105 results about "Confidence factor" patented technology

The invention discloses a weld quality intelligent diagnosis system based on deep learning, and relates to the technical field of welding quality detection, and the weld quality intelligent diagnosis system comprises an image quality evaluation module, a feature alignment module, a deviation detection module, a path reconstruction module, a prior enhancement module and a defect identification module, identifying an area of which the signal-to-noise ratio is lower than a preset threshold value, and constructing a noise interference distribution diagram; and the feature alignment module executes a deformable convolution feature alignment operation with a confidence factor adjustment mechanism based on the noise interference distribution diagram to generate an initial space mapping result. Through mechanisms such as image quality perception, robust alignment, deviation detection, self-adaptive reconstruction and prior enhancement, a closed-loop weld joint intelligent diagnosis process is constructed, false alignment errors are effectively inhibited, the multi-modal fusion stability and the defect recognition precision are improved, and the reliability and the intelligent level of the system under complex working conditions are enhanced.

The invention discloses a medical health cost prediction system and method based on multi-source data fusion, and relates to the technical field of medical data analysis, the system firstly fuses a plurality of heterogeneous medical information sources to generate structured data; then, multidimensional features related to the cost are extracted to construct a feature representation vector; and the cost prediction modeling module constructs a cost prediction model through graph structure modeling and semantic embedding, carries out joint optimization by combining a graph attention mechanism and semantic similarity, and outputs a prediction result by fusing a time sequence and static characteristics. A joint optimization algorithm of graph semantic comparison loss and prediction deviation loss is introduced into model training, and positive and negative sample pairs are constructed through a cost label distance. And the feedback optimization module triggers model updating when the prediction deviation exceeds a threshold value, and dynamically adjusts a model structure and parameters through a deviation index and a sample confidence factor. And the prediction interpretation module analyzes influence factors based on intermediate layer features or gradient propagation, outputs an interpretation report, and improves the transparency and credibility of the model.

The invention provides a landslide risk dynamic early warning method and system based on a Bayesian network, and belongs to the field of geological disaster intelligent prediction, and the method comprises the steps: constructing a disaster-inducing factor data set, and screening key factors based on a Pearson's correlation coefficient and an information gain method; an FP-Growth algorithm is further utilized to extract association rules among high-confidence factors, a Bayesian network structure is guided to be optimized, and a Bayesian network model with a causal relationship is constructed; the model supports an incremental learning mechanism based on a newly added landslide sample, can dynamically update a conditional probability table, and realizes landslide probability prediction and risk grade division in combination with a Bayesian forward reasoning result. The method has the advantages of being high in causal reasoning ability, high in model structure expression ability, excellent in prediction precision and capable of supporting real-time updating and risk partition, and is suitable for an intelligent risk assessment and early warning system for landslide disasters.

The invention relates to the technical field of wind turbine generator state monitoring, in particular to a wind turbine generator state monitoring method and system based on multi-source heterogeneous data fusion, and the system comprises a data collection unit, a dynamic noise processing unit, a damage feature analysis unit and a health state output unit. A data acquisition unit synchronously obtains blade strain, unit rotating speed and environment vibration signals through hardware timestamp alignment, and a dynamic noise processing unit removes rotating speed coupling noise by using a two-parameter coupling model, a temperature-pitch angle three-dimensional correction curved surface and closed-loop feedback in combination with variable step blanking and local band elimination protection. The damage feature analysis unit extracts microcrack features by adopting multi-scale wavelet packet decomposition and kurtosis detection, and performs cross validation by fusing a vibration mode confidence factor, and the health state output unit generates a topological graph containing a crack position, an expansion trend and an alarm confidence level, so that the problems of insufficient multi-source data fusion and false and missing judgment of damage are solved, and the safety of the system is improved. And the monitoring precision is improved.

The invention relates to the technical field of image recognition, in particular to a fire behavior recognition method and system based on video monitoring. The technical problem that the early warning reliability of a video monitoring system on fire smoke is insufficient is solved. The method comprises the following steps: acquiring a monitoring video stream of a monitoring area and time sequence data acquired by a sensor in the monitoring area; determining an evaluation value of the to-be-detected area based on the image feature and the morphological change of the to-be-detected area; determining a confidence factor based on the form change trend of the to-be-detected region and the change trend of the time series data; determining a smoke authenticity value based on the evaluation value and the confidence factor; and adjusting an alarm threshold value of the smoke alarm based on the smoke authenticity value. The method is used for fire smoke early warning and monitoring scenes.

The invention relates to the technical field of industrial measurement and control, in particular to a multi-source data fusion system operation monitoring system which comprises a jump behavior recognition module, a confidence coefficient screening module, a behavior consistency extraction module, a power recovery anomaly detection module and an operation state monitoring output module. According to the method, the sudden change trend in the current and voltage signals of the operation equipment is extracted, the response dislocation condition between the signals is combined, the behavior change of the equipment can be accurately described, potential abnormal fragments are identified, the signal fragments are screened by means of the multi-dimensional confidence factor, and low-reliability data content is removed; the method comprises the following steps: establishing a characteristic time section with representativeness through time interlacing characteristics between triggering behaviors and behavior type consistency judgment, and realizing accurate marking of abnormal behaviors in a power recovery process of equipment by combining trend delay and fluctuation amplitude change of a power signal in a voltage rising process.

The invention provides a metal ore artificial intelligence sorting method based on lithofacies analysis, and relates to the technical field of data processing, and the method comprises the steps: obtaining a unified image of an ore sample, extracting a crystal structure contour, and calculating texture direction consistency, particle distribution density and color concentration degree in each region; constructing a lithofacies analysis model based on a two-channel neural network; training the lithofacies analysis model according to the training data set to obtain a fusion recognition model; obtaining an ore image to be sorted, and inputting the ore image to be sorted into the fusion recognition model to obtain predicted mineral distribution data; and extracting a boundary confidence factor and a region type probability, carrying out re-calibration on predicted mineral distribution data, generating a sorting control instruction, and driving sorting equipment to carry out physical sorting operation. The rock facies analysis is carried out on the ore image sample through artificial intelligence, and the types of symbiotic minerals can be accurately distinguished.

The invention discloses a supplier material market price evaluation method and system based on AI networking search, belongs to the technical field of electronic commerce and data processing, and provides a double-weight confidence model based on time sequence fluctuation and statistical deviation, data are grouped according to platform types and suppliers, a price fluctuation oscillogram is generated for each group of data, and the price fluctuation oscillogram is calculated. Calculating average price and standard deviation in the group; the global average price is introduced, the confidence coefficient of a single group of data is dynamically quantified through a normalization formula, low weight is given to high-fluctuation and high-deviation data, noise interference is effectively suppressed, and the reference of the finally output optimized market price is improved; in addition, structured data with timestamps are generated and the grouping average price and confidence factor of each platform are synchronously output, so that purchasing personnel are assisted to transversely compare the quotation rationality of suppliers; and finally, the output optimized market price is fused with multi-source high-confidence data, so that enterprises are helped to accurately grasp the market price interval, and the bargaining capability and the purchasing efficiency are improved.

The invention provides an intelligent door and window control method and system, and the method comprises the steps: S1, obtaining multi-source environment data; s2, inputting the multi-source environment data into a fuzzification module, and converting the multi-source environment data into a fuzzy variable through a preset ambiguity function; s3, dynamically generating an environment parameter weight vector according to the real-time scene parameters; s4, inputting the fuzzy variable and the environmental parameter weight vector into a fuzzy inference engine, executing logical operation of a fuzzy rule base, and outputting comprehensive evaluation values of window opening suitability, window closing urgency and shading demand; and S5, generating a final control instruction through a defuzzification strategy, and executing the final control instruction. According to the method, the problem of frequent invalid operation caused by fluctuation of the environmental parameters near the boundary value is solved, the environmental parameter weight vector is dynamically generated based on the time parameters, the user preset mode and the meteorological data confidence factor, adaptation to seasons, time periods, user states and other scenes is achieved, and decision logic can be flexibly adjusted according to actual requirements; and a contradictory decision under a traditional fixed rule is avoided.

The invention belongs to the technical field of power distribution automation of a power system, and particularly relates to a self-adaptive load control method and system for an intelligent cable branch box, and the method comprises the steps: collecting and preprocessing the current data of a branch loop and the temperature data of a cable joint in real time, obtaining a data confidence factor, and extracting a current change rate and a temperature rise rate; obtaining a load prediction value in a future set time period through a load trend prediction model; calculating the residual heat tolerance time for reaching the limit tolerance temperature; calculating an output load control adjustment coefficient based on the adaptive load control decision model and a particle swarm optimization algorithm; and judging a current state interval according to the load control adjustment coefficient, and controlling an execution end to execute a grading response strategy of through-flow maintenance, short-time overload monitoring and early warning and breaking current limiting in combination with the residual heat tolerance time. According to the method, the residual heat tolerance time is calculated in combination with load prediction and a transient thermal circuit model, and hierarchical response of load control is realized through a particle swarm optimized radial basis function neural network decision.

The invention discloses an underground safety level intelligent evaluation response method and system fusing fuzzy credibility weighting and Bayesian reasoning, and relates to the technical field of underground safety monitoring. According to the method, a multi-source heterogeneous sensor is deployed to collect data, a fuzzy membership matrix is obtained through fuzzy membership function normalization, a credibility factor matrix is constructed by combining time sequence stability, spatial neighborhood consistency and node long-term health weight, and the two are subjected to weighted fusion to obtain a fusion index vector. And inputting the Bayesian network fused with the credible nodes, reasoning to obtain security level posterior probability distribution, and triggering a linkage response. The system correspondingly comprises a plurality of modules for implementing the steps. According to the method, the dynamic evaluation and credibility quantification of the data quality of the underground multi-source sensor are realized, and an intelligent evaluation mechanism for deeply integrating the credibility of the data source into a reasoning structure is constructed, so that the risk evaluation robustness and accuracy are improved, the closed-loop management from evaluation to response is realized, and the underground safety is guaranteed.

The invention discloses a freight logistics track abnormity processing method and system, and belongs to the technical field of intelligent logistics monitoring, and the method comprises the steps: obtaining a track original data set of a target freight vehicle; constructing a behavior evolution model based on the historical trajectory data, wherein the behavior evolution model is used for describing conventional operation boundaries of the vehicle under different road types and time periods; performing multi-source fusion correction on the trajectory data to generate a trajectory point sequence; calculating credibility factors of the track points based on GPS stability, historical track goodness of fit and road matching degree; constructing an anomaly judgment graph, and generating an anomaly distribution function by adopting a recursive scoring mechanism; identifying a track abnormal fragment based on a dynamic adaptive threshold value and outputting an abnormal label; generating a scheduling suggestion according to the abnormal label and a vehicle scheduling rule, and pushing the scheduling suggestion to a vehicle-mounted system in real time; according to the invention, individuation, high precision and real-time performance of track abnormity identification are realized, and the method is suitable for an intelligent logistics management and dynamic scheduling system in a complex scene.

The invention discloses an aircraft performance constraint compliance control method for contact type nondestructive testing, and belongs to the field of aircraft control. The implementation method comprises the following steps: introducing a saturated contact force controller into a position ring, and dynamically adjusting a confidence factor based on a position error to adaptively adjust normal rigidity and tangential flexibility; a zero-order control barrier function ZOCBF restrains the force controller, a three-dimensional dynamic safety boundary is established by using a sampling data mechanism, and the position, speed and contact force of the unmanned aerial vehicle are optimized and limited through quadratic programming; designing a hybrid torque controller in the attitude ring, and dynamically selecting a balance point through a binary logic switching strategy; the controller performs safety limitation on the attitude and angular velocity of the unmanned aerial vehicle in combination with zero-order control obstacle function constraint; monitoring a quaternion scalar component symbol in real time by defining a manifold switching set and a jump set; by fusing position loop and attitude loop control logic, compliance control during contact-type nondestructive testing of the unmanned aerial vehicle is realized.

An infrared-spectroscopy-based method for monitoring temperature-induced deformation of a component of an intelligent moxibustion robot. The method comprises: collecting an infrared spectrum and a frequency spectrum of each detection position on a target component of a moxibustion robot; on the basis of frequency differences between peaks and troughs in the frequency spectrum, constructing a motion influence confidence factor of each detection position; using a box-counting method to acquire a scale relationship graph of the infrared spectrum of each detection position; on the basis of scale relationship graphs of all the detection positions, determining an overall absorbance difference index of the target component; on the basis of the overall absorbance difference index and the motion influence confidence factor, using a local outlier factor (LOF) outlier detection algorithm to calculate an LOF of each detection position in a thermal data sequence; and on the basis of a thermal alarm threshold value, determining a temperature-induced deformation risk of the target component. The method can eliminate spectral difference variations caused by shadows and reflectance properties, thereby improving the precision of determining high-temperature deformation of components.

The invention relates to the technical field of intelligent power grid abnormity diagnosis, in particular to an intelligent electric energy meter operation data abnormity diagnosis method and system. The method comprises the steps of establishing a sliding window, collecting magnetic induction intensity and load current data, maintaining references of the magnetic induction intensity and the load current data according to historical data, calculating the change direction and intensity of a sampling point relative to the references, screening data with enhanced magnetic induction intensity and reduced load current, and carrying out weighted covariance calculation by utilizing an effective mask. Constructing a negative correlation response coupling index; constructing an abnormal definite diagnosis confidence reasoning model; calculating a causal confidence factor based on the coupling index; calculating an aging confidence factor based on the duration of an abnormal state; according to the scheme, industrial interference and electricity stealing attacks can be accurately distinguished, hidden attacks are captured, the false alarm rate is reduced, and diagnosis blind areas are eliminated.

Exemplary systems and methods are directed to reducing processing complexity for command and control of a remote autonomous object in response to natural language commands. A processor receives a user command in a natural language format and extracts designated parameters for matching with a control prompt stored in a library of control prompts. The processor determines a confidence from a result of the operation. The confidence factor is used to selectively route the user command to a simple command processing path to generate a simple command message or a complex command processing path having one or more neural networks to generate a complex command message. The processor compares the simple command objective message, or the complex command objective message generated from the selective routing operation with known capabilities of the remote autonomous object to identify a remote autonomous object command, which is formatted and transmitted to the remote autonomous object.

The invention discloses a natural road feature-based vehicle-mounted radar external parameter calibration method and device, a storage medium and a program product. The method comprises the steps of obtaining synchronous radar point cloud data and visual image data; determining a visual lane line according to the visual image data, and determining a radar lane line according to the radar point cloud data; lane line transverse deviation is calculated according to the visual lane line and the radar lane line, and deviation confidence is calculated according to the credibility factor and the factor weight; determining a deviation estimated value based on the effective deviation and the deviation confidence by adopting a one-dimensional Kalman filter; the deviation estimation value is evaluated according to preset calibration threshold values, the preset calibration threshold values comprise the minimum calibration threshold value and the maximum calibration threshold value, when the deviation estimation value is larger than the minimum calibration threshold value and the maximum calibration threshold value, the radar coordinates are calibrated based on the deviation estimation value, and when the deviation estimation value is larger than the maximum calibration threshold value, an early warning mode is started. And outputting early warning prompt information in response to the early warning mode, and recording a fault code.

The invention relates to the technical field of robot navigation, in particular to a robust visual inertial navigation method for dynamic and static factor decoupling in a high dynamic environment, and the method comprises the following steps: obtaining prior pose information and visual feature data of an inertial measurement unit from a navigation system; the method comprises the following steps: generating a stable confidence factor and a stable inertia factor of a visual feature based on priori pose information and visual feature data of an inertial measurement unit, and generating a double-factor residual error at the same time; based on prior pose information and visual feature data of the inertial measurement unit, effective dynamic features are identified, and a dynamic effect residual error is generated; and in combination with the basic residual error, the two-factor residual error and the dynamic effect residual error of the navigation system, decoupling dynamic and static residual errors and carrying out joint optimization to obtain an optimized navigation track. According to the method, the robustness of the visual inertial navigation system in a complex environment can be improved through dynamic and static factor decoupling and dynamic compensation constraint, and scene specific training is not needed.

The invention relates to the technical field of main power distribution network monitoring, in particular to a main power distribution network integrated dispatching monitoring method and system, and the system achieves main power distribution network integrated dispatching monitoring through the method steps of main network and distribution network data acquisition, data preprocessing, data fusion, main power distribution network fusion topology model establishment and the like. When the operation data of the main power distribution network is fused, the traditional Bayesian fusion algorithm is improved by using a confidence factor, so that the heterogeneous data sources can be more effectively integrated, and more accurate information is provided for the operation and maintenance of the main power distribution network; the fused state estimation value reflects the most probable value of the state parameter of the main power distribution network under the condition that all observation data and priori knowledge are given, the information of all data sources is integrated, and the reliability of different data sources is constrained through confidence factors, so that the state estimation value is more accurate and reliable.

The invention relates to the technical field of instrument checking, and discloses a power plant chemical instrument checking method and device capable of automatically adjusting the temperature, and the method comprises the following steps: collecting the temperature of a main water sample and a reference water sample, monitoring the mixed water temperature, and starting checking when the temperature difference exceeds a temperature threshold value; setting a gradient temperature check point, and calculating a theoretical conductivity value; controlling the temperature of the main water sample and the reference water sample by adopting a multi-stage flow path; when the water temperature reaches a steady state, sodium standard liquid is injected, and theoretical sodium concentration increment is calculated; converting the potential signal into a sodium concentration measured value by adopting a difference algorithm; constructing a temperature-concentration double-variable high-order response curved surface model; performing back-stepping to obtain simulation concentration, calculating confidence factors, and triggering grading response; according to the method, a temperature-concentration linkage response curved surface model is constructed, all working conditions of temperature and concentration are covered, confidence factors are introduced to quantify calibration reliability, and an adaptive closed-loop compensation mechanism is established for iterative optimization, so that chemical verification of the power plant is realized.

The invention relates to a variable-frequency adaptive protection control method and system based on dynamic vector correction, and belongs to the field of power protection control. The method comprises the following steps: measuring an output instantaneous frequency through three-mode fusion; performing dynamic correction on the rotating coordinate system based on the instantaneous frequency to obtain a correction amplitude; performing three-domain confidence factor correction based on the correction amplitude to obtain a comprehensive confidence coefficient; cooperative compensation is carried out based on the comprehensive confidence coefficient and the instantaneous frequency, and the cooperative compensation comprises protection constant value adaptive compensation and excitation-synchronous cooperative compensation which are respectively used for compensating an over-current protection constant value and excitation voltage; and obtaining an error index, carrying out real-time closed-loop verification based on the error index, and optimizing cooperative compensation. Frequency conversion adaptive protection control is realized by means of dynamic vector correction, three-domain confidence evaluation, excitation-protection cooperative compensation and the like.

The invention relates to the technical field of intelligent power grid fault analysis, in particular to an intelligent power grid fault analysis method and system based on big data. Comprising the following steps: analyzing a power grid physical wiring diagram to construct a node admittance matrix, and extracting modal feature vectors through feature decomposition to generate a topological fingerprint database; synchronously collecting wide-area phasor data streams, dynamically intercepting a data time window, extracting resonance frequency band signals, and generating a fault feature data set through feature enhancement; a modal confidence factor is dynamically evaluated, when the dominant modal confidence coefficient is lower than a threshold value, redundant modals in the node-modal association map are activated, and fault feature vectors are fused and reconstructed; and matching the reconstruction vector with a fingerprint database, performing triple verification, and outputting a fault positioning coordinate or starting a safety protection instruction in combination with a confidence scoring decision. By introducing a dynamic confidence mechanism and a redundancy modal compensation strategy, the accuracy and fault-tolerant capability of fault identification in a new energy disturbance environment are improved.

In certain embodiments, a surgical system includes a camera and a computer. The camera captures a site image of a surgical site. The computer receives the site image from the camera, accesses a stored image of the surgical site, and registers the site image and the stored image according to a registration process. The computer then identifies one or more confidence factors of the registration of the images, determines a confidence level of the registration according to the confidence factors, and outputs the confidence level via an output device.

The application discloses a method for determining the control range of bolt connection pre-tightening force, which comprises the following steps: firstly, establishing a finite element model of bolt connection under the condition of applying pre-tightening force; calculating the maximum equivalent stress of the connection area under different pre-tightening force conditions; constructing a radial basis function proxy model based on the pre-tightening force and the equivalent stress; considering the uncertainty of the pre-tightening force, obtaining a plurality of groups of sample points of the pre-tightening force, and calculating the corresponding equivalent stress through the radial basis function proxy model; calculating the reliability confidence factor of the equivalent stress, and taking the bolt pre-tightening force when the reliability confidence factor is greater than 1 as the control applied pre-tightening force range. Starting from the essence of the uncertainty of the pre-tightening force, the uncertainty of the pre-tightening force is described through the mean value and the multiple relationship of the pre-tightening force, the safe working range of the structure is determined according to the reliability confidence factor of the equivalent stress, the pre-tightening force control range is obtained, the traditional pre-tightening force control is expanded from a single deterministic value to a range interval, and the reliability requirement of the structure in the range is ensured.

Disclosed is a system (100) and a method (300) for determining a confidence factor to evaluate network performance. The method comprises initiating, by a test unit of a user device (110), a request for performing a network speed test. The method further comprises receiving, by a receiving unit of the user device (110) from a network, values of a plurality of network performance metrics based on results of the network speed test. The method further comprises determining, by a determining unit of the user device (110), a confidence factor based on the values of the plurality of network performance metrics.

The embodiment of the invention provides a risk assessment method and device for a bridge strut, equipment and a storage medium. Under the condition that the bridge supporting columns comprise a plurality of single supporting columns, first point cloud data, visible light images and positioning data corresponding to each single supporting column are obtained; constructing a strut three-dimensional model of each single strut according to the data; then determining a defect severity factor, a deterioration development trend factor and a detection confidence factor of each single pillar according to the pillar three-dimensional model; determining a first risk level of each single pillar according to the factors; and finally, determining a second risk level of the bridge strut according to the first risk levels of all the single struts. According to the method, the problems that the assessment dimension is single and the assessment result is not accurate enough when risk assessment is carried out on the bridge strut only depending on two-dimensional image data are effectively solved, and more comprehensive assessment on the risk of the bridge strut can be realized through multi-dimensional data.

The invention belongs to the field of information security, particularly relates to an identity authentication method and system, electronic equipment and a storage medium, and aims to solve the problem that balance among security, flexibility and accuracy is difficult to obtain due to lack of comprehensive analysis capability for dynamic behaviors of a user in the prior art. The method comprises the following steps: acquiring user authentication data; uploading the user authentication data to a preset graph database, and representing a corresponding relationship between the account information and the authentication factor by using the graph database; performing matching verification on the account information according to a high confidence factor in the graph database, and determining and distributing a first joint ID; performing cross validation on the account information which is not allocated with the first joint ID according to the middle and low confidence factors in the graph database, and determining and allocating a second joint ID; and uploading the first joint ID and the second joint ID to a preset ID library. The method is higher in accuracy and effectiveness when dealing with fuzzy matching or incomplete information.

This invention belongs to the field of image processing technology, specifically relating to an automatic measurement method and system for material quantity in a silo based on machine vision. The method includes: acquiring depth and infrared intensity images of the silo; preprocessing these images to generate a valid binary mask; determining a geometric confidence factor based on the deviation between the depth gradient and the natural angle of repose of the material being measured, as well as local curvature; and determining an environmental confidence factor by combining infrared intensity and measurement distance; repairing invalid pixels using anisotropic diffusion; predicting depth values ​​based on first-order Taylor expansion and multi-dimensional confidence weighting; converting the repaired depth image into physical height and projected area; and obtaining the total volume of material in the silo through infinitesimal element accumulation. This invention solves the problem of data loss in complex environments and improves measurement accuracy.

The invention discloses a function handling method, device and equipment based on a life cycle and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of obtaining a confidence factor set corresponding to a to-be-selected evaluation function in a current function evaluation service; performing quantitative judgment on the confidence factor set based on the target low-efficiency affirmation model to obtain a quantitative judgment result, and determining a corresponding intermediate evaluation function based on a preset function category field and the quantitative judgment result; generating a qualitative affirmation starting instruction based on the intermediate evaluation function, and determining a qualitative affirmation result corresponding to the current function evaluation service based on the qualitative affirmation starting instruction and a preset qualitative affirmation rule; and performing function disposal on the intermediate evaluation function based on the qualitative affirmation result, and generating a function disposal result corresponding to the to-be-selected evaluation function. According to the technical scheme of the invention, the method can achieve the accurate recognition and disposal of the low-efficiency function, improves the recognition efficiency and accuracy of the low-efficiency function, and reduces the maintenance and operation cost of a system.

To improve accuracy of data extraction from unstructured text data.SOLUTION: An entity extraction system includes: a candidate recognition unit that recognizes an entity candidate as an entity candidate from a processing object sentence as an unstructured sentence; a confidence factor determination unit that evaluates one or more features for each of the entity candidates to determine a confidence factor on the basis of an evaluation result; and an entity selection unit that selects an entity from the entity candidates on the basis of the confidence factor.SELECTED DRAWING: Figure 1