3749results about "Specific gravity measurement" patented technology

The invention relates to the technical field of concrete pouring, in particular to a concrete pouring homogeneity detection system and method based on sensing data, and aims at achieving efficient and accurate concrete pouring homogeneity evaluation and defect detection. The system comprises a multi-sensor sensing module, a multi-sensor data conversion and transmission module and a concrete homogeneity evaluation module, the multi-sensor sensing module comprises a temperature sensor, an ultrasonic sensor and a vibration sensor; the multi-sensing data conversion and transmission module is used for acquiring multi-sensing data and performing data type conversion and transmission on the multi-sensing data; and the concrete homogeneity evaluation module is used for constructing a concrete pouring homogeneity evaluation model to detect and evaluate the homogeneity and defects in the concrete pouring process. According to the invention, detection and evaluation of the concrete pouring homogeneity are realized through combination of multi-sensing data, and the accuracy of detection of the concrete pouring homogeneity is improved.

The invention relates to the technical field of nondestructive testing, solves the problems of insufficient flaw detection precision and adaptability of a metal component caused by restriction in the prior art, and particularly discloses a flaw detection system and method for the metal component. The flaw detection system comprises a multi-modal sensor group, a cross-modal feature alignment module, a self-adaptive weight distribution module, a defect decision module and a closed-loop control unit, the flaw detection method comprises the following steps: S100, synchronously acquiring data through the multi-mode sensor group; s200, performing space-time registration on the multi-source data of the metal component by using the dynamic calibration matrix; s300, evaluating the confidence coefficient of the sensor in real time, and generating a defect feature map; s400, inputting the defect feature map into a defect classification network, matching a defect map database, and outputting a defect type and a quantization parameter; and S500, automatically adjusting the detection motion track and the sensor according to the defect parameters. According to the multi-method combined detection for the metal component, the detection precision is improved, and a detection result with higher accuracy can be obtained through comprehensive evaluation according to multi-source data.

The invention relates to the technical field of material detection, in particular to a building engineering material detection method and system. The method comprises the following steps: performing multi-area scanning on a building decoration wall coating through an ultrasonic scanner to obtain a wall coating sound wave data set, identifying non-uniform distribution of coating texture, then calculating density distribution difference of an inner coating and an outer coating between layers according to the coating texture data, and further performing quantitative simulation on water resistance permeation loss to obtain the water resistance permeation loss of the building decoration wall coating. The method comprises the following steps: acquiring water-resistant permeation loss data, then inferring a permeation mechanism periodic aging process of the coating on the basis of the water-resistant permeation loss data, detecting the tolerance performance of a coating material in combination with permeation mechanism aging data, acquiring material tolerance performance data, and comprehensively evaluating the quality and durability of the coating. According to the invention, the detection technology is optimized, so that the detection technology is more perfect.

The invention relates to the technical field of corn kernel quality detection, in particular to a corn kernel quality detection model construction method and system based on multi-source data fusion, and the method comprises the following steps: S1, multi-source data collection: collecting apparent morphology data of corn kernels through a multi-spectral imaging device, synchronously utilizing a near-infrared spectrometer to obtain spectral data of internal components of the corn kernels, and measuring structural density characteristic data of the corn kernels in combination with a sonic sensor; s2, multi-modal feature construction: forming a multi-modal feature matrix; s3, dynamic weighted fusion: generating a mixed feature vector; s4, constructing a dual-channel neural network: constructing a dual-channel deep neural network based on the generated mixed feature vector; and S5, model compression and deployment optimization: generating a lightweight detection model suitable for the embedded device. According to the invention, multiple requirements of an agricultural field on real-time performance, precision and deployment flexibility are met.

The invention discloses a textile fabric defect detection system, relates to the technical field of textile fabric defect detection, and is beneficial to comprehensively evaluating the appearance quality of textile fabrics by collecting the number of hair balls, a color difference value and the area of a color difference region in an external defect parameter collection process. The pilling phenomenon on the surface of the textile fabric can be accurately recognized by collecting the number of the hair balls, the overall appearance and the service life of the textile fabric can be affected by the hair balls, non-uniformity of the textile fabric in the dyeing process can be revealed through measurement of the color difference value, and the severity degree of the color difference problem can be determined through statistics of the area of the color difference region. In the external defect evaluation process, whether the textile fabric meets the quality standard or not can be objectively judged by analyzing the external defect evaluation index of the specified textile fabric, and in the structural defect analysis process, the structural performance of the textile fabric can be comprehensively known by collecting the fiber breaking strength, the fiber density and the porosity.

The invention relates to the field of aramid fiber band defect detection, and discloses an X-ray-based aramid fiber band defect online detection method and device. The method comprises the following steps: carrying out potential defect analysis on a physical characteristic image and a material parameter distribution diagram to obtain spatial distribution data of a potential defect region; physical characteristic quantitative analysis is carried out on the spatial distribution data of the potential defect area, a defect physical descriptor is obtained, and the defect physical descriptor comprises a density anomaly feature, a molecular chain orientation disorder degree and a thermal anomaly feature; and performing multi-dimensional feature extraction and weight distribution on the defect physical descriptor according to a preset material performance standard to obtain a quality score and a physical performance parameter of the aramid fiber band, and generating a defect detection report according to the quality score and the physical performance parameter. According to the invention, the comprehensiveness, accuracy and reliability of aramid fiber band defect detection are improved.

The present invention relates to the field of Internet of Things monitoring technology, specifically a Chinese herbal medicine drying monitoring system based on the Internet of Things, the system includes a gas component monitoring module, a density measurement and analysis module, a data anomaly correction module, a drying endpoint determination module, and a terminal data storage module. In the present invention, by acquiring gas concentration data within a fixed time window and analyzing the changing trend of the gas component ratio curve, the sensitivity to changes in the gas environment is improved, the density measurement adopts a multi-monitoring point method, the density change rate is calculated, and the gas component data is combined to form a multi-dimensional drying state analysis, reduce the impact of local errors on the overall judgment, detect abnormal fluctuations through trend consistency calculation, correct mutation data, prevent sudden measurement errors from interfering with the drying state determination, combine the gas component ratio change trend and the density change trend, adjust the drying endpoint determination basis, adapt to the drying characteristics of different batches of Chinese herbal medicines, and improve the accuracy of the drying endpoint determination.

The invention provides a segmental bridge wind vibration performance evaluation method and system, and relates to the technical field of segmental bridge wind vibration performance. Laser scanning is used for obtaining geometric parameters of a segmental bridge and collecting material characteristics, the geometric parameters and the material characteristics are imported into finite element software, a basic model is established, and boundary conditions are set; modal analysis is executed in finite element software, the natural vibration frequency and the vibration mode are calculated through a Lanczos method, and basic data are provided for wind load analysis; simulating a wind load by using a Davenport wind spectrum, setting a wind speed and a wind direction, and then applying the wind speed and the wind direction to the model; finally, the wind load condition is adjusted, the performance of the bridge under different wind loads is tested, and the displacement, acceleration and stress distribution of the bridge are analyzed and calculated through dynamic response to complete the wind vibration performance evaluation of the segmental bridge.

The invention discloses a seed quality evaluation system and device based on multi-modal data fusion and a control method, and relates to the technical field of agricultural engineering. The system comprises a data acquisition module, a data processing module, a quality evaluation module and a control module. The data acquisition module acquires multi-modal data; the data processing module performs preprocessing, feature extraction and fusion on the multi-modal data to generate a comprehensive feature vector; the quality evaluation module performs seed quality evaluation based on the comprehensive feature vector by using a machine learning model; the control module controls all the modules to operate and outputs results. The device comprises a closed box body, a temperature and humidity control unit, an electronic scale, a hyperspectral imaging unit, a visible light imaging unit, a mechanical arm and a main control unit. The method realizes rapid, accurate, lossless and automatic evaluation of seed quality, overcomes the defects of low efficiency, strong subjectivity, easy damage of seeds and the like in a traditional method, and improves the accuracy and efficiency of seed quality evaluation.

The invention relates to an intelligent compaction monitoring method and system for a highway subgrade pavement. The method comprises the following steps: collecting construction site data of the highway subgrade pavement; wherein the construction site data comprises soil mass density, water content and temperature; inputting the construction site data into a compactness prediction model to obtain the compactness of the roadbed and the pavement; wherein the compaction degree prediction model is constructed based on a quantitative relation between construction site data and radar signals; and carrying out early warning on the compaction degree. According to the invention, intelligent and precise monitoring of the compaction degree of the roadbed and the pavement can be realized, the accuracy and the reliability of compaction quality evaluation are improved, data support is provided for optimization of a compaction construction process, and the roadbed and pavement engineering quality is effectively guaranteed.

The present invention relates to the technical field of geotechnical engineering safety monitoring, and provides a geotechnical engineering safety monitoring method based on muon detection technology. The method comprises: arranging a plurality of muon detection devices around or inside a geological body to be observed to form an array, and measuring in real time the muon observation flux of multi-angle cosmic rays passing through said geological body; converting the muon observation flux into the density length, and obtaining the three-dimensional density length distribution of said geological body; and analyzing the stress, strain, and deformation. In the present invention, muon particles are used to perform geological detection of geotechnical engineering, and the muons are particles having extremely high penetration capability, and can penetrate through the thick layer of the earth, so that detailed information about the interior of the geotechnical engineering is provided. In addition, the present invention has the advantages of being free of vibration and electromagnetic noise interference, and can implement accurate and rapid dynamic monitoring of the geotechnical engineering. The present invention has a wide application prospect in the fields of geological exploration, mineral exploration, underground resource development, and the like.

The invention relates to a soil dry density and moisture content calibration and measurement method and device based on an active heating optical fiber, computer equipment and a computer readable storage medium. The method comprises the following steps: configuring first test soil samples with specified dry densities and different mass moisture contents; obtaining temperature characteristic values of the first test soil samples under the conditions of specified dry density and different mass moisture contents; fitting a temperature characteristic value-mass moisture content calibration formula; respectively heating and testing the second test soil samples with different mass moisture contents under different dry density conditions to obtain a temperature characteristic value-mass moisture content calibration formula under different dry density conditions; establishing a calibration correction model which simultaneously considers two parameters of dry density and mass moisture content; and obtaining the dry density or the mass moisture content of the to-be-detected soil body. By adopting the method, the limitation that a traditional model is only suitable for a single dry density condition can be broken through, and accurate measurement on the in-situ soil body under different dry density and mass moisture content conditions is realized.

The invention relates to the technical field of water conservancy environment survey, in particular to sampling equipment for water conservancy environment survey and a sampling method thereof.The sampling equipment comprises a chassis, a sampling mechanism is installed at the top of the chassis, the sampling mechanism comprises a first motor, and the first motor is fixedly installed at the top of the chassis; a first threaded rod is fixedly mounted at the output end of the first motor, the supporting columns are fixedly mounted at the top of the chassis, the number of the supporting columns is two, a mounting plate is slidably mounted on the surfaces of the two supporting columns, the mounting plate is in threaded connection with the first threaded rod, and the second motor is fixedly mounted at the top of the mounting plate. Through arrangement of the sampling mechanism, disturbance of samples can be effectively reduced. When the first piston is inserted into sludge, the first piston can block mixing of surrounding sludge to a certain extent, the purity of the sample is kept, interference of external factors on the sample can be further reduced, and the obtained sludge sample can reflect the in-situ state more truly.

The invention relates to a roadbed compaction quality detection method and system based on multi-dimensional data, and the method comprises the steps: obtaining sensor data of a roller of a road roller in real time, and obtaining pavement images and positioning data of a plurality of continuous time points; when the road surface is flattened, the roadbed structure is gradually stable, so that the corresponding pressure data and vibration data are gradually stable. By analyzing the stability of the pressure data and the vibration data, the stability of the pressure data and the vibration data is determined. According to the method, vibration data, stable data and temperature data of a road section at a stable time point are extracted, and then a target data reference range corresponding to a corresponding temperature grade and a roadbed material is found from a pre-constructed data model, so that the target data reference range can verify the vibration data and the stable data at the stable time point; therefore, whether the road roller performs the road rolling operation in a reasonable range or not is judged. In addition, the road surface is detected by using the road surface image.

The invention discloses a multi-modal intelligent wood defect detection and performance evaluation system and a detection and evaluation method, which integrate multiple sensing technologies and deep learning algorithms to realize wood defect detection and quality evaluation. The system comprises a wood transmission module, a multi-modal data acquisition module, an information processing module and an intelligent grading module, and surface and internal characteristics of wood are obtained through visible light, infrared, near-infrared, ultrasonic and microwave sensors. A feature pyramid network (FPN) and an instance segmentation technology are utilized to accurately identify defects such as cracks, wormholes, knot scars and the like, and ARIMA and LSTM models are combined to predict the moisture content, so that the quality evaluation precision is improved. The method can intelligently classify wood, improves the detection efficiency, and is suitable for the fields of wood processing, furniture manufacturing and the like.

The invention relates to a cutter suction dredger reamer suction port position density forecasting method, and belongs to the field of engineering ship automation systems.The method includes the steps that firstly, a suction vacuum value, a density value and a flow velocity value are collected in real time, and data are processed and stored in real time; secondly, performing time shifting operation on a suction vacuum value by using the length of a pipeline from a suction vacuum sensor to a densitometer and the flow velocity, performing time dimension matching on the suction vacuum value and a density value, and after matching succeeds, respectively extracting the suction vacuum value and the density value by using a weighted average algorithm as characteristic values for storage; fitting operation is carried out on the feature statistical data; a fitting equation is obtained through a fitting operation result, and a density value corresponding to the position of a reamer suction opening is predicted in advance through a suction vacuum value obtained in real time; and meanwhile, fitting correction is performed on the fitting equation in real time. By accurately forecasting the position of the suction port of the cutter of the cutter suction dredger in real time, the dredging construction control precision of the cutter suction dredger can be remarkably improved, and the dredging construction efficiency is guaranteed.

The invention provides a pole piece detection method, a detection device, detection equipment and a storage medium. The pole piece detection method comprises the following steps: acquiring first detection data corresponding to a composite coating on a pole piece irradiated by a first light beam and second detection data corresponding to the composite coating irradiated by a second light beam; based on the first detection data and the second detection data, determining the proportion of a target component in the composite coating; determining the surface density of the composite coating based on the second detection data; determining the surface density of the target component based on the proportion of the target component and the surface density of the composite coating; the correlation coefficient between the absorption coefficient of the first light beam and the atomic number is larger than the correlation coefficient between the absorption coefficient of the second light beam and the atomic number. Based on the mode, the surface density of the target component in the composite coating can be determined.

The invention discloses a pole piece surface density measurement method based on a double-index model, which comprises the following steps: firstly, starting an X-ray source, and measuring the ray intensity when a pole piece is not placed; secondly, replacing standard to-be-measured pole pieces with different surface densities, and measuring ray intensity; obtaining mass attenuation coefficients corresponding to different coating surface densities according to an attenuation formula, and performing double-index model correction on the mass attenuation coefficients according to the calibration data; and finally, measuring the intensity of rays passing through the pole piece to be measured, and measuring the surface density of the pole piece according to a ray intensity attenuation model and the calibrated mass attenuation coefficient. According to the method, the surface density of the pole piece is measured by using a method for correcting the quality attenuation coefficient of the pole piece through the double-index model, and compared with a method for directly calibrating the measured ray intensity and the standard surface density value, the problem that the measurement of the surface density of the pole piece coating is affected by X-ray beam hardening and Compton scattering is solved; and the surface density measurement precision of the pole piece is improved.

The invention relates to the technical field of metal injection molding, in particular to a method for detecting uniformity of metal powder injection molding feed, which comprises the following specific steps: randomly extracting a plurality of samples from the same batch of metal powder injection molding feed at different positions, weighing a plurality of groups of samples from each sample, recording the weight of each group of samples as m1, and recording data, the apparent weight of each group of samples is obtained through a density meter and an electronic balance by utilizing a drainage method and is recorded as m2, data is recorded, the volume and density of the samples are calculated, then the standard deviation of the density of the samples is calculated according to a sample standard deviation calculation formula, then a variable coefficient is calculated, and the feeding uniformity is evaluated according to the variable coefficient. The method is simple in process, convenient to operate and short in consumed time, the uniformity of the feed is evaluated through sampling, weighing and density calculation, the uniformity of the metal powder injection molding feed can be visually and quantitatively detected, and the method is suitable for detecting the uniformity of the metal powder injection molding feed.

The invention discloses a comprehensive detection method for detecting the quality of a traditional Chinese medicine capsule, and relates to the technical field of medicine quality detection.According to the method, the quality fluctuation of a shell can be effectively recognized by monitoring the elasticity modulus of the shell of the capsule in real time and constructing a variable coefficient; and secondly, further monitoring is carried out aiming at capsule filling types and density uniformity, so that the uniformity of medicine filling is ensured, the medicine effect fluctuation caused by non-uniform filling is avoided, and the release consistency of the medicine is improved. And then, based on dissolution rate gradient monitoring under different temperature and pH value conditions, the dissolution characteristics of the capsule in various environments can be comprehensively evaluated, so that the dissolution performance stability of the capsule is guaranteed. And finally, accurately calculating a dissolution stable factor by combining the detection of trace dissolution substances with a dissolution characteristic analysis model, and further optimizing the quality evaluation of the capsules by combining a dissolution rate change coefficient, thereby reducing unqualified batches caused by uneven dissolution.

The invention relates to an SF6 density table data abnormity self-diagnosis method, a storage medium and equipment, and the method comprises the following steps: obtaining a data collection time sequence corresponding to a to-be-diagnosed SF6 density table based on the uploading data of the to-be-diagnosed SF6 density table, and at each detection moment, obtaining a data collection time sequence corresponding to the to-be-diagnosed SF6 density table based on the real-time density time sequence; judging whether the change of the density measurement value exceeds a set fluctuation threshold value or not, and if so, judging the change direction of the density measurement value; judging whether air leakage exists or not based on the air pressure time sequence, and if yes, generating an air leakage alarm signal; and calculating a correlation coefficient of the real-time environment temperature time sequence and the density time sequence, judging whether the density value change and the environment temperature change have high negative correlation or not, and if so, judging that the density value change is caused by the fact that the environment temperature at the installation position of the density meter is higher or lower than the air chamber temperature of the electrical equipment body. Compared with the prior art, the method has the advantage that the change reason of the SF6 density value can be accurately and quickly judged.

The invention relates to a poplar property high-throughput accurate quantitative identification method, system, equipment and medium, the innovation of the method lies in that spectral data analysis and automatic script processing are combined, efficient data acquisition, import, matching and storage are realized, abnormal point detection is carried out by introducing principal component analysis and anti-noise mahalanobis distance, a data set is optimized, and the identification accuracy of the poplar property is improved. Building and training a model to realize high-flux accurate prediction of the material property and the density; a sample control module, an acquisition test module, an automatic processing module, a data set determination module and a result output module are integrated on the system, so that full-process automatic and intelligent management is realized; the equipment comprises a database and a memory, and the method is cooperatively executed through the database and the memory to ensure efficient operation of the method; a computer readable medium stores executable instructions, which is convenient for wide deployment and application. The method is excellent in high-throughput analysis, cost saving, environmental protection and data accuracy, and development of the poplar property identification technology is effectively promoted.

Globally our environment comprises structures built to perform a meet different requirements including residential, commercial, retail, recreational and service infrastructure. Whilst, millions of tons of construction materials are deployed annually the quality control procedures in many instances have not changed to reflect today's demands. Accordingly, it would be beneficial to provide construction companies, engineering companies, infrastructure owners, regulators, etc. with means to automated testing / characterization of construction materials during at least one of its manufacture, deployment in construction and subsequent infrastructure life. It would be further beneficial for such automated methods to exploit self-contained data acquisition / logging modules allowing them to be employed with ease at the different points in the life cycle of a construction material and / or construction project.

The invention relates to the technical field of seawater quality detection, and discloses a seawater quality monitoring method based on automatic multi-source data assimilation, which comprises the following steps: acquiring a satellite remote sensing image of a seawater monitoring area, and carrying out eutrophic hierarchical division on the seawater monitoring area to obtain an eutrophic divided ocean area; optical sensing data of the seawater monitoring area are collected, and the suspended particulate matter concentration of the seawater monitoring area is determined; collecting temperature sensing data of the seawater monitoring area, and analyzing the water quality change level of the seawater monitoring area; collecting salinity data and dissolved oxygen data of the seawater monitoring area, and analyzing the ecological structure change degree of the seawater monitoring area; and based on the eutrophication divided ocean area, the suspended particulate matter concentration, the water quality change level and the ecological structure change degree, constructing an ocean multi-attribute comprehensive graph, and giving an early warning to the corresponding monitoring user of the seawater monitoring area. According to the invention, the comprehensiveness of seawater quality detection can be improved.

The invention discloses a grain quality detection device and method, a control device and a storage medium, and the device comprises a grain sample separation device which is used for dividing a target grain sample into two paths and conveying the target grain sample to a first discharge port and a second discharge port; the appearance quality detection device is used for detecting an appearance detection result; a volume weight detection device for detecting a target volume weight; the internal quality detection device is used for detecting an internal quality detection result; the safety index detection device is used for detecting a safety index detection result; a recovery device; the conveying device is used for sequentially conveying the target grain sample among the first discharge port, the appearance quality detection device and the recovery device and among the second discharge port, the volume weight detection device, the internal quality detection device, the safety index detection device and the recovery device; and the control device is used for determining grade information of the target grain sample according to the appearance detection result, the target volume weight, the internal quality detection result and the safety index detection result. The detection efficiency and the accuracy of the detection result can be improved.

A method for analyzing density-type coal quality parameters on the basis of big data, and an analyzer and a system. The method comprises: on the basis of (II) and a large number of collected coal quality parameters A, M, V and FC, and collected Q and S data, establishing a coal quality parameter database and an identification and analysis software package; and on the basis of a coal quality parameter change signal U measured by a coal quality parameter measurement apparatus (1), the software package performing analysis, so as to obtain coal quality parameters A, M, V, FC, Q and S of coal. With regard to the coal quality parameter analyzer that uses the method, the analyzer is at least composed of a coal quality parameter measurement apparatus (1) and a control and calculation unit (2), wherein the coal quality parameter measurement apparatus (1) measures a coal quality parameter change signal U, the control and calculation unit (2) receives the signal U, and all coal quality parameters are rapidly analyzed by means of an identification and analysis software package. Compared with the establishment of a mathematical model for measurement, the method in the present invention is not affected by changes in coal types and coal quality, and has high measurement accuracy. The present invention is suitable for the online and offline measurement of coal quality parameters.

The invention provides an ultrasonic-based surface density measurement method and system and electronic equipment, and relates to the technical field of signal processing. According to the method, an integral value of voltage and time of an ultrasonic pulse signal is monitored in real time to serve as a reference value of the ultrasonic pulse signal; and then the duty ratio of the ultrasonic pulse signal is adjusted by using the adjustment strategy determined by the reference value, so that the output voltage of the ultrasonic pulse signal is adjusted by using the integral value, the stable output of the ultrasonic pulse signal is realized, and a data drift error caused by external factors can be effectively inhibited.

The method for detecting the comprehensive working performance of the fresh concrete provided by the invention comprises the following steps: pouring the fresh concrete into a certain channel in a detection container which is provided with a plurality of channels and the bottoms of which are communicated with one another, and detecting the comprehensive working performance of the fresh concrete when the liquid level height of the fresh concrete in each channel is kept unchanged, measuring the liquid level height of the concrete in each channel and detecting the quality of the concrete in the container; a discharging opening in the bottom of the detection container is opened, the concrete in the detection container is discharged by means of self weight, in the discharging process, the mass of the discharged concrete or the discharging flow speed of the concrete is measured, and after discharging is completed, a concrete discharging mass-time curve or a concrete discharging flow speed-time curve is obtained; according to a measurement result, a series of indexes including the fluidity, viscosity, uniformity and apparent density of the concrete are obtained through arrangement and conversion and serve as a comprehensive evaluation result of the working performance of the fresh concrete. According to the method, multiple working performance indexes of the concrete can be given at a time, and mutual complementary interpretation can be carried out.

The invention provides a submarine sedimentary environment dynamic change characteristic inversion method based on three-phase potential. The method comprises the steps of in-situ data processing, electrical signal calibration, mathematical model establishment and dynamic change analysis. A seabed electrode array is arranged, signals of natural potential, resistivity, oxidation-reduction potential and the like are collected in real time, and seabed sedimentary environment characteristics such as seabed interface position, suspended particle concentration, sediment density, porosity, water content and the like are deduced by adopting an inversion algorithm. The method is combined with multi-frequency signal analysis, and can monitor seabed sedimentary environment changes in real time, identify sediment movement and the like. Compared with the prior art such as CN118153411B and CN110411923B, more abundant inversion contents are covered, the monitoring precision and efficiency are improved through the complementarity of potential signals, and higher adaptability and reliability are achieved. The method has low equipment and maintenance cost, is suitable for large-range and long-term monitoring, and can provide an innovative solution in the fields of deep sea monitoring, environment evaluation, resource exploration and the like.

The invention relates to the technical field of asphalt performance analysis, in particular to a method for detecting and analyzing physical properties of polymer modified asphalt. According to the invention, the collected asphalt sample is subjected to pretreatment means of vacuum degassing, multi-angle image bubble monitoring and constant-speed stirring, the uniformity of the asphalt sample is judged according to physical state parameters at different positions, the reliability of subsequent detection data is further improved, and a plurality of uniformly-divided target asphalt samples are respectively placed in the constant-temperature containers; the method comprises the following steps: recording the heat conduction temperature of each target asphalt sample at different monitoring points in a constant-temperature water bath in real time, carrying out penetration detection on each target asphalt sample after the sample reaches heat balance, carrying out penetration index analysis according to the incidence relation between the temperature and the penetration, and evaluating the physical properties of the polymer modified asphalt based on the penetration index. The penetration data under different temperature gradients can be accurately obtained, the physical properties of the polymer modified asphalt can be comprehensively and accurately evaluated, and a reliable basis is provided for engineering quality control.