31results about How to "Less input data" patented technology

The invention discloses a method for constructing an atmospheric pollutant diffusion model. The method includes the following steps of establishing three-dimensional space rectangular coordinates, determining factors which influence atmospheric pollution diffusion, simplifying a fundamental form of a Gaussian model, and obtaining the atmospheric pollutant diffusion model. The method can be widely applied to survey regions with various dimensions, and is particularly suitable for medium and small dimensions. Besides, the method is high in computational efficiency, low in requirement to meteorological data, and capable of reflecting a diffusing process of atmospheric pollution to the greatest extent in a simple mode. Under the conditions that a rang is small, a weather field is simple, the underlying surface and topographic conditions are relatively not complex, and secondary pollutant is not considered or processed simply, a concentration field calculated through the method and a concentration field calculated through a complicated numerical method are comparatively fit, errors are mainly derived from input parameters of the model (such as precision degree of pollution source data), the model is simple, input data are less, and programming and developing are convenient.

The invention belongs to the technical field of image information processing, and discloses a fire image recognition method based on depth learning. The method comprises the following steps: collecting smoke pictures and normal pictures in the early fire stage as a training set and a test set of a convolution neural network; Obtaining dark channel images of each image to compose a final training set and a test set; constructing Convolution neural model to determine whether smoke could be detected or not. Obtaining The smoke detection model by training the neural network, and testing the reddest pair model and evulating its performance. Compared with the prior art, the method of the invention improves the accuracy of smoke detection in a single image by utilizing dark channel images and depth learning, simultaneously improves the detection speed, and can be practically applied to the fire detection work of cities or forests.

A method for detecting small objects in a structured road based on deep learning comprises the steps that image data, containing the small objects, on the real structured road are collected, and the positions and the category information of the small objects in the structured road are marked through a manual method; Constructing a deep convolutional neural network suitable for small object detection in the structured road and a corresponding loss function; Inputting the acquired image and the labeled data into the convolutional neural network constructed in the previous step, updating the parameter value in the neural network according to the loss value between the output value and the target value, and finally obtaining an ideal network parameter. The invention provides a brand new network structure for the problem that the current neural network is poor in small object detection. On the premise that the calculated amount is not increased basically, the performance of small object detection is greatly improved, and the method can be conveniently deployed in an existing intelligent driving system, so that an intelligent driving automobile can detect dangerous objects on a road in along distance and respond in time, and the safety in the driving process is improved.

The invention discloses a test-case selection method based on user sessions and a hierarchical clustering algorithm. The method includes the following steps: acquiring server access logs, and carryingout sorting according to time; carrying out preprocessing and clustering to form a user session sequence set; calculating similarity distances among all user session sequences through using an improved user-session-sequence comparison algorithm; employing the improved condensing hierarchical clustering algorithm to cluster the user session sequences, and outputting final clustering results of test cases; and optimizing selection of the test cases through deleting redundant test cases. According to the method of the invention, representative user operation sequences can be quickly mined from the large number of server access logs to use the same as test cases, automation of test-case generation and optimization of test-case selection are realized, and subsequent work of automated functiontests of a server, performance tests, user behavior analysis and the like is facilitated.

The invention relates to an intersection short-lane queue overflow identification method based on electric police data. The intersection short-lane queue overflow identification method based on the electric police data comprises the steps of: (1), obtaining the electric police data, and calculating the departure time headway of a vehicle; (2), in combination with the timestamp information that thevehicle passes through a parking line and the departure time headway obtained by calculation, describing double characteristics of proximity spacing and time headway fluctuation, so that a departuretime headway sequence variation point is identified, and estimating the vehicle queue length on each lane; and (3), analyzing a possible queue overflow type of an intersection in a condition that a left-turn short lane and an adjacent straight lane are different in queue length; and furthermore, performing subdivision by judging whether driving of a vehicle on an adjacent lane is blocked or not. The queue length of each lane is obtained by calculation based on a variable point analyzing theory; under different phase release rules, queue overflow identification algorithms based on rules are respectively established; therefore, queue overflow states in different types can be identified; and the intersection short-lane queue overflow identification method based on the electric police data inthe invention has the advantages of being definite in queue overflow classification, little in input data, wide in applicability and the like.

The invention relates to the technical field of cooling towers and discloses a three-dimensional design method for a water collection apparatus of a high-level water collection cooling tower. The method comprises the following steps of S1, inputting design parameters of the water collection apparatus; S2, selecting a quadrant arrangement axis net; S3, determining an inner and outer zone boundary on the axis net; S4, arranging structural members on the axis net to form a simplified model with member occupation; S5, arranging the water collection apparatus based on the simplified model; S6, building a water collection apparatus model according to a symmetric relationship of quadrants; S7, generating a drawing and a material statistics report; and S8, generating a video animation. Through the design method, the three-dimensional video animation of a water collection apparatus arrangement scheme can be automatically generated only by inputting necessary basic parameters, so that a large amount of cooling tower design time is shortened in the whole process, the manual input error probability is reduced, and the working efficiency is improved.

The invention discloses an IP network fault locating method based on a static Bayesian model. On the one hand, a suspected fault filtering module is additionally arranged; therefore, influence of network noise on a detection result is eliminated; the fault locating accuracy is greatly improved; on the other hand, a fault pre-processing module is additionally arranged; therefore, the optimal fault set is calculated; the complexity of the existing algorithm is greatly reduced; and thus, the IP network fault locating method disclosed by the invention is suitable for large-scale network topology.

The special yarn manufacturing apparatus of the present invention performs the speed change of the front roller based on a plurality of pattern data indicating changes in the thickness of the spun yarn, and manufactures a special yarn corresponding to the above pattern data including at least information on the distance between thick pitches. Wire. The CPU controls the first servomotor in accordance with an execution program for executing a repeating pattern of one cycle consisting of pattern data larger in number than the pattern data based on the pattern data stored in the working memory. , thereby spinning a spun yarn that performs a repeated pattern structure of one cycle.

A building contour model generation method, system, device and readable storage medium are disclosed, which is applied to the field of assembled build. The method includes extracting attribute information of a target component model and configuration information of a standard layer from a bottom drawing; by using the preset classification information and the attribute information of the target component model, the target component model is classified, and a variety of component model categories are obtained; by using attribute information and configuration information, Boolean operations are performed on the target component models of each component model category according to preset Boolean operation rules, and the target Boolean models of each standard layer are obtained. The refined contour model is obtained by using all target Boolean models and non-Boolean component models in the base map. The present application is more accurate and specific, and establishes a Boolean model at each standard layer for various component models, thereby reducing the amount of Boolean operations and making the results of the Boolean operations more accurate.

The invention discloses a combined anti-theft lock and a time alertor thereof. The technical scheme includes that an electronic lock is additionally arranged and installed in a lock body of a common mechanical lock so that the combined anti-theft lock is formed, and an anti-theft function and an alarming function at the time are achieved through match of the combined anti-theft lock and the time alertor. The combined anti-theft lock is integrally and structurally composed of a lock body (1), a marble spring bolt (2), a main spring bolt (3), a doorknob poking fork (4), a motor (5), a transmission worm wheel (6), a drive worm (7), a locating cam (8), a position-locking switch (9), a connecting bar (10), an electronic spring bolt (11), a key cylinder (12), a key cylinder fixing bolt (13), an auxiliary spring bolt (14), a counter lock poking fork (15) and a support bearing (16). When a theft invades, under the control of the time alertor, a mechanical spring bolt is locked through the electronic lock so that a door cannot be opened. The combined anti-theft lock and the time alertor can be widely applied to products such as emergency doors, security doors, safe boxes and safe cases.

The invention discloses a short-term power load prediction method based on dimensionality reduction and an improved neural network, and relates to the technical field of power load prediction, and themethod comprises the steps: firstly enabling high-dimensional load related meteorological data to be mapped to a low dimension through affine transformation through employing an SNE algorithm; and then taking the output weight and threshold of the ELM as optimization variables through a moth flame optimization algorithm, taking the mean square error value of the load prediction result as an optimization result, finding out and feeding the training weight and threshold corresponding to the optimal prediction result back to the ELM, and obtaining the improved neural network prediction model; and jointly inputting the dimension-reduced meteorological data and the power load data into an improved neural network for data training and load prediction. According to the invention, the short-termpower load prediction process of the neural network is improved, and the precision of power load prediction is greatly improved.

The embodiment of the invention belongs to the technical field of information, and relates to a cloud native resource dynamic prediction method, which comprises the steps of performing relevancy sorting on acquired to-be-predicted resource data and performance index data based on a Pearson correlation coefficient to obtain a correlation relationship between the to-be-predicted resource data and the performance index data; defining a correlation threshold based on the correlation relationship; taking the performance index data greater than or equal to the relevancy threshold value as performance index time series data; performing transverse data expansion on the performance index time series data to obtain training data and test data; inputting the training data into a constructed time sequence neural network model for training to obtain a trained time sequence neural network prediction model; and inputting the test data into the time sequence neural network prediction model for prediction operation to obtain a resource prediction result. The invention further provides a cloud native resource dynamic prediction device, computer equipment and a storage medium. The prediction complexity can be reduced, and the prediction accuracy can be improved.

The invention discloses an intelligent unattended sensor system, which comprises a power supply conversion unit, and a seismic detection unit, a signal processing unit, a data processing unit and a communication unit which are connected in sequence, the seismic detection unit is used for converting a vibration signal into an electric signal, the signal processing unit is used for converting the electric signal into a digital signal, and the data processing unit is used for analyzing and processing the digital signal to obtain a target identification result. The invention also discloses a target identification method based on the system. The method comprises the following steps: S1, the seismic detection unit is used for converting a vibration signal into an electric signal; s2, the signal processing unit is used for converting the electric signal into a digital signal; s3, the data processing unit is used for processing the digital signal to obtain an identification result; and S4, the communication unit is used for outputting a target identification result. The system has the advantages of flexible layout, good concealment, high recognition rate, multiple recognition types, strong interference resistance, good integration, all-weather work and the like.

The invention relates to a signalized intersection lane queuing length estimation method based on single-section electronic police data. The method comprises the following steps that: (1) the vehicledeparture time headway information of each lane is acquired through electronic police data, wherein the electronic police data is the time stamp information of vehicles passing a stop line, includingdeparture time, direction number, lane number and vehicle model parameters; and (2) according to the timestamp information and a vehicle departure time headway sequence, for a single-lane layout condition and a multi-lane layout condition, a feature function is defined to describe the double characteristics of the close distance and the time headway fluctuation, the change points of the departuretime headway sequence are identified, and the vehicle queuing length and the omission ratio on each lane in the period are estimated. Compared with the prior art, the method considers the error and missing detection phenomena of electronic police equipment, and has the advantages of complete scene coverage, less input data, wide applicability and the like.

The invention relates to the technical field of information processing and particularly to a method for establishing a stock volatility prediction model. The method comprises the following steps of: 1, reading original data from a database, the original data including quotation data and financial statement data relating to stock; 2, constructing a risk factor according to the original data; 3, storing the factor constructed in the second step in a factor database; and 4, establishing a volatility prediction model by using the risk factor determined in step three. The method can perform decomposition according to different factors, can not only predict and quantify the risk and improve the stability of the risk prediction, but also decompose an investment risk into different sources so that investors can choose undertaken risks and avoided risks in a more pertinent way.

The invention discloses a real-time weather prediction algorithm based on an LSTM-DNN (Long Short Term Memory Network-Deep Neural Network) network model, which is mainly applied to speculation of future weather conditions. The algorithm is divided into two networks, namely a deep neural network and a long-short-term memory network. The deep neural network performs manual classification on input data according to various meteorological elements and current weather conditions, and builds a deep full-connection network for speculating weather conditions according to weather elements to classify the input data. The LSTM network carries out normalization and vectorization processing on input data so as to predict change values of various meteorological elements in a future time period. The output of the LSTM network is used as the input of the trained DNN network, and after the two networks are connected to form the LSTM-DNN network, the future weather condition can be speculated based on the current meteorological elements. According to the invention, the LSTM-DNN network model is applied to the meteorological field, and a real-time weather condition prediction method capable of being realized is provided.

The invention discloses a multi-category target recognition method and system for an unattended sensor system. The method comprises the following steps: S1, data preprocessing: framing original data collected by unattended sensor equipment; s2, signal feature extraction: extracting multiple segments of independent time domain features of the framed data at the same time; s3, convolutional neural network model training: taking the multiple segments of independent time domain features obtained in the step S2 as training samples, inputting the training samples into a convolutional neural network for training, and establishing a convolutional neural network model; and S4, signal recognition: recognizing the signal through the convolutional neural network model established in the step S3 to obtain an recognition result. The invention has the advantages of being easy and convenient to operate, small in calculation amount, high in recognition rate, capable of being applied to an embedded system and the like.

The invention discloses an object tracking method, a multi-target tracking model training method and related equipment. The method comprises the following steps: sequentially inputting each image frame of a target video into a multi-target tracking model to obtain target information of a target object corresponding to each image frame; tracking the target object according to the target information between the continuous image frames; the multi-target tracking model is used for performing feature extraction processing on the Nth image frame to obtain a first feature map, performing sampling processing on the first feature map to obtain a second feature map, fusing the second feature map and the target feature map to obtain a third feature map, and performing regression processing on the third feature map to obtain target information; under the condition that N is 1, the target feature map is empty; under the condition that N is greater than 1, the target feature map is a third feature map obtained by processing the (N-1) th image frame by the multi-target tracking model.

The special yarn manufacturing apparatus of the present invention performs the speed change of the front roller based on a plurality of pattern data indicating changes in the thickness of the spun yarn, and manufactures a special yarn corresponding to the above pattern data including at least information on the distance between thick pitches. Wire. The CPU controls the first servomotor in accordance with an execution program for executing a repeating pattern of one cycle consisting of pattern data larger in number than the pattern data based on the pattern data stored in the working memory. , thereby spinning a spun yarn that performs a repeated pattern structure of one cycle.

The invention discloses a prediction method for the net photosynthetic rate of a population and aims to overcome the problems that it is difficult to establish a prediction regression equation for the net photosynthetic rate of a population, considerable related data are needed in establishment of a prediction model for the net photosynthetic rate of the population and prediction accuracy is not high. The method comprises the following steps: 1, acquiring proportioning data of spectral radiance of every wave band of visible light in a region, wherein a portable multispectral radiometer is installed at a height H higher than the canopy of a plant in the test region with an area of S, the proportioning data M_D of spectral radiance of every wave band of visible light in the test region in different periods is obtained, and [0,1] normalization processing is carried out; 2, acquiring data of the net photosynthetic rate of the population; 3, constructing a bionic kernel function; 4, establishing an SVM training set and an SVM prediction set; 5, carrying out tool selection and parameter optimization on a prediction model; and 6, predicting the net photosynthetic rate of the population, which comprises obtainment of the prediction model Model, obtainment of a predicted value Predict of the net photosynthetic rate of the population and determination of reliability of the prediction model Model.

The invention discloses a method for calculating the forced excitation hotspot temperature of the air-cooled hidden pole synchronous condenser rotor. By inputting the calculation model and the forced excitation parameters, the model geometry is firstly divided into discrete heat flow node topology connection graphs; each heat flow node is gradually determined. Parameters such as area, depth, thermal resistance, resistance, heating power, etc., list the energy conservation equations of each node with the node temperature as the unknown variable and form a system of equations, and obtain the forced hot spot temperature of the rotor winding of the air-cooled camera by solving the equations . Only need to input 24 parameters, it can automatically calculate the rotor forced excitation hot spot temperature, and automatically draw the heat flow node topology diagram, which can well meet the requirements of air-cooled hidden pole synchronous motor rotor forced excitation hot spot temperature calculation and curve drawing, to achieve convenience, The purpose of quickly and accurately obtaining the forced excitation hot spot temperature of the air-cooled rotor. It has the characteristics of less input data, simple operation, quick calculation and drawing, concise and intuitive topological connection diagram of heat flow nodes, etc.

The invention discloses a method for detecting a periodic defect of a calendered material surface in a calendering process. The method comprises the following steps: S1, collecting a defect source image of the calendered material surface to be detected; S2, extracting image features of the defect in the defect source image; S3, pre-judging the periodic defect of the defect source image; and S4, matching adjacent suspected periodic defects in the current longitudinal region by adopting an image relevance matching method one by one to determine the periodic defect. The method can be used for quickly and accurately detecting the periodic defect of the calendered material surface, so that the omission ratio and pseudo-flaw false drop rate of periodic defect detection under various environments can be improved. The method is applicable to any material.

The present invention relates to a method for estimating the queuing length of lanes at signalized intersections based on single-section electric police data. The time stamp information of the vehicle passing the stop line, including the departure time, direction number, lane number and vehicle model parameters; 2) According to the time stamp information and the time sequence of the vehicle leaving the headway, for single-lane layout conditions and multi-lane layout conditions, pass The characteristic function is defined to describe the dual characteristics of approaching distance and headway fluctuation, identify the change point of the departing headway sequence, and estimate the vehicle queuing length and missed detection rate in each lane within the period. Compared with the prior art, the present invention takes into account the phenomenon of false and missed detection in electric police equipment, and has the advantages of covering all scenarios, less input data, wide applicability, and the like.

The invention provides a method for plotting a water temperature-power output curve of a generator. The method is characterized by comprising the steps of: calculating a allowable largest excitation current value at a water temperature point with a certain number; setting a rated power output value as an initial power output value of the generator; calculating an excitation current value according to the power output value of the generator; increasing the power output of the generator if the calculated excitation current is larger than the allowable largest excitation current at the water temperature point with the certain number, otherwise, decreasing the power output of the generator; calculating a new excitation current value according to the new power output of the generator; obtaining the power output value of the generator corresponding to the water temperature point with the certain number when the error between the new excitation current and the allowable largest excitation current at the water temperature point is smaller than 1A; and plotting the water temperature values of all water temperature points and the corresponding power output values to obtain the water temperature-power output curve of the generator. The method provided by the invention can overcome the disadvantages in the prior art, and has the advantages of being few in input data, convenient to operate, and high in curve plotting speed.

The invention discloses a spherical panorama generating method based on an overlapping way, comprising the following steps: (1) establishing a target spherical spatial model on the basis of two half-spherical spaces formed by two fisheye images; (2) calculating the mapping relation between the points in the target spherical space and the points in the half-spherical spaces; (3) calculating pixel values corresponding to the points on a spherical surface O according to a fisheye lens projection model and the mapping relation between the points in the spherical space and the points in the half-spherical spaces; and (4) mapping the points in the spherical space to a spherical panorama according to the principle of spherical panorama generation. According to the invention, panoramic stitching is realized with less input data, stitching is carried out with only two fisheye images, the data source is simple, and the cost is low. In the processing process, the location relationship of overlapping areas is introduced effectively, and the relative positions of pixels are quantitatively calculated according to the relationship, which simplifies the calculation process and speeds up stitching. Finally, image fusion is performed by a gray maximum fusion way, and the quality of panoramic stitching is improved.

The invention relates to a method for estimating transit time of a car on a mixed-traffic site road section based on a reliability model. The method comprises the steps of: the step 1: taking each carpassing a bus stop road section as an observation sample, taking the moment when a car arrives at a bus stop starting point as a keyword, and matching buses, cars and non-motor vehicles within a period of time before the moment into the observation sample to construct car road section transit time sample data; the step 2: building a bus road section transit time reliability model at the bus stopin a mixed-traffic environment, estimating parameters of the reliability model, and finally obtaining a final mathematical expression of the car road section transit time reliability model; and the step 3: on the basis of the step 2, obtaining the real-time bus arrival flow, the car arrival flow and the non-motor vehicle arrival flow as the input variables of the car road section transit time reliability model to estimate or predict the currently arrived car road section transit time.

The invention belongs to the technical field of image information processing, and discloses a fire image recognition method based on deep learning, including: collecting smoke pictures and normal pictures in the early stage of fire as the training set and test set of convolutional neural network; The dark channel images constitute the final training set and test set; construct a convolutional neural model that can detect smoke; train the neural network to obtain a smoke detection model, and test and evaluate the performance of the model. Compared with the prior art, the present invention improves the correct rate of smoke detection in a single image by using dark channel images and deep learning methods, and at the same time increases the detection speed, and can be actually applied to fire detection in cities or forests.

A method for estimating the range of a vehicle (1) when driven by means of a power supply is described. The method comprises the steps of: determining a current longitudinal acceleration (23) of the vehicle by triggering a request (21) to update the range estimate; determining a current driving resistance value (25) of the vehicle if the determined magnitude of the current longitudinal acceleration of the vehicle is less than a predetermined limit value; transmitting (13, 31) the calculated current driving resistance value and the current position of the vehicle to the cloud (2); determining acurrent driving resistance value of the plurality of vehicles (1) by means of the cloud, and comparing the same with a standard driving resistance value; determining a correction index (11) based onthe comparison; and calculating a range of the vehicle based on the correction index (29).

The invention discloses a method for calculating the rotor forced excitation hot spot temperature of an air-cooled non-salient pole synchronous phase modifier, and the method comprises the steps of inputting a calculation model and forced excitation parameters, and dividing a model geometry into a discretized heat flow node topological connection diagram; and gradually determining the area, depth,thermal resistance, resistance, heating power and other parameters of each heat flow node, listing the energy conservation equation of each node by taking the node temperature as an unknown variable,forming an equation set, and solving the equation set to obtain the rotor winding forced excitation hot spot temperature of the air-cooled phase modifier. According to the invention, only 24 parameters need to be input, the rotor forced excitation hot spot temperature can be automatically calculated, the heat flow node topological graph can be automatically drawn, the requirements for air coolingnon-salient pole synchronous motor rotor forced excitation hot spot temperature calculation and curve drawing can be well met, and the purpose of conveniently, rapidly and accurately obtaining the air cooling rotor forced excitation hot spot temperature is achieved. The invention has the characteristics of less input data, simplicity and convenience in operation, quickness in calculation and drawing, concise and intuitive heat flow node topological connection diagram and the like.