1078results about How to "Quick analysis" patented technology

The invention provides a ship remote real-time early warning system based on cloud computing, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting ship operation data, environment state data and external interaction data; the data preprocessing module is used for generating standard processing data; the data grouping module is used for dividing a priority data group and a common data group; the data transmission module is used for transmitting the data to the cloud analysis module; the cloud analysis module is used for carrying out multi-source data aggregation processing on the priority data set and the common data set, carrying out real-time evaluation on the ship operation state, identifying abnormal behaviors and predicting emergencies, and generating corresponding final early warning instructions including an avoidance operation instruction and an air route adjustment suggestion; the early warning feedback module is used for sending the early warning instruction to the ship terminal; the local response module is used for executing instant triggering operation on the instruction classification data; according to the invention, autonomy and accuracy of remote real-time early warning of the ship are improved.

The invention belongs to the technical field of intelligent document management and auditing in the electric power industry, and provides an electric power document autonomous review method and system fusing causal reasoning and compliance review. According to the technical scheme, obtained electric power document data is subjected to semantic analysis, and semantic representation of a document is obtained; based on the semantic representation of the document, performing event extraction on each sentence recognized in the document, and encoding each event to obtain the semantic representation of the event; constructing an initial event causal chain based on the semantic representation of the event; verifying the constructed initial event causal chain by adopting a causal reasoning algorithm, comparing each event in the verified causal chain and the incidence relation between the events with a power industry compliance rule, calculating a compliance score, and calculating the incidence relation between the events of which the compliance score is lower than a set score value or the incidence relation between the events of which the compliance score is lower than the set score value; and generating a correction suggestion, and generating a review report. And the efficiency and the accuracy of document review are obviously improved.

The invention discloses a distribution transformer fault diagnosis method based on full-band multi-modal data fusion. The method comprises the following steps: collecting different feature maps of a transformer; performing feature extraction and weighted summation on the collected different feature maps to obtain a multi-source fusion feature vector; performing data preprocessing on the multi-source fusion feature vector, and performing normalization processing after data preprocessing; training an improved probabilistic neural network by using the normalized multi-source fusion features; optimizing the polynomial order of the trained improved probabilistic neural network by adopting an improved parrot optimization algorithm; and carrying out transformer fault diagnosis by adopting the optimized improved probabilistic neural network. According to the method, the improved parrot-shaped optimization algorithm is adopted, the foraging behavior of the parrot-shaped algorithm is simulated, the polynomial order of the improved probabilistic neural network is optimized by effectively combining global search and local search strategies, and the accuracy of transformer data fusion diagnosis and the generalization ability of the probabilistic neural network are improved.

The invention relates to the technical field of geological disaster monitoring, and discloses a debris flow monitoring and early warning system based on Internet of Things, comprising a multi-source data acquisition module used for acquiring environmental data related to debris flow; the data transmission module is used for transmitting the environment data to a data center or a cloud end from a collection point; the data preprocessing module is used for carrying out abnormal value cleaning, time synchronization and normalization processing on the environment data; the deep learning analysis module is used for analyzing the preprocessed data so as to predict the occurrence risk of debris flow; and the dynamic early warning and publishing module is used for generating a multi-stage early warning signal according to an output result of the deep learning analysis module and publishing early warning information to a user in multiple modes. The accuracy, the real-time performance and the adaptability of the debris flow early warning system are improved through multi-source data fusion, edge calculation and a dynamic learning mechanism so as to cope with debris flow disasters in a complex geological environment.

The invention relates to the technical field of hydraulic engineering, geotechnical mechanics and geological disaster prevention and control engineering, and discloses a landslide surge physical and numerical hybrid model construction method and system and application. Analyzing the landslide instability evolution motion process, the landslide warehousing first wave height and the surge propagation process of the landslide surge; performing a numerical simulation test according to a second result of the landslide surge numerical model test; adjusting the particle size and the kernel function in the landslide surge numerical simulation test, so that the first wave height, the opposite-bank arrival height and the surge pressure distribution in the landslide surge numerical simulation test and the landslide surge physical model test are consistent; and taking the adjusted landslide surge numerical model as the obtained landslide surge hybrid model. The system comprises a first result acquisition module, a second result acquisition module, a test parameter adjustment module and a hybrid model acquisition module. According to the method, different calculation working conditions can be conveniently analyzed.

The invention discloses an intelligent bid evaluation method and system based on a large model, and a related product, relates to the technical field of purchase bid evaluation systems, and solves the problems that an existing intelligent bid evaluation system is difficult to understand a complex logical relationship and semantic connotation and cannot provide a comprehensive and accurate scoring result. According to the technical scheme, the method comprises the steps that S1, bidding and tendering files are obtained; s2, performing disassembly processing based on the bidding and tendering file to obtain a plurality of bidding and tendering data pairs; s3, performing instruction construction on the bidding and tendering data pair based on a historical bidding and tendering database to obtain a prompt instruction of the bidding and tendering data pair; s4, processing the prompt instruction of the bidding and tendering data pair based on the large model after training and fine tuning to obtain the score of the bidding and tendering data pair, and accumulating the scores to obtain the score of the bidding file; through the method based on the large model, the scoring effect is effectively improved in the intelligent bid evaluation work, the efficiency is improved, manpower and material resources are saved, and meanwhile interference caused by human factors is avoided.

The invention relates to the technical field of short message fraud recognition, and discloses a short message fraud recognition method and system based on machine learning and computer equipment, and the method comprises the steps: carrying out the preprocessing of collected fraud and non-fraud short message samples, and obtaining structured short message data; performing multi-dimensional feature extraction to obtain an initial feature vector; constructing a short message fraud multi-mode knowledge graph; performing feature enhancement to obtain a knowledge enhancement feature vector; inputting the knowledge enhancement feature vector into a plurality of machine learning models for training to obtain an integrated short message fraud recognition model; and performing real-time analysis on the newly received short message based on the integrated short message fraud identification model to obtain a fraud risk prediction result, so that the newly received short message can be quickly analyzed, the real-time requirement in practical application is met, and a risk threshold classification mechanism is adopted, so that the fraud risk prediction result is more interpretable and operable, and the fraud risk prediction accuracy is improved. And subsequent risk management and disposal are facilitated.

The invention provides an ultrafast laser heat dissipation state real-time monitoring system based on cloud computing, and relates to the technical field of laser heat dissipation, and the system comprises a laser monitoring module which is used for transmitting ultrafast laser and is internally provided with a temperature sensor so as to monitor the working temperature of a laser in real time; the data acquisition module is used for being connected with the laser monitoring module, acquiring temperature data and key parameters, including laser power and working current, of the temperature sensor in real time, and uploading the temperature data and the key parameters to the cloud server; and the cloud analysis module is used for receiving and analyzing the temperature data and the key parameter data, identifying an abnormal temperature mode, predicting a potential overheating risk and calculating heat dissipation efficiency to obtain an analysis result. According to the invention, comprehensive, real-time and intelligent management of the heat dissipation state of the laser is realized, the operation stability and safety of the laser are effectively improved, the overheating risk is reduced, the heat dissipation efficiency is optimized, and remote monitoring and data analysis services are provided for users.

The invention belongs to but is not limited to the technical field of software development, and particularly relates to a multi-round reasoning and feedback optimization method and system for industrial software codes, and the method comprises the steps: S1, vector code library establishment: the establishment of a vector code library is composed of three parts of feedback establishment, vector database establishment and format summarization; s2, code reasoning optimization: the code reasoning optimization mainly comprises four aspects: control flow graph analysis, multi-query generation and reciprocal ranking fusion retrieval, reasoning generation subtask and code chain submodule generation, and code combination completion; and S3, feedback optimization: the feedback optimization is composed of score feedback optimization and log feedback optimization. According to the method, an automatic verification mechanism is introduced, logic consistency check and performance evaluation are carried out on the generated and optimized codes, and the correctness and execution efficiency of the codes are ensured. The verification mechanism can find and correct errors in real time in each stage of generation, so that the cost of manual verification is reduced, and the development efficiency is improved.

The invention relates to the technical field of leakage traceability, in particular to a water transfer project leakage traceability identification method and system, and the method comprises the following steps: setting monitoring points in a water transfer project, collecting the temperature, pressure and flow velocity data of the key monitoring points, carrying out the time sequence analysis of parameters, analyzing the water flow dynamic state and environment change in real time, and generating state monitoring data. According to the method, the environment change near the leakage is analyzed by using the state monitoring data, the abnormal change is identified through simulation comparison analysis, the leakage point is predicted and positioned more accurately, the energy state before and after the leakage is evaluated through entropy increase calculation, and the leakage position and time are further predicted accurately; by simulating energy flow under differentiated environment conditions, the accuracy of seepage path identification is improved, and more efficient seepage source positioning is realized, so that the whole seepage traceability identification process is more systematic and scientific, and the safety supervision and maintenance efficiency of water conservancy facilities is greatly enhanced.

The invention relates to the technical field of peanut oil production control, in particular to an intelligent optimization system for a peanut oil production control process, which comprises a process parameter modeling module for collecting peanut oil production parameters, performing linear combination on the peanut oil production parameters, analyzing the correlation of the peanut oil production parameters to quality indexes, and calculating the quality indexes of the peanut oil production parameters; and obtaining a process parameter mapping matrix. According to the method, through deep correlation analysis of multiple parameters in the production process, the mapping relation between the process parameters and the oil quality indexes is established, and the precision and reliability of quality control are improved. In the process control, the specific influence of each production link on the quality index is accurately reflected through standardized and linear combinatorial analysis on the parameters such as squeezing temperature, pressure, time, stirring speed and the like. By predicting the quality index under the current production condition, the response speed to the quality change is improved, so that the quality index deviation can be quickly analyzed and evaluated in real-time data monitoring.

The invention discloses an operation and maintenance platform data acquisition system based on big data, and relates to the technical field of big data, and the system comprises the steps: capturing a data packet from a network flow of an operation and maintenance platform, and storing the data packet in a buffer area; performing preliminary screening on the data packets in the buffer area, and allocating priorities for the data packets based on a dynamic weight scheduling mechanism to complete preliminary preprocessing of the data packets; based on a circular sorting and block optimization technology, distributing the plurality of decomposed analysis tasks to a plurality of threads for parallel processing; formatting the analysis result, and performing batch parallel processing according to the data scale of the analysis result; formatting the analyzed data, writing the formatted data into a time sequence database according to a time sequence, and generating an index based on the tag field; through a dynamic cache management mechanism, the cache size is dynamically adjusted according to the throughput and the maximum delay of the network flow, and the problems of data loss and processing delay are avoided.

The invention provides a power distribution automation terminal debugging system and method based on an IEC104 protocol, and the method comprises the steps: inputting an IP address, a port number and a channel address of a device as parameters after a manufacturer selection module selects a manufacturer name, transmitting IEC104 message data information through a message communication module, a message sending module and a message receiving module, and transmitting the IEC104 message data information to a power distribution automation terminal. An IEC104 message is analyzed through a man-machine interaction interface, the original message and the message meaning represented by each byte of each original message are displayed through the man-machine interaction interface, finally, an information value in the analyzed IEC104 message corresponds to a substation point table, and real-time feedback and display of data are achieved. And meanwhile, a debugging log is recorded. And the method is adaptive to a plurality of manufacturers, so that the same software is used to debug terminal equipment of different manufacturers. The problem that multiple sets of software are used when a user debugs multiple manufacturer devices is solved, and the daily working efficiency is improved.

The invention relates to the field of biological detection, in particular to a primer probe combination and a kit for HIV-1 (human immunodeficiency virus-1) nucleic acid detection. The primer combination provided by the invention comprises a primer group I and / or a primer group II, the primer group I has a nucleotide sequence as shown in any of SEQ ID NO: 1 to SEQ ID NO: 4; the primer group II has a nucleotide sequence as shown in any of SEQ ID NO: 5 to SEQ ID NO: 8. The kit provided by the invention can be stored for 1 year at 2-8 DEG C, and has good stability. Meanwhile, the detection method and the kit provided by the invention have the advantages of strong specificity, high sensitivity, simplicity and convenience in operation, clear result, high reliability and wide coverage. The invention has potential application prospects in detection and identification of HIV-1 and auxiliary diagnosis of HIV infected persons, and has great influence on prevention and treatment of AIDS and public health.

The invention relates to the technical field of network security, and discloses an industrial log noise reduction processing method and system based on an artificial intelligence algorithm, and the method comprises the steps: collecting historical alarm data of a device / application, carrying out the preprocessing, and dividing the preprocessed data into a training set and a test set; constructing a convolutional neural network model, and training and testing the convolutional neural network model based on the training set and the test set to obtain an alarm log noise reduction model; operating parameters of the alarm log noise reduction model are configured and integrated with a dynamic sensing system; receiving real-time alarm data of the equipment / application, and inputting the real-time alarm data into the alarm log noise reduction model to obtain alarm features; classifying according to the alarm features, and determining the category of the real-time alarm data; and carrying out storage and visual analysis based on the alarm features and categories. According to the invention, repeated or irrelevant alarm information can be accurately identified from a large number of alarms, and noise reduction processing is carried out, so that the system burden is reduced.

The invention belongs to the technical field of image segmentation, and discloses a multi-modal medical image automatic segmentation method and system based on a three-dimensional reconstruction model.The multi-modal medical image automatic segmentation method comprises the steps that multi-modal image data are acquired, and local features of the multi-modal image data are extracted; identifying a patient tissue structure in the multi-modal image data based on the local features; segmenting the tissue structure of the patient in the multi-modal image data by using a segmentation technology, and reconstructing a three-dimensional virtual model based on the tissue structure of the patient; and fusing a three-dimensional virtual model body drawing result and a three-dimensional virtual model surface drawing result to realize three-dimensional display of a segmentation result. According to the method, the influence of human errors and subjective judgment is reduced by identifying and segmenting the tissue structure, so that boundaries of various tissues are identified more accurately, automatic image segmentation can be completed in a short time, and subsequent diagnosis and analysis can be performed more quickly.

The invention provides a method for quickly training an LSTM (Long Short Term Memory) model by time series data, which relates to the technical field of data processing, and comprises the following steps: S1, a data integration stage: efficiently processing and utilizing telemetering real-time stream data through Kafka and Flink, s2, a data storage stage: storing the time sequence data processed in the integration stage into a high-performance database IoTDB specially designed for the time sequence data according to a time sequence, and S3, an algorithm application stage: constructing an LSTM model, setting parameters, optimizing the LSTM model, and performing time sequence data prediction by using an LSTM algorithm model. According to the method for rapidly training the LSTM model through the time series data, the time series telemetry data generated in the test flight process are collected and processed through Kafka, real-time data of an aircraft can be rapidly captured, processed and analyzed, data transmitted by a sensor and an algorithm model are decoupled through the Kafka, data output by the sensor can be directly sent to the Kafka, and the LSTM model can be rapidly trained through the Kafka. And it is avoided that an algorithm model needs to be concerned to process data.

The invention relates to the technical field of data analysis, and discloses a consumer demand analysis method and device based on a multi-objective optimization algorithm, and the method comprises the steps: obtaining and preprocessing a plurality of pieces of consumer comment data, and obtaining a plurality of pieces of effective comment data; analyzing the effective comment data by using a preset recognition model to obtain theme probability distribution and emotion probability distribution of the effective comment data; combining the topic probability distribution and the emotion probability distribution to obtain an overall joint probability matrix; calculating by using a multi-objective optimization algorithm to obtain an optimal weight matrix; and multiplying the overall joint probability matrix by the optimal weight matrix to obtain a demand analysis result. According to the method, by obtaining the comment data of the consumer and analyzing the theme and emotional tendency of the consumer, the coverage and real-time performance of data collection are improved, the demand and preference information of the consumer is deeply mined, the demand preference of the consumer for the target product is comprehensively and dynamically mastered, and a more accurate and timely basis is provided for the market decision of an enterprise.

The invention discloses an intelligent data analysis method based on spatio-temporal big data, and belongs to the technical field of spatio-temporal big data, the method comprises the step of obtaining original spatio-temporal data of different sources in real time, and the method comprises the steps of performing feature extraction on preprocessed spatio-temporal data, including unification of a time format, spatial coordinates and a data unit, deduplication fusion and the like, so as to obtain the spatio-temporal data of different sources. According to the method, multi-source spatio-temporal data is formed, a spatio-temporal data analysis model is constructed based on deep learning, spatio-temporal dynamic changes in data can be captured, modes in the spatial data can be effectively extracted through models such as a convolutional neural network, classification is performed through cross entropy loss, high-precision spatio-temporal data analysis is achieved, and the spatial data analysis efficiency is improved through the trained spatio-temporal data analysis model. According to the method, new spatio-temporal data are analyzed in real time, abnormal modes and trend changes in the data are rapidly identified, an anomaly detection threshold value is set, model output is compared with the set threshold value, comprehensive judgment is carried out in combination with context information, and the accuracy and efficiency of anomaly detection are improved.

The invention discloses an existing building outer wall internal and external heat insulation energy consumption optimization method based on a building model, and belongs to the technical field of building design. In order to solve the problems of insufficient data comprehensiveness and incomplete thermal insulation scheme evaluation, the thermal insulation performance of a building can be evaluated more accurately by checking an outer wall structure on site, recording material types, damage degrees and the current situation of a thermal insulation layer and combining scientific methods such as thermal resistance testing and heat transfer coefficient measurement; digitization and visualization of building information are achieved through the BIM technology, historical energy consumption data are collected from multiple data sources and systematic arrangement and classification are carried out, the readability of the data is improved, rapid import and analysis can be conveniently carried out in energy consumption simulation software, and therefore the energy consumption condition of a building is reflected more accurately; according to the method, comprehensive coverage of multiple possibilities is achieved, the influence of different heat preservation schemes on energy consumption can be evaluated more comprehensively, differentiated design is conducted on the thickness of the heat preservation layer based on the energy consumption simulation result, and the heat preservation performance of a building is effectively improved.

The invention discloses a case anomaly detection method and system based on deep learning, and relates to the technical field of medical images. Comprising a data collection and preprocessing module, a multi-modal feature extraction module, a feature fusion module, a context information integration module, an anomaly detection model module, a model training and optimization module, a model iteration training and verification module and a real-time monitoring and feedback module. The method further comprises the following steps: step 1, collecting medical image data from different modalities, and carrying out denoising and standardization processing on the medical image data; 2, extracting features of different modal images by using a deep convolutional neural network, and fusing the features to form a comprehensive feature vector; 3, combining the clinical information of the patient with the fusion features to generate a comprehensive feature vector; 4, analyzing the fused feature vector through the trained deep learning model, and outputting an anomaly detection result; 5, the system monitors a new case in real time, automatically prompts abnormity and continuously updates the model according to feedback of a doctor.

The invention discloses a power Internet of Things data transmission method and device and a medium, and belongs to the field of power network networking, and the transmission method comprises the steps: collecting power data such as current, voltage and power in real time based on a sensor, and carrying out the de-noising of the data through Kalman filtering; layering packaging is carried out according to the data type, a data compression algorithm is applied to compress the data, the transmission bandwidth is reduced, the compressed data is encrypted, and integrity verification information is added; dynamically selecting a transmission channel by using a reinforcement learning algorithm according to a real-time network state; the data are packaged and sent to the cloud through the selected transmission channel, the data are received at the cloud, and the power data are stored and managed by using a time sequence database; and analyzing the received data, generating a real-time report, carrying out anomaly detection and load prediction through a random forest algorithm, and carrying out adaptive adjustment according to the real-time monitoring data. By dynamically selecting a transmission channel and a self-adaptive transmission strategy, network instability is dealt with, and timely transmission of data is ensured.

The invention discloses an energy storage regulation and control method and system for a virtual power plant, and the method comprises the following steps: S1, data collection: a metering sensor and metering equipment are installed at DERs to collect the data of the generating capacity, the energy storage state and the load state, the DERs are various distributed energy equipment or systems forming the virtual power plant, and the DERs are distributed energy equipment or systems forming the virtual power plant; a wired or wireless communication network is configured to connect the metering sensor and the metering device to ensure that data can be transmitted from the DERs to the central control system. The method has the advantages of high efficiency and stability, and solves the problems that in the energy storage regulation process of an existing virtual power plant, efficient and reliable energy storage regulation is inconvenient to carry out, unbalance of power regulation is easily caused, maximization of economic benefits and environmental protection benefits are affected, and stability of energy storage regulation is reduced.

The invention discloses a multi-center inherited metabolic disease information integration platform, and relates to the technical field of medical treatment, according to the multi-center inherited metabolic disease information integration platform, the normalization and availability of data are improved through a data standardization module; data from different centers are integrated into a unified database through the data integration module by utilizing a data fusion technology, redundant data are removed, the problem of heterogeneous data integration is effectively solved, and the integrity and consistency of the data are improved; the privacy protection module avoids data leakage and unauthorized access; through the resource calculation management module, the resource utilization efficiency is effectively improved, and the calculation cost is reduced; and secondly, the storage resources are dynamically adjusted and allocated according to the evaluation result of the storage index Cczs, so that the optimal management of the storage resources is realized, the problems of resource waste and resource insufficiency are avoided, and the efficient operation of the system is ensured.

The invention belongs to the technical field of stratum recognition, and particularly relates to a shield parameter geological recognition calculation method and system based on an unsupervised clustering algorithm, and the method comprises the steps: selecting a plurality of groups of data containing factors affecting stratum category recognition; pCA converts a group of complex related variables into fewer and irrelevant variables, and the new variables are called as main components and can capture most of information in original data; determining the clustering number of K-means + + by adopting an elbow method; determining the clustering number of a hierarchical clustering method by adopting a hierarchical tree; k-means + + and hierarchical clustering are respectively used for clustering the data, and evaluation indexes such as identification precision or contour coefficients of the data and the data are calculated, so that potential modes in the data are analyzed and mined more comprehensively. The invention aims to deeply explore the internal relevance between shield tunnel parameters and geological types, provides an innovative and robust solution for real-time detection of geological features in shield construction by overcoming the limitation of the prior art, and has wide application potential.

The invention discloses a self-supervision enhanced semi-supervised wafer failure prediction method and system, and belongs to the field of wafer failure detection. Obtaining wafer acceptability test / probe test data to construct a training set; carrying out self-supervision pre-training on the wafer feature encoder; a pre-training wafer feature encoder is used to generate original wafer coding features of each sample, strong / weak noise is added, data enhancement constraint is introduced when the strong noise is added, and loss is calculated; using a wafer failure classifier to predict whether the wafer fails according to the wafer coding features before and after the strong / weak noise is added, and calculating consistency loss; in addition, the supervision loss of a small amount of labeled data needs to be calculated; and semi-supervised training is performed on the pre-trained wafer feature encoder and the wafer failure classifier by combining the loss, and the semi-supervised trained model is used for actual wafer failure detection. According to the method, a large amount of wafer label-free data and a small amount of wafer label data are fully utilized, and the prediction accuracy of wafer failure is improved.

The invention discloses a lightweight crack penetration degree detection method based on ground penetrating radar and Swin Transform optimization, and the method comprises the following steps: 1, collecting ground penetrating radar data, and carrying out the preprocessing and feature extraction; step 2, constructing a multi-feature fusion data set; step 3, designing a two-stage through crack detection model based on Swin Transform optimization; 4, a first-stage through crack detection model optimized on the basis of Swin Transformer-YOLOv8 is designed, and a second-stage through crack detection model is designed on the basis of Swin Transformer-YOLOv8 optimization; and step 5, training and predicting the data of the training set and the test set according to the model architecture in the step 3 and the step 4. According to the invention, the accuracy of crack penetration degree detection is improved, and a scientific basis is provided for road maintenance; the detection speed is greatly improved, and the requirement of large-area rapid detection is met; the calculation complexity is reduced, the equipment performance pressure is reduced, and the detection efficiency is improved; the generalization ability of the model is enhanced, and the method can adapt to different road conditions; and the development of an intelligent road detection technology is promoted.

The invention discloses a medicine bottle defect detection method and system based on machine vision and deep learning, belongs to the technical field of defect detection, and solves the problems that a twin defect classification network is sensitive to a highlight area caused by light reflection in appearance image information and the defect detection accuracy of the twin defect classification network is interfered in the existing method. The method comprises the following steps: constructing a defect detection model based on deep learning in combination with a YOLOV5 model, carrying out conjoint analysis detection on a time sequence image set by the defect detection model in combination with a medicine bottle simulation model, carrying out defect identification on the time sequence image set by the defect detection model, positioning a medicine bottle defect area, and carrying out defect detection on the medicine bottle. Classifying and marking defect types of the defect areas of the medicine bottles in a short circuit and random vector machine combined mode; according to the invention, the mean filter is introduced into the defect detection model, so that background filtering can be carried out on the image acquired under machine vision under the condition of light reflection of the medicine bottle, effective information of a small-size target image is effectively reserved, and the defect classification and recognition accuracy is ensured.

The invention provides a link tracking method and system for a service cluster based on log burying points, and belongs to the technical field of distributed monitoring. According to the method, a link tracking ID is generated through a snowflake algorithm at a starting point of service calling, and service IDs are distributed to micro-service nodes participating in service calling; setting log burying points at the entrance, the exit and the key logic processing points of each service according to preset burying point logic; asynchronously recording link tracking information contained in the service node through the log burying point, and uploading the information to a storage system for storage; according to the link tracking ID and the service ID in the link tracking information, the link tracking information is connected in series, a complete calling link called by the service is restored, and meanwhile a service calling topological graph is generated. According to the method, real link information can be quickly and accurately tracked, and quick retrieval and analysis of a large amount of real-time link data can be supported.

The invention discloses a petroleum pipe microscopic fracture morphology identification method, system and device and a medium, and belongs to the technical field of failure analysis image process.The method comprises the steps that a deep learning classification network with a residual module is constructed and trained, and a deep learning classification network loaded with a network connection weight matrix is obtained; and finally, the deep learning classification network is utilized to efficiently and accurately identify the to-be-tested object region image. According to the method, massive data sets can be quickly processed and analyzed by using the high-speed processing capability of a modern computer, so that the efficiency is greatly improved. Compared with a traditional method, the automatic fracture classification method can reduce subjective interference of people and enhance reliability and consistency of results. And meanwhile, the identification method is simple, engineering application integration is facilitated, and good practicability and expansibility are achieved.