1141 results about "Allocation method" patented technology

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.

The invention provides a supercomputing resource intelligent allocation method and system based on AI load monitoring, and the method comprises the steps: firstly obtaining a real-time load data set of a plurality of computing nodes in a supercomputing system, including a resource occupancy rate index and a task queue state parameter, then carrying out the load feature extraction processing of the real-time load data set, generating resource occupancy fluctuation characteristics and task queue evolution characteristics, performing resource demand analysis on the load characteristic set by using a pre-constructed resource demand prediction model, predicting resource demands of each computing node in a subsequent time window, determining resource allocation priorities and migration strategy parameters based on prediction results, and performing resource allocation according to the resource allocation priorities and the migration strategy parameters. The resource dynamic allocation instruction is generated and sent to the target computing node, the resource reallocation operation is triggered, intelligent and efficient allocation of super computing system resources is achieved, the resource utilization rate and the task execution efficiency are improved, and the overall energy efficiency ratio and stability of the system are optimized.

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

The invention discloses an intelligent load management and allocation method for an electric meter box, relates to the technical field of intelligent power grid management and energy optimization, and solves the problems that the adaptability of load management in an existing electric meter box is insufficient, the prediction accuracy is influenced by data prejudice, and the dynamic adjustment capability is limited. According to the method, diversified data are collected and analyzed through a real-time data interface and a gradient boosting tree technology, a power consumption prediction model is constructed in combination with a long-short term memory (LSTM) network, and meanwhile, prediction performance is optimized by adopting a data enhancement algorithm and an adaptive enhancement algorithm; fast response and dynamic adjustment are realized by using a sliding window prediction method based on Kalman filtering, a load distribution strategy is optimized through an adaptive depth deterministic strategy gradient algorithm, and collaborative distribution scheduling and management are performed on cross-regional electric meter boxes through a distributed collaborative scheduling system; according to the invention, the stability and efficiency of the power system are greatly improved, and the energy utilization efficiency and economic benefits are remarkably improved.

The invention discloses a calculation task allocation method and device, equipment, a storage medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-executed calculation task, and extracting the features of the to-be-executed task to obtain a feature label; decomposing the to-be-executed calculation task based on the feature labels to obtain sub-tasks; constructing a directed acyclic graph based on the dependency matrix of each sub-task, and performing topological sorting on each sub-task to obtain a priority and an execution sequence; according to the method, a to-be-executed calculation task is decomposed into a plurality of sub-tasks through feature labeling, a directed acyclic graph is constructed according to the dependency relationship among the sub-tasks, and the sub-tasks are distributed according to the reference execution time, the priority and the execution sequence. The priority and the execution sequence of each sub-task are determined by utilizing topological sorting, and the sub-tasks are allocated to each GPU for execution in combination with the reference execution time of each sub-task on different GPUs, so that the calculation efficiency of the calculation task is effectively improved.

The invention discloses a heterogeneous computing resource-based dynamic task scheduling and allocation method, which comprises the following steps of: S1, creating a plurality of resource pools, and defining structure attributes of the resource pools; s2, classifying all the heterogeneous computing resources, and distributing the heterogeneous computing resources to corresponding resource pools; s3, classifying the target calculation task, scheduling the target calculation task to a resource pool matched with the target calculation task, decomposing the target calculation task into a plurality of sub-tasks, and scheduling the plurality of sub-tasks to a calculation unit in the resource pool; s4, judging whether the load data of the resource pool is greater than a preset threshold value or not, and if the load data is greater than the preset threshold value, migrating the target calculation task; and S5, continuously monitoring the load data of all heterogeneous computing resources, and dynamically adjusting a target computing task scheduling strategy by the scheduling module according to the load data. According to the method, the parallelism is improved through a fine-grained scheduling mode, overload of some resources is avoided, and the pressure of each computing unit is balanced.

The invention discloses a computing power distribution method based on a dynamic window and multi-version correction, and relates to the technical field of intelligent resource scheduling, the method comprises the following steps: constructing a scheduling state set by monitoring a to-be-scheduled task set and a platform resource state of a cloud platform resource scheduling system in real time; based on time window dynamic judgment, extracting a stable and effective task set; calculating a scheduling priority coefficient in combination with task resource intensity, urgency and historical completion performance, determining a priority scheduling set and executing a first version scheduling scheme; the difference between the current scheduling version and the historical optimal scheme is evaluated through the residual vector and the offset coefficient, and whether version correction needs to be carried out or not is judged; and based on resource use feedback in the scheduling execution process, calculating a feedback change rate and judging whether a scheduling execution state is stable or not, and if the scheduling execution state is unstable, dynamically adjusting scheduling parameters. According to the method, accurate allocation of computing power resources, adaptive scheduling optimization and abnormal risk control are realized.

The invention relates to the technical field of resource optimization, in particular to an unmanned aerial vehicle distribution task real-time optimization scheduling management system, which comprises a bat sound wave calculation module for performing echo positioning frequency conversion calculation on the distance between the current position of an unmanned aerial vehicle and a target distribution point, a time window and a carrying weight upper limit value through a bat algorithm, and obtaining a task sound wave waveform matrix. According to the invention, the application of the bat algorithm enables the unmanned aerial vehicle to accurately calculate the task search space when the unmanned aerial vehicle executes the distribution task, and avoids the resource waste caused by a static distribution mode. The task search vector is formed based on the comparison operation of the task waveform amplitude and the distribution distance, so that the unmanned aerial vehicle can quickly adjust the task priority in a dynamic environment, and the execution accuracy of the distribution task is improved. The task group is optimized through the subspace decomposition operation, so that the unmanned aerial vehicle can execute multiple tasks in a parallel mode, the load pressure of a single unmanned aerial vehicle is reduced, and the cooperation efficiency of the overall tasks is improved.

The invention discloses a task allocation method and system based on a hybrid game genetic algorithm, and belongs to the field of intelligent manufacturing, and the method comprises the following steps: obtaining task allocation data based on an industrial Internet of Things and an edge computing technology; constructing a task allocation-path planning-energy consumption control collaborative optimization model based on the task allocation data; the collaborative optimization model comprises a three-dimensional optimization objective function and a constraint system; the constraint system comprises an equipment health state constraint, an AGV dynamic motion constraint, a process connection space-time constraint and a multi-agent game equilibrium constraint; and solving the collaborative optimization model based on a hybrid game genetic algorithm to obtain a task allocation optimal solution. Through deep coupling of a multi-agent game and a dynamic evolutionary algorithm, the problem of decision dimension splitting of traditional single-target static scheduling is solved.

The invention provides a computing power resource management and distribution method and system and a medium. The method comprises the following steps: integrating various computing power resources in a private network, establishing a computing power resource information base, analyzing a computing power resource request, establishing a service demand model, optimizing and matching a computing power resource configuration template, obtaining a computing power adaptation template, and establishing a service demand model; optimal computing power resource adaptation is carried out according to dynamic information of target computing power resources matched with the computing power adaptation template, isolation processing is carried out at the same time, monitored performance index data are processed, a load trend is predicted, a corresponding computing power resource adjustment strategy is obtained, and the computing power resources are optimized and expanded according to big data analysis; therefore, efficient utilization and elastic expansion of the computing power resources of the private network are realized by constructing the dynamic computing power resource pool and the resource scheduling model and monitoring, adjusting and optimizing an expansion mechanism, and the processing efficiency and security of big data services are remarkably improved.

The invention discloses a power consumption distribution method and device, a storage medium and a program product, and relates to the technical field of servers, and the method comprises the steps: dividing a plurality of time slots, and in each time slot interval, according to the predicted load demand of each node and the task priority weight of a task executed by each node in the corresponding time slot, determining the task priority of each node; according to the method, the dynamic priority of each node is determined, and then the power consumption of each node is allocated based on the dynamic priority and the resource budget of each node in each time slot interval, so that the method can flexibly adapt to different load requirements and execution of tasks with different priorities in different time periods, and the tasks with high priorities can guarantee more resources; according to the method, resource waste is avoided, reliable decision support is provided for power consumption management of the server, system stability and task execution efficiency are effectively balanced, and double breakthrough of the data center in the aspects of energy conservation and performance optimization is promoted.

The invention discloses a task allocation method, device and equipment, a storage medium and a computer program product, and relates to the technical field of computers.The method comprises the steps that reasoning task information in a first preset time period and computing power information of all computing cards in a heterogeneous computing system are obtained; predicting a predicted number of the reasoning requests received in a second preset time period based on the reasoning task information; and according to the reasoning task information, the computing power information of each computing card and the predicted number, allocating a pre-filling task or a decoding task to each computing card, and determining the number of tasks allocated to each computing card, so that the time for the heterogeneous computing system to respond to the predicted number of reasoning requests is shortest. According to the method, the optimal matching of the heterogeneous resources and the reasoning tasks is realized, the utilization rate of the heterogeneous resources is improved, and the execution efficiency of the reasoning tasks is improved.

The invention discloses a workshop task allocation method and system based on multiple dimensions, and relates to the technical field of production control management. Comprising the steps of obtaining order data and then performing preprocessing; based on the preprocessed order data, an advanced machine learning algorithm is adopted to construct a decision tree model, and the processing complexity of each order is identified; determining a task allocation scheme by using an optimization algorithm according to the identified order processing complexity and the current state of the production line; monitoring the actual operation condition of each production line in real time, starting a dynamic adjustment mechanism when the work load of a certain production line is unbalanced, and re-evaluating and adjusting task distribution; through intelligent task allocation and resource optimization, the production efficiency and customer satisfaction are improved, and meanwhile, dynamic adjustment and intelligent management of the production process are realized.

The invention provides a distributed reasoning task allocation method for an edge computing large model, which belongs to the technical field of mobile edge computing and comprises the following steps of: considering a large language model distributed deployment scene, constructing a workflow for cooperatively finishing a reasoning task by a plurality of edge servers, and adopting accurate secondary reconstruction by considering the generation type characteristic of the reasoning task; the uncertainty of reasoning task resource occupancy is solved, mathematical model expression is constructed for the edge reasoning task allocation problem in the scene, the aim is to maximize the service provider income, the constructed mathematical model is reconstructed into a combination selection problem, an approximate solution with theoretical guarantee is given by adopting a dual method, and the problem of edge reasoning task allocation is solved. And the reasoning task distribution decision of the service provider is optimized, so that the income of the service provider is maximized.

The invention discloses a memory allocation method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The method comprises the steps that when a memory application of a requester is received, the memory size requested by the memory application is determined; if the memory size is smaller than or equal to the first threshold value, responding to the memory application based on the memory page which is not completely allocated; wherein the size of the memory page is larger than or equal to a preset value, and the first threshold value is smaller than the size of the memory page; if the memory size is greater than the first threshold and less than or equal to a second threshold, responding to the memory application based on an idle memory page in a preset memory page set; wherein the second threshold value is larger than the size of the memory page; and if the memory size is greater than the second threshold value, responding to the memory application based on the unreleased idle memory page of the cache and / or the idle memory page in the operating system. The memory allocation efficiency is improved.

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.

The invention provides a server GPU computing power allocation method and system and a server, belongs to the field of server resource management, and solves the problems of low utilization rate and inflexible decision of traditional allocation resources. The method comprises the steps that a central agent constructs a directed weighted game diagram of tasks and a GPU cluster, and a global allocation strategy is solved based on Nash equilibrium; a local agent distributes tasks to a specific GPU through reinforcement learning, intelligent migration is achieved in combination with a dynamic threshold value and anomaly detection, and a strategy is iteratively optimized through federal learning. The system comprises a central agent, a local agent, a GPU cluster and an experience pool module, and a server carries the system execution method. According to the scheme, through double-layer agent cooperation and multi-algorithm fusion, the task cluster adaptation relation is quantified, the allocation strategy is dynamically adjusted, the resource utilization rate and the task processing efficiency are remarkably improved, and the adaptivity and reliability in a complex scene are enhanced.

The invention discloses an intelligent computing power cluster task allocation method and system based on cloud edge collaboration, and relates to the technical field of computing power task allocation, and the method comprises the steps: extracting a first task in to-be-allocated tasks, collecting and obtaining a first multi-dimensional feature parameter of the first task, and obtaining a first feature curve; performing clustering analysis on the to-be-allocated tasks to obtain a clustering result; judging whether distributed edge computing power in a cloud edge computing power cluster meets the first resource requirement or not; if yes, performing comparative analysis to obtain first fitness of the first clustering cluster; descending the computing power nodes to obtain a target computing power node; and performing task processing of the first clustering cluster. According to the method and the device, the technical problems of low task allocation efficiency and poor task and computing power node adaptability in the cloud-side collaborative environment in the prior art are solved, and the technical effects of realizing efficient allocation of the tasks in the intelligent computing power cluster in the cloud-side collaborative environment and improving the task processing adaptability and efficiency are achieved.

The invention belongs to the technical field of computing power distribution, and particularly relates to a dynamic computing power distribution method and system based on reinforcement learning, and the method comprises the following specific steps: S1, covering cloud, edge and end full-node scenes, and collecting computing power resource states, task demand features and cross-domain network condition data in real time; s2, on the basis of standardized data output by a cross-domain computing power sensing module, by constructing a state space fusing computing power, tasks and a network, defining an action space of computing power scheduling direction and proportion, and designing a multi-target reward function for balancing the resource utilization rate, the task satisfaction rate and long-term conflict avoidance; and realizing self-learning and self-iteration scheduling strategy generation based on a reinforcement learning algorithm. According to the invention, the reinforcement learning agent autonomously learns the computing power demand of the emergency scene and the new type of task, the rule does not need to be manually preset and modified, and the method has the advantage of realizing dynamic adaptation of computing power distribution to complex and changeable scenes.

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 invention relates to a task distribution method and system based on a zero-work market, and a terminal, and relates to the technical field of the zero-work market, and the method comprises the steps: obtaining a preset trigger signal of a mobile terminal; when the trigger signal is consistent with a preset search signal, controlling the mobile terminal to carry out article shooting prompt to obtain article image information; determining article features in the image according to the article image information; determining whether a preset domain database contains an article corresponding to the article feature in the graph or not; if the domain database contains an article corresponding to the article feature in the graph, determining a specific shot article according to the article feature in the graph; determining an appropriate working field and an appropriate field number according to the specific shot object; and when the proper field number does not exceed the preset field excessive number, controlling the mobile terminal to display the working content of the proper working field according to the proper working field. The task allocation method and device have the effect of improving the task allocation efficiency.

The embodiment of the invention discloses an address allocation method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: receiving a first request of first equipment, the first request being used for requesting to occupy a target address of intelligent equipment; under the condition that the intelligent device communicates with the second device through the target address, returning a retry instruction to the first device; receiving a second request of the first device; and under the condition that the second request is still used for requesting to occupy the target address, sending a redistribution instruction to the second equipment, and communicating with the first equipment through the target address. According to the address allocation method provided by the embodiment of the invention, the address can be re-allocated according to the request occupation degree of other equipment on the target address under the condition that the target address used for communicating with the other equipment conflicts, so that simultaneous communication with the other equipment is realized.

The invention relates to the technical field of electric vehicle charging pile management and distribution, and discloses a new energy charging pile management and distribution method and system, and the method comprises the steps: collecting charging user data, position data and environment data, and carrying out the preprocessing; and analyzing the preprocessed data, and constructing a prediction model of the charging demand of the user. And calculating the load condition of each charging pile according to a demand result predicted by the model, and adjusting a charging pile distribution strategy. According to the invention, through the steps of data preprocessing, charging demand prediction, load calculation and distribution strategy optimization, real-time monitoring and feedback and the like, intelligent management of charging pile resources is realized. The method plays a key role in improving the efficiency of the charging service, reducing overload and idle phenomena, optimizing the user experience and the like. A more intelligent, efficient and personalized charging pile management and distribution system is provided for electric vehicle users, the utilization rate of charging pile resources is effectively improved, the waiting time of the users is shortened, and the flexibility and stability of the system are enhanced.

The invention relates to the technical field of equipment management, and discloses a multi-unmanned equipment task dynamic allocation method and system, and the method comprises the steps: collecting the initial state information of an unmanned equipment group and a to-be-allocated task set, analyzing the load balancing demands of equipment based on the initial state information, and extracting the task priority in the to-be-allocated task set, obtaining an initial task allocation scheme; monitoring the execution progress of the initial task allocation scheme to form execution progress data, and analyzing a task dependency relationship in the execution progress data to identify key task nodes; starting local negotiation between devices according to the priority of the key task node, processing device state information through a game coordination mechanism to form a coordination decision weight, and generating a dynamic adjustment instruction based on the weight; according to the method, the overall execution efficiency of the cooperative task of the multiple unmanned devices can be improved, meanwhile, reasonable configuration of device resources is achieved, the situation that the resources are idle or overloaded is avoided, and the stability and reliability of task execution are enhanced.

The embodiment of the invention provides an annotation task allocation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a plurality of annotation tasks to be distributed; determining task portraits of the plurality of to-be-distributed annotation tasks according to the plurality of to-be-distributed annotation tasks; obtaining capability portraits of a plurality of annotation personnel and capability portraits of a plurality of data annotation models, wherein the data annotation models are used for data annotation; according to the task portrait, the ability portraits of the plurality of annotation personnel and the ability portraits of the plurality of data annotation models, constructing an optimization model used for determining an allocation relationship among the to-be-allocated annotation task, the annotation personnel and the data annotation models; and solving the optimization model by using a solving algorithm to generate a target task allocation scheme.

The invention discloses a cargo distribution method and device and a computer program product. The method comprises the following steps: determining a target cargo hold of a passenger plane loaded with a to-be-distributed cargo set, and obtaining cargo characteristics of each cargo and cargo hold characteristics of the target cargo hold; obtaining a plurality of cargo loading constraint conditions, and constructing a search space of a heuristic algorithm based on the plurality of cargo loading constraint conditions, the cargo hold features and the cargo features; generating cue words based on the cargo loading constraint conditions, the cargo hold features and the cargo features, and inputting the cue words and the search space into a target model to obtain a reduced search space; randomly extracting N initial cargo distribution schemes from the reduced search space, and screening out a target cargo distribution scheme from the N initial cargo distribution schemes based on a target evaluation function; and distributing the cargo to the target cargo hold based on the target cargo distribution scheme. According to the invention, the problem of low efficiency of distributing the goods to the passenger plane cargo hold in the prior art is solved.

The invention provides an intelligent terminal task allocation method based on AI edge decision and a related device, and the method comprises the steps: obtaining environment data and task attribute data in a target power operation region, obtaining target environment data and m pieces of task attribute data, transmitting the target environment data to n intelligent terminals, calculating according to the target environment data through n intelligent terminals to obtain n pieces of first calculation performance data, returning the n pieces of first calculation performance data to the electronic equipment, and determining a first adaptation degree matrix of tasks and intelligent terminal performance according to the n pieces of first calculation performance data and the m pieces of task attribute data, determining a target task allocation strategy according to a preset constraint condition and the first adaptation degree matrix; and controlling the n intelligent terminals to execute the edge decision according to the target task allocation strategy. In an electric power operation scene, the accuracy and robustness of task allocation for distributed terminal decision making can be improved.

The invention provides an intelligent property management and allocation method and system based on big data, and the method comprises the steps: carrying out the data compression and anomaly detection of the operation state information of property facilities and user demand feedback through an edge computing technology, obtaining screened data, constructing a facility health assessment model, and carrying out the data compression and anomaly detection. The method comprises the steps of generating a facility health assessment report, constructing a service response mode library based on the facility health assessment report in combination with user demand feedback to generate an optimal service response scheme and generate a task work order, performing monitoring and assessment by using a big data analysis technology and a machine learning algorithm, and collecting actual effect feedback. According to the technical scheme provided by the invention, the data transmission quantity is effectively reduced, the response speed of the system is improved, the system can be ensured to continuously evolve and adapt to new situations, and the management efficiency and the service quality are further 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.