1160results about How to "Maximize Utilization" patented technology

The invention relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

The invention discloses an energy storage cluster dynamic optimization control system based on edge computing, which relates to the technical field of energy storage clusters and comprises the following steps: acquiring historical data of an energy storage cluster and local data of an edge node; and performing time sequence prediction by using an ARIMA model, performing nonlinear error identification and correction by using a BP neural network, and outputting a final power prediction value. According to the method, through short-term trend prediction of the ARIMA and error correction of the BP neural network, power fluctuation can be responded more quickly, power distribution can be optimized, stable operation of the energy storage system in the power grid is ensured, local BP neural network model training is performed on each edge node, distributed computing resources can be fully utilized, and the calculation efficiency is improved. Under the synergistic effect of the double-layer optimization model, a final power distribution scheme and a final transmission path optimization result are output, and efficient operation, energy consumption reduction and maximum utilization of renewable energy sources can be ensured under the fluctuation of load requirements and power supply.

The invention relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

The invention discloses an electric power and electric quantity balancing method and system, and relates to the technical field of electric power management. S2, demand prediction; s3, evaluating the power supply capability; s4, carrying out resource optimization configuration; s5, integrating renewable energy sources; s6, demand side management; s7, performing real-time monitoring and adjustment; and S8, performing performance evaluation and feedback, and constructing an electric power and electric quantity balance system based on the method. According to the electric power and electric quantity balancing method and system, the accuracy and efficiency of electric power and electric quantity balancing can be remarkably improved, various electric power demand changes can be effectively handled, and stable operation of a power grid is ensured. Meanwhile, by dynamically adjusting access and demand side management of renewable energy sources, the purposes of maximum utilization of energy sources, energy conservation and emission reduction are achieved. Finally, through regular performance evaluation and feedback, the system performance is continuously optimized, a closed-loop optimization process is formed, and powerful support is provided for sustainable development of a power system.

The invention relates to the technical field of chip task allocation, and discloses a task allocation optimization method and device for a multi-core heterogeneous ASIC (Application Specific Integrated Circuit) computing mainboard. Computing core setting information of the computing mainboard is collected, a mainboard computing performance model is generated through digital feedback processing, an executable task set is obtained, and the computing requirement of each task is analyzed; performing simulation analysis on the calculation demand set based on the performance model by using an optimization algorithm, generating a task allocation plan, identifying and generating task execution characteristics of a currently executed task, allocating the task execution characteristics according to the task allocation plan, and scheduling calculation resources to process the current task. The resource performance is accurately simulated through the performance model, the optimization algorithm ensures efficient resource allocation, responds to task changes in real time, maximizes the utilization of heterogeneous computing resources, improves the overall computing efficiency, and solves the problem of execution delay when real-time task allocation is carried out on the multi-core heterogeneous ASIC computing mainboard in the prior art.

The invention discloses an adjustment method and system for 5G network slices, and relates to the field of 5G networks, and the method comprises the steps: obtaining historical demand information of a target service; determining an initial network slicing strategy based on the historical demand information; determining an initial network slice based on the initial network slice strategy; monitoring performance data of each initial network slice; obtaining priority information of a target service corresponding to each network slice; the initial network slice is adjusted based on the performance data and the priority information to determine an optimized network slice.

The invention relates to a load regulation and control method and system based on reinforcement learning, and the method comprises the steps: 1, decomposing a multi-time scale optimization problem into a plurality of layers in a source network load storage management and control scene through employing a layered optimization frame, enabling each layer to correspond to a different time scale, and carrying out the optimization; a model prediction control algorithm is adopted in the short-term optimization layer, a mixed integer linear programming algorithm is adopted in the medium-term optimization layer, and a reinforcement learning method is adopted in the long-term optimization layer to solve a multi-objective optimization problem; and 2, long-term load regulation and control decision making: controlling the power grid to make different load regulation and control decisions through an actually adjustable reward mechanism based on reinforcement learning, so that the intelligent power grid load regulation and control system adapts to continuously changing environments and targets, and the flexibility and adaptability of the system are improved. According to the invention, the reliability and stability of power supply can be ensured, fine management and regulation of power loads are realized, and the operation efficiency, reliability and safety of a power system are improved.

The invention provides an unmanned aerial vehicle route planning method and device, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: determining initial routes of a plurality of unmanned aerial vehicles according to a first constraint condition of the unmanned aerial vehicles; performing space-time grid division on each initial route to obtain a plurality of airspace grids; determining a safety distance between the unmanned aerial vehicles, wherein the safety distance is expressed as a plurality of airspace grids; and according to the second constraint condition of the unmanned aerial vehicle, determining the simultaneous flight route of the plurality of unmanned aerial vehicles. According to the invention, route planning of simultaneous flight of a large number of unmanned aerial vehicles is realized, the utilization rate of airspace is improved, and maximum utilization of route airspace resources is realized. Meanwhile, the low-altitude air traffic control system can perceive, monitor and control the flight of the unmanned aerial vehicle in real time within the defined route range, and unsafe conditions such as communication loss and out-of-control are prevented. The flight density of the unmanned aerial vehicle is improved on the premise of safe flight by finely dividing the airspace of the flight route, and airspace resources of the flight route are utilized to the maximum extent.

The invention discloses an intelligent networking distributed storage interaction system based on multi-cabin cooperation and a method thereof, and belongs to the technical field of camping intelligent cabin interaction, and the method comprises the following steps: taking each intelligent cabin as an independent node, initializing and accessing an intelligent networking distributed system, and enabling the nodes of the intelligent cabins to establish initial connection with other nodes in the system; each intelligent cabin collects user data, environment data and equipment state data through a built-in sensor and stores the collected data in a local storage unit in a classified mode. According to the method, the data synchronization among the nodes of the intelligent cabin is realized through a distributed database consistency algorithm, the data consistency under the condition of network delay or interruption is ensured, the data conflict and damage are prevented, and the system reliability is improved. Meanwhile, resource allocation is dynamically optimized based on an intelligent scheduling algorithm of machine learning, different load requirements are met, resources are saved, and the system efficiency and the intelligent level are improved.

The invention discloses a multi-link fusion method and system based on an SD-WAN (Secure Digital Wireless Area Network) technology, which can timely discover and deal with the condition of network failure or performance reduction by monitoring network state parameters of a plurality of network links in real time, and is helpful for ensuring reliable network connection at any time point, thereby improving the reliability and the stability of the network, and improving the reliability and the reliability of the network. By dynamically adjusting distribution of data traffic, the optimal link combination can be selected for data transmission, so that congestion is avoided, delay is reduced, the packet loss rate is reduced, the utilization rate of network bandwidth is maximized, the data transmission efficiency is remarkably improved, the routing optimization step can quickly respond to service changes by utilizing flexibility and programmability of the SDN technology, and the service quality is improved. According to the method, the continuity and the high efficiency of services are ensured by optimizing data transmission routes, existing network resources can be fully utilized through multi-link fusion and intelligent scheduling, excessive dependence on a single link or an expensive high-performance link is avoided, and reduction of network construction and operation and maintenance cost is facilitated.

The invention discloses a photovoltaic tracking support gale protection device, which comprises an assembling base, a supporting seat, a photovoltaic tracking support, a mounting seat, an elastic supporting piece, a protection plate and a protection mechanism, and is characterized in that the elastic supporting piece is mounted between the inner wall of the protection plate and the top of the mounting seat, and the protection mechanism is mounted on the side wall of the assembling base. Therefore, when the wind speed reaches a certain speed, under the action of the wind shield, the swing rod deflects by an angle, then one end of the elastic connecting piece can be driven to be subjected to position adjustment in the guide groove, and then one end of the traction assembly on the corresponding side can be separated from the positioning plate; therefore, the elastic supporting piece can be changed from a compressed state to an extended state, the protection plate can be opened, the top height of the opened protection plate is higher than the height of the photovoltaic tracking support, strong wind can be guided, the influence of the strong wind on the photovoltaic tracking support is reduced, and a photovoltaic panel on the photovoltaic tracking support can be still in a sun-facing state.

The invention relates to the field of technical data acquisition, and particularly discloses a data acquisition method and device for an industrial Internet of Things and a storage medium, and the method comprises the following steps: S1, constructing a dynamic sensor network, and collecting the operation data of industrial equipment in real time through a multi-modal sensor; s2, dynamically adjusting a data acquisition frequency # imgabs2 # based on an environment noise coefficient # imgabs0 # and an equipment health index # imgabs1 #; s3, performing hierarchical compression and encryption on the acquired data, and allocating a compression rate # imgabs4 # and encryption strength # imgabs5 # according to data type importance # imgabs3 #; s4, executing load balancing through an edge computing node, and distributing a computing task to an optimal node; and S5, storing the processed data into a distributed database according to spatial-temporal correlation, and generating a multi-copy disaster recovery index. Through dynamic acquisition, intelligent processing, optimized storage and adaptive strategy adjustment, accurate and efficient data acquisition is realized, data security and processing performance are flexibly guaranteed, storage query efficiency and disaster tolerance capability are improved, and storage resources are utilized to the maximum extent.

The invention discloses a network resource optimization method for network slices, which comprises the following steps of: acquiring historical flow data, user behavior data and network topology information of each network slice from each network node in real time by utilizing a distributed data acquisition system, and performing data preprocessing on the historical flow data, the user behavior data and the network topology information to form a feature data set; inputting the feature data set into a prediction model constructed based on an attention mechanism and a mixed density network algorithm, optimizing the network weight through a gradient descent algorithm, and training the prediction model; inputting feature data of the current moment into the trained prediction model, and outputting prediction values of probability distribution of different network slices for future traffic demands; and formulating a resource pre-allocation strategy according to the predicted probability distribution of different network slices for future traffic demands, and dynamically optimizing and adjusting network resources. According to the invention, through accurate resource pre-allocation, network resources are utilized to the maximum extent, the operation cost is reduced, and the resource utilization efficiency is improved.

The invention relates to an intelligent earth-rock filling and digging balance optimization method and system for a water conservancy project. According to the method, firstly, a laser scanner, unmanned aerial vehicle aerial survey equipment and a GPS positioning device are used for carrying out data collection on a construction site, and detailed topographic data and earth-rock distribution information are obtained; subsequently, construction requirements related to the hydraulic engineering are determined, including construction progress, construction sequence, resource availability, environmental limitation, and cost control. And based on the collected data and set construction requirements, a genetic algorithm is used for optimization, and an optimal earth-rock filling and digging balance scheme is generated. The optimization scheme covers a construction route, an equipment scheduling plan and a resource allocation scheme. And finally, hydraulic engineering construction is guided according to the optimization scheme, and efficient balanced filling and excavation of the earthwork are achieved. The construction efficiency is effectively improved, the cost is reduced, reasonable utilization of resources is ensured, and the method is suitable for various complex hydraulic engineering projects.

The invention discloses an equipment scheduling method and system based on dynamic priority and multi-objective optimization, and the method takes the minimization of a waiting time mean square error, the minimization of total waiting time and the minimization of maximum completion time as optimization objectives. A task dependency relationship, an equipment maintenance time window, a time window constraint, a student learning progress priority and a delay compensation mechanism constraint condition are comprehensively considered, a dynamic priority calculation mechanism is introduced, the priority is dynamically adjusted in real time according to task waiting time, deadline and urgency, and a high-quality initial population is generated through CMA-ES; a non-dominated sorting framework optimized by NSGA-II is combined, crossover variation of a traditional genetic algorithm is replaced by PSO updating, and an optimal solution is searched by using an elitism mechanism. The method can effectively improve the utilization rate of equipment resources, reduce the task completion time and optimize the fairness of task allocation, is suitable for equipment sharing and dynamic scheduling scenes, and has high practicability and popularization value.

The invention discloses an efficient Transform model reasoning accelerator deployment method based on Versaal ACAP, and belongs to the field of software and hardware collaborative acceleration numerical calculation. Firstly, load analysis is carried out on the model, and meanwhile, according to hardware performance, the types and scales of operators needing to be borne by hardware of all parts are determined; then, hardware is equipped with a data transmitter, a data receiver, a nonlinear operator and other modules; then, the modules are reasonably deployed and connected in hardware, pipeline parallelization is completed, and calculation of a multi-head attention stage and a feedforward neural network stage is achieved; and finally, integrating the two stages to realize accelerated calculation of the whole encoder / decoder layer so as to accelerate the reasoning speed of the upper layer model. Experiments prove that the throughout of the model is effectively improved, the reasoning delay is reduced, meanwhile, the accelerator has huge advantages in the aspect of batch reasoning, and the accelerator has wide application prospects in the aspects of reducing the model reasoning cost and improving the model reasoning speed.

The invention provides an intelligent drinking water distribution path optimization method based on a mobile terminal, and the method comprises the steps: calculating an order regularity coefficient and a recovery rate, classifying users into four types, and determining the order basic priority according to the four types; and calculating a time urgency degree weight in combination with the expected order delivery time so as to determine a comprehensive priority value of the order. According to the method, real-time road conditions and deliveryman position information of a delivery area are collected, an order density thermodynamic diagram is generated, distribution area grids are adaptively divided based on the thermodynamic diagram, and a high-density area is covered with a smaller grid size. And a multi-objective optimization model is established by combining the vehicle-mounted capacity state of the deliveryman, and optimization objectives comprise weighted delivery time minimization, vehicle-mounted capacity utilization rate maximization, container recovery efficiency maximization and workload balancing. And solving the optimization model by using an ant colony algorithm, adjusting a distribution path in real time, and dynamically updating path planning according to vehicle-mounted capacity change data so as to realize efficient and flexible intelligent distribution management.

The invention discloses an intelligent production management order arrangement system, which belongs to the technical field of production management, and comprises an order management module, an intelligent production arrangement module, a resource integration and optimization module, a real-time monitoring and adjusting module and a data analysis and decision support module, through setting an order management module, an intelligent production scheduling module and a resource integration and optimization module, the order management module manages and grades orders during use, and the intelligent production scheduling module produces an optimal production schedule according to the order and equipment information and an algorithm. The resource integration and optimization module intelligently distributes resources by means of detailed modeling and analysis of resources such as equipment information and manpower, maximum utilization of the resources is achieved, the efficiency of production management scheduling is greatly improved, and the requirements of industry production can be met according to different equipment information.

The invention provides a wind power hydrogen production energy scheduling method and a wind power hydrogen production energy scheduling system. The wind power hydrogen production energy scheduling method comprises the following steps: predicting the wind power, the hydrogen load and the electricity price of a wind power plant in a wind power hydrogen production system in a future time period; and based on the predicted wind power, the predicted hydrogen consumption load and the predicted electricity price, determining energy scheduling among a plurality of components in the wind power hydrogen production system, so that at least one of the net income and the renewable energy consumption rate of the wind power hydrogen production system within the predetermined time period is maximum.

The invention relates to the technical field of boiler energy management, in particular to an energy management system for a boiler, and the system comprises a data collection module which collects fuel input, steam output, heat loss, combustion temperature, excess air coefficient, hearth temperature and flue gas temperature parameters according to the operation data of the boiler, and generates an operation parameter set. In the invention, through data analysis and a predictive maintenance strategy, the energy management efficiency of the boiler is optimized. Data collection can directly reflect the real-time state of boiler operation, key parameters are continuously monitored, energy use is optimized, and it is ensured that the fuel utilization rate is maximized. Deviation analysis and trend monitoring are implemented, so that the understanding of the operation condition of the boiler is enhanced, and the maintenance decision is more scientific and prospective. By means of historical data comparison, fault probability calculation and predictive maintenance, faults can be effectively prevented, so that accidental shutdown is reduced, and the service life of equipment is prolonged.

The invention discloses a mixed utilization method of coal chemical gasification black water high-temperature flash steam, and relates to the technical field of black water flash evaporation. In the high-pressure section flash steam heat exchange process, the heat transfer temperature difference and the flow speed are dynamically adjusted through Bayesian optimization, the heat utilization rate of the high-temperature flash steam is maximized, the heat exchange efficiency is improved, and meanwhile in the catalytic reaction stage, the heat transfer temperature difference and the flow speed are dynamically adjusted through Bayesian optimization; heat input and reaction temperature of the catalytic device are regulated in real time based on the outlet temperature of the heat exchanger, so that heat energy distribution is more efficient, and heat waste is avoided; meanwhile, graded flash evaporation is carried out, steam is divided into high-pressure section heat exchange, medium-pressure section catalytic treatment and low-pressure section condensation recovery, and multi-level gradient utilization of energy is achieved; meanwhile, real-time prediction is carried out based on gas components and catalytic device loads, the feeding amount of the adsorbent is dynamically adjusted, resource waste is avoided, meanwhile, the conversion rate of acid gas is increased, and after low-pressure-section tail gas is subjected to condensation treatment, the condensate recovery rate is remarkably increased.

The invention relates to an acid barrel automatic transportation method, system and equipment and a medium, and belongs to the technical field of intelligent acid barrel automatic transportation. The method comprises the steps of obtaining acid barrel transportation environment information, obtaining the acid barrel transportation information through a data acquisition mechanism, and obtaining an optimal acid barrel transportation path and an acid barrel transportation task allocation strategy through an acid barrel transportation path dynamic planning model according to the acid barrel transportation environment information and workshop position information, and obtaining an unmanned acid barrel transportation device through the acid barrel transportation task collaborative scheduling model according to the acid barrel transportation task allocation strategy, and automatically transporting the acid barrels through the acid barrel automatic transportation model according to the unmanned acid barrel transportation device. The automatic collaborative transportation of the acid barrels is realized, and the transportation safety and the transportation efficiency are improved.

The invention discloses a multi-tenant management method and system based on containers and Kubernetes, and relates to the technical field of computer resource management.Tasks of tenants are packaged into a plurality of container instances, the container instances are submitted to a Kubernetes cluster for calculation, each container instance is subjected to Gang scheduling in the Kubernetes cluster, GPU resources of an AI platform are subjected to critical analysis, and when a critical edge is reached, the Kubernetes cluster is subjected to Gang scheduling in the Kubernetes cluster. All the tenants are classified according to the payment conditions of the tenants, scheduling priority ranking is carried out on all categories in combination with cluster center values, and dynamic allocation and management strategy generation are carried out on GPU resources of each tenant in each category by using a dynamic scheduling algorithm. A distributed deployment architecture is adopted, computing power resource management is performed through a containerization technology and a Kubernetes cluster, GPU resource allocation is performed by using a dynamic scheduling algorithm, and the management efficiency of GPU resources is effectively improved.

The invention discloses a multi-energy complementary heating system for a near-zero energy consumption building, which relates to the technical field of building energy-saving equipment and comprises a microclimate regulation and energy optimization integration module, a multi-energy complementary heating system module, an intelligent control and execution module and a user interaction and feedback module, the microclimate adjustment and energy optimization integrated module monitors microclimate data of the surrounding environment of the building in real time through a high-precision sensor, and the microclimate adjustment and energy optimization integrated module monitors the microclimate data of the surrounding environment of the building in real time through the high-precision sensor; and factors having key influences on the heating effect are accurately extracted by utilizing a Pearson's correlation coefficient analysis algorithm and a neural network algorithm, and the system can dynamically calculate the optimal heating strategy and parameters based on the key factors, so that the operation efficiency of the heating system is remarkably improved, unnecessary energy waste is reduced, and the energy consumption is reduced. Therefore, a remarkable energy-saving effect is achieved.

The invention provides a device for automatically producing a regular positioning net. The device comprises a mechanical arm, a first feeding structure, a second feeding structure, a third feeding structure, a welding table, a welding assembly and a grabbing assembly. The second feeding structure and the third feeding structure are matched with the welding table to complete the welding work of the second transverse rib and the vertical rib; after welding of the second transverse bars and the vertical bars is completed, the second transverse bars and the vertical bars are transferred to the position above the welding assembly through the mechanical arm and are in lap joint to third transverse bars on the welding assembly, a plurality of reinforcing meshes formed by welding of the second transverse bars and the vertical bars are welded through the welding assembly, and a regular reinforcing mesh is formed; the whole production process is unmanned, automatic production of regular reinforcing meshes is achieved, the reinforcing steel bar lap joint and welding process is automatic welding, manpower and time consumption is reduced, the production cost is reduced, and efficiency is high; and meanwhile, potential safety hazards to workers are avoided, the welding error is relatively small, and the yield is increased.

The invention discloses a coal gangue grading and sorting method and system based on LIBS (laser-induced breakdown spectroscopy) and image analysis, and relates to the technical field of coal gangue sorting, the method comprises the following steps: acquiring spectral data of each sample in a mixed coal flow comprising coal briquettes and coal gangue by using an LIBS technology; comparing the obtained spectral data with a pre-established coal briquette spectral database, preliminarily identifying coal briquettes and coal gangue according to a comparison result, and completing preliminary sorting; obtaining a visible light image of the separated mixed coal flow, preprocessing the image, inputting the preprocessed image into a coal gangue detection model based on an improved Mask RCNN, identifying and segmenting all coal gangues in the image, and obtaining the equivalent particle size of each coal gangue; and according to a preset particle size range, the coal gangue is divided into different grades, and the coal gangue in the mixed coal flow is classified and sorted. According to the invention, accurate identification of the coal gangue and grading sorting of the coal gangue can be realized.

The invention discloses a port wharf quay crane control system and a computer program. The port wharf quay crane control system comprises a remote control center, an operation table connected with the remote control center and a single machine PLC arranged on a quay crane. The remote control center is provided with a remote control PLC, and the remote control PLC can distribute quay crane operation tasks; the single-machine PLC of the quay crane is connected with the remote control center; the remote control PLC and the single-machine PLC of the quay crane are provided with platform jumping request and release request interfaces; the operation platform controls the multiple quay cranes by switching the jumping platforms and releasing the jumping platforms. The operating platform is provided with an interactive interface, and a driver can control the quay crane to work through the interactive interface and check related information of the quay crane. According to the technical scheme provided by the invention, a plurality of operation platforms are switched to control a plurality of quay cranes, so that the utilization rate among a driver, the operation platforms and the quay cranes is maximized, the working efficiency is improved, and the operation cost is reduced; through the monitoring module and the talkback module which can be switched synchronously, a driver can check field conditions in real time, contact with field personnel and make corresponding treatment in time.

The invention relates to the technical field of soil and groundwater remediation and treatment, in particular to a method and device for determining a targeted agent during pollution source remediation and computer equipment, and the method for determining the targeted agent during pollution source remediation comprises the following steps: obtaining an analysis result obtained by analyzing a target pollutant in a pollution source; determining an active oxygen species type corresponding to the target pollutant according to an analysis result; and determining the type of a targeted medicament for repairing the pollution source according to the type of the active oxygen species. According to the method, target pollutants are analyzed, the types of active oxygen species which are mainly responded are used as guidance, and targeted agents are screened in a targeted mode, so that when a pollution source is repaired, the targeted agents can generate needed dominant active oxygen species to efficiently react with the target pollutants and accurately oxidize the target pollutants, and repairing treatment of soil and underground water is achieved.

The invention discloses a connection tracking management method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: receiving a virtual machine creation request, and determining a target network to which a to-be-created virtual machine belongs according to the virtual machine creation request; determining the port limit number of the target network by the current host machine, and determining the existing port number of the target network in the current host machine; when the number of the existing ports is lower than the port limit number, determining the total connection tracking limit number of the current host machine, the total connection tracking number of the existing ports and the single-port connection tracking number of the target network; and when the sum of the existing port connection tracking total number and the single port connection tracking number does not exceed the connection tracking limit total number, executing the virtual machine creation request. By applying the technical scheme provided by the invention, the connection tracking in the host system can be managed more reasonably and effectively, the maximum utilization of host resources is realized, and the stable operation of the host system is ensured at the same time.

The invention discloses a method for electric spark machining of a high-carbon high-chromium molybdenum alloy steel chute, a first group of electrodes is adopted to machine the chute to be machined, the first group of electrodes comprises a first electrode, a second electrode and a third electrode, when the first group of electrodes is adopted to machine each chute, the first electrode is firstly used for rough machining, and then the second electrode is used for machining; carrying out semi-finish machining by using a second electrode, and finally carrying out finish machining by using a third electrode; after a plurality of skewed slots are machined, after a first electrode in a first group of electrodes is abraded, a second group of electrodes is adopted to machine the skewed slots to be machined, the second group of electrodes comprises a second electrode, a third electrode and a fourth electrode, when the second group of electrodes is adopted to machine each skewed slot, the second electrode is firstly used for rough machining, and then the third electrode is used for semi-finish machining; and finally, finish machining is conducted through a fourth electrode. And in this way, after the (n-3) th electrode in the (n-3) th group of electrodes is abraded, the (n-2) th group of electrodes is adopted to machine the chute to be machined, the (n-2) th group of electrodes comprises the (n-2) th electrode, the (n-1) th electrode and the nth electrode, when the (n-2) th group of electrodes is adopted to machine each chute, the (n-2) th electrode is firstly used for rough machining, and then the (n-1) th electrode is used for semi-finish machining, and finally, finish machining is conducted through the nth electrode. The invention aims to improve the processing efficiency and reduce the production cost while ensuring the processing quality.