5781 results about "Resource assignment" 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 computer task scheduling method based on artificial intelligence, and the method comprises the following steps: 1, data collection: employing a double-flow feature fusion mechanism, and generating global feature representation containing long-term dependence and an instantaneous state; step 2, generating a global optimization scheduling strategy: constructing a hierarchical federal reinforcement learning system, dividing a cluster into a plurality of super nodes through an enhanced spectral clustering algorithm, independently training a Dueling DQN network by each super node, performing global strategy cooperation by adopting Shapley value weighted aggregation and differential privacy protection, and generating a scheduling strategy of global optimization; distilling a global strategy into a lightweight decision tree through a strategy distillation technology, and deploying the lightweight decision tree to a physical node; 3, task priority control and elastic resource allocation are carried out, wherein elastic control over resource allocation is carried out through a dynamic time slice bank mechanism; and 4, self-adaptive evolution: establishing a closed-loop optimization system, and carrying out strategy self-evolution by adopting a double-layer optimization architecture.

The invention discloses a distributed component dynamic resource allocation method based on multi-objective optimization, which is characterized in that a PPO algorithm is introduced into a distributed system, dynamic adjustment is carried out aiming at a plurality of optimization objectives to optimize the overall configuration of resources, and the system firstly collects the real-time state, the task demand and the resource use condition of a distributed component; and then training an intelligent agent by using a PPO algorithm to gradually optimize a resource allocation strategy according to environment feedback, and finally realizing long-term optimization of a resource scheduling process by continuously interacting with the environment and continuously adjusting the strategy through the PPO algorithm. According to the method, the PPO algorithm in reinforcement learning is combined, efficient resource allocation of the distributed components in the complex dynamic environment is achieved, different from an existing rule driving or static optimization method, the allocation strategy can be adjusted in a self-adaptive mode according to task requirements, resource use conditions and system loads which change in real time, the resource utilization rate is increased, and the resource utilization rate is increased. And the system burden is reduced, and efficient operation of the system under variable conditions is ensured.

The invention relates to the technical field of intelligent scheduling, in particular to an enterprise production real-time monitoring and intelligent scheduling system based on artificial intelligence, which comprises a multi-source heterogeneous data fusion unit, a priority resource coupling decision unit, a bottleneck prediction and tracing unit and a scheduling instruction generation unit, the multi-source heterogeneous data fusion unit collects multi-dimensional data such as equipment vibration, temperature, order delivery time and the like in real time and constructs a joint feature vector, and the priority resource coupling decision unit dynamically adjusts task priority and resource allocation through a dual-channel depth Q network to cope with order insertion tasks and equipment health degree fluctuation. The bottleneck prediction and tracing unit predicts production bottlenecks and traces root causes by using a process dependency graph, a multi-modal fusion model and a causal discovery algorithm, and supports preventive maintenance and dynamic scheduling, and the scheduling instruction generation unit synthesizes a preorder result to generate an adaptive scheduling instruction. And enterprise production equipment utilization rate and production efficiency are improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention relates to the technical field of data processing, and discloses a heterogeneous computing acceleration method and system based on a deep learning framework network. The method comprises the following steps: acquiring performance parameters of heterogeneous computing equipment to obtain an equipment characteristic data set; receiving a calculation task and analyzing the calculation task into an operation sequence; the operation sequence is coded into a gene sequence, task decomposition is optimized through a genetic recombination algorithm, and a subtask set marked with acceleration characteristics is obtained; performing matching analysis on the sub-task set and the equipment characteristic data set to obtain a task allocation scheme; deploying the subtasks to corresponding equipment according to the allocation scheme to obtain a distributed execution framework; and monitoring the running state of the framework in real time, and dynamically adjusting resource allocation to obtain a calculation result of speed-up ratio improvement. According to the method, a self-adaptive task decomposition and resource allocation and dynamic load balancing mechanism can be realized, and the execution efficiency and the resource utilization rate of the deep learning task are improved.

The invention relates to the technical field of data processing, and discloses an automobile manufacturing industrial data distributed processing method and system based on digital twinning. The method comprises the following steps: monitoring and collecting production abnormal events, decision emergency degree parameters and simulation task types based on service states to generate a service priority evaluation data set, and performing priority ranking on a plurality of simulation tasks to form a dynamic priority queue and a resource demand matrix, the queue is input into a distributed simulation engine for CPU, memory and GPU load balancing processing to generate a node resource allocation scheme, and preemptive scheduling is executed to migrate low-priority tasks to idle nodes to form a task execution mapping table; and monitoring simulation progress and resource consumption through an adaptive scheduling mechanism to obtain scheduling performance feedback data, and updating the dynamic priority queue. According to the method and the device, the problem of lack of an intelligent scheduling mechanism during concurrent execution of multiple simulation tasks in the prior art is solved, and the response speed of the key simulation task and the utilization efficiency of computing resources are improved.

The invention belongs to the technical field of resource scheduling, and particularly discloses a resource scheduling method and system based on reinforcement learning, and the method comprises the steps: describing the dependency and conflict relation between tasks through constructing a task causal relation graph which can be dynamically updated; constructing a state space and an action space based on a current task state and a causal relationship, training an intelligent agent by adopting a reinforcement learning model in combination with a multi-target reward function, and generating a scheduling and resource allocation strategy; linear programming and heuristic joint resource allocation are carried out under strategy guidance, and task priorities and causal relationships are dynamically adjusted in combination with task states; by collecting execution data and analyzing strategy deviation, a model structure and parameters are further adjusted, and a complete closed-loop optimization process is formed. According to the method, task priority dynamic adjustment, resource allocation strategy self-adaptive updating and scheduling process closed-loop optimization are realized, and the method is suitable for complex project management scenes under multi-task and multi-resource constraints.

The invention discloses an intelligent flow arrangement method based on fusion expert network and deep reinforcement learning, which comprises the following steps: collecting network node and link state data in real time, and constructing a time sequence input vector and a topological graph structure; a time sequence neural network and a graph neural network are used for extracting traffic spatial-temporal features and node topological features respectively, future traffic is predicted through a classification network after fusion, and coarse-grained arrangement of network slices of different service levels is completed; modeling resource scheduling into a multi-agent Markov decision process, and designing a state space, an action space and a reward function; a deep reinforcement learning agent is initialized, and training is carried out through interaction experience; fusing a pre-trained expert strategy network, and constructing a total loss function to optimize network parameters; and finally generating an intelligent strategy capable of dynamically optimizing the flow path and resource allocation according to the real-time state. According to the invention, efficient resource scheduling under multi-service differentiation service quality requirements can be realized.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention relates to the technical field of wireless communication, and discloses a multi-dimensional resource management joint optimization method based on a wireless edge network. The method comprises the following steps: acquiring multi-dimensional resource state information of each node in the wireless edge network in real time; calculating a corresponding resource index based on each piece of resource state information; based on each resource index, determining an initial resource allocation strategy of each node through reinforcement learning, so that each node in each node constructs and trains a local first strategy model based on the initial resource allocation strategy, and generates a local resource allocation strategy of the node; and based on the local resource allocation strategy and the emergency task queue length of each node, updating the local resource allocation strategy through federated learning, and performing multi-dimensional resource allocation based on the local resource allocation strategy. By adopting the method, collaborative scheduling of multi-dimensional resources such as calculation, storage, frequency spectrum and power in the wireless edge network can be realized, and the overall efficiency of the network is improved.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention relates to the technical field of vehicle scheduling, in particular to a multi-terminal vehicle scheduling system based on reinforcement learning. The system comprises a heterogeneous data fusion module, a resource allocation module, a hierarchical reinforcement learning module, an optimization feedback module and a man-machine cooperative control module. Data of vehicle operation, operation tasks, environment monitoring and the like are collected and uniformly packaged into a structured data set, an upper-layer manager model generates a global scheduling instruction set based on a PPO algorithm, and a lower-layer worker model outputs a specific vehicle control instruction based on multi-agent reinforcement learning. The system also evaluates and optimizes a historical scheduling execution effect through an NSGA-II algorithm, selects a Pareto optimal solution set, and realizes continuous iteration of a scheduling strategy. The man-machine cooperative control module supports visual display and manual intervention operation, and improves the adaptability and controllability of the system in a complex operation scene.

A submerged arc furnace intelligent inspection system based on cloud side-end cooperation belongs to the technical field of submerged arc furnace intelligent robot inspection, and comprises an end side device, an edge side platform, a cloud platform and a cross-layer cooperative processing module, the end side device is in communication connection with the edge side platform, and the edge side platform is in communication connection with the cloud platform; and the cross-layer cooperative processing module is used for intelligently deploying global resources of the end-side equipment, the edge-side platform and the cloud platform, predicting a future load trend by adopting a dynamic sparse LSTM model algorithm, inputting a prediction result into a security constraint PPO reinforcement learning algorithm to adaptively adjust a task and a computing power allocation proportion, and carrying out dynamic resource allocation according to an output result. According to the invention, the cross-layer cooperative processing module interacts and communicates with the cloud side, the edge side and the end side, so that a dynamic allocation strategy of resources is realized, and the problems of low resource utilization rate and poor real-time performance caused by a rigid resource allocation strategy during routing inspection are avoided.

The invention provides a big-data-combined bus system full-data comprehensive management analysis method and system, and the method comprises the steps: collecting a real-time operation data set uploaded by a plurality of bus terminals in a target region, carrying out the multi-dimensional feature extraction, generating a bus operation feature set, carrying out the abnormality recognition based on a preset abnormality detection model, and obtaining a bus operation feature set; determining an abnormal operation event set; performing time-space association mapping on the bus operation feature set and the historical operation data set, constructing a bus operation knowledge graph, performing dynamic path planning and resource allocation analysis on the graph, generating a target scheduling strategy set, and performing priority adjustment on the abnormal operation event set based on the target scheduling strategy set. Generating a bus scheduling optimization instruction set and issuing the bus scheduling optimization instruction set to a corresponding bus terminal; and updating a weight parameter and a historical operation data set of the multi-task optimization model based on the execution feedback data. According to the method, multi-source real-time data can be integrated, and a scheduling strategy is dynamically optimized, so that the problems of data islands and strategy stiffness are solved.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention relates to the technical field of campus management, and particularly discloses a smart campus management system based on big data, an event-driven data management architecture is used for dynamically collecting, integrating and associating multi-source heterogeneous data in a campus, and generating a standardized event stream; the dynamic resource scheduling engine is in communication connection with the event-driven data governance architecture, generates a resource allocation instruction based on event types and priorities in event streams, and dynamically deploys campus resources; the closed-loop evaluation optimization module receives a resource scheduling result of the dynamic resource scheduling engine and generates a multi-dimensional evaluation index, and the multi-dimensional evaluation index is fed back to the data governance architecture through root cause analysis so as to optimize a subsequent decision; the privacy enhancement processing unit integrates a federated learning framework and a differential privacy algorithm, performs collaborative analysis on cross-system data and ensures the anonymity of individual data; through three core technologies of dynamic data management, intelligent resource scheduling and closed-loop evaluation optimization, intelligent upgrading of the whole campus management process is realized.

The invention relates to the technical field of decoration construction management, and discloses a BIM-based decoration construction scene resource consumption simulation analysis method. The method comprises the following steps: firstly, creating a multi-dimensional BIM resource model, and integrating building information model data, real-time construction data flow and historical resource consumption records; identifying resource consumption characteristics through the model, and generating an initial resource allocation strategy set comprising material allocation, equipment scheduling and process priority schemes; receiving a user interaction instruction, and dynamically adjusting the initial strategy set; and finally, executing the adjusted strategy, and updating the multi-dimensional BIM resource model based on a result. Wherein the resource consumption feature comprises construction stage division, resource node dynamic weight calculation and space correlation correction operation. According to the method, by integrating multi-dimensional data and dynamically analyzing a resource consumption rule, real-time optimization of a resource allocation strategy is realized, and the accuracy and flexibility of decoration construction resource management can be improved.

The invention provides a heterogeneous network resource virtualization modeling and intelligent arrangement method and system based on a knowledge graph, and relates to unified modeling, dynamic retrieval and intelligent resource arrangement of heterogeneous network equipment. The method specifically comprises: 1, a unified modeling method based on a knowledge graph: integrating protocol attributes, dynamic states and topological relationships of heterogeneous devices such as a 5G base station, an SDN switch, a router, an Internet of Things gateway and the like into a structured knowledge graph, breaking the barrier of a manufacturer private data model, and realizing semantic-level collaborative scheduling of cross-domain resources; 2, designing a semantic retrieval engine: querying dynamic conditional reasoning through a natural language, replacing traditional manual rule definition, and improving retrieval response speed and accuracy; and 3, developing a graph-driven intelligent arrangement framework: combining a graph neural network (GNN) and reinforcement learning (RL), automatically generating a resource allocation strategy according to a real-time network state, reducing manual intervention and improving the resource utilization rate.

The invention is suitable for the field of GPU management, and provides an intelligent dynamic management method for GPU computing power and a cloud platform, and the method comprises the following steps: collecting task data and GPU state data, and generating a task queue and a resource allocation strategy in combination with a scheduling plug-in; based on the task queue and the GPU real-time load, dynamically adjusting a resource allocation proportion through reinforcement learning to analyze a task dependency relationship and generate a migration plan; optimizing a communication path and adjusting asynchronous transmission delay according to the task dependency relationship and the GPU communication topology, and outputting a synchronous state mark; and monitoring abnormity in combination with the synchronization state and the GPU hardware state, executing thermal migration according to the migration plan, performing video memory recovery, and updating the resource idle list. According to the invention, through algorithm innovation and hardware collaborative optimization, intelligent, dynamic and efficient resource scheduling in the multi-GPU system is realized.

The invention provides an unmanned aerial vehicle flight path planning method based on air-space linkage, and the method comprises the steps: carrying out the multi-time-domain air-space data fusion, double-layer asynchronous iterative planning, multi-constraint joint optimization, distributed computing resource scheduling, task perception path generation, and other innovative technologies; the problems of limited environment perception capability, high calculation complexity, low resource allocation efficiency, insufficient real-time track adjustment capability and the like in the prior art are solved, and the flight performance and task execution efficiency of the unmanned aerial vehicle in a complex environment are improved.

The invention relates to a resource allocation system for 5G-A Internet of Vehicles network slices, which belongs to the technical field of intelligent traffic systems and comprises a network slice architecture module, a management and control plane module, a programmable module, a safety and fault-tolerant module, a performance monitoring and optimizing module, a mobility management module, a resource arbitration engine and a cross-domain collaborative interface. And the network slice architecture module is used for dividing Internet of Vehicles communication into a plurality of virtual slices based on a 5G-A network slice technology. According to the resource allocation system and method oriented to the 5G-A Internet of Vehicles network slices, accurate control over end-to-end performance indexes is achieved through the slice definition template library and a QoS dynamic mapping mechanism, the problem that key service performance is degraded due to one-step resource allocation of a traditional network is solved, distributed agent collaboration based on the MAPPO algorithm, and the resource allocation efficiency is improved. LSTM load prediction and a heuristic time delay equalization algorithm are combined, so that the system can respond to business demand changes at a millisecond level, and the resource utilization rate is improved.

The invention provides a heterogeneous GPU resource management scheduling method, and relates to the technical field of GPU resource allocation, heterogeneous equipment management and unified abstract modeling are carried out, GPU resources of different architectures are registered to a container arrangement platform, and a unified abstract layer is constructed to shield bottom layer hardware differences; gPU cluster optimization management based on a multi-dimensional real-time monitoring and intelligent scheduling strategy is carried out, GPU operation indexes are collected, priorities are dynamically calibrated for tasks, and task performance portraits are constructed; scheduling decision making is carried out through multi-strategy cooperation, and optimal GPU resources are distributed for tasks; carrying out fine-grained resource allocation, carrying out space or time segmentation on the GPU, and dynamically adjusting resource allocation according to a load state; aPI conversion of cross-architecture tasks is realized through a unified runtime library, and task execution data is collected to feed back an optimization scheduling model; automatic detection, isolation and task migration of GPU faults are carried out, and unified monitoring and alarm are provided.

The invention discloses an unmanned aerial vehicle anti-interference communication link control method based on heterogeneous network convergence, and relates to the technical field of unmanned aerial vehicle anti-interference communication, and the method comprises the steps: S1, interference sensing and network state modeling, S2, intelligent link decision making, S3, spectrum avoidance and resource scheduling, S4, distributed feedback and cooperative anti-interference, and S5, heterogeneous network cooperative relay. S6, resource allocation and energy efficiency optimization; S7, performance evaluation and parameter optimization; and S8, multi-link redundancy backup. According to the invention, through S1, S2 and S7, a sensing, decision-making and optimization closed-loop system is constructed, LSTM interference prediction and three-dimensional network modeling in S1 provide accurate input, a dual deep Q network in S2 realizes low-delay link decision-making, and KPI evaluation and genetic algorithm dynamic parameter adjustment in S7, the signal-to-interference ratio is improved, the anti-interference adaptability is improved compared with a traditional passive response mode, and the method has the advantages that the method is simple and convenient to operate, and the cost is low. The creative breakthrough that interference does not reach strategy precedence is realized; through S3 and S4, an active anti-interference mechanism is formed, and compared with a single avoidance or suppression method, the interference elimination efficiency is improved.

The invention discloses an industrial control data processing system and method based on an embedded real-time operating system, and relates to the technical field of industrial automation control, and the industrial control data processing system comprises a dynamic weight evaluation module, a resource elastic distribution module and a dual self-healing verification module; and the dynamic weight evaluation module is used for generating a dynamic weight value according to the task type, the data security level, the deadline margin and the historical execution abnormal rate. According to the industrial control data processing system and method based on the embedded real-time operating system, the task criticality is quantified in real time through the dynamic weight evaluation module, and the real-time response capability and the resource utilization efficiency of the industrial control system are improved in combination with elastic resource allocation driven by load prediction. A dynamic weight mechanism ensures that the emergency task can instantly preempt resources, and the problem of low-priority task blocking caused by traditional static priority scheduling is avoided.

The present invention relates to a wireless communication system and, more specifically, to a method wherein resource allocation information for downlink reception or uplink transmission is acquired, and if the downlink reception or uplink transmission is required on a sub-band slot / symbol, the downlink reception or uplink transmission is performed in consideration of sub-band operations, and a wireless device therefor.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention belongs to the technical field of mobile communication, and particularly relates to a task unloading and resource allocation method for edge computing. According to the method, a three-layer network structure is established, a distributed decision framework is constructed through reinforcement learning, the task emergency degree is dynamically evaluated, and computing resources are distributed in a differentiated mode; and task unloading and resource allocation are optimized in combination with an edge-cloud collaborative architecture, so that calculation load balancing is realized. According to the method, aiming at a cloud edge-end collaborative edge calculation model, the total cost of a system is defined as a joint optimization problem of task unloading time delay and energy consumption, the problem model is converted into a Markov decision process, and multi-agent and multi-user oriented deep reinforcement learning algorithm agent near-end strategy optimization (MAPPO) is designed; and obtaining an optimal unloading decision through mutual learning among multiple agents. According to the method, the total cost of the system can be effectively reduced, the rationality of edge computing task unloading and resource allocation decision is realized, and meanwhile, the use experience of a user can be improved.

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.