167 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 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.

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 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 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 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.

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 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 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.

The invention discloses a software development application data processing method based on an AI large model, and relates to the technical field of data processing and streaming computation.According to the software development application data processing method based on the AI large model, spatial and temporal features are extracted by means of a pre-trained Transform model, time dependence and spatial association in data streams can be captured more accurately in combination with a self-attention mechanism, and the data processing efficiency is improved. Therefore, the accuracy and real-time performance of anomaly detection are improved, and misjudgment or omission caused by environmental interference or equipment faults is reduced. Secondly, according to the self-adaptive threshold mechanism, by dynamically updating a threshold and adjusting an abnormal score based on statistics in a window, the system can flexibly adapt to the change of data distribution, and the problem of response lag occurring in a high-speed data stream in a traditional fixed threshold method is avoided; besides, a reinforcement learning strategy is introduced, the error, the time delay and the service constraint are comprehensively reconstructed through a state space and a reward function, and a targeted repair action is generated, so that the self-adaptive capability of the system in a complex scene is enhanced, and the manual intervention requirement is reduced.

This invention relates to the field of data transmission, and particularly to a method and server for storing IoT data in charging stations. The method involves receiving IoT data sent by the charging station through a message middleware and transmitting it to a stream computing module. The stream computing module preprocesses the IoT data to ensure it conforms to preset data specifications. For the IoT data, a metadata query is performed on the database to determine if a target data table associated with the IoT data exists. If the target data table does not exist, a new data table is created in the data lake and the data is written. If the target data table exists, the metadata is used to determine if the structure of the target database table is compatible with the IoT data. If compatible, the data is written to the target data table in the data lake; otherwise, the data table structure is modified before data is written. The database provides metadata storage for the data lake. This invention ensures the validity of data writing and achieves automated management of the storage of IoT data from charging stations in the data lake.

The invention relates to the field of multi-modal data analysis, in particular to an emergency team cooperation dynamic resource scheduling method and system based on multi-modal data driving, and the method comprises the steps: collecting patient physiological time sequence data, team behavior interaction data and resource state data in an emergency scene, and forming a heterogeneous data stream; according to the heterogeneous data flow, a topological dependency relationship of a team cooperation link is deconstructed; calculating a collaborative robustness index based on the topological dependency relationship and the heterogeneous data flow; when the collaborative robustness index is lower than a preset index threshold value, executing a conflict resolution decision of multi-modal constraint driving; and monitoring cooperation efficiency change data after resource allocation, and correcting the weight coefficient of the cooperation robustness index and a preset index threshold in combination with a preset optimization target. Key node overload risk probability calculation and cooperative path resource assurance degree evaluation are fused, quantitative system anti-risk capability evaluation is provided, and compared with traditional single threshold alarm, composite collapse risks are recognized earlier.

The application relates to data processing technology in the field of public services and discloses a multi-system dynamic scheduling processing method for real-time streaming data of public services, which comprises the following steps: collecting data from different data sources by a distributed data collection framework through a data collection and preprocessing layer, and performing preliminary format unification and standardization processing; the stream-batch integrated processing of a real-time stream computing engine and a batch processing system in a processing and analysis layer can reduce data redundancy and improve analysis timeliness; the dynamic scheduling in a multi-system scheduling and cooperation layer can adapt to business priority changes to provide scheduling flexibility; and the application and service layer provides an intuitive and convenient service interface for users, assists the users in scientific decision-making and resource optimization configuration, and promotes cross-department and cross-field data fusion innovation. When complex system load and variable business requirements are responded to, the method can improve the real-time performance, reliability and flexibility of data processing.

Techniques for generating audio streams from modified audio streams and information about the modifications to the audio streams are provided. In an example method, a computing system joins a first client device to a video conference, to which a number of client devices are connected. The computing system receives, from the first client device, a modified first audio stream including information about the modification to the first audio stream. The computing system generates a second audio stream using the modified first audio stream and the information about the modification to the first audio stream. The computing system outputs the second audio stream.

The application relates to a batch power flow solving method and device based on a CPU and GPU hybrid architecture, computer equipment, a storage medium and a computer program product. The method comprises the following steps: obtaining a plurality of power flow calculation example groups based on a CPU; performing parallel power flow calculation on the plurality of power flow calculation example groups based on a GPU to obtain a first power flow calculation result set; judging whether there is an out-of-limit power flow calculation example in the plurality of power flow calculation example groups according to the first power flow calculation result set, wherein the out-of-limit power flow calculation example comprises an out-of-limit node; in the case that there is an out-of-limit power flow calculation example in the power flow calculation example group, adjusting the node type of the out-of-limit node in the out-of-limit power flow calculation example based on the CPU, performing power flow calculation on the adjusted out-of-limit power flow calculation example to obtain a second power flow calculation result set, updating the first power flow calculation result set according to the second power flow calculation result set based on the CPU, and obtaining a target power flow calculation result set. The method can improve the power flow calculation efficiency.

The invention relates to the technical field of data driving, and particularly provides a hierarchical data driving method, system and device for digital twin archives and a medium, and the method comprises the steps: dividing physical entity data into three processing levels according to real-time requirements, the first level being the highest in real-time performance, and the third level being the lowest in real-time performance; establishing a parallel and resource-isolated data processing pipeline for each level; first-level data is directly written into a memory database, and a digital twin engine actively queries through a high-frequency timer to drive a virtual model to realize millisecond-level synchronization; performing state aggregation on the second-level data through stream calculation, and pushing the second-level data to an engine to realize second-level state updating; and persistently storing the third-level data in various databases, and accessing the third-level data by an engine when receiving a query request to realize post analysis and backtracking. According to the method, the hierarchical parallel processing assembly line is established, so that accurate optimization of data driving is realized.

The invention relates to the technical field of equipment operation and maintenance management, in particular to a light operation and maintenance method and system for enabling intelligent equipment management, and the method comprises a data collection and perception layer, an infrastructure and platform support layer, an intelligent center and capability layer, an application service layer and a presentation and interaction layer. The data acquisition unit is responsible for acquisition and preliminary access of equipment data, and the infrastructure, namely the service unit, is used for providing basic resources; the platform-as-a-service unit is used for providing an enabling service and comprises a big data processing platform, a stream computing engine, an AI model training and deployment platform and a database service; the intelligent center and capability layer comprises a digital twinning engine unit, an AI analysis engine unit, a knowledge graph engine unit and a rule engine unit, the knowledge graph engine unit is used for carrying out structured and semantic association on knowledge in the operation and maintenance field to form a knowledge graph, and the rule engine unit is used for processing a deterministic task based on predefined logic to form a knowledge graph; the process automation is realized.

The invention discloses a big data integration task management system based on cloud edge collaboration, and the system comprises the following modules: a data feature template library construction module which is used for generating a data feature template library and forming a grassland nutrition degree score set; the goat foraging optimization algorithm candidate task scheduling graph population initialization module is used for dividing a hillside memory area, a valley bottom memory area and a ridge memory area according to node aggregation degrees; the nerve tangent kernel method performance prediction module is used for fusing into a nerve tangent kernel method performance prediction vector; the fitness function construction and optimization module is used for executing global exploration and local development of a goat foraging optimization algorithm population and outputting an iterative optimization result; and executing a directed acyclic graph generation and deployment module for completing initial deployment of a multi-source heterogeneous data source integration task. According to the method, the resource utilization efficiency is maximized, seamless switching among the three mainstream computing engines is realized, and the deployment scheme is elastically adjusted according to the resource load.

The invention provides a data processing method and device, a rule configuration method and device, equipment, a medium and a product, and relates to the field of user interface design, the technical field of streaming data processing and the technical field of digital marketing. The data processing method comprises the following steps: calling a real-time stream calculation engine to calculate a plurality of order transaction data based on a pre-configured conversion rule in response to receiving a plurality of order transaction data for a plurality of to-be-processed orders issued by an order system, and generating conversion data, the conversion data represents a conversion value for converting the transaction resources of the plurality of orders to be processed into special resource consumption, and the conversion rule represents a conversion mode for converting the transaction resources of the orders into special resource consumption.

This invention discloses a fully embedded linear power flow calculation method and apparatus based on adaptive piecewise segmentation. The method includes: processing the system state... S max , S min The power flow solution at point 0 is used as the Germ solution to construct the FFHE-LPF model, update the correction coefficients, and calculate the maximum power mismatch value. When the error first exceeds 0.5%, the model is then used to calculate the maximum power mismatch value. max Record the current system status S 1 and S 2; Change the current system status S 1. S The power flow solutions at points 2 are used as Germ solutions to construct new FFHE-LPF models. The upper and lower bounds of the convergence range of these models are calculated, and the corresponding segment lengths are also calculated. l b1 and l b2 Take the segment length l c =min{ l b1 , l b2} as the basis for partitioning; based on segment length l c Divide the fluctuation range of new energy output into equal parts G The system constructs FFHE-LPF models for different sub-intervals, determines the sub-interval to which a power source belongs based on its current output, and updates the correction coefficients to obtain the power flow solution. This solution is then used to identify line overload or voltage exceedance risks in the power system. The device includes a processor and a memory.

The invention discloses a regional output limit regulation and control method, device and equipment. The method comprises the following steps: acquiring the number of units and the number of output modes of various units in a target region; determining an initial number of output combinations corresponding to the target area according to the number of units and the number of output modes; if the initial number of the output combinations exceeds the calculation threshold value, the output mode number is reduced according to the unit capacity of the unit, and multiple unit output combinations are determined; performing load flow calculation on the target area according to each unit output combination, determining a target unit output combination in a load flow convergence state, performing transient simulation, and determining a stability control result of each target unit output combination; and according to each stability control result, determining an output limit corresponding to the target area and performing regulation and control. Therefore, the unit capacity reduction is combined with the load flow calculation and the transient simulation, the unit output combination is effectively and quickly screened, the precise quantification of the output capacity is further realized, and a precise setting value is provided.