2856 results about "System structure" patented technology

Provided in the present invention is a method for analyzing the vibration of a fluid conveying pipe on the basis of a Fourier featured PINN. The method comprises: constructing a physics-informed neural network (PINN), determining initial conditions and boundary conditions on the basis of a fluid conveying pipe model, and obtaining loss functions corresponding to the initial conditions and the boundary conditions; using a "soft" constraint to express a second-type boundary condition and initial excitation of a pipe into PINN loss functions, and using a "hard" constraint to code a first-type boundary condition and an initial amplitude into a DNN system structure; adding an observation anchor point to the network for correction when a peak value is insufficient during a network model training process; constructing a Fourier feature mapping function, adjusting a neural tangent kernel of the network, training two mapping results by means of a set network, and finally, in the network, connecting spatial and temporal hidden layers by means of point multiplication, so as to establish an FFNN; and by means of training and learning, finally outputting a prediction result by means of a linear layer connection.

The invention relates to the technical field of data security management, and discloses a blockchain-based forestry data security management system and method, and the system comprises a data collection unit which is used for comprehensively obtaining a multi-dimensional information flow of forest ecology and resource states; the data processing unit is used for carrying out deep cleaning, standardized regulation and feature value extraction on the originally collected mass heterogeneous data; the block chain storage unit is used for constructing a permanent, tamper-proof and distributed secure storage infrastructure platform of the forestry core data; a verification unit; the safety control unit is used for constructing a covering data transmission, storage and access full-chain depth defense system structure model; a user interface unit; an auditing unit; and a network communication unit. The method is reasonable in design, and the intelligent analysis module is used for processing to form a forest interannual growth trend prediction function distribution map state visual output result set.

The invention discloses a high-temperature airflow two-dimensional temperature field and velocity field synchronous measurement system and method based on laser-induced phosphorescence, and the system comprises phosphorescence particles, a low-frequency double-pulse laser, an image collection device, a synchronous controller, and an image processing device. The image processing device analyzes the gray intensity ratio of different wave band images acquired by the two PIV cameras at the same time, and combines a temperature response function calibrated by an experiment to realize inversion of a temperature field; meanwhile, a double-frame time-resolved image acquired by any PIV camera is subjected to cross-correlation calculation, particle displacement is extracted, and then a velocity field is reconstructed. According to the invention, synchronous acquisition of temperature and speed based on the same data source is realized, and the system has the remarkable advantages of simple structure, high measurement precision and wide application environment.

The invention provides an anti-condensation temperature and humidity regulation and control system and method for remote deep learning prediction aided decision making, and the method is characterized in that a field controller is connected with a temperature and humidity collection device, obtains the environment temperature and humidity, calculates the dew point temperature, obtains the surface temperature of a protected part, and starts and stops a heating and / or dehumidification device by comparing the difference between the two values with a preset threshold value; the temperature and humidity acquisition device acquires field environment temperature and humidity data for the field controller to calculate the dew point temperature, and the temperature and humidity acquisition device is also used for acquiring the surface temperature of the protected part; the heating device is used for raising environment temperature; the dehumidification device is used for reducing the environment humidity; the communication module is used for data transmission between the field controller and the remote prediction terminal, sending field data and receiving prediction information; the remote prediction terminal obtains field data through the communication module; an anti-condensation system structure combining local autonomous control and remote prediction assistance is established, and redundancy fault-tolerant capability is achieved; even if the remote prediction function fails, the field control can still operate independently and reliably.

The invention relates to the field of milling, in particular to a modeling method and system for a morphology model of a milling surface, and the method comprises the steps of data collection, data preprocessing, feature extraction, model construction and model verification and optimization. According to the modeling method and system, various actual factors in milling are fully considered, including tool abrasion, cutting force vibration, workpiece material non-uniformity and the like, the fitting degree of the model and the actual milling surface appearance is improved, and the microscopic geometrical characteristics of the machining surface can be reflected more accurately; a machine learning algorithm is adopted to construct a model, so that a complex milling process parameter combination relationship can be flexibly processed, the applicability and accuracy of the model under different milling process parameters are improved, and accurate evaluation of part performance and optimization of a machining process are facilitated; the system is complete in structure, all the modules are clear in division of labor, and the construction, verification and optimization process of the morphology model of the milling surface can be efficiently achieved.

The invention provides a green asset management system based on a multi-layer system structure, relates to the technical field of green asset management, and aims at obtaining green asset data; data analysis and behavior simulation verification are carried out based on the carbon emission activity nodes, carbon emission factors are obtained, carbon footprint calculation is carried out, and a first carbon circulation path is obtained; performing economic division on the quota carbon asset data by taking nodes in the first carbon flow path as constraints to obtain a plurality of node quotas; dynamically correcting each node quota in combination with carbon transaction flow and hedging influence to obtain a plurality of node quota threshold values, performing node footprint optimization by taking the node quota threshold values as a target, generating a second carbon flow path, performing association clustering on nodes in the second carbon flow path, obtaining a plurality of emission reduction management stages, and performing emission reduction management and quota optimization updating by taking the node quota threshold values as a strategy target. The problems of poor flexibility and dynamic nature of carbon asset management and untimely market response are solved, and the effects of improving the efficiency and adaptability of carbon asset management and enhancing economic benefits are achieved.

The invention discloses a neural network extension execution computer system based on RISC-V. The neural network extension execution computer system comprises an RISC-V processor based on an out-of-order execution system structure, a neural network hardware accelerator, an instruction memory, a data memory and a dynamic scheduler. The dynamic scheduler is used for switching different working modes for the computer system according to the current calculation load, and distributing a calculation task to be executed to the RISC-V processor or the neural network hardware accelerator for execution; the working modes comprise a standard calculation mode, a neural network acceleration mode and a low power consumption mode; the computing tasks comprise a general computing task and a neural network computing task. According to the technical scheme, the intelligence of the computer system is improved, and the calculation efficiency, the resource consumption and the power consumption are well balanced.

The invention relates to a temperature field and deformation field synchronous measurement system and method based on laser speckle DIC and temperature field algorithm temperature measurement. The system comprises a 3CCD color camera, a lens, a three-band filter, a monochromatic light source, a mechanical support adjusting platform, a triangular support and a computer. A test piece is actively illuminated by blue laser, and a color image containing three channel sub-images of red, green and blue on the surface of a high-temperature sample before and after deformation is acquired by a 3CCD color camera. A temperature field algorithm is used for analyzing sub-images of the R channel and the G channel after deformation to calculate a temperature field of the surface of the test piece, and a digital image correlation method is used for analyzing sub-images of the B channel before and after deformation to obtain a strain field of the surface of the test piece, so that synchronous measurement of the temperature field and the deformation field of the surface of the test piece is realized. The principle is simple, the system structure is compact, temperature field measurement is not interfered by speckles, and non-contact, high-precision and synchronous measurement of a material deformation field and a temperature field under the action of force and thermal loads can be achieved.

The invention discloses a design and control method of a wheel-leg balance robot capable of actively jumping. The design of the wheel-legged balance robot is based on a planar parallel five-bar mechanism, the leg structure design and the wheel train structure design of the wheel-legged balance robot suitable for active jumping are comprehensively considered, and various sensor modules are adopted for sensing, so that the robot can autonomously jump on complex obstacle road sections; meanwhile, the diameter of the wheel and the torque of the motor are designed in a matched mode, so that the robot has high trafficability; a joint motor connecting structure is improved, so that the design strength of a leg structure is improved; an inverted pendulum model containing a wheel-leg mixed structure is used for fitting robot motion, an LQR control algorithm is adopted for processing a nonlinear system through piecewise linearization, and reference input is added to achieve trajectory tracking.

The invention discloses a short message template intelligent identification method and system based on semantic similarity calculation, relates to the technical field of information and communication, and realizes automatic matching of a request short message and a template library through semantic vectorization and similarity calculation; semantic extension, structural constraint and a dynamic feedback mechanism are combined, so that the matching accuracy and robustness are improved; an optimization process driven by a multi-level model and category features is introduced, and an identification-feedback-optimization closed-loop system is formed; the system structurally comprises a semantic modeling module, a similarity calculation module, a dynamic feedback module and a template optimization module, automatic and efficient short message template recognition and updating can be achieved, and the requirements for real-time performance and accuracy in a large-scale application scene are met.

The invention discloses a wide-speed-domain position-sensorless control method for a permanent magnet synchronous motor, and the method comprises the steps: building an extended state observer of the permanent magnet synchronous motor, and obtaining the counter electromotive force of the motor through the extended state observer; a self-adaptive vector filter is adopted to filter out higher harmonics in the back electromotive force obtained by the extended state observer, estimated back electromotive force is obtained, the obtained estimated back electromotive force serves as input, position information contained in the estimated back electromotive force is demodulated through a phase-locked loop position observer, and the rotating speed and the position angle of the motor are obtained; and controlling the permanent magnet synchronous motor according to the obtained rotating speed and position angle. The position observer is used for extracting position information from the rotor back electromotive force, a mechanical position sensor is omitted, the system structure is simplified, cost is reduced, the self-adaptive vector filter is used for filtering harmonic waves for estimating the back electromotive force, and estimation errors of the rotating speed and the position angle of the motor are reduced. And the high efficiency and the stability of position-sensorless control of the motor in a wide speed range are improved.

The invention provides a computer hardware performance evaluation method and system based on an analogue simulation model, and relates to the technical field of computer performance evaluation. According to the method, hardware component models such as a processor, a memory and I / O are constructed by analyzing a system structure description file, semantic analysis is performed on an instruction stream or a parallel computing graph of a to-be-evaluated application, and a task load model is formed. The model is input into an event-driven scheduling module, simulation is executed on the hardware component model, task-resource mapping is generated, and task time delay, communication traffic and power consumption are recorded. And calculating average response time delay, data bandwidth and unit energy consumption according to the simulation data, and outputting an evaluation report compared with the performance baseline. According to the method, real load-oriented multi-dimensional performance prediction can be realized, and the method can be used for architecture optimization and scheme selection.

An energy-saving optimization method for a building cold source system comprises the following steps: S1, acquiring a topological graph of a target building cold source system, analyzing a system structure, and collecting historical operation data of the system; s2, constructing a cold source system energy consumption model, and performing parameter identification on the cold source system energy consumption model to improve the precision; s3, energy consumption is calculated through the cold source system energy consumption model by means of the cold load and the system operation parameters, and the optimal operation state of the system is searched for with the minimum system energy consumption as the target through an LECALO algorithm; and S4, outputting the minimum energy consumption and the optimal operation state of the building cold source system, and issuing the minimum energy consumption and the optimal operation state to the building cold source system software control platform. According to the method, the operation efficiency of the building cold source system can be remarkably improved, and energy consumption and carbon emission are effectively reduced.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention discloses a hydrogen-lithium dual-power system structure optimization design and integrated control method applied to an unmanned aerial vehicle. The method comprises the following steps: constructing a hydrogen-lithium dual-power system of the unmanned aerial vehicle; setting an integrated control strategy according to the hydrogen-lithium dual-power system; the integrated control strategy comprises an energy management strategy and a life and safety management strategy; the energy management strategy comprises a rule-based strategy, a power following strategy and a self-adaptive optimization strategy; the service life and safety management strategy comprises a hydrogen fuel cell protection strategy, a lithium battery health management strategy, a thermal management strategy, a dual-power coupling safety management strategy and a redundancy and fault processing strategy. By optimizing the power system design and the energy management strategy, the mass of the unmanned aerial vehicle device is reduced, the cruising ability, the load capacity and the environmental adaptability of the unmanned aerial vehicle are improved, and optimization processing of the control strategy is achieved.

A calibration assembly, including: a base; and a plurality of calibration wires dispersedly connected to the base. An absorption capacity of the calibration wire for X rays is greater than that of the base for X rays. Through a specific structure design of a plurality of calibration wires in the calibration assembly, the calibration wires are dispersedly connected to the base, and taking advantage of the characteristics that the absorption capacity of the calibration wire for X rays is greater than that of the base for X rays, the calibration wires are applied to the calibration phantom, and the calibration phantom is scanned in the scanning system. By continuously adjusting the geometric parameter values in the imaging method, the optimal geometric parameter values that are closest to the real scanning system structure may be obtained, thereby improving the imaging effect of the scanning system.

The invention discloses a remote disturbance test system and method in primary frequency modulation, and relates to the technical field of power system control, and the method comprises the steps: obtaining a primary frequency modulation intelligent control system, and configuring a master-slave redundancy control device; generating and verifying a primary frequency modulation test disturbance signal, and triggering a remote disturbance test according to a verification result; the master and slave control devices respectively respond to the test and collect the power grid state signal flow, and analyze and generate a master and slave frequency modulation load instruction; evaluating and screening the master-slave load instructions by using a hardware high-selection module, and determining a target frequency modulation load instruction; and sending the target instruction to the DCS and the DEH for test recording and performance evaluation, and optimizing a frequency modulation strategy according to a test result. The technical problems of low test efficiency and insufficient accuracy caused by system complexity in an existing primary frequency modulation remote disturbance test system are solved, and the technical effects of simplifying the system structure, improving the test efficiency and remarkably enhancing the test accuracy are achieved.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

The invention discloses an optimized scheduling method and system for an electricity-hydrogen coupling system, and electronic equipment. The method comprises the following steps: acquiring a system structure of the electricity-hydrogen coupling system; based on a coupling relationship among energy storage equipment, a generator set, hydrogen production equipment and a power grid in a system structure, constructing a first scheduling model comprising a cost function and constraint conditions, the constraint conditions at least comprise operation constraints among the hydrogen production equipment, energy storage equipment constraints, power grid energy interaction constraints and generator set operation constraints under different working conditions; obtaining day-ahead generator set output data and load demand data of the electricity-hydrogen coupling system, and performing optimization solution on the first scheduling model based on the day-ahead generator set output data and the load demand data to obtain a first scheduling strategy of the electricity-hydrogen coupling system; and performing day-ahead optimization scheduling on the electricity-hydrogen coupling system based on the first scheduling strategy. Through the method, the stability and reliability of the system can be improved.

The invention relates to the technical field of computer system structures and storage, in particular to a non-volatile memory quick recovery method based on metadata priority and on-demand loading. The method comprises the following steps: in response to a system fault signal detected by a voltage monitoring unit, freezing a processor context and traversing a page table structure to extract system configuration information; writing the system configuration information and business data codes in the volatile memory into a nonvolatile medium to generate a persistent state mirror image; analyzing the persistent state mirror image, extracting address conversion metadata, and reconstructing a mapping relation from a virtual address to a physical page frame in a volatile memory; and generating an address mapping table, wherein the physical page frame pointed by the address mapping table is set to be in an existing state but is not associated with the effective service data. According to the method, decoupling of the control flow and the data flow is realized by constructing a virtual ready state, and quick starting of the system and immediate response of key services are realized on the premise of not depending on the total capacity of a memory.

The invention discloses a segmented energy storage direct-hanging convergence method based on photovoltaic direct-current networking, and relates to the technical field of segmented energy storage of photovoltaic direct-current networking, and the method comprises the steps: constructing a direct-current networking system, collecting the operation data of the direct-current networking, and constructing a data set; based on the data set, dynamically adjusting the output power of a plurality of energy storage modules in real time through an adaptive power distribution algorithm, and balancing each energy storage module according to the use frequency and depth; and constructing a long-term optimization strategy of the direct-current networking system based on real-time adjustment and control of the direct-current networking system, and continuously optimizing system configuration and algorithm performance. By combining direct current networking, segmented energy storage and an artificial intelligence algorithm, the electric energy conversion efficiency is remarkably improved, the equipment loss is reduced, load variable frequency driving is realized, the system structure is simplified, and the fault rate and the equipment cost are reduced. And meanwhile, energy storage utilization is optimized, the response speed and stability of the system are improved, and access of renewable energy sources is better adapted.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention provides a node task migration and scheduling system based on digital twinning, and relates to the technical field of computer system structures and data processing. Computing resource interference in a multi-tenant sharing environment is quantized by sensing a cross-tenant noise coefficient and a state synchronization complexity entropy in a node micro-architecture; extracting track features of the mobile terminal, calculating spatial discrete variance, generating a self-adaptive migration decision hysteresis factor, and converting the migration decision hysteresis factor into decision damping to inhibit invalid high-frequency reciprocating migration; constructing a digital twin sandbox before physical cutover, cooperating with a chaos scene injection engine to inject a composite fault operator into a bottom layer, and performing actuarial calculation on service continuity retention after risk adjustment by using a fidelity integrator; and finally, a bottom layer controller is linked through safety baseline comparison to execute physical flow switching. According to the method, network boundary deduction is completed on the premise that physical bandwidth is not consumed, the interruption risk caused by state hard switching is avoided, and smooth transition of stateful services is effectively guaranteed.

The invention discloses an E-SOP-based interconnected power distribution area voltage optimization regulation and control method, and the method comprises the steps: carrying out the analysis of an E-SOP-containing power distribution area interconnection system structure, and constructing an E-SOP mathematical model; establishing a voltage-power sensitivity calculation model, and determining the optimal access position of the E-SOP; an E-SOP interconnected power distribution area planning model with the lowest comprehensive cost as the target is constructed, and the optimal capacity configuration of the E-SOP is determined; establishing a multi-objective function E-SOP interconnection power distribution area operation optimization model and solving the model; and determining the optimal position, capacity and operation scheme of the E-SOP according to the solving result. According to the method, a three-terminal SOP (E-SOP) containing an energy storage system is applied to an interconnection system of a power distribution area, the flexibility and rapidity of a power distribution network can be improved from the multi-dimensional aspects of space, energy and power of a power distribution system, and voltage optimization of the power distribution area is achieved.

The invention discloses a joint beam forming optimization method for an RIS auxiliary low-altitude communication system. The problem of performance bottleneck caused by interaction of active beam forming and passive beam forming is solved by adopting a genetic algorithm based on a dynamic adaptive theory. Aiming at the limitation of a standard optimization method, a hybrid strategy of genetic algorithm optimization and a full-connection neural network is explored, and intelligent collaborative setting of system parameters is realized. Simulation results show that the hybrid algorithm not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. On the basis, the signal transmission quality is improved, interference is reduced, and it is guaranteed that the RIS-assisted low-altitude unmanned aerial vehicle intelligent communication system stably and efficiently operates in a complex wireless transmission environment.

The invention discloses a personalized semantic understanding system for assisting an autonomous mobile platform, and the system comprises a user personalized word meaning library which is used for recording the mapping relation between the user habit expression and the corresponding operation; the fuzzy semantic analysis module is used for identifying fuzzy expression in a natural language instruction of a user and generating a plurality of candidate targets or operation options of semantic generalization; the multi-round context modeling module is used for establishing a semantic context based on session history and performing disambiguation and complementation on current semantics; and the intention generation module is used for converting the processed semantic information into a structured instruction. The personalized semantic understanding system is reasonable in structural design, all the modules cooperate with one another, the effect that 1 + 1 is larger than 2 can be achieved, fuzzy semantic understanding and multi-round context modeling can be conducted, the personalized requirement of a user can be met, semantic understanding precision is high, user adaptability is good, and the personalized semantic understanding system is suitable for man-machine interaction of an autonomous mobile platform.

The invention provides a compressor and heater control integrated system and a vehicle. The control integrated system comprises a heat management controller, a compressor and a heater, the integrated controller is integrated with a compressor controller and a heater controller, and the integrated controller is in communication connection with the heat management controller; the integrated controller is in communication connection with the compressor pump body and the heater core body and used for controlling the compressor pump body and the heater core body, and the integrated controller and the compressor pump body are integrally installed to form a compressor assembly or the integrated controller and the heater core body are integrally installed to form a heater assembly. Through the innovative design of the integrated controller, centralized control over the compressor and the heater is achieved, the number of controllers and wire harness connection are reduced, the hardware cost and the system complexity are reduced, meanwhile, a software high-voltage interlocking function is adopted to replace hard wire interlocking, the system structure is further simplified, and the reliability and safety of the system are improved.

The invention provides a non-intrusive diagnosis method for typical rotor faults of a synchronous phase modifier, and the method comprises the steps: carrying out finite element modeling and fault working condition setting, carrying out simulation to obtain magnetic field distribution data under each working condition, carrying out the extraction and feature analysis of stray magnetic field signals, extracting time domain and frequency domain signals of a stray magnetic field under healthy and various fault working conditions, and carrying out the diagnosis of the typical rotor faults of the synchronous phase modifier. Fundamental wave, odd-even harmonic components and signal change trends are compared and analyzed, stray magnetic field characteristics corresponding to faults are extracted and recognized, diagnosis criterion establishment and fault recognition are carried out, diagnosis criteria and threshold values of rotor turn-to-turn short circuit, dynamic eccentricity and shaft-diameter composite eccentricity faults are established according to the stray magnetic field characteristics, and the fault diagnosis accuracy is improved. And when the specific harmonic amplitude or change characteristic of the stray magnetic field signal exceeds the criterion threshold, determining the rotor fault of the corresponding type and degree. Equipment dismounting or structure reconstruction is not needed, the detection system is simple in structure, internal fault characteristics of the phase modifier can be reflected in real time, and high sensitivity and practicability are achieved.

The invention relates to a self-adaptive visual training method based on eye features, and belongs to the technical field of visual health and artificial intelligence. In order to solve the problems that traditional visual training equipment is heavy in structure, single in training mode and lack of personalized regulation and control and concentration state monitoring, the visual training equipment obtains eye images of a user in real time through an image acquisition module, adopts visual intelligent analysis, extracts double features of eye contours and pupil positions, calculates the visual concentration degree, and improves the visual training efficiency. And a concentration state is judged by combining a dynamic self-adaptive threshold value. When the concentration degree is insufficient, the control module triggers intervention mechanisms such as prompt or training pause and the like, and the remote interaction module uploads data to the cloud platform to generate a personalized training scheme. Intelligent and personalized regulation and control of the training process are achieved, the training effect and compliance are improved, the system is compact in structure and suitable for various scenes, and an efficient solution is provided for vision health.

The invention discloses a damping device, a refrigerating machine assembly and a refrigerating system, and relates to the technical field of refrigerating system structures.The damping device is used for being connected between a machine head and a Dewar of a refrigerating machine, the damping device comprises a first damping structure and a second damping structure, the first damping structure comprises a mounting structure and a first elastic piece, and the second damping structure comprises a second elastic piece; the installation structure is used for being installed on the Dewar. One end of the first elastic piece is installed on the installation structure. The second vibration reduction structure comprises a second elastic piece, the second elastic piece and the first elastic piece are oppositely arranged in the first direction in a spaced mode, and one end of the second elastic piece is used for being installed on the Dewar; wherein the other end of the first elastic piece is used for being connected with one end, in the first direction, of the machine head, and the other end of the second elastic piece is used for being connected with the other end, in the first direction, of the machine head. The invention aims to solve the problem of operation vibration when a low-temperature system is built for an existing refrigerator.