502 results about "Smart computing" patented technology

The invention relates to an intelligent computing power and storage scheduling method and system for a multi-service system, and belongs to the technical field of intelligent computing resource scheduling and storage management, and the method comprises the steps: collecting the real-time monitoring data of each node in the multi-service system, and carrying out the feature extraction; predicting a computing power demand value and a storage demand layering strategy in a future preset time window; dynamically correcting the prediction result, and generating a dynamic resource demand table and a cross-service priority weight matrix; generating a task allocation scheme through a hybrid scheduling algorithm; generating a storage data migration instruction through a storage scheduling engine; executing calculation node capacity expansion, task migration and storage data redistribution operation; and based on the executed latest state snapshot of the resource pool and the scheduling failure case library, updating a weight parameter of the time sequence prediction model and a scheduling strategy rule library to obtain an updated strategy version, and applying the updated strategy version to a next scheduling period. According to the invention, efficient scheduling of resources can be realized in a multi-service system environment.

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 method for realizing agent development MCP service through computing power of an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, determining interface information of a target application programming interface API, the target API being an interface of a target platform; s2, generating a first coding task based on the interface information through an intelligent agent of the intelligent computing center; s3, generating a first MCP service component corresponding to the first coding task through the intelligent agent; and S4, registering the first MCP service component to the intelligent computing center. According to the invention, the development efficiency of the MCP service of the intelligent computing center can be greatly improved.

The invention provides a method and device for an intelligent computing center to provide computing power resources through computing power packages, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the steps: S1, determining a computing power package corresponding to a to-be-processed computing power task under the condition that the computing power resources need to be called, the computing power task is a task needing to call computing power resource processing, and the computing power package is used for metering the computing power of the intelligent computing center according to the calculated amount; s2, computing power resources are called based on an application program interface corresponding to the computing power package, the application program interface comprises a code program corresponding to operation for executing the computing power task, and the computing power resources called by the application program interface are matched with the operation environment of the computing power task. According to the method, the scheduling efficiency of the computing power resources can be improved, the computing power is metered according to the calculated amount through the computing power package, and wide application of the general computing power is achieved.

The invention discloses a battery state dynamic monitoring and balancing method and system and electronic equipment, and belongs to the technical field of battery balancing management. The method comprises the following steps: calculating an initial health score of each single battery in a battery pack; taking the single battery corresponding to the initial health score lower than a preset initial health score threshold as a first target equalization battery, and calculating an initial equalization current of the first target equalization battery; calculating the initial equalization time of the first target equalization battery; controlling an equalization circuit to carry out current equalization on the first target equalization battery according to the initial equalization current until the initial equalization time is reached; and generating a first balanced data set, and transmitting the first balanced data set to the cloud platform. According to the scheme, the state of each single battery of the battery pack is monitored in real time, the equalization current and equalization time are intelligently calculated, the battery state change can be dynamically adapted, the battery health management is optimized, the charging and discharging efficiency is improved, and the battery life is prolonged.

The invention provides a computing power intelligent scheduling method and device oriented to a common computing power intelligent computing center, and relates to the technical field of computing power infrastructure, and the method comprises the steps: S1, obtaining first sensing data which is used for calculating the load condition of each computing resource in a computing power resource pool in a target time period, the computing power resource pool comprises a reasoning resource pool, a training resource pool and an elastic resource pool; s2, inputting the first perception data into a pre-trained calculation model, and determining a first target proportion in a target time period; and S3, scheduling the computing resources corresponding to the resource pools based on the first target proportion. In this way, idle or overload of the reasoning task and the training task caused by static allocation of the computing resources is reduced, and the utilization rate of the computing resources of the intelligent computing center is increased; according to the method, redundant computing resources reserved for coping with peak values are reduced, the cost of model development is greatly reduced by dynamically adjusting hardware resource allocation, and wide application of the cost benefit is achieved.

The invention provides a computing power management method of an intelligent computing center and a related device, and the method comprises the steps: S1, carrying out the standardized conversion of heterogeneous computing resources of the intelligent computing center, and obtaining heterogeneous computing power resources represented in a standardized manner; the heterogeneous computing power resources represented in a standardized manner comprise a resource type, a total capacity and a current usage amount; s2, determining a computing power resource demand of a multi-modal computing task on the intelligent computing center; and S3, allocating the heterogeneous computing power resources to the multi-modal computing task according to a matching corresponding relationship between the resource types and modal data of the multi-modal computing task, the total capacity and the current usage amount of the heterogeneous computing power resources of each resource type, and the computing power resource demand. According to the method, the resource utilization rate of the heterogeneous computing power resources of the intelligent computing center in the multi-modal computing task can be improved.

The present application relates to the field of data processing, and in particular to an arithmetic unit chip configuration method, a computing subsystem, and an intelligent computing platform. The method comprises: in response to a configuration instruction for a target chip, generating an initial chip population corresponding to the target chip; performing strategy optimization on the initial chip population by using a dynamic evaluation and optimization model, so as to obtain an optimal initial individual in the initial chip population; performing crossover and mutation operations on the optimal initial individual so as to obtain an offspring chip population of the target chip; and performing iterative loop on the offspring chip population until an offspring individual meeting a chip optimization objective is selected, or until a preset iteration stop condition is met, and configuring chip parameters of the target chip on the basis of the optimal offspring individual in a chip population of a last generation. The method can improve the chip optimization efficiency, reduce the power consumption of arithmetic units, optimize the energy efficiency of arithmetic units, achieve the balance of low-energy-consumption and high-performance for arithmetic units, and improve the operation efficiency of devices.

The invention relates to the technical field of intelligent industrial manufacturing and automation, in particular to a carton production dynamic adjustment control system and a carton production dynamic adjustment method thereof. The problems of parameter lag, process coupling interference, quality tracing difficulty and the like in traditional carton production are solved.

The invention provides a method for debugging an intelligent agent through computing power of an intelligent computing center, which comprises the following steps: S1, a debugging intelligent agent receives task information provided by a user through an interactive interface, and the task information is used for indicating to debug a debugged object; the debugged object is a debugged intelligent agent or a function module or a tool function of the debugged intelligent agent; s2, the debugging agent determines a debugging requirement according to the task information; s3, the debugging agent determines a debugging method according to the debugging requirement, deploys a debugging environment according to the debugging method, generates a running instruction and debugs the debugged object; s4, the debugging agent obtains the operation information of the debugged object and analyzes the operation information, and whether the debugging requirement is met or not is evaluated according to the analysis result; s5, if not, the debugging agent checks the code of the debugged object, modifies the code according to the check result, and returns to the step S3 until the condition is met; and S6, the debugging agent feeds back a debugging result to the user through the interactive interface.

The invention provides a computing power resource allocation method of an intelligent computing center and a model training method and device.The allocation method comprises the steps that S1, operation data of all computing tasks running on the intelligent computing center at the current moment are obtained, and operation features are extracted from the operation data to obtain operation feature vectors; s2, inputting the operation feature vector into a computing power resource allocation large language model deployed in the intelligent computing center to obtain a computing power resource allocation strategy; the computing power resource allocation strategy represents a computing power resource type and a computing power resource quantity allocated to each computing task by the intelligent computing center; and S3, indicating the intelligent computing center to execute the computing power resource allocation strategy. According to the method, the computing power resource allocation strategy can be efficiently formulated, and the management efficiency of the computing power resources is improved.

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 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 provides an intelligent computing center model development method and device oriented to the common computing power, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. The method comprises the steps that S1, a scheduling instruction is received, and the scheduling instruction comprises a model code; s2, computing power prediction is carried out based on the data size of the model code, and required computing power information is obtained; s3, acquiring a target virtual accelerator card based on the required computing power information, wherein the target virtual accelerator card is a virtual accelerator card created based on a part of GPU computing power resources of a GPU accelerator card of the first intelligent computing center; and S4, creating a first container based on the target virtual accelerator card, wherein the first container is used for executing the model code. According to the method, the computing power resource utilization rate of the intelligent computing center can be greatly improved, and the economic cost of model development is greatly reduced.

The invention provides a computing power management method of an intelligent computing center and a model training method and device.The computing power management method comprises the steps that S1, multi-modal operation data of a multi-modal computing task operated on the intelligent computing center at the current moment are obtained, extracting context features of each modal operation data from the multi-modal operation data, and combining all the context features to obtain feature vectors; the multi-modal operation data comprises modal operation data of at least one of the following modals: texts, monitoring indexes and visual data; s2, inputting the feature vector into a multi-modal resource allocation large language model deployed in an intelligent computing center to obtain a multi-modal resource allocation strategy for allocating computing power resources to a multi-modal computing task; and S3, indicating the intelligent computing center to execute the resource allocation strategy. According to the method, the multi-modal resource allocation strategy can be efficiently formulated for the multi-modal computing task, and the management efficiency of the computing power of the intelligent computing center is 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 relates to the technical field of vehicle-mounted intelligent computing, in particular to a hybrid key task scheduling method, system and equipment based on a virtualization technology. The method comprises the following steps: firstly, carrying out hybrid criticality grading on vehicle-mounted intelligent cabin tasks, and establishing a priority table; then, creating a dynamic resource partition based on a QNX Hypervisor platform, monitoring a system load state in real time through a hardware performance counter, and predicting a computing power demand of a large model; dynamically calculating an allocation scheme in combination with the task criticality level and the system energy efficiency state; then determining an optimal calculation unit selection strategy and a model precision grade by adopting an energy consumption-time delay joint optimization model; and finally, executing encrypted storage and integrity verification of large model parameters in a trusted environment, and completing end-side deployment and security verification. According to the method, the dynamic resource scheduling of the mixed key task is realized, the resource utilization rate is improved, the real-time requirement of the safety key task is ensured, and the dynamic balance between the performance and the energy efficiency is realized.

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 provides an intelligent computing center multi-agent sharing method and device oriented to common computing power, and relates to the technical field of computing power infrastructure, and the method comprises the steps: S1, receiving an execution instruction sent by a first terminal; s2, creating an execution task based on the execution instruction, and making an execution plan of the execution task; s3, the execution task is distributed to at least one agent, the execution plan is sent to the at least one agent, and the at least one agent comprises at least one of an agent created based on the computing power of the intelligent computing center and an agent created based on the computing power of a second terminal; and S4, receiving an execution result of the at least one agent, and sending the execution result to the first terminal, the execution result being a result obtained by the at least one agent executing the execution task based on the execution plan. The method can improve the utilization rate of the computing power resource corresponding to the intelligent agent.

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 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 relates to the technical field of computing power infrastructure, and provides a method and device for supporting elastic expansion and contraction capability of computing power resources of an intelligent computing center, and the method comprises the steps: S1, monitoring the calling frequency of a target service; s2, according to the calling frequency, the current service level of the target service is judged, and the service level comprises a high-frequency service, an intermediate-frequency service, a low-frequency service and a cache-free service; and S3, running the target service according to the service level to which the target service belongs currently. Therefore, the intelligent computing center can dynamically adjust the allocation of the computing power resources according to the actual demand of the service, realizes the elastic expansion and contraction capability of the computing power resources, avoids the starting delay of the service, improves the user experience, avoids the waste of the computing power resources, improves the utilization rate of the computing power resources of the intelligent computing center, and improves the user experience. And the performance of the high-performance computing power resource of the intelligent computing center is 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 a computing power resource management method of an intelligent computing center and a related device. The method comprises the following steps: S1, predicting a computing power demand of the intelligent computing center at a future moment by adopting a computing power demand prediction large language model deployed in the intelligent computing center; s2, determining a computing power resource scheduling scheme from the current moment to the future moment according to the computing power demand; and S3, executing the computing power resource scheduling scheme until the future moment. According to the method, the computing power resources can be managed in time, and the management efficiency is improved.

The invention provides an intelligent computing center computing power resource container network card configuration method and device, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, the method comprises the following steps: S1, creating a first virtual network card based on first information, the first information being used for indicating a first network segment and a first container, the first network segment is one of a plurality of network segments included in the container network, the container network is a network used when the container cluster distributes the network segments, the container cluster comprises a plurality of computing power resource containers, the first container is one of the computing power resource containers, and the first virtual network card is used for accessing the first network segment; s2, determining a first container in the plurality of computing power resource containers based on first information; and S3, migrating the first virtual network card into the first container, and executing network card configuration operation in the first container based on the first virtual network card. The network segment expansion efficiency of the container can be improved.

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.