162 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 cyclic utilization and purification treatment system for slurry in pile foundation engineering construction. According to the invention, the purification treatment system is utilized to realize the double breakthrough of environmental friendliness and high resource efficiency in the construction process through the deep enabling of the intelligent regulation and control module. The system constructs a closed-loop management chain of the slurry life cycle by relying on full-process data perception and dynamic optimization capability. The intelligent regulation and control module captures fine changes of physical and chemical characteristics of the slurry in real time and automatically regulates the operation mode of the treatment equipment, so that the purified slurry always keeps the optimal engineering performance index, and the risk of pile body defects caused by quality fluctuation of the slurry is greatly reduced. By means of the intelligent design of the waste slurry recycling path, pollution sources in traditional construction are converted into reusable engineering materials, damage to the ecological environment caused by outward transportation and landfill of waste is remarkably reduced, and an engineering site is promoted to move towards the near-zero emission target.

The invention is suitable for the technical field of network scheduling, and provides a network scheduling method and system for hybrid service transmission based on RDMA, and the method comprises the steps: receiving a to-be-transmitted service demand instruction, and determining a corresponding service type according to a service quality parameter in the service demand instruction; matching a corresponding virtual transmission channel for the service demand instruction according to the service type, wherein the virtual transmission channel is used for isolating hardware-level resources of different types of traffic; selecting an optimal transmission path for the flow of each virtual transmission channel according to a preset path evaluation model; analyzing the congestion risk of the optimal transmission path by adopting a prediction model; and dynamically adjusting a flow transmission path according to a congestion risk analysis result, and ensuring the transmission quality of the highest priority service through a hardware-level scheduling strategy. According to the invention, the optimal balance between the service quality and the resource efficiency can be realized in a complex mixed service environment.

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 belongs to the technical field of cloud computing and communication resource management, and discloses a communication equipment resource dynamic allocation method and system based on cloud computing, and the method comprises the steps: obtaining biological migration tide data, communication network load historical data and environmental parameter data, constructing a migration trajectory prediction model and a resource demand prediction model, and carrying out the prediction of a migration trajectory; generating an elastic resource allocation scheme; base station group division is carried out on a communication network coverage area, resource demand space-time analysis is carried out based on a migration trajectory prediction result, and a resource utilization rate index is calculated; generating a resource scheduling instruction set based on the resource elastic allocation scheme, performing priority ranking, optimizing resource allocation in combination with a resource utilization rate index, and generating a base station parameter configuration instruction; adjusting the resource configuration of the base station according to the configuration instruction, monitoring the operation state, evaluating the resource efficiency, performing feedback tuning and applying to the resource elastic allocation scheme; and the equipment operation and maintenance complexity and the manual intervention requirement are effectively reduced.

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 the field of electric digital data processing, and discloses a data processing method for dynamically updating a knowledge graph, which comprises the following steps of: presetting a basic weight for a knowledge unit, calculating an activity index of the knowledge unit attenuating along with time, and broadcasting an updating pulse signal to a neighbor unit when the activity index exceeds a threshold value, and the neighbor unit autonomously judges whether to trigger updating or continue to propagate the signal according to the signal attribute. According to the method, system resources are naturally focused on a high-value area through a life-driven self-organizing updating mechanism in the knowledge unit, global scanning redundancy is avoided while the knowledge timeliness is guaranteed, efficient transmission of key updating and reliable awakening of a dormant area are achieved by means of signal priority coupling and a neighborhood activity detection mechanism, and the reliability of the system is improved. And the resource efficiency contradiction in dynamic updating of the knowledge graph is solved.

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 network security, and discloses a network security event script development method and device and a storage medium, and the method comprises the steps: obtaining a corresponding attack scene, a defense strategy and a development node from a preset script material library; running a preset development model, inputting the attack scene, the defense strategy, the node type, the node function and the development node into the development model, and generating a trial version event script; and verifying the defense effect of the trial version event script through script trial operation, dynamically replacing the development node based on a verification result, and outputting a formal version event script. The device and the storage medium correspond to the method. Through the closed-loop design of development model construction, intelligent script generation, multi-dimensional defense evaluation and dynamic replacement optimization, the problems of high manual dependence, low response speed, one-sided defense effect and the like in traditional network security script development are solved, and the automation level, real-time adaptability and resource efficiency of network security event response are improved.

An efficient open set encryption traffic identification method for super-large scale traffic relates to the technical field of data security, solves the technical problems of insufficient resource efficiency, feature expression and open set identification in the prior art, and comprises the following steps: S1, inputting a network traffic data set, and generating dimension reduction data of an optimal dimension through a PCA dimension reduction algorithm; s2, inputting the dimension reduction data of the optimal dimension into an improved MobileNetV3 model, and in combination with an improved SE attention mechanism and a composite structure of Dense connection and residual connection, extracting features and outputting a classification result; s3, judging whether a classification result belongs to a known category, a drift sample or an unknown category by using an open set recognition algorithm based on confidence; s4, if the classification result is a drift sample or an unknown category, incremental learning based on knowledge distillation is started, parameters of the improved MobileNetV3 model are updated, and old knowledge is reserved. According to the invention, the network security protection capability can be effectively improved.

The invention discloses a state compression and unlabeled reward-based reinforcement learning method and system and electronic equipment, and the method comprises the steps: receiving a user prompt outputted by a model, and converting the user prompt into a structured state vector which comprises a task target, a function call sequence and an environment feedback result; mapping the function call sequence into a complex plane vector, and automatically generating an unlabeled reward value based on the complex plane vector; based on the unmarked reward value, a low-rank adaptive network is adopted in a model training engine to carry out parameter fine tuning on the model, and batch training is carried out through a token gradient optimization strategy; and identifying and extracting a function call instruction from a text generated by the model, converting the function call instruction into tool API call, and injecting an execution result of the tool API call into a next round of model input in real time. According to the scheme, the decision quality is improved, the resource efficiency is optimized, the implementation cost is reduced, and the application scene can be expanded.

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 invention relates to the technical field of service function chain migration, in particular to a service function chain migration method and system based on a user movement scenario, and the method comprises the steps: obtaining migration request information of a to-be-migrated service function chain in response to a service function chain migration demand caused by user movement, a migration decision task is determined for each virtual network function in each service function chain to be migrated, and on the premise of meeting resource requirements and performance indexes of the service function chain, comprehensive minimization of migration cost, migration delay and end-to-end delay of the service function chain after migration is defined as an optimization target. And performing optimization calculation on the determined migration decision-making task, and outputting an optimal service function chain migration decision-making scheme. According to the method, the migration cost, the migration delay and the subsequent end-to-end time delay are comprehensively minimized, and the decision-making quality, the resource efficiency and the user experience of SFC migration are remarkably improved.

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 discloses a load-balanced low-orbit satellite general computation resource collaborative allocation method and system, and belongs to the technical field of low-orbit satellite resource allocation, and the method comprises the steps: building a model with the maximization of the task completion rate as the target, obtaining a computation scheduling sub-model and a flow scheduling sub-model through Lagrange dual decomposition decoupling, and achieving the collaborative allocation of low-orbit satellite general computation resources. The calculation scheduling sub-model determines a calculation decision through linear solution and node preliminary screening, the flow scheduling sub-model is converted into a maximum flow model based on a storage time aggregation graph, a link rate and a calculation frequency distribution decision are obtained through solution, continuous iteration is carried out to convergence by utilizing a gradient method, and an optimal decision of calculation scheduling and flow scheduling is obtained; according to the method, the problem of load imbalance of the low-orbit satellite is solved, space-time joint scheduling of communication and computing resources is realized, network throughput and resource efficiency are improved, congestion and overload are avoided, the task completion rate is remarkably improved, and the performance is superior to that of an existing algorithm.

The invention discloses a planting method for increasing the content of anthocyanin in purple rice grains, which comprises the following specific steps: sowing purple rice seeds from May 20th to May 30th every year, and setting the nitrogen application amount to be 24kg N / mu and the planting density to be 1.58-2.22 million holes / mu at the same time. According to the method, the sowing time, the nitrogen application amount and the planting density are precisely and cooperatively regulated and controlled, resource distribution in the key period of purple rice anthocyanin synthesis is directionally optimized, the content of paeoniflorin and cyanidin in grains is remarkably increased, meanwhile, yield stability and efficient resource utilization are both considered, and through cultivation technology innovation which is low in cost and easy to popularize, the yield of purple rice is increased. The multi-target of directional enhancement of functional components of purple rice, stable yield and high resource efficiency is achieved, a technical normal form is provided for green transformation of functional agriculture, remarkable economic, ecological and social benefits are achieved, and the important requirement for green and high-efficiency development of functional agriculture is met.

The invention relates to the field of industrial automation, and discloses an AToT-based material process mobile management and control method and system, and the method comprises the following steps: collecting a station process state in real time through an AToT network, and constructing a dynamic weight factor generation model; task allocation weights are dynamically optimized based on deep reinforcement learning, and a distributed bidding mechanism is driven to realize cooperative task matching of multiple mobile devices; according to the path curvature and the historical congestion degree, elastic fragments are dynamically divided, and a driving path taking control precision and resource efficiency into consideration is generated; and forming a closed-loop resource scheduling link through a priority-driven fragmentation preemption mechanism and predictive path re-planning. According to the method, quick response of high-priority tasks, dynamic optimal configuration of path resources and real-time resolution of system conflicts can be realized, and the method is suitable for high-dynamic industrial scenes such as automobile manufacturing and electronic assembly.

The invention discloses a causal influence discovery-based disguised topological element attack method. The method aims at solving the problems that an existing attack method does not distinguish a disturbance total graph, neglects attack budget and node differences, does not select key topological elements, is low in resource utilization rate, is limited in success rate and the like. According to the method, graph structure elements susceptible to attack are discovered and perceived by means of causal influence, fragile nodes are identified by using a layered filtering mechanism, and accurate allocation of resources is realized; integrating causal effect evaluation to determine key nodes; and carrying out optimal element operation by adopting a joint loss optimization disturbance method so as to maximize the attack effect in a prediction. Experiments show that CTEA is superior to an existing method in efficiency and effect, and the CTEA shows adaptability, robustness, accuracy and resource efficiency on multiple GNN architectures and data sets.

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 relates to the technical field of resource optimization, in particular to a laboratory resource optimal distribution system based on cloud computing and big data analysis, which comprises a resource state monitoring module, a resource demand analysis module, a resource dynamic adjustment module, a resource efficiency analysis module and a resource optimization execution module. According to the method, through time interval grouping and interval difference calculation, in combination with periodic reagent consumption trend analysis, high-demand and low-demand intervals of laboratory resources are accurately positioned, the accuracy and pertinence of resource allocation are optimized, meanwhile, excess resources are recycled and stored, and meanwhile, insufficient parts are supplemented; the conditions of excessive consumption and unused resources are concluded, the recognition and response capabilities of differences between resource allocation and actual demands are improved, resource waste or insufficient allocation caused by static allocation is reduced, and high adaptability of resource utilization is realized by optimizing the equipment starting sequence and the reagent allocation rhythm. Powerful support is provided for the continuity and the high efficiency of the experiment process.

The invention relates to the technical field of enterprise resource management, in particular to an enterprise management optimization method and system based on big data, and the method comprises the following steps: capturing data of employee skills, a project execution process and customer interaction according to enterprise resource planning in an enterprise, analyzing the captured data, and carrying out the multi-dimensional information classification processing; and constructing an enterprise resource comprehensive data set. According to the method, the flow and utilization efficiency of the resources is improved by establishing the graphic database to integrate the enterprise resources and converting the data elements into the network nodes and the connecting edges, and the resource flow and utilization efficiency is improved by identifying and analyzing the association strength of the nodes and the weights of the edges in the resource network. Through detailed flow path analysis, an enterprise can formulate a scientific resource allocation strategy according to market requirements and current resource situations, resource allocation and use are optimized after dynamic resource allocation is implemented, the strategy is adjusted in time by comparing and analyzing resource efficiency and cost effectiveness before and after optimization, and the market response speed and competitiveness of the enterprise are remarkably 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 multi-task cooperative processing method and system for an industrial vehicle-mounted computer, and relates to the field of data processing, and the method comprises the steps: extracting an original time sequence, and carrying out the preprocessing of the original time sequence, and obtaining a smooth driving behavior sequence; extracting original features and performing component analysis to obtain a driver habit feature vector; a plurality of driving behavior modes are obtained through classification, and a priority distribution rule is constructed; extracting real-time driving characteristics, matching the real-time driving characteristics with the driving behavior mode, and calling a priority allocation rule to obtain a first task execution sequence; adjusting the first task execution sequence to obtain a second task execution sequence, allocating resources according to the second task execution sequence and a priority allocation rule, and performing resource optimization to obtain an optimized resource allocation scheme; and adjusting the second task execution sequence to obtain a third task execution sequence. The real-time performance of the key task is ensured, the resource utilization rate of the system is improved, and the real-time performance and the resource efficiency of the vehicle-mounted system are effectively balanced.

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 a vehicle door handle accessory manufacturing management system for an electric vehicle, particularly relates to the field of intelligent manufacturing, and is used for solving the problems of production chain stability and efficiency caused by order insertion task disturbance. Production chain coupling constraint is converted into a quantum optimization model for global solution, and process collapse caused by traditional priority coverage is avoided. The anti-disturbance capability is evaluated based on process topological characteristics, local optimization of fragile links is locked, and the calculation overhead of whole-chain reconstruction is reduced. Digital twinning real-time verification and calibration ensure equipment load, material supply and process stability and balance, and reduce the risks of product overstock and equipment idling. By means of time window elastic adjustment, integration of an order insertion task and steady-state scheduling is achieved, continuity of production takt is maintained, emergency order response speed is improved, toughness of a manufacturing system is enhanced, manual intervention and trial and error cost are reduced, and reliable delivery and resource efficiency of multi-variety orders are guaranteed.