22 results about "Resource use" patented technology

The invention relates to the technical field of computers, and provides a resource use method, device and system, and the method comprises the steps: determining a resource use plan of a tenant according to the resource demands of the tenant and the resource configuration of a plurality of resource points, the resource demand of the tenant comprises the resource demand quantity of at least one resource demand point in a plurality of resource demand points associated with a plurality of resource points, the resource configuration of the resource points comprises resource capacity and resource price, and the resource use plan of the tenant is used for indicating that the resource demand of the tenant is met. At least one target resource point in the plurality of resource points provides the resource usage amount for at least one resource demand point. And using the resources of the at least one target resource point according to the resource use plan of the tenant. The resource usage plan determined according to the method can indicate that the resources of one or more target resource points are selected and used in the plurality of resource points, and the resource usage amount of the used target resource points can guarantee the resource requirements of the tenants.

The invention relates to the technical field of medical management, in particular to an emergency department emergency disposal decision system which comprises a resource distribution analysis module, a task allocation engine module, a hierarchical structure mapping module, an execution degree screening module and an abnormity regulation and control module. According to the method, through processing steps of resource distribution analysis, task optimization matching, hierarchical structure division, execution degree screening and the like, configuration and scheduling of emergency resources of the emergency department are optimized, high-efficiency and low-efficiency areas, abnormal points and logic differences of resource clusters are identified, resource allocation is more targeted and efficient, and the resource allocation efficiency is improved. Reasonable allocation of resources on different scales and levels is achieved through hierarchical structure division and fluctuation change recognition, resource use is maximized, decision making efficiency and response speed are improved, low efficiency and misjudgment of traditional manual decision making are effectively avoided through an intelligent scheduling method, the emergency response capability and the resource utilization rate are improved, and the resource utilization rate is increased. And rapid and accurate execution of the emergency task is ensured.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance / digital medical treatment, and discloses a data pipeline exception processing method and device, electronic equipment and a storage medium. The method comprises the steps of monitoring a task execution state in a data pipeline, and collecting abnormal log information and abnormal context information when execution abnormity occurs; performing semantic analysis on the exception log by using a pre-trained large model, determining an exception reason type and generating an exception processing strategy; executing automatic repair or strategy execution operation according to the exception handling strategy; the task scheduling priority, the execution sequence and the resource allocation are optimized in combination with the task running state and the resource use state in the exception processing process, and the overall processing efficiency is improved; and performing associated storage on the exception log, the exception reason, the processing strategy and the processing result, and continuously updating the exception processing strategy library. According to the method, intelligent diagnosis, self-repairing and continuous stability optimization of data pipeline abnormity are realized.

The invention provides a resource quota configuration method and device and a training method of a credit evaluation model, relates to the technical field of data processing, and can optimize the quota of resource application of a stock user, improve the passing rate of resource application of the stock user and reduce the mobility of the stock user. According to the specific technical scheme, credit data such as business conditions of stock users, historical resource application performance data, credit evaluation data and resource management scale AUM data are collected; and evaluating the credit rating of the stock user according to the credit data by using a pre-trained credit evaluation model. And determining a resource limit coefficient of the stock user in combination with the credit rating of the stock user, the service demand information of the resource application service and the multi-dimensional risk assessment parameter. And determining an initial quota, a first configuration quota and a second configuration quota based on the resource quota coefficient, the historical resource application performance data and the application time, and determining a resource use quota which can be applied by the stock user according to the data.

The embodiment of the invention provides a resource management method and a related device, and the method comprises the steps that a management device is used for managing a resource pool, the resource pool comprises a plurality of resources and a plurality of resource logic isolations, the plurality of resources can be used by a plurality of devices, and each device can use the bound resource; the management device can distribute the resources used by one device to the other device for use based on the requirements of the devices, and can also distribute new resources in the resource pool for one device, and through redistribution of the resources in the resource pool by the management device, the distribution efficiency of the resources in the resource pool is improved, and the resource utilization rate is improved. And the utilization rate of the resources in the resource pool is improved.

The application discloses a data resource health maintenance method, device and equipment, and the data resource includes data computing resource and data storage resource. A health evaluation index for evaluating the health of the data resource is determined, and the index includes resource effective use degree and resource risk level. The resource effective use degree is used to represent the degree of effective use of the data resource, and the resource risk level is used to represent the degree of negative impact of the use of the data resource on the business. The resource effective use degree is negatively correlated with the increase rate of the resource risk level. The weights corresponding to the two indexes are determined, and the health of the data resource is evaluated based on the two indexes and the weights corresponding to the two indexes. In this way, the health of the data resource is evaluated based on the degree of effective use of the resource and the degree of negative impact of the use of the data resource on the business, which can more accurately reflect the health of the resource. In addition, the data resource can be managed to improve the health of the data resource.

Embodiments of the disclosure describe system and methods to perform Intra-gNodeB-Central Unit (CU) handover by a gNodeB(gNB) is disclosed herein. The method includes determining, by the gNB (502), whether resource usage at a first gNB-CU-User Plane(gNB-CU-UP) (506) associated with the gNB-CU (504) of the gNB exceeds a predefined threshold value. Upon determining that the resource usage at the first gNB-CU-UP exceeds the predefined threshold value, the method includes detecting, by the gNB, whether one or more User Equipment (UEs) (510a and 510b) connected with the gNB experiencing performance degradation based on the resource usage at the first gNB-CU-UP. Further, the method includes initializing, by the gNB, a handover of at least one of the one or more UEs from the first gNB-CU-UP to a second gNB-CU-UP (508) associated with the gNB-CU of the gNB.

The application belongs to the technical field of cloud computing and container orchestration resource management, and discloses a Kubernetes resource portrait and cost optimization method and system, which comprises the following steps: obtaining container resource index original data; performing aggregation to obtain aggregated data; using a resource portrait engine to classify services and generate service resource portraits; performing multi-dimensional anomaly detection according to the service resource portraits to obtain an abnormal service list; generating adjustment suggestions based on the abnormal service list to obtain an optimization suggestion set; adopting a differentiated merging request submission strategy according to the risk level of the optimization suggestion set to output a merging request; comparing resource usage data and service running indexes before and after merging to generate an optimization effect verification report; and using a capacity predictor to predict resource demand of the service resource portrait, and generating a capacity warning and calibrating the predictor when the predicted value exceeds a threshold value. The application solves the deficiencies of the prior art in pattern recognition, risk management and capacity planning.

The under-utilized resources are attributed to various reasons such as over-provisioning of resources, diminishing use of resource, application upgrades. The present disclosure identifies one or more under-utilized resources from a set of resources by (i) deriving most recent steady state in utilization of metrics specific to set of resources, (ii) deriving one or more temporal patterns by analyzing derived most recent steady state in utilization of metrics specific to set of resources, (iii) computing a representative maximum utilization of metrics specific to set of resources for each of derived one or more temporal patterns, (iv) deriving headroom based on computed representative maximum utilization, (v) forecasting future behavior of utilization, (vi) deriving time to saturation for metrics specific to set of resources, and (vii) identifying one or more under-utilized resources based on derived time to saturation. One or more recommendations for optimizing identified one or more under-utilized resources are generated.

The invention discloses a scene cost adjusting method and device, equipment and a storage medium. The scene cost adjusting method comprises the steps that the cost of a sub-scene is determined according to actual service parameters of business services provided by all large models under current resources and actual resource use information; determining the cost of a total scene of a large model cluster formed by the large models according to the cost of each sub-scene; obtaining a service performance index of each large model for providing a business service, and determining a target large model needing resource updating according to the service performance index and the actual resource use information; and performing resource updating on the target large model, and adjusting the cost of the total scene of the large model cluster according to a resource updating result. According to the actual service providing condition and the resource occupation condition of each model, the sub-scene cost is determined, the actual total scene occupation cost of the sub-scene is determined, and the total scene cost is reasonably adjusted according to the service performance and the resource occupation condition during cost calculation.

A system enables clients to utilize resources that have been committed from one or more third-party resource providers, allowing them to execute various operations on a data platform. The system tracks the aggregate historical use of these resources over a specific period and monitors any unused resources within a first term commitment over an initial time period. The system also tracks requests for additional resources representing times when demand exceeded the baseline commitment and required supplemental, short-term resources. This collected data is then fed into a predictive model. The model analyzes these inputs to generate a forecast of future resource needs, providing an informed view of anticipated demand for upcoming periods. Based on this forecast, the system executes a first term commitment for resources, securing the necessary capacity over a second time period in alignment with the forecasted usage, thus optimizing resource allocation and cost efficiency.

The invention provides a medical resource intelligent dynamic scheduling method and system based on double disease features, and the method comprises the steps: carrying out the matching in a preset resource demand tensor according to the information of a patient, and obtaining a standard template value; and according to the inspection index set, the historical index mean value, the historical index standard deviation and the disease type superposition indication variable, constructing an individual correction item, and combining the standard template value, the individual correction item and a preset resource adaptation matrix to obtain a resource use prediction matrix. And constructing a scheduling objective function according to the resource scheduling matrix, the resource use prediction matrix, the individual correction term, the disease superposition indication variable, the resource conflict matrix and the dynamic resource load index. And maximizing the scheduling objective function to obtain an optimized resource scheduling matrix. The scheduling task is screened out from the optimized resource scheduling matrix, an execution command structure is constructed according to the scheduling task, the task scheduling time and the resource metadata, resource scheduling is conducted according to the execution command structure, and intelligent dynamic scheduling of the medical resources is achieved.

A system for remotely managing mental health of a user includes a tag, a user device, and a server. The user device receives a uniform resource locator (URL) from the tag and renders a web resource for providing digital mental health resources corresponding to the URL. The server receives user input data associated with the mental health of the user from the user device and determines the digital mental health resources for the user. The server further monitors interactions of the user with the web resource and the digital mental health resources to generate behavioural data and resource usage data, and dynamically updates, using machine learning models, the digital mental health resources provided to the user based on identified unlabelled clusters, patterns, or relationships in the user input data, the behavioural data, and the resource usage data.

The invention relates to a resource configuration optimization method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the data preprocessing of resource use data, and obtaining a standard resource use data set; performing clustering analysis on the standard resource use data set to generate a dynamic label set; performing feature extraction on the dynamic label set to obtain resource classification features; performing time sequence analysis on the resource classification features and the service context information to obtain a multi-dimensional time sequence data set; performing resource classification optimization on the multi-dimensional time series data set based on a long short-term memory network to obtain an optimized resource classification result; and performing cost distribution calculation and resource allocation optimization according to the optimized resource classification result and the multi-dimensional time sequence data set to obtain resource optimization configuration information for system optimization. The resource allocation method and device can be applied to application scenes of a financial service system and a digital medical system, and the resource allocation of the system can be effectively optimized according to the current system resource use condition.

The invention belongs to the technical field of artificial intelligence, and discloses a resource optimization method and device, equipment, a storage medium and a product. According to the invention, data integration is carried out on the collected customer data and market data to obtain the integrated data set, and then the initial health management plan, the initial dynamic insurance policy and the initial resource usage amount corresponding to the customer are determined according to the integrated data set; and then constructing a target function based on the health management plan adjustment amount, the dynamic policy adjustment amount and the resource optimization adjustment amount corresponding to the client, and optimizing the initial health management plan, the initial dynamic policy and the initial resource usage amount based on the target function. According to the method, the interaction relationship can be maintained in the process of adjusting the dynamic insurance policy and the health management plan, so that the dynamic insurance policy and the health management plan do not deviate from each other or contradict each other, the optimization result of the resource usage amount is not easy to deviate from the basic health condition and the basic risk condition of the customer, and the resource usage amount of the customer can be effectively optimized.

The invention discloses a metering system for department management, and relates to the technical field of department management.The metering system specifically comprises a data acquisition module, a control unit, a communication module, a display module and an alarm module.Original isolated and fuzzy resource consumption in a department is converted into accurate, real-time and analyzable digital information flow; fuzzy experience is replaced with accurate data, tedious manual operation is replaced with an automatic process, comprehensive digital and refined management of hospital department resources is achieved, data circulation and instruction collaboration of all the modules are achieved through the control unit, the management efficiency is remarkably improved, and the management cost is reduced. According to the method, the medical institution is assisted to realize refined cost control and medical quality improvement, the labor cost is reduced through comprehensive monitoring and management of resource use conditions, and the method is suitable for resource use monitoring and statistics of various departments in the medical institution and is helpful for realizing a more refined and scientific management process of various departments in a hospital.

The invention relates to the technical field of big data, can be applied to the technical field of medical health and finance, and discloses a log sampling dynamic aggregation method and device, computer equipment and a storage medium. The method comprises the following steps: when a service of an application node changes, obtaining log information collected by a log collector deployed on the application node; performing multi-dimensional importance scoring on the log information according to abnormal semantic features, business criticality, influence range and transmissibility and index mutation relevance to obtain a scoring result; and uploading the log information with the scoring result meeting the requirement and the corresponding scoring result to a log server. By implementing the method provided by the invention, a multi-dimensional strategy can be fused, a context sensing capability and a dynamic response mechanism are provided, the resource use efficiency can be improved, the fault response speed can be accelerated, the traceability of logs can be enhanced, and the method can better serve a complex and sensitive business environment.

The embodiment of the invention provides a physical resource capacity expansion method and device, electronic equipment and a storage medium, belongs to the technical field of load distribution, and can be applied to the fields of finance and medical treatment. The method comprises the following steps: acquiring the resource utilization rate of each piece of load balancing equipment; if the first resource utilization rate of the first load balancing device is greater than or equal to a first preset utilization rate, recording a first forwarding configuration of a first virtual service in the first load balancing device; determining a second load balancing device in the load balancing devices; creating a new first virtual service in the second load balancing device, and setting the forwarding configuration of the new first virtual service as the first forwarding configuration; and refreshing an address resolution protocol of the second load balancing device, and deleting the first virtual service in the first load balancing device. According to the invention, automatic capacity expansion of the load balancing equipment can be realized, and the efficiency and accuracy of physical resource capacity expansion are improved.

The invention relates to the technical field of data analysis, in particular to a teaching resource management system and method for nursing teaching. The method comprises the following steps: acquiring multi-modal data of teaching resources, and establishing multi-dimensional indexes covering resource use, teaching effects and the like; performing data cleaning on the multi-modal data to obtain real-time processing data of the teaching resources; carrying out resource efficiency analysis on the real-time processing data to obtain a dynamic recommendation sequence of teaching resources, and reflecting the change of teaching resource effectiveness along with time and environment; and establishing a resource management model based on the dynamic recommendation sequence, identifying the change of the environment where the teaching resources are located, and determining resource management logic. According to the method, the resource management model is established based on the dynamic recommendation sequence, the resource management logic is determined to accurately perform big data analysis, innovative management of teaching resources is realized, and the nursing teaching quality is improved.

The present application belongs to the field of medical and health informatization technology, and discloses an infectious disease prevention and control big data AI resource scheduling system and method; by constructing a load temperature field model of a computing power node cluster, the node load intensity is mapped to a temperature distribution, and the temperature gradient between adjacent nodes is calculated to identify hot and cold node pairs. Based on the data dependency topology, the energy consumption cost and time delay penalty of task migration are analyzed, and a multi-path heat diffusion network is constructed combined with the temperature gradient to realize the optimized shunting of tasks from hot spots to multiple cold nodes. The system analyzes the periodic fluctuations and sudden peak characteristics of resource occupation through wavelet transform, dynamically adjusts the task decomposition granularity and load distribution weight, and adapts to changes in resource usage patterns. The present application realizes global heat balance and efficient utilization of computing power resources in epidemic analysis, improves computing efficiency, and enhances the system's ability to respond to sudden computing demands.

The application belongs to the technical field of cloud computing and container orchestration resource management, and discloses a Kubernetes resource portrait and cost optimization method and system, which comprises the following steps: obtaining container resource index original data; performing aggregation to obtain aggregated data; using a resource portrait engine to classify services and generate service resource portraits; performing multi-dimensional anomaly detection according to the service resource portraits to obtain an abnormal service list; generating adjustment suggestions based on the abnormal service list to obtain an optimization suggestion set; adopting a differentiated merging request submission strategy according to the risk level of the optimization suggestion set to output a merging request; comparing resource usage data and service running indexes before and after merging to generate an optimization effect verification report; and using a capacity predictor to predict resource demand of the service resource portrait, and generating a capacity warning and calibrating the predictor when the predicted value exceeds a threshold value. The application solves the deficiencies of the prior art in pattern recognition, risk management and capacity planning.