228results about How to "Avoid the influence of subjective factors" patented technology

The invention discloses an electrochemiluminescence sensor for detecting trace pesticide residue and a method for detecting pesticide residue. An electrode preparation method (figure attached) comprises the following steps: selecting an identifier of the pesticide residue; preparing quantum dots and graphene nano material according to documents; and modifying the nano material, the quantum dots and the identifier onto the electrode surface of the sensor by the electrode surface modification technology. The method for detecting the trace pesticide residue comprises the following steps: connecting the modified electrode to an electrochemiluminescence instrument and detecting the pesticide residue in the sample extract. The electrode in the invention has high specificity and sensitivity capable of reaching ng level, a basic detection process is finished only within 3-5 min, and the cost is low. The method for detecting the pesticide residue by utilizing the electrode is fast and simple to operate, and the reaction is automatically finished and the result is automatically recorded by an apparatus.

The invention discloses a method for detecting an electrode of a molecular imprinting membrane of EDCs and the EDCs; the preparing method of the molecular imprinting membrane electrode comprises the following steps: a functional monomer is selected; template molecule of the EDCs, the functional monomer, a crosslinker, a porosity-making agent, an initiating agent and organic solvent are mixed evenly according to a certain molar ratio so as to prepare MIPs solution; nanometer material solution is prepared; the nanometer material and molecular imprinting polymer MIPs are modified on the surface of the electrode of a sensor by using the electrode surface modifying technology; a method for detecting trace amount of the EDCs comprises the following steps: the electrode of the molecular imprinting membrane is connected with an electrochemical working station and detects the EDCs in the environment sample extract; the electrode of the molecular imprinting membrane of the invention has strong specificity and high sensitivity which can reach ng level; a basic detection process is completed by only 1 to 2 minutes; the cost is low and the detection of one sample needs only a plurality of cents; the method for detecting the secretion in the environment by the electrode of the molecular imprinting membrane has fast and simple operation and the reaction and result are completed and recorded automatically by instruments.

The invention discloses a user abnormal behavior detection method based on neural network clustering. The method comprises: firstly, performing SVD decomposition and denoising on a behavior data set matrix of a user, and inputting the denoised matrix to an input layer of a neural network, and then calculating weight of all attributes of each user in a hidden layer of the neural network, and standardizing the weight, outputting the weight in an output layer, and finally respectively calculating similarity value of each user and each user in a normal behavior model database and a threshold value, if the similarity value is larger than the threshold value, behavior being abnormal behavior, and carrying out corresponding prompt and precautionary measures, otherwise, the behavior being normal behavior, and combining the behavior in the normal model database to upgrade the database in real time. Through cooperation of each part, the method effectively realizes high detection rate and low false alarm rate.

The invention discloses a quantum dot molecular imprinting microsphere quartz fluorescent sensor for detecting multicomponent food additives simultaneously and a method for detecting the food additives by the quantum dot molecular imprinting microsphere quartz fluorescent sensor. A method for preparing a quantum dot molecular imprinting microsphere comb quartz plate comprises the following steps of: selecting functional monomers corresponding to the food additives; preparing quantum dots and preparing quantum dot molecular imprinting microspheres according to the literature; and modifying thesurfaces of different probes of the comb quartz plate with the imprinting microspheres of different food additives by utilizing layer-upon-layer accumulated surface modification technology. The method for detecting the trace multicomponent food additives simultaneously (which is shown as the figure) comprises the following steps of: immersing the modified quartz plate into simply-pulpified food solution, arranging the quartz plate on a sealing quartz vessel, and detecting the food additives in a sample. The quantum dot molecular imprinting microsphere quartz fluorescent sensor has high specificity and sensitivity, short detection time and low cost; and in a method for detecting pesticide residues by fluorescent light, the operation is simple and quick, and reactions and results are completed and recorded automatically by instruments.

The invention discloses a molecular imprinting electroluminescent sensor for detecting veterinary drug residue by taking a battery as power and a method for detecting veterinary drug residue. A preparation method of an electrode (a schematic diagram is shown in the figure) comprises the following steps of: preparing MIP (Molecularly Imprinted Polymer)s sol of the veterinary drug residue; preparing a carbon dot and preparing a graphene nano material according to documents; and modifying graphene, the carbon dot and the MIPs sol onto the surface of the electrode of the sensor. The method for detecting the trace veterinary drug residue comprises the following steps of: connecting the modified electrode to an electrogenerated chemiluminescence instrument, and detecting the veterinary drug residue in a sample extract by taking the battery as the power. The molecular imprinting electroluminescent sensor disclosed by the invention has the advantages that the specificity of the electrode is strong and the sensitivity is high and can be up to a ng grade; only 3-5 minutes are spent on a basic detection process; and the cost is low. The method for detecting the veterinary drug residue by adopting the electrode is quick and easy to operate, the reaction is automatically completed by instruments and results are automatically recorded by the instruments.

The invention discloses a Chinese composition automatic testing method and system. The method includes: step A: input the corpus of the composition text to be evaluated, and preprocess the corpus of the composition text to be evaluated; Extracting subject content features, organizational structure features, and language expression features; Step C: Calculate the extracted subject content features, organizational structure features, and language expression features with scoring equations to obtain automatic scoring results for Chinese compositions. The invention reduces the input of manpower and material resources in the large-scale Chinese composition proficiency test, and realizes the objectification of scoring.

The invention provides an endocrine interferent molecularly imprinted membrane substrate in a trace environment with rapid detection speed and high sensitivity, the preparation method and the application thereof. The molecularly imprinted membrane substrate comprises a gold quartz crystal substrate, and multi-layer nano-materials that alternate with each other and environmental endocrine interferent molecularly imprinted polymer are formed on the surface of the gold quartz crystal substrate. The preparation method comprises the following steps: selecting functional monomer that can be synthesized with the environmental endocrine interferent into the molecularly imprinted polymer; preparing the molecularly imprinted polymer solution; and modifying the nano-material and the molecularly imprinted polymer on the surface of the gold quartz crystal substrate. The invention applies the surface modification technology to the preparation of the molecularly imprinted membrane substrate, so as to allow the controllability over the preparation of the nano-scale synergetic EDCs molecularly imprinted membrane substrate. A method for detecting the environmental endocrine is rapid and simple, and the reaction and the result are automatically recorded and completed by an instrument with good repeatability, thereby facilitating the on-site detection.

The invention discloses a process for preparing a trace amount mycotoxin molecular imprinted column, which comprises the following steps of: selecting a functional monomer; evenly mixing a mycotoxin template molecule, the functional monomer, a cross-linking agent, a pore-forming agent, an initiator and an organic solvent by a certain molar ratio to prepare a molecular imprinting polymer solution; preparing a nano-solution; and dressing the nano-material and a molecular imprinting polymer on the internal surface of a glass pipe column. A method for detecting the trace amount mycotoxins includes: pumping a chemiluminescence system solution and a sample solution into a chemiluminescence analyzer respectively and detecting the mycotoxins in the sample. The molecular imprinted column of the invention has advantages of high sensitivity and accuracy. The nano-enhanced trace amount mycotoxin molecular imprinted column obtained in the invention is connected to the chemiluminescence analyzer for detecting mycotoxins, and purposes of high specificity, high sensitivity and fast detection to mycotoxins in the sample can be realized.

The invention relates to a method for evaluating damage degree of hinge joints of plate girder bridges. Shear force and rigidity of a hinge joint are obtained according to information such as load size, position, actually-measured plate girder defection and the like in a bridge static loading test by means of the relation among the hinge joint relative displacement, the hinge joint shear force and the high joint rigidity. A hinge joint force transmission capacity index lambda I and a hinge joint rigidity ratio index lambda i are defined, and corresponding method for evaluating damage degree of the hinge joint is provided. When the lambda I>=0.95 or the lambda i>=20, the hinge joint is determined to be perfect; when the lambda I<=0.9 or the lambda i<=10, the hinge joint is damaged and supposed to be reworked; and when 0.9<lambda I<0.95 or 10<lambda i<20, the hinge joint is damaged and supposed to be maintained instead of being reworked. The method automatically gives consideration to the interaction among the hinge joint shear force, multiple loads and eccentricity of the loads, is applicable to automobile loading, and can provide optimal estimation values of rigidity parameters of multiple tests. Further, using the method can quantitatively judge the damage degree of the hinge joint and avoid influences of subjective factors.

The invention provides a drift fault recognition method of hot-rolling strip steel based on sound signals. The method comprises the following steps of: preprocessing a signal by combining the features of a hot-rolling production line and adopting a resampling and framing technology, carrying out feature extraction on the signal by adopting a Mel frequency cepstrum technology and carrying out feature selection and recognition by adopting a principal component analysis method and a multivariate statistic process T2 control diagram so as to realize the diagnosis of drift faults of the hot-rolling strip steel. The invention has the advantages that the drift phenomenon can be judged accurately and rapidly on line by utilizing an acoustic detection method, the roller replacement time can be reasonably arranged, the roller consumption can be effectively reduced, the production cost can be controlled, and the quality defect prevention capacity and the production operability of the product can be improved.

The embodiment of the invention discloses a risk assessment report generation method and device, equipment and a storage medium. The method comprises the steps of determining a business scene classification of a target object according to the target object and a report purpose; obtaining interaction data of the target object based on the business scene classification; extracting risk data with risk characteristics from the interactive data; determining a risk assessment model corresponding to the business scene classification, and processing the risk data through the risk assessment model to obtain a risk conclusion of the target object; and filling the structured template corresponding to the business scene classification through the risk data and the risk conclusion to obtain a structured report, calling a natural language processing model to process the risk data to generate an unstructured report, and generating a risk assessment report by using the structured report and the unstructured report. The method disclosed by the embodiment of the invention can realize automatic generation of the risk assessment report in different scenes.

The invention relates to an electroencephalogram feature selection approach based on a decision-making tree. Firstly, collected multi-channel electroencephalograms are preprocessed; secondly, feature extraction is conducted on the preprocessed electroencephalograms by means of the principal component analysis method to obtain feature vectors; thirdly, the feature vectors obtained after the step of feature extraction is conducted are input into the decision-making tree, and superior feature selection is conducted; fourthly, superior features selected by the decision-making tree are reassembled; finally, the reassembled superior feature vectors are input into a support vector machine, and electroencephalogram classification is conducted to obtain the classification correct rate. According to the electroencephalogram feature selection approach, the decision-making tree is used for superior feature selection, operation is simple, manual participation is not needed, and time and labor are saved. The decision-making tree is used for superior feature selection, so that influence of subjective factors of people is avoided in a selection process, selection is more objective, and the classification correct rate is higher. Experiments show that the average accuracy is 89.1% by using the approach to conduct electroencephalogram classification and is increased by 0.9% compared with a traditional superior electrode reassembling method.

The invention provides a tunnel lining crack image segmentation method, and belongs to the technical field of image recognition. The method comprises the following steps: firstly, dividing a directlycollected original crack image into a plurality of image blocks, and selecting the image blocks with cracks and corresponding binary images as a training sample set; performing de-mean normalization processing on the image blocks with the cracks in the training sample set; training the training sample set subjected to mean value removal normalization processing by using a convolutional neural network and combining a loss function to obtain a crack segmentation model; and carrying out mean normalization processing on the crack image to be segmented, inputting the crack image to be segmented into the crack segmentation model, and outputting a segmentation result image. According to the method based on the deep convolutional neural network, the image is automatically identified, human intervention is reduced, the influence of subjective factors of a detector is avoided, the identification precision and the identification efficiency are improved, and the false alarm rate is reduced.

The invention provides a product life cycle identification method and device, which can identify life cycle and improve identification accuracy. The method comprises the following steps: obtaining sales data of products in a sales system, and extracting a life cycle pattern set from the data according to a product life cycle pattern extracting method, a product life cycle stage division method and sales curve similarity calculation method; determining the position of life stage of each life cycle pattern in the life cycle pattern set according to the product life cycle stage division method; calculating similarity between a sales curve of a new product and each life cycle pattern according to the sales curve similarity calculation method to obtain a similarity set; serving the life cycle pattern corresponding to the largest similarity in the similarity set as the life cycle pattern of the new product, and recording relative movement position of the sales curve of the new product when the similarity is the largest and the life cycle pattern of the new product; and determining the life cycle of the new product according to the position of life stage of the life cycle pattern of the new product and the relative movement position.

The invention discloses a fully-automatic measurement method of plant heights of crops and a device. The method comprises steps of firstly establishing a camera coordinate system, a ground coordinate system and a calibration board plane coordinate system; after establishing a system model, calibrating device parameters in advance, wherein the device parameters comprise inner parameters of a camera, installation parameters of the camera and linear laser plane parameters; acquiring images, and transmitting the acquired images to a control processor through the camera, wherein the control processor controls on and off of a linear laser and processes the images; and according to image processing results and the device parameters, calculating and outputting the plant heights of the crops through the control processor. According to the invention, manual measurement is not required, so subjective factor effects caused by manual measurement are avoided; and non-contact and automatic measurement of the plant heights of the crops can be simply and easily achieved.

The invention discloses an ensemble learning-based type 2 diabetes kidney disease risk assessment system, which comprises a data collection module, an influence index data extraction module and an ensemble learning module, wherein the data collection module acquires a group of clinical index data of a to-be-assessed patient from a hospital database; the influence index data extraction module extracts influence index data from the group of clinical index data acquired by the data collection module; the ensemble learning module inputs the influence index data into an ensemble learning model, andthe ensemble learning model outputs a judgment result about whether the to-be-assessed patient suffers from the type 2 diabetes kidney disease or not. The system can simply, conveniently and valuablyobtain a judgment result of whether the patient suffers from the type 2 diabetes kidney disease or not, is beneficial to screening out high-risk groups with the diabetes kidney disease, helps doctorsto carry out auxiliary diagnosis, and has important significance in early diagnosis, prevention and delay of DKD, reduction of cardiovascular events, increase of the survival rate of the patient andimprovement of the life quality.

The invention discloses a structural deformation monitoring method based on contour line image stacking difference analysis. The complete image of a structural body is formed by image splicing, and the contour line image of the structural body is acquired by utilizing the complete image; the contour line images acquired in different periods are stacked so that a stacking difference image is formed; and thus relative deformation data of the structural body measured in different periods are acquired. The structural deformation monitoring method is used for detecting and evaluating the safety of a bridge and other structural bodies so that data acquisition time is short, and the structural deformation monitoring method has the advantages of being high in working efficiency and low in cost and can realize high-frequency wide-range bridge safety monitoring and enhance the safety early warning capacity of the bridge and other structural bodies. The structural deformation monitoring method is different from the existing point monitoring method having incompleteness and used for monitoring the bridge and other structural bodies so that all the contour line data of the whole structural body can be rapidly acquired and structural deformation of parts which can be photographed in the images can be acquired through the stacking difference image, and the structural deformation monitoring method is used for evaluating the safety of the structural body and truly and objectively avoiding the influence of artificial subjective factors to the greatest extent.

The present invention discloses a preparation method of an electrogenerated chemiluminescence sensor with a quantum dot modified nano porous carbon paste electrode for detecting trace antibiotic residues. The preparation method for the carbon paste electrode comprises the following steps: synthesizing nanoporous gold and quantum dot solution according to the existing method; modifying light-emitting quantum dots into the surface and the porous channels of the nanoporous gold material layer by layer by adopting the layer-by-layer self-assembly modification technology; doping the modified nanoporous gold material into graphite powder. The method for detecting the trace antibiotic residues comprises the following steps: connecting the modified carbon paste electrode with an electrogenerated chemiluminescence instrument, and detecting the trace antibiotic residues in dairy and meat products. The sensor has high sensitivity and a high detection speed, and one basic detection process can be completed within only 2-5 minutes. Moreover, the sensor has a low cost, and is economical and practical. The instrument is easy to operate and can automatically record and analyze experimental results, the effect of subjective factors is avoided, and good repeatability is achieved.