118 results about "Statistical correlation" patented technology

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 relates to the technical field of image recognition and understanding, and discloses a high-speed rail platform intrusion early warning system based on image processing, which comprises a main analysis unit, an interference source sentry unit and a collaborative decision controller, and the collaborative decision controller not only dynamically adjusts a time sequence association window according to the apparent velocity estimated by the interference source sentry unit, but also performs self-diagnosis on the health state of the interference source sentry unit by monitoring long-time-history statistical correlation. The technical bottleneck of mutual restriction of sensitivity and anti-interference performance in traditional statistical flow analysis is solved, so that high detection capability and extremely low false alarm rate of a weak shielding target can be kept under the working conditions of complex interference and sensor failure.

The application discloses a kind of cognitive impairment early screening and diagnosis system based on multi-modal data, including following module: (1) multi-modal data acquisition module, for obtaining image data, gene data, behavior data and clinical narrative information;The clinical narrative information includes at least two kinds of patient complaint text, interference factor hook item, symptom time axis;(2) causal purification module, for filtering the pseudo-correlation between multi-modal data by clinical narrative anchoring and reverse intervention deduction, including: narrative anchoring unit: based on preset causal conflict rule base, through structured clinical narrative field identification data conflict, trigger freezing or weight reduction operation;Intervention deduction unit: generate the reverse intervention verification package containing low-cost intervention measures and review plan, and update causal rule base based on review data;The application breaks through the statistical correlation limitation of traditional technology, realizes the upgrade of diagnosis and treatment paradigm from "correlation" to "causality".

The invention relates to the technical field of electrochemical detection and food quality safety, and particularly discloses an edible oil rancidity degree rapid detection system based on an electrochemical sensor, comprising: a data acquisition module for receiving an original electrochemical response signal and performing format standardization, abnormal data rejection and integrity verification; the data preprocessing module adopts a statistical optimization algorithm to reduce noise, correct baseline drift, generate a unified dimension data set and complete stability verification; the feature analysis module is used for extracting rancidity related feature parameters through statistical correlation analysis, and carrying out fusion calculation after significance verification to obtain feature values representing rancidity; and the evaluation and judgment module inputs the characteristic value into a pre-training model, outputs a quantitative index through multivariate statistical calculation, dynamically sets a safety threshold in combination with the edible oil type, and outputs a rancidity judgment conclusion after comparison. The method depends on statistical data processing and analysis, the detection reliability and accuracy are improved, and the method is suitable for edible oil quality safety rapid screening and quality monitoring.

The invention discloses a multi-channel measurement time difference fusion method for Kalman filtering, and the method comprises the steps: obtaining the historical data of multi-channel measurement time differences, and obtaining a series of historical mean values; monitoring multi-path measurement time difference input, judging interruption when any path of judgment observation value is missing or invalid based on a historical mean value, performing interruption compensation, and dynamically adjusting process noise covariance according to interruption duration; based on historical data, dynamically distributing weights; and fusing the multiple paths of time difference data through Kalman filtering, and outputting a smoothed time difference measurement result. Through an interruption compensation strategy (virtual observation and weight adjustment) and sliding window smoothing, even if single-path or multi-path input is interrupted, continuous fusion metering time difference can still be output; the dynamic weight distribution mechanism makes full use of statistical correlation of multi-path time difference, and the interrupt path dynamically adjusts the weight according to real-time reliability; and dynamically updating the process noise covariance Q so as to adapt to a time-varying noise environment and realize adaptive noise processing.

The invention discloses a risk early warning method and device, electronic equipment and a storage medium, and relates to the technical field of risk early warning. According to the risk early warning method and device, personalized learning is carried out based on a medical knowledge base in combination with multi-modal semantic embedding, and a user exclusive causal map is generated to consider individual differences; the model is updated in real time, the health state posterior probability is accurately calculated through a filtering algorithm, and misjudgment is reduced; a target event probability and a high-probability causal path are deduced, a hierarchical intervention instruction and an active disturbance calibration mechanism are matched, a user feedback dynamic optimization map is combined, and medical rationality and personalized adaptation are guaranteed, so that the problems that individual differences are ignored, high false alarms and missing alarms are likely to be generated, and the accuracy and the reliability of medical analysis are poor in existing threshold judgment or statistical correlation analysis can be solved. According to the technical scheme of the invention, the technical problems in the prior art are solved, and the technical effects of reducing the false report and missing report rate of health risks, enhancing the individual suitability, maintaining the medical rationality, and improving the continuous effectiveness of the home health service and the user credibility are achieved.

The invention discloses a power supply cell state diagnosis method and device, a storage medium and a program product, and belongs to the technical field of battery safety management. The method comprises the following steps: acquiring internal stress, temperature, terminal voltage and charge and discharge current values of a battery unit; based on the parameters, constructing a thermal runaway symptom index through a statistical correlation algorithm; inputting the historical state parameters into a long-short term memory (LSTM) network model, and predicting a future terminal voltage value; calculating the residual error between the predicted terminal voltage and the actual terminal voltage, and carrying out anomaly detection on the residual error; and performing comprehensive state diagnosis by combining the thermal runaway symptom index and the anomaly detection result. Internal stress parameters are introduced, LSTM prediction and OC-SVM detection are combined, early degradation symptoms can be captured from mechanical and electrochemical double angles, and the accuracy and advance of thermal runaway early warning are improved.

The invention relates to the field of internet security, and discloses a big data risk early warning and evaluation method based on artificial intelligence, comprising the following steps: step S1, collecting original security data from a heterogeneous data source; s2, constructing a global causal model; s3, constructing and evolving an event causal evolution diagram so as to establish directed edges with weights among the nodes; s4, dynamically evaluating the ability level of the attacker; s5, performing adversarial intention projection; s6, calculating a dynamic risk score; and generating an early warning when the score exceeds an early warning threshold. According to the method, asymmetric information flows among event types are quantified through transfer entropy, and a context evidence fusion mechanism is combined, so that a real causal relationship and a simple statistical correlation can be distinguished; the defect that a high false alarm rate is easily generated based on rule or simple threshold matching is overcome, so that the event causal evolution diagram can accurately reflect the internal logic and time sequence characteristics of an attack behavior, and the accuracy of complex threat perception is improved.

The invention provides a ship power device cross-domain fault diagnosis method based on a disturbance consistency confrontation mechanism, and the method comprises the steps: obtaining an original multi-source time sequence signal, carrying out the depth feature modeling and confidence guidance, and obtaining a global feature vector; performing disturbance consistency evaluation on the global feature vector, and performing statistical correlation alignment of domain distribution features to obtain alignment features; designing consistency supervision loss, and performing adversarial learning by using a domain discriminator; constructing a joint optimization objective function, and alternately optimizing the classifier and the domain discriminator to obtain a reasoning model; reasoning and fault category judgment are carried out on the new label-free data of the target domain through the reasoning model, and a fault diagnosis result is obtained. According to the method, the excellent cross-domain adaptive capacity is shown under the setting that the target domain has no label and the source domain is single, and the cross-domain migration intelligent diagnosis precision and adaptability can be comprehensively improved.

The invention belongs to the technical field of medical data processing, and discloses a preoperative multi-complication risk prediction method and system based on structured clinical data, and the method comprises the steps: inputting the causal association between risk factors and complication nodes into the edge of a knowledge graph, calculating the statistical correlation between all complications, and supplementing the statistical correlation into the knowledge graph, and performing network embedding training on the knowledge graph to form a first-stage model, performing preliminary risk assessment on complications, modeling the knowledge graph in a graph neural network mode, and performing joint training with the first-stage model to form a second-stage model to output a final complication probability. According to the method, the interpretability and cross-domain consistency of the model can be improved through deep fusion of the medical knowledge graph and the multi-relational graph convolutional network, stability and calibration performance are still kept in a specialist with scarce sample size, and the problem that a traditional black box model cannot be interpreted is avoided; and the practical application value can be evaluated conveniently.

The invention provides a closed watershed lake water level evolution prediction method based on multi-source data, and relates to the technical field of hydrological resources. The method comprises the following steps: firstly, collecting natural environment and social economic multi-source data of a closed drainage basin, and constructing a normalized multi-source feature input matrix; carrying out total element analysis on the features and extracting a nonlinear response threshold value; and meanwhile, Mantel statistical test is executed on the feature matrix to obtain a statistical correlation coefficient. And judging the characteristics which simultaneously meet the statistical significance and mechanism threshold conditions as real causal driven factors, and eliminating false related factors which are statistical significance but have no physical threshold. And finally, reconstructing a prediction model based on the screened causal-driven feature set, and outputting a water level evolution prediction value. Through'statistics-mechanism 'dual verification, interference of false correlation is effectively eliminated, the problem that a traditional black box model cannot distinguish causal and coincident trends is solved, and physical interpretability and robustness of water level prediction are remarkably improved.

The invention relates to the technical field of medical information processing, in particular to a gastric cancer / pancreatic cancer risk assessment method and system based on periodontal data, and the method comprises the steps: obtaining periodontal index data and clinical index data of a user, and extracting a periodontal image feature vector in the periodontal image data; fusing features reflecting similar information in the periodontal index data, the periodontal image feature vector and the clinical index data, and mapping the fused data into nodes in a heterogeneous graph; on the basis of statistical correlation, domain knowledge and biological association, defining connecting edges between different nodes, and constructing initial heterogeneous graph structure data; carrying out aggregation updating processing on the initial heterogeneous graph structure data by utilizing a heterogeneous graph neural network model to obtain feature representation of each node; and performing classification processing based on the updated feature representations of all the nodes, and outputting a risk assessment result that the user belongs to gastric cancer, pancreatic cancer or non-cancer people. And constructing a relational graph model by using the multi-modal periodontal data to realize quantitative evaluation of cancer risks.

The invention discloses a power demand prediction system and method based on a deep fusion network, and the method comprises the steps: carrying out the standardization preprocessing of multi-source data, introducing a self-adaptive weighting mechanism based on relation complexity, and dynamically fusing a plurality of statistical correlation coefficients to precisely recognize significant influence factors, the technical problem that traditional fixed weight screening is difficult to consider linear and nonlinear relationships is solved. Then, an Almong polynomial distribution lag model and coupling coordination degree analysis are utilized to quantify time lag contribution of influence factors and process feature interaction, and a final feature set including a dynamic conduction mechanism is constructed. And finally, through a deep fusion model integrating the convolutional neural network and the bidirectional long-short-term memory network, multi-scale local features and bidirectional long time sequence dependence are extracted in parallel, so that high-precision prediction of the power demand is realized, and the defects that an existing model is weak in generalization ability and difficult to capture a complex time sequence rule are overcome.

The present application relates to the technical field of intelligent tool parameter optimization, and particularly relates to an intelligent tool adaptive parameter optimization method and system, comprising: acquiring a multi-dimensional parameter set and state data in a running process, constructing a parameter influence relationship graph through causal inference and statistical correlation analysis; performing multi-level influence diffusion simulation based on the graph, identifying sensitive propagation links and oscillation loops, and constructing a constraint space topology; solving a multi-objective constraint satisfaction problem according to a current state and a target constraint, and generating a parameter configuration scheme with causal consistency; applying the scheme to an intelligent tool and collecting feedback, and updating influence weights and coupling coefficients in the graph using reinforcement learning. The present application realizes adaptive and accurate optimization of intelligent tool parameters, and improves running stability and performance.

The application discloses a differential privacy data publishing method based on risk self-adaption and structured Gaussian noise and relates to the technical field of data mining and privacy protection. The method comprises the following steps: preprocessing original table data and initializing a Rényi differential privacy accountant; calculating sample risk based on self-information and mutual information, and evaluating individual identification and structured correlation risk; constructing a structured dependence relationship between features by using a total variation distance and a spectral decomposition dependent projection algorithm, and generating a noise transformation matrix; constructing a synthetic data pool without real individual mapping based on a noisy edge distribution; and finally performing risk self-adaptive data cleaning and publishing. In the publishing stage, a dynamic camouflage mechanism is implemented according to the distance between the risk score and the sensitive threshold: structured noise is injected into high-risk samples to protect feature dependence, K-nearest neighbor camouflage replacement is performed on risk boundary samples by using a synthetic data pool, and independent noise is injected into low-risk samples. The application effectively retains the statistical correlation of data under the premise of strict privacy protection by fine risk measurement and structured noise injection, and significantly improves the utility of data in machine learning modeling.

The invention discloses an intelligent substation equipment fault diagnosis method, device and equipment, and relates to the technical field of intelligent substation fault diagnosis, and the method comprises the following steps: carrying out the space alignment of a power sampling signal based on an equipment connection relation, and obtaining a structured graph signal; performing feature transformation on the structured graph signal to obtain a statistical incidence matrix and mapping the statistical incidence matrix into real-time state feature points in a Riemannian manifold space; according to curvature characteristics of the Riemannian manifold space, obtaining geometric deviation between the real-time state feature point and a preset ideal state point by adopting a logarithm mapping operator; and calculating the fault contribution degree of each device by using the geometric deviation, determining a fault device, and outputting a device diagnosis result. The method is used for solving the problems that weak fault sensing sensitivity is insufficient and faults are difficult to trace accurately under complex working conditions in the prior art.

The invention relates to a data asset intelligent recommendation method based on security classification constraint and multi-factor fusion, and belongs to the technical field of data management and information. The method comprises the following steps that a user logs in an asset portal, the asset portal initiates a recommendation request, an intelligent recommendation engine receives the request, request analysis and context extraction are carried out, real-time authority authentication is carried out, metadata is obtained, rigid security level filtering is carried out, a security candidate set is generated, a multi-path recall strategy is carried out, recall results are aggregated, fusion sorting is carried out, and results are generated. And returning asset portal interface rendering, and viewing a result by the user. According to the invention, built-in security level security constraints are adopted, so that deep collaboration of recommendation and security is realized; multi-dimensional service features are fused, and the crossing from'statistical correlation 'to'service usefulness' is realized; and an elastic and extensible recommendation architecture is adopted to adapt to complex constraints of a high-security environment.

The application relates to the technical field of data authenticity judgment, and particularly discloses a data monitoring method and system for auditing business software, which comprises the following steps: obtaining to-be-audited data based on a data statistical template; converting the to-be-audited data according to preset data conversion rules to obtain transit data; calculating the correlation characteristics of different to-be-audited units based on the transit data to obtain a correlation matrix; statistically analyzing the correlation matrix according to time sequence; determining the data change rate of each to-be-audited unit according to the statistical correlation matrix; and marking a target to-be-audited unit according to the data change rate. The to-be-audited data is converted, the correlation relationship of the converted data of different units is calculated, then it is judged whether the correlation relationship changes at each moment, and the unit for priority analysis is positioned according to the change of the correlation relationship; the positioning function is added to the original technical scheme, and the work efficiency of the staff can be effectively improved.

The invention relates to the field of respiratory dynamics image evaluation, and provides a dynamic DR-based respiratory dynamics analysis method, which comprises the following steps of: acquiring a dynamic X-ray video sequence of a chest through dynamic DR equipment; a pre-trained deep learning model is adopted to segment the lung field area and eliminate rib interference, and a rib-free image is generated; generating initial grid points covering the lung field based on a first frame image of the rib-free image; intercepting a template area by taking a previous frame of grid point as a center, intercepting a search area at a corresponding position of the target image, and calculating grid point displacement through pixel intensity statistical correlation matching to obtain grid point displacement data; performing polynomial fitting correction on the grid point displacement data according to grid coordinates to obtain single-frame displacement; according to the frame rate of the dynamic X-ray video sequence, single-frame displacement is converted into a real-time speed field, and the displacement is accumulated to generate a displacement field relative to the initial frame; and superposing velocity field or displacement field visualization results in the dynamic X-ray video sequence.

This invention relates to a geochemical prospecting method based on graph neural networks and spatial features, enabling geochemical prospecting under different geological environments based on accurately predicted background concentrations of different chemical elements. The method includes: preprocessing raw geochemical multi-element data and separating it from background data to obtain background area data; constructing spatial context statistical features for each sampling point of the geochemical multi-element data according to a preset spatial scale, and fusing them with the original environmental features of that sampling point to generate an enhanced environmental feature vector for each sampling point; constructing a graph topology based on the statistical correlation between chemical elements in the background area data using the maximum spanning tree algorithm; constructing a graph neural network based on the graph topology, inputting the enhanced environmental feature vector of each sampling point into the graph neural network, outputting the predicted background concentration of each chemical element at each sampling point, and calculating a mineral prediction map.

The invention discloses a water conservancy project monitoring data pseudo-correlation identification method and system. According to the method, monitoring data, space coordinates and structure segmentation information of a monitoring point are obtained, a standardized time sequence is obtained through preprocessing, and a monitoring point space topological structure is constructed; performing correlation analysis on the time sequence, identifying candidate correlations, and extracting time-statistics correlation features; constructing a space attenuation expectation in combination with the space distance, and extracting a space inconsistency feature; extracting structure inconsistency characteristics by combining the structure segmentation information; constructing a correlation transfer expectation based on the indirect correlation path, and extracting correlation transfer inconsistency features after spatial distance correction; and performing multi-dimensional fusion on the time, the space, the structure and the transmission characteristics, calculating a pseudo-correlation risk index, identifying a pseudo-correlation relationship, and updating the monitoring correlation network. The reliability of data association analysis is improved.

The application discloses a piezoelectric guided wave online damage alarm method for an aircraft test flight structure, and belongs to the field of aircraft structure health monitoring, and comprises the following steps: constructing a guided wave sensing network; obtaining a reference feature sample set by acquiring a reference feature through a variable step window sliding method; performing probability statistical modeling on the reference feature sample set, obtaining a probability statistical correlation feature and a reference feature sample set residual error, and taking an absolute value to obtain a reference alarm feature; establishing a monitoring feature sample set, multiplying the monitoring feature sample set and the probability statistical correlation feature to obtain a monitoring feature sample set residual error, and taking an absolute value to obtain a structure damage alarm feature; performing network weighted fusion on the structure damage alarm feature, determining a damage high-probability channel according to a channel screening threshold, calculating a structure damage alarm threshold of the damage high-probability channel, and realizing structure damage alarm. The method is simple and efficient in implementation process, and realizes reliable alarm of structure pit damage under the influence of time-varying factors.

The invention discloses a short critical strong earthquake occurrence time prediction method and device, a medium and equipment, and relates to the technical field of earthquake prediction. The method comprises the following steps: performing standardized preprocessing on an atmospheric tidal gravity signal in a preset time window, and after an atmospheric tidal gravity abnormal event is detected, inputting a plurality of target days into a pre-constructed prediction model respectively to obtain the probability of strong shock in each target day after the atmospheric tidal gravity abnormal event occurs; the prediction model is obtained by training according to statistical correlation characteristics between the occurrence time of the atmospheric tidal gravity abnormal event and the occurrence time of the strong earthquake; the strong earthquakes represent earthquakes of which the magnitude is greater than a preset threshold value; and determining a strong earthquake occurrence time prediction result according to the probability of occurrence of the strong earthquake in each target day after the abnormality occurs. According to the method, quantitative probability prediction of the strong earthquake occurrence time is realized, and the method has the advantages of interpretability, calibration and online updating.

The application discloses a Kalman filtering multi-path measurement time difference fusion method, comprising the following steps: acquiring historical data of multi-path measurement time difference and obtaining a series of historical mean values; monitoring multi-path measurement time difference input, determining as interruption when determining observation value missing or invalid for any one path based on the historical mean values, performing interruption compensation, and dynamically adjusting process noise covariance according to interruption duration; dynamically allocating weights based on historical data; fusing multi-path time difference data through Kalman filtering and outputting smoothed time difference measurement results. Through interruption compensation strategy (virtual observation, weight adjustment) and sliding window smoothing, even if single-path or multi-path input is interrupted, continuous fusion measurement time difference can still be output; the dynamic weight allocation mechanism fully utilizes the statistical correlation of multi-path time difference, and dynamically adjusts the weight according to real-time reliability; the process noise covariance Q is dynamically updated to adapt to the time-varying noise environment, and adaptive noise processing is realized.

The invention relates to the technical field of time-domain electromagnetic geological exploration, in particular to a time-domain superconducting electromagnetic multi-effect multi-parameter extraction method and system, and the method comprises the steps: calculating time-domain superconducting electromagnetic induction-polarization-magnetization multi-effect response, and constructing a data error fitting term based on the multi-effect response and the residual error of measured data; constructing a constraint matrix of multi-parameter correlation coefficients including resistivity, polarizability and magnetic susceptibility, and constructing a multi-parameter statistical constraint term for describing statistical correlation among multiple parameters based on the constraint matrix; constructing a regularization objective function; an improved Gaussian-Newton optimization algorithm is adopted, the regularization objective function is minimized, the optimal value of the regularization objective function is iteratively solved, and the resistivity, the polarizability and the magnetic susceptibility are obtained through synchronous inversion. According to the invention, multi-parameter efficient and accurate extraction imaging in the deep mineral exploration and geological structure identification field is realized.

The invention discloses a fault early warning method and system fusing SAW temperature and UHF partial discharge, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: obtaining a surface acoustic wave temperature signal and an ultrahigh frequency partial discharge signal of power equipment, generating original time sequence data, respectively generating a temperature change rate parameter and a partial discharge activity intensity parameter, and carrying out the fault early warning according to the temperature change rate parameter and the partial discharge activity intensity parameter. Calculating a state correlation coefficient; comparing the state correlation coefficient with a linkage threshold value to generate a hierarchical fusion early warning signal; and analyzing the graded fusion early warning signal, and generating and outputting equipment fault early warning information containing fault property inference. Through a mode of synchronously acquiring temperature and partial discharge signals, calculating and analyzing statistical correlation of time sequences of the temperature and the partial discharge signals, and constructing a linkage threshold value to generate a graded fusion early warning signal, the thermoelectric coupling type fault can be accurately identified, the fault property is deduced, and the reliability and the intelligent level of early warning are improved.

The application discloses a virtual power plant multi-element resource dynamic aggregation method considering source-load correlation, and belongs to the technical field of power supply and distribution. The steps comprise the following steps: a source-side output feature and a load-side demand feature are extracted from a resource time sequence feature set, statistical correlation and time sequence correlation are calculated, and a source-load correlation feature is generated; a scene set reflecting a source-load joint change rule is constructed; time-effect layering and feasible region integration are performed on resource constraints; optimization and risk checking are performed in the aggregated feasible region to form a reliable dynamic aggregation instruction; and finally, correlation weight and resource boundary set are corrected by using execution feedback data. The method of the application can respond to power grid peak shaving demand with high reliability and economy by adopting the source-load correlation feature construction and joint change constraint generation coupling scene, and by fusing time-effect layering boundary to depict the aggregated feasible region.

The invention discloses a human resource intelligent information screening system and method based on data analysis, and particularly relates to the technical field of human resource information processing, and the method comprises the following steps: constructing a data offset weight recognition difference influence based on historical candidate samples, screening offset abnormal samples, and classifying the screened offset abnormal samples into an auxiliary verification set; after fields highly related to sensitive label fields are removed, non-sensitive attributes are extracted, candidate ability expression vectors are generated through semantic mapping, model training is completed, a disturbance comparison control mechanism is introduced, and finally, ability matching calculation is executed on a current applicant group, and an output result is calibrated in combination with an auxiliary verification set; according to the method, the quality of a training sample is controlled by constructing a data offset weight, sensitive features are eliminated based on statistical correlation to guarantee feature fairness, disturbance control and a result calibration mechanism are introduced to doubly improve structural fairness, and fairness and stability of intelligent screening are realized from a data source, a training process and a screening output full link.

This invention provides a method, apparatus, device, and storage medium for inferring a single-sample ncRNA regulatory network, relating to the field of gene identification technology. The specific implementation includes: acquiring ncRNA and target gene transcriptome data of a matched sample; for each target sample in the matched sample, calculating a first regulatory relationship strength matrix and a second regulatory relationship strength matrix of ncRNA and target gene before and after removing the target sample using a preset statistical algorithm; obtaining the fusion regulatory relationship strength matrix of ncRNA and target gene corresponding to the target sample based on the first and second regulatory relationship strength matrices; and inferring the single-sample ncRNA regulatory network of the target sample based on the fusion regulatory relationship strength matrix of ncRNA and target gene corresponding to the target sample. This invention can reflect the regulatory relationship strength between ncRNA and target gene at the single-sample level by constructing statistical correlation values, thereby inferring the single-sample ncRNA regulatory network.

The invention relates to the technical field of environment monitoring and data processing, and discloses a high-speed rail reinforcing steel bar production workshop air quality real-time monitoring method and system, and the method comprises the steps: constructing a unified state vector comprising a pollutant concentration field, a potential unknown source item, sensor zero offset and response gain; obtaining the original data of the dense static module and the reference value of the sparse mobile anchor point as observation vectors; based on a data assimilation algorithm, correcting the prediction state set by using an observation operator to obtain an analysis state set; and extracting a monitoring result from the analysis state set. According to the scheme, high-precision information of sparse moving anchor points is synchronously transmitted to sensor parameter estimation by utilizing statistical correlation in a unified state vector, so that real-time online calibration of dense low-cost sensors is realized, and the problems of zero drift and sensitivity attenuation are solved; meanwhile, multi-source heterogeneous data are fused, high spatial resolution and high measurement accuracy are both considered, unknown pollution sources can be dynamically compensated, and the robustness of the system is improved.