222 results about "Streaming computation" patented technology

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 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 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 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 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.

The application discloses a kind of analytic expression probabilistic power flow calculation method, device and storage medium.The application is determined after the mapping relationship between the injection power of node and the operating state of power system by linear power flow model, using first-order polynomial fitting correction method to revise the operating state of power system, make the operating state of the power system after revision be closer to real operating state, according to the mapping relationship between the injection power of node and the operating state of power system after revision, update linear power flow model, carry out analytic expression probabilistic power flow calculation using updated linear power flow model, to effectively improve the accuracy of probabilistic power flow calculation.

The invention discloses an operation mode optimization method and device based on an optimal power flow algorithm. The method comprises the following steps: sending a basic operation mode to an analysis module, and generating operation modes in corresponding scenes according to the basic mode through different scene boundary conditions; sequentially verifying whether the operation modes converge or not through a Newton-Raphson method; constructing an AC power flow original model, and obtaining a linear form of a power flow equation to form a linearized AC power flow algorithm engine; and establishing a four-step priority adjustment rule based on the linearized alternating current power flow algorithm engine, and adjusting non-convergent operation modes step by step until all the operation modes converge. According to the invention, through a high-precision high-convergence load flow calculation engine, a load flow non-convergence automatic adjustment algorithm is constructed, and the problem of non-convergence is accurately positioned; forming an optimal power flow algorithm according to different scene boundaries; whether the power flow converges or not is verified through Newton-Raphson method circulation, and finally convergent operation modes in different scenes are generated, so that the convergence stability is enhanced.

The invention discloses an AC / DC system load flow calculation method and device based on trajectory uniformity, and belongs to the technical field of power system calculation and analysis. The method comprises the following steps: inputting topological parameters of an AC / DC system, node power injection data and boundary constraints of variables; according to the topological parameters and node power injection data, an AC / DC system feasible power flow solution model containing voltage constraints is constructed, and the voltage constraints are converted into equality constraints through slack variables; constructing a QGS based on a feasible power flow solution model, wherein a stable equilibrium point of the QGS corresponds to a solution or a least square solution of a power flow equation; tracking a QGS system track by adopting a numerical integration method, and carrying out integration from an initial point until a convergence condition is met; and judging whether the QGS system converges to a feasible power flow solution or a least square solution according to an integral result, and outputting a corresponding result. According to the method, any initial point in the power flow solution neighborhood can be reliably converged to the power flow solution, and the robustness and practicability of power flow calculation are remarkably improved.

The invention discloses a multi-level collaborative reconfiguration method based on fine-grained state transition, electronic equipment and a storage medium, and belongs to the technical field of stream computing platform design. In order to avoid unnecessary reconfiguration overhead, the method comprises the following steps of: constructing a formalized model of a multi-stage collaborative reconfiguration optimization problem based on a built-in reconfiguration mechanism of a stream computing platform by combining characteristics of an edge computing environment and overhead of three levels of partitioning, scheduling and elasticity in a reconfiguration process; on the basis of the constructed formal model of the multi-stage collaborative reconfiguration optimization problem, constructing a granularity adjustment method for fine granularity state transition, and independently solving the selection of the optimal transition state granularity in the multi-stage collaborative reconfiguration optimization problem; and designing a multi-stage collaborative reconfiguration method based on a second-order heuristic algorithm, obtaining a multi-stage collaborative reconfiguration solution integrating three levels of partitioning, scheduling and elasticity, and obtaining an approximate optimal solution in polynomial time. According to the invention, the number of reconfiguration times can be effectively reduced, and unnecessary reconfiguration overhead is avoided.

The invention provides a workflow calculation task high-dynamic scheduling method and system. The method comprises the steps that a task scheduling event is monitored; under the condition that the task scheduling event is triggered, a simulation optimization task queue to be scheduled is obtained according to the triggering condition of the triggered task scheduling event, and the simulation optimization task queue comprises a plurality of workflows and the task requirement of each workflow; obtaining a plurality of currently available servers and resource characteristics of each server; according to the task requirement of each workflow and the resource characteristics of each server, a scheduling optimization problem is constructed, an optimization algorithm is selected, and the scheduling optimization problem comprises a constraint condition and an iteration target; according to the scheduling optimization problem, obtaining a coding mode and a decoding mode for an optimization algorithm; and determining a scheduling mode of the simulation optimization task queue to be scheduled by adopting an optimization algorithm, a coding mode and a decoding mode by taking a constraint condition and an iteration target of the scheduling optimization problem as iteration conditions.

The invention relates to the technical field of power system fault monitoring and diagnosis, and discloses a power distribution network fault accurate positioning visualization system based on a real-time flow calculation technology, and the system comprises a data collection and preprocessing module which employs a real-time flow calculation framework to extract multi-dimensional fault features; constructing a multiplexing attribute network containing explicit and implicit association by using a multiplexing graph construction module; carrying out differential feature aggregation on different sub-graphs by utilizing a type perception feature aggregation module; and through an adaptive attention fault judgment module, attention modes are dynamically switched according to feature distribution, a sparse index set is generated, and accurate positioning of fault nodes is realized. Implicit dynamic association between equipment can be mined by constructing a multiplexing attribute network and an adaptive attention mechanism, and the accuracy of the fault nodes is improved. And the problems of data processing lag and implicit correlation modeling deficiency in a traditional method are avoided.