70results about How to "Avoid irrationality" patented technology

The invention discloses a detecting method of a weak periodic signal based on a chaotic system and wavelet threshold denoising, which comprises the following steps of: firstly carrying out wavelet decomposition on collected information, and determining a decomposition scale according to an actual signal-to-noise condition; denoising a wavelet high-frequency coefficient after the wavelet decomposition, wherein in the process of wavelet threshold denoising, the selection of a threshold is an important problem and directly influences a denoising result, so that the invention firstly provides a method for determining the threshold according to the scale for carrying out coefficient threshold processing to improve the denoising effect; reconstructing a signal after denoising, merging the signal to be detected after wavelet denoising reconstruction as one part of the driving force of the chaotic system into a chaotic detecting system, further inhabiting noise interference by utilizing the characteristics of the chaotic system for strong noise immunity, periodic weak signal sensitivity and the like, and effectively extracting the weak signal. The invention improves the detection threshold and the signal-to-noise ratio purely based on the chaotic detecting system.

The invention provides a ship energy efficiency management and control platform based on fuzzy clustering and a genetic algorithm. The ship energy efficiency management and control platform comprises a sensor group, a lower computer system and an upper computer system, wherein the upper computer system comprises an upper computer communication module, a data storage module, a data processing module and a ship energy efficiency comprehensive management and control module; the data processing module is used for calling parameters in the data storage module, resolving all the received parameters and providing the resolved parameters to the ship energy efficiency comprehensive management and control module; the ship energy efficiency comprehensive management and control module is used for computing the current energy efficiency operation index of a ship in real time based on fuzzy clustering and the genetic algorithm by using the resolved parameters in order to provide references for a ship operator; through the system, optimal navigational speed, power supply form and load working state of the ship can be determined, and control commands can be output automatically. According to the ship energy efficiency management and control platform, the energy consumption state of the ship can be monitored in real time, the influences of environmental factors on the energy consumption of the ship can be eliminated or lowered, and the irrationality of the navigational speed can be set in advance, so that the aims of saving energy and reducing emission are fulfilled.

The invention discloses an unmanned aerial vehicle system health status evaluation device and is applied to the evaluation of the health status of K unmanned aerial vehicles. The unmanned aerial vehicle system health status evaluation device is characterized by comprising an information collecting module, an information processing module, an evaluation processing module, a health status score displaying module and a health status information database; the information collecting module is used for obtaining the flight data of the unmanned aerial vehicles in flying processes; the information processing module is used for processing the flight data to obtain standard flight data blocks; the evaluation processing module processes the standard flight data by using an entropy coefficient G1 power evaluation method according to a set health status evaluation index system, and the weight of each evaluation index and the score of unmanned aerial vehicle system health status are obtained by the evaluation processing module and sent to the health status score displaying module for being displayed. The unmanned aerial vehicle system health status evaluation device can conduct colleting, monitoring and processing on the flying data of the unmanned aerial vehicles to obtain the visual unmanned aerial vehicle health status scores for unmanned aerial vehicle decision support.

The invention relates to an image splicing method based on an optimal splicing plane and a local feature. The method comprises the following steps: S1: estimating the depth information of an image; S2: segmenting the image based on the depth information, dividing the original image into an area to be registered and a non-registration area, and determining the optimal splicing plane by using two segmentation lines as sides; S3: calculating rotation angles by using the optimal splicing plane as a reference and using the respective segmentation lines as axes, and transforming the image to be in a reference plane; S4: extracting FAST feature points and the feature description of the feature points in an area to be spliced; S5: calculating Hamming distance for feature point matching, and performing precise matching by using use depth information and an RANSAC algorithm to obtain matching point pairs; and S6: computing a transformation matrix by using the registered feature points to obtain a transformed registration area image, and obtaining a spliced image by using a weighted fusion method based on focus distance. The method can overcome tensile distortion caused by the global correction under a large viewing angle difference, and obtains the spliced image more conforming to human vision.

The invention discloses a mechanical arm robust optimization design method based on interval and bounded probability hybrid uncertainty. The method comprises the following steps: considering two kindsof uncertainty of interval and bounded probability distribution influencing the mechanical arm performance, describing the bounded probability distribution as a random variable obeying generalized beta distribution, and establishing a mechanical arm robust optimization design model; direct solution is carried out based on a genetic algorithm; the robustness of a population individual constraint performance function is analyzed by utilizing the uncertainty boundness, and the feasibility of individuals is judged; for a feasible individual, calculating a mean value and a standard deviation of anobjective function of the feasible individual by adopting a Monte Carlo method based on multi-layer encryption Latin hypercube sampling; and then sorting the individuals of the current population according to the total feasible robustness index and the negative ideal solution approaching distance of the constraint performance function to obtain robust and optimal mechanical arm parameters. The mechanical arm robustness optimization model established by the method truly reflects uncertainty distribution, the optimization process is intelligent and efficient, and the method has good engineeringapplicability.

The invention discloses a data dimension reduction method based on neighborhood similarity and mainly solves the problem that an existing method only uses the Euclidean distance to measure a sample neighborhood structure, so that recognition results are non-ideal when a data structure is not balanced. The data dimension reduction method comprises the following realization steps: (1) inputting and normalizing data and randomly initializing a basis matrix and a coefficient matrix; (2) calculating a diagonal covariance matrix of a sample; (3) calculating KL (Kullback-Leibler) divergence through the diagonal covariance matrix; (4) calculating neighborhood sample similarity through the KL dispersion degree; (5) calculating a neighborhood class label distribution matrix of the sample; (6) calculating neighborhood class label similarity through the neighborhood class label distribution matrix; (7) calculating neighborhood similarity through the neighborhood sample similarity and the neighborhood class label similarity; (8) applying to iterative criterions according to the neighborhood similarity to obtain the basis matrix and the coefficient matrix after dimension reduction. The data dimensionality reduction method is high in accuracy rate and can effectively perform feature extraction and dimensionality reduction on data and be used for data and image processing.

The invention provides a route programming method and an apparatus thereof. The method comprises the following steps: according to a received starting point and a terminal point, programming a publictraffic travelling route from the starting point to the terminal point; selecting a walking road which is characterized in that at least one distance is greater than a distance threshold from the walking road included in the public traffic travelling route as a correction road; by aiming at the correction road, determining whether a specific site of a specific public traffic tool is existed in thestarting point and the terminal point of the to-be-corrected road in respective predetermined scopes or not, if the result is positive, according to the starting point and the terminal point of the to-be-corrected road, and the specific site of the specific common traffic tool, programming the correction route from the starting point and the terminal point of the to-be-corrected road, and replacing the correction route by the to-be-corrected road. The invention provides the reasonable and practical programming method and the apparatus for a travelling route, so that rationality of route programming is increased.

The invention discloses an optimization method for a temperature cyclic stress acceleration model of a semiconductor device, and belongs to the field of integrated circuits. The method comprises the steps of performing a normal stress degradation test on a group of samples by utilizing a cyclic stress, and performing an accelerated degradation test on another two groups of samples by utilizing anincreased constant temperature cyclic accelerated stress; performing detection and determination on sample sensitive parameters every a certain temperature cyclic frequency, obtaining a degradation path model, obtaining a pseudo-life and a distribution type, and performing fitting to obtain distribution parameters; calculating average lives of the samples in the normal stress degradation test andthe accelerated stress test, and calculating model parameters and acceleration factors of a to-be-assessed temperature cyclic stress acceleration model; and inferring the lives of the samples in a normal cyclic stress condition, and performing comparison with sample lives obtained by performing extrapolation through adopting different to-be-assessed stress acceleration models, wherein the model with an optimal life value is an optimal model. According to the method, the optimal temperature cyclic stress acceleration model can be accurately judged.

The invention provides a cluster scheduling method, a load balancer, a node and a clustering system. The cluster scheduling method applied to the load balancer comprises the steps as follows: the load balancer receives operation information transmitted by each node in a cluster; the load balancer analyzes the operation information of each node and determines the operation information transmitted by each node is normal operation information or abnormal operation information, wherein if the operation information is normal operation information, the normal operation information contains the processor utilization rate of the node; when a current task to be scheduled is received, for each first node that has transmitted the normal operation information, the load balancer uses the processor utilization rate P of the node contained in the normal operation information to calculate a load value of the first node, and select one node from the first nodes according to the calculated load value; and the load balancer distributes the current task to the selected node. According to the solution, the cluster scheduling is more reasonable.

The invention provides a Brazilian green propolis quality control method. The Brazilian green propolis quality control method is implemented through the following steps that a test propolis sample is ultrasonically extracted with ethyl alcohol, filtered and detected through high performance liquid chromatography, the content of six evaluation index ingredients is calculated according to a standard curve, and whether the quality of Brazilian green propolis is high or low is evaluated through the content of the six ingredients, that is to say, if the total sum of the content of the six ingredients is larger than 10%, the Brazilian green propolis is high-quality propolis, and if the total sum of the content of the six ingredients is smaller than 6%, the Brazilian green propolis is low-quality propolis. According to the method, the chemical ingredients of the Brazilian green propolis are combined with the antioxidant activity, the antioxidant material basis of the Brazilian green propolis is illustrated, the defect that some main ingredients do not achieve the high pharmacological activity is avoided, and the antioxidant activity of the Brazilian green propolis can be reflected more truly; the design is reasonable, the irrationality that in an existing method, total flavonoid is taken as the quality control index of the Brazilian green propolis is overcome, the defect that content detection of multiple flavones or phenolic acids has no correlation with the activity is overcome, and a novel method is provided for Brazilian green propolis quality control.

The invention discloses a shale gas reservoir formation pressure calculation method and a computer readable storage medium. The shale gas reservoir formation pressure calculation method includes the following steps that: a wet clay elasticity tensor and a sandy mixture elasticity tensor are obtained based on logging data; the elasticity tensor of equivalent shale is obtained based on the wet claytensor elasticity tensor and the sandy mixture elasticity tensor; acoustic wave time difference is obtained based on the elasticity tensor of the equivalent shale; and shale gas reservoir formation pressure is acquired based on the acoustic wave time difference. With the shale gas reservoir formation pressure calculation method adopted, the uncertainty of the establishment of a trend line under acondition that measured pressure data are insufficient can be lowered, the influence of lithology on the trend line is effectively considered, and the accuracy of pressure prediction can be finally improved.

The invention discloses a pulse regression model-based electrocardiography data correction method and system. The method includes the steps of: constructing a regression model by using an artificial neural network; performing training on the regression model by utilizing acquired pulse signal data and acquired electrocardiography data to obtain trained regression model; serving the pulse signal data as a network input of the trained regression model; serving an output as estimated electrocardiography data; and then correcting the electrocardiography data according to a value difference between the acquired electrocardiography data and the estimated electrocardiography data. The arithmetic speed of the method is far above that of the conventional methods.

The invention discloses an ion chromatography method for simultaneous determination of oxalic ion, succinate ion, sulfate ion and phosphate ion in reconstituted tobacco The method established in the invention uses methanesulfonic acid as an extraction liquid to guarantee the extraction efficiency of each group to be tested in a sample reach above 99.7%; a mixed solution of sodium carbonate and sodium bicarbonate is used as a mobile phase, in order to effectively eliminate the effects of degradation of vitamin C on the quantification of oxalic acid; sample treatment and sampling at a time can simultaneously detect four components including oxalic ion, succinate ion, sulfate ion and phosphate ion in reconstituted tobacco; and the relative standard deviation of content of each group to be tested is 0.27%-0.99%, the adding standard recovery is 99.3%-106.2%. The method is accurate, simple and reliable, and is applicable to the determination of relative groups in tobacco samples and reconstituted tobacco samples.

The invention discloses a chemical storage tank abnormity detection algorithm research based on FCM-LSTM, relates to the chemical equipment and neural network fields. According to the method, a multi-layer network architecture model is used as a fault diagnosis method, the advantages of supervised learning and unsupervised learning are combined, and a fault diagnosis mechanism based on data driving is adopted. The method comprises the following steps that firstly, mass data is clustered by using an unsupervised tool class FCM algorithm; according to a specified similarity standard, datasets are divided, the normal data and the fault data belong to different class clusters; a PSO algorithm is used for avoiding random selection of initial values and accelerating the clustering process, a small amount of mark data is obtained to improve the detection performance, then an LSTM is used for carrying out training network on each cluster and offline historical data, finally, multi-subnet parallel learning is carried out, then results are subjected to fitting combination and integrated analysis, and the generalization capacity of the network is improved. The data size processed by the method is larger, more information can be processed, and the application range is wider.

The invention provides a method for recovering gas content of shale. According to the method, the original gas content of the shale is recovered according to adsorption gas content and pore volume gas content, wherein on the theoretical bases of Polanyi adsorption potential, the London chromatic dispersion interaction potential energy theory and a gas state equation in combination of a core column methane isothermal adsorption curve, test data of gas components as well as porosity and water saturation of the shale, recovery of the adsorption gas content is acquired through adsorption gas content change of gas at the formation temperature and at the temperature of 95 DEG C in the analytical process and; recovery of the pore volume gas content is acquired by adopting test data of the porosity and the water saturation through the state equation. According to the method for recovering the gas content of the shale, the theoretical bases are further perfected, the original content of each gas component of the shale can be recovered, and mineability of a shale gas reservoir can be evaluated more accurately, more objectively and more comprehensively.

The invention discloses a reciprocating oscillation flapping energy conversion device. The device comprises a frame, a flapping wing, a dip angle adjusting device, two slide blocks, two slide columns, a connecting rod mechanism, a small belt wheel, a controller, and a power generation device; the two slide columns are respectively fixed at two ends of the frame; the two slide blocks are respectively sleeved on the two slide columns to form slide pairs; the flapping wing is connected with the two slide blocks through a flapping wing rotating shaft; the dip angle adjusting device is a servo motor connected with the flapping wing rotating shaft; limiting blocks are arranged at the middle parts of the slide columns; position inductors are respectively arranged at the tops and the middle parts of the slide columns; the slide blocks are connected with the small belt wheel through the connecting rod mechanism; the up-down sliding of the slide blocks is converted to the rotation of the small belt wheel through the connecting rod mechanism; the small belt wheel is connected with the power generation device through a belt; signals of the position inductors are all accessed into the controller; and the controller controls the flapping wing rotating shaft to rotate through the servo motor so as to change an attack angle of the flapping wing. The device is simple in structure, is high in energy conversion efficiency through transmission of the slide pairs and connecting rods, and is low in loss.

The invention discloses a prediction method for a zinc powder effectiveness coefficient in a wet zinc smelting and copper removal process. The prediction method for the zinc powder effectiveness coefficient during copper removal is established by integrating a chemical reaction stoichiometry model and a continuous stirred tank reactor material balance model in the wet zinc smelting and copper removal process and according to process parameters of copper ion concentration at an outlet and an inlet, addition quantity and flow of zinc powder, a content test value of cuprous oxide in copper slag and the like. A zinc powder effectiveness coefficient database is established based on industrial operating data, and the current zinc powder effectiveness coefficient is predicted on line by means ofa time series prediction method and an adaptive window length adjustment algorithm, so that the problems of high fluctuation of the copper ion concentration at the outlet and low qualified rate due to excessive addition or insufficient addition of the zinc powder caused by lack of measure of impurity removal efficiency in a copper removal reaction are avoided, and a necessary condition is provided for implementation of stable control of the copper ion concentration at the outlet in the in a wet zinc smelting and copper removal process.

The invention discloses a sample pretreatment method for simultaneous determination of oxalic acid, succinic acid, sulfuric acid and phosphate radicals in regenerated tobacco leaves through the ion chromatography. According to the method, regenerated tobacco leaf samples are subjected to vibrating extraction by using methanesulfonic acid as extraction liquid and then are filtered by a filter membrane, and filter liquor, namely liquor to be detected, is collected. By the adoption of the method, the methanesulfonic acid is used as the extraction liquid, it is guaranteed that the extraction efficiency of all the components to be detected in the samples can reach 99.7%, and realizing of the technical scheme that the four components of the oxalic acid, the succinic acid, the sulfuric acid and phosphoric acid in the regenerated tobacco leaves can be detected at the same time accurately is effectively ensured. The method is simple, reliable and suitable for determining the related components in tobacco samples and the regenerated tobacco leaf samples at the same time.

The invention discloses a cargo transport vehicle remote online monitoring management cloud platform based on the logistics industry. Comprising a cargo transportation company basic information acquisition module, a vehicle and personnel information acquisition module, a cargo transportation order information acquisition module, a cargo transportation order distribution module, a truck entrance and exit monitoring module, a maintenance vehicle display and a storage database. A preferred influence coefficient is obtained through comprehensive analysis on two aspects of driver driving experience and a driver fatigue index, and meanwhile, a vehicle maintenance coefficient is obtained through comprehensive analysis on two aspects of vehicle body wear degree and a vehicle body sag index, so that irrationality caused by the problem of incomplete consideration during truck driver allocation is avoided; a powerful data basis is provided for vehicle maintenance, timely maintenance and adjustment of the truck are achieved, and potential safety hazards of a truck driver in the driving process are effectively reduced.

The invention provides a data backup method and device, equipment and a storage medium. The method comprises the steps: obtaining a data increment of a to-be-backed-up device at a current backup moment relative to a previous backup moment, a data volume at the current backup moment, a data volume at the previous backup moment, and a resource quantity of a host device at the current backup moment, the to-be-backed-up equipment runs on the corresponding host equipment, and a backup terminal is installed on the host equipment; according to the data increment, the data volume at the current backup moment, the data volume at the previous backup moment and the resource quantity of the host equipment at the current backup moment, determining a backup strategy of the to-be-backed-up equipment at the current backup moment, the backup strategy comprises at least one of a to-be-backed-up device disk file merging direction, a disk file merging depth and selection of a target host device; According to the backup strategy, sending an instruction for backing up the to-be-backed-up device to a backup terminal in the target host device. According to the method, the data backup success rate can be improved, and the data backup duration is shortened.

The invention proposes a biostimulation feedback treatment method and system, computer equipment, and a computer readable storage medium, wherein the method comprises the steps: obtaining treatment information at a terminal; performing data training of the treatment information to obtain treatment parameters; and sending the treatment parameters to the terminal so that the terminal controls the biostimulation feedback device to treat the patient according to the treatment parameters. According to the invention, the data training of the treatment information is carried out to obtain the treatment parameters so as to determine a treatment scheme, thereby achieving a purpose of determining the treatment scheme according to different persons, and preventing the manual selection of the treatment parameters from causing an unreasonable treatment scheme. Moreover, there is no need to store many treatment schemes, thereby reducing the requirements for the performances of treatment equipment.

The invention discloses a method for optimum design of a planar variable-pitch grating and relates to the spectrum technical field. By the adoption of the method, the problem that the groove density function of a variable-pitch grating manufactured with existing methods is quite different from an expected groove density function is solved. According to the method, the meridian expected groove density function of the planar variable-pitch grating is obtained by means of a spherical wave exposure system, the deviate focus, meridian coma and spherical aberration of the planar grating are obtained according to the eikonal function of the variable-pitch grating, the variable-pitch grating is made to focus according to a grating equation, the expected groove density function, the value of the deviate focus F20, the value of the meridian coma F30 and the value of the spherical aberration F40, the meridian coma and the spherical aberration are zero, the expected groove density function coefficient optimized spherical wave logging parameters of the planar variable-pitch grating are obtained, the integral function of the square of the difference between expected groove density and designed groove density of each point on the surface of a substrate of the grating serves as an objective function and is optimized, the minimum value is obtained, and then optimum design of the planar variable-pitch grating is achieved. By the adoption of the method, computing time is shortened, and computing efficiency is improved.

The invention provides an improved multidimensional scaling heterogeneous cost-sensitive decision tree building method, which comprises the steps of selecting splitSi from a candidate attribute according to a target function f(Si) of an attribute Si, extending branches meeting the condition splitS=splitSi from a node, supposing that the number of the branches meeting the condition is k and adding a blank node to the node, namely determining the number of the branches of the current node to be k+1; and simultaneously carrying out pruning operation on leaf nodes by using a first pruning technology, carrying out pruning while building the tree, and stopping building the tree when two conditions are met as follows: (1) Yi is supposed to be a sample set meeting the condition splitS=splitSi in a training dataset, if Yi is null, one leaf node is added and the sample set is marked as the most common type in the training dataset; and (2) all examples in the node belong to the same type. According to the method provided by the invention, the classification accuracy is improved; the bias problem in the classification process is solved; and multiple cost impact factors and the blank node in the branches of a decision tree are considered, and the next step of classification operation can be continued through the blank node if an unknown classification result does not conform to a current model.

The invention discloses an automatic filling system for a gas well defoamer, which comprises a differential pressure sensor, a processor and an injection pump, wherein the differential pressure sensor is used for detecting liquid flow pressure in a gas production pipeline and transmitting the detected pressure signal to the processor; the processor has a preset liquid flow pressure trigger value, and the received liquid flow pressure value is compared with the trigger value, wherein if the real-time value is higher than the trigger value, the processor conducts the power supply of the injection pump to inject the defoamer, and if the real-time value is lower than the trigger value, the processor disconnects the power supply of the injection pump to stop the injection pump; and thus the injection pump is controlled by the processor to start or stop the addition of the defoamer. The automatic filling system for the gas well defoamer is capable of filling the defoamer according to the liquid production condition of a gas well in real time, thereby ensuring that the defoamer can play a role in time when liquid is produced; and when no liquid is produced, the addition of the defoamer can be quickly stopped, thereby avoiding untimely and unreasonable conditions caused by artificially starting pump injection, saving a large amount of the defoamer and improving the defoaming effect.

The invention discloses a dangerous driving behavior-based vehicle insurance accounting method. The method comprises the following steps that: dangerous driving behaviors are graded; dangerous drivingbehavior information is obtained; and vehicle information is obtained. According to the method, the dangerous driving behavior information is analyzed and graded, and with the vehicle information used in combination, vehicle insurance expenses are calculated. The dangerous driving behavior information of drivers is collected and analyzed; the dangerous level weighs of the dangerous driving behaviors are divided; and therefore, the dangerous driving behavior-based vehicle insurance accounting method can be realized. The rationality of the insurance accounting method is improved. With the method adopted, the drivers can be urged to reduce dangerous driving behaviors, so that traffic accidents can be reduced.

Embodiments of the invention provide a work time distribution method and an apparatus thereof. The method comprises the following steps of receiving work time to be distributed, wherein the work time includes one or more time periods; determining a user quantity matching with the one or more time periods; acquiring user identifications of one or more users; according to the user identifications, extracting time period distribution state information of one or more users, wherein the time period distribution state information includes information of the time periods to be distributed and corresponding frequencies to be distributed; and aiming at the user quantity, the information of the time periods to be distributed and the corresponding frequencies to be distributed, distributing the work time for one or more users so as to acquire time period distribution results of the one or more users. Automatic distribution of the work time is realized and a problem that work efficiency is low because of unreasonable manual work time distribution is avoided.

The invention discloses a method for analyzing a traversal rate and a phase optimization method in a non-orthogonal multiple access (NOMA) system based on a simultaneous transmission and reflection reconfigurable intelligent surface (STAR)-RIS (simultaneous transmission and reflection reconfigurable intelligent surface) assisted non-orthogonal multiple access) system of a statistical channel, and relates to a method for analyzing the traversal rate and a method for optimizing the phase of the STAR-RIS assisted non-orthogonal multiple access (NOMA) system of the STAR-RIS assisted non-orthogonal multiple access (NOMA) system of the STAR-RIS assisted non-orthogonal multiple access (NOMA) system. Aiming at an STAR-RIS-assisted NOMA downlink system, a system model of the system under a Rician fading channel is established, the signal to interference plus noise ratio of a user is calculated, a reflection phase of STAR-RIS is designed and optimized by utilizing statistical channel information, and an approximate expression of a system traversal rate and a progressive expression under a high signal to noise ratio condition are given; through simulation verification, the performance analysis method provided by the invention can effectively evaluate the performance of the system, and the phase optimization method provided by the invention can effectively improve the traversal rate of the system.