644 results about "Normal state" patented technology

The invention provides an aero-engine state monitoring method and device based on sound and vibration fusion and a computer readable storage medium, and relates to the technical field of aero-engine state monitoring. The method comprises the following steps: acquiring signals respectively acquired by a vibration sensor and a sound sensor; respectively intercepting a vibration signal low-frequency component and a sound signal high-frequency component based on the frequency response characteristic difference of the sensor; performing normalization processing on the intercepted signal to eliminate amplitude difference; constructing a transition region through an interpolation method, and splicing the continuous mixed frequency spectrum to obtain a sound-vibration fusion spectrum; and establishing a health benchmark based on the normal state fusion spectrum, and realizing abnormity monitoring and alarm through characteristic difference analysis. Advantages and characteristics of a low-frequency band of the vibration sensor and a high-frequency band of the sound sensor are fully utilized, frequency response limitation of a traditional single sensor is broken through, deep fusion of sound and vibration signals is achieved, the effective detection frequency range is remarkably widened, and accuracy and robustness of recognition of early faults such as aero-engine blade breakage are improved.

The invention discloses an intelligent operation and maintenance system, method and equipment for steam turbine lubricating oil and a storage medium, and relates to the technical field of industrial digital intelligent operation and maintenance. Zero loss and low delay of data are guaranteed through the data transmission module, and the data fault problem of traditional manual meter reading is solved; static parameters are converted into dynamic trend, residual life and abnormal early warning through a hybrid model of a visual cloud operation and maintenance module, and intelligent conversion from data to decision is realized; and the oil filtering execution module automatically responds to the instruction to complete closed-loop treatment from polluted oil liquid to clean oil liquid without manual intervention, so that real-time monitoring and intelligent analysis of the running state of the steam turbine are realized, an oil filtering system is automatically started when a set threshold value is reached, the oil liquid is ensured to be in a normal state, the maintenance cost is reduced, and the operation and maintenance efficiency is improved.

The invention relates to an anti-ant large-core-number fiber ribbon cable comprising a cable core and an outer sheath. The cable core includes loose tubes; and fiber ribbons are sleeved inside the loose tubes. The outer sheath is an outer nylon sheath; and a non-metallic reinforcement layer is arranged between the outer sheath and the cable core. According to the anti-ant large-core-number fiber ribbon cable provided by the invention, with the outer nylon sheath, ants can not bite the sheath, so that the damages on the optical cable by ant biting in a pipe at an area with a serious anti problem are prevented, the normal state of the cable is ensured, and the application region of the cable is extended. The cable based on a simple and reasonable structure has advantages of small cable diameter, high fiber density, large fiber capacity, and low duty ratio and can be laid in a pipe conveniently; and the bending performance is good. During branched connection, 4 to 24 fibers can be melted once, so that the construction time is saved and the manpower cost is lowered, and the construction efficiency is improved. On the basis of the full drying type dielectric structure, branched connection is realized conveniently and usage becomes convenient.

The invention provides an equipment state deviation identification method based on self-supervision and incremental learning, and the method comprises the steps: S1, obtaining time sequence data of equipment in a fault-free state, and constructing a normal state model; s2, during operation, deviation detection is carried out on real-time data through the normal state model, and a deviation degree index is obtained; s3, comparing the deviation degree index with a preset threshold value, and judging an abnormal event; s4, determining new normal state data by manually verifying the abnormal event; and S5, updating the normal state model according to the new normal state data. The method does not need to depend on a fault sample, establishes an equipment normal behavior model through self-supervised learning, introduces a deviation index to quantify a state difference, and combines manual feedback and incremental learning to form a closed loop, so that the model has self-adaptability and long-term evolution ability.

The invention discloses an intelligent aeration control system and method for a sewage treatment plant, and relates to the technical field of sewage treatment control. Comprising a data acquisition module, an aeration test module, a demand prediction module, a normal state analysis module and an aeration control module, wherein the data acquisition module is used for acquiring real-time working data of a primary sedimentation tank, test data of an aeration tank, historical working data and real-time working data of the aeration tank and preprocessing the data; the technical key points are as follows: an aeration tank is scientifically divided into multiple sections, professional monitoring equipment is deployed in each section, multi-dimensional data is acquired, the data of each section is independently acquired and tested, the operation condition and the processing effect of the aeration equipment in each section can be accurately mastered, and on the basis of the accurate data and in combination with an aeration efficiency evaluation model and a regulation and control strategy, the aeration efficiency of the aeration tank is evaluated. And fine adjustment can be performed according to actual requirements of each section, so that resource waste or poor treatment effect caused by one-step regulation and control is avoided, efficient and accurate operation of the aeration equipment is realized, and the aeration equipment has a good use prospect.

The invention discloses an electric power digital anomaly identification method and system based on digital twinning, and relates to the technical field of electric power. Comprising the following steps: constructing a digital twinborn model according to power data of a plurality of time periods in a current detection period; obtaining multi-source operation data in the power system, and preprocessing the multi-source operation data; according to actually collected data, inputting the data into a digital twin model for simulation, and generating virtual operation data; calculating a data deviation value between the virtual data and a normal state according to a dynamic deviation coefficient formula; on the basis of the deviation value, abnormal features are extracted, and the abnormal features are judged; and generating a corresponding early warning response according to a judgment result. According to the method, through accurate simulation of the digital twin model and comprehensive analysis of multi-source data, abnormal feature extraction and grading judgment are combined, the abnormal condition of the power system can be identified more accurately, and misjudgment and missed judgment are reduced.

The invention discloses an abnormality diagnosis method for an electric energy metering device, and the method comprises the steps: building a dynamic baseline model which reflects the real normal state change rule of a target user through the full mining of the historical multi-dimensional and fine-grained data of the target user, and carrying out the refined comparison through the data collected in real time at high frequency, thereby achieving the abnormality diagnosis of the target user. Sensitive and reliable anomaly criteria are formed by comprehensively considering absolute and relative deviations, so that possible problems of the metering device can be quickly and accurately identified in an actual operation environment. Through the mode, the adaptive capacity of the diagnosis result to individual differences and behavior change trends is remarkably improved, and efficient data security guarantee and operation and maintenance support are provided for a large-scale intelligent distribution network system.

The invention relates to a safety air bag ignition control method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps that the running state of the automobile safety air bag control system is configured through the safety control state machine; under the condition that the running state is the normal state, the ignition function of the automobile safety air bag control system is configured to be in a forbidden state through a safety control state machine; in response to the fact that the automobile safety air bag control system is powered on for the first time and / or a watchdog error is detected, the running state is switched to a watchdog error protection state from a normal state through a safety control state machine; and under the condition that the operation result of the sensor data processing unit exceeds a preset threshold value, the operation state is switched from the normal state to the safety air bag ignition enabling state through the safety control state machine, and the ignition function is switched to the starting state. By adopting the method, the low-delay and high-precision response requirements of the chip on the ignition control instruction can be met.

The invention discloses an anomaly detection method, system and device based on a multi-mode visual large model and a medium, and the method comprises the steps: carrying out the shooting of an image at a designated point through an inspection device, comparing a template image in a normal state with a detection image collected in real time, whether abnormity exists or not and the position of the abnormity in an image are judged through the following steps that S1, the image is registered; s2, identifying a target in the image by using an open set target detection large model so as to detect various abnormal parts; s3, performing image segmentation by using the large segmentation model; s4, performing IOU comparison on contours generated by segmentation of the template image and the to-be-detected image, removing part of differences, performing area-by-area analysis, and setting the contours with IOU smaller than a specified threshold value as possible abnormal positions; and S5, carrying out similarity comparison on the different contours, removing contours with the similarity greater than a preset threshold value, and taking the rest contours as abnormal areas. The method does not need to perceive a specific abnormal type in advance, and has the capability of identifying and segmenting multiple types of targets.

The invention provides a production control method and system based on SPC alarm linkage process locking, and relates to the technical field of quality management and control, and the method comprises the steps: deploying an SPC sensing node used for collecting technological parameters at a cutting edge position where a machining tool of production equipment is in contact with a workpiece, or at a contact point where a probe of online measurement equipment is in contact with the surface of the workpiece; the method comprises the following steps: acquiring process parameter data of a key production process in real time through an SPC sensing node deployed at a cutting edge position or a contact point; inputting the process parameter data as real-time data or small sample expansion data into an SPC judgment engine; and performing statistical process control analysis on the real-time data or the small sample expansion data through an SPC judgment engine so as to model data clusters which are densely distributed and have stable parameters in the data into a virtual reference curved surface representing a normal state domain. According to the invention, the accuracy of abnormity determination can be improved, and timely management and control of quality abnormity and minimization of production loss can be realized.

The invention provides an equipment fault early warning method and system, and belongs to the technical field of fault prediction, and the method comprises the steps: obtaining the real-time working condition parameters of equipment; the real-time working condition parameters comprise real-time operation parameters and environment parameters; extracting a first feature vector based on the real-time working condition parameter; respectively calculating the similarity between the first feature vector and a plurality of second feature vectors in a model library; determining the corresponding normal state model with the maximum similarity as a target model; and inputting the real-time working condition parameters of the equipment into the target model to obtain the deviation between the real-time working condition parameters and the normal parameter range, and carrying out fault early warning on the equipment based on the deviation. According to the equipment fault early warning method and system provided by the invention, the accuracy of equipment fault early warning can be improved.

The invention aims to provide an aircraft blade detection probe and method based on flexible double-frequency differential eddy current, and relates to the technical field of nondestructive testing, when defect detection of an aircraft blade is carried out, a flexible substrate can be adaptively attached to a complex curved surface of the blade; the double-frequency excitation coil cooperatively works through high frequency and low frequency and respectively captures surface micro-cracks and subsurface corrosion defects, and when differential signal amplitude fluctuation occurs in the differential receiving coil, it can be judged that abnormal damage exists in a corresponding blade area; if the coil induction signal is kept stable and unchanged, the corresponding area is in a normal state, and the geometric characteristics of the damaged part can be quantitatively analyzed by observing the numerical range of the amplitude change; specifically, the intensity of signals output by the induction lines and the integrity of the blade structure are in positive correlation, and the larger the fluctuation value is, the more serious the damage degree is.

The invention relates to the technical field of mechanical equipment fault diagnosis, and particularly discloses an attention mechanism CNN-based electric actuator fault feature extraction method and system. The method comprises the following steps: through a mode of combining simulation modeling and field acquisition, obtaining a fault feature of an electric actuator; obtaining multi-source data of the electric actuator in a normal state, a known fault state and an unknown fault state, and preprocessing and enhancing the multi-source data; and constructing a lightweight convolutional neural network model containing a cross-modal attention mechanism. According to the method, multi-source signals such as current, voltage and displacement can be effectively fused, weights of different modes and characteristics are automatically distributed, and the extraction capability of key fault characteristics is enhanced; accurate distinguishing between known faults and unknown faults is realized through multi-scale feature complexity measurement and a dynamic discrimination mechanism; in combination with domain self-adaption and an incremental learning mechanism, the model can migrate between simulation and real working conditions, and has self-expansion capability for a novel fault mode.

The invention discloses an anti-blocking optimization pneumatic conveying intelligent control method and system, and the method comprises the steps: enabling the system to receive a starting instruction, initializing system parameters, and entering a standby state; the system enters a conveying state, the opening degree of the valve is calculated according to initialized system parameters, and meanwhile pressure data and flow data are collected in real time and stored in a storage; after the transmission process is finished, feature extraction is carried out on the collected data, and historical trend parameters are stored; dividing the conveying state into a normal state, an early warning state and a blockage critical state, selecting key operation variables influencing blockage from system parameters, processing the key operation variables to form a blockage risk scoring model so as to obtain a risk score, judging the current conveying state of the system, and executing corresponding measures; when the number of times of conveying reaches the preset number of times, the system conducts conveying optimization according to the pipe blocking rate and executes in the follow-up conveying process, and the system has the advantage that the conveying process can be accurately monitored and intelligently optimized.

To provide a game machine that improves management of game media number display according to the game state.SOLUTION: A game machine of the present invention has three game states: a first normal state, a second normal state, and a specific game state. Control can be performed so that the degrees of advantage for auxiliary games differ between the first and second game states. A game machine information notification command containing information indicating the game state can be generated and transmitted to virtual game media number control means. Standby processing can be executed when a situation in which a response command from the virtual game media number control means cannot be received occurs a predetermined number of consecutive times. When it is determined that a power interruption has occurred, after storing a checksum in a used area of the memory of main control means and a back flag to be referenced when recovering from the power interruption, the standby processing can be executed. A power-on operation accompanied by a RAM clear clears the backup information in the main control means, but does not clear the virtual game media number which can be played.SELECTED DRAWING: Figure 40

The invention discloses a monitoring and early warning method and system for replacement of a powder feeding pipe for 3D printing, and the method comprises the steps: carrying out the quantitative calibration of the convergence of the powder feeding pipe in a normal state, and obtaining the initial light powder overlapping length d of the powder feeding pipe; and the width W of the molten pool and the melting deposition height D are measured multiple times in real time, W and D are calculated respectively, if W and D are abnormal, it is judged that abnormal points exist in the powder feeding pipe, and the number of the abnormal points is accumulated and counted. And if the number of the abnormal points is larger than or equal to a set threshold value, forming is paused, the actually-measured light powder overlapping length value dn is collected, and if the difference value of dn and d exceeds the set threshold value, replacement of the powder feeding pipe is prompted. According to the invention, indirect judgment is carried out by adopting the melting width and melting height obtained by the laser coaxial measurement system, and direct judgment is carried out by adopting the lateral photographing data acquisition system, so that timely early warning and reconfirmation are realized, manual monitoring and judgment errors are reduced, the precision of monitoring the service life of the powder feeding pipe is improved, and the practicability is relatively good.

The invention provides a monitoring data determination method, a simulation device, a simulation device test method, electronic equipment and a storage medium, and relates to the field of data processing, the simulation device comprises a displacement device, a plurality of target anchor chains and sensors corresponding to the target anchor chains, the monitoring data determination method comprises the following steps: acquiring displacement parameters of a displacement device; controlling the displacement device to displace in a preset space range at a preset speed, wherein the preset space range is determined based on the displacement parameter; a top end tension data set of the corresponding target anchor chain is collected through a sensor, the top end tension data set comprises top end tension of the target anchor chain in multiple different target states, and the target states comprise at least one of a normal state, an anchor dragging state and an anchor breaking state; and storing the top tension data set, wherein the top tension data set is used for monitoring the target anchor chain in the actual environment.

Provided in the embodiments of the present disclosure are a file processing method, a file system in user space, a device, and a computer program product. The file processing method is applied to a file system in user space, wherein the user state file system comprises a file system process and a file system in user space (fuse) process which is in communication connection with the file system process. The file processing method comprises: a fuse process acquiring a file processing request; the fuse process caching the file processing request, wherein the fuse process comprises a caching request queue used for caching the file processing request; and when a file system process exits, the fuse process determining a request processing state of the file processing request to be a suspension state, such that a file processing link where the fuse process is located maintains a normal state. In the embodiment, when a file system process exits, a fuse process may cache a file processing request, thereby ensuring the normal operation of a user process.

To increase the degrees of design freedom of a game machine.SOLUTION: In a game machine, stipulated loading numbers are different between a normal state and a bonus state, a small role that becomes a lottery object in the normal state is won in a bonus state at a probability that is higher than that in the normal state, internal lottery is conducted so as to acquire a winning mode of the small role without fail in the bonus state, and when the small role is won in the bonus state, game values fewer than the stipulated loading number are put out. When a winning mode including small roles 3, 4 of which the put-out numbers of game values in the bonus state are different from that in the normal state is acquired in the bonus state, the game machine draws a common pattern in each winning form of the small roles 3, 4 with a first stop reel to display a pattern configuring a winning form of the small role 4 with a second stop reel in an effective line 15 preferentially to a pattern configuring a winning form of the small role 3 and to enable the small role 4 to be won irrespective of timing of a third stop operation.SELECTED DRAWING: Figure 18

The invention discloses a silicon-based new material compressor fault detection method based on multi-source heterogeneous data, and the method comprises the steps: firstly obtaining normal historical operation data of vibration, pressure and temperature through a compressor state detection platform, carrying out the data preprocessing, and constructing a training set; then, a star-graffiti optimization algorithm is adopted to carry out joint optimization on variational modal decomposition (VMD) parameters and decompose signals, and modal features are extracted; secondly, realizing cross-channel adaptive fusion through convolutional neural network (CNN) multi-scale coding and introduction of channel and time attention; performing time sequence modeling on the fusion feature sequence by adopting a bidirectional long short-term memory network (BiLSTM), and training an anomaly detection model through training set data; and finally, inputting the test set data into the initial anomaly detection model through the same process, and realizing fault judgment by calculating the deviation from a normal state. And when the evaluation index of the test set reaches the standard, solidifying the model into an optimal anomaly detection model for on-line reasoning application of on-site real-time data.

Provided in the present disclosure is a data stream processing method. The method comprises: in response to the failure of a first load sharing link in a load sharing link group, determining an actual load sharing ratio of each second load sharing link among at least one second load sharing link, wherein the second load sharing link is a link in the load sharing link group which is in a normal state, and the actual load sharing ratio is used for representing an actual ratio of the second load sharing link in a forwarding table of the load sharing link group; on the basis of the actual load sharing ratio of each second load sharing link, determining at least one target load sharing link from among the at least one second load sharing link; and processing a data stream on the first load sharing link by means of the at least one target load sharing link. Further provided in the present disclosure are an electronic device, a computer-readable storage medium and a computer program product.

The invention discloses a chaotic engineering fault drilling method and device, a storage medium and electronic equipment, and relates to the field of chaotic engineering, and the method comprises the steps: injecting an nth fault factor into a to-be-tested system, and obtaining an nth fault response result of the to-be-tested system under the action of the nth fault factor, and determining the current operation state of the to-be-tested system according to the target dimension and the nth fault response result, adjusting the nth fault factor into the (n + 1) th fault factor based on the fault adjustment rule and the nth fault response result when the current operation state is a normal state, and then injecting the (n + 1) th fault factor into the to-be-tested system. And circularly executing the steps to realize the chaotic engineering fault drilling of the to-be-tested system. Through the method and the device, the problem of low accuracy of a drilling result of chaotic engineering fault drilling caused by performing chaotic engineering fault drilling through a static fault injection strategy in related technologies is solved.

The invention discloses a paper jamming monitoring method and paper processing equipment, paper is driven by a driving part to advance, and when the scalar A0 output by the driving part is larger than a first threshold value, it is preliminarily judged that the paper is abnormal at the moment. According to the scheme, when the output scalar A0 is larger than the first threshold value, the rotating speed V0 is obtained, the output scalar A1 of the driving part and the rotating speed V1 of the driving part are obtained again in the first period, if A1 is also larger than the first threshold value and V1-V0 is smaller than the second threshold value, it is judged that paper is stuck, otherwise, if A1 is smaller than or equal to the first threshold value, it is judged that judgment is misjudged for the first time; or if V1-V0 is greater than or equal to the second threshold value, the rotation speed increase caused by the output scalar meets the expectation, and the state is a normal state. According to the mode, whether the paper is stuck or not is judged by combining whether the rotating speed is increased abnormally or not, the paper jamming phenomenon can be judged within a short time without waiting for complete jamming, the obtained result is more accurate through the two times of judgment, and efficiency reduction caused by misjudgment is prevented.

The invention provides a vehicle control method and device, electronic equipment and a storage medium, and the vehicle control method comprises the following steps: when it is detected that a user triggers a crawling mode activation instruction for a target vehicle, determining state detection information of the target vehicle; when the state detection information of the target vehicle represents that the vehicle is in a normal state, responding to the crawling mode activation instruction to enable the working mode of the target vehicle to be switched into a crawling mode; and when the state detection information of the target vehicle represents that the vehicle is in an abnormal state, refusing to respond to the crawling mode activation instruction. On one hand, when the vehicle is in the normal state, the crawling mode activation instruction can be normally responded, so that the vehicle can achieve the crawling mode, on the other hand, when the vehicle is in the abnormal state, the crawling mode activation instruction can be refused to be responded, a user is prevented from mistakenly touching the crawling mode, and therefore the driving safety of the vehicle is improved.

The invention discloses a direct current transformer based on a quantum Hall effect, and belongs to the technical field of direct current transformers. Comprising a lower shell which is hinged with an upper shell; the positioning mechanisms are arranged in a mirror image mode and are arranged on the lower shell, and each positioning mechanism comprises a guide column fixedly connected to the lower shell; the movable frame is connected to the guide columns arranged in a mirror image mode in a sliding mode; the fixed shell is fixedly connected to the movable frame; the overturning shell is hinged to the fixed shell, and the fixed shell and the overturning shell are jointly and fixedly connected with an expansion shell; and the driving mechanism is arranged on the lower shell and is used for enabling the expansion shell to expand and clamping and pulling the cable. According to the invention, the expansion shell in mirror image arrangement expands and clamps and pulls the cable, so that the normal state of the cable is ensured, and the situation that the measurement result obtained when the cable is measured is inaccurate due to non-uniform magnetic field distribution caused by bending of the cable in the measurement process is prevented.

The invention discloses an early fault monitoring method, device and equipment for a transformer and a storage medium, and the method comprises the steps: arranging magneto-optical measurement sensors at the two ends and the middle part of a winding in an iron core, obtaining the magnetic leakage induction intensity of the sensors and the current of a high-voltage winding, constructing a coefficient analysis model, and calculating the fault of the transformer; and identifying each coefficient of the current coefficient analysis model through a least square parameter algorithm to obtain an identification result, calculating an identification coefficient change rate based on the identification result and a leakage magnetic induction coefficient of the winding in a normal state, and when the transformer is not no-load and the iron core is not magnetized and saturated, if the identification coefficient change rate is greater than a change rate threshold value, determining that the transformer is in a non-load state. And determining that the transformer has turn-to-turn short circuit. It can be seen that the coefficient analytical model reflects the analytical calculation relation between the current and the magnetic flux leakage induction intensity, through the combination of the analytical model and the recursive least square algorithm, signal offset caused by load fluctuation and power grid disturbance is effectively filtered out, and the stability and accuracy of transformer early-stage turn-to-turn short circuit recognition are improved.

The embodiment of the invention provides a fault processing method and device for a control system. The method comprises the following steps: determining a current running state of the control system; under the condition that the current running state accords with the target abnormal state, target fault configuration information corresponding to the target abnormal state is obtained, and the target fault configuration information is configured with at least one fault reason in the target abnormal state and fault recovery programs corresponding to the fault reasons respectively; according to the target fault configuration information, a target fault reason of the control system is determined, a target fault recovery program corresponding to the target fault reason is executed, the target fault recovery program is used for enabling the control system to recover the normal state, and the target fault reason of the control system is determined according to the target fault configuration information. And the target fault recovery program corresponding to the target fault reason is executed, so that the system can be helped to perform autonomous recovery, the system is recovered to a normal state, the system reliability is improved, and the train driving safety and the passenger travel safety are guaranteed.

The invention relates to a blast furnace ironmaking abnormal data screening method and device, electronic equipment and a storage medium, an original data sequence collected in the blast furnace ironmaking process is obtained, and the data state of each piece of original data in the original data sequence is determined based on a preset state judgment algorithm, or a normal state and a damping-down state during a damping-down period. Segmenting the original data sequence according to the data state corresponding to each piece of original data to obtain at least one sub-data sequence comprising the same data state data, and removing disturbance data in each sub-data sequence to obtain a corresponding target sub-data sequence, and abnormal data fragment identification is carried out on each target sub-data sequence according to an abnormal data determination rule, and abnormal data fragments are extracted. According to the invention, the abnormal data can be automatically identified through the preset abnormal data determination rule. And the original data in different states are segmented to perform abnormal data identification, so that the accuracy of an identification result is improved.

The invention discloses a high reactive overload control method and device for an improved active neutral point clamped topology. The method comprises the following steps: obtaining an output voltage and a phase angle according to a three-phase voltage and a three-phase current of a grid-connected point of the topology; obtaining a delay angle according to the total loss of the auxiliary diodes in the topology; obtaining a zero-sequence component according to the delay angle and the phase angle; obtaining d-axis and q-axis target current components according to the output voltage and the phase angle; comparing the q-axis target current component with a preset threshold value to obtain an overload flag bit; performing amplitude limiting on the d-axis target current component and the q-axis target current component according to the overload flag bit to obtain a first current instruction and a second current instruction; obtaining a three-phase modulation signal according to the first current instruction and the second current instruction; and when the overload flag bit displays that the topology is in a normal state, modulating a switching signal of the IGBT tube according to the three-phase modulation signal, otherwise, injecting a zero-sequence component into the three-phase modulation signal and then performing modulation.

Disclosed are an intelligent monitoring method and system for a condenser vacuum system of a thermal power unit. The method comprises: collecting operating data of a condenser vacuum system, and performing preprocessing; extracting features from the preprocessed data, and performing anomaly recognition; and on the basis of an anomaly detection output result, adjusting operating parameters of the vacuum system. In the monitoring method and system provided in the present invention, temperature features reflect the heat exchange effect of a system, quality features reflect the operating load and cooling effect of the system, and vacuum system features reflect the overall operating state of the vacuum system; when an anomaly score S exceeds 0.3, the automatic adjustment of the system is realized; and an adjustment amount is calculated on the basis of the current anomaly score and the features, thereby ensuring that the system quickly restores to a normal state.