524 results about "Event stream" patented technology

The invention relates to a military training-oriented dynamic multi-modal knowledge graph retrieval method, which comprises the following steps of: acquiring a multi-source data set to generate a unified original event stream; based on the original event flow, preliminarily constructing a knowledge graph by adopting a Bayesian prior-based tetrad model, locally embedding and updating through a military training event incremental graph neural network, and dynamically re-estimating the confidence of nodes and edges through a Bayesian evidence propagation algorithm; a pre-training language model in the military field is adopted for analysis and semantic coding, a query semantic vector is generated, a retrieval module is constructed, and a retrieval result is output based on the knowledge graph; and constructing a military retrieval strategy optimization network based on a reinforcement learning framework, dynamically adjusting a retrieval strategy, writing back user feedback data, and optimizing node embedding and retrieval strategy parameters. According to the method, multi-source heterogeneous data can be integrated, a dynamic credible graph is constructed, retrieval accuracy, timeliness and self-adaption are improved, and military training decisions are supported.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

The invention provides a user behavior tracking and portrait generation system, which is characterized in that multi-source original behavior data is acquired through a data acquisition module, and a standard behavior event stream is generated through cleaning and standardization; the behavior modeling module is used for carrying out cross-event association and self-defined time window combination modeling on a standard event flow based on a configurable rule engine to generate a complex event flow; the real-time processing and calculation module performs real-time aggregation calculation on the complex event stream, generates a real-time behavior index, a trigger signal and an incremental portrait snapshot in combination with a preset rule, and pushes a behavior trigger signal to a downstream service system; the user portrait management module dynamically updates user tag weights and values according to the real-time indexes, and generates a target user portrait tag library and a lightweight tag change event stream; the data storage and query module stores data of each link; and the visual configuration and operation and maintenance module issues a configuration instruction through a visual interface, and monitors full-link operation and task scheduling. And real-time accurate portrait construction and instant service response are realized.

The invention discloses a cross-channel marketing automation method and system based on real-time behavior triggering. The method comprises the following steps: capturing a user behavior event flow in real time through a front-end acquisition unit; performing real-time mode matching on the event stream by utilizing a complex event processing engine, and generating a marketing trigger signal when a preset rule is matched; dynamically selecting an optimal channel for the user based on a real-time scoring model in response to the signal; the marketing content is sent to the selected channel through the unified execution module; and finally, tracking user feedback and updating model parameters and user preferences in real time to form closed-loop optimization. The corresponding system comprises a front-end data acquisition module, a streaming event processing module, a real-time intelligent decision-making module, a multi-channel execution module and a closed-loop optimization module which are used for implementing the steps. According to the method, millisecond-level intention perception, cost and experience optimal channel intelligent decision and data-driven adaptive optimization closed loop are realized, and the problems of delayed response, extensive channel and lack of self-adaption of traditional marketing are solved.

The invention relates to the technical field of multi-terminal collaborative management, in particular to a multi-terminal collaborative intelligent office internal management system, which comprises a terminal behavior acquisition unit, a user behavior analysis unit, a state synchronization protocol unit and a terminal collaborative unit, a terminal behavior acquisition unit captures an operation behavior to generate an event stream containing an operation type and a sensor state; a user behavior analysis unit identifies a non-editing action sequence, generates an implicit intention abandoning label and triggers a conflict signal when detecting a multi-terminal space-time overlapping conflict; a state synchronization protocol unit dynamically broadcasts an authority instruction according to the label; according to the system, the problems that intention recognition is fuzzy, authority synchronization lags behind and conflict processing is missing are solved, and the office efficiency and the data security 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 discloses a smart park alarm method and system based on an intelligent gateway, and the method comprises the following steps: accessing a heterogeneous terminal through a multi-protocol southbound interface, generating a standardized event set, and building a spatial topology table; performing quality evaluation on the standardized event set to form a sequential cleaning event stream; target detection is carried out based on the sequential cleaning event flow, and a semantic event flow is output; performing space-time matching on the semantic event flow to obtain a fusion event set; according to a preset grading standard, a grading warning set is generated, and a linkage instruction set is generated; scheduling and executing the execution end according to the linkage instruction set to form a processing state feedback; integrating the grading warning set and the processing state feedback into an evidence packet, and reporting the evidence packet in parallel through the main and standby reporting links; and archiving and indexing the evidence packet and the reported result, generating an auditable record, and finishing synchronous updating with an upper platform. According to the invention, the smart park alarm method and system are realized.

The invention relates to the cross technical field of AI multi-modal analysis and dialect processing, in particular to a dialect content crawling and auditing system and method based on AI analysis, a simulation terminal generates a real person behavior entropy interval event stream through a Markov chain and reinforcement learning hybrid model, a dialect exclusive operation library and a differentiated interest strategy are integrated, and a real person behavior entropy interval event stream is generated through a real person behavior entropy interval event stream; anti-crawling is avoided in combination with a proxy IP pool and a Bezier curve trajectory, a search unit realizes directional crawling of dialect keywords through a hot updateable script, a blind patrol mode locates high-risk content depending on a triple knowledge graph and risk prediction, whole-course block chain evidence storage is performed, and a collaborative analysis unit analyzes dialect audio and video features in a layered architecture. The multi-modal large model is combined with the professional small model, the audio spectrum, the text translation and the video picture are fused to realize cross-modal violation detection, the application service unit alarms violation content in real time, a supervision report containing a violation distribution thermodynamic diagram is generated, and the accuracy and traceability of dialect violation content crawling and auditing are improved.

The invention discloses a robot dynamic path planning method and system fused with lightweight LLM. The robot dynamic path planning method comprises the following steps of dynamically mapping a grid map, constructing a semantic analysis model, performing global path planning by adopting an improved A * algorithm, and performing local path optimization by adopting an improved DWA algorithm. Specifically, according to the method, the high-frequency capture and real-time updating of the dynamic obstacle are realized by introducing the fusion of the event flow of the event camera and the depth information of the laser radar; dynamic matching of semantic instructions and planning parameters is achieved by introducing lightweight LLM, meanwhile, sight distance penetration rate optimization path connectivity is fused in global planning, local obstacle avoidance adopts a semantic adaptive weight adjustment mechanism, the high-precision perception advantage of multi-sensor fusion is guaranteed, task adaptability and scene pertinence are improved, and the method is suitable for large-scale popularization and application. And the complex requirements of logistics scenes are met.

The invention relates to the technical field of intelligent prediction, and discloses an emergency resource reserve intelligent prediction system and method based on a graph neural network, and the system comprises a space-time diagram construction module which collects port, berth, channel segment, emergency warehouse and ship information, passing time window information and emergency information, and constructs an event flow heterogeneous space-time diagram; the backbone screening module is used for screening backbone sub-graphs and setting a relation feature conversion rule and a time feature representation rule; the graph memory updating module is used for updating node and edge features; the multi-task prediction module is used for outputting quantile demand prediction, reachability scores, transportation timeliness and safety stock threshold values; the consistency constraint training module is used for executing consistency constraint training; the inventory allocation decision-making module is used for generating allocation distribution quantity and outputting predicted arrival time; and the berth time window recharging module is used for generating a berth operation time window and recharging the actual arrival time. According to the invention, emergency resource demand prediction, inventory threshold calculation and allocation and berth operation linkage decision are realized.

The invention discloses an event camera target tracking method and device, a storage medium and electronic equipment, and relates to the technical field of information. The method comprises the following steps: acquiring an original event stream of a moving target collected by an event camera; based on a space-time neighborhood consistency strategy and / or a polarity time difference constraint strategy, performing noise reduction on the original event stream to obtain a noise-reduced event stream; dynamically adjusting an event aggregation time window based on multi-dimensional statistical characteristics, and aggregating the de-noised event streams based on the adjusted event aggregation time window to obtain aggregated event streams; performing motion compensation on the aggregated event stream to obtain an event stream after motion compensation; and predicting trajectory information of the moving target according to the event stream after motion compensation and a preset motion trajectory prediction model. The method can avoid the loss of space-time information, and accurately and stably extracts the target trajectory.

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 relates to the technical field of data processing, and discloses an advertisement putting state real-time monitoring method and system. The method comprises the following steps: collecting an advertisement putting event stream, and extracting an instantaneous index and a fluctuation rate; counting historical abnormal conduction time delay and intensity, and constructing a conduction matrix; when single-point abnormity is detected, downstream abnormity is predicted according to the conduction matrix, the deviation degree is calculated, and an abnormal conduction chain is searched; conducting chain features are extracted to form abnormal fingerprints, fingerprint database similarity is matched, high similarity is directly confirmed, and low similarity is injected into probe flow verification. By constructing the real-time monitoring method based on the abnormal conduction matrix, the problems that in the prior art, the conduction relation between indexes cannot be quantified, the downstream abnormal moment cannot be predicted, the false report and missing report rate is high, and the abnormal confirmation response is slow are solved, and the accuracy, the real-time performance and the intelligent level of advertisement putting abnormal detection are improved.

A computer-implemented system (100) for real-time detection of supply chain disruptions and forecasting of the financial impact in a computer-driven ERP environment, wherein the system (100) comprises: a data acquisition interface (1) configured to continuously capture structured and unstructured operational data from one or more ERP modules, supply chain execution systems, and enterprise event streams; a data management and harmonization engine (2) configured to validate, normalize, deduplicate and semantically map the captured data into a unified, time-aligned canonical enterprise schema; a disturbance signal fusion and anomaly detection engine (3) configured to fuse signals from multiple sources and detect disturbance events using one or more machine learning models to output a disturbance value, event class and a list of affected nodes; a supply chain dependency graph and a digital twin builder (4) configured to create and update a dynamic dependency graph representing suppliers, facilities, transportation routes, SKUs, orders, contracts and lead times, and propagate disruption effects across the graph; a financial impact forecasting engine (5) configured to estimate the real-time and forward-looking effects of the disruption on one or more financial measures, including sales, margin, working capital, cash flow, service level penalties and inventory holding costs, and furthermore the effects on the cost of goods sold (COGS) and / or the landed costs using a Kl-based forecast with a range of uncertainty; a module for coordinating corrective actions and automating workflows (6) configured to generate ranked corrective actions and initiate ERP workflow steps, including reordering, reassignment, alternative sources of supply, production rescheduling and logistics diversions, wherein the ranked corrective actions include recommendations to resolve the disruption by using available stock, reassigning low-priority shipments or diverting shipments that can be delivered later within a lead time window; an explainability, audit trail and compliance logging module (7) configured to record model inputs, feature assignments, decision justifications, scenario assumptions and action results as an immutable audit trail; and a visualization, alerting and collaboration interface (8) configured to output real-time alerts, dashboards and scenario comparisons to authorized users and downstream systems via APIs and role-based access controls, wherein the interface (8) generates a quick report view that displays early disruption signals and impact on manufacturing costs, delivers notifications to mobile devices, triggers a special urgent notification if the disruption affects a high-priority trading partner, and accepts user responses on mobile devices that automatically and without delay trigger corrective actions or updates in the ERP system.

The invention discloses a ward dynamic rule decision-making method based on the Internet of Things and multiple agents, and the method comprises the following steps: collecting related data of a ward, and carrying out the data standardization, and obtaining a standardized event stream; constructing three types of agent agents, establishing an intention and instruction channel, and forming an inter-agent communication channel; semantic fusion is executed based on the inter-agent communication channel, and a disposal trigger condition is generated; arranging and forming a rule template set according to a processing trigger condition and a specified field; based on the rule template set and a preset security constraint, a trinuclear-GR (1) reaction formula is called for synthesis, and a dynamic rule set is generated; executing mutual exclusion and resource constraint check to form a disposal scheme; performing formalized security verification on the disposal scheme to obtain a verified disposal decision; and translating the verified disposal decision into an equipment protocol instruction, issuing the equipment protocol instruction, and recording a trigger event and a compliance log. According to the invention, ward dynamic rule decision is realized.

In some implementations, the techniques described herein relate to a method including: receiving a stream of security events from a plurality of event generators; generating security signals based on the security events, the security signals including a subset of the security events; aggregating and correlating the security signals using a signal source; evaluating the security signals and determining risk levels using a plurality of independent processors; determining that action is required based on a combined output of the security signals and determined risk levels; and executing a security response responsive to the action.

The invention relates to the technical field of network security and auditing, and discloses an auditing workflow of an auditing network security agent and a construction method of the auditing workflow. The method comprises the following steps: receiving a security task request, generating a unique identifier and creating a session context containing multiple types of policy rules; analyzing the task, and generating a tool calling sequence through tool routing matching; performing risk level evaluation on each tool call in the sequence, and triggering an approval process when a threshold value is exceeded; dynamically selecting an isolation environment and executing a tool sequence according to an approval result and a sandbox strategy; capturing output and a system state in real time in an execution process, recording a state difference through a difference tracking algorithm, and generating an audit event flow; and finally, integrating all events, approval records and results to form a structured audit log, and carrying out associative storage. According to the invention, dynamic approval triggering based on real-time risks and fine-grained auditing of influence on a tool operating system level are realized.

A method for performing attribute constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. The method further includes determining singleton events in the dataset, where each singleton event is associated with an attribute. The method also includes generating event pattern sets corresponding to different distinct singleton events and that maintain a fixed positional order for each event sequence. The event pattern sets include pattern occurrence data for each event sequence positioned according to the fixed positional order. The method also includes identifying the fixed positional order maintained by each event pattern set, and generating an attribute constraint search structure, such as a B-tree, B+tree, or table, that includes keys and pointer values. The method may also include searching the search structure to identify key values satisfying a received attribute constraint, and outputting the sequence identifiers corresponding to the identified key values.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a personalized display recommendation method based on multi-dimensional behavior characteristics. The method comprises the steps that S1, multi-dimensional behavior data of a user is collected and analyzed into a cognitive discharge event; s2, performing time sequence fusion and dynamic attenuation processing on the event stream, and constructing a user cognitive vector; s3, using a ColBERT model to encode display content, and constructing a content semantic field tensor; s4, calculating a semantic association degree based on the tensor set, and constructing a content universe network; s5, mapping the user cognitive vector and the content universe into an improved HMC algorithm, introducing a cognitive inertia factor, simulating a particle dynamics trajectory, and calculating resonance intensity; s6, inputting the resonance intensity list into a prediction model to generate a predictive recommendation list; and S7, selecting and rendering a final display form according to the list. According to the method, deep modeling of the cognitive state of the user and accurate quantification of the content value are achieved, and the recommendation accuracy and timeliness are remarkably improved through dynamic simulation and trend prediction.

A method for generating an inverted search index for event sequence visualization may include determining, from an event sequence dataset containing event sequences and events, event pair combinations based on identified unique singleton events. The method may further include establishing a positional ordering for each singleton event and each event pair combination and generating a respective event sequence set for each event sequence. The method may also include determining whether each unique singleton event and each event pair combination exists or does not exist within each event sequence, and populating, for each respective event sequence set and according to the positional ordering, data indicative of whether each unique singleton event and each event pair combination exists or does not exist within the event sequence for the respective event sequence set. The method may also include generating the inverted search index including each respective event sequence set for each event sequence.

The invention discloses an event camera semantic segmentation method based on double-teacher-assistant cross-modal distillation. The method comprises the steps of data preprocessing, double-teacher-assistant distillation framework construction, double-teacher-assistant cooperative pre-training of an event student model and supervised fine tuning. Firstly, an input image and an event stream are processed, the event stream is coded into a 3D voxel grid, and the 3D voxel grid and an image frame are subjected to data enhancement. Secondly, constructing a framework comprising an image teacher model, an event teacher model, two teacher assistants and an event student model, and enabling the teacher assistants to learn knowledge of the teacher model in the corresponding mode through first-stage training; and utilizing the trained image and the double-teacher assistant of the event domain to cooperatively pre-train an event student model through cross-modal multi-scale semantic guidance and an event perception relation constraint mechanism. And supervised fine tuning is carried out on the pre-trained student model. And finally, performing semantic segmentation on the voxelized event data by using the trained student model, and outputting a prediction result.

The invention discloses a full life cycle management system for an enterprise product supply and demand order contract, relates to the technical field of enterprise supply and demand contract management, and is used for solving the problem of low efficiency of supply and demand performance collaboration. Through multi-source business event collection and unified coding time reference, records dispersed in a business system are integrated into an event stream sorted according to time, a supply and demand event chain and a life cycle unit corresponding to a contract are automatically identified, the performance state is visible and the abnormity is traceable, and the efficiency is improved. The performance terms and the settlement terms are analyzed into computable constraints, cooperative control of a performance task network and resources is driven, resource demand intervals are generated according to uncompleted tasks and constraints, key resource states are compared to identify resource conflicts, and processing control items such as production, delivery, purchase or term adjustment are generated, so that stockout and delay risks are reduced; and the performance stability and the resource utilization efficiency of the enterprise are improved.

The invention provides an intelligent time sequence security analysis method and device based on a large language model, and relates to the technical field of information security, and the method comprises the steps: collecting an original log from a multi-source heterogeneous system to obtain a standardized log event; inputting the standardized log event into a preset large language model to generate a historical semantic log event; constructing a user digital portrait based on the historical semantic log event, and establishing a dynamic behavior baseline model associated with the user digital portrait by using a long-short term memory neural network model; obtaining a semantic log event stream output by the large language model in real time, and querying a dynamic behavior baseline model of a user corresponding to the semantic log event stream; and evaluating whether the current semantic log event is an abnormal event: if the current semantic log event is an abnormal event, calling a large language model to generate a semantic alarm, and injecting the semantic alarm into a rule engine. The technical problems that a traditional static rule system is poor in adaptability, high in false alarm rate and high in maintenance cost are effectively solved.

The invention provides an intelligent caching method and device based on a self-adaptive caching strategy, and the method comprises the steps: collecting an access event flow of each caching key in real time through a sensing probe embedded in a caching client, the event flow comprising Key, operation type, timestamp, request source, hit state and response time; running a streaming processing engine at a client based on an access event stream, and calculating indexes such as instantaneous QPS, continuous miss times and short-term access frequency by adopting a sliding time window; a calculation result is input into a rule engine for online matching, a decision instruction for a single Key is generated, and the decision instruction comprises distributed mutual exclusion lock addition, local TTL dynamic adjustment and read-write mode switching; a decision instruction is directly executed in a client memory through a strategy executor, and nanosecond strategy effectiveness is achieved; and finally, collecting system feedback data after strategy execution, inputting the system feedback data into a machine learning module for online learning, and dynamically optimizing a rule threshold value or recommending a new strategy.

The invention discloses a teaching resource intelligent management system and method based on big data, and relates to the technical field of teaching resource intelligent management. Constructing a teaching resource arrangement model based on the mastery index of the micro knowledge points; solving the teaching resource arrangement model by adopting a two-stage heuristic decomposition algorithm to generate an optimized learning arrangement sequence; sequentially pushing the assembled micro learning units to a user terminal in a streaming manner according to the sequence of the optimized learning arrangement sequence, and obtaining process interaction data of a user; extracting process interaction data to analyze answer accuracy and response time stability; updating the total mastery index according to the two-dimensional normal probability model; and triggering re-optimization of the subsequent optimization learning arrangement sequence based on the updated total mastery index. Instant refreshing of the state is realized; the analysis efficiency is improved.

The invention relates to a dynamic health record automatic updating and management method based on a cloud platform. The method comprises the following steps: acquiring multi-source heterogeneous data and performing event packaging to generate a standard health event stream; performing cross-modal time sequence alignment processing on the standard health event stream to form a time sequence event sequence; an event-state double-layer time sequence knowledge graph is constructed, and real-time updating of health state vectors is achieved by means of the dynamic reasoning ability of a graph neural network in combination with a time sequence event sequence; and dynamic updating of the archive is completed through vectorization archiving. By the adoption of the method, the defects of traditional health archives in the aspects of data fusion depth, updating timeliness and state characterization precision can be effectively overcome, an accurate and real-time data basis is provided for chronic disease management and other long-term health intervention scenes, and the initiative and individuation level of health management is remarkably improved.

The invention relates to an AI-driven intelligent work reporting method, system and device and a medium. The method comprises the steps of collecting original operation data of production equipment and performing anomaly detection to generate a standardized anomaly event stream; carrying out unified representation on the abnormal event and the production context thereof, and generating a space-time aligned feature sequence; calculating causal intensity between abnormal events based on a preset model and constructing a dynamic time sequence causal graph; back diffusion iteration is carried out by simulating abnormal influence to trace root cause nodes, and a root cause score sorting vector is generated; positioning a key root cause and tracing a propagation path of the key root cause, analyzing backlog data for root cause types such as network interruption, and performing compensation work reporting; and finally, optimizing causal intensity model parameters by utilizing artificial feedback. The method overcomes the defects that in the prior art, abnormal events are analyzed in an isolated mode, and causes and effects cannot be correlated, intelligent root cause diagnosis and work report data self-correction of a complex abnormal chain can be achieved, and therefore production data reliability and operation and maintenance efficiency are improved.

The invention discloses an asynchronous task message passing method, device and equipment based on an MCP protocol and a storage medium, is applied to a calling end registered as an MCP endpoint, and relates to the technical field of computer networks, and the method comprises the following steps: establishing a full duplex communication session with an execution end based on instance information issued by a service registration center, establishing task semantic information based on the initial task request by using an MCP protocol layer; a target task request is sent to the execution end through the session, and the execution end executes a task by using the Nacos service according to the target task request to generate an event flow. And when the network connection is abnormal, the calling end locates the position of the event successfully received last time based on the global unique event identifier of the event stream, generates a reconnection request and issues the reconnection request to the execution end, and the execution end returns the event stream in sequence according to the reconnection request. And the calling end sends the confirmation instruction to the execution end, the execution end generates the event confirmation sequence number and returns the event confirmation sequence number, and the calling end judges that task message transmission is completed after receiving the sequence number, so that the task message transmission efficiency is improved.

The invention discloses an accounting risk identification and auxiliary decision-making method and system, and relates to the technical field of accounting information processing, and the method comprises the steps: carrying out the cleaning and standardization of an accounting related data flow, and generating an accounting event flow; pre-screening the accounting event flow in real time, and determining the accounting event to be verified and a pre-screening result; evidence is called according to an association rule to form a structured evidence packet, cross-modal consistency verification is executed, and contradictory evidence features are extracted; updating the entity relation graph based on the accounting event flow and the contradictory evidence feature increment, constructing a local sub-graph, and reasoning to obtain a graph risk score and a risk association path; and fusing the multi-source results to determine a risk type and a risk level, and outputting an auxiliary decision result. According to the method, the real-time performance and the accuracy are both considered, the hidden association and network type fraud recognition capability is improved, false alarm and missing alarm are remarkably reduced, manual retrieval and comparison are reduced, the time delay of evidence calling and rechecking is shortened, and the conclusion interpretability and the audit traceability are enhanced through evidence positioning and association paths.