77results about How to "Simple parameter setting" patented technology

The invention relates to the field of communication, and discloses a configuration method of a wireless router based on a mobile data network and a system thereof. The user can solve the configuration problem simply by accessing the website, and does not need to access the incomprehensible IP address; and the configured router parameters are not lost due to the router replacement, factory setting recovery and the like. According to the invention, the method comprises the following steps that: a user terminal accesses a cloud server through Internet, and configures the router parameters of the wireless router on the cloud server; and the cloud server sets the router parameters to the wireless router through the mobile data network.

In order to simply and easily implement an elimination of a color deviation and a color adjustment for a color correction to a favorable color in an arbitrary portion of a color region in a color reproduction range, a video processing circuit 2 corrects a luminance, a saturation and a hue of a color by the correction quantity corresponding to a reference correction coefficient with regard to a signal value of an input video signal indicating a color within a designated color region defined by a designated range of the luminance, the saturation and the hue when the color indicated by the signal value is a color in the center of the designated color region or at a core for occupying a predetermined range around the center. Otherwise, the video processing circuit 2 corrects the luminance, the saturation and the hue of the color of the signal value by the correction quantity gradually smaller as a position in a color space of the color indicated by the signal value approaches a boundary position of the designated color region.

The invention provides a scene classification method based on Gist characteristics and an extreme learning machine. The scene classification method comprises the steps of firstly extracting the Gist characteristics of images, performing convolution operation on the scene images by use of Gabor wavelet, next, extracting Gist vectors as characteristic descriptions of the scene images and applying the characteristic descriptions to scene classification. According to the scene classification method, a comprehensive cognition is generated on images by use of the Gist characteristics, and five natural attributes, namely naturalness, opening degree, roughness, expanding degree and bumpiness, are comprehensively described, and compared with the traditional scene classification method, setting different parameters and threshold according to various practical situations can be avoided. The technical problem that the classification threshold must be adjusted continuously under the condition of many change conditions during traditional classification is solved; the scene classification method is high in operation speed, excellent in generalization ability, and excellent in expansibility, and shows better superiority with the increase of the classification situation complexity and the expansion of the scale.

The invention discloses a method for automatically detecting terminals in batch based on an IEC62056 protocol, and the method comprises the following steps: (A) physical setting: connecting a terminal with an electric meter through RS485 or carrier waves; (B) configuration setting: configuring parameters of the terminal correspondingly; (C) establishing connection, connecting a worktable with the terminal through an RS232 serial port or TCP/UDP; (D) detecting by the worktable in an automatic detection method or an independent detection method; and (E) forming a detection report by data generated in the former steps. The method for automatically detecting the load control, low-voltage meter collecting and distribution transform terminals in batch based on the IEC62056 protocol solves the problems of low efficiency and poor stability in the prior art when detecting the terminal.

The invention provides a discharging test device for a full online storage battery, mainly comprising a DC-DC host operating power supply, a control unit, a safeguard circuit, an automatic limited current charging and equipotential connection safety control circuit, a constant current discharge load intelligent control circuit and a discharge load circuit, wherein the safeguard circuit and the automatic limited current charging and equipotential connection safety control circuit are connected in parallel; and the constant current discharge load intelligent control circuit and the discharge load circuit are connected in series. The discharging test device for the full online storage battery of the invention has a safe energy saving discharge function of the full online batteries on load of a communication device and a function of intelligently controlling and regulating discharge load current through an online constant current discharge load, is suitable for constant current discharge test of the online storage battery for the current in different communication device loads in the existing network and automatically auxiliary regulation of operating current in the discharge load, and has manual, automatic and remote control functions. By using the discharging test device, the full online constant current discharge capacity test for the storage battery can be achieved.

The invention relates to an implementation method for automatically controlling starting, stopping and exhaust amount of a range hood; the range hood comprises a smoke collecting cover, an exhaust fan, an air amount controller of the exhaust fan, and a smoke discharging pipe; a first temperature sensor is arranged outside the upper part of the smoke collecting cover; a second temperature sensor is arranged in the smoke discharging pipe; a third temperature sensor is arranged below the smoke collecting cover; and the air amount controller of the exhaust fan controls the starting, the stopping and the air feeding amount of the exhaust fan according to signals of the first temperature sensor, the second temperature sensor and the third temperature sensor. The range hood disclosed by the invention can be controlled in a full-automatic manner, and convenient to operate and has the energy-saving effect; aiming at cooking operation in a relatively closed space, the carbon monoxide poisoning phenomenon is difficult to happen; the use of a stove is not limited, and therefore, the range hood is wide to use; and, compared with an automatic range hood with a sensor using infrared rays and the like, after being converted into a product, the range hood disclosed by the invention has the advantages of being low in production cost, easy in parameter setting, convenient to debug and calibrate, difficult in pollution to the sensor, steady and reliable in property, and steady to work for a long time.

The invention relates to a method used for avoiding overhigh local overlapping degree of unmanned aerial vehicle images of zones with large altitude fluctuating, and belongs to the technical field of remote sensing image processing. According to the method, under the assist of POS data, topographic reference data is obtained via cutting, the overlapping degree among images is calculated, intelligent analysis is adopted, and automatic processing of the overhigh local overlapping degree of unmanned aerial vehicle images of zones with large altitude fluctuating is completed. The method is capable of removing excess redundant images with ensuring the overlapping degrees of all images are higher than a minimum threshold value under simple parameter assignment. Simplifying of original data is capable of accelerating subsequent DOM manufacturing efficiency. The method is simple in parameter assignment, and is capable of increasing DOM manufacturing efficiency without reducing DOM manufacturing precision.

A grid-based data clustering method comprises: a parameter setting step, a partition step, a searching step, a seed-classifying step, an extension step, and a termination step. Through the above-mentioned steps, data in a data set are disposed in a plurality of grids, and the grids are classified into dense grids and uncrowded grids for a cluster to extend from one of the dense grid to gradually combine data in other dense grids nearby. Consequently, convenience in parameter setting, efficiency and accuracy in data clustering, and performance in noise filtering are achieved.

The invention discloses a polygon object change detection method and device. The method comprises following steps: under a coordinate of a map of the object, a boundary buffer area and an integral buffer area of the object are constructed on the basis of GIS data of the object; a pixel point set of the boundary buffer area and a pixel point set of the non-boundary of the integral buffer area are extracted; a boundary point gradient set of the boundary buffer area and a non-boundary point gradient set of the integral buffer area are extracted from a gradient image of the object on the basis of the extracted pixel point sets; a boundary significance value of the object is calculated by use of a z value test method on the basis of the extracted gradient sets; and if the calculated boundary significance value is less than a set boundary significance threshold value, the object is determined as changed. The provided method and device are simple and have high efficiency, high detection precision, high accuracy, and high automation degree.

The invention relates to the technical field of petroleum coke calcining in the carbon product industry, in particular to an intelligent temperature control system and method for a calcining furnace kiln. The intelligent temperature control system comprises a plurality of calcining furnace burden tanks; each calcining furnace burden tank is provided with a temperature adjusting and controlling device, and comprises an air door control device and a fire discharging port control device; each temperature adjusting and controlling device controls the actions of the corresponding air door control device and the corresponding fire discharging port control device so as to adjust the temperature of the corresponding calcining furnace burden tank; each temperature adjusting and controlling device performs remote adjusting and controlling setting through an intelligent adjusting and controlling terminal; and the calcining furnace burden tanks are arranged in a parallel connection manner. According to the intelligent temperature control method, the real-time temperature in the calcining furnace burden tanks is compared with a preset temperature interval periodically, the opening states of anair door and a fire discharging port are automatically controlled, it is achieved that the temperature of a calcining furnace is accurately controlled in time, it is avoided that high temperature damages the calcining furnace or low temperature affects the product quality, the service life of the calcining furnace is greatly prolonged, the percent pass of products is greatly increased, and the labor intensity of workers is relieved.

The invention provides a fast response mixed signal generator and an implementation method thereof. The fast response mixed signal generator comprises an interface circuit, a micro controller unit (MCU), a low-pass filter, an FPGA unit and a DAC unit, wherein the micro controller unit is electrically connected with the FPGA unit, a keyboard display circuit and a reset unit respectively; the FPGA unit is electrically connected with a power supply circuit, a JTAG unit, a crystal oscillation unit and the DAC unit respectively; the DAC unit is electrically connected with the low-pass filter. The fast response mixed signal generator overcomes defects in the prior art, and has the characteristics that the resultant velocity is high, the waveform data is less, the integration is easy, the circuit is simple, all parameters are set independently, and the parameters can be changed conveniently and quickly.

The invention discloses a block-based robust tensor principal component analysis method. By introducing a cascade operation, the whole tensor is divided into cascades of several block tensors of the same size, and an image denoising experiment is carried out in a tensor of a more suitable size. Alternating direction multiplier method divides the optimization model into two sub-problems of low rankcomponent approximation and sparse component approximation. Iterative tensor singular value soft threshold operator and iterative soft threshold operator are used to solve these two sub-problems. Thelow rank component is denoised image, and the sparse component is noise. The invention is used for extracting the low-rank component and the sparse component of the multi-path data. By introducing the block idea and adding the sparse constraint, the low-rank component is extracted from the smaller block tensor, and more accurate and clear details can be obtained.

The invention provides a method for detecting the position of a sensorless rotor of a PMSM for a blower fan. The method includes the steps of converting three-phase current into two-phase d-axis q-axis current through Clarke conversion and Park conversion when a sensor detects the three-phase current of a motor, sending d-axis voltage generated by a d-axis current PI controller and containing rotor position error information to a phase-locked loop rotor position tracking proportional integral estimator, controlling gain parameter according to the PI in work frequency selection estimator of an inverter switch, and estimating the position and rotating speed of a rotor by controlling the d-axis current to be zero. Only permanent magnetic linkage is needed, no other motor parameters are needed, and the algorithm is simple. The position and rotating speed of a rotor can be accurately detected when the rotating speed reaches over 20% of the rated rotating speed. The method is applicable to PMSMs for heating ventilation and industrial purifying blower fans with wide power levels.

The invention relates to a generative adversarial network embedded learning representation method, which is applied to the technical field of network entity alignment. According to the invention, embedded representation of a network and cross-network entity alignment tasks are fused in a unified manner; network features are extracted through a graph convolutional neural network; meanwhile, generative adversarial learning is introduced to guide learning of domain invariant features, the influence of domain dependent features in the embedded representation learning process is avoided, on this basis, the graph convolutional network for sensing the direction is proposed to better optimize the structure information of the directed network, and based on the features of the graph convolutional network, the cross-network embedded representation learning efficiency is optimized through the weight sharing skill of the graph convolutional network. Compared with the prior art, the method effectively solves the problem that the alignment effect is poor due to the existence of domain features in the entity alignment tasks, obtains domain invariance features more beneficial to the entity alignment tasks through domain adversarial learning, and improves the entity alignment effect.

The invention discloses a target behavior mode online classification method based on multi-dimension features. The method comprises a step 1 of defining correlation variables, a step 2 of using one class of training track data sets and calculating the p value of a current track point in a testing track, a step 3 of repeating the step 2, and calculating p values of the current track point corresponding to other classes, a step 4 of comparing p values corresponding to all classes, and determining a target behavior mode class corresponding to the current track point, a step 5 of updating the training track data sets after target behavior modes corresponding to all track points of the current testing track are classified, a step 6 of updating a multi-factor Hausdorff distance matrix, and a step 7 of classifying target behavior modes corresponding to a next testing track. The method comprehensively utilizes the position, speed and course features of targets, is advantaged by simple parameter setting, high accuracy and good timeliness, and has a wide application prospect in the early warning surveillance field.

The invention provides a step-type landslide displacement prediction method based on a gradient elevator and quadratic programming, and the method comprises the steps: enabling the accumulated displacement of a step-type landslide to be decomposed into trend term displacement and periodic term displacement, and carrying out the fitting of a trend term displacement change curve through a polynomial model least square algorithm; then, obtaining a gradient elevator-quadratic programming model with high accuracy through combination of multiple normalization modes; and finally, superposing a trend term displacement change curve fitting result and an output result of the gradient elevator-quadratic programming model to obtain a step type landslide displacement prediction curve. Therefore, the analysis and prediction models of the landslide trend term displacement and the periodic term displacement can be respectively obtained, the step-type landslide total displacement prediction value can be automatically obtained, and the method has the advantages of being simple in parameter setting and suitable for a low-dimensional feature matrix; the defects of nonlinear model parameter setting and data preprocessing in landslide displacement prediction are effectively overcome.

The invention discloses a modular intelligent control system and method of a crane. The modular intelligent control system comprises a control unit and power units controlled by the control unit. The control unit is characterized by comprising a signal acquisition module, a central processing unit and a logic output module, wherein the signal acquisition module is used for acquiring setting signals given by a user and action feedback signals fedback by all the power units controlled by the control unit in real time, processing the obtained singles and transmitting the processed signals to the central processing unit; the central processing unit is internally embedded with multiple motor control mode programs, and can be used for switching and selecting according to set signals, and operating the motor control module programs corresponding to the set signals to realize the logic control on the power units, and simultaneously, the central processing unit is also used for monitoring operating states of the power units in real time and carrying out fault alarming when an abnormal condition occurs; and the logic output module is used for controlling the operation of the power units connected with the logic output module according to logic control signals outputted by the central processing unit. The modular intelligent control system and method disclosed by the invention have the advantages that the control cost is low, the control loop is simple, the fault rate is low, the maintenance is convenient, the service life of the whole system is prolonged, the design and the production are simple and convenient, and the like.

The invention discloses an automatic ultrafiltration system which comprises a diaphragm pump, an ultrafiltration membrane module and a PLC (Programmable Logic Controller) controller. The ultrafiltration membrane module is provided with a liquid inlet, a liquid outlet and a liquid return port; a first pressure sensor is connected between the outlet end of the diaphragm pump and the liquid inlet of the ultrafiltration membrane module; the liquid return port of the ultrafiltration membrane module is connected with a second pressure sensor; the second pressure sensor is connected with a shutoff valve; the liquid outlet of the ultrafiltration membrane module is connected with a third pressure sensor; the first pressure sensor, the second pressure sensor, the third pressure sensor and the shutoff valve are respectively connected with the PLC controller. The invention also discloses a method for implementing ultrafiltration under a constant transmembrane pressure by utilizing the automatic ultrafiltration system. The ultrafiltration system implements continuous operation under the constant transmembrane pressure and at constant flow, obtains the most reasonable ultrafiltration conditions, implements the continuous ultrafiltration process and improves ultrafiltration efficiency. Compared with the ultrafiltration efficiency of an existing ultrafiltration system, the ultrafiltration efficiency of the automatic ultrafiltration system can be improved by 30 to 50 percent.

The invention discloses a building energy consumption prediction method based on a longicorn beard optimization algorithm and a neural network, and the method comprises the following four main steps:1), collecting building energy consumption related data, and carrying out the principal component analysis and normalization preprocessing of the data; 2) determining input and output items and a network structure of a multilayer feedforward neural network model with an error back propagation learning function; 3) optimizing the connection weight and threshold of the BP network by using a longicorn beard algorithm; and 4) performing short-term prediction on the building power consumption by utilizing the optimized BAS-BP prediction model. According to the method, principal component analysis is carried out on the pre-input variables by utilizing a principal component analysis algorithm, and the variables meeting the principal component extraction requirement are selected, so that the inputdimension is reduced; and the structure and parameters of the neural network model are optimized by using the global optimization capability of the longicorn beard algorithm. Compared with an existing building energy consumption prediction method, the prediction model has the advantages of being simple in structure, high in prediction precision, short in operation time and the like.