48 results about "Identification criteria" patented technology

The invention provides a flying dust online detection method and system based on laser, and the method comprises the following steps: obtaining laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data before and after the abnormality when the flying dust concentration of a flying dust monitoring region is monitored to be abnormal; calculating laser scattering energy and a particulate matter concentration change rate to obtain a flying dust concentration change coefficient; and judging an abnormal type according to the change coefficient and an abnormal identification criterion, and if the flying dust concentration in the region exceeds the standard, starting an early warning action to realize management of the flying dust monitoring region. According to the invention, the abnormal type of dust raising can be accurately identified, early warning is started in time, and the environment management and control capability of a dust raising monitoring area is improved.

The invention relates to the technical field of load management, in particular to a model and data driving combined user side load non-intrusive identification method. Comprising the following steps: extracting steady-state features and transient-state features of typical electric equipment; constructing a user side load non-intrusive identification architecture based on decision tree classification; the invention discloses a user side load non-intrusive identification algorithm based on multi-feature fusion. According to the design of the invention, through extraction of steady state features and transient state features of typical electric equipment, multi-dimensional feature quantity identification is carried out on load samples based on a decision tree classification criterion, and non-intrusive load identification is realized through feature quantity matching of similar loads; an identification criterion based on decision tree classification is established, and various types of electric equipment can be quickly decomposed from a user sample; the steady-state and transient-state multi-feature fusion of the electric equipment is applied to a load non-intrusive identification algorithm, so that the accuracy of the algorithm can be improved, and the calculation amount and time of the algorithm can be greatly reduced.

The invention relates to the technical field of agricultural microbial monitoring, in particular to a microbial action monitoring and analysis system which comprises a pollution detection module, a distribution recognition module, a state analysis module, a period extraction module and a node traceability module. According to the method, a pollution identification basis is constructed based on a multi-channel conductance deviation trend and a synchronization feature, flora distribution information is established by introducing a feature grouping mode of image texture, gray level and structural density, and consistency judgment is performed by combining direction deviation of three types of response data of conductance, gas release concentration and pH; the continuity and the staged response precision of microbial function state recognition are enhanced, and meanwhile, a linked list structure index path is constructed according to sampling time, position and channel sequence, so that a continuous mapping process can be formed by a front-back sequence and behavior transition relation of a state period; the structure expression capability and the rhythm construction efficiency of the multivariable monitoring data in the microbial action stage analysis are improved.

The invention discloses an underground cave depot slurry diffusion and solidification state space-time identification method and system, and belongs to the technical field of underground cave depot grouting, and the method comprises the steps: taking an on-site grouting influence region as a monitoring region, and detecting the apparent resistivity of the monitoring region at different grouting stages in real time in a cave depot grouting process; drawing visual cloud charts of the apparent resistivity of the monitoring areas at different grouting stages; and based on the visual nephograms of the apparent resistivity of the monitoring areas at different grouting stages and in combination with a preset grout solidification degree judgment criterion, judging the grout diffusion range, the grout solidification degree, the grout solidification time and the grouting water plugging effect. According to the scheme, the slurry diffusion range, the solidification degree, the solidification time and the grouting water plugging effect in the cave depot grouting process can be intelligently and accurately judged, so that monitoring of the grouting process and improvement of the grouting effect are achieved, the grouting precision and efficiency are improved, and the grouting cost is reduced.

The invention discloses a geological comprehensive information analysis system and method for gold mine exploration, and relates to the technical field of information analysis, and the analysis method comprises the following steps: extracting the geological type and geological features of an exploration region, and generating a corresponding collection record; carrying out quality evaluation on the geological information which is randomly collected and recorded; performing comparative analysis on any two acquisition records with the same geological type to obtain difference features, and performing deviation degree analysis on each difference feature; constructing an anomaly evaluation model and setting an anomaly identification standard; performing geological feature extraction on geological information acquired in real time in the exploration area to generate a real-time acquisition record; performing anomaly identification on the presentation condition of each geological feature; by performing multi-dimensional analysis on the geological information collected in the exploration process, errors existing in traditional single index judgment can be eliminated, quality judgment can be more accurately and effectively performed on the geological information, and the exploration efficiency is improved.

The invention discloses an illumination temperature control transmitter monitoring system and method, and relates to the technical field of transmitter monitoring, and the method comprises the steps: constructing a multi-feature extraction and classification mechanism based on an instantaneous fluctuation recognition criterion, and dividing the operation state into three types: a stable type, a critical fluctuation type and a severe abnormality type, therefore, a standby acquisition channel can be started for critical fluctuation to perform high-speed sampling, data aligned and fused through a time axis is generated, a distorted or drifting section in a main channel is replaced, continuity and accuracy of monitoring data are ensured, fused monitoring data and a reference operation model are continuously compared, fitting analysis is performed on an error trend, and the accuracy and accuracy of the data are ensured. According to the method, the monitoring threshold value and the response delay can be dynamically adjusted, differential control aiming at different fluctuation levels can be realized, execution of high-priority alarm or intervention instructions can be delayed in an acceptable fluctuation range, and during the period, the acquisition and processing control parameters are dynamically adjusted to inhibit fluctuation diffusion, so that the operation stability of the illumination temperature control transmitter is further improved.

The invention discloses a tunnel unfavorable geologic body catastrophe identification method based on an alteration geological evolution sequence, and relates to the technical field of tunnel disaster prevention and reduction, and the method comprises the steps: carrying out the comprehensive advanced geological forecast of an unexcavated section of an excavated tunnel where an unfavorable alteration geologic body is found until a geophysical prospecting abnormal region is found; recording measuring points are arranged in the tunneling direction of the side wall of the excavated section of the tunnel, the three-dimensional coordinates of the measuring points and the tunnel mileage are positioned, and recording data are recorded; carrying out structural plane plotting, rock strength test, underground water state measurement and mineralogy test at the catalog measuring point to obtain in-situ test data; enabling the catalog data to correspond to the in-situ test data according to the tunnel mileage, extracting an undesirable alteration quantitative index, and drawing an alteration evolution sequence diagram; and constructing a bad alteration identification criterion, and if the identification criterion is met, performing trend extrapolation on the bad alteration quantitative index, and constructing a catastrophe evaluation system. According to the method, the engineering attributes and catastrophe of the poor alteration geologic body in front of the tunnel face can be effectively identified.

This invention discloses a wearable intelligent joint range of motion measurement device for forensic clinical identification, belonging to the field of forensic identification equipment technology. The device includes a wearable fixation component, a posture sensing unit, a data processing unit, a storage and interaction unit, and a power supply unit. It synchronously collects limb posture data from both sides of the joint using dual inertial sensors, calculates high-precision joint range of motion, and automatically matches the data using a built-in disability assessment standard library to output a preliminary suggestion for the disability level. This invention offers objective measurement, convenient wear, and traceable data, specifically addressing the problems of large errors, low efficiency, and susceptibility to subjective interference in traditional forensic manual measurements. It is suitable for limb injury disability assessment and possesses strong practicality and professionalism.

The application discloses a method and device for identifying gas-bearing reservoirs, and the method comprises the following steps: establishing a reservoir longitudinal identification criterion according to well logging data and seismic data of a study area; extracting a plurality of seismic attributes from each seismic survey line in two-dimensional seismic data of the study area; determining optimal seismic attributes from the plurality of seismic attributes through comparative analysis; and determining a favorable reservoir distribution area according to the reservoir longitudinal identification criterion and the optimal seismic attributes.

The present invention discloses a method for identifying the fault risk mode of a motor shafting based on the interval evidence fusion of a normal cloud model. This method is based on the analysis of typical fault data to construct cloud models of various fault template modes; according to the data of different fault characteristics of the online monitored motor shafting, a cloud model of the fault to-be-detected mode is constructed; the cloud model of the to-be-detected mode is matched with the cloud models of various fault template modes to obtain the matching degree interval of the to-be-detected mode for different fault template modes; the obtained matching degree interval is normalized to obtain the interval evidence for fusion; the interval evidence is fused, and according to certain identification criteria, the fault risk mode is determined. The present invention utilizes the advantages of the normal cloud model in describing the fuzziness and randomness of objective objects, and obtains more accurate interval evidence by constructing the cloud models of the to-be-detected mode and the fault template mode, so that the identification result of the fault risk mode obtained based on the interval evidence fusion is more in line with the actual situation.

The application provides a rock slice image data management method, device and equipment, and relates to the technical field of geological exploration. The method comprises the following steps: acquiring image data and original identification data of a rock slice to be managed, which have first annotation information; identifying key features of the rock through machine learning based on the first annotation information and an identification standard to obtain second annotation information, wherein the second annotation information contains information carrying a first annotation information label; preliminarily checking the original identification data by using the second annotation information carrying the first annotation information label to obtain a checking result and annotation deviation information; performing secondary checking on the original identification data by using the second annotation information not carrying the first annotation information label to obtain annotation missing information; and managing the image data based on the checking result, the annotation deviation information and the annotation missing information in combination with double-expert collaborative identification. The application can improve the accuracy of rock slice image data management.

The invention provides a mining face coal wall caving monitoring and early warning method based on multi-modal information. The mining face coal wall caving monitoring and early warning method specifically comprises the steps that a high-definition image of a coal face coal wall is collected, and environment noise and interference of the image are removed; establishing a mining face fracture and face guard image segmentation data set, training a Mask R-CNN-based instance segmentation algorithm, and automatically segmenting key areas such as a coal wall fracture and a face guard by the algorithm; establishing a coal wall caving identification criterion based on the area of the face guard plate, the coal wall crack and the electric signal of the hydraulic support; calling the electric signal of the hydraulic support and judging the supporting condition of the hydraulic support; extracting different instance face guard plate areas under the stable supporting condition, and calculating the area of the areas; acquiring a coal wall fracture segmentation condition; the coal wall caving state is judged according to the multi-modal information and the judgment rule, and early warning of coal wall caving is achieved; and when the risk of wall caving of the coal wall is monitored, a real-time alarm is pushed to remind miners or related workers to carry out further inspection or take protective measures.

The invention discloses a method for counting the number of wild rock sheep based on a microsatellite molecular marker. The method comprises the following steps: preprocessing a rock sheep manure sample and extracting DNA (Deoxyribonucleic Acid); performing double PCR amplification on the microsatellite marker, wherein each group of microsatellite primers comprises two pairs of specific primers; carrying out electrophoresis separation on a PCR product by using 12% polyacrylamide gel, and reading the size of an allele fragment of each microsatellite site through GeneMapper software after silver staining and developing; analyzing the acquired allele data by using software, and counting the number of different wild rock sheep individuals in the region according to an individual identification standard; the individual identification standard is as follows: if the genotypes of all 10 microsatellite loci are completely the same, or only one allele of one locus is different, the same individual is determined. The method is particularly suitable for non-invasive individual identification, genetic relationship identification and population quantity statistics of plateau wild rock sheep populations, and provides technical support for rock sheep resource protection and dynamic monitoring.

To prevent invasion of privacy of a subject that may appear in a captured image when an aircraft takes an image during flight. [Solution] The information processing device 1 has an acquisition unit 131 that acquires captured images taken by an imaging unit equipped in the aircraft while the aircraft is in flight; a judgment unit 133 that determines whether a specified object is captured in the captured image while the aircraft is in flight; a decision unit 134 that determines an identification criterion that serves as a standard for whether a specific part of the object can be identified, and that indicates that the specific part can be identified from the captured image provided that the identification criterion is satisfied; and an aircraft control unit 132 that controls the aircraft so that the identification criterion is not satisfied, provided that the judgment unit has determined that an object is captured in the captured image.

The present invention provides a PRI estimation and pulse sequence extraction method and device, belonging to the field of radar signal sorting. The method comprises the following steps: obtaining a TOA sequence and a preset PRI value range of a pulse stream; calculating a PRI spectrum based on the TOA sequence, comparing the amplitude of the PRI spectrum with a detection threshold, and obtaining a potential set of non-jittered PRI values; segmenting the TOA sequence, calculating the amplitude of the PRI spectrum for each segment of data, and superimposing the PRI spectrum to obtain a segmented superimposed PRI spectrum, which is then compared with the detection threshold to obtain a potential set of jittered PRI values, and determining a current round of estimated PRI values ​​based on a PRI value identification criterion; and separating corresponding sequences of the estimated PRI values ​​using an improved DAG algorithm. The present invention accurately estimates potential PRI values ​​in a mixed radar signal based on the PRI spectrum, and the PRI spectrum has strong robustness against pulse omissions, false pulses, and noise uniformity, and has good adaptability to different types of PRIs.

This invention discloses a method for identifying catastrophic changes in unfavorable geological bodies in tunnels based on alteration geological evolution sequences, relating to the field of tunnel disaster prevention and mitigation technology. The method includes: conducting comprehensive advanced geological forecasting on the unexcavated section of an excavated tunnel where unfavorable altered geological bodies have been discovered, until a geophysical anomaly zone is found; setting up logging points along the excavation direction of the tunnel sidewalls of the excavated section, locating the three-dimensional coordinates of the logging points and the tunnel mileage, and recording the logging data; conducting structural surface mapping, rock strength testing, groundwater state measurement, and mineralogical testing at the logging points to obtain in-situ test data; correlating the logging data with the in-situ test data according to the tunnel mileage, extracting catastrophic alteration quantification indicators, and drawing an alteration evolution sequence diagram; constructing an identification criterion for unfavorable alteration bodies; if the identification criterion is met, extrapolating the trend of the catastrophic alteration quantification indicators to construct a catastrophic evaluation system. This invention solves the problem of effectively identifying the engineering properties and catastrophic changes of unfavorable altered geological bodies ahead of the tunnel face.

The invention discloses a numerical simulation-based reinforced soil retaining wall damage simulation mode discrimination method, and relates to the technical field of civil structure safety prediction, and the method comprises the steps: collecting three-dimensional numerical simulation data, simulating the interaction between a soil body and a reinforced material, setting a boundary condition and a loading condition, and generating a standard simulation data set; identifying a working condition boundary condition and a working condition loading mode from the standard simulation data set through an adaptive density clustering algorithm, obtaining an enhanced simulation data set, and performing data fusion on the enhanced simulation data set and the standard simulation data set to generate an extended simulation data set; and according to the damage development trend and the real-time environmental condition change, carrying out dynamic optimization adjustment on the damage mode discrimination standard, carrying out damage mode classification through a new damage mode discrimination standard, and generating a damage mode discrimination report. According to the method, the precision and reliability of failure mode prediction are improved, and effective technical support is provided for failure detection and early warning in a complex soil body structure.

The invention discloses a threshold determination and identification method suitable for power semiconductor bonding wire fracture identification. The method comprises the following steps: counting an average value of total resistance of a bonding wire and a power source lead terminal of a brand new power semiconductor; aiming at the conditions that any one bonding wire is broken, any two bonding wires are broken and any three bonding wires are broken, counting the average value and the standard deviation of the total resistance change values of the bonding wires under the three conditions; determining a probability density function of a change rate threshold value of three aging values relative to a reference value based on an average value of total resistance of a bonding wire and a power source lead terminal of the brand new power semiconductor and an average value and a standard deviation of total resistance change values of the bonding wire under three conditions; and three change rate thresholds are determined through a probability density function. According to the invention, the accuracy of the identification criterion can be effectively improved, and the online identification of the fracture condition of the bonding wire can be realized.

The invention relates to the technical field of power systems, and discloses a Saggob base group extreme turning weather output characteristic and risk assessment method, which comprises the following steps: constructing a multi-source data fusion and extreme scene identification criterion system, and constructing a multi-dimensional meteorological factor matrix; establishing a plurality of extreme meteorological scene criteria; constructing an output factor matrix, setting a low output coefficient threshold value and a ramp rate threshold value, and establishing an output abnormity criterion; identifying a plurality of predefined extreme turning weather scenes; constructing a spatial weight matrix; and performing time autocorrelation function and partial autocorrelation function analysis on the occurrence sequence of each base in each extreme meteorological scene by using the spatial weight matrix and the output factor matrix, and constructing a multi-dimensional risk indicator system. The method has the advantages that a meteorological criterion-output response-space-time analysis-risk assessment framework is constructed, and the problems of inaccurate extreme weather output characteristic capture, insufficient space-time association description depth, single risk assessment dimension and the like are solved.

The invention relates to the field of power system relay protection, and provides a transformer inrush current identification method and system based on sampling value image space distribution characteristics, a voltage and current sampling sequence is introduced, a # imgabs0 # sampling value image space distribution concept is defined, image space distribution characteristics of # imgabs1 # sampling values of a transformer under different working conditions are analyzed, and the transformer inrush current identification method and system based on the sampling value image space distribution characteristics are obtained. According to the symmetry and the shape difference of image space distribution, a transformer inrush current identification and closing fault identification criterion based on # imgabs2 sampling value image space distribution characteristics is provided, the current type of a transformer is identified according to the identification criterion, different operation conditions of the transformer are set respectively to carry out example simulation on the provided method, and the method is used for identifying the inrush current of the transformer and identifying the inrush current of the transformer according to the shape difference of the # imgabs2 sampling value image space distribution characteristics. The result shows that the identification method is accurate and reliable, the inrush current can be effectively identified, and differential protection misoperation is prevented.

This invention discloses a method for identifying failure modes of reinforced soil retaining walls based on numerical simulation, belonging to the field of civil structure safety prediction technology. The method includes: acquiring three-dimensional numerical simulation data, simulating the interaction between soil and reinforcement materials, setting boundary conditions and loading conditions, and generating a standard simulation dataset; identifying the boundary conditions and loading methods from the standard simulation dataset using an adaptive density clustering algorithm to obtain an enhanced simulation dataset; fusing the enhanced simulation dataset with the standard simulation dataset to generate an extended simulation dataset; dynamically optimizing and adjusting the failure mode identification criteria based on the failure development trend and real-time environmental changes; classifying failure modes using the new criteria; and generating a failure mode identification report. This invention improves the accuracy and reliability of failure mode prediction, providing effective technical support for failure detection and early warning in complex soil structures.

Techniques for reducing resources used for uploading a video to be indexed for searching at a cloud search service are provided. A file containing the video is received at an on premise client application. The application processes the file with an event detector to detect at least one event of interest based on a first set of event detection criteria. The at least one event of interest is sent to the cloud searching service. The cloud searching service classifies at least one object in the at least one event of interest. The cloud searching service identifies a second set of event identification criteria based on the classification. The cloud searching service sends feedback to the application for event detection and sending events based on the second set of event identifying criteria. The application modifies the first event detection criteria based on the feedback to create new event detection criteria.

The invention discloses a biomarker MSLN positive circulating tumor cell for ovarian cancer diagnosis and application of the biomarker MSLN positive circulating tumor cell. CTC detection and counting are carried out on an ovarian mass blood sample collected prospectively, an optimal cutoff value for distinguishing benign and malignant ovarian lesions is found by using an ROC curve and a maximum approximate sign index, it is found that the diagnosis specificity can be improved by adding MSLN identification on the basis of a traditional CTC identification standard, the specificity of MSLN (+) CTC is higher than that of CA125 and HE4, and it is found that the MSLN (+) CTC can be applied to diagnosis of benign and malignant ovarian lesions. By establishing a joint scoring diagnosis model, it is found that when the MSLN (+) CTC serves as an auxiliary diagnosis index and is combined with CA125 for diagnosis, the excellent diagnosis efficiency is achieved, and the sensitivity and the specificity can both reach 90% or above. A new biomarker is provided for early diagnosis of ovarian cancer, the sensitivity and specificity of the biomarker are excellent, the diagnosis index shows a good diagnosis effect, and the biomarker has excellent clinical application prospect and value.

The present invention relates to the field of power system relay protection and provides a transformer inrush current identification method and system based on the image space distribution characteristics of sampling values. The method introduces voltage and current sampling sequences, defines the concept of image space distribution of #imgabs0# sampling values, analyzes the image space distribution characteristics of #imgabs1# sampling values of transformers under different operating conditions, and proposes a transformer inrush current identification and fault closing identification criterion based on the image space distribution characteristics of #imgabs2# sampling values based on the symmetry and shape differences of the image space distribution. The current type of the transformer is identified according to the identification criterion, and the proposed method is simulated under different operating conditions of the transformer. The results show that the identification method proposed in the patent is accurate and reliable, can effectively identify inrush current, and prevent differential protection malfunction.

The invention provides a method and a system for automatically identifying abnormity of a secondary system of a transformer substation. The method comprises the following steps: acquiring voltage and current monitoring data and recording data of secondary equipment; performing primary processing on the voltage and current monitoring data to obtain voltage and current data corresponding to primary equipment and secondary equipment; performing primary processing on the recording data, and extracting primary equipment information and corresponding voltage and current instantaneous value data; based on a steady-state anomaly identification criterion, carrying out steady-state anomaly identification on the voltage and current data, and sending an anomaly alarm to equipment meeting the steady-state anomaly identification criterion; and on the basis of a transient anomaly identification criterion, performing transient anomaly identification on the voltage and current instantaneous value data, and giving an anomaly alarm to a channel meeting the transient anomaly identification criterion. The problems that an existing secondary equipment and loop abnormity online identification method is high in data quality requirement and not high in abnormity identification accuracy, and which equipment is abnormal and which type is abnormal cannot be identified specifically are solved.

The application discloses a kind of based on multi-source data fusion's pump station dynamic regulation method and system, belong to agricultural water conservancy engineering and fluid metering technical field.The water pump start-stop event is trigger source, synchronous acquisition flowmeter output signal and pump group electrical parameter, utilize short-time Fourier transform to extract water hammer transient process main oscillation frequency, amplitude attenuation coefficient and initial phase, construct and the time-varying compensation function of strict correspondence of pipeline wave propagation equation, real-time frame compensation is carried out to flowmeter original output and is completed by integration cumulative error correction.For the water hammer scene of broken flow closure, set three conditions joint identification criterion and isolation processing branch, adopt interpolation reconstruction to replace invalid flowmeter reading.System also has pipeline characteristic self-identification ability, and is automatically completed pipeline characteristic identification by the spectrum statistics of initial start-stop event, and continuously optimizes compensation precision by self-learning mechanism of parameter library maintenance independently according to flow grading.

The application relates to a low, slow and small target tracking method based on identification information, which obtains the target type of each track by utilizing track features and target identification criteria, further establishes a height constraint condition based on the target type of each track, and updates the target state estimation value according to the target state estimation value at the current moment and the space constraint condition, so that the tracking performance of the low, slow and small target can be effectively improved. Therefore, the application realizes a target tracking method capable of improving the tracking performance of the low, slow and small target.

The invention relates to the technical field of slope engineering management and computer vision, and discloses a slope support progress calculation method based on image processing. The method comprises the following steps: firstly, obtaining a slope support field picture with a timestamp According to a feature point identification criterion, through an image identification technology, obtaining feature points in the on-site picture; correcting the on-site picture into an on-site picture in an orthogonal view form through homography transformation by taking the feature points in the on-site picture as base points and combining with a slope support design drawing; in the field picture in the orthogonal view form, obtaining the area of the slope supporting structure through a similarity algorithm; and finally, calculating the slope supporting progress based on the timestamp and the area of the slope supporting structure. According to the method, the support progress change is efficiently, flexibly and dynamically captured, and the response speed of engineering management is improved.

This invention relates to a method for classifying and identifying anomalies in underground radio wave imaging based on differences in electrical parameters, belonging to the field of underground geological exploration and ore body exploration technology. This invention aims to solve the problem of misjudgment of anomalies caused by the similarity in signal responses between water bodies and ore bodies in large-scale water-bearing metal mines. The method includes: constructing a dedicated electrical parameter database; using a radio wave imaging system, setting up measuring points according to specifications and selecting a 0.3MHz frequency to collect data, and obtaining a standardized dataset after preprocessing; establishing the correspondence between differences in electrical parameters and radio wave anomaly characteristics, and delineating anomaly areas through inversion; accurately classifying ore bodies and water-rich anomalies based on mechanistic differences, verifying and optimizing the identification criteria; and outputting classification results and detection reports. This invention uses the essential constraints of electrical mechanism for inversion interpretation, improving the resistance to water interference and classification accuracy. It is less subjective and more practical, providing support for mining design and water hazard prevention in large-scale water-bearing metal mines.

This invention provides a hydrogen pipeline damage state detection system and method based on nonlinear ultrasound. The system's nonlinear ultrasonic detection device includes a signal excitation module and a waveform receiving module. The signal excitation module uses pulsed ultrasonic signals to ultrasonically excite the test sample, achieving non-contact ultrasonic propagation to acquire the vibration signal of the detection response. The waveform receiving module analyzes the nonlinear response waveform information. The feature parameter calculation module is configured to calculate state feature parameters based on the nonlinear response waveform information. The evaluation matrix formulation module establishes hydrogen damage identification criteria based on the correlation between state feature parameters and hydrogen damage status. Furthermore, the hydrogen damage assessment module comprehensively evaluates and provides early warning of the pipeline's hydrogen damage status based on the hydrogen damage identification matrix combined with the state feature parameters of the detection response. This approach overcomes the limitations of existing technologies in terms of application scenarios and accuracy, providing a comprehensive and reliable evaluation of hydrogen damage status.