2585 results about "Kalman filter algorithm" patented technology

The invention belongs to the technical field of data processing, and particularly relates to a well deviation azimuth measurement system based on a high-precision compensation algorithm. The system comprises a reference vector construction part, a compensation part and a Kalman filtering measurement part. The benchmark vector construction part is used for determining a reference benchmark of a geomagnetic field and a gravitational field in the underground environment; the compensation part is used for compensating original sensor data measured by the underground sensor group, and estimating an underground initial attitude angle according to compensated accelerometer data and compensated magnetometer data in the compensated data; calculating attitude change data; according to the attitude change data, constructing a local interference field model to obtain corrected magnetometer data; and the Kalman filtering measurement part is used for modeling all error sources into state vectors, correcting measurement errors in real time through a Kalman filtering algorithm and outputting a final well deviation direction. According to the invention, the precision, the stability and the robustness of well deviation azimuth measurement are obviously improved.

The invention provides a battery health state assessment method and system based on a BMS, and the method comprises the steps: presetting different voltage sampling ranges and resolutions for different load current intervals, judging the current interval to which the load current belongs when the load current changes, and dynamically switching to the corresponding range and resolution; when the current changes violently, the voltage sampling frequency is improved, more data points are obtained, and when the current changes gently, the sampling frequency is reduced, and data redundancy is reduced; filtering and fusing the internal resistance estimated value by adopting a Kalman filtering algorithm, dynamically tracking the change trend of the internal resistance, and adaptively adjusting the parameters of a filter according to the working state and the aging degree of the battery; in combination with working condition parameters including the temperature, the current and the SOC of the battery, a multiple regression model of the internal resistance and the working condition parameters is established, the multiple regression model is trained and optimized through a machine learning method, and online self-adaptive correction of the internal resistance is achieved.

The invention provides a multi-target tracking detection method and system based on cascade matching and trajectory confirmation, and relates to the technical field of digital video monitoring and tracking, and the method comprises the steps: obtaining a personnel construction video, and decomposing a video frame into a plurality of images; identifying a target in the current frame of image based on an improved YOLOv5 network, obtaining a target detection frame, and obtaining a prediction trajectory frame of the current frame through a Kalman filtering algorithm based on the trajectory frame of the previous frame; performing multi-target tracking on the target detection frame and the prediction trajectory frame of the current frame by using a DeepSort algorithm, respectively obtaining motion features and appearance features of the target detection frame and the prediction trajectory frame, performing cascade matching on the motion features and the appearance features of the target detection frame and the prediction trajectory frame by using a Hungary algorithm, and performing matching optimization by using an IOU matching algorithm; and carrying out fusion updating on the successfully matched target detection frame and prediction trajectory frame by using a Kalman filtering algorithm, and replacing the original prediction trajectory frame to obtain a tracking result.

The invention relates to the technical field of liquid cooling servers, and discloses a liquid cooling server safety management system and method, and the method employs a multi-mode sensor network to synchronously collect the thermodynamic parameters of a liquid cooling system at 100 Hz, and constructs a three-dimensional thermal field digital twinborn model after the processing of an extended Kalman filtering algorithm. A distributed cooling strategy is designed based on a federated learning framework, and each node locally trains a thermal dynamic prediction model and is optimized by a central aggregator. A dynamic control instruction is generated by using a near-end strategy optimization algorithm, and cooling liquid flow distribution is optimized in combination with a quantum derivative simulated annealing algorithm. And designing a dual-threshold phase change control mechanism, establishing a block chain log to ensure traceability and tamper resistance of the instruction, and realizing closed-loop feedback control through a CAN bus. The system and the method can accurately monitor and intelligently control the liquid cooling system, improve the heat dissipation efficiency, reduce the power consumption, and guarantee the data safety and the system stability.

The invention relates to the technical field of municipal road construction, and discloses a municipal road construction section scheduling optimization method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-source dynamic data of a construction region through Internet of Things equipment and a satellite remote sensing technology, and constructing a time sequence database; a multi-objective optimization model is established, and an initial scheduling scheme is generated by adopting a non-dominated sorting genetic algorithm with the goal of minimizing construction period, resource waste and traffic jam influence. Dynamically adjusting the priority of the construction section by using a reinforcement learning algorithm, and updating a construction task sequence; a graph neural network is utilized to detect space-time conflicts of construction sections, and conflict-free scheduling constraint conditions are generated; and optimizing resource allocation based on mixed integer linear programming, simulating a construction process in combination with a digital twinborn technology, correcting a construction progress deviation by using a Kalman filtering algorithm, and outputting a final scheduling instruction. According to the invention, optimization of municipal road construction section scheduling is realized, the construction period is effectively shortened, resource waste is reduced, and traffic jam is relieved.

The invention relates to the technical field of welding, in particular to a corrosion steel welding cooperative control method and system. Comprising the following steps that welding seam geometric parameters, molten pool dynamic characteristic parameters and welding heat input parameters in the corrosion steel welding process are collected in real time through a multi-dimensional sensor array; constructing a corroded steel welding seam feature space model based on the welding seam geometric parameters, and determining material corrosion grade distribution and mechanical property parameters of a welding seam area in combination with a preset corroded steel material database; a molten pool form evolution prediction model is established through an adaptive Kalman filtering algorithm by utilizing the dynamic characteristic parameters of the molten pool and the welding heat input parameters, and the solidification behavior and the welding seam forming trend of the molten pool are predicted in real time; according to the material corrosion grade distribution, the mechanical property parameters and the molten pool forming trend, a dynamic adjustment strategy of the welding process parameters is generated through a multi-objective optimization algorithm; the reliability and safety of the corrosion steel welding joint can be improved.

The invention provides an aircraft and satellite cluster collaborative navigation positioning accurate control method and system, and relates to the technical field of aviation, and the method comprises the steps: an aircraft receives a multi-system navigation signal from a satellite cluster, carries out the fusion processing of multi-source navigation information through employing a self-adaptive Kalman filtering algorithm, and carries out the navigation positioning of a satellite cluster based on the fused multi-source navigation information. Calculating the accurate position of the aircraft by adopting a differential positioning technology; and transmitting the optimal control instruction to a flight control system of the aircraft based on the accurate position of the aircraft in combination with a preset flight plan, calculating a correction instruction by using a satellite-ground cooperative trajectory correction algorithm when trajectory deviation is detected, and transmitting the correction instruction to the aircraft through a high-frequency broadband communication system, and closed-loop accurate control of the track of the aircraft is realized.

The invention discloses a method for realizing data communication by a multi-band adaptive antenna based on 5G communication. The method comprises the steps of dynamic sensing and multi-mode signal fusion, multi-band intelligent analysis, hybrid beam forming and dynamic reconstruction, cross-layer parameter joint tuning, real-time monitoring and self-learning compensation, multi-band adaptive switching and fault diagnosis and error calculation. The problems that in traditional 5G communication, the antenna frequency band is fixed, the beam adjusting capacity is limited, the anti-interference performance is poor, and the communication performance is unstable in a complex environment are solved. According to the method, the channel state is accurately evaluated and predicted through fusion of the extended Kalman filtering algorithm and the convolutional neural network, and frequency band resources are intelligently allocated and optimized through the deep Q network DQN and the non-orthogonal multiple access NOMA principle, so that omnibearing optimization and management of the antenna are realized, the communication efficiency and stability are improved, and the method is suitable for large-scale popularization and application. And the efficient and stable operation of the communication system in various complex environments is ensured.

The invention provides an underwater vehicle multi-source information fusion accurate navigation method and system. The method comprises the following steps: firstly, acquiring inertial navigation data, DVL data and ocean current information of an underwater vehicle and observation information of a distance between the underwater vehicle and a beacon, and preprocessing the inertial navigation data, the DVL data and the ocean current information; then, establishing a state model according to the kinematics principle of the underwater vehicle, and establishing an observation model by combining various types of information; carrying out fusion processing on the state model and the observation model by adopting a self-adaptive Kalman filtering algorithm; and finally, estimating the current position of the underwater vehicle according to the fused state vector, comparing the current position with a preset target position to calculate a deviation, and correcting the deviation by adjusting a rudder angle and the rotating speed of a propeller. According to the method, multi-source information is integrated, innovation is carried out on construction of a state model and an observation model and position estimation and deviation correction, adaptive Kalman filtering is adopted, and the method has the advantages that the navigation precision is improved, the ocean current influence is considered, adaptive adjustment is realized, and real-time navigation is realized.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention discloses a coastal protection dam settlement monitoring method, and belongs to the technical field of hydraulic engineering safety monitoring. The method comprises the steps that a longitudinal monitoring section is arranged on the slope surface of the back sea side of a dam, a three-measuring-line fiber grating sensor array is arranged, and vertical displacement and horizontal dip angle data are periodically collected through a synchronous triggering unit; establishing a vertical displacement-horizontal dip angle joint analysis model by using a multi-source data fusion module, and eliminating tide level interference through a Kalman filtering algorithm to generate a settlement distribution curve; and triggering third-level to first-level early warning signals based on the grading early warning rule, and transmitting the signals to the terminal equipment. The problems that a traditional monitoring method cannot effectively separate tidal interference, the real-time performance is poor, and multi-dimensional data collaborative analysis is insufficient are solved, high-precision settlement monitoring, complex environment anti-interference and rapid emergency response are achieved through multi-measuring-line sensor deployment, dynamic filtering optimization and a graded early warning mechanism, and the method is suitable for large-scale popularization and application. And the reliability and timeliness of dam safety monitoring are obviously improved.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention discloses an automatic identification and expansion communication method based on an RS485 interface, and relates to the technical field of industrial automation communication. The problems that an existing RS485 interface is complex in configuration, poor in protocol compatibility and high in data transmission delay are solved. According to the scheme, the terminal matched resistance and the bias current are adjusted in real time by dynamically measuring the bus equivalent distributed capacitance value, and signal attenuation caused by the capacitance effect is inhibited; a silicon controlled rectifier trigger type charge discharge mechanism is adopted to force bus residual charge to return to zero quickly, and an idle state is accurately judged in combination with a hysteresis comparator; dynamically compensating signal edge distortion and calibrating a sampling window time sequence based on a pre-emphasis filtering and Kalman filtering algorithm; multi-node stable communication is realized through capacitance value driven conflict detection threshold adaptive adjustment and a Poisson back-off model; according to the invention, the real-time performance, the anti-interference capability and the multi-node expansion reliability of RS485 communication in a complex industrial scene are obviously improved, and the debugging and maintenance cost of the system is reduced at the same time.

The invention provides a lithium ion battery health state estimation method based on a TSLANet network, and belongs to the technical field of power batteries, and the method comprises the steps: obtaining multi-dimensional time sequence data of a lithium ion battery in an actual operation process, and carrying out the preprocessing of the multi-dimensional time sequence data; constructing a battery health state estimation model by using the preprocessed multi-dimensional time sequence data based on a TSLANet network and in combination with a neural network; inputting the operation condition of the current lithium ion battery system into a battery health state estimation model, and estimating the health state of the current lithium ion battery; the method is based on the TSLANet network, combines the Kalman filtering algorithm, the IC curve, the health factor and the neural network, constructs the battery health state estimation model to estimate the health state of the lithium ion battery, correctly reflects the long-term change trend of the battery performance, is excellent in noise data processing performance, and reduces the calculation complexity and the resource consumption.

The invention relates to the technical field of VR display control, in particular to an LBE enhanced multi-mode perception interaction VR display control method and system, and provides the following scheme that multi-mode perception data of a user is collected through VR head display equipment and a hand controller, position data and action data are fused by adopting a Kalman filtering algorithm, and the multi-mode perception interaction VR display control method and system are obtained. And calculating a continuous motion track of the user, dynamically adjusting the activity range based on the continuous motion track, and generating a directional dynamic range. And performing synchronization, feature extraction and weighted fusion processing on the multi-modal data to generate an interaction state vector. And analyzing real-time behaviors, action intentions and physiological feedback of the user according to the interaction state vector, judging the operation sensitivity and the emotional state of the user, and dynamically adjusting response parameters of the VR equipment. According to the method, the real-time performance, the flexibility and the immersion of large-space VR interaction are effectively improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention discloses a heavy-load automatic guided vehicle repeated positioning precision self-correction method and device, and the method comprises the steps: obtaining AGV operation state data and environment data collected by different sensors, and forming a multi-source data set; preprocessing the multi-source data set, constructing an environment model, and calculating attitude information in real time; based on an environment model, determining a positioning error and carrying out error modeling, correcting the weight of each sensor, each filter parameter, AGV position information and AGV attitude information in real time, and optimizing an AGV advancing path; fusing the multi-source data set by using an improved Kalman filtering algorithm, optimizing a Kalman filter in real time, and correcting a positioning error so as to further correct AGV position information; correcting an AGV advancing path in real time according to the image data of the surrounding environment of the AGV; and a regular correction mechanism is set, and the AGV is controlled to regularly execute a specified correction action. According to the method, the repeated positioning precision is improved, and stable operation of the AGV in a dynamic environment is ensured.

The invention relates to the technical field of building engineering, and discloses a building robot space positioning method based on a BIM model, which comprises the following steps: S1, establishing the BIM model of a building, and in the construction process, integrating real-time sensor data of a building site, and dynamically updating the BIM model to reflect the real-time state of a construction environment; and S2, setting multiple reference points at the construction site, performing coordinate calibration on the reference points by using a total station or a GPS system, aligning a coordinate system of the BIM model with a coordinate system of an actual construction site, and matching a building structure in the BIM model with a building structure in an actual environment through the reference points. Through the fusion of detailed building geometric information and real-time sensor data in the BIM model, the system can realize high-precision space positioning, the pose of the robot is corrected in real time by using an extended Kalman filtering algorithm and a multi-source data fusion technology, and the operation efficiency and the construction precision are effectively improved.

The invention discloses a fault detection method and system for a mine unmanned vehicle, and relates to the field of positioning navigation, and the method comprises the steps: collecting the data of each sensor; according to the laser radar data, a slam point cloud map is constructed through a cartographic algorithm; according to the IMU data and the vehicle speed data, performing fusion through a Kalman filtering algorithm to obtain odometer data; the preprocessed laser radar data, IMU data, vehicle speed data, camera data and odometer data are converted to a slam point cloud map coordinate system through coordinate conversion, fusion is carried out through a Kalman filtering algorithm, and state estimation of the unmanned vehicle is obtained; taking the state estimation of the unmanned vehicle as a predicted value, comparing the predicted value with laser radar data, IMU data, vehicle speed data and camera data, and calculating an observation residual error; and performing unmanned vehicle fault detection according to the observation residual error. Aiming at the low positioning precision of the mine unmanned vehicle in a complex environment in the prior art, the positioning precision is improved through multi-sensor fusion, fault-tolerant switching and the like.

The invention discloses a coal mine pressure risk assessment method based on complex system dynamics, belongs to the technical field of mine pressure risk assessment, is used for mine pressure risk assessment, and constructs a complex system dynamics model, combines various sensor data, and carries out real-time monitoring and risk assessment of mine pressure changes. The method comprises the steps of geological data acquisition, sensor data real-time monitoring, data preprocessing, mine pressure prediction model construction, model training and optimization, real-time risk assessment and the like, data of a stress sensor, a displacement sensor and an acoustic emission sensor are comprehensively utilized, and data preprocessing is performed by adopting a Kalman filtering algorithm; and optimizing the mine pressure prediction model by using a graph convolutional network and an attention mechanism, and performing real-time risk assessment through an adaptive graph neural network. According to the method, the adaptive graph neural network is adopted for real-time risk assessment, potential risks in mine pressure changes can be found in time, risk scores are generated, and scientific decision support is provided for coal mine managers.

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention discloses a mode switching control method and device for an air-sea amphibious unmanned aerial vehicle, and the method comprises the steps: monitoring the unmanned aerial vehicle through a sensor, and obtaining target collection data; on the basis of an extended Kalman filtering algorithm, state estimation is carried out on the unmanned aerial vehicle through the target acquisition data, and a state estimation result is obtained; monitoring the mode switching state of the unmanned aerial vehicle according to the state estimation result to obtain a state monitoring result; and executing a mode switching strategy according to the state monitoring result to realize mode switching control of the unmanned aerial vehicle. The method can improve the stability and accuracy of mode switching control of the air-sea amphibious unmanned aerial vehicle, and can be widely applied to the technical field of unmanned aerial vehicles.

The invention discloses a ship intelligent collision avoidance decision-making method and system, and relates to the technical field of ship intelligent navigation, and the method comprises the steps: collecting radar data and AIS information of a ship; building a ship dynamic prediction model according to the radar data and the AIS information, predicting the future navigation trajectory of the target ship through a Kalman filtering algorithm, and calculating the meeting parameters of the two ships in combination with the navigation parameters of the ship; based on the meeting parameters, establishing a risk assessment matrix, and performing risk level judgment on the current navigation situation by using a fuzzy neural network algorithm; based on the risk level judgment result, collision avoidance maneuvering parameters are calculated through an adaptive particle swarm optimization algorithm, and a collision avoidance action scheme is output. According to the invention, the whole process automation from information perception, situation prediction, risk assessment to decision execution is realized, the workload of navigation personnel is greatly reduced, and the navigation safety level is improved.

The invention discloses an environment-adaptive high-precision Beidou visual SLAM (Simultaneous Localization and Mapping) cooperative localization optimization method. The method comprises the following steps: step 1, acquiring environment characteristic data, visual SLAM data and Beidou system data; 2, constructing an environment self-adaptive model, wherein the environment self-adaptive model adjusts the weight distribution of the visual SLAM and the Beidou system according to the environment characteristic data; and step 3, performing data fusion on the visual SLAM data and the Beidou system data according to respective weight distribution to form a unified positioning result. Through the environment adaptive model, the Kalman filtering algorithm is improved, and the mechanism is adjusted in real time, so that the cooperative positioning precision and robustness of the visual SLAM and Beidou system are effectively improved.

The invention provides a power transformation equipment digital twinborn part time-varying consistency self-adaptive maintaining method and system, and relates to the technical field of power grids, and the method comprises the steps: collecting the real-time operation data of power transformation equipment, constructing a physical model, generating a digital twinborn model, and obtaining the initial state parameters of parts through deep learning training. And establishing a time-varying feature vector matrix based on the initial state parameters, performing real-time updating by using an adaptive Kalman filtering algorithm to obtain a dynamic feature model, and calculating a state deviation value of the digital twin component and the physical equipment. And when the deviation value exceeds a preset threshold value, triggering a self-adaptive consistency keeping mechanism, generating a state compensation parameter through deep reinforcement learning, applying the state compensation parameter to a digital twin model, and realizing dynamic synchronization of the digital twin component and the physical equipment. According to the invention, the state consistency of the digital twinning component and the physical equipment can be adaptively maintained, and the precision and reliability of digital twinning are improved.

The invention discloses a shield attitude control method and system based on machine vision, and the method comprises the steps: constructing a motion state model of a shield tunneling machine according to the motion track of a feature point, and carrying out the prediction and correction of a motion state through a Kalman filtering algorithm, and obtaining the real-time attitude data of the shield tunneling machine; vibration data of the shield tunneling machine are obtained, the vibration data are obtained based on a vibration compensation model of an inertial measurement unit, the vibration data and visual sensor data are combined, a data fusion algorithm based on particle filtering is adopted to suppress vibration interference, and stable attitude information is obtained; and constructing a multi-modal data fusion model based on a graph convolutional neural network, and fusing the visual data, the inertial data and the motion state data to obtain a high-precision attitude estimation result.

The invention provides a ship fault prediction system based on multi-source data fusion, and relates to the technical field of ship fault prediction, and the system comprises a data collection module which is used for obtaining original signal data; the data preprocessing module is used for aligning the original signal data from different sources, and after data alignment, preprocessing the original signal data after data alignment according to a preprocessing step to obtain preprocessed data; the data fusion module is used for performing multi-source fusion on the preprocessed data by using an improved Kalman filtering algorithm to obtain fused data; the feature engineering module is used for performing multi-dimensional feature extraction and feature selection on the fused data, and fusing the selected features to obtain fused features; and the fault prediction module is used for receiving the fusion features and performing ship fault prediction according to the fusion features based on a pre-trained fault prediction model. According to the invention, accurate prediction of ship faults can be realized, and finally the safety and reliability of ship operation are improved.

The invention relates to the technical field of AUVs (Autonomous Underwater Vehicles), in particular to a real-time communication and positioning data fusion processing method for AUV (Autonomous Underwater Vehicle) collaborative operation, which comprises the following steps of: deploying a multi-modal sensor, acquiring communication data, kinematics data, acoustic positioning data and visual data required by an AUV, and preprocessing the data; the method comprises the following steps: fusing multi-modal data by using an extended Kalman filtering algorithm, calculating three-dimensional position and attitude information of the AUV in real time, carrying out dynamic path planning by using an ant colony optimization algorithm based on fused positioning data, optimizing a collaborative operation path between the AUVs, and sharing the positioning data and a path planning result in real time through an underwater communication network. And dynamically adjusting a task allocation strategy by using an improved task allocation algorithm, optimizing the cooperation efficiency, and finally, storing all data, path planning records and task allocation schemes in a database. According to the invention, the accuracy and stability of AUV collaborative operation are improved.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.