118 results about "Knowledge rule" patented technology

An automated method and / or system for identifying suspected lesions in a brain is provided. A processor (a) provides a magnetic resonance image (MRI) of a patient's head, including a plurality of slices of the patient's head, which MRI comprises a multispectral data set that can be displayed as an image of varying pixel intensities. The processor (b) identifies a brain area within each slice to provide a plurality of masked images of intracranial tissue. The processor (c) applies a segmentation technique to at least one of the masked images to classify the varying pixel intensities into separate groupings, which potentially correspond to different tissue types. The processor (d) refines the initial segmentation into the separate groupings of at least the first masked image obtained from step (c) using one or more knowledge rules that combine pixel intensities with spatial relationships of anatomical structures to locate one or more anatomical regions of the brain. The processor (e) identifies, if present, the one or more anatomical regions of the brain located in step (d) in other masked images obtained from step (c). The processor (f) further refines the resulting knowledge rule-refined images from steps (d) and (e) to locate suspected lesions in the brain.

The system comprises preprocessing board connecting key phase signal to fast signal, key phase board controlling whole period of sampling, high speed collecting board for vibration signal, low speed data collecting board for graded signal. In the method, they are connected to down level server to fulfil data collection, signal analysis, and automatic identification as well as picking up characteristic parameters of operatino of machine set and down level server can make high speed communication with up level server through network card or IP protocol as up level server is equipped with an intelligent reasoning machine integrating professional knowledge rule, fuzzy logic and neural network in one component to carry trend prediction and intelligent diagnosis for operation and failure of machine.

The invention relates to a storage and processing method and a system of rail transit monitoring data. The method comprises the steps that centralized monitoring systems are established in a station, an electric service section and a railway management department; a centralized monitoring system of the station acquires various monitoring data; the centralized monitoring system of the electric service section acquires the monitoring data acquired by the centralized monitoring system of the station; an HDFS (Hadoop Distributed File System), an Hbase database, an Oracle database and a MapReduce parallel processing framework are established on a comprehensive operation and maintenance platform to form a cloud computing platform; the monitoring data is stored, analyzed and mined to generate knowledge rule data and various statistical information; and the centralized monitoring system of the railway management department generates and feeds back a dispatch control command to the centralized monitoring system of the electric service section. According to the method and the system, a cloud storage technology and the parallel processing framework are adopted, the storage, analysis and processing efficiency of rail transit resources and data is improved, and the safety and high usability of the data are ensured.

The automatic fingerprint distinguishing method based on template learning includes the following steps: registering fingerprint figure and recording the fingerprint template and characteristic information of the fingerprint figure to be matched; distinguishing fingerprint figure to judge whether the characteristic information of the fingerprint figure is the same as that of some template information in the system data base; feeding back detail information in fingerprint figure via knowledge rule to perfect corresponding reliable detail points in templates. The present invention classifies detail points of fingerprint templates, records the kernel of classes, interval and other information and has one coarse matching link before common matching. The present invention can extract and repair the characteristic information of multiple templates, raise the distinction effect and performance of automatic fingerprint distinguishing system, and has important application in biological distinction.

The invention discloses a forest classification method based on an object-oriented high-resolution remote sensing image. The method is based on the high-resolution remote sensing image, an object-oriented image classification method is used, an orienting remote sensing forest second-level classification system is established, a forest remote sensing classification auxiliary data set and an integrated image are created, key indexes which can distinguish forest types are selected, and a layered step-by-step classification extraction method is provided to be used for establishing information extraction knowledge rules of various forest types. The processes of the method are suitable for middle-small-scale forest resource remote sensing monitoring in a zone, good operability and repeatability are achieved, and efficiency and accuracy of forest remote sensing monitoring in the zone can be effectively improved.

The invention relates to a rules-based intelligent system for grid fault diagnosis, comprising a real-time data acquisition module, a data receiver, a preprocessing module, a working database, a knowledge base, an inference engine and a command generator. The real-time data acquisition module acquires real-time data information of a grid and sends it to the data receiver; the data receiver performs reasonableness testing and encoding; the preprocessing module performs formatting and information fusing and sorting and stores the information in the working database; the working database sends a command to the inference engine; the inference engine sends an inference verification request to the knowledge base; the knowledge base acquires data from the working database and performs data matching on the basis of a grid CIM (common information model) according to knowledge rules in the knowledge base built; the inference engine performs inference analysis according to the data in the working database, system state and knowledge in the knowledge base. The system has the advantages that a self-learning function is achieved, checking and error correcting is facilitated and grid fault diagnosis is more efficient.

A system for fault prediction in a home network includes: a context generator for generating context information based on status data collected in real time about components of the home network; a specification interpreter for generating knowledge rules for fault detection by using specifications of the components of the home network; a context analyzer for analyzing if the context information meet the knowledge rules to classify the context information into normal situation contexts and abnormal situation contexts; a context pattern learner for generating new knowledge rules based on the abnormal situation contexts and fault rules corresponding to the abnormal situation contexts; a knowledge rule database for storing and managing the knowledge rules and the new knowledge rules; and a fault predictor for analyzing a correlation between the knowledge rules or the new knowledge rules and the generated context information, thereby predicting faults to be generated.

The invention provides a method for extracting the profile of a building from a satellite remote sensing image, which comprises the following steps of: integrating the full-color wave band image and the multi-spectrum wave band image of the same area and generating a multi-spectrum integrated image; carrying out contrast enhancement processing on the integrated image; carrying out image division on the integrated image and constructing an image object; carrying out supervised classification on the image object based on the spectral characteristic to obtain a waterproof land type distribution map; and constructing a knowledge rule aiming at the waterproof geographic distribution map by utilizing a fuzzy logic classifier and combining the characteristics of the spectrum attribute, the space attribute, the texture attribute and the context attribute, classifying according to the knowledge rule and extracting the roof type of the building. The invention can realize the extraction of the profile of the building aiming at an irregular or complicated city proper scene, does not need auxiliary data and does not need to subdivide other waterproof land types except for the roof type in classification, the processing procedure has practicability and flexibility, and the extraction accuracy is high.

The fingerprint image enhancement method based on knowledge includes the steps: image pre-treatment; direction field estimation to calcualte the direction of each pixel in fingerprint image; binarization to change fingerprint image into black-white image according to pixel direction; fining the binary image; detail post-treatment to eliminate burr, spot and bridge in fined image to avoid pseudo detail; feedback enhacement with the knowledge rules; and extracting detail points including crest line end points. The said technology can be used to extract the details in fingerprint image and has important application value in biological recognition.

The invention relates to an electric power control system with simulation training function, and a simulation training method thereof. The system comprises a control host and controllers installed on electric power devices. The controllers are electrically connected with the control host; the control host is internally provided with an accident processing module, a daily operation module, and a basic knowledge and electric power equipment preventive experiment module. The method comprises the following steps: performing accurate three-dimensional modeling on the electric power devices in all transformer station chambers; performing format conversion on generated models, and then performing DOF node and LOD special efficacy node arrangement; rendering the models, and constructing scenes; representing the change of the states of the electric power devices by use of a texture switching technology; and determining and displaying fault situations of the electric power devices by use of knowledge rules in an expert knowledge base. Compared to the prior art, the system and the method, provided by the invention have the advantages of real-time performance, reduced work load of workers, simulation of power network accidents, convenient operation, and the like.

The invention provides a multi-feature-parameter-based comprehensive fault diagnosis method of a transformer. The method comprises: partial discharging of a transformer is classified into different partial discharge classes; a partial discharge signal is obtained by a partial discharge analyzer; feature extraction is carried out on the signal; a specific partial discharge type is identified by using a Fisher classification method; a vibration signal of the transformer is monitored and analyzed to determine the state of the transformer; if a partial discharge signal is monitored, a diagnosis iscarried out based on partial discharge signal diagnosis and DGA diagnosis knowledge rules; and if a partial discharge signal and a vibration signal are monitored simultaneously, a diagnosis is carried out based on the vibration signal, the partial discharge signal, and the DGA diagnosis knowledge rules and comprehensive diagnosis is carried out according to a collaborative diagnosis rule. The diagnosis method has high openness; and when a new monitoring quantity is added, the diagnostic processes of other monitoring data do not need to be changed.

The invention provides a virtual electric power urgent repair plan training system, which comprises a three-dimensional model library component, a model building component, a knowledge rule library component, an inference engine component, a script drive component, a plan library component, a test question library component and a marking system component, wherein the three-dimensional model library component is the basis of the model building component and the knowledge rule library component; the inference engine component needs to carry out inference according to the knowledge rule library component and the plan library component; the corresponding control and execution are completed through the script drive component; the marking system needs to carry out marking according to a plan flow process in the plan library and test questions in the test question library; and the test questions in the test question library component and plans in the plan library component also have the corresponding relationship. The virtual electric power urgent repair plan training system uses a three-dimensional model, an inference engine, a plan library and a script drive as the basis to be combined with other components; various plan training items for electric power system urgent repair are simply and fast built; the training goal is achieved; a great amount of labor and materials are saved.

The present invention discloses a multi-attribute decision tree power grid stability margin assessment method based on linear discrimination analysis. a key variable discovery model is established based on the offline simulation data and the real-time monitoring data of a power grid to perform effective screening of historical sample data to reduce the data dimensions, a combination relation model among key variables is established to discover the association relation among the variables, extract combination features capable of reflecting important degree contrast of each variable, establish the association relation between the power grid operation state and the transient stability margin, determine the main reasons of system stability level changing, form a concise and accurate knowledge rule base and regulate the decision reference so as to rapidly assess the current stability level according to the system operation state, provide quantification information support for operators' auxiliary decisions and improve the standardization, the rapidity and the adaptive capability of the power grid stability assessment, and therefore the multi-attribute decision tree power grid stability margin assessment method based on the linear discrimination analysis has wide application prospects.

The invention belongs to the field of intelligent transport study and relates to an adaptive intersection signal control method considering supply side constraint. The problem that consideration on intersection exit ramp transportation supplies is absent in the existing signal control system and study is solved. The method comprises the following steps: 1, detecting and processing intersection traffic information, and acquiring traffic demands of an intersection entrance ramp and traffic supplies of the exit ramp; 2, establishing a time-varying relationship of intersection traffic supplies, and constructing an intersection supply-demand matrix; 3, establishing an intersection signal timing dial optimization model for quintuple descriptions on problem description, initial state, target state, action set and knowledge rule based on intelligent planning; and 4, searching the target state by adopting a decision-making tree method, and generating a corresponding timing plan. Compared with the prior art, the method disclosed by the invention has the advantages that active control of intersections is realized, the intersection downstream exit ramp queue overflow can be avoided, and intersection 'deadlock' is avoided.

The invention discloses an equipment fault, alarm, prediction and processing mechanism based on data mining. The equipment fault, alarm, prediction and processing mechanism based on data mining comprises a fault relevance description and fault prediction and processing mechanism obtained based on data mining. Because the amount of fault alarm information is huge nowadays and is continuously updated every day, time cost will be increased in a geometric mode through an ordinary traversal algorithm, data relevance mining efficiency is greatly reduced, real-time requirements can not be met in reality, and accordingly fault information association rules are mined from a fault alarm information database according to a clustering distribution type parallel algorithm to form a knowledge base. When a kind of fault alarm information is generated, the probability of happening of relevant faults is quickly predicted in real time by the fault predicting and processing mechanism according to the association rules in the knowledge rules to guide fault processing.

The invention relates to the technical field of geographic space information systems, and discloses an immersive visual analysis method for spatio-temporal data on the basis of a multilayer association network. The invention has the concept that: firstly, abstracting and extracting an object node related to a task from a scene; then, analyzing and calculating an association relationship among nodes, and carrying out hierarchical division on the association relationship among multi-granularity spatio-temporal objects; according to a spatial topological relationship, a semantic association relationship layer and a multi-modal characteristic association relationship layer, constructing a multilayer association network, and carrying out collaborative visual processing on the constructed multilayer association network and a scene; and finally, through a human-computer interaction operation, realizing a final purpose that inferential reasoning and discovery ability of a human brain can be combined and a potential and complex association relationship is assisted in discovery. Therefore, the method is favorable for people to quickly discover a new knowledge rule, and the progress and the development of human scientific knowledge are accelerated.

A mangrove extraction method based on a hierarchical decision tree belongs to the technical field of mangrove information extraction based on remote sensing data. On the basis of the deep analysis on the spectral signature and the spatial distribution regulation of mangrove wetland types, a decision tree classification model of a multi-knowledge rule is built, and mangrove is extracted from a remote sensing image; according to judging criteria described by the decision tree, each image element in the image is identified and classified layer by layer, a target is separated according to multiple times of middle distinguishing and masking is performed, as a coverage, the interference in and the influence on the extraction of other targets by the target are avoided, and mangrove information is extracted. The mangrove extraction method based on the hierarchical decision tree has the advantages that on the basis of the deep analysis on the spectral signature and the spatial distribution regulation of the mangrove wetland types, the decision tree classification model of the multi-knowledge rule is built, and the mangrove can be accurately and quickly extracted from the remote sensing image.

This invention provides a method of building an intelligent electronic shopping search system that helps a customer search and shop items which have the best match for him / her using a programmed computer, CD-ROM, television, Internet or other electronic medium such as video. The method comprises receiving personal information including body characteristics, personal preferences and style from the customer; making the customer selects the item he wants to shop or search; using expert and fashion knowledge rules which contain information of what works best for people along with his personal preferences to find the items in the descending order of their personal scores for the customer; and presenting the shopping search items to the customer in the descending order of their personal scores, such that the items with higher personal score come before the items with lower scores.

Knowledge based Interface Method and System between Analysis and geometric Model, the method comprises steps of removing geometric errors both at native CAD level and neutral CAD level, and improving a geometric model based on engineering knowledge rules. Engineering rules are either previously available for known component or are built for new component. The interface system which is a computer program embodied on a computer readable medium, interacts with any out of a number of CAD platforms and CAE platforms, thus offering un-restricted selection of the combination of CAD and CAE platforms which is based on need and or preference.

The invention discloses a big data-based electric cable well state evaluation and early warning method, comprising the following steps: processing data collected in real time to obtain a new trainingsample data set; performing running state evaluation on the data to obtain a comprehensive score of the current electric cable well running state; assessing the risk level of the current electric cable well running state according to the comprehensive score; and carrying out displaying and warning by an operation control system according to an assessment result. According to the method in the invention, multiple indicators affecting the running state of the electric cable well are firstly selected and synthesized through a principal component analysis method so as to obtain a new training sample data set, and then, a gene expression programming algorithm is used for mining a functional relationship between the comprehensive score and each index data, and finally, the current electric cablewell running state data is taken as the input of the functional relationship to calculate the comprehensive score, the calculation result is submitted to a risk early warning module, a risk level knowledge rule base is queried to evaluate the risk level of the current electric cable well running state, and feasible reference comments are given out.

The invention discloses a threat estimation and situation assessment method based on a genetic fuzzy logic tree. The method comprises the following steps: reasoning target characteristics and attributes by adopting a fuzzy logic method to obtain a threat estimation result; inputting the threat estimation result and the environmental influence factors into a fuzzy inference device of situation assessment, performing situation assessment and obtaining a preliminary situation assessment result; and according to the real-time change of the environmental influence factors of the situation assessment, continuously optimizing the fuzzy inference device by combining a genetic algorithm, and outputting to obtain a final situation assessment result. According to the invention, a cascaded double-layer fuzzy logic tree is formed; fuzzy logic techniques are adopted, the method is suitable for processing a complex and nonlinear dynamic system which cannot be mathematically described; the method is combined with a genetic algorithm, the capability of updating a knowledge rule base is achieved, accordingly, intelligent threat estimation and situation evaluation can be realized, the computational complexity can be greatly reduced while fuzzy logic adaptive force and robust performance can be reserved, and the timeliness of threat estimation and situation evaluation can be improved.

The invention discloses a mathematical problem answering method and device, and the method comprises the steps: generating one or more problem solving paths of a mathematical problem to be solved according to the mathematical problem to be solved and a pre-constructed knowledge rule library; wherein the question-solving path represents that the mathematical question to be solved is reasoned step by step until a complete question-solving process of a final answer is solved; and outputting one or more question-solving paths. Through the mathematical problem answering method provided by the invention, the user can experience the application skills of the knowledge points from the problem solving path, so that the understanding degree of knowledge and the learning efficiency are improved.

The disclosed method to auto design and adjust engineering graph comprises: building and storing the 2D master control graph with some elements set as the controllable variable parameters and generated variable parameters, and creating the relation formula between parameters as knowledge rule design method; recalling the graph to input new value for controllable parameters and calculate new parameter values by computer, and then auto generating the production graph by a computer. This invention can improve 10-100 times for the design speed compared to the 1-2 months time by common technical staff.

The invention provides a boundary digitizing method of a cadastral plot, which comprises the following steps of: input step: inputting the scanning grid image of an existing paper cadastral map; division step: dividing the scanning grid image into a plurality of image objects to be classified; extraction step: building knowledge rules, applying an object-oriented analytic classifying method, and extracting a plot object from a plurality of image objects; vectorization step: vectorising the extracted plot object; and comprehensive summarization step: comprehensively summarizing the outline of the plot object, and finally obtaining the boundary element of the vectorization cadastral plot. The invention is used for solving the problem that the existing cadastral map digitizing method has low automation degree, large man-computer interaction workload and low efficiency.

The invention discloses an intelligent optimization method and system for ecological operation of cascade hydropower stations, belonging to the technical field of efficient utilization of water resources and optimal operation of hydropower systems. The method comprises the following steps: establishing a cascade hydropower station reservoir operation model which takes the minimum ecological watershortage as an objective function and considers a complex constraint set; Knowledge rules are used to dynamically generate the initial scheduling process, and the initial population is generated by random disturbance in its neighborhood. All individuals are modified to feasible search space in turn, individual fitness value is calculated by penalty function method, individual position and velocityare updated dynamically, and neighborhood search is carried out by adaptive mutation strategy. The search process described above is repeated until the termination condition is met. The invention hasthe advantages of clear and simple principle, fast convergence speed and the like, and the obtained result is reasonable and feasible, and can provide strong technical support for rational allocationof hydropower resources.

The invention belongs to the technical field of nuclear power plant control systems, and in particular to an online management method for temporary control changes of a nuclear power plant control system. The method comprises the following steps: 1) acquiring data, converting DCS configuration data into configuration text information, converting DCS function drawing from a CAD format file to a JPG image format; 2) establishing a signal mapping relation and a configuration data module state table; 3) establishing a data sheet of risks associated with instrumentation and control equipment, and determining a risk knowledge rule between the module state of software corresponding to instruments and an equipment state; 4) integrating the data and the drawing by using the signal mapping relation and the configuration data module state table as a relational model; 5) during change initiation, check and approval, prompting workers in respective links of change management the influence of the change on the control system; 6) enabling a user to inspect a field configuration data module state and to import the configuration data module state information inspection result into the system; and 7) designing a conflict detection module.

The invention belongs to the technical field of alloy cast steel rolls, and relates to a method for establishing an alloy cast steel roll material designing expert system on the basis of a neural network. The expert system comprises an integrated database, an inference machine, a knowledge base, a performance optimization module, a man-machine interface and other modules; sample sets for training and testing the neural network are stored in a database of the expert system; knowledge rules of components, processes and properties obtained in a process of training the neural network are stored in the knowledge base in a form of matrix to provide knowledge reserve for inference of the system; the performance optimization module automatically judges performance prediction results and returns to the integrated database of the system; each module of the expert system has a friendly man-machine interface. The expert system constructed by the artificial neural network in the invention has advantages of convenient knowledge maintenance, fast inference and the like, also reduces blindness in an alloy cast steel roll material designing process, and saves time and costs.

The invention provides an expert system knowledge acquisition method based on Weka software. The method comprises the followings steps of: creating a knowledge library; extracting rules from samples by using the Weka software, and verifying the rules; and storing the verified rules into the expert system knowledge library for diagnosis of an expert system. By the method, the knowledge rules of the expert system can be automatically acquired, so that the expert system knowledge library is updated in real time; and knowledge is acquired reliably, the method is high in practicality, and the rules can be extracted by selecting appropriate data mining methods for various data.

The invention discloses a vector element change comparison method based on similarity degree. The method comprises the following steps: first, data being normalized so as to have the same spatial reference; second, establishing a knowledge rule base and determining knowledge rules, which mainly includes data analysis and change definition; third, data alignment and updating. The invention has thebeneficial effects that the accuracy and efficiency of the target identification are improved by combining the buffer area, the spatial index, the key attribute and the spatial similarity, and the reliability of the change detection result is ensured; at the same time, the invention proposes a whole set of element comparison and linkage update flow based on similarity, which can directly guide vector element comparison and update production activities.

The invention discloses a numerical control machine tool fault monitoring and diagnosis system based on data mining. The system comprises a data management module, a data interpretation module, an intelligent fault diagnosis module, a data mining module and a man-machine interaction module. The data management module supports real-time importing of current data and batch importing and exporting ofhistorical data; the data interpretation module performs preprocessing, feature extraction and symptom extraction on the data; the intelligent fault diagnosis module is used for reasoning system-level faults by synthesizing the obtained symptoms and diagnosis knowledge; the data mining module autonomously mines an unknown fault mode alarm criterion and a diagnosis knowledge rule, and updates an alarm criterion library and a diagnosis knowledge base; the man-machine interaction module realizes setting control of fault monitoring and diagnosis processes and real-time display of results; the system has the advantages that machine tool operation is fully utilized to naturally generate data, deep modeling of a machine tool with a complex mechanism is not needed, the knowledge bottleneck of a traditional expert system type diagnosis system is broken through, the diagnosis process is automatic, and diagnosis is rapid and accurate.