1444 results about "Relational model" patented technology

The invention relates to the field of exercise equipment, in particular to an intelligent running machine. The intelligent running machine comprises a running machine body, a tablet personal computer which is arranged on the running machine body, a flexible array pressure sensor which is arranged on the running machine body, a control module which is arranged on the tablet personal computer, a data acquisition module and a motor drive module. An internet function is integrated in the control module of the running machine, and each running machine uploads a relationship model and a training program which are established according to individual characteristics through network to a server for storage so as to realize identity authentication. Multiple kinds of training modes and virtual scenes are integrated in the control module; the training program which meets an exerciser is customized in a personalized way; foot pressure distribution information of the exerciser, which is detected bythe flexible array pressure sensor, and the real-time heart rate which is sent by a heart rate monitoring module are received in real time; running speed, gradient and the like can be adjusted automatically; safety of the exerciser can be guaranteed; and training modes can be enriched and training efficiency can be improved.

The invention belongs to the petroleum exploration field, and concretely relates to a compact sandstone reservoir complex netted fracture prediction method. The method comprises the steps of: building a geological structure model and a fracture growth model; testing magnitudes and directions of ancient and modern crustal stresses; completing a rock mechanic parameter experiment; testing rock mechanic parameters and fracture stress sensitivities; developing a fracture rock multistage composite rupture criterion; performing a rock deformation physical test to obtain a peak value intensity; building a relation model between single axle state stress-strain and fracture bulk density; building a relation model between triaxial state stress-strain and fracture bulk density and occurrence; building a relation model between single axle state stress-strain and fracture bulk density; calculating and stimulating fracture parameters under modern conditions; and verifying the reliability of a fracture quantitative prediction result. The method can accurately obtain compact sandstone reservoir complex netted fracture parameters, and perform quantitative characterization, is suitable for quantitative prediction of any fracture mainly with a brittle reservoir, and reduces exploitation risks and costs.

The invention provides a non-contact free space eye-gaze tracking method suitable for man-machine interaction. The method comprises the steps of positioning and tracking a face and eyes in real time, extracting eye movement biological characteristic information, building an eye movement model based on the eye movement biological characteristic information, building a mapping relational model of the eye movement model and an eye-gazed object and the like. The non-contact free space eye-gaze tracking method suitable for the man-machine interaction relates to multiple crossed fields of image processing, computer vision, pattern recognition and the like and has wide application prospect in the fields of new-generation man-machine interaction, disabled people assisting, aerospace relating field, sports, automobile and airplane driving, virtual reality, games and the like. In addition, the method has great practical significance for improvement of the life and self-care level of disabled people, building of a harmonious society and improvement of independent innovative capability in the national high-and-new technical fields of the man-machine interaction, unmanned driving and the like.

A Relational Database Management System (RDBMS) having any arbitrary structure is translated into a multi-dimensional data model suitable for performing OLAP operations upon. If a relational table defining the relational model includes any tables with cardinality of 1,1 or 0,1, the tables are merged into a single table. If the relational table is not normalized, then normalization is performed and a relationship between the original table and the normalized table is created. If the relational table is normalized, but not by dependence between columns, such as in the dimension table in a snowflake schema, the normalization process is performed using the foreign key in order to generate the normalized table. Once the normalized table is generated, OLAP measures are derived from the normalized relational table by an automated method. In addition, OLAP dimensions are derived from the normalized relational table and the results of the OLAP measures derivation by an automated method according to the present invention. According to an aspect, it is possible to associate a member of a dimension to another member of the same or another dimension. According to another aspect, it is possible to create a new dimension of analysis, the members of which are all the different values that a scalar expression can take on. According to yet another aspect, it is possible to access the various instances of a Reporting Object as members in an OLAP dimension. According to the yet another aspect, it is possible to apply opaque filters or a combination of them to the data that underlies analysis.

A method of translating a relational database into an XML document comprises generating an Extended Entity Relationship model from relational schema associated with the relational database and applying a schema translation process to the Extended Entity Relationship model to map the relational schema into a Document Type Definition (DTD) of an XML schema. An XML Tree Model is then generated from the Document Type Definition, the XML Tree Model being representative of one or more data semantics of the relational schema which are preserved as one or more data semantics in the XML document. Relational data from the relational database is then converted into the XML document using the relational schema and the XML schema from the Document Type Definition and / or the XML Tree Model. There is also described a method of translating an XML database into a relational database which comprises the reversal of the steps of the aforementioned method, and a system for implementing both methods.

A Relational Database Management System (RDBMS) having any arbitrary structure is translated into a multi-dimensional data model suitable for performing OLAP operations upon. If a relational table defining the relational model includes any tables with cardinality of 1,1 or 0,1, the tables are merged into a single table. If the relational table is not normalized, then normalization is performed and a relationship between the original table and the normalized table is created. If the relational table is normalized, but not by dependence between columns, such as in the dimension table in a snowflake schema, the normalization process is performed using the foreign key in order to generate the normalized table. Once the normalized table is generated, OLAP measures are derived from the normalized relational table by an automated method. In addition, OLAP dimensions are derived from the normalized relational table and the results of the OLAP measures derivation by an automated method according to the present invention. According to an aspect, it is possible to associate a member of a dimension to another member of the same or another dimension. According to another aspect, it is possible to create a new dimension of analysis, the members of which are all the different values that a scalar expression can take on. According to yet another aspect, it is possible to access the various instances of a Reporting Object as members in an OLAP dimension. According to the yet another aspect, it is possible to apply opaque filters or a combination of them to the data that underlies analysis.

The invention relates to a three-dimensional intelligent video monitoring method, which comprises the following specific steps of: firstly, performing three-dimensional modeling on an actual monitoring area and displaying a three-dimensional virtual scene corresponding to the actual monitoring area by using software; secondly, calibrating a camera to acquire the internal and external parameters and distortion correction parameter of the camera and establishing a two-dimensional image and three-dimensional space one-to-one mapping relational model; thirdly, intelligently analyzing a monitoring video acquired by the camera to acquire moving target information comprising a target category, a moving path and the like; fourthly, positioning target information in a two-dimensional image in a three-dimensional space and displaying the target information in the three-dimensional virtual scene in real time; and finally, storing a target two-dimensional video and the moving path in the three-dimensional space synchronously for system retrieval and playback. In the method, a two-dimensional moving target is positioned in the three-dimensional virtual scene by fully utilizing the real three-dimensional space feeling of the three-dimensional virtual scene, so that the ability of monitoring personnel of quickly positioning and processing an alarm event is improved and the workload of the monitoring personnel is effectively relieved.

The invention relates to a patent literature similarity measurement method based on ontology, and relates to the technical field of natural language information processing for the ontology. The method comprises the following steps: extracting a core technical scheme according to the structural features, the position features and the keyword features of patent literatures; constructing a model for the relation between thematic terms of patent classes; constructing a field dictionary according to the model for the relation between the thematic terms of the patent classes and segmenting terms and removing stop terms for the core technical scheme; extracting keywords and weight by combining the relation between the thematic terms to TF-IDF as TextRank term initial weight; training a FastText model, and generating a term vector; and calculating an EMD distance to obtain a semantic distance according to keywords, term weight and term vector. Compared with the prior art, the patent literature similarity measurement method based on the ontology solves the problem that the similarity is low due to the fact that the structural features, the field features, the term relation features and the semantics approximate expression of the patent literature are not fully considered.

The invention relates to a method for estimating an image relation model for two or more related images based on a plurality of point correspondences. The method includes using a weighted random sampling algorithm to repeatedly draw subsets of point correspondences for generating model estimates, evaluating said model estimates for the full plurality of the point correspondences, and updating weights associated with each of the point correspondences in the random sampling algorithm based on information obtained in said evaluating.

System and method for a content management system are described. The content management system takes complex hierarchically represented content structures and represents the hierarchical model by way of a relational model that creates node tables and edge tables to represent various content structures. Moreover, the content is separated from the structure such that the same content units may be used by multiple content structures.

The invention discloses an indoor positioning method for goal nodes of a mobile social network. The indoor positioning method comprises the following steps of: respectively placing at least three access points AP in different positions, using known coordinate points as anchor nodes, obtaining a relational model between signal strengths and distances of all the anchor points, obtaining the signal strength value of each signal by using a mobile smart terminal, selecting the anchor nodes with the strongest signal strengths as the nodes needing to particle in positioning, respectively putting the three anchor nodes into the relational model between the RSSI (Received Signal Strength Indication) and the distances of the corresponding anchor nodes, and working out the distances from the mobile smart terminal to the corresponding anchor nodes; and working out the position coordinates of the mobile smart terminal by using an angular positioning algorithm. According to the indoor positioning method, the positioning of mobile nodes can be realized in a mobile social network environment, the impact of the environment on positioning is overcome to a certain extend, thereby the positioning accuracy is improved, and the positioning requirement can be basically satisfied in the mobile social network environment.

An indoor positioning method on the basis of a rotary antenna relates to a mobile terminal indoor positioning technology, and comprises that step 1, a reference node and a node to be tested are set, a two-dimensional coordinate system which takes the reference node as an origin is built, and comprises a horizontal axis and a longitudinal axis, the node to be tested is required to be located in the plane of the two-dimensional coordinate system, the included angle between a connecting line of the reference node and the node to be tested and the horizontal axis is a positioning angle, and the distance between the reference node and the node to be tested is a positioning distance; step 2, the positioning angle and the positioning distance are measured; and step 3, through an algorithm of a distance and an energy relational model, and the positioning angle, the two-dimensional coordinates of the node to be tested on the plane of the two-dimensional coordinate are calculated, so the node to be tested is positioned. The method has the advantages of avoiding error superposition, realizing point positioning, high precision and the like.

The invention provides a question pushing method and system, which are implemented based on pre-established user models, and the user models include at least two of the following models: an interest model, an attribute model, a behavior model and a relation model, wherein the interest model is established by exploiting questions asked by users and answers thereof, the attribute model is established based on user attributes, the behavior model is established by carrying out statistics on user behaviors, and the relation model is established based on relations between different users. The method comprises the following steps: A, carrying out text analysis on a question to be answered so as to extract question features; B, matching with the user models by using the extracted question features, and carrying out sequencing on users according to the matching degree of the question features with the users in each user model and a preset sequencing weight of each user model; and C, pushing the question to be answered to the front M users in the obtained sequence, wherein M is a preset positive integer. Through the method and system disclosed by the invention, the answer providing efficiency and quality of a knowledge questioning and answering system can be improved.

The invention discloses a mura defect detection method based on sample learning and human visual characteristics, which belongs to the TFT-LCD display defect detection field. According to the invention, the method comprises the following steps: firstly, utilizing the Gaussian filter smoothing and Hough transform rectangle to preprocess the TFT-LCD display image, removing a large amount of noise and segmenting the image areas to be detected; then, using the PCA algorithm to conduct learning to a large amount of defect-free samples; automatically extracting the differential characteristics between the background and the target and re-constructing a background image; and then, thresholding the differential characteristics between a testing image and the background; through the reconstructing of the background and the threshold calculating, jointly creating a model. According to the invention, based on the training sample learning, a relationship model between the background structure information and the threshold value is established; and a self-adaptive segmentation algorithm based on human visual characteristics is proposed. The main purpose of the invention is to detect different mura defects in a TFT-LCD, to raise the qualification rate and to increase accuracy for the detection of mura defects.

Application code maintenance often imposes rebuilding and / or a patch kit update from a source code vendor. Users of such applications often find such conventional rebuilds and install efforts intrusive. An extensible object-relational (O-R) model operable for dynamic additions to an existing baseline of modeled data specifications mitigates interim patches and builds by employing a scripted modeling form and a corresponding relational form. The scripted modeling form, such as an XML file, is readily modifiable from existing data specifications, such as those driven by a recognized standard, while the relational form is seamlessly updated within an existing application environment without requiring rebuilding or patch kits to implement. The extensible O-R model defines data characteristics of data specifications, and the relational model is updated by instantiating an object or entry into the modeled relational specifications based on the extensible O-R model. In this manner, an incremental update to a set of relational specifications implements an addition or modification to a data specification rather than requiring a more global build, update, or install kit.

The invention discloses an NRIET fog forecasting method based on machine learning. The method comprises steps that historical and forecast data for model training and operational forecasting in and around the forecast area is collected; the collected data is analyzed and processed to be a sample set for training a machine learning model; for problems, an xgboost model algorithm based on a decisiontree is selected, key influence factors generated by the airport fog are screened, and a relationship model between the fog and the influence factors is established; a training set and a verificationset are trained; according to the parameter adjusting sequence, model parameters are repeatedly adjusted till a forecasting model with optimal performance is acquired; an integrated learning method is utilized, and intersect verification and iteration training are carried out for the model to further improve model forecasting performance; observed real-time data is inputted to the forecasting model to acquire the fog forecasting result.

The invention belongs to the control field of electric power electron converters and relates to a model prediction control method for a three-level voltage-source-type converter. The model prediction control method for the three-level voltage-source-type converter comprises the following steps: step one, building models of relations between currents of the alternating current side of the three-level voltage-source-type converter and switch functions; step two, building models of relations between capacitance voltage offsets of the direct current side and switch functions; step three, obtaining current instruction values through outer voltage loops and obtaining a current instruction value of the next moment by calculating current instruction values of several moments before; step four, setting value functions of a model prediction control algorithm for the three-level voltage-source-type converter; step five, calculating on-off state subsets; step six, achieving real-time prediction control. The model prediction control method for the three-level voltage-source-type converter is simple in algorithm, easy to achieve and capable of being adopted in a higher-level converter, and has generality.

The invention discloses a power transmission line fault season identification method based on high and low frequency wavelet feature association, which comprises the steps of extracting fault phase current samples of N failure types, and building a sample database; performing high frequency and low frequency data sampling on a fault type T from the sample database, and respectively performing wavelet decomposition; performing feature extraction on a high frequency data wavelet coefficient and a low frequency data wavelet coefficient; S4, building an association relationship model of the fault type T; determining the association relationship model of the N fault types; S6, performing wavelet decomposition on sample data whose fault reason is to be tested, and extracting a feature vector; substituting the feature vector of the sample whose fault reason is to be tested to the association relationship models of the N fault types, and judging the fault type of the sample data whose fault reason is to be tested. According to the invention, fault phase current is analyzed and processed through combining a wavelet transform theory and an information entropy, thereby not only being capable of effectively analyzing suddenly changed signals, but also being capable of achieving a purpose of information fusion, and improving the identification accuracy.

The invention discloses a multi-rotor unmanned aerial vehicle (UAV) pose acquisition method based on markers in shape of a large and a small square. The method is as below: first determining target markers based on the relative height of the UAV and the markers, wherein the markers comprise a small square and a large square with side length 10 times of that of the small square; second, according to the determined target markers, establishing a relationship model between a CCD camera of the UAV and the target markers; and finally, according to the established model, employing a direct linear transformation method to obtain an azimuth angle of the UAV and location information of the UAV at the corresponding time. The method can accurately obtain the pose of the UAV.

The invention relates to a method and a system for predicting remaining service lives of different lithium ion batteries of the same type. The method comprises the following steps of 1) extracting health factors capable of reflecting the performance degradation of lithium ion batteries; 2) establishing a health factor prediction model, wherein the health factor prediction model is a relation modelof battery health factor service life early stage and service life later stage and constructed by utilizing a neural network; 3) constructing a battery capacity prediction model, wherein the batterycapacity prediction model is a relation model of the heath factors and the battery actual capacity and constructed by utilizing a neural network; and 4) taking the service life early stage health factors of different batteries of the same type to be predicted as input, obtaining a battery service life later stage battery capacity prediction value based on the health factor prediction model and thebattery capacity prediction model, and then working out the remaining service life value of each battery at the current moment. The method has the advantages that the accuracy and the adaptability are relatively high in the prediction of RUL of different batteries of the same type.

The invention discloses a method for establishing a relation between a scene stereoscopic depth and a vision difference in a binocular stereoscopic vision system. the method comprises the steps of firstly, solving inner parameters, and relative rotary matrixes and translation vectors of left and right cameras; then, analyzing a main error source and an error model of the binocular stereoscopic vision system; then, analyzing influences of the main error on a base line length and the vision difference of the parallel binocular stereoscopic vision system; then, establishing a common relation model of the scene stereoscopic depth and the vision difference in the binocular stereoscopic vision system; obtaining depth information by a laser distance measuring instrument by selecting a certain amount of demarcation points and carrying out demarcation based on a least square method to solve a relation model between the scene stereoscopic depth and the vision difference in the binocular stereoscopic vision system; and finally, solving the vision difference in left and right images through a corresponding fixed matching method so as to realize accurate recovery and three-dimensional reestablishment of the scene stereoscopic depth. The method has the beneficial effect of directly, simply, accurately and extremely improving the accuracy of the stereoscopic depth recovery and the three-dimensional reestablishment.

The invention discloses a matrix converter switch open circuit fault diagnosis method based on prediction control. The method comprises the following steps of under the condition that a matrix converter works normally, establishing relation models between an output voltage and an input voltage, and between an input current and a load current; determining numbers of all the switch combination states of the matrix converter; establishing a state space model and acquiring predicted values of the load current, the input current and the input voltage in a next sampling period; determining an evaluation function; using a finite set model prediction control strategy to select a switch state capable of making an evaluation function value be minimum in all the switch combination states in each sampling period and using the switch state as the switch state of the next sampling period; establishing the state space model and acquiring a change rule of a fault phase load current after a switch open circuit fault is generated; carrying out on-line monitoring on the load current, according to a finite set model, predicting and controlling the selected switch state so as to carry out fault diagnosis, and identifying a fault switch position. By using the method, an open circuit fault can be timely diagnosed.

The invention relates to one visual frequency compression codes macro block coding mode selection method, which comprises the following steps: predicting move and processing rapid speed and lose estimation after exchange and volume; during computation, processing exchange parameters as zero; in speed estimation computation, establishing non-zero parameters sum, and number and journey sum and relation module; defining one index function to meter non-zero parameter number and journey sum impact on speed with each real code for one block to update index list according to low pass filter; according to speed and aberration estimation to process macro coding module selection.

A system or method for identifying a plurality of entities in a first dataset that satisfy a predetermined target attribute by deploying on the first dataset a relationship model generated from a second dataset having a plurality of entities not in the first dataset.

The present invention relates to a method for predicting hot-rolled Nb-contained band steel tissue and mechanical performance. Said method is characterized by that it uses physical metallurgical model as basis, adopts a method of making thermal simulation experiment be combined with industrial production actually-measured data analysis, creates mathematical models for hot-rolled Nb-contained band steel tissue and mechanical performance, including temperature model, austenite recrystallization model on the hot-rolling line, phase change model in cooling process and tissue and performance relationship model and selects VB language to originate predictive software for hot-rolled Nb-contained band steel tissue and mechanical performance.

The invention belongs to the medical detection field, and discloses a method and a system for noninvasive detection of fetus chromosome aneuploid. The disclosed detection method and system also relate to a method and a system for elimination of sequencing GC preference in chromosomes and among chromosomes and a method and a system used for the relation model of the Z values of X and Y chromosomes in a normal male fetus. Through elimination of influences of sequencing GC preference in chromosomes and among chromosomes, the relation model of the Z values of X and Y chromosomes in a normal male fetus is built, and the determination threshold of difference between the theoretical value and the actual value of the Z value of the X chromosome is built. The accurate detection of fetus chromosome aneuploid, especially sex chromosome aneuploid is achieved.

The invention discloses a method for jointly estimating vehicle mass-road gradient under the consideration of influence of braking and turning. The method includes steps of 1, acquiring data, to be more specific, 11, acquiring vehicle driving state data by data acquisition devices, 12, combining the vehicle driving state data and vehicle intrinsic parameters with one another and computing relevant parameters required by models; 2, building relation models of vehicle mass, road gradient and the vehicle driving state data on the basis of vehicle dynamic models; 3, building least square mass estimation models and Kalman filter gradient estimation models on the basis of the relation models of the vehicle mass, the road gradient and the vehicle driving state data; 4, jointly estimating the vehicle mass and the road gradient by means of nested loop iteration; 5, eliminating the influence of braking and turning in state maintenance modes. The method has the advantages that the influence of braking and turning is considered in real vehicle driving procedures, and accordingly the vehicle mass and the road gradient can be dynamically jointly estimated in real time.

The invention discloses a short-term load forecast method based on meteorological factor sensitivity. The short-term load forecast method comprises the following steps: obtaining a forecast air temperature of a date to be forecast and actual air temperatures of a plurality of historical dates; modifying the forecast air temperature of the date to be forecast and the actual air temperatures of the plurality of historical dates; obtaining the power loads of the plurality of historical dates; fitting the modified actual air temperatures of the plurality of historical dates with the power loads so as to obtain a relation model of the actual air temperatures and the power loads; and forecasting the power load of the date to be forecast. According to the short-term load forecast method, the influence of the error of the weather forecast and the air temperature accumulation effect is taken into account in the short-term load forecast, the forecast air temperature of the date to be forecast and the actual air temperatures of the historical dates are firstly modified, and subsequently the forecast is carried out; and the forecast precision of the short-term power load is improved, the reference for scheduling and controlling a power grid is provided, and the safe operation of the power grid is guaranteed.

The invention provides a construction method of an evidence chain relation model for a Chinese judgment document. The process of the construction method is shown in the figure and mainly comprises thesteps that text preprocessing is performed on the Chinese judgment document; an XML-formatted file obtained after processing is read, and an evidence chain body set and a fact set are extracted; a keyword set of each evidence chain body and a keyword set of each fact are obtained according to a 4W1H key element extraction strategy; linking in the evidence chain relation model is constructed by calculating key element relevancy to complete model construction; the constructed model is made into an Excel table; and a structured model is displayed as a visual model. Through the method, mainly fora construction task of the evidence chain relation model in argument evaluation of the legal judgment document, according to the characteristics of judgment document arguments and the characteristicsof court document vocabularies, the judgment document preprocessing method is improved, the key element extraction technology is optimized, the correct rate of calculation of linking in an evidence chain is increased, and the method can be effectively used for evaluating the fact argument level of the judgment document.

The invention discloses a self-adaptive speed regulation system of running device based on a flexible array pressure transducer, comprising the flexible array pressure transducer, a data collection transmission module, a tablet computer, a computer control program, and a motor drive module, wherein the motor drive module is connected with a motor for driving the motor to rotate, and the motor drives the running belt of the running device to rotate. In the invention, stride frequency, stride length, ground-touching time, air time, rearward thrust and other parameters of a runner when in running can be obtained by installing the flexible array pressure transducer on the upper side of the running plate of the running device; acceleration can be calculated by an established relational model of difference of acceleration and the step length between two adjacent steps, the step frequency, the component force of the rearward thrust along the moving directing of the running belt, time for completing two steps (the ground-touching time + the air time), and the weight of the runner; a control command is sent to the motor drive module according to the obtained acceleration; and the motor rotation speed is adjusted; therefore, the speed self-adaption of the running device is realized.