98 results about "Resource analysis" patented technology

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 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 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 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 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 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.

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, 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 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.

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.

The invention relates to the field of human resource management, in particular to a human resource management method based on multi-modal data fusion and adaptive learning, and adopts the technical scheme that the method comprises the following steps: constructing a time sequence data portrait of a target object through static attribute data, dynamic behavior data and external environment data; and inputting the multi-modal data into a preset feature extraction model for feature extraction to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a pre-trained human resource analysis model to obtain an analysis result, and finally executing a corresponding human resource management operation according to the analysis result. The personnel and post matching degree score, the demission risk probability and the recommended development path are analyzed, intelligent resume screening and personnel and post matching optimization can be carried out, employee demission risk prediction and intervention suggestion can be carried out, personalized employee development path planning can be carried out, and the employee development path planning efficiency is improved. And deep learning and dynamic optimization capabilities are improved through an algorithm framework of multi-modal data fusion and dynamic adaptive learning.

This application provides a method, apparatus, and electronic device for determining satellites used for storing and forwarding data, relating to the field of communication technology. The method is applied to a satellite resource analysis network element and includes: acquiring demand information, wherein the demand information includes: data transmission requirements of the transmitting end, the location information of the transmitting end, resource usage information of the satellite network, ephemeris information, and the location information of the receiving end; determining, based on the demand information, a set of target satellites from the satellite network for storing and forwarding data transmitted by the transmitting end; and transmitting information carrying the target satellite set to the transmitting end. Based on the above method, satellites that meet the actual data transmission requirements of the data transmitting end can be accurately determined, improving the space utilization rate of satellite storage and avoiding situations where satellites cannot provide services due to insufficient space, even though there are actually idle satellites in the satellite network.

The invention discloses a Yellow River downstream city landscape ecological risk analysis method based on high-score data, and relates to the technical field of resource analysis, and the method comprises the steps: obtaining land utilization classification data, dividing an ecological risk evaluation price network based on the land utilization classification data, carrying out the statistics of landscape pattern indexes in each grid, and carrying out the calculation of the ecological risk evaluation price network. Generating landscape interference degree space distribution data; obtaining multi-dimensional geographical environment factor data of the research area, performing spatial correlation analysis on the multi-dimensional geographical environment factor data and the land utilization classification data, and screening out dominant environment factors; generating landscape vulnerability spatial distribution data based on a spatial association relationship between the dominant environmental factors and the land utilization classification data; according to the method, all technical links are connected in series by taking a uniform grid space as an analysis reference, so that refined space representation and scientific coupling fusion of the landscape interference degree and the landscape vulnerability are realized, and the space accuracy of a landscape ecological risk analysis result is greatly improved.

The present application relates to the technical field of dynamic load balancing. The present application relates to a unified scheduling and monitoring system for computer room computing power resources based on dynamic load balancing. It includes a computing power node setting module, a running prediction analysis module, a demand resource updating module, a demand resource analysis module, a standby resource setting module and a resource scheduling module. The computing power node setting module is used to collect computer room computing power resources, which are divided into to-be-allocated computing power resources and used computing power resources. The present application builds a bounded computing power node state prediction system, taking the latest running state of the computing power node as the prediction center, setting the upper and lower limit boundaries of the prediction in combination with the total available computing power of the computing power resource pool, avoiding the invalid results caused by boundaryless prediction, and based on the change trend and fluctuation law of the historical running state sequence, the prediction result is more in line with the actual running characteristics of the computing power node, providing a reliable state basis for the analysis of subsequent demand computing power resources.

The invention relates to the technical field of power systems, in particular to a novel adjustable resource fault early warning method for wide-area measurement data anomaly detection, and the method comprises a data collection unit which is used for obtaining original measurement data from a wide-area measurement system; the data processing unit is used for realizing data preprocessing, anomaly detection, fault risk assessment and adjustable resource analysis and early warning functions; the communication unit is responsible for data transmission and information interaction with a wide-area measurement system and a power system dispatching control center; and the display and alarm unit presents the fault early warning information and the adjustable resource adjustment suggestion in a visual mode, and triggers sound-light alarm when the fault risk reaches the early warning level. According to the method, the deep auto-encoder is combined with the adversarial training mechanism to realize wide-area measurement data anomaly detection, high-dimensional complex data can be effectively processed, the accuracy and robustness of anomaly detection are remarkably improved, and the inherent limitation of a traditional statistical analysis and machine learning method is overcome.

The application provides a data trusted computing framework based on a consortium blockchain, entities include a data demander, a data provider, a consensus node and a certificate authority; a structure is divided into a data registration layer, a data trusted layer, a data consensus layer, a data processing layer and a data delivery layer. The data registration layer adopts a data architecture paradigm based on the consortium blockchain, divides data into identification, characteristic data and data entities to standardize a data registration process, provides a unified access mode for heterogeneous, heterogeneous and heterogeneous data resources and integrates data resources; the data trusted layer uses a data trusted computing mechanism, so that the data provider provides a data resource analysis and processing result for the data demander without contacting original data; the data consensus layer includes a data trusted traceability mechanism, guarantees safety and integrity of data flow; the application can change a traditional data hosting / exchange as a service mode into a data processing as a service mode and realizes data "available but invisible".

The invention relates to a resource analysis method and device, computer equipment, a storage medium and a program product. The method comprises the steps that in response to a thermodynamic diagram display operation for the hybrid cloud platform, an embedded vector corresponding to a resource node in a resource topological graph of the hybrid cloud platform is obtained, and the embedded vector of the resource node is generated by performing embedded coding based on an operation index of a resource represented by the resource node; based on embedded vectors corresponding to resource nodes in the resource topological graph, a thermodynamic diagram of the hybrid cloud platform is obtained through rendering, the thermodynamic diagram comprises node icons corresponding to the multiple resource nodes respectively, and icon colors of the node icons are related to processor use conditions and / or storage resource use conditions. The side boundary visual information of the node icon is related to network bandwidth and / or delay. By adopting the method, the application scene can be widened.

This invention belongs to the field of enterprise resource planning (ERP) technology and discloses an intelligent matching method and system for policy application conditions based on enterprise resource analysis. By constructing a dedicated extraction model for policy application elements, it performs structured parsing and industry-customized four-level hierarchical decomposition of policy texts, transforming natural language application conditions into executable logical expressions. It accurately handles complex rules such as AND, OR, non-nested logic, numerical thresholds, and temporal constraints. A four-level progressive matching and verification mechanism is adopted, prioritizing matching according to the order of prohibition (one-vote veto), rigid access, core scoring, and auxiliary bonus points. It incorporates industry-specific rules and enterprise lifecycle adaptation logic to avoid invalid matching and screening biases, upgrading policy matching from superficial textual association to deep compliance verification, thus improving matching accuracy and adapting to the application and screening needs of various complex preferential policies for enterprises.

A method for managing data protection includes monitoring, by a data protection service, data protection requests issued by a set of client devices to obtain telemetry data, wherein the set of client devices are associated with a geographic location, performing a resource analysis on the telemetry data to determine a centralized backup system (CBS) instance allocation for a CBS instance in the geographic location, making a determination, based on the resource analysis, that the CBS instance is to be instantiated in the geographic location, based on the determination, deploying the CBS instance in a production environment located in the geographic location, and initiating a replication of backup data from a CBS to the CBS instance.

The invention provides a wind power resource analysis and evaluation method and system for a new energy base, and belongs to the technical field of safety evaluation, and the method specifically comprises the steps: determining the change data of wind directions in different target time ranges according to the analysis results of wind power monitoring data in different target time ranges, and obtaining the change data of the wind directions in the different target time ranges; a wind direction variation time range within a target time range is determined on the basis of a wind direction variation interval, and configuration data of an available power generation period within an aggregation time interval within the wind direction variation time range is determined. And combining with the time interval data between the different aggregation time intervals and the distribution data of the time periods in the preset wind speed interval in the different aggregation time intervals, the evaluation and analysis method of the wind power resources of the new energy base in the wind direction change time range is determined, and the reliability degree of evaluation, analysis and processing of the wind power resources is improved.

A method for managing data protection includes monitoring, by a data protection service, data protection requests issued by a set of client devices to obtain telemetry data, wherein the set of client devices are associated with a geographic location, performing a resource analysis on the telemetry data to determine a centralized backup system (CBS) instance allocation for a CBS instance in the geographic location, making a determination, based on the resource analysis, that the CBS instance is to be instantiated in the geographic location, based on the determination, deploying the CBS instance in a production environment located in the geographic location, and initiating a replication of backup data from a CBS to the CBS instance.

The invention relates to a novel security industry data resource analysis method, and belongs to the technical field of security, efficient processing and intelligent decision making of security data are realized through an end-side cloud cooperation architecture, real-time target detection is performed through deploying a lightweight YOLOv8 model through an edge end, and preliminary anomaly recognition is completed; an LSTM neural network is trained on the cloud platform based on historical alarm data, and high-risk area prediction in the next 24 hours is realized; a DBSCAN clustering algorithm is combined to carry out spatial correlation analysis on the personnel trajectory, and a suspicious gang is automatically identified; and finally, an entity relation network is constructed through the knowledge graph, and cross-system event reasoning is supported. According to the technical combination, the system has the capabilities of full space-time coverage, full factor fusion and full process intelligence, a closed-loop safety prevention and control system is formed, and the automation level and the actual combat capability of the security and protection system are effectively improved.

The invention relates to the technical field of distributed resources, in particular to a distributed resource adjustment capability analysis method and system based on historical data. Comprising the following steps: setting multiple resource categories based on equipment parameters of distributed resources; generating a plurality of analysis scenes according to the historical data, and generating an auxiliary data packet of each analysis scene; establishing a capability analysis model according to all the auxiliary data packets, and generating an adjustable resource analysis table according to the capability analysis model; obtaining a feedback data packet according to the preset update time node, and judging whether a correction instruction of the capability analysis model is generated or not according to the feedback data packet; according to the method, multiple resource categories are constructed according to different distributed device types, multiple operation states are constructed based on deep analysis of historical data, corresponding analysis scenes are established according to characteristics of different operation states, and accurate analysis of the distributed resource calling capability is realized.

The invention relates to the technical field of human resource analysis, in particular to a talent post matching method and system based on big data. The method comprises the following steps: acquiring on-duty data of a trainee, constructing a trainee portrait, setting an updating time period, acquiring on-duty data of the trainee in the updating time period, and dynamically updating the trainee portrait; acquiring current post data, extracting post demand data, and outputting the post demand data; respectively obtaining trainee portraits and post demand data, judging the matching degree, and outputting matching data; the system comprises a personnel portrait updating module, a post demand obtaining module and a post matching module. By obtaining the trainee portrait in the updating time period and judging the matching condition between the trainee portrait and the post demand data, the matching degree between the trainee and the post is judged in the internship period, and the talent post matching accuracy in the internship period is improved.