268results 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 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 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 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.

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 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 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 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.

The invention discloses a traffic police gesture recognition method and apparatus, and relates to the field of computers. The method comprises the following steps: processing an image containing a traffic police gesture, and recognizing the traffic police gesture through a pre-established traffic police gesture segmentation model. By adoption of the method, real-time recognition of traffic policegestures can be performed by a traffic police without wearing a sensor device, so that the recognition cost is low, the implementation is relatively convenient, the traffic police gesture segmentation model is established in a traffic policemen gesture segmentation manner, and the traffic police gesture can be recognized by the traffic police gesture segmentation model via a real-time image, so that the recognition accuracy is relatively high.