252 results about "Causal analysis" patented technology

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably improved.

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention relates to the technical field of intelligent control of test equipment, and discloses a semiconductor device test equipment control system and method based on industrial data processing, and the method comprises the steps: collecting multi-source heterogeneous data of semiconductor device test equipment, and carrying out the preprocessing; constructing a structural causal model, and performing root cause identification through anti-fact reasoning by using the structural causal model; constructing an abnormal test fingerprint and a knowledge base; generating an intervention scheme, evaluating the generated intervention scheme, and selecting an optimal intervention scheme; processing the detected anomaly, and generating and implementing a preventive control strategy based on historical anomaly data and a causal analysis result; according to the invention, by introducing innovative technologies such as causal inference, anti-factual inference, abnormal test fingerprint identification and Monte Carlo tree search, intelligent control of semiconductor test equipment is realized.

The invention relates to an intelligent delivery decision-making method and system based on multi-dimensional index association, and the method comprises the following steps: S1, collecting user, advertisement and context multi-dimensional data, and carrying out the preprocessing of the data to generate standardized features; s2, performing fusion calculation on the multi-dimensional standardized features, extracting key features, and constructing and generating a user-advertisement-context joint feature set; s3, constructing a multi-task prediction model, and training according to a user-advertisement-context joint feature set; s4, according to a prediction result and real-time features of the trained multi-task prediction model, rapidly matching an optimal advertisement for a given user in a real-time bidding process; and S5, performing causal analysis according to the exposure / click log of the optimal advertisement, verifying the real effect of the advertisement, correcting the index, and feeding back to the feature engineering in the S2 and the model training step in the S3 according to the corrected index. According to the invention, the advertisement putting efficiency and effect are effectively improved.

The invention belongs to the technical field of artificial intelligence and engineering, and particularly discloses a tunnel deformation prediction method and system based on causal and space-time mixture graph attention, and the method comprises the steps: receiving monitoring data of a tunnel section, carrying out the data preprocessing of the monitoring data, and obtaining a time sequence; fusing the spatial adjacency relation of the monitoring points and the causal analysis result of the time sequence, generating a graph structure containing physical association and causal dependence, and constructing a weighted adjacency matrix in combination with the geological similarity of the monitoring points; inputting the weighted adjacent matrix and the time sequence into the hybrid network model, extracting spatial features and time sequence features, splicing the spatial features and the time features, inputting the spliced features into a full connection layer, and outputting a prediction result; and carrying out interpretability analysis on a prediction result, dynamically adjusting an early warning threshold value based on statistical distribution of prediction errors, and triggering a graded early warning signal for prompting. According to the invention, the prediction precision of tunnel deformation can be improved.

The invention relates to the technical field of data visualization and integration, in particular to a power equipment fault positioning and maintenance decision-making method based on knowledge graph reasoning, which comprises the following steps of: acquiring a structure parameter generation node, constructing a causal path positioning source component and outputting a maintenance link structure. In the invention, the time attribute is introduced through the node set constructed by the structure number and the operation parameter to strengthen the time sequence characteristic of the power equipment state identification, and the logic response sequence between the nodes is sorted through the causal relationship path chain to improve the accuracy of fault chain reasoning. A multi-path screening mechanism effectively avoids the problems of large path hop count and inconsistent response directions, path dominance identification ensures convergence and pertinence of fault positioning, and key components and maintenance suggested nodes are spliced so that a link structure not only has traceability, but also has operability. An ordered closed loop from structure identification, causal analysis and maintenance output of the power equipment is realized, and the fault processing accuracy and the performability of the power equipment are improved.

The invention discloses a multi-mode intelligent linkage 3D visual data center operation and maintenance system and method, and relates to the technical field of data center operation and maintenance. The system comprises a multi-modal data fusion module used for mapping physical sensor data and video monitoring and operation and maintenance logs to a unified three-dimensional coordinate system and constructing a multi-modal fusion feature tensor; the three-dimensional particle modeling module is used for constructing a particle space structure based on a Voronoi diagram and Delaunay triangulation and adaptively adjusting the particle resolution; the multi-modal score analysis module is used for calculating a particle multi-dimensional score and generating a semantic heat map to recognize an abnormal region; the structured noise prediction module is used for simulating future responses under different instructions based on a diffusion model; the instruction generation and regulation module is used for realizing causal analysis and instruction optimization in combination with a knowledge graph; and the three-dimensional visual interaction module supports real-time rendering and interaction operation at a client. According to the method, the intelligence, the real-time performance and the visualization level of data center operation and maintenance can be improved.

The invention relates to the technical field of data analysis, in particular to a city physical examination accurate analysis method and system based on AI multi-modal data collaboration, and the method comprises the following steps: constructing a self-adaptive fractal space-time grid, and mapping a city multi-modal data source into a space-time coding vector with a fractal dimension; establishing a physical field driven cooperative resonance network, and extracting an abnormal cooperative mode exceeding a normal resonance threshold; calculating a modal entropy chain value according to the real-time metabolic rate of the urban system, and generating an optimal weight matrix through a non-equilibrium thermodynamic model; and fusing the abnormal cooperation mode and the dynamic weight matrix, and generating a space-time causal map for displaying a fault source and a propagation path by using a causal discovery algorithm. According to the method, the multi-modal data collaboration and causal analysis technology is utilized, the emergency response capability of an urban system in the case of sudden failures is improved, the stability of urban operation is guaranteed, and the space-time causal atlas provides comprehensive visual information for the failure propagation process.

The invention discloses an industrial data analysis system and method based on digital twinning and causal inference, and the system comprises a physical sensing layer which is used for collecting multi-source heterogeneous data of an industrial site; the digital twinborn platform layer is used for constructing and operating a virtual twinborn model corresponding to the physical entity; the intelligent analysis engine layer is integrated with a causal analysis module and a federal learning module which are associated; the application and interaction layer is used for visualizing the analysis result and issuing a control instruction; wherein the causal analysis module is used for constructing a causal graph based on the multi-source heterogeneous data and performing causal inference. Through federal learning, on the premise of protecting data privacy of all parties, cross-organization and cross-region collaborative modeling and knowledge sharing are realized.

The invention discloses an industrial process supervision and management system based on multi-modal data processing, which comprises a data acquisition module (100), a data fusion module (200), an anomaly detection module (300), a decision optimization module (400) and a visual interaction center (500), and is characterized in that the data acquisition module (100) transmits multi-source sensing data to the data fusion module (200) in real time through an industrial Ethernet; the data fusion module (200) forms a multi-modal feature vector and sends the multi-modal feature vector to the anomaly detection module (300), and the anomaly detection module (300) presets a threshold value and sends a joint alarm signal to the decision optimization module (400) and the visual interaction center (500). The method has the advantages that breakthrough is achieved in the aspects of detection speed, positioning precision and decision reliability through multi-modal data fusion and dynamic modeling, and the technical bottlenecks of time sequence misalignment, single-source misjudgment, insufficient causal analysis and the like existing in a traditional industrial monitoring system are effectively solved.

The invention discloses a material performance spectrum detection system and method based on AI, and relates to the field of artificial intelligence, and the system comprises a spectrogram embedding module, a feature fusion module, a performance prediction module, an uncertainty evaluation module and an output analysis module. According to the method, multi-source information such as spectral data, microstructure images and material composition is fused, material performance characteristics are modeled through a multi-modal attention mechanism, and the integrity of characteristic expression and the accuracy of a prediction model are improved. A heterogeneous neural network structure is constructed, effective representation of high-dimensional spectrogram data is realized, and the feature learning ability is enhanced through a residual fusion mechanism. Through an attention hot area map, an attention distribution map and an LIME causal analysis method, key feature interpretation of each prediction result is provided, and scientificity and reliability of a prediction model are enhanced.

The invention discloses a power system fault tracing method and system based on data analysis, and relates to the technical field of fault detection, and the method comprises the following steps: collecting multi-source operation data in a target power dispatching system, carrying out the abnormal mode preprocessing, and generating a local abnormal feature sequence of each subsystem; constructing an abnormal feature incidence matrix based on the local abnormal feature sequence, and identifying matrix features based on causal correlation analysis of multi-source operation data to distinguish homologous anomalies and pseudo-synchronization anomalies; based on the identification result, fault boundary identification and anomaly tracing are carried out, and the anomaly level of each subsystem and the corresponding fault source node are output; and generating fault response indication information for scheduling control according to the abnormal level and the fault source node. According to the method, fault traceability and response indication generation are realized through feature association and causal analysis of subsystem abnormity, so that the abnormity judgment precision is improved, and the problems of inaccurate traceability and untimely response of a traditional method are solved.

The invention relates to the technical field of intelligent mine and environmental protection crossing, and discloses a mine development ecological data fusion and causal mining method based on a double-lineage knowledge graph. The method comprises the steps of constructing a double-pedigree domain ontology model comprising a production activity pedigree and an ecological response pedigree, carrying out multi-modal data space-time alignment and standardization, constructing a dynamic map reflecting the real-time state of a mine, predicting future ecological indexes by using a time sequence diagram neural network introduced with physical constraints, and carrying out real-time monitoring on the real-time state of the mine. And calculating the contribution degree of each production link to abnormity by utilizing anti-fact reasoning so as to lock a disaster-causing source, and generating a production regulation and control instruction according to a causal analysis result, and feeding back and executing the production regulation and control instruction. According to the method and the system, full-process traceability and accurate regulation and control of mine environment problems such as surface deformation and water and soil pollution are realized, so that the problems that data multi-source heterogeneous is difficult to fuse and the causal relationship is difficult to confirm in mine ecological environment monitoring are solved.

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data processing, and the method comprises the steps: extracting low-altitude multi-target decision features of a low-altitude semantic decision map, carrying out the fuzzy reasoning of the low-altitude multi-target decision features, and generating an anti-interference control instruction set; based on the anti-interference control instruction set, fault logs and task execution data during operation of the unmanned aerial vehicle are collected, and an evolution strategy library is generated through association rule mining; and performing causal analysis according to the evolution strategy library, generating a cluster coordination rule upgrade package, performing dynamic verification on the cluster coordination rule upgrade package, and outputting a self-healing strategy. According to the method, the reliability and the safety of task execution of the unmanned aerial vehicle are improved by constructing the semantic association large model and generating the anti-interference control instruction set.

The invention provides an intelligent identification and alarm method for respiratory suppression events in an anesthesia revival period, which comprises the following steps of: continuously acquiring high-frequency physiological data such as respiration, blood oxygen and electrocardio of a patient through multi-channel equipment, and establishing a dynamic causal network model fusing medical priori knowledge and clinical guidelines after standardized processing and feature extraction; a Granger causal test and a dynamic time warping algorithm are combined, a significant causal relationship among key physiological parameters is dynamically identified, a causal network structure is updated in real time, a causal analysis result is further input into a time sequence Bayesian network, calculation of a respiratory suppression event occurrence probability and reasoning path tracing are realized, and the probability of occurrence of a respiratory suppression event is calculated. According to the method and the system, the probability score is calculated, an interpretable medical logic evidence chain and thermodynamic diagram visualization are automatically generated, and if the probability score exceeds the limit, multi-mode alarm and data locking are synchronously triggered, so that the timeliness, intelligence and interpretability of respiratory suppression detection are improved, and clinical precise intervention is facilitated.

The invention discloses a multi-network cascading failure propagation prediction method based on a heterogeneous graph neural network under a disaster. The method comprises the following steps: acquiring multi-source data of disaster, electric power, communication and traffic networks; constructing a four-layer heterogeneous graph model, and defining multi-type interlayer edges; establishing a mapping relation between disaster physical quantities and physical node health states, and predicting an initial fault state at a disaster impact moment; defining a multi-scale and multi-mechanism propagation rule based on interlayer edges, and simulating a cascading failure process; a space-time heterogeneous graph neural network model is constructed, a heterogeneous graph neural network module and a gating circulation unit module are stacked in the model, and a double-end prediction layer is adopted to predict the continuous operation state and the discrete function state of nodes in an autoregression mode; and post-processing a state time sequence output by the model, and reconstructing a cross-domain cascading failure propagation path through a causal attribution algorithm. According to the method, the whole evolution process from disaster occurrence to multi-network cascading failure can be accurately simulated, and multi-scale failure prediction and causal analysis are realized.

The invention discloses an object full-cycle analysis method and system based on an artificial intelligence large model and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: receiving identification information, and constructing an object digital twinborn body; obtaining and analyzing historical data to obtain an original design intention and a boundary condition of the to-be-analyzed object, and generating an analysis result; the analysis result is written into the historical background dimension of the object digital twinborn body, and provenance analysis is completed; acquiring real-time data and performing suitability comparison on the real-time data and the boundary conditions to obtain a suitability evaluation result; writing the suitability evaluation result into the object digital twinborn body to complete today analysis; generating a risk simulation scene based on the suitability evaluation result; simulation calculation is executed in the risk simulation scene, a deduction result is obtained, and future analysis is completed; the analysis results of the provenance analysis, the today analysis and the future analysis are fused, and a causal analysis chain is established according to the analysis results; and generating a full-cycle analysis report based on the causal analysis chain.

The invention discloses a deep forgery detection interpretable method, system and device based on causal analysis and a medium, and belongs to the field of face deep forgery detection. The method comprises the following steps: acquiring a multi-source deep counterfeiting data set, extracting a face region, carrying out key point alignment, and carrying out preprocessing; constructing a structured causal model, abstracting the deep counterfeiting detection model into the structured causal model, and defining an endogenous variable and an exogenous variable; and inputting the forged data sets with different depths into the structured causal model, calculating the average causal effect of each neuron in the deep forging detection model, identifying the neuron having important potential for the generalization ability of the model, and calculating the intersection of the first n contribution neuron of the detection model on different data sets to obtain the depth of the deep forging detection model. Generalization neurons shared across the data sets are screened; and carrying out face deep forgery detection by using the final deep forgery detection model. According to the method, the detection precision of the deep forgery detector is improved, and the method has important potential to adapt to unknown forgery data sets.

The invention relates to the field of multi-modal neural network model compression, and particularly discloses a multi-modal model compression and distillation method and system based on causal reasoning, and the method comprises the steps: constructing a comprehensive causal discovery module, identifying a causal dependency relationship among the multi-modal features through information theory measurement, Granger causal analysis and intervention-based verification; executing an adaptive compression engine, and performing pruning, mixing precision quantification and low-rank decomposition based on a causal relationship; a cross-modal distiller is applied, and multiple loss function combinations are adopted to maintain the relationship between modals; and implementing a dynamic optimizer to carry out hardware perception and context-sensitive reasoning optimization. According to the method, the compression decision is guided through causal reasoning, the high compression rate is achieved while the key causal path is kept, and the deployment problem of the multi-modal model in the resource-constrained environment is effectively solved.

The invention discloses a heterogeneous data processing method and system for energy big data, and the method comprises the steps: obtaining different types of energy data, inputting the different types of energy data into a pre-trained directed association graph model, and obtaining a cross-type dynamic contribution flow network node forest; a cross-type linkage report is obtained, and a basic directed positioning dynamic correction chain is constructed through report analysis positioning and causal analysis; and inputting the cross-type dynamic contribution flow network node forest and the basic directed positioning dynamic correction chain into a pre-trained positioning correction path model, and performing real-time positioning error correction adjustment on the cross-type linkage report to obtain an updated and adjusted cross-type linkage report. According to the invention, cross-type linkage analysis and report real-time error correction of the energy data are realized, and the accuracy and efficiency of energy data processing are improved.

The invention discloses a water body health assessment system based on big data, relates to the technical field of water body health assessment, and realizes time synchronization, space mapping and quality assessment and restoration of multi-source data, calculation of water quality semantic indexes, data fusion, health level output and sampling work order generation. According to the method, deep fusion and standardized processing of multi-source heterogeneous monitoring data are realized, data islands are broken through, through high-precision space-time alignment and intelligent quality control, data reliability is ensured, human activity events are creatively fused for causal analysis, result interpretation is enhanced, multi-scale dynamic evaluation and adaptive threshold determination are supported, accuracy is improved, and the method is suitable for popularization and application. A closed-loop mechanism of sampling verification is evaluated, the unmanned ship is used for actively checking a suspicious area, secondary judgment is triggered, the response speed and result reliability of emergencies are remarkably improved, the whole-process compliance recording and standardized interface guarantee process is traceable, results are easy to share, and key technical support is provided for fine management of a water body.

The invention provides a knowledge tracking method and device based on a large model and causal analysis, and relates to the technical field of knowledge tracking, and the method comprises the steps: driving a large language model to carry out the multi-dimensional analysis of historical answer content through zero sample prompt learning, and generating the attribution explanation of the historical answer content; performing quantitative analysis on association between attribution explanations of the to-be-tested question and the historical answer content by using a time sequence causal attention mechanism to obtain a causal explanation set comprising a causal explanation subset and a background explanation subset; processing the causal explanation subset and the background explanation subset through a causal intervention method to generate a plurality of virtual anti-fact sequences, and performing representation learning on the anti-fact sequences by using a hierarchical aggregation network to obtain global representation for the to-be-tested question; and predicting a reaction result of a target user on the to-be-tested question based on a multi-task learning mechanism in combination with the global representation, thereby realizing mining of deep causes after answering.

The invention relates to the technical field of data analysis and artificial intelligence, in particular to a two-channel heterogeneous time sequence causal analysis method based on characterization embedding and time delay pairing, which comprises the following steps: collecting state data of a power battery to obtain multi-scale features; performing iterative optimization based on the multi-scale features to minimize a loss function to obtain a first edge type confidence probability of the power battery state causality graph; determining a target-explanatory variable pair, and randomly extracting data from the multi-scale features for multiple times to form a sampling set; performing conditional independence test on the target-explanatory variable pair based on a sampling set to obtain a second edge type confidence probability of the power battery state causality graph; based on the first and second edge type confidence probabilities of the power battery state causal diagram, obtaining a fusion causal diagram and carrying out acyclic processing to obtain an acyclic power battery state causal diagram as an analysis result; according to the method, the causal discovery robustness, interpretability and efficiency can be enhanced.

The invention discloses a mine gas monitoring and early warning method and system based on a large model, and the method comprises the steps: collecting the multi-modal data under a mine in real time, carrying out the cleaning, denoising, alignment and standardization processing of the multi-modal data, and obtaining the standard multi-modal data; establishing a mine safety knowledge graph, performing SFT supervised fine tuning and field adaptability pre-training by using the mine safety knowledge graph on the basis of the general large language model, and constructing a large model for mine field knowledge enhancement; standard multi-modal data are input into the large model, a multi-modal encoder is used for unified understanding and encoding to extract key information, and panoramic situation description is generated; and based on panoramic situation description, performing causal analysis and risk deduction by using a large model, and generating a mine safety early warning report in combination with a mine safety knowledge graph. The mine gas concentration can be accurately predicted, and the emergency disposal efficiency and effect are improved.

The invention relates to the field of constructional engineering, and discloses a dynamic risk prediction system, which generates space-time alignment input through multi-source data fusion, adopts tensor field modeling to embed contract constraint to construct a risk dynamic model, and solves and outputs a continuous risk field through a partial differential equation; a propagation path is analyzed in combination with asymmetric causal analysis, model parameters are adjusted in real time through a dynamic optimization algorithm, and closed-loop optimization of a risk field is achieved; and finally, through four-dimensional thermodynamic diagram interaction early warning and resource intelligent scheduling, a whole-process closed-loop system of data modeling-causal analysis-dynamic optimization-visual management and control is formed. According to the method, dynamic optimization of risk field parameters is realized through adjoint equation back propagation, and the modeling precision of a complex scene is improved; a four-dimensional space-time thermodynamic diagram rendering technology is innovated to solve the problem of fragmentation of multi-modal information expression, and risk disposal response is accelerated; key task resource supply is guaranteed by combining video memory preemption and containerization scheduling strategies, and the system stability bottleneck in a high-load scene is overcome.

The embodiment of the invention provides a circuit board deep blind hole machining control method and system, and the method comprises the steps: obtaining a historical machining monitoring data set containing a plurality of machining batches, carrying out the multi-dimensional feature fusion of the historical machining monitoring data set, generating a machining influence feature set, inputting the machining influence feature set into a preset precision optimization model, and carrying out the machining of a deep blind hole. Performing feature causal analysis through the precision optimization model to obtain a feature influence result; generating a processing parameter adjustment strategy based on the feature influence result; and adaptively adjusting the path planning parameters and the energy input parameters of the current machining process according to the machining parameter adjusting strategy. By means of the scheme, the association rule and the causal relationship of the multi-dimensional data can be effectively utilized, the influence of a single parameter is considered while the blind hole depth precision, the hole position precision and the hole wall quality are improved, the synergistic effect between the parameters is also considered, and therefore the more comprehensive and more reliable machining control effect is achieved.

The invention discloses an urban operation monitoring method based on multi-modal data analysis. The method comprises the steps of collecting urban operation monitoring data including event logs, disposal effects and citizen satisfaction; adopting a PC algorithm to construct a causal directed acyclic graph to generate causal analysis data; based on a PPO algorithm, performing dual-objective optimization on the disposal effect and the degree of satisfaction of citizens to generate dynamic weight data; key influence path features and an index weight matrix are extracted, and a weight adjustment basis is generated through SHAP value analysis; a federated learning framework is adopted to fuse differential privacy and security multi-party calculation, and a cross-domain knowledge sharing network is constructed to generate enhanced evaluation data; performing space-time trend prediction based on a GraphSAGE + TCN hybrid model to obtain a final evaluation result; and outputting a visual report including performance prediction, risk early warning and resource suggestion. The deep fusion of causal reasoning and dynamic weight is realized, and the accuracy, adaptability and interpretability of urban operation monitoring are remarkably improved.

The invention discloses a security event traceability and response management platform suitable for a wind and light storage station. The platform comprises a station edge layer and a cloud middle platform layer. The station edge layer comprises an acquisition and isolation unit, a time synchronization and evidence shaping unit and a credible evidence storage and equipment snapshot unit; the evidence storage module is used for underlying data acquisition, time synchronization, evidence shaping and credible evidence storage to form original event data and an encrypted evidence chain; the cloud middle platform layer comprises a cross-domain causal atlas and root cause analysis engine, a control physical consistency verification model, a hierarchical response and energy storage security state control unit, a drill and strategy verification unit and a redisk and adaptive optimization unit; the method is used for cross-domain causal analysis, control consistency verification, hierarchical response decision and strategy redisk optimization. Unified acquisition, credible recording and cross-domain traceability of security events are realized, event sources and propagation paths can be accurately identified in a complex operation environment, and closed-loop control of security response is supported.

Accuracy, reliability, and response speed improvements for software applications executed by a computing system or platform are provided herein. There are provided systems and methods for multidimensional error causal analysis for error intercorrelations that impact application availability. A service provider may utilize different computing services for data processing to provide different computing services to users, such as via websites and / or applications of the service provider. Due to errors, users may be unable to utilize applications or may face decreased performance and application availability. To improve application performance, error causal analysis may be performed that identifies error intercorrelations that impact application availability and other performance by identifying error effects on each other. Causal statements may be intelligently generated to then identify error intercorrelations. Once generated, these statements may be tested and verified to allow debugging teams and others to fix errors that reduce application performance and availability.

The invention discloses a commodity sales volume prediction method and system based on an improved neural network, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing a dynamic heterogeneous graph network, calculating the time sequence dynamic association strength, and quantifying the correlation between a node relation and sales volume; based on a self-adaptive space-time attention mechanism, a seasonal mode and a sudden mode in the sales volume data are identified, and the influence of emergencies on the sales volume is quantified; applying a bidirectional causal analysis framework to evaluate the direct, indirect and interactive influence of each node on the sales volume; generating a cross-domain commodity embedding representation based on an evaluation result, and establishing an association bridging mechanism of long-tail commodities and hot-sell commodities; and carrying out sales volume prediction through a multi-level cooperation mechanism, and outputting a sales volume prediction result of the target commodity. According to the method, a time sensing gating mechanism and a heterogeneous graph network are combined, modeling is carried out through a commodity-user-promotion-event quaternary relationship, a dynamic change rule among sales volume influence factors is effectively captured, and the adaptability of a prediction model to market fluctuation is improved.