522 results about "Computing center" patented technology

The invention provides a supercomputing center computing task dynamic scheduling method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a current to-be-processed computing task request data set of a supercomputing center, and extracting a task feature set of each computing task; calling a pre-trained task scheduling strategy decision model to analyze the task feature set, generating a real-time scheduling priority parameter and a resource allocation constraint condition set of the calculation task, and calculating the task feature set based on the real-time scheduling priority parameter and the resource allocation constraint condition set of the calculation task; according to the method, the dynamic resource allocation instruction set of the computing nodes of the super-computing center is generated, the computing nodes are controlled to execute the task scheduling operation, intelligent and efficient scheduling of the computing tasks of the super-computing center is achieved by updating the resource occupancy state of the computing nodes and the task queue execution progress in real time, and the resource utilization rate and the task execution efficiency are remarkably improved.

The invention discloses a satellite collision avoidance system and method based on a satellite edge computing network. The system comprises a space monitoring network, a space traffic management subsystem and the satellite edge computing network. The space monitoring network is used for observing the running state of a space target to obtain multi-source observation data; the space traffic management subsystem is used for integrating the multi-source observation data to generate space target orbit data; the satellite edge computing network further comprises the steps that a GEO satellite broadcasts space target orbit data to an LEO satellite constellation and forwards collision risk screening information to a ground cloud computing center; the LEO satellite constellation calculates collision risk screening information according to the space target orbit data and formulates a collision avoidance control strategy; the LEO / GEO earth station is used for data transceiving and protocol conversion among the GEO satellite, the LEO satellite constellation and the cloud computing center; and the ground cloud computing center stores constellation orbit data through protocol conversion and satellite communication. According to the invention, the real-time performance of satellite collision avoidance is improved.

The invention relates to a bridge and tunnel disease detection method and system based on unmanned aerial vehicle-edge computing collaboration, and solves the problem that the detection efficiency is limited due to the lack of systematic design of a collaboration mechanism of an unmanned aerial vehicle and edge computing. The method comprises the following steps that: a distributed edge computing node fuses multi-source monitoring data to obtain a health index, compares the health index with a multi-level threshold value, generates a message containing space coordinates, levels and characteristics when the health index is abnormal, and transmits the message to an edge computing center; the center screens adaptive unmanned aerial vehicles, plans an optimal path, dispatches collected data, and preliminarily screens diseases through a parallel model; determining disease complexity and types in combination with abnormal features, and establishing a collaborative detection unit to specially collect multi-source data; centimeter-level positioning is realized through BIM registration and SLAM, and an accurate detection report is generated. The method has the following effects: accurate scheduling, real-time processing and centimeter-level positioning of disease detection are realized, an intelligent detection closed loop is constructed, and the accuracy and efficiency of bridge and tunnel operation and maintenance are greatly improved.

The invention relates to the technical field of intelligent traffic, and discloses a vehicle and road cloud data processing method and system based on an edge computing center, and the method comprises the steps: deploying a sensing module, collecting vehicle, road and environment data, and carrying out the standardization and alignment; constructing a three-dimensional tensor, and calculating a local gradient to reflect data aggregation; evaluating a node collaboration demand, and constructing a graph structure and a collaboration weight based on a gradient; constructing an optimization objective function, and balancing delay, resources and task importance scheduling; when the gradient exceeds a threshold value, nodes are selected, and state saving and task migration are executed; when communication is abnormal, autonomous reasoning processing is completed based on a local model and features; the system comprises a sensing module, a tensor modeling module, a resource collaborative evaluation module, a task scheduling module, a task migration module and an autonomous reasoning module. According to the method, the three-dimensional tensor structure is introduced to express the multi-dimensional information of the vehicle, the roadside equipment and the environment sensing terminal, the multi-dimensional information is fused and modeled, and the purpose of automatically extracting the area aggregation situation is achieved.

The invention relates to the technical field of collaborative construction, in particular to a collaborative construction method and system based on BIM, and the method comprises the following steps: obtaining component information and a construction path, calculating the center distance, the number of intersection lines and the blocking ratio, evaluating the density and the staggering degree, generating an urgent distribution set and a task list, extracting spatial parameters, and calculating the overlapping level. And judging the compression feasibility of the time window, and generating a non-parallel task list. According to the method, through quantitative evaluation of blocking risks between construction nodes, a dynamic sorting list generated based on urgency degree values and construction plan binding tasks, conflict levels between component pairs are divided through an operation space overlapping degree index, compression judgment is performed on an adjustable time window, and a minimum interference construction time period is allocated; strong interference task pairs are calibrated to avoid parallel construction, the sorting rationality of construction tasks and the space decoupling efficiency of job scheduling are effectively improved, and fine matching and efficient cooperation of component scheduling sorting and resource allocation in a dynamic scene are achieved.

The invention provides a super-computing center battery charging and discharging strategy optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining a real-time operation data set of a super-computing center battery system in real time, and carrying out the load feature extraction of the real-time operation data set; the method comprises the steps of generating a load feature set of a battery system, inputting the load feature set into a pre-trained reinforcement learning strategy network, performing charge and discharge strategy priority matching processing on the load feature set, generating a priority ranking result of a target charge and discharge strategy, adjusting a current charge and discharge strategy set according to the priority ranking result, generating an optimized charge and discharge strategy set, and outputting the optimized charge and discharge strategy set. And performing charging and discharging operation based on the optimized charging and discharging strategy set, and collecting battery performance feedback data after the charging and discharging operation so as to iteratively update strategy matching parameters of the reinforcement learning strategy network. According to the method, the problems of over-charging and over-discharging and power oscillation caused by lagging response of environmental parameters in a traditional fixed strategy are solved, and autonomous decision-making of multi-target optimization can be realized without depending on artificial experience rules.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The invention discloses a method and a device for supporting elastic expansion capability by computing power resources of an intelligent computing center. The method comprises the following steps: monitoring a QPS index of a Kubernetes cluster service in real time, and dynamically dividing service state grades after smoothing data through a sliding window; dynamically adjusting a QPS threshold value based on the historical data and the cluster load; the HF service dynamically expands and shrinks the capacity according to the QPS, the MF service keeps a single instance, and the LF service releases the instance and pre-caches resources to the SSD node; the service life cycle is managed through queue state transition, and cache is optimized in combination with an LRU-LFU mixed elimination strategy and a disk pressure trigger mechanism; the device comprises a monitor, a scheduler, a router, a three-level cache system, a fault-tolerant processing unit, a processor and a memory. And a three-level cache system is adopted to accelerate cold start, so that the fault recovery time is effectively shortened, and the problems of high loading delay, low resource utilization rate and cross-node scheduling of the AI large model are solved.

The invention relates to the technical field of spacecraft orbit control and space safety, and discloses a space debris orbit prediction and avoidance method, which comprises the following steps that: a ground computing center screens candidate targets and generates enhanced data packets for uploading; the spaceborne computer combines real-time navigation data to carry out geometric feature screening, and an instant high-risk target list is generated; controlling the star sensor to execute optical observation, and resolving an optical measurement relative orbit state by utilizing displacement superposition and Kalman filtering; selecting a high confidence coefficient or a conservative probability threshold according to the optical measurement state acquisition condition, and generating an avoidance instruction when the collision probability exceeds the limit; and the propulsion system responds to the instruction to execute avoidance, verifies the effect by confirming the semi-major axis variation and executes track recovery by selecting an aircraft. According to the method, the ground-air collaborative screening and shift superposition enhancement technology is adopted, the problems that the low-precision ephemeris false alarm rate is high and the detection capability of a satellite-borne sensor on a dark and weak target is insufficient are solved, and the accuracy of evasion decision making and the execution reliability are improved.

The invention discloses a multi-chain collaborative center node switching method for ensuring authentication efficiency and data reliability, which is applied to a system comprising an identity chain, an authentication chain, an edge computing center, a key generation center, a plurality of unmanned aerial vehicle clusters and at least two aerial directors, and is characterized in that the identity chain is used for identity management of all the unmanned aerial vehicle clusters; the authentication chain is jointly maintained by all the unmanned aerial vehicle clusters and all the air directors. According to the multi-chain collaborative central node switching method for ensuring the authentication efficiency and the data reliability provided by the invention, the secure communication between the clusters can be quickly established in the authentication process of the unmanned aerial vehicle clusters, and the switching of the central nodes can be quickly carried out when the central nodes are threatened and attacked in a multi-chain manner; the new central node continues to execute the flight task, and the forward and backward security is ensured in the switching process, so that the problems of low authentication efficiency of the unmanned aerial vehicle cluster, strong dependency of the central node and insufficient security in the switching process of the central node are solved.

The invention provides a computing power routing addressing 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 infrastructures, and the method comprises the steps: S1, obtaining the configuration information of the computing power service of a user; s2, generating computing power routing information based on the configuration information; and S3, based on the computing power routing information, distributing the flow corresponding to the computing power service to at least one computing power service instance corresponding to the computing power service. According to the invention, the efficiency of computing power routing addressing can be improved.

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 provides an edge calculation optimization method for a seawater parameter monitoring Internet of Things system, and belongs to the technical field of edge calculation, and the method comprises the steps: constructing a seawater parameter monitoring sensor network, and calculating an energy consumption evaluation reference value based on a data change rate and a disorder degree; performing feature extraction on the seawater parameter data; deploying a double-model collaborative architecture comprising a terminal lightweight model and a center complete model; setting a dynamic energy consumption threshold judgment mechanism to allocate calculation tasks; executing lightweight edge calculation at the sensing nodes; transmitting the processed data through a low-power wide area network; the calculation center verifies and fuses the data; constructing a seawater parameter space-time distribution model; dynamically adjusting an energy consumption evaluation reference value by using a Pareto optimal solution set method; and periodically evaluating system energy consumption efficiency and updating model parameters. According to the method, dynamic balance of energy consumption and calculation efficiency of the seawater parameter monitoring system is realized, and the continuous operation capability and the monitoring reliability of the system are remarkably improved.

Embodiments of the present disclosure provide a method for dynamic computing resource allocation based on an IIoT data center, the method comprising: receiving, via an IIoT user platform 110, a business demand of an enterprise user, and sending the business demand via an IIoT service platform 120 to a data computing center of a IIoT management platform 130; monitoring, by a monitoring module, resource data of a business management sub-platform; determining, based on the business demand, a resource demand feature of the enterprise user; determining, based on the resource demand feature and the resource data of the business management sub-platform, a resource allocation parameter; and, generating, via the control center, a resource allocation instruction based on the resource allocation parameter.

The invention provides a method and device for an intelligent computing center cloud platform to adjust model training parameters according to computing power operation states, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. The first working component is a component deployed in a node in the intelligent computing center cloud platform, the target link comprises a data preparation link, a model training link, a model evaluation link and / or a model test link in the first working component, and the target link performs data processing or model training based on preset hyper-parameters; s2, calculating target correction parameters corresponding to the operation related parameters in a preset period based on a preset regulation and control model; and S3, adjusting the preset hyper-parameter based on the target correction parameter to obtain a target hyper-parameter. The computing power utilization rate of the intelligent computing center cloud platform can be greatly improved, and then the model training efficiency is greatly improved.

The invention provides a method and system for an intelligent computing center cloud platform to realize agent self-evolution through computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, the method is executed by an embedded agent in a self-evolution agent, and the method comprises the following steps: S1, determining a function module needing to be developed and determining a development demand, the self-evolution intelligent agent is a target intelligent agent embedded with the development intelligent agent; s2, determining a design scheme of a functional module according to development requirements; s3, generating a code of the function module according to the development requirement and the design scheme; s4, debugging codes of the function modules; when the development requirement is not met, checking the design scheme and the code of the function module, returning to the step S2 if the design scheme needs to be modified, and returning to the step S3 if the code needs to be modified until the development requirement is met or the debugging frequency exceeds a preset threshold value; s5, feeding back development result information to the user; and S6, an intelligent agent online function module is embedded.

The invention relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, and discloses a method and a device for distilling a reinforcement learning data set by an intelligent computing center for providing computing power, and the method comprises the steps: obtaining an original data set, generating at least one candidate answer corresponding to the original data set based on the original data set and a large model, respectively generating an answer thought corresponding to each candidate answer; based on the answer thought and the large model, generating an examination point corresponding to the answer thought; based on the answer thought, the examination point and the large model, generating a scoring standard corresponding to the examination point; and combining the original data set, the at least one candidate answer, the answer thought, the examination point and the scoring standard to obtain a reinforcement learning data set. Therefore, the reinforcement learning data set has diversity and complexity, and the large model can be trained subsequently according to the reinforcement learning data set, so that the expression and development potential of the large model in complex tasks and real application scenes is effectively enhanced, and the performance of the large model is improved.

The invention belongs to the technical field of crossing of artificial intelligence and computer system architecture, particularly relates to a computing power center equipment power supply intelligent system of a knowledge graph, and aims to solve the problem of low energy efficiency caused by extensive power supply management, unbalanced energy consumption and lagged load response. The system comprises a task analysis module, a dynamic load prediction module, a multi-source power supply scheduling module, an equipment-level power supply control module and a feedback optimization module, through load prediction and multi-source power supply cooperative scheduling driven by task semantics, voltage and current dynamic adjustment and sleep wake-up control are achieved, and a strategy is optimized online based on operation feedback. The scheme improves energy efficiency, equipment life and system stability.

The invention provides a computing power resource dynamic adjustment method and device of an intelligent computing center, and relates to the technical field of computing power infrastructure, and the method comprises the steps: receiving a computing power operation task execution request of a user; according to the type of the computing power operation task, a computing power resource adjustment strategy corresponding to the computing power operation task is configured, and the computing power resource adjustment strategy comprises a computing power instance number range and a computing power resource use threshold value; and dynamically adjusting the computing power resources allocated to the computing power operation task according to the computing power resource use condition, the computing power instance quantity range and the computing power resource use threshold value in the execution process of the computing power operation task. Therefore, based on the type of the computing power operation task, the computing power resource adjustment strategy of the computing power operation task is configured in a targeted manner, and furthermore, the dynamic adjustment of the computing power resource of the computing power operation task can be realized according to the use condition of the computing power resource in the execution process of the computing power operation task and the computing power resource adjustment strategy; the performance of the computing power operation task can be ensured, and the utilization rate of computing power resources can be improved.

The invention discloses an exhibition information synchronization and real-time updating management method and platform based on cloud computing. The method comprises the following steps: collecting multi-source heterogeneous exhibition information in real time through edge computing nodes; transmitting the collected heterogeneous data to a cloud computing center, carrying out format standardization, and carrying out semantic extraction on an unstructured text to generate a unified exhibition information data pool; periodic synchronization and instant synchronization based on event driving are carried out; when it is detected that multiple versions of the same information are updated, historical operation records of blockchain evidence storage are called, and a final effective version is generated in combination with preset priority rules and timestamp verification; and generating an adaptive information push packet according to the display capability and the interaction permission of the target terminal type. The method is used for solving the technical problems that in traditional exhibition information management, multi-source heterogeneous data integration is difficult, real-time updating response is insufficient, the unstructured text semantic processing capacity is weak, multi-source updating conflict resolution is not credible, and multi-terminal information pushing adaptability is poor.

The invention discloses a heat flow dynamic tracking AI prediction type server temperature control system and device. The system comprises an AI prediction model module, a dynamic temperature control execution module and a data acquisition and feedback module. The AI prediction model module is connected with the output end of the data acquisition and feedback module through a data bus, and the input end of the dynamic temperature control execution module is connected with the output end of the AI prediction model module; the input end of the data acquisition and feedback module is connected with a temperature sensor group deployed in a server, the input data of the AI prediction model module comprises a server real-time load, an environment temperature and a historical temperature time sequence, and the output data of the AI prediction model module is a temperature prediction value in the future 5 minutes. According to the method, the time advantage is established through AI prediction, space buffering is established through PCM cold storage, a three-dimensional regulation and control mechanism which is precisely executed is achieved through pump valve cooperation, optimization is achieved in the heat dissipation stability, the energy efficiency, the transient response, the self-adaptive capacity and the operation and maintenance mode, and intelligent temperature control is provided for the high-density computing power center.

The present invention discloses an intelligent terminal monitoring system and method based on edge computing, which relates to the technical field of terminal monitoring. By obtaining the monitoring area data of traffic monitoring cameras corresponding to each faulty edge computing center, the importance coefficient of the cameras is calculated; and a target edge computing center capable of receiving the data of the faulty edge computing center is screened out from each non-faulty edge computing center; the adaptation coefficient between the target edge computing center and the faulty edge computing center is calculated; according to the adaptation coefficient and the importance coefficient, the final target edge computing center of each faulty edge computing center is determined, and the traffic monitoring camera data is transmitted to this center for analysis; the final target edge computing center analyzes the data in combination with the importance coefficient of the camera to monitor whether the camera is faulty; it ensures that the traffic monitoring camera data can be processed in a timely and efficient manner, and at the same time ensures that the edge computing center can reasonably allocate the load, guaranteeing the stability and accuracy of the monitoring system.

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 a real-time collection and fusion method for highway multi-source heterogeneous toll data, and particularly relates to the technical field of highway toll data management, comprising the following steps: transmitting data to a computing power center deployed at the edge through a data link and network equipment for preliminary fusion to obtain a preliminary fusion data set; marking a computing power center deployed at the edge of each toll station as an edge node, predicting a future traffic flow change trend through machine learning, generating flow prediction distribution, extracting a computing power demand contour of each edge node based on the flow prediction distribution, analyzing nonlinear fluctuation characteristics of the computing power demand contour by using a chaos dynamics model, and calculating the traffic flow of each toll station. Chaos attractors of high-load nodes are identified, a cluster overall computing power boundary is generated, and computing power is dynamically scheduled based on a chaos control strategy; when a multi-source data fusion conflict is detected, a fusion strategy is optimized and adjusted based on the dynamic trust degree and the game theory; and the problem of insufficient computing power or resource waste in a high-load scene is effectively solved.

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.

Disclosed in the present invention are a method for constructing a channel fingerprint twin, and a system. In the present invention, a coarse-grained channel fingerprint and a fine-grained channel fingerprint are respectively regarded as a physical object and a digital twin object, and image super-resolution technology is used to construct a relationship between the coarse-grained channel fingerprint and the fine-grained channel fingerprint. In the present invention, on the basis of variational inference and a re-parameterization theory, an evidence lower bound of a fine-grained channel fingerprint twin likelihood is derived to serve as an objective function, and the coarse-grained channel fingerprint is introduced as side information to design a conditional generative diffusion model for generating the fine-grained channel fingerprint, wherein the conditional generative diffusion model can be deployed in a core computing center of a channel fingerprint twin. In addition, in the present invention, a one-shot pruning algorithm and multi-objective knowledge distillation technology are further introduced to acquire a lightweight conditional generative diffusion model. The method for constructing a channel fingerprint twin provided in the present invention not only ensures the reconstruction accuracy, but also has relatively strong scalability and generalization capability in wireless communication scenarios with different fine-grained channel fingerprints.

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 embodiment of the invention discloses a photovoltaic module diagnosis method and device, a storage medium and computer equipment, and relates to the field of photovoltaic fault detection. According to the invention, through the mutual cooperation of the Stable Diffusion algorithm, the DINO algorithm and the CLIP multi-mode large model, the fault detection accuracy of the photovoltaic module can be greatly improved. After it is determined that the photovoltaic module breaks down, the projection coordinates of the center position of the photovoltaic string image can be accurately calculated by obtaining the unmanned aerial vehicle attitude parameters associated with the photovoltaic string image where the module image is located. And calculating the distance between the projection coordinate of the central position and each string coordinate in a position database by combining the string number and geographic coordinate of each photovoltaic string pre-stored in the position database, thereby accurately determining the string coordinate closest to the central position and the string number corresponding to the string coordinate. And accurate determination of the geographic coordinates and serial numbers of the fault photovoltaic strings is realized.

The invention discloses an intelligent unmanned aerial vehicle flight control method based on a 4D flight path, and the method comprises the steps: obtaining real-time airspace data for an initial four-dimensional flight path set, carrying out the preprocessing of environment information collected by a sensor through a data compression algorithm, transmitting the information to a calculation center through a satellite-based communication link, and obtaining a compressed real-time airspace data package; according to the corrected track sequence, the track adjustment speed of the track segment is optimized in the satellite-based computing center, a millisecond-level adjustment instruction set is generated for the obstacle avoidance requirement in the complex airspace, and a final track sequence containing the adjustment speed is obtained; and for the control sequence adjusted in real time, uploading actual track data to a computing center, and updating a task database through comparative analysis of the actual track data and the initial four-dimensional track set to obtain a track data set for subsequent task optimization. According to the invention, accurate flight path planning and dynamic optimization of the large-scale unmanned aerial vehicle group are realized, and the flight efficiency and safety in a complex airspace are 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.