245 results about "Resource coordination" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention provides a workshop multi-agent deep reinforcement learning scheduling method based on real-time production conditions, and relates to the technical field of production scheduling and industrial automation, and the method comprises the steps: collecting production data in real time through a distributed sensor network, and constructing a multi-dimensional time sequence matrix; establishing a three-dimensional decision space containing time, resource and task dimensions, and adopting an improved multi-agent depth deterministic strategy gradient algorithm; dynamically calculating the real-time production urgency degree of the production batch, and analyzing cross-unit cooperation to generate a cooperation efficiency factor; the two are combined to optimize a scheduling scheme through a course learning strategy; and verifying the scheme by using a virtual twin environment and feeding back and updating model parameters. The method breaks through the limitations of insufficient dynamic adaptability, low efficiency of multi-target coordination and the like of traditional scheduling, realizes real-time response to dynamic production conditions such as equipment faults and order adjustment, balances time efficiency, energy consumption economy and abnormal fault tolerance through a multi-agent coordination and closed-loop verification mechanism, and improves resource coordination efficiency under complex production constraints.

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 discloses a small file distributed high-concurrency aggregation storage method and system, and the method comprises the steps: collecting performance index data, and transmitting the data to a resource coordination server; calculating a queuing rate; determining a target server according to the queuing rate, the file reading concurrency and the disk residual space; determining a first-level directory hash value; selecting a first-level storage directory and a second-level storage directory according to the first-level directory hash value; creating a target aggregation file in the secondary storage directory, generating a 2-byte file format field according to the compression identifier or the non-compression identifier, and generating a 4-byte file length field according to the size of the to-be-written file; writing the file format field, the file length field and the to-be-written file into a target aggregation file in sequence, and recording the initial offset of writing; and generating a file name containing the storage position information, and returning the file name to the client. According to the invention, the efficiency and effect of small file storage are optimized.

The invention relates to a priority scheduling generation method and system for Beidou satellite short message communication, and the method comprises the steps: collecting multi-dimensional features, such as message service types, user identities, spatio-temporal information, channel states and the like, dynamically calculating the priority score of each message through a configurable weighting model, and then, carrying out the dynamic calculation of the priority score of each message; the method comprises the following steps of: abstracting a message into a conflict graph vertex, establishing an edge according to a receiving conflict relationship, distributing candidate message sets which are not conflicted with each other for each scheduling time slot by adopting an independent set selection algorithm, and performing joint optimization on a message sending sequence and power distribution by using a genetic algorithm in each time slot by taking a maximized comprehensive utility value as a target, and generating a final scheduling instruction. Through dynamic evaluation, conflict avoidance and resource collaboration, intelligence and self-adaption of a scheduling strategy are realized, and the guarantee capability of a system for key messages and the overall resource utilization efficiency are remarkably improved.

The invention provides a resource collaborative scheduling system and method for a virtual power plant. The method comprises the following steps: determining space-time probability distribution of wind and light output in the virtual power plant through historical meteorological data and historical illumination data; determining space-time load distribution of an electric vehicle cluster in the virtual power plant, and constructing a source-load interaction scene set under multiple space-time scales in the virtual power plant by fusing the space-time probability distribution and the space-time load distribution; determining a multi-objective optimization function of the virtual power plant according to the price demand signal of the electric energy service market and the source-load interaction scene set; and performing optimization solution on the multi-objective optimization function to obtain a collaborative scheduling plan of the virtual power plant, decomposing the collaborative scheduling plan into a control instruction sequence, and issuing the control instruction sequence to a local controller of each distributed resource. According to the scheme of the invention, a multi-target optimization system considering the operation benefit and the renewable energy power abandonment rate can be constructed through the source-load interaction scene under multiple spatial-temporal scales, so that the closed-loop management and control of the resource scheduling of the virtual power plant can be realized.

The invention discloses a micro-grid automatic power regulation and control method, and relates to the technical field of micro-grid power regulation and control and resource management. In order to solve the defects of poor new energy consumption capability, high distributed resource management difficulty, poor power grid regulation and control flexibility, poor resource cooperation effect and poor power and load prediction accuracy of an existing micro-grid power regulation and control method, grid-connected and off-grid flexible switching of a power grid A and a power grid B is realized by constructing an integrated construction framework. And multi-source data are integrated, and new energy power generation and load demands are intelligently predicted through data center management. And during grid connection, the distribution and dispatching integrated system determines power dispatching, and the micro-grid management system B makes a dispatching plan. And during off-grid, power is supplied according to the user priority. And in the grid-connected and off-grid mode, power distribution, load regulation and control and resource optimization configuration are carried out, and distributed resources are regulated and controlled in real time through the coordination controller, so that stable operation of the power grid is ensured. The method is mainly used for flexibly regulating and controlling the micro-grid.

The invention discloses a multi-objective optimization-based enterprise resource coordination and distribution method, which comprises the following steps of: S1, acquiring multi-dimensional description information of enterprise task requirements, and constructing a requirement diagram; s2, based on the demand graph structure, performing clustering division on task nodes by adopting a self-organizing clustering algorithm, generating task clusters, generating a candidate resource demand set, and constructing a resource node set; s3, constructing a resource scheduling candidate topological graph; s4, defining a scheduling adaptability function containing a multi-target index, wherein the multi-target index comprises cost, period, resource utilization rate and resource scheduling distance; s5, extracting an optimal scheduling sub-graph from the candidate scheduling topology by adopting an improved heuristic aggregation algorithm; and S6, outputting a corresponding resource allocation scheme to complete resource allocation. According to the method, task structure modeling and multi-target scheduling optimization are fused, the precision and adaptability of resource allocation are improved, and the method is suitable for an enterprise resource management system in a complex scheduling scene.

The invention discloses a multi-agent asynchronous collaboration method and system under a centralized architecture, and is suitable for task scheduling and resource coordination of heterogeneous agents in an intelligent Internet of Things environment. According to the method, a main scheduling node centralized control mechanism is adopted, a scheduling priority function is calculated in combination with a multi-agent game model according to real-time state information of a plurality of heterogeneous agents, and asynchronous allocation and feedback control of tasks are achieved. Meanwhile, an asynchronous and synchronous window mechanism is introduced, the scheduling continuity and efficiency can still be kept under the condition of incomplete information, and fairness and robustness of task distribution are achieved through agent feedback delay modeling and revenue function design. The system throughput and the response speed are improved, the isomerism and the expandability are considered, and the method is suitable for multi-agent cooperation scenes such as disaster emergency, industrial manufacturing, smart home and intelligent traffic.

The invention discloses a method and a system for cooperatively controlling post-disaster recovery and multi-source resource coordination of three networks, and belongs to the technical field of post-disaster recovery and toughness improvement of a multi-infrastructure system. The method comprises the following steps: obtaining a node importance evaluation function through multi-index fusion, and calculating a recovery priority score of a grey starting power supply; estimating the node state probability to obtain the node state probability; setting boundary conditions of recovery feasibility of the subarea power grid; optimizing a recovery sequence through a grey starting power supply-load-net rack path matching model, and generating a recovery path set; a reinforcement learning algorithm is adopted, node states, resource availability and a path connection matrix are used as state spaces, and an optimal recovery sequence and a resource scheduling scheme are output; and generating a three-network recovery path recommendation, a partition power grid recovery strategy and a multi-element flexible resource scheduling sequence. According to the invention, efficient scheduling and cross-network cooperative control of multi-source flexible resources are realized, and the recovery efficiency and toughness of three networks in a post-disaster scene are effectively improved.

The invention discloses a U.2 interface-based server intelligent integrated hierarchical storage system and method, relates to the technical field of computer server storage, and is used for solving the problem of optimization of multi-type SSD data access efficiency and storage resource utilization rate. Firstly, a multi-protocol parallel channel is established by redefining U.2 interface reserved pins, signal integrity parameters are monitored in real time and fused with equipment I / O access features, and an I / O feature sequence of physical layer state information is generated; then, based on a time sequence convolutional neural network, extracting access mode multi-scale features, calculating a data block access heat probability, mapping and determining a target storage hierarchy, and generating a scheduling instruction containing a source physical address, the target hierarchy and a migration priority; the migration execution module realizes zero-copy migration of the data block between the shared DRAM pool and the SSD according to the instruction; and the resource coordination module monitors system load in real time, dynamically adjusts migration concurrency and an I / O sampling strategy, and realizes self-adaptive optimization of storage performance and resource utilization.

The invention discloses a multi-resource coordination control method and system in a new energy high-proportion power grid, and the method comprises the steps: collecting power grid data which comprises new energy output data, meteorological data, power grid load data, power grid operation state data and energy storage system operation parameters; constructing a new energy output prediction model based on the power grid data; calculating quantitative cost based on the power grid data and the new energy output prediction model, wherein the quantitative cost comprises energy storage system cost, power grid operation cost and power grid stability cost; constructing an energy storage charging and discharging strategy model based on the quantitative cost; obtaining an optimal energy storage charging and discharging strategy based on the energy storage charging and discharging strategy model; and constructing a multi-resource cooperative scheduling model based on the optimal energy storage charging and discharging strategy, and outputting a scheduling plan. According to the method, the energy storage depreciation, the power grid loss and the stability risk are quantified into specific cost indexes by calculating the quantitative cost, hidden loss can be avoided, and the operation cost of the system is minimized.

The invention relates to the technical field of front-end application development, in particular to a micro-front-end architecture-oriented cross-application state management, style isolation and resource collaborative loading method, which comprises the following steps of: constructing a state management module, establishing a unified state communication protocol based on a publishing / subscribing mode, and realizing safe state synchronization through an authority control mechanism; a style isolation module is constructed, a Shadow DOM and CSS rule rewriting double isolation strategy is adopted, dynamic style elements are automatically processed, and the problem of style pollution is thoroughly solved; constructing a resource coordinator module, and realizing dependent intelligent management and multi-version coexistence based on semantic version specifications; and the configuration management center uniformly coordinates the work of each module. According to the method, safe communication, reliable isolation and efficient resource utilization between micro front-end sub-applications are realized, three core problems of difficult state management, pattern conflict and resource redundancy in the prior art are solved, and the maintainability, the stability and the development efficiency of a large front-end project are remarkably improved.

The invention discloses a coordination control method, system and device for distributed resources of a power distribution network and a medium. The method comprises the steps of obtaining a topological structure, electrical characteristics and resource distribution conditions of the power distribution network; dividing the power distribution network into a plurality of control regions by adopting a self-adaptive partitioning algorithm to obtain a self-adaptive partitioning result; based on the self-adaptive partitioning result, a hierarchical control framework is constructed; different types of distributed resources in the power distribution network are coordinated and optimized through an intra-region resource coordination control algorithm and an inter-region coordination mechanism; and obtaining an optimal power distribution network distributed resource coordination control strategy through a distributed optimization algorithm and a self-adaptive control strategy. Effective control technical support is provided for friendly access of large-scale distributed resources to the power distribution network, and the method has important practical value for improving the bearing capacity of the power distribution network and optimizing the resource utilization efficiency.

The invention discloses a task hierarchical scheduling method for an intelligent computing fusion network, belongs to the technical field of the intelligent computing fusion network, and designs a double-layer task scheduling mechanism (ICCN-DSF) for the intelligent computing fusion network to effectively cope with task scheduling challenges in a large-scale heterogeneous resource environment. Wherein the global task scheduling layer is based on a CIEK-Means algorithm, integrates geographic position constraint and group intelligent optimization, and realizes efficient clustering and dynamic matching of resource ethnic groups; and the local task scheduling layer constructs a three-dimensional state action space and a multi-target reward function through a reinforcement learning algorithm to complete fine-grained resource allocation. According to the method, the scheduling complexity is remarkably reduced, meanwhile, the cross-domain connection requirement is reduced through a layering mechanism, global resource coordination can be achieved only through a small number of WAN links, the operation and maintenance cost and the safety risk are reduced, meanwhile, the cooperation potential of the computing power and the network is fully excavated, and efficient and robust scheduling support is provided for intelligent services in complex scenes.

The invention provides a real-time data processing method and system based on edge computing, and relates to the technical field of data processing.The data validity period is calculated through a composite decision model of terminal equipment physical sampling frequency constraint and preset service rules, the signal restoration accuracy requirement and the service scene emergency degree are effectively balanced, and the real-time data processing efficiency is improved. Overtime failure of key data or occupation of non-key data resources is avoided; the data transmission integrity in a bandwidth fluctuation environment is improved through a dynamic window cutting mechanism driven by a real-time network state; optimizing edge computing power load distribution in combination with a routing decision mapping strategy and a processing priority resource ratio; the terminal instruction reliability is improved based on hierarchical control of historical data abnormal value purification and geometric distance measurement; a complete technical chain from data acquisition, self-adaptive transmission cutting, aging precise arbitration and routing resource collaboration to closed-loop control feedback is formed, and the transmission efficiency, the resource utilization precision and the control stability of high-concurrency real-time data processing are enhanced.

The invention discloses a power distribution network toughness improvement method based on virtual power plant cooperation and transaction settlement, and the method comprises the steps: S1, constructing a multi-type virtual power plant which comprises a virtual power plant management platform, a power transaction platform and a scheduling platform, and enabling the multi-type virtual power plant to participate in a power grid scheduling and power transaction whole-process management frame, so as to carry out the plan of a VPP disaster accident; s2, the scheduling platform constructs a pre-disaster and post-disaster multi-type VPP collaborative optimization power distribution network power supply recovery model so as to formulate a network reconstruction strategy of each time period, a real-time scheduling strategy of emergency resources and an adjustment strategy of the VPP; and S3, according to the VPP actual response amount issued by the scheduling platform, and the declaration response amount and the VPP historical participation degree reported by the virtual power plant management platform, the power transaction platform calculates the power supply recovery participation excitation unit price and compensation income of each VPP. According to the invention, through the whole-process closed-loop management integrating pre-disaster plan generation, post-disaster dynamic adjustment and transaction settlement, the key problems of insufficient resource collaboration, stage splitting, excitation stiffness and the like are solved.

The invention relates to the technical field of resource scheduling, in particular to a building construction resource optimization allocation system which comprises a resource demand prediction module, a progress state monitoring module, a dynamic offset evaluation module, a resource allocation module and an early warning prompt module. According to the resource allocation method and device, through precise matching of the operation process types, the operation sections and the predicted duration time in the construction stage, a staged resource combination list covering the whole construction process can be formed, resource allocation has higher perspectiveness and accuracy, and the efficiency of resource allocation is improved through comparison of the process completion progress and the set resource combination. The progress offset and the resource offset are quantified in time, rapid response to construction site changes is achieved, resource allocation has real-time perception, dynamic correction and priority ordering capabilities by means of an adjustable human list, a mechanical distribution diagram and a material inventory table, the management efficiency and the resource coordination capability of the construction site are greatly improved, and the resource allocation efficiency is improved. And the construction risk caused by plan errors or field changes is reduced.

The invention provides a networking scene-oriented multipath service optimization method and system, and relates to the technical field of network scheduling optimization, and the method comprises the steps: obtaining topological structure information and service demand information of a communication network in a networking scene, and employing a depth-first search algorithm in combination with a pruning mechanism to generate a candidate path set; on the basis of a marginal value contribution degree theory, calculating a transmission value factor and a path complementary factor of each candidate path in the candidate path set, calculating marginal value contribution degrees of different candidate path combinations according to the two factors, and taking the path combination with the highest marginal value contribution degree as an optimal path combination; and aiming at each path in the optimal path combination, establishing a multi-objective optimization model, and obtaining a final scheduling scheme of the service flow among the paths in the optimal path combination. According to the invention, the problems of lack of differentiated path selection mechanism, poor fault tolerance of single-path transmission, insufficient resource coordination capability and the like in multi-service transmission in the prior art can be solved.

The invention relates to a power distribution network reactive power resource coordination configuration method and device, equipment, a medium and a product. The method comprises the following steps: carrying out quantitative evaluation on the utilization rate of the reactive compensation equipment of the power distribution network according to preset indexes to obtain an evaluation value of each preset index; for evaluation objects of different types of power distribution networks, based on a power distribution network reactive power compensation equipment utilization rate grading evaluation index system of an improved radar map method, evaluating the evaluation value of each preset index, and obtaining a reactive power compensation equipment utilization rate comprehensive evaluation result of each evaluation object; constructing a distribution network reactive power compensation equipment configuration optimization model according to the reactive power compensation equipment utilization rate comprehensive evaluation result of each evaluation object; and aiming at different severe scenes, according to each constraint condition in the distribution network reactive power compensation equipment configuration optimization model, determining a distribution network two-stage reactive power collaborative optimization scheduling strategy. Therefore, the voltage quality of the power distribution network under the power conversion single supply working condition is improved, and the reactive equipment utilization rate and the system operation economy are improved.

The invention provides a total element-oriented multi-agent collaborative resource optimization method in an industrial internet, and aims to solve the key problems of difficulty in cross-layer coordination, low scheduling efficiency and the like caused by sensing, decision-making and execution of network work at remarkably different time elements (second level, minute level and hour level) in a multi-networked industrial system. According to the method, an MTPHN (full time element prediction hierarchical network) framework is constructed for a multi-networked industrial system environment with time isomerism, three core modules of feature pyramid prediction, cross-time element synchronization and cooperative scheduling are provided, and effective modeling and vertical-horizontal two-dimensional resource coordination of heterogeneous time streams are realized. According to the method, the production efficiency, the equipment utilization rate and the production value in different operation scenes can be effectively improved, and the method is suitable for dynamic resource scheduling and cross-level collaborative optimization application in a multi-networked industrial system.

The invention discloses a management system for an integrated circuit test, and relates to the technical field of integrated circuit test management, and the system comprises a task scheduling module which is used for receiving a plurality of test task requests, and generating a test task scheduling sequence based on the priority parameter of each test task, the current state of a test resource, and a historical execution result; and the resource coordination module is used for acquiring the running states of the plurality of test devices, executing resource allocation operation according to the test task scheduling sequence, and generating a coordination abnormal event if resource conflict or scheduling failure is detected in the execution process. According to the invention, a dependency graph between test equipment and bottom hardware resources is constructed through the resource coordination module, and accurate modeling of a resource mutual exclusion relationship, concurrent capacity and conflict risk is supported, so that resource conflicts are pre-judged before scheduling, and the problems of resource scrambling and deadlock are effectively avoided.

The invention belongs to the technical field of scenic spot management, and particularly relates to a scenic spot global perception and service system based on agent cooperation. The system can collect and analyze tourist touring information in real time, the tourist touring information comprises real-time positions, moving tracks and regional staying time of tourists, so that tourist bearing pressure values of all touring sub-regions in a future time period can be accurately predicted, and in specific implementation, a trend analysis module recognizes time change characteristics of tourist distribution based on a global people flow thermodynamic diagram, so that the tourist bearing pressure values can be accurately predicted. And the resource coordination module generates a resource scheduling instruction according to the regional bearing pressure value, dynamically coordinates the service resources in each touring sub-region to ensure the balance of the service capability of each region, and collects tourist feedback information and evaluates the resource coordination effect through the feedback optimization module. And on the basis of the resource scheduling correction instruction, resource scheduling is optimized and adjusted, so that the service resource configuration efficiency and the tourist satisfaction degree are continuously improved.

The invention relates to the technical field of house renting management, and discloses a long-term house renting monitoring management system and method.The method comprises the steps that monitoring units in the same physical space are divided into equipment groups, state jump timestamps of the equipment groups are recorded, behavior logic templates are automatically precipitated based on a historical timestamp sequence, and the equipment groups are stored; through logic self-consistency comparison of a real-time sequence and a template, risk early warning is generated, the system has the capability of understanding equipment collaborative behavior logic, the behavior template is constructed by multiplexing a basic switch signal, cognitive transition from physical quantity monitoring to behavior logic verification is realized, and the risk early warning effect is improved. And meanwhile, an anti-interference data quality firewall and system-level resource coordination capability is naturally formed under a centerless architecture by utilizing a state credibility evaluation and distributed priority arbitration mechanism, so that the economy and the reliability of intelligent reconstruction of the old house are remarkably improved.

There is provided a method for resource coordination in a first donor Integrated Access Backhaul (IAB) node connected to a first parent IAB node which is connected to an IAB node, the IAB node further being connected to a second parent IAB node which is connected to a second donor IAB node. The method comprises: sending, to the second donor IAB node, an indication of a first resource configuration for the IAB node, the first resource configuration determined by the first donor IAB node; receiving, from the second donor IAB node, a request for a second resource configuration for the IAB node; and determining a resource configuration for the IAB node based at least on one of the first resource configuration and the second resource configuration.

The invention relates to the technical field of big data network communication, and discloses a big data network communication coordination method and device, and a storage medium. The method comprises the following steps: acquiring original communication data, cleaning and classifying the original communication data, and generating an initial coordination scheme; generating a parallel influence data set based on entropy quantization and a PageRank algorithm; generating a bandwidth demand value through normalization processing and dynamic correction; executing multi-level threshold judgment and selecting a content optimization or data compression strategy; integrating the initial scheme and the correction parameters to carry out strategy fusion and simulation evaluation, and generating a final coordination scheme; and monitoring a resource state through a protocol and generating a feedback optimization parameter. According to the invention, the data processing efficiency and the resource coordination capability of network communication are improved, and the system adaptability and the transmission stability are enhanced.

The invention discloses a logistics planning toughness MILP optimization method for an emergency scene, and the method comprises the specific steps: 1, defining an emergency material transportation parameter system, and fusing a vehicle dynamic starting point, a goods yard supply capability, a demand priority weight, and a road network blocking variable; and 2, constructing a mixed integer linear programming model, coupling a timeliness objective function and a toughness constraint, and integrating path connectivity, time window limitation and a redundant path rehearsal mechanism. And step 3, converting the objective function through the auxiliary variable, and realizing collaborative optimization of the transportation efficiency and robustness in the most unfavorable scene by the inner layer and the outer layer. And 4, solving the model to generate a dynamic scheduling scheme, outputting a vehicle path plan, a goods yard loading strategy and a distribution time sequence, and ensuring reliable delivery of high-priority goods and materials in a golden time window. According to the method, the anti-interference capability of the emergency logistics system in a dynamic disaster environment can be remarkably improved, and the timeliness guarantee rate of key material transportation and the toughness level of a scheduling scheme are improved by optimizing scheduling multi-resource coordination, path redundancy design and time-sensitive demand matching. Compared with an existing method, the method is more scientific, foresight and robust, can be widely applied to the fields of post-disaster emergency management, resource scheduling optimization and the like, and provides a scientific basis for emergency decision making.

The invention discloses an edge federated learning communication method combining dynamic sampling and bandwidth optimization, which relates to the technical field of edge computing, and comprises the following steps of: collecting the sample size, training time and network bandwidth of terminal equipment through a monitoring tool, classifying and extracting bandwidth labels after preprocessing, and optimizing by adopting dynamic sampling and asynchronous communication to obtain an edge federated learning result. Gradient compression based on bandwidth perception is combined, important gradients are preferentially transmitted, convergence speed is evaluated, resource collaboration is optimized, an MEC server asynchronously distributes parameters, a queue is managed and updated, and a model is aggregated. By combining dynamic sampling and bandwidth optimization, the edge federated learning communication efficiency is remarkably improved, the transmission cost is reduced, the sampling strategy is dynamically adjusted, the important gradient is preferentially transmitted, the model convergence is accelerated, and the equipment resource collaboration and asynchronous parameter distribution and queue management of the MEC server are optimized; the timeliness and accuracy of model updating are ensured, and the practicability and flexibility of edge learning are enhanced.

The invention discloses a cooperative game and adaptive learning-based distributed reactive power resource coordination method, which comprises the following steps of: (1) collecting voltage, current and reactive power contribution data of equipment in real time through a sensor and a monitoring system; (2) constructing a game and income balance model based on voltage, current and reactive power contribution data, and training the game and income balance model; (3) optimizing the model by utilizing meta learning; (4) generating a distribution scheme of reactive power resources; according to the invention, the overall coordination of the system is improved.