157 results about "Resource analysis" patented technology

The invention relates to the field of heterogeneous computing resource management, in particular to a CPU (Central Processing Unit), GPU (Graphic Processing Unit) and NPU (Network Processing Unit) resource allocation method and system of a training and reckoning all-in-one machine. The invention discloses a CPU, GPU and NPU resource allocation system of a training and reckoning all-in-one machine. The system comprises a schedulable resource analysis module, a load analysis module and a resource allocation module. According to the method, the reference parameters and the real-time dynamic indexes of heterogeneous hardware are fused, so that deep perception and efficient quantification of computing power resources are realized; on the basis of an NPU temperature attenuation experiment, calibrating a computing power loss coefficient, and dynamically correcting available weight values of a CPU, a GPU and an NPU in combination with suitability analysis of a GPU stream processor utilization rate and a task batch processing demand and collaborative efficiency evaluation of a CPU dominant frequency and an IPC value; and the accuracy of resource availability prediction is improved, so that the system can still guarantee the stability of computing power output in complex environments such as high temperature and high concurrency.

The invention discloses a heterogeneous GPU resource management scheduling method, a computer device, a medium and a product. The method comprises the following steps: acquiring computing power resources of each node, including a GPU model, a GPU video memory, a GPU number, a computing power segmentation scheme and a GPU use condition; automatically segmenting the heterogeneous GPU of each node according to the computing power segmentation scheme of each node to obtain resource segmentation information; obtaining an expected computing power and an expected video memory of a to-be-executed task; screening out target nodes meeting the expected computing power and the expected video memory according to a node analysis strategy; and according to the sub-resource analysis strategy, screening out sub-resources meeting the expected computing power and the expected video memory, recording the sub-resources as target sub-resources, and allocating the to-be-executed task to the target sub-resources. According to the method, GPU resource fragmentation is effectively reduced through global and node resource conjoint analysis scheduling, meanwhile, pooling management, dynamic configuration and intelligent scheduling of heterogeneous computing equipment can be achieved, and the resource utilization rate is effectively increased.

The invention provides a computer big data storage control system, and relates to the technical field of computers, and the system comprises a data evaluation module which is used for comprehensively calculating the real-time popularity of data, and the real-time popularity is obtained through the comprehensive calculation of the data access frequency and the access duration; performing quantitative evaluation on the real-time popularity of the data in combination with a multi-dimensional factor to obtain a value evaluation value of the data; analyzing the system resource information of the storage device on the computer to obtain a resource analysis result; and the intelligent contract execution module is used for carrying out comprehensive analysis and judgment on the real-time popularity, the value evaluation value and the system resource analysis result of the data so as to trigger adjustment of a storage strategy. According to the method, the real-time popularity model is constructed, the analytic hierarchy process is combined to achieve multi-dimensional value evaluation, the intelligent contract dynamically adjusts the storage strategy according to the evaluation result and the resource state, the data value and the storage medium are accurately matched, the problems that a traditional system is extensive in evaluation and rigid in strategy are solved, and efficient and accurate scheduling of storage resources is achieved.

The invention discloses a real-time game image optimization method based on intelligent layered rendering, and particularly relates to the technical field of rendering, which comprises the following steps: carrying out frame rate monitoring on a real-time game image through a frame rate monitoring tool, and preliminarily identifying whether a rendering strategy is adaptive or not; extracting features of the images with the unmatched signs to obtain a feature group I and a feature group II; the first feature group generates a rendering complexity index through a pre-trained machine learning model, and the second feature group performs resource analysis to generate a resource load index; determining an optimization strategy type by using fuzzy logic in combination with the rendering complexity index, the resource load index and the target frame rate; the problem that the rendering strategy is not matched can be found in time, the rendering complexity and the resource consumption condition of the current game image can be known through the rendering complexity index and the resource load index, the rendering optimization strategy is intelligently determined, the capability of adapting to different hardware platforms is achieved, and the method and the device are convenient to use. And thus, players can obtain smoother experience in the game process.

The invention provides a management scheduling system and method for computing power resources, relates to the field of management scheduling of computing power resources, and solves the technical problems that a large number of resources are idle or wasted and efficient resource allocation and scheduling are difficult to realize due to the fact that a resource allocation mode in the prior art cannot be dynamically adjusted according to actual requirements. The method comprises the following steps: a cloud server extracts a current idle computing power node and a corresponding computing power resource, and analyzes the computing power resource of the idle computing power node; the client obtains the computing power demand task, and calculates a difference index between the computing power demand task and the idle computing power node; based on the difference index, obtaining a matching computing power node of the computing power demand task; and when the plurality of computing power demand tasks correspond to the same matched computing power node, the priorities of the plurality of computing power demand tasks are analyzed, and the matched computing power node sequentially executes the tasks based on the priorities of the computing power demand tasks. The method and the device are used in a computing power resource management scheduling process.

The invention discloses a river and lake backflow recognition and driving mechanism analysis method based on interpretable machine learning, and relates to the technical field of hydrology and water resource analysis and artificial intelligence application, and the method comprises the steps: collecting long-sequence hydrology data of a river and lake system; taking operation nodes of the large hydro-junction project as boundaries, and dividing the time sequence into different characteristic periods; constructing a multi-dimensional input feature set; a class weight balance strategy and a Bayesian optimization algorithm are adopted to train a backward flow recognition model based on a gradient lifting decision tree; a marginal contribution value of each hydrological driving factor is calculated by using an SHAP interpretability method, a physical threshold for inducing backward flow is identified by combining an SHAP dependency graph with a binary box plot, analysis results in different periods are compared, and an evolution rule of a backward flow driving mechanism is revealed. The method effectively solves the problems that a traditional method is difficult to capture nonlinear hydrological response and the black box model lacks physical mechanism explanation, and can accurately recognize the backward flow event.

The invention discloses a service scene-oriented resource fault root cause analysis system. The system comprises a service link management module and a fault root cause analysis module, the service link management module is used for monitoring a service system, generating panoramic service link conditions, monitoring operation states of all service links and counting operation indexes; the service link management module is in signal connection with the fault root cause analysis module and is used for carrying out panoramic resource analysis on fault information generated by the service link management module after a service link reports an error, positioning a fault point and issuing fault related information; the invention further discloses a service scene-oriented resource fault root cause analysis method. According to the invention, deeper, comprehensive and accurate operation and maintenance management of the service system is realized.

The invention discloses an RAG-oriented embedded service flexible deployment method, and belongs to the technical field of computer resource configuration. The method comprises the following steps: firstly, obtaining an embedding model suitable for an RAG scene, constructing an embedding service, testing different resource configuration parameters in batches through automatic resource analysis, and screening out optimal resource configuration meeting service quality requirements; secondly, an RPS monitoring and dynamic batch processing mechanism is integrated, and a GPU resource fragmentation optimization algorithm and a three-level candidate GPU scheduling strategy are combined; triggering a hybrid capacity expansion and contraction mechanism according to a reasoning request load to form cooperative scheduling; and finally, reasoning requests are distributed through a polling mechanism, and service performance is continuously optimized. Through the synergistic effect of automatic resource analysis, GPU resource fragmentation optimization and hybrid capacity expansion and contraction strategies, the GPU resource utilization rate and embedded service performance can be remarkably improved, and the high-concurrency and low-delay reasoning requirements in an RAG scene are effectively met.

The invention discloses a paperless conference management system and method for power plant office, and the system can give an early warning in time when a network goes wrong by collecting the network state information in real time and calculating the network quality degradation index, thereby achieving the active monitoring and response to the network abnormality, and reducing the risk of conference interruption. In the conference process, the communication module continuously monitors the network performance, and the resource analysis module optimizes the conference network prediction model through the machine learning technology, thereby effectively improving the audio and video synchronization and resource management capability of the conference. And the conference management module dynamically generates a cold start optimization strategy and a cache preheating strategy according to the conference performance evaluation coefficient, so that the conference start speed and the resource utilization efficiency are improved. And an audio and video synchronization mechanism is adjusted according to the real-time jitter information, so that high-quality synchronization of the audio and the video is ensured, and the usability and experience of the conference are further improved.

The invention provides a science and technology resource data extraction system based on multi-source data fusion, and the system comprises a science and technology information publishing module which is used for a science and technology institution to publish science and technology resource information and a science and technology resource demander to submit science and technology data demands; the intelligent matching module is used for matching the demand of the science and technology resource demander with the science and technology resources; the data tracking module is used for tracking use data of the science and technology resources in real time, wherein the use data comprise the downloading amount, the reference frequency and the updating state; the quality evaluation module is used for evaluating the reliability of the scientific and technological resources through a neural network to obtain a quality evaluation value; the report generation module is used for generating a resource analysis report and / or a quality evaluation report; according to the method, the scientific and technological resource data extraction efficiency can be effectively improved, the labor cost is reduced, and personalized scientific and technological resource data management and data extraction are realized.

The invention relates to the technical field of heterogeneous computing power allocation, in particular to a heterogeneous computing power dynamic allocation method based on big data analysis, which comprises the following steps: analyzing a source, a target, a type and dependency of a data task, normalizing parameters and extracting features, identifying a key task, combining a computing power resource queue and historical scheduling efficiency, optimizing an allocation state, and allocating the heterogeneous computing power according to the allocation state. And screening the computing power resources which run normally, analyzing the dominant frequency temperature and response performance, adjusting the distribution relation, and forming a dynamic distribution sequence. According to the method, standardization, normalized mapping and feature aggregation are carried out on the data task multi-dimensional labels, deep mining and classification of task features can be achieved, dynamic linkage of task requirements and computing power resource states is completed according to big data analysis, relevance between the tasks and heterogeneous computing power resources is finely quantified, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. And in combination with a resource response expression and grading mechanism, a task allocation strategy is automatically adjusted, and the resource adaptation degree and allocation sequence matching during task processing are guaranteed.

The invention provides a node resource processing method and apparatus, a device, a medium and a program product, which are applied to a cloud computing platform architecture comprising N nodes, one node is deployed with an agent component, and one node comprises one or more resource nodes. The method comprises the following steps: when a resource scheduling request is received, acquiring a node resource topology information set; based on the resource scheduling request and the node resource topology information set, performing resource analysis processing on the N nodes, and determining a target node; resources in the target node are pre-allocated based on the resource scheduling request, resource annotation information is generated based on a resource allocation result of the target node, and the resource annotation information is used for indicating that the allocated resource node is bound with the resource object in the target node. According to the method, the node resources can be allocated and scheduled according to the node resource topological structure and the resource scheduling request, so that the node resources can be reasonably allocated, the scheduling result meets user requirements, and the resource scheduling effect is improved.

The invention relates to the technical field of computers, and discloses a data backup method, device and equipment and a medium, which are applied to a data backup system, and comprise a data label generation module, a cloud resource analysis module, a cross-cloud scheduling module and a data backup module. Generating structured label data based on the key information of the to-be-backed-up data through a data label generation module; determining a target access frequency and a target confidentiality level of the structured tag data through a cloud resource analysis module, and determining path information; generating backup summary information based on the path information and the to-be-backed-up data through the cross-cloud scheduling module, and sending the backup summary information to the data backup module; and carrying out structured storage on the backup summary information through a data backup module, and storing the backup summary information to a distributed block chain so as to back up the to-be-backed-up data. The method can be applied to business program systems such as financial science and technology, medical health care and the like, and the efficiency, accuracy and compliance of data backup can be improved.

The invention provides a teaching platform resource optimization system based on cloud computing, and belongs to the technical field of electrical digital data processing and product data management, and the system comprises the steps: obtaining resource use data of a teaching platform through a resource collection module, and transmitting the resource use data to a resource analysis module; obtaining feedback opinion data of the trainees and the instructors through a feedback module, and processing the feedback opinion data to obtain a correlation matrix between each feedback data point and the resource state dimension; calculating a resource state index through a resource analysis module according to the resource use data and the correlation matrix, and predicting the state of each resource dimension in the future time according to an analysis result; the resource scheduling module determines the priority of the resource demand end according to the analysis result to generate a resource allocation priority table, judges low-efficiency resources according to the comparison between the utilization rate of the resource demand end and a preset threshold value, identifies and releases the low-efficiency resources, and adjusts the allocation of computing resources and bandwidth according to the resource allocation priority table and the released resources.

The invention provides a common routing hidden danger analysis method and system in a communication service reinsurance period and a storage medium, and relates to the field of analysis of resources occupied by communication services. The method comprises the following steps: selecting a hidden danger analysis mode; selecting a reference object and an analysis object; according to the service type, the association relationship between the professional relay and the transmission circuit is inquired, and the non-transmission professional circuit needs to establish the association relationship with the transmission circuit for subsequent analysis; according to the resource occupation relation of the transmission circuit, performing recursive query on a system route of the transmission circuit; according to the transmission circuit resource occupation relation, medium routing of the transmission circuit is inquired in a recursive mode; calculating whether the reference object and the analysis object have the same system hidden danger according to the system route of the transmission circuit; and according to the medium route of the transmission circuit, calculating whether the reference object and the analysis object have the same-channel, same-cable and cross route hidden dangers.

The present invention discloses a method and system for assessing fishery resources in the entire water layer using fishery acoustic methods. This method uses a multi-band sonar system to pre-detect each water layer of the target water body, obtain sonar echo data, analyze the sonar detection quality, and determine the optimal sonar frequency range. Water quality characteristics are detected based on this frequency, and receiving parameters are optimized to obtain fishery resource detection data and optimize data accuracy. Based on a deep learning algorithm, a fishery resource analysis model is constructed to assess the status of fishery resources in the target water body. The assessment results are integrated to generate a spatial distribution change map of fishery resources and predict resource change trends. The system includes modules such as sonar detection, data optimization, analysis and evaluation, and resource prediction, and can accurately assess and predict fishery resources in the entire water layer, improving the scientific nature and sustainability of fishery resource management.

The invention relates to the technical field of constructional engineering, and discloses an intelligent management system for constructional engineering, which comprises a data acquisition unit, a data preprocessing unit, a progress analysis unit, a quality analysis unit, a safety analysis unit, a resource analysis unit and a result display unit. According to the invention, in project management, the progress analysis unit accurately controls the construction progress through key path analysis, progress deviation analysis and overall construction period influence evaluation, and ensures that a project is completed on time; the quality analysis unit starts with building materials and construction processes, monitors quality in real time, and effectively avoids reworking caused by quality problems according to comparison between indexes and a standard range; the safety analysis unit carries out safety analysis from the three aspects of environment, personnel and equipment, and safety accidents can be remarkably reduced according to comparison between various data and safety thresholds; and the resource analysis unit reasonably deploys human resources and reasonably arranges personnel according to a comparison result of demands and the number of people on site, so that the construction efficiency is improved.

This invention relates to the field of aquatic organism management technology, specifically disclosing a method and system for aquatic biological resource analysis based on big data. This invention involves planning multiple monitoring points for interconnected monitoring; performing data cleaning and standardization; calculating and statistically analyzing relatively important indicators; conducting comparative analysis of ecological health based on big data technology; and interactively displaying relatively important information and ecological health information. It enables the planning of multiple monitoring points for interconnected monitoring of aquatic organisms, calculation and statistical analysis of relatively important indicators, comparative analysis of ecological health, and interactive display of relatively important information and ecological health information. This allows for high-precision and diverse sampling, effectively and accurately acquiring basic data, improving the comprehensiveness and accuracy of resource analysis, fully reflecting the ecological health characteristics of aquatic ecosystems, and providing a foundation for accurately assessing the potential for sustainable resource utilization.

The invention discloses a media resource optimization management system based on an intelligent server, which comprises a resource acquisition module, a resource analysis module, a resource storage module, a resource transmission module, a resource management module and an intelligent decision module. And intelligent management of media resources is realized. The intelligent server can quickly process a large amount of media data, powerful support is provided for operation of various optimization algorithms, the system can operate efficiently and accurately, and the high requirement of modern media services for resource management is met.

The invention, which relates to the technical field of sludge intelligent monitoring, discloses an intelligent monitoring system for dry and wet sludge in a power plant, comprising a data acquisition unit, a demand analysis unit, a processing and adjusting unit, a resource analysis unit and an abnormity early warning unit. The treatment adjusting unit receives the drying treatment data and performs analysis and calculation to obtain a drying demand index of the wet sludge, and the drying demand index is used for reflecting the drying treatment efficiency of the wet sludge; the resource analysis unit receives the resource utilization data and performs analysis and calculation to obtain a drying demand index of the wet sludge, and the drying demand index is used for reflecting the utilization value of the dry sludge; a resource utilization index model is innovatively introduced through the resource analysis unit, the calorific value, the organic matter content and the heavy metal content of the dry sludge are comprehensively considered, a scientific value grading mechanism is established, it is ensured that the high-value sludge is preferentially used for fuel replacement or building material production, and the low-value sludge triggers a harmless treatment process in time through the abnormity early warning unit; the environmental pollution risk is effectively reduced.

Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

The invention discloses a power grid multi-resource joint collaborative optimization peak-load regulation scheduling method and a power grid multi-resource joint collaborative optimization peak-load regulation scheduling system. The method comprises the following steps: S1, collecting power grid data, and analyzing a support mechanism and a mutual coupling influence mechanism of source network load storage multi-resources on power grid peak regulation and supply guarantee according to the power grid data to obtain a resource analysis result; s2, establishing a peak regulation effect evaluation index system according to a resource analysis result in S1; s3, according to the resource analysis result in the S1 and the evaluation index system in the S2, establishing a power grid multi-resource joint collaborative optimization peak regulation scheduling model; and S4, substituting the resource analysis result in the S1 into the power grid multi-resource joint collaborative optimization peak regulation scheduling model in the S3, and solving to obtain an optimal scheduling plan of the power grid under the condition of difficult peak regulation and supply guarantee. According to the method, the scheduling result can be evaluated from multiple aspects of safety, adequacy, economy and high efficiency, and a scheduling scheme optimization reference is provided for scheduling personnel.

The invention relates to the technical field of data processing, in particular to a data processing method and system based on machine learning and computer equipment. Comprising the steps of obtaining multiple pieces of initial data based on multiple data sources; obtaining the data closeness degree of each piece of initial data, and classifying the initial data based on the data closeness degrees to obtain a plurality of data classification libraries; and performing data division on each data classification library based on frequency band distributed nodes to obtain a plurality of data calculation nodes, and performing data processing on each data calculation node based on machine learning parallel data processing to obtain a first frequency band data set. Initial data are obtained from multiple sources, information of different channels is integrated, limitation of a single data source is avoided, a comprehensive and rich data basis is provided for spectrum resource analysis, reliability and integrity of an analysis result are guaranteed, and through data closeness degree classification, frequency band distributed node division, parallel processing and the like, the spectrum resource analysis efficiency is improved. And the data processing speed and the spectral analysis accuracy are improved.

The application discloses a data intelligent service method and device under a mixed model, equipment and a storage medium, relates to the technical field of artificial intelligence technology, and comprises the following steps: acquiring user question and answer information and user search information; identifying at least one of a corresponding query intention category, an inquiry intention category, an exploration behavior category and a search behavior category based on the user question and answer information or the user search information, and determining at least one of query intention category information, inquiry intention category information, exploration behavior category information and search behavior category information; predicting a corresponding service demand based on at least one of the query intention category information, the inquiry intention category information, the exploration behavior category information and the search behavior category information, and determining service demand prediction information; and analyzing a corresponding data resource based on the service demand prediction information, and determining a data resource analysis result. The application identifies query, inquiry, exploration and search intentions, performs data interpretation, and adapts intelligent service.

The invention discloses a business processing method and device, equipment and a readable storage medium, and the method comprises the steps: obtaining the historical business data of a business object in a first business mode, carrying out the resource analysis of the equipment transaction data in a historical time period based on a resource transaction rule of a second business mode, and obtaining the business data of the business object; obtaining predicted business data of the business object in the second business mode; searching N target service packets of which the service parameters are matched with the equipment service parameter set in the historical time period from the service packet set in the second service mode; and based on the business service parameters, the historical business data and the predicted business data of each target business packet, carrying out statistics on resource optimization information of each target business packet and pushing the resource optimization information to the business object. The method can be applied to the map field, the traffic field, the automatic driving field, the vehicle-mounted scene, the cloud technology, artificial intelligence, intelligent traffic, auxiliary driving and other scenes, the subscription adaptation degree of the cloud platform is improved, and the subscription cost is reduced.

The invention provides a project resource analysis method and device, equipment, a medium and a program product. The method comprises the steps that a trigger instruction of resource analysis is received, an analysis interface is displayed, and the analysis interface comprises a problem input area and an analysis display area; in response to a reference instruction received in the question input area, a reference interface is displayed, and the reference interface comprises at least one reference content; in response to a reference operation on a target reference content in the at least one reference content, adding the target reference content to a resource analysis problem of the problem input area; and in response to sending of the resource analysis problem, displaying a resource analysis result corresponding to the resource analysis problem in the analysis display area. According to the invention, the resources can be analyzed through the large model in project management, the current situation and problems of the project resources can be accurately known, and valuable insight is provided for project managers.

The invention relates to the technical field of resource processing, provides a resource data fluctuation analysis processing method and device, a storage medium and a program product, and can automatically perform resource data fluctuation analysis processing. The method comprises the following steps: in response to a data extraction trigger event, extracting first resource data and corresponding first service volume data of each service in an analysis period from a multi-service system; classifying the first resource data of each service and the corresponding first service volume data according to preset multi-class resource inspection tasks to obtain second resource data and corresponding second service volume data under each class of resource inspection tasks; for each type of resource inspection task, acquiring fluctuation information of the second resource data and the corresponding second business volume data under each preset resource analysis dimension and / or each resource analysis dimension combination, and obtaining a fluctuation analysis result under the resource inspection task according to the fluctuation information; and triggering creation of a corresponding resource processing task according to a fluctuation analysis result.

The invention relates to the field of human resources, in particular to a multi-mode human resource analysis system and method based on a large model, and the system comprises a user interaction module which is used for inputting an instruction and displaying an analysis result; the mode selection module is used for providing selection of an analysis mode and transmitting a user instruction to a locally deployed large model inference engine; the data loading module is used for loading staff related information and investigation detailed rule related information from local storage equipment; the large model reasoning module is used for calling a locally deployed large model to generate a comprehensive analysis result according to the employee related information and the investigation detailed rule related information; the design does not need to depend on an external network or cloud service, data privacy and safety are ensured, the large model reasoning speed is remarkably increased, large-scale employee data and complex rule files can be processed, and enterprise-level application requirements can be met.

A report generator is provided that produces informational reports during an identified event or incident. For example, for a given a declared event, such as a flood warning, relevant data is gathered, such as the event declaration, current and forecast weather, power outages and predictions, news and social media. This data is sent to the report generator for analysis, which is a large language model. A defined or selected area may be used so that the data is relevant to the selected area, which may also be subject to the identified event. Users are able to view the generated reports on a platform or have the reports sent via alerts as soon as they are created. The generation of summaries of discussions in a collaboration forum are also provided. Incident resource analysis techniques provide determinations of, for example, resource-at-risk needs for an event.

A system includes a processor that executes computer executable components stored in a memory. The computer executable components include an application identification component that identifies a new imperative application where recovery workflow for the new imperative application is unavailable. The computer executable components include an analysis component that analyzes the new imperative application to determine resources required to be protected; the analysis component determines an order of steps to be performed within a workflow to guarantee recovery of the new imperative application. The computer executable components include a recovery design component that utilizes a subset of legacy recovery plans to create, at least in part, a preliminary recovery plan for the new imperative application; the recovery design component tests the preliminary recovery plan and iteratively updates the preliminary recovery plan until a failure-free recovery plan is developed for the new imperative application.