398results about How to "Calculation speed" patented technology

The invention discloses a gas pipeline and subway safety distance optimization method based on numerical simulation, and relates to the technical field of subway tunnel safety, and the method comprises the steps: simulating gas pipeline explosion data through a KFX Exsim gas explosion model; establishing a fluid-solid coupling model, a soil dynamic model and a thermodynamic coupling model, and coupling the three models to obtain a multi-physics coupling model; based on a multi-physics field coupling model, performing decomposition and region division on a numerical simulation task in combination with a parallel computing framework, and simulating the influence of gas pipe explosion on the subway; based on a numerical simulation result, evaluating the safety of the tunnel under the action of the explosion load; based on a safety evaluation result, optimizing a safety distance between the gas pipeline and the tunnel; according to the method, the fluid-solid coupling model, the soil dynamic model and the thermodynamic coupling model are established and coupled to form a multi-physical field coupling model comprehensively covering the explosion environment, and accurate simulation of complex behaviors of the subway tunnel in the explosion environment is achieved.

The invention discloses a multi-scale digital rock core rapid reconstruction method based on an agent model, and belongs to the field of digital rock core rapid reconstruction. According to the method, through combination of multiple steps and the model, all-around support is provided for construction of a multi-scale digital core, key links of data acquisition, feature extraction, model construction and multi-scale information fusion are covered, a basis is provided for reservoir physical property evaluation through accurate prediction of permeability, and the reservoir physical property evaluation accuracy is improved through combination of microscopic images and macroscopic attributes. Effective connection of cross-scale information is realized, finally, a digital core with a real microstructure is generated, and a high-fidelity data model is provided for oil-gas exploration.

The invention discloses a method for predicting the heavy metal adsorption capacity of micro-plastics, which comprises the following steps of: screening heavy metal and micro-plastic adsorption data from literature and professional databases, and arranging the data into a sample data set; performing preprocessing including data cleaning, missing value processing and data standardization on the sample data set; constructing an adsorption capacity prediction model, and performing training and evaluation to determine an optimal prediction model; using random search, Bayesian optimization or grid search to adjust hyper-parameters of the model; and carrying out adsorption capacity prediction on microplastics and heavy metals with unknown adsorption capacities by using the adsorption prediction model. The method can be practically applied to research on adsorption of the heavy metal ions to the micro-plastics, interaction between the heavy metal ions and the micro-plastics can be visually understood, and ecological risks caused by coexistence of the heavy metal ions and the micro-plastics can be evaluated.

The invention provides an active load shedding optimization control method, system and device of a wind power plant and a storage medium, and belongs to the technical field of active power control of the wind power plant, and the method comprises the steps: constructing and training an SAC intelligent agent through an SAC algorithm; acquiring a wind power plant state quantity; inputting the state quantity of the wind power plant into the SAC intelligent agent to obtain a vector # imgabs0 # composed of active power initial reference values of all wind turbine generators; inputting the # imgabs 1 # into a constraint controller for secondary calculation to obtain a vector # imgabs 2 # composed of active power optimization reference values of the wind turbine generators; the wind power plant executes # imgabs3 #; and repeating the steps, and adjusting the active power of each wind turbine generator in real time. The model is high in generalization, stability, reliability and learning efficiency, real-time online calculation of the equivalent fatigue load can be achieved, reasonable distribution of the active power of the wind power plant can be achieved under the condition that the active power constraint of the wind power plant is met, the equivalent fatigue load of the whole wind power plant can be reduced, and the maintenance cost can be reduced.

The invention discloses a self-adaptive Monte Carlo macro particle simulation method and system based on parallel computing, and relates to the technical field of particle simulation, and the method comprises the steps: initializing parameters, if the current step number is not the maximum step number, storing node data, balancing particle loading, and calculating charge density and battery density; using macro particles to represent a group of real particles, updating the positions and speeds of all the particles in parallel, and calculating new charge distribution; setting adaptive grid configuration, and dynamically adjusting grid density according to electric field intensity and local density of charge distribution; and based on the updated particle position and speed, evaluating and processing a collision event, storing collision data, re-calculating surface charge density data, adding 1 to the current step number, and circularly executing to finish simulation when the maximum step number is reached. According to the method, the accuracy of a simulation result is improved through the improved collision model, electrical discharge behaviors under different conditions are better predicted, and more reliable data support is provided for design and safety evaluation of high-voltage equipment.

The invention discloses a method and device for detecting the center position in a laser spot image, and the method comprises the steps: obtaining a laser spot image which is a fuzzy circle / circular ring light spot, and solving the center-of-gravity coordinate of the fuzzy circle / circular ring light spot as an initial center position; calculating radial distances from the initial circle center position along a plurality of different directions of the fuzzy circle / circular ring light spot, and calculating an average value of the radial distances in the different directions as an average radius; based on the average radius, utilizing a least square method to fit a new circle center position; and taking the new circle center position as an initial circle center position, repeating iterative calculation until the difference between the current fitting new circle center position and the previous fitting new circle center position is smaller than a preset threshold value, and determining the current fitting new circle center position as a final circle center position. The method is more accurate in circle center position detection and higher in calculation speed.

The invention discloses a computer and large model interaction system based on an artificial dictation command, and the system comprises a control unit which is used for controlling and managing the whole information collection and conversion process; the acquisition module is responsible for capturing voice signals, recognizing the voice signals and converting the voice signals into readable text data for a computer; the analysis module is used for analyzing the text data and converting the text data into a readable instruction format for a large model; the interface module is used for receiving and processing an instruction format and realizing a corresponding function; and the feedback module is used for adjusting and optimizing the accuracy of the voice recognition system and the instruction analysis module according to the feedback result of the user. According to the method, through a deep learning algorithm based on a large model technology, commands dictated by a user are accurately recognized, texts are analyzed and key information is extracted through a large model, and the texts are converted into executable instruction formats, so that seamless joint of man-machine interaction is realized.

The invention discloses a runoff prediction method based on a bidirectional long-short-term memory recurrent neural network, and belongs to the technical field of runoff prediction. The method comprises the following steps: preprocessing original data to obtain a data set, and dividing the data set into a training set and a verification set; constructing a runoff prediction network model, wherein the runoff prediction network model comprises a forward LSTM module, a reverse LSTM module, an Adam optimizer, a Dropout regularization layer and a Dense full connection layer which are connected in sequence; setting a hyper-parameter and a loss function, and training the constructed model by adopting the training set to obtain a trained runoff prediction model; and inputting verification set data into the trained runoff prediction model to obtain predicted runoff. According to the method, the association between the past and future runoff time series data can be mined, the problem of poor feature extraction and memory effect of early learning part data caused by unidirectional learning is effectively avoided, and the prediction precision is improved while better applicability is achieved.

The invention belongs to the technical field of data analysis, and particularly relates to an information analysis method for bidding documents. The information analysis method comprises the following steps: S1, obtaining a newly added bidding and tendering file, and sending the file into a preprocessing module; s2, the preprocessing module outputs corresponding preprocessing data to a classification module; s3, the classification module divides the content in the current preprocessed data into valid tables, core blocks and independent blocks, and then the valid tables, the core blocks and the independent blocks are sent into a table analysis module, a core analysis module and an independent analysis module respectively; and S4, the table analysis module, the core analysis module and the independent analysis module respectively output the first analysis data, the second analysis data and the third analysis data to an integration module for integration to obtain core information and output the core information. According to the method, the core information of various bidding and tendering files can be accurately and efficiently analyzed.

The invention discloses a method and device for accelerating a homomorphic encryption algorithm by using fast number theory transformation and a medium. The method comprises the following steps: initializing homomorphic encryption parameters and generating a public key, a private key and a relinearization key; encrypting a plaintext to be operated by using the public key to generate a corresponding ciphertext polynomial; performing fast number theory transformation on the ciphertext polynomial to transform the ciphertext polynomial into a point value form; performing homomorphic multiplication on the two point value form ciphertexts to obtain three ciphertexts; using a relinearization key to transform and decompose one ciphertext by using a fast number theory and accumulate the decomposed ciphertext into the other two ciphertexts to obtain two similar primary ciphertexts; using fast number theory inverse transformation to transform the first-order ciphertext from a point value form into a polynomial form; and decrypting the ciphertext polynomial by using the private key to obtain a plaintext multiplication result. The method adopts fast number theory transformation and inverse transformation, improves the polynomial multiplication calculation efficiency through the processes of primitive root sampling, bit inverse sequence and the like, is suitable for the field of polynomial operation, and improves the polynomial operation efficiency.

The invention provides a consistency diagnosis method and device for an energy storage battery, and belongs to the field of intelligent diagnosis of an energy storage battery system, and the method comprises the steps: collecting original data of the energy storage battery at a set time interval, and sorting the original data according to a time field; cleaning the original data to obtain cleaned data; extracting all static time segments of the energy storage battery according to current information in the cleaned data, and screening out long static segments; screening the highest voltage and the lowest voltage according to the voltage information in the long standing segment, converting the highest voltage and the lowest voltage into corresponding highest capacity and lowest capacity, calculating a difference value between the highest capacity and the lowest capacity, and when the difference value is greater than a set threshold value, determining that the battery is an abnormal battery; screening out single batteries with outlier capacities from the abnormal batteries, and carrying out abnormity alarm; the capacity-based consistency diagnosis can more accurately evaluate and judge the energy performance of the energy storage unit in the charging and discharging process, and the accuracy of consistency detection is improved.

The invention discloses an automatic code vectorization optimization method and device and a medium, belongs to the technical field of high-performance computing, and aims at solving the problems that existing automatic code vectorization is inconvenient, the manual operation proportion is large, vectorization of irregular codes or codes with a load dependency relationship is difficult to support, and the vectorization efficiency is high. And improvement of calculation performance and reduction of energy consumption are not facilitated. The method comprises the steps of obtaining a scalar instruction diagram of a calculation task according to a calculation kernel of parallel calculation; performing protocol conversion processing on the scalar instruction diagram to obtain a protocol instruction diagram; according to the high-level organization structure of the instruction, grouping processing related to mapping vectors is carried out on the protocol instruction graph, and a grouping graph is obtained; based on the limited vector length of hardware, performing splitting matching processing on instruction vectors in the grouping graph to obtain a vector matching grouping graph; and executing configuration on each group of elements in the vector matching grouping graph to obtain a vector operation graph.

The invention discloses a V2X cooperative sensing method and system based on a lightweight domain adapter. According to the invention, a lightweight domain adapter network is placed in a feature map input branch of each cooperative vehicle of a first V2X cooperative sensing network carried by an own vehicle to form a second V2X cooperative sensing network, and cooperative sensing of the own vehicle and the cooperative vehicles is realized by using the trained second V2X cooperative sensing network. According to the invention, by introducing the domain adapter network, collaborative 3D target detection under the condition of heterogeneous feature maps of the own vehicle and the collaborative vehicle is successfully realized. According to the method, under the condition that the whole basic cooperative detection network does not need to be retrained, the domain adapters corresponding to different feature maps are inserted, so that the domain difference between heterogeneous feature maps is effectively reduced, the detection performance and scene adaptability of cooperative 3D target detection based on V2X are greatly improved, and the detection efficiency is improved. The method has a high practical value for improving the collaborative perception performance of automatic driving.

The embodiment of the invention provides a battery system health detection method, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring voltage lag data of each battery module in the battery system and state parameters of the battery system; establishing a standard lag model corresponding to each battery module based on the state parameters and the voltage lag data; according to the state parameters and a standard lag model, standard lag time corresponding to each battery module is obtained through calculation; analyzing health state data corresponding to each battery module according to the standard lag time and the voltage lag data of each battery module; the method effectively reduces the calculation and analysis complexity, improves the analysis precision, can achieve the real-time monitoring and calculation, and directly reflects the dynamic performance and health condition of the battery system.

The invention relates to an interferometer, in particular to a method for monitoring imaging position deviation of a Doppler differential interferometer, which is used for overcoming the defect of image surface drift of the Doppler differential interferometer caused by a thermal drift effect. According to the Doppler differential interferometer imaging position offset monitoring method, interference pattern signals are extracted and expanded, a groove distribution model D (x) and a scale signal distribution model I (x) are constructed, model parameters are optimized through multiple calibration interference patterns, and simulation scale signals are generated; and in actual measurement, the first pulse position a0 is optimized by minimizing an error between a simulation scale signal and an actually measured scale signal, so that accurate monitoring of the imaging position offset of the Doppler differential interferometer is realized.

The invention provides a system software design method and system for manufacturability verification of an integrated circuit, and relates to the technical field of integrated circuits, and the method comprises the following steps: constructing a multi-level verification model based on integrated circuit design data, carrying out parallel computing processing on the multi-level verification model, generating a manufacturability prediction result, and identifying potential manufacturing defects. Based on the potential manufacturing defects, an optimization suggestion is automatically generated, and the optimization suggestion comprises a layout modification scheme and a process parameter adjustment scheme; applying the optimization suggestion to the integrated circuit design data to generate optimized design data; iterative verification is carried out on the optimized design data until preset manufacturability indexes are met, and the manufacturability indexes comprise the yield, reliability and performance parameters; and transmitting the integrated circuit manufacturing data packet to a manufacturing execution system for guiding the actual manufacturing process of the integrated circuit.

The invention aims to provide a white blood cell detection method based on FBDM-YOLOv8s, which comprises the following steps: A, constructing an improved YOLOv8s neural network model, and the improved YOLOv8s neural network model comprises a Backbond network, a Neck network and a Head network; the Backbond network takes a plurality of groups of Fasnet modules as a main framework, and finally, a group of BRA modules is arranged; b, the improved YOLOv8s neural network model is trained, and a trained improved YOLOv8s neural network model is obtained; and C, introducing a to-be-detected leukocyte image into the trained improved YOLOv8s neural network model, and sequentially processing the to-be-detected leukocyte image through a Backbond network, a Neck network and a Head network to obtain a detected image. According to the method, the edge detection precision can be improved while model parameters are optimized, the calculation speed is increased, and the problems that image cells are incomplete and two cells are mutually overlapped are better solved.

The invention discloses a low-voltage transformer area topology modeling method and system based on hierarchical clustering, and the method comprises the steps: building a sensitivity relation between voltage and current, calculating a sensitivity coefficient of voltage to current, and calculating a sensitivity matrix according to the resistance value and reactance value of each line in transformer area topology; subtraction is carried out on the electrical quantity under the continuous time section, a sensitivity relational expression is converted, and an associated impedance matrix is obtained and comprises an associated resistance matrix and an associated reactance matrix; based on an improved hierarchical clustering algorithm, inputting an associated resistance matrix or an associated reactance matrix to generate a tree diagram; and identifying hidden nodes according to the tree diagram, determining a generation sequence of the hidden nodes and a subordinate connection relationship between the nodes, and constructing a transformer area topology. By implementing the method, not only can the correlated impedance between the end nodes be accurately calculated, but also all hidden nodes in the network can be identified based on the correlated impedance matrix and the network topology can be correctly deduced, high accuracy and robustness are shown, and the cost is low.

The invention relates to the technical field of electric power system automation, and particularly discloses an electric power system wind power consumption capability limit evaluation method considering transient stability, and the method comprises the steps: carrying out the sampling in a feasible space of an electric power system, and generating a sample data set; using the sample data set to offline train a deep neural network as a transient stability evaluator, and predicting a transient stability index of the power system; based on the wind power output short-term prediction data and the load prediction data, considering the transient stability constraint of the power system, and determining a day-ahead scheduling plan of each generator in the power system; and constructing an evaluation model of the wind power consumption capability limit under the current scheduling plan, and accelerating the solving process by means of constructing an initial solution in an auxiliary manner through constraint preprocessing and a heuristic algorithm. The method has the advantages that the calculation speed of the evaluation model is effectively improved, the transient stability of the power system is considered, the wind power consumption capability limit is quickly evaluated, and auxiliary opinions and decision support are provided for real-time dispatching of dispatchers.

The invention discloses an underwater small target detection method based on feature screening and context fusion, and the method comprises the steps: improving a YOLOv3 network model, carrying out the feature screening and context fusion in a backbone network, constructing a target detection YOLOv3-CTE network model, carrying out the sparse training and optimization processing of a pruning network model of the target detection YOLOv3-CTE through a sample training set, and carrying out the detection of an underwater small target. Obtaining an optimal target detection network model; the sample verification set is adopted to verify the optimal target detection network model, a network model verification result is obtained, and the optimal target detection network model is updated according to the network model verification result; and predicting the sample test set based on the updated optimal target detection network model to obtain a detection result of the underwater small target. According to the method, in the underwater small target detection process, the parameter quantity can be reduced to a great extent, meanwhile, the calculation speed of the model is improved, and the classification and recognition accuracy of the underwater small target is further improved on the premise that the detection efficiency is guaranteed.

The invention relates to an FPC-based flexible intelligent sensing electronic skin, belongs to the technical field of integrated circuits, and solves the problem of poor contact sensing stability in the fields of robots, medical health and the like in the prior art. The skin comprises a flexible touch sensor array, a row selection module and a sensor interface circuit. The flexible touch sensor array is used for sensing an external pressure signal and converting the pressure signal into a current signal; the row selection module is used for selecting rows of the flexible touch sensor array through high and low levels; and the sensor interface circuit is used for selecting columns of the flexible tactile sensor array and converting the current signals of the array elements corresponding to the selected rows and columns into digital signals which are used for representing the pressure value of the flexible tactile sensor array. The characteristics of softness, stability and safety of the flexible sensing electronic skin are realized.

The invention relates to a method and a device for generating a panoramic curved surface fault slice based on a CBCT image of three-dimensional segmentation, and the method comprises the steps: S1, carrying out the three-dimensional segmentation of teeth and a mandible in the CBCT image through a deep learning algorithm, and obtaining a tooth and mandible segmentation result; s2, acquiring a reference dental arch line for representing the dentition condition of the patient based on the segmentation result; s3, adjusting the dental arch line of each layer of slice containing the segmentation result based on the segmentation result and the reference dental arch line; and S4, extracting panoramic slice data from the CBCT image according to the dental arch line of each layer of slice, and generating a panoramic curved surface fault slice. According to the method, by means of the segmentation result, the oral cavity panoramic image with the target anatomical structure is accurately extracted from the CBCT image, a dentist can conveniently measure the length, the angle and the like, the adjacent relation of the dentition periphery can be conveniently observed, meanwhile, radiation absorption of secondary shooting is avoided, and the imaging accuracy is improved. And the problems of poor patient cooperation, inaccurate focusing positioning and the like during panoramic actual shooting are solved.

The invention discloses an equipment trusted boot verification system, method and device and a chip, and belongs to the technical field of information security. The system comprises a main control chip used for reading firmware in a memory, calculating a target metric value of the firmware and sending the target metric value to a security chip; and the security chip is used for verifying the target metric value according to the reference metric value of the firmware. According to the method and the device, the firmware in the memory is read through the main control chip, the firmware does not need to be read by a security chip, various different external memories can be adapted, and the verification efficiency is improved; the main control chip calculates the target metric value of the firmware, the calculation speed is higher than that of a security chip, and the verification efficiency is further improved; besides, according to the cooperative mode of the main control chip and the security chip, the security chip does not need to read firmware from the memory in the verification process, so that a switch selection circuit between the security chip and the memory is reduced, and the overall cost is saved.

The invention relates to the technical field of radiation dose calculation, in particular to a radiation dose calculation method and device based on a V-Net network. The method comprises the following steps: acquiring a CT image and light beam flux information for a focus area of a patient to be treated, wherein the light beam flux information represents parameter information of a light beam for performing radiotherapy on the patient to be treated; and on the basis of the CT image and the beam flux information, through a pre-trained V-Net network, performing radiotherapy dose calculation for a focus area of a patient to be treated, and generating a dose distribution diagram. According to the method, the dose distribution diagram can be automatically generated, human intervention is reduced, and the clinical working efficiency and the adaptability of a treatment plan are improved. Moreover, the pre-trained V-Net network can effectively capture features in the CT image, the calculation speed is greatly improved, the precision of the generated dose distribution diagram is equivalent to the calculation precision of the Monte Carlo method, and both the speed and the precision are taken into account.

The invention discloses an automobile windshield glass defrosting and demisting area identification method, which comprises the following steps of: determining a defrosting and demisting area through a three-dimensional single-arm measurement scriber, and drawing the defrosting and demisting area on a drawing; setting image acquisition equipment, and putting the drawing into a preset acquisition area of the image acquisition equipment; acquiring a drawing through the image acquisition equipment; establishing an image correction model, and correcting the image; and the area of the corrected defrosting and demisting area is calculated, and a result is generated. Through the technical scheme of the invention, the defrosting and demisting area identification process of the automobile windshield glass has the advantages of high automation, intelligence, high accuracy, relatively high calculation speed and the like, and the defects of large error, low efficiency, low automation and intelligence degrees and the like in the existing data processing link are improved.

The invention discloses a lightweight radio frequency fingerprint identification method for a variable-length signal, and relates to the technical field of fingerprint identification. Comprising the following steps: S1, acquiring different Internet of Things devices for training, and collecting labeled signal data packets; s2, preprocessing the signal data packet to form a training data set; s3, constructing a depth separable convolutional neural block-SCU module based on depth separable convolution, and designing and constructing an SCU-Net network based on the SCU module; s4, training the SCU-Net network by using the training data set to obtain a preliminary neural network, expanding data by using a mode of manually adding noise and simulating a multi-path effect, and retraining the neural network to obtain a trained convolutional neural network; and S5, obtaining a Lora signal of a to-be-identified device, preprocessing the Lora signal, and inputting the Lora signal into the trained convolutional neural network to obtain an identification result corresponding to the to-be-identified device. According to the invention, the defects of high cost and high equipment requirement of the existing identification scheme can be solved, and the design requirements of low cost and light weight are met.

The invention discloses an industrial process monitoring method based on a lightweight deep principal component analysis-auto-encoder model, and the method comprises the steps: collecting the related process variable data of an industrial process on line, carrying out the preprocessing of the collected data, inputting the preprocessed data into a trained lightweight deep principal component analysis-auto-encoder model, and carrying out the recognition of the related process variable data. The T2 statistical magnitude and the SPE statistical magnitude are obtained and compared with corresponding control limits, and a monitoring result is output; the lightweight deep principal component analysis-auto-encoder model uses a PCA dimension reduction module for dimension reduction to obtain the data score, inputs the score into the auto-encoder, and outputs the reconstruction data. According to the method, the calculation efficiency of the model is improved, compared with a traditional deep network model, the calculation speed is obviously improved, compared with a single multivariate statistical model, the strong feature extraction capacity of the deep hierarchical cascade model structure is incomparable to that of the single multivariate statistical model, and the problem of complex industrial process monitoring is effectively solved.

The invention discloses a coaxial contra-rotating propeller design method and system based on a rapid pneumatic calculation method, and relates to the technical field of aircraft design, and the method comprises the steps: obtaining the layout and parameter design of a coaxial contra-rotating propeller, and determining the blade load distribution in a cruising state through a Goldstein theory; calculating the thickness distribution and chord length distribution of the blades according to the dimensionless spanwise positions of the blades and the diameter of the propeller disc; an airfoil lift coefficient and an airfoil distribution are determined based on these data. The torsion distribution of the blades is calculated through the blade element theory, and the sweepback / sweepback distribution is determined according to pneumatic and structural requirements. A rapid momentum source pneumatic calculation method is used for evaluating whether the pneumatic performance meets the cruise and take-off states or not, and when the pneumatic performance meets the cruise and take-off states, coaxial contra-rotating propeller design is completed; and if not, updating chord length distribution, airfoil profile distribution and torsion distribution of the blades until the design requirements of the cruise and take-off states are met. According to the method, rapid design of the coaxial contra-rotating propeller can be achieved.

The invention relates to the field of pavement detection, and discloses a lightweight unmanned aerial vehicle pavement disease detection method and device and a storage medium, and the method comprises the steps: obtaining a pavement image shot by an unmanned aerial vehicle; constructing an improved unmanned aerial vehicle pavement disease detection network LHP-YOLO based on YOLOv8, and performing pre-training by using the data set to obtain a trained network; and inputting a pavement image shot by the unmanned aerial vehicle into the trained network to obtain an unmanned aerial vehicle pavement disease detection result. The technical problems that a current pavement disease detection method is difficult to deploy at a mobile terminal and low in precision are solved.

The invention relates to an oracle denoising method and device based on channel attention and font aggregation, and a medium. The method comprises the following steps: obtaining an oracle image containing noise; the oracle bone image is input into a denoising model, the denoised oracle bone image and a character skeleton are output, the denoising model comprises an input mapper, an output mapper, an ECharFormer module and a font corrector, the input image is converted into shallow layer features through the input mapper, the shallow layer features are converted into reconstruction features and font features through the ECharFormer module, and the reconstruction features and the font features are reconstructed through the output mapper. The reconstruction features are converted into denoised oracle images through an output mapper and output, and the font features are converted into character skeletons through a font corrector and output. Compared with the prior art, the method can achieve a rapid and good denoising effect while keeping the oracle alphabet fonts.