161 results about "Association model" patented technology

An apparatus and method for executing an application may execute a text string selected by a user or an application associated with a type of text string to input the text string to increase the user's convenience. The apparatus of executing an application includes a text string recognizer to determine a text string, a determiner to determine one or more candidate applications related to the text string, and an input location of the selected text string based on a type of the selected text string and the association model, an application list provider to generate and display a list of the candidate applications, and an application executer to execute a candidate application selected from the list and to input the selected text string into the input location of the candidate application.

The invention discloses an information push method and an apparatus. One specific embodiment is characterized by acquiring customized description information of a user; carrying out content analysis on the customized description information and extracting a description characteristic; based on an association model trained in advance, determining association description information associated to the customized description information according to the description characteristic; based on a matching relation of the association description information and each candidate push information stored in advance, selecting at least one candidate push information to carry out information push. By using the method and the apparatus, validity of the information push can be increased.

A method and apparatus to facilitate pairing between a first wireless device and a second wireless device includes a pairing apparatus for determining (304) an address of the second wireless device and writing the address to the first wireless device to bypass an address discovery process between the first and second wireless devices. The pairing apparatus also initiates (312) removal of an input and output capability indication in at least one of the first wireless device or the second wireless device to control an association model type that is applied during a pairing process, between the first and second wireless devices, which uses the address of the second wireless device. The removal of the input and output capability indication is reversed (318) upon confirming (316) a successful pairing between the first and second wireless devices.

The invention disclosed provides an improved Bluetooth™ system with increased security and privacy for “device association” (“pairing”) of two Bluetooth™ devices where one device has no display. Heretofore, only Secure Simple Pairing (SSP) Association Model “Just Works” is used if one device is display-less. Unfortunately, Just Works is vulnerable to exploits known as “Man-In-The-Middle” (MITM) attacks. By contrast, my invention implements uses Numeric Comparison (heretofore, unusable when one of the pairing devices are display-less). Numeric Comparison is more secure than Just Works, because both agree to a randomly-generated 6-digit number. My innovation allows display-less Bluetooth™ devices (headsets or ear buds) to pair with display-bearing devices, because the matching 6-digit number on the displaying device is “spoken” or sounded (voice recordings, beeps, tones) into a displayless device user's ear. This allows displayless device users to hear sounds (spoken or beeped) equivalent to displayed 6-digit numbers to facilitate comparison, matching, and secure Bluetooth pairing. This reduces or eliminates risk of MITM exploits.

The invention relates to a method for intelligently diagnosing an electric energy meter. The method includes the following steps: 1. acquiring data of an electric energy meter and event data through field detection and an electric energy information acquiring system; 2. analyzing single faults the data of the electric energy meter and the event data which are acquired from 1, single faults analysis including 6 types of intelligent diagnosis analysis models of which each includes a plurality of intelligent diagnosis themes; 3. analyzing fault association of the association relations among different intelligent diagnosis themes and establishing an association model so as to obtain the result of further intelligent diagnosis; 4. and outputting the result of the single fault diagnosis of the intelligent diagnosis themes from 2 and the result of further intelligent diagnosis from 3 to a field detection worker, helping the field detection worker to detect and diagnose the electric energy meter. According to the invention, the method diagnoses and analyzes the operation of the electric energy meter through data comparison, statistics analysis and data mining, which can effectively determine whether the electric energy meter works normally.

The invention discloses a multi-platform avionics big data system. The multi-platform avionics big data system comprises a data acquisition module, a data storage module, a data association analysis module and a data association analysis application module. The data acquisition module acquires a pcap data packet file from a data source 1, after acquisition and classification, the pcap data packet file is sent to the data storage module, and the data storage process is completed; the data association analysis module acquires training data from a data source 2, and establishes a data association model, and provides the data association model for the data association analysis application module for use, real-time prediction is completed, the result is displayed on the screen, and the data association analysis application module completes a real-time storage function by means of a cloud storage function implemented by the data storage module. In addition, the invention also discloses an implementing method for the multi-platform avionics big data system. The multi-platform avionics big data system is integrated with functions of data acquisition, data classified management, data storage, data analysis and the like, multi-source heterogeneous data is acquired and managed in a classified manner, and is stored to a resource cloud platform in real time, and the data real-time performance is guaranteed.

The invention relates to a micro-service intelligent monitoring method for abnormal propagation, which comprises the following steps of: monitoring service calling information based on an agent technology, and establishing a micro-service calling topological graph to describe an abnormal propagation relationship among micro-services; a Lasso regression modeling interface is adopted to call correlation with measurement, and abnormal micro-service is detected by monitoring the change of a correlation model; based on the PageRank algorithm, the abnormal degree of the micro-service and the callinginterface of the micro-service is evaluated, transparent service monitoring is achieved, automatic metric value prediction is achieved to find abnormal service, and the abnormal degree of nodes in agraph is intelligently evaluated to detect the problem root cause.

The invention relates to a subject-oriented large-scale geographic spatial data dynamic fusion method. The method comprises steps as follows: Step a), firstly, establishing a matching model and an association model of geographic spatial data; Step b), then constructing a subject-oriented geographic spatial data fusion frame; Step c), establishing a subject-oriented geographic spatial data fusion rule base in combination with a geographical element matching technology of multi-source heterogeneous geographic spatial data; Step d), finally, realizing the subject-oriented large-scale geographic spatial data dynamic fusion method under the support of geographic raster data information fusion, geometric information fusion and attribute information fusion methods.

The invention discloses a method of item-all-weighted positive or negative association model mining between text terms and a mining system applied to the method. The method comprises the following steps of preprocessing by using a Chinese text preprocessing module to establish a text database and a feature word item library; mining item-all-weighted feature word candidate item sets from the text database by utilizing a feature word frequent item set and negative item set mining implementation module, calculating a weight dimension ratio, and cutting out uninteresting item sets by adopting a multi-interestingness threshold value pruning strategy to obtain an interesting item-all-weighted feature work frequent item set and negative item set model; mining an effective item-all-weighted positive or negative association rule model from frequent item sets and negative item sets by utilizing an item-all-weighted positive or negative association rule mining implementation module between terms, and outputting the mined positive or negative association rule model to a user by utilizing an item-all-weighted association model result display module between terms. By applying the method and the system, unnecessary frequent item sets, negative item sets and association rule models can be greatly reduced, Chinese feature word association rule mining efficiency is improved and a high-quality association model between Chinese terms is obtained.

A reverse engineering support system is provided which has a high abstract degree of an analysis target system and supports high level understanding. The reverse engineering support system stores a physical model which is a graph having as vertexes a program and input / output physical data, a business model which is a graph having as vertexes a business function and input / output logical data and an association model which is an association table indicating association of the business function with the program function and association of the logical data with the physical data, calculates a subgraph corresponding to the business function specified by a user by analyzing the corresponding physical model, displays a comparison with the subgraph of the physical model, and receives a modification order of the business and association models from the user.

The invention discloses a method and a system for analyzing the health degree of the equipment running state based on data driving. The method comprises the following steps: S1, monitoring equipment in real time, determining suitable equipment according to a PHM principle P-F interval curve, and acquiring suitable equipment measuring point data; S2, processing the acquired measuring point data; S3, establishing an equipment data association model, and establishing an equipment running framework model according to a data characteristic model in the equipment data association model; S4, monitoring according to the equipment running framework model, and performing early warning for a monitored abnormality or fault; and S5, diagnosing and processing according to the early warning result. The method and the system have the beneficial effects that early warning about the abnormality or fault of the equipment can be realized; the time and location of a fault being about to occur are predicted; the equipment maintenance and repair can be carried out pertinently; and the requirement of data integration at the industrial enterprise group level can be met.

The invention relates to a battery set service life prediction method based on probability statistics according to user use habits of different types of battery cars. The method comprises steps that step 1), statistics and analysis on user habits influencing battery car battery service life are carried out, and two important factors include mileage and the battery discharging time; step 2), classification of battery car types is carried out according to voltage values, and a battery car user use habit and battery service association model is established; step 3), parameter estimation is carried out for the association model through utilizing a training sample set; and step 4), to-be-tested new user mileage and the accumulative discharging time are substituted to a corresponding battery service life state function for calculation, a standard quantity of the battery service lift state is acquired, through comparison with a set threshold, a battery service life state conclusion of a present battery car user is acquired.

The invention discloses a learning system of a multi-source track association machine, belongs to the field of multi-source information fusion, and mainly solves the problem that the existing track association model requires a lot of manual repeated debugging and is difficult to be directly applied in the actual engineering application. Firstly, historical track data of information sources are collected and an association relation is manually analyzed and studied and judged to form an original database. Then, vector formation of training samples is set, data of associative and non-associative samples are calculated and generated, track association training data are formed, a training data set is preprocessed and then a standard training data set is generated. Finally, the model is trained, verified and overparameterized and tuned by using a learning model of a binary classification machine and adopting appropriate training and verification methods, so that the track association model is generated. The system automatically trains and generates the track association model, completely avoids a large amount of manual debugging for model parameters, and has the advantages of high model generation speed, good practical effect and the like.

The invention provides a knowledge graph completion technology based on a neighborhood structure, aiming at solving the problem that a triple lacks in a knowledge graph. According to the technology, based on information such as entity neighborhoods, relation neighborhoods and corresponding relations between entities of the knowledge graph, modeling is conducted on relation elements and entity elements of the knowledge graph. The method mainly comprises the following steps: (1) establishing a model based on a neighborhood structure of an entity in a map, and mapping entity elements into an entity vector space; (2) establishing a model based on a neighborhood structure of relation elements in the map, and mapping the relation into a relation vector space; and (3) mapping the entity representation into a corresponding relation space by adopting a relation mapping matrix, and establishing a triple association model. In order to more effectively train the model, the invention provides a negative sample sampling algorithm based on a neighborhood structure, performs joint training on entities and relations, and predicts an unknown triple based on a training result. The contribution of theinvention lies in providing an effective knowledge graph completeness technology based on a neighborhood structure.

The invention discloses an electric power multi-source information fault positioning and pre-judging method. The electric power multi-source information fault positioning and pre-judging method comprises the steps of: 1, generating a complex event model in a format required by complex event processing engine analysis; 2, summarizing expert experiences as a complex event form, and inputting regulation and control problems to generate a complex event rule matching model; 3, determining whether to start an offline self-learning thread; 4, outputting reliable events; 5, establishing a Petri inference rule association model; 6, reading the Petri inference rule association model in a Petri inference thread, and monitoring complex event sets in real time; 7, and triggering a fault recovery and processing thread, directly calling a disposal plan or finding all possible power supply recovery paths, and providing a scheme. The electric power multi-source information fault positioning and pre-judging method ensures the rapid aggregation and accuracy of electric power complex events, mitigates the combinatorial explosion problem of Petri fault inference, processing item omitted reporting caused by identification protection, switch rejection action and maloperation as well as communication problem by means of a CEP engine, simplifies the complexity of the Petri inference process, and positions the faulty equipment quickly by means of the Petri inference rules and diagnostic models.

An authentication scheme is provided for securely establishing an association with a second device over a wireless communication link. A cryptographic key exchange is performed between a first device and a second device, wherein cryptographic information for the first and second device is obtained. The first and second devices may independently generate a confirmation value based on the cryptographic information. Each device may obtain a confirmation image based on their respective confirmation values. A confirmation image is uniquely associated with a confirmation value so that no two confirmation values can be associated with the same confirmation image. The images for both the first and second devices are provided to an operator for authentication. If the confirmation images are identical, an association between the first and second devices may be confirmed by the operator. Comparing confirmation images may increase the reliability of operator authentication and is more efficient than comparing values.

The invention provides a sparse representation image reconstruction method based on Gaussian scale structure block grouping. The method comprises the following steps of using the non-local self-similar model trained from the natural image, mixing the non-local similar blocks into the group obtained by the priori model method, and extracting the optimal block grouping model by the search method; combining the block grouping model and the non-local extension Gaussian scale mixing model, using the alternating minimization method for synchronous sparse coding, and solving the update image block; associating the block grouping model and the Gaussian scale mixing model to a coding framework, using the selected training dictionary to calculate the image reconstruction update solution obtained by the association model, sending the update solution value to the block grouping model for carrying out the step one and the step two again, repeating the steps until the optimal solution is obtained, and outputting the optimal solution of image reconstruction. The reconstructed image obtained by the method has good maintenance performance of details such as the edge and the texture, and has excellent the peak signal to noise ratio quality.

The invention discloses a remote sensing video vehicle target detection and tracking method based on a dynamic association model, and solves the problems of low tracking precision, poor stability andinflexible algorithm. The method comprises the following steps: intercepting an image frame by frame, performing moving target detection on a first frame image, and creating a storage space storage target; and for subsequent frame images, detecting candidate moving targets, selecting historical target estimation positions from the storage space and matched with the candidate moving targets, then updating historical target states, arranging the storage space and storing newly-appearing targets. Interference outside a road area is filtered by using a road mask, targets are flexibly added and deleted by using dynamic association, the state estimation of a disappearing moving target is optimized by using a group effect, and the tracking precision is improved by using a trajectory optimizationmethod. Simulation experiments also prove that the method reduces the calculation amount, improves the tracking precision and stability, and is used in the fields of traffic flow monitoring, driving route analysis and military intelligence acquisition.

The invention provides a method and a device for recommending input association based on historical search of a user. The method includes the steps: receiving search terms inputted by the user; acquiring a preset input association candidate set; acquiring hit historical search terms of the user if analyzing that parts or all of keywords of the current search terms hit historical search terms of the user in the input association candidate set; recommending the input association according to the acquired historical search terms of the user. The efficiency of the method for recommending the inputassociation based on historical search of the user is improved. The user can accurately think of proper search terms to express search intention without manually rewriting the search terms for searchagain, and accuracy of the personalized input association recommendation method can be improved. By the aid of an existing input association model, the search cost of the user can be reduced, the user can be helped to rapidly and conveniently find out search results, the deriving effect of a whole search box can be improved, and search experience is further improved.

The invention discloses a remote diagnosis method for the motion behavior of a protection element at a dispatching terminal. The remote diagnosis method is characterized in that the method comprises following steps: S01: establishing an association model of primary equipment and relay protection; S02: transmitting and collecting relay protection intermediate logic node information, SOE information, protection motion information, telecommand deflection information, and fault record files from a substation to the dispatching terminal; S03: determining whether a power grid has faults; S04: determining a fault point via protection motion information, breaker deflection, and protection fault range-measuring information and obtaining association relay protection if the power grid has faults; S05: extracting intermediate logic node files of association relay protection; S06: comparing and analyzing each protection member according to self-consistency and consistency of each protection member and motion strategy; and S07: determining whether all the protection member motions are correct. According to the method, hidden problems of the protection members are discovered in advance, and necessary data is provided for remote operation and maintenance of relay protection.

The invention relates to a sintered ore sintering model determination method and system based on material genes. The method includes the steps: acquiring a sintering raw material gene database, a sintering process parameter gene database and a sintered ore gene database; determining a sintering raw material, sintering process parameter and sintered ore association model according to the sinteringraw material gene database, the sintering process parameter gene database and the sintered ore gene database; determining optimized sintering raw material gene data in the sintering raw material genedatabase and sintering process parameter gene data in the sintering process parameter gene database in the association model according to performance requirements of sintered ores; determining a sintering model according to sintered ore gene data representing the performance requirements of the sintered ores, the optimized sintering raw material gene data and the sintering process parameter gene data. The optimal sintering process can be rapidly set according to the performance requirements of the sintered ores, and the quality of the sintered ores is ensured.

The embodiment of the invention provides a multi-index association model training method and a multi-index anomaly analysis method. The method comprises the following steps: acquiring a historical keyperformance indicator KPI of a wireless network cell and fault information of the wireless network cell; determining association relationship information between abnormal historical KPIs in the historical KPIs and the fault information; and according to the association relationship information, determining a multi-index association mode, wherein the multi-index association model can improve the analysis efficiency of association between the network index abnormality and the wireless network cell fault and quickly determine the cause of the wireless network cell fault.

The invention relates to a recommending method, apparatus, a server and a storage medium for network resources, belonging to the information recommending field. The method comprises the following steps of: combining the user characteristics of a user account with a plurality of resource characteristics associated with the user characteristics respectively to obtain a plurality of joint characteristics; determining an association degree corresponding to the plurality of joint features according to an association model, wherein the association model is used for determining a probability of association of user features and resource features in any of the joint features; obtaining a target joint feature from the plurality of joint features according to an association degree corresponding to the plurality of joint features, and obtaining a matching network resource from an alternative network resource according to the target joint feature; According to the obtained network resources, the user account is recommended. The application can recommend the network resources with high heat degree among the network resources browsed by the similar users to the target users, thereby improving thediversity of the recommended contents, thereby improving the browsing interest of the users and the effectiveness of the recommendation.

The invention discloses a northwest Pacific Ocean ommastrephidae bartramii winter-spring stock colony abundance prediction method based on a gray system. The northwest Pacific Ocean ommastrephidae bartramii winter-spring stock colony abundance prediction method comprises steps of (1) obtaining nino3.4 anomaly, PDO data, sea surface temperature SST and chlorophyll concentration ch1-a, and four sea environment and climate factors, (2) performing gray correlation analysis on four sea environment and weather factors, (3) choosing four factors having a highest association degree according to a gray association analysis result, wherein the four factors include: average sea surface temperature of an egg laying field in march, an interdecadal oscillation index of the pacific ocean in January, nino3.4 anomaly in march and average chlorophyll concentration in march; (4) establishing 8 prediction models based on the gray system according to four chosen factors; and (5) performing effective examination on 8 prediction models to choose a gray association model GM (1,4) structure as the prediction method for the northwest pacific ocean ommastrephidae bartramii winter-spring stock colony abundance. The prediction accuracy of the prediction method of the invention reaches more than 90%.

The invention discloses a power distribution network contact effectiveness evaluation method. The power distribution network contact effectiveness evaluation method comprises five steps: step 1, dataacquisition; step 2, data preprocessing; step 3, construction of an association model; step 4, value analysis; and step 5, visual displaying of the data. The power distribution network contact effectiveness evaluation method has the advantages that multi-dimensional data such as power failure frequency and new energy power generation are acquired by utilizing multiple data platforms such as an electric SCADA system, a power utilization information acquisition system, a PMS system and an OMS system; analysis is carried out from five perspectives of distribution line open capacity, voltage quality, protection sensitivity, power supply reliability and new energy access; and an existing power distribution network frame is diagnosed and deeply excavated, so that the contact perception capability of the power distribution network is improved, and limited resources are utilized, and support is provided for contact construction of the power distribution network to the maximum extent, and powercustomers are better served, and the industry competitiveness of companies in the incremental power distribution network market is improved, and a virtuous circle is formed.

The invention discloses a soft measuring method and a soft measuring device for effluent COD concentrations of a rural domestic sewage A2O treatment terminal. The soft measuring method regards effluent ph, incoming water conductance, effluent conductance, effluent turbidity, incoming water ammonia-nitrogen concentrations and effluent ammonia-nitrogen concentrations as input indicators for effluentCOD concentration prediction, and constructs an association model between the input indicators and the effluent COD concentrations by utilizing a BP neural network. Through simulating and verifying the BP neural network model, the test indicates that the neural network model established by the invention can predict the effluent COD concentrations in the same region quickly and precisely, and themodel has reliable prediction value for the effluent COD concentrations of A20 facilities.

The invention discloses a target damage assessment method for bullet-mesh intersection of a near-burst fragment distribution field. The method comprises the steps of obtaining a centroid pixel coordinate of a fragment image; Calculating spatial three-dimensional position coordinate parameters of the fragments; Establishing coordinate systems respectively; Obtaining a fragment static distribution function at the moment of bullet explosion; Obtaining a fragment dynamic distribution function; Establishing a target damage mathematical model based on a rectangular unit; Obtaining an association model between the position, the speed, the direction vector, the fragment size and the shape of the fragment and the target damage; And obtaining the damage probability and penetration thickness of the target by the fragment field. According to the method, the calculation method for the random target damage by aiming at the fragment field can be obtained, and a theoretical basis is provided for further research on target damage assessment.

The invention provides a target feature-assisted multi-source data correlation method, aiming to provide a correlation method with high utilization rate of measurement parameters and capable of improving the correlation accuracy of a radar and electronic support measures (ESM). The target feature-assisted multi-source data correlation method is realized through the following technical scheme thataccording to the correlation between heterogeneous features, a correlation classification rule of heterogeneous sensor data is determined, a mapping correlation model of a target motion feature space,a target recognition feature space and a target type space is established, a category identification frame is established, K neighbors being at a distance from target features are found according toa K-K-nearest neighbor-NN rule, and trust assignment is constructed based on the distance between a target and the neighbors of the target, an acceptance threshold value and a rejection threshold value; the features of the target at each sampling time are obtained, and then BK-NN training is carried out on the target features at each t time, local static evidences at corresponding times of the categories are obtained and are integrated to generate a static criterion; and the comprehensive results of dynamic classification of different features are calculated, and the correlation filtering results are obtained.

A video sequence classifying method based on a tensor time domain association model comprises the steps of representing an original video sequence in a three-grade video tensor manner; performing tensor Tucker decomposition on the three-grade video tensor for obtaining a latent kernel tensor; applying an autoregression model on the time domain of the obtained latent kernel tensor for establishing relevance between adjacent time slices; dynamically studying the front process, updating the result until an algorithm is convergent, and obtaining an optimal result. The video sequence classifying method ensures time domain relevance and dependence of the video sequence after dimension reduction through limiting the time domain of the video sequence. The video sequence classifying method has advantages of sufficiently utilizing latent useful information in the video, eliminating redundant information in the video, ensuring high continuity of the video sequence in time domain, reducing classification difficulty of the video sequence, and improving classification accuracy of the video sequence. The video sequence classifying method is better than a traditional video sequence classification method and greatly improves classification precision.