2251 results about "Distribution method" patented technology

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The invention discloses a power distribution network protection resource dynamic allocation method, system and device and a storage medium, and relates to the field of power system network security protection, and the method comprises the steps: collecting the operation data of a power distribution network in real time, constructing a multi-dimensional fusion data set, and predicting a potential attack path through an attack path prediction model in combination with a historical attack mode library; performing risk assessment on the predicted potential attack path, and calculating the protection resource demand quantity of each region of the power distribution network in combination with the topological structure of the power distribution network and the importance of key nodes; according to a risk assessment result, dynamic allocation and real-time scheduling of protection resources are realized in combination with a resource constraint condition; the method can grasp the operation of the power distribution network in real time, accurately predict the potential attack path, accurately evaluate the risk and calculate the protection resource demand. Dynamic allocation and real-time scheduling of protection resources are realized, the resources are reasonably utilized, the cost is reduced, the protection effect is improved, and safe and stable operation of the power distribution network under complex network attacks is ensured.

The invention discloses a multi-objective optimization-based enterprise resource coordination and distribution method, which comprises the following steps of: S1, acquiring multi-dimensional description information of enterprise task requirements, and constructing a requirement diagram; s2, based on the demand graph structure, performing clustering division on task nodes by adopting a self-organizing clustering algorithm, generating task clusters, generating a candidate resource demand set, and constructing a resource node set; s3, constructing a resource scheduling candidate topological graph; s4, defining a scheduling adaptability function containing a multi-target index, wherein the multi-target index comprises cost, period, resource utilization rate and resource scheduling distance; s5, extracting an optimal scheduling sub-graph from the candidate scheduling topology by adopting an improved heuristic aggregation algorithm; and S6, outputting a corresponding resource allocation scheme to complete resource allocation. According to the method, task structure modeling and multi-target scheduling optimization are fused, the precision and adaptability of resource allocation are improved, and the method is suitable for an enterprise resource management system in a complex scheduling scene.

The invention relates to the technical field of energy storage systems, and provides a self-adaptive power distribution method and system for multiple energy storage units of an island microgrid. According to the invention, a discrete consistency algorithm is adopted to realize coordination control of multiple units, each unit only exchanges information with adjacent units, and power distribution targets of all the units are converged to a consistent value through iterative computation, so that the communication bottleneck and the single-point fault risk of centralized control are avoided; according to the invention, a complete fault-tolerant processing mechanism is designed, the operation state of each energy storage unit is monitored in real time, topology updating and power redistribution are automatically carried out when a unit fault is detected, and the self-healing operation of the system is realized. Experiments show that compared with a traditional method, the method has remarkable advantages in key performance indexes such as capacity balance among energy storage units, system operation efficiency, communication overhead and fault recovery time, and the operation efficiency and reliability of the island microgrid system are remarkably improved.

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

The invention provides a credible industrial agent task distribution method and system, and relates to the technical field of data processing, and the method comprises the steps: receiving a registration request sent by an industrial agent developer, and storing the metadata information of an industrial agent into a distributed account book based on the registration request; and based on the metadata information, receiving a task distribution request sent by a task requester, and matching candidate industrial agents and unique identifiers meeting function requirements in a distributed account book. Credible agents are screened through distributed account book evidence storage core information and dynamic trust evaluation, the credibility level is iteratively updated after execution, and credibility traceability of the whole task distribution process and dynamic optimization of the credibility of the agents are achieved.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

A charging pile group dynamic power distribution method and system based on load prediction relates to the field of electric digital data processing, and the method comprises the following steps: obtaining charging data of charging piles in real time, and when a new vehicle requests to be charged, calculating the difference value between the required power and the current available power to obtain a power gap; and if the power gap exists, calculating a power amount which can be reduced by each charging vehicle according to the charging curve parameters and a preset rule. And calculating the charging time extension amount of each vehicle after power reduction, and calculating the pile position occupation time sensitivity in combination with the original predicted completion time. And sorting the sensitivities from small to large to form a power recovery candidate queue. And selecting proper vehicles from the candidate queue to form a recovery scheme, calculating the total pile position occupation time of the system corresponding to the scheme, and selecting the scheme with the minimum total time as the optimal scheme. And reducing the charging power of the selected vehicle according to the optimal scheme, and distributing the recovered power to the new vehicle. By implementing the method, the pile position utilization rate can be increased.

The invention belongs to the technical field of computer storage, and particularly relates to a solid state disk data management and efficient storage distribution method, which comprises a data characteristic sensing and classifying unit, a data characteristic sensing and classifying unit, a data processing unit, a data storage unit and a data distribution unit, wherein the data characteristic sensing and classifying unit is configured for analyzing write-in request data flow entering a solid state disk in real time or periodically, extracting various characteristic parameters associated with data blocks and storing the data blocks; classifying the data on the basis of the characteristic parameters, and distributing one or more data classification labels for each data block to be written or stored; and the self-adaptive storage allocation strategy unit is configured to be used for dynamically selecting an optimal physical block for data writing according to the data classification label and in combination with the physical block state information of the current flash memory medium. According to the invention, the solid state disk can better adapt to various dynamic and variable workloads, and especially shows more excellent continuous write-in performance, lower delay jitter and obviously prolonged service life when processing a large amount of random write-in, small block write-in and write amplification sensitive applications.

The invention belongs to the technical field of charging pile power distribution, and discloses a charging pile power distribution method and system based on a depth-first algorithm, and the method comprises the steps: building a host power topology adjacency matrix as an initial adjacency matrix; obtaining a host fault state, and optimizing the initial adjacency matrix to obtain a real-time adjacency matrix; when a host fault state or charging gun power demand change is detected, each charging gun is taken as a starting point, the real-time adjacency matrix calculates each charging path and path efficiency through a depth-first algorithm, the charging paths with the path efficiency lower than a preset value are eliminated, weighting priorities of the remaining paths are calculated, and the charging paths are calculated according to the weighting priorities; selecting the charging path with the highest weighting priority as a target path; and performing cross verification on the target path and the parallel contactor path, if verification succeeds, outputting the target path, otherwise, outputting the path successfully verified last time, so that power distribution can be dynamically and flexibly adjusted according to the charging power demand changing in real time.

The embodiment of the invention discloses a logistics distribution method and device based on an unmanned aerial vehicle cluster, equipment and a medium. The method comprises the steps of obtaining to-be-processed order information and state information of the unmanned aerial vehicle cluster; determining a to-be-delivered task based on the order information, and determining a target unmanned aerial vehicle from an unmanned aerial vehicle cluster based on the delivery information and the state information; determining a flight strategy of the target unmanned aerial vehicle based on a preset relay link, and controlling the unmanned aerial vehicle to execute a distribution task according to the flight strategy; and monitoring real-time state information of the target unmanned aerial vehicle, and determining a real-time control strategy of the target unmanned aerial vehicle based on the real-time state information so as to adjust a flight strategy of the target unmanned aerial vehicle based on the real-time control strategy. According to the embodiment of the invention, an efficient, flexible and safe unmanned aerial vehicle distribution system is constructed, and a solid technical support is provided for large-scale commercial application.

The invention discloses a flexible dynamic distribution method and system for a power module based on a charging pile, and relates to the field of power distribution, and the method comprises the steps: collecting dynamic demand parameters of a charging terminal in real time, and calculating a target power demand in combination with a battery safety constraint; obtaining the total number of available power modules and rated power, and generating an effective module set; calculating the minimum module demand number according to the total demand and the module average power; if the modules are sufficient, an efficiency priority strategy is adopted, and distribution is optimized through a greedy algorithm; if not, high-power terminals are preferentially distributed, and then residual power is distributed in a balanced mode; through real-time data, distribution is dynamically adjusted when a threshold value is exceeded; starting dormancy for the module with the load rate lower than 30%, migrating the terminal to other modules, and awakening when the load rises to 40%, so as to improve the efficiency and the resource utilization rate. The method has the advantages that through real-time monitoring and dynamic distribution of the power modules, the charging efficiency and safety are optimized, and the resource utilization rate and the system reliability of the charging pile are remarkably improved.

The invention provides a distributed countering unmanned aerial vehicle group cooperative expelling path dynamic allocation method and system. The method comprises the following steps: firstly, acquiring a real-time motion track and a formation topological structure of an invading unmanned aerial vehicle group, then identifying a cooperative flight mode of the invading unmanned aerial vehicle group as a dynamic confrontation game behavior, then activating a countering unit combination adaptive to a current threat, and then integrating the countering unit combination into a dynamic available expelling resource. And according to the real-time motion trail and the dynamic available expelling resources, a multi-target allocation strategy is synchronously generated among the distributed computing nodes through a consensus protocol, and finally, a collaborative expelling action is executed according to the multi-target allocation strategy. According to the technical scheme provided by the invention, the real-time response capability and combat reliability of the system to the non-cooperative unmanned aerial vehicle group are improved, and cooperative combat and adaptive confrontation of multiple countering units in a complex environment are also realized.

The invention discloses a distributed intelligent power distribution method and system for a power network, and belongs to the technical field of power distribution control, and the method comprises the steps: collecting multi-dimensional electrical parameters of a power generation node in real time, and carrying out the dynamic power monitoring at a feeder terminal of a transformer substation through an edge calculation node; executing line loss calculation and load prediction in the power distribution unit, generating a demand feedback instruction to a feeder terminal node, and triggering an adaptive power distribution strategy; and on the basis of a digital twin system of a regional dispatching center, credibility verification is carried out on operation decisions of all edge nodes, and global optimization compensation control is executed. The method has the remarkable advantages in the aspects of optimizing power distribution, reducing line loss, improving system stability and safety and achieving efficient energy utilization, and intelligent management of a power network becomes possible.

The invention discloses a bandwidth dynamic allocation method and device, a program product and a storage medium, and relates to the technical field of computers. According to the method, bandwidth use data of a network outlet are collected in real time, the residual bandwidth capacity of the outlet is determined, and the distributable dynamic bandwidth capacity of each interface is calculated by combining an interface weight parameter and an interface stability coefficient calculated based on bandwidth use fluctuation characteristics, so that the residual bandwidth of the network outlet can be reasonably distributed. Meanwhile, a dynamic additional bandwidth mechanism is introduced outside the fixed basic bandwidth, and the dynamic bandwidth allocation weight is determined based on the bandwidth use stability of the authentication terminal and the current bandwidth use state, so that the terminal is not limited by a preset fixed bandwidth upper limit any more, more bandwidth resources can be obtained when the exit bandwidth is sufficient, and the bandwidth utilization rate of the terminal is improved. Therefore, the utilization efficiency of bandwidth resources is improved.

The invention discloses a data distribution method and device, electronic equipment and a storage medium, the data distribution method is applied to a distribution server deployed at a network side, and comprises the following steps: obtaining distribution characteristic statistical data of a target data packet to be sent by terminal equipment; inputting the distribution characteristic statistical data of the target data packet into a first data distribution decision network to obtain a first distribution confidence coefficient of the target data packet matched with each transmission path; inputting the distribution characteristic statistical data of the target data packet into a second data distribution decision network to obtain a second distribution confidence coefficient of the target data packet matched with each transmission path; and matching the first distribution confidence coefficient and the second distribution confidence coefficient of each transmission path based on the target data packet, and determining a target distribution strategy of the target data packet. By applying the technical scheme provided by the invention, the distribution strategy of the uplink data packet of the terminal equipment can be accurately and effectively determined, and the data transmission efficiency is improved.

The invention provides an unmanned aerial vehicle cooperative scheduling community delivery method and system, and the method comprises the following steps: receiving an order delivery task submitted by a user, and storing the order delivery task in an order pool; a multi-agent game reinforcement learning model is constructed based on a reinforcement learning algorithm, real-time and historical unmanned aerial vehicle delivery state information and order data features are fused and input into the model, and a task allocation matrix is output and order delivery tasks are allocated to each unmanned aerial vehicle with the optimal overall delivery efficiency as a target; constructing a three-dimensional grid map, and generating a planned route with the optimal path cost for the unmanned aerial vehicle for task allocation based on a path planning algorithm; according to the method, dynamic cooperative task allocation is realized through multi-agent reinforcement learning, and the path conflict rate is remarkably reduced; a multi-sensor fusion and self-adaptive filtering technology is adopted, so that the positioning precision in a complex environment is improved; and in combination with real-time obstacle detection and dynamic local path planning, high efficiency and safety of distribution are ensured.

The invention discloses a cloud management and intelligent distribution method for classroom teaching resources, and relates to the technical field of cloud computing software. The classroom teaching resource cloud management and intelligent distribution method comprises the steps that S1, multi-source teaching resource data and auxiliary distribution data are acquired and preprocessed, and a knowledge content database is constructed; s2, performing semantic fusion analysis through text semantic association, structural consistency and professional term coverage data; s3, carrying out dependency evaluation by fusing pre-association, knowledge co-occurrence, sequence association and semantic fusion data; s4, performing resource adaptability distribution analysis through the maximum knowledge dependence value in combination with the learning state data and the historical feedback data; and S5, evaluating the teaching resource quality by collecting classroom feedback, teacher evaluation and resource use data, and implementing content optimization. The problems of semantic distortion, path breakage, distribution mismatching and resource updating inconsistency in the teaching resource data fusion and intelligent resource distribution process are solved.

The invention relates to a distributed system multi-copy distribution method based on RDMA (Remote Direct Memory Access), belongs to the technical field of distributed systems, and solves the problem of relatively high write-in time delay of distributed system multi-copy distribution in the prior art. The method comprises the following steps: a client writes data corresponding to a plurality of I / O requests into a predetermined memory slot position of a master node and predetermined memory slot positions of slave nodes; the client sends the global metadata to the main node; the master node sends the local metadata aiming at the master node to the master node, and sends the local metadata aiming at each slave node to each slave node; each slave node persists data stored in a predetermined memory slot of the slave node to a nonvolatile storage medium of the slave node; and the master node persists data stored in a predetermined memory slot of the master node to a non-volatile storage medium of the master node, and the data persisted to the non-volatile storage medium of the master node and the data persisted to the non-volatile storage medium of each slave node are copies of each other. And the write-in time delay is reduced.

The invention discloses a low-altitude ground collaborative distribution method and system considering a real-time traffic state of a road, and relates to the technical field, and the method comprises the steps: constructing a traffic speed function of a driving time function of a distribution vehicle along with the change of time and a road section in a segmented manner; constructing an energy consumption function for the unit time power consumption rate of the unmanned aerial vehicle; constructing a mixed integer programming model according to the passing speed function and the energy consumption function; generating an initial solution; performing variable neighborhood search of multi-thread parallel disturbance in the search space based on the initial solution, and performing iterative solution to obtain an optimal solution of the mixed integer programming model; resetting the optimal solution to obtain a primary optimal solution; and optimizing the primary optimal solution to obtain a final solution. According to the invention, through constructing the mixed integer programming model, the distribution efficiency can be improved in a scene of cooperative distribution of the distribution vehicle and the unmanned aerial vehicle.

The invention relates to the field of manufacturing and production process management, in particular to a multi-laser SLM layer vector data multi-source collaborative dynamic allocation method. The method comprises the following steps: processing a three-dimensional model according to a production order containing commercial constraints to generate a hierarchical process topology set carrying process rules; fusing the static attribute and the real-time process state of the topology set to generate a dynamic collaborative matching matrix, and dynamically scheduling multiple laser production resources according to the dynamic collaborative matching matrix; the real-time process state is compared with a preset space-time process potential field graph, a correction strategy is called, and closed-loop management of the manufacturing task is achieved. According to the method, the dynamic decision model is constructed, so that the defect that a static distribution strategy cannot cope with dynamic working conditions in the background technology is overcome, intelligent optimal configuration of production resources under the condition that order business constraints are met is realized, and the quality, efficiency and cost effectiveness of complex component manufacturing are improved.

The invention discloses a cross-platform content generation and distribution method based on a multi-modal AI, and belongs to the technical field of cross-platform content generation and distribution, and the method comprises the steps: carrying out the content analysis and feature extraction of an original material based on a multi-modal AI model, and generating a structured content label and a semantic vector. By combining a vector matching degree formula of target portrait features and platform features, an adaptation strategy is dynamically generated, it is ensured that content not only conforms to platform rules, but also can accurately reach a target group, the conversion rate and user viscosity are finally improved, visual, text and semantic vectors are aligned through a Transform multi-modal fusion model, a cross-modal joint representation vector is generated, and the user experience is improved. And in combination with an adversarial generative network, differentiated variants of the same theme are generated in batches, and a content diversity score mechanism ensures that generated contents are balanced between creativity and compliance.

The invention discloses a dynamic power distribution method and device for a direct-current micro-grid hybrid energy storage system, and relates to the technical field of electric energy storage management, and the method comprises the steps: collecting the power parameters, load demand fluctuation data and renewable energy power generation output data of each energy storage unit in the hybrid energy storage system in real time, calculating a multi-dimensional power entropy index, and calculating the power entropy index of the hybrid energy storage system; based on the indexes, power distribution weight coefficients are dynamically generated, power dynamic distribution is carried out, a charging and discharging strategy is generated, charging and discharging control of the hybrid energy storage system is executed, and power distribution is dynamically adjusted in combination with a real-time feedback mechanism. The technical problems that an existing energy storage system power distribution method lacks flexibility and cannot effectively cope with load fluctuation and unstable renewable energy sources, so that the system operation efficiency and stability are poor are solved, the charging and discharging strategy of the energy storage unit is dynamically adjusted through the multi-dimensional power entropy index and the real-time feedback mechanism, and the energy storage efficiency is improved. The technical effect of efficient and stable operation of the energy storage system is achieved.

The invention provides a key generation and issuing method and device based on a block chain, electronic equipment, a medium and a program product, relates to application of a large model in a financial science and technology scene, and can be applied to the technical field of artificial intelligence and the technical field of block chains. The method comprises the following steps: acquiring key application information, and performing multi-modal analysis on the key application information to obtain key analysis data; submitting a block chain evidence containing the key analysis data to a target block chain network, and obtaining returned evidence reference information; quoting the key analysis data based on the evidence quoting information, performing security policy and environmental risk analysis on the key analysis data, and constructing a target encryption scheme based on an analysis result; and generating target key data by using the target encryption scheme, performing key negotiation with a target terminal based on the target key data, and establishing a secure session key.

The embodiment of the invention discloses a cooling liquid flow distribution method, device and equipment for a liquid cooling server and a storage medium, and the method comprises the steps: collecting the core temperature and power consumption data of each calculation unit in the server in real time, and continuously recording the temperature time sequence change; based on real-time heating power calculation fused with temperature and power consumption information, the heating power proportion of each unit is determined; further, for the unit with the heating power exceeding the threshold value, a dynamic weight factor is introduced to adjust the corresponding expected flow proportion; a closed-loop controller is adopted to adjust the opening degree of a liquid cooling branch valve, so that the actual flow accurately tracks the expected value; and finally, performing adaptive optimization on the dynamic weight factor according to the temperature change rate to form closed-loop control. Therefore, the method effectively solves the problems of local overheating and flow waste caused by uneven load in a multi-heat-source environment, the heat dissipation energy efficiency is remarkably improved, the system reliability is remarkably improved, and the service life of hardware is remarkably prolonged.

The invention, which relates to the low-altitude technology and artificial intelligence field, discloses a large-model-driven unmanned aerial vehicle relay mode vehicle-vehicle-electricity cooperative distribution method comprising the following steps: analyzing order demand information through a multi-mode large model, performing distribution mode decision making in combination with an environment state and constraints, and outputting an optimal distribution mode. The orders are aggregated and combined according to the characteristics of time, semantic similarity and the like. And based on the real-time state and risk assessment, a deep value learning model optimizes a distribution path. Unmanned aerial vehicle tasks are executed according to distance sorting and feasibility matching, the state is updated in real time during execution, and the optimization model is fed back. According to the method, global collaborative optimization of vehicles, machines and electricity is realized under dynamic order and energy constraints, and the service rate and the total income are remarkably improved.

The invention relates to the technical field of retail supply chain and store resource management, and discloses a service reservation data-based store resource dynamic allocation method, which comprises the following steps of: constructing a coupling association graph of a service item and commodity combination by adopting a graph optimization algorithm fused with sparse perception constraint; according to a core service item determined by the coupling correlation graph, obtaining a reservation fluctuation mode of the core service item by using a time sequence decomposition method of an embedded state feedback mechanism; constructing and training an attention convolutional neural network model combined with local feature self-correction, and predicting service resource demand distribution of the store by using the model; generating a store resource collaborative configuration strategy based on the service resource demand distribution and the associated commodity combination real-time inventory data; according to the invention, accurate coupling prediction and collaborative dynamic allocation of the service reservation demand and the commodity inventory demand are realized, so that the efficiency and accuracy of store resource allocation are remarkably improved.

The invention belongs to the technical field of computers, and particularly relates to a resource allocation method and device, equipment and a storage medium. The method comprises the following steps: S1, acquiring a target resource occupation task and real-time state data of a plurality of clusters; s2, calculating a cluster resource utilization rate based on the total resource configuration amount and the resource usage amount; calculating a residual resource proportion of the cluster based on the resource residual amount and the resource demand parameter; calculating a load sensitivity coefficient of the target resource occupation task based on the task priority, the resource demand parameter, an execution duration threshold and a task dependency identifier; and S3, screening the clusters according to preset screening conditions corresponding to different task guarantee levels, determining the clusters meeting the preset screening conditions as candidate clusters, monitoring in real time, triggering resource migration based on quantitative migration conditions, and forming a closed-loop process of data acquisition-index calculation-screening distribution-preemption coordination-monitoring migration.

An information processing system includes a distribution terminal configured to distribute first data and second data, the first data including image data of a game being executed, the second data including captured image data obtained from a camera and area data indicating an area of a person included in the captured image data, and a reception terminal configured to receive the first data and the second data from the distribution terminal and generate based on the area data, a distributed display image in which the area of the person in the captured image data is shown superimposed on an image of the game being executed in the distribution terminal.