88 results about "Resource efficiency" patented technology

The invention discloses a virtual power plant capacity configuration and regulation operation optimization method, which comprises the following steps: constructing an aggregation model and physically consistent digital twinning, establishing a linearized power distribution network model containing voltage and power flow constraints, and depicting resource efficiency and time delay characteristics; forming a time-varying uncertainty set through quantile calibration and set drift constraint; establishing a capacity-operation joint double-layer optimization model, realizing capacity configuration under the constraint of the whole life cycle cost, and obtaining a rolling scheduling strategy through distributed robust optimization; before issuing, control shielding and formalized constraint are adopted to ensure the safety of the power grid; a multi-variety collaborative quotation is generated on the market side through opportunity constraint and risk measurement; hierarchical adaptive re-optimization is realized based on a trigger criterion, and a twin model is continuously calibrated by using a hardware-in-the-loop experiment; model updating is realized by adopting federated learning and differential privacy; executing degradation control under an abnormal condition, and performing smooth rollback after recovery; and finally, the capacity and operation parameters are evaluated and corrected through performance and service life linkage.

The invention discloses an AI-based distributed big data intelligent storage management method, and relates to the technical field of storage management. The method comprises the steps of generating an initial feature matrix by obtaining observation data of all storage devices in a distributed storage cluster, and inputting the initial feature matrix into a pre-training life prediction model to obtain residual life; equipment with the service life lower than a first safety threshold value is judged as high-risk equipment, and data of the high-risk equipment is included in a to-be-migrated source data set; the devices with the service life higher than a second safety threshold value are included into a to-be-migrated target device set; and generating a migration plan according to the source and target data sets and executing migration. By predicting the residual life of the equipment, advanced identification and active data migration of the high-risk equipment are realized, the data loss risk is reduced, and the utilization rate of the health equipment and the overall cluster storage resource efficiency are improved.

The invention discloses an intelligent triage dynamic priority judgment method for triage, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: respectively collecting the visual sign characteristics, voice complaint information characteristics and voice vital sign characteristics of a patient based on a machine vision module and a voice interaction module; performing fusion analysis on the multi-modal data through a deep learning model to generate a patient injury condition feature vector; then, in combination with the real-time resource occupation condition of the medical institution, the priority scores of the patients are dynamically adjusted, the final triage level is generated, and the patient list sequence is updated in real time; according to the method, the triage efficiency can be effectively improved, the waste of medical resources is reduced, particularly in an emergency resource allocation mode, priority allocation is further optimized through the resource efficiency coefficient, and it is ensured that the most needed patient is treated preferentially when resources are insufficient.

The invention relates to the technical field of AI computing power scheduling, in particular to an AI computing power sharing scheduling system based on a cloud native architecture, which comprises a data acquisition and convergence module, a resource portrait construction module, a resource efficiency optimization module, an operation and maintenance self-healing module and a closed-loop feedback module, the data acquisition and convergence module is responsible for acquiring cluster dynamic operation data and AI task life cycle data; a resource portrait construction module respectively constructs resource portraits for the computing nodes and the AI tasks based on the data; the resource efficiency optimization module executes a resource optimization decision through a Kubernetes scheduler and a cluster management queue according to the resource portrait; the operation and maintenance self-healing module executes a predictive operation and maintenance decision through a Kubernetes API based on the resource portrait; and the closed-loop feedback module feeds back the execution result of the optimization and self-healing decision as new data to the front-end module, so that continuous updating of the resource portrait and self-optimization of the system are realized, and a complete intelligent closed loop is formed.

The invention provides a self-adaptive data packet scheduling method based on approximate quantiles and multiple queues on a programmable data plane, and aims to solve the problem that the scheduling performance and the resource efficiency are difficult to balance in the prior art. According to the method, lightweight ranking scoring, dynamic queue mapping and a cascade enqueue strategy are combined. A relative priority score of the data packet is efficiently calculated through a minimum-maximum normalization mechanism; adaptively mapping the data packets to one of a plurality of FIFO queues according to the scores and real-time congestion conditions of the queues, and providing a fast channel for extremely high priority traffic; and when the target queue is full, trying to store the data packet into a queue with a lower priority in a cascading manner so as to avoid unnecessary discarding. According to the method, resource consumption and implementation complexity are remarkably reduced while the approximate ideal PIFO scheduling performance is achieved, and the method is particularly suitable for resource-limited environments such as a software-defined network and edge computing.

The invention discloses an SLAM system optimization method and device based on a layered anchor point diagram structure and a storage medium, and the method comprises the steps: collecting and preprocessing sensor data, constructing a layered anchor point architecture composed of a structure anchor point, an appearance anchor point, a global anchor point and a dynamic anchor point, gaussian point distribution is restrained through anchor points, camera poses are mapped to appearance codes, absolute coordinates are associated, dynamic interference is processed, and efficient sharing is achieved through an anchor point diagram. According to the method, the dynamic environment adaptability is enhanced, the resource efficiency is optimized, the fidelity and robustness of the map are improved, the multi-machine cooperation bandwidth pressure is reduced, and the method is suitable for automatic driving, robot navigation and other scenes.

The invention relates to the technical field of cloud computing resource scheduling, in particular to a resource allocation method and device and electronic equipment, and the method comprises the steps: inputting current time sequence feature data and current static feature data into a pre-trained resource demand prediction model, and outputting a predicted overflow probability and a predicted overflow probability confidence interval of the first resource pool within a preset duration, and if it is judged that the first to third resource pools meet a preset resource dynamic allocation condition based on the predicted overflow probability and / or the predicted overflow probability confidence interval, determining a dynamic allocation strategy according to the priority of the current to-be-processed task, and distributing the current to-be-processed task to at least one resource pool for task processing. Therefore, the problems of resource response lag, task processing delay, difficulty in optimizing the overall resource efficiency while guaranteeing the key task and the like due to the fact that a static priority mechanism cannot dynamically adapt to the vehicle type strategic weight and the data timeliness and resource allocation lacks an elastic mechanism in the prior art are solved.

The invention relates to the technical field of storage virtualization, in particular to a hospital business-oriented server and storage virtualization system integration and disaster recovery reconstruction method, which comprises the following steps of: simulating layered integrated construction deployment of a system, and constructing a resource pool infrastructure which is deeply coupled with calculation and storage and is optimized aiming at medical business load; on the basis of a progressive online migration technology of a business portrait, risk-controllable and smooth seamless migration of a business system from a physical platform to a virtual platform is realized; the old machine room is reconstructed into a degraded disaster recovery center, and the old machine room is transformed into a disaster recovery site which is economical and practical and meets a core business recovery target; the method has the beneficial effects that the resource efficiency and economy are remarkably improved, the average resource utilization rate is improved from less than 20% to more than 60% through deep resource pooling, the purchase quantity of new equipment is reduced, meanwhile, old assets are fully utilized, and the TCO is reduced.

The application discloses a kind of control system cloud edge commissioning methods based on industrial network, comprising: using the large bandwidth of 5G network, low latency and network slice capacity, unified management in cloud multiple control system commissioning tasks;Each physical control system is registered to cloud by the unique device identification that it is independently generated in its built-in lightweight debugging agent module;Planning debugging channel, allow multiple low-priority control system to share channel to improve resource efficiency, high real-time control system then monopolizes channel to guarantee performance;All commissioning data carry device identification transmission, each control system built-in debugging agent module strictly checks device identification, only handles the instruction that matches this system, realizes the on-demand acquisition of multiple control system state information, accurate distribution and collaborative diagnosis, significantly improve the concurrency capability of remote debugging, resource utilization and system reliability.And adapt to the remote maintenance, efficient commissioning and real-time response demand under the multi-device collaborative scenario in industrial automation.

The present application relates to the technical field of real-time monitoring, in particular to a real-time monitoring system for road and bridge construction progress, which comprises a dynamic relationship analysis module, an event response decision module, a structure health monitoring module, a risk management and prediction module, a progress prediction and adjustment module, a resource optimization configuration module, an environmental impact assessment module and an overall progress management module.In the present application, the dynamic relationship in construction is captured through the graph neural network, the graph attention network and the dynamic graph convolution network, the construction model is updated in real time, the accuracy of progress analysis is ensured, the event response decision module quickly optimizes the construction plan through the predictive control algorithm, the strategy flexibility is improved, the structure health monitoring and risk management module combines the Markov decision process and the Monte Carlo tree search, effectively prevents risks and ensures safety, the progress prediction, resource optimization and environmental impact assessment module improves resource efficiency and environmental adaptability, and the application of comprehensive evaluation method realizes efficient and high-quality construction management.

The invention discloses a tunnel structure deformation dynamic measurement method and system, and relates to the technical field of data measurement, and the method comprises the steps: obtaining a reference axis according to the head end and the tail end of a tunnel, obtaining a to-be-measured region and a to-be-processed reference point, and obtaining a distance data sequence, comparing the distance data sequences of the to-be-processed reference point to obtain an associated fluctuation parameter of the to-be-processed reference point; obtaining the reference point to be processed with the minimum associated fluctuation parameter as a target reference point, and obtaining the dynamic measurement interval duration of the target reference point between the adjacent measurement time points in the current measurement period according to the associated fluctuation parameter of the target reference point; and obtaining a measurement strategy of the to-be-measured area based on the target reference point and the corresponding dynamic measurement interval duration, and completing deformation measurement of the to-be-measured area. The method has the advantages of stable and reliable measurement, optimized resource efficiency and dynamic adjustment of measurement frequency.

Example embodiments of the present disclosure relate to enhancements in sidelink (SL) positioning reference signal (PRS) transmissions for sidelink positioning. In one aspect, a first terminal device obtains configuration information on a resource pool for sidelink (SL) positioning reference signal (PRS) transmission. And then, the first terminal equipment sends the at least one SL PRS to the second terminal equipment based on the configuration information. Thus, embodiments of the present disclosure provide a more flexible SL PRS transmission scheme to flexibly multiplex SL PRS transmissions from different UEs for improving interference mitigation and increasing resource efficiency.

The invention discloses an edge-end collaborative continuous learning method and system based on multi-step knowledge distillation. The method is executed on the basis of an edge-end collaborative architecture comprising an end side (a preset student model) and an edge side (a preset teaching assistant and teacher model): the end side collects a data frame, part of the data frame is transmitted to the edge side according to a dynamic frame unloading rate, and the end side and the edge side are reasoned through corresponding models respectively; the edge side monitors the difference between the inference results of the assistant model and the teacher model, and retraining is started if the difference is lower than a preset threshold value; through hierarchical knowledge distillation, the scale gap between teacher and student models is filled up, and the training and reasoning effects of the student models are effectively improved. And then the incidence relation between the reasoning precision and the GPU resources is determined through mathematical modeling, an optimal resource allocation scheme is solved by means of a dynamic programming algorithm, and the precision benefit is maximized. According to the continuous learning scheme, computing resources can be flexibly allocated in a dynamic environment, the demand of reasoning precision maximization is accurately met, and the continuous learning scheme is suitable for scenes with double requirements for resource efficiency and reasoning performance.

This invention discloses a large-scale model defense method based on semantic logic. By employing semantic logic analysis and model learning techniques, it systematically identifies and blocks the confusion effect of bias, thereby reducing the impact of bias in pre-training data on the guidance process. It includes an interpretable component that provides insight into the alignment between the generated output and the desired direction, enhancing the interpretability of the guidance process, increasing model transparency and user trust. This method may reduce reliance on manual annotation and computational resources, thus improving resource efficiency. Through unbiased guidance representation, it improves the model's generalization ability across different tasks and domains, contrasting with the limited generalization capabilities of existing technologies, exhibiting high scalability and adaptability. It can be applied to different language models, providing in-depth understanding of the consistency between the generated output and the desired direction, thereby enhancing the interpretability of the steering process while mitigating the impact of bias.

A computer-implemented method that identifies resource efficiency deficit score infrastructure resources in cloud environments based on attributes that influence carbon emission controls at least one of the infrastructure resources based on the infrastructure resource efficiency deficit score of that the infrastructure resource. The infrastructure resource efficiency deficit score can equal BVt / (WBt+BVt), where BVt is a best value point and WBt is a worst value point. According to other illustrative embodiments, a computer system and a computer program product for identifying resource efficiency deficit score infrastructure resources in cloud environments are provided.

Embodiments of the present application provide a communication method and a communication apparatus. The method comprises: a terminal device receiving resource configuration information, the resource configuration information including information of K resource sets, the K resource sets being in one-to-one correspondence with K coverage levels, and the K resource sets corresponding to M random access channel formats, wherein each resource set corresponds to one of the random access channel formats, the M random access channel formats include a first random access channel format and a second random access channel format, the two formats correspond to different numbers of occupied frequency domain resource units, and K and M are positive integers; the terminal device determining a first coverage level corresponding to the terminal device, the first coverage level corresponding to a first resource set among the K resource sets; and on at least one random access resource in the first resource set, sending a random access request message using a random access channel format corresponding to the first resource set. The method can not only improve resource efficiency, but also ensure the reliability of a communication link.

Example embodiments of the present disclosure relate to bandwidth part selection for random access procedure. According to embodiments of the present disclosure, a terminal device receives configuration information from a network device. The configuration information indicates a set of BWPs and configurations of the set of BWPs. When a random access procedure is triggered for a certain feature combination, the terminal device determines whether a condition for switching to a target BWP is met. If the condition is met, the terminal device switches to the target BWP. In this way, resource efficiency is improved.

The invention discloses a data collection method and device and electronic equipment, the data collection method can be applied to the technical field of automatic driving, and the method comprises the steps: obtaining original data related to a to-be-executed data collection task; extracting feature information from the original data based on a preset feature dimension; based on the feature information, the contribution degree of the original data in a preset evaluation dimension is quantified, and a trigger priority score of the data acquisition task is obtained; determining a target trigger level matched with the trigger priority score in preset trigger levels; different trigger levels in the preset trigger levels correspond to different resource allocation rules; and based on a resource allocation rule corresponding to the target trigger level, allocating system resources to the data acquisition task. According to the method, a complex driving environment is described through multi-dimensional feature fusion, collaborative optimization classification logic is provided, accurate resource allocation according to classification values is realized, and collaborative improvement of dimension, precision and resource efficiency of automatic driving data acquisition is achieved.

The invention discloses an airport group construction method for improving regional guarantee efficiency. By collecting operation data of each airport in a region, a multi-dimensional feature set is constructed, and a capacity, resource and location feature system is formed. And establishing a hierarchical index system covering task execution capability, resource guarantee capability and structural toughness, quantifying toughness based on a single airport coercivity evolution curve and a group collaborative relationship, and realizing quantitative characterization of regional airport group guarantee efficiency by utilizing clustering analysis and weighted scoring. And constructing an airport group model with multiple constraint conditions, and generating an optimal airport group scheme with hierarchical function positioning and collaborative guarantee capability by taking region overall guarantee efficiency maximization as a target and adopting a multi-layer optimization algorithm. According to the method, while task completion, resource efficiency and balance and structural toughness are considered, the task scheduling efficiency and the resource utilization level of the regional multi-airport system are remarkably improved, and the task continuity and the system obstinate storage capability under complex conditions are enhanced.

The real-time workload of the application is converted into a capacity unit value. The trained machine learning model receives capacity unit data as input and generates a prediction of future capacity usage. Based on the prediction, additional clusters may be assigned to the application. Data sets used to predict application future capacity units may be classified into two categories. The first category includes time series data. The second category includes a service type, a target user type, and other attributes with discrete characteristics. Discrete attributes are incorporated into a wide section, while time series data is integrated into a deep section. The wide and deep models combine the results of the wide and deep sections to generate a prediction of capacity units used by the application for a future time period. In response, the distribution system distributes a corresponding number of clusters.

A terminal according to one embodiment of the present disclosure is provided with: a reception unit that receives first downlink control information (DCI) based on a sequence without using a demodulation reference signal (DMRS); and a control unit that, on the basis of the first DCI, controls reception of a second DCI using a DMRS. According to one mode of the disclosure, the resource efficiency can be improved.

The invention provides a self-adaptive alliance forming method for resource competitive tasks in multiple industrial chains, and aims to solve the problems of low task completion rate and low benefit caused by poor alliance forming sequence, resource redundancy and high transportation cost. The method comprises the following steps: firstly, establishing an alliance formation model considering resource transportation cost and redundancy cost; secondly, resource efficiency is calculated according to resource requirements, scarcity and expected benefits of the tasks, and task priorities are determined in a self-adaptive mode according to the resource efficiency; when an alliance is established, the method comprises the following steps: firstly, based on situation resource adaptation degrees, selecting an alliance leader with the highest adaptation degree as the alliance leader; and then, selecting the alliance members according to the coordination resource adaptation degree until the task requirements are met. According to the method, by dynamically optimizing the task sequence and accurately constructing the alliance, the resource consumption cost can be effectively reduced, and the task completion rate and the total income of the multi-industrial-chain network are remarkably improved under the condition that the resources are limited.

The invention provides a server instance cost optimization system, and relates to the technical field of resource optimization. In the application, the cost optimization system comprises a data acquisition module, a data fusion module, a utilization rate analysis module, a cost analysis module and an optimization suggestion module. Through the cost optimization system, the data fusion module fuses the data of each dimension, so that the problem that the cost analysis is difficult to comprehensively reflect the actual resource efficiency due to the data of a single source is solved, the cost analysis of the server instance based on each analysis dimension is realized, the cross-business and cross-department cost analysis is realized, and the efficiency of the server instance is improved. The optimization suggestion module is determined based on the resource dimension and the budget dimension, so that the accuracy and practicability of the cost optimization suggestion are ensured, resource waste and budget hyper-branched can be avoided, early warning of the server instance and optimization of resources and cost corresponding to the server instance can be realized based on the cost optimization suggestion, and the user experience is improved. And thus, refined cost control of the server instance is realized.

The invention relates to the technical field of production scheduling management, and discloses a production management method and system for cooperative scheduling of a torpedo ladle distributing machine and intelligent vibration, and the method comprises the steps: 1, determining the driving time and the pouring duration, and building batch space mapping; 2, the pouring completion moment is determined, and a feeding continuous window and a quality time window are generated; step 3, determining a lexicographical order target as a quality time window default partition number, a quality time window default amplitude and a resource efficiency cost, and determining an insertion cost; step 4, generating a baseline time sequence table according to the quality time window and the insertion cost; 5, discrete events are set, a time sequence instruction is issued, and time correction is carried out by receiving and reporting; 6, when the deviation exceeds an allowable deviation threshold value, scheduling and repairing are conducted again; and 7, checking a re-scheduling result, forcibly reordering the minimum disturbance of the default template partition, and generating a four-dimensional track. According to the invention, collaborative production scheduling management of torpedo ladle feeding and distributing operation and intelligent vibrating operation is realized.

Example embodiments of the present disclosure relate to a solution for transport block size (TBS) determination for sidelink communication. In this solution, a first terminal device determines a sub-channel comprising a first number of resource block (RB) interlaces and each of the first number of RB interlaces having a target interlace size. The first terminal device determines a transport block size (TBS) for at least one transmission of a transport block (TB) for SL-U communication based on a target criterion associated with the sub-channel. The first terminal device then transmits the at least one transmission of the TB to a second terminal device based on the TBS. In this way, the interlaced RB-based transmission and contiguous RB-based transmission in SL-U are improved in terms of resource allocation, TBS determination, blind decoding as well as resource efficiency.

The invention provides a link migration scheme generation method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining network state information, initial configuration data and target configuration data of a target physical network; determining an initial virtual link path and a target virtual link path based on the network state information, the initial configuration data and the target configuration data; constructing a mixed integer linear programming model; and inputting the network state information, the initial virtual link path and the target virtual link path into a mixed integer linear programming model to generate a target link migration scheme. Through a closed loop of'perception-modeling-optimization-execution ', transition of virtual network link migration from'experience drive' to'model drive and optimization drive 'is realized, the performance guarantee capability, resource efficiency and automation level of a migration scheme are remarkably improved, personalized migration can be realized by adjusting a mixed integer linear programming model, and the migration efficiency is improved. The practicability of the scheme is greatly improved, and the engineering practical value is achieved.

Aspects presented herein may improve a sidelink discovery procedure for resource efficiency and / or power savings, where power consumption at a receiving sidelink device and / or a transmitting sidelink device may be reduced when the receiving sidelink device and / or the transmitting sidelink device is performing the sidelink discovery procedure. In one aspect, an apparatus sends an indication to a second wireless device that indicates in physical layer signaling whether a PSSCH carries a discovery message. The apparatus transmits the PSSCH (e.g., including a discovery message or a communication message) based at least in part on the indication.

The invention discloses a conference system and method, electronic equipment and a storage medium, and belongs to the technical field of video conferences. The system integrates a strategy making layer and a plurality of functional modules. The strategy making layer dynamically responds to the target conference mode, and a processing assembly line is assembled according to needs; the asynchronous message transmission mechanism realizes decoupling transmission between modules through an independent message queue; and the function module orderly processes the conference sensing data based on the uniform interface, and finally outputs the optimized conference data. Dynamic assembly line assembly replaces a traditional fixed framework, modules are flexibly combined according to actual requirements, and resource occupation and system complexity are remarkably reduced. Asynchronous message passing ensures independent operation of the module, and the concurrency efficiency is improved. Redundancy is avoided through dynamic assembly, and the resource efficiency is improved. A unified interface and an asynchronous mechanism support independent development and testing, and maintainability is enhanced. And the strategy making layer realizes rapid configuration and adapts to multi-scene differentiation requirements.

Real-time workload for an application is converted into a capacity unit value. A trained machine-learning model receives the capacity unit data as input and generates a prediction of capacity usage for the future. Based on the prediction, additional clusters may be allocated for the application. A dataset for use in predicting future capacity unit usage by an application may be classified into two categories. A first category comprises time series data. A second category comprises service types, target user types, and other attributes with discrete characteristics. Discrete attributes are incorporated into a wide section and time-series data is integrated into a deep section. A wide and deep model combines results from the wide section and the deep section to generate a prediction of capacity units used by the application in future time periods. In response, an allocation system allocates a corresponding number of clusters.