86results about How to "Accurate discrimination" patented technology

The invention discloses a time series anomaly detection method based on reconstruction and prediction, and particularly relates to the field of time series data analysis of the Internet of Things, comprising the following steps: step 1, data preprocessing; 2, constructing a reconstruction network based on an auto-encoder architecture and a time convolution network; step 3, constructing a prediction network based on LSTM and an attention mechanism; and step 4, end-to-end model training is carried out, and anomaly detection performance is evaluated. According to the method, a reconstruction network based on an auto-encoder architecture is constructed, a time convolution network TCN is taken as a core, multi-scale features are extracted by using expansion causal convolution, and input data are reconstructed to obtain reconstruction errors. A prediction network is constructed to capture a long-term dependency relationship in time sequence data, and weights of different time steps are dynamically allocated, so that the modeling capability of a model to a complex mode is enhanced. By integrating the reconstruction error and the prediction error, an abnormal score is constructed, and a threshold value is automatically determined by using a POT algorithm, so that accurate discrimination of abnormal data points is realized.

The invention discloses a power distribution station high-temperature alarm system and a processing device, and relates to the technical field of high-temperature alarm, and the system comprises a sensor deployment and control module, a dynamic threshold discrimination module, a hierarchical linkage disposal plan library and a disposal backtracking monitoring module. The sensor deploying and controlling module is used for constructing a three-dimensional model of the power distribution station through laser scanning and photogrammetry; according to the invention, the three-dimensional model of the power distribution station is constructed by using the laser scanning and photogrammetry technology through the sensor deployment and control module, the heating part of the equipment is accurately analyzed, and the installation feasibility and accessibility of the sensor are evaluated, so that the reasonable layout of the sensor is realized, the comprehensiveness of temperature monitoring is improved, and the accurate temperature measurement of key parts is ensured; in addition, through a dynamic threshold value judgment module, a deep learning algorithm is combined with transfer learning and generative adversarial network technologies, the limitation of a fixed threshold value is broken through, temperature abnormity is judged more accurately, tiny faults are positioned more accurately, and the situations of false alarm and missing report are effectively reduced.

The invention provides a bonding wire defect detection method based on deep learning, which can be used for identifying the defect type of a bonding wire and automatically detecting the bonding quality problem of the wire in an integrated circuit product, and aims to solve the problems of high labor intensity, low detection efficiency and the like. The process comprises the following steps: establishing a bonding wire acquisition physical platform, acquiring a depth image of a bonding wire through a stereo camera, preprocessing the acquired depth image to enhance the image quality, generating a normal diagram corresponding to the depth image through geometric transformation, and stacking the normal diagram and the depth diagram to obtain a final bonding wire data set. Performing data enhancement on the bonding wire data set to expand the data scale; a Depth-YOLO network model is provided, an input layer of an original YOLO model is reconstructed, and an input feature enhancement module is designed; and finally, training a Depth-YOLO model by using the self-built data set, and carrying out end-to-end bonding wire defect detection by using the model.

The invention proposes an entity extraction method and system based on a dynamic window attention mechanism, and the method comprises the steps: data collection, data preprocessing, and automatic labeling training, and during the automatic labeling training, based on the dynamic window attention mechanism, through improving the attention extraction weight of a TMN frame, the automatic labeling training is carried out. Trigger training, entity recognition model training and entity recognition model testing are carried out. From the perspective, the problem of key information omission or redundancy possibly caused by a fixed window is avoided by introducing a dynamic window attention mechanism, so that the model is more efficient when processing long sentences and complex sentence patterns, and the text information capture capability and the feature extraction effect of the model are remarkably improved. Besides, by combining the similarity matching optimization between the trigger and the sentence, the distinguishing capability of the model in a trigger classification task is remarkably improved, so that the distinguishing of a matched sample and an unmatched sample is more accurate.

The invention discloses a warehouse material storage capability assessment method and device. The method comprises the steps of obtaining a warehouse material storage capability information set; the warehouse material storage capacity information set comprises warehouse material storage capacity information; the warehouse material storage capacity information comprises sampling values of all sampling moments of each warehouse material storage capacity; preprocessing the warehouse material storage capacity information set to obtain a preprocessed warehouse material storage capacity information set; and performing evaluation processing on the preprocessed warehouse material storage capability information set to obtain a warehouse material storage capability evaluation value. According to the invention, effective data screening and cleaning processing is carried out on the performance test data of the warehouse in various logistics scenes generated in the material storage, circulation and test application process, the accuracy of the original data is guaranteed, and an effective premise is provided for the development of capability assessment.

The invention relates to the field of factory management, and provides an intelligent management system for a smart factory, and the system comprises a smoke foreground and background distinguishing module, a smoke detection module, a smoke region heating power recognition module and an alarm management module which are carried on an MES platform. The foreground and background distinguishing module is used for carrying out smoke foreground and workshop background distinguishing on workshop video images collected by the visible light camera; the smoke detection module generates a smoke recognition frame from the smoke foreground image through a smoke detection model based on improved YOLOv5, the smoke detection model is combined with an attention mechanism, and an improved loss function combined with an improved frame difference method is adopted; the smog area thermodynamic identification module generates a smog thermodynamic diagram representing smog temperature distribution from the smog identification frame through a smog segmentation network based on UNet; and the alarm management module displays the smoke thermodynamic diagram and manages fire alarm areas and grades according to the smoke thermodynamic diagram. According to the invention, visual intelligent management of the fire smoldering stage of the smart factory is realized.

The invention discloses individualized variable parameter closed-loop ultrasonic stimulation equipment and method based on deep learning, and belongs to the technical field of transcranial ultrasonic stimulation. The individualized variable parameter closed-loop ultrasonic stimulation equipment comprises an electroencephalogram signal acquisition module, an electroencephalogram signal processing module, an electroencephalogram signal deep learning judgment module and an ultrasonic stimulation module; the electroencephalogram signal acquisition module acquires electroencephalogram signals of an experimental subject within a specified finite time and transmits the electroencephalogram signals to the electroencephalogram signal processing module; the electroencephalogram signal processing module preprocesses and displays the electroencephalogram signals in real time, splits the electroencephalogram signals according to specified time, and stores all electroencephalogram signal segments in a picture format; the electroencephalogram signal deep learning discrimination module discriminates an electroencephalogram abnormal level and appoints effective ultrasonic stimulation parameters; and the ultrasonic stimulation module performs variable parameter ultrasonic stimulation on the experimental subject. According to the method, deep learning training is carried out on large sample electroencephalogram signals, an electroencephalogram signal deep learning model is established, the electroencephalogram abnormal level of any other experimental subject is judged, and variable parameter closed-loop ultrasonic stimulation is carried out.

The invention discloses an operation method of a high-voltage transformer substation tubular bus drainage wire detaching and erecting robot, and relates to the technical field of power construction. Comprising the steps of obtaining parameter data outside a tubular busbar drainage wire; a positioning model is constructed, the positioning model is based on the parameter data, an operation plan of the disassembly and assembly robot is output, and the operation plan comprises an operation position, an operation path and operation time; constructing a detection model, and obtaining environment data influencing the operation of the dismounting and building robot outside the tubular busbar drainage wire based on the detection model and the parameter data; obtaining attribute data of the disassembly and assembly robot; and constructing an influence discrimination model. According to the method, the attribute data and the environment data are efficiently matched through the feature tree diagram to form an accurate mapping relation, so that it is ensured that the disassembly and assembly robot can accurately recognize and understand the working environment in the working process, the data processing efficiency is improved, and the adaptability of the robot to the working environment is further enhanced.

The present application relates to the technical field of sleep monitoring, and provides a method, device, equipment and storage medium for detecting K-complex waves during sleep, wherein the method comprises: inputting EEG data of N channels into a K-complex wave detection model; wherein the K-complex wave detection model comprises a convolutional neural network, a downsampling network with skip connections, a feature fusion extraction network, a normalized stream and a classifier; the EEG data of N channels are processed by the convolutional neural network, the downsampling network with skip connections and the feature fusion extraction network in sequence, and then a first latent code is output; the vector space where the first latent code is located is transformed using the normalized stream to obtain a second latent code that obeys a standard Gaussian distribution; the second latent code is input into a classifier for processing, and after processing, a judgment result of whether a K-complex wave exists in the current EEG segment is output. The present application can improve the accuracy and speed of K-complex wave detection.

The invention provides an error evaluation method, device and equipment of a current transformer and a medium, and belongs to the technical field of transformer evaluation. The error assessment method comprises the following steps: acquiring secondary current data of a current transformer to be assessed; decomposing the secondary current data into an intrinsic mode component and a residual component by using an ensemble empirical mode decomposition method; reconstructing the intrinsic mode component to obtain a high-frequency component and a low-frequency component; and inputting the secondary current data, the high-frequency component, the low-frequency component and the residual component into an error evaluation model, and evaluating the error state of the current transformer. In order to solve the problem of large fluctuation of the current data, the secondary current data of the transformer is decomposed and reconstructed, the reconstructed data is processed in a layered iteration fusion mode, and finally the evaluation result of the current transformer is obtained, so that the evaluation precision is high.

The invention discloses a rapid detection method of limestone mineral powder, and belongs to the technical field of road engineering material detection. The rapid detection method comprises the following steps: respectively acquiring a diffraction spectrum and a fluorescence spectrum of a mineral powder sample by using XRD and XRF scanning equipment and an image processing technology; determining mineral composition according to characteristic peaks in an actually measured diffraction spectrogram of the mineral powder sample, and calculating relative mass percent as an index for identifying whether the mineral powder sample is limestone mineral powder or not; the method comprises the following steps: carrying out comparative analysis on a characteristic peak in an actually measured fluorescence spectrum of a mineral powder sample and a standard fluorescence spectrum, and taking the relative mass percent of calcium oxide (CaO), magnesium oxide (MgO) and silicon dioxide (SiO2) in the mineral powder sample as an index for judging the quality of the mineral powder sample. According to the method, the property and the quality of the asphalt mixture mineral powder can be quickly and accurately judged, the operation is simple, effective supervision on the lithology of the rock processed by the limestone mineral powder is effectively realized, and a solid foundation can be laid for subsequently providing a limestone mineral powder quality grading method.

The invention discloses a method for determining tunnel rock mass stability advance support based on a laser instrument, and relates to the technical field of tunnel rock mass stability analysis. The determination method comprises the following steps: firstly, determining a stress peak position and a protolith stress position by judging a stress environment; then identifying a tunnel surrounding rock structure, identifying lithology by using a laser instrument, constructing a rock stratum three-dimensional model, and analyzing a rock stratum damage degree; carrying out pre-treatment on the surface damage degrees of the rock stratums at the two sides; carrying out normalization treatment; comparing the surface damage degrees of the rock stratums at the top plate and the bottom plate; and finally, judging a support structure, judging support effectiveness according to a stress environment and an underground surrounding rock structure, and determining a support length. The invention provides a novel method for jointly determining a supporting structure by using a stress environment and a surrounding rock structure. The novel method is different from a rigid body limit equilibrium method, a numerical method, an analytical method and a reliability analysis method in the prior art. The method can ensure that the supporting structure meets the requirements of a mechanical foundation and actual engineering.

The present invention discloses an APT detection method that integrates cloud-network-end logs and threat knowledge. The cloud-network-end fusion logs of multiple platforms are collected and processed into a four-tuple sequence in process units and time order to construct an origin graph of the cloud-network-end. Attack entities and entity relationships are extracted from network threat intelligence to form an attack causal graph, which is used in cloud-network-end APT detection. The present invention collects cloud-network-end fusion logs of multiple platforms and extracts attack causal graphs from network threat intelligence. It does not rely on manual work, thereby avoiding the formulation of a large number of rules, helping security industry personnel to more accurately and efficiently identify cloud-network-end fusion APT attacks, thereby achieving effective detection of cloud-network-end fusion APTs.

This invention discloses a graphic calibration system and method for CNC mold machining. The system includes: a data acquisition module configured to receive CNC machining tasks and acquire graphic data and measurement data of the mold to be machined based on the machining tasks; a data calibration module configured to perform geometric feature calibration on the graphic data based on preset calibration data, generate calibrated calibrated graphic data, and spatially register the measurement data and calibrated graphic data to obtain deviation data; a data processing module configured to map the deviation data to a machining parameter library in the machining task, calculate the correction values ​​of each machining parameter to obtain calibration parameters; and a data feedback module configured to feed back the calibrated graphic data and / or calibration parameters to the system, and perform the remaining graphic data input and machining parameter input for the machining task. This invention significantly improves mold machining accuracy and production efficiency, providing a high-confidence graphic input benchmark for CNC mold machining.

The present invention discloses a time difference culture device, which relates to the technical field of biological tissue culture; the time difference culture box is adapted to be equipped with a light source assembly, an observation assembly and a gas supply system; the light source assembly includes a first light focusing part, a light decaying sheet is arranged on the light incident side of the first light focusing part, and the light decaying sheet is provided with a plurality of light transmitting parts in sequence along the length direction of the light decaying sheet, and the light transmittance of the plurality of light transmitting parts increases in sequence along the length direction of the light decaying sheet; the observation assembly includes a microscope camera and a linear drive mechanism; the gas supply system includes a first pressure switch, a first reversing pressure relief valve and a first gas storage tank, the first pressure switch is used to monitor the air pressure in the time difference culture box and control the action of the first reversing pressure relief valve, and the outlet of the first gas storage tank is connected to the inlet of the first reversing pressure relief valve after passing through a pressure reducing valve. The present invention can ensure the clarity of the captured image, and the image has a three-dimensional sense and a sense of hierarchy, so as to accurately judge the morphology of biological tissues and ensure the accuracy of biological tissue evaluation, analysis and screening.

The application provides an image recognition-based personalized gait intention recognition method and system, and relates to the technical field of gait intention recognition.The application collects an infrared image pair and a distorted infrared image through a binocular infrared camera integrated with an equipment, respectively calculates and generates two depth maps, and performs weighted fusion to obtain a three-dimensional depth map of an environment under the feet of a user; multi-scale geometric analysis is carried out on the three-dimensional depth map, geometric features are extracted, and a multi-scale environment geometric feature tensor is generated; the tensor is input into a multi-task processing link, and user landing area semantic segmentation is executed in parallel, foot trajectory prediction is combined with real-time posture and historical gait of the user; according to the matching relationship between the semantic segmentation graph and the trajectory prediction sequence, the exploratory or confirmatory landing intention of the user is judged, corresponding intention instructions are generated, the personalized gait landing intention of an exoskeleton wearer can be accurately and robustly recognized, and reliable instruction basis is provided for intelligent control of the exoskeleton.

The application discloses a rock mass characteristic excavability prediction method, and belongs to the technical field of TBM and shield construction prediction. In view of the problems of low accuracy, difficulty in quantifying rock mass structure effect and inability to effectively support full-cycle decision of an existing prediction method, the method comprises the following steps: geological survey is performed on a tunneling section to obtain uniaxial compressive strength, rock mass brittleness index and volume joint number; a rock mass characteristic excavability index is calculated based on the above parameters; the tunneling section is divided into groups according to the index, and tunneling parameters are preset, and equipment selection is performed based on the thrust demand range predicted by the index; during the tunneling process, the actual thrust is compared with the predicted thrust converted based on the index to verify the accuracy, and the coupled function is corrected according to the actual data to realize dynamic optimization. The application is mainly used for equipment selection, tunneling parameter pre-planning and dynamic optimization in the construction process before tunneling, and can significantly improve the prediction accuracy and engineering guidance value.

The invention relates to the technical field of electric power system management, and provides an electric power system inertia safety domain evaluation method, device, equipment and medium, and the method comprises the steps: obtaining each generator speed regulator model parameter of an electric power system, and constructing an electric power system frequency dynamic response model based on each generator speed regulator model parameter; acquiring an active power disturbance quantity after active power disturbance occurs in the power system; on the basis of the active power disturbance quantity and a power system frequency dynamic response model, calculating critical inertia, including a frequency safety critical inertia value and a regulation economy critical inertia value; dividing an inertia operation domain of the power system into a plurality of inertia operation intervals according to the critical inertia; and the current inertia level of the power system is evaluated based on the system real-time inertia and the plurality of inertia operation intervals of the power system. According to the embodiment of the invention, objective quantification and multi-dimensional analysis of the inertia level of the system are realized by constructing a dual evaluation system fusing the frequency safety and the regulation economy.

The invention discloses a permafrost region tower footing stability discrimination method based on an IABC-RF model, and the method achieves the fitting of factors affecting the stability of a tower footing in a permafrost region through the use of an improved IABC algorithm and an RF model, and obtains the IABC-RF model. Therefore, on-site workers can effectively predict the horizontal displacement and vertical settlement of the tower footing by detecting the stress condition of the tower footing, the environment temperature and the ice content of frozen soil on site, and finally the stability of the tower footing is judged. According to the method, the search efficiency of the IABC algorithm is fully utilized to optimize the hyper-parameters, so that the fitting effect of the random forest algorithm prediction model is relatively good, prediction and judgment of the stability of the tower footing can be relatively accurately realized, a reliable basis is provided for construction and maintenance of the tower footing in the permafrost region, and the maintenance time and labor cost of the tower footing are reduced.

The invention relates to the technical field of motor control, and discloses an electrical transmission system parameter self-tuning system based on a neural network, and the system comprises a state monitoring and gating module which is used for activating the system when the high-frequency energy characteristic of a torque instruction exceeds a threshold value; the homomorphic mapping and reference reconstruction module is used for constructing a homomorphic digital filter to convert a torque instruction into a homomorphic instruction aligned with a feedback speed time sequence; the momentum residual calculation module is used for calculating the difference value between the theoretical impulse and the actual momentum increment based on the momentum theorem; the morphological decoupling and reasoning module is used for calculating morphological similarity between the residual vector and the reference vector by using the neuron structure so as to output confidence; and the parameter updating module is used for adjusting the adaptive updating gain according to the confidence coefficient and iteratively correcting the inertia parameter. According to the method, through signal homomorphic reconstruction and waveform form reasoning, the influence of loop delay and load disturbance is effectively inhibited, and the accuracy and robustness of parameter setting are improved.

The invention provides a salted pork quality evaluation method based on flavor substance analysis, and the method comprises the following steps: S1, measuring the content of characteristic flavor substances of to-be-detected salted pork, taking OAV of the characteristic flavor substances as an independent variable, taking the flavor difference between the to-be-detected salted pork and representative Zhenjiang salted pork as a dependent variable, and adopting an OPLS-DA method to establish a discrimination model, obtaining a VIP value graph and a standardized regression coefficient graph; and S2, according to the positive and negative correlation of the characteristic flavor substance indexes in the standardized regression coefficient graph in the step S1, referring to the VIP value graph, taking the obtained VIP value as the weight of each characteristic flavor substance index, and adopting a weighted TOPSIS analysis method improved based on a grey correlation coefficient to finally obtain a relative closeness value. According to the method, the internal quality of the salted pork can be comprehensively evaluated, subjective assume is effectively eliminated, the reliability and accuracy of the evaluation result are enhanced, and a basis can be provided for quality control and protection of georationally marked products.

The invention relates to the field of image processing, in particular to a smoke image generation method and device, equipment and a storage medium. The method comprises the following steps: obtaining a composite image through a generator; inputting the composite image and a preset real smoke image into more than two discriminators, and outputting a discrimination result of whether the image is the real smoke image through the discriminators; and updating parameters of the generator according to the accuracy rate of the judgment result, obtaining a composite image again according to the updated generator until the judgment result of the composite image and the real smoke image meets a preset requirement, and generating a smoke image according to the generator when the judgment result meets the preset requirement. More than two discriminators are used for discriminating, the probability of misjudgment can be effectively reduced, the learning efficiency of the generator is effectively improved, and the simulation degree of the synthesized smoke image is improved through a one-to-many and high-pressure game strategy, so that a smoke recognition model can recognize smoke more accurately.

The invention discloses a nematode survival rate identification method based on the combination of bright field and dark field images, belonging to the field of biotechnology processing technology. The technical problem to be solved by the invention is how to accurately identify whether the nematodes are dead from experimental images and accurately calculate the nematode survival rate. The adopted technical scheme is as follows: the method is to place the nematodes in a 384-well plate, add PI dye solution to the 384-well plate after a preset time interval, and respectively take bright field images and dark field images of the nematodes; the bright field images and the dark field images are respectively segmented by an image segmentation algorithm based on background brightness estimation, and then the segmented bright field images and dark field images are combined to identify the total number of nematodes in the 384-well plate, the number of dead nematodes and the number of surviving nematodes, and then calculate the nematode survival rate.

The application discloses a kind of owner actual residence big data detection method and system for smart city, it is related to data processing technical field.The method includes: collecting image to be detected;Obtain target building window area image;Significance detection is carried out, and it is judged whether it is bright window;If yes, then the target building window area image is marked;Otherwise, then obtain bright window template image and dark window template image;The similarity of target building window area image and bright window template image and dark window template image is calculated, and then it is judged whether it is bright window, if yes, then mark;Otherwise, then the corresponding target building window area image is handled using low-light image enhancement method, and then it is judged again.The application combines multiscale target detection method, multiple significance detection model and multiple region color histogram comparison method, and combines low-light image enhancement method, to realize the accurate discrimination of window area image.

The invention relates to the field of geological disaster recognition based on intelligent hidden danger judgment, in particular to a geological disaster monitoring system based on intelligent hidden danger judgment, which comprises an intelligent acquisition module, a displacement analysis module, a soil humidity analysis module, an intelligent hidden danger judgment module and a remote monitoring module, the soil humidity analysis module analyzes the hidden danger of the current geological disaster prone area according to the received soil humidity value, and the displacement analysis module analyzes the mountain displacement change of the current geological disaster prone area and sends the analysis result to the intelligent hidden danger judgment module; the intelligent hidden danger judgment module carries out feature extraction on the current geological disaster and judges the type of the current geological disaster, and the system can adapt to different geological conditions and environment changes and can be widely applied to monitoring and treatment work of various geological disaster prone areas.

The invention discloses a heavy metal element standard exceeding judgment method based on an EADC-Net model, and belongs to the field of element detection. The method comprises the following steps: S1, acquiring a soil sample, and determining an element set and sample information; s2, building a heavy metal element standard exceeding judgment model based on EADC-Net, and determining input and output of the model; s3, a heavy metal element standard exceeding judgment model based on EADC-Net is trained; and S4, judging whether the concentration of the heavy metal element exceeds the standard or not, and finishing qualitative analysis on the standard exceeding of the heavy metal element in the test sample. The method has the characteristics of being scientific, reasonable, simple in process, relatively low in cost, non-destructive, high in accuracy and the like, can solve the problems of X fluorescence energy spectrum peak value overlapping interference, low accuracy of a traditional method and the like, and can effectively, quickly and accurately judge whether the concentration of the heavy metal elements contained in the to-be-detected sample exceeds the standard or not.

The invention discloses a lesion discrimination method and system based on an internal image of a digestive tract, and relates to the technical field of medical image processing, and the method comprises the steps: collecting an endoscopic image containing intracavity pressure change, and carrying out the illumination standardization and polar coordinate system mapping of the image, so as to eliminate the illumination difference and the shooting angle influence. And through low-amplitude step inflation stimulation, obtaining a response frame of an inflation stage, and substituting the response frame into the health potential energy model to generate an anti-fact health image. Thirdly, deformation increment and non-conservation residual errors are calculated based on optimization of a joint energy function, topological difference analysis is conducted through multi-stage pressure trajectory analysis, and a focus area is judged according to structural connectivity changes. And in combination with persistence and abnormal intensity analysis, outputting a lesion judgment result. The problem that illumination change and image quality are inconsistent in a traditional method is solved, accurate judgment of the lesion area is achieved, accuracy and stability of lesion detection are improved, and the method has high clinical application value.

The invention discloses a virus inactivation evaluation method based on image recognition and data processing, and relates to the technical field of biotechnology and medical detection, and the method comprises the following steps: preprocessing a collected microscopic image, and adopting a gray conversion and contrast adjustment method to enhance the light and shade difference between virus particles and polysaccharide residues in the image, so as to obtain a virus inactivation evaluation result; obtaining first image data after primary processing; according to the virus inactivation evaluation method based on image recognition and data processing, through extraction and analysis of morphological characteristics such as boundary integrity and shape regularity of virus particles, objective judgment of a virus inactivation state is realized, so that uncertainty caused by manual subjective judgment is reduced, misjudgment and missed judgment are reduced, and the accuracy of virus inactivation evaluation is improved. The accuracy, the stability and the repeatability of a virus inactivation evaluation result are improved.

The invention provides a batch bar code data acquisition method, system and device and a readable storage medium. The method comprises the following steps: acquiring a to-be-acquired bar code set, a coding type of each bar code and a scanning time interval between adjacent bar codes; analyzing the logic continuity of each bar code; determining the bar code of which the logic continuity does not meet the preset requirement as an abnormal bar code; obtaining a first product batch code of the abnormal bar code; the prefix field of the first product batch code comprises a first production time field, a first production line number field and a first serial number field; according to the prefix field, checking the product batch to which each abnormal bar code belongs; marking barcodes which do not belong to the same product batch in the abnormal barcodes; and rejecting the marked bar codes so as to realize acquisition of batch bar code data. According to the invention, the validity of batch bar code data acquisition results can be improved, and the deviation of the bar code data on the overall level is reduced.

The invention belongs to the field of cables, and provides a high-voltage cable insulation eccentricity quality detection method and a deviation adjusting tool, and the method comprises the steps: S1, drawing an inner identification line at the outer side of a semi-conductive wrapping tape; s2, intercepting a detection sample section; s3, drawing a main outer identification line and an auxiliary outer identification line in the axis direction of the detection sample section; s4, cutting a plurality of test pieces, and insulating the thinnest point position and the thickest point position; and S5, observing the orientation distribution of the thinnest point and the thickest point of each test piece. Compared with the prior art, the method has the advantages that the inner marking line is applied before the conductor enters the three-layer co-extrusion machine head, the main / auxiliary outer marking line with the space direction definition is established on the surface of the take-up sample section, and multi-point slicing detection and space direction reduction are combined, so that the appearance quality of a certified cable is not influenced, and meanwhile, the production efficiency is improved. And reasons causing poor insulation eccentricity can be visually distinguished, so that an adaptive deviation adjusting mode can be selected according to the actual eccentricity condition, and accurate positioning of the real insulation eccentricity position and accurate judgment of causes are guaranteed.