95 results about "Logical expression" patented technology

The invention relates to a public multi-mode cloud network resource software security enhancement method based on neural symbol fusion reasoning, which comprises the following steps: an intermediate representation generation stage: generating and optimizing an intermediate representation of a program through a fine-tuned large language model, and establishing semantic mapping from a source code to a structured logic representation; in the symbol language conversion stage, the intermediate representation generated by the large language model is converted into a domain-specific language fact set which can be recognized in the symbol logic reasoning stage, and formal and logic expression of program semantics is achieved; and a symbol logic reasoning stage: matching the fact set with the rule base through a symbol logic reasoning engine, performing detection and verification according to the safety rule, generating a structured report, and feeding back a result for optimization. The method is suitable for security enhancement of various core software systems in a public cloud network multi-modal network environment, high-precision security analysis is carried out on cross-modal and cross-subsystem fragmented codes in a compiling-free environment, and verifiable technical support is provided for public cloud network security control.

Techniques for a natural language interface for searching and retrieval of digitally-stored information are disclosed. In one embodiment, a computerized method is disclosed comprising receiving, at a computing device, natural language input from a user; generating, by the computing device, a prompt using the natural language input; submitting, by the computing device, the prompt to a large language model (LLM); receiving, by the computing device, from the LLM, a response to the submitted prompt, the response comprising a logical expression of the natural language input generated by the LLM using the prompt; generating, by the computing device, for a data source, a query using the logical expression; retrieving, by the computing device, a query result set from the data source using the query; and generating, by the computing device, a response to the natural language input using the query result set.

The invention discloses a large language model knowledge verification and improvement method and device and a medium. The method comprises the steps that task description, specific problems and example data input by a user are received; in combination with a preset heuristic cue word and structured processing, generating a logic expression, and removing redundant contradiction rules to form a logic knowledge base; inputting specific questions into the large language model to generate preliminary answers, and performing symbolization conversion on the preliminary answers to extract entities, relationships and attributes so as to obtain symbolized propositions; matching the symbolized proposition with the logic knowledge base, if a conflict is matched, generating a candidate correction scheme through deductive reasoning, determining an optimal scheme according to a predefined correction cost function, feeding back the optimal scheme to the large language model, and regenerating an answer; and iterating the related steps until the answer passes verification, and outputting a final answer, a confidence score and an interpretability report. Through the deductive constraint and bidirectional feedback mechanism, the LLM output reliability is improved, the knowledge base cost is reduced, and dynamic updating and interpretability of knowledge are realized.

The invention discloses an expressive control dynamic display system and method for a semiconductor design form, and the system comprises an expression configuration module which is used for receiving a logic expression inputted by a user, and prompting available variables in the form in real time; the variable dependency analysis module is used for identifying dynamic variable reference in the logic expression; the change monitoring and scheduling module is used for monitoring a form data change event, acquiring an affected control set when variable value change is detected, and merging and optimizing expression calculation requests; the expression analysis service module is used for performing grammar check and dangerous operation filtering on the logic expression, processing an expression calculation request and returning an analysis result; and the state updating execution module is used for comparing the difference between the analysis result and the current control state according to the analysis result, and judging whether the current control needs to be updated or not according to the difference. According to the method, the problem of performance waste caused by global monitoring data change is effectively solved.

The embodiment of the invention discloses an intelligent number asking method and system based on dynamic feedback, a storage medium and electronic equipment. The method comprises the steps that a subject domain and a semantic model are constructed, a data view is established, data mapping is configured, and a natural language problem and a standardized index are analyzed; generating an SQL (Structured Query Language) logical expression based on the Few-shot example and the large model, and carrying out semantic correction on the SQL logical expression; sQL query is executed, dynamic feedback storage is carried out based on an execution result, and SQL generation parameters are updated by utilizing a self-adaptive learning mechanism. According to the method, through semantic modeling and data mapping, precise alignment of a natural language and a database structure is achieved; few-shot examples and large model generation are combined with semantic correction rules, so that the accuracy and stability of SQL generation are improved; and a continuously optimized intelligent number-asking closed loop is constructed by executing a feedback and adaptive learning mechanism, so that the self-learning and evolution capabilities of the system are improved.

The invention provides a complex scene visual recognition rapid construction method based on natural language description, and relates to the technical field of computer vision and natural language processing, and the method comprises the steps: obtaining natural language description information inputted by a user; performing language analysis and element extraction on the natural language description information to obtain a scene semantic table; decoupling the scene semantic table into M atomic conditions, and obtaining an atomic instruction set; performing visual feature extraction on the to-be-recognized image by using a visual encoder to generate an image feature vector; converting the atomic instruction set into an atomic instruction vector set, and outputting an atomic instruction verification set; generating a relation verification matrix; and executing global logic expression verification operation based on the atomic instruction verification set and the relationship verification matrix, and outputting comprehensive confidence. The technical problems that in the prior art, semantic understanding depth is insufficient, complex scene understanding is limited, and self-adaptive capacity is insufficient are solved.

The invention discloses a chemical industry park environmental pollution risk intelligent assessment method and system, and relates to the technical field of environmental risk management, and the method comprises the steps: extracting an association rule of a risk event based on a real-time data stream, coding the association rule into a symbolic logic expression, constructing a mixed neural symbol architecture, and carrying out the construction of a hybrid neural symbol architecture; optimizing the symbolic logic expression and the mixed neural symbolic architecture parameters through joint training, and outputting a preliminary risk level assessment result; according to the real-time data stream, extracting a'risk event-cause-consequence 'triple, generating a risk knowledge graph, and performing graph reasoning in combination with a preliminary risk level assessment result to obtain a risk event causal chain and a diffusion path; according to the method, efficient integration and dynamic optimization of the multi-source heterogeneous data are achieved by constructing the mixed neural symbol architecture, and the problem that space-time relevance is weakened due to data splitting type processing in an existing method is solved.

The invention discloses a logic knowledge construction method and device based on a large language model and logic programming, electronic equipment and a storage medium, and belongs to the field of artificial intelligence knowledge engineering. According to the method, automatic conversion from multi-source heterogeneous knowledge to machine reasonable logic knowledge is realized through combination of semantic understanding capability of a large language model and standardized processing of logic programming. The method specifically comprises the steps of obtaining unstructured / semi-structured original knowledge data, performing entity relationship recognition and predicate logic detection by utilizing a large language model to generate a preliminary logic expression, performing grammar normalization processing to form standard logic facts and rules, and constructing a consistent logic knowledge base through a conflict detection and resolution mechanism. According to the method, a mixed conversion framework of natural language and formalized logic is innovatively provided, and the maintainability and expandability of the knowledge base are remarkably improved by introducing timestamp metadata, a rule dependence graph and an incremental updating mechanism. The technology provides structured logic support for subsequent large model reasoning, and can be widely applied to intelligent questions and answers, decision support and other scenes requiring precise logic reasoning.

The invention discloses a summary generation method and a related device, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-source information of a target conference, obtaining a summary generation prompt instruction according to the multi-source information, the summary generation prompt instruction is used for instructing a summary generation large model to generate a summary according to the content of the multi-source information, and generating a structured conference summary by referring to the hierarchical structure in the conference presentation, and inputting a summary generation prompt instruction into a pre-trained summary generation large model to obtain the conference summary of the target conference. According to the method, the hierarchical structure of the conference summary can be reconstructed based on the hierarchical structure of the conference presentation, so that the content of the multi-source information can be presented as a more logical expression in the conference summary, and the readability of the conference summary is improved.

The invention discloses an index system early warning method and device, equipment, a storage medium and a program product, and relates to the technical field of operation and maintaining.The method comprises the steps that a logic expression input by a user in a similar natural language form is analyzed, an abstract syntax tree is generated, recursion processing is conducted through a recursion decline interpreter, and an executable rule object is obtained; scheduling the executable rule object according to a preset priority weight to obtain a total scheduling result, the preset priority weight being a weight allocated for the priority of each early warning rule; and when the early warning rule is triggered, carrying out early warning on the abnormal indexes in the logic expression. According to the method, the logic expression is introduced to dynamically define the index, then the logic expression is analyzed into the abstract syntax tree, and dynamic compiling of the abstract syntax tree is realized by using the recursive decline interpreter, so that the flexibility of index early warning can be improved through the introduced logic expression in a complex monitoring scene, and the early warning efficiency is improved. Therefore, the efficiency of index operation and maintenance response is improved.

The invention relates to the technical field of information security, in particular to a trusted data space-based data exchange security verification method, which comprises the following steps of: identifying punctuation marks of request data, splitting the request data into a plurality of data segments, identifying logic expression words in each data segment, and then identifying parallel information in the data segments, so as to verify the security of the data exchange. The method comprises the steps of dividing a data segment into a plurality of pieces of independent local information based on parallel information, carrying out privacy value calculation on each piece of independent local information, setting a corresponding security verification level for the local information based on the privacy value of the local information, and carrying out security verification on each piece of local information based on the security verification level, thereby avoiding an overall verification vulnerability; according to the method, the privacy value and the verification level of each piece of local information are dynamically evaluated, and the circulation efficiency is improved while compliance is ensured.

The invention discloses a wide table data analysis method and system based on semantic association, and the method comprises the steps: receiving a natural language query demand input by a user, carrying out the semantic understanding through a pre-training language model combined with a financial knowledge graph, and generating a corresponding semantic expression; performing semantic analysis on the semantic expression, and disassembling the semantic expression into a logic expression containing a query demand and an analysis demand; based on the logic expression of the query demand, matching a corresponding wide table and field information through a preset index library, generating an executable query statement, and obtaining target data from the wide table; and according to the logic expression of the analysis demand, generating a corresponding data analysis code by using the code generation model, and analyzing the target data to generate an analysis result. The limitation of a traditional wide table fixed association mode is broken through, efficient integration and intelligent analysis of wide table data are achieved, the flexibility and accuracy of data processing are improved, and the limitation of wide table application in a fixed scene is broken through.

The invention discloses a judgment logic configuration method and device, a readable medium, electronic equipment and a product, and the method comprises the steps: displaying a judgment logic configuration page, and displaying an input region which is used for a user to construct a judgment logic expression in a mode of inputting a judgment variable name in the judgment logic configuration page; in response to a variable input operation in the input area, displaying a variable name of a candidate judgment variable corresponding to the variable input operation; in response to a selection operation on a target variable name in the variable names of the candidate judgment variables, displaying the target variable name in the input area; and in response to an input operation of inputting a logic operator and a target judgment parameter in the input region, obtaining a target logic expression comprising the target variable name, the logic relational symbol and the target judgment parameter. A user can construct the judgment logic expression in the mode of inputting the variable name in the input area, operation is easy, errors are not prone to occurring, and the editing efficiency of the judgment logic expression can be improved.

The application belongs to the technical field of natural language processing and knowledge graph, and discloses a kind of multi-modal acquisition and evolution graph analysis method and system of policy text. In view of the passive decision problem caused by the rigidity of existing policy analysis, semantic understanding and lack of dynamic correlation, the application adopts visual and DOM feature weighted fusion and reinforcement learning agent to realize multi-modal adaptive acquisition and joint alignment in anti-crawling environment;Use large language model for semantic deconstruction, and convert implicit logic into computer-readable explicit logic expression through thought chain reasoning;Construct a policy evolution knowledge graph with time and validity level attributes, perform satisfiability analysis to output conflict detection results;Map the target object portrait to the graph and the logic expression to evaluate item by item, and output intelligent decision deduction results combined with quantitative dimensions. The application realizes the whole life cycle intelligent analysis of policy data acquisition, deep logic analysis and active deduction.

The invention belongs to the technical field of intelligent question answering, and particularly relates to an experimental question answering method and system based on a knowledge graph. The method comprises the following steps of: generating a candidate combination category grammatical analysis tree for an input experimental question statement, and when vocabularies outside a preset dictionary are encountered, assigning a temporary combination category in combination with word vectors and adjacent entity types; calculating the syntactic probability of the analysis tree and the node intensity of the entity subgraph in the tree, determining a target syntactic analysis tree, and converting the target syntactic analysis tree into a logic expression; extracting an anchor point entity and a semantic relationship, determining a search depth according to sub-graph intensity, and constructing an initial graph query statement by utilizing bidirectional breadth-first search; if the query result does not exist, differential query reconstruction is carried out according to an intensity threshold value, when the intensity is high, only the semantic relation is replaced with a parent class, otherwise, the parent class and brother class relation is considered at the same time, and a sub-graph or an entity obtained through query is output as an answer. According to the method, the suitability, accuracy and fault tolerance of the experiment question-answering system can be improved, and the query efficiency and the recall rate are balanced.

The embodiment of the invention provides a digital pilot model generation method and device, equipment and a medium. The method comprises the steps that a digital pilot is constructed; according to the model element logic expressions of the digital pilots corresponding to the different flight sorties, the model definitions of the digital pilots corresponding to the different flight sorties and the flight data corresponding to the flight sorties, action parameters of the digital pilots corresponding to the flight sorties are determined; constructing a digital pilot model training data set according to the behavior action, the action parameter and the operation safety value of the digital pilot of each flight sortie; and training the initial digital pilot model based on the constructed digital pilot model training data set, and determining a target digital pilot model. And a basis is provided for subsequent comprehensive and efficient identification of safety risks existing in the flight process.

The application relates to the fields of data query and blockchains, and particularly relates to a query method and device based on a Chinese logical expression. The method comprises the following steps: receiving a query request carrying Chinese query information from a user end; generating a Chinese logical expression according to the Chinese query information carried by the query request; querying a pre-constructed block information table according to the Chinese logical expression to obtain a query result; and returning the query result to the user end. The embodiment of the application can enable users without a learning basis of a traditional SQL query language to conveniently query block information.

The invention discloses a railway interlocking system-oriented test case generation method, which comprises the following steps of: S1, reading a logic configuration file through a logic analysis and modeling module, constructing a Boolean logic expression, and simplifying the Boolean logic expression into a disjunctive normal form to explicit all logic branches; s2, running the test case set through a test execution and tracking module to track and record the state change of each logic variable in the Boolean logic expression; s3, based on the logic branches and the state change of each logic variable, identifying the logic branches activated after being covered by the test case through a coverage analysis and mapping module, counting the coverage rate of the logic branches, and identifying uncovered logic branches; s4, generating an input state set capable of triggering uncovered logic branches through a satisfiability solver; and S5, converting the input state set into a supplementary test case through an automatic case generation module. According to the method, quantitative analysis, visual diagnosis and supplementary case generation of the logic branch coverage condition are reliably realized.

The invention discloses a logical operation method based on depth-first traversal and stack recursion, and belongs to the technical field of computer logical operation and data processing. The method aims at solving the problems that in the prior art, when a complex nested logic expression is processed, operator priority judgment is complex, short circuit evaluation is difficult to achieve, and code maintainability is poor. The core of the method is that firstly, a nested logic expression to be calculated is abstracted into a tree-shaped or nested array data structure; secondly, a depth-first traversal algorithm is adopted to conduct recursive scanning on the data structure, and in the traversal process, encountered logic operators and basic judgment conditions (calculation factors) or intermediate calculation results of sub-expressions are sequentially pressed into a main stack according to the traversal sequence; when traversal of one sub-expression is completed, an actuator module is automatically triggered; and the executor module pops up a logic operator of the current action range from the main stack.

The invention relates to the technical field of cross-border appeal text generation based on LLM, and discloses an LLM-based cross-border appeal text generation method, device and equipment and a storage medium, the method comprises the following steps: extracting case information, mapping the case information to a legal knowledge graph, generating a logic expression, and carrying out conflict detection, so as to identify conflicts among legal clauses, and finally generating a cross-border appeal text. And generating a complaint text according to the conflicted target legal clause information. The method has the advantages that logic conflicts among related legal clauses are recognized in time, legal risks caused by understanding deviation or improper clause selection are reduced, complaint text compiling efficiency is remarkably improved, case information is automatically extracted, corresponding complaint texts are generated, and working time is shortened.

The invention discloses an intelligent auditing method and system based on machine learning, and relates to the technical field of intelligent auditing methods.The intelligent auditing method comprises the steps that a multi-source heterogeneous auditing time-space diagram composed of a plurality of time slice diagrams is constructed, each time slice diagram comprises an enterprise entity node, an account node, a personnel node and a document node, the nodes are connected through heterogeneous relation edges with timestamps; converting a preset auditing rule into structured logic expressions, generating a corresponding learnable rule embedding vector for each structured logic expression, and fusing the learnable rule embedding vectors with the original features of the heterogeneous relationship edge to obtain a rule perception edge representation; inputting a multi-source heterogeneous auditing time-space diagram and the rule perception edge representation into a time sequence diagram neural network, and updating the representation of each node through cross-time slice message transmission to obtain an abnormal sensitive representation; and inputting the anomalous sensitive representation into a classifier in which a random discarding mechanism is introduced.

The circuit verification method comprises the steps that a Boolean logic expression is obtained on the basis of a corner joint Miter circuit, and the Miter circuit is obtained on the basis of a circuit to be verified and a standard circuit; processing the Boolean logic expression by using a graph neural network to obtain a probability that a variable associated with each character in the Boolean logic expression is located in a conflict kernel; and verifying the circuit to be verified based on the probability that the variable associated with each character in the Boolean logic expression is located in the conflict kernel. The method can improve the circuit verification efficiency.

The invention discloses an enterprise-level form generation method and system supporting low-code development, and relates to the technical field of enterprise form generation, and the method comprises the following steps: building a semantic security construction layer, analyzing an original character string logic expression generated by a low-code logic composer, analyzing the logic expression into a plurality of structured semantic nodes, and storing the structured semantic nodes in a database; the action range of the variables is limited based on the structured semantic nodes, so that the logic generation process is expanded in the closed action range; and constructing a dynamic action domain isolation container based on the generated structured semantic nodes, and distributing each structured semantic node to an independent context space. According to the method, closed operation of logic generation and execution is realized through semantic security construction and dynamic action range isolation, variable pollution is prevented, and semantic stability is ensured; through cooperation of namespace control and a limited execution environment, logic operation forms a safe closed loop, and the operation stability and safety of the low-code form are improved.

The application provides a logic standardization processing method of a distributed power distribution terminal, and belongs to the technical field of information processing, and comprises the following steps: obtaining original logic configuration of core functions of the power distribution terminal; performing syntax normalization conversion on a private logic expression, extracting a self-defined operator, a composite variable and an operation relationship, and mapping the conversion result and the extraction result to a preset standardized logic meta-phrase set to generate a standardized logic topology expression describing a connection relationship of logic units, wherein the standardized logic meta-phrase set corresponds to the core functions; performing lexical and syntax analysis on the standardized logic topology expression, extracting non-divisible logic atomic units, and matching a physical quantity mapping relationship of a hardware interface and a communication interface of the corresponding power distribution terminal; and based on logic interaction rules among multiple power distribution terminals and in combination with the physical quantity mapping relationship, a logic execution dependency graph is constructed to realize collaborative work of the distributed power distribution terminal. Cross-terminal logic standardization is realized, and processing efficiency is improved.

The invention relates to a MC / DC coverage rate determination method and device based on a virtual machine and terminal equipment. The method comprises the following steps: analyzing an application source code file to obtain all conditional statements and conditional information, and sorting and simplifying the conditional statements and the conditional information to obtain an independent conditional list and corresponding conditional statement information; analyzing the executable target code file to obtain the address of the conditional statement and the symbol table information of the application program; adding an MC / DC coverage rate recording task in a virtual machine running environment, reading the address of each conditional statement by the task, calculating the truth value of each independent conditional simple logic expression and the truth value of the whole conditional judgment expression when the virtual machine runs to the address, and recording the coverage rate of each conditional statement; recording a file name, a function name, a line number, a judgment expression and a current execution truth value list result of the current statement into a specific file; and analyzing the record file after the operation of the virtual machine is finished, and calculating to obtain the MC / DC coverage rate of the conditional statements in the execution process and the MC / DC coverage rate of all conditional statements in the source code file of the application program.

A cause-effect graph generation device according to one aspect of the present disclosure comprises: a requirement analysis unit that uses a specification in which a requirement relating to a development target is described in natural language as input and uses a large language model to generate a condition relating to business logic represented by the specification and a truth table representing a relationship between the authenticity of the condition and the authenticity of results related to the business logic; a logical expression generation unit that uses the condition and the truth table as input and uses the large language model to generate, for each of the results, a logical expression which derives the results from the condition; and a cause-effect graph generation unit that uses the logical expression as input to generate a cause-effect graph which visualizes a relationship between the condition and the results.

The invention discloses an automatic checking method and device for a secondary circuit of a transformer substation, and the method comprises the steps: analyzing a logic relation between devices in a running state of the secondary circuit, extracting a rule between the devices in the running state of the secondary circuit, converting the rule into a structured logic expression, verifying the structured logic expression, and storing the verified structured logic expression in a rule library; analyzing and extracting device attribute information in the digital drawing, and binding device attributes in the digital drawing with rules in a rule base to generate a mapping table; forming a logic association model between the secondary system devices in the digital drawing based on the binding result; and based on the logic correlation model, performing simulation operation calculation on the secondary system to generate a simulation result, and automatically checking the correctness of the content of the secondary drawing according to the simulation result. According to the method, subjective errors and workload of manual checking can be reduced, the checking efficiency is improved through an automatic process, and the operation reliability of a secondary system device is guaranteed.

The application discloses a kind of based on formalization method's trusted machine learning model explanation method and device, it is related to artificial intelligence technical field, the method includes obtaining target machine learning model and its input characteristic value;Input characteristic value is compiled using formalization method, to obtain continuous variable;Continuous variable is discretized, to obtain the input characteristic value after discretization;The logic variable compilation of characteristic value after discretization is carried out, to obtain the variable after formalization compilation;According to the decision rule of model, the logic expression after formalization compilation is simplified, to obtain the rule reason of the expression of implication, that is, decision;Rule reason is obtained using natural language representation, to obtain the explanation result of model.The application uses formalization method to explain trusted machine learning model, compared with traditional explanation method, can more accurately reveal the internal logic of model decision, to improve the reliability of model explanation.

This invention belongs to the field of enterprise resource planning (ERP) technology and discloses an intelligent matching method and system for policy application conditions based on enterprise resource analysis. By constructing a dedicated extraction model for policy application elements, it performs structured parsing and industry-customized four-level hierarchical decomposition of policy texts, transforming natural language application conditions into executable logical expressions. It accurately handles complex rules such as AND, OR, non-nested logic, numerical thresholds, and temporal constraints. A four-level progressive matching and verification mechanism is adopted, prioritizing matching according to the order of prohibition (one-vote veto), rigid access, core scoring, and auxiliary bonus points. It incorporates industry-specific rules and enterprise lifecycle adaptation logic to avoid invalid matching and screening biases, upgrading policy matching from superficial textual association to deep compliance verification, thus improving matching accuracy and adapting to the application and screening needs of various complex preferential policies for enterprises.

Design assistance facility (300), including: a conversion unit (312) that refers to a state transition graph representing a plurality of state transitions for a plurality of states of an autonomous vehicle, and to convert the state transition into a logical expression for each state transition in the state transition graph; a transition system creation unit (314) to construct, as a logical system of the state transition graph, a logical system from a multitude of logical expressions corresponding to the multitude of state transitions; a recognition unit (315) for non-derived rules to determine, for each traffic rule in a rule set which is a set of traffic rules expressed by logical expressions, whether the logical expression of the traffic rule is derived from the logical system of the state transition graph or not; and a display unit (316) to display a traffic rule corresponding to a logical expression that cannot be derived from the logical system of the state transition graph.