77 results about "Threshold model" patented technology

The invention discloses a ship collision risk and risk degree evaluation method capable of associating with avoidance behaviors. The method comprises the following steps that: establishing a ship potential risk evaluation threshold value SDA (Safe Distance of Approach) quantization model, a ship potential risk evaluation model, a ship collision risk degree judgment threshold value model and an initial reference ship judgment model; through the established models, carrying out avoidance effect prediction on an approach situation and an avoidance scheme; if the avoidance effect is under a safe,secondary safe or under-safe level, proving that the avoidance scheme is feasible; and if the avoidance effect is under an unsafe level, starting an immediate risk coordination avoidance scheme. Through the above method, an urgent degree of a current ship to adopt a measure under a current approach situation can be evaluated, and the avoidance effect of an avoidance decision formed through a PIDVCA (Personifying Intelligent Decision-making for Vessel Collision Avoidance) decision algorithm can be evaluated to guarantee that the current ship is safely avoided. Objectivity and subjectivity are combined, the practical approach scene is comprehensively considered through ship relative motion vector geometric graphic analysis, a redirection avoidance behavior is associated, and a final avoidance decision is safe, economic and scientific.

The invention discloses an isomerous data fusion based coordinated gesture recognition method and a system of a sensor. The method comprises the steps of 1, training a gesture motion recognition model based on isomerous data collected by a camera and a sensor; 2, constructing a threshold model by the gesture motion recognition model; and 3, segmentally recognizing input continuous motion sequences based on the gesture motion recognition model and the threshold model. The method and the system overcome the problem that commonly, vision based technology is of strong dependency on surrounding environment and position, and insensitive to overturn. Meanwhile, the method and the system solve the problem that the gesture recognition precision and efficiency are not high in sensor based gesture recognition and erroneous judgment and lost judgment occur. The invention provides the gesture recognition method which is high in recognition accuracy, strong in robustness and reliable in recognition of gesture motion and the system thereof.

The invention discloses a gesture segmentation recognition method capable of detecting non-gesture modes automatically and a gesture segmentation recognition system. The gesture segmentation recognition method includes multiple steps, a first step is that a gesture recognition model is trained based on heterogeneous data acquired by a camera and a sensor, the gesture recognition model is used for constructing a threshold model, and the gesture recognition model and the threshold model constitute a gesture segmentation model; a second step is that the gesture segmentation model is used for automatically detecting non-gesture modes from an input continuous action sequence; a third step is that the non-gesture modes are used for training a non-gesture recognition model; and a fourth step is that the gesture segmentation model is expanded based on the non-gesture recognition model and used for segmentation recognition of the input continuous action sequence. Due to the gesture segmentation recognition method capable of detecting the non-gesture modes automatically, the gesture segmentation recognition system can well represent the non-gesture modes, the probability that the non-gesture modes are misjudged to gesture modes is reduced, and accuracy of a gesture segmentation algorithm is improved.

The invention discloses a service monitoring system threshold setting and anomaly detection system and a service monitoring system threshold setting and anomaly detection method. The system comprisesan index data module used for acquiring index data of a monitored index item; a threshold model setting module used for setting a corresponding threshold model by using a specific algorithm accordingto the index data of the index item acquired by the index data module; an actual index data collection module used for collecting real-time index data of the monitored index item, and sending the real-time index data to an anomaly detection module; the anomaly detection module used for detecting whether the index data input in real time accords with the set threshold model; and an alarm module used for giving an alarm when the index data input in real time does not accord with the threshold model. The invention provides the intelligent service monitoring system threshold setting and anomaly detection system and method which are flexible and accurate in threshold setting, and have threshold anomaly detection and alarm functions.

To solve the difficult problem about fine association of tracks of various objects in a group under the system error, based on the characteristics of group tracks and in combination with an error estimation technology and a track association technology, the invention provides an error compensation-based group track fine association algorithm. The algorithm is characterized in that first, group identification is carried out on tracks obtained by various sensors based on a circulating threshold model, and overall pre-association is carried out on the group tracks based on a group center track, then, a pre-association group track most approximate to a resolution state is searched or established based on a group track state identification model, afterwards, the final error estimated value is obtained based on a group track system error model and an error confirmation model, and the error compensation is completed, and finally, fine association is carried out on the group tracks by utilizing a traditional track association algorithm. Compared with a fuzzy track alignment association algorithm based on unchanged-object information amount, a track alignment association algorithm based on track iteration and a corrected weighting method, the error compensation-based group track fine association algorithm provided by the invention has the advantages that the comprehensive performance is more excellent, and the requirements of engineering for precise association of tracks of objects in a group under the system error are well met.

The embodiment of the invention provides a vibration state monitoring method and device for a variable speed rotating machinery. The method comprises steps: during a process of increase to an upper limit rotating speed from a lower limit rotating speed in a normal state of the machinery, a vertical vibration speed, a horizontal vibration speed and a rotating speed of the machinery are acquired synchronously; the vertical vibration speed and the horizontal vibration speed are integrated, a vibration root mean square value of the integrated vibration speed is extracted, and mean and variance ofthe vibration root mean square value are determined; relation curves between the mean and the variance and the rotating speed are fit respectively to acquire corresponding continuous functions; the continuous functions are substituted to a preset decision function to acquire an alarm threshold model; and the vibration root mean square value and the rotating speed of the machinery currently are acquired in real time and are substituted to the alarm threshold model to acquire the current vibration state of the machinery. The embodiment of the invention can improve the variable speed rotating machinery vibration state alarm accuracy.

The invention discloses a panoramic video coding-oriented quick global motion estimation method. The method comprises two steps of: 1, using a brightness residual threshold value model, which is based on the combination of statistic characteristics and mean of threshold values, to approximately divide a local motion area and a global motion area, and obtaining an approximate set of a global motion estimation area; and 2, adopting the motion vector residual grading threshold value technology to gradually detail the set of global motion pixels in a minimum iterative process of the energy residual function and finally separating the complete global motion area so as to achieve an aim of quick motion estimation. The method has the advantages of taking both accuracy and robustness of the global motion estimation into consideration, greatly saving cost of calculating the motion estimation under the condition of ensuring the quality of the re-constructed image does not decline, but is improved a little bit, and having effects of low algorithm complexity and accurate estimation of motion parameters on the global motion estimation of the panoramic video sequence.

The present invention relates to the field of electric power management, and especially relates to construction of an electric power transaction supervision risk early warning system based on a unified and interconnected electric power market and a realization method thereof. The electric power transaction supervision risk early warning system comprises an electric power transaction supervision risk index system, a threshold system and a threshold model. The realization method comprises the concrete steps as follows: a step S01 of identifying risk indexes and determining electric power transaction supervision risk early warning indexes through expert experience; a step S02 of collecting correlation information of the risk early warning indexes; a step S03 of calculating corresponding risk assessed values of the acquired transaction processing information according to the risk indexes corresponding to risk models in combination with corresponding comprehensive fuzzy evaluation; and a step S04 of dividing risk early warning grades according to the risk assessed values in combination with the threshold system and giving proper risk early warning modes. According to the present invention, the electric power transaction supervision risk index system is established based on the unified and interconnected electric power market at present. In the environment of the unified and interconnected electric power market at present, the index system established in the present invention can be well adapted to the electric power market at present.

The invention discloses an automatic detection alarm method and device for clogging of a high pressure airtight filter; and the method and device are designed for automatic detection and alarm of a clogging situation of the high pressure airtight filter. An outlet channel and an inlet channel of the high pressure airtight filter are respectively provided with an outlet pressure detector, an inlet pressure detector and an inlet mass flowmeter, which are connected with a core controller. The core controller employs an ''experimental experience set clogging alarm threshold model'' or a ''Darcy-Weisbach formula calculation clogging alarm threshold mode'' to determine a clogging alarm threshold; and based on the actual measurement data, the core controller calculates a pressure difference value between the inlet and outlet of the filter, and compares the value with the clogging alarm threshold, so as to realize real-time alarm sound by the alarm when the filter is severely blocked, and prompt the drilling technical personnel to provide maintenance. The invention provides important safety guarantee for operation safety of control pressure drilling pipe and other high pressure oil and gas transportation pipeline.

The invention discloses an intelligent substation network anomaly traffic detection model construction method based on a probability. According to the method, by carrying out repeated FARIMA simulation on acquired intelligent substation station control layer communication traffic data, a station control layer normal communication traffic threshold model is obtained; and a network anomaly case shown on the station control layer traffic level is subjected to key analysis, KDD99 anomaly data traffic is extracted and overlaid into original background traffic, anomaly network traffic is generated and used as a threshold datum thereout, and an evaluation index and an algorithm of an intelligent substation communication network are designed to analyze an operation condition of a power communication network. According to the invention, detailed algorithm description is given out by actual acquired traffic of a station control layer of a certain substation in Tianjin as test data, and examples are designed to carry out detection and early warning on substation communication network anomaly traffic.

The invention provides an operation and maintenance monitoring management system which comprises a monitoring center used for monitoring the health degree of each application service and operation instance and the basic index information of each instance; an alarm center which configures a new alarm rule or edits an existing alarm rule, wherein the alarm rule comprises an automatic selection model, a static threshold model, a dynamic threshold model, a mutation threshold model and a neural network model; and a system management module which establishes a task plan for all tasks in the system,operates the user operation record of the audit record login system, and retrieves the operation record of the user of the audit record login system on the real-time retrieval page. The service condition can be monitored in real time, the service condition can be known in time, the system log can be acquired in time and the application and data security and reliability are ensured.

The invention discloses a credit control prompting method and device and relates to the technical field of credit control. The method includes the following steps: the account information of users isobtained, whether the users are credit monitoring objects or not is judged, the contrast threshold is obtained according to the overdue sum monitoring range, contrast is performed according to the preset threshold and the arrearage quota to judge whether the user are in arrearage or not, grade assessment and credit prompting are performed on the users, and the credit grade is generated according to the contrast result of the arrearage quota and the preset threshold. Through accessing an information input interface module and an information output interface module to the credit control prompting device, the credit grade assessment model database of different users is established and the preset threshold model database matching the account information of the users, different monitoring plansare adopted for different users, the user payment reminder message and the credit prompting information are transmitted to the clients through various forms and devices of the Ethernet, and the credit control prompting speed and the diversity of the mode and platform of the arrearage and credit information pushing are improved.

The invention discloses an abnormal data detection method, system and device, and the method comprises the steps: obtaining access data in a specified time period, obtaining a threshold model based onthe training of the access data, and judging whether the access data of a target time node is abnormal data or not according to the threshold model; if the access data of the target time node is abnormal data, determining a detection interval, counting the distribution of access data samples in the detection interval, and judging whether the access data of the target time node is abnormal data ornot again; and if the access data of the target time node is judged to be abnormal data again, obtaining a convergence rule and an amplitude threshold corresponding to the access data of the target time node, and judging whether the access data of the target time node is abnormal data to be processed or not based on the convergence rule and the amplitude threshold. According to the technical scheme provided by the invention, the abnormal data detection accuracy can be improved.

The invention discloses a monitoring data exception identification and processing method and system, and the method comprises the steps: predicting to-be-analyzed and judged monitoring data through employing a Kalman filter data prediction principle, and preliminarily judging whether the monitoring data is exceptional or not through the comparison of a prediction value and a monitoring value; performing secondary correction on the preliminarily judged abnormal data through manual supervision, and performing updating processing of a filter and updating of a threshold model on a correction result. The method is different from general data model filtering, considers the change condition of equipment in time, increases the weight of a current normal monitoring value, has time correlation, meets the requirements of scenes such as environment change monitoring and instrument loss monitoring.

The present invention provides a Spark semantics based data reuse method. The method comprises a semantic information collection step of collecting semantic information during the operation of a Spark application; a semantic maintenance step of maintaining the semantic information collected from the semantic information collection step; a data active caching step of caching data that is not explicitly cached by a user program according to the semantic information and a preset threshold model; and cached data migrating step of carrying out migration on the cached data between a dynamic random access memory and a fixed memory according to the semantic information and the preset threshold model. The present invention further provides a Spark semantics based data reuse system. The technical scheme provided by the present invention can reduce the repetitive data calculation, improve the calculation efficiency, and effectively avoid the dependence on the experience of developers.

The invention discloses a construction method for a communication traffic threshold model of an intelligent substation based on FARIMA. The method comprises the following steps: analyzing the stability and autocorrelation of a traffic sequence; designing an FIR filter according to Hurst parameters of the sequence, and filtering data; performing order determination for an ARMA (p, q) model by usingAIC information criteria, and then performing residual test for a predicted value of an FARIMA (p, d, q) model; and simulating the FARIMA model for multiple times, performing short-term prediction, and designing an algorithm to generate traffic threshold models with different confidence levels. According to the construction method disclosed by the invention, a detailed algorithm description is provided by taking the traffic actually acquired by a station control layer of a certain substation in Tianjin as test data, and the communication traffic threshold model of the station control layer ofthe intelligent substation is also obtained through a series of experiments.

The invention provides a defect diagnosis method and device for a photovoltaic power station. The method includes the following steps: collecting test data during the operation of a photovoltaic powerstation to be diagnosed; according to the test data, determining an annual performance PR value of the photovoltaic power station to be diagnosed, a PR value of each power generation unit of the photovoltaic power station to be diagnosed, and the PR value, weighting efficiency, parallel mismatch loss or series mismatch loss of components included in each power generation unit; and diagnosing positioning defects of the photovoltaic power station to be diagnosed layer by layer based on a PR evaluation threshold model and according to the PR value of the photovoltaic power station to be diagnosed, the PR value of each power generation unit of the photovoltaic power station to be diagnosed, and the PR value, weighting efficiency, parallel mismatch loss or series mismatch loss of the components included in each power generation unit, wherein the PR evaluation threshold model is predetermined based on actual measured data of the photovoltaic power station on the scene. According to the above technical scheme, the defect detection of the photovoltaic power station from the operation level can be implemented, and the efficiency of defect diagnosis of the photovoltaic power station can beimproved.

The application provides a method and device for recognizing a passive user, and a server. The method can comprise the steps that target operation index data of a target user generated during execution of a target interaction task is acquired; the target operation index data and a preset operation index threshold are compared, wherein the operation index threshold is obtained by a preset index threshold model based on sample statistic analysis of multiple user operation index data samples which are input into the model; and if the target operation index data is not higher than the preset operation index threshold, the target user can be deemed as a passive user and processing aiming at the passive user is conducted. The passive user can be recognized by the method provided by the application.

The invention discloses a method for dynamically setting a step counting threshold, which is suitable for being executed in a mobile terminal with a pedometer. The method comprises: acquiring weight and height information input by a user, and acquiring first thresholds of step counting parameters corresponding to the weight and height information according to a step counting threshold model; acquiring a motion mode set by the user in the mobile terminal, performing statistics on gait fluctuation data, collected by an acceleration sensor, of the user during exercising in different motion modes, and performing statistics to obtain a second threshold of each step counting parameter; and for each step counting parameter, calculating a first difference between the first threshold and the second threshold respectively, and if the first difference of each step counting parameters is less than a corresponding first difference threshold, setting the step counting threshold of each step counting parameter corresponding to the movement mode as the first threshold of the corresponding parameter, otherwise, setting as the second threshold. The invention further discloses the corresponding mobile terminal.

A method for establish a threshold model for monitoring that integrity risk of a Beidou foundation enhance system comprises the following steps: a) identifying a satellite number, and inputting all monitoring statistical data of a satellite period; B) uniformly converting all recorded monitoring statistical data from an initial equidistant measurement to an equi-proportional measurement; C) calculating correlation coefficients between pairs of monitoring statistics to determine degrees of freedom and weight coefficients required for hypothesis testing; D) calculating the observed value of thechi-square test statistics; E) judging whether the observed value of the chi-square test statistic is within the rejection range; (f) calculating that risk rate; (g) calculating the reliability value;(h) utilizing the inverse transformation of the conversion unified measurement to obtain a threshold model. The invention obtains an effective integrity risk monitoring threshold model aim at the Beidou foundation enhancement system from the collected monitoring quantity data, and can effectively reduce the missed alarm probability and the false alarm probability in the integrity monitoring process.

The invention provides an automatic driving drive test data analysis method and device, and the method comprises the steps: obtaining CAN data corresponding to an active takeover moment of a driver inautomatic driving, carrying out the key data extraction of the CAN data in an automatic takeover scene, and obtaining a CAN data set; drawing a scatter diagram according to the CAN data set, and performing regression curve fitting based on the scatter diagram to obtain a driver active takeover threshold model; and generating vehicle AEB braking rules under different driving conditions according to the driver active takeover threshold model. According to the scheme, the problem of high cost of the existing threshold test is solved, the threshold extraction test cost can be reduced, and the vehicle driving safety is guaranteed.

The invention relates to a metagenome sequencing quality control prediction evaluation method and model, and belongs to the technical field of gene detection. The method comprises the following steps:a Q20 threshold system evaluation process: obtaining sequencing parameters, constructing a Q20 threshold model, and inputting the sequencing parameters into the Q20 threshold model for solving to obtain a relationship between Q20 proportion and correct rate; a sequencing data volume threshold system evaluation process: acquiring data parameters, constructing a sequencing data volume threshold model, and inputting the data parameters into the sequencing data volume threshold model for solving to obtain a relationship between the sequencing data volume and the detected strain unique region; anda sequencing fragment length threshold system evaluation process: acquiring strain mutation parameters, constructing a sequencing fragment length threshold model, and inputting the strain mutation parameters into the sequencing fragment length threshold model for solving to obtain a relationship between the sequencing fragment length and the strain reduction accuracy. The method can be applied tometagenome detection as preset quality control standard evaluation.

The invention provides an adaptive CU splitting decision method based on deep learning and multi-feature fusion. The method comprises the steps: firstly, calculating the texture complexity SD of a current CU through standard deviation, building a threshold model through a quantization parameter function and a depth function, and dividing the current CU into a complex CU and a uniform CU; secondly,if the complex CU belongs to the edge CU, judging whether the complex CU is split or not by utilizing a CNN structure based on multi-feature fusion; otherwise, judging whether the complex CU is splitor not by utilizing the self-adaptive CNN structure. According to the method, deep learning and multi-feature fusion are combined, and the problem of coding complexity is solved. The CNN structure based on multi-feature fusion and the CNN structure based on self-adaptation can successfully process the training samples, calculation of rate distortion RDO of all CU and complex CU is avoided, and therefore the calculation complexity is reduced, and the coding time is saved.

The invention discloses a determination method of planar potential replenishment quantity of underground water based on an isotope rainfall effect. The method comprises the steps that a vadose zone interception threshold model is established; a determined rainfall material and an underground water material are brought into the vadose zone interception threshold model for solving; vadose zone interception water quantity, namely minimum replenishment rainfall of the underground water is obtained; and then the potential replenishment quantity of the underground water is calculated. The method achieves replenishment of hydrogeological underground water, provides a novel reliable method, promotes development of a hydrogeological discipline, scientifically and reasonably determines the planar potential replenishment quantity of regional underground water, facilitates optimal allocation of an underground water resource, provides reliable data for water resource management, provides more detailed scientific support for further establishing economic development and ecological protection measures, can be directly applied to calculation and evaluation of a regional underground water resource, improves the applicability of a determination result, and avoids a possible error in a scale conversion process of the determination result.

The invention relates to the field of Internet service system monitoring, in particular to an abnormality monitoring and alarming system and method for a transportation management system. The system comprises an index data module which is used for obtaining historical index data and real-time index data; the threshold model setting module is used for setting a threshold model according to the historical index data; the anomaly detection module is used for detecting whether the real-time index data conforms to a set threshold value model or not; determining misreported abnormal information according to the characteristic parameters of the wave crest and eliminating the misreported abnormal information; the exception classification module is used for carrying out statistical classification on the rejected exception information and sending a classification result in a mail form; and the alarm module is used for receiving the classification result and sending alarm information. According to the invention, before the abnormal information is alarmed, the abnormal information which may cause false alarm is filtered, and the abnormal information is classified by adopting a statistical algorithm; not only is false alarm prevented, but also abnormal condition processing efficiency is greatly improved.

The invention discloses a bayes method for joint estimation genomic of continuous traits and threshold traits based on genomic estimated breeding value. The bayes method is a new bayes method based on a line-threshold model, called as LT-BayesC pi used for joint estimation of continuous traits and threshold traits. Simulation data and the fourteenth QTL-MAS international symposium public data are used for verifying LT-VayesC pi. Accuracy of genomic prediction and BayesC pi and Bayes TCpi based on a single character model are compared. Factors influencing performance representations are also researched. The result of the bayes method shows that genomic prediction of threshold traits with LT-BayesCpi is more accurate than that with BayesTCpi. However, accuracy of continuous traits is the same as that of BayesCpi.

The invention discloses an elastic optical network spectrum allocation method based on multi-hop routing, and belongs to the technical field of elastic optical network and spectrum resource allocation. The method comprises the following steps: establishing a hop count threshold model of a multi-hop route under the upper limit of energy consumption, selecting a group of intermediate multi-hop nodesas virtual routes, carrying out spectrum allocation on services, and the like. According to the method, firstly, an intermediate multi-hop node threshold value is calculated under the constraint of energy consumption, then, an intermediate multi-hop routing node is reasonably selected, the modulation format grade of each section of short optical path is flexibly selected between source sink nodesaccording to the transmission distance, spectrum resource distribution is optimized, and the purpose of improving the overall resource utilization rate of the network is achieved under the constraintof energy consumption.

The invention discloses a foundation augmentation system integrity monitoring method based on stable distribution. Through a method of combining stable distribution with maximum likelihood parameter estimation and quantile calculation, threshold modeling is performed on monitoring quantities such as code / carrier divergence values and carrier-to-noise ratios involved in a ground station module ofa GBAS system based on H2 risk hypothesis, and the accuracy of threshold modeling is improved in a mode of dividing model intervals according to elevation angles and selecting confidence parameters ina monitoring process of the ground station module in the modeling process. The method solves the problems that a traditional threshold model depends too much on an empirical formula, the model is notaccurate enough and the applicability is low, and the credibility and availability of the GBAS integrity monitoring system are improved through more accurate threshold modeling.

The invention discloses an intra-frame prediction mode selection method in combination with texture spatial correlation. The method comprises the following steps: S1, carrying out statistical analysison the utilization rate of each prediction mode when different quantization parameters are adopted under different prediction units in an intra-frame prediction mode selection process, and selectingfour main texture prediction directions in a texture prediction direction based on a statistical result; s2, analyzing each main texture prediction direction and calculating an image pixel value deviation of a prediction unit, judging whether a prediction unit is smooth or not by utilizing the image pixel value deviation, and when the prediction unit is judged to be smooth, using a DC mode and a Planar mode as the input of a rate distortion optimization stage to obtain an optimal prediction mode; and if not, constructing a texture prediction candidate set based on a multi-reference line technology as the input of a rate distortion optimization stage, and then terminating rate distortion optimization in advance by sequentially utilizing a first adaptive threshold model constructed based onSATD cost and a second adaptive threshold model constructed based on RD cost to obtain an optimal prediction mode.

The invention discloses a thermal power plant furnace tube leakage accident early warning method and device. The thermal power plant furnace tube leakage accident early warning method comprises the steps that S1, furnace tube prediction models based on ridge regression algorithm linear fitting working conditions and furnace tube temperature data are called; s2, utilizing the model to predict the temperature of the furnace tube in real time, wherein the input of the model is real-time time sequence working condition data; the output of the model is furnace tube time sequence temperature data predicted according to the time sequence working condition data; and S3, fuzzy threshold alarm: when the average value of the difference values of the real-time timing sequence temperature data of the furnace tube and the timing sequence temperature data predicted by the prediction model is greater than a first threshold value and the average value of the difference values of the real-time timing sequence temperature data of the adjacent furnace tube and the real-time timing sequence temperature data of the current furnace tube is greater than a second threshold value, performing fuzzy threshold alarm. And early warning of a furnace tube leakage accident is carried out. The boiler four-tube wall temperature fuzzy threshold model early warning method based on time correlation and spatial correlation is provided for the first time, and the method has the advantages of being high in accident recognition accuracy and long in accident early warning time.