2230 results about "Majorization minimization" patented technology

The invention discloses a BIM-based electromechanical installation project progress planning and resource scheduling method and system, and belongs to the technical field of intelligent construction. The method comprises the following steps: 1) collecting multi-dimensional parameters (real-time construction parameters, prediction model parameters and external constraint parameters) in construction in a classified manner, and constructing a dynamic knowledge graph; 2) detecting progress and resource deviation based on a preset threshold value of the BIM model, and dynamically correcting a resource demand curve through the model; 3) in combination with constraint conditions such as policies and weather, optimizing an equipment scheduling path by adopting an algorithm, and screening compliance candidate schemes; 4) performing multi-objective optimization (minimizing progress deviation, maximizing resource utilization rate and controlling supply chain risk) on the scheme by using a genetic algorithm, and verifying through simulation iteration; and 5) outputting the optimal scheme and synchronizing the optimal scheme to a visual interface. According to the invention, real-time closed loop of data is realized through hardware-algorithm cooperation, and an efficient, dynamic and extensible intelligent management scheme is provided for electromechanical engineering.

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

A source-grid-load-hydrogen storage multi-stage planning method and system considering flexible resources, relating to the technical field of source-grid-load-hydrogen storage system planning. The method comprises: collecting source-grid-load-hydrogen storage data for data preprocessing; constructing a flexibility supply and demand characteristic model and a source-grid-load-hydrogen storage multi-stage dynamic planning model; calling a solver to solve the source-grid-load-hydrogen storage multi-stage dynamic planning model to obtain an optimal solution; and outputting a multi-stage source-grid-load-hydrogen storage investment result, a multi-stage source-grid-load-hydrogen storage operation policy, and a multi-stage flexibility supply evaluation result within a planning period. Using the minimization of investment costs, operation costs, and insufficient flexibility penalty costs within the whole planning period as target functions, various constraints such as a new energy permeability constraint and a load loss rate constraint are introduced, a dynamic planning method is proposed, and a source-grid-load-hydrogen storage multi-stage planning solution that has sufficiently economical planning operation and is sufficiently flexible is obtained. According to a processing method based on piecewise linearization, the model is simplified, and the computation speed is increased.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention relates to the technical field of software defined networks, in particular to an SRv6-based SDN (Software Defined Network) data center load balancing method and system, and the method comprises the steps: obtaining node real-time load and SRv6 routing available resource information through an SDN controller, constructing a load balancing model, and recognizing an overload node and a congestion link; generating a flow scheduling path in combination with segmented routing characteristics; in combination with network state information, monitoring the load condition in real time when the node is unbalanced or the link packet loss rate is gt; when 5%, a load balancing adjustment mechanism is triggered, unbalance types are distinguished, a related flow set is determined, optimization is performed by using a DQN algorithm, and a flow scheduling strategy is generated and dynamically adjusted by taking throughput maximization and unbalance degree minimization as targets; and issuing the scheduling strategy to the data center network equipment, updating the flow table through the Netconf protocol, and redistributing the flow. Therefore, the problems of low load balancing efficiency, insufficient dynamic adaptability, low resource utilization rate and the like in the prior art are solved.

The invention relates to the technical field of cloud computing and distributed storage, and discloses a distributed metadata management method and storage system based on cloud computing, and the method comprises the following steps: S1, collecting the access frequency of a plurality of metadata objects in a set time period; s2, performing weighting processing on the access frequency, and constructing access heat distribution under multiple time scales; s3, obtaining the available cache capacity of a plurality of cache nodes, and constructing cache resource weight distribution; s4, assessing the access cost and migration cost between each metadata object and each cache node; and S5, generating a scheduling strategy between the metadata object and the cache node based on a preset optimization target, wherein the optimization target is access cost minimization. According to the method, a heat, resource and cost joint modeling mechanism is constructed, and optimal scheduling and feedback adaptive control are combined, so that accurate decision, dynamic adjustment and migration cost controllability of copy deployment are realized, and the performance and stability of distributed metadata management are effectively improved.

The invention discloses a multi-source reactive power compensation control method, system, program, medium and device, and relates to the technical field of power grid systems, and the method comprises the following steps: extracting the dominant influence characteristics of the reactive power of a power grid as the input of a CNN-LSTM network, predicting the vacancy interval of the reactive power of the power grid, and taking the vacancy interval as the power distribution target of the multi-source reactive power compensation device; a power distribution model of the multi-source heterogeneous reactive compensation integrated system is constructed by taking minimization of system network loss and voltage drop and stability of the multi-source heterogeneous reactive compensation integrated system as optimization targets. The influence factors of the reactive power of the power grid are researched, and the influence characteristics are used as neural network input to predict the vacancy interval of the reactive power of the power grid, so that the spatial-temporal distribution characteristics of the reactive power of the power grid can be reflected, the change trend of reactive requirements is provided for power grid operators, and corresponding reactive compensation measures are taken in time.

The invention relates to a multi-vehicle intelligent driving cooperative training method based on a block chain in the Internet of Vehicles, and belongs to the technical field of mobile communication. Aiming at the problems of data privacy leakage, too high synchronous federated learning time delay, inaccurate node contribution degree evaluation, insufficient single-chain block chain expandability and the like existing in intelligent driving cooperative training in the Internet of Vehicles, an asynchronous federated learning framework with fusion of a cloud-edge-end three-layer architecture and a double-layer block chain is constructed. A local training strategy is optimized through multi-agent reinforcement learning to minimize the total time delay of a system, a dynamic reputation evaluation mechanism based on training interaction timeliness, confirmed site occupancy and model quality contribution is designed to screen high-reputation nodes, and asynchronous model verification and PBFT main chain global aggregation are realized by adopting a DAG block chain. According to the method, on the premise of guaranteeing data privacy, the model sharing efficiency is improved by more than 30%, the training time delay is reduced by 40%, the convergence stability of a global model is effectively enhanced, and the expansibility bottleneck of a traditional architecture is solved.

The invention belongs to the technical field of distributed energy storage control, and discloses an optimized operation method and system for a multi-network-constructed energy storage system, and the method comprises the steps: carrying out the equivalence of the multi-network-constructed energy storage system, and obtaining an equivalent energy storage system; wherein the equivalent energy storage system comprises an isomorphic network-following converter, an equivalent impedance R and an ideal voltage source which are sequentially connected in series; based on the equivalent energy storage system, obtaining a small signal stability boundary condition by adopting a Routh criterion; and taking the minimum operation cost as an objective function, solving the objective function under operation constraints including small signal stability boundary conditions, and obtaining J and D of each network construction type converter at different moments, so that each network construction type converter operates under the corresponding J and D. The method is suitable for optimized operation configuration of an energy storage system comprising a plurality of network following-constructing converters, and can effectively promote advantage complementation of renewable energy sources and energy storage, thereby improving system stability and operation economy.

The invention discloses a cross-chain fragmentation scheduling method based on a DAG, and belongs to the technical field of cross-chain fragmentation scheduling. Comprising the following steps: constructing a transaction dependency graph DAG model; a node selection algorithm based on out-degree is adopted, and nodes with high priorities are selected from nodes without preorder in the directed graph for scheduling; the FAST-MCTS algorithm is adopted to allocate transactions to the optimal fragment, and the number and delay of cross-fragment communication are minimized; and traversing the dynamic DAG by adopting an ODS-BFS scheduling algorithm, and preferentially processing nodes with relatively high degree. According to the method, cross-chain transactions are modeled through the dynamic DAG, the dependency relationship between different transactions is captured, it is ensured that the transactions are executed according to the dependency sequence, and the rollback probability occurring in the cross-chain transaction process is avoided; according to the method, the transactions with the dependency relationship are scheduled to the same fragment, so that the conflict of cross-fragment transactions is reduced, the method adapts to the number of fragments of different scales, and a lower cross-fragment proportion and higher throughput are kept.

The invention discloses a virtual power plant global optimization scheduling method, system and device based on cloud edge collaboration, and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: based on a cloud edge coordinated regulation and control framework comprising a cloud layer, an edge layer and an end side layer, taking minimization of the total operation cost of a system as a target, comprehensively considering a power balance constraint, a main network interaction constraint, a distribution network transmission constraint, a distributed resource operation constraint, an energy storage equipment constraint and a renewable energy consumption constraint; establishing a global optimization scheduling model; edge collaborative optimization is realized by adopting an alternating direction multiplier method, a global coupling problem is decomposed into local optimization sub-problems and a cloud coordination problem of each region, and aggregation power information is sent to the cloud after the local optimization sub-problems are solved in parallel in each region; and the cloud performs global coordination optimization to generate an optimal scheduling strategy, and issues a scheduling instruction to the edge layer to control the actual operation of the distributed power supply, the energy storage equipment and the controllable load, thereby realizing the collaborative optimization scheduling of the virtual power plant. The problems that in virtual power plant large-scale distributed resource coordination optimization, calculation complexity is high, communication burden is heavy, and real-time performance and global optimality are difficult to consider at the same time are effectively solved.

The invention relates to the field of power systems and automation thereof. The invention relates to a power distribution network energy storage optimization configuration method considering dynamic uncertainty and multi-target cooperation. The method is characterized by comprising the following steps: 1) constructing a two-stage robust optimization model: constructing the two-stage robust optimization model with a min-max-min structure; in the first stage, the energy storage construction position and capacity are determined with the lowest annual investment cost of energy storage as the target; in the second stage, the system scheduling cost is minimized in the worst new energy output scene; 2) convex relaxation processing of network constraint; 3) implementation of an iterative solution algorithm: based on a KKT principle and a column constraint generation algorithm, decomposing an original problem into a mixed integer linear main problem and a sub-problem; the main problem optimizes an energy storage configuration scheme, and the sub-problems solve a scheduling strategy in the worst wind and light output scene and feed back to the main problem through cut plane constraint; and carrying out iterative calculation until the solutions of the main problem and the sub-problem converge, and obtaining an optimal energy storage configuration scheme. According to the method, more accurate and efficient energy storage planning can be realized.

The invention discloses an intelligent operation and maintenance distributed cloud native application computing method, and relates to the technical field of cloud computing, and the method comprises the steps: collecting heterogeneous data of an application layer, a middleware layer, a container layer and a service governance layer, constructing a dynamic multilayer dependency graph, and explicitly modeling a cross-layer dependency relationship and implicit dependency caused by resource competition; based on the spatio-temporal features of the spatio-temporal diagram neural network learning map, predicting a cascade fault risk and outputting a high-risk sub-graph; candidate operation and maintenance operations are generated through reinforcement learning, indirect negative influences of the operations on non-target components are evaluated in combination with a causal inference model, and risk filtering operations are verified through a multi-dimensional safety threshold value; and after the safety operation is executed, continuously optimizing the model and the atlas through a feedback closed loop. According to the method, deep insight, accurate risk prediction and self-adaptive decision making of side effect minimization can be carried out on a complex dependency relationship, and the system stability and the operation and maintenance efficiency are remarkably improved.

The invention relates to a transmission and storage multi-stage collaborative planning method considering power transmission network structure optimization, belongs to the technical field of power system planning, and solves the problem that an existing planning scheme is not high in feasibility and reliability. The method comprises the following steps: predicting a wind-light output sequence and a load sequence of each time period in a planning period and putting the sequences into a data set; a clustering algorithm is used for clustering the data set to obtain a plurality of operation scenes, and the occurrence probability of each operation scene is calculated; constructing a first target function and a first constraint condition for minimizing the sum of the total input and storage investment construction cost and the total annual operation cost as an upper layer model; constructing a second objective function and a second constraint condition for minimizing the total annual operation cost according to the wind and light output sequence, the load sequence and the occurrence probability of each operation scene, and taking the second objective function and the second constraint condition as a lower-layer model; the upper-layer model and the lower-layer model are combined into a transmission and storage double-layer model; and iteratively solving the transmission and storage double-layer model to obtain a transmission and storage multi-stage collaborative planning scheme. The feasibility and reliability of the planning scheme are improved.

The invention discloses a park integrated energy scheduling method and system based on a master-slave game model, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting basic operation parameter data in a park integrated energy system; constructing an upper-layer game model taking an operator as a dominant, setting constraints, and making an electric heating and cooling price strategy and a unit output plan by taking operator profit maximization as a target; constructing a lower-layer game model, and making a load energy consumption strategy by taking user energy consumption cost minimization as a target; and converting the upper-layer game model and the lower-layer game model into a mixed integer linear programming problem, and obtaining an electric heating cold price scheduling scheme and an output scheduling scheme of each unit by adopting an optimization solution algorithm. The building thermal inertia and demand response mechanism is introduced, the KKT condition, the linear relaxation technology and the strong dual theory are used for converting the complex problem into the MILP model easy to solve, optimization of operator and user benefits is achieved, and the calculation speed and the solving efficiency are remarkably improved.

The invention discloses a dynamic network heterogeneous resource management and task allocation method, system and device. The method comprises the following steps: constructing a plurality of constraint conditions; constructing a first objective function for maximizing task execution utility and minimizing resource consumption and energy consumption; on the basis of the first objective function and the multiple constraint conditions, a static task allocation strategy is obtained through Lagrange relaxation solution; constructing a dynamic task allocation strategy when resource adjustment is performed on the static task allocation strategy, and based on the dynamic task allocation strategy, adjusting resources of each time step by adopting a Markov decision process to obtain a target resource adjustment amount of each time step; constructing a static task updating strategy according to the static task allocation strategy and the target resource adjustment amount; and based on the static task allocation strategy, the dynamic task allocation strategy and the static task updating strategy, adopting alternate optimization to obtain a target task allocation strategy. The overall resource utilization rate can be improved, and the task execution efficiency is improved.

The invention relates to the technical field of power system source-grid-load-storage collaborative planning, and discloses a source-grid-load-storage collaborative planning method, system and device and a medium, and the method comprises the steps: obtaining the structure, operation and economic parameters of a power system; dividing extreme scenes and establishing a frequency response model; setting a frequency security constraint based on the extreme scene; a three-layer optimization planning model is constructed, the three-layer optimization planning model comprises an objective function and constraint conditions, and the third layer comprises frequency security constraints; the model is solved to determine a planning scheme. According to the method, the minimization change point detection technology is utilized, artificial truncation of the wind-light load output features in sample selection is avoided, and the extreme scene is accurately extracted. The constructed frequency response model comprises nine frequency modulation resources and comprehensively reflects the frequency modulation capability of the novel power system. According to the provided three-layer iterative planning method, the construction investment is properly increased, so that the frequency safety of a power supply, a power transmission line, energy storage and a demand side response scheme in an extreme scene is ensured, and the frequency instability risk and the economic loss are avoided.

The invention discloses a scheduling optimization method for an aviation composite material manufacturing hot-press forming process, which comprises the following steps of: 1, establishing a hot-press forming production plan scheduling objective function by taking the minimum total delay time of a workpiece as an objective according to an aviation composite material manufacturing hot-press forming production plan; 2, establishing a two-dimensional hot press molding process batch scheduling model considering a workpiece release time window and machine applicability; and step 3, taking the objective function as an optimization objective, taking the two-dimensional hot-press forming process batch scheduling model as an optimization model, and utilizing a genetic algorithm embedded with a self-adaptive domain search strategy to solve and obtain a hot-press forming production plan scheduling scheme. According to the method, the total delay time of workpieces can be remarkably shortened while the machining batch of the workpieces is minimized, the occurrence of prepreg deterioration conditions is reduced, and conditions are created for improving the hot press molding production efficiency and promoting digital and intelligent transformation and upgrading of composite material manufacturing enterprises.

The invention discloses an active power distribution network optimal scheduling method and system considering a multi-agent cooperative game, and relates to the technical field of power distribution network optimal scheduling, and the method comprises the steps: considering the interaction among multiple market agents in an active power distribution network, and constructing a multi-agent cooperative transaction architecture; based on a multi-subject collaborative transaction architecture, taking cost minimization as a target, and combining constraint conditions to build an operation and income model of each subject; wherein a distributed energy storage operator is used as one of main bodies, and the operation of distributed energy storage is restrained by using a distributed energy storage distribution strategy based on distributed energy storage SOC balance; according to the Nash bargaining theory, a multi-subject operation and income model is integrated, and a multi-subject cooperative game model is constructed; and solving the multi-agent cooperative game model based on the actual energy demand quantity of the user side to obtain an optimal scheduling strategy of the energy in the active power distribution network, and performing energy scheduling according to the scheduling strategy. The charging and discharging power balance between the DES units can be realized, and the economic benefit is improved.

The invention relates to a ship port resource coordinated scheduling method and device, and belongs to the technical field of port resource optimized scheduling, and the method comprises the steps: building a target function with the maximization of the anchor ground utilization rate, the maximization of the berth utilization rate and the minimization of the ship waiting time as targets; solving the objective function by using MILP and a particle swarm algorithm to obtain an initial scheduling scheme meeting constraint conditions; the initial scheduling scheme comprises anchor ground distribution, berth distribution, berthing time and leaving time; clustering the initial scheduling scheme based on the multi-core weighted distance of the initial scheduling scheme to obtain a clustered scheduling scheme; and optimizing the clustered scheduling scheme based on a game theory model to obtain a target scheduling scheme. According to the ship and port resource coordinated scheduling method provided by the invention, global optimization of ship berth allocation, channel use and anchor ground scheduling is realized, and high efficiency and safety of ship operation are ensured while channel resource conflicts are reduced.

The invention discloses a production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention belongs to the technical field of logistics information, and discloses a multimodal transport trusted data space construction method based on privacy calculation and a block chain. The method comprises the following steps: deploying a modular intelligent data gateway at a core node of a multimodal transport line, unifying the format of multi-source heterogeneous original data, and chaining a hash value; after the multi-source heterogeneous data is subjected to customization processing, key metadata attributes of all the data are registered and linked in real time; constructing a core ontology model, and establishing a cross-domain semantic interoperation capability; establishing a privacy calculation and block chain collaborative dual-channel architecture, and cooperatively training an ETA prediction model in a local data isolation environment by adopting transverse federal learning to ensure that original trajectory data is not out of a domain; constructing a dispute arbitration and credible verification system, and realizing an arbitration process of judicial verification when a transportation dispute occurs; data minimization disclosure and compliance sharing are achieved through dynamic access control. According to the method, multimodal transport data can be available and invisible, and the whole operation process can be audited.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention discloses a flexible job shop scheduling method based on deep reinforcement learning, and belongs to the field of intelligent manufacturing and intelligent scheduling, and the method comprises the steps: firstly, taking the minimization of the maximum job completion time and the maximization of the average machine utilization rate as optimization targets, and obtaining a target model; then representing a flexible job shop scheduling problem by using a disjunction graph model to obtain a scheduling state; according to the scheduling state and the target model, an end-to-end learning framework is constructed, and the framework is combined with a multi-attention network for deep feature extraction and a decision network for extensible decision, and a Markov decision process is utilized to obtain a job scheduling model; and finally, the multiple attention network constructs a production adaptability operation-AGV-machine scheme by using the complex relationship among the operation, the AGV and the machine to support the decision of the decision network, and obtains a scheduling schedule by using the job scheduling model to complete flexible job shop scheduling. According to the method, complex modes and relations in data can be better captured, and the performance and generalization ability of the model are improved.

The invention discloses an electric vehicle V2G micro-grid energy storage capacity optimization method based on dynamic planning, particularly relates to the technical field of micro-grid energy storage optimization, and aims to solve the problem of energy storage capacity optimization defects caused by spatial dynamic characteristics caused by mobility of an existing electric vehicle. Performing geographic grid division on the node position, generating a space-time matrix by combining with a timestamp, and identifying a high-frequency access node; constructing a state transition equation based on the inter-node impedance matrix, and setting a node-level safety boundary by taking a line capacity change rate as a dynamic constraint; a staged reverse dynamic programming algorithm is adopted, spatial dimension optimization is preferentially executed on the high-frequency access nodes, and charging and discharging constraints fusing voltage deviation and capacity overrun penalty terms are generated; carrying out global optimization by taking the minimization of the total operation cost as a target after the constraints are integrated, and outputting an energy storage capacity configuration strategy of each time period; and finally generating an energy storage deployment scheme.

The present application discloses an optimization method and apparatus for a transportation strategy under a feeder logistics network, an electronic device and a storage medium. The method comprises: acquiring logistics information under a feeder logistics network, wherein the logistics information comprises information of each logistics node; inputting the logistics information into a digital twin model, by means of the digital twin model and by taking transportation rules of the feeder logistics network as a constraint condition and transportation cost minimization as an objective, performing route planning to generate a transportation strategy to be optimized, and simulating a transportation process of goods between the logistics nodes on the basis of the transportation strategy to be optimized, so as to obtain a simulation result; and on the basis of the simulation result, optimizing the transportation strategy to be optimized, so as to obtain an optimized transportation strategy. In the present application, on the basis of the computing power of the electronic device, the transportation strategy during logistics transportation can be quickly obtained; and by simulating the logistics transportation process, the reliability of the transportation strategy can be improved.

The invention discloses a high-reliability low-delay sky-ground network computing unloading method for a terminal direct connection satellite, and the method achieves the weighted sum minimization of long-term energy consumption and queuing time delay of a system through the joint optimization of an equipment terminal task unloading decision, task segmentation, power control and edge computing resource allocation. The method comprises the following steps: step 1, establishing a sky-ground communication system model of a ground terminal direct connection satellite, and deducing the weighted sum of energy consumption and queuing time delay of the sky-ground communication system; 2, describing an optimization problem of combining ground terminal task unloading and segmentation, power control and edge computing resource allocation under high-reliability and low-delay constraints by taking system long-term energy consumption and queuing time delay weighted sum minimization as a target; and step 3, adopting a multi-agent deep reinforcement learning method to solve the joint optimization problem of unloading decision, task segmentation, power control and computing resource allocation. According to the method, joint optimization of terminal task unloading, segmentation, power control and edge computing resource allocation under high-reliability and low-delay constraints of the sky-ground network facing the terminal direct connection satellite is realized, and weighting and minimization of long-term energy consumption and queuing delay of a communication system are realized.

The invention relates to the technical field of digital intelligent supervision, in particular to a supervision method based on industrial chain digital collaboration, which comprises the steps of realizing efficient and credible collection and fusion of industrial chain heterogeneous data through a block chain and federated learning technology, ensuring data privacy and constructing a unified data basis; the graph neural network is used for dynamically learning an industry chain entity relationship, a key path and a vulnerability node are accurately identified, a hidden risk structure is revealed, and the risk positioning capability is improved; by fusing policies, behaviors and topological data, the risk entropy value is calculated in real time, early warning is generated, multi-dimensional risk quantification and dynamic monitoring are achieved, and the early warning accuracy is enhanced; a supervision instruction matched with the risk is automatically triggered based on the intelligent contract, so that timeliness and accuracy of supervision measures are ensured, and human intervention errors are reduced; map and risk model parameters are dynamically adjusted through a reinforcement learning mechanism, and continuous optimization of a supervision strategy and minimization of service interference are realized.

The invention discloses a non-ideal ISAC system beam forming method with an anti-eavesdropping capability, which is characterized in that a base station is intelligently optimized to transmit beam forming so as to ensure that radar sensing requirements are met under the condition of limited power resources, and attacks of potential eavesdroppers and guards are effectively resisted, so that the anti-eavesdropping capability of the non-ideal ISAC system is improved. Therefore, the safety communication and covert communication performance of system communication users is obviously improved. Specifically, according to the method, a safe ISAC scene which has potential eavesdroppers and guards at the same time, legal nodes of a system have hardware damage, and CSI of illegal nodes cannot be obtained perfectly is considered, and in order to guarantee safe communication and sensing performance, a legal signal energy minimization optimization problem is established. And then, the original problem is converted into a convex optimization problem by utilizing a positive semidefinite relaxation technology and an S process technology, and the convex optimization problem is solved by utilizing a CVX toolbox, so that the strategy effectively ensures the safety communication and target sensing performance of the system under limited power resources, and the complexity of the problem is reduced.

The invention relates to the technical field of intelligent workshop scheduling, and provides a job shop scheduling method and system based on a principal component heterogeneous graph neural network, and the method comprises the following steps: determining a constraint condition of a distributed flexible workshop, taking the minimization of the maximum completion time as a control target, and building a problem model according to the constraint condition; obtaining a current state of workshop operation, performing embedding based on a graph attention network to obtain an embedded representation of the state, and extracting an embedded representation of a key state based on principal component analysis after fusion; the obtained embedded representation of the current key state serves as input, the problem model is solved through a strategy network, and probability distribution, distributed to a specific machine tool, of each working procedure serves as an initial solution; and based on the obtained initial solution, performing iterative optimization by adopting an enhanced local search heuristic algorithm to obtain an optimal solution. According to the method, the high efficiency and accuracy of the final solution can be ensured, the defects of a traditional GNN-based method in a subsequent optimization mechanism are overcome, and the method is suitable for the scheduling problem of various types of distributed flexible workshops and has high expansibility and adaptability.