237 results about "Smart computing" patented technology

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention provides a full-autonomous cell operation system based on body intelligence, and relates to the field of cell operation, the system comprises a system hardware architecture and a system software architecture; the system hardware architecture comprises a micromanipulation execution system, a precision positioning system, a multi-mode sensing system and an intelligent calculation and control center. The micromanipulation execution system is used for cell environment interaction, the precision positioning system is used for sample bearing and environment control, the multi-modal sensing system is used for providing key information, and the intelligent calculation and control center is used for operation decision making; the system software architecture comprises a sensing layer, a planning layer, an execution layer and an interaction layer. According to the application, cell recognition, tool selection, path planning, operation execution and result feedback can be automatically completed without manual intervention; through tool autonomous switching and strategy dynamic adjustment, various operations such as cell clamping, injection, controllable deformation and micro-assembly can be completed, and the problems that traditional cell operation depends on manual work, efficiency is low, and consistency is poor are solved.

The invention provides a computing power metering method and device of an intelligent computing center cloud platform based on a multi-dimensional performance index, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a first task type of a first computing power operation task; s2, determining a plurality of resource indexes corresponding to the first task type and a weight coefficient corresponding to each resource index; s3, collecting a parameter value corresponding to each resource index in each of the N periods; s4, calculating a computing power value corresponding to the first computing power operation task based on the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods; and S5, generating a first verification data packet based on the first task identifier of the first computing power operation task, the weight coefficient corresponding to each resource index and the parameter value corresponding to each resource index in N periods. The computing power credibility of the intelligent computing center cloud platform can be greatly improved.

The invention relates to a data processing method, a processor, a chip, a display card and electronic equipment, and relates to the field of intelligent computing, and the method comprises the following steps: reading a part of a first matrix from a local memory or a register of a computing unit in response to M tensor computing engines in a tensor computing engine cluster, reading the second matrix from the tensor memory, and executing matrix operation by the M tensor calculation engines according to part of the first matrix and the second matrix respectively to obtain operation results corresponding to the M tensor calculation engines; and the M tensor calculation engines respectively write operation results corresponding to the M tensor calculation engines into registers of the M tensor calculation engines. According to the embodiment of the invention, the computing power can be increased under the condition that the sizes and the bandwidths of the local memory and the register in the computing unit are not increased.

The invention provides a heterogeneous computing power normalization metering method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the steps: S1, obtaining a parameter value of a first related parameter of a first GPU and a parameter value of a second related parameter of a first TPU, the first GPU and the first TPU are used for executing a first computing power operation task; s2, determining a first weighting coefficient of the first GPU, and determining a second weighting coefficient of the first TPU; s3, based on the first weighting coefficient, the parameter value of the first related parameter and a preset reference value, calculating to obtain a first computing power value of the first GPU; s4, based on the second weighting coefficient, the parameter value of the second related parameter and a preset reference value, calculating to obtain a second calculation force value of the first TPU; and S5, based on the first computing power value and the second computing power value, calculating to obtain a third computing power value of the first computing power operation task. According to the method, the computing power metering accuracy of the computing power operation task is greatly improved.

The invention discloses an intelligent collaborative scheduling system and method for a general computing and intelligent computing fusion scene, and relates to the technical field of computing power resource management and scheduling. In order to solve the problem that isomerous computing power resource islands are difficult to collaborate, the system comprises a computing power access layer used for executing specified operation on isomerous computing power resources through a standardized access template, abstracting the isomerous computing power resources into a unified logic computing power unit and registering the unified logic computing power unit into a computing power pool; the resource management layer is used for continuously monitoring and collecting static attributes and dynamic states of computing power resources to form a global resource real-time view; the business processing layer is used for receiving the business submitted by the user, analyzing and identifying the business demand, converting the business demand into a demand vector, and generating a dynamic arrangement scheme based on the real-time view and the multi-target strategy library; and the collaborative scheduling execution layer is used for converting the dynamic arrangement scheme into an instruction adaptive to various APIs and completing computing power resource allocation and service starting. According to the invention, unified management and intelligent cooperative scheduling of computing power resources can be realized.

The invention provides a method and a device for dynamically metering computing power based on computing power operation tasks of an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing cloud. The method for dynamically metering the computing power of the intelligent computing center cloud platform based on the computing power operation task comprises the following steps: S1, acquiring a parameter value of first operation data of a first GPU in a first time period, wherein the first GPU is a GPU for operating a first computing power operation task; s2, determining a first task type of the first computing power operation task based on a parameter value of the first operation data; s3, determining a weighting coefficient corresponding to the first task type; and S4, based on the weighting coefficient and the parameter value of the first operation data, calculating to obtain the calculation power value of the first calculation power operation task in the first time period. According to the method, the computing power metering accuracy of the computing power operation task in the intelligent computing center cloud platform can be greatly improved.

The invention discloses a fire-fighting smoke exhaust pipeline laying planning system based on an industrial cloud platform, and relates to the technical field of pipeline laying planning, and the system comprises a building information collection module which collects building data; the cloud edge cooperative computing module is used for constructing a smoke exhaust simulation model by using distributed computing resources of an industrial cloud platform and carrying out real-time parameter iteration through edge nodes; the pipeline path planning module is used for generating a plurality of candidate laying paths by using an improved algorithm and carrying out path optimization based on a pipeline resistance coefficient and a construction cost constraint condition; and the scheme verification and optimization module is used for carrying out smoke flow dynamics simulation verification on the optimal path scheme, identifying scheme defects and carrying out iterative optimization on path parameters through a genetic algorithm. According to the invention, through a cloud-edge collaborative intelligent computing architecture, a multi-dimensional constrained dual-target optimization mechanism and a dynamic adaptive path planning and verification system are fused, and collaborative improvement of fire-fighting smoke exhaust pipeline laying planning in accuracy, efficiency and compliance is realized.

The invention provides an intelligent computing center path distribution method, a data transmission method and a network system. According to the technical scheme, based on the time sequence characteristics of the AI training data flow and the characteristic that each set communication library only selects one communication algorithm to drive data flow transmission in the same communication period, the data flow identification capable of identifying the data flow and the communication algorithm corresponding to the data flow identification are obtained, and path planning is carried out based on the communication algorithm. In this way, during actual communication, data streams driven by different communication algorithms on the same path cannot be transmitted at the same time, the link congestion risk is reduced, and idle links are reserved for subsequent data streams of the same communication algorithm; different links are allocated to data flow identifiers with the same source IP address or the same destination IP address corresponding to the same communication algorithm in the same set communication domain, multi-path load sharing is carried out, data flow dispersed transmission based on the same communication algorithm drive is ensured, the congestion risk of a single link is further reduced, and the transmission efficiency is improved. The communication efficiency of an intelligent computing center network is expected to be improved.

The invention provides a computing power routing gateway configuration method and device for an intelligent computing center cloud platform, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, employing an agent module to collect the operation state data of a computing power routing gateway deployed on the same server node as the agent module, the operation state data of the computing power routing gateway is reported to the analysis service module; s2, the analysis service module analyzes the received operation state data of the computing power routing gateway, judges whether the computing power routing gateway has a crash risk or not based on an analysis result, takes the computing power routing gateway with the crash risk as a to-be-adjusted computing power routing gateway, and sends the to-be-adjusted computing power routing gateway to the computing power routing gateway; determining a configuration adjustment operation executed by the computing power routing gateway to be adjusted; and S3, the analysis service module calls the intelligent computing center cloud platform management system to perform configuration adjustment on the computing power routing gateway to be adjusted based on the configuration adjustment operation.

The invention discloses a discrete manufacturing full-life-cycle intelligent computing system and method, the system comprises a data knowledge layer, a computing adaptation layer and a parallel service layer, a virtual matching mechanism is used between the data knowledge layer and the computing adaptation layer, that is, primary mapping is formed according to task demand description and manufacturing resource description in a twin world, an optimal algorithm is called for a specific task, the most reasonable resources are adapted, an optimal service strategy is obtained, and a mapping process from a service demand to the service strategy is realized; a virtual-real parallel computing mechanism is used between a computing adaptation layer and a parallel service layer, that is, a mutual linkage relation is established in a physical world according to a service execution strategy obtained in a twin world, so that the requirement of discrete manufacturing full-life-cycle intelligent service is met, and the mapping process from a service strategy to service execution is achieved. And through two times of mapping, autonomous computing adaptation of task-knowledge-algorithm-resource in the whole life cycle of discrete manufacturing is realized.

The invention provides a method and a device for performing large-model self-distillation absorption depth reasoning by an intelligent computing center cloud platform through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, and the method comprises the following steps: S1, obtaining multiple groups of initial sample data; s2, constructing multiple groups of first sample data and multiple groups of second sample data based on the multiple groups of initial sample data; and S3, training the initial large model based on the multiple groups of first sample data and the multiple groups of second sample data to obtain a first large model. According to the method, the displayed natural language deep reasoning step is distilled to a continuous hidden space, and the first model can still ensure complete and excellent reasoning ability under the condition of not displaying and generating the middle deep reasoning step, so that resources occupied by a large model can be reduced; therefore, the large model service provided in the intelligent computing center cloud platform is greatly increased.

The invention relates to a dynamic extensible heterogeneous collaborative intelligent computing platform and method for edge-end equipment, and relates to the technical field of edge intelligent computing and computer hardware, and the platform comprises a hardware infrastructure layer and a master control module for realizing signal transmission and resource sharing; the basic software layer is used for managing hardware resources and software services; the application support layer is used for running computing resources and realizing preliminary compatibility; the task management and resource scheduling layer is responsible for task arrangement and management and task scheduling decision; the application layer is used for providing various application services; the data transmission layer is responsible for transmitting data among different platforms; the computing resource interface layer provides an interface interacting with heterogeneous computing resources; and the computing resource platform is composed of different heterogeneous computing platforms and provides computing resources and computing power. The method has the capabilities of intelligent information and signal processing, diversification of application scenes, efficient parallel operation, rapid iterative development, universal flexible deployment and the like.

The multi-model photovoltaic module array control system comprises an inverter, an intelligent calculation module, a controller and a plurality of paths of photovoltaic module strings, the controller is used for reading the actually measured voltage of each path of photovoltaic string, comparing the actually measured voltage with a voltage threshold value, adjusting a pwm parameter and outputting the actually measured voltage to the inverter; calculating the time average voltage of each path of photovoltaic string, and transmitting the time average voltage to the intelligent calculation module; a voltage threshold update value transmitted by the intelligent calculation module is received, and the voltage threshold is updated; the inverter is used for receiving the actually measured voltage, calculating group average voltage and transmitting the group average voltage to the intelligent calculation module; and the intelligent calculation module is used for receiving the time average voltage and the group average voltage, calculating a voltage threshold update value and transmitting the voltage threshold update value to the controller. Pwm parameters are adjusted according to the comparison result of the actually measured voltage and the voltage threshold value, the intelligent calculation module calculates the updated value of the voltage threshold value, current backflow is actively inhibited, energy backflow caused by mismatching of component parameters is avoided, and the voltage threshold value is dynamically adjusted.

The invention relates to the technical field of spiking neural networks, in particular to a spiking neural network conversion method and system based on differentiable adaptive optimization, and the method comprises the steps: constructing and pre-training a target ANN model; in a pre-trained ANN model, performing simulation processing on the activation value of each layer by using a global differential value representation operator; on the basis of a loss function, an original weight parameter in the target ANN model and a trainable excitation threshold in a global differentiable value representation operator are placed in the same optimization framework for joint fine tuning, and an optimal weight and an optimal threshold learned by each layer are obtained; and an equivalent SNN model is constructed. According to the method, precise correspondence between continuous activation and pulse sequences can be optimized, the method is suitable for multiple pulse coding modes, quantization errors are reduced to the maximum extent, the deployment requirements of neuromorphic hardware and an edge intelligent computing platform can be deeply adapted, and an efficient and universal model conversion solution is provided for the neuromorphic computing industry.

The invention provides an engineering mechanical equipment remote monitoring system and method based on the Internet of Things, and relates to the technical field of equipment operation monitoring, and the method comprises the steps: carrying out the cross-scale feature response of a dynamic impact signal of engineering mechanical equipment through an intelligent calculation node, and obtaining a frequency domain migration feature and an energy attenuation feature; carrying out gradient operation on the operation thermal field data of the engineering mechanical equipment to obtain heat flow evolution characteristics; utilizing a coupling analysis model dynamics interaction map; performing space-time resonance convolution prediction on node states and edge relations of the dynamic interaction atlas based on the displacement track to obtain a state prediction curve of the engineering mechanical equipment; and according to the state prediction curve and a preset scheduling instruction, an adaptive feedback network is constructed at an edge computing node of the Internet of Things, and then remote monitoring scheduling is performed on the engineering mechanical equipment based on the adaptive feedback network, so that operation state prediction and scheduling closed-loop control of the engineering mechanical equipment can be realized; therefore, the remote monitoring stability of the engineering mechanical equipment is improved.

The invention provides an intelligent agent training method of an intelligent calculation data center and an air conditioner control method, relates to the technical field of intelligent calculation data centers, and is used for optimizing an air conditioner control scheme of the intelligent calculation data center. The method comprises the steps that air conditioner historical operation data and computing power cabinet historical operation data of all cooling areas are obtained; based on the historical operation data of the air conditioner and the historical operation data of the computing power cabinet of each cooling area, cooperatively training a first intelligent agent of each cooling area, a second intelligent agent of each machine room and a third intelligent agent of an intelligent computing data center; wherein the first intelligent agent is used for being responsible for controlling the air conditioner of the corresponding cold supply area; the second intelligent agent is used for balancing the temperature of each cold supply area in the corresponding machine room; the third intelligent center is used for balancing chilled water header pipe return water temperature of each machine room; the first agent, the second agent and the third agent are constructed based on a reinforcement learning model.

The invention provides an integrated storage and sterilization device for multi-specification cell culture apparatuses, and belongs to the technical field of crossing of biomedical engineering and laboratory intelligent equipment. The device comprises a multi-specification cell culture appliance information acquisition module, a sterilization parameter intelligent calculation module, a sterilization process execution and real-time monitoring module, a sterilization effect intelligent judgment module and a multi-specification cell culture appliance storage adaptation module. According to the invention, through integration of an integrated architecture and an algorithm, sterile treatment, intelligent management and full-process tracing of multi-specification cell culture apparatuses are finally realized, high requirements of biomedical experiments on sterility, adaptability and controllability of the apparatuses are met, meanwhile, the operation efficiency of a laboratory is improved, and personal errors and pollution risks are reduced.

The invention provides a heterogeneous computing power resource management method and device for an intelligent computing center cloud platform, and the method comprises the steps: S1, registering a plurality of first computing power resources corresponding to a plurality of different operators in a management platform, and obtaining a plurality of registered second computing power resources; s2, receiving a user use request input by a user on the management platform; and S3, performing matching in the plurality of second computing power resources based on the user use request to obtain at least one third computing power resource, and scheduling the at least one third computing power resource to process the computing power operation task. According to the invention, the complexity of management, operation and maintenance is greatly reduced, and the unified management and scheduling of various heterogeneous computing power resources managed by the intelligent computing center cloud platform are realized.

The invention provides a method and device for an intelligent computing cloud platform to realize agent self-evolution through computing power, and relates to the technical field of intelligent computing cloud platforms and computing power infrastructures, and the method comprises the steps: S1, an agent obtains target behavior data of a user using the agent; s2, the intelligent agent analyzes the target behavior data by adopting a large language model to obtain behavior statistical characteristics; s3, the intelligent agent adopts a large language model to identify user preference features according to behavior statistical features; s4, determining a quality evaluation result of the intelligent agent by the intelligent agent according to the behavior statistical characteristics by adopting a large language model; and S5, the intelligent agent determines an evolution scheme of the intelligent agent according to the quality evaluation result and the user preference characteristics by adopting a large language model, and the intelligent agent is optimized based on the evolution scheme. According to the invention, the development cost of the intelligent agent can be effectively saved, and the optimization of the intelligent agent better meets the actual use demand of a user.

The invention provides an enhanced training method and device for an intelligent computing center cloud platform to perform embedded chain self-reward signal through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers, computing power infrastructures and intelligent computing clouds, the method comprises the following steps: S1, predicting a first question based on an initial strategy model to obtain a plurality of predicted answers; s2, generating a first strategy parameter based on the plurality of prediction answers through a preset reference model, wherein the first strategy parameter is used for optimizing parameters in the initial strategy model; s3, calculating an embedded chain score corresponding to each prediction answer in the plurality of prediction answers; s4, generating a second strategy parameter based on the embedded chain score corresponding to each prediction answer in the plurality of prediction answers, wherein the second strategy parameter is used for optimizing parameters in the initial strategy model; and S5, optimizing the initial strategy model based on the first strategy parameter and the second strategy parameter. The application range of the model can be greatly expanded.

The invention provides a method and a device for an intelligent computing center cloud platform to cache an accelerated diffusion model to generate an image through a computing power computing result, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, the method comprises the following steps: S1, receiving an image generation task sent by a client, the image generation task is used for indicating generation of a first image; s2, inputting the image generation task into a pre-trained diffusion model, wherein the diffusion model comprises a plurality of neural network layers connected in sequence; s3, the image generation task is executed based on each neural network layer in a plurality of sequentially connected neural network layers, the first image is obtained, and when the residual value between the jth neural network layer and the (j-1) th neural network layer is smaller than a preset threshold value, the first image is generated. And the jth neural network layer performs the image generation task based on a preset computing power calculation result cache. According to the invention, the image generation speed can be improved.

The invention belongs to the field of edge computing, and discloses an edge intelligent computing device and method supporting multi-level storage and computing integrated acceleration, and the device comprises a main processor system, a near memory computing module and a near flash memory computing module. The near memory calculation module integrates an NPU (Network Processing Unit) with a distributed SRAM (Static Random Access Memory) and an HBM DRAM (Dynamic Random Access Memory) with an MAC (Media Access Control) array, and realizes high-throughput reasoning acceleration And the near flash memory calculation module is used for tightly coupling the SSD controller and the retrieval acceleration engine to realize the nearest retrieval acceleration of mass data at the SSD level. All the modules are interconnected through a PCIe bus, a three-level storage and calculation integrated framework of SRAM-HBM DRAM-SSD is constructed, cross-level data migration is effectively reduced, and the bottleneck of the PCIe bus is avoided. According to the method, high-throughput video reasoning and TB-level data retrieval tasks on the edge side can be efficiently supported at the same time, and the calculation energy efficiency ratio and the system throughput rate are remarkably increased.

The invention provides a route selection method and device for a computing power network of an intelligent computing center cloud platform based on a predefined rule, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. Obtaining a destination address carried in the computing power operation data packet, wherein the destination address is a network address of the target computing power application; s2, the gateway queries a computing power network routing table based on the network address of the target computing power application, and obtains a plurality of candidate next-hop routing addresses corresponding to the network address of the target computing power application and a routing priority of each candidate next-hop routing address; s3, the gateway selects an optimal next-hop routing address from a plurality of candidate next-hop routing addresses corresponding to the network address of the target computing power application based on a predefined rule; and S4, the gateway sends the computing power operation data packet to the optimal next-hop routing address.

The invention provides a method and a device for automatically detecting the injection-writing consistency of automobile drawings through computing power by an intelligent computing cloud platform, and relates to the technical field of intelligent computing cloud platforms and computing power infrastructure, the method comprises the following steps: constructing a sample data set based on a preset consistency semantic tree, and dividing the sample data set into a training set and a verification set; the intelligent computing cloud platform performs N times of iterative training on the initial model based on the training set by using computing power, and determines the initial model after the Mth time of iterative training as a final model; and deploying the final model on the intelligent computing cloud platform so as to carry out injection-writing consistency detection on the received automobile drawings in batches. Therefore, the model can accurately position the injection and writing position and identify the abnormal injection and writing in the automobile drawing based on the characteristics fully trained in the early stage, thereby reducing the understanding difference in the process of manually reviewing the injection and writing consistency of the automobile drawing, and improving the detection efficiency and accuracy.

The invention provides a modularized positioning navigation time service system and a positioning navigation time service method, the modularized positioning navigation time service system comprises a plurality of PNT assembly modules and an information fusion module, and the modules realize efficient data interaction through a standardized interaction protocol. The PNT component module is composed of a sensing element, an edge calculation chip and an information transmission interface, the standardized information extraction sub-module is deployed in the edge calculation chip, comprises a parameter setting unit, a parameter characteristic extraction unit, a constraint construction unit and a statistical characteristic extraction unit, and converts output information of a PNT component into a standard form; and the information is transmitted to the information fusion module. The information fusion module comprises an information transmission interface and an intelligent computing platform, intelligent optimization and fusion processing of multi-source PNT information is achieved through an information monitoring sub-module, a parameter adjustment sub-module, a space-time unification sub-module and an intelligent fusion sub-module which are deployed in the intelligent computing platform, and state estimation information is output.

AI-DRIVEN SMART COMPUTER DEVICE FOR LOAN APPROVAL AND CREDIT EVALUATION.

The application relates to a digital twin architecture and an intelligent control method of an electric vehicle thermal management system, and belongs to the electric vehicle thermal management field. The application utilizes a digital twin technology to establish digital twins corresponding to each module and each component of physical real vehicle thermal management in a cloud platform thermal management digital space; real-time operation data of the physical real vehicle is collected through a sensing device and uploaded to a cloud platform digital space digital twin engine; the digital twin engine utilizes the real-time operation data to realize accurate mapping of the digital twins and the physical real vehicle; an intelligent computing module in the digital twin engine realizes real-time calculation of an optimal electric vehicle thermal management strategy through coupling of the digital twins and operation results; the physical real vehicle receives the optimal thermal management strategy from the digital space, and completes one-time vehicle thermal management control. The application can improve the control efficiency and precision of integrated thermal management of the electric vehicle, improve the cruising range of the electric vehicle, and reduce the risk of battery thermal runaway.

The application provides a method and device for adjusting large model training tasks based on computing power use states of an intelligent computing center cloud platform, and relates to the technical fields of intelligent computing centers, intelligent computing centers, computing power infrastructure and intelligent computing cloud technology. The method for adjusting large model training tasks based on computing power use states of an intelligent computing center cloud platform comprises the following steps: S1: obtaining a computing power use state, which is used to represent the use state of computing power in the intelligent computing center cloud platform during the training of a large model; S2: determining whether the computing power use state matches an expected state; and S3: adjusting the training task of the large model according to the computing power use state in the case where the computing power use state does not match the expected state. In this way, the phenomenon of wasted computing power can be reduced, and the training efficiency of the large model can be improved.

This invention discloses a ship cloud computing load balancing method based on a hybrid intelligent algorithm, relating to the field of ship intelligent computing and cloud computing integration technology. The method includes the following steps: task classification modeling, edge-cloud resource status monitoring, multi-objective optimization decision-making, and dynamic load adjustment. This invention reduces the load standard deviation of ship edge-cloud nodes through a multi-objective optimization algorithm and a dynamic adjustment mechanism. It optimizes task processing efficiency, reducing the average latency of real-time tasks and shortening the total processing time of non-real-time tasks. It reduces system energy consumption by optimizing the energy consumption coefficient, thereby reducing the total energy consumption of edge nodes and cloud servers. It adapts to ship scenarios, improving the robustness and adaptability of scheduling strategies to address the heterogeneity of ship tasks and network dynamics.