4491 results about "Data sequences" patented technology

The invention provides a flowmeter online calibration system and method, and the method comprises the steps: firstly collecting a real-time operation data set of a target flowmeter in a pipeline operation environment, and covering dynamic monitoring data and environment state data; preprocessing the data set to obtain an operation data sequence capable of describing the change trend of the operation state of the flowmeter at multiple continuous time nodes, inputting the operation data sequence into a pre-trained calibration parameter generation model, and generating a calibration parameter set comprising a flow correction coefficient, an environment compensation factor and an error adjustment threshold value; and adjusting and measuring output data in real time based on the calibration parameter set, generating a calibrated flow measurement result, comparing the calibrated flow measurement result with a standard flow reference value for verification, and if the deviation is greater than an error adjustment threshold value, regenerating the calibration parameter set and performing iterative adjustment until the deviation is within a threshold value range, thereby realizing high-precision and self-adaptive online calibration of the flowmeter.

The invention provides a vehicle collision early warning method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a target vehicle multi-source sensor data set which comprises vehicle operation state, environment perception and driving behavior data sequences, each sequence is composed of standardized monitoring data, then carrying out the dynamic spatial-temporal feature extraction of the multi-source sensor data set, and carrying out the early warning of vehicle collision. The method comprises the following steps: generating dynamic space-time coding characteristics containing characteristics such as a vehicle movement trend, calling a pre-trained collision risk prediction model to carry out multi-dimensional risk association analysis on the dynamic space-time coding characteristics to obtain a risk prediction result containing a longitudinal collision risk level and the like, and constructing a real-time early warning strategy set based on the risk prediction result. And finally, deploying the set to a vehicle-mounted decision-making system, establishing a control instruction mapping relation with a vehicle execution mechanism, triggering real-time risk intervention operation, and realizing effective collision early warning and risk intervention.

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

The invention relates to a meter state adaptive estimation method supporting data incomplete completion. The method comprises the following steps: acquiring continuous measurement data of a meter under a unified time reference to form an original measurement data sequence; generating a state parameter set for describing the current working state based on the original measurement data sequence; dividing an original measurement data sequence into a plurality of data windows, and predicting an expected measurement value in each window according to the variation amplitude and trend direction of collected data in adjacent time periods; comparing the predicted measurement value with an actual measurement value, and identifying positions with difference values exceeding a set threshold value to form an incomplete position index set; aiming at the incomplete position index set, in combination with the change trend and continuity of adjacent data, searching a complementation value in a preset reasonable numerical value interval, and generating a complemented measurement data sequence; according to the invention, the integrity and accuracy of the meter data under the condition of loss or abnormity are improved.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention relates to the technical field of exception prediction, in particular to a storage cabinet exception trend prediction system based on time series data analysis, which comprises a state monitoring module, an interval sensing module, a path reconstruction module, a symptom activation module and an evolution prediction module. According to the method, the state vectors including the temperature, the voltage, the current and the door lock state are constructed and combined with the timestamp information to form the time sequence data sequence, and the dynamic expression mode of state change is established; a jump characteristic is analyzed by using a ratio of a time interval to a state change amplitude, a short-time disturbance path and a trend evolution path are distinguished by combining a jump rate statistical index, and an evolution activation signal is identified based on trend maintenance and non-fallback characteristics. On the basis, a neural network structure with long-time dependent learning ability is introduced to capture an aperiodic thermal anomaly trend in a state sequence, and the accuracy and timeliness of anomaly recognition are improved through multi-dimensional parameter cooperative processing and path construction logic.

The invention relates to a power adapter charging protocol identification and compatibility self-learning optimization method. After the adapter establishes physical connection with a target terminal, a voltage signal output by the target terminal is collected, a handshake data sequence is formed, feature extraction is performed on the handshake data sequence, and a first protocol feature vector is generated. And performing similarity matching on the feature vectors in a protocol feature library to determine a historical protocol category and extract a corresponding historical charging parameter template. And during voltage and current step-by-step adjustment, collecting target terminal load impedance change data, extracting impedance spectrum features, and fusing the impedance spectrum features with the first protocol feature vector to form an enhanced protocol feature vector. And based on the enhancement protocol feature vector and the historical parameter template, predicting negotiation parameters supported by the target terminal, generating a predicted negotiation parameter set, and applying the predicted negotiation parameter set to a charging negotiation process, thereby realizing stable charging output with the target terminal. According to the method, the compatibility and the self-adaptive capability of the power adapter to various fast charging protocols can be improved.

The invention discloses a transient electromagnetic three-dimensional dike leakage hidden danger detection method based on unmanned aerial vehicle carrying, and the method comprises the steps: S1, scanning a detection region range, obtaining dike terrain remote sensing image and point cloud image data, and setting a flight path of an unmanned aerial vehicle according to a preset detection region range; s2, performing task flight, and transmitting transient electromagnetic pulse signals with different frequencies to the underground of the dike through a transmitting coil according to a transmitting current frequency range; s3, a receiving coil is used for synchronously collecting secondary electromagnetic response signals generated by underground medium induction, discretization sampling is conducted on the collected signals according to the sampling time interval, and a sampling data sequence is obtained; s4, constructing a three-dimensional resistivity model of the dike underground medium through a three-dimensional inversion algorithm; and S5, through the constructed three-dimensional resistivity model, multi-dimensional features of the leakage hidden danger are extracted, and an area with the leakage hidden danger in the dike is identified. The leakage hidden danger area can be rapidly identified, and the detection efficiency and precision are improved.

The invention provides an intelligent power grid load dynamic monitoring method and system, and the method comprises the steps: collecting real-time power consumption data, and generating a real-time power consumption data sequence; constructing a time sequence model based on historical synchronous data, generating an expected power load data sequence, and comparing the real-time power consumption data sequence with the expected power load data sequence to obtain a load trend prediction result; analyzing by applying a self-adaptive dynamic clustering algorithm based on a graph neural network, generating a configuration scheme of a clustering center and a clustering number, determining an optimal power distribution scheme in combination with a bilevel programming model and a robust optimization algorithm, and evaluating the risk of the optimal power distribution scheme to generate a power distribution strategy; based on a power distribution strategy, a demand side management strategy based on the game theory is formulated to generate an intelligent load response strategy so as to realize dynamic load monitoring; according to the invention, the management level of the smart power grid is improved, and a solid foundation is provided for future energy management and optimal scheduling.

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention relates to a self-adaptive control rewinding machine tension and coiled material deviation collaborative optimization method in the field of intelligent manufacturing, and the method comprises the steps: deploying a distributed tension sensor network at a key position of a rewinding machine coiled material path, collecting the tension value of each measurement point in real time, and generating a multi-point tension distribution data matrix arranged according to a time sequence; processing the multi-point tension distribution data matrix by adopting a sliding window time sequence analysis algorithm, detecting tension fluctuation abnormity, and if a tension value exceeds a preset threshold range, recording a tension abrupt change timestamp and a change amplitude, and generating tension abrupt change data; based on the working condition state description, the rolling diameter real-time change data sequence and the tension sudden change data, a prediction model reflecting rolling diameter change and tension fluctuation is constructed in real time, and a predicted tension trend is obtained; and comparing the predicted tension trend with a preset ideal tension range through a model prediction control algorithm, and generating a multi-target optimization instruction which comprises a dynamic torque regulation and control quantity and a floating roller position set value.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention relates to a wind power prediction method and device based on a pre-trained large language model, and belongs to the field of wind power generation, and the method comprises the steps: dividing a wind power data sequence into a plurality of time sequence patches; dividing the time sequence patch into a plurality of equal-length subsequences; extracting data features of the equal-length subsequences; converting the data features into semantic cue words; adding a text instruction to the semantic prompt word to obtain a semantic instruction; splicing the time sequence patch and the corresponding position code, time code and semantic instruction to obtain a fusion sample corresponding to the time sequence patch; inputting the plurality of fusion samples into a large language model for pre-training, and performing parameter adjustment on the large language model to obtain a wind power prediction model; and inputting the fusion sample of the new wind power data into the wind power prediction model, and outputting a wind power prediction value. According to the method, under the condition that the number of historical operation data samples is small, the large language model is pre-trained through the fusion samples, and high-precision wind power prediction is achieved.

The invention provides a scene perception method and device based on multiple modes, electronic equipment and a storage medium. According to the method provided by the embodiment of the invention, image features of a multi-view image sequence and radar features obtained by a 4D radar data sequence are firstly extracted, and spatial-temporal evolution of a dynamic scene and a static scene in a BEV space and a voxel space is modeled by using historical radar features and current radar features to obtain dynamic scene features and static scene features; and performing cross-modal interaction fusion on the image features, the dynamic scene features and the static scene features to obtain multi-modal fusion features, wherein the multi-modal fusion features can be directly used for 3D target detection, semantic occupancy prediction and / or motion state estimation. According to the invention, high-precision and high-efficiency scene understanding can be realized in a complex environment.

The invention relates to the technical field of noise maps, in particular to a noise map dynamic deduction method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: carrying out the space-time alignment of a multi-cluster low-dimension feature vector in an edge calculation node, and obtaining a multi-cluster low-dimension feature vector; generating a three-dimensional sound field reconstruction parameter of the first target data sequence and a spectrum energy distribution parameter of the second target data sequence; based on the three-dimensional sound field reconstruction parameter and the spectrum energy distribution parameter, dynamically controlling the sensor cluster to sleep and generating a space-time difference parameter; hierarchical encryption is carried out on the time-space difference parameters and the two types of data sequences in the dormancy period, and a basic ciphertext sequence and a feature ciphertext sequence for verification are generated; according to the method, the noise deduction map with space-time continuity is generated, high-precision dynamic deduction and energy efficiency optimization of the noise map are achieved, the evolution rule of noise along with time and space is effectively reflected, the real-time performance and accuracy of noise data are improved, and the actual situation of the noise can be reflected more truly, efficiently and in real time.

The invention belongs to the technical field of intelligent traffic system and artificial intelligence crossing, and particularly relates to a traffic multi-agent simulation decision-making method and system based on a large language model. Road network state data are coded into a three-dimensional feature matrix containing channel dimensions, time dimensions and space dimensions, and joint representation of numerical road network data and text event reports is achieved through a hybrid embedding model. The decision-making layer comprises a dynamic Prompt generator which generates a candidate scheme set based on a four-layer progressive prompt structure; the Monte Carlo tree search multi-objective optimization is executed in cooperation with the decision core module; and the execution layer comprises a cross-language communication bridging device which adopts a gRPC bidirectional stream communication protocol and a Protobuf data serialization scheme to realize millisecond-level data interaction between services and support a hot plug mechanism of a strategy injection interface. According to the invention, dynamic optimization of urban traffic resource allocation and breakthrough improvement of simulation deduction efficiency are realized.

The invention relates to a multi-source data fusion-based urban physical examination evaluation index calculation method and system. The method comprises the steps of extracting a multi-source data sequence; identifying a data source of the urban physical examination index set, and extracting an independent time sequence data sequence; calculating the information entropy of the independent time sequence data sequence, and distributing a basic fusion weight; calculating a dynamic state evaluation value of the independent time sequence data sequence, and performing weighted fusion on the basic fusion weight and the dynamic state evaluation value to obtain a comprehensive state evaluation value; obtaining a distribution variance of the basic fusion weight, inputting the distribution variance into the uncertainty quantification model, and obtaining an index calculation result containing uncertainty measurement; the real-time performance of the evaluation result is enhanced through an aging attenuation mechanism, and the latest state of the city system is accurately reflected; the output uncertainty measurement index provides a quantitative basis of result credibility for a decision maker, and the decision risk caused by a data fusion error is reduced.

The invention provides a data processing system for cooperative operation of an intelligent combined fleet, and relates to the technical field of data processing, and the system comprises an acquisition module which is used for acquiring basic state data of a ship; the prediction module is used for generating state trend data in combination with the historical motion trail and the near-time data sequence, and the state trend data comprises a speed change rate and a course angle change trend in a future short time period; the sensing fusion module is used for receiving the state trend data of other collaborative ships, aligning the state trend data with the state trend data of the ship, and performing synchronous calculation to generate fleet collaborative prediction data; the adjusting module is used for recognizing an included angle error between the current ship thrust applying direction and the future resultant force direction of the target structure and calculating the attitude adjusting amount; the control output module is used for mapping into a thrust adjusting instruction and sending an execution instruction to a power system of the ship; according to the invention, autonomy and accuracy of fleet collaborative operation are improved.

The invention relates to the technical field of engineering construction supervision, and discloses a building engineering construction supervision system based on big data analysis, and the system comprises a multi-modal data collection module which is used for collecting multi-source data of a construction site, and the multi-source data comprises structure sensor data, environment monitoring data, video image data, construction log data and building information model (BIM) state data; performing standardization processing and time synchronization on the data to generate a construction state data sequence; and the construction event modeling module identifies key events in the construction process based on the construction state data sequence and constructs a construction event graph, and the construction event graph is composed of event nodes representing construction events and event edges representing event collaboration or time correlation. By introducing an event atlas construction mechanism based on multi-source construction data driving, structured expression and semantic association mapping of key behavior units of a construction site are realized, and the problem of insufficient non-structured information processing capability in construction monitoring is overcome.

The invention provides a rainfall flood resource retaining structure displacement intelligent monitoring method, and relates to the technical field of data processing, and the method comprises the steps: obtaining a second source point cloud and a second target point cloud of a clay region, and building a spatial distribution map; calculating a global rigid transformation matrix based on the second source point cloud and the second target point cloud; performing rigid-non-rigid mixed registration according to the spatial distribution map and the global rigid transformation matrix to obtain a registered point cloud; calculating a corrected displacement field sequence based on the registered point cloud; and carrying out time-frequency domain analysis on the displacement correction factor and the corrected displacement field sequence, extracting a characteristic frequency and a damping ratio to construct a space-time incidence matrix, and generating a displacement data sequence containing geological parameter space-time variation characteristics. According to the invention, the precision and reliability of displacement monitoring are improved.

The invention relates to a multi-element load combined prediction method fusing a decomposition reconstruction technology and multi-task learning, and the method comprises the steps: firstly obtaining a historical load sequence of an electric load, a cold load and a heat load and a data sequence of influence factor characteristics, and forming a historical multi-element load sequence library and an influence factor characteristic library; performing preliminary decomposition on data in the historical multivariate load sequence library through a variational mode decomposition method, performing reconstruction by using a mixed characteristic evaluation method, and updating the historical multivariate load sequence library; the historical multivariate load sequence library and the influence factor feature library are remodeled into three-dimensional feature tensors, and a CNN-ECA-MMoE multi-task learning model is constructed and used for outputting feature sharing and feature extraction results; and constructing a CNN-ECA-MMoE-LSTM multi-task learning model, carrying out model training by using the three-dimensional feature tensor, and outputting prediction results of an electric load, a cold load and a thermal load through the trained model. According to the method, the prediction precision of the multi-element load is improved.

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention discloses a neurology patient rehabilitation nursing method based on multi-modal data analysis, and relates to the technical field of medical health, and the method comprises the steps: extracting neural function features through a multi-modal data fusion algorithm, and carrying out the calculation through a weighted fusion and statistical analysis method, and obtaining a neuroplasticity index vector; combining the neuroplasticity index vector with the unified multi-modal feature representation, carrying out multi-modal abnormal mode recognition analysis, obtaining a rehabilitation risk early warning signal and a personalized intervention suggestion, and generating a personalized rehabilitation scheme; performing dynamic optimization and self-adaptive adjustment on the individualized rehabilitation scheme by using a feedback loop mechanism to generate an optimized individualized rehabilitation scheme; physiological signals in individualized rehabilitation training based on the optimized individualized rehabilitation scheme are collected in real time and preprocessed, and a standardized physiological response data sequence is generated. According to the invention, the scientificity, timeliness and individual adaptability of regulation and control are improved, so that nerve function remodeling is accelerated and the rehabilitation risk is reduced.

The invention relates to the technical field of image recognition, and provides a video target recognition method and device based on artificial intelligence and a storage medium, and the method comprises the steps: obtaining a video frame data sequence of a target video, carrying out the multi-dimensional analysis of the video frame data sequence, obtaining global video frame information and local region-of-interest information, and generating a target feature map based on the global video frame information and the local region-of-interest information, then carrying out time sequence mode analysis to obtain time sequence evolution features, combining the generated semantic representation vector, inputting the semantic representation vector into a preset adaptive Transform model to carry out target recognition, and obtaining a target recognition result. A semantic representation vector is generated through feature fusion, and a self-adaptive Transform model is used for target recognition, so that the target recognition precision in a complex scene is improved, and the problems of low detection precision and insufficient time sequence information utilization during complex scene processing, dynamic change and long-time sequence analysis are solved.

The invention relates to the technical field of electric data processing, and provides a reliability evaluation method and system for a high-power charging module, and the method comprises an obtaining module which is mainly used for obtaining a multi-dimensional data sequence of the high-power charging module in a plurality of operation states, then carrying out the preprocessing, and obtaining a multi-dimensional data sequence; a standardized sequence set is obtained to construct a multi-dimensional fault trend evolution graph and a transient anomaly distribution graph, an early warning sensitive area and an early warning steady-state area are generated based on the multi-dimensional fault trend evolution graph and the transient anomaly distribution graph, scale fusion is conducted on the early warning sensitive area, a steady-state offset path is obtained, and the early warning sensitive area and the early warning steady-state area are obtained. And performing time consistency matching on the steady-state offset path and the early-warning steady-state region to generate a reliability evaluation curve. The reliability evaluation curve is generated through multi-dimensional data sequence collection and standardization processing of the high-power charging module in multiple operation states, the accuracy and reliability of evaluation are improved, and the problem that under the complex operation condition, an existing evaluation method cannot fully reveal the potential fault evolution trend is solved.

The invention relates to the technical field of data processing, in particular to a monitoring method and system for electrical equipment, and the method comprises the steps: enabling real-time data of any monitoring index of the electrical equipment and historical data in a historical time period to form a data sequence, and according to the change rule of each piece of data in the data sequence under different conditions, obtaining a data sequence; obtaining three abnormal characteristic values of each piece of data; for any abnormal characteristic value, obtaining similar historical data similar to any abnormal characteristic value of the real-time data, and obtaining an abnormal reaction degree of any abnormal characteristic value according to relevance between the real-time data and each similar historical data under other abnormal characteristic values; according to the abnormal reaction degree of each abnormal characteristic value and the three abnormal characteristic values of each piece of data in the data sequence, the abnormal degree of any monitoring index is obtained, abnormal early warning is carried out on the electrical equipment according to the abnormal degree of each monitoring index, and the accuracy of abnormal early warning on the electrical equipment is improved.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention discloses a steel structure quality defect tracing and analysis method based on deep learning, and the method comprises the following steps: collecting image data, sensor data and construction log information of a steel structure member, and generating a tracing identifier; performing alignment based on the traceability identifier to generate an alignment data sequence; based on the aligned data sequence, outputting a defect segmentation result by using an SE (3) isovariant graph neural network; performing continuous coherence topology analysis on a defect segmentation result, and outputting topologically continuous defect areas and severity scores; extracting process parameters of the defect area, calculating statistical dependency by utilizing an independence criterion, and screening out a paired sample set; based on the sample set, performing stability screening on the causal edges to form a causal graph for output; calculating a causal contribution score output by the causal graph, and outputting a liability sorting list; and filing the responsibility sorting list, and visually outputting a defect traceability analysis atlas and report at the same time. According to the invention, steel structure quality defect tracing and analysis are realized.

The invention discloses a water conservancy multi-modal intelligent decision-making method based on a model association protocol, and the method comprises the following steps: obtaining hydrological, meteorological and remote sensing data, and carrying out the preprocessing and feature extraction respectively; performing structured coding on the extracted multi-modal features by using a data serialization protocol to generate corresponding serialization codes, and performing packaging and transmission by using an MCP protocol; at a receiving end, de-encapsulation is carried out by using a corresponding MCP protocol, the data is restored to original multi-modal features, and each modal feature is fused through a dynamic weight distribution mechanism and a cross-modal attention mechanism; inputting the fused features into a big decision model driven by a knowledge graph, and generating a flood risk level and a corresponding decision through mixed decision reasoning by means of the knowledge graph and a rule engine; according to the method, the problem of data islands is solved, efficient fusion of multi-source data is realized, the decision process is more intelligent and interpretable, and the flood risk level and the prediction accuracy of the corresponding decision are improved.