516results about How to "Representative" patented technology

The invention belongs to the technical field of natural gas detection and particularly relates to an automatic natural gas continuous-sampling control system. The system includes a reducing valve anda sampling probe connected with a natural gas pipe. An inlet of the reducing valve is connected with an outlet of the sampling probe, an outlet of the reducing valve is divided into two paths, one path is connected with a pneumatic membrane sampling pump, and the other path is connected with outlet ends of a first constant-pressure sampling steel bottle and second constant-pressure sampling steelbottle. The system also includes a full-automatic natural gas replacement system. The automatic natural gas continuous-sampling control system can achieve continuous sampling. Since sampling is conducted once every certain time interval until the constant-pressure sampling steel bottles are fully filled, sampled gas can completely represent a fluid medium in a whole production cycle and have representativeness.

The invention provides a method for preparing neodymium iron boron through regenerating a waste material containing neodymium, iron and boron. The method comprises steps of (1) preprocessing the waste material; (2) correcting the components of the waste material; (3) crushing by hydrogen; (4) preparing into powder; (5) molding under a magnetic field; and (6) vacuum sintering. The method fully uses the waste material recycled in a production process, has high recovery rate of the waste material, can produce high performance product, has a simple and controllable flow, has high operability, uses no strong acid and strong base polluting the environment, is environment-friendly and energy-saving, and has high social and economic benefits.

The invention discloses a user closeness-based mixed recommending system and a user closeness-based mixed recommending method, which are functional in recommending interesting projects for users in social network sites. The system comprises a user closeness determining module, a user closeness-based recommendation result generating module, a collaborative filtering-based recommendation result generating module, a content-based recommendation result generating module, and a result integrating module. The method is carried out by steps of determining the user closeness, acquiring user closeness-based recommendation result, acquiring collaborative filtering-based recommendation result, acquiring content-based recommendation result, and integrating the results. According to the method, data in the social network sites are fully utilized to make up the disadvantage of a traditional recommending system, therefore, the system application has the advantages of strong practicality, high accuracy and convenient implementation.

The invention relates to a resident trip information obtaining method based on an intelligent mobile phone. The method comprises the following steps: (1), a mobile phone client detecting whether a GPS and a Network are opened; (2), the mobile phone client uploading user registration information and home / work place coordinate information to a web server; (3), the web server receiving the registration information and the coordinate information; (4), the mobile phone client acquiring tracing point latitude and longitude information and time information of a user in accordance with specific time intervals and uploading the information to a local place; (5), the mobile phone client automatically uploading the tracing point information stored in the local place at user specific time or uploading the information when the user clicks a hand-operated transmission button; (6), the web server receiving tracing point data and storing the data in a traveler locus table in a database; and (7), the web server determining an algorithm according to information compiled in advance, and through combination with POI information, analyzing to obtain a trip chain of a resident. Compared to the prior art, the method provided by the invention has the advantages of wide application scope, low cost, simple process, high information accuracy and the like.

The invention relates to a method and a device for evaluating the pilling resistance of a yarn by an on-line inlet and outlet measurement mode. The method comprises the steps of input pre-evaluation, uniform winding forming, preparation of a test sample by carrying pressurizing friction, stable backing-off and on-line measurement, comparison and evaluation. The device comprises a yarn supply and recovery device, a yarn pilling condition measurement device and a winding friction mechanism. The yarn pilling condition measurement device comprises a capacitor plate, a static electricity generation device and a yarn hairness measurement system. The static electricity generation device comprises a prepositive static electricity generator and a post static electricity generator. The prepositive static electricity generator and the post static electricity generator are respectively arranged at both sides of the yarn hairness measurement system. The capacitor plate is arranged at the rear sideof the post static electricity generator. The winding friction mechanism comprises a winding drum. The separation position of the unwound yarn and the winding drum is provided with a backing-off tension measurement mechanism. The winding friction mechanism is connected with a reciprocating mechanism. In the invention, the yarn is used for directly testing and panel knitting is not used; and by taking a base yarn as the contrast, the method and the device are more accurate.

The invention relates to an industrial control system anomaly detection method based on a dual-contour model. Involved industrial control network equipment comprises a security gateway, a programmable logic controller (PLC), onsite sensor equipment, a security management platform, and an engineer station. The method comprises the following steps of S1, the engineer station configures and operates a system, the PLC of each region discriminates controlled equipment connected to an IP module of the PLC, an information list is distributed to the controlled equipment, and a periodic communication mode is formed for master and slave stations; S2, the PLC timely feeds back data information to the security gateway, a data packet deep analysis system of the security gateway extracts data features and eliminates superfluous attribute features, and only features related to a system behavior mode are left, wherein the features related to the system behavior mode comprises protocol features, data packet transmission direction features and register value variation rules based on the communication frequency; and S3, an anomaly detection subsystem carries out anomaly detection and sends an alarm to the security management platform for an abnormal result.

The invention discloses a human detection method. The human detection method includes the following steps that gradient histogram features are extracted in the depth color direction by the cooperation of color information with depth information; detection models are trained; region segmentation is conducted on test images, and the images are detected based on a segmentation result to obtain a candidate region (SR); the candidate region (SR) is verified based on edge detection. The human detection method based on color information and depth information is achieved, the obtained features comprise color information and depth information by the cooperation of feature extraction, outline edge information is enhanced, and then the features are more representative. Detection strategies based on different features and different classification models are used for different parts, and the defect that accuracy of long-distance objects shot by a depth camera is low is overcome. The verification method based on edge information is adopted, and then the detection result is more accurate.

The embodiment of the invention provides an image splicing method and a system thereof; the image splicing method comprises the following steps: matching feature points are extracted from at least twopairs of synchronous frames, and the synchronous frame pairs are provided with an image overlapping region obtained by two different angles; homography matrix is solved according to the matching feature points; the homography matrix is applied to carry out image splicing to the synchronous frame pairs obtained by two different angles. In the embodiment of the invention, the matching feature points are extracted from a plurality of the synchronous frame pairs, so as to obtain a plurality of matching feature points, compared with the prior art, the matching feature points are extracted from a pair of the synchronous frame pairs, the method in the invention has representativeness, and the obtained homography matrix is reliable and accurate, thereby obtaining better image splicing effect.

The invention relates to a pre-assembly method of a construction steel structure, in particular to a computer simulation pre-assembly method of the construction steel structure. The method comprises the following steps: drawing out a three-dimensional standard map of all members of a construction structure according to a design drawing through computer software and establishing a three-dimensional model drawing and a standard map library; using a 3D optical scanning measuring system to carry out three-dimensional raster scanning on the members, measuring the entity members, and introducing into a computer for obtaining a three-dimensional stereo image; using the computer software to process the measured entity members, and being compared with the three-dimensional model drawing for inspecting whether the members are qualified or not; using the actually measured members to carry out simulation assembly on the whole construction structure; finding out amount of deflection of connecting parts between the actually measured members and the simulation assembly members; and providing a member simulation assembly detection parameter report after the simulation assembly of the actually measured members is qualified, and guiding the installation and the field construction. The method has simple operation, can cover the full process of the member overall part of the construction steel structure, and has representativeness and guidance; and one-time development can be utilized repeatedly, and the waste of resources can be further avoided.

The invention discloses an intelligent diagnosing and deciding support system of blast furnace in the automatic technical domain of iron-smelting course, which comprises the following parts: 5 operating parameter detecting and control PLC of blast furnace as bulk PLC of blast furnace, hot wind furnace PLC, feed PLC, coal spraying PLC, assay chamber PLC, PLC and server, wherein the server communicates customer end through industrial Ethernet, which constitutes hardware platform of system; the software mode and database are operated on the server, which reserves disposed PLC detecting parameter in the database; the intelligent diagnosing and deciding support system of blast furnace are operated on the customer end; the data gathering software mode on the server and intelligent diagnosing and deciding system of blast furnace also adopt two-layered structure of program-database.

The invention discloses a high-resolution SAR image individual building extraction method, and the method comprises the steps: firstly carrying out domain ontology modeling through combining the imaging features of a high-resolution SAR image individual building and the characteristics of a complex morphological structure, and constructing an individual building body semantic model in a high-resolution SAR image; secondly obtaining image regions with the good homogeneity and clear boundary through employing SAR image segmentation based on an object, wherein the image regions are the basic processing units extracted by building base units; combining with the related rule of the building base units in the body model, wherein the extracted image object characteristics comprise regional features, shape features, geometric features, texture features, and topological features; forming the object expression of body semantic description according to the body semantic rule and the object features, guiding the image objects to be automatically recognized as the building base units, and achieving the large-scale individual building recognition which takes the semantic knowledge as the center. The method can extract a large-scale individual building in the high-resolution SAR image accurately and quickly.

The invention discloses a key point and local characteristic-based three-dimensional human face identification method. The method comprises the steps of preprocessing a three-dimensional human face model by human face region cutting, smoothing processing and pose normalization, and arranging all human faces in a pose coordinate system; detecting key points according to valuable contour lines and an average curvature; constructing a spatial structure of local characteristics in the form of a DAISY descriptor; adopting a shape index histogram, a slant angle histogram and a directional angle histogram as the local characteristics; and performing key point matching, and measuring the similarity of curved surfaces of two human faces by using the number of successfully matched key points. The method is relatively good in identification performance and has the robustness for expression change to a certain extent.

The invention discloses a method for classifying weibo accounts. The method comprises the following steps: obtaining unknown weibo data corresponding to weibo accounts; extracting feature data of weibo data to obtain weibo data features; utilizing an established classification model based on weibo data features according to the weibo data features to determine types of weibo accounts. The method for classifying weibo accounts is utilized so that ordinary accounts and abnormal accounts can be accurately classified and types of accounts can be recognized.

The invention discloses a preparation method of a sample with a rust layer for in-situ observation of a transmission electron microscope. The method comprises the following steps of: (1), integrally packaging an obtained sample with a rust layer by a cold embedding method; 2), cutting the sample according to the sample size facilitating observation of the transmission electron microscope; 3), thinning the sample primarily; 4), forming a thin sample area which can be observed by the transmission electron microscope; and 5), bonding the prepared sample to a phi3mm round copper mesh, so as to obtain the sample with the rust layer for the in-situ observation of the transmission electron microscope. By adopting the preparation method, the initial shape of the rust layer is remained completely, and the method is very suitable for preparing of the sample with the rust layer for observation of the transmission electron microscope; and the prepared sample is representative, can truly indicate the whole characteristics of the analyzed material, and has great significant in the corrosion resistance research of steel and iron materials.

The invention relates to a pulse helium ionized gas phase chromatographic instrument which comprises a gas carrying unit, a sampling unit, a gas path control unit, a circuit control unit, a separation unit and a test unit; the invention is characterized by further comprising a tail gas processing unit and a signal collection and data processing unit; the tail gas processing unit receives the waste gas discharged by the separation unit; the signal collection and data processing unit is a countercharge chromatograph workstation; the gas carrying unit is constructed by a solid state filter and a helium purifier to provide high purity carrying gas for the separation unit; the sampling unit is used for sampling and automatic sampling injection; the separation unit is comprises a switching procedure of three valves and four columns; the test unit is a pulse helium ionized tester. The stable path control and the circuit control parts of the invention can ensure the instrument to work for a long time and can show good response to all the impurities in the high purity gas, the measurement result can meet the gas standard requirement, the operation is simple and the whole process is in automatic control.

The invention discloses a method for automatically acquiring post professional ability requirements based on internet, which acquires recruiting information from recruiting websites through an automatic mode, stores the recruiting information into a local database, extracts the recruiting information aiming to a specific job, and automatically extracts professional ability requirements of the specific job by utilizing natural language processing technology and data mining technology. Compared with a traditional method, the method for automatically acquiring post professional ability requirements based on the internet has the advantages that 1 the method for automatically acquiring post professional ability requirements based on the internet is an automatic process, and is rapid in time, high in efficiency and low in cost; and 2 the method for automatically acquiring post professional ability requirements based on the internet is large in acquired data samples, updates data in time, and has representativeness in analysis results.

The invention provides a vehicle-mounted movable full-automatic coal sampling and sample preparation machine and a coal sampling method of the vehicle-mounted movable full-automatic coal sampling and sample preparation machine. The sampling and sample preparation machine comprises an automobile chassis (1), an auxiliary frame, hydraulic supporting legs (4 and 8), a sampling device (7), a sample preparation device (3), a sample collecting device, a sample discarding box (2), a hydraulic oil tank (5), a cooler (6) and an electric control cabinet (9), wherein the auxiliary frame is arranged on the automobile chassis (1); and other parts are arranged on the auxiliary frame. The coal sampling and sample preparation machine integrates the functions of sampling, sample feeding, sample preparation, sample collecting and sample discarding, can provide an accurate and reliable sample for the coal quality analysis and test, and has the advantages of high working efficiency, wide application range, high degree of automation, safety and convenience in operation and the like.

The invention relates to a production method of a embedded-sensor simulation software dummy for a clothing pressure test. The production method includes regarding real body data, in a certain sample size, of humans of a certain age as samples, and acquiring intermediate body forms and sizes of corresponding parts by means of mathematical statistics; selecting a model close to the intermediate body in the sizes of height, waistline, hipline, abdominal perimeter and the like, extracting a three-dimensional human body model, and producing a three-dimensional human body model form of the intermediate body by the reverse engineering; according to human soft tissue thickness, producing a three-dimensional model of a hard inner shell of the model form by narrowing inwards the intermediate body; printing the hard inner shell of the model form by the rapid proto-typing technology, with a model form soft tissue made of human body silicon, and human body skin substitute material which is smooth elastic fabric; selecting sensor embedding points, fixing sensors onto the surface of the human body soft material, allowing guide lines to penetrate the soft material and the hard inner shell to be connected with an outside data collecting device outside the body. The production method is convenient and effective to use to evaluate body pressure comfort.

The invention relates to a local solar energy resource abundance evaluating system for design of a photovoltaic power station, which can be widely applied to scientific research and engineering application in the fields related to solar energy. In the system, a data acquisition module is used for acquiring various indexes of four meteorological parameters from a meteorological observation system; an integrated operation module is used for operating and analyzing the acquired meteorological data by a representative meteorological year and month method; an evaluation and quantization value acquisition module is used for stacking annual daily total solar radiation data generated in the integrated operation module to acquire an evaluation and quantization value; and a solar energy resource evaluation module is used for comparing the quantization value with an evaluation system to acquire an abundance evaluation result of the solar energy resources. The system can be widely applicable to various areas and climate conditions, the generated annual daily solar radiation data have representativeness, the evaluation result is objective and reasonable, and the system can meet the requirements on design engineering application of the photovoltaic power station.

The invention relates to a pH value detection method and a pH value detection device based on image processing, wherein a pulse signal sent by a single-chip is sent to a stepper motor, a connection rod is driven to rotate by the stepper motor, fluid to be detected is injected to pH test paper through an injection orifice of the fluid to be detected when an external end of the pH test paper falls to the center of the injection orifice of the fluid to be detected, and the pH test paper is shot by a CCD (charge coupled device) sensor under the light condition of an illuminating lamp when the pH test paper continuously falls to the center of a through groove in an image acquisition device box, so that the image acquisition of the color change signals is finished; and the acquired images are input into the single-chip for preprocessing and then are converted into an HIS color space, three characteristic value data of the color change blocks including H, I and S in the space are extracted, three characteristic values are input into a relation model both of a pre-built color block and the pH value, and the pH value is calculated, so that the online automatic detection of the pH value is realized and the influence of the human factors in the artificial reading process is avoided.

Provided is an automatic sampling device for malodorous gas. The automatic sampling device comprises a seal box, a measurement control unit and a gas concentration monitoring and alarming unit; the gas concentration monitoring and alarming unit comprises a concentration measurement module and an alarming module, and the alarming module sends an alarming signal to the measurement control unit after the odor concentration exceeds the set threshold value concentration; the seal box is internally provided with a gas bag collecting sampling gas, the gas bag is provided with a gas bag gas nipple which penetrates through the seal box to be connected to one end of a two-way solenoid valve, and the other end of the two-way solenoid valve is connected with a sampling inlet; the measurement control unit comprises a monitoring and alarming signal processing module receiving the alarming signal, a solenoid valve control module and a sampling pump control module, and the solenoid valve control module is electrically connected with the two-way solenoid valve so as to control switching-on and switching-off of the two-way solenoid valve. The sampling device is used for achieving on-site automatic identifying and sampling of the malodorous gas, and the problem that the best opportunity of sampling is missed due to the fact that a traditional method depends on artificial olfaction is solved.

A method for classifying enterprise domains and screening enterprise keywords is disclosed by the invention. The method obtains the feature words of the related fields through training the enterprisedomain corpus as a classification dictionary, and then uses the classification dictionary to classify the enterprise profile documents. After classification, the method can also extract the industry label representing the enterprise domain from the enterprise profile documents. In addition, the method can overcome the influence of most noisy words in Chinese text processing, and has low error rate, high classification efficiency and excellent comprehensive performance.

The invention belongs to the field of mode identification, relates to a human behavior video identification method, and aims to enable an extracted key frame to be more representative and greatly increase and improve video identification speed and precision. The method comprises the steps of extracting a key frame of a human body video; and improving neighbor affine clustering by adding an adaptive weighted factor to obtain an adaptive weighted neighbor affine clustering algorithm, wherein the process of extracting the key frame of the human body video comprises the following steps of (1) performing human behavior representation through energy characteristics; and (2) obtaining the key frame by adopting SWAP clustering. According to the method, the SWAP clustering algorithm is applied to the extraction of the key frame of the human behavior video, so that the problem of differentiation of different human physiological structures is solved; different participation effects of the different human physiological structures on human behaviors are well reflected; the key frame can be quickly and reliably extracted; and the method has the great benefits for human behavior quick identification, video quick browsing and the like.

The invention relates to an industrial equipment remaining useful life prediction method and system and electronic equipment. The method comprises the following steps: a, performing normalization processing on original vibration signal data of equipment; b, after feature expansion is conducted on the normalized vibration signal data in an empirical mode decomposition mode, extracting data featuresof the vibration signal data; c, constructing a time sequence convolution network according to the extracted data features; and d, outputting a residual effective life prediction result of the equipment by using the time sequence convolution network. According to the method, the data characteristics of the original signals are decomposed, extracted and enriched through the empirical model, and then the sequential convolutional neural network is used for training and predicting to obtain the residual effective service life prediction model, so that the prediction speed and prediction precisionof the residual life of the industrial equipment can be greatly improved, and the method has realizability in the actual manufacturing process.

The invention relates to a mobile bird recognition method based on birdsong. A birdsong signal acquired by a piece of LabVIEW software is preprocessed; the birdsong signal after preprocessing is processed by an AOK time-frequency analysis method programmed by the LabVIEW software and a piece of MATLAB software in a mixed way; and finally, an AOK time-frequency spectrogram is processed by the MATLAB software to realize feature value extraction. Model training is performed on feature values of birds of known bird species to generate training templates and data is stored, model training is performed on the feature value of a bird of a bird species to be recognized to generate a test template, and the bird is recognized by matching the test template with the training templates based on a DTW algorithm. The mobile bird recognition method based on birdsong of the invention well makes up for the shortcomings of the existing bird recognition method such as a large amount of computation and a large amount of data. A bird recognition method which has the advantages of a small amount of computation, a small amount of data, and high identification rate is provided.

The present invention discloses a fossil power generation unit coal blending scheme acquisition method. According to the method, a coal blending ratio is used as a target function; coal blending parameters obtained by calculating mixed coal coal quality parameters and calculating boiler checking heat are used as input quantities; safety and environmental friendliness of unit operation and practical indexes on site are used as constraint conditions; a coal blending ratio digital model which meets all the constraint conditions is established and obtained; and by means of economic analysis, experimental verification, introduction of auxiliary engine power consumption and the like, the coal blending ratio with the optimal economy is selected and an optimal and reliable coal blending scheme is obtained. According to the present invention, various coal blending schemes obtained by model calculation are subjected to economic analysis and the relatively optimal coal blending scheme is selected according to different constraint conditions; the application range of a boiler for coal types can be enlarged; under the condition of ensuring safe and environmental-friendly operation of a unit, flexibility of coal purchase is improved; and production cost of a power plant is effectively reduced.

The invention discloses a solar power generation capacity prediction method for a photovoltaic power station. The method includes the steps of firstly, calculating the annual average peak sunshine hours of recent years of the region where the photovoltaic power station is located and the installed capacity of the photovoltaic power station; secondly, calculating the solar power generation capacity Wn of the nth year of the photovoltaic power station and the total solar power generation capacity Wtotal of set years of the photovoltaic power station according to the annual average peak sunshine hours, the installed capacity and photovoltaic module attenuation rates. The method has the advantages that the method is applicable to various regions and meteorological conditions, the generated daily average irradiance of each month, annular average irradiance, installed capacity, annual solar power generation capacity in N years and total solar power generation capacity are representative, solar irradiation data accuracy of regions away from a local solar irradiation observation station or regions without a solar irradiation observation station is increased, and prediction and evaluation results are objective and reasonable.

The invention relates to a method for detecting the pulmonary artery blood pressure by using a heart sound analysis method of a multilayer feedforward network. The method comprises the steps of: extracting a heart sound signal; preprocessing the heart sound signal by fast Fourier transform and normalized average Shannon energy distribution to obtain heart sound signal features; filtering the heart sound signal features by using a principal component analysis method; and performing training learning on the filtered hear sound signal features by using a perceptron neural network or a multilayer perceptron feedforward neural network to obtain an optimal neural network between the heart sound signal features and a pulmonary artery blood pressure value. The heart sound signal features are signal features of a first heart sound and a second heart sound; the heart sound signal features comprise crest frequency, average frequency, crest amplitude, average amplitude, duration and time interface of heart sounds. By adopting the method for detecting the pulmonary artery blood pressure by using the heart sound analysis method of the multilayer feedforward network, provided by the invention, the pulmonary artery blood pressure can be easily and conveniently detected at low cost in a non-intrusive and no risk manner.

The invention discloses a dynamic gesture recognition method and system based on a two-dimensional convolutional network, and the method comprises the steps: collecting an actual dynamic gesture video, and carrying out the framing processing of the video; carrying out frame sampling on the actual image after framing; encoding the actual image after frame sampling to obtain an actual feature vectorof the actual image; fusing the actual feature vectors to obtain an actual feature matrix; and inputting the actual feature matrix into the trained two-dimensional convolutional neural network, and outputting a gesture recognition result. By processing a source video stream into a frame of image, and sending the frame of image into a two-dimensional convolutional network, a classification resultof a gesture action is obtained; wherein the image generated by the video contains the spatial feature information and the time sequence information of the video. According to the method, the calculation complexity of gesture recognition is effectively reduced.