471 results about "Communication bandwidth" patented technology

The invention provides an emergency command converged communication system resource dynamic scheduling method based on artificial intelligence. The method comprises the following steps: S1, acquiring a plurality of sensors and log data of an emergency command system; constructing a dynamic graph neural network optimization communication topology, and selecting an optimal communication path; combining resource prediction and the optimized communication topology to formulate a global optimal resource scheduling strategy, and generating an optimal resource allocation scheme for each task; the resource pool comprises available computing resources and communication bandwidth; dynamically adjusting resource allocation and a communication path in a task execution process, and generating task execution feedback of a corresponding task; the system state is monitored in real time, and resource prediction, topology adjustment and scheduling strategies are optimized based on execution error feedback of task execution feedback. According to the method, efficient and intelligent dynamic resource scheduling is realized in an emergency command converged communication system through resource prediction of self-supervised learning, self-adaptive topological optimization of a variable topological graph neural network and global scheduling of reinforcement learning driving.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention discloses a computing node, a computing system, a control method, a control device, equipment and a medium, and relates to the technical field of computers, a plurality of acceleration cards are interconnected through an Ethernet switching module, and low-order channels of serializer / deserializer interfaces of the acceleration cards are connected with the Ethernet switching module through retimers. A high-order channel of at least one serializer / deserializer interface of each accelerator card is connected with the Ethernet switching module through a retimer, and the working mode of the retimer is configured through a first controller on a computing node according to the signal rate of the accelerator cards, so that a framework that communication interfaces between the accelerator cards are fully interconnected is realized. Any two acceleration cards can be directly connected, the communication bandwidth between the acceleration cards is improved, meanwhile, the number of the acceleration cards can be flexibly expanded, longitudinal expansion and transverse expansion of a computing system can be conveniently achieved, the problem that a traditional acceleration card interconnection scheme is not easy to expand is solved, and the method can adapt to service requirements and flexibly adjust working loads.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention discloses an intelligent management method and system for offshore low-broadband data transmission, and the method comprises the following steps: S1, dividing an offshore communication network into a plurality of bandwidth sensitive intervals, collecting dynamic environment data, and marking a network operation mode state; s2, associating and matching the dynamic environment data with a network operation mode state; s3, generating a data transmission strategy group based on the network state feature set; s4, simulating a path selection behavior and generating a path load characteristic spectrum; s5, performing clustering processing on the path load characteristic spectrum to form a path strategy optimization library; s6, determining a data transmission strategy and path combination by using a double-library dynamic mapping mechanism; and S7, monitoring the network state in real time, and dynamically updating the path strategy optimization library and the network state feature set. According to the invention, the maritime communication bandwidth utilization efficiency and the transmission stability are improved.

The invention relates to the technical field of wireless communication, and relates to a multi-terminal data processing method, a multi-terminal data processing device, communication equipment and a computer readable storage medium. The method comprises the following steps: an on-satellite UPF receives to-be-processed data sent by a terminal through a wireless access network, the to-be-processed data carries a joint task identifier, and the terminal is any one of a plurality of terminals corresponding to the joint task identifier; the satellite UPF sends at least part of data in the to-be-processed data to a ground UPF for processing to obtain ground processing data, and stores other data of the to-be-processed data; and the on-satellite UPF performs cooperative processing on the other data corresponding to the joint task identifier in the storage to obtain on-satellite processing data, and sends the on-satellite processing data to the ground UPF, so that the ground UPF performs forwarding based on the ground processing data and the on-satellite processing data. The data transmission amount can be reduced, the communication bandwidth can be saved, the utilization of resources can be optimized, and the data processing efficiency and timeliness can be improved.

The invention relates to the field of train informatization, in particular to a train running state evaluation system based on data analysis. The train braking stress diffusion abnormal tendency value is determined by setting the braking information responding to the train, calling the train body image of the corresponding braking time period, extracting the train shaking characteristics and combining the discrete difference of the loads of all the carriages in the braking time period, and then the data time period type is marked. And the data in the time period is classified, uploaded and stored according to different marks, so that the real-time bandwidth requirement is reduced. According to the invention, the inter-wheel temperature difference characteristic and the in-wheel temperature difference characteristic of each wheel are considered and extracted, the wheel end abnormal risk characterization value is calculated and compared with the early warning threshold value, and the wheel end data is analyzed in a targeted manner, so that the data processing efficiency of a large amount of train data is improved, the real-time communication bandwidth demand is reduced, and the identification accuracy is improved.

The invention discloses a V2V-based intelligent networked automobile road adhesion coefficient estimation method and system, and relates to the technical field of intelligent networked automobiles. The method comprises the steps that physical parameters and motion state information of a front vehicle are received based on V2V, an event triggering communication mechanism is adopted in the sending process of the physical parameters and the motion state information of the front vehicle, and meanwhile data packet loss is considered; constructing a three-degree-of-freedom vehicle dynamics model of the front vehicle; and establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss. According to the method, the front vehicle is regarded as a sensor of the vehicle, the parameters and motion information of the front vehicle are obtained through vehicle-mounted wireless communication, the problems of data packet loss and limited communication bandwidth during communication are considered, the robustness of an estimator to model parameter perturbation is improved through a strong tracking algorithm, and the estimation accuracy is improved. And finally, establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss, and ensuring that the road adhesion coefficient is accurately obtained in advance.

The invention relates to an internet of vehicles edge computing task unloading and resource scheduling optimization method, which comprises the following steps: constructing an internet of vehicles edge computing system model fusing a task jump mechanism and dual-priority scheduling, the system model comprising a road test unit deployment model, a communication model, a computing model and a task jump model; establishing a joint optimization function aiming at minimizing the total time delay and the total energy consumption of the system; decomposing a joint optimization problem into three sub-problems of unloading decision, computing resource allocation and communication strategy selection; a deep reinforcement learning algorithm is adopted to intelligently sense a road environment state, an optimal communication priority strategy combination is dynamically selected, and a task unloading decision and computing resource allocation are collaboratively optimized; and in each scheduling time slot, the edge server allocates communication bandwidth and computing resources to the vehicle tasks according to the selected strategy, and triggers a task preemption and cross-server jump mechanism, thereby realizing maximization of system throughput and minimization of task processing time delay.

A georegistration (a.k.a. georectification) of an image captured by a camera in an aerial vehicle, such as a satellite, is based on identifying multiple features using descriptor sets, and sending to a ground station only the descriptors of the identified features and the associated locations in the captured image, without sending of the captured image itself, thus requiring a low communication bandwidth. Using a database of geosynchronized reference images, the ground station uses the received descriptors sets and the associated image locations to localize the features on a selected geosynchronized reference image from the database, and forms a mapping function that map any locations in the captured image to geographical coordinates on Earth. The mapping may be used to geosynchronize an additional feature identified in the aerial vehicle, or to geo synchronize a region that may be cropped from the captured image and sent to the ground station.

The invention discloses a method of an optical anti-turbulence device for polarization multiplexing spatial optical communication. The mode demultiplexer is connected with the optical fiber; the single-mode optical fiber array is respectively coupled with each spatial intrinsic mode decomposed by the mode demultiplexer; the polarization CBC chip is connected with the other end of the single-mode optical fiber array; the driving control module is connected with the polarization CBC chip; the polarization multiplexing optical communication digital coherent receiver (Rx) is connected with the polarization CBC chip through a single-mode optical fiber; through the arrangement of the polarization CBC chip, the compatibility of a polarization multiplexing technology and a mode multiplexing coherent beam combination technology is realized, the problem of polarization dimension information loss or strong crosstalk caused by the use of a polarization state rotator (PSR) and a polarization maintaining optical fiber in the prior art is solved, modulation information of two orthogonal polarization dimensions is completely reserved, the communication bandwidth is improved, and the communication efficiency is improved. And the communication efficiency is greatly improved.

A dielectric waveguide structure of a semiconductor photonics device is formed prior to formation of the semiconductor photonics components of the semiconductor photonics device. This enables high-temperature processing techniques to be used to form the dielectric waveguide structure without concern for potential damage that might otherwise be caused to the semiconductor photonics components if the dielectric waveguide were to be formed above the semiconductor photonics components. The use of the high-temperature processing techniques may enable low optical loss to be achieved for the dielectric waveguide structure in that the high-temperature processing techniques may be used to achieve a low hydrogen concentration in the dielectric waveguide structure. The low hydrogen concentration in the dielectric waveguide structure enables higher performance to be achieved for the dielectric waveguide structure, including greater operating efficiency and increased communication bandwidth.

The invention provides a method for testing the communication bandwidth of a 10-gigabit network between an FPGA (Field Programmable Gate Array) and a CPU (Central Processing Unit), which is applied to a radar system. The FPGA completes a function of regularly generating data and a function of downlink transmission of the data; the 10-gigabit network exchange board card completes data transmission and sends data to the CPU board card through a 10-gigabit network; and the CPU board card receives the data and calculates the communication bandwidth and the network quality. According to the invention, the limitation of traditional single-port point-to-point transmission or simple multi-port parallel transmission on synchronization, load balancing or protocol overhead is solved, and a solution is provided for how to effectively utilize multiple ports, enable the multiple ports to simultaneously and efficiently work and ensure parallel transmission of data without mutual interference.

The invention relates to a model training method and device based on a heterogeneous GPU cluster and a storage medium. The method comprises the steps of obtaining hardware index data of each GPU in the heterogeneous GPU cluster; obtaining a plurality of operation types corresponding to the deep learning algorithm in the to-be-trained model, and measuring operation performance data of operation corresponding to each operation type executed by each GPU; acquiring communication bandwidth multidimensional features and a load state sensing strategy of each GPU; according to the hardware index data, the operation performance data, the communication bandwidth multi-dimensional features and the load state sensing strategy corresponding to each GPU, constructing a multi-dimensional operation performance matrix of each GPU; according to the multi-dimensional operation performance matrix corresponding to each GPU, the GPU is allocated to each structure layer in the to-be-trained model for model training to obtain a target model, the training efficiency of the model is improved, and the training time of the model is shortened.

The invention discloses a digital in-memory computing architecture compiling and simulating method and a tool chain, and belongs to the technical field of in-memory computing. The method comprises the following steps: acquiring a target deep neural network model and a target in-memory computing architecture configuration parameter adapted to the target deep neural network model, the target in-memory computing architecture configuration parameters comprise at least one of the following parameters: the number of cores, the on-chip communication bandwidth between the cores, the size, the number and the bit width of a storage unit of each core, and the scale of a computing unit of each core; configuring a digital in-memory computing accelerator according to the target in-memory computing architecture configuration parameters; compiling the target deep neural network model into a target optimization instruction sequence based on a digital in-memory calculation accelerator; simulating and executing the target optimization instruction sequence, and performing performance evaluation according to performance indexes in the process of executing the target optimization instruction sequence.

The invention discloses an unmanned aerial vehicle group elastic path planning method and system for resisting an intelligent jammer, which can ensure the accurate synchronization of an interference strategy while realizing the efficient compression and transmission of information and reducing the communication bandwidth demand by an interference party by means of an encoder-decoder communication mechanism, and cooperate with a preferential experience playback technology to realize the flexible path planning of the unmanned aerial vehicle group. Learning is rapidly carried out from past experiences, convergence of an interference strategy is accelerated, efficient cooperative interference is achieved, and a strong threat is formed for a data collection task of the unmanned aerial vehicle group; at the defense party, an attention reviewer module is constructed based on an advanced attention mechanism, information having key influence on unmanned aerial vehicle decision making is accurately focused, the attention weight between intelligent agents is calculated, the weight is reasonably distributed for a state-action joint vector, and a more targeted decision is made in a complex environment; a double-commentator module is introduced, a value function is independently estimated by using two commentator networks, and the reliability and the stability of the algorithm are improved through smooth function fusion output and stable value function estimation.

The invention discloses a transmission type communication system and method based on low-frequency carrier waves and high-frequency modulation. The transmission type communication system and method are suitable for complex medium environments such as deep sea, strata and biological tissues. Aiming at the contradiction that the traditional low-frequency communication bandwidth is insufficient and the high-frequency communication penetrating power is poor, according to the scheme, a low-frequency carrier wave (30Hz-100kHz) and a high-frequency modulation signal (such as OFDM (Orthogonal Frequency Division Multiplexing) and 256QAM (Quadrature Amplitude Modulation)) are fused through a nonlinear signal superposition technology to generate a composite carrier wave signal, long-distance penetration is realized by utilizing the low attenuation characteristic of the low-frequency carrier wave, and meanwhile, the data rate is improved through high-frequency modulation. Application scenes comprise deep sea detection (300-meter high-definition video return), a brain-computer interface (implantable antenna transmission neural signal) and an intelligent power grid (underground pipe network monitoring). Through the physical layer-information layer collaborative design, the physical boundary limitation of traditional communication is broken through, and high penetrability, high bandwidth and strong anti-interference capability are achieved.

The invention relates to the technical field of artificial intelligence and unmanned aerial vehicles, and discloses a multi-unmanned aerial vehicle cooperative navigation method based on deep reinforcement learning, the method is suitable for multi-agent task execution in a complex dynamic environment, the method is constructed by adopting a multi-agent dual deep Q network model, a centralized training and distributed execution architecture is combined, and in a training stage, the multi-agent dual deep Q network model is constructed; through a global state optimization cooperation strategy, each unmanned aerial vehicle autonomously decides according to local observation in an execution stage, and dynamically adjusts an information synchronization frequency based on a communication load sensing mechanism so as to reduce communication bandwidth occupation and improve system real-time performance, and the method also introduces an experience playback and priority sampling mechanism to improve model stability and convergence speed. The system can sense obstacle information, energy consumption level and other unmanned aerial vehicle states in real time, realizes path dynamic optimization and formation keeping, has good task adaptability and expandability, and is suitable for collaborative navigation application of a heterogeneous unmanned aerial vehicle system.

The invention discloses a communication efficient data opening and sharing method and system based on privacy set intersection. In order to solve the problem that when the number of participants is large, the communication bandwidth of the participants in a privacy calculation scene is limited, the invention proposes that different element insertion sequences have influences on the calculation result of the confusion Bloom filter for the first time; an efficient optimal element insertion sequence search algorithm assisted by a counting bloom filter is provided based on a greedy strategy, and meanwhile, a theoretical lower bound and a theoretical upper bound for improving the storage space of the confusion bloom filter under a new algorithm are analyzed; a secret sharing principle of an improved confusion bloom filter is changed from XOR calculation to additive calculation, so that a related algorithm of the improved confusion bloom filter can perform homomorphic calculation in a ciphertext encrypted by a public key; based on the improved confusion Bloom filter, a multi-party privacy set intersection protocol with high communication efficiency is realized, and the protocol can be used for calculating a multi-party intersection and also can be used for calculating a threshold multi-party intersection of a self-defined threshold.

The invention provides a collaborative collision and obstacle avoidance strategy of an underactuated unmanned ship considering input and state quantification. The collaborative collision and obstacle avoidance strategy comprises the following steps: establishing a mathematical model of the underactuated unmanned ship; a distributed guidance rule is constructed in the USV kinematics subsystem by using an auxiliary variable method, and a repulsive force function of an improved artificial potential field method is introduced to reconstruct the guidance rule, so that collaborative collision avoidance and obstacle avoidance at the kinematics level and expected trajectory tracking of the underactuated USV are realized; designing an extended state observer to solve the influence of unknown items in the system and quantitative state variables in the communication process on the formation control system; a linear time-varying model is adopted to describe a quantizer, so that a bottom-layer quantization controller does not need to predict specific information of quantization parameters; and proving the stability of the constructed USV formation tracking control system according to an input-state stability theory. According to the invention, starting from two aspects of input quantization and state quantization, the problem of USV formation tracking control in the marine environment with limited communication bandwidth and complex quantization environment is solved.

The embodiment of the invention relates to the technical field of vehicle control, in particular to a vehicle control method, a control device and a storage medium, and the method comprises the steps: carrying out the preprocessing, feature alignment and preliminary fusion of first data obtained by all sensors, obtaining third data, and processing the third data according to a communication bandwidth demand, according to the invention, the system performance and the data transmission efficiency are improved, and it is ensured that the fourth data of each sensor are efficiently and accurately fused to generate the environmental perception data, so that the vehicle driving is controlled, the transmission delay of the vehicle control information is reduced, and the occurrence of traffic accidents is reduced.

The invention provides a multi-underwater-robot cooperative target tracking control and decision-making method and system. The method comprises the following steps of: detecting and identifying an underwater dynamic target and predicting behaviors; a step of high-precision cooperative tracking under a weak communication condition; and performing target tracking control under a weak perception condition. According to the invention, aiming at a deep sea weak perception weak communication environment, an underwater dynamic target detection and identification technology based on multi-underwater robot perception information is researched, and accurate perception and accurate prediction of ocean dynamic target state information are realized. A multi-underwater-robot cooperative tracking technology under a weak communication condition is researched, and the problem of multi-underwater-robot cooperative target tracking due to communication delay and communication bandwidth limitation is solved.

The invention provides a chip routing method and device, electronic equipment and a storage medium, and relates to the technical field of chip design and manufacture, and the method comprises the steps: carrying out the first-stage interleaving among a plurality of input and output modules based on a memory address of a current data request, and determining a target input and output module; in the target input and output module, performing second-level interleaving among the plurality of links based on the memory address, and determining a target link; and performing third-level interleaving among the plurality of point-to-point channels of the target link, and determining the point-to-point channel of the current data request. According to the method and the device provided by the invention, a three-level progressive interleaving architecture which performs dynamic calculation completely based on the memory address is constructed, the problem that hardware resource consumption is sharply increased due to chip interconnection scale enlargement is solved, the chip cost and power consumption are remarkably saved, and through a step-by-step refined load balancing mechanism, the load balancing efficiency is greatly improved. The routing efficiency in the chip is improved, and the overall communication bandwidth and system performance of chip interconnection are improved.

The invention relates to an unmanned aerial vehicle cluster area search planning method, device, equipment and medium, and the method comprises a task pre-planning stage and a dynamic re-planning stage: in the task pre-planning stage, a ground control center carries out the grid division and numbering of a target area according to the unit time search capability of each unmanned aerial vehicle, and distributes the target area to each unmanned aerial vehicle; maintaining the distributed task state information in each unmanned aerial vehicle; in the dynamic re-planning stage, each unmanned aerial vehicle carries out autonomous search according to the network and the number, and task state transmission and cluster distributed information maintenance are carried out by adopting a long intermittent communication mode in which period triggering and event triggering are combined. Through combination of ground pre-planning and aerial distributed dynamic adjustment, synchronization and efficient and robust collaborative search of cluster task states are realized, the problem that unmanned aerial vehicle cluster area search excessively depends on communication bandwidth and a center node is effectively solved, the requirements for communication bandwidth and frequency are greatly reduced, and the method is suitable for large-scale unmanned aerial vehicle cluster area search. Therefore, the device can work stably in a severe electromagnetic environment.

The present application relates to the technical field of optical communication. Disclosed are an optical chip and a preparation method therefor, and an electronic device. The preparation method comprises: acquiring a substrate, wherein the substrate comprises a first base and a first device layer that are stacked, and the first device layer comprises a detection portion for preparing a photoelectric detector; forming a first waveguide on the substrate, removing the first base, and bonding a second base, wherein the first device layer and the first waveguide are located on the same side of the second base, and the resistivity of the second base is greater than the resistivity of the first base; and forming a first electrode of an electro-optic modulator on the first waveguide, and forming a second electrode of the photoelectric detector on the detection portion. By means of the preparation method and an optical chip obtained thereby, the process compatibility of the optical chip can be enhanced, the loss of optical signals is reduced, the communication bandwidth of the optical chip is increased, and the communication performance of an electronic device is thus improved.

The invention relates to a multi-agent reinforcement learning training method, system and device driven by an unreal engine and a medium. The method comprises the following steps: acquiring core attribute data of multiple agents, including real-time communication bandwidth, initial communication topology configuration, training stage progress, single agent computing power and cluster overall computing load state; analyzing the real-time communication bandwidth and the initial communication topology configuration to form an optimized topology configuration scheme; in combination with an optimized topology configuration scheme and a training stage progress, generating a cooperative training progress state by quantifying progress differences among intelligent agents; extracting load balance data according to the single agent computing capability and the cluster load state; integrating load balance data, a collaborative training progress state and an optimized topology configuration scheme, and constructing a multi-layer collaborative training framework; and integrated optimization is carried out to generate a cooperative training scheme for guiding multi-agent communication, training and resource allocation, so that the training efficiency is effectively improved, the resource load is balanced, and the cooperative synchronism is enhanced.

The invention discloses an electric vehicle transportation state real-time monitoring method based on multi-sensor fusion, and belongs to the field of electric vehicle transportation state real-time monitoring. A comprehensive state index is obtained through integration of the multi-source data fusion model; acquiring a data transmission priority in the comprehensive state index, and if the data transmission priority is higher than a communication bandwidth threshold, allocating an additional transmission time sequence to obtain a dynamic communication scheduling sequence; carrying out resource allocation on the vehicle-mounted communication system according to the dynamic communication scheduling sequence, and judging whether key safety data such as battery overheating early warning is in the first position of the sequence or not so as to determine a real-time transmission path; and monitoring a communication congestion index in the real-time transmission path, and if the communication congestion index exceeds a threshold value, activating a standby data compression mechanism to obtain a reliable transmission data packet, and sending the reliable transmission data packet to a monitoring center.

The invention provides a switch cabinet partial discharge on-line monitoring method based on cloud-side cooperation, and belongs to the technical field of switch cabinet partial discharge detection. Comprising the steps that an edge computing device performs partial discharge preliminary monitoring by using discrete signals collected by an ultrasonic sensor and a ground wave sensor on a switch cabinet; if the partial discharge event is preliminarily monitored, discrete signals collected by all ultrasonic sensors and ground wave sensors on the corresponding switch cabinet before and after partial discharge are sent to cloud computing equipment; and the cloud computing device carries out partial discharge type identification and partial discharge position positioning according to the received discrete signals. According to the method, a collaborative architecture of edge preliminary monitoring and cloud deep analysis is adopted, and lightweight real-time monitoring is executed on the edge side; according to the invention, an intelligent trigger uploading mechanism based on an event is adopted, and the original sensing data before and after the event occurrence moment are uploaded only after the partial discharge event is preliminarily confirmed at the edge side, so that the communication bandwidth demand and the cloud processing load are greatly reduced.