161 results about "Statistical correlation" patented technology

The invention discloses a knowledge construction method and system based on a large model and an RAG technology, and belongs to the technical field of large models.According to the knowledge construction method and system based on the large model and the RAG technology, through cross-text-block mutual information calculation and RAG enhanced reasoning, the system can quantify statistical correlation between entities, and in combination with context information of an external knowledge base, potential incidence relations of cross-paragraphs or documents are mined, so that the knowledge construction efficiency is improved. A dynamic semantic segmentation strategy is adopted to ensure that semantics in text blocks are consistent, context continuity is maintained by retaining overlapped parts, multiple expressions of the same entity are comprehensively judged through a multi-dimensional anaphora resolution mechanism, the error resolution rate is reduced, dynamic segmentation and mixed retrieval are combined, and the problem of large model input length limitation is solved. And through semantic retrieval and keyword matching complementation, the recall rate is improved, and the key problems of semantic fracture, cross-text association missing, low entity alignment precision and the like in traditional knowledge construction are remarkably solved.

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 fault diagnosis, in particular to a contact mechanical wear and fault diagnosis system which comprises a data acquisition module, a wear diagnosis module, a contact surface detection module, a buffer judgment module and a composite diagnosis module. According to the method, the three-phase opening time sequence, the resistance instantaneous value, the pressure signal and the shock wave envelope are aligned and fused according to the millisecond timestamp, the multi-source data time sequence consistency and the linkage feature recognition capability are improved, the statistical correlation between the operation time and the resistance difference value is calculated through covariance, the dynamic judgment precision of the abrasion process is enhanced, and the accuracy of the abrasion process is improved. Based on pressure quadrant division and gradient ratio extraction, spatial positioning features of contact surface depression are determined, the ratio of shock wave change rate to duration is extracted, sensitive recognition of buffer performance changes is enhanced, multiple types of diagnosis results are fused to form an overlapping analysis path, and accurate recognition and logic attribution of composite fault types are achieved.

The invention relates to the technical field of pipeline safety detection, in particular to a pipeline hydrogen-doped leakage real-time monitoring and early warning method and system based on Fluent simulation, and the method comprises the steps: obtaining leakage diffusion data of gas in a pipeline detected by multiple sensors, and carrying out the preprocessing of the data; extracting data features: carrying out conjoint analysis on a time-frequency domain to obtain a time-frequency matrix; by calculating mutual information evaluation correlation of different sensor data, feature statistical correlation is realized, and features with non-significant spatial correlation are removed; the pressure data, the flow data and the concentration data are subjected to sub-channel filtering and fusion processing based on federated Kalman filtering (FKF); establishing a pipeline model based on Fluent, and simulating a pipeline hydrogen-doped leakage process by taking the extracted features as input conditions; a risk model is constructed based on real-time sensor data, simulation prediction data and environmental parameters, and a hierarchical early warning mechanism is established. Through cooperative monitoring of multiple sensors, leakage characteristic data can be accurately captured, and safety and reliability of the system are ensured.

The invention provides a collision-free global path planning method for a mobile robot, belongs to the field of mobile robot path planning, and is used for solving the problems of intention misjudgment, low role allocation efficiency and high collision risk in a dynamic scene caused by dependence on statistical association in related technologies. According to the method, agent intention reasoning is achieved by constructing a mental atlas containing a causal relationship, efficient role allocation is performed based on a quantum role superposition state, path search is optimized in combination with a conflict risk field, role collapse is triggered when a conflict risk exceeds a threshold value, and cooperation of adjacent agents is guaranteed through a quantum entanglement cooperation state. According to the scheme, intention prediction accuracy and role allocation efficiency are improved, and collision-free and efficient global path planning in a dynamic environment is realized.

The invention provides an intelligent heat dissipation system of an oil-free digital energy air compression station, which relates to the technical field of heat dissipation of air compression stations and comprises a sensor module, a correlation analysis module and a heat dissipation execution module which are in communication connection. Statistical correlation, causal correlation and time correlation between each operation parameter and temperature are analyzed, a physical mechanism is introduced, a hybrid correlation model is constructed, a predicted temperature change trend is combined, specific operation parameters influencing the temperature change at the current moment are analyzed, specific equipment is positioned according to the operation parameters, and a heat dissipation strategy is executed. Through a closed loop of model prediction, parameter attribution, equipment positioning and accurate heat dissipation, data-driven correlation analysis and causal reasoning of a physical mechanism are combined, rapid positioning from temperature abnormity to a specific heating source is realized, and low efficiency of traditional equipment-by-equipment troubleshooting is avoided.

The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis model construction method based on distributed causal discovery and federated learning. According to the method, a covariance tensor containing statistical association information of an observation variable and an agent variable is obtained, a global causal graph is obtained by combining a federal causal discovery method introducing the agent variable, then a converted causal intensity matrix is embedded into a graph convolutional neural network, and finally a personalized and global diagnosis model is trained by using a FedAvg framework fused with a Dito algorithm. According to the method, distributed heterogeneous data can be effectively processed while data privacy is protected, model interpretability is improved by mining a causal relationship between variables, global generalization and client personality requirements are considered, the accuracy and generalization ability of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The method is especially suitable for fault diagnosis of industrial bearings and other scenes needing dispersed sensitive data processing.

The invention relates to the technical field of agricultural seedling optimization decision, and discloses a seedling optimization decision method and system based on a knowledge graph, and the method comprises the steps: constructing a causal graph, and recognizing the causal dependence relation between variables through a PC algorithm; carrying out anti-factual reasoning by adopting docalculus, and predicting the performance of the seedlings under the environment intervention condition; constructing a Bayesian network model, and carrying out uncertainty quantification by adopting variational reasoning; establishing a risk assessment index, and generating a seedling optimization strategy by applying a multi-objective optimization algorithm; performing comprehensive reasoning on the knowledge graph to generate a seedling optimization decision report; according to the method, by introducing the causal reasoning algorithm, the real causal relationship between the seedling characteristics and the environmental factors can be identified, and the method does not only depend on statistical correlation, so that the environmental adaptability prediction is more accurate.

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 a science and technology project evaluation method and system, and relates to the technical field of multi-index decision and intelligent modeling. Comprising the steps of collecting multi-dimensional data of finance, intellectual property, teams, historical projects and the like of science and technology projects; constructing an evaluation index system; constructing a cognitive map by taking the indexes as nodes and combining statistical correlation and expert rules; a graph attention network (GAT) is utilized to train the graph, and structural causal weights among indexes are extracted; fusing the structure weight and the original fuzzy relation matrix to form a structure enhanced fuzzy relation matrix; and in combination with a weight vector determined by a fuzzy analytic hierarchy process (FAHP), performing fuzzy weighted comprehensive evaluation, and outputting a project grade and an interpretable result. According to the method, the accuracy, self-adaptability and interpretability of science and technology project evaluation are effectively improved, and the method is suitable for complex project evaluation tasks in a multi-source heterogeneous data environment.

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.

Methods and systems are provided for a histogram model configuring a computing system to derive an indicator of compromise signature based on a sliding window index of identified malware samples, and a matching rule constructor configuring a computing system to generate matching signatures by selecting statistically relevant n-grams of an unidentified file sample. A matching rule constructor configures the computing system to construct a matching rule including, as a signature, 32 n-grams found in the unidentified file sample which occur most frequently, and another 32 n-grams found in the unidentified file sample which occur least frequently amongst records of the threat database across 32 discrete file size ranges. These functions can configure backend operations to a sample identification operation performed by a user operating a client computing device, in a fashion that does not require a user to manually discern strings from the unidentified file sample to derive a signature for the matching engine to search against the threat database.

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.

A multi-instance learning and weakly supervised BGP anomaly detection framework is provided, that detects and analyzes significant statistical correlations across multiple data sources such as model driven telemetry (MDT), network messages, event data logs, and / or device configuration data for network topology. Specifically, methods are provided that involve obtaining, from a plurality of data sources, data related to operation or configuration of Border Gateway Protocol (BGP) in an enterprise network and extracting one or more BGP features based on at least one correlation among the data from the plurality of data sources. The methods further involve detecting one or more network anomalies by performing a weakly supervised machine learning of the one or more BGP features and providing information about the one or more network anomalies for performing one or more actions associated with the enterprise network.

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 belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is 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.

A method for providing automated agent performance improvement includes obtaining a ranking for each agent of a plurality of agents; obtaining customer-agent interactions for each agent; determining a task label for each of the customer-agent interactions; selecting a first set of customer-agent interactions from the customer-agent interactions corresponding to a first task label; receiving application event streams corresponding to the first set of customer-agent interactions and comprising agent application usage data; analyzing the application event streams for statistically relevant differences for one or more activities performed by a first group of agents compared to a second group of agents; determining at least one activity of the one or more activities that is performed by the first group of agents differently than the second group of agents; generating guidance corresponding to the least one activity for the second group of agents; and deploying the guidance to the second group of agents.

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.

A machine learning (ML) system is provided for integrating common sense into the ML system. In contrast to existing machine learning algorithms that search for statistical correlations between concepts, the ML system is configured to learn the semantic relationships or causality between the concepts. This may be accomplished by training an algorithm or data structure to learn similar vector representations of words present in the same context (e.g., that are present together in the same sentence). The resulting AI / ML structure may be used to guide the generation of a causal graph having predictive capabilities. This causal graph may represent semantic relationships and / or causation between concepts, and hence may be employed to introduce a degree of common sense in the machine learning system.

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 belongs to the technical field of space meteorological prediction, and discloses a global ionosphere plate thickness prediction method based on multi-source data. According to the method, firstly, key input features are screened based on a physical mechanism and statistical correlation, then a data set containing time parameters, space parameters, ionosphere characteristic parameters and external environment parameters is constructed based on the key input features and a multi-source data fusion mechanism, and data preprocessing is performed on the data set; and then, constructing an ionosphere plate thickness prediction model based on a machine learning algorithm, and revealing regular changes of the ionosphere plate thickness prediction model along with solar activity, geomagnetic disturbance, latitude and seasons. According to the method, the machine learning algorithm is applied to global ionosphere plate thickness prediction for the first time, the spatial and temporal change rule of the global ionosphere plate thickness is effectively captured through feature conversion processing by means of multi-source data, the prediction precision of the global ionosphere plate thickness is improved, and the method shows high robustness under the extreme space weather condition.

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 provides a high-precision ghost diffraction encryption method for a dynamic target, and the method comprises the steps: carrying out the observation of the moving number of interference fringes of an object through a Michelson interferometer, carrying out the phase calculation, obtaining a set of observed phase change values, carrying out the conversion of the set of observed phase change values through a conversion function, and carrying out the encryption of the ghost diffraction. And taking the conversion function as a first-level key, establishing a ghost diffraction light path based on the optical element and the first-level ciphertext, and performing ghost diffraction operation on the first-level ciphertext and the four reference quantities respectively to obtain a second-level ciphertext and a second-level key respectively. According to the method, the ghost diffraction technology is introduced into the encryption process, ghost diffraction performs imaging by using the statistical correlation characteristic of the light field, and the image can still be restored through the statistical correlation characteristic under the condition that part of the light path is shielded or is interfered by noise, so that the anti-interference performance of the encryption method provided by the invention is effectively enhanced.