282results 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 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 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 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 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 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 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 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 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.

The embodiment of the invention provides a data retransmission method, device and system. According to the embodiment of the invention, retransmission is carried out on a physical layer based on a data slice, the data slice comprises a code block carried by at least one FEC code word, and a corresponding serial number is allocated to each data slice in data. A sending device can store sent data slices and corresponding serial numbers in a buffer area while sending data. And after receiving the data, the receiving equipment distributes a corresponding serial number for each data slice according to the same way as the sending equipment. And furthermore, the receiving equipment sends a retransmission request carrying the serial number corresponding to the data slice after finding the uncorrectable error, and the sending equipment extracts the corresponding data slice from the buffer area according to the retransmission request and starts retransmission. It can be seen that the number of code blocks borne by the data slices is small, and compared with message-based retransmission, retransmission based on the data slices is more beneficial to reducing the transmission delay of retransmission and reducing the space of a buffer area.

The invention discloses a modularized intelligent identification and configuration system for a portable Internet of Things electric energy metering box, and relates to the technical field of Internet of Things. Electric energy data are collected in real time through an integrated sensor network, and are cleaned, compressed and preliminarily filtered by a data processing module; a deep learning algorithm is adopted to perform feature extraction and abnormal power consumption behavior detection on the data, and rapid response is ensured; a graphical operation interface is provided through the user interface module, and a user is allowed to update configuration immediately according to requirements; the link state is monitored through the LACP, the optimal link is automatically selected, and stable data transmission is ensured; temporarily storing data through a local cache and backup module when the network fluctuates, and automatically synchronizing the data to the cloud after the network is recovered; the system load is monitored through the self-adaptive optimization module, resource allocation is dynamically adjusted, and efficient and stable operation of the system is guaranteed; the data processing speed of the metering box equipment of the electric power internet of things is greatly improved, and the flexibility and the stability are enhanced.

The invention provides a resource scheduling method and device, electronic equipment and a storage medium, relates to the technical field of data processing, and can solve the problems of low network resource utilization rate and low network performance. The method comprises the steps that service information is obtained according to a scheduling request sent by a terminal, a target scheduling algorithm matched with the service information is determined based on a scheduling algorithm model and a scheduling algorithm library, and the scheduling algorithm model is obtained based on real service information, simulation service information and the scheduling algorithm library training; the scheduling algorithm library comprises scheduling algorithms corresponding to a plurality of service scenes, the target scheduling algorithm is used for indicating a scheduling mode of network resource information matched with the service information, and the network resource information comprises cloud nodes, edge nodes and transmission paths; and scheduling the corresponding target processing node and the target transmission path for the service information. The network resource utilization rate and the network performance can be improved.

The invention discloses a three-dimensional reconstruction method based on an unmanned aerial vehicle and edge calculation, and relates to the field of unmanned aerial vehicle technology, edge calculation and three-dimensional scene reconstruction. The method comprises the following steps: an unmanned aerial vehicle carries a monocular camera to collect a standard-definition video stream from a target scene; the edge computing device receives and processes the standard-definition video stream in real time to generate preliminary low-precision three-dimensional reconstruction; the edge computing device identifies a defect area by analyzing a low-precision reconstruction result, and optimizes the flight path of the unmanned aerial vehicle; the unmanned aerial vehicle collects a high-definition video stream from the target scene according to the optimized path; and the edge computing device performs off-line high-precision three-dimensional reconstruction on the high-definition video stream so as to improve the accuracy and detail expression capability of a reconstructed scene. Through cooperative work of the unmanned aerial vehicle and the edge calculation end, real-time data processing, feedback and decision making in the collection process and high-precision three-dimensional reconstruction result storage in the off-line stage are achieved, and the real-time performance and accuracy of three-dimensional reconstruction are effectively improved.

The invention discloses an edge calculation control method and equipment for a power transmission line image acquisition device, and the method comprises the steps: deploying a plurality of edge calculation nodes at a preset position node of a power transmission line, and enabling all edge calculation nodes to be connected with a management system of a cloud server; the control method comprises the following steps: preferentially processing tasks with high priorities and high security levels by adopting a dynamic resource allocation strategy according to the priorities and the security levels of image acquisition tasks; the tasks can be distributed to adjacent idle nodes through the centralized management system; or uploading the data corresponding to the processing task of the edge computing node to a cloud server for collaborative analysis; and an analysis result can be sent to a monitoring center and maintenance personnel in real time. According to the method, edge computing resources are reasonably optimized, so that edge computing nodes can perform data processing locally; edge-edge and edge-cloud cooperative processing can be realized through control; the system can cope with complex and changeable field environments, and provides powerful guarantee for safe and stable operation of a power transmission line.

The invention discloses an Internet of Things authentication method and system, and relates to the technical field of Internet of Things. The system comprises a quantum machine learning behavior analysis module, a multi-factor authentication module, a block chain and intelligent contract module, an edge computing authentication processing module, a self-adaptive authentication strategy and risk assessment module, a privacy protection and data desensitization module and a cross-platform interoperation module. According to the invention, the security, efficiency and flexibility of the Internet of Things authentication system are significantly improved, so that the system can efficiently identify abnormal behaviors of equipment, effectively prevent potential security threats, significantly reduce authentication delay, and improve the response speed and user experience of the system. Therefore, the problems existing in the aspects of safety, authentication efficiency and expandability of a traditional authentication method are solved, and a safer, more efficient and more extensible authentication mechanism is provided for the Internet of Things equipment.

The invention relates to the technical field of tunnel engineering monitoring, in particular to a tunnel health monitoring method and system based on edge computing, and the method comprises the steps: a data collection module obtains the original state data of a tunnel through a sensor deployment network, carries out the primary processing of the original state data based on edge computing, and obtains the actual state data; during the period, the data acquisition module performs data filtering on the original state data based on a state data threshold value, rejects invalid data in the original state data and marks abnormal data in the valid data according to an anomaly detection algorithm, and the remaining unmarked original state data is the actual state data of the tunnel; and the monitoring cloud platform obtains the actual state data every a preset period, and carries out data summarization, data storage and data analysis processing on all the actual state data. The system has the beneficial effects that part of calculation tasks are issued to the data acquisition module, so that the processing burden of the monitoring cloud platform is reduced, and the real-time performance and effectiveness of monitoring and early warning are improved.

The invention relates to the technical field of equipment fault detection, and discloses an equipment fault prediction and diagnosis method which is applied to an ATE system, and the method comprises the steps: collecting the real-time operation data of equipment through a plurality of sensors, and dynamically scheduling the sensors through an adaptive algorithm, so as to adjust the data collection frequency and range according to the state of the equipment; the collected data is preprocessed; training a plurality of machine learning models based on the historical fault data and the operation data, and deploying the models to an ATE system for real-time analysis; predicting an equipment fault according to the model, generating alarm information when a potential fault is detected, and sending the alarm information to an operator through a user interface or a notification system; generating a fault diagnosis report according to an analysis result and providing a repair suggestion; recording a fault instance and data, and updating and retraining the machine learning model to improve prediction and diagnosis accuracy; and generating a digital twin model of the equipment based on real-time and historical data, simulating the running state of the equipment, predicting future faults, and evaluating the health condition of the equipment.

The present invention relates to the field of smart grid technology, specifically a distribution terminal communication network system based on WAPI, the system includes a distribution data collection module, a distribution data compression module, a data encryption processing module, a network parameter adjustment module, a distribution anomaly identification module and a distribution scheduling adjustment module. In the present invention, by introducing real-time data processing and encryption technology based on WAPI, the security of data and the efficiency of the network are improved, the data encryption processing ensures the security of information during transmission, enhances the system's control ability over power network operation and monitoring, and adjusts the sending frequency of data packets according to the network status, dynamically optimizes network parameters, improves the network's adaptability and stability, and implements anomaly identification and fault response to ensure the continuity and safety of power grid operation. By optimizing the data processing process and strengthening safety measures, the overall performance and reliability of the power system are significantly improved.

The invention provides a distributed photovoltaic energy efficiency dynamic monitoring method based on time sequence analysis, and relates to the technical field of photovoltaic power generation, and the method comprises the steps: deploying an edge calculation node, regularly receiving the electrical operation parameters of a photovoltaic inverter, and recording a monitoring node; predicting photovoltaic output power according to the electrical operation parameters, and obtaining predicted output power; analyzing the power supply log, obtaining actual output power under the monitoring node, forming energy efficiency analysis data based on the monitoring node, the predicted output power and the actual output power, and storing the energy efficiency analysis data in an energy efficiency analysis database; performing power prediction deviation analysis by using the energy efficiency analysis database, and constructing a time period-deviation comparison table; and when grid-connected scheduling is carried out on the distributed photovoltaic equipment, power output compensation is carried out based on the time period-deviation comparison table. The technical problem that the power prediction precision is insufficient due to the fact that a traditional time sequence analysis method lacks accurate analysis for different time periods is solved, and the power prediction accuracy and the grid-connected dispatching quality are improved.

The invention relates to the technical field of carbon emission management, in particular to edge calculation carbon emission equipment with a display screen. The technical problems are that a traditional carbon emission monitoring method is difficult to realize real-time monitoring and quick response, during large-scale data processing, calculation bottleneck is often faced, analysis efficiency and precision are influenced, intelligent analysis means are lacked, and valuable carbon emission modes and trends are difficult to mine from mass data; according to the technical scheme, the edge calculation carbon emission equipment with the display screen comprises a data acquisition module, an edge calculation module, a cloud calculation module, an intelligent decision early warning module and an API interface, the carbon emission management device integrating real-time monitoring, intelligent analysis and early warning decision is developed, and the intelligence and the high efficiency of carbon emission management are remarkably improved by integrating multiple modules of data acquisition, edge calculation, cloud calculation, intelligent decision early warning and an API interface.

The invention provides a data transmission method and device for ocean perception data and electronic equipment, and relates to the technical field of communication networks. In the application, through the at least one sensing node, the intermediate node and the computing node included in the node set corresponding to the target sea area, a multi-source target ocean sensing network can be constructed, higher requirements of ocean meteorological prediction on data transmission and processing are met, and a future intelligent ocean meteorological prediction scene is enabled. Moreover, at least one data transmission link meeting the preset data transmission link condition is selected from the target ocean sensing network, so that the optimization of link resources in the target ocean sensing network is realized, the link configuration in the target ocean sensing network is dynamically adjusted, the transmission delay of the ocean sensing data is reduced, and the transmission efficiency of the ocean sensing network is improved. Therefore, the overall transmission efficiency of the target ocean sensing network is improved. The rationality and timeliness of ocean sensing data transmission can be improved, and the performance and real-time forecasting capability of an ocean sensing network are further improved.

The invention particularly relates to a gas station oil gas recovery online monitoring system, method and device and a medium, the monitoring system comprises a sensor network, an edge controller and a cloud computing monitoring platform, and the sensor network is used for collecting working parameters at key nodes in the oil gas recovery process. And the edge controller is used for performing preliminary data processing based on the working parameters to obtain oil gas recovery performance parameters and refueling equipment early warning information. And furthermore, the cloud computing monitoring platform is used for acquiring the oil gas recovery performance parameter sent by each edge controller in the gas station, and performing multi-dimensional data analysis based on each oil gas recovery performance parameter to obtain an oil gas recovery multi-dimensional monitoring result. By executing multi-dimensional data analysis, abnormal conditions in the oil and gas recovery process can be found and fed back in time, efficient oil and gas recovery work is ensured, meanwhile, potential fault points can be recognized in advance, the downtime and maintenance cost caused by faults are reduced, and the stability and reliability of the oil and gas recovery process are improved.