857 results about "Normal state" patented technology

A driving state monitoring and feedback method and system based on multi-modal human factors intelligent data analysis, and an edge computing terminal device. The method comprises: receiving multi-modal human factors data of a tested driver that is collected in real time (S110); pre-processing the multi-modal human factors data, wherein the pre-processing comprises denoising processing and data normalization processing (S120); sending the pre-processed multi-modal human factors data into a pre-trained first state recognition model, so as to obtain a real-time recognized driver state, wherein driver states include a normal state and abnormal states, and the types of the abnormal states include a plurality of states such as a fatigue state, a distracted state and an angry state (S130); and when it is recognized that the driver state is an abnormal state, generating, for different categories of abnormal states, driving state feedback instructions to a driving intervention system, such that the driving intervention system performs state feedback adjustments on the driver on the basis of the received driving state feedback instructions (S140). The method and system can recognize different driving states of a driver in real time and then perform processing on the basis of different driving states, thereby avoiding the occurrence of traffic accidents.

The invention discloses a multivariate time series data anomaly detection method based on VAE and association difference, which enables a model to effectively capture the anomaly change essence in a time series by introducing the association difference, and enables the model to quantify the change of the association degree between variables through the calculation of the association difference, thereby more accurately distinguishing the normal state from the abnormal state. Meanwhile, the time sequence is reconstructed by using a variational auto-encoder, so that the distribution rule of the data can be learned, and the factor which can best represent the essential characteristics of the data can be found out. And the association difference of the reconstructed time sequence is further learned, so that the anomaly distinguishing capability of the model is enhanced and generalized. In addition, the mean square error, the KL divergence and the correlation difference are combined to serve as the abnormity judgment standard, and the abnormity detection capacity of the model is greatly improved.

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 relates to the technical field of data monitoring, in particular to an automatic monitoring system for plasma meltallizing coating process data, which is characterized in that a parameter association relationship analysis module performs association analysis based on real-time parameters in a plasma meltallizing coating process, identifies an association relationship among the parameters and generates a process parameter association analysis result; according to the method, through multi-dimensional correlation analysis and path screening of the technological parameters, key factors influencing the coating quality and action links of the key factors are defined, and the abnormal fluctuation state and the transfer path can be accurately predicted in combination with parameter priority dynamic adjustment and real-time data feedback, so that the stability and continuity in the technological process are ensured. In the scheme, a dynamic weight distribution strategy is adopted, so that key parameters in an abnormal state occupy a higher priority position, parameter distribution is rebalanced when the normal state is recovered, and excessive interference of a single parameter on the whole process is avoided.

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.

To provide a game machine that can increase the interest of a game.SOLUTION: In a Pachinko machine 10, when the result of a variable display of a third symbol on a display screen 42a of a third symbol display device 42 indicates a time-shortening win, it is possible to generate a consecutive win zone that is more advantageous to a player than a normal state as the subsequent state. A CPU of a sub-control device has a performance execution function that can execute a predetermined performance on the basis of the result of a lottery performed by a CPU of a main control device. Since the result of a special symbol 2 lottery based on the entry of a game ball into a special symbol 2 start winning device 33b is set to be easy to win, the CPU of the sub-control device can execute a consecutive win suggestion performance indicating that the result of the special symbol 2 win may occur consecutively on the basis of the result of the special symbol 2 lottery when start information based on the entry of a game ball into the special symbol 2 start winning device 33b is reserved in the consecutive win zone, by the performance execution function.SELECTED DRAWING: Figure 297

To provide a game machine for increasing fun of a game.SOLUTION: A CPU of a main control device can generate a first special RUSH state when a variation display of a special pattern 1 based on first start information acquired based on entry of game balls into a special pattern 1 start winning device 33a results in a time-shortening (2) jackpot in a normal state. The CPU of the main control device also generate a second special RUSH state when a variation display of a special pattern 2 based on second start information acquired based on entry of game balls into a special pattern 2 start winning device 33b result in a time-shortening (2) win in the first special RUSH state and when game balls enter a V winning hole 31b. The CPU of the main control device allows value of the first special RUSH state to differ from value of a second special RUSH state.SELECTED DRAWING: Figure 309

To provide a game machine for increasing fun of a game.SOLUTION: A game machine can generate at least a normal state, a jackpot state for opening or closing a variable winning device 32, and a time-shortening state more advantageous to a player than the normal state after completion of the jackpot state during a game, can perform setting such that game balls cannot easily enter a special pattern 2 start winning device 33b in a normal state and game balls easily enter the special pattern 2 start winning device 33b in a time-shortening state, and performs setting such that a time-shortening (2) state (special RUSH state) can be generated more easily when game balls enter the special pattern 2 start winning device 33b in a time-shortening state than when game balls enter a special pattern 1 start winning device 33a in a normal state.SELECTED DRAWING: Figure 268

To provide a game machine for increasing fun of a game.SOLUTION: A game machine has a higher game value when a variation display of a third pattern display device 42 based on satisfaction of entry into a special pattern 1 start winning device 33a results in a win having time-shortening in a normal state than when a variation display of the third pattern display device 42 based on satisfaction of entry into a special pattern 2 start winning device 33b results in a time-shortening state in a time-shortening state, and more easily leads to a win to which time-shortening (2) in a more advantageous time-shortening state is granted when a variation display of the third pattern display device 42 based on satisfaction of entry into the special pattern 1 start winning device 33a results in a win in a normal state than a variation display of the third pattern display device 42 based on satisfaction of entry into the special pattern 2 start winning device 33b results in a win in a time-shortening state.SELECTED DRAWING: Figure 268

The invention provides a fault detection method based on the combination of a Transform model of VMD and a BiGRU, and the method comprises the steps: firstly collecting voltage and current signals, and carrying out the preprocessing of the voltage and current signals; then, decomposing the preprocessed signal into a plurality of modal components by using VMD, and performing normalized coding to form a feature vector; the feature vectors are then input into a Transform model, deep features are extracted, and residual connection and layer normalization processing are carried out; the output of the Transform model is input into the BiGRU network, time sequence change characteristics are captured, and a characteristic vector with a fixed length is output; and finally, inputting the output of the BiGRU network into a softmax classifier, outputting probability distribution through linear transformation and a softmax function, and selecting a fault type with the maximum probability as a final result. And if the fault type is not a normal state, generating early warning information and giving an alarm. The method has the advantages of high-precision fault detection, strong robustness, strong adaptability and good real-time performance, and can effectively improve the accuracy and efficiency of fault diagnosis of the power system.

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 provides a transformer on-load tap-changer on-line monitoring and fault diagnosis method, and relates to the technical field of electrics. The method comprises the following steps: acquiring electric arc energy and on-load tap-changer contact position information generated during the switching action of a transformer on-load tap-changer; determining a switching performance score; acquiring operation data of the on-load tap-changer of the transformer; inputting the operation data into a trained fault probability prediction model, and obtaining predicted fault probability data during the switching action of the transformer on-load tap-changer; determining whether the transformer on-load tap-changer meets a preset target condition or not; if the on-load tap-changer of the transformer meets the preset target condition, determining that the state of the on-load tap-changer of the transformer is a normal state; and if the transformer on-load tap-changer does not meet the preset target condition, determining that the state of the transformer on-load tap-changer is a fault state. According to the invention, the state of the on-load tap-changer can be comprehensively monitored according to the switching performance and the predicted fault probability.

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 present invention discloses a single-photon detector refrigeration control method and system that is adaptive to temperature and TEC performance changes. The method limits the maximum operating voltage of TEC, monitors the TEC operating voltage and refrigeration temperature in real time, and automatically adjusts the target temperature and APD working bias voltage according to the monitoring results, effectively ensuring that the single-photon detector can still work stably when the working environment temperature is too high or the TEC performance is degraded. At the same time, when the ambient temperature is allowed to return to the normal temperature range, the TEC's refrigeration target temperature can be restored to the initial refrigeration target temperature and the APD's working bias voltage can be adjusted synchronously, so that the performance indicators of the single-photon detector, such as the detection efficiency, dark count rate, and after-pulse probability, will be synchronously restored to normal. In this way, the service life of TEC and single-photon detector equipment can be effectively improved, and the environmental adaptability of the single-photon detector can be improved.

The invention discloses an open-circuit fault diagnosis method for a switching tube of a dual-active bridge converter. The open-circuit fault diagnosis method comprises the following specific steps: modeling the dual-active bridge converter; analyzing voltage and inductance waveform characteristics of the dual-active bridge converter in a fault state of each switching tube on the primary side and the secondary side; comparing and analyzing fault features of each switching tube, and selecting a difference value between an inductive current average value and a bridge arm midpoint voltage average value in a fault state and a normal state as a fault feature; and designing a fault diagnosis algorithm according to the selected fault features. Compared with a traditional fault diagnosis method, only the voltage of one bridge arm of the primary side and the voltage of one bridge arm of the secondary side are selected as fault features, fewer sensors are needed, and real-time online fault diagnosis is more accurate and rapid.

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 relates to the technical field of fault detection, in particular to an intelligent fault detection and diagnosis method and system, and the method comprises the following steps: extracting and classifying fault feature vectors item by item based on the normal state and abnormal state operation data of a vehicle, transmitting the fault feature vectors to a fault coding layer through coding, inputting a potential space, and carrying out the clustering of feature points, and obtaining a preliminary fault identification feature. According to the method, the accuracy and the efficiency of fault diagnosis are remarkably improved by refining fault feature extraction and expanding sample analysis, and the accuracy and the efficiency of fault diagnosis are remarkably improved by analyzing normal and abnormal state data, extracting fine fault feature vectors, performing preliminary identification by utilizing a clustering technology, further expanding a sample set by simulating samples and deepening fault mode learning. And graded fault detection is introduced, response is adjusted according to fault severity, early warning is realized through dynamic monitoring, the equipment operation period is effectively prolonged, the shutdown risk is reduced, the maintenance cost is saved, and the system reliability is improved.

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 relates to a fault arc-caused fire early warning method based on multi-sensor fusion, and belongs to the field of fault arc fire. The method comprises the following steps: establishing an arc electrical fire cause fault simulation test platform, selecting a typical single load and a combined load to perform a test before normal operation of a line and the occurrence of an arc cause fire, and establishing a fault arc waveform database and an arc fire database; the power-on detection method for the fault arc detection device is based on dictionary learning, current characteristics are represented through a sparse matrix, and fault classification is carried out in combination with an SVM. The fluctuation range of a plurality of characteristic quantities of the line in a normal state and when a fault arc occurs is analyzed, a threshold value is determined according to the periodic increase ratio of the characteristic quantities in different states, a final combined load detection threshold value is formed by complementation of a plurality of threshold values, and fault arc detection is realized; and based on the arc fire database, training a double-layer LSTM model under Bayesian optimization, and identifying multi-sensor signals in a time window to realize fire early warning.

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 a fault processing method of a multi-device system, the multi-device system comprises a plurality of devices, a plurality of edge controllers and a cloud, and the fault processing method comprises the following steps: the cloud regularly trains and updates fault prediction models of the edge controllers and the devices based on historical data; after the latest fault prediction model is obtained, the cloud end predicts the fault levels of the edge controller and the equipment thereof at regular intervals; the fault level comprises at least one of a normal state, a quality reduction state, a quality early warning state and a fault state; and when the fault level is a quality reduction state or a quality early warning state, the cloud and the edge controller verify the fault through a corresponding heartbeat communication mechanism, and if the corresponding fault is verified, the cloud restarts and recovers a control program of a corresponding device in the edge controller. According to the invention, software faults of multiple devices can be timely handled.

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.

The invention discloses a centralized heating data analysis system for dispersed users based on the Internet of Things, relates to the technical field of centralized heating, and solves the technical problem that it is difficult to process and analyze sampled data of a heating system for each area and time stage of the heating system; the heating data is divided into different subsets according to the startup stage, stable operation stage and shutdown stage of the heating system through a data segmentation processing module; a normal data recognition model for each stage is established by extracting features from each subset of historical heating data; there may be significant differences in heating characteristics and demands in the startup stage, stable operation stage and shutdown stage, and these differences can be better captured by modeling each stage separately, thereby improving the prediction accuracy of the model; at the same time, in the stable operation stage, the state of the system is relatively stable and the data noise is less, which helps to improve the model's recognition ability for the normal state.

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