133 results about "Resource efficiency" patented technology

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention discloses a scheduling system of a content distribution network based on edge computing, and relates to the technical field of computer networks. An edge resource sensing module; an NLP semantic analysis module; reinforcing a learning scheduling engine; a semantic and resource linkage module; and a federation collaboration module. According to the scheduling system of the content distribution network based on edge computing, semantic understanding is carried out on a request of a user to analyze a deep intention of the user, edge node multi-dimensional state sensing data and a semantic label vector are integrated, a scheduling action is generated based on a node state and the user intention, and the scheduling effect is improved. Therefore, each edge node is fully utilized while user requirements are met, the edge nodes have caching, computing and scheduling capabilities, the limitation of surface feature matching of a traditional CDN is broken through, technical upgrading from traditional file type recognition to intention understanding and from a fixed rule to dynamic intelligence is realized, and finally, the resource efficiency and service quality of the CDN are improved.

The invention belongs to the technical field of knowledge graph and big language model combination, discloses a big language model inference enhancement method based on structure perception knowledge graph representation, and provides a structure perception knowledge injection framework which utilizes a graph neural network to combine with multi-source structure information coding to enhance entity representation. And through a contrast learning mechanism, realizing alignment of semantics and a structure space, and meanwhile, designing an adaptive robustness fusion pruning training strategy, dynamically screening high-quality evidence sub-graphs, and guiding a language model to carry out structured reasoning. According to the method, deep integration and optimal utilization of the structure information and the semantic information are realized, the accuracy, robustness and resource efficiency of the model under a complex query task are remarkably improved, and a stronger embedding basis is provided for generation quality, so that the generation accuracy, stability and efficiency under a complex problem are improved.

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.

A system is provided for dynamic scheduling and optimisation of diagnostic tasks in a networked computing environment. The system associates issue tickets with a diagnostic task matrix comprising probable causes, diagnostic tasks, probability values, outcome expectations, and resource parameters. A task scheduling controller generates optimised task sequences based on task success likelihoods, cost, technician availability, and evidentiary sufficiency. As tasks are completed, outcomes are used to update the diagnostic model, enabling automatic self-improvement. A statistical learning model, such as aBayesian or neural network, refines diagnostic probabilities using historical data. Integration with calendaring systems allows real-time rescheduling based on personnel availability. A graphical interface supports live drag-and-drop reconfiguration of task associations, with immediate propagation of updates to task probabilities and cost metrics. The system thereby enhances resolution speed, accuracy, and resource efficiency across evolving operational contexts.

The invention relates to the technical field of AR glasses data flow scheduling, in particular to an AR glasses data flow scheduling method and system for a replaceable module, and the method comprises the steps: carrying out the dynamic topology perception of a module, and monitoring the connection state and function attributes of the module in real time to generate a topological graph; data flow path modeling: constructing a virtual data flow path diagram and marking constraint conditions; generating a multi-target scheduling strategy, and generating a dynamic strategy by taking time delay, power consumption and bandwidth utilization rate as targets in combination with task requirements; strategy distributed execution: decomposing the strategy into sub-instructions and distributing the sub-instructions to each module for execution; the system applies the method and comprises the hot-pluggable module group, the central scheduling controller and the local scheduler. According to the invention, the dynamic adaptation of the data stream in the hot plug scene of the module is realized, the user experience and the system resource efficiency are balanced, and the flexibility, the reliability and the industrialization adaptability of the AR glasses are improved.

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 relates to a park green land service level optimization evaluation method and device, a terminal and a medium, and the method comprises the steps: coding environment monitoring data and tourist positioning data into a unified space-time grid through a multi-source data fusion technology, and constructing a dynamic supply and demand prediction model; based on collaborative analysis of vegetation irrigation capability and tourist gathering intensity, quantifying a time-space conflict degree between a resource strategy and peak activities; a dynamic weight distribution mechanism is adopted to optimize collaborative parameters of an irrigation period and a diversion path, and an adjustment scheme considering both resource efficiency and tourist experience is generated; and finally, realizing closed-loop optimization of the park service level through multi-dimensional index fusion evaluation. According to the method, the problems of resource supply and tourist demand separation, response lag, experience decision deviation and the like in a traditional management mode are effectively solved, the irrigation conflict rate is remarkably reduced, the tourist satisfaction degree is improved, and a decision basis is provided for intelligent park management.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an action queue control method, device, equipment and medium, and the method comprises the steps: obtaining initial observation data, sending the initial observation data to a strategy server, and receiving a generated initial action queue; executing the action in the action queue; when the residual amount of the queue is lower than a threshold value, obtaining new observation data; judging whether action prediction is triggered or not based on the similarity between the data and historical observation data; if yes, the new observation data are sent to the strategy server in a non-blocking mode, and a new action block is generated; and receiving a new action block, integrating the new action block with the action queue to form an updated action queue, and repeatedly executing the updated action queue as a new action queue until all actions are completed. According to the method, the action blocks are generated in parallel in the action execution process in an asynchronous reasoning mode, control blind areas and waiting delay are reduced, response timeliness and continuous control capacity are improved, and the requirement of edge equipment for resource efficiency is met.

The embodiment of the invention provides an application program optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: collecting resource calling data of an application program on user equipment; identifying a to-be-optimized resource set based on the resource calling data; executing optimization processing on the to-be-optimized resource set to obtain an optimized target application program; wherein the optimization processing comprises resource removal or resource relocation. Compared with a traditional static analysis method, the method has the advantages that through a data analysis mode driven by user behaviors, the risk of function missing possibly caused by blind deletion of resources is avoided, and more accurate and safer application program optimization is realized. Besides, for the resources which are used at low frequency but are still necessary to be reserved, the optimization of the initial packet volume is realized while the functional integrity is maintained through a processing mode of resource relocation instead of simple deletion, and the relationship between the user experience and the resource efficiency is balanced.

The invention relates to the technical field of computing power distribution, and discloses a data hotspot identification and computing power dynamic reservation scheduling method. According to the method, suspicion hotspot data are monitored and identified in real time, a shadow execution copy with a minimum resource quota is constructed for the suspicion hotspot data, part of real-time flow is mirrored to the shadow copy for pressure testing without influencing a main service, and after a pressure verification score is calculated to confirm a hotspot, shadow copy resources are improved to a high-performance level and directed diversion is carried out; meanwhile, the method comprises the steps of self-adaptive threshold adjustment and hot spot cooling and capacity reduction, interaction is carried out on the basis of a centralized controller, a container arrangement platform and a service grid, hot spots can be accurately recognized, the computing power can be dynamically adapted, the resource utilization rate is increased, and the service stability and the resource efficiency are balanced.

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 discloses a development environment and production environment configuration difference automatic comparison and synchronization method. The method comprises the following steps: S1, configuration data acquisition; s2, importing the configuration data into a difference analysis engine; s3, synchronizing based on the difference level label and a user preset synchronization rule; s4, executing an automatic synchronization item and recording a log; S5, verifying the synchronization consistency through a bidirectional verification mechanism; according to the method, a plug-in probe technology is adopted, static configuration files, dynamic runtime parameters and dependent component version information are comprehensively covered, compared with an acquisition mode only aiming at single-type configuration in the prior art, accurate capture of multi-dimensional and full-amount configuration data is achieved, and meanwhile, through sensitive parameter encryption and dynamic acquisition frequency adjustment, the acquisition efficiency is improved. Safety and resource efficiency are both considered, and the problem that a traditional method is insufficient in comprehensive configuration data processing capacity is solved.

The invention discloses a multi-dimensional monitoring evaluation method and device, a server and a computer readable storage medium. According to the method, service attribute data and historical fault data are used as original data, the position of a monitored object in a service architecture and the performance of the monitored object in a fault scene can be accurately determined, and misjudgment of monitoring importance caused by data missing is effectively avoided; besides, quantitative scoring is carried out from multiple dimensions, the business value dimension is used for measuring the business criticality of a monitored object, the operation and maintenance utility dimension is used for quantifying the actual value of monitoring in fault processing, and the resource efficiency dimension is used for balancing the monitoring precision and resource consumption. The subjective operation and maintenance experience can be converted into objective data indexes through multi-dimensional cross scoring, the monitoring task can be comprehensively evaluated, various to-be-optimized scenes can be found through data cross analysis, and then the adaptive capacity and operation and maintenance efficiency of a monitoring system to a distributed system are improved.

The invention relates to a resource-constrained unmanned aerial vehicle cluster network data privacy protection method, which comprises the following steps of: establishing a mathematical model, and describing by using an undirected graph; the method comprises the following steps of: randomly generating two different system security parameter bit big prime numbers by each unmanned aerial vehicle in a cluster according to a preset system security parameter; calculating a public key and a private key according to a Paillier key generation algorithm, and encrypting the public key and the private key; and the unmanned aerial vehicles calculate homomorphic products of the encrypted random numbers sent by all neighbors, decrypt the homomorphic products by using own private keys, calculate offset initial position coordinates and control input according to decryption results, and repeatedly execute until all the unmanned aerial vehicles in the cluster converge to the same position. The problem that an unmanned aerial vehicle cluster network data privacy protection method is difficult to effectively balance in the aspects of resource efficiency, privacy security and accurate convergence is solved, the privacy of the initial position of the unmanned aerial vehicle is effectively protected, and the accuracy of task execution and the strong anti-eavesdropping capability are ensured.

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.

The present invention relates to the technical field of data stream scheduling for AR glasses, and in particular to a data stream scheduling method and system for AR glasses with replaceable modules. The method includes dynamic module topology perception, real-time monitoring of module connection status and functional attributes to generate a topology diagram; data stream path modeling, constructing a virtual data stream path diagram and marking constraints; multi-objective scheduling strategy generation, combining task requirements with latency, power consumption, and bandwidth utilization as targets to generate dynamic strategies; distributed strategy execution, decomposing strategies into sub-instructions and distributing them to each module for execution; the system applies the above method, including a hot-swappable module group, a central scheduling controller, and a local scheduler. The present invention realizes dynamic adaptation of data streams in module hot-swappable scenarios, balances user experience with system resource efficiency, and improves the flexibility, reliability, and industrial adaptability of AR glasses.

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.