step 1: performing data acquisition, preprocessing, and storage based on a fifth-generation telecommunication technology standard distributed computing framework terminal network architecture, wherein step 1 comprises following steps:

step 1.1: performing data acquisition, which comprises: building an aero-engine gear fault simulation platform, and arranging, by using an edge computing technology, a base station in an edge network close to the aero-engine gear fault simulation platform, and acquiring vibration signals of gears in different positions and directions by acceleration sensors mounted on the aero-engine gear fault simulation platform, and converting the vibration signals into voltage signals, wherein the base station, by configuring a timeslot number K contained therein, ensures that all terminal devices in each aero-engine meet a delay constraint of service transmission:

wherein T_I is a duration during which each terminal device transmits data for a first time, T_R is a duration during which each terminal device retransmits the data after failing to transmit the data for the first time, T_n(1≤n≤N_Tot, n∈N⁺) is a time interval between a moment at which a terminal device n fails to transmit the data for the first time and a moment of retransmitting the data next time, N_Tot is a total number of the terminal devices in a terminal device group, and T_Threshold is a delay constraint of service transmission;

the base station allocates independent initial data transmission resources to each terminal device according to a number of the terminal devices in a same group, and after failing to transmit the data for the first time, the terminal devices in the same group retransmit the data after the base station configures retransmission resources;

the contained timeslot number K is the number of timeslots of each group the timeslot number K of each group is set to:

wherein T_Threshold is the delay constraint of the service transmission, T_S is a length of a data transmission timeslot, and T_I is the duration during which each terminal device transmits the data for the first time, and T_R is the duration during which each terminal device retransmits the data after failing to transmit the data for the first time; and

step 1.2: establishing an aero-engine fault database management system, and preprocessing and storing the data;

step 2: constructing a machine learning module in an edge cloud, wherein historical data stored in the aero-engine fault database management system is used as training samples of the machine learning module, and the machine learning module predicts and infers a behavior of an aero-engine through a one-dimensional convolutional neural network (1D-CNN) model by using the data from the aero-engine fault database management system, and performs joint optimization allocation on communication and computing resources, wherein step 2 comprises following steps;

step 2.1: building the 1D-CNN model, wherein the 1D-CNN model comprises one input layer, five convolutional layers, five pooling layers, one fully-connected layer, and one output layer, and performing feature extraction and type recognition on the vibration signals by using the 1D-CNN model, and outputting probability values of the vibration signals under various fault types, as a recognition result;

step 2.2: training the 1D-CNN model and visualizing a result of the 1D-CNN model, which comprises: inputting the processed vibration signals of the aero-engine into a to-be-trained 1D-CNN model, setting a ratio of training sets to test sets, a number of iterations of the model, a batch size of data sent into the model for a single-time training, a number of training batches, and network parameters, and monitoring recognition accuracy of the 1D-CNN model and a change in a loss function value in real time; and outputting a recognition result of the 1D-CNN model; and

step 2.3: using a following model for implementing resource joint optimization allocation used when joint optimization allocation is performed on communication and computing resources:

wherein ε is a tolerable maximum value of error probability for a data packet of ultra-reliable and low-latency communication (URLLC) service, D is an actual delay of data packet transmission, P_Suc (D>D_threshold)≤ε is a probabilistic delay constraint, and η is a resource ratio of the URLLC service; and

step 3: performing self-management of the aero-engine gear fault simulation platform and the aero-engine fault database management system, which comprises: providing a decision center inside the aero-engine gear fault simulation platform, wherein the decision center receives an output from the machine learning module, and analyzes and makes decisions on a machine learning result of the machine learning module; and the decision center also manages the aero-engine fault database management system, and instructs the aero-engine fault database management system to cache in advance.