756 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 discloses an intelligent review system and method for a large model auxiliary compliance report. The system comprises a report processing module, a rule base management module, a knowledge graph engine, a large model reasoning module, an automatic review and feedback module and a problem publication and approval interface. The method comprises the steps of data acquisition and preprocessing, blocking and field extraction, rule matching and knowledge association, multi-dimensional inspection, problem identification and positioning, report generation and process triggering. Through deep fusion of the large model and the knowledge graph, intelligent and automatic compliance examination of the report is realized, the method has the advantages of high examination efficiency, high accuracy, good interpretability and the like, and efficient and reliable technical support is provided for compliance management of enterprises and examination work of supervision organizations.

The invention provides a marketing strategy optimization method based on big language model analysis and evaluation driving, and the method comprises the steps: obtaining historical data including user behavior data, label data and statistical data, carrying out the analysis, and constructing a basic marketing strategy; user content data are collected in real time through Kafka, deep analysis is conducted on user content through a large language model, and a personalized marketing scheme is matched from a knowledge base based on the deep analysis result of the large language model and basic rule matching; through a feedback circulation mechanism, feedback of the user to marketing activities is collected, and a feedback result is stored in a knowledge base and used for optimizing a marketing strategy and a large language model; meanwhile, the decision-making process is logged, textualized and vectorized and is stored in a vector library, and the decision-making process and the marketing scheme are optimized in combination with a continuously optimized large language model, so that dynamic adjustment and matching are realized. According to the invention, the accuracy and effect of the marketing strategy are improved by using the large language model.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

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 relates to the technical field of industrial automation and intelligent manufacturing, in particular to a rubber tube production line monitoring method and system.The method comprises the steps that multi-mode production data of a production line is collected and preprocessed, a virtual production line model is generated through a dynamic twin model and compared with actual data, and virtual-real difference data is generated; performing anomaly detection on the virtual-real difference data based on a classification algorithm and a rule matching structure, and optimizing parameters of an anomaly detection model; generating abnormal trend prediction data by using the optimized model, and proposing a system correction suggestion according to the prediction data; and dynamically adjusting the operation parameters of the equipment through a closed-loop feedback mechanism. According to the invention, through innovative dynamic twin modeling, deep learning optimization and closed-loop control technologies, real-time monitoring and intelligent adjustment of the production line are realized, and the production efficiency and the product quality are effectively improved.

The invention discloses an off-site derivative cross-border supervision intelligent adaptation system based on heterogeneous data fusion, and relates to the technical field of big data processing. A data acquisition module is set to obtain off-site derivative transaction data, and an original data set is generated; setting a data structured processing module to perform semantic analysis on the original data set by adopting a financial text-oriented text model to generate a transaction information table, setting a transaction type classification module to judge whether cross-border transactions are involved or not according to transaction types in the transaction information table, and if the cross-border transactions are involved, setting a cross-border compliance evaluation module to perform cross-border compliance evaluation on the cross-border transactions. Performing dynamic rule matching on the transaction information table to generate a compliance report; otherwise, a risk prediction module is set, risk prediction is conducted on the transaction information table through a pre-trained transaction graph neural network model, a risk early warning signal is generated, and corresponding early warning measures are executed. The compliance cost is reduced, and the risk management and control capability is improved.

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 an intelligent archive content evaluation system based on deep learning, which relates to the technical field of deep learning, and comprises the steps of inputting a standardized feature vector into a deep learning model, calculating a time sequence waveform factor and a semantic kurtosis factor in combination with a rule matching reference, and generating an evaluation confidence coefficient matrix; the cosine similarity and the Spearman correlation coefficient are included, and the comparison index threshold triggers abnormal marking and grading early warning. According to the method, a multi-model fusion deep learning evaluation model is constructed and comprises a semantic understanding model, a risk evaluation model and a value classification model, split attributes are dynamically optimized in combination with an evaluation rule decision tree, file contents can be comprehensively evaluated from multiple dimensions of semantic understanding, risk evaluation, value classification and the like, and the evaluation efficiency is improved. And an accurate comprehensive evaluation result and a rule matching reference are generated, so that the comprehensiveness and scientificity of file content evaluation are improved.

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 discloses a geological domain named entity accurate recognition and classification method based on thinking chain enhancement and hybrid expert architecture, which is characterized by comprising the following steps: firstly, extracting geological document text data through an OCR (Optical Character Recognition) technology, and extracting structured entity data by utilizing a locally deployed large language model; then calling a local large model to generate a diversified sentence pattern template according to language styles in the geological field, filling the template with the extracted professional entities, and constructing an instruction fine tuning data set; further constructing a thinking chain (CoT) enhanced data set on the basis, and explicitly simulating an expert reasoning process; efficient fine tuning is carried out on the large model by innovatively combining a low-rank adaptation (DoRA) technology and a hybrid expert (MoE) architecture, the DoRA technology carries out dimension reduction decomposition and orthogonal transformation on weight matrixes of a decoder layer and a multi-layer perceptron, and the MoE architecture constructs a plurality of special sub-networks to enhance the multi-task processing capability; and finally, performing entity extraction on the geological document by using the fine-tuned model, outputting an identification result containing a reasoning process, and filtering and perfecting the result through a rule matching mechanism. According to the method, the problems of fuzzy boundary, indefinite semantics, difficulty in classification and the like of the named entities in the geological field are effectively solved, the recognition and classification accuracy of the named entities in the geological field is remarkably improved, and key technical support is provided for downstream applications such as geological resource exploration and mineral evaluation.

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 discloses an environment protection monitoring method and system based on the Internet of Things, and relates to the technical field of environment monitoring of the Internet of Things, and the method comprises the steps: building a joint optimization model based on a directed causal network diagram and a constraint function, synchronously training the edge weight parameters of the joint optimization model through an alternating direction multiplier method, and obtaining a dynamic constraint coefficient; and inputting the real-time standardized time-space sequence data into the joint optimization model, extracting causal features through a dynamic constraint coefficient, calculating pollution contribution degree scores of each monitoring point, and generating a dynamic treatment optimization instruction set according to contribution degree sorting. According to the method, a data-driven initial causal network is generated through a PC algorithm, a correction direction is matched in combination with a rule of a Neo4j knowledge graph, redundant paths are eliminated, a process mechanism is fused, the problem of misjudgment of a causal direction in a complex scene is solved, and a directed network with high credibility is generated.

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 discloses an egg quality detection system and method based on multi-sensor fusion, and relates to the technical field of multi-dimensional data process.The method comprises the steps that texture features are extracted from visible light images, and feature wavelengths are extracted from spectrums; pressure distribution and shell deformation in the pressing process are synchronously collected; recording a pressure-time curve and a deformation-time curve, extracting characteristic parameters, and constructing a three-dimensional dynamic model; performing wavelet transformation on the response curve of each sensor, extracting time-frequency domain characteristics, and generating a smell fingerprint spectrum; scanning the surface of the egg to excite resonance response, recording a sound pressure signal, and converting the sound pressure signal into frequency domain data through fast Fourier transform; and presetting a business rule, and generating a final decision by combining the model output with a rule matching result. The pressure feature extraction module, the smell feature extraction module and the crack degree quantification module are arranged to realize data depth association, and the quality inspection credibility is improved through an interpretable decision model.

The invention discloses a natural language intention recognition system based on rule and large model dynamic collaboration, and relates to the technical field of artificial intelligence, and the method comprises the following steps: an input preprocessing layer is used for converting a user dialogue voice stream into text information; the complexity judgment module is used for judging whether the text information is complex or not, and the matching module is used for performing rule matching on high-priority rule base rules under a multi-level rule tree architecture; the rule engine module is used for performing intention recognition on the text information based on the rule matching method selected by the matching module under the condition that the text information is not complex so as to obtain an intention recognition result, and the large model processing module is used for performing intention recognition on the text information under the condition that the text information is complex so as to obtain an intention recognition result; and the output layer is used for outputting an intention recognition result. The intention recognition method is beneficial for solving the problems of relatively low intention recognition accuracy, relatively slow response speed and relatively poor processing capability for services in a specific field in the prior art.

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 discloses a quality data management method for a prefabricated part, and the method comprises the steps: carrying out the data verification, formatting and uplink processing according to the collected quality data of the prefabricated part, and storing the data to a block chain network; obtaining an abnormal data set through rule matching and checking based on data stored in the block chain network according to a preset intelligent contract rule in the block chain network; and based on the abnormal data set, the abnormal data in the abnormal data set is notified to the corresponding responsible party through an abnormal data processing algorithm. Through data verification, formatting and block chain storage, it is ensured that full-life-cycle data of the prefabricated part is real, uniform and non-tampering, a data island is broken, and traceability association is achieved. Abnormal data is automatically detected based on the intelligent contract rule, the efficiency is improved, and the missing detection risk is reduced; through accurate matching of responsible parties and automatic notification, rapid response and auditing closed loop are realized, and quality control efficiency and reliability are significantly improved.

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 provides a flow processing system, a processing method and a flow processing cluster, and the system comprises a converging and shunting module which is used for receiving mirror image flow from a communication link, and carrying out the rule matching, data copying and label marking processing of the mirror image flow, so as to generate a first data flow carrying flow feature information; the integrated exchange processing module is used for receiving the first data stream and forwarding the first data stream to the corresponding deep packet detection module based on the flow characteristic information of the first data stream; the deep packet detection module is used for receiving the second data stream forwarded by the integrated exchange processing module and executing protocol analysis, content identification and behavior log generation processing on the second data stream; and the backboard connection module is used for providing power connection and data communication interconnection among the functional modules. And the converging and shunting module, the integrated exchange processing module and the deep packet detection module are integrated on the same system platform, so that the data circulation efficiency and the resource cooperation capability in the system are improved.

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.