131 results about "Streaming computation" patented technology

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 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 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 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 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 provides a double-engine performance automatic test system and method, and the system comprises a pressure test management and control platform which is used for receiving a user instruction, arranging a test task based on test assets, and generating a task scheduling instruction; the pressure measurement execution engine is used for receiving the task scheduling instruction, applying pressure to a tested system, injecting a tracking identifier for a pressure measurement request in the pressure applying process, and generating a first performance data flow containing the tracking identifier and a corresponding transaction performance index; the collector cluster is deployed in the tested system and is used for collecting internal running state data of the tested system in real time and generating a second performance data flow containing a system observation index; and the streaming computation engine is used for receiving the first performance data stream and the second performance data stream in parallel and carrying out real-time association and computational analysis on performance indexes in the two paths of data streams based on the tracking identifier so as to realize cross-layer performance analysis and root cause positioning.

The invention discloses a power distribution network load flow calculation method based on graph synchronization parallel and forward-backward substitution, and the method comprises the steps: building a graph model in a graph database based on a power network, and enabling the graph model to be divided into a plurality of sub-graph models; setting an initial state for nodes in the sub-graph model, traversing the sub-graph model in a graph node parallel computing mode, and recording a node layer number and a node path of the sub-graph model; and in combination with the sub-graph model, completing graph calculation through forward tracking current calculation and backward tracking voltage calculation in sequence by utilizing a forward-backward substitution method, and judging whether an absolute value of a voltage difference value of nodes in the sub-graph model is smaller than the maximum iteration convergence precision or not. According to the method provided by the invention, the graph model is established, and graph calculation is carried out in a graph parallel mode in combination with a forward-backward substitution method, so that the calculation speed can be obviously improved.

The embodiment of the invention discloses a video coding method and device, a video decoding method and device, a code stream, computer equipment and a storage medium. The video coding method comprises the following steps: receiving a polygon mesh, wherein the polygon mesh comprises a plurality of vertexes; predicting a vertex by using an original vertex in the plurality of vertexes; determining a residual error between the predicted vertex and the original vertex; determining a residual context from the plurality of residual contexts; encoding the residual using the residual context, and determining a plurality of indices identifying the residual context, where (a) when the residual is associated with a geometric position of the predicted vertex, the plurality of indices includes three indices; (b) when the residual is associated with an attribute of the predicted vertex, the plurality of indices comprises two indices.

The invention relates to a thick and thin grid fast interpolation technology for wall turbulence calculation, and aims to solve the problems of large calculation amount and complex implementation process of an existing interpolation method. Half of the minimum grid spacing of the fine grids is used as a judgment standard, and interpolation modes are intelligently switched. For each fine grid point, when the distance between the fine grid point and the nearest coarse grid point is smaller than the standard, the physical value of the nearest coarse grid point is directly given to the fine grid point; otherwise, if the distance is greater than or equal to the standard, interpolation is carried out by adopting an inverse distance weighted average method. According to the method, half of the minimum fine grid scale is defined to serve as a standard for judging whether the fine grid point is directly assigned by the physical quantity on the nearest coarse grid amplitude point or not, and on the premise that it is ensured that physical information is not distorted, the complex calculation problem is simplified. The tedious calculation process in a traditional method is avoided, the algorithm complexity is effectively reduced, and the interpolation calculation speed and efficiency are remarkably improved.

The invention discloses a distributed stream computing method, device and equipment, a medium and a product. The method is executed by a distributed node cluster, and comprises the following steps: if a configuration node detects that a formula configuration condition is met, determining configuration information corresponding to a configuration formula, and broadcasting the configuration information to the distributed node cluster; the routing node obtains routing index data sent by the data access node and sends the routing index data to the corresponding computing node; and the computing node determines a target formula executed by a target according to the configuration information sent by the broadcast, and executes a computing operation according to the routing index data and the target formula when detecting that a computing condition is met according to the routing index data and the target formula sent by the routing node. According to the invention, the calculation task is carried out through interaction among the configuration node, the data access node, the calculation node and the routing node, so that the real-time performance, the configurability and the resource utilization efficiency of the multi-index complex formula calculation task in the Internet of Things environment can be improved.

The invention discloses a load flow calculation method, system and device based on quantum singular value transformation and a medium, and belongs to the technical field of load flow calculation, and the method comprises the steps: obtaining the initial power parameter of each node of a power system and the given power of each node, and determining the power unbalance amount; constructing a node admittance matrix according to the initial power parameters, constructing a correction equation of Newton method load flow calculation, and converting the correction equation into a linear equation set for expression; coding the coefficient matrix into a unitary matrix by using block coding, and executing polynomial transformation based on quantum singular value transformation to construct a quantum circuit representing an inverse matrix of the unitary matrix; encoding the power unbalance amount into a quantum initial state, and enabling a quantum circuit to act on the quantum initial state and performing measurement; and determining the correction of the node voltage based on the measurement result, judging whether to iterate based on the convergence condition, and outputting a load flow calculation result. By utilizing the characteristics of quantum parallelism and quantum singular value transformation, the time consumption of large-scale power system load flow calculation is remarkably reduced.

The invention discloses a data stream computing system and method. The system comprises a plurality of routing nodes, a plurality of computing nodes and a data transmission link, the computing node is connected with at least one routing node through a data transmission link; the routing node comprises a plurality of flow counters and a routing decision unit; the traffic counter is used for acquiring the data traffic of the transmission port according to a preset time interval and transmitting the data traffic to the routing decision unit; the routing decision unit is used for performing flow state analysis on the transmission port based on the data flow; and if it is determined that the transmission port in the congestion state exists, selecting a transmission port in an idle state and a to-be-transferred data stream based on a preset routing adjustment strategy, and performing data transmission on the to-be-transferred data stream through the selected transmission port. According to the invention, a self-adaptive rerouting mechanism based on the real-time data flow state and the field information can be realized, and the data transmission efficiency and the link load balancing capability in a high-concurrency scene are improved.

The invention discloses a cross-domain anomaly detection and anti-cheating method and system based on multi-modal behavior analysis, and relates to the technical field of artificial intelligence and network security. The method comprises the following steps: under a strict privacy protection framework, synchronously acquiring and registering user operation behaviors, network communication and external equipment data, and clearly excluding audio, video and biological characteristic data; time sequence, space, semantics and collaborative behavior characteristics are extracted based on a multi-level deep learning network, and a dynamically updated personalized behavior baseline is constructed; through a shared encoder obtained through adversarial training, different field behavior characteristics are mapped to a unified hidden space for similarity calculation, and cross-field collaborative cheating detection is realized; a risk level is output by adopting a multi-stage discrimination mechanism fusing real-time stream calculation, wavelet analysis and integrated learning; and a closed-loop anti-cheating system is formed in combination with distributed Byzantine fault-tolerant verification and block chain evidence solidification. According to the method, the cross-platform distributed cheating detection problem is effectively solved, and the compliance of a data processing process and the legal potency of evidence are ensured while the detection precision is improved.

The invention provides a distributed computing system, method and device based on a hybrid cluster and a storage medium, the distributed computing system based on the hybrid cluster comprises a cluster manager used for distributing a stream computing topology configuration file to a task manager; the cluster coordinator is used for collecting the states of the task process and the task manager, sending the state of the task process to the task manager and sending the state of the task manager to the cluster manager; the task manager is used for creating a task process based on the flow calculation topology configuration file, establishing a flow calculation topology based on the task process and managing the task process based on the state of the task process; wherein the task process serves as a node of the stream computing topology, and the task process is used for executing distributed stream computing and runs in an independent process mode. According to the method, the task process is limited to run in an independent process mode, resource scrambling and logic pollution caused by multi-service task sharing processes are avoided, and the stability of the whole computing cluster is improved.

This invention discloses a cluster resource scheduling method, system, terminal, and storage medium. Based on mainstream AI computing platforms, it innovatively designs a resource manager, task lifecycle manager, and scheduler. When the job to be processed is an elastic training job, the allocated computing resources for the elastic training job are determined by the predicted idle resources of the cluster within a first preset time period and the required computing resources of the elastic training job. Based on the real-time idle resources of the cluster and the allocated computing resources, a job node is allocated to the job, and the job node is invoked to execute the elastic training job within a second preset time period. Based on the predicted idle resources of the cluster in the next first preset time period, the allocated computing resources for the elastic training job are adjusted. Based on the real-time idle resources of the cluster and the adjusted allocated computing resources, a job node is reallocated to the job, and the job node is invoked to continue executing the elastic training job in the next second preset time period, effectively improving cluster utilization.

The invention provides a legacy system non-intrusive mimicry interaction method and system based on screen pixel recognition, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting screen pixel flow data, extracting interaction element features, constructing an interface element knowledge graph, and storing hot area coordinates; pixel streams are continuously collected, differential values are calculated, dynamic matching is carried out, and the hot areas are updated; generating an operation instruction according to the hot area coordinate and the time sequence parameter; and adjusting the control parameters in real time based on resource occupation and outputting an optimal operation sequence. According to the method, a legacy system source code does not need to be modified, stable and efficient anthropomorphic interaction can be realized, the automation efficiency is improved, and the system resource consumption is reduced.