101 results about "Enhancement Technologies" patented technology

The present application discloses a low-light image enhancement method based on wavelet transform and Retinex-Net, and belongs to the field of image enhancement technology. In order to solve the problems of colour distortion and lack of details such as edges and textures when Retinex-Net processes some images, the present application adds the wavelet transform, fuses the high-frequency components with regional characteristics, and processes only the low-frequency component with Retinex-Net, to retain more details of edges and textures in the image, and transfers the low-frequency and high-frequency components from the HSV space to the value space for value fusion and stretching, to effectively improve the colour distortion of the image, and also improve the expression of details of the image. The present application retains more detail information while avoiding colour distortion, and the enhancement effect is satisfactory.

The invention discloses a building structure crack intelligent detection method, which comprises the steps of S1, constructing a multi-dimensional intelligent sensing array, and obtaining four-dimensional spatio-temporal data including vision, stress, vibration and temperature; s2, performing feature enhancement processing on the multi-modal data; s3, realizing cross-modal crack identification and positioning based on a space-time attention neural network; s4, adopting a sub-pixel edge detection and ultrasonic genetic inversion algorithm; s5, constructing a coupling dynamics prediction model; s6, establishing a dynamic threshold evaluation and multi-dimensional self-calibration mechanism; 0.05 mm micro-crack identification and 3mm depth detection precision are achieved and are improved by 50% compared with a traditional method, the detection accuracy under the complex environment is larger than or equal to 98% through the multi-modal data fusion and GAN enhancement technology, the RMSE is smaller than or equal to 0.03 mm through short-term prediction, the accuracy is larger than or equal to 85% through long-term prediction, double guarantees of a physical mechanism and data driving are established, and the detection accuracy is improved by more than or equal to 98% through the multi-modal data fusion and GAN enhancement technology. And the system has self-calibration, self-adaptive sampling and multi-modal fusion decision-making capabilities, so that the manual intervention cost is greatly reduced.

The invention discloses a quantum dot enhanced dynamic power consumption management method, a quantum dot enhanced dynamic power consumption management system and related equipment. Aiming at the problem of insufficient optimization caused by difficulty in comprehensively, accurately and dynamically sensing the internal energy distribution and conversion process of a chip by a traditional method, the method comprises the following steps of: acquiring the quantum state of the chip in real time by utilizing a quantum dot sensing array, and constructing an adaptive power consumption prediction model by combining multi-source data fusion and feature extraction; and an optimal management strategy is generated based on a multi-objective optimization framework, an execution feedback and self-learning mechanism is introduced, and finally a complete closed-loop control system is formed. The method can be widely applied to various processor chips in the fields of high-performance calculation, the Internet of Things, artificial intelligence and the like, dynamic power consumption management of the chips is achieved through the quantum dot enhancement technology, the performance is guaranteed, meanwhile, energy consumption is minimized, the battery endurance time is prolonged, the heat dissipation cost is reduced, and the energy efficiency of the chips can be remarkably improved.

The invention relates to the technical field of aero-engines, and discloses an automatic design method for aero-engine turbine blades, which analyzes design requirements through a language large model, combines a retrieval enhancement technology and a turbine blade expert knowledge base, performs interdisciplinary coupling verification by utilizing a step-by-step reasoning template, and generates and optimizes part design schemes and parameters. And then calling parametric modeling and an AI simulation agent model to carry out multi-physics field prediction, comparing a result with a requirement, if the result meets the requirement, outputting design, otherwise, automatically iterating and adjusting parameters until the parameters reach the standard or reach the maximum number of iterations. According to the method, end-to-end intelligent closed-loop design from design requirements to design results can be realized, manual intervention is reduced, a design scheme meeting performance requirements can be quickly generated, the design efficiency can be improved, the design cost can be reduced, and the design quality can be improved.

The invention discloses a panoramic video frame insertion method based on a potential diffusion model. The method comprises the following steps: compressing an input image to a potential space by using a panoramic perception vector quantization variational auto-encoder to obtain potential features; constructing initial noise, and fusing the motion features extracted by the panoramic optical flow adapter to obtain condition information; performing iterative denoising operation on the potential features of the intermediate frame to obtain final potential features of the intermediate frame; and restoring the final potential features of the intermediate frame into a frame insertion image of a pixel space through a condition decoder. According to the panoramic video frame interpolation method provided by the invention, the potential diffusion model and the panoramic characteristic enhancement technology are combined, so that the perception quality of a frame interpolation result can be remarkably improved, details and complex textures of a pole region can be better reserved, and a high-quality time interpolation solution is provided for immersive panoramic video application.

The invention provides a cell culture medium aided design system and method based on a large oracle model, and belongs to the technical field of artificial intelligence. A plurality of candidate culture medium formula schemes meeting user requirements are generated by obtaining culture medium design parameters and requirements and designing structured cues and inputting the designed structured cues into a large language model; a substance matched with the structured cue word is retrieved by utilizing a retrieval enhancement technology, a replacement substance is recommended for a user to refer, the user selects and purchases a required experimental material according to own requirements, a candidate culture medium formula scheme is fed back and evaluated in an actual experiment, and the system performs iterative optimization based on the feedback and evaluation, so that the accuracy of the experiment is improved. And a more appropriate candidate culture medium formula scheme is provided. According to the cell culture medium aided design system and method, intelligence, scientization and individuation of culture medium formula scheme design are achieved, experiment cost is reduced, repeatability and stability of cell culture experiments are improved at the same time, and therefore wide application of culture medium design in the field of biological manufacturing is promoted.

The invention discloses a biological fermentation intelligent optimization method based on multi-objective optimization, and the method comprises the following steps: 1) collecting whole-process parameters of a biological fermentation process, including but not limited to raw material parameters, process parameters, regulation and control parameters and result parameters; 2) carrying out data cleaning and preprocessing on the collected parameters, and carrying out data enhancement by adopting a generative adversarial network (GAN) so as to solve the problem of insufficient samples and improve data representativeness; by integrating various advanced optimization algorithms and data enhancement technologies, the efficiency and benefits of the biological fermentation process are remarkably improved. Meanwhile, various control parameters are optimized, and the stability and precision of the process are ensured. Besides, through Pareto frontier analysis, the optimal balance among a plurality of targets can be realized, the production efficiency and the cost are further optimized, and the technical scheme is proved to have remarkable advantages in the aspects of improving the yield, reducing the cost and simplifying the process.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

The invention belongs to the field of computer vision, and particularly relates to an edge detection technology based on Transfomer. Firstly, according to the method, a scale enhancement technology is adopted, and an input image is divided into a plurality of scales with different sizes, so that detail information in the image can be better processed; secondly, according to the method, a feature enhancement technology is adopted, and the features extracted by the network are enhanced, so that feature information is utilized more fully, and the accuracy and robustness of edge detection are improved. And finally, a feature fusion technology is introduced in the method, and features of different scales and different stages are fused, so that the feature expression capability is further enhanced, noise generation is effectively inhibited, and a clear edge is generated. Through comprehensive application of the processing technology, the edge detection method can effectively avoid the problems of loss of partial details of the edge, rough lines, disordered content, noise and illumination interference and the like when a complex background is processed, the accuracy and reliability of edge detection are improved, and meanwhile, the efficiency of edge detection is improved.

The invention discloses a patch position correction method and system, relates to the technical field of biomedical engineering and digital health, and adopts a hardware-level time synchronization and motion-artifact coupling modeling technology to effectively solve the problems of time delay drift and motion interference of signal acquisition in a dynamic environment and remarkably improve the basic quality of electrocardiosignals. Secondly, by establishing a body surface reference coordinate system and a pose compensation parameter generation algorithm, the micro-displacement state of the patch can be accurately recognized, and visual position adjustment guidance is generated, so that the patch is always kept at the optimal measurement position, and the phenomenon of signal attenuation caused by poor contact is fundamentally reduced; a signal quality multi-dimensional evaluation system adapts to quality discrimination requirements in different motion states through an intelligent weighted fusion mechanism, and combines an adaptive filtering and deep learning enhancement technology, so that the morphological integrity of a QRS waveform is kept, various interference components are effectively inhibited, and the system can still keep excellent electrocardiosignals in a strenuous motion state.

The invention belongs to the field of optical transmitter and receiver fault detection, and particularly relates to an optical transmitter and receiver fault detection method, which comprises the following steps: acquiring optical transmitter and receiver data in real time, and preprocessing the optical transmitter and receiver data; inputting the preprocessed data into an LSTM (Long Short Term Memory) model into which an attention mechanism is introduced to obtain dynamic features and static features; fusing the dynamic features and the static features, and inputting the fused features into a Bayesian network to obtain a fault detection result; according to the method, the data enhancement technology is realized, and the training effect is improved by adopting the LSTM model in which the attention mechanism is introduced, the mixed time sequence feature and causal feature model and the Bayesian network.

The invention relates to the technical field of speech enhancement, in particular to an audio optimization method and system applied to speech recognition, and the method comprises the steps: collecting audio data in real time, and uniformly dividing the audio data into audio frames; for each audio frame and each neighbor frame of the preset audio frame, evaluating whether a noise spectrum is re-selected when the audio data is enhanced by adopting a spectral subtraction method; if reselection is carried out, each mode of the audio frame is acquired; selecting a noise mode from the modes of the audio frame, and obtaining the number of lagging frames of other modes; obtaining a frequency spectrum change characteristic value and a voice characteristic value of each mode, and selecting a main voice mode; acquiring audio mode characteristic values of other modes except the main voice mode; and a noise frame and a new noise spectrum are obtained. The invention aims to improve the accuracy of voice recognition by improving the accuracy of noise frame selection and enhancing the audio enhancement effect of the spectral subtraction for the voice features of the audio frames.

The invention provides a vulnerability repair method based on a dynamic denoising and hybrid enhancement technology, which comprises the following steps: S1, performing dynamic denoising and hybrid enhancement on an original vulnerability repair model to obtain an optimized vulnerability repair model; s11, deriving approximate distribution of clean data and noise data from the training loss of the original vulnerability repair model by using an expectation maximization algorithm; s12, calculating the weight of the clean data and the weight of the noise data by using a confidence coefficient function of distribution perception, and re-weighting the approximate distribution by using the weight of the clean data and the weight of the noise data; s13, generating a mixed sample by using the interpolation coefficient and reconstructing a loss function to obtain a total loss value; s14, repeatedly training the vulnerability repair model by using the value of the total loss until the value of the current total loss is not reduced in continuous n rounds or reaches the total number of rounds of training, and outputting the optimized vulnerability repair model; and S2, performing vulnerability repair by using the optimized vulnerability repair model. The software development efficiency and quality are improved, and software vulnerabilities are reduced.

The invention discloses a network security detection system, which comprises an intention perception layer, the intention perception layer is connected with a dynamic immune module, the dynamic immune module is connected with a time management and strategy layer, and the time management and strategy layer is connected with an ethical and transparent decision engine. According to the system, multiple advanced technologies are combined, the network security capability is improved, through deep intention analysis, the system can rapidly recognize complex threats, reduce false alarms and effectively cope with unknown attacks, the self-adaptive immune response function enables the system to dynamically adjust defense strategies according to threat changes, continuous protection is ensured, and meanwhile, the system has a good application prospect. The time-dimension enhancement technology helps to identify advanced threats with a long latency period, the visual interface and report improve the operation convenience, a security team can rapidly understand and respond to the threats, in addition, the modular design ensures the high expansibility of the system, is convenient to upgrade and integrate new functions, and the system has the multiple advantages of accurate detection, rapid response and simple and convenient operation.

The invention discloses a method for intelligently controlling a digital human through an action tag, and relates to the technical field of intelligent control. The method comprises the following steps: S1, determining the range and purpose of actions, classifying the actions, and determining which specific application scenes and target user groups are to be used by an action library; s2, designing a technical label system used for describing various action features and providing a basis for technical action recognition and analysis; s3, establishing a behavior control model of the digital character according to the tagged action description; and S4, through cross validation, dividing the data set into a plurality of subsets for multiple times of training and verification, and adjusting the structure of the model according to an evaluation result. According to the method, the naturalness and fluency of digital character expression can be improved, the flexibility and customization capability of the technology are enhanced, and intelligent action selection and transition are realized, so that better user experience and technical support are provided for various application scenes.

The invention belongs to the technical field of robot and human voice interaction, and particularly relates to a human voice interaction method and system. The method comprises the following steps: step 1, an intelligent agent enables radio duration to realize self-adaption according to environment loudness; 2, designing a multi-agent framework with a large model and a small model cooperating with each other; and step 3, based on the framework designed in the step 2, analyzing the radio in the step 1, and using a retrieval enhancement technology based on a tree-shaped document to improve the question and answer reliability of the intelligent agent and realize the voice interaction of the intelligent agent. The method is used for solving the problems that in the prior art, most voice interaction technologies can only achieve single-round interaction and cannot remember context content of interaction, and pure human voice is difficult to obtain when the traditional voice interaction technology is directly transplanted to a robot which can move and makes noise by itself.

The invention discloses an AI service intelligent fault tolerance and degradation method, device and equipment based on a Java bytecode enhancement technology, a medium and a product, and relates to the technical field of AI service integration. According to the method, in a class loading period of Java application starting, a loaded class is scanned to identify target methods marked with declarative fault-tolerant annotations, a bytecode operation framework is utilized to enhance the target methods so as to implant a unified fault-tolerant logic interception entry, and then in a running period, when the target methods are called, the target methods are subjected to fault-tolerant annotation processing. And triggering a fault-tolerant interceptor through the interception entrance to analyze the annotation to obtain a fault-tolerant strategy, allocating an independent fault-tolerant component instance to the fault-tolerant interceptor based on the unique identifier of the method, constructing and executing an enhanced call chain containing a fusing check and retry mechanism, and finally, if the execution of the call chain fails, executing the fault-tolerant interceptor. And if yes, a preset degradation method in the fault-tolerant strategy is automatically executed, so that complete non-intrusive decoupling of AI service fault-tolerant management and service logic can be realized, and the reliability of the system is remarkably improved.

The invention discloses an electric power system operation maintenance method and system based on a retrieval enhancement technology, and the method comprises the steps: carrying out the weight analysis of each layer of a preset large model in the training process of the preset large model, and carrying out the dynamic freezing of the layers meeting a preset condition in the preset large model, so as to obtain a full-parameter fine-tuning large model; based on a Mangban architecture base retriever, introducing a KAN network to optimize each layer parameter in the base retriever to obtain a first retriever; performing integrated optimization on the first retriever through a gating expert integrated network to obtain a second retriever; thirdly, an enhanced data set is constructed according to a result output by the full-parameter fine-tuning large model, and a second retriever is trained according to the enhanced data set to obtain a target retriever; and integrating the target retriever and the full-parameter fine tuning large model to obtain a target operation and maintenance large model, and executing a matched operation and maintenance strategy according to an operation and maintenance result output by the target operation and maintenance large model so as to maintain the stability of the power system.

Methods for reticle enhancement technology (RET) for use with variable shaped beam (VSB) lithography include inputting a substrate design, wherein a low-pass filter is applied to the substrate design to form a target wafer pattern. An initial mask pattern is determined from the target wafer pattern. An initial set of VSB shots is determined based on the initial mask pattern. A substrate pattern is calculated from a simulated mask pattern calculated with the initial set of VSB shots. The target wafer pattern is compared with the substrate pattern, and the initial set of VSB shots is adjusted until the substrate pattern and the target wafer pattern are within a predetermined tolerance. The adjusting of the initial set of VSB shots creates an adjusted set of VSB shots.

The invention discloses a semi-supervised remote sensing image change detection method and system, and relates to the technical field of image processing, and the method comprises the steps: firstly introducing a channel-level data enhancement technology to improve a strong enhancement effect and improve consistency regularization, thereby solving a problem of insufficient feature representation; secondly, a self-adaptive threshold value is adopted to dynamically adjust the threshold value in the training process so as to balance the quality and the number of the pseudo labels, and therefore the training process is optimized; finally, a self-adaptive category weight mechanism is provided to relieve the influence caused by category imbalance, and the learning ability of the model to change categories is effectively enhanced. Through the above three strategies, the utilization rate of the model for the label-free data set is improved, and the change detection performance of the model is further enhanced.

The invention discloses a deep forged audio identification method based on hybrid enhancement and a contrast loss function. The method comprises the following steps: step 1, preprocessing an original audio training data set by using a sliding window technology and a hybrid enhancement technology; step 2, for the preprocessed original audio training data set, using a contrast loss function to constrain and forge feature vector spacing of real audio training data in a high-dimensional space; and step 3, using a sliding window to divide the audio to be detected into a plurality of audio clips to be detected with the total length of 64600, and if any audio clip to be detected is detected to be forged, determining that the audio to be detected is forged. According to the method, the analysis and identification capability of the algorithm on weak and local counterfeit traces is improved, the accuracy, stability and generalization of the deep counterfeit audio identification algorithm are ensured, the extraction and analysis capability of the algorithm on essential counterfeit traces is improved, and the interference of environmental noise is reduced.

The invention discloses a crop planting scheme generation method and device based on a large language model, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the natural language understanding of to-be-processed agricultural information, obtaining language understanding information, and converting the language understanding information into an information vector; constructing an agricultural knowledge graph based on the to-be-processed agricultural information; fusing the information vector and the agricultural knowledge graph to obtain a fusion result, and determining a retrieval result which corresponds to the agricultural knowledge graph and comprises a target entity and entity relationship path in the agricultural knowledge graph by utilizing a knowledge retrieval enhancement technology and based on the fusion result; determining a target context based on the retrieval result and an initial context corresponding to the large language model, and generating a crop planting scheme for the target context by using the large language model to obtain a target crop planting scheme; the target crop planting scheme comprises the explanation information corresponding to the planting suggestions, so that the efficiency of generating the crop planting scheme is improved.

The invention provides a speech enhancement model training method and device, electronic equipment and a storage medium, and relates to the technical field of speech enhancement. Audio data at least including a first audio including environmental noise and a second audio not including the environmental noise are acquired; extracting a first audio feature of the first audio and a second audio feature of the second audio; performing noise reduction on the first audio feature and the second audio feature by adopting a preset first large model to obtain a first noise reduction feature of the first audio and a second noise reduction feature of the second audio; converting frequency domains of the first noise reduction feature and the second noise reduction feature to obtain a first noise reduction signal corresponding to the first audio and a second noise reduction signal corresponding to the second audio; calculating a loss function between the first noise reduction signal and the second noise reduction signal; and adjusting enhancement parameters of the first large model by using the loss function, and generating a speech enhancement model for eliminating environmental noise in the audio data.

The invention belongs to the technical field of RAG memory enhancement, and particularly relates to a self-adaptive RAG memory enhancement method, which comprises the following steps of: 1, determining an application scene and task requirements; 2, a backbone LLM is initialized and configured; step 3, attribute mining and identification; step 4, attribute labeling granularity selection and labeling; step 5, evaluating and sorting attribute priorities; step 6, constructing memory instance embedding representation; step 7, implementing a retrieval strategy based on attributes and embedding; step 8, integrating and optimizing retrieval results; and step 9, performing experimental evaluation and continuous improvement, namely determining the specific application scene of the self-adaptive RAG memory enhancement technology, retrieving appropriate text content for recommendation according to the theme and requirements input by the user, and determining the specific requirements of the task according to different scenes. According to the method, memory information is enriched by deeply mining and marking key attributes, and meanwhile, two efficient retrieval means are adopted to ensure quick positioning of related information.

The application belongs to the field of chemical mass and heat transfer enhancement technology, and discloses a macro-quantity structured micro-scale vortex packing structure, method and application. The packing structure is a packing with a multi-scale microstructure on the surface. The microstructure causes boundary layer separation and vortex shedding when fluid flows through, generates stable micro-scale vortexes at the microstructure, and forms a macro-distributed vortex group in the packing. The vortex can cut the boundary layer, stretch and fold the fluid micro-element, and promote the periodic update of the interface, thereby improving the gas-liquid mass and heat transfer efficiency. The application significantly reduces the pressure drop. Under the same operating conditions, the pressure drop of the packing structure of the application is reduced by more than 50% compared with the conventional packing, and the pressure drop increment caused by the vortex structure is less than 15%, realizing the synergistic optimization of mass transfer enhancement and low pressure drop.

The invention relates to a layout generation method and device, equipment, a storage medium and a program product. The layout generation method comprises the following steps: acquiring a target graph and target layout precision; determining a numerical value effective standard according to the target layout precision; and based on the numerical value effective standard and the geometric information of the target graph, generating a periodic arrangement graph of the target graph, and obtaining a target layout. According to the method and the device, the periodic arrangement graph with the target layout precision can be generated, so that advanced resolution enhancement technologies such as reverse photoetching technology correction and the like can be conveniently implemented on the full-chip layout.

The present invention discloses a method for constructing an EEG data learning model for de-individualizing differences. EEG data with corresponding calibration values ​​are obtained through preprocessing. Data enhancement technology is used to enhance the calibrated EEG data to prevent distortion while increasing the data volume, facilitating subsequent operations and improving the accuracy of subsequent model operations. A loss function is constructed to obtain a loss baseline value in the calibration process. After obtaining the loss gain coefficient, the EEG data is adjusted. The EEG data is continuously cleaned and adjusted according to the model to present differential integration. After the data is proportionally unified, a data learning model is constructed based on the data after differential integration, thereby removing the differences between the original data and being able to uniformly incorporate the EEG data, so that the error between the output result of the final data processing model and the actual output result is controlled within a small range.

The present invention discloses a testing method and a testing device, relating to the field of computer technology. A specific embodiment of the method includes: receiving a test request, obtaining a to-be-tested application and test cases according to the test request, and adding a Java Agent to the virtual machine parameters corresponding to the to-be-tested application; before loading the class files of the to-be-tested application, modifying the bytecode of the target classes of the to-be-tested application according to the Java Agent and based on the bytecode enhancement technology, wherein the target classes include: classes for remote calls and classes involved in persistent data; during the process of testing the to-be-tested application using the test cases, calling the target methods included in the target classes, and obtaining the running data corresponding to the target methods; determining the test data corresponding to the test cases according to the obtained running data, and storing the test data. This embodiment can conduct tests deep into the program, improve the testing efficiency, have a low testing cost, and is also applicable to the testing of complex scenarios.

This invention discloses a method, apparatus, device, and medium for implementing an intelligent quality valve in the requirement submission process, relating to the field of requirement process quality review in fintech. The method includes: acquiring a requirement description and uploading it to a created large-scale intelligent platform; expanding the document based on comparison with existing similar requirements and performing standardization processing to fill in missing elements, generating a requirement document; acquiring voice data from requirement review meetings and uploading it to the large-scale intelligent platform; analyzing the voice data to generate a requirement review meeting summary and submitting the summary to a DevOps platform; creating a requirement review model, which uses retrieval enhancement technology to acquire private domain data in real time to review the requirement document currently awaiting review. This application enables automated review operations, significantly reducing the burden of manual review and improving the accuracy and efficiency of the review process.

The invention relates to the technical field of PCB (Printed Circuit Board) detection, in particular to a method and a system for detecting the edge deviation of the appearance of a PCB based on a welding component. The polarization characteristic pattern is generated by acquiring the multi-spectral original image and the polarization state parameter of the to-be-detected area of the PCB, the reflection exposure abnormal area and the reflection exposure normal area can be effectively positioned, the fusion weight is automatically adjusted according to the spectral response characteristic of the signal area through the self-adaptive spectrum fusion enhancement technology, and the detection accuracy is improved. The problem that in a traditional method, image enhancement is not fine enough is solved, the adaptive fusion enhancement technology enables the multispectral image to better conform to the actual situation during enhancement, more detail information is reserved, key features can be accurately extracted by generating a semantic segmentation map and combining three-dimensional standard coordinate information, and the image enhancement accuracy is improved. The detection precision and reliability of the welding component and the appearance edge of the PCB are effectively improved, the deviation can be accurately positioned and quantified, and the quality control level in the production process of the PCB is improved.