56 results about "Automated reasoning" patented technology

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches. The invention uses hierarchical and fuzzy deontic logic implementations alongside connectionist AI / ML to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration. In at least one embodiment, the invention operates through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining coherence, consistency and supporting compound workflows. The invention provides a framework for AI systems to make logically consistent, ethically-aware decisions by combining deontic reasoning with multi-agent coordination, token space communications and knowledge, including on intermediate results, enabling automated decision-making for a variety of applications.

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches alongside quantum-inspired token management. The invention uses hierarchical and fuzzy deontic logic implementations and quantum-inspired state representations that combine complex amplitudes and phase information to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve complex goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration and information-theoretic metrics. The platform is capable of operating through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining system coherence and logical consistency using quantum-inspired token operations and phase alignment transformations for optimizing information transfer between states.

The invention discloses a technology development situation awareness system and method, and belongs to the field of information processing, and the system comprises a data input module, a field overview module, an intelligence task question and answer module and a data output module. The method comprises the steps of 1, receiving a user question, and performing deep semantic analysis; 2, constructing a thinking chain and cue word engineering system based on metadata; 3, executing thinking chain reasoning based on large language model driving and generating a first draft of a report; step 4, executing metadata consistency checking of the first draft of the report, executing report content updating, and presenting a research report and evidence; and 5, archiving high-quality scientific data in the research report. According to the method, key feature rules in technology development can be effectively mined, comprehensive, convenient, efficient and intelligent perception of the technology development situation is realized, automatic reasoning of a technology situation perception result is provided, and efficient and credible intelligence reference is provided for decision making.

Computer-implemented techniques for verifying translated access controls for application modernization include an application modernization service of a provider network obtaining a source access control. The service translates the source access control to a target access control. The service compiles the source access control and the target access control into respective automated reasoning solver encodings. The service uses the automated reasoning solver encoding to query an automated reasoning solver such as a Satisfiability Modulo Theories (SMT) solver to determine whether the source access control is less or more permissive than the target access control representing a security issue or an availability issue with the target access control, respectively.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

The invention relates to the technical field of bidding document auditing, and particularly discloses a bidding document compliance auditing method based on a multi-modal knowledge graph, which comprises the following steps: extracting a standard reference file from an authoritative data source, and constructing the multi-modal knowledge graph in combination with natural language processing and image recognition technologies; through modal separation and structured processing, texts, tables and images in the bidding document file are converted into structural features, and standardized expression of an unstructured document is achieved; through semantic mapping and node matching, a one-to-one correspondence relationship is established between text modal features of a bidding document file and standard term nodes in a knowledge graph, and text difference features are extracted, so that the accuracy and pertinence of an auditing result are improved; based on an automatic reasoning mechanism of a knowledge graph logic rule, automatic compliance judgment is completed, and subjectivity and omission risks of manual auditing are reduced; the bidding document file is subjected to hierarchical labeling through cross-modal consistency verification, a compliance evaluation result is formed, and the intelligent level of auditing is improved.

The invention discloses a forest management data processing method driven by a large language model, and belongs to the technical field of forest resource management and artificial intelligence data processing crossing. The method comprises the steps of multi-source data acquisition and preprocessing, forest management knowledge graph construction, large language model fine adjustment and retrieval enhancement generation, data semantic fusion and understanding and management strategy reasoning, wherein the large language model performs automatic reasoning based on fused data, management intention and industry knowledge to form a management strategy conforming to a forest growth law and multi-target balance; operation plan text generation: on the basis of completing strategy and space matching, compiling a forest operation plan text meeting forestry industry specifications and management requirements, and man-machine interaction and continuous optimization: collecting feedback information of forestry workers on forest operation plans through a man-machine interaction interface, and systematic evaluation is carried out on the generated operation scheme from forest resource sustainability, ecological function improvement, operation goal achievement degree, risk controllability and policy compliance.

The invention relates to the technical field of data processing, in particular to a data mining system based on artificial intelligence. According to the method, a finer computing resource regulation and control mechanism is constructed through links of inter-task correlation analysis, load task priority adjustment, computing cost evaluation, abnormal task identification, resource use trend analysis and the like. A topological structure is constructed for the relevance between the tasks through a graph neural network, and fuzzy matching is performed in combination with the computing resource sharing degree, so that automatic reasoning of the task dependency relationship is realized, and the intelligence of scheduling optimization is improved. The task priority adjustment adopts a mode of combining a CPU utilization rate and a calculation time proportion, load tasks are dynamically marked, a scheduling sequence is optimized in combination with a task time overlapping degree, and calculation resource conflicts are effectively reduced. According to calculation resource consumption analysis, task calculation load influence coefficients are calculated from multiple dimensions of task execution time, data reading amount and I / O operation times, and the reasonability of calculation resource allocation is improved.

The invention belongs to the field of software engineering, and particularly relates to a deep learning compiler test case generation method based on automatic reasoning of a computational graph structure, which can be used for generating a deep learning compiler test case so as to improve the test efficiency of a deep learning compiler. The method is divided into two stages: in a computational graph structure learning stage, a text generation model is adopted to learn structure information of a computational graph; the relationship between operators in the calculation graph is the same as the relationship between words in a natural language, and each calculation path is similar to a sentence; and then all calculation paths in the calculation graph are extracted, so that the existing calculation graph structure information can be learned by utilizing an efficient text generation model, and the method is applied to generation of a new calculation graph. In the calculation graph reasoning generation stage, the calculation graph structure reasoning model obtained in the calculation graph structure learning stage is utilized, so that the next node is automatically reasoned according to the information of the current node, and a complex and diversified deep neural network model is formed.

The invention relates to the technical field of text data processing, in particular to a thesis withdrawal reason automatic classification system which comprises a task division module, a model construction module, a multi-task joint module and an inference analysis module. According to the method, the content in the draft withdrawal statement is subdivided, and specific information is extracted and analyzed, so that the accuracy and depth of draft withdrawal reason classification can be remarkably improved, and through automatic text analysis and reasoning, the draft withdrawal reasons can be quickly identified and can be deeply understood, so that the accuracy and the accuracy of draft withdrawal reason classification are improved. For example, legal dispute and periodical operation problems in a draft withdrawal statement are analyzed, a data processing flow is optimized, efficient analysis of the draft withdrawal statement is achieved, manual intervention is reduced, the processing speed is increased, the transparency and processing efficiency of the draft withdrawal flow are improved, automatic reasoning analysis further enhances exploration of the relation between draft withdrawal reasons and potential laws and editing problems, and the method is suitable for popularization and application. And more accurate decision support can be provided for the publication, so that the future manuscript withdrawal risk and related disputes can be reduced.

The invention discloses an agent training and evaluation method and system under typhoon disasters. Constructing a typhoon disaster dispatching knowledge graph according to the power grid typhoon emergency plan and the historical dispatching log; and a large language model is selected and is optimized through double-stage training of supervised learning and reinforcement learning. Generating a disposal flow reasoning plan by using the optimized model, and inserting an original sample to form a complete reasoning sample; analyzing the disaster description, extracting an inference target, generating a multi-hop inference chain according to the disposal priority and the logic constraint, and mapping the multi-hop inference chain to a knowledge graph; and recognizing subject entities in the reasoning process, and outputting answer entities in combination with parallel and sequential sampling and semantic similarity expansion reasoning paths. And finally, a question and answer simulation platform and a question library are built, the reasoning agent is questioned, and the reasoning ability of the reasoning agent is evaluated by adopting multi-angle similarity. According to the method, the automatic reasoning and decision-making level of power grid emergency disposal in a typhoon scene can be improved.

The invention relates to the technical field of large language model optimization, and discloses a thinking chain generation method, device and equipment and a storage medium, and the method comprises the steps: constructing a parameter difference matrix and extracting a parameter change direction vector through comparing the parameter difference between a basic model for generating a long thinking chain and a target model for generating a short thinking chain; establishing a controllable continuous reasoning space; a low-rank matrix aligned with a direction vector is generated by utilizing a low-rank fine tuning technology, and the updating weight of the low-rank matrix is controlled by adjusting a normalization factor, so that the basic model can dynamically generate a thinking chain with adaptive length under different task complexity, and the optimal matching of the reasoning depth and the task requirement is realized. According to the method, the problems of redundant calculation and inference insufficiency caused by fixed threshold truncation and parameter stiffness in the prior art are solved, and the efficiency and correctness balance capability of the model in scenes such as intelligent question answering and automatic inference is remarkably improved.

The invention provides a ship structure assembly process reasoning method and system based on a knowledge graph. The method comprises the following steps: constructing an assembly process knowledge graph library; the assembly process knowledge graph database comprises assembly element data, assembly process data and an association relationship established between the assembly element data and the assembly process data; establishing an assembly process reasoning model according to the incidence relation between the assembly element data and the assembly process data; acquiring and determining assembly element data; based on the assembly element data, establishing and sending an assembly process reasoning request; and in response to an assembly process reasoning request, receiving and extracting assembly element data, and reasoning based on the assembly process reasoning model and the assembly process knowledge graph database to obtain corresponding assembly process data. According to the method, tight coupling of the assembly element data and the assembly process data is achieved by constructing the assembly process knowledge graph database, the assembly process data is obtained through automatic reasoning based on the assembly element data, and the design standardization degree and design efficiency of the assembly process are improved.

The invention relates to the technical field of block chains, and provides an EVM instruction set abstract semantic description method and device based on a theorem prover, electronic equipment and a computer program product. The method comprises the steps of obtaining a to-be-processed EVM instruction set; and converting the EVM instruction set into a corresponding abstract semantic description based on an inductive definition method in the theorem prover. According to the method, a theorem prover with high credibility is utilized, and instruction behaviors during operation of the EVM instruction set can be accurately and completely described mathematically, so that the accuracy of abstract semantic description for the EVM instruction set is improved, and a solid mathematical basis is provided for automatic reasoning and formalized verification of the smart contract.

The invention belongs to the technical field of ecological data processing. The invention provides an ecological environment admission intelligent analysis risk assessment method and system based on a large model. An integrated scheme of multi-source data acquisition and targeted preprocessing, large model driven knowledge extraction, dynamic ecological environment access knowledge graph construction, project information intelligent analysis, multi-dimensional intelligent matching, risk inference engine iterative reasoning, structured report generation and knowledge and rule base continuous optimization is adopted. A full-process automatic ecological environment admission analysis and risk assessment system is constructed, the core problems that traditional ecological environment admission assessment highly depends on manpower, is low in efficiency and is non-uniform in standard are solved, and the technical defects that an existing system lacks deep text understanding and automatic reasoning ability, knowledge updating lags behind and complex correlation analysis is difficult are overcome. Through close connection and data circulation of all links, the automation degree and operation efficiency of the assessment process are greatly improved, the consistency and objectivity of assessment standards of similar projects are ensured, and the comprehensiveness and accuracy of risk identification are improved.

The present invention proposes an intelligent agent automatic reasoning method and system based on task-dependent planning, belonging to the technical field and system of model automatic reasoning. The method includes: according to the status information of the current task, using a forward reasoning module to predict the subtasks to be executed and generate the dependency relationship between the subtasks and the status; according to the dependency relationship between the subtasks and the status, using backward reasoning to generate the confidence score of the dependency relationship between each subtask and the status; by performing statistical analysis on the parameter uncertainty of the predicted output subtasks, generating the probability distribution of the results and quantifying the uncertainty of the subtasks; by performing decision uncertainty analysis on the historical behavior and current status of the executor, evaluating the credibility of its decision. It solves the problem that the predefined task dependency relationship cannot adapt to different task inputs and topological structures, and avoids the problem that the large language model is too confident when expressing oral uncertainty.

The invention discloses a traffic law enforcement virtual simulation training method based on a dynamic scene and cognitive evaluation, and the method comprises the steps: generating an initialization parameter according to a trainee portrait and a training target, and driving an emergence type scene generation engine to synthesize a dynamic three-dimensional virtual training scene containing composite illegal elements; in scene operation, multi-modal interaction behaviors and physiological response data of the trainees are synchronously collected to form a data stream; through real-time analysis of the data flow, a cognitive behavior analysis report for evaluating attention, cognitive load and decision intention is generated; constructing a space-time knowledge graph based on the student operation sequence, performing automatic reasoning and contradiction detection in combination with formalized legal rules, and outputting an evidence chain logic verification result; and fusing the report and the result to carry out multi-dimensional weighted evaluation, and generating a comprehensive score and personalized feedback. According to the method, normal form transformation from preset process training to open cognitive evaluation is realized, and the authenticity, pertinence and evaluation depth of training are improved.

Methods, systems, and computer program products for generating computer source code products. According to one method, an engineer creating or modifying an implementation of computer source code selects design patterns from a catalog of pre-existing design patterns. The design patterns are known abstractions that solve specific problems of computer program design and implementation. The selected design patterns are then bound to elements within a representation of the implementation known as a model. The model is then converted into a representation using elemental design patterns, and this abstraction model is used to generate the computer source code in any programming language. The abstraction model is also suitable as input for an automated inference engine for validation or verification purposes prior to or after the generation of the computer source code.

To provide a device, a dataset and a computer-implemented method for testing automated reasoning, in particular, automated tabular data reasoning, for determining an answer to a question based on a table including contents of cells in a tabular structure.SOLUTION: The method includes: providing a table, a question, and an answer (300); determining a prediction for an answer by automated reasoning based on contents of cells of the table and the question (304-2, 306-2, 308-2); and determining, depending on a difference between the prediction and the answer, at least two of a robustness metric of automated reasoning, in particular, automated tabular data reasoning, with respect to changes in the tabular structure, an attention metric of automated reasoning, in particular, automated tabular data reasoning, with respect to at least one cell of the table, and a robustness metric with respect to changes in numerical contents of at least one cell of the table (304-3, 306-3, 308-3).SELECTED DRAWING: Figure 3

The invention provides an advanced instruction understanding and long-range navigation method based on a visual language model, and the method comprises the steps: carrying out the semantic object reasoning and spatial position prediction based on an advanced instruction and a global three-dimensional scene with annotations through a visual language model, and obtaining a target region; determining a target waypoint in the local boundary of the target area through a random sampling mode; and based on the panoramic view of the target waypoint, through the intelligent model with the body, carrying out detection and positioning to obtain the position information of the target object. Compared with the large difference between a navigation task and a real world demand in the prior art, the method comprises the following steps: performing semantic object reasoning and spatial position prediction through a visual language model, and determining the position of a target object; based on the panoramic view of the target waypoint, through the intelligent model with the body, detection and positioning are carried out, and a target object is determined; understanding of a high-level abstract instruction is realized, and a target object and a possible position are automatically reasoned; and the navigation capability of the robot in a complex real environment is obviously improved.

The invention provides a multi-source document automatic abstracting method and device, equipment and a storage medium, and the method comprises the steps: extracting information from a multi-source document, and constructing an initial knowledge graph; obtaining an inference rule based on the initial knowledge graph, performing automatic inference according to the inference rule, and adding or labeling an inference result in the initial knowledge graph to obtain a first knowledge graph; taking an entity needing to generate an automatic abstract as a key entity, extracting an event corresponding to the key entity from the multi-source document, constructing a relationship between the extracted event and the key entity, and adding the constructed relationship into the first knowledge graph to obtain a second knowledge graph; traversing all events associated with the key entity in the second knowledge graph, calculating importance scores of all the events, and screening part of the events as initial abstracts according to the importance scores; and inputting the initial abstract into a pre-trained editing network to obtain a final abstract.

A large language model driven forest management data processing method belongs to the cross technical field of forest resource management and artificial intelligence data processing. Multi-source data collection and preprocessing, forest management knowledge graph construction, large language model fine tuning and retrieval enhancement generation, data semantic fusion and understanding, management strategy reasoning: based on the fusion data, management intention and industry knowledge, the large language model automatically reasons to form the management strategy conforming to the forest growth law and the multi-objective balance, management scheme text generation: on the basis of completing the strategy and space matching, the forest management scheme text conforming to the forestry industry specification and management requirement is compiled, human-computer interaction and continuous optimization: through the human-computer interaction interface, the feedback information of the forest management scheme of the forestry workers is collected, and the generated management scheme is systematically evaluated from the forest resource sustainability, ecological function improvement, management target achievement degree, risk controllability and policy compliance.

Techniques disclosed herein for diagnosing faults include receiving a description of a sensor set. The techniques further include decomposing the sensor set, constructing a data-driven model for each sensor subset, and determining a fault association for each data-driven model residual. Using sensor measurements of a first sensor subset, the techniques further include calculating residuals of (i) the data-driven model and (ii) a physics-based model, determining a fault of a component or a sensor of the first sensor subset based on the residuals, and generating an alert indicating that the fault is present in the component or the sensor. These disclosed techniques advantageously integrate conventionally independent diagnostic techniques into a single diagnostic framework that outperforms such conventional configurations. Moreover, the disclosed techniques enable the integration of additional diagnostic techniques into well-established and / or otherwise currently implemented diagnostic approaches for a particular system, which was previously unachievable in conventional diagnostic systems.

The invention discloses an automatic reasoning acceleration method for a deep learning model and a related device, and relates to the technical field of software, and the method comprises the steps: obtaining an original deep learning model, and exporting the original deep learning model as a deep learning model in an ONNX format; performing low-rank quantization processing on the deep learning model in the ONNX format to obtain an optimized deep learning model; and based on a dynamic concurrent processing mechanism, performing deployment and reasoning on the optimized deep learning model. By exporting the deep learning model into the ONNX format, it can be ensured that the model can achieve efficient reasoning on various hardware platforms, and through low-rank quantization optimization and dynamic concurrent processing, the reasoning speed can be remarkably increased and computing resource consumption can be reduced while high precision of the model is kept.

The invention provides a mathematical knowledge reasoning verification method and system based on mathematical text formalization, and belongs to the field of mathematical knowledge reasoning check, and the method comprises the steps: S1, converting an input English text describing mathematical knowledge into a formalized formula list which can be recognized and processed by a computer; and S2, performing logical reasoning verification and algebraic calculation verification on the formalized formula, and judging correctness of a reasoning process and a conclusion. According to the method, an English text describing mathematical knowledge is automatically converted into a formalized language with complete semantic information, and automatic reasoning verification of the mathematical knowledge is realized on the basis of the formalized language; the method does not depend on a data set, does not need model training, and has wide application scenes and remarkable values in the fields of mathematical theory research, computer-aided proof, mathematical software development and the like.

Techniques are described for providing a SAT-based solver for a quantifier-free theory of strings and bit vectors. The solver can be used by an automated reasoning service of a cloud provider network to analyze policies and policy consequences. The solver reduces an input formula to a Boolean satisfiability problem by encoding the input formula into an equivalently satisfiable propositional formula, where the satisfiability of the equivalently satisfiable propositional formula is determined by a SAT solver. Instead of using a traditional DPLL(T)-style algorithm, the solver described here limits the length of variables in an input formula and reduces the problem to a single formula, which can then be solved using incremental SAT solving.The solver can be used independently or as part of a portfolio of solvers to determine the satisfiability or unsatisfiability of certain formulas corresponding, for example, to questions about user policies within a cloud provider network.

The present invention discloses a technology development situation awareness system and method, which belongs to the field of information processing. The system includes a data input module, a field overview module, an intelligence task question and answer module, and a data output module. The method includes: step one: receiving user questions and performing deep semantic analysis; step two: building a thinking chain and prompt word engineering system based on metadata; step three: executing thinking chain reasoning driven by a large language model and generating a draft report; step four: executing metadata consistency check of the draft report, performing report content update, presenting the research report and evidence; step five: archiving high-quality scientific data in the research report. The present invention can effectively mine the key characteristic laws in technology development, realize comprehensive, convenient, efficient and intelligent perception of technology development trends, provide automated reasoning of technology situation awareness results, and provide efficient and reliable intelligence reference for decision-making.

The application relates to the technical field of ship process design, in particular to a ship complex structure three-dimensional process design method, a computer storage medium and equipment. The application extracts key process features and processes them by interpreting the three-dimensional model of the ship complex structure to be subjected to process design, then obtains process design knowledge rules of the model process features based on complex structure process knowledge system reasoning, then performs process intelligent design based on the process design indication rules, realizes process automatic reasoning and design, improves the knowledge reuse rate, reduces repetitive labor, shortens the manufacturing cycle of products, and solves the technical problem of low process design efficiency of the ship complex structure.

The present invention discloses a method for automatically designing a controller for a swarm aircraft system, comprising the following steps: 1. Initializing swarm parameters, establishing a kinematic model and a dynamic model for the swarm aircraft system, and clarifying the formation control objectives; 2. Performing theoretical analysis of the swarm aircraft system's formation control based on general Lyapunov function theory; 3. Combining the sum-of-squares optimization method in automated reasoning technology to transform the formation control problem into a corresponding sum-of-squares optimization problem for solution; and 4. Using a designed efficient optimization algorithm, integrating controller design with verification and parameter optimization, the method completes the automatic design of a collaborative controller for the swarm aircraft system. The method implemented by the present invention not only solves controller design challenges in the prior art but also significantly improves the control efficiency of the swarm aircraft system in intelligently completing its target tasks.

The invention discloses a task scheduling method and system for SMT solving, and relates to the technical field of automatic reasoning, and the method comprises the steps: carrying out the feature extraction of an SMT problem instance training set, and obtaining the feature vector of each SMT problem instance; clustering a feature vector set formed by all feature vectors by adopting an unsupervised clustering algorithm to generate a plurality of instance families, and storing a clustering center of each instance family; aiming at each instance family, constructing a hyper-parameter space of a solver sequence and time distribution; for each instance family, searching in a hyper-parameter space through a Bayesian optimization algorithm of a hybrid agent model to obtain an optimal hyper-parameter corresponding to the instance family, the optimal hyper-parameter being used for generating a solver scheduling scheme of the instance family; and for an input SMT problem instance, extracting features and matching the features to the nearest clustering center, and calling a solver scheduling scheme corresponding to the optimal hyper-parameter of the instance family to execute SMT solving, so that the overall performance and the resource utilization rate of an SMT solving system are improved.