429 results about "State recognition" patented technology

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention discloses a distribution cable branch box state monitoring method based on state recognition, and particularly relates to the technical field of power monitoring, and the method comprises the steps: collecting multi-dimensional operation data of a plurality of branch boxes in a continuous time period, and constructing a state evolution sequence; calculating a state offset score and an adjacent equipment state consistency score to judge whether an evaluation process is triggered or not; after triggering, constructing a state influence probability map, identifying an abnormal influence path, extracting a state disturbance diffusion index and a cooperative behavior deviation index, inputting into a pre-trained risk identification model, generating a risk level interval and a cause probability distribution vector, executing an influence regulation and control measure, and updating a state identification logic; according to the method, dynamic perception of the state of the branch box is realized by constructing a state evolution sequence, combined judgment of individual and group behaviors is realized by combining a state offset score and an adjacent equipment state consistency score, and a risk level interval and cause probability distribution vector are used for driving regulation and control strategies and map updating. And the monitoring precision and the self-adaptive capability of the system are improved.

The invention relates to the technical field of construction data modeling, in particular to a BIM (Building Information Modeling)-based construction progress dynamic optimization system, which comprises an abnormal event identification module used for detecting local abnormity based on multi-source data and marking an abnormal area; the local component reconstruction and dynamic fusion module is used for reconstructing an abnormal region component model and performing space mapping to obtain a global BIM model; the component-semantic double-layer graph modeling module is used for constructing a component topological dependency graph and a component semantic relation graph and jointly reasoning a state transition sequence of the component; the state recognition and self-adaptive classification module is used for fusing the geometric features of the component and the graph structure features and executing state recognition and judgment boundary adjustment; and the graph evolution calculation module is used for performing multi-round state propagation deduction to generate a component state evolution result. According to the method, in a heterogeneous information modeling environment, dynamic processing and state intelligent identification of component-level information are realized, and the data calculation precision and response capability under three-dimensional model driving are improved.

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

The invention discloses an abnormity early warning method and system based on artificial intelligence, and relates to the technical field of abnormity early warning. The method comprises the following components: S1, brain wave data acquisition and preprocessing, S2, emotional state recognition and feature extraction, S3, specific business data acquisition and arrangement, S4, data fusion and abnormal feature construction, and S5, abnormal early warning decision and notification. According to the method, the brain wave emotion features and the specific business data are fused, abnormal feature construction is carried out by adopting a fusion algorithm of multi-scale feature matching and a support vector machine algorithm based on quantum annealing optimization, the abnormal feature rule can be mined more accurately through the comprehensive data processing and analysis mode, and the accuracy of abnormal feature extraction is improved. Therefore, in the real-time data monitoring and analysis process, the accuracy of abnormal early warning is improved, meanwhile, the system can dynamically adjust the monitoring frequency according to the fluctuation characteristics of the data, the timeliness of early warning information is ensured, and related personnel can take countermeasures in time.

The invention discloses an elevator running state multi-source sensing internet-of-things inspection system which comprises the following steps: S1, a multi-mode sensing acquisition module for synchronously acquiring data and generating a multi-channel sensing data packet; s2, a data preprocessing module used for preprocessing the data packet; s3, the edge computing node cluster is used for executing preliminary anomaly judgment on the structured multi-modal data sequence and generating local event description information; s4, a multi-modal feature fusion module which is used for receiving local event description information from each edge computing node and generating a unified feature vector sequence; s5, a heterogeneous sensor collaborative scheduling module enables different sensors to be coordinated and consistent in triggering conditions, data frequencies and data transmission paths; and S6, the state recognition and exception labeling module is used for outputting the corresponding elevator running state label and the exception event identification code. The method has the advantages of being comprehensive in sensing dimension, low in response delay and high in anomaly recognition precision.

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention relates to the technical field of image state recognition, in particular to an unhooking and rehooking AI accurate recognition grabbing system and method based on pattern recognition, in the system, node construction is conducted through contour changes, boundary difference values and gray level dynamic states of a hook assembly in an image sequence frame, edge displacement accumulation analysis is combined, meanwhile, through a graph neural network, an image sequence frame is obtained, and the image sequence frame is obtained. Cosine values and coordinate difference values between nodes are subjected to combined comparison, and a path hopping sequence is constructed, so that the response sensitivity to state abrupt change is enhanced, a key path of morphological evolution can still be stably extracted under the condition of complex background interference or local shielding, the anti-interference performance and fault tolerance of space path identification are effectively improved, and the space path identification accuracy is improved. Statistical modeling is further carried out on state rate sudden change points through a hidden Markov model, paragraph merging and invalid fragment removing operation are carried out on abnormal point segments by matching a standard state mode, a state label sequence is constructed, and accurate division of high-confidence and multi-segment continuous states is achieved.

The invention provides a self-adaptive adjustment control system and method applied to natural gas flow calibration, and relates to the technical field of automatic control and adjustment systems.The method comprises the steps that real-time pipeline operation data are collected, and real-time flow state recognition is conducted to obtain real-time flow state data of a preset type; the instruction generation module is used for determining a trigger coefficient, judging whether a trigger condition is met or not, and generating a calibration trigger instruction if the trigger condition is met; the data optimization module is used for receiving a calibration trigger instruction, acquiring a current compensation parameter, generating a preliminary correction coefficient, calling reference data and determining a final optimization correction parameter set in combination with the calibration data packet; the calibration updating module is used for updating the real-time compensation algorithm, determining the deviation degree, and re-triggering the calibration process if the deviation degree exceeds a preset allowable range; and the process optimization module is used for extracting current key calibration data, calling historical calibration data, determining an optimal calibration period and optimizing the calibration process based on the optimal calibration period after the calibration process is finished. And the calibration precision and efficiency of the flowmeter are improved.

The invention provides a construction state monitoring and risk assessment method and device based on BIM multi-mode conversion, and relates to the technical field of building information models. According to the method, a standardized image mode is generated by analyzing and extracting component information of a BIM model, and a BIM text mode is generated by using natural language description; constructing a graph structure mode based on space and construction logic, and realizing unified alignment and deep fusion of multi-modal data through multi-level modal alignment and a cross-modal attention mechanism to obtain a cross-modal fusion representation which is used for inputting a state recognition model and automatically detecting an execution deviation so as to monitor a construction state; and then introducing a deviation conduction mechanism to quantitatively calculate a comprehensive risk index of the component so as to carry out risk assessment. According to the method, the fusion representation which not only keeps semantic consistency but also conforms to construction logic can be obtained, the abstract cross-modal semantic features are converted into quantifiable and interpretable construction states and risk indexes, and powerful support is provided for intelligent analysis and application in a construction scene.

The invention relates to the technical field of state monitoring, in particular to a special equipment state real-time detection system and method, and the system comprises a data processing module, an association construction module, an abrupt change extraction module, a state recognition module and a trend prediction module. According to the method, by combining signal continuity analysis and denoising processing, the original data quality is improved, the influence of noise interference on state evaluation is reduced, multi-cycle data feature extraction and clustering analysis are utilized, a dynamic correlation model is constructed, the state classification accuracy under different working conditions is enhanced, and a trend direction and fluctuation form combined analysis mechanism is adopted; the method is advantaged in that abnormal signal period characteristics are accurately captured, early-stage identification efficiency of potential faults is improved, a multi-dimensional correlation characteristic comparison verification method is integrated, a composite state discrimination system is established, single-dimension misjudgment risks are reduced, amplification proportion sequence and persistence analysis technologies are fused, and a probabilistic prediction model is combined. And early warning of the abnormal state time node of the special equipment is realized.

The invention discloses a deep learning-based valve opening and closing state recognition and diagnosis method, which comprises the following steps of: acquiring and synchronizing multi-source signals, and generating a standardized multi-mode time sequence sample; extracting modal features by multiple branches and fusing the modal features into a joint feature vector sequence; the combined features are input into a phase change layered decoder, and a layered recognition result is output; constructing a plurality of types of abnormal events in a point process layer modeling phase change stage, and outputting an event modeling result; constructing a condition reversible generation diagnostor, and outputting a consistency checking result; and fusing a result output state and diagnosis information, and executing alarming and filing. Through multi-mode deep learning feature fusion, phase change hierarchical decoding and conditional modeling, accurate recognition of the opening and closing state of the valve, fine division of the phase change stage and intelligent diagnosis of early faults are achieved.

The invention discloses an equipment working state recognition method based on a voiceprint recognition model, and relates to the technical field of industrial equipment operation state recognition. The equipment working state recognition method based on the voiceprint recognition model comprises the following steps: collecting operation audio waveform data of target equipment, extracting acoustic representation data containing parameters such as short-time energy, a frequency spectrum centroid, a spectrum flux, MFCC and a zero crossing rate, inputting the acoustic representation data into a pre-trained voiceprint recognition model to extract voiceprint feature representation vectors, and carrying out voiceprint feature representation on the target equipment; according to the method, the audio signal is divided into the frames, the acoustic features such as short-time energy, spectrum centroid, spectrum flux, MFCC and zero crossing rate are extracted, the inter-frame evolution relation is modeled in combination with the bidirectional neural network, and the attention mechanism is introduced to highlight the key frame segment, so that the real-time performance of the audio signal is improved, and the real-time performance of the audio signal is improved. The recognition capability of working conditions such as fuzzy state boundary or unobvious transition is effectively enhanced, and the time sequence analysis and state judgment precision is improved.

According to the equipment state recognition method and device, the equipment and the storage medium provided by the embodiment of the invention, the multi-modal data in the operation process of the target equipment is acquired, and the multi-modal data comprises the image data, the text data and the temperature data; performing feature extraction and embedded coding on the multi-modal data through a feature extraction model to obtain embedded vectors of all modals, and aligning the embedded vectors of all modals to a unified target semantic embedding space; through a modal attention mechanism, the fusion weight of each modal is adjusted according to the state feature of the target device, and the embedding vectors of each modal are fused according to the fusion weight of each modal to construct a multi-modal fusion vector; and recognizing the state of the target equipment and / or performing abnormal risk early warning through a recognition model according to the multi-modal fusion vector. By introducing a modal attention mechanism, dynamic weighted fusion of image, text and temperature modals is realized, the accuracy and generalization ability of state recognition are remarkably improved, and the accuracy and robustness of the model under complex working conditions are improved.

The invention belongs to the technical field of precise and intelligent breeding, and provides a sheep apparent state recognition method and system based on an improved supervised learning network, and the method comprises the steps: apparent state category determination, data collection, dynamic trajectory prediction and filtering noise reduction, superposition windowing processing and feature extraction, data merging, data set grouping and model training. According to the method, dynamic trajectory prediction and filtering noise reduction are carried out on the collected data, and superposition windowing processing and feature extraction are carried out on the preprocessed data, so that the noise of the training data is reduced, the identification precision of the model is improved, scientific, accurate and rapid identification of the sheep apparent state is realized, and the identification efficiency of the sheep apparent state is improved. And breeding personnel can be helped to know the physiological and health conditions of the sheep in time.

The invention discloses an intelligent office table self-adaptive control method and system based on artificial intelligence. The method comprises the following steps: S1, multi-modal data acquisition; s2, data preprocessing; s3, feature extraction; s4, data fusion and state identification; and S5, executing an intervention strategy. Three types of data including visual information, physiological signals and pressure distribution are fused to perform multi-dimensional state sensing of multi-modal data fusion, sitting posture key points are extracted based on a convolutional neural network (CNN), real-time recognition of bad sitting postures is realized, time sequence characteristics of pressure distribution are analyzed through a long-short-term memory network (LSTM), behavior modes such as sedentariness and heeling are recognized, and the sitting posture recognition accuracy is improved. According to the method, the association between operation habits and fatigue is mined, HRV frequency domain features are extracted by using Fourier transform, and the fatigue state is comprehensively evaluated in combination with multiple indexes, so that three-dimensional description of different states of different users is realized, and whether the users are fatigued or not is judged to provide more refined health management.

The invention discloses a power transmission line on-line monitoring management system, belongs to the technical field of monitoring, and remarkably improves the state monitoring efficiency of a power transmission line through multi-dimensional technical innovation: multi-dimensional state collaborative perception of voltage data, current data, temperature data, vibration data and the like is realized, and equipment faults of the power transmission line are effectively captured; a corresponding relation between a state data set and a state type is constructed for correlation analysis, so that the abnormal state recognition accuracy under a complex working condition is improved, and misjudgment and missed judgment caused by environmental interference or single parameter fluctuation are reduced; based on high-precision time alignment and analysis, the anomaly identification efficiency is improved, and a precise reference is provided for fault traceability; a multi-stage state subdivision and cross-parameter logic judgment system is established, accurate recognition from state recognition to specific fault types is achieved, a monitoring closed loop is formed through multi-terminal real-time early warning, the problems of early warning lag and recognition fuzziness of a traditional system are solved, and the operation and maintenance decision efficiency and the operation reliability of a power transmission line are remarkably improved.

The invention provides a language learning dynamic resource configuration and interaction system based on an internet platform, relates to the technical field of data processing systems, and provides a double-layer cascade diagnosis mechanism. Through a first calculation module, a state anomaly score is calculated based on the stability of user interaction behaviors instead of simple correctness, so that beneficial struggling and harmful fatigue are accurately distinguished; when the score exceeds a threshold value, a second calculation module is activated, a specific learning fragment is analyzed in combination with eye movement trajectory data, and a cognitive deviation value for quantifying specific cognitive impairment is calculated; on one hand, through accurate state recognition, wrong intervention during deep thinking of the user is avoided, and the learning heart stream is effectively protected; and on the other hand, through accurate cognitive attribution, the system can provide targeted accurate assistance, the tutoring efficiency and the learning effect are improved, and intelligent teaching upgrading is realized.

The invention discloses a transformer state recognition method and device based on multi-scale time-frequency characteristics. The method comprises the steps that voiceprint signals generated when a transformer operates are collected; processing the voiceprint signal, and extracting a multi-scale time-frequency feature to obtain a time-frequency tensor; the time-frequency tensor is input into a pre-constructed multi-scale time-frequency model, and the multi-scale time-frequency model is constructed based on multi-scale convolution, a Transform neural network and a double-branch attention mechanism; and outputting a transformer state identification result through the multi-scale time-frequency model. According to the method, the voiceprint feature expression capability can be remarkably improved by extracting the multi-scale time-frequency features and the multi-scale time-frequency model, so that the transformer state recognition precision and speed are improved, and stable real-time recognition of the transformer state can still be realized in a complex noise environment.

The invention discloses an intelligent diagnosis method for power equipment primitive state recognition and rule fusion, and relates to the technical field of power system dispatching automation. The method is used for substation dispatching master station monitoring picture equipment primitive state recognition and abnormity diagnosis, video and graph model library data acquisition, preprocessing denoising, frame synchronization and ROI extraction. Using the improved YOLOv8-Tiny to extract a multi-modal feature to identify a primitive state; establishing an expert experience rule base, a graph-model association rule base and an anomaly judgment rule base; performing primary diagnosis on the forward chain reasoning fusion rule; performing cross checking on telecommand consistency, telemetering relevance and graph model library integrity; and outputting a result coexistence log. The problems that manual checking is low in efficiency and prone to omission are solved, the reliability of primitive recognition and correlation checking is improved, a D5000 system and a domestic operating system are adapted, complex monitoring scenes are coped, the operation and maintenance burden is relieved, it is guaranteed that monitoring pictures of a dispatching master station are accurate, and safe operation and maintenance of a power grid are facilitated.

The invention relates to the technical field of artificial intelligence and data processing, and discloses an energy storage equipment operation state monitoring method based on Internet of Things acquisition, which comprises the following steps: constructing a vibration data sample; performing state labeling on the vibration data sample to form a labeling database; performing sliding mean filtering and linear interpolation processing on the vibration data sample; carrying out adaptive spectrum noise suppression by adopting a dual-threshold wavelet packet noise reduction function; carrying out frequency band division by adopting a weighted multi-scale spectral entropy feature enhancement method; constructing a deep neural network model for classifying the running state of the energy storage equipment, and training the deep neural network model by using a labeling database; and inputting preprocessed vibration data acquired in real time into the trained deep neural network model, and performing state identification and fault alarm according to a category probability prediction vector output by the model. The running state of the energy storage equipment can be accurately monitored in real time, potential faults can be found in time, and measures can be taken.

The invention discloses a historical building digital monitoring protection system based on multi-scale features. The system comprises a building data acquisition module, an original data optimization module, a feature extraction model construction module, a state recognition model construction module and a building digital monitoring module. The invention relates to the technical field of historical building digital monitoring, in particular to a historical building digital monitoring protection system based on multi-scale features, and the method comprises the steps: obtaining original data through building data; a data optimization method of data alignment, data cleaning, data standardization and data set segmentation is adopted; a deep learning model is adopted as a feature extraction model, and deep correlation features of building time-varying behaviors and structural damage are learned through feature decoupling driven by time sequence modeling and physical laws; an improved clustering model is adopted as a state recognition model, and robust recognition of the building state is achieved by introducing physical threshold constraint spectral clustering, a dynamic time kernel function and a multi-scale probability fusion mechanism.

The invention relates to a municipal water distribution pipe network data analysis system and method and electronic equipment, and relates to the technical field of municipal engineering technology.The municipal water distribution pipe network data analysis system comprises a data acquisition and preprocessing module, a state recognition module, an anomaly detection module and an intelligent scheduling module; and the long and short-term memory network is used for analyzing the pipe network time sequence data and predicting the future pipe network state. According to the method, dynamic prediction of the pipe network state is realized by introducing the gated neural network, anomaly recognition is performed in combination with the statistical residual threshold, and the prediction precision and the anomaly detection capability are effectively improved. Meanwhile, self-adaptive generation of a scheduling strategy is realized by adopting a particle swarm optimization algorithm, and water supply safety and energy efficiency balance are ensured. The modular design of the electronic equipment integrates data acquisition, intelligent analysis and control output, an integrated closed-loop system is constructed, and high responsiveness and deployment flexibility are achieved.

The invention relates to the technical field of short message transmission optimization, and discloses an intelligent short message resending system and method based on receipt state recognition. A communication data acquisition module of the system obtains historical short message transmission records in a preset time period of a target communication area and generates a communication state data set; a feature extraction storage module performs receipt feature extraction on the data set to obtain a historical receipt feature set and establish a receipt state change matrix; the topology modeling module constructs a terminal state atlas according to the matrix, and calculates the topology aggregation degree of the historical short message failure events to determine an abnormal characteristic index set; a real-time analysis module extracts real-time receipt features of the current short message transmission data to generate real-time state vectors, and calculates a topology correlation degree with the abnormal feature index set in a terminal state graph to generate real-time risk factors; and the decision generation module generates a short message resending strategy instruction according to the real-time risk factor, the historical receipt feature set and the receipt state change matrix.

The invention relates to the technical field of artificial intelligence, provides an edge device gateway-based inspection system, and aims to improve the operation perception capability and intelligent inspection efficiency of a heating ventilation air conditioning system. The system comprises a data acquisition module, an edge gateway module, an inspection decision module and an inspection task management module, the data acquisition module is used for acquiring multi-source operation data, the edge gateway module is responsible for data preprocessing and homology feature extraction, the inspection decision module generates an optimization strategy based on multi-scale dynamic modeling and graph convolution reasoning, and the inspection task management module realizes task scheduling and execution feedback; the system has the capabilities of high-precision state recognition, abnormity early warning and energy efficiency optimization, and is suitable for intelligent routing inspection application of the heating ventilation air conditioner in a complex building environment.

The invention provides a gas turbine power plant rotating equipment state monitoring method and system based on voiceprint recognition, and relates to the technical field of power plant equipment monitoring, and the method comprises the steps: collecting an original voiceprint signal through deploying an array type acoustic sensor network; performing signal preprocessing, generating a voiceprint signal map, extracting Mel amplitude spectrum and phase spectrum features, constructing a multi-scale feature set, and generating a multi-dimensional voiceprint feature vector after screening; constructing an adaptive feature fusion network based on an attention mechanism, and fusing the multi-dimensional voiceprint feature vectors to generate voiceprint fusion features; inputting the voiceprint fusion feature into a preset voiceprint classification network, and outputting a real-time state recognition result, a confidence value and a fault prediction trend; according to the method, the early warning level is determined through the preset multi-level early warning mechanism, and the early warning information and the state monitoring report are output, so that high-precision state identification of the gas turbine power plant rotating equipment in a complex noise environment can be realized, and the accuracy and the anti-interference capability of fault diagnosis are remarkably improved.

The invention relates to a stateful protocol fuzzy test method and system based on response information. The method comprises the following steps: S1, a fuzzy test preprocessing stage; s2, a basic state identification stage; s3, a double-layer state subdivision stage; s4, a state evaluation and selection stage; s5, a fuzzy test execution stage; and S6, a feedback analysis stage. The system comprises a state division module, a state evaluation module, a test execution module and a feedback analysis module. According to the method, the efficiency and the accuracy of protocol fuzz testing are remarkably improved through a double-layer state recognition and dynamic evaluation mechanism. In the aspect of state recognition, a basic state identifier is firstly constructed based on the combination of a response code and a control field, and then state subdivision is realized through path feature analysis, so that state confusion and redundancy are effectively avoided. In the aspect of state evaluation, the key test of the key control node is realized by analyzing the topological characteristics, the historical income and the transition probability of the node.

The invention discloses a rolling linear guide pair performance degradation state identification method under variable working conditions, and relates to the technical field of rolling linear guide pair performance state monitoring. Vibration signals at different operation speeds and different positions in the whole-life periodic performance degradation process of the rolling linear guide rail pair are collected; the vibration signals are preprocessed by using a combined denoising method based on CEEMDAN and a wavelet adaptive threshold value; extracting time domain, frequency domain and multi-scale fuzzy entropy features from the denoised vibration signals to construct a feature set; constructing a field adaptive degradation state recognition model based on a one-dimensional convolutional neural network; and carrying out degradation state identification tests of multiple groups of variable working conditions, randomly selecting data of at least two groups of working conditions as labeled source domain training data, taking data of other working conditions as unlabeled target domain test data, and identifying the performance degradation state of the rolling linear guide rail pair by utilizing a trained model. The method is suitable for linear guide pair performance degradation state recognition under various working conditions.