14results about How to "Efficient resource allocation" patented technology

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention provides a multi-unmanned aerial vehicle edge network task unloading and resource allocation method based on MAPPO-Att, and relates to the technical field of unmanned aerial vehicle resource allocation. The method comprises the following steps: firstly, constructing a multi-layer heterogeneous edge computing network model, and computing average path loss between an unmanned aerial vehicle group and Internet of Things equipment; then designing an Internet of Things task unloading model, and calculating the total delay cost of the unloading task of the Internet of Things equipment; designing an energy cost and cost target optimization function model of the unloading task of the Internet of Things equipment; an MAPPO-Att algorithm is adopted to determine an optimal selection strategy of task unloading and resource allocation, the task unloading and resource allocation problems in an A2G network are solved, and the optimal strategy is searched to maximize long-term return, so that the calculation cost and effective resource allocation are reduced, and the overall training cost is reduced through a centralized training and decentralized execution technology; and completing unmanned aerial vehicle cluster resource allocation simulation until the task completion rate reaches a set value.

According to the high-frequency communication system assisted by the multiple intelligent reflecting surfaces and the deployment optimization method provided by the invention, the communication performance in a complex environment is improved by optimizing a multi-IRS (intelligent reflecting surfaces) assisted MIMO (multiple-input-multiple-output) system. The system solves the problem that a base station-user link is blocked, reduces path attenuation by using IRS single reflection, and introduces an orthogonal deployment strategy to ensure that an independent propagation path maximizes the system capacity. IRS element allocation and power allocation are optimized through an SCA (continuous convex approximation) algorithm, and channel capacity maximization is achieved. The method improves the system capacity, is efficient in optimization algorithm, low in deployment cost, green and environment-friendly, and is suitable for a next-generation efficient wireless communication system.

The invention relates to the technical field of server hardware resource optimization, in particular to a server hardware resource intelligent optimization method and system based on an adaptive neural network, and the method comprises the steps: obtaining hardware resource information and system performance indexes of a server; constructing topological resource mapping based on the hardware resource information and the system performance indexes; executing a group theory resource scheduling algorithm according to the topological resource mapping; performing matrix resource state prediction based on a result of the group theory resource scheduling algorithm; executing number theory resource allocation optimization according to a result of matrix resource state prediction; updating the weight of the adaptive neural network based on a number theory resource allocation optimization result; according to the method, an optimized server hardware resource configuration scheme is output, and by introducing topological resource mapping, the method can comprehensively and accurately represent a complex relationship among multi-dimensional resources, so that a solid foundation is provided for subsequent optimization decision making; the application of the group theory resource scheduling algorithm enables the resource scheduling operation to be more flexible and systematized.

This invention relates to a robust image semantic perception adaptive masking transmission method for vehicle-to-everything (V2X) networks, comprising: Step 1: The transmitting end performs semantic analysis on the original input image, extracts high-precision semantic features, and identifies the regions of interest (ROI) and non-ROI regions of interest (NROI); Step 2: A Boolean mask is dynamically generated based on the semantic segmentation result and SNR; Step 3: The ROI features are directly masked based on the Boolean mask; Subsequently, the processed feature map is fed into a semantic encoder to extract multi-scale semantic features; Step 4: Power normalization is performed, dynamic feature encoding is performed, and then the encoded signal is transmitted through a typical analog wireless channel, and noise is superimposed before reception and recovery; Step 5: The receiving end receives the noisy feature signal, performs channel fading compensation and preliminary denoising, and sends the decoded features into a semantic decoder; Through progressive upsampling and multi-level semantic reconstruction, multi-scale features are fused, and finally, a high-quality reconstructed image is recovered and output.

The invention discloses a data center joint optimization method and system, belongs to the technical field of data center management, and can solve the technical problems of inaccurate load prediction and low resource allocation efficiency of a data center. The method comprises the steps that the load condition of a data center in a historical period is obtained, a data center load data set is formed, a time sequence is obtained through the data center load data set, a GM model is established, the GM model comprises a GM model difference equation and a GM model prediction equation, the GM model difference equation is obtained through the time sequence through the GM model, and the GM model prediction equation is obtained through the GM model. The GM model difference equation comprises an adjustment parameter, a bird flock algorithm is used to obtain an optimal value of the adjustment parameter, the optimal value is substituted into the GM model prediction equation in the GM model, a prediction sequence is obtained, and a data center optimization strategy is executed according to the prediction sequence.

The invention provides a quantum computing auxiliary analysis and marketing prediction method for private domain traffic pool data, which comprises the following steps of: acquiring and processing user behavior data, coding the user behavior data into a quantum state by using a quantum algorithm, extracting user behavior characteristics by using a quantum support vector machine (QSVM), and analyzing a periodic mode through quantum Fourier transform, so as to realize the quantum computing auxiliary analysis and marketing prediction of the private domain traffic pool data. According to the method, a marketing prediction model is constructed based on a quantum algorithm, model parameters are optimized by using a quantum annealing technology, the prediction accuracy is improved, and the method further comprises the step of carrying out classical verification and optimization on a quantum calculation result so as to realize personalized marketing. According to the invention, efficient analysis and precise marketing prediction of the private domain traffic pool are realized, and the pertinence and effectiveness of enterprise marketing strategies are improved.

A control device controlling allocation of resources in a radio access network includes: a resource usage rate acquiring unit configured to acquire a resource usage rate for each service; a total received data volume information acquiring unit configured to acquire total received data volume information indicating a total received data volume for each service received by user equipment via an interface with a base station; a total traffic volume information acquiring unit configured to acquire total traffic volume information indicating a total traffic volume for each service for the user equipment in the base station via the interface with the base station; a communication quality degradation degree calculating unit configured to calculate a degree of degradation of communication quality indicating a degree of degradation of a throughput based on a ratio of the total received data volume to the total traffic volume for each service; and a control unit configured to control a margin for a requested amount of resources on the basis of the resource usage rate and the degree of degradation of communication quality in the past for each service.

The present invention belongs to the field of digital chips, and relates to a universal verification system for integrated circuit chip verification, wherein the top-level code directory includes a bin directory, a lib directory, and a pub directory, and the directory of each verification platform of the universal verification system includes a tb directory, an env directory, a testcases directory, an etc directory, and an inst.fl directory; after the simulation starts, all configuration information under each etc directory is collected, and then the compilation parameters inherited by the corresponding test case are selected according to the verification case to be simulated specified by the user to compile the verification platform; after the compilation is completed, the simulation parameters specified by the user are added for simulation; after the simulation is completed, the log file of the simulation is checked according to the inspection rules specified in the build to determine whether the verification case is passed. The present invention has the advantages of unified verification environment, flexible test case management, real-time verification result display, low learning cost, high resource utilization, cross-module collaboration, unified parameter management, and strong versatility.

The invention relates to the technical field of informatization software, and particularly provides an intelligent application recommendation method and system, and the method comprises the following steps: S1, data collection and preprocessing; s2, feature extraction and feature combination; s3, model training; and S4, generating a recommendation result. Compared with the prior art, the method and the device have the advantages that the time required by a user to find a proper application is shortened, and the satisfaction of the user is improved.

The present invention relates to the technical field of digital information transmission, and specifically provides a cloud-edge multiple unloading method and system for deep neural network tasks. The method of the present invention includes obtaining a deep neural network task; based on the deep neural network task, generating tasks of multiple different task stages corresponding to the deep neural network task; substituting the deep neural network task and the tasks of the multiple different task stages into a task unloading optimization module to generate an unloading strategy for tasks of each task stage; selectively dynamically optimizing the unloading strategy of the tasks of each task stage to obtain a dynamic unloading strategy for the tasks of each task stage; and performing multi-stage unloading of deep neural network tasks based on the dynamic unloading strategy for the tasks of each task stage. Flexible allocation of resources and optimization of unloading performance in multi-task scenarios improve time efficiency, energy consumption management, and resource utilization.

The invention relates to the field of chip integrated design, in particular to an integrated design method and system for a gene sequencing chip. The method comprises the following steps: acquiring an initial design drawing of a gene sequencing chip; carrying out multi-level chip framework design on the initial design drawing so as to construct a multi-level chip simulation framework; probe array arrangement multi-parameter design and channel distribution feasibility adjustment are carried out based on a gene sequencing chip design drawing, so that a plurality of chip space arrangement schemes are obtained; extracting an optimal chip space arrangement scheme based on the plurality of chip space arrangement schemes; performing spatial arrangement optimization on the multi-level chip simulation framework to obtain a spatial optimization simulation framework; and performing full-cycle gene sequencing simulation based on a space optimization simulation framework, collecting gene sequencing simulation data, and performing noise suppression circuit design, thereby obtaining a noise suppression circuit strategy. According to the invention, by optimizing the layout in the chip design, the precision and reliability of the chip integrated design are improved.

The invention discloses a comprehensive energy system optimization operation method considering electricity market transaction. According to the method, a green certificate-carbon transaction interaction mechanism is comprehensively considered, P2G segmented operation is refined, and a heat-to-electricity ratio adjustable technology is adopted, so that an optimized operation target with minimum energy purchasing cost, carbon transaction cost, green certificate transaction cost and energy abandoning cost is constructed, segmented processing is performed on a traditional power-to-gas system, green hydrogen generated in the process is refined and utilized, and the energy utilization rate of the system is improved. Secondly, the heat-to-power ratio adjustable technology of combined heat and power generation and hydrogen fuel cell equipment is researched, the system flexibility is improved, and the energy abandoning cost is reduced; and finally, a green certificate-carbon transaction interaction mechanism is introduced, a linkage relationship between carbon emission transaction and green certificate transaction is established, and the new energy consumption rate and the renewable energy utilization rate are improved in a marketization form.

The invention discloses a teaching resource scheduling system and method driven by a multi-parameter optimization algorithm. The method comprises the steps of collecting teaching subject types, time consumption of each subject, the total class number, the student number of each class, the classroom capacity and the teaching subject of each teacher in a semester; setting a target condition of the teaching resource scheduling scheme, wherein the target condition comprises a maximum resource utilization rate; setting constraint conditions of the teaching resource scheduling scheme, wherein the constraint conditions comprise a classroom capacity constraint, a teacher time constraint and a classroom uniqueness constraint; according to the target condition and the constraint condition, utilizing an optimization heuristic algorithm to generate an optimal teaching resource scheduling scheme; the resource utilization rate is maximized, the number of times of violating teacher time constraints is reduced, the teacher interval time is reasonably arranged, it is ensured that the teachers have enough lesson preparation and rest time, and the teaching efficiency, quality and teacher satisfaction are improved.