50 results about "Closed loop analysis" patented technology

The invention relates to a gold mine air-ground-in-well rapid collaborative exploration method, belongs to the field of geological exploration, and aims to solve the technical problems of data splitting, low target area positioning precision, insufficient deep prediction capability and the like in traditional gold mine exploration. By integrating air remote sensing, surface geological survey and underground detection data, a multi-source data standardization processing flow is established, and a progressive exploration system from wide-area screening to accurate positioning is realized by combining geological, geophysical and geochemical comprehensive analysis methods. The system adopts a dynamic feedback mechanism to optimize an exploration process, forms closed-loop analysis and prediction, improves exploration efficiency and accuracy, and reduces resource waste and exploration cost. The method is suitable for gold ore resource exploration under complex geological conditions, has remarkable economic benefits and application value, and can be widely applied to the field of mineral resource development.

The invention relates to the technical field of fund information management, in particular to a fund exchange information management system based on big data analysis. The system comprises a multi-source heterogeneous data acquisition and fusion module, an enterprise dynamic behavior portrait construction module, a hidden association network mining module and a trade background authenticity dynamic evaluation module. The method comprises the following steps: firstly, collecting and processing data from a plurality of data sources to generate a fused fund exchange record; constructing an enterprise dynamic behavior portrait based on the record; furthermore, a hidden association network graph is constructed based on the data, the data are received at the same time, the transaction behavior deviation degree is identified through behavior consistency check, fund closed-loop analysis is carried out on abnormal transactions, and finally a trade background authenticity index and a risk decision signal are generated. According to the invention, dynamic evaluation and risk early warning of supply chain financing trade background authenticity are realized, and the risk control capability of a financial institution is effectively improved.

The invention discloses a cross-platform income data closed-loop analysis method, and particularly relates to the technical field of data closed-loop analysis. The method comprises the following steps: collecting original multi-platform interaction data, and generating a cross-platform interaction data set; executing multi-layer anonymous identifier mapping, and outputting a unified user link identifier sequence and event sequence mapping table; calculating an observability matrix of each platform event chain to obtain a cross-platform observability score set; a cross-platform implicit path sequence set is mined based on a probabilistic graph reasoning method, and a backflow path complete sequence is constructed in combination with a cross-platform observability score set; performing income event association calculation and link credibility weight normalization, and outputting a closed-loop income attribution result; the closed-loop income attribution result is fed back to the cross-platform putting strategy database, closed-loop optimization is completed, accurate reduction of the real conversion path of the user and closed-loop quantitative analysis of multi-platform income contribution are achieved, and the optimization effect and the actual income performance of the cross-platform putting strategy are improved.

The invention discloses a new-generation emergency command system scene analysis system integrated with a convolutional neural network, particularly relates to the technical field of computer vision and intelligent emergency command, and is used for solving the problem that the dynamic association and situation evolution logic analysis capability among emergency scene elements is insufficient in the prior art. Visual element features are extracted from a video stream through a feature extraction module by using a convolutional neural network, an association graph construction module constructs a space-time association graph based on the features, a movement evaluation module calculates element group movement feasibility indexes, and a situation analysis module analyzes divergence features and antagonism degrees in combination with movement track convergence. And the risk identification module identifies a derived risk aggregation mode according to the situation prediction, and the semantic generation module finally outputs high-level semantic description information containing situation prediction to realize closed-loop analysis from scene perception to cognitive decision.

The invention discloses an electric energy metering error evaluation method and system for a direct current charging pile, equipment and a medium, and the method comprises the steps: constructing an electric energy disturbance response function based on transient voltage and transient current, and effectively quantifying the transient metering deviation caused by measurement lag, voltage drop, current peak and other factors in the actual operation of the direct current charging pile. Error reconstruction modeling has higher dynamic adaptive capacity and physical rationality, the limitation that in the prior art, the influence of the high-variable-load working condition on metering cannot be reflected is broken through, and the accuracy and timeliness of direct-current charging pile error monitoring are greatly improved. Besides, a standard error is introduced to carry out fitting calibration on a reconstruction error and carry out energy conservation verification, thereby realizing verifiability on a physical level and controllability in engineering implementation, enhancing credibility and authority of an evaluation result, constructing a closed-loop analysis mechanism from error evaluation to metering correction and then to conservation verification, and improving reliability and reliability of the evaluation result. And the closeness and the adaptive capability of the overall error evaluation are improved.

The invention discloses a remote sensing time series data analysis method and system considering geographic object space-time correlation, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring and preprocessing a multi-source multi-temporal remote sensing image and auxiliary data to generate a consistent time sequence data stack; dividing the image into voxels in a three-dimensional time-space domain, carrying out adjacent tracking, identifying a geographic process object with an evolution behavior, and recording the attribute of the geographic process object; judging a space-time topological relation between geographic process objects, and generating a unified space-time relation table through composite reasoning; constructing a geographic process object space-time diagram model by taking the geographic process object as a node and the space-time relationship as an edge, and extracting and standardizing attribute features of the node and the edge; a graph convolutional network and a topology dynamic mechanism are used for joint modeling, and the importance index of each node is calculated. According to the method, closed-loop analysis from data processing, relation modeling and importance evaluation to decision support is realized, and the interpretability of remote sensing time sequence change detection and decision effectiveness are improved.

The invention relates to the technical field of touch control of touch all-in-one machines, in particular to a touch control processing system of a centralized control type touch all-in-one machine and a control method, and aims to solve the problems that in the prior art, touch events and track characteristics cannot be accurately fused, gesture types cannot be accurately recognized, and the touch control efficiency is high. Operation modules cannot be accurately divided, behavior dynamics cannot be described, and closed-loop analysis from touch input to behavior intention is difficult to realize; a gesture behavior analysis module fuses a touch event and track features, constructs a multi-dimensional vector, recognizes a gesture category through a regression model, aligns an interaction and physiological sequence, calculates a Pearson's correlation coefficient, combines a filtering threshold value to generate an emotion trend, and utilizes a dynamic time warping matching template and a clustering division operation mode. And calculating an evolution rate to describe behavior dynamics, outputting structured data, and realizing closed-loop analysis from touch input to behavior intention.

The invention belongs to the technical field of PCB detection, and particularly discloses a PCB copper thickness and line width uniformity detection method and system, and the method comprises the steps: dividing a PCB surface into a plurality of detection regions, carrying out the detection of the copper thickness uniformity and the line width uniformity in each detection region, constructing a uniformity evaluation mechanism of dual dimensions of space correlation and time continuity, and carrying out the detection of the copper thickness uniformity and the line width uniformity. Dynamic uniformity judgment of each detection area in the electroplating process is realized, early abnormity can be effectively identified, and meanwhile, after copper thickness and line width uniformity judgment is completed and an abnormal detection area is identified, current density of the abnormal area is dynamically associated with copper thickness fluctuation characteristics; and lagging correlation analysis is carried out on the plating solution flow rate and the line width fluctuation characteristics, so that main influence factors causing nonuniformity are determined, closed-loop analysis from anomaly detection to root cause tracing is realized, and clear directional guidance is provided for process parameter tuning.

The invention discloses a URL (Uniform Resource Locator) security analysis method and device for an end-side large language model and a medium, and belongs to the field of network and information security. The method comprises the steps of collecting multi-modal data such as a URL analysis structure, DNS / certificate metadata, front-end code sampling, an OCR text and a dynamic behavior event sequence; performing exponential decay weighted aggregation on the dynamic events in the sliding time window; generating a static structure feature, a text semantic feature and a threat intelligence similarity based on the multi-modal data; fusing the multi-modal features by adopting a gating attention mechanism; inputting the fusion features and the readable context into an end-side large language model, and calling a security analysis tool set under limited decoding; a risk score and a minimum sufficient evidence are obtained through two-stage scoring; and executing an end-side security policy according to the scoring threshold value and leaving a trace by using a JSONL structure. According to the method, in-end closed-loop analysis is realized, the detection accuracy is remarkably improved, and the method is suitable for various deployment scenes such as mobile terminals and edge gateways.

The invention relates to the technical field of vehicle energy consumption analysis, and discloses a vehicle energy consumption closed-loop analysis method based on big data, which comprises the following steps: data acquisition and scene recognition, clustering and reference calculation, anomaly recognition, and hierarchical root cause diagnosis on vehicles with abnormal energy consumption, comprising macroscopic state comparison and microcosmic operation parameter comparison, and if no suitable vehicles with normal energy consumption in the same track exist, self historical comparison analysis is carried out; and improvement and tracking verification: based on the diagnosis result, generating improvement measures and implementing the improvement measures, and performing multi-dimensional effect tracking verification on the vehicle after the improvement measures are implemented. The invention further discloses a vehicle energy consumption closed-loop analysis system based on the big data. According to the vehicle energy consumption closed-loop analysis method and system based on the big data, the problems of reference static state, low diagnosis efficiency and flow splitting in the prior art are effectively solved.

The invention discloses an intelligent warehouse management and resource optimization method. The method comprises the steps of S1, multi-source data acquisition and fusion processing; s2, performing real-time analysis and dynamic prediction on the inventory state; s3, intelligently generating storage tasks and scheduling priorities; s4, collaborative path planning and resource optimization allocation are carried out; s5, task execution monitoring and real-time dynamic re-optimization are carried out; s6, performing task closed-loop analysis and strategy optimization; and S7, performing storage layout and resource configuration simulation optimization. According to the method, the storage data is collected in real time through the Internet of Things equipment and the sensor, intelligent collaborative optimization of the storage resources and the operation tasks is achieved in combination with the dynamic analysis model, the inventory state and the equipment operation condition can be monitored in real time, the storage tasks are automatically generated and adjusted, the equipment path and resource allocation are optimized, and the operation efficiency is improved. And the warehousing operation efficiency and the resource utilization rate are remarkably improved.

The invention discloses a rehabilitation equipment fault data monitoring method and system, and particularly relates to the technical field of electric digital data processing, comprising the following steps: S1, synchronously acquiring equipment operation time sequence data and patient interaction time sequence data, S2, performing feature extraction and adaptive fusion on the two types of data, and S3, acquiring the data of the equipment operation time sequence and the patient interaction time sequence; s3, equipment health prediction and rehabilitation curative effect deviation prediction are executed in parallel based on the fusion features, and the relevance of the equipment health prediction and the rehabilitation curative effect deviation prediction is analyzed through a cross attention mechanism, and S4, a personalized dynamic baseline model is combined, and an equipment fault risk value, a curative effect deviation degree and a relevance confidence degree are integrated to generate hierarchical monitoring feedback. According to the invention, by constructing an equipment-patient-curative effect ternary closed-loop analysis framework, the spanning from pure fault early warning to treatment effectiveness guarantee is realized, the problems of single monitoring dimension and unhooking with clinical curative effect in the prior art are solved, and the method has the outstanding effects of strong early warning perspectiveness, low false alarm rate and support of root cause analysis.

The invention provides a meta universe user behavior analysis closed-loop method and system based on big data and a medium. The method comprises the following steps: acquiring multi-modal data of meta universe users, performing preprocessing according to a preset scenarized data preprocessing standard rule to obtain corresponding standard data, performing data bit correction processing, generating user behavior archive data, and performing funnel model disassembly and user traceability grouping sorting processing to obtain user behavior archive data. Generating a business process funnel report and a user grouping and value analysis table, performing comprehensive processing to obtain a real-time index monitoring report and abnormal log record data, inputting the data into a preset behavior prediction model for processing to obtain user individual behavior prediction data and group user prediction trend data, and finally optimizing an initial business scheme. Obtaining an optimized service scheme; according to the method and the device, closed-loop analysis of meta-universe user behaviors is realized through accurate acquisition and efficient integration of data of an adaptive scene, behavior rule mining, dynamic monitoring and closed-loop iterative optimization.

This utility model relates to the field of dispensing equipment technology, and in particular to a dispensing height detection device. The dispensing height detection device includes a calibration base, a distance detection component, an imaging component, and a reading component. The calibration base is positioned below the dispensing head, and the distance detection component is fixed to the dispensing head. The distance detection component is used to detect the vertical distance between its detection end and the upper surface of the calibration base. The imaging component is configured to capture images of the dispensing head and the calibration base in a horizontal direction. The reading component is communicatively connected to the imaging component. The reading component can read the images captured by the imaging component and measure the vertical distance between the dispensing head's outlet and the upper surface of the calibration base, obtaining the actual value of the vertically extending distance between the dispensing head's outlet and the product. It also performs closed-loop analysis of the theoretical relationship between adhesive viscosity, dispensing valve orifice size, dispensing pressure, and dispensing height to ensure the dispensing quality of the product.

The invention belongs to the technical field of low-voltage power distribution networks, and discloses a transformer area line loss anomaly analysis method based on a segmented metering mode and a related device.A transformer area anomaly reason is preliminarily diagnosed through a pre-trained line loss anomaly analysis model: after a power grid structure and meteorological parameters of a transformer area to be analyzed are input, the model outputs a line loss preliminary conclusion; if the result is fuzzy judgment, starting a step-by-step sectional metering method, in the first level, comparing the power supply quantity and the power sale quantity of each branch under the transformer outlet bus by group, and if the difference value exceeds a preset first threshold value, marking an abnormal branch group, terminating the detection of the level, and turning to a second level; and the second level repeatedly compares the sub-branch groups of the abnormal branch group, accurately locates the fault point, and finally locks the minimum abnormal unit. According to the method, through dynamic threshold management and grouping progressive detection, closed-loop analysis from model inference to physical troubleshooting is realized, and efficiency and precision of transformer area line loss anomaly analysis are both considered.

The invention discloses a design method of a three-order active filtering automatic test system, and the method comprises the steps: controlling a PXI platform through LabVIEW programming to achieve the generation of a sweep frequency signal and the collection of filtering response, and completing the signal processing, characteristic analysis and result visualization. The system integrates six core technologies, namely a modularized hardware architecture, automatic process control, frequency domain parameter precision measurement, multi-order characteristic synchronous detection, software and hardware collaborative linkage and simulation data comparison. On the basis of retaining traditional functions such as setting waveform type, amplitude, sweep frequency range, sampling rate, trigger mode, recording cut-off frequency, gain and other parameters, exponential sweep frequency accurate excitation and adaptive signal acquisition are innovatively realized, link interference is reduced through matrix switch multi-channel scheduling, a LabVIEW parallel thread and a feedback node mechanism are combined, and the adaptive signal acquisition is realized. And closed-loop analysis of amplitude-frequency / phase-frequency characteristics and real-time drawing of a Bode diagram are completed.

The application provides a sound stimulation-brain electrical response closed-loop analysis system and method, relates to the technical field of brain electrical data analysis, and comprises the following steps: obtaining real-time brain electrical data under sound stimulation based on a standardized interface, and performing artifact removal and data standardization operations to obtain standard brain electrical data; performing response latency analysis on the standard brain electrical data to obtain response latency data; performing response significance analysis on the standard brain electrical data to obtain response significance data; extracting an abnormal response event set from the standard brain electrical data, and performing context feature modeling and abnormal root cause analysis on the abnormal response event set based on the response latency data and the response significance data to obtain abnormal root cause data; and generating a brain electrical response analysis graph interface corresponding to the response latency data, the response significance data and the abnormal root cause data based on neural signal analysis logic, and visually displaying the brain electrical response analysis graph interface.

The application discloses a fault analysis method, device and equipment and a computer readable storage medium. The fault analysis method comprises the following steps: performing data analysis based on a cloud database to determine a fault battery pack in which a single-cell voltage inconsistency phenomenon has occurred; comparing a state-of-charge extreme single cell and a health degree extreme single cell in the fault battery pack, and determining a cause of the single-cell voltage inconsistency phenomenon and an inconsistent single cell according to a comparison result; and when it is determined that the cause is an abnormally large self-discharge, determining a root cause of the abnormally large self-discharge according to a state-of-charge change trend of each single cell in the fault battery pack. The application can accurately locate a failure cause leading to single-cell voltage inconsistency, and simply and conveniently identify a root cause of abnormally large self-discharge of a single cell, so that closed-loop analysis of a fault cause can be quickly realized, product and process improvement is facilitated, and a series of problems possibly caused by root cause analysis through disassembly of a single cell are avoided.

The embodiment of the invention provides a visual analysis method for diagnosis and optimization of an urban drainage system and related equipment, and belongs to the technical field of smart city and data visualization. According to the method, a closed-loop analysis framework formed by collaboration of a map view, an analysis view and a comparison view is constructed. The map view integrates a hydrological and hydraulic model simulation result, visualizes a drainage system state and supports interactive modification; the analysis view is used for extracting a key drainage subsystem based on rules, and performing causal visualization by adopting an optimized mulberry-based graph integrated with terrain elevation information, so that overflow causes are accurately positioned; a comparison view is visualized through a parallel histogram and version inheritance relation, and multi-scheme comparison and iterative optimization management are supported. According to the method, the problems of difficulty in causal analysis, poor interpretability and lack of iterative optimization support in the prior art are solved, the whole-process integrated support from problem identification and diagnosis to optimization verification is realized, and the analysis efficiency and decision scientificity of the urban drainage system are remarkably improved.

The application discloses a deep evaluation model and analysis method based on a computing power application scene, relates to the field of information technology and digital infrastructure planning, and specifically relates to a deep evaluation model and analysis method based on a computing power application scene. The model is constructed from ten dimensions of policy adaptability, regional industry integration, infrastructure support, construction operation mode, implementation strategy landing, operation and maintenance service, energy consumption control, cost return calculation, market supply and demand matching, and risk prevention and control system, taking actual landing requirements as the core. The importance of each item is graded and quantitatively scored, a feasibility determination standard is set, a full-closed-loop analysis system is formed, full-dimension and hierarchical deep analysis of the feasibility of computing power project construction and operation is realized, and support is provided for project decision-making.

The application discloses a kind of rehabilitation equipment failure data monitoring method and system, specifically related to electric digital data processing technical field, including S1, synchronous acquisition equipment operation time series data and patient interaction time series data, S2, two kinds of data are extracted and adaptively fused with features, S3, based on fusion features parallel execution equipment health prediction and rehabilitation curative effect deviation prediction, and the relevance of both is analyzed through cross attention mechanism, S4, combined with individualized dynamic baseline model, comprehensive equipment failure risk value, curative effect deviation and correlation confidence degree generate graded monitoring feedback.The application realizes the leap from simple failure early warning to treatment effectiveness guarantee by constructing equipment-patient-therapy ternary closed-loop analysis framework, solves the problem that the monitoring dimension of prior art is single and decoupled from clinical efficacy, and has the outstanding effect of strong early warning foresight, low false alarm rate and supporting root cause analysis.

The present invention discloses a method and system for optimizing the generation of cosmetic adverse reaction analysis reports, specifically relating to the technical field of cosmetic safety supervision, and is used to solve the problem of inaccurate adverse reaction attribution caused by dispersed supply chain data and inconsistent batch labels in the prior art. By integrating full-process data of raw material procurement, production and processing, warehousing and logistics, and adverse reaction events, a batch labeling system with a unified index is constructed, and cross-link data deviations are repaired based on timestamp conflict detection and conditional generative adversarial networks to generate complete batch data after elimination. Further, a causal network is constructed through an attribute-constrained variational autoencoder to extract potential risk factors, and a spatiotemporal knowledge graph is constructed by combining supply chain disruption events and user location trajectories to identify abnormally associated batches. Finally, causal weights and spatiotemporal probabilities are integrated to generate a batch risk quantification report, realizing a closed-loop analysis from data conflict resolution to accurate attribution.

The application relates to the technical field of soil analysis, in particular to a cane field soil organic matter influencing factor analysis method and system based on multi-source data, which comprises the following steps: acquiring the spatial distribution of all sampling points, using different colors to distinguish different years and different cane fields; calculating a spatial autocorrelation index to determine whether the sampling point distribution is aggregated, discrete or random; dividing the cane fields and analyzing the soil nutrients of each cane field for comparative analysis; constructing a random forest regression model; extracting feature data of each cane field and using SHAP values for interpretation; drawing a single feature data of each cane field and a corresponding SHAP value relationship scatter diagram to obtain a SHAP dependence diagram; using a PLS-SEM algorithm to estimate the random forest model to obtain direct effects or indirect effects; and verifying the importance ranking of SHAP by comparing the total effects of different driving factors. The application realizes closed-loop analysis from spatial pattern identification to machine interpretation.

The invention provides a deep rock dynamic energy consumption analysis and implementation method based on improved near-field dynamics, relates to the field of geotechnical engineering, and overcomes the defects of a traditional bond-based near-field dynamics model in the aspect of describing complex mechanical behaviors of rock materials by introducing a nonlinear damage softening law, a Weibull distribution function and an S-type rate correlation effect criterion. The accuracy and the reliability of the model in the process of simulating the dynamic fracture of the deep underground engineering material are obviously improved; through systematic numerical value verification, the model can be suitable for both two-dimensional and three-dimensional conditions, can accurately simulate material mechanical response and energy evolution rules under different confining pressure conditions, and proves the effectiveness and practicability of the model in solving actual deep underground engineering problems; according to the method, a Hopkinson impact test model is combined with an improved near-field dynamic model, and a complete closed-loop analysis framework of energy input-internal dissipation-energy output is constructed.

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention provides a protein data analysis and processing method and system and a server, and relates to the field of protein data analysis and processing, the scheme realizes a standardized and automatic analysis process, and the whole process from original mass spectrum data import, database searching and matching, quantitative analysis, annotation enrichment, visual display to report generation is automatically completed, so that the analysis efficiency is improved. An efficient closed-loop analysis system of'delivery after off-machine 'is realized, and the efficiency, depth and stability of blood protein data processing are remarkably improved.

The invention discloses a vehicle formation dynamic modeling and simulation method based on physical coding deep learning. The method comprises the steps that driving data of a vehicle formation are collected and preprocessed; based on the preprocessed data, constructing a parameter coding calculation graph of physical constraints; constructing a multi-scale trajectory feature learning network to learn behavior features of the vehicle formation from the preprocessed data; constructing a non-autoregression parameter decoder to map behavior characteristics of the vehicle formation into time-varying parameters required by a physically constrained parameter coding calculation graph, and iteratively calculating a future trajectory of the vehicle formation in combination with a pilot vehicle state; and in combination with the future trajectory of the vehicle formation, model training is performed through preprocessed data, and a formation dynamic model with both precision and physical interpretability is generated. According to the method, the defect that a traditional data driving model lacks physical interpretability and analyzability is overcome, meanwhile, the conservative limitation of a pure physical model is avoided, the real formation speed and spacing can be reproduced with high precision, and closed-loop analysis of formation stability is supported.

The application provides a large-span steel structure closed loop analysis method and system, the analysis method comprises the following steps: constructing a finite element model and a design parameter space, identifying key design parameters and key regions affecting the first N modal strain energy of the structure through initial sensitivity and modal analysis, and establishing a mapping relationship between the two; according to the parameter sensitivity contribution, a non-uniform sampling strategy is formulated, the sampling is encrypted in the high sensitivity parameter subspace, an initial sample set is generated, based on the sample and the finite element response, a hierarchical mixed kernel Kriging surrogate model containing a global long-range kernel and a multi-region local short-range kernel is constructed, the local kernel weight increases as the input point approaches the high sensitivity region, the iteration termination and the performance limit state are set, the composite point adding criterion integrating the prediction variance, the limit state distance and the cross-validation error is used to iteratively update the sample and the model, and after the condition is met, the structure performance analysis is completed by using the surrogate model.

The present application relates to a kind of compressor blade grid parameterization joint call analysis method.The present application is with MATLAB software as total control hub, by the flow control and data interaction ability of MATLAB, the grid parameterization deformation technology of HyperMesh software and the parametric finite element solution technology of ANSYS software are organically integrated together, construct a full-automatic closed loop analysis method and system from variable input to result output.The method realizes the quick response and batch calculation of several kinds of uncertain variables such as geometry, material, load, and is especially suitable for the fatigue life reliability analysis of compressor blade.

This application discloses an event evolution reasoning and prediction method based on cognitive-driven intelligent generation. First, it utilizes a domain-wide language model to extract expert knowledge points, analysis processes, analytical perspectives, and logical reasoning from event research reports, constructing an interactive thought chain. It then extracts causal reasoning and constructs a causal reasoning network structure. Cognitive logical knowledge is integrated with relevant facts of the event to be predicted and embedded into a prompt template to construct a prompt model. This allows the domain-wide language model to generate event evolution reasoning and prediction results based on expert analysis dimensions and processes. Finally, conflict detection is performed on event elements through closed-loop evidence chain analysis, and credible conclusions are selected to obtain credible prediction conclusions. By constructing prompt templates to guide the large model through cognitive logical learning and combining multi-level credible verification and screening, the technical problem of excessive prediction errors caused by illusions generated by the large model is solved, improving the accuracy and credibility of event evolution prediction.