76 results about "Optimal management" patented technology

The invention provides a Docker Registry management optimization mode. The Docker Registry management optimization mode comprises the steps that based on Docker Registry management requirements, a Docker Registry native management mode corresponding to the management requirements is determined; based on the management requirements, a corresponding optimization management mode is determined; optimization is conducted on the native management mode through the optimization management mode, and corresponding Docker Registry management operation is conducted through an optimized operational order. According to the embodiment, the problems that the native Docker Registry control operational mode is unfriendly, management operation is complex, and the network operation and maintenance cost is too high are solved. For example, storage catalogs of mirrored documents are changed to cluster-type share mirrored storage equipment from a local storage area, namely, single-machine mirrored storage is changed to cluster-type mirrored storage, and on the condition of large-scale cluster-type machinery equipment, the mirrored document maintenance cost can be greatly lowered. For another example, by means of the mode that multiple Registry service operation is simplified, the operating difficulty of Docker Registry management is lowered, and the network maintenance operational experience is improved.

The invention discloses an energy consumption optimization management system and method, and particularly relates to the field of energy management, and the system comprises an intelligent information collection terminal collection module, an energy data preprocessing module, an intelligent prediction module, a multi-target optimization scheduling module, a real-time feedback module, an abnormity alarm module, and a scheduling scheme output module. According to the energy consumption optimization management system and the method thereof, data are acquired through the intelligent information acquisition terminal acquisition module by means of various intelligent information acquisition terminals, so that efficient management of energy consumption is realized; dynamic prediction, optimal scheduling and efficient management of energy consumption are realized through the intelligent prediction module, and the accuracy of energy consumption trend prediction is improved. And a multi-target optimization scheduling scheme is obtained by applying a multi-target optimization algorithm through the multi-target optimization scheduling module, so that the scientificity and rationality of energy scheduling are improved, and energy waste caused by unreasonable scheduling is reduced.

The invention discloses a quantum dot enhanced dynamic power consumption management method, a quantum dot enhanced dynamic power consumption management system and related equipment. Aiming at the problem of insufficient optimization caused by difficulty in comprehensively, accurately and dynamically sensing the internal energy distribution and conversion process of a chip by a traditional method, the method comprises the following steps of: acquiring the quantum state of the chip in real time by utilizing a quantum dot sensing array, and constructing an adaptive power consumption prediction model by combining multi-source data fusion and feature extraction; and an optimal management strategy is generated based on a multi-objective optimization framework, an execution feedback and self-learning mechanism is introduced, and finally a complete closed-loop control system is formed. The method can be widely applied to various processor chips in the fields of high-performance calculation, the Internet of Things, artificial intelligence and the like, dynamic power consumption management of the chips is achieved through the quantum dot enhancement technology, the performance is guaranteed, meanwhile, energy consumption is minimized, the battery endurance time is prolonged, the heat dissipation cost is reduced, and the energy efficiency of the chips can be remarkably improved.

The invention discloses a medical equipment full-life-cycle management system based on a digital twinning technology. The medical equipment full-life-cycle management system comprises four modules. The digital twinborn model construction module constructs and updates a simulation medical equipment digital model in real time; the intelligent data acquisition and monitoring module is responsible for data acquisition, transmission, monitoring and interface design; the intelligent decision and maintenance module realizes fault prediction, maintenance plan generation and record management; and the comprehensive analysis and optimization management module performs data analysis, optimization decision and life cycle planning. The system has the remarkable advantages that when equipment is used, the running state of the equipment is adjusted in time through real-time monitoring and intelligent analysis, and the fault rate and the maintenance and replacement frequency are reduced; in the aspect of cost control, a maintenance strategy and a purchase decision are optimized, and expenditure is reduced; at the medical service quality level, stable and efficient operation of equipment is guaranteed, patient safety is improved, and reasonable utilization of medical resources is achieved.

The invention discloses a power system optimization scheduling method based on artificial intelligence, and relates to the technical field of power system fault testing. The method comprises the steps of obtaining production monitoring data, operation data and management data under a target power system as a power data set, and performing preprocessing; constructing a load prediction model, inputting historical load data, user power consumption behaviors, meteorological data and calendar characteristics, and outputting a predicted power grid load curve; the technical key points are as follows: through combination with an equipment health state evaluation model, dynamic adjustment of health scores and subsequent real-time scheduling optimization, an optimal management scheme with extremely high linkage is formed, the effect of accurately identifying potential fault risks is realized, operation and maintenance personnel can take prevention measures in advance, and the risk of potential faults is prevented. The situation of misjudgment or missing report caused by neglecting external factors is avoided, and the pre-judgment and response capability of the whole scheduling scheme to the equipment health risk is enhanced.

The invention discloses a safe penetration type management method and system for an engineering construction project, and relates to the technical field of constructional engineering, and the method comprises the steps: collecting engineering entity data in real time, carrying out the preprocessing, and generating an engineering state vector; based on the engineering state vector, constructing a digital twinborn model, calling multi-physics field coupling simulation to simulate the whole construction process, and generating a risk early warning report; uploading the risk early warning report and historical construction data to a federated platform, and constructing a dynamic security knowledge graph; performing hierarchical feature learning on nodes in the dynamic security knowledge graph, simulating decision behaviors of a manager, and generating an optimal management and control rule; based on the optimal management and control rule, driving a construction site management mechanism to operate, and forming a closed loop report; by using the digital twin model and the multi-physics field simulation technology, the accurate prediction of potential safety hazards possibly occurring in the construction process is realized, and the accuracy and timeliness of risk early warning are greatly improved.

The invention discloses an intelligent combustion optimization control method, which comprises the steps of obtaining historical operation data and historical control data of different operation conditions, and determining associated operation data and associated control data of a preset evaluation index according to a first association degree and a second association degree; generating a first evaluation value corresponding to a preset evaluation index according to the real-time associated operation data, and generating a comprehensive evaluation value of the boiler according to the first evaluation value; judging whether to optimize the real-time associated control data based on the comprehensive evaluation value, if so, obtaining a real-time optimization strategy, and obtaining a plurality of optimization control data intervals according to the real-time optimization strategy; and determining a standard optimization control data interval based on the optimization control data interval, carrying out analogue simulation on standard optimization control data, carrying out optimization effect evaluation according to an analogue simulation result, and determining optimal control data, so that optimization management of the boiler combustion process is realized, and efficient and stable operation of the boiler is ensured.

The invention belongs to the technical field of energy management, and particularly relates to a regional energy management optimization method based on edge computing and multi-objective optimization, which comprises the following steps of: determining edge equipment with qualified processor performance based on processor performance parameters of the edge equipment, establishing an edge equipment site diagram based on geographical sites of the qualified edge equipment, applying the initial multi-objective optimization scheme to the regional energy simulation model, obtaining an energy efficiency evaluation value and an energy efficiency comprehensive difference value, comparing the source efficiency comprehensive difference value with an energy efficiency comprehensive difference standard value stored in a database, matching a pre-selected management optimization scheme, and applying the pre-selected management optimization scheme to the regional energy simulation model, and based on the optimal management optimization scheme, adjusting an optimization scheme decision according to a real-time evaluation result. According to the invention, the flexibility of overall regional energy management can be improved, and sustainable development of long-term regional energy management can be maintained.

The embodiment of the invention discloses an expressway lane management and control method and system based on real-time traffic flow. The method comprises the steps that a plurality of intelligent agents based on reinforcement learning are constructed, and prediction durations corresponding to different intelligent agents are different; selecting a target agent corresponding to a prediction duration, and predicting an optimal management and control measure in the prediction duration in the future; simulating a traffic flow data time-varying curve of the controlled road section under the optimal control measure by using a simulation model; the optimal management and control measure is utilized to carry out actual management and control on the management and control road section; if the difference between the actual traffic flow data at a certain moment and the time-varying curve exceeds a set range, recording the actual management and control duration up to the certain moment; performing feedback reward on the target agent according to the actual management and control duration; and according to the actual management and control duration, the prediction duration is selected again, and a new round of prediction is started. According to the embodiment of the invention, the accuracy and anti-interference performance of highway management and control are improved.

The invention discloses a management method and system for coping with nursing rehabilitation service, and relates to the technical field of nursing management. The method comprises the following steps: collecting and maintaining personal information of all users; health assessment is conducted on the target user regularly, and a rehabilitation state data set is obtained; the technical key points are as follows: the daily average nursing duration fine adjustment operation when the nursing grade reaches the limit is constructed, and the Barthel index, the PEI value and the ideal range deviation degree of the physical state evaluation value are introduced, so that the whole scheme can flexibly adjust the nursing duration to adapt to the rehabilitation trend change on the premise that the nursing grade is not changed or cannot be continuously adjusted, and the rehabilitation effect is improved. On one hand, personalized nursing resource configuration is carried out according to functional state differences, service flexibility is enhanced while nursing grade stability is maintained, on the other hand, the nursing disjunction phenomenon caused by grade limitation is avoided, and optimal management operation on nursing operation is completed.

The invention relates to the technical field of low-carbon parking lots, in particular to a parking lot management method and system based on carbon point tendency, and the method comprises the steps: obtaining the basic information of a parking lot; calculating the minimum carbon emission and the maximum carbon emission corresponding to the parking lot based on the basic information of the parking lot in combination with a preset carbon factor database; calculating a dynamic carbon integral state corresponding to each parking space; obtaining the current carbon emission, and judging the position of the current carbon emission between the minimum carbon emission and the maximum carbon emission to obtain a first factor; obtaining a current earned carbon integral, and calculating a relation between a ratio of the earned carbon integral to the number of vehicles in the field and a preset value to obtain a second factor; and calculating a carbon score based on the first factor and the second factor, judging a low-carbon management tendency based on the carbon score and the state information of the parking lot in the current parking lot, and generating a corresponding optimal management scheme based on the low-carbon management tendency. The method has the effect of improving the carbon emission management capability of the parking lot.

The invention provides an intelligent construction site equipment remote monitoring management method and system based on the Internet of Things, and relates to the technical field of monitoring management, and the method comprises the steps: collecting the operation parameters of target construction site equipment through an Internet of Things sensor; constructing a life monitoring model in combination with the operation parameters; performing remote monitoring on the target construction site equipment based on the life monitoring model to obtain an equipment operation condition; and performing equipment maintenance management according to the equipment operation condition. According to the invention, the technical problem that the construction progress and the engineering efficiency are further influenced due to the lack of personalization and precision of maintenance decisions caused by insufficient management of the life cycle of construction site equipment in the prior art can be solved, the technical targets of intelligent monitoring and optimal management of the whole life cycle of the equipment are achieved, and the purposes of improving the operation efficiency of the equipment and reducing the construction cost are achieved. And the technical effect of finishing the project on time is ensured.

The invention provides a transformer area flexible interconnection system optimization management method based on fault self-healing capability, and the method comprises the steps: carrying out the following self-healing strategies through a transformer area flexible interconnection system fault self-healing model: calculating the energy storage state of an energy storage system through an optimal power flow algorithm in a grid-connected mode before network disconnection, and charging the energy storage system, running in an island mode; when the fault of the main network is detected, the power supply time of the key load is maximized by using the source load storage resources, and optimal scheduling is carried out according to the current operation state; and after the transformer area flexible interconnection system enters the island mode, non-critical load removal is executed, and power supply of critical loads is guaranteed preferentially.

The present invention provides a service optimization management system for telemedicine services, which relates to the field of data processing technology. The system includes: retrieving multiple queue information sets based on the reception window, extracting multiple queue personnel characteristics for differential identification of consultation time, generating multiple consultation time prediction data, monitoring queue conflicts based on the appointment queue information set and the online queue information set, building a queue time prediction model, generating a consultation queue time data set, formulating multiple exclusive channels, performing feedback response through the user terminal, optimizing the multiple consultation time prediction data according to the feedback response results, updating the consultation queue time data set, and realizing the optimization management of telemedicine services. The technical effects of more accurate resource allocation and service optimization, improving service efficiency and quality, reducing patient waiting time, and improving patient satisfaction are achieved.

The application discloses a kind of management methods and systems to cope with nursing rehabilitation service, it is related to nursing management technical field;The method includes the steps of: collecting and maintaining the personal information of all users;Regular health assessment is carried out to target user, and obtains rehabilitation state data set;Its technical points are: the daily nursing time length fine tuning operation when the nursing level has reached the limit is constructed, and the ideal range deviation degree of Barthel index, PEI value and body state evaluation value is introduced therein, so that the overall scheme can flexibly adjust the nursing time length under the premise that the nursing level is unchanged or cannot continue to adjust, to adapt to the rehabilitation trend change, on the one hand, individualized nursing resource allocation is realized according to functional state difference, while maintaining the stability of nursing level, enhance service flexibility, on the other hand, avoid the nursing disjointedness phenomenon caused by level limit, complete the optimization management operation of nursing operation.

We provide the system. [Solution] A means of communication for receiving information on the health status of residents, An analytical means for analyzing the aforementioned health status information and extracting estimated diseases, A means for presenting management methods that proposes the optimal management method based on the analysis results, A monitoring system for collecting residents' biometric information and detecting abnormalities, A warning generation means that generates a notification when an anomaly is detected, A report generation means for generating an evaluation report including the aforementioned analysis results and management method, A system that includes this.

The invention provides a data center pipeline optimization management method and system, and belongs to the technical field of optimization management, and the method specifically comprises the steps: according to the coincidence condition of the distribution data of a harmonic monitoring abnormal time period and the data processing busy time period of a corresponding server, when it is determined that the optimization processing of a matched optical cable line does not need to be carried out, carrying out the optimization processing of the matched optical cable line; and determining an abnormal condition of a data processing result of the corresponding server in the harmonic monitoring abnormal time period based on the distribution data of the harmonic monitoring abnormal time period, and determining an interference risk server in the servers based on the abnormal condition, according to the method, the optimal management method of the matched optical cable line is determined according to the interference risk server data in different harmonic monitoring abnormal time periods and the data processing busy time periods of different interference risk servers, so that the operation reliability of the servers is improved, and the risk of electromagnetic interference is reduced.

The system according to the embodiment aims to simulate multiple scenarios and derive an optimal management pattern. [Solution] A system according to an embodiment includes a setting unit, a judgment unit, an evaluation unit, and a derivation unit. The setting unit sets a job title, personality, and judgment tendency. The judgment unit makes a management decision based on the job title, personality, and judgment tendency set by the setting unit. The evaluation unit evaluates the management decision made by the judgment unit. The derivation unit derives an optimal management pattern based on the evaluation results obtained by the evaluation unit.

The invention provides an ultrasonic amplitude-change pole optimization management method and system. The method comprises the following steps: receiving a performance target parameter set which is set when the system is initialized; dividing the amplitude-change pole into an input section, a middle transition section and an output section to construct a structure design model, and reversely optimizing the structure design model through a performance prediction module to generate a corresponding parameter set; sLM process parameters are collected, the parameter set is combined to be input into a pre-constructed error prediction module, and manufacturing error prediction is generated; real-time amplitude-change pole performance data are collected and combined, the manufacturing error prediction and the performance offset are combined, an individual final performance value is generated through Bayesian weighted fusion, and an individual performance label of each individual amplitude-change pole is constructed; the tissue electrical impedance, the cutter head temperature and the ultrasonic echo signal are collected in real time in the operation, the driving voltage of the transducer is dynamically adjusted by combining the individual performance label, and closed-loop control over the amplitude is carried out. According to the invention, a safer and more intelligent energy output scheme is provided for a high-complexity operation scene.

A method and device for optimal power flow calculation in power systems based on generalized Nash equilibrium are disclosed. The method comprises: determining first optimal power flow model corresponding to distribution network and second optimal power flow model corresponding to each microgrid; constructing a third optimal power flow model based on the first optimal power flow model, each second optimal power flow model and boundary coupling constraint condition; determining generalized Nash equilibrium solution corresponding to the third optimal power flow model; determining generalized Nash equilibrium constraint condition corresponding to collaborative objective function and the Nash equilibrium solution; determining fourth optimal power flow model corresponding to the collaborative objective function, with constraint condition including the generalized Nash equilibrium constraint condition; determining Pareto optimal solution corresponding to the fourth optimal power flow model to determine an optimal power flow. Accordingly, effectiveness of optimization management is improved.

The present invention relates to optimizing a management environment and a quality control, by adjusting quality criteria of an abnormality detection model applied to an edge device according to a management situation. In addition, an objective of the present invention is to establish optimal quality criteria by comprehensively calculating a production disruption caused by increasing quality criteria of a model, a loss amount of a sales profit according to a missed sales opportunity due to the production disruption, and a risk of quality costs that may occur when the quality criteria is lowered.

We provide the system. [Solution] A means including multiple artificial intelligences that collect data related to urban management and perform analyses on energy, transportation, and the environment based on said data, A means for conducting virtual meetings among multiple artificial intelligences, integrating urban management information, and generating an optimal management plan, A means for automatically executing urgent management instructions based on the generated management plan, A means of notifying the user of the results of the execution and enabling them to make further decisions, A system that includes this.

The invention relates to the technical field of mine field meeting before class, solves the technical problems that in the prior art, data analysis is low in accuracy and robustness, and the analysis process is tedious and difficult to learn, and particularly relates to a meeting before class intelligent management control method and system. Pre-processing the reference data Ck to obtain pre-processed data; s2, calculating an environment equipment personnel index Hsrh according to the preprocessed data; the optimal management control scheme is obtained through calculation of the cross experiment value, the process is simple and efficient, workers can rapidly obtain analysis results of the conference before class, the working efficiency of the conference before class is improved, the complexity can be reduced, rapid learning of the workers is facilitated, the global search ability is high, the convergence speed is high, and the method is suitable for popularization and application. And the accuracy of an analysis result and the working efficiency are greatly improved.

The invention discloses an intelligent decision support system based on big data, which relates to the technical field of decision support systems, and is characterized in that limiting elements are generated based on historical planting scenes, a plurality of parameter spaces are generated by using a random generation tool according to a preset space number, and a loss degree value of each parameter space is calculated through an information function; the loss degree values of all the parameter spaces are substituted into a dynamic screening algorithm for calculation, after part of the parameter spaces are screened out, content randomization operation is carried out on the reserved parameter spaces, space sets are established for all the parameter spaces completing the randomization operation, multiple sets of space sets are output, loss degree value comparison is carried out on the multiple sets of space sets, and the loss degree values of all the parameter spaces are obtained. And selecting the parameter space with the minimum loss degree value as a management scheme of the current planting scene of the enterprise. According to the support system, through dynamic scheme generation and screening, the optimal management scheme can be globally selected to adapt to the current planting scene of an enterprise, the adaptability is high, and the decision making efficiency is improved.

The invention discloses a multivariate spatial data management control method and device, and the method comprises the steps: obtaining multivariate spatial data in the operation process of a power system; performing correlation analysis on each external data and the power load data to obtain a correlation coefficient of each external data and the power load data; constructing a correlation coefficient matrix according to the correlation coefficient of each external data and the power load data; traversing the correlation coefficient matrix, and selecting external data of which the correlation coefficient is greater than or equal to a preset correlation threshold value to obtain an external data set mainly correlated with the power load data; and on the basis of the power demands of different power regions, predicting the power consumption required by the target power region according to the external data set mainly associated with the power load data so as to determine an optimal management scheme of the multivariate spatial data. According to the invention, the management efficiency and precision of the multivariate spatial data can be improved.

The present invention discloses a method for optimizing energy management of interconnected microgrids. Multiple rounds of optimization management are performed in one day. In each round, the power of the power-controllable device is adjusted according to the state of the interconnected microgrid, and then the power that has not been absorbed is dispatched between the microgrids according to the topological structure of the interconnected microgrid to achieve the supply and demand balance of the interconnected microgrid. The method mainly includes the following steps: the devices in the microgrid are divided into power-controllable devices and power-uncontrollable devices, and a cost function of energy optimization management of a round of interconnected microgrids is constructed. The method does not need to establish a complex physical model, and the network is trained with data as the driving force, so that the network gradually learns the optimal control strategy for achieving the supply and demand balance of the interconnected microgrid at the minimum cost, thereby avoiding the waste of resources.

The invention relates to a construction management system and method for a complex construction scene, and relates to the technical field of construction management, and the system comprises a monitoring module which obtains the construction progress and construction monitoring information of the construction scene in real time, and a digital twinborn model recognition module which builds a three-dimensional digital twinborn model and determines the risk recognition result of equipment and personnel; the decision module determines a plurality of initial management strategies based on the risk identification result and a pre-constructed construction safety knowledge graph; and based on the construction progress, the construction plan, the environment information and a preset multi-objective optimization algorithm, optimizing the plurality of initial management strategies, determining an optimal management strategy, and sending the optimal management strategy to a corresponding management terminal in the optimal management strategy. The method has the effects of effectively identifying the construction risk, optimizing the management strategy, guaranteeing the construction safety and improving the construction efficiency.

This invention discloses an intelligent management system for distribution network diagram and model data, belonging to the field of distribution network diagram and model data management technology. It includes an application analysis module, an optimization analysis module, and an optimization management module. The application analysis module performs application analysis on the diagram and model data to determine optimization items, which are data items requiring optimization of diagram and model data transmission. The optimization analysis module performs optimization analysis on the optimization items, obtains diagram and model flow association data for each optimization item, generates a diagram and model flow information diagram for each optimization item based on the association data, identifies the transmission items present in each optimization item based on the flow information diagram, determines the baseline optimization method for each transmission item, determines the evaluation order of each transmission item based on the baseline optimization method, performs optimization analysis based on the evaluation order, and forms an optimization combination curve. The optimization management module manages the optimization items for each diagram and model.

The invention discloses a distributed power supply grid-connected optimization management system and a control method thereof, and belongs to the technical field of distributed power supply grid-connected management, and the control method comprises the steps: judging the operation state of distributed power supply equipment according to natural microenvironment data and operation state data in real time, and when the distributed power supply equipment is judged to be abnormal, executing the operation of the distributed power supply equipment; and issuing a control instruction of equipment shutdown to the distributed interface equipment in the region, predicting the output state of each region based on the operation state judgment result of the distributed power supply equipment in the region, and comprehensively considering the power market transaction and power selling market conditions. And the output state of the distributed power supply equipment in each region is coordinated and distributed based on the overall optimization target, so that the whole distributed power supply grid-connected optimization management system achieves optimal scheduling. According to the method, the comprehensive effect of multiple influence factors can be comprehensively considered, the operation state of the equipment can be accurately judged in a complex operation environment, and then the optimal management strategy is formulated according to different conditions.

This invention provides a distributed energy optimization management method and system for rapidly deploying mobile makeshift hospitals. In this embodiment, the invention dynamically adjusts energy allocation by constructing a multi-layered virtual energy network model for the mobile makeshift hospital; it uses time series forecasting technology to predict energy consumption demand and power generation potential, generating an energy supply and demand balance plan; it introduces a multi-objective optimization algorithm to intelligently schedule the charging and discharging process of the energy storage system, obtaining the optimal charging and discharging plan; it collects and analyzes the operating data of the distributed energy system at preset time nodes, uses a machine learning model to predict fault risks from the operating data, and optimizes the optimal energy allocation strategy and multi-objective optimization algorithm based on the results, forming an adaptive and self-learning energy closed-loop management system. The technical solution provided by this invention enables dynamic optimization management of energy in mobile makeshift hospitals, ensuring efficient, flexible, and adaptive energy allocation and scheduling, and improving the overall performance and reliability of the system.