1602 results about "Data error" patented technology

A codeword read from memory includes data blocks including data and supplemental blocks including error correction code (ECC) symbols for detecting and correcting data errors. Metadata can be stored in the supplemental blocks to increase memory utilization but using bits of the supplemental blocks for metadata leaves too few bits remaining for the ECC symbols. To maintain error protection, the supplemental blocks include ECC symbols to protect a first data portion of the codeword and parity bits configured to protect a second data portion of the codeword. Errors in the first data portion can be located and corrected using the ECC symbols. Errors in the second data portion can be detected by the parity. For example, the first data portion is encoded based on the second data portion, so locations of parity errors correspond to locations of symbol errors, and parity errors can be corrected.

The invention discloses a multi-level energy management system based on multi-dimensional data, and relates to the technical field of energy intelligent management, and the system comprises a multi-source data collection module which collects power utilization, environment and equipment state data in real time; the data fusion processing module is used for processing abnormal values through an algorithm and fusing multi-scale data features; the energy state evaluation module is used for realizing equipment state evaluation and early warning by using a fusion algorithm and a prediction model; the multi-level energy scheduling module adopts an optimization algorithm to balance the energy cost, the production efficiency and the carbon emission, and dynamically adjusts the strategy; the energy performance analysis module is used for developing an analysis tool and an evaluation model; and the decision support module is used for configuring an expert knowledge base and developing a fault diagnosis system and a knowledge graph. Through multi-dimensional data acquisition and multi-level management, the energy data error is greatly reduced, the comprehensive energy cost and carbon emission are remarkably reduced, the cost of participating in enterprise operation is reduced, and the accuracy, safety and sustainability of energy management are improved.

Disclosed in the present invention is a power distribution network point cloud model construction system based on three-dimensional visualization, the system comprising: a data collection module, which collects model data of a power distribution network by means of receiving a signal reflected by a LiDAR scanner; a data denoising module, which performs denoising processing on the collected model data, so as to eliminate a data error caused by noise; a point cloud reconstruction module, which simplifies denoised point cloud data, screening important features in the power distribution network, and constructing a point cloud model using the screened data; a visualization module, which uses a visualization technique to present the constructed model, so as to realize an interactive visualization operation; and an operation state evaluation module, which evaluates the safety performance of the power distribution network on the basis of fault data and a historical operation record of the power distribution network. In this way, the operation condition of the power distribution network can be visually known by means of a three-dimensional visualization interface, and thus the operation and maintenance efficiency is improved, and accurate data support is provided for the management and operation and maintenance of the power distribution network.

The invention provides a sensor error calibration method and system for a smart watch, and is applied to the field of data processing. The intelligent watch is used for monitoring the sensor in real time, collecting trend sensing data and detecting whether the data shows the deviation trend or not based on the preset standard, so that the accuracy of sensor anomaly detection is improved, when data deviation is detected, abnormal values are recognized through standard deviation calculation and classified, and the accuracy of sensor anomaly detection is improved. Comprising short-term sudden anomalies, long-term drift and outliers, so as to accurately analyze error sources, then judge whether abnormal values can be repaired or not, if yes, adopt linear, nonlinear or periodic drift compensation according to abnormal types, and dynamically adjust sensing error coefficients in combination with environmental factors to realize self-adaptive calibration, so that sensor drift can be effectively detected, and the detection accuracy is improved. Data error accumulation caused by long-term use is prevented, it is ensured that the smart watch can maintain high-precision measurement in various environments, and user experience and equipment reliability are improved.

The invention relates to the technical field of corrosion detection, in particular to a grounding grid corrosion intelligent monitoring system which comprises an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability evaluation module and a monitoring strategy optimization module. According to the method, data errors and abnormal values are filtered and corrected, the overall quality of data is improved, the accuracy of analysis results is ensured, and in corrosion behavior analysis, the future trend of corrosion is effectively predicted through dynamic change detection of electrochemical properties and calculation of the corrosion rate; the time sequence analysis of the electrochemical impedance data not only enhances the prediction accuracy, but also improves the understanding depth of corrosion behaviors, the corrosion probability evaluation module adds consideration of random changes to the whole prediction model through the application of the Markov chain, so that the model is closer to the uncertainty of the real world, and through the optimization of the processing flow, the prediction accuracy is improved. The real-time performance and the early warning capability of the monitoring system are obviously improved, and the reliability and the operation accuracy of the system are enhanced.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention relates to the technical field of ruin three-dimensional construction, in particular to a ruin three-dimensional model construction method fusing SLAM point cloud and image features, which comprises the following steps: acquiring a depth image and RGB data, aligning and eliminating inconsistent data according to a timestamp, calculating a frame rate to node ratio, establishing a binding relationship between an image and a point cloud, and extracting three-dimensional coordinate points. Recognizing and classifying geometric structures, evaluating texture and edge response, adjusting resolution, analyzing static and dynamic structures, eliminating dynamic interference, calculating geometric curvature and fitting error, optimizing three-dimensional model display, and obtaining detail reduction degree scores. According to the method, through accurate synchronization of the point cloud data and the RGB image, strict verification of timestamp matching and data consistency is allowed, data errors and noise are significantly reduced, the accuracy and reliability of the model are ensured, structural elements are identified, dense areas of geometric structures are refined and analyzed, richer data level support is provided, and the method is suitable for large-scale popularization and application. And higher-quality data is provided for archaeological research.

The invention relates to a water plant intelligent chemical adding amount prediction method based on data preprocessing, and belongs to the technical field of deep learning and intelligent chemical adding. Calculating a theoretical dosage based on historical flow, pH, water temperature and turbidity; dividing a plurality of clusters and splicing to query historical dosage data; weighting and fusing the theoretical dosing amount and the inquired historical dosing amount as a pre-treatment dosing amount; learning the relationship among the flow, the pH, the water temperature, the turbidity and the pretreatment dosage to perform forward feedback optimization; building an alumen ustum image recognition model, classifying alumen ustum, and associating the alumen ustum with corresponding dosage to form a dosage feedback algorithm model; building a dosage feedback model based on the sedimentation tank outlet water quality monitoring data; and correcting the weight in the preprocessed dosage based on the adjusted data of alumen ustum identification and water quality feedback on the dosage. The method can effectively reduce the influence of the dosage data error on the effectiveness of the model, can reduce the complexity of the algorithm model, and improves the robustness of the model.

The invention discloses a dynamic three-dimensional ultrasonic spine detection method and imaging system, and belongs to the field of medicine.The method adopts a mature three-dimensional spine ultrasonic imaging system to be matched with a wearable wireless ultrasonic probe to collect real-time ultrasonic images, a depth camera records space coordinate changes of spine feature points in real time when the spine of a patient is bent, and the dynamic three-dimensional spine ultrasonic imaging system is obtained. A three-dimensional ultrasonic image, a real-time ultrasonic image and a dynamic three-dimensional image of a back form are fused to obtain a dynamic three-dimensional ultrasonic spine image, and a deep learning algorithm is combined according to the dynamic three-dimensional ultrasonic spine image to carry out Lenke typing parameter measurement and calculation and carry out dynamic comprehensive evaluation on the spine. Scoliosis typing is improved from a traditional X-ray plane evaluation mode based on radiation hazards to non-radiation dynamic three-dimensional ultrasonic analysis, and the problems that in the prior art, the error rate of data collected under the static condition is high, and collection parameters are insufficient are solved.

The invention discloses a multi-node collaborative logistics data real-time synchronization method and system, and particularly relates to the technical field of logistics data synchronization management. Round-trip delay between logistics nodes and a cloud platform is measured, delay jitter data is collected and analyzed, network transmission environment stability of the nodes is evaluated, when a network is unstable, initial time difference is recorded, the change trend of a clock drift value is evaluated, clock synchronization accuracy is analyzed, and the network stability and the clock synchronization accuracy are integrated. The method determines the severity of the time synchronization deviation between the logistics nodes, divides the time synchronization deviation into a slight deviation, a moderate deviation and a serious deviation, and generates a graded early warning signal to take a targeted treatment measure, thereby effectively solving the problems of data error sequence and scheduling error caused by the time synchronization deviation in the prior art, and improving the reliability of the system. The time synchronization accuracy, the network transmission stability and the overall operation efficiency of the logistics system are improved, and the accuracy and the reliability of logistics scheduling and event processing are remarkably improved.

Provided herein are systems and methods for configuring integrity constraints (including a check constraint) and row violation logging using error tables. An example method includes decoding a query received at a network-based database system. The query includes a command to perform an operation on a base table. An integrity constraint associated with the base table is retrieved. The integrity constraint specifies a desired configuration for the base table. A verification of the integrity constraint is performed to detect erroneous data of the base table that violates the desired configuration. The erroneous data is input into an error table that is configured as a nested object of the base table. A notification that the erroneous data is available in the error table is generated and output.

Provided herein are systems and methods for configuring integrity constraints (including a check constraint) and row violation logging using error tables. An example method includes decoding a query received at a network-based database system. The query includes a command to perform an operation on a base table. An integrity constraint associated with the base table is retrieved. The integrity constraint specifies a desired configuration for the base table. A verification of the integrity constraint is performed to detect erroneous data of the base table that violates the desired configuration. The erroneous data is input into an error table that is configured as a nested object of the base table. A notification that the erroneous data is available in the error table is generated and output.

The invention discloses a reservoir multi-target optimization scheduling decision-making method based on digital twinning and AI simulation, and relates to the technical field of reservoir scheduling, the method effectively solves the problems of data fragmentation and information redundancy in traditional scheduling by constructing a multi-source data fused reservoir digital twinning body, realizes accurate virtual-real mapping of reservoir working conditions, and improves the scheduling efficiency. A high-reliability simulation basis is provided for subsequent optimization scheduling, and decision errors caused by data errors or model deviation are avoided; four core targets of flood control, power generation, ecology and water supply are considered, an engineering safety bottom line and rigidity requirements are defined through a layered constraint system, constraint satisfaction degree screening is enhanced by means of an improved multi-target optimization algorithm, and target weights can be dynamically adjusted according to a real-time scene. And after the non-dominated scheme set is optimized and output, candidate schemes are screened in combination with real-time demand quantitative scores.

The invention relates to the technical field of digital information transmission, in particular to a secondary water supply pump room equipment safety monitoring system based on the Internet of Things, which comprises a data acquisition module for acquiring energy consumption data of pump room equipment, including current value, voltage value and operation time value, acquiring environmental parameters, including temperature value, humidity value and pressure value, and acquiring the energy consumption data of the pump room equipment, including current value, voltage value and operation time value; the method comprises the following steps: collecting user demand parameters including a water consumption value and a water consumption time value; according to the method, the optimal communication channel is selected, so that accurate transmission of the monitoring data is ensured, and data errors and transmission delay caused by poor channel quality are reduced. In addition, in combination with energy consumption real-time monitoring data and external factors such as power grid electricity price and weather forecast, the power demand and electricity price change can be dynamically predicted. The prediction enables the operation strategy of the pump room to be adjusted according to the prediction result, optimizes the energy use, reduces the meaningless energy consumption, and reduces the cost.

The invention relates to the field of numerical weather forecast and deep learning, and discloses a numerical forecast wind field data error correction method and system based on a neural network, a medium and equipment. A data set obtained after data preprocessing is divided into a training set, a verification set and a test set, and space-time matching and normalization are conducted on the data set; establishing a many-to-many variable mapping relationship in a horizontal space by using the training set, expanding a time dimension on the basis of the established multiple mapping relationship, and establishing a 3D U-Net-based deep learning model; performing training and parameter tuning on a 3D U-Net-based deep learning model by using the training set and the verification set, and constructing a deep learning correction model of multi-element multi-time forecast; and correcting the test set by using the trained deep learning correction model. According to the method, the problem of multi-forecast aging and multivariable collaborative correction can be solved.

Disclosed in the present application is a chip hybrid simulation method based on general verification methodology and an image processing algorithm. The method comprises: upon receiving a chip simulation task, starting a verification platform; inputting excitation data into a design module under test, and performing simulation processing on a target chip, so as to obtain simulation output data; transmitting to an image processing model the simulation output data and the excitation data to be processed, and converting the formats thereof, so as to obtain a simulation data code stream and an excitation data code stream; processing the excitation data code stream, so as to obtain an image algorithm output data code stream; and comparing the simulation data code stream with the image algorithm output data code stream, so as to obtain a verification result. The present application solves the technical problem that chip simulation verification modes in the prior art isolate the interconnection and connectivity between image algorithm models and UVM simulation verification platforms, are thus prone to cause data errors and algorithm processing flow errors, and therefore reduce the simulation efficiency and accuracy of the UVM platforms.

The invention discloses a method and a system for quickly generating a battery test scheme, particularly relates to the field of battery test optimization, is used for solving the problem of test process stiffness, and is characterized in that key is disassembled and normalized through hierarchical feature extraction and nonlinear mapping, and is converted into dynamic weight and embedded with priority marks; therefore, a standardized demand vector reflecting the real-time battery state is constructed; the candidate modules are coded by adopting binary feature codes, the modules meeting core test requirements are accurately screened through cosine similarity matching, and comprehensive coverage of key indexes is realized; a multi-objective optimization algorithm is utilized, conflict modules are automatically eliminated under the dual restrictions of test coverage rate gain and resource occupation penalty coefficient, the test precision and hardware resource allocation are balanced, and data errors and resource preemption risks are effectively reduced; and an independent thread task scheduling and temperature rise rate real-time monitoring mechanism is used for rapidly switching a standby module to complete data supplementary collection when abnormal temperature rise is detected, so that continuous and reliable test data is ensured.

Provided in the present application are a data re-read method, an electronic device and a storage medium. The method comprises: recording information from multiple previous re-reads; on the basis of a plurality of re-read reference voltages and a plurality of pieces of column height information from multiple re-reads conducted at each read level, estimating state wave information of each read level; and on the basis of the estimated state wave information, determining a next re-read reference voltage of each read level, so as to carry out the next re-read. On the basis of the information from previous re-reads, quantity information of difference data between two consecutive re-reads is obtained, such that, on the basis of the quantity information, a read offset direction for reducing the data re-read error rate can be determined; thus, on the basis of the read offset direction and the re-read reference voltages from previous re-reads, the next re-read reference voltage can be determined, so as to carry out the next re-read, enabling the error rate of the next re-read to be reduced; that is, the success rate of data read is improved, and the time consumed by data read is thereby reduced.

The invention discloses a multi-dimensional fusion medical resource intelligent monitoring and early warning method. According to the method and the device, the integrity and the accuracy of the medical resource data are improved through a multi-dimensional data fusion and dynamic calibration mechanism. Multi-source data of a medical institution system, a regional health platform, wearable equipment and the like are integrated, comprehensive data coverage and timely updating are ensured, and resource monitoring blind areas caused by information islands are reduced. Through the processing flows of noise filtering, missing value filling, abnormal value identification and the like, the influence of data errors on resource evaluation is reduced, quantitative analysis of key indexes such as medical resource supply and patient demands is closer to the actual situation, and the timeliness of medical resource risk early warning and the effectiveness of decision support are enhanced. The hierarchical early warning model constructed based on the multi-modal feature matrix can identify potential risks from multiple dimensions of resource exhaustion, service saturation, regional propagation and the like, and ensures reasonable distribution and efficient utilization of medical resources in different scenes.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

The invention discloses a method for switching between a first communication mode and a second communication mode, and the method comprises the steps: determining a dual-mode phase difference between a first mode baseband signal and a second mode parameter based on the first mode baseband signal and the second mode parameter; generating an interleaved phase coding sequence in response to the dual-mode phase difference; pre-modulating the first mode signal in parallel before switching by using the coding sequence, and generating a pre-activated second mode virtual signal; switching from the first mode to the second mode is executed based on a parallel fusion phase track generated after the phases of the two preprocessed signals are subjected to time-varying weighted fusion. According to the method, a smooth switching track is constructed through parallel phase preview and phase domain fusion, phase continuity and frequency stability in the switching process are ensured, switching time delay and the data error rate are remarkably reduced, and the communication service quality of the heterogeneous network is improved.

The invention relates to the technical field of geographic information processing, in particular to a map updating system based on cloud computing, which comprises a global offset correction module, a regional error division module, a local feature correction module, a residual error optimization fitting module and a computing resource scheduling module. According to the method, through offset calculation and hierarchical division of real-time geographic data, offset characteristics of multiple geographic areas are accurately analyzed, dynamic distribution optimization of data errors in a global range is achieved, hierarchical error division is combined with geographic boundary lines and multi-level data characteristics, difference values are accurately extracted, and area division is adjusted; the local feature correction optimizes error matching under a complex terrain by integrating a slope, a land type and a terrain parameter relationship, enhances terrain adaptability and detail precision, ensures high efficiency of large-scale data processing through nonlinear relationship fitting of residual values and optimization of a resource allocation proportion, and improves the accuracy of data processing. And meanwhile, the map updating response speed is increased.

The invention provides an enterprise tax declaration data automatic verification and intelligent error correction method and system, and relates to the technical field of tax information processing, and the method comprises the steps: receiving original voucher data, constructing a voucher association diagram, recognizing an abnormal transaction chain, and generating financial accounting data; comparing the financial accounting data with the tax payment rules, identifying violation data items and distributing risk weights; and constructing a data balance constraint matrix based on the risk weight, forming a data correction cluster, and executing grouping iteration correction. According to the invention, declaration data errors can be automatically identified, intelligent correction is provided, declaration accuracy is improved, and manual intervention is reduced.

The invention discloses an enterprise carbon emission supervision and early warning system based on data collection, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting the real-time state data of an enterprise carbon emission monitoring node, constructing a carbon emission time sequence, and executing the abnormality elimination to generate effective data; the carbon emission fitting calculation module is used for evaluating a multi-source data error index, distributing a weight and outputting a fitting value sequence; the emission offset detection module is used for recognizing a nonlinear severe offset event based on the fitting track; the trend prediction module is used for extracting trend components by adopting an improved Kolmogorov-Arnold network and generating a sudden change early warning mark; and the carbon emission risk early warning module is used for integrating the offset event and the trend mutation, generating a structured risk report and pushing the structured risk report to a supervision system. According to the invention, the carbon emission abnormity identification precision and the early warning response efficiency are improved, and intelligent perception and linkage regulation and control of enterprise carbon emission risks are realized.

The invention discloses a cross-device memory dynamic complementary utilization method based on CXL protocol interconnection and a computer system. The method comprises the following steps that an ECC module of a memory detects data in the data transmission process; when a data error is detected, the error is positioned by the ECC module, the error counting module corresponding to the position is updated, and an error report is generated; when the counting value of the error counting module reaches a set threshold value, the area corresponding to the position is marked as a failure area; evaluating the states of all memories based on the spatial dimension, the health dimension, the time delay dimension and the load dimension, and selecting a target memory according to an evaluation result of the states of the memories; distributing a new logic address for the failure area based on the error report and a selected target memory; the logic address is transmitted to a target memory through a CXL protocol, and an address mapping table is updated at the same time; and after the mapping is successful, migrating the data of the failure area to the healthy area of the target memory by the system through a CXL protocol.

The invention discloses a data error correction method and device based on low-density parity check, equipment and a medium, and relates to the technical field of data error correction, and the method comprises the steps: reading data to be corrected from a data memory, and carrying out the pre-verification of the data to be corrected and a verification matrix of a preset low-density parity check code, and obtaining a pre-verification result; based on the pre-verification result, determining a first error position set in a code word position set; performing bit flipping operation on code words indicated by the first error position set to complete pre-error correction; and inputting the code word after pre-error correction processing into a low density parity check decoder for decoding, outputting data after error correction, pre-detecting the error position of the code word, and performing bit flipping on the code word at the error position, thereby reducing the number of error bits entering LDPC decoding, and improving the decoding efficiency. Therefore, the number of error bits does not exceed the decoding capability limit of hard decoding, the probability that the system enters soft decoding is reduced, and the decoding throughput rate of the system is ensured.

The invention relates to the technical field of solid state disks, and provides a solid state disk data storage security test method and device, and the method comprises the steps: generating original test data of a solid state disk in a preset test environment; performing a simulation attack on the solid state disk according to a preset strength grade and preset duration, and obtaining a data block after the simulation attack of the original test data after the simulation attack is finished; recovering the data block after the simulation attack based on the verification data, and obtaining a primary data recovery rate, a secondary data recovery rate and a data error rate of the data block after the simulation attack in the recovery process; calculating a data storage security score of the solid state disk according to the primary data recovery rate, the secondary data recovery rate and the data error rate; and determining a data storage security level of the solid state disk according to the data storage security score. According to the method, the data storage security of the solid state disk under various complex environments and security threats can be comprehensively, systematically and deeply evaluated.

The invention provides a data error compensation method for a biological net port flowmeter, and belongs to the technical field of ocean detection. A multi-dimensional sensor is arranged at a biological net port to collect fluid parameters, a deep learning model is used for recognizing multi-phase flow characteristics, and a corresponding compensation strategy is selected; based on a correlation analysis algorithm, compensation processing frequency is dynamically adjusted to achieve self-adaptive balance of precision and efficiency, the flow state change degree is monitored through the Euclidean distance, compensation parameters are hierarchically adjusted to ensure system stability, a parallel processing architecture is adopted to execute coarse compensation and fine compensation algorithms at the same time, and results are fused according to weights. Finally, dynamic and accurate compensation of the measurement error of the biological net port flowmeter in the complex multiphase flow environment is achieved, and the technical problem that in the prior art, the biological net port flowmeter cannot achieve dynamic and accurate error compensation in the complex multiphase flow environment is solved.

The invention relates to the technical field of unmanned aerial vehicle electronic data processing, and provides an unmanned aerial vehicle flight risk assessment and quality monitoring method, which comprises the following steps of: acquiring diversified data acquired in a flight process of an unmanned aerial vehicle, establishing a data optimization group, performing optimization correction, extracting features and constructing a risk assessment model; real-time flight monitoring data is classified and corrected and then substituted into the model, and a comprehensive flight risk assessment result is obtained. The problems of sensor data errors and noise are solved, the evaluation accuracy is improved, decision support is provided for safe flight of the unmanned aerial vehicle, the flight safety and efficiency of the unmanned aerial vehicle are remarkably enhanced, and the flight risk evaluation and quality monitoring accuracy of the unmanned aerial vehicle is improved.