1635results about How to "Versatility" patented technology

The invention provides a marine environment time sequence prediction method and system based on a recurrent neural network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining marine environment historical observation data, carrying out the preprocessing of the marine environment historical observation data, and removing abnormal values and missing values from the marine environment historical observation data; according to the preprocessed marine environment historical observation data, a recurrent neural network model is constructed, and the recurrent neural network model comprises an input layer, two stacked long short-term memory (LSTM) network layers and an output layer; acquiring marine environment observation data at the current moment, and inputting the marine environment observation data into the trained recurrent neural network model; predicting marine environment data of a plurality of time steps in the future through a recurrent neural network model, and obtaining a marine environment prediction result in a preset time; and according to the marine environment prediction result, identifying a hot spot region of marine environment change, and generating a visual result representing a dynamic change rule of the marine environment in the prediction time period.

According to the goaf detection method based on transient electromagnetic and seismic wave field joint inversion, data acquisition and joint inversion are carried out on the goaf through the combination of the transient electromagnetic detection system and the reflection seismic detection system, the complementary advantages of the two detection methods are played, the bottlenecks of a single method in the aspects of resolution and signal-to-noise ratio are broken through, and the goaf detection method based on transient electromagnetic and seismic wave field joint inversion is realized. Fine characterization of the structure, the physical property and the stability of the goaf is realized; a machine learning classification algorithm is introduced to analyze a goaf multi-attribute feature data set, and intelligent discrimination of the type, scale and stability of the goaf under complex geological conditions is realized; establishing a quantitative response relationship between goaf physical property parameters and goaf electrical and elastic difference characteristic parameters, constructing a goaf transient electromagnetic and seismic wave field joint forward modeling numerical model, and constructing an electromagnetic-seismic joint inversion objective function; and calculating and solving to obtain goaf resistivity, seismic wave velocity three-dimensional distribution and uncertainty quantitative evaluation results, and reducing blind areas of goaf judgment.

The invention discloses a primary helium fan health state evaluation method based on multi-sensor data fusion. According to the method, a vibration sensor, a temperature sensor, a current sensor and the like are used for comprehensively monitoring the primary helium fan, a deep learning model is used for carrying out feature extraction and fusion on multi-source heterogeneous data, a fault diagnosis knowledge base based on a knowledge graph is constructed, and real-time and accurate evaluation of the health state of the primary helium fan and intelligent diagnosis of a potential fault mode are achieved. Meanwhile, a reinforcement learning algorithm is introduced in the method, the maintenance strategy of the main helium fan is automatically optimized, the equipment reliability is improved to the maximum extent, and the whole life cycle cost is reduced. The method provided by the invention has good adaptability and expandability, can be popularized and applied to nuclear power and other industrial fields, and provides important technical support for improving the intelligent level and operation safety of key equipment.

One or more embodiments of the invention provide a load-aware scheduling method of an inference system and the inference system. The method is applied to a global scheduler in the inference system. The inference system further comprises an inference engine. The inference engine comprises at least one computing instance deployed on each computing node in a computing cluster; computing resources of the computing examples comprise GPUs carried on the computing nodes where the computing examples are located; the global scheduler maintains dynamically updated GPU load information of each calculation instance; the method comprises the steps of obtaining a to-be-executed target reasoning request; based on the maintained GPU load information of each calculation instance, determining a target calculation instance of which the GPU load meets a preset condition; and sending the target reasoning request to the target calculation instance, so that the target calculation instance executes the target reasoning request.

The invention discloses a new energy automobile battery fault identification and early warning method based on hybrid deep learning. The method comprises the following steps: firstly, defining a fault type and a judgment standard, and generating a label of training data; and then constructing a hybrid deep learning model comprising a convolutional layer model and a bidirectional gating cycle unit layer, respectively extracting spatial features of battery data and a long-term dependency relationship in a time sequence, and encoding state information of the battery. Then, the extracted features are input into a fault recognition module for fine adjustment of model parameters, optimization of a cross entropy loss function of a model and minimization of an error between model prediction and an actual fault, so that the calculation time is shortened, and the training efficiency is improved; and finally, based on the trained hybrid deep learning model, detecting the fault probability of the battery data, predicting target feature data in a period of time in the future by using the model, and generating fault early warning information. According to the method, the accuracy of battery fault identification and the reliability of early warning can be improved.

One or more embodiments of the invention provide a load-aware scheduling method of an inference system and the inference system. The method is applied to the inference system comprising a global scheduler and an inference engine. The inference engine comprises a Prefill engine and a Decode engine; the Prefill engine and the Decode engine respectively comprise at least one calculation instance; computing resources of the computing instances comprise GPUs carried on the computing nodes where the computing instances are located; the method comprises the following steps: a global scheduler obtains a target reasoning request to be executed, determines a first calculation instance in a Prefill engine of which the GPU load meets a first preset condition based on maintained GPU load information, and sends the target reasoning request to the first calculation instance to execute reasoning calculation in a Prefill stage; and a first local scheduler corresponding to the first calculation instance in the Prefill engine determines a second calculation instance in the Decode engine with the GPU load meeting a second preset condition based on the maintained GPU load information, and sends an inference calculation result of the Prefill stage to the second calculation instance to execute inference calculation of the Decode stage.

The invention provides a soil nutrient detection method based on a gamma-ray energy spectrum and an XGboost algorithm, and the method comprises the steps: constructing a training sample for soil nutrient detection, the training sample comprises radioactive element data, and the label of the training sample is soil nutrient data with the same soil point location as the radioactive element data; the training sample is input into an XGboost model, the XGboost model extracts features of the training sample and predicts soil nutrients of the training sample, the soil nutrients serve as nutrient predicted values, a loss function is constructed according to the nutrient predicted values and labels of the training sample, the XGboost model is trained, and a soil nutrient detection model is obtained; acquiring soil radioactive elements of to-be-detected soil through a gamma-ray energy spectrometer, and inputting the soil radioactive elements into the soil nutrient detection model to obtain a nutrient detection result of the to-be-detected soil.

The invention relates to a method for measuring the spatial pointing precision of a panoramic camera turntable, which comprises the following steps of: firstly, establishing an initial coordinate system for an azimuth axis and a pitch axis, performing data measurement on the two axes during rotary motion through a laser tracker, and calculating circle center coordinates and circular surface direction vectors of the two rotary axes through a spatial circle fitting method of sphere fitting and plane fitting; the actual position relation of the two coordinate systems can be obtained, then the offset and the angular deviation between the two coordinate systems can be calculated through the actual position relation of the two coordinate systems, and calibration parameters are obtained; and solving a transformation matrix between the reference coordinate system and the measurement coordinate system by using the actual position relation of the two coordinate systems, obtaining a coordinate vector of a measured point in the reference coordinate system, and solving a space included angle between the two vectors according to the coordinate vector and a theoretical position coordinate vector of the measured point to obtain a space pointing error. The method adopts a space coordinate calculation mode of a robotization method, not only has universal significance, but also can be popularized to precision measurement of other two-axis universal two-dimensional space pointing mechanisms.

The invention provides a tunable micro-nano laser based on a perovskite phase change material and a preparation method thereof, and belongs to the technical field of micro-nano photoelectron, the tunable micro-nano laser comprises a substrate, and a distributed Bragg reflector, a gain dielectric layer and a reflector which are sequentially arranged on the substrate from bottom to top; wherein the gain medium is a perovskite material with a crystal phase transition characteristic. The substrate is made of a material which is transparent or low in loss to pump light and emitted light; the distributed Bragg reflector is formed by alternately arranging high-refractive-index layers and low-refractive-index layers; the gain dielectric layer is a perovskite thin film layer with a crystal phase transition characteristic, and a perovskite material has a spectrum tuning function in the laser resonant cavity; the reflecting mirror is composed of a metal film. According to the invention, the perovskite material is used as a gain medium and a phase change material at the same time, the wide-range tuning of laser wavelength is realized, and the tuning range can reach several nanometers or even wider; the energy utilization efficiency can be improved, the stability is high, and the method is suitable for various types of tunable micro-nano laser applications.

The invention discloses a traction converter multi-fault intelligent diagnosis method based on edge AI, and the method comprises the following steps: building a permanent magnet motor mathematical model and a three-level active neutral point clamped converter mathematical model, and carrying out the simulation and experiment of a permanent magnet motor control system fed by a three-level ANPC converter, acquiring a three-phase current signal, a midpoint voltage signal and a rotating speed signal to form an original data set; performing multi-source and multi-dimensional information fusion and normalization on simulation and experimental data, and dividing a training set, a cross validation set and a test set; constructing a two-dimensional convolutional neural network model, and performing offline training; and carrying out lightweight design and edge deployment on the trained neural network model to realize real-time diagnosis of multiple fault modes of the converter under different working conditions. According to the method, the edge calculation efficiency is improved through the lightweight design of the network model, intelligent diagnosis of multiple faults can be realized, and the method has relatively high diagnosis precision and diagnosis speed.

The invention relates to a preparation method of a universal super-hydrophobic fluoride-free waterproof agent with strong washability. According to the method, a polymeric monomer and a polymerizable silane coupling agent are subjected to free radical copolymerization or polycondensation to obtain an emulsion A, and a silanol-based compound formed by hydrolysis of the silane coupling agent interacts with the surface of a fiber base material to enhance the adhesive force of microspheres on the fiber surface; meanwhile, organic silicon in the emulsion B is cured on the surfaces of the polymer microspheres and the fibers to form a film, so that the firmness of the fabric is further enhanced, and the washability of the fabric is remarkably improved while an excellent hydrophobic effect is ensured. According to the invention, simplification and batch production of the production process are effectively realized, and meanwhile, the requirements of modern industry on economic and green production are met.

The invention discloses a GPC3 derived antigen peptide vaccine, an mRNA vaccine, a saRNA vaccine and a preparation method and application thereof. The amino acid sequence of the antigen peptide vaccine is one or any combination of more of amino acid sequences shown as SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5 and SEQ ID NO: 6. According to the invention, an efficient pGPC3 model antigen peptide is screened on the basis of GPC protein, and pGPC3 mRNA and pGPC3 saRNA are designed according to a pGPC3 sequence example. The pGPC3saRNA (at) Lipi vaccine is prepared through a liposome technology, and due to the fact that limiting conditions such as nonapeptide and high-affinity binding with H-2I type molecules (H-2Kb) are adopted during pGPC3 polypeptide antigen screening, APC cells are facilitated to present antigens through an MHCI restrictive pathway, CD8 + T cells are activated, and GPC3 specific CTL is triggered to efficiently kill liver cancer cells. Finally, the pGPC3saRNA (at) Lipi vaccine can completely inhibit the growth of the liver cancer, the inhibition rate reaches 99.8%, and the liver cancer of 4 / 8 mice completely fades down. In addition, the method can be used for vaccine adjuvant and carrier research on a liver cancer subcutaneous transplantation tumor model.

The invention belongs to the field of software security and vulnerability detection, and particularly discloses a method and system for automatically generating and labeling a vulnerability program, and the method comprises the steps: determining a vulnerability repairing program of obtained vulnerability data, extracting a code mode containing a specific type from the vulnerability repairing program, and obtaining a screened vulnerability mode; the step of extracting the code mode of the specific type comprises the substeps of extracting a repair statement of a single modification block, generating a symbolized repair statement, removing repeated repair statements, identifying a key variable and describing a vulnerability triggering statement; according to the vulnerability mode, accurately injecting the vulnerability mode into a normal program in combination with a code analysis tool, and generating a vulnerability program containing a specific vulnerability type; and performing vulnerability type labeling on the vulnerability program in combination with static analysis and a large language model. According to the invention, the detection accuracy of the vulnerability detection model can be improved.

The invention relates to a construction method of a sectional type universal potential sliding surface model, and belongs to the field of traffic slope engineering. According to the method, different shear failure mechanisms and different shear failure characteristics of rock-soil bodies at different parts due to different stress states of all parts of the slope are considered, a three-section type universal potential sliding surface is adopted to simulate the complex failure form of the slope, and the constructed sectional type universal potential sliding surface comprises the possible type of the existing sliding surface and the possible type of the existing sliding surface; the reasonability of the shape of the sliding surface can be ensured under the condition that the concave characteristic of the curve of the sliding surface and the consistency of the motion directions of the connecting points of the adjacent sections of sliding surfaces are required, and the shape of the sectional type universal potential sliding surface can be controlled by a plurality of easily determined parameters simply and conveniently and is generated in a discrete mode; therefore, multiple advantages of universality, accuracy, usability and the like of potential sliding surface determination in actual slope engineering under complex conditions are taken into consideration, and the accuracy of slope stability evaluation is further improved.

The invention relates to an electric energy quality comprehensive optimization method based on multi-resource cooperative control, and belongs to the field of electric energy quality comprehensive optimization. According to the method, operation of two kinds of differential regulation and control characteristic equipment of the SVG and the multifunctional grid-connected inverter is coordinated, and comprehensive treatment of three kinds of typical electric energy quality problems of harmonic distortion, voltage deviation and three-phase imbalance is considered. Wherein the SVG device is used as a common reactive compensation device and only participates in compensation of voltage / reactive power problems, and the multifunctional grid-connected inverter needs to meet active power output and also considers regulation and control of harmonic wave and reactive power problems. According to the invention, the problems of insufficient cooperative control and poor comprehensive treatment effect in the current electric energy quality regulation and control method can be solved.

The invention discloses an intelligent recommendation method and system for an industrial process monitoring model, and the method comprises the steps: firstly, solving a problem that industrial time series data lacks visual features; extracting data set features of each case from four aspects of general statistical features, distribution shape features, task related features and exogenous knowledge embedding by adopting a data set feature engineering method; secondly, designing a mapping relation learning strategy based on width learning, mapping the data set feature of each case with the corresponding model score vector, and thus endowing the width learning network with the capability of predicting the model performance; and finally, providing a triple increment strategy for driving multiple increment demands of an industrial scene, and designing an updating strategy of a wide learning network for monitoring case increment, candidate model increment and recommendation node increment respectively, thereby continuously expanding and enhancing the recommendation capability of the method. According to the method, the detection model with the optimal performance can be recommended for the industrial process.

The present invention relates to the field of disposable sanitary product production equipment, and specifically to a pull-up pants ultrasonic flip welding device, comprising a frame, a rotating shaft and an ultrasonic welding mechanism are mounted on the frame, the ultrasonic welding mechanism comprises a welding machine body and a pressing plate, the materials to be welded are arranged and transported in an assembly line, when the pressing plate contacts the welding machine body, the materials to be welded are sandwiched between the pressing plate and the welding machine body, the frame is provided with a flip mechanism for driving the pressing plate to approach / move away from the welding machine body, the flip mechanism comprises a groove wheel, a rotating frame, a guide rail, a slider and a connecting rod mechanism, the circumferential outer surface of the groove wheel is provided with a guide groove, the lower end of the slider is provided with a guide rod embedded in the guide groove so that the slider can be displaced as the position of the guide rod changes. The present invention is applied to the welding process of waist circumference and other positions in the production of sanitary products.

The invention relates to the technical field of information security, in particular to an abnormal network traffic detection method. Comprising the following steps: S1, obtaining to-be-detected network flow data, and performing preprocessing to form a to-be-detected data set; s2, constructing an abnormal network traffic detection model, specifically, S21, acquiring original abnormal network traffic data, and performing preprocessing to form an original data set; s22, constructing a teacher model, selecting a BiLSTM model as a basic structure of the teacher model, and training the BiLSTM model by using the original data set to obtain the teacher model; s23, constructing a student model, selecting a one-way LSTM model as the student model, and training the one-way LSTM model by adopting a knowledge distillation algorithm to obtain an abnormal network traffic detection model; s3, inputting a to-be-detected data set into the abnormal network traffic detection model, and outputting a detection result; and the detection speed and the operation efficiency are improved.

The invention relates to a non-contact multi-target heart rate variability (HRV) detection method based on adaptive variational mode decomposition (VMD). The method comprises the following steps: firstly, performing multi-target detection on human body targets in a detected scene by using a radar technology so as to determine an accurate distance window of each target; then, a signal phase is extracted from the distance window through a differential and cross multiplication algorithm, and then the thoracic cavity displacement signal of each person to be detected is obtained. According to the method, an improved GOA algorithm is adopted to optimize VMD parameters, a new adaptive function HIE (combining permutation entropy, mutual information amount and energy loss rate) is provided to serve as a target function of GOA, and complete self-adaptive VMD decomposition of chest displacement signals of each to-be-tested person is achieved. Through the decomposition, the heartbeat signals and the breathing signals are extracted respectively, so that the heart rate variation characteristic of each person to be detected can be accurately detected. The method provides a high-precision and non-contact HRV detection means, and is suitable for a multi-target scene.

The invention belongs to the technical field of environmental health risk identification and supervision, and discloses an environmental health risk assessment method and system for combined pollution of a metal smelting area, and the method comprises the following steps: S1, constructing an environmental health risk assessment system; s2, acquiring and inputting data; s3, heavy metal environmental health hazard identification; s4, evaluating receptors of heavy metal environmental health; s5, heavy metal environmental health hazard assessment; s6, evaluating the exposure of the heavy metal environment health; s7, comprehensive evaluation of heavy metal environment health; and S8, judging the supervision priority. Aiming at various heavy metal environmental health risks under multiple exposure media in the production activity of the metal smelting area, based on multiple steps, multiple models and graded evaluation, the method considers comprehensiveness and differentiation of evaluation results, determines a preferential supervision area, carries out targeted differential supervision, and improves the safety of the production activity of the metal smelting area. The efficiency of identifying and supervising the environmental health risk of the metal smelting area is improved.

The invention relates to a preparation method of an irradiation-resistant and accident-resistant SiC / SiC nuclear fuel cladding. The preparation method comprises the following steps: (1) alternately carrying out the following step (A) and step (B) from step (A) to step (A) to obtain a tubular SiC fiber preform with at least two PyC interface layers and at least one SiC nanowire interface layer: (A) depositing the PyC interface layers on the surface of the tubular SiC fiber preform through a chemical vapor deposition process; (B) loading a catalyst on the PyC interface layer through vacuum impregnation, and generating a SiC nanowire interface layer on the surface of the PyC interface layer in situ through a chemical vapor deposition process; (2) carrying out densification treatment on the tubular SiC fiber preform by adopting a chemical vapor infiltration process; and (3) depositing a SiC coating on the surface of the tubular SiC fiber preform by adopting a chemical vapor deposition process to obtain the SiC / SiC nuclear fuel cladding tube.

The invention relates to a functional security cloud platform and a control method for a rail transit real-time control system, the cloud platform follows a fault-security principle during design and meets the requirements of EN50129 functional security SIL4 level, and the cloud platform comprises a communication gateway used for providing a unified access entrance; the safe real-time virtualization system is used for virtual management, clock self-inspection and interrupt distribution; the security middleware runs in a virtual machine distributed by the security real-time virtualization system and is used for security clock verification, CPU memory self-check result verification, security communication, security voting, cycle control and application management; and the cloud management platform is matched with a virtual machine management module in the secure real-time virtualization system to jointly realize management of virtualization resources. Compared with the prior art, the method has the advantages of high safety, high reliability, high availability, high maintainability, high performance and the like.

An atmosphere water collection coupling water electrolysis hydrogen production system comprises an electro-catalysis hydrogen production subsystem, an atmosphere water collection subsystem and an electro-catalysis oxygen production subsystem, and the atmosphere water collection subsystem is located between the electro-catalysis hydrogen production subsystem and the electro-catalysis oxygen production subsystem; a moisture absorption material is placed in the atmosphere water collection subsystem, and one side of the moisture absorption material is in contact with a cathode catalyst placed in the electro-catalysis hydrogen production subsystem to generate an electro-catalysis hydrogen production reaction; the other side of the moisture absorption material is in contact with an anode catalyst placed in the electro-catalysis oxygen production subsystem to generate an electro-catalysis oxygen production reaction; the cathode catalyst and the anode catalyst are respectively connected with two electrodes of the energy input subsystem through wires, and under the driving of the energy input subsystem, an atmospheric water electro-catalytic decomposition reaction is carried out; the atmosphere water collection technology is coupled with electro-catalytic water decomposition, atmosphere water is decomposed to produce hydrogen while atmosphere water collection is achieved, the utilization rate of atmosphere water and the efficiency of electro-catalytic decomposition of atmosphere water to produce hydrogen are improved, and the in-situ hydrogen production device has the advantages of being environmentally friendly, capable of saving energy, sustainable and the like and is an ideal in-situ hydrogen production mode.

The invention belongs to the technical field of virtual power plant resource scheduling, and discloses a virtual power plant flexible resource calling method and device based on a power grid visual angle and a medium, so as to solve the problems that the contribution of flexible resources to the state change of a power distribution network is ignored, the performance evaluation is inaccurate, the response result is not in place, and the response intention is not clear. The method comprises the following steps: acquiring real-time measurement data of each flexible resource of each virtual power plant and grid-connected point topological structure data, and establishing a power grid view angle; obtaining the response capability and historical response data of each flexible resource of each virtual power plant to calculate the contribution degree to a power grid node, the excitation response intention, the response qualification rate, the response rate, the response preparation time and the response duration to construct a user portrait; and obtaining a response demand or an auxiliary service interaction adjustment demand generated by the power grid, and calling flexible resources to execute an interaction response in combination with the constructed power grid view angle and the user portrait so as to meet whole-network and local real-time demands of the power grid, thereby completing flexible resource calling.

The invention relates to the technical field of simulation regression systems, and provides a parallel simulation regression method and system based on cluster servers. The method comprises the following steps: classifying simulation tasks according to task types, and determining a dependency relationship among a plurality of simulation tasks; generating a parallel simulation task list through a cluster server management tool according to the dependency relationship among the plurality of simulation tasks; and delivering the parallel simulation task list to a cluster server, and running simulation tasks by the cluster server to generate a simulation result. The parallel simulation regression system is conveniently constructed by utilizing the scheduling function of the cluster server, the dependency relationship of various types of tasks can be processed, the degree of parallelism of the simulation regression system is increased to the maximum extent, and the method has universality and can be widely applied to different scenes.

The invention belongs to the technical field of vibration isolation testing, and particularly relates to a method for testing the vibration transmissibility of an autonomous excitation active vibration isolator. The method comprises the steps of excitation signal generation and input, vibration signal acquisition, signal coherence analysis and vibration transmissibility calculation. Due to the fact that ultra-precision equipment is sensitive to environmental vibration, an active vibration isolator is usually needed when the ultra-precision equipment is used, and low-frequency vibration such as ground vibration is isolated. In order to evaluate the vibration isolation performance of the active vibration isolator, the vibration transmissibility of the active vibration isolator needs to be tested and analyzed. According to the test method, an actuator and a sensor of the active vibration isolator are adopted, and an additional excitation device and a test sensor are not needed. The method provided by the invention is simple and convenient to implement, short in time consumption and good in test effect, and can solve the technical problem of testing the vibration transmissibility of the active vibration isolator.

The utility model belongs to the field of automatic production of products, and discloses a product shape self-adaptive tray for an automatic production line, which comprises a base (1), a support head (2), a support leg (3), a locking spring (4), a support spring (5) and a release rod (6), the supporting head (2) is a cylinder with a thick end and a thin end; a plurality of vertical holes are formed in the base, a supporting head and a supporting spring (5) are arranged in each hole, and the thin end of each supporting head (2) is arranged in the base; one end of the support spring contacts with the thin end of the support head. The device is simple in structure, low in manufacturing cost and easy to operate, and guarantees accurate positioning of parts, rapid product changing, economical efficiency of different types of trays and the like; different types of trays do not need to be replaced during production changing, one-time placement forming and positioning can be achieved according to parts of different external dimensions, accurate positioning of different products and parts of different specifications and external dimensions is met, and certain universality is achieved.

The invention discloses a honeycomb type combined loading pad and a method. Comprising a soft double-bottom honeycomb bag (1) and a U-shaped elastic cushion block (2), the soft double-bottom honeycomb bag (1) comprises an upper bonding surface (4) and a lower bonding surface (5) which are arranged in parallel, and a pair of honeycomb cavities (6) are transversely arranged between the upper bonding surface and the lower bonding surface; and two U-shaped arms of the U-shaped elastic cushion block (2) are respectively inserted into the corresponding honeycomb cavities (6). The device is high in universality and low in use cost.

The invention discloses a metal piece service life prediction method and device, a controller and a storage medium, relates to the technical field of metal material performance prediction, and at least solves the problem of low prediction result precision of a prediction model constructed on the basis of uniaxial smooth sample test data. The method comprises the following steps: determining a first pure fatigue life of a target metal piece in a notch structure in a non-notch smooth structure state, target holding time of creep and a target notch stress concentration coefficient; obtaining a corresponding target preset model associated with the target metal material of the target metal piece; the target preset model represents the stress concentration coefficient, the load holding time, and the mapping relation between the pure fatigue service life and the creep-fatigue service life in the unnotched smooth structure state; and based on the target preset model, obtaining the target service life of the target metal piece under the first pure fatigue life, the target holding time and the target notch stress concentration coefficient.

The utility model discloses a buried oil tank of a double-layer gas station, and relates to the technical field of double-layer oil tanks. The device comprises a base, a first connecting assembly is arranged on the outer surface of the base, and an adjusting assembly is fixedly connected to the outer surface of the first connecting assembly. Through the connection of the adjusting assembly and the second connecting assembly, after the positions of the first connecting assembly and the adjusting assembly are fixed, the second connecting assembly can be driven to move up and down by changing the length of the adjusting assembly, so that the second connecting assembly can move to the same height as a fixed steel belt; when the fixing steel belt is located on the double-layer oil tank with the large size, even if the position of the fixing steel belt is close to the upper portion, the fixing steel belt can be connected with the fixing steel belt through movement of the second connecting assembly, and therefore the fixing steel belt can be conveniently connected. Different second connecting assemblies and fixing steel belts do not need to be selected according to the size of the double-layer oil tank, certain universality is achieved, and use is convenient.