1591 results about "Resource consumption" patented technology

The invention discloses a communication engineering construction dynamic optimization method based on multi-dimensional perception, and the method comprises the steps: collecting the multi-dimensional perception data of a construction site in real time through a multi-source heterogeneous sensor network, including environment parameters, equipment operation states, construction progress and personnel behavior data; performing space-time alignment processing on the multi-dimensional sensing data, and constructing a space-time associated dynamic construction digital twinborn body; establishing a dynamic optimization model based on a reinforcement learning algorithm, taking construction efficiency maximization, resource loss minimization and safety risk minimization as target optimization functions, and embedding risk constraint conditions; inputting the dynamic construction digital twin into the dynamic optimization model, and outputting a multi-dimensional parameter optimization instruction set comprising equipment scheduling parameters, construction path parameters and resource configuration parameters; and performing real-time performance evaluation on the multi-dimensional parameter optimization instruction set through edge computing nodes, and dynamically adjusting weight parameters of the model to form closed-loop feedback control. The purpose of cooperatively improving the construction efficiency, the safety and the economical efficiency is achieved.

The invention provides a business data mining method and system applied to intelligent port scheduling, and the method comprises the steps: firstly obtaining real-time operation data containing an equipment operation state, a transportation task queue and environment monitoring information during the loading and unloading operation of a target port, then carrying out the dynamic preprocessing of the real-time operation data, screening and marking conflict tasks according to a preset rule, and obtaining effective operation data; and then performing multi-dimensional feature coding on the effective data to generate an operation feature set containing equipment operation features, task distribution features and environment constraint features, and based on the operation feature set, generating a port equipment scheduling strategy meeting balance of operation efficiency and resource consumption through a pre-training model, covering an equipment start-stop sequence and the like. And finally, dynamically adjusting port equipment to execute tasks according to a scheduling strategy, and updating real-time operation data, thereby realizing efficient and reasonable scheduling of the intelligent port.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to the technical field of data processing, and discloses an automobile manufacturing industrial data distributed processing method and system based on digital twinning. The method comprises the following steps: monitoring and collecting production abnormal events, decision emergency degree parameters and simulation task types based on service states to generate a service priority evaluation data set, and performing priority ranking on a plurality of simulation tasks to form a dynamic priority queue and a resource demand matrix, the queue is input into a distributed simulation engine for CPU, memory and GPU load balancing processing to generate a node resource allocation scheme, and preemptive scheduling is executed to migrate low-priority tasks to idle nodes to form a task execution mapping table; and monitoring simulation progress and resource consumption through an adaptive scheduling mechanism to obtain scheduling performance feedback data, and updating the dynamic priority queue. According to the method and the device, the problem of lack of an intelligent scheduling mechanism during concurrent execution of multiple simulation tasks in the prior art is solved, and the response speed of the key simulation task and the utilization efficiency of computing resources are improved.

The invention belongs to the technical field of intelligent electric power inspection and multi-agent cooperative control, and provides an electric power multi-agent dynamic cooperative inspection method and system, and the technical scheme is that a path planning scheme is generated by using a reinforcement learning algorithm and a large language model, so that an agent can dynamically adjust an inspection path according to real-time data and task requirements, and the inspection efficiency is improved. Therefore, the minimization of the path and the maximization of the inspection efficiency are realized, and unnecessary travel and resource consumption are reduced. Meanwhile, a dynamic task redistribution mechanism in the system ensures that the intelligent agent can quickly respond and redistribute tasks under the conditions of equipment failure, environment change or task priority adjustment. And real-time communication and data synchronization among the intelligent agents ensure information sharing, so that the system still keeps task continuity and reliability in emergency.

A method for inversing a depth of a subsurface chlorophyll-a maxima (SCM) of an oceanic water body based on remote sensing reflectance is provided. The method includes: band selection, R value calculation, data collection, model construction, and ZSCM calculation. This method can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentrations, sea surface temperature data and sea surface height data, and reduce the calculation resource consumption of running machine learning, but also effectively improve the inversion accuracy, and effectively reduce the calculation resource consumption and inversion difficulty of inversing the SCM.

The invention discloses a fusion question and answer method, device and equipment of a hybrid expert large language model and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a question input by a user into a pre-trained routing model, obtaining k target expert models which are selected from the N expert models by the routing model and are used for answering the questions input by the user; and answering the question input by the user through the k target expert models, fusing expert answers output by the target expert models, and outputting the fused expert answers. The answers of the k target expert models are screened through the routing model and are fused and output, and the output of the model focuses on the results of the k expert models with relatively high task correlation, so that the resource consumption is reduced, the response time is shortened, and the question and answer fusion efficiency is improved. In addition, expert model outputs from different task fields are fused, so that the generalization ability of the model is enhanced, and the reliability of fused questions and answers is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an intelligent optimization method, device and equipment for query statements and a medium. The method comprises the steps of recording execution plan information and resource consumption information of an original query statement, generating original execution information, preprocessing the original query statement and the original execution information to generate preprocessing data, extracting feature information from the preprocessing data, generating optimization information through a pre-trained optimization model based on the feature information, and sending the optimization information to a server. And according to the optimization information, adjusting the content or execution mode of the original query statement, and generating an optimization query instruction. According to the method, the feature information is extracted by monitoring the actual query behavior and combining the execution performance, and the optimization strategy is generated by utilizing the training model, so that the automatic optimization of the original query statement is realized, the manual intervention cost is reduced, and the intelligent adaptation capability of the query performance is improved.

The invention relates to the technical field of project management, in particular to a BIM-based building project resource scheduling optimization method, which comprises the following steps: based on the current progress state of a construction task, collecting the consumption of constructors, mechanical equipment and materials, comparing the task plan progress with the real-time progress, screening out construction links with the backward task progress, and establishing a construction project resource scheduling optimization model; and judging a resource type, and establishing a task progress offset. According to the method, the construction progress is accurately monitored, real-time evaluation of resource consumption is achieved, waste and scheduling lag are avoided, the construction path is optimized by means of the BIM model, the key path monitoring precision is improved, progress management and control effectiveness is ensured, key path progress deviation is analyzed, the resource shortage risk is recognized, stable supply of construction resources is guaranteed, and on the basis of the influence of resource shortage, the construction efficiency is improved. The resource distribution sequence is optimized, the resource utilization efficiency is improved, the supply chain delivery cycle is matched, the resource scheduling is optimized, the influence of resource supply and demand imbalance on the construction progress is reduced, and the continuous and stable construction progress is ensured.

The invention relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

The invention discloses a project progress and resource early warning method and system based on dynamic regulation and control, and the method comprises the steps: 1) collecting project progress and resource data in real time, and constructing a multi-source data fusion model; 2) based on the constructed multi-source data fusion model, judging whether the construction progress and the resource consumption are abnormal or not, and if so, executing the step 3); otherwise, continuing conventional construction progress tracking and resource management; 3) triggering a grading early warning mechanism, generating and executing an optimization regulation and control strategy, and adjusting the construction progress and resource configuration; and 4) evaluating the regulation and control effect, and returning to the step 2) after collecting feedback data. According to the method, the actual construction progress deviation and the resource supply and demand imbalance condition are comprehensively considered, the current overall operation condition of the construction project is analyzed, the construction management strategies are flexibly switched, the management parameters are flexibly adjusted, the construction progress can be rapidly and effectively guaranteed, resource utilization is optimized, and the construction efficiency is improved. And the intelligent level and the response efficiency of construction management are obviously improved.

The invention relates to the field of cloud computing and distributed computing, and discloses a cloud data processing system based on an artificial intelligence algorithm, which comprises a data input module, a task decomposition module, a resource scheduling module, a parallel computing module and a result reconstruction module, the data input module receives high-dimensional data and converts the high-dimensional data into a tensor structure; the task decomposition module decomposes the tensor, generates a core tensor and an orthogonal matrix, and generates a plurality of sub-tasks; the resource scheduling module is used for carrying out resource allocation and scheduling on the sub-tasks, and task execution time and resource consumption are minimized; the parallel computing module distributes subtasks to the computing nodes for execution, and the task execution sequence is guaranteed; and the result reconstruction module combines and verifies the execution results of the subtasks to generate a final calculation result. The cloud data processing efficiency and the resource utilization rate are effectively improved, the accuracy and consistency of calculation results are ensured, and the method is suitable for the fields of artificial intelligence training, large-scale data analysis, scientific calculation and the like.

The invention belongs to the technical field of electromagnetic modeling of a target in a layered medium, particularly provides a planar multilayer medium electromagnetic scattering modeling method based on a layered medium Green function, and is used for solving the problems that the number of unknown quantities of a system is large and the calculation efficiency is low when electromagnetic scattering characteristics of the target are analyzed in the layered medium. The method comprises the following steps: firstly, carrying out mesh generation on a medium target in a planar multilayer medium; then, a layered medium Green function is calculated based on a space list and an interpolation method, a surface integral equation of the target is solved by adopting a moment method, and a matrix equation of target current and magnetic current is constructed; and finally, solving a matrix equation by applying an improved laminated off-diagonal low-rank matrix, and calculating a target scattering field. According to the method, a direct solving calculation method is adopted, and the problem that an ill-conditioned matrix is difficult to converge in a conventional iteration method is effectively solved; especially for a multi-port excitation problem, repeated calculation is not needed, the solving efficiency of a large-scale problem is improved, and calculation resource consumption is reduced.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

Systems for explainable large language model routing with immutable audit trails are disclosed. The system receives a query and determines its characteristics including complexity, domain, regulatory constraints, and performance requirements. It retrieves profiles for multiple LLMs from a model matrix containing performance attributes, resource consumption, and compliance parameters. The system selects a particular LLM by balancing resource consumption with performance requirements, evaluating regulatory compliance, ranking LLMs based on these factors, and prioritizing models with successful processing history. The system generates a human-readable explanation of the selection including decision factors, rationale, and alternatives considered. Finally, it records the selection and explanation in a tamper-evident, immutable audit trail data structure.

The invention discloses a power enterprise intelligent collaborative office method and system based on artificial intelligence, and relates to the technical field of power informatization. According to the intelligent collaborative office method and system for the power enterprise based on artificial intelligence, request data is received from a user terminal through a deep learning model, a service demand type, an emergency degree and task complexity are identified, a service priority index is generated, classification and priority distribution are carried out, and based on the service priority, the user terminal is subjected to intelligent collaborative office. The method comprises the following steps of: matching associated tasks and resource requirements through a model, retrieving related historical data to optimize a resource configuration path, dynamically tracking task progress, execution state and resource consumption, identifying a bottleneck in an execution process and generating an optimization instruction, and finally, dynamically adjusting model parameters and resource configuration rules through an adaptive learning algorithm. And a closed-loop feedback mechanism is formed. Information flow and resource configuration are effectively integrated, the flexibility and response speed of collaborative office are improved, and the development requirements of smart grids and smart cities are met.

The invention relates to the technical field of decoration construction management, and discloses a BIM-based decoration construction scene resource consumption simulation analysis method. The method comprises the following steps: firstly, creating a multi-dimensional BIM resource model, and integrating building information model data, real-time construction data flow and historical resource consumption records; identifying resource consumption characteristics through the model, and generating an initial resource allocation strategy set comprising material allocation, equipment scheduling and process priority schemes; receiving a user interaction instruction, and dynamically adjusting the initial strategy set; and finally, executing the adjusted strategy, and updating the multi-dimensional BIM resource model based on a result. Wherein the resource consumption feature comprises construction stage division, resource node dynamic weight calculation and space correlation correction operation. According to the method, by integrating multi-dimensional data and dynamically analyzing a resource consumption rule, real-time optimization of a resource allocation strategy is realized, and the accuracy and flexibility of decoration construction resource management can be improved.

Reducing resource consumption spikes in an anomaly detection framework, including: gathering information describing historical job execution associated with a plurality of customers; assigning, based on the information describing historical job execution, each of the plurality of customers to a particular grouping of a plurality of groupings, wherein each of the plurality of groupings corresponds to a particular time offset within a time interval; and scheduling, for each grouping of the plurality of groupings, at a time offset of a corresponding grouping, execution of one or more jobs associated with one or more customers of the corresponding grouping.

The invention relates to the technical field of digital twinning, and particularly discloses a historical building structure evaluation system based on digital twinning, which comprises a multi-source data acquisition module, a dynamic digital twinning modeling module, a structure health evaluation module, an intelligent maintenance decision module and a collaborative optimization analysis module. According to the scheme, geometric, material and environment information of the historical building is captured, omnibearing monitoring of the historical building is realized through a digital twin modeling technology, an intelligent evaluation model is constructed in combination with finite element analysis and an SVM algorithm, a high-risk area is rapidly identified, and authenticity and accuracy of an evaluation result are ensured; a reinforcement learning algorithm is used to adapt to complex and changeable environmental requirements of historical buildings, a maintenance strategy is automatically generated, cooperation efficiency between modules is continuously optimized through collaborative optimization analysis, the overall operation performance is improved, evaluation precision is guaranteed, and meanwhile system resource consumption is effectively controlled.

The invention relates to the technical field of environmental impact assessment, in particular to an intelligent environmental impact assessment method and system, and the method comprises the following steps: collecting environmental data including pollutant discharge, water resource consumption, air quality change and biodiversity, building an impact factor data set, and segmenting the data set to obtain an impact factor data set; and calling a kernel function type to carry out numerical prediction on pollutant emission. According to the method, by integrating data processing and machine learning technologies, the accuracy and efficiency of environmental impact assessment are improved, numerical prediction is performed on environmental data by using a kernel function and a support vector regression model, the adaptability and precision of a prediction model are enhanced, model parameters are optimized through automatic population generation and crossover mutation operation, and the accuracy and efficiency of environmental impact assessment are improved. The model can better learn and adapt in a complex data environment, sensitivity and response speed to environmental changes are further improved through dynamic clustering analysis and time sequence adjustment, and scientific support is provided for formulating timely and effective environmental protection measures.

The invention relates to the technical field of artificial intelligence, in particular to a scene application operation maintenance platform based on an artificial intelligence model, which comprises a reasoning state sensing module, a load balancing scheduling module, an intelligent resource allocation module, a deployment configuration adjustment module and a scene feedback integration module. According to the method, by sensing and reasoning process resource consumption and response time migration trend, abnormal tasks caused by resource fluctuation can be accurately recognized, the state monitoring sensitivity is improved, task allocation records are analyzed to construct conflict structures, the resource conflict recognition and visualization capability is enhanced, the model operation level and the GPU vacancy rate are combined, a task migration path is optimized, and the task migration efficiency is improved. Node resource balance is realized, storage frequency, memory margin and network delay are fused during deployment, migration stability is guaranteed, drift tasks are identified through response logs and call records, an operation and maintenance list is generated, operation and maintenance accuracy and multi-scene matching capability are improved, and reasoning task stability and resource scheduling intelligent level are enhanced.

The invention relates to an intelligent computing resource allocation method based on reinforcement learning. According to the technical scheme, the method comprises the steps that monitoring data from different sources including hardware monitoring, software logs and network bandwidths are fused, and a multi-dimensional time sequence state space is formed; carrying out dimensionality reduction and de-noising processing on the multi-modal data by using a depth auto-encoder; multi-task learning MTL is introduced into time sequence modeling, and resource requirements and state evolution of multiple tasks are predicted at the same time; generating a plurality of predictive resource scheduling strategies by using a GAN (Generative Adversarial Network), and dynamically selecting a strategy scheme when a load changes; each strategy is realized by an independent sub-network, and part of core knowledge is shared; through a strategy evolution mechanism, according to a historical feedback optimization strategy combination including task completion time and resource consumption, in a heterogeneous resource environment including a plurality of cloud computing platforms and edge computing nodes, based on difference of resource types, fine-grained scheduling of strategies is carried out; and scheduling the decision by using a distributed Q-learning mechanism in the reinforcement learning model.

The invention relates to the technical field of front-end development, in particular to an application fusion method based on a micro-front-end technology, which comprises the steps of sub-application registration, state management and synchronization, dynamic routing and navigation, cross-platform compatibility and adaptive design, resource optimization and loading strategy, performance monitoring, fault processing and the like. Registration and management of a plurality of sub-applications are realized through a uniform interface specification, a distributed state management mechanism is adopted to update and synchronize a global state, a dynamic routing system loads and renders the sub-applications as required according to user operation, and a cross-platform compatible design ensures consistent experience on different devices. According to the resource loading strategy, pre-loading and lazy loading are carried out by intelligently predicting user behaviors, unnecessary resource consumption is reduced, and the performance monitoring module monitors the running state of the system, automatically processes exceptions and provides a fault recovery mechanism. According to the method, the performance and the cross-platform adaptation capability of the micro-front-end application are effectively improved, and the method is suitable for development and deployment of complex enterprise-level applications.

The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance management system and method based on artificial intelligence, and the system comprises an operation baseline modeling module, a resource consumption prediction module, an automatic resource allocation module, a data correlation analysis module, an abnormal data detection module and a fault path analysis module. According to the method, accurate data support is provided for resource management through real-time monitoring of operation data and baseline performance modeling, resource demands are analyzed and predicted in combination with a time sequence, shortage and excess in resource configuration are identified, automatic allocation of resources is achieved, optimal matching between resource supply and demands is ensured, and the optimal resource allocation efficiency is improved through construction of a dependency graph. The interaction between the data stream and the application is analyzed, abnormal data detection and fault path analysis are helped, fault problems are found in real time, reasons are identified, the downtime of the system is shortened, the continuity of services and the integrity of data are ensured, and the automation degree and the intelligent level of operation and maintenance management are improved.

The present invention belongs to the technical field of network function virtualization and relates to a method and apparatus for optimizing a cross-domain deployment of an SFC (Service Function Chain) based on VNF (Virtual Network Function) migration, a computer device, and a storage medium. The method includes: acquiring a node resource of VNFs and a resource requirement of flow processing; performing the cross-domain deployment of the SFC according to the node resource and the resource requirement; searching for an equilibrium point between a delay and a resource consumption rate of the SFC in combination with the delay and the resource consumption rate of the SFC; and performing optimization processing on the VNF migration according to the equilibrium point to reconstruct the cross-domain deployment of the SFC. First of all, combined with an original deployment of the SFC in a model, whether the VNFs are migrated in the SFC is determined.

The invention provides an AI model training acceleration method and system based on computing power service scheduling, and relates to the technical field of computing power service.Firstly, historical training process data of an AI model and structural features of a current training task model are collected, a computing power demand prediction model is constructed, the structural features of the current training task model are input to generate a computing power resource demand distribution sequence, and a computing power demand prediction model is constructed; the method comprises the following steps of: training a demand change curve of each stage of calculation core quantity, memory bandwidth and data transmission rate, screening a matched computing power resource combination scheme from a computing power service cluster according to a sequence, scheduling computing nodes to execute a training task, and collecting actual computing power resource consumption data in real time; and finally, comparing actual data with the prediction sequence, generating a deviation value, and dynamically adjusting the number of enabled computational nodes, thereby realizing accurate prediction and dynamic optimization scheduling of the computing power demand, improving the utilization rate of computing power resources, and accelerating AI model training.

In various examples, each user of a hosted application may be modeled with a corresponding user-specific resource consumption model that predicts a measure of that user's anticipated (e.g., processing, memory or storage, and / or networking) resource utilization at some future time based on an input representation of one or more features of the user's current session, application (e.g., game) setup, skill or experience level, social media activity, some time series representing feature(s) for a series of interactions, the time of day, and / or other characteristics. As such, user-specific models may be used to predict resource demands at a future time and schedule resource allocations accordingly. The present techniques may be used to manage and reallocate resources for applications such as game streaming applications, remote desktop applications, simulation applications (e.g., an autonomous or semi-autonomous vehicle simulation), virtual reality (VR) and / or augmented reality (AR) streaming applications, and / or other application types.

The invention provides an intelligent maintenance method and system for the wall of a squat silo, and the method comprises the steps: applying controllable environment parameters to a concrete test block group with the same proportion through an environment simulation device, and obtaining test block deformation data, moisture content data and stress data; performing time sequence correlation mapping on the test block deformation data, the moisture content data and the stress data and environmental parameters to obtain a maintenance decision data set; acquiring a real-time environment vector through a sensor network deployed on the site of the squat silo; inputting the real-time environment vector into a maintenance decision model, and generating a spraying control instruction by matching the maintenance decision data set; and driving a spraying system to execute operation according to the spraying control instruction, and dynamically correcting spraying parameters based on moisture monitoring data. According to the method, through data-driven intelligent maintenance decision and dynamic closed-loop control, the crack risk of the wall of the squat silo is remarkably reduced, meanwhile, water resource consumption is reduced, and double breakthrough of maintenance quality and resource efficiency is achieved.

The invention relates to the technical field of plant extraction, in particular to an intelligent plant extraction control method and system, and the method comprises the following steps: based on historic plant extraction data, including temperature, pressure and pH value during extraction, analyzing the long-term trend of parameters, predicting the requirements of plant extraction conditions by simulating parameter reactions under various conditions, and obtaining a plant extraction result; and obtaining a parameter prediction result. According to the method, long-term trend prediction of parameters such as temperature, pressure and pH value is realized through analysis of historically recorded plant extraction data, so that the parameters of extraction equipment are adjusted in advance, changes of different plant materials and environmental conditions are effectively adapted, the extraction efficiency is optimized, and by dynamically simulating the expression of the extraction efficiency in various time periods, the extraction efficiency is improved. According to the innovative scheme, an optimal extraction time window is mined, resource consumption and extraction time in the extraction process are reduced, and meanwhile the quality and consistency of extracts are kept.

Techniques associated with categorizing simulation instances associated with simulation testing comprise categorizing simulation instances that produce unique or otherwise interesting results for further testing, review, and validation. In some cases, the system may be configured to determine low period of resource consumption and to schedule simulation testing during those periods of time.