66results about How to "Fully consider the impact" patented technology

The invention relates to a driving safety auxiliary system based on road surface image analysis. The driving safety auxiliary system comprises a visual monitoring module, a classification bank module, a vehicle parameter monitoring module, a road surface allowed-maximum-friction-force calculating module and a displaying and prompting module. According to the driving safety auxiliary system based on road surface image analysis, by utilizing the method of image processing, monitoring is conducted on the characteristics (maximum friction factors) of the road surface appearing at present and the front, allowed maximum friction force of the present road surface and the front road surface is calculated, and moreover a safe drive (tractive force / brake force) prompt is provided for a driver so that the driver can programme the tractive force or the brake force of a vehicle timely to enable speed change of the vehicle to meet the requirements for safe driving and timely following-change of a vehicle behind.

The invention discloses a public bicycle dynamic scheduling method based on site short-term demand prediction. The method comprises the steps of collecting a site free pile number time sequence, constructing a neural network model to predict the short-term demand of each site, calculating the scheduling time window and demand on the basis, constructing a dynamic scheduling model minimizing the cost of scheduling and maximizing satisfaction, solving a scheduling scheme, and adjusting the scheduling plan through information updating. By combining site short-term demand prediction and dynamic scheduling, the efficiency and accuracy of public bicycle scheduling are ensured, the service level of public bicycles is improved, and public bicycles are made more attractive.

The invention discloses a runoff change attribution recognition method considering land utilization spatial-temporal evolution. The method comprises the steps that a research period is divided into a natural period and a change period according to runoff sudden change characteristics; the change period is segmented according to land utilization evolution characteristics; a basin SWAT model is established; model parameters are calibrated; and basin runoff change is calculated in a segment-by-segment item-by-item rolling and attribution quantification mode. Through the method, the change period is finely divided according to the evolution characteristics of land utilization to fully consider land utilization evolution conditions, basin underlying surface characteristics can be accurately described and hydrologic process characteristics can be effectively simulated based on the SWAT model, a basin distributed hydrologic model is established to describe the hydrologic process, and influences of land utilization evolution on runoff are fully considered in an attribution quantification recognition result obtained by calculating the runoff change in the basin research period in a segment-by-segment item-by-item rolling mode.

The invention discloses an abstracting method for mass-text quick understanding. The abstracting method includes the steps that the content of text is obtained; the text is subjected to pretreatment operating such as word segmentation, coreference eliminating, redundant information removing and analysis unit dividing; the content of the text is subjected to subject analysis with a topic model to obtain subject distribution in the text; a graph model is built according to subject incidence relationships between analysis units, and weights of all directed edges in the graph model are calculated; the graph model is calculated with the contribution iterative method till is converged, and the text summarization with the suitable length is generated as requirement. According to the text abstracting method, mass unstructured text data can be automatically analyzed, the text summarization with which a core topic can be fully covered is obtained to serve as replacement of mass original data, and therefore the quick understanding aim is achieved.

The invention discloses a method for processing and tracking heart rate and respiration rate signals. The method comprises the steps of signal processing, wherein an acceleration sensor is used for collecting heart rate and respiration rate acceleration original signals, and a wavelet method is adopted for removing noise signals to obtain denoised signals of the heart rate and the respiration rate; signal separating, wherein based on the denoised signals of the heart rate and the respiration rate, a signal smoothing method is adopted for obtaining respiration rate signals, and then a signal model is adopted for removing the respiration rate signals from the denoised signals of the heart rate and the respiration rate in real time to obtain heart rate signals; signal tracking estimation, wherein the signal tracking estimation step is used for tracking the respiration rate signals and the heart rate signals, instant respiration rate signals and instant heart rate signals are obtained within the set time period, and signal forecasting is carried out under the circumstance of signal loss.

The invention discloses a protein lysine malonylation site prediction method based on deep learning, and relates to the technical field of biological information. The method comprises the steps: converting the character information of a protein sequence into a numerical vector by adopting an enhanced amino acid composition, a grouped enhanced amino acid composition, a dipeptide deviation expectedaverage value, a K neighbor score and a BLOSUM62 matrix feature extraction algorithm, performing fusion and obtaining a feature space, wherein the influence of each potential feature on a prediction result is fully considered; performing calculating by using a linear convolutional neural network to obtain malonyl site specificity characteristics; selecting related features and reducing feature dimensions through a maximum pooling layer, classifying malonylation sites and non-malonylation sites in combination with a multi-layer deep neural network, constructing a protein malonylation site prediction model DeepMal, and evaluating prediction performance by using 10-fold cross validation and an independent test data set. The model DeepMal is remarkably improved in evaluation indexes, and further promotion of application of deep learning in protein function prediction is facilitated.

The invention discloses a flight airport scene operation optimization method considering runway port waiting time and belongs to the flight airport scene optimization technology field. The method comprises the following steps of firstly, establishing a MAGMO model and simultaneously optimizing slipping time and runway port waiting takeoff time; then, designing an RSS strategy so as to distribute paths for all the flights and distributing launch time for a departing flight; simultaneously, according to a waiting node, distributing a strategy HNAS, selecting the mode in the path for the flight and distributing waiting time so as to obtain an optimal solution to double target airport scene optimization. Priorities of the flights, fairness among flights and an influence to environment are fully considered, and safety, orderliness and high efficiency of a scene flight flow are ensured. By using the method of the invention, safety of the whole flights can be increased and flight conflicts can be effectively avoided to some extent. Simultaneously a potential safety hazard, fuel oil consumption and environment pollution brought by long time waiting of a lot of flights can be prevented.

The invention relates to robot equipment for automatically and precisely grinding ancient building bricks. The robot equipment comprises a six-degree-of-freedom industrial robot, an electric servo grinding tool, a vision detecting and positioning device, a rotating disc type workbench, brick block positioning fixtures and a dust collector; the six-degree-of-freedom industrial robot is a movement mechanism for brick block grinding; the electric servo grinding tool is used for grinding the surfaces of a brick block to achieve grinding of all the surfaces of the brick block; the vision detectingand positioning device is used for detecting the initial geometric size of the brick block; the rotating disc type workbench is fixed to the ground through a support frame and driven by a servo motorto rotate and change the position, and then one-time feeding and in-turn grinding of multiple brick blocks are achieved; the brick block positioning fixture is installed on the rotating disc type workbench, the four groups of fixtures are evenly distributed at the periphery of a rotating disc to position the brick block, and grinding of the six surfaces of the brick block is completed through twotimes of clamping and position changing; and in the grinding process, the dust collector is used for collecting grinding dust, and environment pollution is avoided. According to the robot equipment, automatic grinding can be achieved, the efficient and the grinding consistency are improved, and the cost is reduced.

The invention discloses a modeling method of a flux linkage adjustable permanent magnet synchronous reluctance motor. The method comprises the following steps: extracting relation data of change of motor stator flux linkage and electromagnetic torque along with armature current and rotor position angle through finite element modeling analysis, solving an inverse function through a pair flux-current relationship function, constructing an armature current database taking stator flux linkage and rotor angle as parameter variables and an electromagnetic torque database taking armature current androtor angle as parameter variables, and further obtaining change rate data of the armature current and the electromagnetic torque of the motor along with the temperature through finite element analysis at different temperatures. Meanwhile, a motor loss real-time calculation formula is obtained in a finite element simulation and experiment fitting mode, on this basis, motor loss equivalent currentis solved, and the motor control mathematical model is further corrected. According to the method, the influence of multiple factors such as space magnetic field harmonic waves, magnetic saturation, quadrature/direct axis coupling, motor loss and temperature change is comprehensively considered, and an accurate calculation model of the motor is established.

The invention relates to an SOC-OCV curve cluster calibration method, an SOC correction method and an SOC correction device of a lithium battery. The calibration method comprises that standing time isset as first setting time, and a charging SOC-OCV curve charging A and a discharging SOC-OCV curve discharging A within the standing time of the first setting time are obtained; and according to thecharging SOC-OCV curve charging A and the discharging SOC-OCV curve discharging A within the first setting time, corresponding charging SOC-OCV curve and discharging SOC-OCV curve when the standing time is greater than the first setting time are calculated within the set standing time range, and further an SOC-OCV curve cluster of the lithium battery is obtained. The charging SOC-OCV curve and thedischarging SOC-OCV curve corresponding to different standing time are obtained according to the different standing time, the SOC-OCV curve cluster of the lithium battery is formed, influence of thestanding time on the open circuit voltage OCV of the battery is taken into full consideration, and the accuracy of the SOC-OCV curve is improved effectively.

The invention discloses a cooperative cognitive approach based on wireless mobile network, comprising the following steps: an unauthorized system dividing each two unauthorized clients existing in the system into one group, and determining the data reliability of respective unauthorized client in each group contained in the dividing result, and acquiring the environmental information of each group acquired on the basis of the cooperative cognition according to the data reliability, and combining the information to acquire the final environment sensory result; and the data reliability in inverse proportion to the distance between the unauthorized client and the authorized base station is used for regulating the value of environment information acquired by the unauthorized client. Thus, thereliability of the environment information acquired on the basis of cooperative cognition is improved after that each two unauthorized clients are combined; on the basis, the environment information acquired by the respective combination are combined to acquire the final environment sensory result, and the accuracy of environment sensory result is further improved. The invention also discloses a communication apparatus and a communication system.

The invention discloses an orderly power utilization load index distributing method and system. According to the invention, an existing load index distributing mode that the load index is distributed from cities to counties and then the load index is distributed from the counties to the towns is broken; a load index distributing mode that the load index is directly distributed from cities to users and user indexes are gathered to form town indexes and county indexes is used, so that the index distributing process is transparent and operable, and the fairness and impartiality of the index distributing result in the city range are guaranteed at a finest granularity. Meanwhile, the total load index is distributed by dividing into 3 types aiming at different user groups, and the effect of the response behavior of the orderly power utilization of the user groups are taken fully into account.

A system for predicting tumors based on next-generation sequencing, including an anticoagulant blood collection tube for collecting blood, a DNA extraction kit, a next-generation sequencing device, and a gene mutation evaluation system. Extract whole blood DNA, the second generation sequencing device detects the sequence of the whole blood DNA, the gene mutation evaluation system analyzes the situation of gene mutation according to the sequence detected by the second generation sequencing device, and according to the tumorigenicity grade of the mutated gene And the dominant recessiveness of the gene predicts the risk level of tumor occurrence. The present invention adopts the second-generation sequencing platform second-generation sequencing device to sequence the gene, which is fast, so that the gene can be comprehensively sequenced, the tumor can be comprehensively screened, and the risk level of the tumor can be predicted through the gene mutation evaluation system, so that customers can do it as soon as possible. get ready. Analyze the situation of gene mutation, and predict the risk level of tumor occurrence according to the tumorigenicity level of the mutated gene and the dominant recessiveness of the gene, and fully consider the impact of the mutated gene on the occurrence of tumor, thus making the prediction more accurate.

The invention discloses an analytic hierarchy process based load fusion modeling method. The method comprises: acquiring actual measurement data or experimental simulation data of a monitoring point of a power system; establishing an analytic hierarchy structure comprising a target layer, a criterion layer and a scheme layer, wherein the scheme layer comprises two load models which are a load model considering a power distribution network and a load model of a tractive power supply system respectively; identifying model parameters: performing parameter identification on the two models to obtain a power fitting curve with model power changed with voltage; forming a determining matrix according to parameter identification results, wherein the determining matrix represents relative importance degrees among factors in the same level in a matrix form; calculating a weight and a weighted factor of the determining matrix; and calculating a weighted model fitting result, comparing errors, determining whether a fitting error is reduced, and if the fitting error is reduced, outputting the identification parameters, the weighted factor and the power fitting curve of the model. The method further fully considers load randomness and variability factors; and the method is high in fitting precision and reduced in error.

The invention relates to a water quality space-time correlation prediction method based on long and short term memory and a radial basis function neural network. A water quality space-time fusion prediction model based on LSTM-RBF is provided by considering space-time characteristics and multivariate correlation of water quality changes. The method is mainly divided into five processes. The method comprises the following steps: 1) cleaning data, and respectively constructing a time data set and a space data set; 2) performing grey correlation analysis on the time data set obtained in the step 1), and performing spatial feature dimension reduction by using depth self-coding; 3) constructing an LSTM water quality time dimension prediction model according to the parameters after feature extraction in the step 2) to obtain a time dimension prediction result; 4) taking a result after feature dimension reduction in the step 2) as input of a spatial dimension RBF model, establishing an RBF spatial dimension water quality prediction model, and obtaining a spatial dimension prediction result; and 5) taking the time prediction result and the space prediction result as two new features, and fusing by using the model tree to obtain a time-space fusion water quality parameter prediction result.

The invention discloses a small-interference stability analysis method for a time-delay power system based on low-order IGD-IRK. The small-interference stability analysis method comprises the following steps: establishing a mathematical model of the time-delay power system; according to whether state variables of the time-delay power system are related to time delay, rewriting differential equations of the time-delay power system as a part related to the time delay and a part unrelated to the time delay; obtaining an infinitesimal generator corresponding to a state equation of the time-delay power system after recombination; aiming at the infinitesimal generator, performing low-order discretization based on the implicit Runge-Kutta method to obtain a low-order discretized approximation matrix of the infinitesimal generator; aiming at the low-order discretized approximation matrix of the infinitesimal generator, performing inverse displacement change to obtain a low-order discretized approximation inverse matrix of the infinitesimal generator; and aiming at the inverse matrix, performing sparse eigenvalue calculation, and then performing inverse transformation and Newton's check onthe sparse eigenvalues to obtain the exact eigenvalues of the time-delay power system.

The invention relates to an in-vivo neuron modeling method based on neuromorphology. The in-vivo neuron modeling method comprises the following steps: step 1, constructing a dendritic single compartment model; step 2, constructing a multi-chamber cascade model; according to the specific morphological characteristics of the neuron dendrites; connecting single-chamber linear-nonlinear systems corresponding to N nodes according to a connection mode of the corresponding nodes by using a multi-chamber cascade model including a dendritic distribution region, a branch number and a topological structure, and establishing a multi-chamber cascade model for keeping original morphological characteristics of neuron dendrites to simulate characteristics of complete dendrites of neurons; step 3, fittingthe parameters of the in-vivo neuron model.

Provided is a radial rotation error prediction method for a static air pressure main shaft. The method conducts prediction in consideration of micro-scale effects of air film flow in a static air pressure bearing and nonlinear dynamic characteristics. According to the structure and a principle of the static air pressure bearing, flow factors embodying the micro-scale effects are introduced to establish a mathematical model of the air film flow of the static air pressure radial bearing under micro-scale; an air film is transformed into a spring damping system with two freedom degrees perpendicular to each other, the flow model is calculated, and nonlinear dynamic stiffness and damping coefficients are assigned to the spring damping system under the micro-scale; according to the structure and working principle of the static air pressure main shaft, a bearing-rotor system model composed of the air film and a rotor is established; based on the characteristics of main shaft vibration, a mathematical model of dynamic vibration of the bearing-rotor system is established; various vibration errors of the static air pressure main shaft are obtained, and total radial rotation errors of the main shaft are obtained by integrating the various errors.

The invention relates to development and testing of BMSs, and discloses a matching test method and system for a BMS alternating current charging coupler. The matching test system comprises a temperature control module, a model simulation module, a communication module, a coupler module and a master computer. The matching test method comprises the steps of acquiring and storing corresponding relations between ambient temperatures of different models of charging piles and a preset charging current, separately inputting a temperature signal indicating a preset temperature, an identification signal indicating the model of the corresponding charging pile, an electronic interlocking signal and a handshaking signal into the BMS alternating current charging coupler, receiving a requested chargingcurrent fed back from the BMS alternating current charging coupler, and judging whether the BMS alternating current charging coupler can pass a matching test according to the corresponding relations.The matching test method and system are not limited by a whole vehicle and the real charging piles, and take into account the influence of the ambient temperatures on charging currents output by the charging piles, thereby improving the test efficiency and the accuracy of test results.

The present invention discloses an orbit dynamics assisted dynamic pulsar signal processing method, which comprises: calculating the pulsar estimation phase at a time ti according to the rotating frequency and the direction vector of the pulsar, the estimation position of a spacecraft relative to the earth at the time ti, and the position of the earth at the time ti; after setting the order K of the frequency coefficient as 2, calculating the spacecraft detection phase at the time ti according to the phase of photons received by the spacecraft and the pulsar estimation phase at the time ti; according to the pulsar flow rate, the noise flow rate and the number of the photons received within the observation period, calculating to obtain an initial phase; and according to the initial phase, the earth velocity at the time t0 and the estimation velocity of the spacecraft relative to the earth, calculating the pulse arriving time at the time t0. According to the present invention, the method is provided under the premise of consideration of the orbit motion of the spacecraft, the operability is strong, and the practicality is provided.

The invention provides a nonlinear runoff probability prediction method. The method is characterized by comprising the following steps that 1, unit lines which are generated by net rainfall within the drainage basin time frame (j, j+delta t) and submit to distribution G(.); k unit line impact factors tau j,1, tau j,2,..., tau j,k within the time frame (j, j+delta t) are selected, and a correlation H(.) between the unit line impact factors and theta j is built; 2, runoff of the net rainfall generated at the moment T within the time frame (j, j+delta t); 3, it is assumed that true runoff of the net rainfall generated at the moment T within the time frame (j, j+delta t) shows normal distribution and fluctuates vertically relative to a predicted value; it is further assumed that the true runoff, the predicted value and the impact factors tau j,1, tau j,2,..., tau j,k conform to the formula shown in the description; 4, the true runoff at the moment T is obtained; and distribution which QT meets is obtained; 5, a likelihood function of the flood is obtained according to the drainage basin, a likelihood function of multiple floods is calculated, and the maximum likelihood method is used for estimating parameters; 6, according to the obtained parameters and the runoff models built in the steps 1-4, prediction is performed.

The invention discloses an LSTM-based charging and battery swap station battery swap load prediction method and related components thereof, and the method comprises the steps: collecting the number ofhistorical battery swap vehicles of a charging and battery swap station, and selecting the number of battery swap vehicles in a preset period in a period of time and corresponding related variables as sample data; carrying out normalization processing on the sample data and then carrying out segmentation by adopting a sliding window algorithm to obtain training samples; training the LSTM model byusing the training sample to obtain a battery replacement vehicle number prediction model; and obtaining the number of the battery replacement vehicles in the specified period according to the battery replacement vehicle number prediction model, and obtaining the total battery replacement load corresponding to each vehicle in the specified period in combination with a state-of-charge probabilitydensity distribution function based on Gaussian distribution. According to the method, the power exchange vehicles in the specified period are predicted by using the power exchange vehicle number prediction model, and the power exchange load corresponding to each vehicle in the specified period is acquired in combination with the state-of-charge probability density distribution function, so that the influence of related variables on sample data is fully considered, and the prediction accuracy is improved.