151results about How to "Improve comparability" patented technology

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information data processing. Combining meteorological data and water quality monitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learning algorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.

The application relates to the technical field of bimetal part manufacturing, and particularly discloses an aluminum-magnesium interface intermediate layer high-flux screening device and method for quantitatively delivering magnesium droplets, which comprises an inert atmosphere experiment cavity; a bearing platform arranged in the cavity and used for fixing an aluminum alloy plate, wherein the bottom of the bearing platform is connected with a two-dimensional moving mechanism and is provided with a heating element; a wire-powder coaxial composite device, which comprises a laser emitter, a beam deflection mirror, a focusing lens and a plurality of independently controlled powder device slots, and a laser beam acts on the surface of the aluminum alloy plate after passing through the focusing lens; a magnesium wire spool and a wire feeding roller, which feeds the magnesium wire from the magnesium wire spool to the heating area of the laser emitter. The device uses a plurality of powder device slots to sequentially deposit a plurality of intermediate layer coatings on the same aluminum alloy plate, and realizes the quantitative delivery of magnesium droplets by combining the quantitative wire feeding and laser heating, so that a large number of aluminum-magnesium interface preparations of different intermediate layers can be completed on the same substrate in parallel, and the screening efficiency and data comparability are effectively improved.

The invention provides a post-retinal detachment reduction body position management method and system and wearable equipment, and relates to the technical field of medical monitoring. The method comprises the following steps: continuously acquiring head motion data through an inertial measurement unit integrated on head-mounted equipment, resolving the data through an embedded processor to obtain a real-time head posture angle, and comparing the real-time head posture angle with a preset rehabilitation position angle range to generate a posture comparison result; meanwhile, neck strain signals are collected through a flexible strain sensor array, and biomechanical characteristic signals are obtained through signal conditioning; and finally, the two types of data are fused, and a judgment result of the consistency of the overall body position of the user is generated through a classification algorithm, so that multi-modal data fusion analysis is realized, the postoperative body position state can be accurately monitored in real time, immediate feedback is provided, and the accuracy of body position management and patient compliance are effectively improved.

The invention relates to the technical field of medical informatics, in particular to a pain management-oriented physiological signal real-time monitoring and intervention system, which comprises a determination module for determining the abnormal tendency of a stimulation window updated in real time at each updating moment to obtain an abnormal tendency sequence; the determination module is also used for determining similar fluctuation segments in the abnormal tendency sequence; the determination module is also used for determining an abnormal process factor of the physiological sensing signal at the current moment based on the abnormal tendency difference value and the length of each similar fluctuation section; the determination module is also used for determining a trigger factor for evaluating the abnormal pain state at the current moment according to the abnormal process factors of each physiological sensing signal at the current moment and the previous moment, and performing normalization processing on the trigger factor to obtain a normalized trigger factor; and the intervention module is used for carrying out visualization and intervention on the pain degree of the target user based on the normalized trigger factor. According to the invention, the physiological signal abnormity identification accuracy and sensitivity are improved.

The invention discloses an S-type pitot tube calibration coefficient prediction method based on CFD, and the method is realized through the following steps: S1, carrying out CFD numerical simulation based on parametric modeling, and obtaining a calibration coefficient set of an S-type pitot tube under different installation angle deviations, flow velocity range conditions and environmental parameters; s2, integrating simulation data, and constructing a feature vector data set of the key influence parameters; s3, dividing the data set into a training set and a test set; s3, based on an RBF algorithm, constructing a high-precision nonlinear prediction model by using the training set; and S4, verifying the performance of the model by using the test set, and judging whether the prediction accuracy meets the expectation or not. A CFD simulation technology and a machine learning algorithm are integrated, the problem that a traditional real flow calibration device is limited in capacity and cannot reproduce on-site complex conditions is solved, and a solution is provided for high-precision prediction and correction of calibration coefficients under on-site complex installation conditions and a non-uniform flow field.

The invention discloses a photoelectric sensor system conversion efficiency calibration system and method based on a reverberation chamber, and belongs to the technical field of photoelectric sensor conversion efficiency calibration, the system comprises an electric wave reverberation chamber, a transmitting antenna, a reference antenna and a photoelectric field sensor system; the photoelectric field sensor system is composed of a photoelectric sensor, a laser source, a photoelectric detector, a polarization maintaining optical fiber and a single-mode optical fiber. In the calibration process, the reference antenna and the photoelectric sensor system are used as receiving ends respectively, receiving power at different stirring positions in the reverberation chamber is sampled to obtain two groups of transfer functions, and the total efficiency of the photoelectric sensor system is determined by comparing the two groups of transfer functions under the condition that the radiation efficiency of the reference antenna is known. The conversion efficiency of the photoelectric sensor system is obtained by combining the relation among the power, the field intensity and the total efficiency in the free space; and the effective averaging of the incident direction and the polarization state is realized by using the uniform and isotropic electromagnetic field characteristics of the reverberation chamber.

The application discloses an analysis method for silicon and manganese alloy element enrichment behavior of hot-rolled plate coil oxide scale, and belongs to the technical field of hot-rolled plate coil surface oxide scale detection and evaluation, comprising: sampling, preparing a sample for a test section, identifying the hierarchical structure of the oxide scale, the oxide scale-matrix interface and the abnormal area, determining the measurement path and / or measurement area across the thickness direction of the oxide scale and the oxide scale-matrix interface; screening and positioning silicon (Si) and manganese (Mn) in the test section, quantitatively performing line scanning and / or area scanning on the test section by using EPMA to obtain concentration distribution data of target elements; establishing a coordinate system, determining the reference concentration of the target elements, and calculating the enrichment index according to the concentration distribution data; comparing the enrichment index with a pre-established pickling defect risk threshold value, and outputting the result. The application solves the problems of insufficient authenticity of the test section, non-uniform measurement path, difficult comparison of the results and insufficient stability of the determination basis in the prior art.

The invention discloses a financial analysis report generation method, and relates to the technical field of natural language processing and information extraction, and the method comprises the steps: enabling natural language analysis to be a core position before generation, carrying out the differential analysis of a criterion text, and extracting the constraints such as a disclosure item, a metering caliber, a period, a unit and currency; then, structured and normalized alignment with multi-source heterogeneous financial data is completed, so that the response of the system to external criterion change does not depend on a large amount of instant annotation or frequent whole-model fine tuning any more; on the basis of a consistency verification score and an adaptive threshold value calculated by a constraint graph, rigid violation and tolerable deviation are distinguished and can be quantitatively positioned, and all-link backtracking of evidence anchor points is matched to ensure that each paragraph and each conclusion have audible sources, so that compliance risks caused by outdated calibers and disclosure missing are remarkably reduced.

The invention relates to the technical field of gas detection, in particular to a gas recognition device, a gas recognition method and an electronic nose system.The gas recognition device comprises a constant-temperature gas circuit unit, a sensor array, a calibration unit and a data processing unit; the constant-temperature gas circuit unit is used for receiving to-be-tested gas and providing a temperature-controllable testing environment for the to-be-tested gas; the sensor array is used for acquiring information of to-be-detected gas and converting the information into an electric signal; the calibration unit is connected with the data processing unit and is used for generating a reference signal, transmitting the reference signal to the data processing unit and providing a calibration reference for the electric signal of the sensor array; the data processing unit is connected with the constant-temperature gas circuit unit and is also connected with the sensor array; and the data processing unit is used for controlling the formation and switching of the test environment, receiving the electric signal and identifying the information of the gas to be tested.

The invention discloses a heating and ventilation outdoor unit health degree prediction method based on infrared imaging of an unmanned aerial vehicle, and particularly relates to the technical field of heating and ventilation outdoor unit health degree prediction. Comprising the following steps: acquiring a first infrared temperature frame and a second infrared temperature frame which are continuously acquired according to an air route in the same inspection task, and a first visible light frame and a second visible light frame which are respectively aligned in time, and outputting an inspection frame set; based on two frames of visible light, an outdoor unit start-stop state, a fan gear state and a spraying partition start-stop state are extracted to form a two-time operation element set, consistency verification is carried out with the condition that the three states are consistent one by one as a contrast establishment condition, a supplementary collection mark is output and recollection is triggered if the condition does not meet the condition, and a contrast failure mark is still not met if the preset number of times of recollection is exceeded. The output contrast frame pair is satisfied; the method comprises the following steps: constructing a contrast frame pair which meets the requirement of consistent operation elements in the same inspection task, and executing combined processing of eliminating operation disturbance terms and writing degradation deviations into digital twin state records on partition response differences.

The application provides a three-dimensional reconstruction and quantitative analysis consistency control method and system of multi-center CT blood vessel imaging data, an electronic device and a storage medium, and relates to the technical field of medical image processing. The method comprises the following steps: first, synchronously acquiring original projection data of multi-center CT blood vessel imaging, blood vessel center line topological data and blood vessel respiratory motion displacement data, generating a blood vessel deformation correction matrix based on the respiratory motion displacement data, and dynamically registering the blood vessel center line data by using the matrix to obtain corrected center line data; then, inputting the corrected center line data and the original projection data into a multi-modal feature fusion network for three-dimensional reconstruction to generate an initial blood vessel model; then, performing morphological parameter quantitative analysis on the initial model; finally, comparing the analysis result with a clinical standard and adjusting the model parameters to output a target blood vessel three-dimensional model. The application improves the geometric accuracy and clinical applicability of the blood vessel model reconstruction.

The invention discloses an under-crown temperature inversion method and system and application, and relates to the technical field of forest ecology and microclimate inversion, and the under-crown temperature inversion method comprises the following steps: obtaining flux tower observation data; constructing and training a baseline prediction model to obtain a baseline prediction value of the vertical temperature difference; constructing and training a gating correction function based on the stability index; adding the baseline predicted value and the output value of the gating correction function to obtain a final predicted value of the vertical temperature difference; and obtaining the under-crown temperature through inversion by subtracting the final predicted value of the vertical temperature difference from the on-crown temperature. The invention provides a flux tower scene-oriented under-crown temperature inversion method and system and application, and the method comprises the steps: reconstructing a vertical temperature difference through employing the conventional environment elements of a flux tower and carrying out the inversion of the under-crown temperature under the condition of lacking of the on-crown / under-crown surface temperature or lacking of temperature profile observation; therefore, the availability, comparability and interpretability of the microclimate data of the site are improved.

This invention discloses a method and system for embryo quality detection, relating to the field of embryo detection technology, including the following steps: S1: fertilized embryos are continuously cultured in an in vitro culture system containing specific metabolic marker detection components, and continuous dynamic imaging of the embryos is performed to obtain a complete dynamic developmental image sequence; S2: the dynamic developmental image sequence is segmented, and temporal developmental features are captured, identifying key time points such as pronucleus appearance and disappearance, cleavage, and blastocyst formation, and generating a segmentation mask corresponding to each frame of the dynamic developmental image sequence; this invention achieves full-process time-difference imaging to capture the temporal features of embryonic development, without trauma or interference, avoiding subjective bias in static observation; by segmenting the dynamic developmental image sequence and capturing temporal developmental features, the segmentation accuracy is high and the anti-interference ability is strong.

The application relates to the technical field of active sonar detection, in particular to a transmission waveform selection method for target wake field disturbance detection, which comprises the following steps: applying uniform engineering constraints to selected engineering realizable waveforms to construct a candidate transmission waveform set of a detection system; equivalent the influence of target wake field disturbance on sound propagation to a time delay perturbation process of multi-path propagation, and establishing a target wake field disturbance sensitivity model accordingly; using the target wake field disturbance sensitivity model, calculating the sensitivity indexes of each candidate transmission waveform in the candidate transmission waveform set and sorting, taking the candidate transmission waveform with the largest sensitivity index as the optimal transmission waveform most suitable for target wake field detection; instructing the transmission end of the detection system to transmit a sound wave signal based on the optimal transmission waveform. The method can improve the observability of weak propagation time-varying characteristics caused by wake disturbance, and make the selection of the transmission waveform have stronger explainability and scene adaptation ability.

The application belongs to the technical field of computer hardware testing, and discloses a multi-sensor integrated detection dock system for notebook computer performance testing, which comprises a multi-sensor module, a data acquisition and interface module, a main control module and a hardware component. The application has the following remarkable advantages: high integration and improved efficiency; data synchronization precision and improved analysis value; high automation degree and reduction of human error; consistent test conditions and strong result comparability.

The present application relates to a tire rubber performance detection method, and particularly relates to a method and system for measuring a low-temperature network dissipation index for characterizing the low-temperature rolling energy consumption tendency of rubber tire tread rubber. The present application performs a strain amplitude scanning under constant frequency at T0+ / -DeltaT three temperature points by RPA / DMA, calculates the area type Payne value A(T) of the logarithmic strain coordinate and the temperature derivative thereof at T0, and outputs an index; the mixed area can be normalized or G'' is introduced. Through regression calibration k of the whole tire rolling resistance according to ISO 28580 and a threshold value, fast, comparable and quantifiable prediction of low-temperature energy consumption of the material grade is realized, and the present application is suitable for formula development, incoming material release and process control.

The invention discloses a calligraphy stroke feature extraction method and system based on a fixed-size grid. The method relates to the technical field of stroke feature extraction, and comprises the following steps of calligraphy stroke image preprocessing and grid division, grid-based stroke space distribution feature extraction, size feature extraction, grid-based stroke direction feature extraction and feature fusion and output. The method comprises the following steps: S1, normalizing a binary image of calligraphy strokes to a fixed canvas and dividing the binary image into grids; s2, calculating the stroke density of each grid and constructing a density feature vector; S3, refining the image to obtain a skeleton, calculating a direction field and generating a direction feature vector; and finally, the two vectors are connected in series and fused, and a joint feature vector is output for a subsequent task, so that the calligraphy stroke feature extraction accuracy is improved, and the problem of low calligraphy stroke feature extraction accuracy caused by insufficient understanding of dynamic essence, complex form and high-level semantics of calligraphy art in the prior art is solved.

This invention belongs to the field of medical image processing technology, specifically a system for aortic motion feature extraction and disease risk prediction based on medical image analysis. The system includes modules for acquiring multi-temporal image sequences and clinical data, dynamic segmentation of aortic anatomical structures, construction of a dynamic relative coordinate system, trajectory preprocessing and standardization, trajectory segmentation analysis based on physiological phases, multidimensional dynamic feature extraction, and risk stratification prediction model construction. This invention integrates multidimensional motion features with clinical indicators to construct a risk model based on machine learning or statistical regression, outputting a predicted value for the individual's cardiovascular disease risk. This invention can reduce global interference caused by respiratory and cardiac translation, improve the biological specificity and inter-individual comparability of features, achieve fully automated processing, and enhance the risk prediction ability for cardiovascular events such as heart failure, atrial fibrillation, and aortic disease, showing promising application prospects.

The invention discloses a cultivated land non-agrochemical intelligent monitoring method and system based on multi-temporal SAR and optical image fusion, and belongs to the technical field of remote sensing monitoring and land resource management. The method solves the problems of information loss and misjudgment caused by cloud and mist shielding or noise interference of a single remote sensing data source. According to the scheme, the method comprises the following steps: acquiring and preprocessing a multi-temporal SAR and an optical image sequence; carrying out space registration; constructing a double-branch deep neural network, respectively extracting radar scattering features and spectral texture features, and fusing the radar scattering features and the spectral texture features by adopting a cross attention mechanism; generating a change intensity graph based on a time sequence change detection module; and extracting non-agrochemical suspected pattern spots through threshold segmentation, and aggregating and outputting a final monitoring result. The method is mainly used for realizing the automatic and high-precision monitoring of the non-agrochemical change of the cultivated land.

A kind of submarine landslide disaster chain test system and monitoring method for realizing bottom erosion belong to the field of marine engineering, geotechnical disaster physical simulation and test technology, including water tank main module, source item generation module, bottom erosion-shallow buried pipeline response module and multi-source monitoring and synchronous acquisition module.Firstly, the conditions of setting water depth, flow rate and tank body inclination are formed by water tank main body and circulating water supply module;Secondly, the underwater slope sliding or landslide material continuous release boundary is formed by source item generation module, and the landslide material moves along the water tank and then enters the erosion reaction zone, which produces scouring, erosion and bed surface incision effect on the bottom model;At the same time, the overlying water body is disturbed by the movement of landslide material and the process of bottom erosion, and surge response is formed;Finally, synchronous monitoring and unified analysis are carried out by multi-source monitoring and synchronous acquisition module.The present application can realize the test simulation of submarine landslide-bottom erosion damage-shallow buried pipeline response-surge response disaster chain process.

This invention discloses an adaptive thermally excited infrared thermal wave nondestructive testing method and device. The method includes: querying a pre-stored database based on the scanned position signal to obtain the theoretical standard energy distribution spectrum under the current pose; triggering pre-aiming sensing; acquiring and generating the actual pre-aiming energy distribution spectrum; comparing the two to calculate the actual energy non-uniformity, and comparing it with a preset threshold to decide whether to initiate compensation; driving the actuator to perform physical adjustments and confirming the adjustments; and finally synchronously triggering the main thermal excitation and infrared thermal image acquisition. The device includes a scanning motion and pose feedback module, a high-precision multispectral pre-aiming sensing module, an intelligent decision-making and synchronous control module, a finely adjustable main excitation execution module, and a data management and communication interface module. This invention can proactively correct the uneven excitation energy distribution caused by scanning geometric changes from the physical source, significantly improving the quality and reliability of the detected image.

This invention relates to the fields of medical data analysis and artificial intelligence, proposing a dual-latent variable decoupled data analysis method based on plasma Netrin-1 and clinical scale features. First, plasma Netrin-1 test data and clinical characteristics such as UPDRS scale scores and disease duration are acquired from the subjects. Batch effect correction is applied to the raw test values, and an input feature vector is constructed. Then, a variational autoencoder model is established. By setting orthogonal constraints, Netrin-1 regression constraints, and disease duration regression constraints, the latent space is structurally constrained, enabling the model to learn latent variables D representing disease progression and C representing adaptive changes during training. Based on this, an efficiency index η composed of latent variables C and D is calculated, and relevant trend indicator parameters are obtained by combining longitudinal follow-up data. This method can achieve decoupled representation of disease-related features and adaptive change features in multi-source clinical data, providing a new data analysis tool for statistical analysis and research of neurodegenerative disease-related data.

The invention relates to a system and method for testing the high-temperature creep performance of a thermoplastic non-crosslinked insulating material, and relates to the technical field of cable material performance test.The system comprises a temperature control box, a sample bearing frame and a pressure loading mechanism; the temperature control box body is provided with a test chamber which can be closed, a temperature control module is arranged on the temperature control box body, and the temperature control module is used for controlling the temperature of the test chamber and realizing air circulation in the test chamber; the sample bearing frame is fixedly arranged in the test chamber and is used for horizontally bearing at least one sample to be tested; the pressure loading mechanism comprises a pressure applying unit and a weight assembly, the pressure applying unit is arranged on the upper side of the sample bearing frame, and the weight assembly is connected with the pressure applying unit, so that the pressure applying unit applies constant vertical acting force to the surface of the to-be-tested sample. Through a high-precision temperature control and forced circulation system integrated by the temperature control box body and the temperature control module, the temperature fluctuation and gradient in the box are effectively reduced, a more stable thermal environment is provided, and a test temperature field is more uniform.

The invention discloses a traditional Chinese medicine residue sorting and quality grading method based on machine vision recognition. The method comprises the steps that an image sequence is collected, and exposure, white balance gain and light source driving quantity are recorded; a standardized image sequence is obtained through preprocessing, and a working condition drift tensor is constructed through batch difference; segmenting the target area, constructing an equivalent refraction-scattering weight map based on a particle size distribution parameter, a fiber orientation anisotropy parameter and a water-containing scattering indication parameter, and generating a physical token sequence; adopting improved VMama asymmetric scanning to obtain an anisotropic state characterization sequence; a prediction entropy is calculated, a physical consistency loss is constructed through a working condition drift tensor alignment residual error, and a self-adaptive normalized parameter set is obtained through TENT online updating; and reasoning to obtain a target sorting category and a target quality grade, and generating a shunting control instruction and grading record data for sorting and grading of a conveying line. According to the method, unmarked cross-working-condition sorting and consistent grading are achieved.

The present application relates to a kind of creep-fatigue test methods in molten salt environment, it includes to be measured alloy material and is processed into integrated molten salt tank smooth bar sample;To the molten salt tank is filled with molten salt, so that sample gauge section is completely immersed in molten salt;The sample of assembly completion is transferred to fatigue testing machine and installs clamp and measuring device;Test container is evacuated and filled with inert gas, and temperature is raised to test temperature;Using axial strain control mode, trapezoidal wave cyclic load is applied to sample, only at the tensile peak strain setting, the test process is real-time collected test data, until sample reaches failure judgment condition.The present application relies on integrated molten salt tank structure and exclusive loading waveform, so that sample is continuously in molten molten salt environment, can simultaneously carry out creep, fatigue, corrosion coupling test, truly restore material service condition, effectively make up the defect that traditional test cannot simulate multiple damage synergism.

The application discloses a function function and requirement consistency verification method and system, and the method comprises the steps of obtaining a function function requirement document described in natural language, parsing the function function requirement document based on a requirement document analysis model, generating corresponding requirement semantic vectors, and forming a requirement semantic vector set; obtaining a to-be-tested function source code, performing static analysis on the to-be-tested function source code, constructing an intermediate representation, determining a plurality of code segments based on the intermediate representation, generating a code semantic vector corresponding to each code segment, and forming a code analysis result; determining a candidate matching relationship between requirement constraint units and code segments based on the vector space similarity between the requirement semantic vector set and the code semantic vector set, performing logical implication checking based on the candidate matching relationship, and forming a consistency comparison result; and determining the deviation type and deviation code segment range between the function function requirement document and the to-be-tested function source code based on the consistency comparison result.

The invention provides a strabismus screening system based on multi-dimensional dynamic eye movement tracking. The strabismus screening system comprises a data acquisition module, a data preprocessing module, a data analysis module and a result feedback module. A cartoon experimental normal form is adopted for an experiment through a domestic Pupil.IO AIO eye tracker, so that the eye movement track is monitored, the stability of a fixation point is dynamically quantified, the dynamic change during jump reaction is captured, multi-dimensional eye movement feature calculation such as sight line difference analysis is carried out, and finally dichotomy result judgment is carried out. The problems of inherent limitation, high equipment price, insufficient clinical suitability of a data analysis model and lack of a standardized process of a traditional inspection method are effectively solved.