47results about How to "Recognition stability" patented technology

The invention discloses a method for realizing signal identification of a frequency hopping unmanned aerial vehicle based on wireless spectrum analysis, which comprises the following steps: acquiring image transmission data and frequency hopping remote control data of the unmanned aerial vehicle, acquiring 50 parts of data as original data by each unmanned aerial vehicle, converting an acquired one-dimensional unmanned aerial vehicle time domain signal into a two-dimensional time frequency signal through short-time Fourier transform, the method comprises the following steps: performing signal calibration on data, constructing a model framework, extracting features of unmanned aerial vehicle signals by means of a multi-layer convolutional network of a vgg model, performing feature extraction through reasonable design of a convolution kernel and the number of network layers, and after a stable model is trained, performing feature extraction on the unmanned aerial vehicle signals. An output result of the model is converted into possible position information of each signal element of the frequency hopping unmanned aerial vehicle signal in time axis prediction on a time-frequency graph, accurate signal element positions obtained by signal matching are detected one by one to ensure independence of the signal elements, and data subjected to signal verification is subjected to data arrangement; and removing statistically redundant and repeatedly detected elements, and classifying.

The invention relates to the field of methylation sequencing, in particular to a screening method of methylation sequencing fragments. The method comprises: 1) using a healthy sample, establishing a signal baseline by constructing an n-order Markov chain model, the healthy sample containing a methylation sequencing result, and a sequencing fragment of the healthy sample originating from an rDNA region; and 2) filtering a to-be-detected sample by using the established signal baseline, the to-be-detected sample containing a methylation sequencing result, and a sequencing fragment of the to-be-detected sample being derived from an rDNA region. According to the method disclosed by the invention, background noise from normal tissues is effectively filtered, and methylation signals (especially rDNA methylation signals) related to diseases are effectively screened out.

The invention discloses a ship water gauge identification method, system and equipment based on deep learning. The method comprises the following steps: acquiring a real-time image of a ship water gauge; preprocessing the real-time image of the ship water gauge; inputting the preprocessed real-time image of the ship water gauge into a pre-trained deep learning network model, and identifying the water line and the water gauge scale of the ship water gauge by using the deep learning network model to obtain the real-time draught state of the ship; and storing the identified water line and the water gauge scale of the ship water gauge in a database. According to the application, the deep learning network model is adopted, and the ship water gauge scale and the waterline can be accurately identified in a complex environment; through an image preprocessing technology, identification of the ship water gauge can adapt to various light conditions and complex sea conditions, stable identification in severe weather is guaranteed, and environmental adaptability is improved. In addition, historical data are stored through a cloud database, real-time query and data backtracking are supported, and traceability and accuracy are improved.

According to the multi-two-dimensional-code data array generation and identification method and system provided by the invention, a plurality of two-dimensional codes are arranged in an array form, and through a corresponding generation and identification algorithm, the original advantages of the two-dimensional codes are maintained, and the information storage density and the identification speed are remarkably improved; the spatial relevance between the two-dimensional codes is fully considered in the multi-two-dimensional code data array generation process, the data storage efficiency is improved, and the error code diffusion condition in the recognition process is avoided; by introducing an error correction extreme value with high robustness and an optimization recognition algorithm, stable recognition of the multi-two-dimensional code data array in a complex environment is ensured; the limitation of an existing two-dimensional code in the aspects of information transmission and processing is effectively solved, a large amount of complex information is transmitted at a time by generating a multi-two-dimensional-code data array, the information storage capacity is greatly improved, more complex scene requirements are met, the scanning frequency is reduced, the information obtaining efficiency is improved, the high recognition accuracy is guaranteed, and the misreading risk is reduced.

The invention discloses a tip cutting and sugarcane tip prediction method for a sugarcane harvester, and the method comprises the steps: (1), adjusting the initial position of a camera after the position of the camera is obtained, and adjusting the initial position of the camera to one side without sugarcane interference; (2) obtaining sugarcane tip position data through a camera, processing the data collected in a camera picture by using a sugarcane tip identification model, and preliminarily judging whether a cutter head is too high or too low by using the obtained sugarcane tip position information; (3) performing control coarse adjustment on a tip cutting device through a control algorithm of a single-chip microcomputer according to the position information of the sugarcane tips; and (4) acquiring the position of the sugarcane tip by using a camera, acquiring accurate sugarcane tip position information from the data through a sugarcane tip identification model, judging according to the sugarcane tip identification model and a prediction result, and finely adjusting the tip cutting device through a control algorithm of a single chip microcomputer. According to the method, the impurity rate and the loss rate of the sugarcane harvester during tip cutting can be reduced, and meanwhile a large amount of manual labor is saved.

The invention relates to the field of image processing, in particular to an automobile part defect detection method and system based on image recognition, and the method comprises the steps: obtaining a part surface image, matching the part surface image with a standard contour to determine an optimal position, and carrying out the proportional adjustment of the standard part contour based on the optimal matching position, calculating burr coefficients under different proportions to form a burr coefficient sequence; taking any burr coefficient in the burr coefficient sequence as a target value, traversing the target value from large to small, setting a traversal stop condition, and finishing traversal in response to the stop condition reaching a minimum value; and obtaining the standard part contour of the adjustment proportion corresponding to the minimum value of the stop condition as a burr range, calculating a defect evaluation value according to the burr range, and determining that the automobile part has defects when the defect evaluation is greater than a defect threshold value. According to the invention, proportion adjustment is carried out on the contour of the standard part through the optimal matching position, the burr range after proportion adjustment is obtained, and the burr detection accuracy is improved.

The invention discloses an intelligent detection method and system for garment quality inspection, and relates to the technical field of computer vision and intelligent manufacturing. The method comprises the following steps: carrying out pixel-level feature analysis on two-dimensional image data, identifying a sewing line and a printing area, and mapping the sewing line and the printing area to a unified space coordinate system; reconstructing the three-dimensional surface form of the garment based on a stereoscopic vision algorithm, and calculating the dimensional deviation of key parts; converting the spectral data into a CIELAB color space, and carrying out optical distortion correction on local chromatic aberration in combination with a three-dimensional curvature; and under a unified space coordinate system, performing correlation analysis on the stitches, the dimensional deviation and the corrected space distribution, identifying a composite defect which cannot be judged by a single defect, generating a correlation defect identifier, and finally forming a quality report. The technical problems that in the prior art, local chromatic aberration is easily influenced by three-dimensional curved surface deformation and optical distortion of ready-made clothes, information such as dimensional deviation, sewing and printing defects and the local chromatic aberration is isolated, comprehensive correlation analysis is lacked, and consequently the detection accuracy is insufficient are solved.

The application provides a three-dimensional part PMI automatic labeling method based on a graph neural network, belongs to the technical field of aero-engines, and comprises the following steps: initialization configuration based on a UG NX environment; B-Rep topological analysis is performed on an imported three-dimensional part model, and the three-dimensional part model is converted into attribute graph structure data; feature learning and semantic classification are performed on the attribute graph structure data based on a graph neural network, high-dimensional feature fingerprints representing overall engineering semantics of the part and labeling graphs are extracted, and prototype clustering analysis is performed to form a knowledge base; structured recognition is performed on a newly input three-dimensional part model, similar prototype matching and rule calling are performed based on the knowledge base, a parameterized labeling action sequence is generated, the parameterized labeling action sequence is mapped into PMI creation commands, and the labeling of the new model and the establishment of related annotation objects are automatically completed. The scheme realizes the automation of the labeling process and the leap-forward improvement of the efficiency, and improves the labeling accuracy.

This invention discloses a method for identifying the connection location of underground dissimilar pipelines based on 3D image reconstruction, belonging to the field of location recognition technology. It addresses the problems of distorted spatial structure representation and insufficient accuracy in determining connection locations. By segmenting the underground pipeline, fusing ground-penetrating radar reflection data and electromagnetic detection results, abrupt gradient coefficients are calculated to mark potential material abrupt change segments. 3D scan data of the marked segments are retrieved, and combined with scan density and segment structure information, connection candidates are generated. Enhanced scanning is performed on the candidates, and the pit landing point is determined through spatial coarseness features. The existence of a connection interface is determined based on local point clouds, regional point clouds are extracted, and axis fitting is performed on the regional point clouds to obtain 3D misalignment data of the pipelines on both sides. The construction area is divided based on the soil obstacle density around the connection interface, and the connection center point is output based on misalignment constraints. This significantly improves construction accuracy and reduces the risk of blind excavation.

The application belongs to the technical field of vehicle information anonymization detection, and particularly relates to a multi-type image anonymization annotation dataset construction and target coverage rate determination method. The method is based on a face and license plate image dataset with double annotation of theoretical anonymization region and anonymization features, introduces an anonymization feature extraction branch in an improved YOLOv5-L model and performs cross-modal feature fusion, combines a plurality of loss functions with theoretical anonymization region positioning loss as the core and a two-stage progressive training and difficult example mining mechanism, and realizes precise learning of the anonymization features. After normalizing the input anonymization image, the model inference obtains the theoretical anonymization region coordinates and maps them back to the original size, and through non-maximum suppression and matching of the IoU threshold, the region coverage rate is calculated to determine the positive detection, missed detection and statistical false detection rate. The application effectively overcomes the feature dependency failure and model robustness problem, and realizes high-precision, low-misjudgment anonymization detection and evaluation under various anonymization conditions.

This invention relates to a method and system for cross-domain open set person re-identification based on unsupervised domain adaptation. The method includes: constructing a cross-domain feature adversarial alignment module including a feature extractor and a cross-domain discriminator; constructing an integrated binary classification bias module including multiple binary classification bias modules; alternately optimizing the cross-domain feature adversarial alignment module and the integrated binary classification bias module in an adversarial manner by combining multi-class cross-entropy loss, difference loss, and bias loss in the source and target domains; after the model converges, determining whether the person in the current test image belongs to a known object or an unknown object based on the re-identification result and bias score output by the model; this invention uses a joint training method to simultaneously perform cross-domain feature distribution adversarial alignment and unknown object identification, ensuring efficient re-identification of known objects while also identifying unknown objects with high accuracy.

The application discloses a kind of photovoltaic panel intelligent cleaning trolley stain identification and accurate cleaning method, belong to photovoltaic operation and maintenance technical field, including cleaning trolley initialization and moves to photovoltaic panel starting detection position, completes attitude calibration and detection system preheating, the present application is by fusing multispectral image acquisition, deep learning identification model and self-adapting cleaning control strategy, constructs a set of set stain identification, classification determination, path planning and accurate cleaning in one integrated intelligent cleaning method, realizes the high-precision identification of photovoltaic panel surface stain and differentiates accurate processing, significantly improves cleaning effect and energy utilization efficiency;Stain is divided into mild, moderate, severe three grades, which provides a scientific basis for subsequent differentiated cleaning;Overall identification process is fast, stable, supports real-time processing, which greatly improves the intelligent decision-making ability of cleaning system.

The application discloses a detection method for a QUIC protocol denial of service attack (QDA) in a backbone network. The method can realize detection of a composite denial of service attack under the QUIC protocol. The detection method comprises an offline training stage and an online detection stage. In the offline training stage, according to the attack characteristics of the composite QDA, representative one-way traffic feature data is extracted, and corresponding feature groups are constructed. These features can effectively deal with the case that there is a large amount of one-way traffic in the backbone network node. Finally, a specific machine learning method is used for training to obtain an attack detection model. The application can be used for QDA attack behavior detection and early warning in a massive traffic scene such as a backbone network, and provides a basis for maintaining the security of the network.

The invention provides a self-adaptive grid generation method and device, electronic equipment and a storage medium. The method comprises the steps that a water depth prediction field is obtained on a coarse grid through model deduction; the water depth gradient of the coarse grid is calculated, and an encryption index is weighted in combination with the distance to the dike and the prediction uncertainty of the model; performing local encryption on a to-be-encrypted parent unit determined according to the encryption index to generate a subunit; the water depth on the parent unit is transmitted to the subunits to serve as initial water depth, and local fine water depth is obtained on the subunits through model deduction; at the junction, the local fine water depth and the water depth of the coarse mesh are fused; transmitting the water depth of the fused subunits back to the parent unit according to area weighted average, and correcting the water depth of the subunits according to the water depth of the parent unit; and when the parent unit does not change any more, outputting the grid structure at the moment. According to the method, the problems that a grid encryption area is unstable and engineering reusable grids are difficult to output are solved.

The invention discloses a road marking identifying and spraying method and system based on image processing, and the method comprises the steps: collecting a lane line image through a camera, and converting the lane line image into an aerial view; establishing rigid calibration of the pose relation between the nozzle and the camera to realize coordinate registration; converting the aerial view from an RGB space to an HSV space, introducing a self-adaptive threshold adjustment mechanism on this basis, and obtaining a lane marking area image from the lane line image according to self-adaptive segmentation; a lane image is converted from a pixel coordinate space to a parameter space, and automatic detection of lane edge lines and marked lines is realized through Hough transform; in combination with the detected lane marking area, the detection area is classified through a gray threshold value or a brightness feature, and the offset of a marking center line is calculated; the transverse position of the spraying mechanism is adjusted in real time according to the offset of the center line of the marked line, dynamic opening and closing control over the spraying head is achieved in combination with the vehicle running speed and the recognition signal state, and it can be guaranteed that the spraying track and the recognition center line are kept consistent.

This application discloses an obstacle recognition method and an automatic cleaning device. After acquiring multiple initial point cloud data frames from a ranging sensor, this application filters each initial point cloud data frame to obtain a target point cloud data frame corresponding to the obstacle. Then, based on the point cloud features of the point cloud dataset within a preset distance range in each target point cloud data frame, the obstacle category of each point cloud dataset is determined. Based on the obstacle categories of all point cloud datasets, the target obstacle category corresponding to the area where each point cloud dataset is located is determined. Finally, the automatic cleaning device is controlled to avoid obstacles based on the target obstacle category. The automatic cleaning device of this application can utilize the laser emitted by the ranging sensor to acquire environmental information over a large area to identify obstacles, eliminating the need for multiple retraction actions to scan complete obstacle information, thus improving the efficiency of obstacle recognition and overall work efficiency.

The present application provides a kind of termite indicator identification method and system based on optical and infrared image data, it is related to image recognition technical field, the present application obtains the gray optical image of current dam, and the spectral analysis of each pixel in it is carried out, to obtain the optimal direction and optimal micro-period texture response of the pixel, filter out the pixel of optimal micro-period texture response qualified as candidate pixel, based on the path expansion rule of optimal direction setting, to form multiple indicator direction chain with candidate pixel;Setting extension pixel judgment rule, based on the corresponding indicator chain of setting extension pixel judgment rule connection, based on the abutment relationship between indicator direction chain, indicator direction chain is merged, and based on the size of merged area, the optical image of dam is divided into indicator region, indicator pending area and other regions, obtain infrared image data, and analysis is carried out, to further identify indicator pending area.

The invention discloses a same-supplier power grid equipment operation reliability prediction method and system based on deep learning, and relates to the field of power grid technology and artificial intelligence. The prediction method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data covering full life cycles of equipment design, production, factory test, online operation and the like so as to construct a standardized equipment feature matrix; performing multi-dimensional feature screening and dimension reduction on the feature matrix to extract a core feature subset with the highest prediction value; a deep learning reliability prediction model is constructed and trained, and the model takes the core feature subset as input and objective performance indexes reflecting the historical batch operation stability of the equipment as labels for supervised learning so as to autonomously learn a nonlinear mapping relation between the core features and the operation reliability of the equipment; and finally, according to the trained model, carrying out reliability prediction on new to-be-predicted equipment, and outputting a reliability prediction index of long-term operation of the new to-be-predicted equipment.

The application relates to the technical field of body sense interaction and virtual character control, and discloses a surfboard body sense input and character linkage system, which comprises a board body, a tension detection unit, a posture detection unit and a central processing unit. The system collects cable tension through the tension detection unit, collects an original pitch angle and a roll angle through the posture detection unit, and a decoupling module is built in the central processing unit. A mechanical coupling pitch angle is calculated according to the cable tension and a preset mechanical coupling coefficient, the original pitch angle is subtracted by the coupling pitch angle to obtain a net pitch angle, so that the physical torque interference is eliminated and a speed instruction is generated. Meanwhile, the system dynamically adjusts a roll dead zone threshold according to the tension intensity by using a dead zone module, and identifies a drift state based on a motion time window and a multi-dimensional parameter threshold through a judgment module. The application effectively solves the interference problem of the cable tension on posture recognition, and realizes accurate judgment and stable control of complex combined actions.

The invention discloses a ceramic green surface bubble identification method based on machine vision, particularly relates to the field of image processing and surface defect detection, and is used for solving the problem of false detection caused by artifacts generated by bubble brightness overturning and mirror surface reflection of a green glaze surface under different illumination. A first illumination image and a second illumination image are collected, an alignment image group is established, local illumination normalization and peripheral texture compensation are executed to form a highlight texture removal image group, a symmetric residual image and an abnormal response are calculated to obtain a candidate region set, and the candidate region set establishes corresponding association in the alignment image group to obtain a highlight texture removal image group. Extracting a main reflection direction and calculating a slip normalization quantity of the main reflection direction and a slip normalization quantity of a vertical reflection direction, outputting bubble credibility through logistic regression, dividing a bubble region and a reflection artifact region, generating a bubble mask to update a reference texture, updating a highlight compensation model through the reflection artifact region, and performing consistency verification on a compensation result. And outputting a bubble recognition result.

The embodiment of the invention provides a time sequence signal fault prediction method and device, equipment and a storage medium, and relates to the technical field of anomaly prediction. The method comprises the following steps: generating a simulation vibration signal based on the vibration response of a gear, and inputting the simulation vibration signal into a gear fault prediction model for signal prediction to obtain a predicted vibration signal; physical information loss is calculated based on predicted displacement, predicted speed and predicted acceleration in the predicted vibration signals, predicted error loss is calculated based on the simulated vibration signals and the predicted vibration signals, a total loss value is calculated according to the predicted error loss and the physical information loss, parameter adjustment is carried out, and a trained gear fault prediction model is obtained. And inputting a to-be-predicted signal into the trained gear fault prediction model for signal prediction to obtain a reference vibration signal, and obtaining a time sequence abnormal result based on the reference vibration signal and the to-be-predicted signal. The prediction process fits the actual characteristics of the signal and the physical law of gear vibration, and the recognition accuracy and stability are improved.

The present invention relates to a method for detecting wheel slip of a vehicle, wherein at least one rotational speed sensor is assigned to the wheel, and wherein the method is executed with the aid of a control unit of the vehicle, the method comprising the following steps: 1) detecting the rotational speed of the wheel by means of at least one rotational speed sensor; 2a) generating a first signal for the rotational acceleration of the wheel by means of the control unit, wherein the rotational acceleration of the wheel is determined based on the detected rotational speed; 2b) generating a second signal for the rotational jerk of the wheel by means of the control unit, wherein the rotational jerk of the wheel is determined based on the determined rotational acceleration; 3a) monitoring whether the first signal exceeds a first threshold value; 3b) monitoring whether the second signal exceeds a second threshold value; 4) generating a positive detection signal identifying wheel slip by means of the control unit if the first threshold value is exceeded by the first signal and the second threshold value is exceeded by the second signal.

The invention provides a first arrival identification method based on spatial attribute change. The first arrival identification method based on spatial attribute change comprises the following steps: step 1, inputting a seismic trace set of which first arrival needs to be picked up; step 2, inter-track time difference elimination based on a small smooth reference surface is carried out; step 3, reconstructing the gather by using the spatial correlation of the gather by using a PCA method, extracting a wavelet template on the PCA reconstructed gather, and calculating the spatial attribute of each point of the gather by using a correlation coefficient method; and 4, identifying the first-motion wave according to the spatial attribute change. According to the first arrival recognition method based on the spatial attribute change, the problem that first arrival wave recognition is inaccurate under the complex earth surface condition is solved, statistical correlation in the first arrival signal space is fully utilized, element-mode seismic wavelets of seismic exploration are combined, and stable first arrival wave recognition under the complex earth surface condition is achieved.

This application provides a method and apparatus for identifying HPV integration into newly formed chromatin loops, relating to the field of bioinformatics. The method includes: acquiring interaction sequencing read data and determining HPV reference sequences and host reference sequences; identifying HPV integration breakpoints based on chimeric read characteristics, split read characteristics, and abnormal pairing end characteristics; constructing fusion contigs based on breakpoint directions to obtain a breakpoint-aware extended reference set; comparing the interaction sequencing read data with the breakpoint-aware extended reference set, and obtaining high-confidence cross-genomic anchor pairs based on breakpoint-aware alignment constraints; clustering the anchor pairs and screening target HPV-loops; calculating the newborn score of the target HPV-loop and outputting the chromatin loop identification result. This application solves the problem of low accuracy and identification bias that often occurs in traditional interaction localization systems during the identification of newly formed chromatin loops.

The invention discloses a tactile stick obstacle intelligent identification method based on real-time image analysis, and relates to the technical field of barrier-free travel assistance, and the method comprises the following steps: obtaining front road surface image data collected by an image collection device arranged at the front end of a tactile stick in real time, carrying out the distortion removal, brightness normalization and scale standardization processing of the image data, and obtaining the image data; and associating the image data with corresponding image acquisition time information to generate normalized pavement image data. According to the method, the image data of the front road surface is normalized and multi-scale road surface feature analysis is introduced, so that pixel-level accurate identification and type judgment of the blind sidewalk area are realized, and the blind sidewalk can still be stably identified under the conditions of complex illumination, material change and environmental interference; the accuracy and robustness of blind sidewalk detection are remarkably improved, the blind sidewalk continuity score and the blind sidewalk prediction area are constructed based on the blind sidewalk center line data and the advancing direction vector data, and time dimension modeling of the blind sidewalk continuous state is achieved.

The application discloses a formaldehyde concentration detection method based on image recognition and relates to the technical field of formaldehyde concentration detection.The method comprises the following steps: S1, standard solution color development: taking nine colorimetric tubes with consistent specifications, preparing different concentration standard formaldehyde solutions in each colorimetric tube, and adding a color developing solution to develop color. The formaldehyde concentration detection method based on image recognition does not need to rely on large laboratory equipment such as a spectrophotometer, but can complete detection only through standardized image acquisition and an AI model, supports on-site instant operation, greatly improves detection portability and scene adaptability, meets the requirements of rapid detection in multiple scenes such as indoor and in-vehicle scenes, and avoids interference factors such as manual colorimetry, operation methods and environmental illumination through a standardized color development, image acquisition and AI correction process, and the model is optimized with mean square error and mean absolute error as core indexes.

A method, device and system for detecting home intrusion based on WiFi signals, wherein a wireless AP device is set within 6m of a wireless router, and a circle formed by the line connecting the wireless AP device and the router device as the diameter is located in the area where the intruder is predicted to pass; the wireless AP device is connected to the wireless network of the wireless router, the wireless AP device continuously sends ICMP data packets to the wireless router device at equal time intervals, and the wireless router returns the ICMP data packets to the wireless AP device, the wireless AP device parses the returned ICMP packet, obtains the time series data composed of the channel information data of RSSI and CSI, processes the time series data, calculates the stability of the signal-to-noise ratio in the time series data, and determines whether an intrusion has occurred. The invention has a simple structure, a cheap device, no privacy issues, and can accurately identify the occurrence of intrusion.

The invention discloses a fabric surface defect feature detection method based on YOLOv5-Fibre. The method comprises the following steps: step 1, selecting warp knitting fabric pictures with defects from all collected pictures; 2, defective pictures are selected from the warp knitting fabric images obtained in the step 1 and form a warp knitting fabric defect data set with good cloth pictures of different substrates, then a labeling tool is used for labeling, the warp knitting fabric defect data set is expanded, and finally a training set and a test set are obtained; step 3, a YOLOv5-Fibre network model is established; 4, training the data set by adopting the YOLOv5-Fibre network model established in the step 3; 5, inputting a to-be-detected picture, detecting the to-be-detected picture by using the YOLOv5-Fibre network model trained in the step 4, and outputting a detection result; and 6, evaluating the model by using the test set obtained in the step 2. According to the invention, rapid and accurate fabric defect detection is realized.

The application provides a harmful algal bloom area identification method and device based on remote sensing data, and belongs to the field of marine remote sensing and environmental monitoring. The method comprises the following steps: determining a plurality of sensitive bands of harmful algal blooms; acquiring remote sensing data of a to-be-identified area in each sensitive band; determining an identification interference factor of the to-be-identified area; combining each sensitive band according to the relationship between the identification interference factor and each sensitive band; calculating an identification interference factor index corresponding to each combination; correcting the remote sensing data corresponding to the first sensitive band combination and the second sensitive band combination according to the identification interference factor index; calculating a harmful algal bloom index according to the remote sensing data corresponding to the third sensitive band combination after correction; and judging whether the to-be-identified area is a harmful algal bloom area according to the harmful algal bloom index and a dynamic threshold corresponding to the to-be-identified area. The harmful algal bloom area identification method and device based on remote sensing data provided by the application can realize high-precision and high-stability identification of harmful algal bloom areas.

The invention discloses an electric energy meter live and null line positive and negative connection detection system and method based on a PCB capacitor, and the system employs a coupling capacitor formed by reference ground laying copper and the ground to construct a signal backflow path, and outputs high and low level signals in combination with the conduction state change of a triode of a signal processing circuit. Signal isolation transmission is realized through an optocoupler device of the isolation circuit, and finally the MCU judges the connection state of the live line and the null line, thereby effectively detecting the reverse connection state of the live line and the null line of the electric energy meter, avoiding the fire hazard of electric appliances caused by manual missing detection, improving the safety performance of the circuit, and greatly facilitating the application and maintenance of the intelligent electric meter.