121results about How to "Ensure fair distribution" patented technology

The invention discloses a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention relates to a peripheral adaptation system based on a dynamic plug-in loading mechanism, and belongs to the technical field of plug-in management. The system comprises a dynamic plug-in loading management unit, a peripheral adaptation management unit and a front-end management unit, the dynamic plug-in loading management unit is used for managing the loading and hot plug functions of the plug-in and realizing the loading, isolation and unloading of the plug-in when the system runs; the peripheral adaptation management unit is used for communicating with a peripheral, and analyzing and identifying a special protocol and an interface of the peripheral through the intelligent adapter; and the front-end management unit is used for designing an API (Application Program Interface) for a front-end application to call, and performing authority control on sensitive operation through the security access module. Dynamic loading and flexible support of various peripherals are realized, compatibility of the peripherals is optimized, and maintainability and expansibility of the system are improved.

The invention relates to the technical field of freight scheduling, in particular to a freight scheduling information management method and system and a storage medium. The method comprises the following steps that uncompleted order collection is carried out on a freight platform to obtain an uncompleted order set, and the uncompleted order set comprises a plurality of uncompleted orders; each uncompleted order at least comprises an order ID, order release time, target cargo attribute data, publisher identity data, a departure place, a destination, an order state identifier, an order transportation task schedule, cargo stacking attributes, predicted delivery time and cargo expected arrival time; and performing order state classification and marking on the uncompleted order set to obtain a classified order set. By optimizing the loading space utilization rate of the vehicle, the unloaded and half-loaded conditions of the vehicle can be reduced, so that the fuel consumption and the transportation cost are reduced.

The invention relates to the technical field of power distribution cabinet control, in particular to an electrical power distribution cabinet and a control method thereof. The method comprises the following steps: acquiring multi-dimensional data of the power distribution cabinet; performing data preprocessing on the multi-dimensional data of the power distribution cabinet to generate a power distribution cabinet preprocessing data set; performing contribution degree sorting on the preprocessed data set of the power distribution cabinet to generate contribution degree sorting data of the power distribution cabinet; constructing an electric cabinet model according to the contribution degree sorting data of the power distribution cabinet, and generating an electric cabinet power consumption prediction model; acquiring real-time power distribution cabinet data; performing power distribution cabinet power utilization prediction on the real-time power distribution cabinet data based on the power utilization cabinet power utilization prediction model to generate a power distribution cabinet prediction result; therefore, by comprehensively integrating the multi-dimensional data of the power distribution cabinet, intelligent prediction and optimal scheduling are performed, the problems of low prediction precision, non-uniform resource distribution, response lag and the like in traditional power distribution cabinet management are solved, and the operation efficiency and the resource utilization rate of the electrical power distribution cabinet are improved.

The invention discloses a mobile game dynamic control optimization method based on adaptive deep learning, and the method comprises the following steps: S1, collecting player operation data and scene background parameters, and generating feature data of a unified scale; s2, generating a self-adaptive spatial-temporal feature sequence through the spatial-temporal feature self-adaptive convolutional network; s3, constructing an adaptive memory network, recognizing a recent high-frequency operation mode through a short-term focusing mechanism, and generating operation intention features; s4, generating a self-supervised learning task through a self-supervised learning multi-layer perceptron, and adaptively adjusting the feature weight to generate a control parameter; s5, a feedback score is generated in real time through a feedback scoring mechanism, and the learning rate is adjusted; s6, establishing a satisfaction evaluation mechanism based on feedback scores and data analysis; and S7, recording operation feedback and scoring results in real time, and realizing adaptive optimization based on feedback circulation. According to the method, mobile game control is dynamically optimized through the adaptive deep learning method, and high adaptability and excellent user experience are achieved.

The invention relates to the technical field of real-time early warning of risks in the construction process of a subway station, and particularly discloses a real-time visual early warning method for the risks in the construction process of the subway station. The hydrological monitoring data is transmitted to a ground control center through a wireless communication network; processing the hydrological monitoring data of the current subway tunnel construction stage, comprehensively analyzing a possible coefficient of occurrence of a water burst event of the current subway tunnel construction stage, and sending a water burst analysis early warning signal; after the water gushing analysis early warning signal is received, the water gushing early warning risk level of the current subway tunnel construction stage is evaluated, and corresponding processing is carried out; the predicted tunnel water inflow of the subway tunnel in the future period is synchronously predicted, and timely early warning processing operation is carried out; the risk control in the construction process is ensured to be within an acceptable range, and scientific risk assessment and prevention measures are provided for tunnel construction.

The invention relates to the field of robot collaborative optimization, in particular to an industrial robot collaborative optimization system. Comprising the following steps: creating a task distribution matrix, optimizing the task distribution matrix by adopting a multi-objective optimization strategy, setting constraint conditions, and introducing a self-adaptive feedback adjustment mechanism to obtain an optimized task distribution matrix; and performing path optimization based on the optimized task allocation matrix. The problems that a fixed or simple scheduling algorithm is adopted for existing task allocation, and overload of part of industrial robots is likely to be caused; a dynamic feedback adjustment mechanism for resource consumption and state of the industrial robot is lacked, and optimization of task allocation cannot be performed according to real-time load and resource state of the industrial robot; the technical problems that path planning is prone to falling into a local optimal solution, and an industrial robot may frequently select a fixed path when executing a task, resulting in path resource competition and overall efficiency reduction are solved.

The invention provides an edge computing resource allocation method and system based on multi-objective optimization, and relates to the field of data processing, and the method comprises the steps: determining the resource demand feature of each unmanned retail store and the resource demand association feature between any two unmanned retail stores according to the historical computing resource demand information of a plurality of unmanned retail stores, determining an edge calculation layout scheme, and setting a plurality of edge calculation devices; predicting future computing resource demands of the plurality of unmanned retail stores according to the resource demand feature of each unmanned retail store, the resource demand association feature between any two unmanned retail stores and the demand prediction auxiliary information; acquiring resource allocation auxiliary information; according to the method, the edge computing resource allocation scheme is determined according to the future computing resource demands and the resource allocation auxiliary information of the unmanned retail stores, the edge computing resources are allocated to the unmanned retail stores, and the method has the advantage of improving the flexibility of edge computing resource allocation in an unmanned retail scene.

The invention provides a sports student development core accomplishment comprehensive management method and system based on big data, and the method comprises the steps: receiving and integrating multi-channel sports student dynamic data streams, and constructing a personal growth trajectory map; analyzing nodes and edges in the atlas by using an association rule mining algorithm, identifying key factors and turning points influencing growth, and performing optimal matching on educational resources and training plans in combination with a core literacy framework and a particle swarm optimization algorithm to generate a matching result; personalized learning materials and training suggestions are accurately pushed to form a preliminary development path plan; monitoring the execution progress in real time through a reinforcement learning algorithm, and dynamically adjusting the preliminary plan to obtain a target development path plan; and establishing a multi-dimensional feedback mechanism, collecting feedback information of coaches, sports students and third-party reviews, performing analysis processing to obtain an evaluation result, and finally generating an optimized final target development path plan. According to the invention, the management effect of core accomplishment of sports students is improved.

The invention discloses a wireless router access management method and system and a wireless router, relates to the technical field of wireless routers, and remarkably improves network security and resource utilization efficiency through multiple authentication and a dynamic management mechanism. According to the system, first behavior characteristic data and network state data of equipment are collected, intelligent analysis is carried out in combination with a CNN model, and the security of the access equipment is judged in real time. For equipment meeting the security standard, the system automatically allocates bandwidth and provides continuous network service, and for abnormal equipment, the system blocks access of the abnormal equipment in time through multiple rounds of authentication and load evaluation, so that the security and stability of a network environment are ensured. In the aspect of bandwidth management, the system intelligently sorts and dynamically allocates bandwidth resources according to the second access safety index # imgabs0 # and bandwidth requirements of the access equipment, and low efficiency and resource waste of a traditional static allocation mode are avoided.

The invention provides a pipeline valve verification Internet of Things system and method based on intelligent gas safety supervision, and the method comprises the steps: generating a second verification instruction based on a flexible verification batch and a first verification instruction; performing second verification on the gas pipeline and the pipeline valve to obtain a second verification result; based on the first verification result and the second verification result, a to-be-maintained valve is determined; based on the to-be-maintained valve, work order information is determined; determining a work order distribution instruction based on the work order information; and generating a parameter adjusting instruction. The Internet of Things system comprises an intelligent gas government safety supervision management platform, an intelligent gas government safety supervision sensing network platform, an intelligent gas government safety supervision object platform, a gas company management platform, a gas company sensing network platform, a gas equipment object platform and a gas equipment maintenance object platform. According to the method, the to-be-maintained valve with potential safety hazards can be quickly and accurately positioned, and the monitoring frequency of the valve is enhanced before the valve is maintained, so that the gas use safety is ensured.

The invention relates to the field of big data super-concurrent processing, in particular to an education big data super-concurrent processing system and method. The method comprises the following steps: collecting and preprocessing original request data to obtain actual request traffic; using historical data in actual request traffic to train a weighted autoregression model, and predicting the traffic; dynamically adjusting the concurrency based on the obtained flow prediction value, calculating the scheduling priority of the data packets, and sorting and distributing the data packets; a dynamic back pressure control mechanism based on flow prediction is introduced, flow input is dynamically adjusted, and overload is prevented. The problems that a traditional scheduling algorithm and a traditional load control strategy lack enough accurate flow prediction, concurrency cannot be adjusted in time, and system resources are excessively distributed or insufficient are solved; the priority processing of the key task cannot be ensured because the flow fluctuation and the scheduling priority are not fully considered; the problems that flexibility and self-adaptive capacity are lacked, the flow cannot be effectively limited when the load is too high, and the overload risk of the system is increased are solved.

The invention discloses a system for mixing, utilizing and recycling waste heat in urea production, and relates to the technical field of recycling, which comprises the following steps of: before urea production starts, initially setting a heat energy distribution proportion and operating parameters, and collecting urea production data by deploying various sensors; based on urea production data, a heat energy demand prediction model is constructed, and waste heat is divided into different temperature levels; according to the prediction result, the distribution proportion of waste heat of different temperature levels is dynamically adjusted; according to the dynamically-adjusted heat energy distribution proportion, waste heat of different temperature levels is utilized in a gradient mode through a multi-level heat exchanger; waste heat which cannot be utilized immediately is guided into the phase change material to be stored, released according to production requirements and distributed again; through construction of the heat energy demand prediction model and application of the phase change material, effective division and efficient gradient utilization of waste heat of different temperature levels are achieved, and continuous supply of heat energy in the production process is ensured.

The invention belongs to the field of live-action three-dimensional modeling, and discloses a live-action three-dimensional modeling method and device based on cloud native task batch processing scheduling, and the method comprises the steps: calculating aerial triangulation encryption data of a three-dimensional modeling task, and carrying out the block processing of the aerial triangulation encryption data through a task segmentation module; the task scheduling module adjusts operation and task issuing of sub-tasks in real time according to available computing resources of the cloud architecture system, and optimizes the utilization rate of distributed computing resources in real time; the task state monitoring module monitors the execution state of the sub-task in real time; the subtask processing module performs corresponding generation on the image data in each block, and uploads a generation result to a storage resource pool after operation is completed; and after all the subtasks are operated, calling a model merging module to perform overall merging. According to the method, the live-action three-dimensional modeling task and the cloud native task batch processing architecture are combined, operator-level scheduling of the live-action three-dimensional modeling function is achieved through the cloud native system architecture and the resource container pool, and cloud native system resources are efficiently utilized.

The invention discloses a rural photovoltaic grid-connected management method and system, relates to the technical field of energy management, and solves the problem that a harmonic change rate pollution value, an energy loss rate and an environmental influence coefficient are difficult to analyze according to operation data and environmental data. Secondly, potential faults of the photovoltaic power station are difficult to identify and analyze according to video images; thirdly, fault coefficients are analyzed according to the recognition result, and the inspection frequency is planned; and finally, comprehensive analysis is difficult to carry out according to various data, and the inspection frequency is difficult to dynamically adjust after threshold evaluation operation states are divided according to analysis results. According to the invention, real-time fault identification is realized by analyzing operation data and environment data and by means of a deep learning model, and the inspection frequency is planned according to a fault coefficient; the operation condition of the photovoltaic power station is analyzed in multiple dimensions, the operation level is dynamically divided, the inspection frequency is dynamically adjusted accordingly, and the inspection efficiency is improved.

The invention provides a DNN model-based division and reasoning acceleration method and system for vehicle-mounted edge intelligence. The method comprises the following steps: constructing a DAG graph based on a vehicle-mounted DNN model; constructing an auxiliary network flow graph of the DAG; adding a head node representing local calculation of a vehicle and a tail node representing calculation of an edge server or an auxiliary vehicle, and setting the weight of an edge as time delay overhead for segmenting the edge; obtaining different layering modes of an auxiliary network flow diagram of the DAG and corresponding maximum tolerance point sets meeting local time delay, memory and energy consumption limitation of the vehicle; correspondingly modifying the weight of the edge in the auxiliary network flow graph of the DAG based on each maximum tolerance point set, and calculating the maximum flow of each modified network flow auxiliary graph; and selecting a division mode corresponding to the maximum flow as a DNN model division mode. According to the method, the vehicle and road cloud cooperative reasoning is realized through a DNN model division mode, so that the DNN task cooperative reasoning completion time delay is reduced, and the vehicle safety and the user experience are improved.

The invention belongs to the technical field of spaceflight launching, and particularly relates to a primary and secondary satellite in-orbit maneuvering control method and device and a storage medium. The control method comprises the following steps: step 1, controlling a primary satellite and a secondary satellite to enter an orbit; 2, acquiring task requirements in real time, and calculating to obtain an on-orbit maneuvering strategy of the child satellites based on an autonomous maneuvering control algorithm; 3, potential attack and defense threats are analyzed, and the possible influence on in-orbit maneuver control is evaluated; key defense measures of the child satellite and the mother satellite in the maneuvering process are determined, and a coping strategy is formulated; and 4, establishing a real-time monitoring system, continuously collecting state information, orbit data and potential threat information of the child satellites, and adjusting a maneuvering control strategy according to the state information, the orbit data and the potential threat information of the child satellites. According to the method, the operation flexibility and the task response speed in a complex environment are improved.

The invention discloses an IP address management method and system, and the method comprises the following steps: S1, obtaining network equipment information, and obtaining a cluster according to the network equipment information through employing a clustering algorithm; s2, constructing address pools corresponding to the clusters, and allocating IP addresses in the address pools based on the clusters; s3, acquiring real-time network equipment information, and matching the corresponding address pool according to the real-time network equipment information and the corresponding condition of the clustering cluster to adjust the IP address distribution condition; and S4, constructing a recovery priority according to the use proportion of the IP addresses in each address pool, and executing IP address recovery based on the recovery priority. The method has the beneficial effects that the IP address allocation efficiency is improved, and IP address allocation conflicts are avoided. Meanwhile, when the IP addresses need to be recycled, the IP addresses are recycled from the address pool according to the priority from low to high, so that efficient utilization of IP address resources is ensured.

The turbine guide vane front edge air film cooling structure coping with the rotational flow incoming flow influence comprises a turbine guide vane front edge, a through cold air cavity is formed in the turbine guide vane front edge, and a plurality of rows of air film holes are formed in the outer side of the turbine guide vane front edge at intervals. The air film holes are arranged according to the real isobaric distribution characteristics of the front edge of the turbine guide vane under the influence of rotational flow incoming flow, and one end of each air film hole is communicated with the through cold air cavity. The technical problem that a combustion chamber outlet rotational flow field influences the turbine guide vane front edge gas film cooling characteristic is solved, and the turbine guide vane front edge heat insulation gas film cooling efficiency under the influence of rotational flow incoming flow is improved.

The invention discloses a multi-stage coordinated regulation and control method and system for a virtual power plant, and the method comprises the steps: carrying out the quantitative description of an adjustable space of various distributed devices in the virtual power plant through a unified modeling method, and building an evaluation model of real-time output and response capabilities; aiming at the problem of online coordinated scheduling between the virtual power plant and the power distribution network, a centralized-distributed hierarchical scheduling structure is adopted, and the scheduling efficiency of the system and the response capability of equipment are effectively improved through unified modeling and hierarchical optimization. According to the method, the total operation cost of the virtual power plant can be effectively reduced, the problem of comprehensive information sharing needed by traditional centralized scheduling is avoided through privacy protection design, efficient interaction and information safety between the virtual power plant and the power distribution network are achieved, the requirement for real-time economic scheduling in a complex scene can be met, and high expandability and adaptability are achieved. And meanwhile, the method accords with the development direction of optimal dispatching of the current power distribution network, and plays a crucial role in promoting energy structure transformation and accelerating renewable energy consumption.

The invention belongs to the technical field of channel allocation, and particularly relates to a full-duplex D2D (Device-to-Device) communication channel allocation algorithm based on novel graph coloring, which comprises the following steps of: constructing a bidirectional interference graph containing all potential interference links; expressing a channel allocation problem as an interference minimization graph coloring problem; distributing N independent communication channels for all cellular user equipment; a greedy graph coloring algorithm is adopted, and single colors are distributed to the D2D vertexes which are not colored one by one; if the number of D2D vertexes in a certain color set is increased or decreased, the interference level is evaluated again; and traversing each color, allocating a single color to the remaining D2D vertexes, terminating the algorithm after all D2D vertexes are traversed, and generating a conflict-free channel allocation scheme. According to the invention, the channel allocation can be optimized, and the interference in the system can be effectively managed and reduced, so that the access rate of the user and the overall spectrum efficiency are improved.

The invention discloses an intelligent flow monitoring system for network communication, and relates to the technical field of network communication monitoring. Comprising a traffic data acquisition module, a data preprocessing module, a data analysis and storage module, a data result display module, a real-time traffic analysis module, a communication traffic use monitoring module, a security threat detection module and a performance optimization processing module. The flow data acquisition module is used for capturing network communication flow data from data sources of network equipment and a server, an acquisition method of the flow data acquisition module selects one or more of SNMP, Net Flow and s Flow, flow data acquisition is realized by each node in a network, and acquisition equipment comprises one or more of a router and a switch. The intelligent flow monitoring system is composed of a plurality of modules and functions, the modules and the functions work cooperatively, real-time monitoring, analysis and optimization of network flow are achieved jointly, and through the components, a network administrator can effectively manage network resources and guarantee safety and stability of a network.

The invention relates to the technical field of bridge health monitoring, in particular to a multi-point positioning intelligent bridge health monitoring system which comprises a data acquisition module, a water level analysis module, a wind speed analysis module and a correction module. According to the system, the water level sensor, the wind speed sensor, the impact force sensor and the image monitoring technology are comprehensively used, real-time and comprehensive monitoring of the health condition of the bridge is achieved, the accuracy of risk assessment and the timeliness of early warning are effectively improved, the system can automatically recognize abnormal conditions, determine the scour section and give out flood discharge or wind reinforcement alarms in time, and in addition, the system is convenient to use. The system dynamically adjusts the monitoring distance according to the actually monitored erosion condition, so that reasonable distribution of monitoring resources and high efficiency of information collection are ensured, and the problem that the assessment accuracy of the bridge health condition is poor due to the lack of comprehensive assessment capability for various risk factors caused by the fact that alarm information is output only depending on abnormal data is effectively solved.

The invention relates to the technical field of intelligent scheduling, in particular to a new energy charging pile intelligent scheduling system based on big data, and the system comprises an information collection module which is used for obtaining the scheduling period of a charging pile, and collecting the charging amount, power consumption and charging time of the charging pile in each scheduling period; the scheduling identification module is used for identifying a scheduling target and a scheduling price in each scheduling period and checking power consumption distribution in each scheduling period to obtain a consumption evaluation factor; the scheduling adjustment module is used for acquiring a plurality of excitation clusters corresponding to the scheduling targets according to the scheduling targets and the scheduling prices in each scheduling period; each incentive cluster comprises a scheduling target, a scheduling pricing and an incentive effect; clustering the plurality of excitation clusters according to scheduling pricing to obtain excitation factors in each scheduling period, and determining a target set of charging pile scheduling according to the obtained excitation factors; the utilization rate and efficiency of the charging pile are improved, and intelligent scheduling of the charging pile is realized.

The invention discloses a safety shoe wear-resistant material layout method based on an ant colony optimization algorithm. The method comprises the following steps: S1, constructing a safety shoe wear data set; s2, dividing the surfaces of the safety shoes into a plurality of priority areas; s3, distributing pheromone initial concentration according to priority region distribution of the fractal feature value; s4, establishing an optimization objective function; s5, gradually selecting an optimal material layout path of a high-priority region; s6, calculating the fitness value of each path according to the path search result and the optimization objective function, and updating the path pheromone concentration by using the fitness value; s7, a final global optimal path is obtained through multiple rounds of iteration from S1 to S6; s8, outputting a final optimal layout scheme of the wear-resistant material of the safety shoes; and S9, verifying the final optimal layout scheme. According to the method, an efficient and scientific optimization scheme can be provided in the layout design of the wear-resistant material of the safety shoes, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses a multi-underwater robot cooperative path planning method and system and a storage medium, and relates to the technical field of underwater robos.The method comprises the steps that S1, an underwater distribution electronic graph is obtained in advance, a plurality of sets of underwater charging station areas are locked, and meanwhile serial number marking is conducted on the charging station areas and the robots; accurate positioning of the robots and the charging stations in the path planning process is further ensured, the initial precision of path planning is improved through the process, and a reliable data basis is provided for subsequent steps. In the steps S2 and S3, electric energy state data information of a plurality of groups of robots is monitored in real time, an electric energy set is generated, a to-be-charged robot is determined, a task state set is constructed according to related task processing data information, and a relative task urgency coefficient Rhxs is generated, so that the electric energy state and the task urgency degree of the robots are comprehensively considered; and the reasonable arrangement of the charging demand in the path planning process is further ensured.

The invention relates to the field of remote monitoring, and discloses a safety inspection method and system applied to intelligent security, and the method comprises the steps: calculating a potential risk coefficient of a scene element; marking risk nodes of the intelligent security scene, analyzing risk indexes of the risk nodes, defining multi-modal inspection equipment of the risk nodes, and analyzing inspection resistance of the multi-modal inspection equipment to the risk nodes; configuring a mobile inspection device of the risk node, capturing an abnormal behavior of the risk node, locking an abnormal target of the intelligent security scene, analyzing target characteristics of the abnormal target, and constructing a multi-modal tracking path of the multi-modal inspection device and the mobile inspection device; the abnormal development state of the abnormal target is analyzed, when the abnormal development state accords with a preset abnormal development threshold value, the abnormal level of the abnormal target is determined, and abnormal alarm of the intelligent security scene is executed based on the abnormal level. According to the invention, the automatic test efficiency of the X86 tablet interface can be improved.

The invention provides a solid state disk test method and device, electronic equipment and a storage medium, which are applied to a first server, a test management platform is deployed in the first server, the first server establishes communication connection with at least one second server, and each second server is configured with at least one solid state disk. Each second server is used as a test node to be managed by a test management platform, and the method comprises the following steps: in response to a test task, determining a target second server matched with the test task based on the server performance state of the second server managed in the test management platform; and under the condition that the test task is not the solid state disk virtualization test task, calling a test case script matched with the test task from a script library of the test management platform, and distributing the test case script to the target second server to control the target second server to test the solid state disk. Therefore, an efficient and automatic test framework is formed, and the test efficiency and expandability are greatly improved.

The invention provides a centralized and distributed network calculation decision-making method and device, equipment and a medium. The method comprises the following steps: acquiring a time delay demand and a computing power service resource pool demand of a user according to a service request of the user; if the time delay demand indicates that the network time delay requirement of the service of the user is smaller than a first threshold value, selecting a target resource pool for the service of the user from a plurality of computing power service resource pools according to the computing power service resource pool demand; according to the target resource pool, obtaining a first service change time delay of the centralized architecture and a second service change time delay of the distributed architecture; and according to the time delay demand, the first business change time delay of the centralized architecture and the second business change time delay of the distributed architecture, confirming that the business of the user is provided with the centralized architecture or the distributed architecture. According to the method, the network calculation decision-making efficiency and the network performance can be improved.

The invention discloses a hierarchical progressive geometric feature measurement method of a water turbine runner. The method comprises the following steps: S1, establishing a geometric feature hierarchical progressive evaluation system of the water turbine runner; s2, establishing a'global-local-detail 'multi-level progressive measurement strategy, including adopting global photogrammetry to measure reference features, adopting laser tracking to measure main features, and adopting a handheld scanner to measure detail features; s3, real-time data splicing and registration are implemented in the measurement process, and measurement data of different levels are unified to the same coordinate system through a feature point matching algorithm; and S4, performing measurement density adjustment based on the feature level and performing real-time evaluation and supplementary measurement on measurement data. According to the method, different measurement means and progressive measurement strategies are combined, the geometrical characteristic measurement task of the turbine runner can be efficiently and accurately completed, and the measurement efficiency and precision are remarkably improved.