920results about How to "Reduce transmission delay" patented technology

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The invention provides a real-time streaming media transmission method and system based on a cloud game and a storage medium, and the method comprises the steps: monitoring network state parameters in real time, and generating network state data; dynamically adjusting the resolution, the frame rate and the bit rate of the video stream based on the network state; a redundant data packet is inserted, and the redundancy proportion is adjusted according to the packet loss rate and the jitter, so that the data reliability is enhanced; dynamically adjusting the transmission rate by adopting a congestion control algorithm; dynamically selecting an optimal server node according to a network condition and a server load; audio and video processing tasks are shared through edge computing nodes, and the load and transmission delay of a main server are reduced; setting a self-adaptive decoding buffer area, dynamically adjusting the size of the buffer area according to a network state, and reducing playing lagging; through real-time monitoring, dynamic adjustment and edge collaboration, smoothness is guaranteed, reliability is improved, delay is reduced, efficient load distribution and user experience optimization are achieved, and the method is suitable for real-time streaming media transmission in a cloud game scene.

The invention relates to an internet-of-things data processing method, system and device based on edge computing and a medium. The method comprises the following steps: acquiring original data acquired by an internet-of-things sensor, and acquiring resource state data and network topology information of an internet-of-things edge computing environment; preprocessing the original data to obtain preprocessed data, and performing transmission strategy analysis on the preprocessed data to obtain transmitted data; mapping the transmitted data into an edge node, and performing distributed analysis on the transmitted data to obtain a corresponding edge analysis result; performing multi-source data fusion according to the edge analysis result to obtain corresponding global data, and performing deep analysis based on the global data to obtain a corresponding intelligent analysis result; and carrying out resource scheduling analysis according to the resource state data and the intelligent analysis result to obtain edge node scheduling strategies, and carrying out comprehensive analysis on all the edge node scheduling strategies to obtain a global resource optimization strategy. According to the invention, dynamic allocation and optimal utilization of resources can be realized.

The invention provides an unstructured file parallel synchronization method and system, and relates to the technical field of file synchronization, and the method comprises the steps: carrying out the intelligent partitioning of a target file through a pre-training file partitioning model, and generating a double-layer cascade index number and a data hash value; and then, according to the data transmission priority model, transmitting the data sub-blocks to a target end database through a self-adaptive parallel transmission channel, and performing asynchronous verification. And finally, recombining the data sub-blocks based on the double-layer cascade index number and the data block association graph by utilizing a pre-training file recombining model, generating a synchronous target file, and submitting confirmation information to the block chain network. According to the method, the unstructured file synchronization efficiency can be improved, the data integrity and security are guaranteed, and the network transmission pressure and the storage cost are reduced.

The invention discloses a fault detection visualization processing system and method for an industrial switch, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting operation data of an industrial switch from different data sources, performing multi-source data fusion, and constructing a multi-source data fusion data set; extracting abnormal features, training a fault diagnosis model by using a random forest algorithm, inputting real-time data into the model, and performing fault prediction and diagnosis; designing a visual interface, and displaying the operation state and fault information of the industrial switch; functions of real-time refreshing, history backtracking and fault positioning are provided; after a fault is found, a fault source is automatically tracked, a fault propagation path is analyzed, based on a fault type and historical data, a Bayesian network is used to establish a correlation model based on the historical data, and repair suggestions and prevention measures are provided; and analyzing data needing preliminary fault detection, performing preliminary fault detection locally on the industrial switch, and synchronizing edge and cloud data in real time.

The invention discloses a low-orbit communication satellite load multilink cooperative transmission system, and belongs to the technical field of satellite communication. In order to solve the problems of insufficient link reliability, low utilization rate of resources in constellations, weaker emergency response and fault-tolerant capability and higher operation cost, the invention is composed of a plurality of low-orbit communication satellites, realizes global seamless coverage through reasonably deployed constellation configuration, ensures that a user can obtain stable communication service in any area, and improves the user experience. A multi-link cooperative communication mechanism is adopted, through real-time link quality evaluation and dynamic link selection, rapid switching of link faults is achieved, communication reliability is improved, a dynamic path adjustment technology is combined, a forwarding path of data in a constellation is optimized according to an actual link state, data transmission time delay is effectively reduced, and data transmission efficiency is improved. According to the time-space change of the flow demand, communication link resources are scheduled in real time, the utilization efficiency of system resources is optimized, and the communication demand of a high-priority task is guaranteed.

The invention discloses a karst collapse risk analysis method and system based on geological big data mining, and the method comprises the steps: collecting multi-source heterogeneous geological data, and carrying out the data fusion through employing a Tucker decomposition-based tensor decomposition algorithm; cleaning, complementing and standardizing the fused data; feature selection and construction: selecting features related to the karst collapse risk; carrying out feature extraction on the preprocessed data; based on the features extracted by the feature engineering, constructing a risk assessment model by using a graph convolutional neural network, and optimizing model performance through an ensemble learning method; carrying out risk assessment and prediction by utilizing the trained model, and carrying out visualization through a geographic information system; and real-time risk assessment is realized based on an edge computing technology, and the model is continuously optimized. According to the method and the system, complex heterogeneous data can be processed, the time change trend of geological parameters can be accurately captured, the prediction accuracy under complex geological conditions is high, and the timeliness of early warning is greatly improved.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention discloses an Internet of Vehicles multi-parameter monitoring method and system based on 5G multi-access edge computing. The method comprises the following steps: collecting multi-source heterogeneous data in real time by using a vehicle-mounted sensor; transmitting the collected multi-source heterogeneous data to the nearest edge computing node in real time by using a 5G network; preprocessing the multi-source heterogeneous data at an edge computing node; inputting the fused data into a local model deployed on an edge node, and carrying out real-time monitoring and real-time evaluation on a vehicle state and a driver state; the obtained evaluation result is uploaded to a cloud platform, and the cloud platform dynamically adjusts a driving assistance decision and a control instruction according to the vehicle driving working condition and the environment data to generate an optimal decision result; and issuing the optimal decision result to a vehicle-mounted execution unit through a 5G network. According to the method, key parameters such as the vehicle speed and the physiological state of a driver are monitored, and the real-time performance and accuracy of a monitoring system are improved.

The invention discloses an industrial environment information wireless monitoring system based on the Internet of Things and a control method thereof, and relates to the technical field of industrial management, and the system comprises an intelligent edge calculation module, a data visualization module, a self-healing network management module, a precise energy consumption management module, an environment anomaly detection module and a wireless network optimization module. According to the industrial environment information wireless monitoring system based on the Internet of Things and the control method thereof, real-time analysis and decision are realized at a data source by using an intelligent edge computing module, data transmission delay is reduced, response can be quickly made in an emergency, potential faults are predicted through a time sequence abnormity self-learning network model, and the monitoring accuracy of the industrial environment information wireless monitoring system based on the Internet of Things is improved. And network self-healing is actively carried out before a problem occurs, so that the network downtime is effectively reduced. In addition, due to the introduction of a low-power-consumption ad hoc network protocol, the energy consumption optimization management of the sensor nodes is realized, and the service life of equipment is prolonged.

The invention relates to the technical field of wire harness intelligent evaluation, in particular to a wire harness connection performance intelligent test method and system. The method comprises the following steps: collecting wire harness test data generated by a wire harness test device and a wire harness sensor in real time through an edge computing node; performing electrical performance detection extraction and mechanical performance detection extraction according to the wire harness test data to respectively obtain wire harness electrical performance detection data and wire harness mechanical performance detection data; performing electrical performance deep evaluation according to the wire harness electrical performance detection data to obtain wire harness electrical performance evaluation data, and performing mechanical performance deep evaluation on the wire harness mechanical performance detection data to obtain wire harness mechanical performance evaluation data; and performing environment pressure cross reference according to the wire harness electrical performance evaluation data and the wire harness mechanical performance evaluation data to obtain wire harness connection performance test evaluation data. According to the method, the accuracy of wire harness performance evaluation is improved, and reliable performance data can be obtained in different application fields.

The invention provides an MBB multi-channel local aggregation superposition transmission method and an MBB multi-channel local aggregation superposition transmission system. Belongs to the technical field of communication. The method comprises the following steps that: a plurality of physical links are established in a wireless communication system, and the system selects the plurality of optimal physical links for aggregation according to a current network environment, a current equipment state and a current user demand; dividing data to be transmitted into a plurality of data packets, packaging each data packet according to the characteristics of the selected physical link, and transmitting the packaged data packets in parallel through the selected physical link; in the transmission process, the system monitors the transmission state of each link in real time, and dynamically adjusts the transmission rate and power distribution of each link according to the current network load and equipment state. By transmitting a plurality of physical links in parallel, the network bandwidth can be fully utilized, the data transmission rate is improved, and the transmission delay is reduced.

The invention relates to the technical field of data processing, in particular to a real estate surveying and mapping data processing method and system based on intelligent data. The method comprises the following steps: acquiring multi-source real estate surveying and mapping data; performing space-time synchronization on the multi-source real estate surveying and mapping data to generate multi-source real estate surveying and mapping space alignment data; real estate space feature extraction is carried out on the multi-source real estate surveying and mapping space alignment data to obtain real estate space feature data; performing cross-domain feature fusion on the multi-source real estate surveying and mapping data based on the real estate spatial feature data to generate real estate surveying and mapping fusion spatial feature data; public surveying and mapping point confirmation is carried out on the multi-source real estate surveying and mapping data through the real estate surveying and mapping fusion spatial feature data, and real estate surveying and mapping public surveying and mapping point data are obtained. Through multi-source data space-time synchronization, feature fusion, distortion correction, high-dimensional space reconstruction, semantic tag association and chain storage, the efficiency and accuracy of real estate surveying and mapping data processing are improved.

The invention relates to the technical field of industrial internet, in particular to a smart energy management method and system based on the industrial internet, and the method comprises the following steps: S1, data collection: collecting energy consumption data and environment data in a production process in real time; s2, data fusion: carrying out fusion processing on multi-source data by adopting a data fusion technology; s3, distributed energy optimization: optimizing the energy consumption of each node on the distributed nodes in real time by using an edge computing technology; s4, intelligent load balance optimization: dynamically adjusting the operation load of each industrial production device; s5, multi-level fault prediction: performing fault prediction on the industrial production equipment; and S6, control strategy generation: generating a self-adaptive control strategy in combination with the fault prediction result of the industrial production equipment. According to the method, the lowest energy consumption of the optimization result is ensured, the load is uniformly distributed, local overload and energy waste are avoided, and the overall energy utilization efficiency is improved.

The invention provides an Internet of Things gateway control method fusing local communication and AI computing power, and relates to the technical field of data processing, the method comprises the following steps: receiving an original data packet sent by at least one terminal device; protocol analysis, noise filtering and format conversion are carried out on the received data, an AI analysis model corresponding to the equipment type is loaded, standardized data are input into the AI analysis model, an analysis result is sent to target terminal equipment, and the analysis result and a communication result are stored in a gateway cache module; according to a preset time interval, the analysis result is transmitted to a remote cloud server, and synchronization state information and synchronization error log information are generated; according to the synchronization error log information, synchronization error analysis data is generated, network synchronization optimization and data anomaly positioning are executed, and a synchronization optimization result is generated; according to the data type and the error type, abnormal data are processed, and synchronous updating is executed; according to the invention, the data processing efficiency and accuracy are improved.

The invention relates to the technical field of data transmission, and provides a data transmission method and device, electronic equipment, a storage medium and a program product.The method comprises the steps that resources needed by RDMA are initialized, and the initialization process comprises the steps of creating a registration memory of the RDMA and creating a plurality of work queue pairs according to different data transmission logics of a target data packet to be transmitted, each work queue pair corresponds to one data transmission logic; dividing a data buffer area in the registered memory into memory blocks with preset sizes; writing the to-be-transmitted target data packet into a data buffer area by taking a memory block as a unit; and selecting the corresponding target working queue based on the data transmission logic of the to-be-transmitted target data packet, and sending the to-be-transmitted target data packet in the data buffer area to an opposite end, or receiving data of the opposite end to the data buffer area. According to different data transmission logics, different working queues are selected to complete operation on the logic channels serving as data transmission, multiple pieces of data are transmitted in parallel, and transmission delay is reduced.

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 discloses a multi-unmanned ship cluster cooperative tracking control method based on data driving, and belongs to the field of unmanned ship cluster intelligent control. The method comprises the following steps: firstly, constructing a communication topology for cluster distributed cooperative control of multiple unmanned ships; constructing a single unmanned ship speed and course system dynamic linearization data model with interference terms based on unknown external interference and nonlinear uncertainty; constructing a smooth anti-interference trajectory tracking algorithm of a single unmanned ship tracking dynamic target point; constructing a pseudo-Jacobian matrix estimation and distributed disturbance compensation controller for distributed model-free adaptive control of the multiple unmanned ships; constructing a reset algorithm and a controller of an actual unmanned ship; and finally, multi-unmanned ship cluster cooperative tracking control is realized. The method breaks through the limitation of a traditional control method, does not need to depend on complex system model information, only utilizes input and output data of the system, and achieves a complete data-driven intelligent control strategy.

The invention relates to the technical field of data transmission, and discloses an ultrahigh frequency RFID system data transmission method, device and equipment and a storage medium, and the method comprises the steps: carrying out the band-pass filtering and orthogonal demodulation of a multi-tag backscattering signal received by an RFID reader, and obtaining a tag feature data sequence; performing multi-dimensional feature analysis and time sequence correlation calculation to obtain an independent label signal frame; boundary identification is carried out, and a label data frame starting point is determined; carrying out continuous sampling analysis and label signal conflict identification, and outputting a conflict position sequence; performing matrix interleaving transformation and position rearrangement on the data marked by the conflict position sequence to generate an anti-conflict data packet; the label access scheduling queue is constructed based on the transmission time sequence and the priority attribute of the anti-collision data packet, the multi-label concurrent transmission time sequence is output, and the data transmission performance of the ultrahigh-frequency RFID system is improved through multi-stage signal processing and an intelligent scheduling mechanism.

The invention relates to the technical field of material management, and discloses an infrastructure material cyclic application management and control system, which comprises the following modules: an intelligent material identification and real-time data acquisition module, which is used for carrying out automatic identification, classification and real-time data acquisition on infrastructure materials based on various sensor technologies (visual identification, RFID, laser scanning and NFC) and a deep learning algorithm, a digital material file is generated, and dynamic tracking and accurate positioning of materials are achieved; and the block chain-based full life cycle management module is used for carrying out data management and encrypted storage on the full life cycle (from purchasing, transportation, storage, use, maintenance, cyclic utilization to scrapping treatment) of the materials. Through close combination of a multi-mode sensor technology (visual identification, RFID, laser scanning and NFC) and a deep learning algorithm, all-directional and multi-dimensional identification and real-time data acquisition can be performed on capital construction materials in a complex and changeable environment.

The invention discloses a construction deviation detection system and method based on three-dimensional point cloud construction, and belongs to the technical field of constructional engineering measurement and detection. The system comprises a measurement module which is used for transmitting and receiving laser beams, ultrasonic waves and microwave signals so as to measure various deviations in the building construction process; the sensor network module is used for monitoring environment and structure states, collecting stress, vibration, temperature and humidity data and transmitting the data through the Internet of Things module; the data processing and analyzing module is used for processing and analyzing the data acquired from the measurement module and the sensor network module, generating a three-dimensional model of the building, automatically matching the measurement data with the drawing size data, and marking a deviation position; and the augmented reality module is used for providing a real-time AR view. According to the invention, more comprehensive and accurate measurement data can be provided by adopting various measurement technologies such as laser, ultrasonic wave and microwave, and the device is suitable for various complex building structures and materials.

The invention provides a 5G intelligent networking method and system based on edge computing. The method comprises the following steps: deploying edge computing nodes, and designing a hierarchical network architecture and a dynamic routing mechanism; deploying an edge model on the edge computing node, and carrying out real-time processing and anomaly detection; dynamically distributing network resources, and carrying out network switching and load balancing by utilizing an edge model; decomposing the complex task into a plurality of sub-tasks, and performing distribution and parallel processing of the sub-tasks; updating and optimizing the edge model, and dynamically adjusting a networking strategy; and data security protection is realized, and network data and user feedback are monitored. The system comprises an edge deployment module, a data analysis module, a networking strategy module, a collaborative computing module, a collaborative optimization module and a security protection module. According to the invention, network resources can be efficiently utilized, intelligent networking and network switching are carried out, load balancing is realized, the response speed, the processing capability and the adaptive capability of the network are greatly improved, and the experience feeling of a user is improved.

The invention provides a smart power grid data communication optimization method based on edge calculation, and relates to the technical field of smart power grids, and the method comprises the steps: obtaining an inspection topology network of a target power grid; performing multi-angle communication analysis to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples; carrying out edge step identification to obtain M edge topology sub-networks; performing balanced configuration of sub-network service resources to obtain M edge computing servers; performing routing inspection by using a routing inspection unmanned aerial vehicle and a routing inspection robot to obtain routing inspection video data of L sites; and distributing the data to M edge computing servers for data preprocessing, and uploading the data to a cloud computing center for routing inspection data analysis to obtain a routing inspection analysis result. According to the invention, the technical problem of low data processing efficiency caused by data transmission delay and overweight network load due to the fact that power grid monitoring generally depends on centralized data processing in the prior art is solved.

The invention provides a meta-universe scene distributed two-stage cache optimization method combining MADDPG and GCN, and the method comprises the steps: dividing a meta-universe scene in the horizontal and vertical directions, obtaining a preset number of sub-scenes, and carrying out the integration of resources needed by the rendering of each sub-scene; the sub-scenes are compressed through an edge network, and the similarity of user tracks is calculated and obtained through a fast dynamic time warping algorithm; based on the similarity of different track fragments, sub-scene popularity ranking is obtained by weighting, summarizing and sorting the number of access times of a GPU server, sub-scenes predicted to be frequently accessed are requested and cached in advance according to the popularity ranking of the sub-scenes, and a two-stage cache replacement framework combining GCN and MADDPG is adopted to adjust a cache strategy of the server. According to the method, intelligent cache distribution and adjustment can be realized according to sub-scene popularity prediction, the cache hit rate is improved, and the delay response of a user request is reduced.

The invention discloses a high-frequency data remote transmission method and system, and the method comprises the steps: dynamically adjusting the collection frequency and mode according to environment conditions, and obtaining high-frequency data in real time; based on the data type and the transmission network state, a compression algorithm, a filtering mode and a coding strategy are automatically selected; the congestion condition of the transmission network is predicted through a built-in machine learning model, and a packaging strategy and transmission parameters are dynamically adjusted according to network conditions; in the data receiving process, the bit error rate in the transmission process is monitored in real time, abnormity in transmission is automatically detected, repair is carried out through an error correction algorithm, and if repair fails, the system automatically requests the data packet again; and the recovered high-frequency data is classified and stored according to the importance, real-time performance and storage cost of the data, and automatic storage strategy adjustment is realized in a remote database. According to the technical scheme, the high efficiency, reliability and safety of remote transmission of high-frequency data are improved.

The invention relates to the technical field of smart home, and discloses an AI-based fan intelligent temperature regulation control system, which comprises a multi-dimensional data acquisition module for acquiring environment temperature, humidity, air quality, illumination intensity and human body state data; the data fusion and preprocessing module is used for carrying out denoising, standardization and time synchronization on the multi-dimensional data and fusing the multi-source data; the AI decision and control module is used for generating a speed regulation and mode control instruction of the fan based on the human body comfort evaluation model and an AI algorithm; and the feedback optimization and execution module is used for executing the control instruction. Through a multi-dimensional data perception and fusion technology, comprehensive monitoring of environment temperature and humidity, air quality and a human body state is realized. The AI intelligent regulation and control algorithm is combined, the fan speed and mode are dynamically adjusted, and the user comfort and energy consumption are balanced; through edge calculation and cloud optimization, real-time response and continuous adaptive optimization of the system are ensured, and the accuracy and stability of the system are improved.

The invention discloses an efficient real-time voice and video transmission method, which is characterized in that a proper video frame coding mode is selected according to the state of video content, so that a system can effectively reduce the data volume and save the network bandwidth by using a high-compression-rate coding mode in a static state; more video details are reserved by using a low-compression-rate coding mode in a dynamic state, so that the data transmission quantity is reduced to the greatest extent on the premise of not influencing the video quality, and the overall transmission efficiency is improved; meanwhile, due to the fact that the transmitted data size is reduced and the data transmission speed is increased, transmission delay of the audio and video data is reduced, the phenomenon of audio and video asynchronization caused by data packet disorder can be effectively reduced through reasonable buffer area management, it is ensured that the audio and video data are played according to the generated time sequence, the watching experience of a user is optimized, and the user experience is improved. High-quality and low-delay audio and video transmission service is realized, and high requirements of users on real-time performance and definition are met.

The invention discloses a behavior detection method, a storage medium and a computer program product, and relates to the technical field of computer vision, and the method comprises the steps: edge end equipment obtains continuous frame image data collected by image collection equipment, and inputs the continuous frame image data into an image detection model; performing behavior detection based on the continuous frame image data through the image detection model to obtain an initial detection result; and sending the initial detection result to the cloud equipment. And the cloud device performs behavior detection based on the initial detection result through the multi-modal large model to obtain an intermediate detection result, compares the intermediate detection result with the initial detection result to obtain a comparison result, and outputs the initial detection result and the intermediate detection result based on the comparison result. The processing efficiency and the recognition precision of behavior detection based on the image are improved.

The invention discloses a diagnostic data remote transmission method based on a regional remote diagnosis and treatment platform. The method comprises the following steps: S1, collecting diagnostic data of a patient; s2, preprocessing the acquired diagnosis data; s3, carrying out data transmission by utilizing a short-distance wireless communication technology and a long-distance mobile communication technology; s4, performing data encryption and identity verification by adopting AES and RSA encryption algorithms; s5, after the regional remote diagnosis and treatment platform receives the diagnosis data, decryption and identity verification are carried out on the data; s6, performing image data analysis by using a convolutional neural network, processing character data by using a natural language processing technology, predicting and classifying physiological parameters by using regression analysis and a classification algorithm, and performing fusion analysis on multi-modal data in combination with a deep learning model; and S7, transmitting the analyzed and processed diagnosis result back to a local data terminal through a long-distance mobile communication technology. The system has the advantages of high transmission stability, high safety, high transmission efficiency and high diagnosis accuracy.

The invention relates to the technical field of machine learning, and discloses a model file loading method and system, computer equipment and a storage medium, and the method comprises the steps: obtaining an update request when an inference service is started; if it is detected that the updating request carries a local storage path of the model file, taking the local storage path as a target storage volume of a target scheduling unit; mounting the target storage volume in an initialization container of the target scheduling unit, and generating a target mounting item of the initialization container; generating response information based on the target storage volume and the target mount item, wherein the response information is used for updating the target scheduling unit to establish a communication link between the updated target scheduling unit and the local directory; and response information is sent to the first slave node, so that the first slave node updates the target scheduling unit and loads the model file based on the communication link. According to the method, the problems of high transmission delay, redundant occupation of hard disk resources, limitation of namespace and the like in the model file loading process can be solved.