410 results about "Analysis of algorithms" patented technology

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention discloses a new energy state monitoring method based on Internet of Things, and relates to the technical field of new energy, and the system comprises the following components: S100, Internet of Things data acquisition system construction, S200, dynamic adaptive monitoring strategy design, S300, intelligent early warning and fault diagnosis, and S400, monitoring strategy continuous optimization. According to the method, equipment state changes can be monitored more meticulously by collecting equipment operation parameters and environment data in real time and applying a dynamic self-adaptive monitoring strategy, compared with a traditional fixed monitoring mode, the method can discover and early warn potential faults in time, the monitoring efficiency is remarkably improved, and meanwhile, the method is suitable for popularization and application. Through the application of algorithm analysis and a machine learning technology, the method can automatically optimize a monitoring strategy, and sets a more accurate early warning threshold for each monitoring parameter, thereby improving the monitoring accuracy.

The invention discloses an engineering project progress multi-dimensional monitoring system and method based on an information technology, and belongs to the field of monitoring. The engineering project progress multi-dimensional monitoring system comprises an initial construction plan construction module, a data acquisition and environment monitoring module, a data analysis and plan optimization module, a construction progress prediction module and a risk management module. By collecting multi-dimensional data of a construction site in real time and applying the intuitionistic fuzzy reasoning algorithm, a construction plan can be dynamically adjusted to cope with constantly changing site conditions and external environment factors, the intuitionistic fuzzy reasoning algorithm is utilized to analyze requirements and bottlenecks of a construction project, and the construction efficiency is improved by combining construction logic and physical constraints. Resource configuration can be carried out more accurately; fuzzy and uncertain conditions are processed through the fuzzy logic controller, it is ensured that a project meets all related building and safety standards, and project quality and safety are ensured through continuous monitoring and timely adjustment.

The invention discloses a belt conveyor fault identification and prediction method and system, and relates to the technical field of digital intelligent monitoring of belt conveyors, and the method comprises the steps: collecting the operation data of a belt conveyor in real time through various sensing devices arranged on the belt conveyor, analyzing the collected video data and multi-modal data through an AI algorithm, and carrying out the real-time monitoring of the operation data of the belt conveyor; analyzing the operation state and fault prior information of parts and materials of the conveyor, inputting the information into a dynamic simulation model, simulating the common influence of various faults on the belt conveyor through Monte Carlo simulation, and calculating the probability prediction values of the various faults; finally, an analysis result is displayed in real time in combination with modes of graphic display, probability curves and the like, and working personnel are timely notified of occurred and about-to-occur faults; meanwhile, fault probability prediction and operation data serve as training data again to conduct incremental learning on the AI algorithm, and the accuracy of the AI algorithm is continuously improved.

A system for dynamically scaling microservices in cloud environments, including: a predictive scaling module that uses machine learning algorithms to analyze historical and real-time workload data and predict resource requirements; an auto-scaling decision engine configured to determine and execute dynamic scaling actions based on predictions and current system performance; a cloud orchestration module that deploys and manages microservice instances in cloud environments to ensure optimal resource allocation; an intelligent load balancer that dynamically redistributes incoming requests across microservice instances to avoid bottlenecks and maintain service reliability; and a real-time monitoring module configured to continuously collect system performance data, detect workload fluctuations, and provide feedback for adaptive scaling.

The invention relates to the field of computers, in particular to a micro-service fault positioning method and device based on causal inference and a knowledge graph. The method comprises the following steps: firstly, collecting an original calling link, a multi-dimensional time sequence index and log data of a micro-service system, analyzing the correlation of micro-service indexes by using a partial correlation algorithm, then inferring the causal relationship of the indexes by using a structural causal model, and mapping the causal relationship of the indexes to specific service calling by using a principal component analysis method. And constructing a service dependency relationship. And then defining a knowledge graph ontology, constructing a fault dependency graph according to an index causal relationship and a service dependency relationship, and fusing the fault dependency graph with a calling link graph to form a complete fault dependency knowledge graph with multi-level influence. And finally, storing the graph in a graph database, taking exception as a service node starting point, and outputting a fault root cause candidate set through exception detection, random walk and node score calculation. According to the invention, the efficiency and accuracy of fault positioning are improved, and the stable operation of the micro-service system is ensured.

The invention discloses an intelligent detection method for product defects on an automatic production line and a detection system based on machine vision, and relates to the technical field of industrial product quality detection. According to the method, a multi-waveband imaging technology is combined with temperature and chemical component information to generate a multi-dimensional feature data set, and an unsupervised learning algorithm is utilized to perform clustering analysis, so that the limitation of traditional single-waveband imaging is broken through, product features are comprehensively captured, the defect detection range and accuracy are remarkably improved, and a foundation is laid for subsequent analysis and classification; further utilizing an attention mechanism and a deep learning technology to accurately position and classify defects, and triggering deep scanning through a priority index to improve the detection precision and the system adaptability; and finally, combining with a random forest algorithm to analyze defect influence, and feeding back and optimizing production parameters through a dynamic adjustment mechanism, thereby realizing production optimization closed-loop management, and improving production efficiency and product quality detection.

The invention relates to the technical field of data processing and governance, in particular to a monitoring method and system for a data governance process, and the method comprises the steps: constructing a quantum distribution model through extracting multi-modal features, so as to represent a complex relation between feature variables; analyzing the abnormal propagation path based on a causal chain model and a recursive reasoning algorithm, and generating basic abnormal feature data; performing parameter optimization on the governance rule by using a memetic evolutionary algorithm to generate optimized rule data; model parameters of the monitoring system are updated through multi-round iteration, and dynamic optimization of the system state is achieved. According to the method, the depth and precision of anomaly detection are remarkably improved, meanwhile, efficient iteration of a monitoring system is achieved, the treatment requirement under the complex multi-modal data environment is met, and the key problems that in the prior art, the analysis depth is insufficient, the real-time performance is poor, and the traceability is limited are solved.

The invention provides an expectoration auxiliary control method and device. The method comprises the steps that audio signals of breathing sounds and cough sounds at multiple positions of the chest of a patient are collected through an audio sensor array, and a standardized audio feature data set is obtained through noise reduction, segmentation and feature extraction; a dynamic weighted graph model is constructed, and a random edge sampling algorithm and a parallel batch processing dynamic algorithm are combined to analyze and obtain a sputum viscosity index and a sputum distribution position map; based on the result, mapping the vibration parameter space into an unweighted disk diagram, and obtaining a personalized vibration treatment scheme by using a shortest path algorithm; in combination with the real-time breathing cycle of the patient, working parameters of the sound wave vibrator and the negative pressure suction device are synchronously controlled, and a coordinated and consistent multi-mode treatment execution instruction is obtained; and collecting real-time feedback data in treatment, and dynamically adjusting working parameters through reinforcement learning to obtain an optimized expectoration adjuvant therapy scheme. Intelligent adjustment can be achieved based on the real-time breathing state and sputum characteristics of the patient, the expectoration efficiency is improved, and discomfort of the patient is reduced.

The invention relates to the technical field of medicine sales inventory management, and discloses a medicine sales inventory management system based on artificial intelligence, which comprises a multi-modal anomaly detection module, a multi-modal anomaly detection framework is constructed, and medicine sales anomaly is detected by using an isolated forest algorithm, a depth auto-encoder and a sequence anomaly detection algorithm; the causal structure learning and attribution analysis module is used for constructing a drug sales causal structure learning and attribution analysis system and analyzing abnormal reasons by adopting a NOTEARS algorithm and an SHAP attribution algorithm; the intervention strategy generation and evaluation module is used for constructing an intervention strategy generation and evaluation system and simulating different strategy effects through a causal intervention formula; and the inventory strategy optimization and execution module is used for implementing inventory strategy optimization and execution and dynamically adjusting inventory management. According to the invention, automatic detection, cause attribution and intelligent correction of sales abnormity are realized, and a closed-loop management system is formed.

The invention provides an automatic software testing method and system based on artificial intelligence. The method comprises the following steps: analyzing a software demand document by using an NLP technology, converting the software demand document into structured data, extracting a key function point, and identifying a software function point; generating a test case covering all function points through a deep learning model in combination with historical test data training; automatically executing a test operation, analyzing a test result, analyzing a test log by using a machine learning algorithm, identifying and classifying potential defects by using a data mining and pattern recognition technology, and generating a repair suggestion report; and analyzing feedback data in the test process, dynamically adjusting a strategy according to a test result by using a reinforcement learning algorithm, and optimizing a test case and the strategy. According to the invention, intelligent analysis of test data, automatic generation of test cases and automatic execution of a test process are realized; flexible configuration and expansion can be carried out according to the actual condition of software; the test efficiency and quality are improved, and the test cost and manpower consumption are reduced.

The invention provides a fan full life cycle archive management system and a management method thereof, and the system comprises an Internet of Things data collection module which is installed on a key part of a wind turbine generator set and collects vibration, temperature and stress data; the full-life-cycle digital archive management module is used for recording full-process information of the fan from design to operation and maintenance based on a cloud database and a block chain technology, and ensuring data integrity and traceability; the AI predictive maintenance analysis module analyzes data and predicts faults by using deep learning and machine learning algorithms; the intelligent scheduling and maintenance decision support module is used for generating an optimal maintenance plan in combination with external factors; and the responsibility tracing and process optimization module is used for recording maintenance details to optimize the process and spare part management. The management method comprises the steps of data acquisition, storage, analysis, maintenance plan making, task execution and recording, effect evaluation, process optimization and the like. Through cooperative work of all the modules and implementation of the system method, accurate and efficient full-life-cycle management of the draught fan is achieved.

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.

The invention relates to the technical field of rehabilitation training detection, in particular to a rehabilitation training detection method and system based on artificial intelligence, a standard action library is constructed through standard action videos shot at multiple angles, and track and angle features of key joints are extracted for user training comparison; the skeleton key points of the user are extracted in real time through a MoveNet network, and efficient posture recognition in a home scene is achieved; analyzing position difference, angle change and acceleration characteristics by combining a space-time sequence matching algorithm, generating a dynamic matching degree index, and positioning a deviation joint to generate a correction prompt; introducing an attention mechanism model, learning the contribution degree of each joint to cycle recognition, dynamically selecting a dominant joint for action counting, recognizing starting and ending points of an action cycle through an acceleration curve, and finishing effective action statistics in combination with a dynamic threshold value, so that the counting accuracy and the self-adaptive capability are improved; therefore, the training cost is reduced, the evaluation credibility is enhanced, and accurate statistics and analysis of rehabilitation training data are realized.

The invention discloses a security monitoring video intelligent analysis method based on multi-algorithm cooperation and unified architecture, and relates to the technical field of intelligent video monitoring, and the method comprises the steps: collecting a security monitoring video, carrying out the preprocessing, carrying out the spatial-temporal feature extraction and fusion through a CNN-LSTM spatial-temporal fusion engine, and outputting a fusion feature map; performing target detection, behavior recognition and anomaly detection on the fused feature map to obtain a multi-algorithm analysis result; a cross-module fusion mechanism based on an attention mechanism is utilized to perform weighted integration processing on the multi-algorithm analysis result to obtain a fusion event representation vector; performing event type identification and risk level evaluation on the fusion event representation vector to obtain an event classification result and an event risk level; according to the invention, through the CNN-LSTM space-time fusion engine, the front-end perception capability of abnormal behaviors in a complex scene is improved, and the event detection accuracy and the anti-interference capability are improved.

The invention discloses a call center outbound call completing rate improving method, system and device and a storage medium, and relates to the technical field of call center management, and the method comprises the steps: collecting historical outbound call data of a call center; analyzing historical outbound data by using a machine learning algorithm, and establishing an outbound connection prediction model; based on an outbound call connection prediction model, calculating an optimal outbound call time window and an expected connection probability for each number to be called; formulating an outbound strategy according to the optimal outbound time window and the expected connection probability, and generating an outbound task queue according to the outbound strategy; executing an outbound task according to the outbound task queue, and generating an outbound task record; and updating the outbound call connection prediction model and the outbound call strategy by using the outbound call task record, dynamically optimizing the outbound call process, and completing the improvement of the outbound call connection rate of the call center. According to the method, the historical outbound data are analyzed through the machine learning algorithm, the outbound call connection prediction model is established, the prediction accuracy of the outbound call connection rate is improved, and the resource utilization efficiency is also greatly improved.

The invention provides an AI-based pipe gallery fire operation hidden danger automatic identification method and device. The method comprises the steps of collecting data of a pipe gallery fire operation site in real time and preprocessing the data; identifying and comparing the check points by adopting an image detection classification algorithm based on a diffusion model and a large model distillation technology to obtain an identification and comparison result; and calling a pre-constructed LLM large language model, carrying out potential safety hazard identification on the preprocessed multi-modal data and the identification comparison result through a multi-modal input algorithm, and outputting a multi-modal fire operation comprehensive supervision report. A diffusion model and a large model distillation technology are utilized, data augmentation and data preprocessing are provided, image detection and classification of a pipe gallery fire operation site are realized, and real-time monitoring, automatic hidden danger identification and intelligent early warning of the pipe gallery fire operation site can be realized through LLM construction of an overall system framework and multi-modal input algorithm analysis. And the accuracy and efficiency of potential safety hazard identification are improved.

The invention relates to the technical field of machine tool cutter wear monitoring, and particularly provides an integrated multi-dimensional data monitoring system and a monitoring method.The integrated multi-dimensional data monitoring system comprises a multi-dimensional data acquisition module which can acquire multi-dimensional data of the working state of a cutter in real time; the feature information layer and the intelligent analysis layer comprise a multi-source data fusion module, an abnormal state recognition module, a residual life prediction module and a self-adaptive learning module; the decision execution layer comprises an early warning feedback module, a parameter optimization suggestion module and an intervention control module; the knowledge base system comprises a case base module, a rule base module and a model base module, different physical quantities are collected through the multi-dimensional data collection module, full-dimensional sensing of the state of the cutter is achieved in combination with machining features, material information and environment data, multi-sensor collection, physical model construction, feature information integration and intelligent algorithm analysis are combined, and the machining accuracy of the cutter is improved. And comprehensive, real-time and accurate monitoring of various abnormal states of the machine tool cutter is realized.

A method comprises receiving data corresponding to operation of one or more edge devices, and analyzing the data using one or more machine learning algorithms to determine whether the operation of the one or more edge devices is anomalous. In determining whether the operation of the one or more edge devices is anomalous, the one or more machine learning algorithms determine whether the operation of the one or more edge devices deviates from identified normal operational parameters of the one or more edge devices. At least one action to control access to the one or more edge devices is triggered in response to determining that the operation of the one or more edge devices is anomalous.

The invention, which relates to the technical field of traffic management, discloses an intelligent detection system for a vehicle parking space in a passenger and goods mail fusion station, and the system comprises a multi-mode sensing module which is configured to collect vehicle data information, the collection mode comprises the steps that vehicle data information is collected through an enhanced geomagnetic array, an adjustable video monitoring set, a lateral laser radar and a piezoelectric sensor; and the edge computing node module is configured to integrate an AI chip, the AI chip comprises a vehicle classification model, an abnormal parking detection algorithm and a data fusion engine, and the collected data is computed and analyzed. The edge computing node is integrated with an AI chip, and data are analyzed and processed through a built-in algorithm; the hybrid communication network transmits data through an optical fiber backbone network and a specific redundant link, self-adaptive visual adjustment and intelligent algorithm optimization are performed, the problem of complex vehicle type detection in a passenger and goods mail fusion station is solved, and the parking space utilization rate and the operation efficiency are remarkably improved.

The invention discloses a collision identification method and system for a window cleaning robot, and relates to the technical field of robot environment perception, and the method comprises the steps: carrying out the time difference compensation and space synchronization based on a glass material type and collision detection parameters through a distributed perception collaborative analysis method, and generating a three-dimensional space vibration information matrix; enhancing the three-dimensional space vibration information matrix through a dynamic wavelet packet decomposition algorithm and a sliding window analysis method according to the type of a glass material; according to the enhanced three-dimensional space vibration information matrix, collision coordinates are calculated through a time difference positioning algorithm, the characteristic frequency of the vibration waveform is analyzed, and collision characteristic information is obtained; based on the collision feature information, combining safety parameters of glass material types, adopting a hybrid decision algorithm to analyze the collision feature information, and generating and executing a robot obstacle avoidance instruction; according to the method, the high-resolution three-dimensional vibration field is constructed through space-time collaborative modeling, and the collision coordinate calculation precision and the anti-interference capability are improved.

The invention discloses an electric heating deicing system and method based on machine learning prediction and adaptive adjustment, and belongs to the technical field of wind power generation. The system comprises a multi-physical sensor array and a dynamic heating execution module which are arranged on a blade. The multi-physical sensor array and the dynamic heating execution module are both connected with a CAN bus; the CAN bus is connected with an intelligent control module, a cloud collaboration platform and an edge computing node. The method comprises the following steps: acquiring historical operation data of an original unit, and preprocessing the historical operation data to obtain preprocessed data; the original unit historical operation data comprises environmental parameters and blade state monitoring data; analyzing the preprocessed data based on a machine learning algorithm, and establishing a blade icing prediction model; real-time environment parameters and blade state monitoring data are obtained, and the blade surface icing condition is predicted based on the blade icing prediction model; and determining the heating requirement of the blade based on the surface icing condition of the blade, and carrying out deicing.

The invention provides an intelligent comprehensive system and method for steel production process parameter optimization, and the system comprises a data configuration module which is used for collecting quality-related data in a steel production process; the influence factor analysis module is used for processing data related to quality by using a feature engineering technology and identifying key variables; the quality index prediction module is used for constructing a quality prediction model by using a machine learning algorithm and key variables; the process parameter optimization module is used for constructing a process parameter optimization model based on the prediction result and the quality index control target, and analyzing and recommending an optimal process parameter control range by utilizing an intelligent algorithm; and the model monitoring optimization module is used for evaluating and optimizing the operation effects of the quality prediction model and the process parameter optimization model in real time. According to the method, the whole-process intelligent management from data configuration and key factor identification to quality prediction, process parameter optimization and model dynamic monitoring is realized, a closed-loop optimization mechanism is formed, and the production efficiency and the product quality are effectively improved.

The invention discloses a processing cloud platform for dynamically adjusting and optimizing Beidou measurement data. The processing cloud platform comprises an acquisition module which is responsible for acquiring Beidou measurement data from different area measurement points and data of an ionosphere real-time monitoring network in real time; the buffer is responsible for temporarily storing the Beidou measurement data sent by the analysis module and the processing module; the processing module is responsible for processing Beidou measurement data and ionosphere real-time monitoring network data in real time, performing error correction by adopting a dynamic compensation model and utilizing ionosphere data, and adopting an M estimation robust adjustment algorithm in a baseline resolving process; the analysis module is used for performing single-point analysis and precision analysis on the Beidou measurement data, performing reinforcement learning by using a PPO algorithm, constructing a prior knowledge base and accelerating the training process of reinforcement learning; the client module is used for receiving the charts and the reports sent by the analysis module, outputting the charts and the reports and issuing monitoring information; and the database is used for storing Beidou measurement data, ionosphere data and other related information.

The present invention relates to the technical field of cross-border e-commerce supply chain, and discloses a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence. The method has the following effects: The embodiment of the present invention provides a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence. The method is executed by a processor and includes obtaining cross-border e-commerce supply chain data; analyzing through artificial intelligence algorithms to obtain supply chain analysis results for each type of product; obtaining a product number set, matching the product number set with the supply chain analysis results to obtain product number supply chain analysis results; establishing a product supply visualization network model; determining main product nodes in the product supply network model according to the product supply visualization network model; and determining the product supply route between the target product node and the main product node. This method provides an effective high-efficiency transportation method for cross-border e-commerce supply chain through artificial intelligence analysis methods.

The invention provides an artificial intelligence advertisement pushing method based on multi-dimensional historical data analysis, and the method comprises the steps: constructing second-level, minute-level, hour-level and other behavior feature views of different time granularities for extracted original features, and forming multi-time scale feature representation; analyzing feature views under different time scales through a graph mining algorithm, mining an association rule between feature nodes, and constructing a cross-scale feature association graph; in the feature correlation map, according to the spectral clustering result of the feature nodes and the gravitation direction of the feature edges, the temporal correlation degree of different feature combinations is judged; if the time correlation degree of the feature combination exceeds an empirical threshold value, endowing the time weight of the feature combination with a high weight coefficient of power law distribution; and dynamically adjusting the network depth and the connection density of the association atlas according to the vertical dimension and the semantic hierarchy of the features, and optimizing the feature weight distribution in the atlas.

The invention discloses a coal mine underground navigation control system and device based on big data, and particularly relates to the technical field of underground navigation control. The underground roadway waveguide effect grade is judged by monitoring underground radio signals in real time and recognizing a signal reflection mode by combining wavelet transform with a convolutional neural network; meanwhile, three-dimensional point cloud data of the underground roadway are processed through multi-scale curvature analysis and a DBSCAN clustering algorithm, and division of a structure mutation region is achieved; determining a navigation error level according to the waveguide effect level and a structure mutation result, and when the navigation error level reaches a second level or a third level, analyzing historical navigation trajectory data by using a dynamic time warping algorithm, and evaluating a current navigation path deviation degree; comprehensive analysis is carried out through the deep belief network, the reliability of the navigation control system is evaluated, whether a path re-planning instruction is triggered or not is judged, and the underground navigation precision and safety are improved.

The invention discloses an Internet of Things data analysis system based on artificial intelligence, and relates to the technical field of telecommunication, the system analyzes the coupling effect in signal propagation through a path selection and prediction module and an AI algorithm, and outputs an environmental influence function TW; and the dimension reduction feature data set F is combined with the environmental influence function TW to calculate and output a prediction path optimization parameter Pout. The intelligent path selection model not only considers factors such as signal intensity attenuation, noise interference and building reflection, but also combines the influence of terrain and weather, so that the prediction accuracy is greatly improved. And the dynamic path adjustment switching module realizes intelligent adjustment of the communication path based on the quality score comparison of the current path Pcurr and the predicted optimal path Pout and the setting of a threshold value M. And the response speed and the decision quality of the system in the case of network condition change are enhanced.

The invention relates to the technical field of environment traceability, in particular to a Juncao production process environment traceability tracking system which comprises a data acquisition module, a data analysis module and a traceability tracking module. The data acquisition module is used for acquiring environment data and quality data of the Juncao. And the data analysis module is used for constructing an abnormal data analysis model of the isolation forest algorithm optimized by the wolf pack algorithm and constructing an SVM-LSTM network model optimized by the dung beetle algorithm. And the traceability tracking module is used for marking the production process of the Juncao, inputting the abnormal data into the Juncao traceability analysis model, and tracing the source of the abnormal data. According to the method, the isolation forest algorithm is optimized based on the wolf pack algorithm to analyze abnormal data, the method has better distinguishing capability and higher adaptability, the accuracy of anomaly detection is improved, environment tracing tracking is carried out in combination with the SVM-LSTM network model optimized by the dung beetle algorithm, the convergence speed of the model is increased, the training process is more efficient, and the training efficiency is improved. And the overall operation efficiency is improved.

The invention relates to the technical field of machine tool data processing, in particular to a machine tool abnormal operation data edge calculation method and system, and the method comprises the steps: deploying a sensor at a key part of a machine tool for collecting machine tool operation data and transmitting the machine tool operation data to an edge calculation device which has calculation capability and a local database; dynamically optimizing a data transmission path by adopting an SDN network technology in a data transmission process; after receiving the data, the edge computing device performs data preprocessing and abnormal data detection, stores the data and a detection result in a local database and regularly uploads the data and the detection result to a cloud server; and the cloud server analyzes the trend of abnormal operation of the machine tool by adopting a big data analysis algorithm, and feeds back optimization suggestions to the edge computing device according to an analysis result. According to the method, the data processing delay is reduced by combining the edge computing with artificial intelligence, the transmission path is optimized, the cooperation of the edge computing equipment and the cloud is realized, and the processing efficiency of the abnormal operation data of the machine tool is improved.