269results about How to "Reduce identification costs" patented technology

The invention relates to a method for constructing an electronic evidence. The method specifically comprises the following steps: generating a unique length-fixed digital abstract for electronic data of an applicant, and constructing a Merkle tree of an electronic evidence block chain system; constructing the electronic evidence block chain system with a timestamp; and setting an index number for a block by the electronic evidence block chain system, and returning the index number, the generated digital abstract and the timestamp to the applicant together. According to the method and the system disclosed by the invention, all participating parties can construct jointly, maintain jointly and supervise jointly, thereby meeting the right to know and the supervision right of each party; the identification cost of the electronic evidence is reduced, the identification efficiency is improved, and the objectivity and the reliability of the electronic evidence are enhanced.

The invention discloses an identification method and device for an application scenario of a mobile terminal. The mobile terminal comprises a terminal state detection module, and the identification method comprises the following steps: acquiring first time sequence state data acquired by the terminal state detection module in a preset application scenario; segmenting the first time sequence statedata by taking a preset time period as a unit, and obtaining first time window data; setting a corresponding scenario label for the first time window data according to a corresponding preset application scenario; training a deep neural network and obtaining a scenario prediction model; acquiring second time sequence state data acquired by the terminal state detection module in a to-be-predicted application scenario; segmenting the second time sequence state data by taking the preset time period as the unit, and obtaining second time window data; and inputting the second time window data into the scenario prediction model, and obtaining a scenario label of the to-be-predicted application scenario. The identification method disclosed by the invention has the advantages of simple data processing procedure, simple identification algorithm and high identification accuracy rate.

The invention provides a cast-in-place concrete cable conduit construction method. Layered laying of cable conduits and layered pouring of concrete are adopted; and the cable conduits are accurately positioned by adopting a cable conduit anti-floating positioning device when the concrete is poured, so that the cable conduits are prevented from floating and shifting, and the construction quality is effectively improved. The method comprises the following steps of: 1) measuring and paying off; 2) digging a foundation trench; 3) setting a pouring template; 4) pouring a reinforced concrete foundation, and arranging positioning steel bars on the concrete foundation piecewise; 5) positioning the laid cable conduits layer by layer by matching an anti-floating positioning bracket and the positioning steel bars, and pouring concrete layer by layer for fixing to the cable conduits between the positioning steel bars; 6) taking down the anti-floating positioning bracket and the positioning steel bars, and filling the holes at the positioning steel bars by using concrete; 7) checking passing of cables passing through the pipes to prevent blockage; 7) plugging pipe orifices by using plastic cloth to prevent sundries; and 8) backfilling soil to the foundation trench, and finishing the construction.

The invention belongs to the field of a tea quality identifying method, and relates to a method for identifying the quality of jasmine tea. The method comprises the following steps of: 1, measuring the relative content of aroma ingredients by using a method combining the head space solid phase microextraction in combination with a gas chromatography-mass spectrometry, wherein the sweet smell ingredients comprise alpha-farnesene, cis-3-hexenyl benzoate, methyl anthranilate, indol and linalool; 2, computing the aroma index of the jasmine tea; and 3, determining the quality of the jasmine tea according to the aroma index of the jasmine tea. According to the relevency among various main aroma ingredients and the quality of the jasmine tea, the JTF index (relative content specific value) is determined to be equal to [(alpha-farnesene+cis-3-hexenyl benzoate+methyl anthranilate+indol) / linalool]. According to the method, the quality of the jasmine tea can be effectively and objectively determined, the quality of the jasmine tea with accordant tea base levels can be distinguished, the hard work of specialists can be avoided, and the identifying cost can be saved as well.

The invention relates to a Gabor human face recognizing method based on a simplified intelligent single-particle optimizing algorithm, comprising a simplified intelligent single-particle optimizing algorithm and a link of selecting a Gabor filter group by the simplified intelligent single-particle optimizing algorithm, wherein the simplified intelligent single-particle optimizing algorithm comprises the following steps of: carrying out optimizing search on a solution space of a problem function by a particle and respectively carrying out intelligent updating on all dimensionality components of the single particle during iteration; and the link of selecting the Gabor filter group by the simplified intelligent single-particle optimizing algorithm comprises the following steps of: constructing a particle structure according to the quantity of Gabor filters, determining a particle search range and using a Fisher criterion as a fitness function; during the human face recognition, carrying out characteristic extraction on a human face image conforming to the authentication to form a characteristic database; after a new human face image is input, carrying out the characteristic extraction on the human face image by the selected Gabor filter; and comparing the obtained characteristic vector with the characteristics in the database by a minimal adjacent classifier to judge whether the input image is contained in the database or not and a corresponding identity. The invention improves the recognition precision and the characteristic extracting pertinence.

The invention discloses a driver identification method and device, a storage medium, and a terminal. The method includes: acquiring the vehicle travel trajectory data associated with the driver in a first preset period; processing the travel trajectory data to obtain characteristic data of at least one dimension; analyzing the feature data of the at least one dimension based on a preset algorithmmodel to obtain a probability that the driver is a special vehicle driver, wherein the preset algorithm model is obtained by training historical data including a plurality of vehicle travel trajectorydata acquired in a second preset period and driver identification information for driving each vehicle, the identification information including a special vehicle driver and a non-special vehicle driver. The scheme provided by the invention can automatically identify the identity of the driver, and has the advantages of high identification accuracy, high efficiency and low cost.

The invention discloses a circuit for recognizing classes of devices, a circuit board, terminal equipment and a signal controller. The classes of the devices are recognized by measuring resistors, recognizing resistors and the signal controller. The recognizing resistors are connected to one another in series, and values of the resistances of the recognizing resistors which are connected to one another in series are determined by using analog-to-digital detection signals inputted by an analog-to-digital detecting signal port in the signal controller, universal input and output signals outputted by a universal input and output signal port and the measuring resistors, so that the classes of the devices are recognized. The number of elements used for the circuit for recognizing the classes of the devices is small, the line of the circuit is simple, the circuit is easy to measure, the classes of the recognized devices are increased, the complexity of the circuit for recognizing the classes of the device is reduced, and the recognizing cost is reduced.

The invention discloses a household transformer relationship identification method and system based on characteristic current signals. The household transformer relationship identification method comprises the following steps that step 1, a master station issues a household transformer relationship identification command to a smart meter with a characteristic current signal sending function according to an electric meter address; step 2, a current signal of a characteristic code bit is generated in an electric network by using a resistance switching device, and the switching end time is recorded locally; step 3, a target feature code bit on a power line is detected after the household transformer relationship identification command is received; 4, whether the target feature code bit is successfully detected or not is judged by a terminal with an alternating current collection function; and 5, the meter switching time and the terminal identification time are measured by the master station, the phase is identified, and the user transformer relationship is determined according to the time scale comparison. According to the method and the system, power outage is not needed, the influence caused by "co-zero", coupling and crosstalk zone problems can be well overcome by controlling the strength of the sending characteristic current signal, and the accurate identification of the household transformer relationship can be realized.

The invention relates to a metagenome sample library construction method and an identification method based on a nanopore sequencing platform and a kit. The method comprises host removal pre-treatmentand sample library construction on human and animal metagenome samples by combining incomplete host removal based on the nanopore sequencing platform so as to sequence and identify the metagenomes. Compared with a conventional process flow, the method is simpler and rapider, meets the reasonable detection demands, omits flows such as library construction PCR in the identifying process, extrudes deviation caused by PCR amplification, and can be used for identifying metagenome microorganisms more truly and accurately.

The invention discloses a method for quickly extracting fungal DNA for PCR amplification, which comprises the steps of: (1) culturing fungi to obtain fungal mycelia; (2) placing the fungal myceliua into a centrifugal tube filled with a lysate; (3) alternately freezing and thawing the centrifugal tube; and (4) centrifuging the thawed fungal mycelia, taking a supernatant, adding 95 percent ethanol into the supernatant, mixing the mixture evenly, quickly freezing the mixture by liquid nitrogen, centrifuging the mixture, removing a supernatant, and air-drying a precipitate to obtain the fungal DNA. The method has simple operation, is time-saving and labor-saving, reduces the loss of the fungal DNA during the grinding, does not need phenol and chloroform for extraction, and avoids the contact of a toxic reagent. The DNA extracted by the method has good integrity, high purity and high yield, can be directly used for a PCR amplification reaction, and can efficiently amplify a target product for cloning and sequencing. The method improves the extraction efficiency of the fungal DNA, reduces the cost for the extraction of the fungal DNA, and can effectively improve the molecular identification and detection efficiency of the fungi.

The invention relates to an improved variational mode decomposition-based noise source recognition method. The method comprises the following steps of: acquiring single-channel signal noise of an engine under a certain working condition; carrying out preprocessing such as trend term removal and denoising on a noise signal; carrying out improved variational mode decomposition IVMD on the preprocessed signal to obtain signal components of different frequency bands; for output decomposed signal components, screening main noise components by utilizing mutual information, and determining main noisefrequency bands; carrying out mutual information analysis on the noise components and a source signal, and determining several components with highest mutual information values as the main noise frequency bands to carry out subsequent separation and analysis; carrying out blind source separation on the noise components to obtain independent noise components; carrying out time frequency analysis and Fourier transformation on the independent noise components by utilizing wavelet transformation so as to determine features such as a noise component time frequency, a power spectrum and an envelopespectrum of each independent component; and carrying out coherence analysis on the noise components and near-field noise to recognize a component noise signal source.

The invention discloses an identification method of bamboo raw fiber based on infrared and two-dimensional correlation spectra. The method comprises the steps of: separating natural plant fiber with a mixed solution of glacial acetic acid and hydrogen peroxide; grinding the treated natural plant fiber into plant fiber powder, then adding potassium bromide into the plant fiber powder, carrying out tablet-forming to prepare the sample, and measuring the one-dimensional infrared spectrum of the tablet sample; measuring the two-dimensional correlation infrared spectrum of the tablet sample, which is generated by disturbance in any form; comparing the one-dimensional infrared spectrum and the two-dimensional correlation infrared spectrum, thereby enabling direct identification of bamboo raw fiber and other plant fiber. According to the invention, the identification method is simple and feasible, the consumption of sample is small, bamboo raw fiber can be identified accurately and rapidly, the operation is easy, and the identification cost is low.

The invention discloses a commodity information identification method. The method comprises the following steps of: acquiring images in a single wireframe on a price tag; processing the acquired images, and acquiring one-dimensional bar code images and character images in the acquired images; and decoding the one-dimensional bar code images and the character images respectively to obtain commodity information contained in the one-dimensional bar code images and the character images. By the commodity identification method, the identification efficiency can be improved, and the cost can be reduced. In addition, the invention also provides a commodity information identification device.

The embodiment of the invention discloses an abnormal account determination method and a related device, and at least relates to a natural language processing technology and machine learning / deep learning in artificial intelligence, and parallel data calculation in cloud computing, and the method comprises the steps: obtaining the account description information of a to-be-recognized account and the target content published by the to-be-recognized account; and determining an account feature corresponding to the account description information and a content feature corresponding to the target content; calculating similarity information based on the account features and the content features. Whether the to-be-identified account is an abnormal account or not is determined according to the content quantity of the content published by the to-be-identified account and the similarity information corresponding to the to-be-identified account. Based on this, the abnormal accounts which are divergent due to published contents in the we-media accounts in the platform are automatically identified, so that the identification cost is reduced, the identification efficiency and precision are improved, and the overall environment of the platform and the reading experience of subscribers are promoted.

The invention discloses an HTTPS flow identification method, an HTTPS flow identification device, a terminal device and a storage medium. The HTTPS flow identification method comprises the steps of: obtaining HTTPS flow data; calculating each XDR characteristic variable of the HTTPS flow data according to a pre-set calculation calibre, so that all XDR characteristic variables of the HTTPS flow data are obtained; and inputting all the XDR characteristic variables of the HTTPS flow data into a pre-created support vector machine classification model, so that the flow type corresponding to the HTTPS flow data is obtained, wherein the flow type is a picture flow type or a non-picture flow type; the support vector machine classification model is pre-created according to XDR characteristic variable data of pre-established sample flow data; and the sample flow data is data labelled as picture flow and non picture flow. By adoption of the embodiment of the invention, the data obtaining difficulty and the calculation complexity of picture flow identification of HTTPS flow can be reduced; and the identification cost is reduced.

The embodiment of the invention provides a personnel identification method and device and a storage medium. The method includes: setting first database of which the initial state is empty in snapshotequipment; when the snapshot device snapshots a face image, comparing with a local first database; and under the condition of unsuccessful recognition, the snapshot device stores the face image in a first database and sends the face image to a server at the back end, the server recognizes the face image and returns identity information obtained through recognition to the snapshot device, and the snapshot device adds the identity information to the first database. And when the person corresponding to the face image is snapshot again, the snapshot device can complete identification by using thefirst database, and identification by a back-end server is not needed, so that the person identification cost is reduced, and the problem of waste of comparison resources is improved.

The invention relates to a resource allocation method based on a deep learning model and a terminal, and belongs to the field of data processing. The method comprises: the accuracy of using differentOCR deep learning model combinations to identify the to-be-identified character segment image is estimated through a performance function capable of reflecting the relation between the confidence coefficient and the accuracy of using a plurality of OCR deep learning models to identify the character segment image at the same time; further combining different OCR deep learning model combinations with different numbers of artificial identifications; and calculating the accuracy of the combination of various machines and manual recognition, selecting a combination with the lowest cost from the combinations of all machines and manual recognition which can reach an expected accuracy target value, and distributing the number of models and the number of workers according to the selected combination. The identification cost is effectively reduced while the high identification accuracy target value is met.

The embodiment of the invention provides an igneous rock lithology identification method and device. The method comprises the steps of: identifying igneous rocks based on a crossplot method; for lavawhich cannot be identified in the igneous rocks based on the crossplot method, obtaining a logging curve of the lava; converting the logging curve to an intrinsic modal function set; determining the experience modal energy entropy of the logging curve corresponding to each intrinsic modal function in the intrinsic modal function set; and processing the experience modal energy entropies based on apreset sorting algorithm to obtain a lithology identification result of the lava. The igneous rock lithology identification method and device can improve the igneous rock lithology identification precision and can reduce the identification cost.

The invention provides a calligraphy and painting record authentication device based on multistage magnification times. The device comprises a housing, a main board, a display screen, a storage unit and image acquisition devices. The technical key points are that the number of the image acquisition devices, of which the relative distance is fixed and magnification times are same or different, is N, N>=2; and the number of the image acquisition devices, of which the magnification times are different, is M, M>=2, and N>=M. The calligraphy and painting record authentication device has the advantages of simple system, high record and authentication efficiency, reliable authentication result, low authentication cost, high anti-counterfeiting capability and easy popularization and the like.

A portable all-dimensional monitoring device for an examination room comprises an all-dimensional visual sensor used for obtaining overall video information in the examination room, a microphone used for obtaining audio information in the examination room, a digital wired or wireless transmission module used for transmitting a video signal and an audio signal in the examination room, a memory card used for saving video data and audio data in the examination room, as well as a microprocessor. The all-dimensional visual sensor is mounted above the middle of the examination room; a camera is connected with the microprocessor through an A / D interface; the microphone is connected with a voice-controlling chip; the microprocessor is connected with the digital wired or wireless transmission module, which is connected with an antenna; the memory interface for the memory card is arranged on the microprocessor; the memory card is inserted in the memory interface for the memory card. The invention can improve usage rate, be applied conveniently and has lower cost.

The invention provides a method for rapidly, accurately and efficiently identifying the molecules of a broccoli variety Tailv1 hybrid. The method comprises the following steps: extracting genomic DNA from a sample to be detected, carrying out PCR amplification on the genomic DNA by a PCR primer of 5'-TCTCTCTCTCTCTCTCN-3', carrying out gel electrophoresis imaging analysis on the obtained PCR amplification products, and judging the sample to be detected as a Tailv1 hybrid single plant if about 800bp and about 600bp brands are simultaneously obtained through amplification; and calculating according to a formula of genetic purity of hybrid = number of hybrid plants / number of sample plants to be detected *100% to obtain the genetic purity of the sample to be detected. The accuracy of the identification of the purity of the broccoli variety Tailv1 hybrid by an ISSR primer TNISSR15 used in the invention is high, has a 99% consistency with a field identification result, and is reliable; the identification can be completed within 10d, the tedious program of field planting identification is avoided, the labor force and the land resource are saved, and the identification cost is reduced; and the method can determine the genetic purity of seeds in the same year of seed production, is convenient for seed graduation and purchase, and improves the identification efficiency.

The invention discloses a lithium battery pack parameter identification method based on a multi-constraint condition particle swarm optimization algorithm, and relates to the field of lithium ion battery pack electrochemical model parameter identification. The invention aims to solve the problems that only the behavior of a battery monomer can be identified and the overall state of a battery packcannot be predicted in the prior art. The method comprises the steps of 1, establishing an electrochemical model of a lithium-ion single battery; 2, identifying the lithium ion battery monomer electrochemical model by adopting an excitation response method to obtain a model parameter value; 3, setting a parameter value range of the electrochemical model of the lithium ion battery pack according tothe model parameter value obtained in the step 2; and 4, obtaining a model parameter vector of the lithium ion battery pack from a set parameter value range of the electrochemical model of the lithium ion battery pack by adopting a multi-constraint condition particle swarm optimization algorithm. The method is used for detecting the state of the lithium-ion battery pack.

The embodiment of the invention is applicable to the technical field of information. The invention provides a customer intention identification method and device and a customer service system. The method is applied to a customer service system and comprises the steps that a voice flow platform receives seat transfer voice transmitted through a voice transfer channel, the seat transfer voice is converted into text transfer information, and the customer intention contained in the text transfer information is recognized in the mode that text matching is conducted on the text transfer information in a preset intention model; the service system database obtains response information matched with the customer intention and sends the response information to the agent platform; and the agent platform displays the answered information in a display interface of the agent platform. According to the embodiment of the invention, only the keyword transferred by the seat is processed by configuring the voice transfer channel, and compared with a mode of simultaneously collecting two paths of voice streams of the seat and the client or only collecting the voice stream of the seat, the voice stream collection amount is further reduced, and the recognition cost is reduced.

The invention discloses a model training and abnormal account recognition method and device. The method comprises the following steps: firstly, obtaining a pre-labeled first account and an unlabeled second account as training samples; secondly, determining each node in the heterogeneous network according to the account data of each user account; establishing a heterogeneous network according to the incidence relation among the nodes; determining the node attribute of each node in the heterogeneous network; inputting the heterogeneous network and the node attribute of each node into a to-be-trained account identification model; and determining a feature vector and a prediction probability of each user account, finally determining a loss function according to the prediction probability of each first account, the annotation information, the feature vector of each first account and the prediction probability of each second account, and adjusting model parameters by taking the minimum lossfunction as an optimization target so as to identify an abnormal account through the account identification model. Model training is carried out in a semi-supervised mode, so that the labeling time and labeling cost of the user account are reduced, and the recognition cost of the abnormal account is saved.

The invention relates to a brain contrast image processing method and device, a medium and electronic equipment. The method comprises the steps that a brain contrast image is acquired; brain partitionis performed on the brain contrast image to obtain a brain partition image; the edge of each partition in the brain partition image is smoothed to obtain a target brain partition image; and a regionof interest is identified from the target brain partition image. Thus, the acquired cerebral angiography image can be partitioned at first; then region-of-interest identification is respectively carried out on the subarea images; the whole processing method can be automatically executed, manual operation is not needed, a manual method does not need to be adopted for visual evaluation, the recognition difficulty and cost of the region of interest are reduced. The problems that due to visual evaluation conducted through the manual method, consumed time is long, efficiency is low, recognition results are inconsistent, and consequently the reliability of judgment results is affected are solved.

The invention relates to a method and system for recognizing adjacent wireless access points. When signal coverage areas between the wireless access points are overlapped, the first wireless access points receive data packages sent by the second wireless access points, the signal coverage areas of the first wireless access points and the signal coverage areas of the second wireless access points are overlapped, system identifiers and wireless service information can be added to the data packages sent by the second wireless access points so as to recognize the second wireless access points, when the data packages received by the first wireless access points include the system identifiers and the wireless service information, the data packages are sent by the second wireless access points adjacent to the first wireless access points, the second wireless access points adjacent to the first wireless access points can be automatically recognized, so that the position relations between all the wireless access points are determined, and recognition cost is low.

The invention relates to a text recognition method and device, equipment and a medium. The method comprises the steps that a to-be-recognized text image is acquired; the text image is converted into a target one-dimensional tensor through a Hilbert curve, and the target one-dimensional tensor is used for representing the sequence relation between pixels in the text image; and the target one-dimensional tensor is input into a pre-trained recognition network for text recognition to obtain a target character string in the text image. The text recognition cost can be reduced, the text recognition efficiency can be improved, and the text recognition difficulty can be reduced.

The invention discloses a payment method and a payment device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a wirelesssignal in a scanning range, and reading at least one wireless network identifier from the wireless signal; Analyzing the coding information of the wireless network identifier to obtain decoding information, judging whether the wireless network identifier conforms to a coding rule or not according to the code identifier in the decoding information, and if the wireless network identifier conforms tothe coding rule, generating a new verification code by using a verification code generation mode indicated by the code identifier; And judging whether the new verification code is the same as the verification code of the wireless network identifier, and if the new verification code is the same as the verification code of the wireless network identifier, obtaining the payee identifier from the decoding information, so that the payer completes payment according to the payee identifier and the payment amount. According to the method, non-inductive identification of the payer terminal equipment is achieved, it is guaranteed that the transaction information is not forged through the verification code, and consumption settlement between the payer and the payee is safely and rapidly achieved.

The invention relates to a bronchoalveolar lavage fluid sample library building method based on a nanopore sequencing platform, an identification method and a kit. According to the method, based on the combination of the nanopore sequencing platform and incomplete host removal, host removal pretreatment and sample library building are carried out on metagenome samples of human bodies and animal bodies, and then used for sequencing identification of metagenomes. Compared with the traditional process, the method provided by the invention is simpler and faster, meets the reasonable detection requirements, omits the processes of library building PCR and the like in the identification process, eliminates the bias caused by PCR amplification, and can more truly and accurately identify the metagenome microorganisms.

The invention provides an image compression degree identification method, which comprises the following steps: acquiring a target image, and identifying a target object edge region of the target image; carrying out segmentation operation on the target image by using a preset size to obtain a target image block related to the edge area of the target object; inputting the pixel gray scale value of the target image block into a preset neural network model to calculate the definition corresponding to the target image block; and calculating the definition of the target image according to the definition corresponding to the target image block, thereby obtaining the compression degree of the target image. The invention further provides an image compression degree recognition device, the compression degree of the target image is obtained based on the target image block related to the edge area of the target object, the recognition error and the recognition cost of video definition are reduced,and the recognition efficiency of video definition recognition is improved.