10303results about "Material analysis using wave/particle radiation" patented technology

The invention provides a defect detection method and system for integrated circuit manufacturing. Wherein in the integrated circuit packaging stage, packaged chips are divided into a first type of chips meeting a preset physical characteristic threshold value and a second type of chips exceeding the physical characteristic threshold value through a pneumatic sorting device; performing scanning electron microscope imaging on the surface of the second type of chip, generating a three-dimensional topological structure by using an image reconstruction technology, and positioning a micron-sized particle defect area by comparing reference type differences; synchronously applying high-frequency electric signal excitation in the sorting process, detecting impedance response of the second type of chips, and recording an abnormal signal sequence exceeding a preset frequency domain range; performing space-time correlation matching on the micron-sized particle defect area and the abnormal signal sequence, and judging that the defect is a multi-layer composite defect when a preset coupling condition is met; and dynamically adjusting the mesh diameter and vibration frequency parameters of the pneumatic sorting device. According to the technical scheme provided by the invention, accurate identification of the multi-layer composite defect of the chip is realized, and the defect interception efficiency is remarkably improved.

The invention relates to the technical field of battery temperature control early warning, in particular to an energy storage battery temperature control early warning method and system. The method comprises the following steps: acquiring an internal structure diagram of the energy storage battery; performing internal space layout analysis and inter-unit physical connection analysis on the internal structure diagram of the energy storage battery to construct a three-dimensional battery topological structure model; acquiring real-time temperature monitoring parameters of the energy storage battery; dynamic temperature distribution visual rendering is carried out on the three-dimensional battery topological structure model according to real-time temperature monitoring parameters of the energy storage battery, so that thermal state portraits of a plurality of unit areas are constructed; performing multi-period battery operation simulation on the thermal state portraits of the plurality of unit areas, and performing sliding time window temperature change situation prediction to obtain a temperature change prediction situation of each area; and carrying out continuous abnormal temperature rise trend detection on the temperature change prediction situation of each area so as to generate an abnormal temperature rise trend characteristic. According to the invention, safe and efficient pool temperature control early warning is realized.

The invention discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention provides a new energy battery internal microcrack detection method, which comprises the following steps: acquiring a battery internal three-dimensional structure image by adopting a high-resolution X-ray tomography technology, extracting a pole piece area through an image segmentation algorithm, and carrying out bending deformation simulation on a pole piece to obtain a pole piece internal stress distribution cloud picture; and combining the retrained health state evaluation and residual service life prediction model with battery electrochemical performance characterization data, coupling microcrack evolution and battery performance degradation, predicting a future crack propagation trend and a corresponding performance degradation condition of the battery, and guiding quality control in a battery design and production process.

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

The invention relates to the technical field of concrete detection, in particular to a dynamic detection and analysis method and system for early performance of ultra-high performance concrete. According to the technical scheme, the method comprises the steps of establishing a raw material characteristic spectrum library based on multi-physics field coupling, constructing a dynamic detection array based on a time sequence, developing a multi-modal data fusion algorithm, establishing a nonlinear coupling effect decomposition model and implementing dynamic parameter inversion. According to the method, a technical chain of four-dimensional monitoring network-multi-physics field coupling analysis-cross-scale prediction-adaptive regulation and control is constructed, so that dynamic accurate detection and intelligent regulation and control of the early performance of the ultra-high performance concrete are realized, and the technical bottlenecks of a traditional method in the aspects of parameter coupling, cross-scale association and real-time regulation and control are solved; the early performance prediction precision and the maintenance efficiency are remarkably improved, the cracking risk is reduced, and reliable technical support is provided for application of the ultra-high performance concrete in major engineering.

The invention discloses a reef limestone strength prediction method based on pore feature machine learning, and belongs to the technical field of crossing of geotechnical engineering and artificial intelligence, and the method comprises the steps: obtaining a reef limestone three-dimensional digital core, and extracting pore features after preprocessing; establishing a multi-source pore-physical coupling feature data fusion framework based on the pore features; integrating a random forest and a gradient boosting tree, dynamically weighting parameters in the pore features, designing a composite loss function, performing hierarchical training, and embedding physical constraints to construct a reef limestone strength prediction machine learning model; layered K-fold cross validation is adopted, the robustness of the reef limestone strength prediction machine learning model is tested, and interpretability is verified. According to the method, through data processing, the reef limestone strength prediction machine learning model is constructed and optimized, the relationship between the pore characteristics and the reef limestone strength is deeply excavated, the model prediction accuracy, reliability and interpretability are improved, and an effective solution is provided for reef limestone strength prediction.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

Disclosed in the present invention is a macro-micro combination-based method for quantitatively evaluating a battery electrode charging strategy, comprising: constructing an electrochemical-stress model to obtain distribution and evolution rules of an electrode diffusion stress; according to the stress evolution rule in a lithiation process, using a multi-stage constant-current optimization strategy to perform rate optimization on the lithiation process, and on the basis of the stress of material yield strength, designing a rate optimization solution; separately performing macroscopic and microscopic quantitative verification on the rate optimization solution by means of a long-cycle test, an electrochemical impedance spectroscopy test, a scanning electron microscope test and a selected area electron diffraction test; and performing macro-micro combined analysis on macroscopic and microscopic quantitative verification results. By using macro-micro combined evaluation, the impact of charging strategies on microstructures of electrode materials and the cycle performance of batteries is studied, thus quantitatively revealing the optimization effects of different optimization strategies. By combining the microscopic characteristics of electrode materials and the macroscopic performance of charging strategies, the impact of different charging strategies on the performance of batteries is studied so as to optimize the charging strategies, thus improving the performance and prolonging the cycle life of batteries.

The invention belongs to the field of data analysis and processing, and discloses a building facade defect detection system based on unmanned aerial vehicle exogenous thermal excitation compensation. Comprising the steps of generating a building facade three-dimensional model, predefining a heterogeneous modal joint optimization framework, selecting an automatic connection port according to a mode, and operating an intelligent control center; unmanned aerial vehicle formations are deployed based on a master-slave mode, and the formations share detection data through federal learning; the intelligent control center comprises the steps of deploying an airborne lightweight model, processing thermal imaging data and RGB images in real time at an unmanned aerial vehicle end, identifying a suspected defect area, generating a light spot distribution thermodynamic diagram through a pre-detection model deployed by the intelligent control center, and performing a heating task in a dynamic light-heat cooperative and integrated manner; the intelligent control center further comprises ground analysis centralized control, a cloud actuarial model is used for constructing a thermal diffusion space-time map and hollowing expansion trend prediction, a three-dimensional defect distribution map is finally generated and displayed through an interactive interface, and defect detection of the unmanned aerial vehicle on the building facade under exogenous thermal excitation compensation is achieved.

The invention discloses an unmanned aerial vehicle route planning method based on an improved grey wolf optimization algorithm. The method comprises the following steps: 1, environment modeling and parameter setting; 2, designing constraint conditions and a target function; 3, initializing an algorithm and generating a population; 4, improving an algorithm and updating a track; and step 5, iteration termination and result output. The flight path of the unmanned aerial vehicle is efficiently coded in a spherical vector coding mode, and the flexibility and the calculation efficiency of flight path representation are remarkably improved; by introducing the Levy flight strategy, the global search capability of the algorithm is enhanced, and falling into a local optimal solution is avoided; meanwhile, a parameter adaptive adjustment strategy is combined, so that the convergence speed and precision of the algorithm are remarkably improved. According to the method, the problem of flight path planning in a complex mountain environment and a threat area is effectively solved, a safer and more efficient flight path is planned for the unmanned aerial vehicle, and the method has higher practicability and reliability.

The invention relates to the technical field of power equipment monitoring, in particular to a 10kV cable joint temperature inversion correction method based on environmental heat exchange conditions, which comprises the following steps: measuring surface temperature data through a sensor array, and analyzing material layering characteristics in combination with X-ray imaging; then, establishing an electromagnetic-thermal-fluid field coupling model, and calculating a dynamic thermal resistance coefficient and a heat flow distortion index; thirdly, optimizing surface temperature distribution and iteratively updating thermal resistance parameters by utilizing a bidirectional feedback correction strategy, and finally generating a high-precision internal temperature field; through adaptive temperature compensation and multi-source data fusion, the accuracy of temperature prediction is improved, long-term monitoring errors are reduced, the method can be widely applied to intelligent operation and maintenance and fault early warning of cable joints, and the safety and stability of a power system are improved.

The present invention relates to a sodium-ion battery material, and specifically relates to a sodium ferric phosphate pyrophosphate composite material, and a preparation process, evaluation method and process optimization method therefor and the use thereof. The sodium ferric phosphate pyrophosphate composite material has a spinel-type NaFePO4 phase; and in an XRD diffraction pattern obtained by subjecting the sodium ferric phosphate pyrophosphate composite material to XRD diffraction using CuKα radiation, there are a characteristic diffraction peak T of sodium ferric phosphate pyrophosphate and a characteristic diffraction peak O of spinel-type NaFePO4 at 2θ of 33.6±0.2° and 32.8±0.2°, respectively, the peak intensities thereof are respectively IT and IO, and the diffraction peak intensity ratio Ir is equal to IT / IO, wherein 6.19≤Ir≤13.

The invention relates to the technical field of soil remediation, and discloses a remediation treatment method and system for simulating cadmium-contaminated soil and a storage medium. The method comprises the following steps: detecting and analyzing the adsorption performance of the remediation material according to soil cadmium content data to obtain remediation material performance data; based on the soil cadmium content data and the remediation material performance data, carrying out data processing on a pollutant migration rule to obtain pollutant migration characteristic data; carrying out data analysis on the repair material combination by utilizing the pollutant migration characteristic data to obtain material combination effect data; according to the material combination effect data, the repair process is dynamically detected, and repair effect data are obtained; and performing data evaluation on the remediation scheme by utilizing the remediation effect data to generate a cadmium-contaminated soil remediation scheme. According to the method, the performance of the repairing material, the pollutant migration rule and the repairing effect are accurately evaluated, and the scientificity and the reliability of the repairing scheme are improved.

The invention is applicable to the technical field of blast furnace slag analysis, and provides a blast furnace slag harmful element analysis method which comprises the following steps: preparing a sample to be analyzed; collecting LIBS and XRF spectral data of a sample to be analyzed; the collected spectral data are preprocessed; training a machine learning model by using a slag sample with known components, and establishing a mapping relation between spectral data and element contents; inputting the preprocessed spectral data into the trained machine learning model, and outputting a content prediction value of each harmful element; according to the method for analyzing the harmful elements of the blast furnace slag, the LIBS and XRF spectrum technologies are combined, the coverage range of the elements is wider, real-time online analysis and multi-element synchronous analysis are achieved, the detection efficiency is improved, model parameters are automatically optimized through a machine learning algorithm, the method adapts to different slag components, and the method is suitable for popularization and application. The detection precision of trace elements is obviously improved, and automatic data analysis and report generation are supported.

The invention relates to the technical field of monitoring, in particular to a cable aging monitoring and early warning method and system. The method comprises the following steps: acquiring cable monitoring data, and performing electrical characteristic analysis to obtain electrical characteristic data; performing conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; cable image feature extraction is carried out according to the cable monitoring data, cable morphology type division is carried out, and curve segment cable data and winding segment cable data are obtained; performing bending fatigue analysis on the curve section cable data to obtain bending fatigue data; performing winding stress analysis on the winding section cable data to obtain winding stress data; performing material damage evaluation according to the bending fatigue data and the winding stress data to obtain material damage data; and carrying out damage position identification according to the material damage data to obtain damage position data. The cable aging monitoring accuracy and the early warning response rate are improved based on the monitoring technology.

An apparatus includes an x-ray source configured to generate x-rays, at least some of which impinge a sample and at least one diffractor configured to concurrently diffract at least some of the x-rays from the sample in a first direction and in a second direction substantially orthogonal to the first direction. The apparatus further includes at least one two-dimensional (2D) position-sensitive x-ray detector configured to receive at least some of the diffracted x-rays and to concurrently generate first spectral information of the x-rays diffracted in the first direction and second spectral information of the x-rays diffracted in the second direction. The apparatus further includes circuitry configured to receive the first and second spectral information and to generate a single x-ray absorption spectroscopy (XAS) spectrum using the first and second spectral information.

The invention provides a soil nutrient detection method based on a gamma-ray energy spectrum and an XGboost algorithm, and the method comprises the steps: constructing a training sample for soil nutrient detection, the training sample comprises radioactive element data, and the label of the training sample is soil nutrient data with the same soil point location as the radioactive element data; the training sample is input into an XGboost model, the XGboost model extracts features of the training sample and predicts soil nutrients of the training sample, the soil nutrients serve as nutrient predicted values, a loss function is constructed according to the nutrient predicted values and labels of the training sample, the XGboost model is trained, and a soil nutrient detection model is obtained; acquiring soil radioactive elements of to-be-detected soil through a gamma-ray energy spectrometer, and inputting the soil radioactive elements into the soil nutrient detection model to obtain a nutrient detection result of the to-be-detected soil.

The invention provides a waste glass activity rapid comprehensive evaluation method based on dissolution kinetics analysis. According to the method, the silicon ion dissolution characteristic of the waste glass in an alkaline environment is measured, the particle size effect and kinetic parameter analysis are combined, the corresponding relation with the 28-day compressive strength activity index is established, and rapid evaluation of the activity of the waste glass is achieved. The method comprises the steps of dissolution test, kinetic analysis and activity evaluation. Research finds that a power function relationship exists between the reaction rate constant and the particle size, and good linear correlation exists between the dissolution characteristic and the activity index. According to the invention, a complete evaluation system of particle size-reaction rate-activity index is established, the goal of predicting the 28-day activity index from the 24-hour dissolution characteristic is realized, and the error between the prediction result and the measured value is less than 2%. The method is short in evaluation period and accurate and reliable in result, and has important engineering application value.

The invention belongs to the technical field of roadbed engineering, and particularly relates to a construction uniformity evaluation method for a clay improved copper tailing roadbed. The method comprises the following specific steps: S1, determining the proportion of the clay improved copper tailing roadbed filler; s2, applying the blended clay improved copper tailings to roadbed construction; and S3, carrying out construction detection on the clay-improved copper tailing roadbed, calculating a mixing uniformity coefficient, and comparing the mixing uniformity coefficient with a uniformity coefficient in a laboratory to evaluate the construction uniformity of the clay-improved copper tailing roadbed. According to the method, after roadbed construction is completed, a working project of measuring the compactness through a field sand filling method is combined, the mixing uniformity coefficient is calculated with the deviation between the composition proportion of roadbed filler sampled through the sand filling method and a proportion design result as a parameter, and the construction quality can be effectively evaluated conveniently.

The embodiment of the invention relates to the technical field of rock mechanics, in particular to a rock micromechanical property determination method, device and equipment, and the method comprises the following steps: carrying out indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micro-mechanical response data represents the change relationship between the micro-mechanical response of the rock sample and time; scanning a target area on the surface of the rock sample in the indentation test process to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents a change relationship between the microstructure of the rock sample and time; and determining the micromechanical properties of the rock sample according to the micromechanical response data of the rock sample and the corresponding microstructure evolution data. According to the embodiment of the invention, the heterogeneous micro-mechanical property of the rock can be accurately represented based on collaborative analysis of the micro-mechanical response and the microstructure of the rock sample at multiple time points, and dynamic, in-situ and quantitative representation of the micro-mechanical property of the rock is realized.

The invention provides a double-space fatigue electron microscope test system, which is applied to the technical field of material analysis, and comprises an electron microscope module, a fatigue loading module and a vacuum isolation damping module, the vacuum isolation damping module controls two spaces of the electron microscope module and the fatigue loading module to be communicated or independent; the fatigue loading module comprises a hydraulic loading actuator, an electromechanical loading actuator, a loading rod and a fixing clamp, the fixing clamp is used for clamping a to-be-tested sample, and the loading rod is used for stretching or extruding the to-be-tested sample; and the fatigue loading module is driven by the moving mechanism to reciprocate along the X axis to adjust the position of the to-be-tested sample or rotate along the X axis to realize switching of different stations. According to the double-space fatigue electron microscope test system, combined use or independent use of the electron microscope module and the fatigue loading module is realized, and adverse factors such as vibration and pulse generated by the fatigue loading module during electron microscope observation are improved; in-situ compensation is achieved by means of cooperation of loading force and displacement of the fatigue loading module, and the observation effect of the electron microscope is improved.

The invention relates to the field of electronic detectors, and discloses an ultrafast and efficient electronic detector based on nano polycrystalline boron nitride, which comprises a nano polycrystalline boron nitride scintillator, a light guide and a photomultiplier, and is characterized in that the nano polycrystalline boron nitride scintillator and the photomultiplier are respectively connected with the front end and the rear end of the light guide, a nano polycrystalline boron nitride material of the nano polycrystalline boron nitride scintillator is prepared by taking a sapphire single crystal wafer as a substrate, using NH3 and BCl3 or NH3 and BF3 as raw material gases and adopting a chemical vapor deposition furnace for epitaxial growth. In the invention, the nano polycrystalline boron nitride scintillator has short electron decay time and high scintillation light yield, and can realize ultrafast and efficient electron radiation detection when being applied to an electron detector. The electron detector can be applied to secondary electron and backscattered electron detection in an electron beam detection system, and a new choice is provided for the electron beam detection technology.

The invention provides a rock damage evaluation method and device, and the method comprises the following steps: carrying out a compression test on a rock sample until the rock sample is damaged; treating the damaged rock sample to obtain a multi-scale damage fractal dimension of the damaged rock sample; based on the multi-scale damage fractal dimension, calculating a weight coefficient of each scale fractal dimension to the damage of the rock sample through an analytic hierarchy process; constructing a multi-scale rock damage evolution fractal model based on the weight coefficient; and evaluating the damage degree of the rock to be measured based on the rock damage evolution fractal model. The defect that the rock damage degree is determined from a single scale is overcome, rock damage can be more comprehensively and accurately quantified, long-term rock damage and stability evolution are effectively predicted, and reference is provided for comprehensively and accurately representing the rock damage degree and long-term evolution.

The invention provides a method for measuring the content of carbonate minerals, and belongs to the technical field of process mineralogy. The method for measuring the content of the carbonate minerals comprises the steps of ore grinding, reselection, automatic mineral analysis sample preparation, acid treatment, analysis comparison sample preparation, automatic mineralogy analysis testing, data correction and the like. Compared with the prior art, the analysis comparison sample is prepared in a targeted manner by introducing the analysis comparison sample for correction and combining automatic mineralogy analysis data, so that the analysis comparison sample is closer to an actual sample and is more representative, the test data is more accurate, and reliable data support is provided for efficient utilization of such resources.

The invention relates to an automobile bunching belt detection method, system and device and a storage medium, and the method comprises the steps: carrying out the initial detection of a bunching belt through a multi-mode sensing device, obtaining an initial detection result, and enabling the multi-mode sensing device to comprise an industrial CT, a high-frequency ultrasonic phased array and a fiber Bragg grating sensor; designing a multi-physics field cooperative loading working condition of a simulated bunch strip service environment to obtain multi-physics field loading data; constructing a dynamic damage accumulation model based on the multi-physical field loading data and the initial detection result; based on the dynamic damage accumulation model and a machine learning algorithm, predicting the remaining service life of the cable tie, and generating a service life prediction result of the cable tie; and generating a comprehensive detection report in combination with the initial detection result, the multi-physics field loading data and the life prediction result. The method and the device have the effect of improving the detection accuracy of the bunching belt.

The invention discloses a copper-based nano-enzyme, a preparation method thereof and application of the copper-based nano-enzyme in organophosphorus pesticide detection, and belongs to the technical field of pesticide residue detection. The copper-based nano-enzyme has a netted-like structure, a high specific surface area and many active sites, the valence state of a copper element is zero-valence Cu, Cu < + > and Cu < 2 + >, the copper-based nano-enzyme has high peroxidase-like activity and laccase-like activity, the detection limit is low, and glyphosate can be sensitively detected; and glyphosate, glufosinate-ammonium and isazofos can be well distinguished from six organophosphorus pesticides with medium concentration, and the method has high selectivity. The nano enzyme provided by the invention is prepared from ammonium citrate and copper nitrate in an N, N-dimethylformamide (DMF) solution by using a solvothermal method; the preparation process of the nano enzyme is simple.

The invention provides a core permeability prediction method based on time sequence decomposition and multi-dimensional feature interaction, and belongs to the technical field of core permeability prediction. The method comprises the following steps: performing CT scanning on multiple sandstone samples to obtain high-resolution three-dimensional rock core original data; the method comprises the following steps: performing image preprocessing on original data, performing adaptive threshold segmentation, and dividing pores and matrixes to obtain three-dimensional binary image data; performing small window sliding sampling on the binary image data to obtain three-dimensional core small cubes, and calculating the permeability of each core cube as a label to obtain a final experimental data set; a core image sequence is decomposed from an experimental data set, fusion features are extracted, information is extracted from the fusion features from three dimensions of channel, height and width, features of different dimensions are combined, and the permeability is predicted through a full connection layer. According to the method, lightweight design is adopted, multi-dimensional information of the core image is fully utilized, prediction efficiency and precision are remarkably improved, and computing resource consumption is reduced.

A system for saturating a core sample of rock includes a vessel with an inner surface defining a receptacle that receives and holds the core sample and a saturating fluid. The vessel defines an opening into the receptacle at a first end of the vessel. A piston is configured to fit within the opening and the receptacle and travels to seal the vessel. A pump directs hydraulic fluid to the piston, wherein the pump pushes the hydraulic fluid against the piston to maintain a saturation pressure within the receptacle to accommodate the saturating fluid filling pore throats within the core sample. The core sample may include bores that provide additional surface area subject to the saturation pressure. The system calculates a numeric permeability of the core sample and a measured permeability of the core sample while simultaneously saturating the core sample over a time period.

The invention discloses a fire-fighting non-destructive detection method based on multi-modal detection, and relates to the technical field of fire-fighting engineering, and the method comprises the following steps: carrying out the rapid scanning of fire-fighting equipment through an ultrasonic array, analyzing and positioning a suspected defect region based on a time domain reflection signal, and generating a three-dimensional coordinate mapping model; and performing multi-angle X-ray projection acquisition on the positioning area, and generating an internal structure cross-sectional image through a three-dimensional reconstruction algorithm. According to the fire-fighting non-destructive detection method provided by the invention, by integrating various technical means such as ultrasonic array scanning, X-ray tomography, thermal imaging analysis and microwave sealing detection, comprehensive and accurate identification of potential defects of fire-fighting equipment is realized, and the dimensionality and precision of defect detection are improved; the structural integrity of the equipment is kept to the maximum extent, extra risks caused by destructive detection are avoided, and the reliability and accuracy of detection are improved through a data fusion algorithm.