256 results about "Streaming computation" patented technology

The invention provides a power grid energy disturbance suppression method for electric heating gas state information transmission collaboration, and relates to the technical field of information, and the method comprises the steps: obtaining multi-source heterogeneous state information of an electric heating gas energy network; aiming at the multi-source heterogeneous state information, dynamically converting a heterogeneous communication protocol into a unified protocol format by adopting a self-adaptive communication protocol conversion technology; and for the decoupled state information, parallel processing is carried out by adopting a distributed stream computing framework, so that the real-time performance and expandability of state information processing are improved. A distributed stream computing framework is adopted to construct an efficient processing assembly line, the block chain technology is utilized to guarantee the integrity and reliability of data transmission, a deep learning algorithm is applied to construct an intelligent analysis model, a multi-energy-flow coupling relationship and an evolution law are mined, and a multi-energy-flow analysis result is obtained. According to the method, data processing results of all links are fused to form a global state information view, decision support is provided for power grid energy disturbance suppression, and efficient processing, analysis and visualization of the state information of the electric heating gas multi-energy flow system are achieved.

The invention provides an urban inland inundation simulation method based on CPU-GPU heterogeneous parallel calculation, and aims to reduce the number of calculation grids by adopting a self-adaptive non-uniform grid, consider pipe network calculation and improve the calculation efficiency of low-fluctuation urban area flood simulation at the same time, and comprises the following steps: performing one-dimensional pipe network runoff CPU calculation, constructing a rainwater pipe network model, and constructing a rainwater pipe network model. Extracting an overflow process of an overflow node of the pipeline; dividing a non-uniform grid based on the original raster data and a gradient threshold; non-uniform grid parallel calculation is carried out; a CPU-GPU heterogeneous parallel one-dimensional and two-dimensional coupling model is constructed; and coupling water depth calculation. The method can flexibly adapt to topographic changes and reduce the number of computational grids, and solves the limitation that precision and efficiency are difficult to consider in waterlogging simulation of a uniform grid in a low-fluctuation area in combination with CPU-GPU heterogeneous parallel calculation.

The invention discloses an Internet of Things operation management scheduling system based on cloud edge collaboration, relates to the technical field of task scheduling optimization, and aims to solve the problem of adaptability of edge computing and cloud edge collaboration architecture, the Internet of Things perception access technology is adapted and fused with heterogeneous equipment through an architecture adaptation module, various IOT data are stably acquired, and the cloud edge collaboration is realized. The task execution position is dynamically adjusted in combination with the task distribution unit; real-time stream calculation and an ETL processing mechanism are combined to fit a comprehensive scheduling index Zdzh, the utilization rate of calculation resources is increased, and task scheduling is optimized; internet of Things equipment data, internet data and government affair data are analyzed based on the data lake and warehouse architecture, a data access strategy is optimized, and the data fusion efficiency is improved; and calculating and evaluating a sudden anomaly prediction coefficient Tycs, triggering an anomaly early warning mechanism or a sudden anomaly emergency mechanism, optimizing task migration, calculating node load balancing and data traceability analysis, so as to improve the stability and intelligent level of an Internet of Things operation management scheduling system.

The invention relates to the technical field of enterprise finance and taxation management, and discloses an enterprise finance and taxation intelligent management system based on data analysis. According to the method, the problems of data islands, response delay and business disjunction in the prior art are solved by constructing a multi-source data real-time access and dynamic knowledge graph and stream calculation analysis engine collaborative architecture. Specifically, deep fusion and real-time perception of enterprise finance and taxation total data are realized, invoice risks and compliance deviations in a business flow can be actively identified, and the conversion from post-event recheck to in-event intervention is completed; through combination of a dynamic knowledge graph and an AI model, an analysis decision deeply accords with a business scene, a tax preferential policy is matched, and the finance and tax planning efficiency is intelligently improved; meanwhile, the system has self-learning and self-adaptive capabilities and can be continuously optimized along with policy and regulation and business changes, the risk control level and compliance capability of an enterprise are enhanced, and the real-time performance, accuracy and intelligent degree of finance and tax management are improved.

The invention discloses a hybrid power flow calculation method and device based on a cross entropy algorithm, and belongs to the technical field of optimization algorithm and machine learning crossing. The method comprises the steps that system modeling and parameter initialization are carried out, and a state space, an action space and a target function are defined according to a specific application scene; setting and initializing row parameters; entropy algorithm-guided sample generation and elite screening: iteratively optimizing power grid parameters through probability distribution, and quickly approaching a global optimal solution; the strategy optimization target of the strategy gradient algorithm is that a dynamic strategy network is constructed based on optimized power grid parameters, and the running state of the power grid is adjusted in real time to deal with uncertainty; performing sample multiplexing and distribution fusion of importance sampling: setting a double-buffer mechanism and performing sample fusion and multiplexing; and parameter updating and convergence judgment. According to the method, global search is carried out through the cross entropy algorithm, local optimization is carried out through the strategy gradient algorithm, the sample utilization rate is improved through the importance sampling method, and cooperative processing of global optimization and local optimization is achieved.

The invention provides a stream computing resource scheduling method and system based on a dynamic time window, and relates to the field of stream computing resource scheduling, and the method comprises the following steps: constructing a master-slave event time monitoring network to obtain a task feature vector, and establishing an adaptive time window scheduling mechanism to dynamically adjust the window size; and constructing a multi-level task priority queue, carrying out state transition by adopting a remote direct memory access mechanism of incremental transmission, and finally coordinating computing nodes through a distributed task arrangement protocol to complete resource switching. According to the method, computing resource competition can be effectively reduced, the flow computing task processing efficiency is improved, dynamic optimal configuration of resources is realized, and the system throughput is enhanced.

The invention relates to the technical field of medical data processing, and discloses a medical data source real-time processing and quality control method based on distributed computing. The method comprises the steps of obtaining and classifying multi-source heterogeneous medical data streams, constructing a distributed parallel processing framework, setting an initial constraint condition, executing distributed streaming computation to generate a primary processing strategy, and optimizing and generating a global consistency processing strategy by using a federated learning algorithm. The system comprises a plurality of modules such as a data access and classification module and a processing framework construction module. According to the method, multi-source heterogeneous medical data can be efficiently processed, real-time calculation is realized through a multi-dimensional processing space and dynamic task allocation, and the data quality and privacy are guaranteed by utilizing a federal learning optimization strategy. And equipment abnormity can be monitored, quality risks can be predicted, task scheduling can be managed, the accuracy, reliability and availability of medical data processing can be improved, and powerful support can be provided for medical decision making and the like.

The invention discloses a resource collaborative awareness-based Storm flow calculation dynamic scheduling method, and provides the following technical scheme aiming at the problem that the performance of a Storm default scheduling algorithm is reduced in node abnormity, resource fluctuation and rescheduling scenes: firstly, dynamically sensing node busy, downtime and data flow fluctuation events through a real-time monitoring module; triggering a rescheduling process; secondly, constructing a resource collaborative optimization model based on historical task instance resource requirements, node performance indexes and communication overhead, and generating a task instance allocation scheme by taking minimization of inter-node communication cost as a target and combining CPU / memory dynamic threshold constraints; further, a greedy algorithm is adopted to sort high-relevance task instances, the high-relevance task instances are preferentially distributed to nodes with the optimal historical performance, and it is ensured that the node resource utilization rate does not exceed a dynamic threshold value; meanwhile, node load balancing parameters are corrected in real time through time window smoothing processing, and the remaining resource state is updated; and finally, outputting an optimized topology division result, so that the system delay after rescheduling is remarkably reduced, and the throughput is improved. According to the method, rapid recovery and stable operation of the heterogeneous cluster are realized through resource collaborative modeling, historical data driven dynamic scheduling and load balancing optimization.

The invention discloses an import and export risk real-time early warning method and system based on multi-modal data fusion, and relates to the technical field of data analysis, and the method comprises the steps: obtaining multi-modal data in real time, and carrying out the preprocessing; designing a dynamic weight distribution algorithm, and endowing different types of data with differentiated weights to realize dynamic fusion according to quantitative indexes of timeliness, authority and target transaction association degree; fusion data features are extracted through a streaming computation engine, real-time risk calculation is carried out based on an Apache Flink cluster, and an association network is constructed in combination with a six-dimensional knowledge graph; on the basis of the extracted features and the association network, a CEP rule engine quickly identifies and calculates potential risk events and accurately locates risk points by relying on a predefined multi-class composite risk mode; and setting a grading early warning mechanism, enabling the identified risk mode to correspond to a corresponding early warning grade, generating a grading early warning signal, and synchronously prompting a dynamic disposal suggestion. According to the method, the risk identification accuracy can be improved, and the early warning response delay is shortened.

A coordinated control method for an urban rail passenger flow, and an electronic device and a storage medium, which belong to the technical field of rail transit control. In order to realize passenger flow management and control without increasing the transport capacity, in the present application, a rail simulation system is used to deduce a predicted passenger flow of an urban rail line during rush hour, which involves data such as entry station IDs, exit station IDs, entry time periods and passenger flow volumes; performing division on the basis of different time periods t and stations s, so as to obtain a flow of passengers arriving at a station s and preparing to enter the station to take a train within the time period t; on the basis of historical passenger flow distribution data of subway operating companies, compiling statistics on passenger flows in various directions among historical passenger flows, and calculating the proportions of the passenger flows in the various directions among the historical passenger flows; constructing a mixed integer programming model for multi-station passenger flow coordinated control; and using a branch and bound algorithm to solve the mixed integer programming model for multi-station passenger flow coordinated control constructed in step S3, so as to obtain an optimal passenger entry flow scheme for stations. The present application realizes the effect of prioritizing the evacuation in a station with a large passenger flow.

The invention discloses an electric power system load flow calculation method and related equipment, and the speed of electric power system load flow calculation is remarkably improved by using a neural network model. Compared with a traditional physical simulator, the neural network model can complete a large amount of load flow calculation in a shorter time, which is crucial to power grid operation and risk assessment needing quick response. According to the method, the accuracy similar to that of a traditional algorithm based on Newton-Raphson iteration can be achieved. The addition of the physical constraints ensures that the model output result accords with the physical characteristics and operation rules of the actual power grid, and particularly, the projection method strictly ensures the linear equality hard constraints, so that the model almost does not violate the key physical constraints in the training process, and the reliability of the model is improved.

The invention relates to a coal mine equipment cooperative control system based on digital twinning, and belongs to the field of coal mine intellectualization. According to the system, the equipment state, environmental parameters and fault data are acquired in real time through the data acquisition layer; the data processing layer realizes multi-source data fusion and fault prediction by using a deep fusion neural network and a space-time attention network; the digital twinborn layer constructs a three-dimensional dynamic model based on unified geodetic coordinate system coupling equipment and geodetic survey information; and the control layer adopts a distributed stream computing framework to execute collaborative scheduling, active disturbance rejection speed control and fault prediction compensation. The problems that in the prior art, spatial position expression is missing, collaborative delay is high, data are not synchronous, and complex working condition adaptability is poor are solved. The method has the beneficial effects that total-factor high-precision space coupling is realized, the collaborative delay bottleneck is overcome, the adaptability to complex working conditions is enhanced, a closed-loop intelligent optimization system is constructed, and full-chain transparent management and control of the coal mine are promoted.

The invention discloses a quasi-real-time bank reconciliation management and control method and system, and the method comprises the steps: obtaining a data source, carrying out the collection of data to a data storage platform, transmitting the data in the data storage platform to a real-time flow calculation platform, creating a calculation task according to an account checking detection rule, performing abnormality judgment to obtain a transaction recognition result for storage, positioning a business system and a link in which an abnormal transaction occurs, and intelligently analyzing the type of a technical fault causing the abnormality by using a model constructed by a machine learning algorithm; and for the identified abnormal transaction, carrying out message notification on related personnel. According to the invention, quasi-real-time reconciliation detection of fund transactions is realized, and the timeliness of reconciliation detection is improved; the type of a technical fault causing the abnormal transaction is intelligently analyzed through a machine learning model, so that technicians can conveniently and timely check and repair the fault, and a bank is helped to reduce the fund risk.

The invention discloses an optimal power flow solving method and device based on a graph neural network and an extreme learning machine, and relates to the field of power grid power flow calculation, and the method comprises the steps: obtaining the node topology of a to-be-solved power system and the historical operation data of each power node, and constructing a system topological graph according to the node topology and the historical operation data; extracting features of a system topological graph based on a multilayer graph convolutional neural network to obtain first node features; taking the minimum total output of the system as a target, screening first node features based on regularization, and fusing the first node features to obtain second node features; performing multiple times of iterative training on the initial extreme learning machine model according to the second node features, optimizing model parameters based on mean square error loss during iteration, completing training until the model parameters meet preset values, and obtaining a first extreme learning machine model; and obtaining real-time operation data of the to-be-solved power system, and obtaining an optimal power flow solving result of the to-be-solved power system according to the real-time operation data and the first extreme learning machine model.

The invention relates to the technical field of streaming computing, and provides a real-time service computing system based on a dynamic rule engine, which comprises a rule management module and a streaming computing engine, the rule management module is used for providing visual configuration and release functions of service rules and performing versioning storage on the released service rules; the streaming computation engine comprises a rule synchronization unit and a plurality of parallel processing units; the rule synchronization unit is used for acquiring service rules from the rule management module and distributing the acquired service rules to each parallel processing unit based on a broadcast mechanism; and each parallel processing unit is used for receiving the business event flow and performing real-time rule matching on the business event flow according to the received business rule so as to output a decision result. According to the system, complete decoupling of business rule configuration and execution is realized, and the problem that rule updating in a traditional business system needs shutdown deployment is effectively solved.

The invention relates to an electric power marketing business abnormity real-time detection method and system based on stream-oriented computation, and belongs to the technical field of electric power system optimizing.The method comprises the steps that data snapshots are extracted from an electric power marketing business system, difference comparison is conducted on the data snapshots and historical snapshots of an intermediate library, and standardized increment events are generated and stored; capturing an incremental event in real time through a data change capturing tool and pushing the incremental event to a message queue; a streaming computation engine consumes the event stream, sequentially performs data cleaning, association with a static dimension table and sliding window statistical feature calculation, and constructs a feature vector; and performing parallel analysis and weighted fusion on the feature vectors based on a business rule base and an online machine learning model to generate a comprehensive risk score, and outputting an abnormal event when the score exceeds a threshold value. According to the method, the problems of exception identification lagging and complex work order process in a traditional batch processing mode are solved, the crossing of the business risk from hour-level detection to minute-level real-time perception is realized, and the timeliness and accuracy of power marketing risk management and control are improved.

The invention discloses a multi-dimensional operation data analysis and method based on a BI system, and relates to the technical field of multi-dimensional operation data analysis, and the system comprises a data integration module which is in butt joint with real-time data of a business system through a BI platform; the data logic processing module adopts a streaming computing framework to compute and update data in real time, and the timed task layer configures periodic batch tasks and executes historical data aggregation, trend analysis and cross-module association computation; the data warehouse module is used for storing the processed data and carrying out classified storage according to business dimensions to form a unified business data warehouse; the distributed computing engine module is used for scheduling and distributing tasks; and the visual report module is used for generating a multi-dimensional operation analysis report. Through real-time data integration, streaming computation and timed task configuration, efficient data processing and personalized requirements are supported, large-scale parallel computation and load balancing are supported, and disaster recovery capacity is enhanced.

The invention discloses a data processing method, device and system, and relates to the technical field of digital marketing. A specific embodiment of the method comprises the following steps: acquiring a rule comprising a trigger condition and a disposal strategy, generating an execution script corresponding to the rule, and determining an execution logic of a state machine instance corresponding to the rule by utilizing the execution script; in response to the monitored behavior event of the target user, transmitting the behavior event and a historical feature vector of the target user to a state machine instance, updating the historical feature vector of the target user to a current feature vector based on the state machine instance and the behavior event, and transmitting the current feature vector to a preset stream calculation engine; matching the current feature vector with a trigger condition in the rule based on a stream calculation engine; and under the condition that the matching is successful, executing the processing strategy in the matching rule on the target user. According to the embodiment, the user behavior event can be responded in real time, so that business requirements are met.

The power distribution network load flow calculation method based on the hyper-parameter optimization graph attention network comprises the steps of simulating and generating load flow operation data of a system according to historical wind and light output data and load data, and forming graph data by utilizing node characteristic data and topological information of a power distribution network. On the basis, a graph attention network (GAT)-based power flow regression model of the power distribution network is established, and spatial correlation characteristics among nodes are mined through a graph attention mechanism. In addition, when the topology of the power distribution network changes, the model can dynamically adjust the attention coefficient according to the new node connection relation, and rapid adaptation to the new topology is achieved. In view of the fact that the performance of the neural network highly depends on setting of hyper-parameters, an improved seagull optimization algorithm (ISOA) is introduced to carry out automatic optimization on the network hyper-parameters, so that an optimal GAT power flow regression model, namely, ISOA-GAT, is obtained. And finally, a Python simulation platform verifies that the method not only can quickly and accurately calculate the power flow of the power distribution network, but also has relatively strong topology generalization capability.

The quantum Newton-Raphson load flow calculation method and system based on the subspace CKS linear algorithm are provided under the current limited quantum hardware resources, and combination of power system load flow calculation and the quantum calculation technology is achieved. Compared with a classical linear solver, the CKS quantum linear solver has an exponential acceleration effect, greatly optimizes the high dependency relationship of algorithm complexity on calculation precision, accelerates the solving speed of a linear system, and enhances the capability of solving the linear system.

The invention discloses a distributed environment parameter intelligent early warning method based on general control network flow-oriented computation, and relates to the technical field of intelligent early warning of the environment of an active launching platform, and the method comprises the steps: dividing the environment of the launching platform into a plurality of monitoring points, and collecting environment and key equipment operation state data; calculating priority coefficients of the monitoring points, and judging whether the key safety parameters meet requirements or not; establishing minute-level and hour-level sliding windows under a streaming computation framework, extracting multi-time scale parameter fluctuation characteristics, calculating a stability index, and comparing and analyzing the stability index with a threshold value; for adjacent monitoring nodes, multi-dimensional parameter consistency is calculated, an overall consistency index is obtained, and discrimination of a single-point link fault and a global environment anomaly is realized; and a link tracing log is automatically generated, and a basis and parameter optimization correction are provided for abnormal event tracing. According to the method, efficient monitoring and intelligent early warning of multi-point environment and equipment parameters of the movable launching platform can be realized, and the safety protection accuracy and timeliness are improved.

The invention relates to the technical field of databases, and particularly provides a streaming computing method and device for a distributed database, and the method comprises the following steps: S1, receiving time sequence data change captured by a CDC component in real time, and storing the time sequence data change in a local cache; s2, performing stream aggregation calculation of stream calculation; s3, receiving a calculation result of stream calculation, converting a result set into a target table compatible format, and writing data into a target table by using an internal actuator; s4, processing existing historical data during stream calculation creation, breakpoint data generated during a stream calculation pause period and expired data exceeding a time sequence data out-of-order window; and S5, managing the life cycle of the flow calculation in the distributed database cluster. Compared with the prior art, the method has the advantages that the streaming calculation of the time series data of the database can be efficiently completed, the recalculation of the window split data is efficiently completed by using the streaming calculation recalculation module, and the performance attenuation is within 15% of the original storage performance.

The invention discloses a power system probabilistic power flow method and system based on polynomial proxy and graph parallelism, and belongs to the technical field of power system planning and operation optimization. The method comprises the following steps: establishing a probability distribution model of a bus load prediction error to obtain sample distribution of predicted power; representing the output response quantity of the system as an input polynomial function, and establishing an initial polynomial agent model; determining an optimal configuration point set; mapping all the selected configuration points to an input sample space to obtain a small number of sample points of an input variable; performing deterministic load flow calculation on the sample points to obtain a sample vector of output response; calculating an undetermined coefficient in the proxy model according to the configuration point matrix and the sample vector; and solving the statistical characteristic quantity of the power flow response to obtain the probability distribution of the response. According to the calculation method provided by the invention, the probability distribution of the power flow response can be accurately and efficiently estimated, and convenience is provided for probabilistic power flow calculation of a novel power system under the access of a distributed power supply.

The invention discloses a rule engine quality inspection method and device, a storage medium and a computer program product, and relates to the technical field of software testing, the method comprises the following steps: receiving an expected target description input by a user, and determining an executable script code corresponding to a rule engine to be subjected to quality inspection based on the expected target description; extracting parameter information corresponding to the rule definition text of the rule engine to be subjected to quality inspection; embedding the expected target description, the executable script code and the parameter information into a preset cue word template; and calling a quality inspection model to perform quality inspection on the cue word template to obtain a rule engine quality inspection result. The rule engine is converted into the script code based on the mainstream computer programming language from the proprietary grammar, so that the quality inspection efficiency and the quality inspection accuracy of the quality inspection model on the rule engine are improved.

Disclosed in the present invention are a service risk determination method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring a data source relationship graph of a target service, wherein the data source relationship graph consists of nodes and directed edges, one node represents one data source associated with the target service, each directed edge represents a data flow relationship between data sources, the data flow relationship is determined by means of a stream computing task under the target service, and the stream computing task is used for processing data in a data source; and during the execution of the target service, when the execution of a stream computing task is abnormal, determining from the data source relationship graph data sources matching the stream computing task, and positioning a service risk of the target service on the basis of the data sources matching the stream computing task. The present invention solves the technical problem in the related art of it being difficult to position a service risk when a problem occurs in the execution of a stream computing task of a service.

A method for processing of a data stream using a graph-based data model includes receiving a data stream including data messages; disassembling the data messages data elements and metadata; generating a structured data model comprising the set of data elements based on the type of the data elements and the pattern of the data messages; instantiating a workflow to process the structured data model; configuring a CPS-G model sub-graph to add to the CPS-G model based on the type of the data elements and the pattern of the data messages; adding the CPS-G model sub-graph to the CPS-G model to form the CPS-G dataset; and storing the CPS-G dataset including the CPS-G model sub-graph in a data store for further processing by streaming computation and machine learning algorithms.

The embodiment of the invention provides a streaming data processing device, method, equipment and system in a relational database, and relates to the technical field of data processing.The device comprises an optimizer used for receiving input SQL statements for defining streaming calculation in a database, converting the SQL statements into a streaming execution plan capable of being executed by an executor, and outputting the streaming execution plan to the executor; the SQL comprises an upstream database table of the incremental data, processing calculation implemented on the incremental data and a downstream database table; the memory is used for persistently storing related meta-information of stream calculation in the library, and the related meta-information comprises a stream execution plan and progress information of the stream execution plan; and the executor is used for extracting incremental data, carrying out processing calculation on the incremental data and reflecting a processing calculation result into the downstream database table in the process of repeatedly executing the flow execution plan. According to the scheme, the flow calculation process of the incremental data is realized in the same database, the resource overhead is reduced, and the real-time performance is improved.

The invention discloses a GPU parallel solving method and system for linear equations of a power system, and relates to the field of power system automation. In order to solve the defects that an existing GPU parallel solving method for the system of linear equations of the power system has an overlapping calculation problem and is low in calculation result accuracy and low in parallel load flow calculation execution efficiency, an initial node is defined in all nodes of the power system, other nodes are divided into different hierarchies according to the distance between the other nodes and the initial node, and the calculation efficiency is improved. Establishing a sparse decomposition thread tree; child nodes with the same father node in the same hierarchy are called as brother nodes, and a brother node set is established; threads are distributed in the GPU according to the brother node set; and sparse matrix parallel solving calculation is carried out on each node of the same level. The method is mainly used for optimizing the GPU parallel solution of the system of linear equations of the power system.

The invention provides a video stream data processing system and method, and relates to the technical field of edge computing, cloud computing and stream-oriented computing. The video stream data processing system comprises an infrastructure layer used for constructing and managing an edge computing node on which a lightweight container cluster is deployed; the stream computing engine layer is used for calling the container cluster on the edge computing node according to a to-be-processed task so as to process video stream data in parallel; and the task co-processing layer is used for adjusting the distribution of the to-be-processed task on the container cluster according to the resource utilization rate of the edge computing node when the edge computing node processes the video stream data.

The invention discloses a cloud-edge cooperative computing method and system for low-altitude unmanned aerial vehicle supervision space-time stream data, a terminal and a storage medium, and the method comprises the steps: building a cloud-edge integrated cooperative mode of an unmanned aerial vehicle supervision task based on a cloud-edge network structure, and converting a cloud-edge computing resource topological relation into a graph network structure; based on node sequence features of a cloud edge integrated cooperation mode, constructing a cloud edge cooperation allocation strategy of a cross-edge node global task adaptive to a graph network structure, and realizing cross-edge node global task cloud edge cooperation allocation; and establishing a cloud-side resource scheduling adaptive optimization mechanism considering global storage, calculation and transmission low delay cost, and realizing cloud-side resource scheduling adaptive optimization of a space-time flow calculation task on a global level. According to the method, a cloud edge collaborative optimization scheduling mechanism for large-scale unmanned aerial vehicle supervision space-time flow data calculation in a three-dimensional space is established, so that the efficient scheduling requirement of real-time calculation of unmanned aerial vehicle supervision tasks in a cloud edge collaborative mode is met.