498 results about "Data Pooling" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

The invention discloses an intelligent port crane operation monitoring method and system, and relates to the technical field of crane systems.The system comprises a data acquisition unit, an edge computing node, a cloud processor and an early warning monitoring unit, through deep fusion and efficient treatment of multi-source data, a dynamic twinborn data pool architecture is established, and the dynamic twinborn data pool architecture is established; cross-data-type space-time alignment is realized through multi-modal coding, and the feature fusion accuracy is improved; the dynamic twin data pool is intelligently analyzed, and the crane operation path is optimized in real time, so that the track of the crane operation path is dynamically adjusted, and the working efficiency is improved; through a digital twin model and integration of a multi-modal physical engine, the operation physical state deviation of the crane is evaluated, an operation plan is corrected in real time, and the operation precision is improved; video streams are deeply analyzed, virtual-real comparison is carried out, and model prediction video deviation is monitored, so that the analogue simulation precision is improved; by establishing a closed-loop early warning mechanism, full-process digital management and control are realized, and intelligent upgrading of a port is promoted.

The invention relates to the technical field of seed processing analysis, in particular to a big data-based rape seed quality evaluation model, which is characterized in that a data pool is constructed by converging multi-source heterogeneous rape data, and high-quality input is provided for subsequent cross-domain feature association; a cross-domain feature interaction graph is constructed, a graph neural network is used for learning, cross-domain feature interaction is visually presented, and a gene-environment interaction relationship is analyzed; through a four-branch multi-head and cross-modal attention analysis atlas, focusing key features, fusing optimization information and generating a high-quality feature vector, high-order feature nonlinear fusion is realized, and the characterization capability of the model on multi-source heterogeneous data is remarkably improved; an evaluation model is constructed through decoding vectors, and high-yield variety data is extracted, so that efficient screening of rape varieties is facilitated; by deploying the generative adversarial network and transferring the learning adaptive model, the evaluation capability is quickly expanded to a new variety, and breeding intelligence is promoted.

The invention relates to the technical field of oil field databases, and discloses a dynamic data management method, device, equipment and medium for an oil and gas field database, and the method comprises the steps: docking multi-source data of the oil and gas field database, the multi-source data comprising a shaft sensor real-time monitoring data flow, geologic modeling gridding structure data and an equipment management tree coding table; the method comprises the following steps: performing abnormal value cleaning and timestamp alignment on real-time monitoring data flow of a shaft sensor, converting geologic modeling gridding structure data into a reservoir unit set with a coordinate system label, extracting a set membership chain of well IDs and equipment IDs in an equipment management tree-shaped coding table, generating an identifier system with a unified coding format, and storing the identifier system in a database; the identifier system comprises a well identifier, a reservoir unit identifier and a device identifier; generating a structured data pool based on the identifier system; and when the data pool is updated, sending a change instruction to a plurality of subsystems of the oil and gas field database, and updating each subsystem based on the change instruction.

The invention relates to the technical field of operation data analysis, in particular to a multi-dimensional operation analysis index automatic adjustment and optimization management method, which comprises the following steps of: connecting a plurality of service systems through a distributed data acquisition gateway, and constructing a standardized data pool based on a dynamic pattern recognition algorithm; calling a dynamic index calculation engine, performing multi-dimensional feature extraction on the standardized data pool according to a preset index generation rule, and generating an initial index set; inputting the initial index set into an optimization model, generating an index optimization scheme through an association constraint analysis module, and outputting an optimization index set; and establishing an index dynamic update link. According to the invention, the health state of the system is monitored in real time in the release process of each business index, rollback control is triggered based on the change trend of the health index, and the updating safety is improved.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention discloses intelligent knowledge service electronic equipment for information retrieval, and the equipment comprises a data management layer which is responsible for providing original data for a cognitive calculation layer, and carrying out the closed-loop optimization through continuously monitoring the feedback of a man-machine interaction layer; the cognitive calculation layer is responsible for converting original data into a semantic tag system and a dynamic knowledge graph, applying BERT + GNN hybrid analysis, BERT processing text context and GNN analysis entity relationship in the knowledge graph, and dynamically adjusting a reasoning path according to user field preference by combining an SWRL rule engine and reinforcement learning; the service arrangement layer is responsible for extracting data from the multi-modal data pool, solidifying and arranging a complex query stream to perform logic decoupling and efficient multiplexing, and providing a visual interface to support non-technical personnel to configure a new scene; and the man-machine interaction layer is used for constructing a natural interaction bridge through the structured answers and the visual maps output by the service arrangement layer, carrying out personalized interface adaptation based on historical dialogues, and establishing a user feedback closed loop.

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

The invention discloses a garbage collection method and device, equipment and a storage medium, and relates to the technical field of electrical digital data processing.The method comprises the steps that when metadata of a cache pool is updated, internal object abstracts corresponding to the metadata are put into a garbage collection queue; according to the water level condition of the data pool and the disk utilization rate of the object storage device of the data pool, the single-round execution task number of the garbage collection queue is determined; according to the number of single-round execution tasks of the garbage collection queue and the garbage collection priorities of the internal objects, the internal objects are selected to execute garbage collection, so that effective data in the internal objects are aggregated into new internal objects, and the internal objects subjected to garbage collection are deleted, namely, the situation that the water level of the current data pool is too high, and the garbage collection efficiency is improved is avoided. And the overlarge disk utilization rate of the object storage equipment is avoided, and the two are balanced, so that the influence on other businesses is avoided as far as possible on the basis of ensuring the normal recovery of the garbage.

The invention discloses a grid-connected detection method and system for an energy storage converter, and relates to the technical field of electrics, and the method comprises the steps: collecting the operation data of the energy storage converter through a multi-source sensor, and carrying out the preprocessing; fusing the virtual grid-connected scene data with the operation data of the energy storage converter to form an enhanced data pool, and performing feature extraction on the enhanced data pool to form a feature vector; based on the feature vectors, extracting the feature vectors of each time point through a sliding window method, and combining the feature vectors into a three-dimensional array; and based on the three-dimensional array, obtaining a grid-connected state through a grid-connected detection model. According to the method, the operation data of the energy storage converter are collected and preprocessed through the multi-source sensor, virtual grid-connected scene data are generated by utilizing the generative adversarial network, the adaptability of the model to complex working conditions is enhanced through rich data samples, and finally more accurate and more stable grid-connected state detection of the energy storage converter is realized.

The invention discloses a ground engineering construction quality supervision digital delivery system, which relates to the technical field of engineering supervision, and comprises the steps of generation and multi-source data checking, activity record structuring and anti-verification before hiding, space-time reference unification and point cloud fusion playback, rule checking and differential rectification, and object-level marshalling delivery and asset authority management. According to the method, a unified data pool, activities, events and evidence chains are constructed through object codes to achieve time-space consistent association, image, point cloud and detection consistency check is conducted before hiding, blocking difference and unified reference fusion point cloud are generated, indexes support three-dimensional playback, rule base batch check is conducted, and issuing rectification is conducted. And the object-level delivery list and fingerprints support cross-platform identification and auditing to form a traceable link.

The invention relates to the technical field of source network load storage intelligent scheduling, and discloses a source network load storage AI intelligent scheduling method and system, and the system comprises a data collection module, a fluctuation analysis module, a constraint calculation module, a load modeling module, an energy storage analysis module, a decision engine module, and a safety correction module. Output data of a photovoltaic power station, a wind power plant and a traditional power plant are collected in real time through an Internet of Things sensor, and a multi-source heterogeneous energy data pool is constructed; aligning the corrected source load data with the energy storage state and the power grid operation parameters according to a time sequence to generate a four-dimensional optimization combination matrix; matching an optimal scheduling algorithm through a bionic search strategy, and analyzing the data matrix through the deep reinforcement learning model to generate a three-section scheduling instruction; and finally, the power grid control system executes power generation adjustment, load regulation and control and energy storage charging and discharging instructions. According to the system, the edge computing gateway is adopted to realize data acquisition, the new energy consumption capability and the power grid stability are improved, and the risk of source-grid load-storage collaborative failure is reduced.

The invention discloses a reinforcement learning model training method and device based on dynamic data selection and electronic equipment, and the method comprises the steps: carrying out the screening from an original mathematical problem data pool according to preset data quality, diversity and difficulty evaluation indexes, and obtaining a candidate training set; calculating a data validity score of each training sample in the candidate training set; difficulty filtering is carried out on the candidate training set, and an adaptive training course is constructed; further filtering the candidate training set according to the solution reasoning process and the reward score of the candidate training set; dynamically selecting a data subset for the current training period by setting a temperature parameter attenuated along with the training process and a probability threshold based on the further filtered candidate training set and the data validity score; the selected data subsets are put into a reinforcement learning model for training, and the reinforcement learning model is trained in an optimized mode according to the correctness reward and the format reward. The model training period can be remarkably shortened, and the performance equivalent to or even better than that of full data training is achieved.

The invention relates to the field of visual element recommendation, in particular to a personalized visual communication element recommendation method and system based on Internet big data. The method comprises the following steps: acquiring user multi-dimensional internet behavior data, and mining platform user interaction behaviors one by one to obtain a user multi-platform interaction content data pool; performing user interaction intention speculation on the user multi-platform interaction content data pool, and performing personalized interaction interest preference mining to obtain user interaction interest preferences; deep interaction demand mining is carried out according to user interaction interest preference, adaptive portrait evolution is carried out, and a personalized user demand portrait is constructed; and performing visual element analysis and multi-modal visual mining according to the user multi-platform interactive content data pool to obtain multi-modal visual element information of each homepage. According to the method and the device, the accuracy and the quality of visual element recommendation are improved through accurate user aesthetic demand analysis.

The invention provides a strategy model training method and device for a robot and a storage medium, and the method comprises the steps: in an offline learning stage, firstly collecting expert demonstration data, enabling the robot to carry out the imitation learning based on the expert demonstration data, and obtaining an initial strategy model and an offline data pool; and entering an online learning stage, enabling the robot to perform reinforcement learning on the basis of expert strategies, determining training data of a current time step according to manual intervention information, adding the training data into an online data pool, performing model parameter updating according to the offline data pool and the online data pool, and after all tasks are finished, performing online learning. And taking the obtained initial strategy model as a target strategy model. In the online reinforcement learning process of the robot, autonomous exploration and manual intervention of the model are combined, the robustness of the robot strategy network is further improved, and efficient transformation of the robot strategy from demonstration to autonomy is realized.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention provides monitoring equipment and system for monitoring a reactor core of a nuclear power plant. The monitoring device comprises a data preprocessing module, a model updating module, a monitoring module, a shared data pool and a monitoring interface module. On one hand, on-line monitoring of a reactor core is realized by tracking actual operation data of a reactor, and the accuracy of a monitoring result is improved; and on the other hand, the reactor core three-dimensional power distribution reconstruction is carried out in combination with the actual measurement current, so that the monitoring result (such as the second reactor core physical parameter) obtained based on the reconstructed reactor core three-dimensional power distribution is more matched with the actual operation condition of the reactor core, and the accuracy is higher.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention belongs to the technical field of smart cities, and relates to a smart city public service opinion feedback multi-dimensional evaluation system, which comprises a data acquisition and processing module for analyzing object contour features in public service original data to generate a structured data pool; the space-time atlas modeling module is used for generating a demand migration feature vector with a weight coefficient; the dynamic scheduling engine module is used for calculating a municipal resource real-time scheduling path according to the demand migration feature vector, and generating an emergency execution scheme when a resource gap value exceeds a preset threshold value; the task execution monitoring module is used for splitting an emergency execution scheme into interactive task nodes, acquiring running state data of an execution terminal in real time, and triggering an automatic replacement path supplement mechanism when detecting that the overheating duration of equipment exceeds a safety critical value; and the feedback optimization evaluation module is used for generating an urban governance health index report containing a compensation scheme and a priority adjustment factor. According to the invention, the service can be recovered only through manual intervention when abnormal events are solved.

The invention discloses a traffic flow prediction method and device, a storage medium and electronic equipment. The method relates to the technical field of traffic flow analysis, and comprises the following steps: constructing a multi-source traffic data pool, and preprocessing historical traffic data in the multi-source traffic data pool to obtain a target multi-source traffic data pool; performing data enhancement processing on the historical traffic data in the target multi-source traffic data pool by adopting a pre-trained GANs model to obtain an enhanced sample set for training a target traffic flow prediction model; performing model training on an initial traffic flow prediction model by adopting a Dropout method based on the enhanced sample set to obtain a target traffic flow prediction model meeting a preset condition; and performing traffic flow prediction on multi-source traffic data acquired in real time by using the target traffic flow prediction model to obtain a traffic flow prediction result. According to the method, the accuracy of urban traffic flow prediction can be improved.

The invention relates to the technical field of artificial intelligence, and discloses an information security model auxiliary decision-making method and system based on an intelligent knowledge graph, and the method comprises the steps: constructing a multi-modal security data pool of a to-be-decided scene, training a joint extraction model of the to-be-decided scene, and extracting data examples and example relationships of the multi-modal security data pool; constructing a security knowledge graph of the scene to be decided; constructing an attack tactics-technology-process ontology layer of the scene to be decided to calculate a potential attack path of the scene to be decided, and calculating an attack path occurrence probability and an attack influence range of the potential attack path; marking a strategy decision point of the security knowledge graph, and analyzing a Top-K strategy of the strategy decision point by using a preset PPO algorithm; and constructing an attack chain analysis interface of the Top-K policy in the security knowledge graph to generate a policy optimization parameter of the Top-K policy, and executing the information security model aided decision of the scene to be decided based on the policy optimization parameter. According to the invention, the efficiency of security decision-making of to-be-decided scene information can be improved.

The invention discloses an exhibition information synchronization and real-time updating management method and platform based on cloud computing. The method comprises the following steps: collecting multi-source heterogeneous exhibition information in real time through edge computing nodes; transmitting the collected heterogeneous data to a cloud computing center, carrying out format standardization, and carrying out semantic extraction on an unstructured text to generate a unified exhibition information data pool; periodic synchronization and instant synchronization based on event driving are carried out; when it is detected that multiple versions of the same information are updated, historical operation records of blockchain evidence storage are called, and a final effective version is generated in combination with preset priority rules and timestamp verification; and generating an adaptive information push packet according to the display capability and the interaction permission of the target terminal type. The method is used for solving the technical problems that in traditional exhibition information management, multi-source heterogeneous data integration is difficult, real-time updating response is insufficient, the unstructured text semantic processing capacity is weak, multi-source updating conflict resolution is not credible, and multi-terminal information pushing adaptability is poor.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention discloses a power industry language model training data dynamic selection method, system and device, and the method comprises the steps: obtaining original scene corpus data based on a power industry power grid dispatching and fault handling application scene aimed at language model training, and constructing a candidate data pool and a scene verification data set; performing parallel or pipelined multi-dimensional quality evaluation on the candidate data in the candidate data pool, dynamically adjusting the weight corresponding to each dimension in the multi-dimensional quality evaluation based on an adjustment strategy, and calculating to obtain a comprehensive quality score; a failure case library is constructed, in the language model training process, the failure case library is updated regularly, semantic relevance between candidate data and the failure case library is quantified, and batch construction of training data is carried out based on the semantic relevance; and based on the batch construction result, performing language model training feedback circulation. According to the invention, the overall efficiency and utilization rate of training data are effectively improved.

In various embodiments, a system for detecting and preventing equipment failure using a plurality of agents includes an interface configured to receive information about a state of the equipment from a plurality of sources. The system includes a data pool including the information from the interface. The system further includes a plurality of agents configured to access data in the data pool, where the plurality of agents are trained to assess a condition of the equipment based on the data. The system includes an orchestrator configured to: evaluate an assessment of a first agent; access the second agent for further assessment in response to the evaluation of the assessment of the first agent indicating access of a second agent; and cause a modification to be made to the equipment.

The invention discloses a structure construction deviation automatic identification and safety evaluation method, which comprises the following steps: acquiring multi-modal original signals such as space measurement, environment, equipment operation and images from multiple positions of a structure construction site, and establishing a trend drift feature distribution model through time sequence alignment, normalization, noise reduction and feature extraction; then through combination with generative artificial intelligence, virtual samples of historical and extreme working conditions are expanded, and construction of a mixed working condition data pool is realized; through distribution drift analysis and multi-target parameter optimization, a deviation detection threshold value is dynamically and adaptively adjusted, parameters and a judgment result are pushed to a digital twinning and safety control platform in real time, and meanwhile, whole-process log and performance archiving are carried out, so that the sensitivity, adaptability and system safety of structure construction deviation detection are improved, and the construction safety is improved. And the method has relatively high data fusion efficiency and algorithm generalization ability.

The invention relates to the field of geographic information mutual analysis, and discloses a multi-source dynamic collaborative geographic information mutual analysis method and device, equipment and a medium. The method comprises the following steps: collecting time-synchronized static geographic information, dynamic GPS positioning data and a plurality of pairs of starting and ending point unstructured text data sets, and constructing a dynamic spatio-temporal data pool; high-quality spatio-temporal data is obtained through dual anomaly verification and Kalman filtering cleaning correction; optimizing an address longitude and latitude mapping table in combination with a three-level administrative range and a dynamic high-frequency word; a non-standard address is analyzed into a structured text through a BERT-CRF model, and a precise coordinate and a standardized text are obtained through a hierarchical dynamic matching algorithm. By adopting the method, meter-scale positioning is realized, the non-standard address resolution accuracy is greatly improved, the duplicate name address mismatching rate is reduced, a dynamic scene is adapted, the resolution efficiency and reliability are considered, and an efficient technical support is provided for bidirectional accurate mutual resolution of geographic coordinates and text addresses.

The invention discloses a cross-border e-commerce service data management method, and relates to the technical field of service data management, and the method comprises the steps: obtaining commodity service data from a plurality of overseas e-commerce platforms, and caching the commodity service data to an edge data pool according to the attributes of a data source; based on source attributes, updating time and historical accuracy of the data sources and supervision rating of judicial jurisdictions, comprehensive credible weight values of the data sources are calculated, and main data channels are screened out according to the weight values; inputting commodity service data in the main data channel into a multi-language semantic encoder, generating semantic vectors, calculating cross-language semantic alignment, and comparing logic consistency of key structured fields; generating a consistency score according to a comparison result of the semantic alignment degree and the structured field, when the score is lower than a preset threshold value, marking as abnormal and entering a rechecking process, and otherwise, entering a compliance review stage; and calling the regional compliance rule base to carry out regulation matching on the data passing the consistency verification.