15results about How to "Implement dynamic allocation" patented technology

The application relates to a resource scheduling method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: determining a target virtual machine from a plurality of virtual machines to be created, and taking an initial physical machine cluster as a candidate physical machine cluster corresponding to the target virtual machine; determining a candidate allocation physical machine corresponding to the target virtual machine from the candidate physical machine cluster; determining a reference allocation physical machine from the candidate allocation physical machine based on the physical machine cluster fragmentation quantity and the remaining virtual machine quantity generated by allocating the target virtual machine to the candidate allocation physical machine; updating the idle resources of the reference allocation physical machine in the candidate physical machine cluster to obtain an intermediate physical machine cluster corresponding to the target virtual machine; and continuously allocating a next virtual machine on the basis of the intermediate physical machine cluster until a candidate physical machine cluster and a candidate allocation physical machine corresponding to a last virtual machine are obtained, and then determining a target scheduling scheme. The method can improve the resource utilization rate.

The application provides an AI computing power allocation method, system, device, storage medium and program product, and relates to the technical field of data processing. The AI computing power allocation method determines the corresponding target AI algorithm model based on the service demand of the target reasoning service in real time, and then dynamically matches the target computing power resource used to load the target AI algorithm model by combining the processing demand of multiple AI algorithm models, the preset adaptability rules of various heterogeneous computing power resources, and the current use state of the various heterogeneous computing power resources, so as to realize dynamic allocation of AI computing power resources, avoid computing power resource overload or idling, improve computing power resource utilization, reduce task reasoning delay, and thus improve data processing capability.

This application provides a signal communication method for a multi-dimensional domain of a data link, comprising: performing cross-domain correlation deconstruction on the data link's spatiotemporal-frequency multi-dimensional signal and error feature data under an electromagnetic environment to obtain the original spatiotemporal-frequency domain feature set; simultaneously integrating signal propagation characteristic data and error source attribute data to generate a multi-source correlation data cluster to generate a spatiotemporal-frequency multi-dimensional fused feature topology; constructing a multi-dimensional domain collaborative error-resistant model based on the spatiotemporal-frequency multi-dimensional fused feature topology; and performing dynamic weight allocation and error suppression strategy iterative optimization on the spatiotemporal-frequency domain error-resistant weight parameters and error suppression strategy parameters in conjunction with spatiotemporal-frequency domain resource adaptation rules to generate a multi-dimensional domain error-resistant control command; performing spatiotemporal-frequency collaborative optimization processing on the data link transmission signal according to the multi-dimensional domain error-resistant control command to obtain an error-resistant optimized signal; simultaneously collecting transmission quality data of the link feedback signal; and fine-tuning the multi-dimensional domain error-resistant control command parameters in reverse by the deviation between the transmission quality data and a preset quality threshold.

The application provides a digital twin and cloud edge collaborative deep-sea mining system fault diagnosis method and system, and relates to the technical field of deep-sea mining system fault diagnosis. The method comprises the following steps: constructing a digital twin model and performing mechanism monitoring; edge side signal processing and component level intelligent diagnosis; cloud data aggregation; double-engine information fusion and decision making are performed in the cloud diagnosis center; uncertainty quantification and visual output are performed. The application constructs an intelligent diagnosis architecture based on digital twin and cloud edge collaboration, deeply integrates mechanism models and artificial intelligence, realizes early warning of system level chain faults, robust diagnosis of weak component faults in a strong noise environment, and provides high credibility diagnosis conclusions with quantitative confidence and decision support information, thereby significantly improving the safety, reliability and operation intelligent level of deep-sea mining operations.

The application belongs to the technical field of civil aviation airport flight management, and discloses a flight resource dynamic allocation method and system based on data analysis. A resource data set based on resource characteristics is constructed, and a flight history database based on flight characteristics is constructed; a resource allocation scheme sample library containing both flight characteristics and resource characteristics is constructed; an initial resource allocation scheme is established by data analysis using the resource allocation scheme sample library, and the initial resource allocation scheme is further adjusted to form an allocation scheme of allocation resources; flight information is marked with flight characteristics to complete the allocation of flights; meanwhile, corresponding resource characteristic values in the resource data set are matched with resource characteristics in the allocation scheme of allocation resources to complete the allocation of resources. The application realizes dynamic allocation of flight resources, reduces the cost of manual allocation, and improves the efficiency of flight resource allocation.

The invention discloses a two-phase cold plate spraying cooperative battery thermal runaway intelligent hierarchical control method and system, and relates to the technical field of battery thermal control. The two-phase cold plate spraying collaborative battery thermal runaway intelligent hierarchical control method comprises the following steps: S1, collecting and preprocessing thermal state working condition data, component operation data and environment auxiliary data, and constructing a standardized thermal state data set; s2, top evaluation is conducted on the top sudden heat source intensity, and the starting behavior of a spraying device is regulated and controlled; s3, bottom analysis is conducted on the bottom steady-state cooling bearing condition, and the opening degree of a cold plate runner is dynamically adjusted; and S4, performing vertical evaluation on the vertical heat flux coupling characteristics in combination with the local thermal disturbance trend, and performing linkage control on the top spraying frequency and the operation state of the condensation path. The problems that in battery thermal management, a traditional single cooling mechanism is difficult to consider distributed temperature control requirements of bottom steady-state heat absorption and top sudden refrigeration, so that heat flux coupling is uneven, and the local overheating risk is prominent are solved.

The present application relates to dilution refrigerator temperature calibration technical field, and disclose a kind of dilution refrigerator thermometer in situ calibration system based on natural cooling dynamics, comprising: according to the dynamics equation of cooling power and temperature Curvature is calculated, according to the temperature corresponding to thermometer resistance value, the temperature is substituted into temperature curvature, and the calibration function of resistance value and temperature is obtained;Identify the multivalued region of temperature, and evolve multiple branch functions in multivalued region in calibration function, calculate the deviation measure of each branch function and calibration function;In multivalued region, the support degree of each branch function to calibration function is calculated, and real-time data is obtained by adjusting calculation load, to minimize the number of experiments to verify calibration function;According to the branch verification result of multivalued region, dynamically adjust the verification strategy of non-multivalued region, under the premise of not relaxing calibration accuracy, through dynamics branch and load guide, significantly reduce the repeated experiment cost of dilution refrigerator cryogenic calibration.

The application discloses a kind of distributed control network deployment systems based on industrial PON, it includes industrial network link deployment unit, builds industrial network link and equipment, collects power production control signal and power production related data;Wireless communication unit, the power production control signal of real-time acquisition is encrypted transmission, and non-real-time power production related data is transmitted through LoRa module;Intention drive management unit includes semantic analysis module, and the natural language command of power production control signal and power production related data is parsed using BERT model, and dynamic scheduling module is calculated and distributed to link load by dynamic load balancing algorithm;Block chain storage unit, the natural language command storage of power production control signal and power production related data is realized using alliance chain;Security isolation unit, firewall is based on SM4 algorithm of national secret ALC strategy is deployed, and one-way net gate isolates control area and management information area of industrial network link.

The application relates to the field of intelligent analysis, and discloses a LIBS multi-component quantitative analysis method based on spectrum mechanism constraint and feature decoupling. Target spectrum data is acquired and pretreated. Spectrum mechanism perception features are extracted according to the pretreated target spectrum data. Component-specific feature decoupling is performed according to the spectrum mechanism perception features. Adaptive error regulation is performed. Finally, multi-component quantitative analysis results are output. The output multi-component quantitative analysis results are based on concentration range constraint and multi-component consistency constraint. The application realizes spectrum feature decoupling, avoids the problem of insufficient attention to low-content components, and improves the accuracy and stability of analysis.

The application discloses a double-drive privacy budget and decentralized incentive method and system based on a blockchain, relates to the technical field of the blockchain, and comprises the following steps: receiving user privacy demand data and task side drive data, generating a user side privacy demand baseline based on the user privacy demand data, generating a task side drive value based on the task side drive data, weighting and coupling the user side privacy demand baseline and the task side drive value to obtain a privacy budget baseline; dynamically distributing and cutting the privacy budget baseline under a preset global risk constraint and a unified scaling factor to obtain a distributed final round privacy budget, obtaining the data contribution degree of the user, inputting the data contribution degree of the user and the distributed final round privacy budget into a preset incentive function to output a user final reward value, and inputting the user final reward value into the blockchain.

The invention discloses a marine energy storage box battery pack liquid cooling heat dissipation device and method suitable for a sea wave impact working condition. According to the marine energy storage box battery pack liquid cooling heat dissipation device, the total output flow is adjusted through a cooling liquid pump, and the flow of each branch is adjusted through a multi-channel proportional valve; in the strategy adjusting aspect, whether a compensation mechanism is triggered or not is determined according to the calculated comprehensive impact index and the flow channel impact included angle, and the output total flow and the branch flow are adjusted; and when the temperature difference between the adjacent battery packs is greater than a first set value, triggering an overcooling protection mechanism, otherwise, updating the output total flow and the branch flow through a circulation mechanism. Ship movement is sensed in real time through the sensor, active cooling liquid flow compensation adjustment is conducted on impact disturbance, and the problems that heat dissipation of a battery pack is uneven, efficiency is reduced and system reliability is reduced due to sea wave impact of a ship under the complex sea condition can be solved.

This application discloses a method, apparatus, and electronic device for fine-tuning a large model based on hybrid experts, belonging to the field of artificial intelligence technology. It addresses the problem that current large model technologies struggle to meet the application needs of multi-task scenarios in network management. The method includes: initializing a hybrid expert network for a pre-trained large model, the hybrid expert network comprising a task-level gating network and multiple task-level expert groups (each group containing a character-level gating network and multiple character-level experts), the hybrid expert network being embedded in specific layers of the pre-trained large model; inputting training data and a first prompt word into the pre-trained large model, guiding the pre-trained large model to output sample prediction results through the first prompt word; determining the loss result of the pre-trained large model based on the true label results corresponding to the training data, the sample prediction results, and the target loss function; and fine-tuning the network parameters of specific layers of the pre-trained large model based on the loss result to obtain a network management model for multi-task scenarios.

The invention discloses a public network access authority management method and a related device, and the method comprises the steps: firstly obtaining a routing rule corresponding to a target Pod, then configuring the corresponding routing rule for the target Pod through an initialization container when the target Pod is started, enabling the target Pod to have a public network access authority, and enabling the target Pod to have the public network access authority based on the routing rule when the target Pod sends a public network access request. The message corresponding to the target Pod is sent to the server corresponding to the next hop address, the message is forwarded to the public network server after being subjected to SNAT, when the target Pod is deleted, the public network access permission of the target Pod is recycled, resource leakage caused by residual permission is avoided, and therefore the public network access permission is bound with the life cycle of the target Pod, and the service life of the target Pod is prolonged. According to the invention, the dynamic distribution and recovery of the public network access authority are realized, and the security of the public network access authority management of the Kubernetes cluster is effectively improved through the fine-grained management of the Pod level of the public network access authority.

The application provides an LDPC iterative decoding method and device based on cross-layer sensing and system timing, and belongs to the technical field of wireless communication. The total time length of a current OFDM data packet and the total number of LDPC code blocks contained are obtained, and the core parameters of LDPC decoding are preset. Based on a system timing sensing mechanism, the real-time available time margin of the current LDPC code block is calculated in combination with the synchronization information of the OFDM data packet, the real-time state of the memory and the protocol timing constraint. The decoding state of the MAC layer data packet to which the current LDPC code block belongs is determined based on a cross-layer sensing mechanism, the real-time available time margin obtained is combined, the adaptive iteration number of the current LDPC code block is dynamically calculated in different scenes, and LDPC iterative decoding is performed. Therefore, through the fusion of the cross-layer sensing mechanism and the receiver system timing sensing mechanism, the fine and dynamic scheduling of iteration resources is realized, and the high-speed transmission requirement of the 802.11 protocol is adapted.

The invention discloses a regenerative electric energy recovery and optimal utilization system and method for port electrical equipment, and belongs to the technical field of port energy recovery, and the system comprises a regenerative electric energy capturing unit, a bidirectional conversion unit, a hybrid energy storage unit, an intelligent energy management unit and a multi-element utilization unit. The high-power response speed and the high energy storage density are both considered, the adaptability problem of a single energy storage medium is solved, and the regeneration electric energy recovery efficiency is improved to 95% or above. An intelligent energy management strategy based on a fuzzy PID algorithm is constructed, dynamic distribution of regenerative electric energy in a three-level utilization path of equipment self consumption, auxiliary power supply and power grid feedback is achieved, and the method adapts to complex operation conditions of a shore bridge and load changes of a power grid. The bidirectional converter unit integrates harmonic suppression and power factor correction functions, the total harmonic distortion (THD) of electric energy fed back to a power grid is less than or equal to 3%, and impact pollution to the power grid is remarkably reduced.