291results about How to "Allocation is accurate" patented technology

The invention discloses a big data dynamic allocation and optimal scheduling method based on reinforcement learning. The method comprises the following steps: S1, constructing a real-time operation data set; s2, preprocessing the real-time operation data set, and constructing a reinforcement learning environment model; s3, initializing a hierarchical reinforcement learning model; s4, generating an optimal data distribution and scheduling strategy based on the hierarchical reinforcement learning model; s5, adjusting data distribution and task scheduling of each computing node in real time to balance the system load and maximize the resource utilization rate; s6, the hierarchical reinforcement learning model continuously monitors the system state and dynamically updates the strategy, and self-adaptive adjustment is carried out on resource allocation and scheduling in time when the system load or environment changes; and S7, through multi-round iterative optimization, gradually converging the hierarchical reinforcement learning model to an optimal strategy. Through application of reinforcement learning, automatic optimization of resource allocation and scheduling strategies is realized, and the resource utilization rate, the scheduling efficiency and the adaptability of the system are remarkably improved.

The invention relates to the technical field of industrial equipment management, and discloses an equipment management method for a 3D digital factory, and the method comprises the following steps: sensing the operation parameters of a network collection device through the Internet of Things; processing the operation parameters by utilizing a cloud computing or edge computing technology; dynamically generating an equipment health state based on the processed data; the equipment management system of the 3D digital factory comprises an Internet of Things sensing network used for collecting operation parameters of the equipment; the data processing module is used for carrying out cleaning, noise reduction, dynamic modeling and anomaly detection on equipment operation parameters; and the equipment health state generation module is used for generating the health state of the equipment. According to the invention, through the Internet of Things sensing network, dynamic modeling, intelligent scheduling and full-life-cycle management technology, the effects of equipment state real-time monitoring, accurate maintenance, energy efficiency optimization and comprehensive asset management are realized.

The invention discloses an advertisement budget distribution method and system based on machine learning and user behaviors, and the method comprises the following steps: collecting the historical behavior data of a user, and extracting the feature data of the historical behavior data through feature engineering; constructing a user probability prediction model, and training the model by using the feature data; in an advertisement putting process, collecting real-time behavior data of a user, inputting the real-time behavior data into the trained model, and predicting an advertisement clicking probability of the user and an advertisement commodity purchasing probability of the user; a fusion model is constructed, the fusion model calculates the conversion rate of the user according to the advertisement clicking probability of the user and the advertisement commodity purchasing probability of the user, and calculates the channel conversion rate according to the putting channel; distributing a preliminary advertisement budget according to the historical data of the putting channel; and dynamically adjusting the preliminary advertisement budget of the putting channel based on the channel conversion rate. According to the invention, precise distribution of the advertisement budget can be realized, and the overall conversion effect of the advertisement is improved.

The invention provides a task allocation method, device and equipment based on a distributed cluster and a computer readable storage medium. The method comprises the following steps: acquiring to-be-allocated tasks and determining priorities among the to-be-allocated tasks; selecting a to-be-allocated task node from the low-load nodes according to the task processing capability and the task processing type of the low-load nodes; according to the task number and the priority of the to-be-allocated tasks and the task processing capability and the task processing type of the to-be-allocated task nodes, estimating whether the load of the to-be-allocated task nodes after task allocation exceeds a preset load threshold value or not; if the load does not exceed the preset load threshold, establishing a distribution relationship between the to-be-distributed task and the to-be-distributed task node; and distributing the to-be-distributed task according to the distribution relation. According to the embodiment of the invention, tasks can be distributed more accurately, node overload and software crash are avoided, and load balancing is realized.

The invention discloses a configuration system and method for dynamically adjusting a business process, particularly relates to the technical field of business adjustment, and aims to dynamically group users accurately according to the degree of dependence of the users on different network services so as to enable resource allocation to be more accurate, and dynamically adjust a bucket dividing strategy according to the change of dependence of the users on the services so as to improve the efficiency of business process adjustment. According to the method, changes of markets and user behaviors can be quickly responded, and a network service provider can more accurately adjust priorities of the user bucket groups by comprehensively analyzing the hidden danger degree of abnormal user traffic distribution and network resource demands in peak periods; the preventive analysis and adjustment method not only can effectively predict and relieve the potential service interruption risk, but also optimizes the resource allocation, and through the dynamic and comprehensive adjustment strategy, the network operator can improve the service reliability and the user satisfaction, and reduce the service quality problem caused by improper resource allocation at the same time.

The invention discloses an RISC-V-based thread bundle scheduling method and device and a medium, belongs to the technical field of general graphics processors, and aims to solve the technical problem that the execution efficiency of instructions is affected by the fact that an existing thread bundle scheduling module is affected by a synchronous fence mechanism and a thread bundle scheduling strategy, and the thread bundle scheduling efficiency is easily reduced. The method comprises the following steps: performing instruction configuration on a current thread bundle state to obtain a real-time thread bundle state instruction; performing state control on the thread bundle queue to obtain a state change control strategy; performing instruction fetching sequence control on the thread beam queue, and determining a thread beam instruction fetching control strategy; performing address fetch control on the thread bundle queue, and determining an address type control strategy; and performing thread beam scheduling processing based on a thread beam scheduling system architecture on the thread beam queue through the real-time thread beam state instruction and the thread beam scheduler after the strategy configuration is completed to obtain a thread beam scheduling result.

The invention provides an allocation method and system for improving the GPU resource utilization rate, a medium and a program product, and relates to the technical field of data processing. The method comprises the following steps of: constructing a hybrid resource pool which comprises a divided resource pool divided according to set computing power and video memory granularity and a whole spare resource pool retaining a complete GPU resource unit; then, a to-be-processed task is obtained from an interface end, and the computing power scale and continuity demand data of the to-be-processed task are determined through a task analysis model obtained based on multi-factor machine learning training; finally, according to the data, computing power resources are distributed from the hybrid resource pool according to a preset method, so that diversified task requirements are met, efficient and flexible distribution and utilization of GPU resources are achieved, and the overall efficiency is improved.

The invention discloses a multi-machine parallel operation control method for a network-forming type energy storage converter, and relates to the field of electrical engineering. Establishing a sparse communication network; obtaining the frequency, the voltage effective value, the active power and the reactive power of the node and the active power and the reactive power of a neighbor node; constructing a network construction type converter control strategy combining active support and cooperative control, and calculating the output frequency and voltage effective value of each network construction type converter; and obtaining a three-phase AC voltage waveform through dq conversion, modulating the three-phase AC voltage waveform into a three-phase PWM switch control signal, and applying the three-phase PWM switch control signal to the network-forming converter to achieve a control effect. The active support of the frequency and the voltage aims at the deviation of the frequency and the voltage to rated values in the VSG control, the contradiction between the power control and the frequency and voltage control is eliminated by the cooperative control strategy, the defects of the VSG control are made up by the proposed optimization control method, the frequency and the voltage of the network construction type converter are controlled to be the rated values, and meanwhile, the efficiency of the network construction type converter is improved. And the active power and the reactive power can be accurately and reasonably distributed.

The invention relates to the technical field of education course playing, in particular to a multimedia education course resource playing method and device, and the method comprises the steps: reading student identity data based on an electronic student identity card, carrying out the identity verification, and obtaining an access authorization certificate; calling multimedia education course resources associated with student courses from the server, and adjusting playing parameters according to current course requirements of students to obtain personalized playing configuration; after the personalized playing configuration is checked before playing and the content is verified to be appropriate, based on the playing ready state, a multimedia playing process is executed, the playing quality is adjusted in real time, and an operation request of a student is responded to obtain an interactive monitoring record; and based on the interaction monitoring record, evaluating the watching behavior and the interaction quality of the student, optimizing the education course recommendation content in the future time period, and obtaining an education course recommendation content updating result. According to the invention, personalized multimedia education course resource recommendation can be provided according to the specific academic state of the student, so that the education course resource allocation is more accurate.

The invention discloses a multi-strategy collaborative heat supply balance control method based on virtual heat supply network grouping, which relates to the technical field of virtual heat supply networks and comprises the following steps: dividing a heat supply network into a plurality of virtual heat supply network groups according to the similarity of a first parameter set in the heat supply network; estimating the thermal load demand and the heat supply capacity of each virtual heat supply network group through a dynamic thermodynamic model and a thermal load prediction model; calculating a heat load characteristic parameter set based on the heat load demand and the heat supply capacity, and selecting a heat supply strategy of each virtual heat supply network group; and performing coordination control on the heat supply network based on the heat supply strategy. Through real-time monitoring and a dynamic grouping mechanism, abnormal nodes can be quickly identified and adjusted, and the flexibility and adaptability of a heat supply network are ensured.

The invention relates to the technical field of multi-unmanned-aerial-vehicle cooperative operation and three-dimensional modeling, in particular to a multi-unmanned-aerial-vehicle cooperative three-dimensional rapid modeling method for a highway accident scene, and the method comprises the steps: generating a three-dimensional grid map of an accident area through the scanning of a millimeter-wave radar by a main control unmanned aerial vehicle; subareas are divided according to a load balancing strategy and are distributed to slave unmanned aerial vehicles, the slave unmanned aerial vehicles traverse grids along a snake-shaped track, laser radar point clouds and five-view-angle images are synchronously collected, data are bound through double time stamps and space coordinates, the point clouds are preprocessed through edge computing nodes, and the point clouds are stored in a database; the master control unmanned aerial vehicle evaluates quality based on density standard deviation and overlapping matching degree and instructs to reacquire, performs high-precision Poisson reconstruction on an accident core area, performs voxelization processing on a peripheral area, maps image textures, optimizes vehicle deformation details, simplifies a model and retains key element precision, and finally performs data processing. License plate coordinates, a scattered object thermodynamic diagram and an emergency lane occupation state are automatically marked, a visual model is generated, and rapid and accurate restoration of an accident scene is realized.

The invention discloses a hybrid power ship energy management system based on fuzzy control, and relates to the technical field of hybrid power ship energy management, and the hybrid power ship energy management system comprises a state monitoring module, a fuzzy controller module, an energy scheduling module, a power source management module, a monitoring module and a fault diagnosis module. The system has the advantages that intelligent control over hybrid power ship energy management is achieved through the fuzzy controller module in combination with the fuzzy rule base and the membership function, the system can receive data of the state monitoring module in real time, a control decision is generated through fuzzy rule reasoning, and therefore energy of different power sources is accurately distributed, and the energy management efficiency is improved. According to the intelligent energy management and scheduling mode, the energy utilization efficiency of the ship is improved, and the performance of the ship in various sailing states is kept in the optimal state.

The invention relates to the technical field of illumination control, in particular to a public place illumination intelligent regulation and control method capable of distinguishing multiple environments. The method comprises the following steps: firstly, classifying public places, analyzing activity and personnel behavior characteristics of the public places, determining an illumination function and influence variables, and defining a target model; secondly, establishing an analytic hierarchy structure, and constructing a judgment matrix to calculate a weight vector and check consistency by taking an illumination function as a target layer, relevant factors as a criterion layer and public places as a scheme layer, and distributing regulation and control weights; and finally, determining lighting functions and a target model thereof according to the priority, setting an initial value, adjusting variables, solving a current value, judging whether the current value meets a threshold value or not, and regulating and controlling in sequence until all lighting functions are met. According to the method, the requirements of different places can be accurately met, the regulation and control weight is scientifically determined, multi-target regulation and control are efficiently realized, and a flexible regulation and control strategy is also provided.

The invention discloses a remote video monitoring data transmission method, and the method comprises the steps: collecting visual video information through a robot camera, and transmitting the visual video information to a remote power plant monitoring center through a HDMI interface, an encoder, a 5G-CPE, a decoder and other links. The 5G-CPE uses MEC, 5G slicing and SRT technologies to adjust resource allocation according to network conditions and requirements, and defines configuration slices and priorities. A network monitoring component is deployed to monitor slice loads in real time, a load threshold value is set for each slice, and a resource adjustment strategy is triggered when the loads exceed the threshold values. And resources are dynamically reallocated after analysis and evaluation, so that sufficient and optimized utilization of high-load slice resources is ensured. Performance is continuously monitored and feedback adjustment is performed, network adaptability and flexibility are improved, key video streams are ensured to be transmitted preferentially, and power plant inspection requirements are met.

The invention relates to the technical field of sound box synchronous playing, and discloses a Bluetooth earphone and sound box synchronous playing method and system supporting stereo synthesis, and the method comprises the steps: carrying out the digital sampling of an input audio signal, and obtaining high-frequency digital audio data and low-frequency digital audio data; performing scanning and stereo function identification on peripheral equipment based on a Bluetooth 5.0 protocol to obtain a target audio equipment list; distributing a main data buffer area and a standby data buffer area; distributing the high-frequency digital audio data to Bluetooth earphone equipment for transmission, distributing the low-frequency digital audio data to sound box equipment for transmission, and when detecting that clock drift among equipment exceeds a preset target value, adjusting a reading rate of a main data buffer area for clock synchronization to obtain an equipment synchronization state; and the signal gain and the equalization parameter of the equipment are dynamically adjusted, and the target synchronous playing audio is output, so that the accurate distribution of the high-frequency data and the low-frequency data is realized, and the overall playing effect of the system is improved.

The invention relates to the technical field of computing power cabin cluster scheduling. The invention relates to a cluster scheduling control method based on a computing power cabin. The method comprises the following steps: S1, obtaining performance data of a computing power cabin, and then carrying out resource virtualization to form a resource pool; s2, collecting historical operation data and real-time operation data of the service user, and predicting a resource demand of the service user to obtain a predicted resource demand; s3, combining the predicted resource demand with the real-time operation data to carry out predicted volatility analysis, setting an isolation fluctuation threshold value, then comparing the predicted volatility with the isolation fluctuation threshold value, and when the predicted volatility is smaller than the isolation fluctuation threshold value, judging whether the predicted volatility is smaller than the isolation fluctuation threshold value or not; according to the method, resource demand prediction is carried out by using the historical operation data and the real-time operation data, resources can be allocated to the user more accurately, and when the predicted fluctuation rate is smaller than the isolation fluctuation threshold value, the resource instance is automatically started to carry out node isolation on the user, so that the stability of resource allocation is ensured.

The invention discloses a plug-in-based real-time acquisition and manual analysis method for webpage data without a fixed format. Real-time integrated operation of data acquisition, labeling and uploading is realized through a browser plug-in. A user selects target data through a floating window interface, and a plug-in automatically extracts content, identifies a platform source, performs quick labeling in combination with a preset analysis label and uploads the data to a background. According to the method, the flexibility of manual judgment and the high efficiency of an automation technology are fused, and the method can adapt to multiple platforms including high-protection dynamic webpages without developing a customized tool for a target website. The floating window supports operations such as dragging and zooming, so that the user experience is improved; the labeling is completed by data acquisition, so that the processing chain is effectively shortened, and the efficiency and accuracy are remarkably improved. The system supports authority management, encrypted transmission and real-time statistical analysis, and ensures data security and efficient utilization.

The invention discloses a fan blade gas heat deicing system which comprises a heater and an air blower which are installed in a blade, an air inlet of the heater is connected with an air outlet of the air blower, an air outlet of the heater is communicated with an air inlet of an air guide pipe, an air outlet of the air guide pipe points to the blade tip direction, and a hard air pipe is adopted at the front end of the air guide pipe. The rear end of the air guide pipe is provided with a soft air pipe, an air inlet of the hard air pipe is communicated with an air outlet of the heater, an air outlet of the hard air pipe is communicated with an air inlet of the soft air pipe, the soft air pipe is arranged in a blade front edge cavity, a plurality of air guide holes are formed in the position, facing the blade front edge, of the soft air pipe, and when no hot air exists, the air guide holes are communicated with the hard air pipe. And when hot air exists, the flexible air pipe is stretched to be close to the blade tip or the blocked position of the blade tip. The air guide pipe can extend to the blade tip, the deicing effect of the blade tip is enhanced, a new hot air backflow channel can be constructed in the blade blocked by the blade tip, and the air heat deicing effect is achieved.

The invention provides a new energy field station AGC distribution method for power distribution, and the method comprises the steps: obtaining the characteristic information of a target photovoltaic power station, and the characteristic information at least comprises the grid-connected point voltage value of the photovoltaic power station, the environment temperature of the photovoltaic power station, and the solar irradiance task profile data; based on the characteristic information, an improved droop control strategy is adopted to calculate the reactive power of the photovoltaic power station; obtaining a reactive power regulation margin based on the reactive power of the photovoltaic power station; generating an active power distribution instruction based on the reactive power regulation margin, the distribution instruction being used for distributing active power; a photovoltaic power supply fatigue loss model is established, a photovoltaic power supply fatigue loss result is obtained, and photovoltaic power supply loss at least comprises IGBT loss and capacitor loss; based on the photovoltaic power supply fatigue loss result, a photovoltaic reactive power optimal distribution model is established, and based on the photovoltaic reactive power optimal distribution model, a distribution strategy is generated, and the distribution strategy is used for distributing reactive power.

The invention discloses a power dynamic adjustment method and system for a string type photovoltaic inverter matrix, and the method comprises the steps: obtaining a power adjustment value Pset of the matrix and a total power value Preal of an inverter which is in normal communication and is in a power adjustable state, and carrying out the power adjustment: a, when the Preal is a value 0, carrying out the distribution according to a corresponding power distribution expression; b, when the Preal is larger than 0, the communication is normal, and the real-time power values of the inverters in the power adjustable state are all larger than 0, distribution is carried out according to a corresponding power distribution expression; and c, when the Preal is greater than 0, the real-time power values of the inverters in normal communication and power adjustable states are 0, and the total target power is set to be greater than the total real-time power, distributing the target power to the inverters with the real-time power values being 0 according to the condition a, distributing the total target set power values and the total real-time power of the remaining inverters according to the condition a, and distributing the target power according to the condition b. The method has the advantages of accurate power distribution and the like.

The invention discloses a green base station energy-saving method based on self-attention and convolutional deep learning fusion, and solves the problems that base station resources are allocated through figure number statistics in the prior art, figure number statistics is inaccurate, reference resource allocation is affected, limitation exists when model application is adopted for figure recognition, and application in an actual scene is limited. The method comprises the following steps: periodically scanning a time node to collect and monitor a first video image; performing character recognition and counting on the first video image by adopting a character recognition model fusing self-attention and convolutional deep learning; analyzing the character motion trend to dynamically count the number of people in the monitoring area; and calculating the number of users in the monitoring area, and activating corresponding base station capacity resources according to the number of users. According to the invention, the resources of the base station are reasonably allocated by using long-term monitoring equipment, identifying the character condition in the image and combining the related settings of the base station, so that the energy consumption of the 5G base station is further reduced, the overall operation cost is reduced, and the green and energy-saving effects are achieved.

The invention discloses an automatic personnel allocation method and system suitable for an MES system, a medium and equipment, and the method comprises the following steps: S1, defining a skill label: defining the skill label, and obtaining and storing the skill label of an employee; s2, skill corresponding process: establishing a corresponding relationship between the skill label and the process; s3, production data acquisition: acquiring working data of the employees in a production process in real time; s4, data analysis: generating an employee portrait, and generating an employee skill feature median; s5, optimizing process parameters: adjusting process guidance parameters according to the skill characteristics of the employees in the step S4, and optimizing the production process; and S6, automatically allocating processes: recommending an optimal personnel combination according to the employee portraits and the processes involved in the production plan. The system has the advantages that the skills of the employees and the deficient skills are mastered, so that the employees are accurately allocated to proper posts, and the storage of human resources of enterprises and the reduction of labor cost are facilitated.

The invention belongs to the technical field of network security, and discloses an enterprise network security self-checking method and system, and the method comprises the steps: collecting network flow data, equipment parameter data, user behavior data and security situation awareness data; preprocessing the collected network traffic data, equipment parameter data and user behavior data to obtain a network traffic feature data set, an equipment feature data set, a user behavior feature data set and a security situation awareness feature data set; fusing the network traffic feature data set, the equipment feature data set, the user behavior feature data set and the security situation awareness feature data set to obtain a comprehensive feature data set; training according to the comprehensive feature data set to obtain a network security prediction model, and predicting through the network security prediction model to obtain an enterprise network security state; the possibility of occurrence of network security events is reduced, response delay caused by human intervention is reduced through an automatic process, and the detection efficiency is greatly improved.

The invention discloses a heat storage tank-containing heat supply system scheduling method considering heat storage and release role changes, which comprises the following steps of: additionally arranging a heat storage tank in a primary network urban area between an original urban heat supply system pressure separation station and each heating station, and adjusting a supply-demand imbalance operation condition into a supply-demand balance operation condition through the heat storage tank, the urban heat supply system containing the heat storage tank is formed; setting a heat storage tank role change mechanism; based on the corrected heat supply network hydraulic working condition calculation model and a heat storage tank role change mechanism, combining heat load demand change of each heating station and supply and demand imbalance operation working condition identification information, and analyzing the influence on the heat supply network hydraulic working condition during heat supply network flow distribution and heat storage and release of a heat storage tank; the flow of the heat supply network pressure separation station side, the heat storage tank and each heating station in each time period is distributed; correspondingly communicated water pumps and valves are regulated and controlled; and flow distribution and evaluation of regulation and control effects of the water pump and the valve are performed.

The invention relates to the technical field of blood inventory management, in particular to a radio frequency tag reuse method and system for blood inventory management. Comprising the following steps that blood is collected, radio frequency tags corresponding to carriers are distributed, information of the radio frequency tags is read, the blood is put in storage, different kinds of blood are recorded and stored in the corresponding carriers, and rechecking is conducted through the number of the empty carriers; the blood storage information is acquired, the blood information is classified, the ex-warehouse quantity of blood of different blood types every day is acquired, and the blood demand quantity of different blood types is predicted, the demand quantity is predicted based on the ex-warehouse quantity, blood sampling and stock adjustment strategies can be planned in advance, blood shortage or excess is avoided, scientific classification and fine management of blood are achieved, and the blood quality is improved. The clinical blood use requirement is better met, the real-time stocktaking and alarming functions can remind workers to supplement insufficient blood types in time, and the timeliness and stability of blood supply are ensured.

The invention relates to a self-adaptive wavelet packet wind power stabilizing method based on a time scale, and the method comprises the steps: dividing a time domain based on a set time scale, carrying out the self-adaptive wavelet packet decomposition, and carrying out the stabilizing of fluctuation out-of-limit data in adjacent time domains through employing an arithmetic average method; based on the characteristics of the energy storage equipment and the node energy distribution condition, the correlation between the hybrid energy storage power components is analyzed by using the energy entropy difference, the energy type and power type energy storage power components are obtained, and hybrid energy storage capacity optimization is carried out by combining the maximum charging and discharging power and the energy change; the method comprises the following steps: dynamically partitioning an energy storage SOC, improving a fuzzy control membership function, and optimizing a hybrid energy storage power instruction by adopting two-stage fuzzy control so as to reduce SOC out-of-limit of the energy storage system. According to the method, power fluctuation can be effectively stabilized, the hybrid energy storage component can be more accurately distributed, meanwhile, SOC out-of-limit can be effectively reduced, and the electric energy quality is improved.

The invention provides a tissue culture bottle filling assembly line water circulation control method and device and a readable storage medium thereof. The tissue culture bottle filling assembly line water circulation control device comprises a bottle feeding station, a rough washing station, a fine washing station, a water purifier, a main water inlet pump, a circulating pump and other components, wherein the bottle feeding station, the rough washing station and the fine washing station are of specific structures. The bottle feeding station is used for linearly inputting tissue culture bottles into the rough cleaning station through a material shifting disc and a bottle shifting motor; the rough washing station is provided with an electric rotating frame, a washing pipe and a rough washing water tank; the fine cleaning station is similar in structure and is in butt joint with the rough cleaning station through a transition rail. The water circulation control method comprises the steps that the main water inlet pump is controlled to supply water according to different stages, the power of the main water inlet pump, the working state of the circulating pump and the water supplementing strategy are dynamically adjusted in combination with factors such as rough washing and fine washing time, environment temperature, the service life of the water purifier and water pump energy consumption, tissue culture bottles are efficiently washed, water resources are reasonably utilized, and the system stability and reliability are improved.

The invention relates to the technical field of communication, and discloses a network slice resource optimization method, device, system and product. The method comprises the following steps: collecting flow data of each edge node in a target network in a current time step according to the size of the time step; the traffic data comprises short-time traffic data and long-time traffic data of all network slices in the edge node; predicting predicted flow data of each edge node in the next time step based on the flow data of each current edge node; determining a target distribution strategy based on the predicted traffic data of each edge node in the next time step; determining target resource configuration in each edge node in the next time step according to a target allocation strategy and the predicted traffic data of each edge node in the next time step; and allocating network resources to each edge node according to the target resource configuration. By adopting the method, the accurate and efficient dynamic network resource allocation of the whole target network can be realized, and the overall performance of the network is improved.

The invention provides a distributed computing system based on pre-scheduling resource collaborative adaptation, and relates to the technical field of resource collaborative allocation systems. The distributed computing system based on pre-scheduling resource collaborative adaptation aims at solving the problems that in the prior art, resource scheduling is not flexible, and application configuration and resource scheduling are lack of coordination. By introducing a dynamic scheduling mechanism of the resources, organic combination of resource pre-scheduling and flexible allocation is realized. Meanwhile, through cooperation of application configuration and resource scheduling, optimal distribution and execution of calculation tasks are achieved, the system is composed of a server side and a client side, the client side has pre-scheduling resources and is used for feeding back the resource state in real time and receiving and executing the calculation tasks, and the server side is responsible for monitoring and scheduling of global resources and task distribution. And through a designed heuristic collaborative adaptation algorithm, the server can dynamically schedule resources in the pre-scheduling resources, and high efficiency and stability of task execution are ensured.

The invention discloses a multi-inverter parallel connection improved reactive droop control method and system, and relates to the technical field of inverter operation voltage regulation, and the method comprises the steps: obtaining first data of the output side of an inverter, and calculating second data outputted in real time according to the first data of the output side of the inverter; performing droop control on the second data to obtain given values of frequency and voltage, obtaining PWM given voltage through voltage and current double-closed-loop control according to the given values of the voltage, and performing dynamic adjustment on the PWM given voltage through energy storage adjustment; the phase of the inverter 1 is collected in real time through SOGI-PLL pre-synchronization and serves as phase input of the inverter 2. Through the improvement of the variable reactive droop coefficient, the drop phenomenon of the system voltage is relieved, the stability of the output voltage of the inverter is kept, the influence of voltage fluctuation on the system is reduced, and the electric energy quality of the system is improved.