1473 results about "Cross-validation" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

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 a big data privacy protection modeling method and system based on federated learning and a block chain, and relates to the technical field of privacy protection and joint modeling. According to the method, homomorphic encryption, differential privacy, federated learning, secure multi-party computing and block chain technologies are fused, big data privacy protection and joint modeling are realized, encryption and dimensionality reduction are performed on original data through homomorphic encryption and differential privacy, an encrypted training sample of secure privacy is generated, a local model is trained on an encrypted data set through federated learning, and a big data privacy protection result is obtained. The method comprises the following steps: calculating aggregation parameters by using security multiple parties, constructing a verification network in combination with a block chain, ensuring credibility and integrity of model training, and finally, adding noise optimization performance for a global model by using differential privacy, testing generalization ability through cross validation, and determining a deployable privacy protection joint learning model, thereby breaking traditional data islands, promoting cross-mechanism data cooperation, and improving the privacy protection performance. Big data values are released, and data protection regulations and privacy requirements are met.

The invention discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention discloses a bridge construction abnormity monitoring data identification method and system based on big data, and relates to the technical field of bridge construction monitoring, the system comprises a collection module, an analysis module, a process matching module and an execution module, the collection module collects construction data, transmits the construction data to the analysis module, carries out linkage verification on the construction data through an analysis unit, and carries out process matching on the construction data; identifying and outputting abnormal data, transmitting the abnormal data to a process matching module, constructing a judgment standard for dynamic matching through a construction process, comparing the abnormal data with the judgment standard, outputting a preliminary judgment result, transmitting the preliminary judgment result to an execution module, executing multi-stage cross validation on the preliminary judgment result, and outputting a final abnormal judgment result. By responding to multi-source data, missed judgment is prevented, a space-time linkage verification mechanism is constructed to improve anomaly recognition accuracy, a dynamic judgment standard adapts to risks of all stages, false anomalies are filtered through three-stage verification, the efficiency is improved through full-process automation, hidden danger early warning is assisted, accidents are avoided, and bridge construction quality and safety are guaranteed.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention relates to the technical field of transformer monitoring, and discloses a comprehensive online transformer monitoring system which comprises a sensor layer, an edge computing layer, a cloud platform layer and a communication module. The cloud platform layer comprises a data warehouse, a big data processing engine, a comprehensive diagnosis engine, a model training engine and a system management module, the edge calculation layer is in communication connection with the cloud platform layer through a communication module, the comprehensive diagnosis engine integrates a deep neural network and a knowledge graph inference engine, and cross validation of data driving and knowledge guiding is achieved; a model training engine utilizes historical data and online incremental data to continuously optimize a model, and a dynamic knowledge graph updates a fault rule through a real-time diagnosis result and expert feedback, so that the system can adapt to novel faults and complex working conditions, and the diagnosis accuracy and adaptability are greatly improved.

The invention discloses an on-load tap-changer vibration fault diagnosis algorithm based on tensor feature and adaptive weighted Stacking integration, relates to the technical field of on-load tap-changer fault diagnosis, and is used for improving the fault diagnosis precision. Comprising the following steps: S1, data acquisition; s2, feature extraction; the method comprises the following steps: extracting multi-scale time-frequency characteristics of an on-load tap-changer vibration signal by using wavelet scattering transform WST, and realizing low-rank decomposition and dimensionality reduction characterization of high-dimensional characteristics by combining a non-negative tensor decomposition model NTF; s3, fault diagnosis; a multi-base learner Stacking integration framework is adopted, and a prediction matrix is generated through K-fold cross validation; through a swarm intelligent optimization algorithm SRA, hyper-parameters and fusion weights of all base learners are adjusted, L2 regularization suppression over-fitting is introduced, and finally fault classification is realized by adopting a logic regression element learner with Softmax cross entropy. According to the invention, through fault diagnosis of multi-model adaptive fusion and optimization, the fault identification precision, stability and on-line monitoring capability are improved.

The invention discloses a large-model-driven automatic knowledge graph construction method based on a confidence feedback mechanism, and aims to improve the structural accuracy and semantic consistency in a structured triple generation process, and perform structural constraint guidance by using a few-sample prompt mechanism and a cross validation mechanism of a heterogeneous large model. And the control capability of the large language model on the triple format is enhanced, so that format offset and semantic redundancy in the generation process are reduced. And meanwhile, a multi-dimensional confidence evaluation system is constructed, model consensus judgment, semantic rationality analysis and knowledge consistency verification are fused, and refined quantification and screening of triple quality are realized. According to the method, a confidence backtracking feedback strategy is introduced, a generation-verification-optimization closed-loop process is constructed, the expression and correction capability of the system on a complex knowledge structure is enhanced, the dependence on an external API is effectively reduced, the consumption of computing resources is reduced, and the operation efficiency of the system and the feasibility of engineering deployment are remarkably improved.

The invention relates to the technical field of bridge health monitoring, and discloses a method for judging rigidity change of a bridge structure based on bridge health monitoring deformation data. The method comprises the following steps: establishing an initial data set of bridge deformation monitoring data and performing multi-scale decomposition processing to generate deformation component data of different time scales; inputting the deformation component data of different time scales into a pattern recognition engine, and recognizing a characteristic pattern data stream associated with the structural rigidity; constructing a rigidity influence factor sequence based on the characteristic mode data flow, and calculating a statistical characteristic quantity of the rigidity influence factor sequence through a sliding time window; performing multi-dimensional matching analysis on the statistical characteristic quantity and a historical reference database, and outputting a stiffness anomaly probability index; and activating a hierarchical verification mechanism according to the stiffness anomaly probability index, and confirming a stiffness change trend through a cross validation algorithm. Reliable data support is provided for bridge structure health condition evaluation.

The invention discloses a knowledge cross validation question-answering method and system for reducing hallusion of a large language model, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: generating results through multiple times of sampling: when a user puts forward a question, controlling the large model to perform multiple times of sampling, and generating a specified number of results; calculating hidden state related indexes: extracting the hidden state of the last token of the middle layer of the large model corresponding to the result, and calculating covariance matrixes and answer discrete feature values of the hidden states; mLP model prediction: inputting the discrete feature value of the answer and the length of the answer into a multilayer perceptron MLP, and outputting a hallucination-free probability; querying and summarizing a knowledge graph; and calculating a final illusion-free score and outputting a result. According to the method, the answer quality and credibility of a large language model can be remarkably improved, and the method is particularly suitable for application scenes with extremely high requirements on the accuracy of single-mode text generation contents.

The invention discloses a water conservancy facility monitoring method and system based on digital twinning, and relates to the technical field of water conservancy monitoring. Historical operation data of a water conservancy facility are obtained based on a digital twinning model for analogue simulation, and full life cycle data of the water conservancy facility are obtained; and importing the standard operation data into the digital twin model, carrying out data comparison and data compensation on the standard operation data and the full life cycle data, verifying the accuracy of the standard operation data, and generating output verification data after comparison is completed. The verification data is generated by comparing the operation data with the data simulated by the digital twin model and combining cross verification of multiple operation data in the same area, so that data accuracy confirmation and data compensation can be carried out on the data distortion problem caused by abnormal data acquisition of the water conservancy facility operation data acquisition equipment; the reliability of the collected operation data is ensured, and the accuracy of evaluation and decision-making of the subsequent water conservancy facility monitoring operation state is guaranteed.

The invention provides an HCG and LH test paper identification method and system based on optical characteristics and a storage medium. The method comprises the following steps: firstly, collecting and generating a reflectivity curve of a test paper detection area and a quality control area; secondly, extracting a reflection characteristic value of a curve in a detection area for test paper validity verification, and entering a core identification stage if the test paper is qualified; preliminary judgment is carried out by calculating a similarity deviation value of a quality control area curve and an HCG / LH standard curve, when the deviation is significant, a preliminary recognition result is generated and enters a cross validation link, and a result is confirmed by comparing a reflection characteristic value with a dynamic threshold range; when the similarity deviation is not obvious or the cross validation is not passed, starting a pre-trained discrimination model to perform final arbitration; by constructing a quadruple judgment mechanism of validity verification, preliminary recognition, cross validation and model arbitration, the operation complexity and misjudgment risk of manual test paper type selection are overcome, and the automation degree of the detection process and the accuracy and reliability of the recognition result are improved.

The invention discloses an AI-based tendering and bidding full-process intelligent generation and review method and system. The method comprises the steps of tendering full-process intelligent generation, bidding full-process intelligent generation and bidding review. An AI scoring model is adopted for review, the technical scheme of each bidding document is deeply analyzed through a natural language processing technology, and real-time cross validation is carried out with a knowledge graph, so that the score of each sub-item is provided with detailed comments and a definite evidence chain, the objectivity, transparency and credibility of review are greatly improved, and the review efficiency is improved. The review quality is improved from experience judgment to a data and knowledge driven level; secondly, potential violation behaviors are identified through a surrounding bidding risk report generated through multi-dimensional correlation analysis, and the risk prevention and control capability is effectively enhanced; and finally, based on comprehensive data analysis, automatically generating a bid-winning candidate sorting and comprehensive review report, and ensuring that the calibration result is optimal.

The invention discloses a multi-modal content intelligent auditing and violation detection method and system, and relates to the technical field of information processing. The method comprises the following steps: carrying out audio-picture separation on a video stream, and carrying out parallel processing on audio-to-text and visual key frame extraction; a space-time encoder is constructed to record the corresponding relation of the time stamps of all the modes; constructing a multi-modal resource target dictionary, and forming an inter-entity knowledge graph by using relation categories; calculating a confidence coefficient difference index between modals by comparing, learning and training the shared semantic space; and carrying out violation judgment, triggering a sensitive characteristic threshold value for any mode, and starting multi-mode evidence cross validation. According to the method, audio and picture separation is carried out on the video stream, parallel processing is carried out on audio-to-text and visual key frame extraction, a multi-modal resource target dictionary is constructed, violation judgment is carried out according to confidence coefficient difference indexes among modals, and false information auditing efficiency and detection efficiency are improved.

The invention discloses a road three-dimensional intelligent inspection system and method based on multi-source perception, particularly relates to the technical field of traffic intelligent inspection, and is used for solving the problem of low reliability of road multi-source data fusion. According to the invention, camera shooting, thermal imaging and a vehicle or airborne platform are deployed in a road scene to collect multi-source data; boolean consistency gating and mutual exclusion override are executed in a unified time window, high-credibility candidates are generated through cross validation, cross-modal external parameters are solved by means of road geometric anchor points and a random consistency model, and long-term stable alignment is ensured in cooperation with external parameter change rate threshold rollback; then, necessary and sufficient condition confirmation is carried out on foreign matters, accumulated water, smog and marking defect according to an inspection anomaly truth table, and a finite-state machine is used for tracking evolution and confirming upgrading nodes to schedule vehicle and machine platforms based on the shortest path of a topological graph and link field facilities to form a closed loop of discovery and disposal, so that the efficiency and reliability are remarkably improved, the cost is reduced, and misjudgment is reduced. The method has the advantages of intelligence, globalization and self-adaption.

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention discloses a prone position ARDS patient death risk prediction system based on interpretable machine learning, which constructs an interpretable machine learning prediction model based on clinical data of an ARDS patient before the ARDS patient receives prone position ventilation treatment, and is used for identifying high-death-risk crowds in an ICU (Intensive Care Unit). According to the method, an optimal prediction model is determined by performing preprocessing and correlation and collinearity screening on original variables, selecting key features by adopting Lasso regression and combining multi-model training and cross validation. On the basis, an SHAP method is introduced to explain a model decision basis, a column graph is constructed, visual expression of a risk assessment result is realized, and clinical understandability and practicability of the model are improved, so that auxiliary support is provided for precise treatment and resource allocation.

The invention discloses a grouting parameter intelligent feedback method based on rock mass groutability evaluation, which comprises the following steps: collecting geological condition data, rock mass quality data, field test data and grouting process data, and constructing a rock mass groutability grading data set; an SMOTE algorithm is adopted to process data imbalance, input indexes are screened through Pearson correlation analysis, and data are normalized through deviation standardization; an XGBoost classification model is constructed, hyper-parameters are optimized through grid search, the model is trained through K-fold cross validation, and the rock mass groutability grade is evaluated; constructing an XGBoost regression model by taking a rock mass groutability grade, grouting construction data and monitoring data as input, and predicting the maximum value and the minimum value of grouting pressure and slurry density by using an optimal model; and generating a grouting parameter interval according to the predicted maximum value and minimum value, and dynamically feeding back to the grouting equipment to regulate and control parameters. According to the method, scientificity and accuracy of rock mass groutability evaluation and grouting parameter regulation and control can be effectively improved.

The invention relates to the technical field of water supply pipe network monitoring and computers, and discloses a precise water leakage positioning method and system based on water supply pipe network leakage monitoring, and the method comprises the steps: obtaining pipe network time sequence data and a topological structure, and constructing a structured model; performing multi-source data cross validation according to the model, and determining a deviation source node; historical data of the deviation source node are extracted for simulation, and a confirmation result of the leakage event is obtained; analyzing the space-time gradient change of the upstream pipeline flow, and accurately positioning the coordinates of the water leakage point; the water leakage point coordinate enhancement model is fed back, consistency verification is carried out, and accurate coordinates are obtained; and integrating physical details of the pipe network to generate a final positioning result. By establishing a set of intelligent analysis closed loop from data fusion positioning to feedback verification, the problem of insufficient water leakage point positioning accuracy in the prior art is solved.

The invention relates to the technical field of cable fault diagnosis and positioning, in particular to a cable fault positioning method and system based on multi-source data space-time correlation, and the method comprises the steps: obtaining partial discharge, traveling wave and temperature multi-source monitoring data from a high-voltage cable-overhead line hybrid power transmission network; on the basis of space-time correlation analysis of partial discharge and temperature data, heat-electricity associated defect points are calibrated; comparing traveling waves of the cable and the overhead line, and identifying and inhibiting cross-domain propagation interference; positioning the traveling wave data which are not identified to obtain temporary coordinates, judging fault characteristics by fusing information such as sheath ring current and the like, and comparing the temporary coordinates with defect points to generate matching identification; and inputting multi-dimensional information such as fault features, defect matching identification and temperature into the fusion model, calculating the confidence coefficient of a fault mode, and outputting a cross validation positioning result. According to the invention, high-reliability positioning of mixed line faults and active early warning of insulation defects are realized by carrying out space-time correlation and fusion decision on multi-source monitoring quantities such as traveling waves, partial discharge, circulation, temperature and the like.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a bridge maintenance system based on data analysis, which comprises a multi-source data acquisition module, a fatigue degradation monitoring module, a wave velocity anomaly positioning module, a risk collaborative judgment module and a maintenance task planning module. According to the method, structural fatigue characteristics are subjected to cross validation by integrating two-dimensional data of a strain sequence and a stress wave propagation parameter, and a dynamic window range deviation degree is adopted to replace a static threshold value to establish a datum reference linked with a bridge bearing period; a same-path propagation difference value is extracted by using a high-frequency and low-frequency stress wave frequency band energy separation technology to detect continuous overrun, and a degradation trend weight and a priority list of damage space distribution are generated in combination with multi-index coexistence verification and a hierarchical sorting strategy; resource allocation is optimized by integrating a multi-level response mechanism of emergency repair coordinate positioning, directional detection and regular inspection, accurate crack positioning is achieved, the early degradation trend capturing capacity is enhanced, the misjudgment risk is reduced, and an operation and maintenance response chain is shortened.

The invention provides a multi-modal data and interaction quantification-based senile osteoporosis dynamic risk assessment method. The method comprises the steps of screening effective variables in candidate risk factors; collecting structured data and unstructured data, and cleaning and standardizing the data to obtain multi-mode data of a patient; calculating a statistical weight and a clinical weight to obtain a multi-dimensional weight, calculating a dynamic total risk integral of an effective variable, and quantifying a variable interaction effect; and carrying out risk level layering and clinical intervention suggestion to obtain a dynamic risk assessment result, and then carrying out internal cross validation and external data set validation. According to the method, data full coverage, weight self-adaption and risk dynamic interaction analysis can be achieved, scientificity, sensitivity and clinical friendliness of osteoporosis screening are greatly improved, the method is low in cost and high in prediction precision, risk grading is associated with specific prevention and treatment measures, and a prevention and treatment integrated information closed loop is achieved.

The invention discloses a fixed asset investment auditing method and device and a medium, and the method comprises the steps: carrying out the standardization processing of multi-source heterogeneous data of an investment project, storing the processed data in a structured database, and obtaining a standardized data set; performing entity identification and relation extraction on the standardized data set to obtain auditing entities and entity attributes; constructing an audit information atlas by taking the audit entities as nodes and the relationship between the entities as edges; based on the audit information atlas, determining a checking rule of cross-document consistency, executing the checking rule, and performing cross verification on the entity attributes to obtain a verification result; and identifying the auditing doubtful point according to the verification result, and associating the contradictory evidence to generate an auditing report. By constructing the audit information atlas, effective association of audit information dispersed in different stages is realized. By automatically executing a large number of complex cross validation rules, deep and hidden contradictions can be perceived, and the audit coverage and accuracy are greatly improved.

The invention discloses a multi-scale time-frequency network sleep stage classification method based on single-channel electroencephalogram, and belongs to the technical field of medical signal processing. Aiming at the problems of capturing multi-scale time-frequency characteristics, processing variability among subjects and modeling long-range time dependence of a sleep staging method, the method comprises the following steps: firstly, acquiring electroencephalogram signal data, and performing time sequence segmentation, sleep stage category label determination and standardized preprocessing by taking a single-channel electroencephalogram signal as an analysis object; performing data set division by adopting nested cross validation, and constructing a multi-scale time-frequency network model; the model comprises a feature extraction module and a sequence learning module, wherein the feature extraction module comprises a time domain branch and a frequency domain branch; a subject adaptive feature calibration module is proposed to dynamically compensate the influence brought by individual difference and signal quality fluctuation; respectively training a feature extraction module and a sequence learning module by adopting a component type training strategy; and inputting to-be-classified electroencephalogram signal data into the trained model, and outputting a corresponding sleep stage classification result.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.