1615results about "Web data indexing" patented technology

A coherent, intelligent, packet-switched memory fabric enables predictive, cache-coherent access across distributed compute, accelerator, and memory resources using a Memory-Fabric Transaction Layer Protocol (MF-TLP). MF-TLP defines routable packet formats for read, write, vectorized, atomic, reduction, collective, and predictive-prefetch transactions executed by memory-centric network interface controllers (MC-NICs). Each MC-NIC performs packet parsing, address translation, coherence management, and near-memory arithmetic or tensor operations while coordinating with MF-TLP-aware switches providing hierarchical directory control, multi-path routing, and in-network aggregation. Vectorized and multimodal packets encode multiple addresses or tensor offsets to reduce scatter / gather overhead, and programmable caching and quality-of-service modules manage tiered memory and tenant fairness. MF-TLP supports extension headers for predictive prefetch, collective coordination, and tenant governance, operating across hierarchical leaf-spine topologies using Ultra-Ethernet Transport, InfiniBand, or CXL fabrics. The system delivers scalable, low-latency, memory-centric orchestration for large-language-model training, multimodal AI, and data-intensive analytics.

The invention discloses a microblog emotion analysis method based on standard dictionaries and semantic rules. The microblog emotion analysis method comprises the following steps: collecting microblog data and manually labeling and marking the emotion value of each microblog; proposing corresponding standard micrblog emotion dictionaries, and establishing an emotion dictionary database; based on the standard emotion dictionaries, adding the semantic rules for assistance, and performing parameter adjustment and optimization on parameters of the semantic rules; based on a real dataset experiment, acquiring the final classification accuracy and precision. The technical scheme provided by the invention is adopted to well analyze the emotion tendency of each microblog user by introducing the standard emotion dictionaries, microblog expression dictionaries and the semantic rules, therefore, higher classification accuracy and precision are achieved.

The invention discloses a public opinion risk assessment method and system based on multiple agents and a large language model, and belongs to the technical field of network information security. The method comprises the steps of obtaining multi-source public opinion data; reasoning in combination with a language model to obtain a multi-dimensional semantic vector, and clustering to form a plurality of topic clusters; the emotion opposition level, the credibility and the text quantity increment score of each topic cluster are generated based on an intelligent agent, a comprehensive risk value is obtained through weighted fusion, and high-risk topics are screened through a double-threshold retention mechanism; and generating a knowledge graph according to the high-risk topic, determining an associated entity, a propagation link and an intervention node, implementing an intervention strategy, and generating a public opinion intervention report. According to the method, the monitoring problem of multi-source heterogeneous public opinion data can be effectively solved, the accuracy and timeliness of risk assessment are improved, full-link automation from risk identification to accurate intervention is realized, and efficient support is provided for public opinion management and control of governments, enterprises and other mechanisms.

The invention discloses a multi-modal harmful model factor detection method based on a thinking chain, and belongs to the field of harmful model factor detection, and the method comprises the steps: firstly obtaining an image-text model factor, and automatically generating the thinking chain containing a problem summary, an image subtitle, and harmful reasoning and conclusion; constructing an annotation data set according to the annotation data set; then, the multi-modal model is finely adjusted in two stages, in the first stage, the visual encoder and the language model are fully finely adjusted to improve image-text understanding, and in the second stage, the visual encoder is frozen, and the language model is finely adjusted through LoRA to strengthen reasoning; introducing reinforcement learning rewards to optimize implicit harmful discrimination; then extracting an entity relationship to construct a knowledge graph and a causal graph, and embedding into the model; and finally, dynamically updating the atlas through an RAG mechanism, so that a detection result adapts to an emerging model cause in real time.

The invention provides a data analysis mapping method and system based on context awareness, and the method comprises the steps: carrying out the directional crawling of a target website through a crawler instance, so as to obtain multi-format webpage source data, then extracting the structural features, semantic features and source features of the multi-format webpage source data, and constructing a source data context feature set; dynamically matching an adaptive analysis strategy based on the source data context feature set to accurately extract target field data, performing semantic similarity matching on the target field data and a preset service scene standard field library, and performing data format unified conversion in combination with a context format rule to obtain standardized field data; and finally, performing multi-dimensional verification on the standardized field data, and generating an SQL statement after the verification is passed, so as to solve the problems that an existing data analysis mapping scheme depends on a fixed template and cannot dynamically adapt to page structure change of a target website, manual auxiliary field mapping consumes time, and errors are easy to introduce.

The invention relates to the technical field of information processing, in particular to a supply chain cross-packet vulnerability detection method, device and equipment and a storage medium. A vulnerability packet name, a sensitive API, a trigger parameter and vulnerability description are integrated into tetrad information; if so, performing cross-packet call chain analysis on the source code file by using a cross-packet chain reachability analysis algorithm, obtaining a function call sequence of the sensitive API based on a cross-packet call chain analysis result, realizing vulnerability detection on a cross-packet call chain, generating a vulnerability verification code based on tetrad information by using a preset large language model, and performing vulnerability verification on the vulnerability verification code. The method comprises the following steps: establishing a function call sequence of a bug verification code, verifying the accessibility of the bug verification code in the function call sequence, determining the bug confidence according to the energy consumption condition of a large language model, and generating bug alarm information when the accessibility verification result is that the bug is accessible and the bug confidence is high, thereby realizing double judgment of the bug, reducing the false alarm rate of the bug and improving the user satisfaction.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention provides a recommendation method and system fusing big language model reasoning and multi-source trajectory information, and the method comprises the steps: firstly obtaining user historical learning behaviors and static attribute information after receiving a user recommendation request, and constructing a static interest vector; in combination with the initial feature vector of the learned knowledge point and the map enhancement vector of the first-order neighbor node of the knowledge map, generating explicit and map extension interest vectors, and fusing to obtain a user interest vector; screening N unlearned knowledge points to form a candidate set through similarity analysis of user interest vectors and unlearned knowledge point vectors and / or reasoning of a large language model on user association information; and screening the target knowledge points through mastery degree verification of the pre-modified knowledge points, and outputting a recommendation result after sorting. According to the method, recommendation accuracy and suitability are improved, and personalized learning requirements are met.

The invention particularly relates to a policy redemption method based on a large model technology. According to the policy redemption method based on the large model technology, policy original texts of all levels are gathered, key structured information is analyzed and extracted, and the key structured information is stored in a policy knowledge base; enterprise data are automatically collected, and a multi-dimensional enterprise dynamic portrait is constructed; developing an intelligent matching engine, and matching a declaration condition in the policy knowledge base with the enterprise dynamic portrait; automatically pushing policy information to the enterprise meeting the condition based on the bidirectional matching result of the policy and the enterprise; and after an online confirmation instruction of the enterprise is received, the government affair data is automatically called to complete qualification verification, an audit report is generated, and a cashing process is triggered. According to the policy redemption method based on the large model technology, the target enterprise group can be accurately positioned, the phenomena of policy report omission and error report are obviously reduced, the resource allocation efficiency is optimized, the manpower and time cost investment is greatly reduced, and the modernization of the governance capability is improved.

The invention relates to the field of ecological monitoring, in particular to a marine ecological anomaly detection method and system based on multi-source data. Comprising the following steps: acquiring multi-source dynamic data of a target sea area; constructing a dynamic quality evaluation system, inputting multi-source dynamic data, and outputting a quality control report; based on the multi-source dynamic data conforming to the preset quality threshold value, establishing a marine ecological dynamic database in combination with a spatio-temporal indexing mechanism; based on the marine ecological dynamic database, space-time anomaly features are extracted, and candidate anomaly regions are determined; and generating a marine ecology comprehensive abnormal index by adopting the dynamic weight calculation model, outputting a prediction result through a preset abnormal trend prediction model based on the marine ecology comprehensive abnormal index, and generating an early warning measure. Through a distributed crawler and sensor gateway collaborative architecture, comprehensive acquisition of macroscopic data and near-shore real-time data is realized, and high-quality basic data is provided for subsequent detection.

The invention discloses a webpage data collection method and a related device, and relates to the technical field of data processing.The webpage data collection method comprises the steps that a webpage data collection knowledge base containing data collection modes of various webpages is constructed in advance; a data acquisition mode matched with the webpage is obtained from a webpage data acquisition knowledge base, and the data acquisition mode defines that an analysis mode of each field in the webpage is analyzed based on multi-modal features (at least two of DOM structure fingerprint features, visual position features and semantic features of the fields); according to the method, the analysis accuracy of the webpage data can be greatly improved, so that stable and efficient execution of a webpage data acquisition task is guaranteed.

The invention provides an AI knowledge base construction method and system for multi-source data integration, and belongs to the technical field of knowledge base construction. The method comprises the following steps: adaptively pulling unstructured data at an API-free site through a reversible crawler of a heterogeneous data access framework; the mode drift detector senses field change in real time and updates a protocol in a hot plug mode within 500ms, so that zero-stop synchronization is realized; carrying out weighted resolution on conflicts according to source credibility index attenuation, and generating version stamps with digital watermarks and consanguinity URI (Uniform Resource Identifier) for records; uniformly mapping to a shared semantic space through cross-modal comparative learning, and constructing an incremental hypergraph knowledge graph which can be evolved by probability weight; and extracting an optimal sub-graph by using generative adversarial reinforcement learning, writing the optimal sub-graph into a time sequence knowledge warehouse by using a zero knowledge evidence chain and carrying out block chain solidification, and outputting a verifiable certificate. By the adoption of the AI knowledge base construction method and system for multi-source data integration, end-to-end automatic, high-credibility and traceable large-scale knowledge base construction is achieved.

An RAG auxiliary generation type search engine method based on multiple rounds of expert and self-adaptive mixed retrieval comprises the steps that a crawler is used for crawling data, the crawled data are processed, a graph knowledge base is constructed through text partitioning and entity relation extraction, the graph knowledge base is coded into semantic vectors, and a vector database is established; by calculating similarity between subject vectors and literature vectors in a vector database, retrieving related literatures, utilizing a large language model to simulate a multi-expert role to generate differentiated viewpoints, and combining multiple rounds of dialogues and user feedback, generating a multi-view fused article to complete multiple rounds of expert answers; designing a double-layer retrieval mechanism according to different nodes and relationships in the retrieval atlas based on the atlas knowledge base; meanwhile, dense retrieval of a vector knowledge base of text coding is combined and utilized, a map-vector collaborative retrieval mechanism is designed, a self-adaptive mechanism and a session memory function are introduced, and multi-round question and answer response is carried out; according to the method and the system, higher logical contents can be generated.

The invention relates to the technical field of human resources, and discloses a multi-dimensional automatic background investigation method and system based on Ai assistance. The system comprises a multi-source data acquisition module, an intelligent analysis engine module, a real-time interview processing module, a quality verification control module, a dynamic decision support module, a privacy security protection module and a visual report generation module. According to the method, public data of candidates are acquired through a multi-thread crawler technology, and the authenticity of educational background is verified; analyzing the paper certificate by adopting an optical character recognition technology; constructing a space-time relation knowledge graph to detect work experience abnormality; interview content is transcribed in real time, and emotional tendency is analyzed; dynamically adjusting the evaluation dimension weight; and generating a visual risk assessment report. According to the method, the enterprise employment risk is reduced, the comprehensiveness and depth of background investigation are improved, the accuracy and applicability of a background investigation conclusion are improved, a safe and reliable background investigation environment is constructed, and the enterprise human resource allocation effect is optimized.

The invention discloses a battery carbon emission influence factor analysis method and device and a storage medium, and belongs to the technical field of data processing. Comprising the following steps: acquiring life cycle data of a battery at the current moment from a data source, constructing a knowledge fusion atlas database of the battery based on the life cycle data through an atlas construction model, generating a carbon emission prediction model according to the knowledge fusion atlas database, and predicting the carbon emission through a feature contribution degree analysis algorithm. Calculating a prediction contribution degree of each factor variable in the carbon emission prediction model, determining a target factor variable according to the prediction contribution degree, constructing a directed acyclic graph of the target factor variable based on a preset electrochemical constraint condition, and calculating conditional probability distribution of the electrochemical constraint condition in the directed acyclic graph, and forming a coupling relation model of the target factor variables, and calculating the sensitivity coefficient of the factor variables in the coupling relation model to the carbon emission. According to the method, the accuracy of the analysis result is improved by quantifying the sensitivity coefficient of each carbon emission factor under the coupling relationship.

According to the privacy enhancement type (k, d)-truss community search method for the social network, a novel KTG tree structure is constructed by fusing k-truss and G-tree indexes. According to the index structure, hierarchical community decomposition of a social graph and social distance information are fused, a refined boundary vector coding scheme is designed to support efficient distance calculation, and a double-cloud-server non-collusion architecture integrating improved homomorphic encryption and matrix encryption technologies is constructed. Through a two-stage security query process of first structure filtering and then distance verification, on the premise of protecting full-process privacy of a graph structure, a query intention, a distance matrix and an intermediate calculation result, efficient and accurate search of a close community in a large-scale social network is realized. The method is suitable for various scenes such as social recommendation, risk control, public opinion analysis and anti-fraud, and the problems of privacy disclosure and calculation efficiency in social network analysis are effectively solved.

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 bank intelligent operation knowledge base implementation method and system based on a large language model and RAG, and the method comprises the steps: firstly extracting business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructing a business dictionary containing term similarity data and an ontology tree; quantitatively calculating the release mechanism weight, the timeliness coefficient and the punishment association degree of the supervision clauses to obtain authority values, and sorting the authority values; then using a double-constraint loss function to finely adjust the pre-trained large language model as a vector representation model, vectorizing service data, and storing the vectorized service data into a vector database; and finally, aiming at user query, retrieving in a vector database, rearranging in combination with a supervision clause sorting result, and generating a business response after compliance verification. According to the method, the bank unstructured knowledge processing capability and retrieval precision can be improved, and efficient intelligent operation is realized.

The invention discloses an automatic generation method for health science popularization articles, and belongs to the crossing field of computer technology and health science popularization. The method comprises the following steps that S1, health authority information is collected through a multi-source crawler, and an original material pool is obtained through Sension-BERT vectorization duplicate removal and BioBERT medical entity labeling; s2, classifying nine types of health themes by using a RoBERTa fine tuning model; s3, multiple AI model supplementary materials are made into a structured package; s4, predetermining optimal selection questions in combination with AI three-dimensional scoring and editing; s5, calling the multi-dimensional knowledge base to generate a professional knowledge packet; s6, generating an outline and performing three-dimensional five-score system auditing of knowledge point fullness and the like; s7, the LLM expands and writes the text and marks knowledge sources; s8, performing double-stage auditing; s9, anthropomorphic draft moistening; s10, intelligently illustrating the picture; s11, when the matching degree is smaller than a preset threshold value, automatically updating the knowledge base; and S12, integrating and outputting. According to the method, the health science popularization article is generated, the manual participation time is shortened to be within 30 minutes, the medical error rate is reduced to be below 5%, the daily output of a 10-person team is improved by 3-5 times, the annual human cost is reduced by 60% or above, and the large-scale and high-quality science popularization requirements are met.

The invention relates to the field of data analysis and cleaning, in particular to a method and system for automatically collecting and cleaning public opinion experimental data. The method comprises the following steps of performing periodic data crawling on a public information website, performing public opinion hotspot analysis, and extracting a to-be-processed hotspot event stream; performing keyword detection and global content distribution analysis on the to-be-processed hot event stream to obtain a keyword frequency distribution diagram; performing negative emotion deep recognition according to the to-be-processed hot event stream, and extracting negative emotion public opinion data; performing negative public opinion evolution trend prediction based on the keyword frequency distribution diagram to obtain a negative public opinion propagation situation; and performing IP address tracing on the negative emotion public opinion data to obtain the negative public opinion region sensitivity. According to the method, comprehensive and accurate abnormal public opinion detection and deletion are realized, and the network information quality and the public governance level are improved.

The invention discloses an IETM intelligent retrieval method based on multi-modal fusion and a knowledge graph, and the method comprises the following steps: data layer construction: integrating multi-dimensional data, and establishing a multi-modal database; knowledge layer modeling: defining an IETM domain ontology through an ontology modeling tool, constructing a knowledge network, cleaning data and unifying entity annotations by using a rule engine, and forming an RDF triple knowledge graph which can be understood by a machine; and application layer retrieval: based on hierarchical semantic fusion of multi-modal data and a three-level progressive retrieval mechanism, carrying out deep analysis and accurate retrieval on the query intention of the user. According to the method, semantic enhancement is carried out on the text, three-level feature extraction is carried out on the image, and spatio-temporal joint coding is carried out on the video, so that deep semantic alignment and cross-modal semantic consistency improvement of the multi-modal data are realized, and the quality and effect of multi-modal data fusion are effectively improved.

The invention discloses a commodity recommendation method and system based on agent and hotspot information collaboration, and relates to the technical field of intelligent recommendation, and the method comprises the steps: obtaining structured hotspot data, commodity dynamic information and current user data in parallel through a hotspot information collection tool, a commodity information collection tool and a user information collection tool; performing multi-source information fusion based on a large language model, and generating a structured recommendation demand including a target user, a hotspot association direction, a commodity constraint condition, a user preference matching point and a recommendation priority; and matching and screening the commodities meeting the requirements, and performing multi-dimensional priority ranking according to the hotspot association closeness, the user preference matching degree, the commodity cost performance and the inventory sufficiency to generate an optimized commodity recommendation list. According to the method, semantic comprehension and information fusion are carried out based on a large language model through deep collaboration of hotspot information and user requirements, and the accuracy, timeliness and adaptability of commodity recommendation are improved.

The invention provides a digital economic information management method and system based on big data analysis, and the method comprises the steps: integrating the data of a consumption end, a production end and a server end through a unified platform, guaranteeing the transmission safety through the combination of differential privacy and TLS 1.3, recording the full life cycle of data through a segmented block chain, supporting the dynamic authorization and fine-grained authority control, and improving the efficiency. The method comprises the following steps: verifying data purposes by combining zero-knowledge proof, fusing NLP, image processing and time sequence analysis, constructing a knowledge graph, generating dynamic tags and composite features, simulating decision influence through anti-fact analysis, supporting generation of a Pareto optimal solution by an interactive decision tree, and providing a market trend thermodynamic diagram and a supply chain risk graph. Customized suggestions are pushed to an ERP system in real time, sensitive data are protected based on role permission and lightweight homomorphic encryption, abnormal behavior monitoring and zero-knowledge proof compliance review are combined, a service scene is simulated through an A / B sandbox, and low-code community drives demand iteration.

The present application discloses a method, system, and computer system for proactively discovering malicious domains through a guided crawling of attack infrastructure. The method includes (i) determining a set of toxic network neighborhoods on the internet, (ii) expanding one or more network graphs for the set of toxic network neighborhoods; (iii) determining a set of domains expected to be malicious from the set of toxic network neighborhoods, and (iv) performing an action based at least in part on the set of domains expected to be malicious. A particular toxic network neighborhood shares a plurality of hosting environments.

A system and method for verifying the authenticity of information associated with an entity and communicating the verification status of the information across a network. The system facilitates the receipt of data relating to information associated with an entity so that a corresponding data record can be created and stored in memory. A visually perceptible element, which corresponds to the verification status of the data record, is associated with the data record so that the visually perceptible element can be displayed to a requesting computing device to communicate the verification status of the data record. If the verification status of the data record is changed, the visually perceptible element associated with the data record is changed to reflect the new verification status which is automatically changed for any requesting computing devices so that the new verification status is communicated to the public from any platform displaying the visually perceptible element.

The invention discloses a meteorological equipment maintenance knowledge base construction method and device and a medium, and the method comprises the steps: obtaining multi-modal data related to meteorological equipment maintenance, and synchronizing structured data in the multi-modal data to a knowledge base management platform through a Restful API interface; the multi-modal data further comprises unstructured data; analyzing the unstructured data to extract image-text features, and asynchronously transmitting the crawled webpage content to a knowledge base management platform through a message communication protocol; constructing a vector index and knowledge graph association relationship on a knowledge base management platform, and labeling a multi-modal semantic tag of an equipment type and a fault phenomenon; a natural language query request of a user is received, knowledge base fragments are retrieved through keyword retrieval and vector similarity calculation, retrieval results are input into a large language model to generate equipment maintenance suggestions, and associated data sources are output.

The invention provides a public opinion monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining public opinion data of a public opinion event of a vehicle, generating a directed graph of the public opinion event through the public opinion data, and obtaining a public opinion monitoring result based on the directed graph and nodes and edges in the directed graph; and extracting emotional tendency features, propagation rule features and vehicle theme features of the public opinion events from the directed graph, and generating a public opinion monitoring result of the vehicle according to the emotional tendency features, the propagation rule features and the vehicle theme features of the public opinion events. According to the method, the discrete data related to the public opinion events are converted into the structured directed graph, the directed graph is subjected to multi-dimensional feature extraction analysis, features such as themes, emotional polarities, propagation paths and outbreak laws of the public opinion events are accurately recognized, comprehensive and accurate public opinion event monitoring results are obtained, manual intervention processing is not needed, and the method is easy to implement. The vehicle public opinion events are automatically, efficiently and accurately monitored and analyzed, and the efficiency and quality of vehicle public opinion monitoring are improved.

The invention provides an RAG adaptive evidence refining method based on submodule optimization, and belongs to the technical field of artificial intelligence. Collecting data to construct a knowledge base, and generating a candidate document set according to the knowledge base inquired and retrieved by a user; designing an instruction and calling a DeepSeek model to evaluate the answer contribution degree of the candidate document, calculating the relevancy between the query and the candidate document and the information coverage degree score of the optimal document to the candidate document, and constructing a sub-module objective function; calculating a marginal gain threshold value, iteratively calculating optimal documents with marginal gains greater than the threshold value, and generating an optimal document set; and splitting the optimal document set according to the paragraph level, generating a candidate paragraph set, calculating the redundancy score of the optimal paragraph set, constructing a sub-module objective function added with redundancy penalty by multiplexing the method, and performing iterative calculation to generate a final optimal evidence paragraph subset. The method effectively solves the problems of over-high redundancy, over-low information coverage and the like caused by a traditional processing method, so that the final model reasoning performance is improved.

The invention provides an intelligent tax auditing robot system based on NLP and OCR technologies, and relates to the technical field of tax auditing. The data acquisition module collects enterprise tax related data. And the data processing module constructs an enterprise tax knowledge graph. And the risk assessment module generates a risk report and early warning information. And the intelligent interaction module explains the risk assessment result to obtain a decision suggestion. And the report generation module generates an enterprise tax audit report. And the learning optimization module performs real-time optimization and adjustment. According to the method, the dynamic tax rule engine is established through tax regulations and enterprise historical data, and the enterprise tax risk can be accurately identified in combination with the risk assessment model of machine learning. Through real-time comparison and deep analysis, abnormal conditions in the enterprise tax data can be found in time, the risk level is accurately evaluated, tax staff are helped to take measures in advance, enterprise tax risks are reduced, and tax violation behaviors are avoided.

A computer implemented method for the automated analysis or use of data, comprising: learning new information and representing the new information in a structured, machine-readable representation, in which representation of data comprises semantic nodes and passages; and in which each semantic node represents an entity and is represented by an identifier; and each passage is either (i) a semantic node or (ii) a combination of semantic nodes; and where machine-readable meaning comes from choice of semantic nodes and how they are combined and ordered as passages; in which the representation of data uses a shared syntax that applies to semantic nodes and passages that represent factual statements, query statements and reasoning statements, wherein the syntax is an unambiguous syntax comprising nesting of structured, representations of data to a depth; and storing the structured representation of data in a non-transitory storage medium and automatically processing it.