515 results about "Rapid processing" patented technology

Disclosed in the present invention are a fault identification method and system based on the operating condition of a continuous system in an open-pit mine. The method comprises: establishing a simulation model for a continuous system in an open-pit mine, and monitoring device data in real time; pre-processing the data, and performing potential fault identification; on the basis of a system pressure change rate and an adaptive adjustment mechanism of the continuous system in the open-pit mine, optimizing fault identification output; and designing a fault response and real-time adjustment mechanism to prevent fault occurrence. The fault identification method and system based on the operating condition of a continuous system in an open-pit mine provided in the present invention improve the speed and accuracy of fault diagnosis, particularly the rapid processing capability for complex data relationships. A breakthrough is achieved in fault prevention, thus enabling early warning and adaptive adjustment to be implemented before faults occur. Thus, the stability and safety of continuous systems in open-pit mines are significantly improved, and a more efficient technical solution is provided for operation management of modern open-pit mines.

The invention relates to the technical field of data processing, in particular to an LLM-based multi-source heterogeneous data intelligent fusion and self-adaptive processing method, which comprises the following steps of: receiving multi-source heterogeneous data, matching character sequences of mailbox fields and identity card number fields in records one by one by adopting a preset regular expression model, according to a preset key value pair mapping dictionary, items are uniformly converted, data records which cannot pass format verification, type conversion and value domain mapping are marked as doubt, and data records which cannot be processed or are marked as suspicious are separated. According to the method, a hierarchical processing flow is constructed, and deterministic format verification, type conversion and value domain mapping are arranged at the front end of a processing link, so that most simple data quality problems can be quickly processed with low calculation overhead. Then, numeric statistics and character string comparison are used to carry out fuzzy correction on the data, and spelling errors and numeric abnormities which cannot be covered by rules are further solved.

The invention discloses an AI big data real-time processing and analysis method, and solves the problems of insufficient real-time performance and resource waste in traditional processing. The method comprises the steps of collecting heterogeneous data from multiple sources, dividing priorities through feature vector construction and a dynamic evaluation model, and shunting to an edge rapid processing channel, an edge-cloud collaboration channel and a cloud batch processing channel. The edge node preprocesses the high / medium priority data and compresses an analysis result in a layered manner; and the cloud receives the compression result, the middle-priority residual data and the low-priority data, a unified view is established through fusion, and the edge analysis model parameters are iteratively optimized in real time. And finally feeding back the edge preliminary analysis and the cloud depth result to the terminal. According to the method, through dynamic distribution, cooperative processing, differential compression and model iteration, real-time response of high-priority data, efficient resource allocation and continuous improvement of analysis precision are achieved, and the method is suitable for multi-scene heterogeneous data processing.

The invention discloses a rail transit dispatching simulation practical training and automatic examination system integrated with emergency assistance, and relates to the technical field of simulation practical training, and the system comprises a data collection module which is used for collecting basic data, operation data and historical emergency event data of a rail transit system; and the simulation environment construction module is used for constructing a high-fidelity rail transit simulation environment by applying a digital twin technology based on the data acquired by the data acquisition module, mapping a geometric field model, a physical field model, a behavior field model and a rule field model which are required by virtual equipment, constructing a four-model mapping rule base and fusing a multi-field model. According to the invention, the simulation environment construction module relies on a digital twinning technology and real acquired data, combines geometric, physical, behavioral and regular four-field models and a mapping rule base, constructs a high-fidelity simulation environment and meets diversified practical training requirements, and the emergency aid decision-making module introduces a quantum computing technology and can quickly process mass data in a complex scene.

The invention discloses a dynamic priority dyeing task scheduling and conflict resolution method, which comprises the following steps of: pre-checking task conflicts, extracting all step sequences of a newly-added task and reversing, and detecting whether irreversible opposite node conflicts exist between the step sequences of the newly-added task and all the step sequences of the executed task or not; a to-be-updated execution task queue is generated, the task dip dyeing state of each dyeing site is traversed, to-be-updated execution tasks are screened out, task weights are calculated, and the to-be-updated execution task queue is generated according to descending sort of the weights; a dynamic priority decision is made, and the task executed by the mechanical arm in the next step is determined according to the weight sorting result of the to-be-updated execution task and the step precision level; and a mechanical arm operation instruction set is generated, whether space-time conflicts exist or not is judged, if yes, a new to-be-updated execution task is selected to generate the mechanical arm operation instruction set, and if not, the mechanical arm operation instruction set is executed. Multi-task parallel processing is supported, and the rapid processing requirement of large-scale pathological samples is met.

According to the energy storage system control method based on the interrupt task and the energy storage system provided by the embodiment of the invention, the unspecified interrupt vector of the processor in the energy storage system is configured as the target interrupt vector, and the interrupt priority of the target interrupt vector is set as the highest priority of the system; when it is detected that a preset emergency occurs in any energy storage device in the energy storage system, a target interrupt vector is triggered, so that a processor skips to execute a target interrupt service program pointed by the target interrupt vector; wherein the target interrupt service program is used for processing an emergency event of the energy storage equipment, and the target interrupt service program cannot be preempted by other interrupt programs in the execution process. According to the method, the non-specified interrupt vector of the processor is configured as the target interrupt vector with the highest priority, so that the emergency event can be quickly processed under the condition of not influencing other processes, and the service program is not preempted by other interrupts due to the characteristic of the highest priority, thereby ensuring that the emergency task is efficiently completed without delay.

The invention discloses a road and bridge engineering concrete crack detection method and system based on image recognition, and the method comprises the steps: carrying out the global modeling of high-level semantic features through introducing a Transform enhancement module, and obtaining a YOLOv8 crack detection model; carrying out frame selection on a crack region in the preprocessed image data through a YOLOv8 crack detection model, and outputting a crack detection region; the method comprises the following steps: segmenting a detection area by using a U-Net + + network, adjusting a segmentation threshold according to image local gray level distribution in combination with a dynamic threshold segmentation algorithm, generating a segmentation crack mask image, and evaluating a crack grade according to the segmentation crack mask image. The image data can be quickly processed and analyzed, and compared with traditional manual detection, the detection time is greatly shortened, and the detection efficiency is remarkably improved.

The invention relates to the technical field of image processing, and discloses a parallelization connected domain analysis method based on neighborhood labeling, computer equipment and a storage medium, through high parallelization design, the steps of father node, root node and area junction point searching, mapping establishing, re-numbering and the like are all designed into an independent parallel computing mode, and the parallel computing mode is designed into a parallel computing mode. According to the method, the acceleration capability of parallel computing hardware such as a GPU can be fully utilized, the processing speed is remarkably improved, the method is easy to expand, large-scale high-resolution images can be efficiently processed, the area of each connected domain is counted by adopting a Hash optimization method of grouping local reduction and global merging, atomic conflicts are effectively reduced, the statistical efficiency is improved, and the calculation efficiency is improved. The robustness of the algorithm in a parallel computing environment is further enhanced, so that the efficiency of the connected domain analysis algorithm is greatly improved, rapid processing of high-resolution images is realized, and powerful support is provided for real-time performance and large-scale application in the field of computer vision and image processing.

The invention provides a multilayer medium efficient imaging method suitable for frequency modulation continuous wave ice radar data, and belongs to the field of frequency modulation continuous wave ice radar imaging. Comprising the steps of multi-layer medium signal model construction, improved distance-Doppler algorithm implementation, distance migration accurate correction and azimuth focusing processing. According to the method, a multi-layer medium signal model containing refractive index and propagation velocity changes is established for the problem of target distance distortion caused by propagation velocity differences of electromagnetic waves in different media such as ice and snow; based on the model, a traditional distance-Doppler algorithm process is improved, distance migration caused by multiple layers of media is accurately corrected by introducing a medium-dependent distance migration factor, and efficient azimuth focusing is achieved. And finally, a high-resolution ice layer internal structure image can be generated, and the method is suitable for polar region large-range ice cover detection and rapid processing of a large amount of ice radar data.

This invention discloses a method for processing temperature and humidity big data curve trend charts, belonging to the field of data processing technology. The method includes the following steps: initializing a data point set and error threshold; retaining key points by recursively segmenting line segments; detecting out-of-bounds points and marking continuous out-of-bounds intervals; supplementing key points to ensure data feature integrity; merging the thinning results with the supplemented points and outputting the final result in time order. By quantifying the deviation of data points through geometric vertical distance, key inflection points are accurately identified, while out-of-bounds intervals and extreme points are supplemented, effectively avoiding data feature loss. This invention can quickly process millions of data points on ordinary computer devices, with a compression rate exceeding 92%, while preserving the key trends of the curves. It is suitable for temperature and humidity verification needs in fields such as pharmaceutical cold chain logistics, solving the problems of insufficient performance of traditional tools and high costs of professional platforms when processing large-scale temperature and humidity data.

The invention discloses a wetland human interference activity rapid identification method based on remote sensing and deep learning, and relates to the technical field of image identification, and the method comprises the steps: 1, carrying out the radiometric calibration, geometric registration and pixel-level alignment of a multispectral remote sensing image of a target wetland obtained at different time phases, and dividing the multispectral remote sensing image into image tile units according to a preset size; 2, two image tile units of different time phases at the same geographic position are paired to form a tile pair, and after fusion of a spectral coding branch and a time coding branch, a change coding graph is output and used for representing wetland surface change characteristics; step 3, constructing a double-layer adjacent pixel graph by taking the change coding graph as input, and performing segmentation under multiple scales through a progressive form graph segmentation algorithm to obtain communicated change clusters; and 4, comparing the attribute values of the change clusters with a template threshold value, and carrying out type interference activity type judgment on each change cluster. The method has rapid processing capability and good adaptability.

The invention provides a power supply abnormity processing method and device, electronic equipment and a readable storage medium. The method comprises the following steps: in response to a detection request for a power supply state of a target module, matching a target state of a power-on and power-off state machine of the target module with a first feedback signal of a power domain of the target module to obtain a first matching result; when the first matching result represents that the first feedback signal is not matched with the target state, executing an operation corresponding to the target state on the power domain to obtain a second feedback signal of the power domain; matching the target state with the second feedback signal to obtain a second matching result; when the second matching result represents that the second feedback signal is not matched with the target state, the first matching result and the second matching result are sent to the central controller; and performing power supply exception processing on the target module based on the exception solution issued by the central controller. According to the invention, real-time detection and rapid processing of power supply abnormity can be realized.

The invention discloses a clinical laboratory data rapid processing and sharing method based on big data, and relates to the technical field of medical information. Through a big data storage architecture and a parallelization processing technology, the data are stored in a fragmented mode and are cleaned and integrated in parallel, mass data generated by the clinical laboratory can be efficiently processed, the data processing time is remarkably shortened, the timeliness requirement of clinical rapid diagnosis is met, data association is mined through a big data analysis algorithm, and the data association efficiency is improved. Potential laws such as disease trend and treatment effect are extracted from test data, and quantitative support is provided for clinical diagnosis; the visual presentation mode improves the inspection result interpretation efficiency, is convenient for doctors to quickly acquire key information and construct a safe sharing platform, realizes quick and convenient sharing of clinical laboratory data within an authorization range, meets the requirement of quickly acquiring results clinically, meets the medical data compliance requirement, and is constructed based on a big data technology, so that the medical data interpretation efficiency is improved. The method can be conveniently expanded to adapt to the increase of future data volume and the increase of processing requirements.

The invention belongs to the field of electric power system data analysis, and relates to an electric power system electric quantity loss calculation method and device based on a neural network, and the method comprises the following steps: S1, data recording; s2, a step of feature extraction; s3, a causal graph is constructed; s4, a causal inference model is constructed; s5, estimating a causal effect; s6, evaluating the electric quantity income loss; according to the technical scheme of the invention, the method achieves the accurate calculation of the power loss value through employing the block chain technology and constructing the neural network algorithm model for the limitation of an existing power system power loss evaluation system, facilitates the formation of a complete compensation tracing list, and provides a technical guarantee for the quick processing of a case and the timely realization of rights and interests.

The invention provides a rapid processing method for HPV (human papillomavirus) gene sequence data. The method relates to the technical field of data processing, and comprises the following steps: acquiring and integrating HPV sequence data, host transcriptome data and clinical association data to form a comprehensive input matrix; performing feature screening on the comprehensive input matrix by adopting a bee colony optimization algorithm based on reinforcement learning to obtain an optimal feature subset; according to the bee colony optimization algorithm, global search of the bee colony algorithm is combined with a dynamic decision-making mechanism of reinforcement learning, and a feature selection strategy is dynamically adjusted; and constructing an HPV subtype accurate typing and variation detection model based on multi-task deep learning, performing deep coding and weighted fusion on the optimal feature subset through an unstable attention mechanism, and outputting two key prediction results of subtype probability and mutation site probability. According to the method, the accuracy and biological interpretability of virus typing and variation detection are improved, and efficient extraction and multi-task accurate recognition of high-dimensional features are realized.

The invention belongs to the field of machining, and particularly relates to an integrated high-precision nut burying operation machine which comprises a frame body, a displacement mechanism, a nut burying mechanism, a man-machine interaction layer and a data acquisition and processing module. The core of the system is that a multi-source cooperative closed-loop regulation and control system is constructed, a visual detection module integrates a multispectral compound eye bionic system and an improved ICP algorithm, pressure and temperature field adjustment is combined with shape memory alloy, phase change energy storage and fuzzy control, and a process parameter self-optimization and abnormity self-repairing subsystem is matched. The equipment can automatically adapt to workpieces made of various materials, initial optimization parameters are output when the equipment works for the first time, the embedding precision and the pressing-in force stability are effectively guaranteed, the problems of screw loose, deformation and the like are avoided, clamping stagnation, overheating and other abnormalities can be rapidly handled, production interruption is reduced, and high precision, high efficiency and production continuity are considered.

The invention relates to the technical field of equipment operation tests, particularly discloses a simple scene equipment operation action rapid processing method based on a test video, and is used for solving the problems of low action recognition precision, insufficient spatial-temporal feature fusion, low detection speed and more redundant data in the prior art. Comprising the following steps: accurately intercepting motion start-stop frames, extracting the frames according to a preset frame rate, implementing geometric transformation and color augmentation on the frame-extracted images, respectively extracting spatio-temporal dynamic features and static spatial features by adopting a 3D convolutional network and a 2D convolutional network in parallel, integrating the spatio-temporal features by a channel fusion attention module based on a Gram matrix, and extracting the spatial and temporal dynamic features and the static spatial features by adopting the channel fusion attention module based on the Gram matrix. A thermodynamic diagram is generated based on Grad-CAM to assist in key area positioning, a RefineDet network is input to generate a detection result through ARM coarse regression and negative anchor filtering, ODM fine regression and non-maximum suppression in sequence, and efficient and accurate positioning and classification of operation actions are carried out with assistance of a dynamic frame abandoning and time sequence correction strategy; according to the invention, through the multi-scale residual error convolution and the anchor frame network, accurate positioning of slow action of equipment is realized.

According to the technical scheme, the automatic water quality monitoring method is characterized by comprising the following steps that on-site sampling is conducted, and sample information is input into a printing tool and printed on a sample; transporting and handing over the water sample, scanning and inputting all sample information into a management system, and recording the sample entering time; intelligent sample distribution and circulation; performing intelligent water sample detection by using a water quality analyzer; data analysis and transmission and intelligent sample distribution and circulation comprise a sample test distribution strategy and a quality control scheme, in the method, each sample has a unique digital identity card, the system automatically distributes tasks, optimizes a detection path, greatly improves circulation efficiency, avoids deviation caused by manual selection, can continuously run for 24 hours, and has a wide application prospect. Rapid treatment of a large number of samples is achieved, and the high-throughput requirement of modern monitoring is met.

The application discloses an automatic sorting equipment for low-altitude logistics, which comprises a conveying mechanism for conveying packages and a support table, wherein the support table is provided with a sorting mechanism for sorting the packages and a plurality of storage mechanisms, the sorting mechanism is arranged on one side of the conveying mechanism, the plurality of storage mechanisms are used for storing the sorted packages, and the plurality of storage mechanisms are arranged on the side, away from the conveying mechanism, of the sorting mechanism, a mechanical arm is arranged between adjacent sorting mechanisms, a grabbing assembly is installed on the mechanical arm, and a parking table for parking a drone is arranged on one side of the storage mechanism. The application can significantly reduce the waiting time of the drone, improve the operation efficiency of the whole process of "sorting-loading-distribution" of the low-altitude logistics, fully exert the time advantage of the low-altitude logistics, realize the rapid processing of priority packages, meet the efficient sorting demand of hundreds or even thousands of packages per day of the low-altitude logistics, and reduce the labor cost and the sorting delay rate.

The invention discloses a plant three-dimensional reconstruction and phenotype extraction platform and a plant three-dimensional reconstruction and phenotype extraction method, and relates to the field of computer vision. A data acquisition controller in the platform judges whether all target plants enter a shooting window according to the center line deviation of a shooting trolley and the distance between the target plants and a distance measurement module; if yes, a differential motor PID closed-loop control algorithm is adopted to control the shooting trolley to decelerate to a preset shooting speed, the shooting trolley is controlled to shoot the target plant at multiple visual angles at the same time, a processing module adopts a NeRF model to conduct three-dimensional reconstruction on the target plant according to the image set, and a target plant surface point cloud is obtained; processing the surface point cloud of the target plant by adopting a Pointnet + + segmentation model to obtain a point cloud subset of each leaf and a stem point cloud subset; and obtaining phenotypic parameters according to the stalk point cloud subset and the leaf point cloud subset. The method and the device have the advantages of high precision, fast processing and field operation adaptability.

The invention provides an annunciator circuit system comprising a first repeating structure which comprises a first white lamp and a first repeating transformer, and the first white lamp is connected in series with the secondary side of the first repeating transformer; the first main structure comprises a second white lamp and a first main transformer, the second white lamp is connected with the secondary side of the first main transformer and the first white lamp in series, and the secondary side of the first main transformer is connected with the secondary side of the first repeating transformer in series. During normal work, the bulbs with the same current, main indication signals and repeat indication signals in a series loop are lightened at the same time, synchronous display is achieved, the broken wire state can be detected in real time, receiving equipment is not added, the equipment structure is simplified, the equipment complexity is reduced, maintenance work of maintenance personnel is more convenient, faults can be rapidly processed, and the fault processing time is shortened.

The present application discloses a digging bucket type pioneering device, a pioneering machine and a use method thereof, the pioneering device comprising: a supporting top plate, a bucket component and a cutting component; the supporting top plate is fixed with supporting side plates on both sides; the bucket plate in the bucket component is located between the pair of supporting side plates and forms a bucket structure with a certain volume together with the supporting top plate; the cutting component is located at the rear side of the bucket component and has a cutting drum driven to rotate and connected between the pair of supporting side plates; the supporting top plate is driven to rotate circumferentially, so that the bucket component and the cutting component can switch positions circumferentially. The pioneering machine comprises the pioneering device and a mobile host, and the mobile host is movably connected with the supporting hanger through a mechanical arm. The present application has a simple and compact structure, realizes continuous operation of shoveling and cutting, completes rapid processing of the environment, has strong maneuverability, reduces the use of traditional multiple machine tool matching operation, and is suitable for different working environments.

The application discloses a large-format cross-scale adhesive film defect detection method and device. The method comprises the following steps: rough focusing of a camera and capturing an adhesive film image, rough scanning of the adhesive film image and extraction of information of printed graphics in the adhesive film image by using an image difference method, input of an output differential image into an anchor frame algorithm, input of output fine scanning target region coordinates into a genetic algorithm, combination of motor S-curve planning characteristics to output an optimized fine scanning path; driving a motion platform to complete path execution by using a double-shaft linkage control combined with the fine scanning path, focusing of the camera during path execution, collection of high-resolution images of defect regions and registration, image stitching and fusion of the registered images by using an optimal seam line algorithm combined with an improved Laplacian pyramid fusion method, and defect detection on the complete adhesive film image after fusion. The application has high stitching accuracy, fast processing efficiency, and can effectively retain more detailed information.

The present application belongs to the field of mechanical processing and sawing, and relates to a plastic door and window cutting and chamfering integrated saw, which comprises a rack, a horizontal cutting saw, a vertical cutting saw, a first positioning device, a second positioning device and a chamfering cutting positioning assembly. The horizontal cutting saw and the vertical cutting saw are slidably arranged on the rack along the longitudinal cutting direction of the material. At least one first positioning device and at least one second positioning device are arranged on the rack along the longitudinal cutting direction of the material. The first positioning device and the second positioning device are respectively located on the two sides of the longitudinal cutting direction of the material. The chamfering cutting positioning assembly is movably arranged on the first positioning device. The chamfering cutting positioning assembly and the cutting saw are oppositely arranged on the two sides of the material cutting direction. The positioning devices are arranged at intervals in the material cutting direction. The chamfering cutting and the saw cutting are efficiently matched. The problem of batch rapid processing of trapezoidal blocks for plastic doors and windows is solved.

The invention belongs to the field of aerodynamics, and particularly relates to a quick treatment method for a trailing edge step of an airfoil profile. The method comprises the following steps: standardizing airfoil profile data, calculating a thickness distribution curve and a camber distribution curve of the airfoil profile through airfoil profile coordinates, constructing a new continuous disturbance function which is a continuous derivable function, and the disturbance quantity of the function at the rear edge is just the required step thickness. And the smooth and continuous disturbance function is superposed on the thickness distribution, so that local rapid adjustment of the thickness of the step on the rear edge of the airfoil is realized, finally, the coordinates of the airfoil are inversely calculated through the camber distribution and the thickness distribution, and the airfoil profile meeting the requirement is obtained. Due to the fact that the camber distribution and the maximum thickness of the airfoil profile modified through the method are not changed, the requirement for continuity of the appearance is met, and it can be guaranteed that the overall aerodynamic characteristics of the airfoil profile before and after adjustment are basically consistent.

The application provides an intelligent summary automatic processing method for bad asset related cases, and relates to the technical field of data processing, which comprises the following steps: Sp1: collecting original case records of bad asset related cases, wherein the original case records comprise judicial ruling texts, financial reconciliation data and protocol scan images; Sp2: constructing a distributed collaborative analysis unit and receiving the original case records; in the distributed collaborative analysis unit, the original case records are converted into a weighted characteristic matrix through a case characteristic clustering algorithm, wherein the weighted characteristic matrix comprises a core characteristic vector, an auxiliary characteristic vector and a stability factor vector; by introducing the distributed collaborative analysis unit and the k-means clustering algorithm, the technical solution realizes automatic characteristic extraction and clustering processing of large-scale case data. Compared with the inefficiency of manual review, the present application can quickly process hundreds to thousands of cases, significantly shortening the processing time.

This application discloses a method, apparatus, device, medium, and product for batch processing of transaction data, relating to the field of big data technology. The method includes: grouping a first transaction dataset based on account identifier codes to obtain a second transaction dataset corresponding to each account; aggregating transaction data with the same transaction sequence number from all second transaction datasets into a third transaction dataset to obtain a third transaction dataset corresponding to each transaction sequence number; classifying each third transaction dataset based on transaction type to obtain a corresponding fourth transaction dataset; processing the pending transaction data group corresponding to the current transaction sequence number to obtain cross-sectional data for the current transaction sequence number; and sequentially processing the pending transaction data group corresponding to the next transaction sequence number based on the cross-sectional data until all transaction sequences are processed to obtain the final cross-sectional data. This application can meet the need for rapid processing of massive amounts of account transaction data and can improve the processing speed of massive amounts of account transaction data.

The invention discloses a test machine detection system and method, a medium and computer equipment, and belongs to the field of semiconductor tests.The method comprises the steps that a program detection code is run through a test machine; sending an instruction code to the detection board by using the test mother board; executing the instruction code by using the detection board; the detection result of the instruction code executed by the detection board is displayed by using the on-off of the light-emitting diode; a detection result is fed back to the test machine through the detection plate; performing data comparison on the detection result by utilizing the test machine, and if the detection result is abnormal, displaying fail output by utilizing a test interface; and if the detection result is normal, displaying pass output by using a test interface. According to the application of the invention, each module in the system can be detected, the detection result can be synchronously displayed on the test machine and the detection board, the abnormal position and the abnormal type of the abnormal module can be rapidly locked, and the formation of a rapid abnormal processing scheme is facilitated.

The utility model provides an interface signal control terminal for simulating equipment failure, which relates to the technical field of video conference systems, and comprises a control terminal in communication connection with an interactive terminal, the control terminal comprises a main control module, a relay module and an interface module, the main control module is in communication connection with the interactive terminal, and the relay module is in communication connection with the interface module. The relay module is electrically connected with the main control module, and the interface module is electrically connected with the relay module; the main control module can receive a switching value signal sent by the interaction terminal and transmit the switching value signal to the relay module, so that the relay module controls the on-off of the interface module; according to the utility model, the interaction terminal cooperates with the control terminal having the master control module, the relay module and the interface module to control the on-off mode of the corresponding interface and simulate the real fault state and fault environment of the equipment, thereby cultivating the fault diagnosis and processing capability of workers, and enabling the workers to work normally when the equipment has an abnormal fault. And rapid processing can be realized.

The application relates to the technical field of semiconductor polishing processing, and discloses a high-precision semiconductor cavity polishing machine, which comprises a positioning base, a sliding base movably arranged on the positioning base, a polishing mounting base rotatably arranged at the end of a connecting arm, a track-type polishing assembly arranged on the outer side of the polishing mounting base, cleaning members and polishing liquid adding members arranged at the middle and the side of the inner side of the polishing mounting base respectively, and a tightening mechanism arranged on the polishing mounting base and used for adjusting the tightness and distribution state of the track-type polishing assembly and improving polishing precision. The high-precision semiconductor cavity polishing machine improves polishing processing efficiency, facilitates rapid processing of recessed areas such as curved surfaces, can effectively clean the polishing assembly and continuously and stably add polishing liquid to the polishing assembly, and improves polishing precision.