304 results about "Ocean observations" patented technology

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

The invention discloses an internal wave characteristic field information rapid prediction method based on deep learning, and belongs to the technical field of ocean internal wave detection. According to the method, rapid prediction is carried out on the basis of traditional numerical simulation data by using deep learning, so that the efficiency of inner solitary wave prediction is remarkably improved; through multi-channel coupling learning and an iterative prediction strategy, the inner wave field prediction achieves the effect of high precision and high efficiency. Meanwhile, in combination with high-resolution numerical mode data, the consistency of a model prediction result and a physical process is ensured, and rapid extension period prediction of a solitary wave characteristic field in the ocean is realized while low error and high relevancy are kept. The method is suitable for application scenes of real-time ocean internal wave monitoring and early warning and the like, and can be widely applied to the technical fields of ocean environment monitoring and early warning, intelligent ocean observation, ocean engineering safety and the like.

The invention provides an ocean intelligent forecasting large model construction method, and relates to the field of ocean forecasting, and the method specifically comprises the following steps: obtaining multi-source ocean observation data, and constructing a high-resolution ocean analysis data set which is subjected to quality control, space-time registration, standardization and data set division processing through multi-source observation data fusion and numerical mode assimilation; constructing a basic prediction model, and performing multi-scale fusion on the frequency domain enhanced features and the spatial local features by using the basic prediction model; the trained basic prediction model is operated in a set area range, and an output result of the basic prediction model is recovered to an original physical quantity value through inverse standardization; and comparing the rolling output of the basic prediction model with observation data or a high-resolution mode result through a correction module, learning an error, outputting a correction quantity, and superposing the correction quantity with an original prediction result to obtain a prediction field. According to the technical scheme, the problem that the ocean forecasting model in the prior art cannot meet the requirement of a complex application scene is solved.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention discloses an ocean multi-source observation data fusion assimilation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source ocean observation data; uniformly converting the timestamps and the space grids of the multi-source ocean observation data into a data format of an FVCOM numerical model; constructing a data fusion assimilation model; the data fusion assimilation model comprises an FVCOM physical model and an AC-PKAN neural network; training the data fusion assimilation model, and adjusting the weight parameter of the data fusion assimilation model according to the dynamic observation data currently acquired by the edge device; and fusing and assimilating the multi-source marine observation data by using the data fusion assimilation model to obtain multi-source fusion marine observation data. According to the method, the timestamps and the space grids of the multi-source marine observation data are synchronized, and then the data fusion assimilation model is constructed for fusion assimilation, so that the fusion accuracy of the multi-source marine observation data can be improved.

The invention provides an intelligent storm surge prediction method and device based on deep learning and a medium, and relates to the field of storm surge disaster risk assessment, and the method comprises the steps: obtaining multi-source heterogeneous data of a typhoon storm surge disaster, and carrying out the preprocessing; the multi-source heterogeneous data comprises meteorological and marine observation data and high-precision geographic information data; constructing a typhoon and storm surge prediction network; the typhoon and storm surge prediction network comprises a multi-scale spatial-temporal feature module, a physical mechanism enhancement module and a spatial-temporal sequence prediction model based on a fusion attention mechanism and a graph convolutional network; training a typhoon and storm surge prediction network through multi-source heterogeneous data; acquiring meteorological and tide level live data; and inputting the meteorological and tide level actual data into the trained typhoon and storm surge prediction network to obtain a typhoon and storm surge state prediction result. A physical mechanism is introduced into the neural network to predict the states of the typhoon and the storm surge, and the prediction precision and efficiency are remarkably improved.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the field of ocean observation data processing, and discloses an ocean salinity data correction method based on space-time gradient adaptive filtering, which comprises the following steps of: calculating a motion compensation salinity gradient and a motion compensation temperature gradient; constructing a double-gradient driving filtering window, introducing a gradient sensitive weight distribution strategy, and carrying out adaptive dynamic filtering on the salinity data; generating an initial hyper-parameter combination of the GBDT model; inputting salinity data subjected to adaptive dynamic filtering into the model, optimizing an initial hyper-parameter combination of the model by using a grey wolf optimization algorithm, obtaining an optimal hyper-parameter combination through a dynamic search strategy, and obtaining a final salinity correction value at the same time; and carrying out index verification on the final salinity correction value, and when the salinity does not reach the standard, correcting a model hyper-parameter or a self-adaptive dynamic filtering parameter until the salinity reaches the standard. According to the method disclosed by the invention, the physical abrupt change signal of the spring layer region is reserved to the greatest extent while the noise is suppressed, and the accuracy of salinity profile correction is remarkably improved.

The invention relates to the technical field of sea wave height prediction, and provides a sea wave significant wave height prediction method and system fused with multi-source data, and the method comprises the following steps: correcting obtained satellite observation data based on obtained buoy observation data; carrying out preliminary fusion on the obtained reanalysis data and the corrected satellite observation data by adopting an optimal interpolation method; adding a layer of data mask to mark the position of the satellite data in the fused data; and inputting the marked initial fusion data into a VQ-VAE model, compressing the fusion data into discrete potential variables, inputting the discrete potential variables into a GPT model, and generating a prediction result of the significant wave height of the sea wave. According to the method, artificial intelligence, data assimilation and fine tuning technologies are combined, the data assimilation technology is introduced to fuse the multi-source time-space sparse marine observation data and the model simulation result, and the fine tuning technology is used in the training process to amplify the adjustment effect of the observation data, so that the precision and timeliness of sea wave prediction are improved.

The invention relates to an optical satellite absolute radiometric calibration method, device and equipment under ocean low brightness based on an offshore pseudo-invariant field and a medium, and belongs to the technical field of in-orbit absolute radiometric calibration. According to the method, a marine pseudo-invariant field with stable water and atmospheric optical characteristics is determined according to marine observation data of a calibrated optical satellite, the off-water radiance of the marine pseudo-invariant field is calculated according to the chlorophyll a concentration of the marine pseudo-invariant field, the zenith radiance is theoretically calculated, a data source is provided for absolute radiometric calibration of a satellite to be calibrated, and calibration is completed. The method explores the potential of the marine pseudo-invariant field in supplementing the traditional radiometric calibration method, gets rid of the limitation of a fixed and single marine optical buoy MOBY site data source, improves the data volume, the coverage area and the acquisition frequency of effective calibration data, provides guarantee for the continuity of an optical satellite observation task, and has a wide application prospect. And a plurality of calibration sources can be utilized to realize rapid and accurate calibration of the optical satellite, so that the calibration efficiency of the offshore optical satellite is remarkably improved.

The invention discloses a sound velocity distribution forecasting method based on a Mama neural network, and belongs to the technical field of ocean observation. According to the method, the historical profile data of the forecasting task area is used as a reference, the historical profile data are arranged according to the time dimension, and then the sound velocity distribution of the forecasting area at the future moment is forecasted. According to the method, the problem of prediction of underwater sound velocity profile distribution in the time dimension is solved, sound velocity distribution at future moments can be accurately predicted in real time, and sound velocity distribution at specified time resolution intervals in the future is estimated; the problems of insufficient time resolution of ocean sound velocity distribution estimation and lack of future sound velocity forecasting function are solved, and important guiding significance is provided for ocean scientific research.

The invention relates to an internal solitary wave identification and amplitude inversion method in mode data based on machine learning, and belongs to the technical field of ocean observation. In combination with a Mask-RCNN and a variational auto-encoder, firstly, sea surface height data is preprocessed and imaged, texture features are extracted through a local binary pattern algorithm, a labeled data set is constructed, model training is performed, and internal solitary wave stripes and positions thereof are identified from the labeled data set; through extraction and processing of temperature field data, the temperature field at the water depth is inversed by using a variational auto-encoder; and extracting the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. According to the method, large-scale and refined ocean mode data can be efficiently and accurately processed, and the efficiency and accuracy of data processing are remarkably improved. The method has a wide application prospect, and especially in marine disaster early warning, marine scientific research and internal wave mechanics research, the automatic identification and amplitude extraction capability of internal waves can be greatly improved.

The invention belongs to the technical field of ocean observation, and discloses an underwater sound velocity distribution super-resolution construction method based on multi-modal data fusion. According to the method, a sound velocity distribution forecasting model is constructed, the model carries out temperature preliminary reconstruction on original space area temperature data through a Unet neural network, and sea surface temperature features are extracted through a Mama neural network; correcting the preliminarily reconstructed temperature data through sea surface temperature features to obtain high-spatial-resolution temperature data; and finally, obtaining high-spatial-resolution sound velocity distribution data through a sound velocity distribution calculation module. According to the method, the underwater temperature and the sea surface temperature are subjected to data fusion, so that the sound velocity distribution of the whole space area is quickly and accurately forecasted under the condition of original low-spatial-resolution data, and the problem of insufficient spatial resolution of ocean sound velocity distribution estimation is solved.

The invention provides a sparse covariance reconstruction and adaptive grid correction-based DOA (Direction of Arrival) estimation method, and belongs to the technical field of ocean observation. According to the method, snapshot-level signal reconstruction is changed into covariance-level sparse modeling, an alternating optimization strategy is introduced to carry out energy estimation, a Sherman-Morrison matrix lemma is fused to accurately update a covariance inverse matrix, and an adaptive grid continuous correction mechanism is introduced. According to the method, a staged optimization framework is constructed, sparsity and geometric interpretability are fused, DOA estimation precision can be remarkably improved, off-grid error limitation is broken through, robustness under low signal-to-noise ratio and signal correlation is improved, algorithm calculation complexity is remarkably reduced, real-time processing capacity is improved, and the method is suitable for an underwater complex propagation environment.

The invention relates to the technical field of marine environment inversion, and provides a marine environment inversion method and system based on a mooring buoy, and the method comprises the following steps: employing a constructed ConvLSTM model to extract spatial features and time sequence features for the obtained marine environment data collected by the buoy, and obtaining the prediction data of the buoy data; carrying out fusion processing on the predicted data of the buoy data and the obtained data of the plurality of ocean observation data sets by adopting a variational mode decomposition and Kalman filtering fusion method; and constructing a marine environment generation model combining the diffusion model and the U-Net model to obtain a marine environment inversion result. The multi-source ocean data is combined, the precision and the calculation efficiency of the inversion model are improved, the inversion algorithm is optimized, the calculation efficiency is improved, and the inversion result is verified by using the actually measured data, so that the reliability and the applicability of the method under different ocean environment conditions are ensured.

The invention provides a coastal water level prediction method based on multi-modal observation data, and belongs to the technical field of coastal water level prediction.The coastal water level prediction method comprises the steps that multi-modal ocean observation data of different sensors are collected, and a multi-scale space-time registration matrix is established to achieve space-time alignment of heterogeneous data; an ocean dynamic process feature extractor is constructed to recognize astronomical storm surge and wave features and calculate nonlinear coupling parameters, an adaptive space-time fusion algorithm is adopted to dynamically adjust weights according to data quality to generate a space-time consistency data set, and an ocean dynamic coupling strength discrimination model is established to determine a modeling strategy. And finally generating a coastal water level prediction product containing a predicted value confidence interval and risk early warning. The technical problem of insufficient coastal water level prediction precision caused by difficult effective fusion of multi-modal ocean observation data under the condition of spatial-temporal scale mismatching is solved.

The invention discloses a method, a device, equipment, a medium and a product for estimating a sea condition deviation value of a radar altimeter, and relates to the field of ocean observation, and the method comprises the steps: screening L2-level GDR data of the radar altimeter based on a data identification bit and a sea surface parameter editing criterion of the radar altimeter; based on a threshold screening condition, constructing an SNSSB model according to the self-intersection point data set; according to different sea surface parameter combinations, training the SNSSB model, and determining a trained SNSSB model; and estimating a sea condition deviation value according to the trained SNSSB model, and according to the method, the sea condition deviation value of the radar altimeter can be accurately evaluated.

The invention discloses a sound velocity distribution forecasting method based on an Att-KAN neural network model, and belongs to the technical field of ocean observation. According to the method, an Att-KAN neural network model is constructed, prediction time resolution, prediction starting time and prediction maximum time length are specified according to a task, prediction iteration times and historical sound velocity distribution reference data corresponding to the prediction starting time are determined, and the prediction time resolution, the prediction starting time and the prediction maximum time length are calculated. And taking four or more integral change cycle data at the moment before the forecast starting time as a training set of the model to obtain a prediction result, and performing normalization processing on the result. According to the method, the problem of prediction of underwater sound velocity profile distribution in the time dimension is solved, sound velocity distribution at future moments can be accurately predicted in real time, and sound velocity distribution at specified time resolution intervals in the future is estimated; the problems of insufficient time resolution of ocean sound velocity distribution estimation and lack of future sound velocity forecasting function are solved, and important guiding significance is provided for ocean scientific research.

The invention discloses an XBT-T5 observation data deviation analysis and temperature correction method, and relates to the technical field of ocean observation, and the method comprises the steps: obtaining the original temperature observation data of a target sea area based on an XBT-T5 front-end temperature sensor, calculating the depth, generating a temperature-depth profile, and constructing a temperature-depth database in combination with historical depth observation archive data; the method comprises the following steps: synchronously acquiring temperature and depth data by using a CTD temperature-salinity-depth instrument, and carrying out association pairing with a temperature-depth database to generate an XBT-T5 observation data deviation field under the same spatial-temporal scale; based on generation of an XBT-T5 observation data deviation field under the same spatial-temporal scale, designing a dual-channel adaptive encoder, and constructing a temperature-depth collaborative correction model; and according to the temperature-depth collaborative correction model, coupling the temperature-depth database and generating an XBT-T5 observation data deviation field under the same spatial-temporal scale, thereby realizing XBT-T5 observation data deviation analysis and temperature correction. The method improves the quality and application value of observation data.

The invention discloses a wave sensor detection system based on a wave spectrum and a control method, and relates to the technical field of ocean observation, the wave sensor detection system comprises an X-axis motion module, a Y-axis motion module, a Z-axis motion module, an A-axis motion module, a B-axis motion module, a C-axis motion module and a control system; the control system calculates the three-dimensional translational motion of a wave surface point, the two-dimensional form change of a wave surface and the autorotation motion of a sensor carrier based on a P-M wave spectrum, an ITTC direction distribution function, a Gerstner sea wave model and a linear superposition theory; the wave sensor detection system is controlled to simulate the six-dimensional motion state of the sensor in the ocean wave environment so as to detect measurement and verification of the wave sensor on comprehensive characteristic parameters such as wave height, wave period, wave direction, wave energy spectrum and wave direction spectrum.

The present invention relates to a satellite-based method and system for processing ocean observation data. The method includes: obtaining ocean observation data of an ocean observation device within a set time; formatting the ocean observation data to obtain a plurality of data slices; arranging the plurality of data slices based on timestamps, and for each data slice, calculating the difference between this data slice and an adjacent data slice with a smaller timestamp, and defining the difference as the differential slice data corresponding to this data slice; for each differential slice data, performing: preprocessing the differential slice data to obtain preprocessed data, and determining whether the preprocessed data meets the similarity requirement. If so, defining this differential slice data as data to be processed, otherwise defining the preprocessed data as data to be processed; performing compression, encryption, and channel coding on the data to be processed to obtain data to be sent; and sending the data to be sent to a land observation station. The present invention can simplify ocean observation data and improve the security of transmitting ocean observation data.

The invention provides a multi-source data fusion correction method for a marine observation station, which belongs to the technical field of marine observation, and comprises the following steps of: establishing a hierarchical interpolation correction model comprising a weighted moving average algorithm and a space collaborative interpolation algorithm, and constructing a marine environment adaptive threshold adjustment mechanism based on a closed-loop feedback system; a Kalman filter is combined with a multi-factor coupling drift prediction model of a long-short-term memory network, a progressive degradation fusion strategy switched according to environmental conditions is designed, and a biological pollution gradual change detection correction model adopting a liquid neural network structure is constructed. A multi-source data quality evaluation system based on data continuity, numerical stability, consistency and historical reliability is established, high-precision self-adaptive fusion correction of the marine environment multi-source sensor data is achieved, and the technical problem that high-precision self-adaptive fusion correction of the marine environment multi-source sensor data is difficult to achieve is solved.

The invention relates to a wave height parameter correction method and system for low-angle laser radar observation data, and relates to the technical field of ocean observation data processing, and the method comprises the steps: carrying out the smoothing and data cleaning processing of original wave height data, so as to construct a fitting model of polynomial and Gaussian global trend analysis; and then, through double-model dynamic fusion, polynomial short-term fluctuation capture and Gaussian long-term trend capture are deeply combined, so that the generalization ability of the model is improved. And finally, intelligent reference interval positioning is introduced to adapt to wave height distribution changes in different periods and positions, excessive correction is avoided in combination with introduced correction factors, accurate correction data are obtained, the problem that the wave height is attenuated along with the distance is solved, and the efficiency and reliability of correction processing are remarkably improved.

The invention relates to the technical field of ocean observation data, in particular to an offshore ocean observation data quality control method based on time-frequency domain characteristics, which comprises the following steps: extracting the characteristics of first ocean data, including time sequence characteristics, frequency domain characteristics, inner time sequence characteristics and outer time sequence characteristics; fusing the internal time sequence features and the external time sequence features to obtain internal and external time sequence features; fusing the internal and external time sequence features and the frequency domain features to obtain time-frequency domain features; inputting a preset anomaly detection model to obtain abnormal first ocean data; and correcting the abnormal first ocean data to obtain the ocean data after quality control. According to the method, the time sequence features, the frequency domain features and the fusion of the internal and external time sequence features are introduced, the multi-dimensional features of offshore ocean observation data are comprehensively described, and the accuracy of anomaly detection is remarkably improved. A preset anomaly detection model is adopted to identify the time-frequency domain features, and an abnormal data correction mechanism is combined, so that the reliability and stability of observation data are effectively improved.

The invention relates to the technical field of ocean observation, in particular to an intelligent quality control system and method for ocean survey data, which takes whole-process data quality closed-loop management and control as a core, proposes an element-standard-process three-dimensional quality control framework, constructs a real-time and timing dual-mode architecture, and integrates a whole-chain visualization and non-intervention automation technology. By extracting more than 40 differentiated quality control elements of more than 10 types of data, a four-layer quantitative standard of equipment state-parameter setting-data integrity-operation compliance is established, and closed-loop control of the whole process is standardized. The error condition detection rate of the system reaches 89% or above, the manual quality control time cost is reduced by 90% or above, the quality control time efficiency is compressed to the real-time / timing hour level, paradigm innovation from post-event error correction to real-time intelligent management and control is achieved, core support is provided for high-quality collection and high-reliability application of ocean survey data, and the method is suitable for various ocean comprehensive scientific investigation scenes.

The invention provides a parameter self-adaptive adjustment method for a semi-automatic calibration system of a thermohaline depth gauge, and belongs to the technical field of marine survey. The parameter self-adaptive adjustment method comprises the following steps: determining the length of a calibration window by self-adaptive clustering; acquiring original data of the sensor and extracting features; calculating a measurement deviation value; establishing an association data set of the environmental factors and the deviations; constructing and training a multilayer neural network model; dynamically optimizing the calibration window length; verifying and optimizing the parameter adaptive adjustment model; applying an error decomposition function to separate different types of errors; generating compensation parameters by using a deep residual attention network model; and deploying the optimization model into the system to realize real-time adaptive adjustment. According to the method, adaptive data processing and deep learning technologies are combined, the problem that the measurement precision of the temperature-salinity-depth instrument is reduced in a complex and changeable environment is effectively solved through sensor parameter real-time optimization and error accurate compensation, and the reliability and accuracy of ocean observation data are improved.

The invention discloses a land-based data automatic receiving and preprocessing management system and method, and relates to the technical field of ocean observation data processing, and the system comprises a communication service subsystem, a data preprocessing subsystem, an intelligent perception linkage subsystem, and a data management subsystem. And full-process automatic processing of underwater acoustic real-time communication subsurface buoy observation data is realized through modular integration. Automatic receiving, safe storage and efficient preprocessing of deep sea power and biogeochemical element data are achieved, reliable data support is provided for marine scientific research and environment monitoring, and the long-term observation requirement of a 6000-meter water depth subsurface buoy system is met.

The invention relates to the technical field of international marine scientific investigation collaborative technology and mutual identification, and discloses an international marine observation data fusion and achievement mutual identification system suitable for joint scientific investigation. Comprising a multi-source data standardization access module, a polar region adaptive intelligent fusion module, an international achievement mutual recognition standard and verification module, a hierarchical sharing and mutual recognition process management module, a collaborative iteration and operation and maintenance management module, a block chain full-process traceability system and an international joint collaborative operation and maintenance mechanism. According to the system, the problem of international data heterogeneity is solved through multi-source data standardization preprocessing, accurate data fusion under polar region / open sea complex working conditions is realized by utilizing an intelligent fusion technology, a mutual recognition system of bidirectional verification is established, and the traceability of the whole process is ensured by adopting a block chain. The system supports hierarchical sharing and dynamic iterative optimization, is cooperatively operated and maintained by two international parties, guarantees long-term adaptation to standard updating and equipment iteration of the two parties, and effectively improves the joint scientific investigation data sharing efficiency and the result mutual recognition credibility.