347results about How to "Realize adaptive adjustment" patented technology

The invention discloses a self-evolution network security defense strategy generation and dynamic deployment method, which comprises the following steps of: acquiring multi-dimensional data such as original network flow data, and generating an optimized feature matrix through adaptive interval division and symbolization conversion; constructing a feature graph structure based on the optimized feature matrix, extracting threat features by using a graph neural network and an attention mechanism, and generating a threat mode set; analyzing threat propagation characteristics according to the threat evaluation index set and the threat incidence matrix, and generating a threat propagation graph; and generating an initial defense strategy set based on a threat assessment result, and generating an optimized defense strategy set through dependency analysis and conflict elimination. According to the invention, through adaptive feature extraction and dynamic strategy optimization, the intelligent level and protection effect of network security defense are improved.

The invention particularly relates to a document abstracting method based on domain knowledge and a multi-granularity graph network, which comprises the following steps: S1, constructing a knowledge coding module, carrying out domain knowledge graph transfer learning, extracting triple structure features in combination with a capsule network, and generating a text entity coding module; s2, constructing a knowledge fusion module, converting a document text into a semantic vector, and adaptively fusing the semantic vector with external knowledge; s3, constructing a word-level semantic graph, and aggregating neighborhood information through a semantic graph attention mechanism to obtain word embedding representation containing context; s4, constructing a sentence-level semantic graph, performing self-attention calculation on the word-level semantic vector in the S3 to obtain semantic node representation of the sentence-level semantic vector, and performing feature aggregation on sentence-level nodes to obtain global semantic information of the document; and S5, constructing an output layer, and predicting according to the document feature vector in the S4 to obtain a final extraction type abstract result. The method has the advantages that redundant information in the document is removed, and ambiguity of text semantics is eliminated.

The invention provides a data encryption transmission system and method, relates to the technical field of data encryption transmission, ensures basic safety of data transmission through AES pre-encryption processing, further introduces a data acquisition module and a data processing module, can monitor environmental parameters and operation state parameters in different time periods, and analyzes variation indexes of the parameters, so that the data encryption transmission can be realized. Dynamically adjusting an encryption strategy to adapt to different data transmission environments; through a matched decryption mechanism of an encryption lock and a decryption condition, the security of identity verification is improved; and the adaptive adjustment of the conformity threshold value M is realized through the dynamic adjustment module, so that the system can automatically adjust the security level when facing complex attacks, and the security and flexibility in the data transmission process are greatly enhanced.

The invention provides a distributed photovoltaic access distribution network voltage fluctuation evaluation method, medium and system, and belongs to the technical field of distributed photovoltaic, and the method comprises the steps: firstly collecting the parameters of a photovoltaic system and a distribution network, and building a power grid response equation set considering the parameters; more parameter samples are obtained through data augmentation and expansion, and indexes such as voltage, power and harmonic waves of each node are calculated according to the response equation set. Constructing a voltage fluctuation evaluation index matrix including voltage deviation, fluctuation, harmonic waves, short-circuit capacity change and the like; and decomposing the matrix by adopting singular value decomposition to obtain a voltage stability and fluctuation evaluation matrix, and finally calculating a voltage stability index and a fluctuation index. If the voltage fluctuation evaluation index exceeds a preset threshold value, an alarm is triggered, and decision support is provided for distribution network planning and operation. According to the invention, the technical problems of short evaluation time scale and difficulty in capturing the dynamic characteristics of voltage fluctuation in the evaluation of the distributed photovoltaic access distribution network in the prior art are solved.

The invention provides an intelligent bee colony type quality optimization management method and system for multi-source tag data, and relates to the technical field of data tags, and the method comprises the steps: extracting a comprehensive feature vector of a tag through a feature extraction module, carrying out the processing through a deep feature enhancement network, generating a similarity and dependency feature matrix, and constructing a tag quality evaluation model; mapping the label data into bee colony network nodes, performing three-level division, and constructing a node optimization association network; and performing optimization based on the collaborative optimization rule set until the quality improvement value is smaller than a preset threshold value. The label data quality can be effectively improved, the data processing cost is reduced, and the optimization efficiency is improved.

The invention relates to the technical field of industrial equipment fault diagnosis and intelligent control, and discloses an industrial equipment fault diagnosis system and method based on image recognition, and the system comprises a data collection module which is used for collecting the multi-modal data of industrial equipment, and the multi-modal data comprise equipment state data, environment data and image data; and the data preprocessing module is connected with the data acquisition module and is used for preprocessing the acquired multi-modal data, including smoothing, denoising and anomaly detection processing of the equipment state data, the environment data and the image data. The environment data, the equipment state data and the image data are subjected to weighted fusion through an attention mechanism, so that feature weights of different data sources can be dynamically adjusted, the actual state of the equipment can be reflected more accurately, a feedback control module is integrated, the running state of the equipment is dynamically adjusted according to a real-time fault diagnosis result, and the fault diagnosis accuracy is improved. And the closed-loop design realizes self-adaptive adjustment of the equipment state, so that the fault risk is effectively reduced.

The invention provides a YARN cluster node resource dynamic adjustment method and system based on dual-mode perception, and relates to the technical field of data processing, and the method comprises the steps: collecting system index data and application program index data, respectively constructing two deep neural network models for fusion evaluation, calculating a resource dynamic adjustment coefficient, and obtaining a resource dynamic adjustment result; node resources are updated in real time by adopting a sliding time window mechanism, and a multi-dimensional resource allocation optimization model is constructed by utilizing a genetic algorithm to realize optimal resource allocation. According to the invention, the utilization rate of cluster resources can be improved, resource competition is reduced, and intelligent dynamic adjustment of node resources is realized.

The invention relates to the field of process optimization, in particular to a steelmaking process optimization method and system based on a digital twin model. A steelmaking process optimization system based on a digital twin model comprises a converter digital twin model construction module, a physical parameter adjustment module, a converter wear model construction module and a steelmaking process optimization module. By establishing the high-precision converter digital twinning model, heat transfer, flow field change and chemical reaction in the steelmaking process can be accurately described, a physical rule basis is provided for analogue simulation, and then component information of input materials and converter process parameters are optimized and adjusted by utilizing analogue simulation, so that the converter process parameters are optimized and adjusted, and the converter process parameters are optimized and adjusted. During the period, the flame image is used as a process quality evaluation index, so that the accuracy and efficiency of the steelmaking process are remarkably improved, and the dependence on experience of operators is reduced.

The invention provides a welding seam detection method, medium and system for welding of a floating ship plate of an oil storage tank, belongs to the technical field of electric digital data processing, and realizes comprehensive and accurate monitoring through six key matrixes. The method comprises the following steps: starting from a floating ship plate motion characteristic matrix, accurately capturing height, attitude and weld joint displacement data, establishing a comprehensive detection matrix containing ultrasonic flaw detection, X-ray imaging and surface topography through a multi-source detection technology, and comprehensively depicting weld joint microscopic characteristics. Dynamic stress-strain analysis is deeply carried out, a complex stress field distribution model considering floating influence is constructed, multi-dimensional feature matrixes are combined through weights to form a weld quality comprehensive evaluation matrix, and a self-adaptive detection parameter updating mechanism is established. And finally, a welding seam state vector and a safety threshold matrix are constructed, real-time monitoring and early warning are achieved, and the technical problems that in the prior art, only the internal quality of the welding seam can be statically evaluated, and the actual stress-strain state of the welding seam under the dynamic working condition cannot be comprehensively reflected are solved.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent vehicle lamp system and a control method thereof.Optical sensing equipment is used for collecting environment information around a vehicle, a pre-projection image is determined according to the environment information, optimal projection parameters are calculated according to the reflectivity and flatness of a projection plane, and the parameters are sent to a light module; projecting according to the calculated focal length and angle parameters; and adaptively adjusting the frequency of detecting the environment information by the optical sensing equipment by using a cyclic decision detection algorithm to realize adaptive control on different roads, weather and vehicle states. According to the method, the light brightness can be automatically adjusted, unreasonable projection areas can be avoided, safety prompt projection is carried out according to road conditions, the accuracy and safety of vehicle illumination are improved, and the risk of traffic accidents is reduced.

The invention provides a photovoltaic support rapid installation and adaptive terrain adjustment device, and relates to the technical field of photovoltaic supports. The photovoltaic support rapid installation and adaptive terrain adjustment device comprises a supporting plate, an installation mechanism is fixedly arranged at the upper end of the supporting plate, detection mechanisms are fixedly arranged in the middle of the periphery of the upper surface of the supporting plate, and supporting mechanisms are fixedly arranged at the four corners of the bottom face of the supporting plate. An energy storage mechanism is fixedly arranged in the middle of the bottom surface of the supporting plate, the mounting mechanism comprises a mounting frame, a photovoltaic panel is clamped in the mounting frame, hand holes are formed in the two sides of the upper surface of the mounting frame, and a first sliding groove is fixedly formed in one side of the upper surface of the mounting frame; a second sliding groove is fixedly formed in the other side of the upper surface of the mounting frame, and four first fixing rotary knobs are arranged on the periphery of the mounting frame in a threaded mode. And by arranging the mounting mechanism and the supporting mechanism, the mounting and adjusting difficulty of the device in use is reduced, and the overall practicability of the device is improved.

The invention relates to a screw feeding pressure self-adaptive adjusting method, device and equipment. The method comprises the following steps: constructing a Lagrange control model for screw feeding equipment to obtain a control reference parameter group; acquiring a real-time air pressure data flow and calculating an air pressure regulation and control instruction; a torque-rotating speed-temperature mapping function is constructed, and torque output parameters are obtained through calculation; torque compensation parameters are calculated, integral operation is carried out on the torque compensation parameters and locking angle sensor data, and a torque-locking angle characteristic curve is generated; carrying out locking state evaluation to obtain a state evaluation index, carrying out deviation analysis on the state evaluation index and a set locking threshold value, and carrying out multi-dimensional parameter cooperative calculation to obtain a system correction matrix; and inputting the system correction matrix into a Lagrange control model, updating a dynamic weight coefficient, and outputting an optimized control instruction set. According to the screw feeding device, the reliability and the maintenance efficiency of the device are improved, and the technical problems of dynamic control, parameter optimization and fault early warning of a traditional screw feeding device are solved.

The utility model relates to a pipe fitting end grinding device, and belongs to the technical field of pipe fitting machining, the pipe fitting end grinding device comprises a support, a platform mounted on the upper side of the support and a control box mounted on the upper side of the platform, a grinding disc is arranged on the upper side of the platform, a deburring assembly is arranged on the grinding disc, and a driving mechanism is arranged on the outer side of the grinding disc; the deburring assembly comprises two inner grinding blocks arranged on the outer wall of the grinding disc and an elastic assembly arranged between the two inner grinding blocks, outer grinding blocks are arranged on the opposite sides of the two inner grinding blocks, and adjusting assemblies are arranged on the outer sides of the two outer grinding blocks. The elastic assembly comprises a fixed cylinder fixedly connected to the center of the outer wall of the grinding disc. According to the pipe fitting end grinding device, efficient burr removal is achieved, secondary machining is not needed, time and labor are saved, the structure is simple, use is convenient, good practicability is achieved, and the technical problems that in the prior art, time and labor are wasted, and secondary machining is needed are solved.

The invention relates to the technical field of intelligent vehicle lamps, and particularly provides an intelligent vehicle lamp control method based on color avoidance and depth correction, and the method comprises the steps: determining a corresponding heterochromatic region on a pre-projection image according to the color deviation tensor of a projection plane and a global color fitting plane, and controlling a vehicle lamp to carry out the color avoidance projection of the heterochromatic region, according to the depth difference tensor of the projection plane and the ideal depth plane, determining a corresponding depth change area on the pre-projection image, and controlling the vehicle lamp to change the projection size to realize depth correction, so as to ensure that the finally projected image is not interfered by the color of the actual road surface; and discontinuous and unclear projection images in the visual field of the driver due to road surface potholes are avoided. According to the invention, the light brightness and the local projection size of the vehicle lamp can be adjusted according to the collected information, so that the projection of the vehicle lamp is not interfered by the road surface color and the road surface unevenness.

The invention discloses an underwater acoustic communication equalization method based on a leakage normalized least mean square algorithm, and belongs to the technical field of underwater acoustic communication. The method comprises the following steps: acquiring a baseband sampling signal; initializing a weight vector w = 0, an instantaneous power vector p = 0 and an error vector e = 0; and taking the baseband sampling signal x as an input signal, taking the training sequence as an expected signal d, and carrying out gradual leakage updating on the weight of a filter through iterative calculation based on a leakage factor control strategy so as to complete self-adaptive adjustment in a signal equalization process. According to the method, the leakage factor control strategy is introduced, and the adaptive step parameter design is adopted, so that the stability and the convergence speed of the weight of the filter are effectively balanced, finally, the received signal is accurately balanced, and the robustness and the signal quality of the underwater acoustic communication system are remarkably improved.

The invention discloses a display screen brightness control method for an intelligent storage cabinet. The method comprises the following steps: acquiring environment illumination data and time data acquired by a plurality of illumination sensors to generate an environment feature vector; constructing a brightness mapping model by using a neural evolutionary algorithm to obtain an initial brightness value; brightness optimization is carried out by adopting a constraint optimization method based on an Ising machine; and dynamically optimizing the brightness mapping model by using a bilateral structure evolution mode. Intelligent self-adaptive control of the brightness of the display screen of the intelligent storage cabinet can be realized, and the energy consumption is reduced while the visual experience of a user is improved.

The invention discloses a multi-evaporator branch flow self-adaptive distribution two-phase cooling cycle and a control method thereof, the multi-evaporator branch flow self-adaptive distribution two-phase cooling cycle mainly comprises a plurality of parallel evaporators, a condenser, a liquid storage device, a liquid pump and a connecting pipeline, and a ventilation liquid blocking valve is mounted at an outlet of each branch evaporator. The ventilation liquid blocking valve is mainly composed of a valve seat, a valve element and a floating ball, the opening degree of the valve is automatically adjusted according to the dryness of an inlet working medium under the action of gravity and buoyancy, and the larger the dryness is, the larger the opening degree of the valve is. When the heat loads of the branches are not synchronous, the circulation flow can be distributed in a self-adaptive mode according to needs, the larger the heat loads on the branches are, the higher the dryness of the corresponding working media is, the smaller the resistance of the ventilation liquid blocking valve is, and the larger the flow of the branches is. The smaller the thermal load on the branch is, the smaller the dryness of the corresponding working medium is, the larger the resistance of the ventilation liquid blocking valve is, and the smaller the flow of the branch is. The problem that a high-load branch is prone to being overheated and burnt due to small flow is solved, meanwhile, invalid flow of a low-load branch can be effectively reduced, and energy consumption of a liquid pump is greatly saved.

The low-noise environment-friendly speed reducer comprises a speed reduction supporting shell, a driving part is fixedly installed on the outer wall of the speed reduction supporting shell, a speed reduction device is installed at the output end of the driving part, and the output end of the speed reduction device is connected with a load; a stabilizing device and a damping device are installed on the outer wall of the speed reducing mechanism, the stabilizing device comprises a stabilizing base and a first air bag, the first air bag is communicated with the air source assembly, the damping device comprises a damping base and a second air bag, the second air bag is communicated with the air source assembly, a universal joint mechanism is installed on the second air bag, and a first pressure sensor is arranged on the damping base. A deceleration intelligent controller is installed on the deceleration supporting shell, and a main control module, an instruction module, a monitoring module and a decibel meter are arranged in the deceleration intelligent controller. According to the low-noise environment-friendly speed reducer, noise generated by vibration of the speed reducing device is reduced, the heat dissipation efficiency of the speed reducing mechanism is improved, and self-adaptive adjustment of heat dissipation and noise reduction of the speed reducer is achieved.

The invention relates to a self-adaptive driving control method suitable for an H-bridge circuit. The method comprises the following steps: firstly, performing turn-off driving, and loading a target turn-off driving signal to a gate end of a target turn-off power tube based on turn-off information of the current target turn-off power tube so as to drive the target turn-off power tube to be gradually turned off based on the target turn-off driving signal; and when the turn-off information of the target turn-off power tube is at least in a pre-turn-off state, performing turn-on driving on a target turn-on power tube in the target turn-on bridge circuit tube group, and loading a target turn-on driving signal to the gate end of the current target turn-on power tube based on the turn-on state of the current target turn-on power tube so as to turn off the target turn-off power tube. And driving the current target conduction power tube to be gradually conducted based on the target conduction driving signal. Soft switching of the power tube in the H-bridge circuit is controlled, the working efficiency of the H-bridge circuit is improved, meanwhile, self-excitation resonance of the H-bridge circuit is prevented, and the dead time can change along with changes of external application conditions.

The invention relates to the technical field of warehouse logistics, and discloses a carrying method, a carrying device and a warehousing system.The method comprises the steps that the number of first to-be-put-in objects and / or the number of first to-be-put-out objects corresponding to at least one to-be-processed task is determined; according to at least one of the number of the first to-be-put-in objects and the number of the first to-be-put-out objects and the first number, a proportion threshold value corresponding to a cache region of the warehousing system is adjusted; wherein the first number is related to the number of cache bits in the cache region; determining the occupation proportion of cache bits in the cache region; and according to the occupancy proportion and a proportion threshold value, the first carrying equipment is controlled to carry the target warehouse-out object stored in the storage area in the warehousing system to the cache area, and / or carry the target warehouse-in object stored in the cache area to the storage area. According to the invention, adaptive adjustment of the proportion threshold value of the cache region can be realized, and the working efficiency of the warehousing system is improved.

A self-adaptive control method of an aircraft wing and the aircraft wing wherein the self-adaptive control method comprises: constructing a prediction model, inputting a real-time external aerodynamic disturbance parameter to the prediction model, predicting a predicted disturbance error at a next moment, and realizing feedforward prediction compensation of external aerodynamic disturbance. The influence of predictable disturbance on the airfoil profile is eliminated in advance; and then monitoring the airfoil pose to obtain tracking airfoil parameters, and carrying out deviation calculation on the tracking airfoil parameters and the target airfoil parameters to obtain an actual tracking error so as to provide a real-time airfoil pose feedback signal. And finally, the control assembly generates a control instruction based on the predicted disturbance error, the actual tracking error and the target airfoil profile parameter, the control instruction acts on the mechanical neural network to realize adaptive adjustment of the airfoil profile, and the airfoil profile continuously approaches the target airfoil profile neighborhood through a feedforward and feedback composite control mechanism.

The invention discloses a self-adaptive optimization method for energy storage frequency modulation of a super capacitor and a related product, and belongs to the technical field of wind power generation. The invention provides a self-adaptive optimization method for energy storage frequency modulation of a super capacitor, and the method comprises the steps: collecting wind speed data, building an original wind speed sequence, dividing the original wind speed sequence into a prediction set and a verification set, and adjusting and correcting the original data of the wind speed sequence based on an actual value and a predicted value in the wind speed sequence of the verification set; a difference error and a proportion error are calculated, correction parameters are calculated according to the difference error and the proportion error, self-adaptive adjustment of a prediction model is achieved, inherent rules and characteristics of original data of a wind speed sequence are more effectively mined and utilized through iterative optimization of a prediction result, then the accuracy and reliability of wind speed prediction are improved, and the precision of wind speed prediction is ensured. A data basis with higher precision is provided for a super-capacity energy storage system, and energy waste caused by wind energy fluctuation is reduced.

The invention relates to the technical field of computer processing, and discloses a media data playing loudness processing method and device, equipment and a storage medium, and the method comprises the steps: obtaining to-be-played media data and audio features of the to-be-played media data; obtaining loudness demand information, wherein the loudness demand information comprises attributes of a current playing environment and / or playing equipment; determining loudness processing information of the to-be-played media data based on the audio features and the loudness demand information; and processing the to-be-played media data based on the loudness processing information to obtain target media data for playing.

The invention discloses a robot motion control system and control method based on machine vision, relates to the technical field of machine vision, and provides effective data input for an error calculation and correction module by extracting a local feature point set Fp during operation of the system. And the error calculation and correction module calculates an image translation error Et and a rotation error Er based on the feature point set Fp, obtains a translation vector T and a rotation error R by correcting the image position, generates a complete panoramic image P, and provides reliable visual information support for subsequent target positioning and path planning. The motion control and target positioning module is combined with a panoramic image P and a positioning algorithm, so that the target positioning precision is improved, the path planning is optimized, the problems of image dislocation, positioning errors and path instability in a traditional method are solved, and the adaptive capacity and task execution efficiency of the robot in a dynamic environment are greatly improved.

The invention provides a cooling tower and refrigerating machine collaborative optimization method based on a whale algorithm and a control system. The method comprises the steps of collecting operation data of a cooling tower and a refrigerating machine to form an initial state set; initializing an optimization parameter space, and designing a target function of the cooling tower based on the thermophysical constraint model; designing an energy efficiency optimization objective function of the refrigerating machine based on a thermophysical constraint model, and screening out a refrigerant flow and compressor power combination of the refrigerating machine meeting conditions; data are monitored in real time, a collaborative dynamic optimization objective function of the cooling tower and the refrigerating machine is designed according to the real-time data, and the goal of the collaborative dynamic optimization objective function is to maximize the comprehensive energy efficiency ratio of the system. According to the method, a two-stage collaborative optimization framework based on thermophysical constraints is constructed, so that the problems of target conflicts, insufficient physical constraint adaptability, insufficient dynamic adjustment capability and the like in the collaborative optimization problem of the cooling tower and the refrigerating machine are solved.

The invention relates to a turnout detection method based on computer vision and an intelligent monitoring pile. In order to improve the detection precision, an improved YOLOv8 model is provided, an original path aggregation feature pyramid network structure is jointly improved by using a space-depth convolution module (SDC) and a feature enhancement-fusion module (FEF) in a check layer of the network, and a small target detection enhancement pyramid structure is provided to enhance the small target detection capability. In addition, an improved loss function with a scaling factor and an auxiliary bounding box is used to further enhance the detectability of the model. Compared with the prior art, the method has higher accuracy and robustness.

The invention relates to the field of wearable equipment, in particular to a real-time health monitoring and intelligent diagnosis method applied to the wearable equipment. The method comprises the following steps: collecting human body bioelectrical impedance data, and constructing a spectrum data matrix; preprocessing the spectrum data matrix to obtain a standardized spectrum matrix; dynamic spectrum features are extracted from the standardized spectrum matrix and analyzed, and a health state vector is generated; and based on the health state vector, calculating a health risk assessment value, and analyzing and assessing the health condition of the user. The problem that the prediction accuracy and sensitivity are limited due to the fact that complex physiological state changes cannot be comprehensively captured is solved; generally, a linear model is used for analyzing data, a complex non-linear relation between physiological data is neglected, and the real-time adaptive capacity to dynamic change of the data is lacked; the problem that the risk assessment result is not accurate enough due to the lack of comprehensive consideration of historical data, time decay, feature influence and other factors in the prior art is solved.

The invention provides a rigidity valve. The rigidity valve comprises a valve seat, a valve element, fixed iron and a movable sealing rubber part. The valve core is sleeved in the fixed iron and the valve seat; the valve element can move in the axial direction relative to the fixed iron and the valve seat so as to selectively open or close a valve port in the valve seat. The dynamic sealing rubber part comprises a mounting part and a sealing lip; the mounting part is assembled in the first groove of the valve core or the second groove of the fixed iron in an interference manner; the first groove is located in the peripheral wall of the valve element. The second groove is located in the inner circumferential wall of the fixed iron. According to the invention, the structure of the dynamic seal is simplified, the assembly difficulty and the processing cost of the dynamic seal are reduced, and the self-adaptive adjustment of the dynamic seal is realized, so that the sealing effect cannot be reduced after the dynamic seal rubber part is used for a long time.

The utility model discloses a head support for thyroid nodule minimally invasive treatment, which comprises a fixing support, two inclined planes are arranged on the upper side of the fixing support, the connecting position of the two inclined planes is the highest point of the fixing support, the two inclined planes respectively correspond to the shoulders and the head of a patient, and an opening is formed in the highest position of the upper side of the fixing support. A neck supporting mechanism is arranged on the fixing frame and comprises a neck pillow corresponding to the opening and a lifting assembly used for adjusting the height of the neck pillow. The device has the advantages that the fixing frame is arranged to support the shoulders of a patient, the supporting plate supports the head of the patient, the neck pillow is lifted through the lifting assembly, and after the neck of the patient is lifted, the head of the patient drives the head supporting mechanism to move, so that self-adaptive adjustment of the head supporting mechanism is achieved, operation of medical workers is omitted, the workload of the workers is reduced, and the working efficiency is improved. And the follow-up design can ensure that the distance between the head supporting mechanism and the neck supporting mechanism is most suitable for the posture of the patient.

The invention discloses a double-cold-trap operation monitoring method, a double-cold-trap control system, a medium and a product, and relates to electric digital data processing.The method comprises the steps that first cold trap temperature and humidity data, first condensate water cumulant, second cold trap temperature and humidity data and second condensate water cumulant are obtained; determining a first cold trap dehumidification efficiency, a second cold trap dehumidification efficiency, a first temperature gradient, a second temperature gradient and a condensate water cumulant change rate; determining a first operation defect of the first cold trap and / or a second operation defect of the second cold trap according to a preset parameter standard value; and based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, searching a double-cold-trap processing scheme of which the relevancy is higher than a preset relevancy threshold in a preset operation defect processing scheme database. By implementing the method, the accuracy of double-cold-trap operation monitoring can be improved.