364 results about "State recognition" patented technology

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 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 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 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 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 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 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 provides an industrial equipment indicator lamp state identification method based on an image sequence and a Transform network, the method is provided with a target detection network and a state identification network, the target detection network combines all detected indicator lamp sub-images into an image sequence, and all indicator lamp states are simultaneously output at one time; the state recognition network is based on a Transform structure network, respectively calculates the relation between the internal characteristics of each indicator lamp sub-image and the relation between the characteristics of all indicator lamp images, and comprehensively recognizes the state of the indicator lamp; according to the method, images are input into a neural network model in a one-time mode in the form of an image sequence, the state of each indicator light is recognized by learning the image feature relation in a single indicator light and the image feature relation between the indicator lights, the indicator lights are concerned, and comparison between global features and the indicator lights is also considered; therefore, the method has a more accurate recognition effect under a complex illumination condition.

The invention discloses a ring main unit state online monitoring and intelligent operation and maintenance system based on big data, and relates to the technical field of data monitoring, and the system comprises a ring main unit state data collection module which collects operation parameters in real time based on a sensor array disposed in a ring main unit, and generates a standardized feature vector; the equipment health monitoring module is used for performing state recognition and life prediction on the standardized feature vector based on a deep learning model, and generating an equipment health degree index; the intelligent operation and maintenance decision-making module is used for establishing an intelligent operation and maintenance decision-making model based on the health degree index and a preset operation and maintenance strategy library, and generating a maintenance work order based on the output of the intelligent operation and maintenance decision-making model; and the communication module transmits the monitoring data and receives the control instruction. According to the invention, fault early warning, state identification, life estimation and health degree index generation are carried out by combining the sensor array and edge calculation with deep learning, the operation and maintenance strategy is optimized, and the cost and downtime are reduced.

The invention relates to the technical field of machine learning, and discloses a transformer operation state intelligent identification method based on machine learning, which is applied to a 110kV-500kV transformer and an edge-cloud collaborative architecture of an online monitoring system of the 110kV-500kV transformer. According to the method, multi-modal features are extracted based on multi-channel cleaning data, a modal enhancement TCN model fusing kernel feature mapping and a Koopman operator is introduced, and time-frequency coupling modeling and hidden space state representation are achieved. The model realizes online recursion and working condition adaptive tracking through a Sherman-Morrison incremental updating mechanism, outputs an operation state identification result and a risk score, realizes millisecond alarm linkage through an IEC 61850 GOOSE protocol, and provides high-precision and traceable technical support for intelligent monitoring and operation and maintenance of a transformer of a transformer substation.

The invention relates to the technical field of intelligent training and data analysis, in particular to an intelligent model construction method and system for student training data analysis. Comprising the following steps: constructing an SOP time sequence knowledge graph, obtaining a standard operation specification document and expert-level operation demonstration data, and constructing the SOP time sequence knowledge graph; real-time feature extraction: capturing a multi-modal data stream, and generating a real-time behavior feature vector; state recognition and path deduction: inputting the real-time behavior characteristics into an event classifier to recognize a key event, performing path deduction based on deterministic finite state automaton logic in an SOP time sequence knowledge graph, and determining and outputting a current state pointer; deviation analysis and judgment: extracting a compliance mask based on a current state, and injecting the compliance mask into a decoding process of the attention network; and performing feedback and adjustment to generate a corresponding deviation event signal. Through deep analysis, feedback is more targeted, and the accuracy and fineness of training evaluation are remarkably improved.

The invention provides a traffic flow state identification method based on a multi-source heterogeneous data source. The method comprises the following steps: collecting and classifying traffic information data of road traffic; preprocessing the traffic information data; respectively inputting the preprocessed traffic information data into corresponding classification models according to types; outputting an intermediate traffic state corresponding to each type; weight distribution is carried out on each type of intermediate traffic state; and calculating to obtain a final traffic state. The method has the advantages that a customized feature extraction and conversion method is adopted according to the characteristics of different types of sensor data, semantic information of different types of data can be better reserved, and the quality and effect of data fusion are improved; the performance of different models on the verification set is integrated, and the voting weight is dynamically adjusted according to the accuracy of the models, so that the final prediction result is more accurate and reliable.

The invention provides a pump room scene abnormal state recognition system and method based on video monitoring, and relates to the field of monitoring or monitoring activities, in particular to the field of space perception. The system comprises a synchronous trigger device which is used for synchronously triggering the collection of video data and audio data in a pump room in the same monitoring time interval; and the state identification device is used for intelligently identifying a pump room scene abnormity type by adopting an intelligent scene abnormity identification model on the basis of basic contents obtained by analyzing the video data in the pump room and the audio data in the pump room. According to the invention, in order to solve the technical problem that the recognition precision and efficiency of various specific pump room scene abnormal states cannot be considered at the same time, different intelligent scene abnormal recognition models can be designed for different pump room scenes and multi-modal basic contents screened in a targeted manner are introduced; and various specific pump room scene abnormal state identification is completed, so that the technical problem is solved.

The invention relates to the technical field of blowout preventer monitoring, in particular to an AI-based blowout preventer product performance monitoring system and method, and the system comprises a data collection module which is used for collecting the operation data of a blowout preventer and outputting a standardized multi-dimensional time sequence data stream; the feature extraction module is used for performing feature extraction on the standardized multi-dimensional time sequence data stream and outputting a multi-dimensional feature vector matrix; the state recognition module is used for constructing a state recognition model to recognize the current performance state of the blowout preventer according to the multi-dimensional feature vector matrix and generating probability distribution of the performance state; the trend prediction module is used for constructing a trend prediction model according to the performance state probability distribution and the historical state sequence, predicting the future performance degradation track and the remaining service life of the blowout preventer and generating a prediction result; the decision early warning module is used for constructing a multi-level early warning system according to the prediction result and generating a maintenance decision scheme; and the learning optimization module is used for continuously optimizing the AI model performance according to the decision scheme and the actual maintenance result feedback.

The invention discloses a method for connecting an intelligent operation and maintenance large model and a public large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining equipment operation data from a sensor node through a preset data collection frame, and carrying out the preprocessing of the equipment operation data, thereby obtaining a first data set; performing abnormal state recognition on the first data set by using a convolutional neural network and an SVM classifier, extracting an abnormal data subset, and clustering to obtain an abnormal mode; acquiring historical equipment operation parameters and environmental parameters, and performing correlation analysis on the abnormal state, the historical equipment operation parameters and the environmental parameters by adopting a Bayesian network to obtain a potential reason set of the abnormality; constructing an equipment fault knowledge graph to perform path reasoning on reasons in the potential reason set, and generating a fault association mapping table; and a multi-objective optimization function is established based on the fault association mapping table and the real-time state data of the equipment, and an optimal disposal scheme combination is solved, so that the intelligent level of equipment fault diagnosis and operation and maintenance operation optimization is improved.

The invention discloses a training method of a running state recognition model and a running state recognition method and device, and belongs to the technical field of audio processing. According to the invention, the acoustic sensor arranged around the target equipment is used for collecting the acoustic signal, and the action and state of the target equipment are monitored based on the collected acoustic signal. Because the monitoring mode does not need manual participation, compared with a monitoring mode of manual regular inspection, the manpower cost is saved, the efficiency is higher, and the abnormity of the target equipment can be captured in time. Besides, the machine learning model can be trained to recognize the action and state of the target equipment, effective voiceprint features used for operation state recognition can be extracted in the model training process, and the effective voiceprint features comprise frequency band features capable of representing the action features of the target equipment. Therefore, the effective voiceprint features serve as input of the model to participate in model training, and the above processing mode can greatly improve the recognition accuracy of the model.

The invention relates to the technical field of automatic control of road engineering, and discloses a multi-sensor fused cold recycled mixture moisture content regulation and control system and a multi-sensor fused cold recycled mixture moisture content regulation and control method. A data preprocessing and tensor construction module; a tensor analysis and state identification module; a moisture content prediction module; an intelligent decision module; a cooperative regulation and control execution module; the method comprises the following steps: fusing and collecting multi-modal parameters of a mixture, and preprocessing the multi-modal parameters to construct a high-dimensional time sequence tensor; identifying an internal state through tensor analysis, decoupling interference, extracting a pure signal and predicting the future moisture content; based on the predicted value and the internal state, the water adding amount is decided through reinforcement learning, and closed-loop water content regulation and control are accurately executed. According to the method, the problems of inaccurate measurement and control lag of a traditional method are solved, prospective and accurate dynamic regulation and control of the moisture content are realized through prediction and state recognition, and the stability and the engineering quality of a cold regeneration process are remarkably improved.

The invention discloses a flexible operation and maintenance early warning device based on artificial intelligence. The flexible operation and maintenance early warning device comprises a multi-source data acquisition module, a data preprocessing and fusion module, an AI state recognition module, a risk reasoning and knowledge graph module, a flexible strategy engine module and an early warning output module. The device realizes cleaning, noise reduction, time alignment and feature fusion of multi-source data by collecting equipment operation indexes, log behaviors, security events and environmental parameters. The AI state recognition module is used for recognizing an operation state, a behavior mode and potential abnormity, and the risk reasoning and knowledge graph module completes risk source positioning and reason verification based on an entity relationship and a semantic link. And the flexible strategy engine dynamically generates an early warning strategy according to a reasoning result, and pushes early warning information in a multi-channel manner through an early warning output module. The device can realize high-accuracy, interpretable and self-adaptive operation and maintenance early warning capability, and is suitable for intelligent operation and maintenance management of hospital machine rooms and key business systems.

The invention provides a parking state monitoring system based on RISC-V, and relates to the technical field of automatic control. The parking state monitoring system based on the RISC-V comprises the following modules: a data acquisition module, a signal preprocessing module, a fusion judgment module, a state recognition module, a prediction control module, an energy consumption optimization module, a communication transmission module, a cloud analysis module and a feedback regulation module. Through a multi-source sensing synchronous sampling and time sequence correction method, multiple types of sensing signals are subjected to consistent sampling under a unified time reference, and the space-time matching degree of data is remarkably improved; an adaptive filtering and dynamic noise estimation mechanism is introduced in a signal processing link, so that an input signal keeps a high signal-to-noise ratio and stability in a complex environment; in the feature fusion and state recognition stage, probability weighted fusion and correlation matrix analysis are utilized to realize information multi-layer complementation, and the accuracy and reliability of parking space state recognition are ensured.

The invention belongs to the technical field of state recognition, and particularly relates to a truck wheel state recognition method and system, and the method comprises the following steps: S1, obtaining a to-be-recognized truck wheel image, and extracting a hierarchical feature map of a plurality of resolutions through a backbone network; separating each level of feature map into a low-frequency coefficient matrix and a high-frequency coefficient matrix through discrete cosine transform, performing multi-scale low-frequency feature fusion on the low-frequency coefficient matrixes of different levels to obtain a cross-layer low-frequency association map, and performing operation on the high-frequency coefficient matrixes of the same level to obtain an intra-layer high-frequency structure map; s2, determining a candidate region on the hierarchical feature map, detecting angular points in the candidate region, and calculating a covariance matrix of angular point positions; according to the method, the feature response of a key area is enhanced, the interference of a background and an irrelevant area is inhibited, frequency domain analysis and space geometric structure prior are combined, the quality of final fusion features is improved, and therefore the accuracy of truck wheel state recognition is improved.