69 results about "Resource use" patented technology

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

The invention relates to an intelligent management method and system based on a server cluster, and the method comprises the steps: a load balancing module employs a preset mode, and detects the health condition of a server node at preset time intervals; the node monitoring and alarming module monitors the resource use condition of each server node in real time; the intelligent fault detection module detects the resource use condition of the server node based on a pre-trained fault prediction model and outputs a fault prediction result; the resource management module predicts historical resource use data based on a time sequence model and actively adjusts resources; and the background management module dynamically displays a load state and a resource use condition, records a log and processes a fault. The intelligent management system based on the server cluster successfully predicts most faults and automatically repairs the faults, and the availability of the system is improved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a data hierarchical storage method, device, equipment and medium based on access characterization. Writing the data into a matched first target storage device according to a preset device performance corresponding relation; after the data are stored, continuously monitoring subsequent access characteristics of the data, and dynamically judging whether the heat state is changed or not; and if the popularity state is changed and a specific migration condition is met, redistributing the second target storage device and completing data migration operation so as to realize automatic hierarchical storage management of cold and hot data. By acquiring the data access representation and dynamically identifying the popularity state, hierarchical migration of data with different popularity among storage devices with different performances is realized, the resource use efficiency and access performance of a storage system are improved, and the storage cost is reduced.

The invention provides a hospital operation monitoring analysis method and platform based on artificial intelligence, and the method comprises the steps: collecting data from a plurality of data sources of a hospital, and integrating the multi-source heterogeneous data into a unified data platform through a data access gateway; performing cleaning, standardization and feature extraction on the acquired data, and extracting key features by using automatic feature engineering; constructing an AI model according to a hospital operation scene, and training and verifying the model by using historical data; inputting the real-time data into an AI model for analysis, generating a prediction result or a classification label, and performing real-time early warning on an abnormal condition; key indexes and analysis results are displayed through a visual instrument panel, and an operation report is automatically generated. Through the artificial intelligence technology platform, hospital resource use conditions can be analyzed in real time, resource allocation can be dynamically optimized, medical staff scheduling and equipment use plans can be automatically adjusted, waiting time of patients is shortened, and scheduling efficiency and response speed are remarkably improved.

The invention discloses a hospital operation analysis and target management system based on big data, and relates to the technical field of hospital operation management, and the system employs an activity-based cost method ABC, introduces a non-linear sharing function and a multi-dimensional cost sharing model, enables the operation cost to be refined to a specific project of each department, and improves the operation efficiency. Equipment use, medicine consumption and working hours of medical staff are covered, cost allocation and updating are carried out through a dynamic adjustment mechanism and a system according to real-time resource use conditions, hospitals are helped to identify high-cost expenditure points, resource waste is reduced, transparency of operation cost and management efficiency are improved, an automatic feedback mechanism is introduced, and the system is convenient to use. According to the method, the predicted data and the actual data can be compared in real time, the operation abnormity can be identified, the adjustment suggestion is automatically triggered when the abnormity is found, the adjustment suggestion is pushed to the related management personnel for quick response, and compared with a manual examination method, the speed and the accuracy of management response are improved.

The invention discloses a nursing information distribution management system based on big data, and relates to the technical field of information distribution management, and the system comprises a data collection module which is used for collecting original data from a nursing data source in real time and cleaning the original data, the original data comprises patient information, nursing records, resource use conditions and nursing personnel capabilities; the data acquisition module is connected with an external system through an API (Application Program Interface); the nursing information distribution management system based on big data can comprehensively evaluate the skill level and professional ability of nursing personnel, update skill archives, and provide an accurate basis for personnel deployment; appropriate nursing personnel are allocated to appropriate tasks, so that the quality and the specialty of nursing services are improved; optimal allocation of resources and nursing personnel is realized; resource preparation is made in advance, resource waste and shortage are avoided, and the resource utilization rate is increased.

The invention discloses a resource scheduling method and device based on IT asset health assessment, a medium and equipment, and the method comprises the steps: obtaining resource use data, network connection data and abnormal information data corresponding to each node in a distributed system, and carrying out the health degree scoring calculation based on the resource use data, the network connection data and the abnormal information data, obtaining a health degree score corresponding to each node; obtaining the priority of the service adapted to each node, and performing calculation based on the health degree score corresponding to each node and the priority of the adapted service to obtain a comprehensive evaluation value corresponding to each node; and scheduling resources based on the comprehensive evaluation value corresponding to each node. According to the method, the comprehensive evaluation value of multi-modal fusion is calculated, resource scheduling is carried out according to the comprehensive evaluation value, comprehensive and real-time comprehensive evaluation is carried out on the health condition of IT assets, corresponding scheduling processing is carried out according to the comprehensive evaluation condition, and the stability and reliability of the system are remarkably improved.

The invention relates to the technical field of medical resource management, in particular to a pediatric outpatient service resource intelligent scheduling and patient flow prediction management system which comprises a data acquisition module, a flow prediction module, a resource scheduling module, a scheduling optimization module and a feedback correction module. Historical patient flow data, doctor scheduling data, consulting room availability data and patient reservation data are collected in real time, a patient flow prediction result generated by the flow prediction module is combined, data support is provided for resource scheduling, the resource scheduling module generates a preliminary scheduling scheme according to the prediction result and actual data, and the preliminary scheduling scheme is used for resource scheduling. And a scheduling scheme is optimized through a simulated annealing algorithm, and the resource utilization rate is improved. And the feedback correction module monitors patient flow and resource use conditions in real time, and adjusts a scheduling scheme in time to cope with emergencies and expected changes. According to the invention, the scheduling scheme can be dynamically adjusted under different demand fluctuations, the high efficiency and flexibility of resource allocation are ensured, and the service quality of hospitals is improved.

The invention discloses a multi-hospital first-aid collaborative strategy recommendation method and system, and relates to the technical field of pre-hospital first-aid collaboration.The method comprises the steps that spare medical resource information is acquired in real time, and an initial collaborative strategy set is acquired based on the spare medical resource information according to department and hospital positions and basic first-aid configuration; acquiring historical medical resource use information, and constructing a medical resource candidate influence relationship based on the medical resource use frequency and the importance degree according to the historical medical resource use information; and responding to the first-aid information, matching an initial collaborative strategy according to the first-aid information, generating an auxiliary collaborative strategy according to the medical resource candidate influence relationship, and executing collaborative strategy recommendation according to the initial collaborative strategy and the auxiliary collaborative strategy. The method has the beneficial effects that the medical resource scheduling efficiency and the anti-risk capability in the emergency treatment process are improved.

The invention discloses an internet-based physical hospital comprehensive information management system, which relates to the technical field of hospital information management and comprises a data acquisition module, a data processing module, a calculation module and an analysis module. According to the method, factors such as resource types, use frequency and maintenance cost are comprehensively considered, a visual resource utilization situation overview is provided for hospitals, resource allocation can be optimized to a certain extent, resource waste is reduced, the resource use efficiency is improved, and the method combines multiple dimensions such as medical service quality and patient satisfaction, so that the medical service quality and the patient satisfaction are improved. The medical service efficiency is comprehensively evaluated to provide an improvement direction for hospitals, the system comprehensively evaluates the information management efficiency from multiple aspects of information management cost, information processing capability, information security and the like, and the hospitals can clearly see the actual effect of an information management strategy through quantitative analysis, so that the strategy is adjusted, the cost is reduced, and the medical service efficiency is improved. And the information management efficiency is improved.

The invention relates to the technical field of medical management informatization, in particular to a visual multi-dimensional patient flow adjustment system and method, and the system comprises a patient sensing module, an intelligent analysis gateway, an adjustment strategy generation module, a resource dynamic scheduling engine, and a visual positioning platform. Wherein the patient sensing module is used for collecting basic information, doctor seeing states, department resource use conditions and environment data of patients in all areas of a hospital; the intelligent analysis gateway is used for fusing and processing data and predicting the patient flow risk through the gating circulation unit; the adjustment strategy generation module is used for carrying out treatment priority fine classification on the patients and generating a personalized flow adjustment strategy; the resource dynamic scheduling engine is used for performing accurate scheduling through an intelligent allocation algorithm according to the adjustment strategy; the visual positioning platform is used for calibrating patient positions and rendering a hospital-wide patient distribution thermodynamic diagram and a resource occupancy rate in real time. Therefore, the problems of long waiting time, unreasonable resource allocation, single management strategy and the like in the prior art are solved.

Various methods, devices, and systems for task identification and resource management are described. In one aspect, a method includes obtaining information about a subject, obtaining identification of a condition of the subject based on the information, and obtaining identification of a treatment for the subject. The method also includes identifying a set of tasks corresponding to the treatment, generating, via a rules engine, a first set of parameters for the tasks, and generating, via an artificial intelligence (AI) engine, a second set of parameters for the tasks. The method further includes generating a resource allocation schedule for the treatment using the set of tasks, first set of parameters, and the second set of parameters, including assigning one or more tasks to a particular entity, and providing information about the one or more tasks to the particular entity.

The present application discloses a data resource health maintenance method and apparatus, and a device. The data resource includes data computing resource and data storage resource. The method includes: obtaining a health assessment indicator for assessing health of the data resource, wherein the health assessment indicator includes a resource effective utilization degree and a resource risk level of the data resource in a use process; determining a weight corresponding to the resource effective utilization degree and a weight corresponding to the resource risk level; obtaining a health assessment result of the data resource based on the resource effective utilization degree, the resource risk level, the weight corresponding to the resource effective utilization degree, and the weight corresponding to the resource risk level; and obtaining a data resource use event related to the health assessment result, and performing data resource governance based on the data resource use event.

The invention relates to the technical field of health care accompanying robots, and particularly discloses a memory optimization system for an intelligent health care accompanying robot, which comprises the following steps: acquiring state data of a first application program and a time sequence of memory resource usage amount; embedding coding of state data of the first application program and time sequence association of the memory resource usage amount of the first application program are carried out by utilizing a data analysis and coding mode based on deep learning; therefore, whether memory cleaning operation is executed on the first application program or not is intelligently judged according to semantic alignment fusion representation between the application program state embedded coding feature and the application program memory resource usage amount time sequence implicit association feature. In this way, the condition of the application program memory can be accurately recognized, and misjudgment possibly caused by a traditional static rule is effectively avoided. Moreover, the optimization strategy can be automatically adjusted according to different equipment and user behavior modes, and the method does not need to depend on a fixed rule set, so as to better adapt to diversified application scenes.

When deciding which medical treatments and procedures to provide to patients from among the hospital's medical resources, a computer is used to predict how patients will use medical resources, and by systemizing the medical resource decisions, the hospital aims to optimize its profits by making resource use more efficient and profitable. [Solution] Patient information and information on available medical resources that can be provided to patients by medical institutions are stored, and a prediction of the medical resources required by the patient is calculated based on the patient information as predicted patient resource information, and the predicted patient resource information is matched with the available medical resource information (S707). The matching results are presented to medical professionals, and input from the medical professionals is accepted. The predicted patient resource information is changed to planned (decided) patient resource information (S710), allowing the patient to be selected and accepted, and medical resources to be provided to the patient.

The software as a service (SAAS) platform of the present invention analyzes an individual's functional capabilities by comparing self-reported data with professional observations. It utilizes research-based normative data to calculate changes in function across various domains, such as activities of daily living and cognitive function. The system generates reports with graphical representations and detailed analyses of deficits or gains. It also projects long-term status changes and calculates resource requirements, using databases of costs and aids to provide cost analyses. This platform aids in various areas, including disability claims, insurance claims, legal disputes, and rehabilitation planning, by tracking functional changes to optimize resource use and improve outcomes.

The invention relates to a data processing technology, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a resource dynamic adjustment method, device and equipment and a medium, and the method comprises the steps: obtaining the use condition and weight information of each resource group in a current database; generating resource configuration information corresponding to each resource group based on the use condition and the weight information of each resource group; submitting a resource adjustment request to a database front-end node according to the resource configuration information in a hard restriction isolation mode; and forwarding the resource configuration information to a database rear-end node based on the database front-end node, executing the resource configuration information based on the database rear-end node, and storing a resource use condition. Dynamic adjustment of resources is supported, the system resources are fully utilized, the query response time is shortened, and meanwhile the stability of the system can be guaranteed; and for tasks with high weights, resources can be preferentially allocated or allocated more, and the operation and maintenance efficiency is improved.

The invention provides a hospital medical resource optimization allocation method and device. The method comprises the following steps: receiving medical resource use data, wherein the medical resource use data comprises sickbed use rate data and medical equipment use data; determining a first confusion vector, a second confusion vector and a third confusion vector for medical resource allocation, wherein each confusion vector comprises a plurality of random numbers; based on the first confusion vector and the second confusion vector, performing confusion processing on the sickbed utilization rate data to obtain a first confusion matrix; based on the second confusion vector and the third confusion vector, performing confusion processing on the medical equipment use data to obtain a second confusion matrix; and sending the first confusion matrix and the second confusion matrix to operation processing equipment. According to the invention, on the premise that the safety of medical resource use data is protected, optimal allocation of medical resources is realized, and the medical resource use efficiency is improved.

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 discloses a method for quantitatively evaluating the completion degree of a task, which comprises the following steps of: D1, acquiring the planned completion time Tp of the task, the actual completion time Ta of the task, the planned continuity resource usage amount Rp of the task, the actual continuity resource usage amount Ra of the task and the planned one-time resource usage amount Sp of the task, the actual one-time resource usage amount Sa of the task, the subtask number n of the task, the total number m of the indexes, and the expert-evaluated initial value xij of the ith subtask on the jth index are calculated; d2, calculating the time efficiency of the task according to the planned completion time of the task and the actual completion time of the task; d3, calculating the resource utilization rate of the task according to the planned resource usage amount of the task and the actual resource usage amount of the task; d4, calculating the completion quality of the task according to the number of the subtasks of the task; and D5, calculating the completion degree of the task according to the time efficiency of the task, the resource utilization rate of the task and the completion quality of the task.

The invention relates to the technical field of medical resource management, in particular to a medical resource allocation method and device for pre-hospital first aid, electronic equipment and a storage medium. And importing the scoring result of each evaluation sub-item to each medical resource allocation scoring model to obtain an allocation scoring result of each medical resource, obtaining various medical resource scoring results of other patients to be subjected to resource allocation at the current moment, and determining the recommendation probability of the target patient in various medical resources based on a recommendation probability function. Compared with an existing mode of obtaining allocation results of various medical resources by using the same model, the medical resource allocation method disclosed by the embodiment of the invention fully considers influence factors of various medical resources, so that allocation of various medical resources is more reasonable and accurate, idle or overuse of part of medical resources is avoided, and the medical resource allocation efficiency is improved. The effectiveness of patient rescue is ensured, and the rescue success rate is improved.

Methods and systems for continuously and quantitatively assessing the risk to data confidentiality, integrity, and availability on identified on endpoints, servers, medical devices, and “Internet of things” devices in a networked healthcare environment monitor resource requests by user applications running on the various device. A map of resource usage by each application may be generated. Based on the map and a risk model (e.g., the contents of a risk database), application events associated with risks are detected and resources vulnerable to the risk may be identified.

A resource dynamic scheduling adaptation method and system in a limited demand scenario. This method uses historical data of user resource usage to predict the confidence interval of resource demand in the resource usage area, comprehensively considering the high dynamics and uncertainties during the user's resource usage process in the resource interval, and predicts the demand distribution of users in the demand area in the form of a demand interval, transforming the point estimation problem of user demand prediction into an interval prediction problem of demand prediction. At the same time, the prediction model incorporates the characteristics of external factors affecting the current area's resource usage, making a highly robust and accurate confidence interval prediction of the user resource demand situation in the resource usage area. The present invention realizes a better and more optimal efficient resource scheduling plan for the user resource demand distribution in each area under the conditions of overall resource limitation and scheduling capacity limitation, can efficiently and stably generate a resource scheduling strategy for the resource usage area, and improve the effective adaptation efficiency of the overall resources.

The invention discloses a community hospital vaccination outpatient service area measuring and calculating method based on virtual scene behavior simulation, and the method comprises the steps: S1, analyzing the hospital function demands, service object features and regional features of different hospitals, and constructing a preliminary demand framework; s2, collecting basic data of a hospital, establishing a preliminary virtual scene model based on a demand framework, and simulating inoculation processes and resource use under different conditions; s3, according to the characteristics of different hospitals and the virtual scene model, the vaccination outpatient service site layout is designed, and the outpatient service area is adjusted through combination of virtual simulation and genetic algorithm optimizing.According to the method, through comprehensive consideration of hospital function requirements, service object characteristics and regional characteristics, flexible adjustment and accurate application of different hospitals are achieved, and the vaccination outpatient service site layout is achieved. Through combination of virtual scene behavior simulation and a genetic algorithm, the area required by the vaccination outpatient service can be measured more accurately, and reasonable allocation of resources is ensured.

The present invention relates to a system (1) which enables focal agendas of an organization to be visualized; the status of service infrastructures such as growth, bottlenecks and use of resources to be assessed; problems and solutions to be announced to the entire organisation; and automated reporting to be performed.

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 present invention relates to the technical field of nuclear fusion tokamak device control systems, and in particular to a method for predicting resource usage of a plasma control system based on LSTM. The technical solution includes data preprocessing and feature extraction, introduction of an LSTM network for time series prediction, feature embedding and feature separation strategies, standard value prediction and memory leak detection, health index calculation and feedback adjustment, and feedback adjustment within the prediction cycle. The present invention is used to predict the standard value of resource usage during system operation, and by comparing with real-time monitoring data, determines whether resource usage deviates too much from the standard value. It can capture the long-term dependence and nonlinear variation of system resource usage, and is particularly suitable for processing complex periodic waveforms. It can not only improve the accuracy of the predicted standard value, but also more effectively evaluate the system health status and provide early warning of potential abnormal situations, providing important support for the stable operation of the system.

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.