608 results about "Rule matching" patented technology

The invention provides an analyzable anti-attack network security method and system based on an AI unified model. The method comprises the following steps: S1, carrying out attack source tracing and attack mode identification on an input data stream; s2, performing protocol structure analysis and grammar element extraction on the input data stream in a grammar verification layer, and starting a grammar rule matching process to obtain a grammar exception perception set; s3, fusing attack vector information on the basis of a grammar anomaly perception set in a semantic analysis layer, constructing a semantic relation graph, and outputting a semantic risk vector; s4, taking the semantic risk vector as input, combining a business scene, resource constraint and strategy preference, modeling a defense target, and outputting an optimal response path; and S5, forming a model evolution path based on local feedback and global collaboration. Through a three-layer full-information analysis mechanism and behavior feedback driving, interpretable recognition of attack intentions and collaborative optimization of defense paths are realized, attack recognition is comprehensive, response decision is accurate, and strategy evolution is controllable.

The invention relates to the technical field of text classification, and provides a text classification method based on a large model and a rule engine, and the method comprises the steps: S1, storing multi-level rule classification labels, and constructing a classification rule template library; s2, receiving text data from various data sources, and preprocessing the text data; s3, performing rule matching on the text data based on the classification rule through a rule engine, and outputting a rule classification result; and S4, when any one of the following conditions is met, large language model classification is triggered: a, a classification rule is not matched; b, matching a classification rule, wherein the rule confidence is smaller than a rule confidence threshold; c, the text data length exceeds the preset text data length; d, matching a specific business scene label; outputting a model classification result; and S5, when the rule engine classification in the S3 and the large language model classification in the S4 are parallel, executing the strategy. The output reliability and the service continuity are guaranteed, and the method is suitable for scenes with high accuracy requirements such as financial compliance examination and the like.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

The invention relates to a large model business logic processing method and system based on workflow engine and domain knowledge fusion, and is suitable for automatic processing of complex multi-node and multi-branch business processes. According to the method, natural language input of a user is analyzed through a large language model, a service intention is recognized, and the service intention is converted into an executable task process through a workflow engine. And the workflow engine dynamically adjusts an execution path according to rules and data in the domain knowledge graph to realize efficient parallel processing of tasks. The system integrates a workflow engine, a domain knowledge graph and a large language model, supports real-time data processing, rule matching, conflict resolution and decision optimization, is suitable for the fields of water conservancy, medical treatment, finance and the like, automatically generates and executes a complex business process, and optimizes a decision process. The system improves the accuracy and execution efficiency of business decision through deep fusion of domain knowledge.

The invention relates to the technical field of automobile fault detection, in particular to an automobile fault detection and diagnosis case library construction method based on rules and a fault tree, which comprises the following steps: performing context enhancement on original fault data by external environment data to obtain environment enhanced fault associated data; performing fault classification on the automobile fault features to obtain fault classification labels; performing rule matching on the fault classification label according to a preset expert rule base to obtain a matching result; performing case correlation analysis according to the fault propagation chain and a preset historical diagnosis case library to obtain case correlation characteristics; constructing a case library according to the structured case model to obtain a diagnosis case library; and sending the diagnosis case library to a cloud platform to execute automobile fault detection and diagnosis. According to the method, the fault classification labels are subjected to rule matching, the known fault modes can be quickly and effectively identified according to expert experience, and corresponding solutions can be provided for known faults.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention relates to an office process optimization method and device based on artificial intelligence, equipment and a medium. The method comprises the steps of performing cross-modal feature alignment processing according to multi-modal office data to generate a structured data tuple; performing intention classification processing through a preset Transform model, generating and obtaining a node attribute set by querying a preset office process knowledge graph according to an intention classification label, and analyzing office process rule constraints in the set to generate a process node instruction; according to the instruction and the real-time service context, rule matching is carried out through a predefined rule, and when a matching result is that matching is passed, a normal task package is generated; and when the matching is not passed, generating and carrying out conflict analysis processing according to the exception description, and generating a manual intervention request. According to the method, through multi-modal data fusion, dynamic rule matching, systematized conflict analysis and the like, the multi-modal office data processing efficiency and the process decision accuracy are improved, and the adaptability of the office process to a real-time business scene is enhanced.

The invention provides a knowledge graph-based IT operation and maintenance fault tracing method and device, and the method solves the problem of low fault positioning efficiency in a high-complexity heterogeneous network through fusing a topological path and a knowledge graph of a fault propagation mode, and specifically comprises the steps: firstly, carrying out the fusion of the knowledge graph, and carrying out the positioning of a fault through the fusion of the knowledge graph; the incidence relation between the physical connection and the fault logic is represented, so that the reduction accuracy of a cross-equipment fault propagation path is improved; secondly, manual troubleshooting participation degree is reduced through semantic similarity analysis, and priority ranking of fault roots is achieved in a complex network environment; and finally, the reliability of fault positioning is guaranteed through consistency verification, and misjudgment caused by simple rule matching is avoided.

The invention relates to an automatic code auditing method and device, computer equipment and a storage medium. The automatic code auditing method comprises the steps of obtaining a grammar structure, a control flow and a data flow of a to-be-audited code; constructing a context graph of the to-be-audited code according to the grammatical structure, the control flow and the data flow of the to-be-audited code; obtaining a multi-modal collaborative vulnerability detection method, wherein the multi-modal collaborative vulnerability detection method comprises a static analysis method based on rule matching, a symbolic execution method based on a code path, a large model reasoning method based on semantic understanding and weights of the methods; and identifying one or more code vulnerabilities, the vulnerability type of each code vulnerability and the confidence coefficient according to the context graph of the to-be-audited code and the multi-modal collaborative vulnerability detection method. According to the method, the audit codes of various vulnerability types can be processed while the code audit efficiency can be improved.

The present application relates to the technical field of electric digital data processing, and provides a data preparation method, system, and device for AIGC-based interaction analysis, and a medium. The method comprises: by means of a pre-trained text label recognition model, determining a label sequence corresponding to an interaction statement having experienced first word segmentation processing; on the basis of a preset expression construction rule, determining a label combination sequence corresponding to the label sequence and a conditional expression corresponding to the label combination sequence; by means of a preconfigured database candidate text dictionary and a preset matching rule, determining a strictly matched keyword and a fuzzily matched keyword corresponding to the interaction statement having experienced second word segmentation processing; on the basis of the strictly matched keyword and a similar candidate text corresponding to the fuzzily matched keyword, determining a word segmentation result corresponding to the second word segmentation processing; on the basis of a plurality of preset SQL statement rule matching templates, generating an interaction SQL query statement, so as to determine, on the basis of the interaction SQL query statement, interaction response information corresponding to the interaction statement.

The invention discloses an enterprise data asset entry system based on artificial intelligence, and the system comprises an original document receiving module which is used for generating an original document set; the multi-modal data construction module is used for constructing a multi-modal input data set; the artificial intelligence identification module is used for identifying a field set with data asset attributes; the structured asset modeling module is used for converting the identification field set into a structured data asset object; the rule matching and template selection module is used for generating a field mapping rule and table entry trigger configuration; the field standardization mapping module is used for generating a structured data entry record set; the table entry metadata supplementing module is used for adding a template number, a mapping timestamp and an operator identifier for each data table entry record; the ledger writing and auditing module is used for writing the data entry record into the enterprise asset ledger system; and the table entering state management module is used for generating a state mapping table. According to the invention, automatic data asset entry is realized.

The invention relates to the technical field of data processing, and provides a field rule matching recommendation method and device based on multi-dimensional feature fusion. Obtaining user input information, wherein the user input information comprises a target data table and a current business target; performing feature extraction on fields in the target data table through a field feature extraction module to obtain field multi-dimensional features; performing attribute modeling on the data quality rules in the preset rule base through rule attribute modeling to obtain rule multi-dimensional attributes; calculating matching scores of the field multi-dimensional features and the rule multi-dimensional attributes through a multi-dimensional scoring model; and dynamically adjusting the weight of each dimension in the multi-dimensional scoring model according to the service target priority through a rule recommendation engine, and outputting Top-N recommendation rules based on the adjusted weight and the matching score. According to the scheme, the problems of low quality rule matching precision, poor efficiency and insufficient scene coverage of a traditional method can be solved.

The invention relates to the technical field of cross-border logistics, in particular to an artificial intelligence algorithm for cross-border overseas warehouse location storage and picking path optimization, which comprises the following steps: S1, obtaining order details, commodity characteristics and warehouse environment data, and monitoring warehouse states and cargo storage conditions in real time; s2, data preprocessing is carried out, features are extracted, clustering analysis is carried out, and association rules are mined; s3, performing rule matching, constructing a mixed integer programming model, performing constraint processing, and outputting a commodity storage strategy; and S4, converting the storage strategy into a warehousing operation instruction, and executing the warehousing operation instruction. The method has the beneficial effects that the accuracy of goods classified storage and the space utilization rate of the warehouse location can be improved, and the storage cost of the warehouse location is reduced.

The invention discloses a semantic judicial contract compliance automatic generation and examination system based on a DIKWP artificial consciousness model. The system is constructed according to five layers of data, information, knowledge, wisdom and intention, wherein the data layer integrates multi-source information; the information layer enables terms to be semantized and disambiguated; the knowledge layer performs rule matching and compliance judgment by using a legal knowledge graph; the wisdom layer automatically corrects the risk terms based on a fair principle and performs scene simulation; and the intention layer is embedded into targets of all parties to ensure that the contract is consistent with the compliance intention. The system process covers the whole process from intention analysis, knowledge reasoning to intelligent generation and intention negotiation, and finally a compliance contract and an audit report are output. The system is provided with a multi-language semantic adaptation and intelligent negotiation interface, and convenient access can be realized through an API (Application Program Interface). The system realizes integration of contract generation and review, intention driving and decision interpretability, can effectively reduce compliance cost and dispute risks, and is suitable for multiple scenes such as finance, e-commerce, government purchase and the like.

The invention discloses a business data quality treatment method supporting system hidden danger identification, and relates to the technical field of computer information processing, and the method comprises the steps: S1, collecting a real-time batch tracking data stream from an enterprise inventory system through a multi-source data interface, and obtaining an initial data set containing inconsistent formats and missing batch numbers; s2, scanning a data stream according to the initial data set by applying a preset automatic detection rule, judging positions and types of abnormal points by utilizing a rule matching mode and a threshold comparison mechanism to obtain an abnormal point set, and activating a real-time stream analyzer to process field complete verification to generate an abnormal log record; according to the business data quality treatment method supporting system hidden danger identification, real-time and accurate anomaly detection and repair are realized, the consistency and reliability of inventory data are improved, and the enterprise inventory management efficiency is remarkably optimized.

The invention belongs to the technical field of artificial intelligence task planning, and provides a task planning PDDL file automatic generation method based on natural language input, and the method comprises the steps: (10) constructing task environment description words: analyzing environment elements and participants, and designing a diversified scene framework; (20) building a knowledge enhancement fine tuning model: fusing a vector RAG knowledge base retrieval result and pre-training model parameters by the model, and inhibiting logic illusion; (30) PDDL file generation and knowledge constraint: driving the fine tuning model to output a correct PDDL file in combination with scene semantics and knowledge base rules; (40) multi-stage dynamic evaluation: executing a feasibility index verification instruction through a knowledge base rule matching degree; and (50) model closed-loop iterative optimization: updating RAG knowledge base content and model parameters based on execution feedback, and constructing a closed-loop iterative optimization model. The method has the beneficial effects that the instruction logic deviation rate is reduced through RAG knowledge base constraint, the complex scene instruction generation accuracy is improved, and the dynamic decision response time is shortened.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention provides an intelligent assistant method and system based on a large model, relates to the field of large model application, is used for providing a question and answer result for a user question, and comprises the following steps: constructing an entity recognition cue word, and recognizing an entity in the user question through the large model; constructing intention recognition and tool selection prompt words, recognizing user intentions in the user questions through a large model, and determining tools to be used; based on the assembled user questions and entities, related information is retrieved from a pre-constructed vector knowledge base, and a large model is combined with the retrieved related information to enhance and generate an answer text; processing the answer text according to the user intention and the tool, and outputting a final question and answer result; according to the method, the limitation that a traditional intelligent assistant cannot deeply understand user semantics and only can mechanically carry out rule matching is solved, and the requirements of the user can be more accurately grasped to provide a more accurate problem solving strategy.

The invention provides a heterogeneous data stream real-time automatic cleaning and correcting method based on a dynamic rule engine, and relates to the technical field of data processing, and the method comprises the steps: accessing a multi-source heterogeneous data stream; loading a predefined dynamic cleaning rule set to a rule engine; monitoring an input data stream in real time, analyzing data content in parallel through a rule engine, and triggering a cleaning action; dynamically generating a correction operation instruction according to a rule matching result; a data credibility weight factor and a correction utility attenuation coefficient are introduced, and a cleaning strategy is dynamically adjusted; and outputting the cleaned standardized data stream and recording a correction log. Through the dynamic rule engine and the intelligent algorithm, flexible adjustment and real-time processing of the heterogeneous data stream cleaning rule are realized, the efficiency and accuracy of data cleaning are improved, and the problems of insufficient rule flexibility, low processing efficiency and difficulty in ensuring data accuracy in the prior art are solved.

The invention relates to the technical field of engineering technology research and development, in particular to a risk assessment method for personalized marketing content for generative AI output. According to the method, firstly, feature extraction is carried out on marketing content data to obtain structured representation, and then preliminary screening is carried out through a preset rule to obtain a rule matching condition; inputting the feature data and the rule matching result into a trained neural network model for deep evaluation, and outputting a risk score and a risk type label; and calculating a model parameter update value based on a manual auditing result and a business key index. According to the method, the interpretability and the risk identification capability of evaluation are ensured through the combination of the rule and the model, and the dynamic adjustment of the evaluation standard is realized by introducing the business index and the online learning mechanism, so that the risk evaluation better meets the business requirements.

The invention provides an intelligent agent-based automatic verification and rule matching system for insurance-waiting evaluation reports. The intelligent agent-based automatic verification and rule matching system comprises a report acquisition module which pulls to-be-verified and historical insurance-waiting reports; the auditing processing module calls an attention Bi-LSTM model which is subjected to reinforcement learning fine adjustment and is fused with knowledge graph embedding, and scores the report sentence by sentence; the rule management module is used for storing and analyzing an equal-guarantee 2.0 structured machine rule and carrying out secondary accurate matching on low-confidence sentences; the knowledge graph module is used for maintaining a multi-dimensional association graph of a standard family, a control measure and an evaluation item and providing graph reasoning; the agent decision-making module generates actions of passing, returning, manual rechecking or automatic correction according to the auditing state; the feedback learning module collects artificial recheck differences, an incremental training model, a strategy network and atlas vectors; and the storage module stores reports, models, rules, maps and logs in a multi-modal manner through a relation library, a map library and a vector library, and implements data governance and access control. According to the invention, the audit efficiency, accuracy and standard consistency of the evaluation report can be improved.

The invention provides a commodity multimedia recommendation method and system combined with RPA and AI.The method comprises the steps that firstly, a current interaction behavior flow of a user and a commodity multimedia interface is recorded in real time through an RPA interaction capture module, the current interaction behavior flow comprises an operation triggering time sequence and an attention staying track, then an intention evolution track is extracted based on a preset historical interaction mode library, and the intent evolution track is extracted; the method comprises the following steps of: generating a dynamic matching rule set according to an intention evolution track, including an association constraint condition and a priority ranking logic, inputting the dynamic matching rule set into a pre-trained AI recommendation model, performing rule matching screening on a candidate commodity multimedia content set, generating a screening result, and outputting the screening result to a user. And finally, a recommendation sequence is rendered in real time according to a screening result through an RPA display arrangement module, and the display size and the text typesetting style are adjusted, so that the individuation degree of commodity multimedia recommendation and the user experience are improved.

The invention provides a regional wind field multi-source heterogeneous work order data fast structuring system fused with a large language model, which comprises a plurality of modules, and is characterized in that a data input module receives various work order images; the OCR layout recognition module comprehensively recognizes the data, extracts key information, determines a spatial position and outputs text fragments with coordinates and layout anchor point information, and the rule field extraction module recognizes format category fields from the text fragments and calculates rule matching confidence; the semantic field recognition module recognizes descriptive fields from unstructured content and outputs related confidence information, the fusion decision module calculates final confidence of field candidates according to a predetermined function and judges output or complementation according to a threshold value, and the data consistency verification module is connected with an asset database to verify key information. The data complementation module generates complementation candidates and confidence coefficients, the man-machine collaborative closed-loop module pushes suggestions and receives feedback, and the result output module outputs standardized data. According to the method, the multi-source heterogeneous work order data can be effectively processed.

The invention discloses an engineering exhibition digital exhibition space dynamic regulation and control method fused with environment perception, and particularly relates to the technical field of dynamic regulation and control, and the method comprises the steps: constructing an environment perception and control integrated architecture, and enabling a distributed sensor network to collect environment characteristic parameters and equipment operation state information; the control center carries out abnormal value correction, space-time alignment fusion data and space-time feature extraction, and generates a state deviation analysis result and a space dynamic feature map; outputting an environment comfort level, an equipment stability index and an environment-equipment coupling coordination degree based on a multivariable coupling analysis model of a graph convolutional network and an attention mechanism; obtaining final judgment results such as environmental risks and equipment hidden dangers through thresholding processing and rule matching; and generating a differential optimization scheme to trigger regulation and control, a closed-loop iterative optimization algorithm and an early warning threshold. According to the invention, by realizing environment-equipment cooperative regulation and control, the regulation and control precision and the intelligent level are improved, continuous optimization is guaranteed, the energy consumption is reduced, and the risk response capability is enhanced.

The invention discloses an automatic labeling method and system based on a neural symbol combination framework, and belongs to the field of information processing. The method comprises the following steps: S1, inputting multi-modal data, and preprocessing the multi-modal data; s2, respectively extracting features of each mode, and generating a joint embedded vector by adopting a multi-head attention mechanism; s3, logical reasoning is executed based on the domain knowledge base, and interpretable labeling rules are generated; s4, mapping the joint embedded vector to a predicate space of symbol logic for rule matching; s5, detecting whether a matching conflict exists or not based on a logic constraint solving algorithm; if the conflicts exist, manual auditing is triggered, the rule weight is updated based on the Bayesian network, and the steps S3-S5 are executed again; and if no conflict exists, outputting a labeling result. According to the method, collaborative optimization of data driving and knowledge driving is realized by fusing the perception ability of the neural network and the logical reasoning ability of the symbol system.

The invention relates to the field of electric power monitoring, in particular to an electric power monitoring system intrusion detection method and system based on flow analysis. The method comprises the following steps: collecting network traffic, analyzing and recombining to obtain structured session data; time sequence behavior features and function code distribution features are extracted to construct a multi-dimensional feature set; inputting the feature set into a compliance rule base and a behavior baseline model in parallel, and respectively outputting a rule matching result and an abnormal deviation degree score; generating a comprehensive threat index by adopting a weighted decision fusion strategy; and when the index exceeds a dynamic threshold value, intrusion is determined and an alarm is given. According to the invention, the problem of insufficient precision and adaptability caused by single feature dimension and isolated detection mechanism is solved.

The invention belongs to the technical field of code-free development, and particularly relates to a Web3D zero-code interactive design platform and method based on a unified model, and the platform comprises a unified business logic model UBLM and a platform-independent, self-described and structured data container which is used for completely defining all behavior logics in 3D interactive application, including events, conditions, actions, parameters, variables and flow relations; the dual-mode editor comprises a form-type editor and a blueprint-type editor, and the form-type editor and the blueprint-type editor can be used for completely creating, editing and visualizing the same UBLM and realizing real-time state synchronization; and the interpreted rule execution engine IREE is a lightweight engine running in a JavaScript environment of a browser and is responsible for analyzing the UBLM during running and converting the declarative logic into an executable instruction in a 3D scene through a rule matching and function registration mechanism. According to the invention, the development and cooperation efficiency can be improved, and the iterative maintenance process is simplified.

The invention discloses a multi-scene fund flow monitoring method, device and equipment and a storage medium, relates to the technical field of risk control, and discloses a multi-scene fund flow monitoring method comprising the following steps: obtaining transaction flow data of a monitored object, and generating a dynamic label combination according to the transaction flow data; performing combination according to the monitoring object and the dynamic label combination to obtain a current scene; performing rule matching according to the monitoring object, the current scene and a preset scene to obtain a rule matching result; and triggering a scene processing action according to the rule matching result, and completing multi-scene fund flow monitoring. Fund flow multi-scene accurate identification and real-time disposal based on dynamic labels and configurable rules are realized, and the flexibility, accuracy and timeliness of risk monitoring are improved.