93results about How to "Reduce data redundancy" patented technology

The invention relates to the technical field of new energy power generation, and discloses an intelligent panoramic monitoring management system and method for a new energy box transformer substation, and the method comprises the steps: obtaining an original monitoring data set of a target box transformer substation, and carrying out the data cleaning; key features are extracted from the cleaned data, and a weighted feature set is generated; constructing a box transformer substation fault prediction model to obtain a fault risk assessment value; performing graded early warning on potential faults based on the evaluation values; constructing a box transformer fault diagnosis knowledge graph, and performing reasoning and tracing in combination with the knowledge graph during high-risk or medium-risk early warning; through multi-source data fusion and knowledge graph reasoning, comprehensive monitoring of the operation state of the box transformer substation and intelligent fault diagnosis are realized, and the operation reliability and maintenance efficiency of the new energy box transformer substation are improved.

The invention provides an obstacle map marking method and system combined with an unmanned vehicle, and relates to the technical field of map marking, and the method comprises the steps: determining the motion direction and motion path of a mobile unmanned vehicle according to the current position information of the mobile unmanned vehicle and preset target position information; the position information of the static obstacle is determined through a laser radar scanning device installed on the mobile unmanned vehicle; performing dynamic obstacle target detection on the dynamic point cloud data through a pre-constructed target detection model, and performing target tracking on a dynamic obstacle according to a target tracking algorithm to obtain motion information of the dynamic obstacle; through an improved artificial potential field method, a local path for avoiding the static obstacle and the dynamic obstacle is planned in real time in the moving direction of the mobile unmanned vehicle; and generating a motion control instruction of the mobile unmanned vehicle according to the local path.

The invention relates to the technical field of side slope monitoring and early warning, in particular to a mine site side slope remote online real-time monitoring and early warning system which comprises a multi-source data monitoring unit, a data processing and analyzing unit, a side slope instability judging unit, an instability grading early warning unit and an early warning verification pushing unit. The method comprises the following steps: collecting monitoring data of a plurality of data sources to obtain time sequence monitoring data of each data source, carrying out noise reduction on the time sequence monitoring data, then decomposing the time sequence monitoring data, calculating a time-frequency feature vector through an energy density function, and optimizing the time-frequency feature vector through feature screening; calculating a slope stability evaluation index by optimizing the time-frequency feature vector, constructing an instability risk index to judge a slope instability risk level, constructing an early warning index, and combining the early warning index with the slope instability risk level to establish a graded early warning mechanism to obtain a slope instability early warning level; and verifying the slope instability early warning grade, and performing early warning according to the slope instability early warning grade passing the verification. According to the invention, the false alarm rate of slope monitoring and early warning can be reduced.

The invention discloses a multi-thread and LOD-based point cloud rendering method, and relates to the field of video rendering, and the method comprises the steps: dividing point cloud data into n data blocks; calculating an initial position of each data block in the point cloud file, and creating n threads; extracting m pieces of LOD point cloud data with different levels of detail from the octree of each data block; calculating the proportion # imgabs0 # of the size of the current point cloud in a rendering viewport and the distance # imgabs1 # from a viewpoint to a camera, and calculating the detail level # imgabs4 # of the current to-be-rendered LOD point cloud according to a preset viewport proportion threshold value # imgabs2 # and a preset sight distance threshold value # imgabs3 #; according to # imgabs5 #, selecting point cloud data of a corresponding detail level from the m pieces of LOD point cloud data; for each interactive operation event, parallel coordinate transformation and illumination calculation are carried out on the LOD point cloud data to be rendered currently in different threads; submitting a calculation result to a renderer for rendering; aiming at low rendering efficiency in the prior art, the method and the device can efficiently render large-scale point cloud data.

The invention discloses a fault analysis method and system applied to a 5G base station transmission link, and the method comprises the following steps: obtaining alarm information of the 5G base station transmission link through integrating a real-time data stream processing mechanism and an incremental learning model; the alarm information is preprocessed; based on a time dimension, a topology dimension and a text dimension, converting the pre-processed alarm information into multi-dimensional vector representation; grouping aggregation is carried out by combining a DBSCAN algorithm; through an Incident root cause analysis algorithm, performing deep suspected root cause analysis on the grouped and aggregated alarm information, and determining suspected root cause alarms; and performing fault diagnosis through the knowledge graph and the diagnosis rule, generating a repair process and recording the whole process. According to the method, the accuracy and speed of fault positioning can be remarkably improved, the intelligence and automation level of fault analysis is enhanced, and meanwhile, the real-time performance and flexibility of fault processing are ensured, so that the operation and maintenance management of a 5G network is optimized, and a solid guarantee is provided for the stability and reliability of communication service.

The invention relates to the technical field of wireless communication, and discloses a low-power-consumption communication method and device for wireless audio equipment, an SoC chip and a storage medium. The method comprises the following steps: sampling acoustic characteristics of heterogeneous audio data of a plurality of target audio devices, calculating a voice spectrum corresponding to device characteristics, and extracting multi-device acoustic characteristics, performing multi-frequency sub-band speech enhancement, heterogeneous segmentation and grouping of multi-target audio equipment, verification value calculation and differential coding on each extracted acoustic feature sequence to obtain a multi-source coding speech segment; performing transmission unit division and cooperative signal modulation, cooperative resource allocation and data stream transmission of each transmission unit on the multi-source coding voice segment to obtain a transmission voice data stream; and obtaining a transmission voice data stream transmitted with low power consumption and performing audio recovery on the obtained data to obtain a target audio data stream transmitted by the multi-target audio equipment. The communication power consumption of the multiple target audio devices is reduced, and the audio transmission quality is ensured.

The invention discloses a method, a device and equipment for testing the uniformity of a light guide film, and the method comprises the steps: carrying out the dynamic temperature adjustment of the light guide film, and obtaining the multi-wavelength dynamic spectrum data of an edge region; nonlinear optical response characteristics are extracted; generating a dynamic optical characteristic diagram and performing three-dimensional structure analysis; calculating a thermo-optical coupling complexity index; generating dynamic change data of the optical performance of the edge region; and executing multi-dimensional evaluation index processing and time sequence analysis to obtain a dynamic accurate evaluation result. According to the technical scheme, the optical performance of the edge area of the light guide film can be accurately evaluated under the dynamic temperature condition, the influence of temperature change on the uniformity of the light guide film is effectively captured, the accuracy and practicability of a test result are improved, and a more reliable basis is provided for design optimization and quality control of the light guide film.

The invention discloses a pavement disease inspection method based on a vehicle-mounted edge device, and relates to the technical field of pavement disease inspection, and the method comprises the steps: 1, obtaining image data, thermal imaging data and road geometric data of a pavement through an information obtaining module; 2, the information processing module processes the image data to obtain a disease area I; matching the image data with the thermal imaging data to obtain a disease area II; performing height change analysis on the geometric data of the road to obtain a disease area III; 3, the information processing module matches the disease area I, the disease area II and the disease area III with the position of the road surface, and when any position of the road surface is successfully matched with more than two disease areas I, disease areas II or disease areas III, the position of the road is marked as a disease part. According to the method, the multi-modal road surface data are adopted, and various light-weight means are adopted, so that the remarkable optimization effect is achieved in the aspects of performance, efficiency, resource utilization and the like.

The invention discloses an efficient intelligent data compression and feature extraction method and system. The method comprises the following steps: S1, data preprocessing: performing data cleaning, denoising and standardization processing on to-be-processed data; s2, data compression and storage: determining a compression algorithm according to the type of the data, and uploading a compressed data file to a database; s3, feature extraction: deploying a machine learning model, and extracting representative features from the compressed data; and S4, data post-processing: further processing and optimizing the extracted features. According to the method, the data compression technology and the feature extraction technology are combined, the most representative information is extracted from the original data, meanwhile, the data storage and transmission cost is reduced, key features in the data are automatically recognized and reserved through an intelligent algorithm, and therefore efficient data compression and feature extraction are achieved.

The invention provides a cleaning robot environment obstacle marking method and system, equipment and a medium, and relates to the technical field of information, and the method comprises the steps: obtaining sensor data which comprises the position information of an obstacle; for sensor data, marking and clearing obstacle information in the map by adopting a mode of detection point marking, detection line clearing, blind area timeout clearing and vision field timeout clearing to obtain an obstacle grid map corresponding to a sensor; according to the sequence of priority of the obstacles, the second order of the removed areas and the third order of the unknown areas, the obstacle grid maps corresponding to the sensors are merged and compressed, and a comprehensive obstacle grid map is obtained; expansion processing is carried out, the obstacle points are expanded outwards by a preset grid number, exponential attenuation is carried out, and a cost map of the robot is obtained; and the robot autonomously navigates to the target position according to the global path and the local trajectory. The robot can flexibly deal with obstacles in a complex environment, and efficient autonomous navigation is achieved.

The invention discloses a method and a system for rapidly extracting morphological indexes of an erosion gully, and belongs to the technical field of water and soil conservation. The method comprises the following steps: S1, watershed segmentation; s2, identifying an erosion gully object; comprising the following steps: performing spectral feature analysis based on an orthoimage to obtain a first principal component; selecting a texture feature variable and a topographic feature variable; performing multi-scale image segmentation on the first principal component based on the texture feature variable; identifying whether each object is an erosion gully according to the topographic feature variables; and correcting the identification result to obtain a final erosion gully object identification result. S3, channel line extraction is carried out on the erosion gully; extracting a river network water system through hydrological analysis, correcting the river network water system, and taking the corrected river network water system as a channel line of the erosion gully; and S4, extracting the morphological index of the erosion gully according to the erosion gully object identification result and the gully line. The method can effectively improve the extraction precision of the morphological characteristics of the erosion gully, and breaks through the problem of poor extraction precision of the erosion gully.

The invention discloses a ghost imaging target reconstruction method based on FSD-GIRA, and relates to the technical field of digital image processing. According to the technical scheme, a fuzzy clustering algorithm is used for classifying feature values of an optical field intensity measurement matrix of an optical path detector, the feature values are reconstructed through singular value decomposition, dimensionality reduction processing of measurement data of a ghost imaging system is achieved, and the ghost imaging target reconstruction method based on FSD-GIRA is obtained. A WE-FOMP-CG algorithm is proposed, and recovery of sparse signals is optimized by combining generalized orthogonal matching pursuit, a conjugate gradient method and a multi-threshold wavelet noise reduction method; and selecting the most relevant sensing matrix array in each iteration, solving a linear equation set by using PCG, carrying out wavelet preprocessing and multi-threshold de-noising on a result, and updating and recovering estimation. The calculation efficiency and the convergence speed are improved, and a more accurate recovery result is provided in a high-noise environment. The invention also provides a WBDH algorithm, and the algorithm employs a dual denoising mechanism, including wavelet transform and a BM3D method, so as to guarantee that the restored image details and structures are reserved, and further improve the reconstruction quality of the ghost imaging target.

The invention provides a lithology identification method and system for a shale oil reservoir, and relates to the technical field of geological exploration. According to the method, the logging data from multiple sources, the X-ray diffraction (XRD) data and the microscopic image analysis data are fused, so that the influence of complex and changeable mineral components and other physical parameters on lithology identification is effectively overcome, and the accuracy and reliability of an identification result are remarkably improved. Furthermore, dimension reduction is carried out on the fused features by adopting a principal component analysis (PCA), data redundancy is reduced, the most representative features are extracted, and the efficiency of the model is improved. An attention mechanism is introduced into the lithology identification model, the weight of each logging data feature is adaptively adjusted, the influence of key features on the lithology identification result is enhanced, and the interference of useless features is reduced, so that the accuracy and reliability of lithology identification are further improved, and a more accurate basis is provided for distribution prediction of shale oil.

The invention relates to the technical field of picture rendering, in particular to a media picture rendering optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring a terminal video stream; performing RGB format conversion on the terminal video stream to generate an RGB type frame image set; pixel partitioning is carried out on the RGB type frame image set, and RGB pixel matrix blocks are generated; performing color space acceleration conversion on the RGB pixel matrix block to generate a YUV cache data matrix; the YUV storage data matrix is sent to a DSP internal processing unit for key frame selection, and YUV key video frame data and YUV non-key video frame data are obtained; and performing intra-frame coding on the YUV key video frame data so as to generate YUV key frame coded data. According to the method, the media picture rendering efficiency is improved by optimizing data processing, transmission and rendering and combining user visual focus analysis.

The invention discloses a gearbox fault diagnosis method based on multi-sensor adaptive fusion, and the method comprises the following steps: obtaining the monitoring data of a plurality of sensors in a gearbox, and carrying out the adaptive weight fusion processing based on the multi-sensor data of the gearbox, so as to obtain the multi-sensor weight fusion data in different fault modes; and a multi-sensor multi-level data set is constructed based on the weighted fusion data and is used for training a multi-sensor fault diagnosis model so as to carry out fault diagnosis on the gearbox fault. According to the gearbox fault diagnosis method based on multi-sensor adaptive fusion, abnormal values are effectively eliminated, data redundancy is reduced, meanwhile, based on an adaptive weighted data fusion strategy of an optimal weight distribution principle, under the condition of not depending on expert experience, data noise interference is remarkably reduced, and the fault diagnosis efficiency is improved. And the contribution degree of each sensor is effectively balanced.

The invention discloses a dynamic sliding window optimization method for a 3D laser radar inertial SLAM system, relates to the technical field of SLAM, and aims to improve the precision of pose estimation of an existing laser inertial SLAM system in a complex motion state of a robot. According to the method, the key frame is selected according to the displacement difference and the rotation difference, the key frame is further divided into the strong key frame and the weak key frame, when the window slides, a multi-slide-in and multi-slide-out dynamic optimization strategy of the strong and weak key frames is designed, and the dynamic optimization of the strong and weak key frames is realized in combination with double standards of whether the window meets optimization conditions or not. And the SLAM system is endowed with the capability of dynamically adjusting the window size according to different motion states of the robot. Besides, a two-time optimization scheme of key frame optimization in the sliding window and local common frame optimization in the sliding window is designed, a relatively accurate optimization reference can be provided for common frame optimization after key frame optimization, and more reliable marginalized prior information can be provided for next key frame optimization after common frame optimization. Therefore, the pose estimation precision of the SLAM system in the complex motion state of the robot is improved.

The invention discloses a point cloud intelligent cutting and dynamic optimization method based on edge computing, and belongs to the technical field of intelligent traffic systems and edge computing, and the method comprises the steps: collecting three-dimensional point cloud data through a multi-line laser radar disposed on an RSU; generating a three-dimensional bounding box through a three-dimensional target detection network in an RSU edge calculation unit, generating an ROI mask, expanding a buffer region, and performing adaptive region cutting on the point cloud to form an ROI point set and a background point set; a perception-driven cutting decision is realized, and ROI extraction is converted into a self-learning process based on semantic features from fixed threshold judgment; voxelizing the non-uniform resolution point cloud, generating a unique hash index for each voxel unit, comparing a hash set of a current frame with a hash set of a previous frame, extracting newly added and disappeared point sets, and constructing a differential data packet according to a change ratio; while high timeliness is kept, data redundancy is reduced, and the point cloud updating rate and the incremental transmission efficiency are improved; and dynamic optimization of communication-calculation cooperation is realized.

The invention discloses a path planning method, system and device for a complex terrain and a storage medium. The method comprises the following steps: acquiring topographic data of a complex terrain through a laser radar, and constructing a map model based on an irregular triangular network; calculating the topographic cost of each triangle according to the topographic features of the map model, and converting the topographic cost of each triangle into the total topographic cost; and inputting the total terrain cost of each triangle into a Double DQN algorithm, and planning an optimal path. By combining the three-dimensional terrain modeling technology and the reinforcement learning algorithm, efficient, accurate and real-time path planning can be realized in a complex three-dimensional environment, the limitation of the prior art is overcome, and the development of the autonomous navigation technology of the robot is promoted.

According to the gait event detection method based on the built-in sensor of the smart phone and deep learning, diversified gait features can be accurately understood and recognized, high efficiency and convenience are achieved, interference on real gait information is effectively reduced, a more reliable gait event detection result is ensured, and the gait event detection efficiency is improved. And a widely used tool is provided for gait analysis in a non-laboratory environment. The method comprises the following steps: collecting real-time acceleration, angular velocity and equipment rotation angle data generated during walking through a built-in sensor of the smart phone; noise in the original data collected by the sensor is removed; excluding sensor data with small fluctuation amplitude; constructing an input and output data pair by using a sliding window mode; modernTCN is used as a basic model architecture, and a bidirectional gating cycle unit BiGRU and a cross attention mechanism are introduced; training a model by using the constructed data set; and inputting real-time data collected by a built-in sensor of the smart phone into the trained model, and outputting a corresponding gait event detection result by the model.

The invention relates to the technical field of enterprise product analysis, in particular to an enterprise information processing method and device based on a large language model, equipment and a storage medium. The method comprises the steps of obtaining a target page group of a to-be-analyzed enterprise; performing data cleaning on the parent page and the child page of the same target page group, and obtaining page information of the target page of the to-be-analyzed enterprise after data cleaning; constructing an enterprise product tree based on the page information of all the target pages; performing link analysis on the enterprise product tree to obtain a page source code block corresponding to the to-be-analyzed enterprise; and inputting the page source code block into a preset large language model, and performing product information extraction on the page source code block through the preset large language model to obtain enterprise product information corresponding to the to-be-analyzed enterprise. According to the method, the enterprise product information of the to-be-analyzed enterprise can be obtained, so that the problem that an enterprise product library is difficult to construct is solved, the blank of enterprise product library resources on the market is filled, and the beneficial effects of enabling enterprises, industries and industry analysis are finally realized.

The invention discloses a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor, and relates to the technical field of biomedical engineering. The method solves the problem that static scattered light caused by physiological behaviors and electronic noise of imaging equipment have adverse effects on blood flow imaging quality in a traditional laser speckle contrast imaging method, and comprises the following steps: step 1, using a helium-neon laser as a light source, and irradiating a scattering medium with an expanded laser beam to form a speckle pattern; 2, a microfluidic injector is adopted, two parameters of the inner diameter and the flow of the injector are set, and the flow speed range is controlled; 3, asynchronously sensing the change of the light intensity of the speckle pattern by using an event camera, and shooting the speckle pattern by using a frame camera; and 4, acquiring event stream data by the event camera, and processing the data by using an algorithm. Real-time and high-resolution blood flow imaging and flow velocity calculation are achieved, and the inherent limitation of a traditional method is overcome.

The invention discloses an embedded firmware camera data modification method and device, a medium and equipment, and relates to the technical field of computer vision, and the method comprises the steps: collecting image data in real time, and carrying out the primary processing; a frame difference method and a background modeling algorithm are adopted to carry out region division and priority distribution on the image data after primary processing, and a region priority mapping table is generated; extracting feature points of a high-priority motion area in the optimized image data through an optical flow method, and calculating a motion vector between continuous frames; according to the calculated motion vector between the continuous frames, an event-driven frame processing mechanism is triggered, and the frame rate and the processing precision are automatically adjusted; through the motion vector extracted through the optical flow method, the motion speed of the object can be calculated in real time, the frame rate and the processing precision are dynamically adjusted according to the motion speed, it is ensured that enough details are captured in the rapid motion process, and processing resources are saved in the static process.

The invention discloses an agricultural product component content analysis method and system based on multispectral imaging, and relates to the technical field of agricultural product component content detection.The agricultural product component content analysis method comprises the following steps that component content characteristic data of an agricultural product is extracted; obtaining an agricultural product component content change trend in a future time period based on the content prediction model; judging whether the quality of the agricultural product meets requirements; and identifying the agricultural product component content related to the target index. Through the established component content prediction model, the component content change trend of the agricultural product in a period of time in the future can be predicted, so that prediction of nutrition loss or quality change of the agricultural product can be facilitated, and whether the quality of the agricultural product meets requirements can be accurately evaluated based on the component content change trend; the agricultural products can be sold and eaten in an optimal state, so that the quality standard of the whole agricultural products can be improved.

The present invention relates to the field of renewable energy power generation technology, and discloses an intelligent panoramic monitoring management system and method for new energy box transformers. The method comprises: obtaining an original monitoring data set of a target box transformer and performing data cleaning; extracting key features from the cleaned data and generating a weighted feature set; constructing a box transformer fault prediction model to obtain a fault risk assessment value; performing graded early warnings on potential faults based on the assessment values; constructing a box transformer fault diagnosis knowledge graph, and performing reasoning and tracing in combination with the knowledge graph when providing high-risk or medium-risk early warnings; the present invention realizes comprehensive monitoring of the box transformer operating status and intelligent diagnosis of faults through multi-source data fusion and knowledge graph reasoning, thereby improving the operating reliability and maintenance efficiency of the new energy box transformer.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a multi-unmanned aerial vehicle synchronous positioning and mapping method based on block chain information security enhancement. According to the invention, the data uploaded by the unmanned aerial vehicle can be stored in a non-tampering manner at the server side by combining the block chain technology, and the function of the group key is dynamically adjusted according to the state of the unmanned aerial vehicle, so that the unmanned aerial vehicle can be ensured to flexibly and safely join or exit from the network in different states; therefore, the flexibility and fault tolerance of an unmanned aerial vehicle network are improved, a plurality of unmanned aerial vehicles collect and upload local map data at the same time, the server can integrate the data to form a larger regional map, the precision and efficiency of environmental perception are improved, the redundancy is reduced, the reliability is improved, and the unmanned aerial vehicles can realize high-precision map construction. Meanwhile, the chain structure of the block chain ensures the historical traceability of each data block, and ensures that the source of map data is clear and the process is transparent.

The invention belongs to the technical field of water quality monitoring, and particularly relates to an urban water supply network water quality monitoring and early warning method which comprises the following steps: S1, setting water quality monitoring points of a water supply network; s2, acquiring original time sequence data; s3, processing the original time sequence data through complete adaptive noise ensemble empirical mode decomposition CEEMDAN to obtain a plurality of IMF component subsequences; s4, calculating a sample entropy value of each IMF component sub-sequence by using a sample entropy SE, and carrying out merging reconstruction; s5, importing the merged and reconstructed IMF component subsequences into a water quality prediction model to obtain a prediction result of the water quality data; and S6, according to a prediction result of the water quality data, judging whether a water quality early warning condition is met, and if so, generating early warning information. According to the method, the water quality data of the water supply network can be effectively predicted, so that the water quality problem of the water supply network is effectively warned in advance, and the influence of the water quality problem on normal water use of residents is reduced as much as possible.

The invention discloses a three-dimensional modeling method and device, electronic equipment and a readable storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining shot images containing a to-be-reconstructed object under a plurality of visual angles, determining the camera shooting parameters of each shot image, and carrying out the point cloud reconstruction according to each shot image, obtaining sparse point cloud data, wherein the camera shooting parameters comprise camera poses; segmenting a to-be-reconstructed object in each shot image, and determining an object mask of the to-be-reconstructed object in each shot image; and performing three-dimensional reconstruction on the object to be reconstructed according to the sparse point cloud data, the camera shooting parameters, the shot images and the object masks to obtain a three-dimensional model of the object to be reconstructed. The technical problem that the data redundancy of the reconstructed three-dimensional model is high is solved.

The embodiment of the invention provides a multimedia editing method and device and electronic equipment, and the method comprises the steps: generating initial data layer data according to the editing operation of a multimedia material, and the initial data layer data comprises multimedia material track fragment data obtained through the editing operation; determining an editing effect attribute indicated by the editing operation, associating the editing effect attribute with the initial data layer data, and taking the initial data layer data associated with the editing effect attribute as target data layer data; and according to a received display instruction, acquiring multimedia material track fragment data and the editing effect attribute from the target data layer data by utilizing a consumption view layer, and generating multimedia fragment content after editing operation based on the multimedia material track fragment data and the editing effect attribute. And data redundancy occurring in the multimedia editing process is reduced.

The invention discloses an intelligent grouting progress monitoring management and control method and system, and relates to the technical field of intelligent grouting, and the method comprises the steps: obtaining a preset video monitoring frequency range and a preset video monitoring duration range, and obtaining a plurality of random video monitoring vectors based on a random number generation method; analyzing monitoring redundant data and monitoring load data of grouting progress monitoring under different video monitoring vectors; constructing a progress monitoring effect evaluation model based on machine learning according to the monitoring redundant data and the monitoring load data; constructing a video monitoring vector effect display model based on the three-dimensional model according to the output of the progress monitoring effect evaluation model and the corresponding video monitoring vector, and performing data analysis on the video monitoring vector effect display model to obtain an optimal video monitoring vector which is used for managing and controlling the monitoring process of the grouting progress; the method is used for solving the problem of poor monitoring caused by shooting disorder during grouting progress monitoring in the prior art.

The invention relates to a neural network continuous casting billet slag inclusion quality fault diagnosis method based on mutual information and Bayesian hyper-parameter optimization. The method comprises the following steps: firstly, acquiring continuous casting billet production process data, and performing oversampling processing on fault samples to solve the problem of class imbalance; thirdly, calculating mutual information values between the feature variables and the category features, removing the feature variables of which the mutual information values are lower than a set threshold value, introducing agent variables, and performing error discovery rate estimation processing through a neural network to obtain an optimal variable subset; and finally, fault diagnosis is carried out on the data after variable selection by using a neural network, hyper-parameters of the neural network are adjusted by using a Bayesian optimization algorithm, an optimal classification model is obtained, and a classification result is visualized by using a t-SNE algorithm. According to the method, by selecting the high-quality variable subset and the optimal hyper-parameter combination, accurate diagnosis of the slag inclusion quality fault of the continuous casting billet is effectively achieved, and important guidance and support are provided for the production process of the continuous casting billet.