170 results about "Logical reasoning" patented technology

Methodology of implementing complex behavior recognition in an intelligent video system includes multiple event detection defining activity in different areas of the scene (“What”), multiple masks defining areas of a scene (“Where”), configurable time parameters (“When”), and a configurable logic inference engine to allow Boolean logic analysis based on any combination of logic-defined events and masks. Events are detected in a video scene that consists of one or more camera views termed a “virtual view”. The logic-defined event is a behavioral event connoting behavior, activities, characteristics, attributes, locations and / or patterns of a target subject of interest. A user interface allows a system user to select behavioral events for logic definition by the Boolean equation in accordance with a perceived advantage, need or purpose arising from context of system use.

A system and methodology that employs an agent technology logic layer operating in connection with or integral to a controller is provided. The logic layer can be a functional extension of the controller's firmware that facilitates logical reasoning and decision-making with regard a network as a function of individual agent(s) state and / or status. The components of the subject invention can facilitate combining high level reasoning and / or decision making capabilities with conventional control programs to effect agent-based system diagnosis and / or system reconfiguration.

The invention discloses a natural language semantic analysis system and method based on the depth neural network. The method comprises the steps that a knowledge map is built, a training set is inputted, and an N-Gram probability model is obtained, a matrix is obtained as an input by representing words as vectors using the word2vec, a deep belief network model is used for the entity identification and the input validation set, the classifier parameters and the input test set are adjusted, the group abilities of the models are tested, the knowledge graph method is adopted to apply reasoning to the entities in the descriptions of the language, and corresponding conclusions are obtained. Compared with the prior art, the natural language semantic analysis system and method based on the depth neural network uses the knowledge graph method to apply reasoning to the entities in the descriptions of the language and to obtain the corresponding conclusions, so that our natural language understanding abilities are provided not only with the capacity to understand the literal meaning, but also with logical reasoning and the understand of the meaning on a deep level, and the method has promotable and practical value.

A method, system, and computer program product provides simultaneous debugging of multiple OS image and / or system dump pairs in a distributed storage repository. A management console receives a terminal debugging session request and a cluster selection from an interface and starts a debugger instance. The debugger instance autonomously identifies client LPARs and loads the system dump images assigned to the client LPARs. In response to receiving a selection of a first and second client LPARs, the debugger analyzes the first and second system dump images, respectively, and calculates relational information between the first analysis and the second analysis via one or more logical reasoning utilities of the management console. The debugger then loads the relational information to the management console interface with an analysis of one or more similarities between the first and second system dumps.

The invention relates to a method for classifying legal texts by adopting a semi-supervised convolutional neural network (SSC) and belongs to the field of neural networks. According to the method, natural languages are processed by utilizing the SSC to achieve the main objective of a system; and through the processing on the legal case description, the problems that what rights and interests of parties does the legal case description violate or which laws and regulations does the parties violate are preliminarily solved, probably more than one rights and interests of the parties is violated orthe parties violate a plurality of laws and regulations at the same time and multi-label classification is realized. Through the legal service platform, case handlers are helped to efficiently handlevarious legal cases and the various legal cases are subjected to semantic analysis to achieve classification to ensure that not only the ability to understand literal meanings but also the ability tologically reason and understand deep meanings are provided in the aspect of the natural language understanding function.

The invention relates to a modeling and graphical displaying method for a causal relationship reasoning model of unusual working conditions of a chemical process. The method mainly solves the problems that in the prior art, the root causes of the unusual working conditions are not judged accurately and a graphical displaying effect is poor. According to the modeling and graphical displaying method for the causal relationship reasoning model of the unusual working conditions of the chemical process, real-time data of a control system are collected, an expert rule model which is closely related to the judging process of the unusual working conditions is established, a fault tree logical reasoning model for root cause analysis of the unusual working conditions is established according to a root cause analysis mode of the unusual working conditions, an intelligent computing method is adopted, through the fact that threshold value judgment and feature extraction are carried out on key safety technical parameters, feature matching is carried out on the established expert rule model and the established fault tree analysis model, intelligent monitoring and early warning on the unusual working conditions of a production process and root cause analysis are achieved, the problems are solved better through the technical solutions, and the method can be applied to treatment of the unusual working conditions of the chemical process.

The invention belongs to the technical field of computer word representation, and discloses a deep dynamic context word representation model and method. The deep dynamic context word representation model is a masking language model formed by stacking multi-layer bidirectional Transformer encoders with layers of attention mechanisms. The network is a multi-layer neural network, and each layer of the network captures context information of each word in the input statement from different angles. Then different weights are given to each layer of the network through a layer attention mechanism. Finally, different word representations are synthesized according to the weights to form context representations of the words. Words generated by using the model represent three tasks of logic reasoning(MultiNLI), named entity recognition (CoNLL2003) and reading understanding (SQuAD) on a public data set, and the tasks are improved by 2.0%, 0.47% and 2.96% respectively compared with an existing model.

A mining belt conveyor coal amount detection method based on binocular vision depth perception is provided. A speed sensor is installed on a belt conveyor to collect speed signals of a conveying belt in real time and transmit the signals to a host computer. Two parallelly placed cameras are installed above the conveying belt to collect conveyed coal material images in real time and transmit the images to the host computer for image analysis and processing. The specific steps are as follows: a multi-resolution wavelet transform algorithm is used to enhance the conveyed coal material images, and a K-means clustering algorithm is combined to segment the images only with the coal material; a binocular vision method is used to acquire three-dimensional point cloud information of the coal material; and a Delaunay algorithm is used to calculate an initial volume of the conveyed coal material, a T-S fuzzy logical reasoning method is combined to correct the volume of the conveyed coal material, and a coal amount formula is applied to achieve detection of the amount of the coal material. The method of the invention can measure the amount of the currently conveyed coal material in real time according to the characteristics of the coal briquette on the surface of the conveyed coal material, has few errors and high effectiveness, has a practical value and is convenient for popularization.

The invention provides an on-line monitoring and fault early-warning system and method for a traction electric transmission system of a train. The system comprises a signal detection module, a lower computer, a host computer and a monitoring and early-warning result displaying module, wherein the signal detection module obtains the system quantity states to be monitored, classifies the system quantity states and transmits to the lower computer, the lower computer filters and pre-processes the system quantity states, and extracts the time domain characteristic information and frequency domain characteristic information of the system. The main characteristic information of the traction electric transmission system can be obtained by characteristic compression and dimension reduction through fuzzy logical reasoning and PCA principal component analysis. The main characteristic information is input to a SOMNN fault early-warning module, and calculated and processed by using the SOM neural network algorithm. On-line monitoring the current state of the traction electric transmission system of the train is realized, and early warning for future fault is given. Rapid real-time monitoring the state of a traction electric transmission system of a train and fault early-warning can be realized.

A formal verification method for facing design and accomplishment of a wireless sensing network safety protocol, comprises the following steps: (1) a sensing network safety protocol design method based on logical analysis: a designer of the sensor network safety protocol accepting a protocol design goal, and designing a protocol rudiment based on analysis of sensor network environmental factors; establishing a logical reasoning system to accomplish protocol modeling and logical analysis, amending the protocol design plan according to the analysis result, and repeating logical analysis until the logic of the protocol is correct; and giving description of the protocol specification which is not formalized finally; (2) a safety protocol specification description language facing the sensing network developing environment; and (3) a safety protocol code automatic verification method based on model checking. The present invention effectively ensures safety and efficiency of the design and development and has good expansibility.

The invention relates to the technical field of artificial intelligence, and provides a ten-thousand-level intention classification method and device, a storage medium and electronic equipment. The ten-thousand-level intention classification method comprises the steps of obtaining a dialogue statement of at least one round of dialogue with a user; performing context analysis on the dialogue statement to complement the context information of the dialogue; performing semantic analysis on the dialogue statement to obtain a plurality of candidate intentions of the user; and based on the pluralityof candidate intentions and the complemented dialogue context information, determining a real intention of the user by utilizing an intention decision model constructed based on a reinforcement learning algorithm. The method is realized based on a brand-new three-layer man-machine conversation technology framework. According to the method, the plurality of candidate intentions are obtained throughsemantic analysis on a semantic understanding layer, complemented context information is obtained through context analysis on a logical reasoning layer, dynamic decision making is conducted on a decision judgment layer through an intention decision making model according to the candidate intentions and the complemented context information, and the accuracy of intention classification through themethod is high.

System, methods and electronic devices aimed at motivating users to perform physical activity are disclosed herein. A user may be presented with a series of physical and cognitive exercises. For example, an exercise may be locked until completion of an unlocked exercise. Performance in each exercise may be tracked to display feedback to a user and track the user's improvement over time. Physical exercises may comprise challenges to move a particular distance or take a particular number of steps. Cognitive exercises comprise techniques test or train the cognitive abilities of the user in: attention, memory, processing speed, logical reasoning, numerical reasoning, spatial reasoning, verbal reasoning, language, and cognitive executive functions. Performance in one exercise may modify the difficulty or parameters of a subsequent exercise. The tracking of user improvement may comprise a progress or reward system such as the increasing of a user's “level” or awarding of “badges.”

Systems and methods are presented that enable logical reasoning even in the Presence of noisy (inconsistent) data. The knowledge base is processed in order to make it consistent and is also compiled. This processing includes checking and correcting spelling, removing stopwords, performing, grouping words of similar and related meaning, and compacting the knowledge base. A robot can use the processed knowledge base to perform many different types of tasks, such as answering a query, determining a course of action that is designed to achieve a particular goal, and determining its own location.

The invention relates to a mechanical arm kinematics formal analysis method. The method comprises the steps of 1, determining structural parameters of a mechanical arm mechanism; 2, using high-order logical language for building a mechanical arm kinematics logical model; 3, using a logical formula for describing kinematics constraints or attributes needing to be verified; 4, enabling the mechanical arm mechanism kinematics logical model and the kinematics constraints or attributes needing to be verified to form a logical proposition; 5, utilizing a logical reasoning engine for proving whether the logical proposition is true or not; if the logical proposition is true, it represents that the model meets the kinematics constraints or have the attributes; if not, it represents that the model does not meet the kinematics constraints or does not have the attributes. According to formal verification, on the basis of system formal specifications or attributes, the correctness of the system is verified by using the mathematical method, and the verified attributes are precise and complete.

The invention discloses a product allocator and constructing method, which comprises the following steps: building product knowledge base based on logical language; realizing intelligent option and highly effective allocation calculation; designing logic rational regulation and real-time detecting function to realize effectively rapid definition of logic relationship among characteristic parameters; affirming the logic relationship of the parameter not mutually contradiction; making the product allocator have certain rational intelligence; realizing to extract the BOM making information in the allocated BOM. The main content of knowledge base of characteristic parameter contains product element file, allocated BOM, characteristic parameter information and exclusive or coexistent logic relationship information among characteristic parameters.

The invention discloses a multi-source data aggregation sampling strategy based on a big data environment. The method comprises the following steps that on the basis of a multi-source data set encoded by GBK, fusion of multiple attributes in one data set and fusion of different source data sets are achieved, and multi-source data fusion operation is completed; fused files are segmented to form a two-dimensional word frequency matrix of file words; high-frequency words are displayed to a user for reference, and random migratory selection of seed root node words required by snowball sampling is conducted; business-goal-oriented seed root node keywords are selected, and the depth of snowball sampling is input; on the basis of seed root node data, a balance check value is set, and the words are circularly matched for snowball sampling; a directed acyclic graph and an adjacent matrix are constructed; a root node clustering network diagram and a logical reasoning diagram which are related to business goals can be obtained. The interference of sample noise on subsequent reasoning can be reduced through the strategy.

The invention relates to an STEP-NC based intelligent nonlinear process planning method. The method comprises the steps of determining a machining operation method corresponding to a component machining feature type through a pre-trained BP neural network model; sequencing all machining steps in the machining operation method based on pre-defined machining step sequencing principles to obtain a reasonable machining step sequence; and in allusion to each machining step in the machining step sequence, selecting resources matched with the machining step, and optimizing each machining step and process parameters of each machining step by adopting a chaotic genetic algorithm to obtain an optimal machining process plan. According to the method, the chaotic algorithm, the genetic algorithm and the BP neural network are organically combined and applied to process optimization for the STEP-NC, thereby being capable of performing efficient, accurate and intelligent logical reasoning, effectivelysolving a complex process planning problem, and having important significance for the further study of the STEP-NC theory.

The invention belongs to the technical field of computers, relating to fault diagnosis system and method of an encoder based on logic compatibility and aiming to provide an automatic fault diagnosis device of an encoder and a diagnosis solving method thereof. The fault diagnosis system comprises a user interface unit, a model description unit, a language analysis unit, a conflict recognition unit, a diagnosis solving unit, a fault positioning unit and a digital communication unit. The system is specially characterized in that the structure, function and component behavior of the encoder is prescriptively described by using a model description language according to an encoder constitution principle, accordingly, the expected behavior of the encoder under normal circumstances is deduced, differences exist between the expected behavior and an actual observation when a fault occurs, and then a component set triggering the fault is confirmed by using logical deduction. In the invention, from the conflict recognition to the generation of candidate diagnosis, the whole operation process is simple and needs short time, and the discrimination and positioning of the system on a fault point does not depend on the experience of an operator or an expert, thereby the limitations of the traditional diagnosis method are overcome.

The invention discloses a wireless network switching management method and system; the method uses software to define network inherent centralized control and programmability advantages, and fully considers influences on the network switching management strategy performance by multi factors such as the network, users and applications; the method selects a plurality of influence factors as the input parameters, such as user mobility, network states and user preferences, senses network demands in many aspects, and uses fuzzy logical reasoning to obtain the final network scoring result so as to provide a network switching final decision. The method considers objective factors on the network side and the user side, and considers user subjective customized demand preferences, thus providing differentiated network switching management services for different users.

The invention relates to an electric power equipment fault early-warning method which comprises the following steps of acquiring acting quantity information of a power grid in a fault occurrence time, namely switch information and protection action information; acquiring action information from a data acquiring and monitoring system and a circuit breaker, and obtaining fault occurrence time of each action node of a suspected faulted component; discriminating faulted equipment in the power grid in a real-time online manner and generating fault brief information; combining the switch information, the protection action information and the fault brief information by a transformer substation for forming transformer substation fault data information; receiving and analyzing uploaded fault data information by a scheduling center, performing real-time diagnosis on the fault equipment of the power grid according to a logical reasoning algorithm, and performing comparison checking on the diagnosis result and the fault diagnosis result of the transformer substation. The electric power equipment fault early-warning method has advantages of acquiring fault information in the power grid equipment in time, shortening accident processing time, ensuring safe and reliable operation of the power grid, ensuring high safety of power grid equipment and preventing abnormity of the power grid.

The invention discloses an end-to-end dialogue system and method based on dialogue state guidance and knowledge base retrieval. The system comprises: a dialogue historical information encoding moduleused for encoding sentence-level semantic information by adopting a Transformer structure and encoding dialogue-level semantic information by adopting a GRU structure so as to output a dialogue-leveldistributed vector according to input dialogue historical information; a dialogue state tracking module used for extracting round-level information, extracting the inter-round information to obtain the probability distribution of the slot value on the current slot, and taking the maximum probability as the final slot value; a knowledge base retrieval module used for determining an accessed database and determining attribute entries in the accessed database; and a dialogue reply decoding module used for introducing a copy mechanism to decode so as to alleviate the OOV problem, and controlling the source of the copy by using a gating mechanism. The system has the advantages of accurate database positioning, accurate reply and logical reasoning capability.

The invention relates to the field of artificial intelligence, in particular to a knowledge map expansion model, a structured knowledge storage method and device, aiming at solving the problem that the quantized data cannot be stored in the prior art. The knowledge map expansion model of the invention comprises entities and the relationships. The connection between the entities is a directed connection, and the directed connection edges contain the relationships. Each relationship includes one or more optional attributes; each entity, relationship, and attribute has a unique identifier. Each relationship includes one or more optional attributes; each entity, each relationship, and each attribute has a unique identifier; each attribute includes a data type and value, as well as optional dimensions. The storage method of the invention is based on the knowledge map expansion model, stores the structured knowledge as the data table structure of the relational database, stores the quantizeddata into the attribute, facilitates the logical reasoning and the quantified reasoning, and improves the query efficiency.

The invention relates to a high-voltage insulation fault diagnosis method based on heterogenous image temperature rise and partial discharge characteristics. The high-voltage insulation fault diagnosis method based on heterogenous image temperature rise and partial discharge characteristics includes the steps: extracting the contour of an infrared image and the contour of an ultraviolet image; performing image registering based on the contour information to obtain the optimal affine transformation parameter; by means of the optimal affine transformation parameter, performing ultraviolet imagetransformation, extracting the ultraviolet spot contour diagram and the ultraviolet gray level image of the ultraviolet image after transformation, and performing fusion of the infrared image and theultraviolet image; and extracting the greatest temperature rise and the partial discharge characteristics from the fused image, and at the same time constructing a fuzzy logical reasoning system to perform external insulation fault diagnosis. Compared with the prior art, the high-voltage insulation fault diagnosis method based on heterogenous image temperature rise and partial discharge characteristics has the advantages of reducing the information redundancy, being more visual in the images, improving the detection accuracy, being stable in the algorithm, being high in the information reservation degree, being high in the applicability and the like.

The invention discloses an intelligent equipment fault diagnosis method based on semantic reasoning. According to the method, on the basis that a cloud distributed system is used for storing and rapidly processing mass manufacturing data generated by the intelligent equipment, firstly, a Profee graphical modeling tool is used for building an intelligent equipment semantic model, and the semantic model is loaded and edited through a Jena toolkit; reading the real-time data of the equipment through the JDBC, and associating the real-time data of the equipment in the database with the model; self-defining a logical reasoning rule in Jena, and performing logical reasoning by utilizing a reasoning machine to realize fault diagnosis of the intelligent equipment; finally, the fault diagnosis result is intelligently visualized, and a proper solution is recommended. By adopting the method, the fault diagnosis efficiency and level of the intelligent equipment in the intelligent factory can be remarkably improved, the maintenance time of the equipment is shortened, and the production benefit is maximized.

The invention discloses a complex network link prediction method and system based on logical reasoning and graph convolution. The method comprises the following steps: constructing a knowledge graph corresponding to a complex network, and obtaining a training set; performing relation reasoning on each entity pair in the training set through a first-order logical reasoning network with default, and obtaining a relation confidence coefficient matrix through mapping; based on the relation confidence coefficient matrix, performing iterative training on a graph convolutional neural network based on iterative attention through a centralized training decentralized execution mechanism and a local relation attention mechanism to obtain first probability distribution; calculating second probability distribution according to a relation weight matrix and a relation confidence coefficient matrix output by network iteration; obtaining a Wasserstein distance between the first probability distribution and the second probability distribution according to a joint evaluation function; iteratively updating the two networks according to a Wasserstein distance to obtain a link prediction model; and complementing the knowledge graph according to the link prediction model. The link prediction efficiency is high.

The invention discloses a logical reasoning mechanism being suitable for a legal expert system, effectively overcomes the malady that the current legal expert system is based on regulation, and solves the problems that the majority of legal expert systems can not apply judging knowledge for reasoning, only provides simple explanation through regulation feedback, the problems that uncertainty how to stimulate the intuition, experience and reasoning result and other problems are expressed dissatisfactorily. The reasoning mechanism introduces a correlation theory and a method of fuzzy reasoning, abandons simple positive and negative two-value features of traditional logic, regards the logical world into gray level with continuous changes, allows a proposition to be this or that, and exists partial positive and partial negative. The invention provides a more natural and more reasonable operation environment, can describe target system behavior adopting a fuzzy logic technology, imitates people to judge and control behavior technology by cognition and experience, can transform some natural languages into algorithms of computer operation to achieve the purpose of using a simple program to process complex systems which can not be processed or is difficult to be processed by traditional control method. The fuzzy logic correlation theory and method are applied in the legal expert system, can well overcome the shortcomings of knowledge expression, reasoning mechanism and knowledge acquisition and the like of the traditional method in the legal expert system, are more convenient to express numerous uncompleted and inaccurate knowledge and relationship in legal fields, more appropriately describe uncertainty in legal reasoning, makes up for defects of traditional reasoning method, and are suitable for large-scale popularization.

A system and method defining the twelve modular functions of cognition, and using this information to identify individual work style preferences and to facilitate individual and leadership competence development programs. The system and method associates sets of two related pairs of the cognitive functions with each of the three cognitive sectors of Reasoning logically, Relating to others, and taking Action. A preference survey instrument includes sets of any combination of statements, words, questions, or behaviors related to traits common to each of the functions, and defines a set of six preferred functions based on responses to each item in the instrument.

The invention provides a machine reading inference method based on a graph neural network. Overall process is as follows: a proposition judgment module, an entity identification module and an entity chain finger module are obtained through secondary training of a neural network; an information extraction module and a polarity discrimination module are combined respectively; a fact logic relation graph in a reading material and entity and polarity information in a to-be-inferred proposition are obtained, and then the fact logic relation graph, together with an environment knowledge graph, is input into a graph neural network subjected to secondary training together to obtain a final entity logic relation graph; and finally an inference conclusion and an inference route graph are obtained byusing a Bayesian network. According to the method, the graph neural network is applied to machine reading inference for the first time; on the basis of relation inference, the machine logic inferencecapacity is further given, and the automatic case inference process is achieved; and the method has important use value in the fields of criminal investigation, machine questioning and answering andthe like.

The embodiment of the invention discloses a knowledge graph mining method, device and equipment and a storage medium. The method comprises the following steps of generating each candidate entity representation pair according to a candidate feature set of an entity and / or each user dialogue corpus; determining the weight of each candidate entity characterization pair according to entity informationand characterization information included in each user dialogue corpus; and determining the key representation of the entity according to the weight of each candidate entity representation pair. According to the embodiment of the invention, the subjective influence factors when a worker determines the entity and the representation relationship are avoided, the labor experience cost for determining the entity and the representation relationship and the complex logical reasoning process are reduced, and the determination efficiency and the accuracy of effective representation under the entity are improved.