305 results about "Causal reasoning" patented technology

The present invention provides a system and method for causality-augmented generative intelligence capable of autonomously discovering non-obvious actionable insights from heterogeneous and multimodal data sources. The system integrates a data ingestion unit for semantic and temporal harmonization of structured and unstructured datasets, a causal inference processor for constructing a dynamically evolving directed causal knowledge representation using perturbation-based validation, a latent representation processor that combines multimodal semantic embeddings with causal parameters to generate fused latent vectors, and a generative insight processor utilizing causally constrained generative reasoning to synthesize hypotheses anchored to verified cause-effect dependencies. A validation processor performs counterfactual assessment and observational verification to ensure retention of only those insights that remain consistent with causal ground truth.

The invention relates to the technical field of information data analysis. The invention provides a public policy case analysis knowledge graph fusion reasoning method and system. The method comprises the following steps: generating standardized preprocessing data; performing extraction processing on the standardized pre-processed data to generate a structured triple set; processing the structured triple set to generate a multi-dimensional knowledge graph; constructing a hybrid inference engine, and processing the multi-dimensional knowledge graph to generate inference result data; performing incremental updating processing of nodes and relationships on the multi-dimensional knowledge graph, and performing parameter optimization processing on the hybrid inference engine to generate an updated knowledge graph and an optimized inference engine; and reasoning result data are processed, and a visual analysis result is output, so that the problems of limitation of a rule engine on causal reasoning, semantic fuzziness and logic illusion of a large language model in the policy field and insufficiency of a single-field knowledge graph on cross-field interaction influence revelation are solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal causal reasoning and interpretation method, device, equipment and medium, and the method comprises the steps: obtaining original data streams of at least two different modals, and extracting modal features; a cross-modal attention mechanism is utilized to fuse modal features, and causal features are extracted through feature distillation; constructing a dynamic causal graph based on causal features, and updating an edge weight through a causal intensity function; identifying the causal relationship in the dynamic causal graph and performing anti-factual reasoning verification to evaluate the reliability of the causal relationship; and generating a causal interpretation result in combination with the dynamic causal graph and the causal relationship reliability. According to the method, the multi-modal data are fused, the causal features are extracted, and dynamic causal graph updating and anti-factual reasoning verification are combined, so that reliable modeling and explanation of the causal relationship in a complex scene are realized, the defects of single modal or simple fusion in the prior art are overcome, and the accuracy and interpretability of causal reasoning are improved.

The invention discloses an operation and maintenance management method of an integrated management system, and belongs to the technical field of operation and maintenance of systems. The invention discloses an operation and maintenance management method of an integrated management system, and aims to solve the problems of data islands, slow fault positioning, experience dependence on strategies and the like in traditional operation and maintenance. The method comprises the following nine core processes: dynamically accessing multi-source heterogeneous data and carrying out standardization processing; constructing a hierarchical time series data storage structure; generating a modeling dependency and fault path of the equipment knowledge graph; adopting a three-layer anomaly detection model to identify anomaly; fault root causes are positioned through causal reasoning and a Bayesian network; generating an energy efficiency strategy based on reinforcement learning and multi-objective optimization; triggering the self-healing workflow to execute operation; testing the robustness of the system in a sandbox environment; and iteratively updating the knowledge graph and the AI model to form a closed loop. According to the method, automation and intelligentization of the whole operation and maintenance process are realized, and the system availability and the energy efficiency management level are improved.

The invention discloses a question and answer method and system based on a knowledge graph, and the method comprises the steps: outputting a structured query graph of which nodes comprise entities, relationships and semantic weights according to a natural language question input by a user; on the basis of the structured query graph, outputting candidate entity sub-graphs of which the ambiguity is eliminated; outputting a reasoning path set with the highest probability according to the candidate entity subgraph; based on the reasoning path set, outputting candidate answers which conform to logic and are coherent in grammar; and according to the topological consistency of the candidate answers and the knowledge graph, calculating an answer credibility score by using a causal reasoning model, and outputting a final answer and an interpretability verification report by analyzing a logic causal chain implied in the answers and verifying with the knowledge graph. By utilizing the embodiment of the invention, deep integration and value mining of park multi-source data can be realized, and powerful support is provided for fine management and intelligent decision-making of the smart park.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The embodiment of the invention provides a dynamic risk assessment method and system based on perception and causal reasoning, and belongs to the technical field of automatic monitoring. The method comprises the following steps: acquiring a video data stream in real time and analyzing the video data stream into a structured dynamic visual scene representation; through a neural network model used for executing cross-modal semantic alignment, mapping the dynamic visual scene representation into activation evidence of nodes in a pre-constructed causal knowledge graph in a multi-modal embedding space, the nodes of the graph being risk events or risk factors; and performing probabilistic reasoning on the map based on the activation evidence to calculate the posterior probability of the node corresponding to the to-be-predicted risk event. According to the method, the problems that visual perception and causal reasoning cannot be effectively integrated and the evaluation result lacks interpretability in the prior art are solved, obstacles between visual information and abstract causal concepts are overcome by constructing a perception-mapping-reasoning technology closed loop, and the accuracy, transparency and credibility of risk evaluation are remarkably improved.

The invention discloses a power plant secondary circuit fault tracing method based on causal reasoning. The method comprises the following steps: collecting and preprocessing multi-source operation data of a power plant secondary circuit system; based on the secondary circuit schematic diagram, the relay protection configuration table and the signal connection relation, establishing a topology constraint set, and generating an intervention node set; constructing an enhanced causal Bayesian network model based on the topology constraint set and the intervention node set; based on the topology constraint set, carrying out structure learning and parameter estimation on the enhanced causal Bayesian network model; inputting the standardized operation data set into the trained enhanced causal Bayesian network model, and executing reverse causal reasoning; and performing consistency verification on the fault root cause candidate set, the propagation path set, relay protection logic and interlocking rules, and generating a fault traceability report. According to the method, the enhanced causal Bayesian network is adopted, so that the power plant secondary circuit fault can be accurately traced.

The invention relates to a weather situation intelligent analysis method based on artificial intelligence multi-modal causal reasoning, and the method comprises the following steps: obtaining multi-modal meteorological data, and carrying out the preprocessing of the multi-modal meteorological data, and obtaining the processed data; processing the processed data through a pre-constructed multi-modal causal reasoning neural network model to obtain a weather situation intelligent interpretation result; performing feedback correction on the weather situation intelligent interpretation result through an automatic feedback correction mechanism based on a large model so as to update the weather situation intelligent interpretation result; and generating a weather situation analysis result based on the updated weather situation intelligent interpretation result, and visually displaying the weather situation analysis result. Through the cooperative work, the weather system can be automatically identified, the causal relationship between the systems can be analyzed, a professional weather situation analysis report can be generated, and multi-mode forecast comparative analysis is supported. According to the invention, the efficiency and accuracy of weather situation analysis can be improved, and reliable technical support is provided for weather forecast service.

The invention discloses a large model fine tuning method based on a causal graph and thinking chain enhancement and a related device, and relates to the technical field of large model fine tuning in the power industry, and the method comprises the steps: carrying out the causal mining of power equipment data, and constructing a power equipment causal graph containing causal weight information; disassembling the input of the large model into a thinking chain, correspondingly generating a chain type causal pair according to the thinking chain, and performing path retrieval matching and causal consistency check through the chain type causal pair and the constructed electrical equipment causal graph to realize alignment of the reasoning process; and exciting a reinforcement learning process through an alignment result of the reasoning process, optimizing a pre-established reinforcement learning reward model, constraining a thinking chain generation process, guiding the large model to generate a thinking chain under a causal constraint condition, and realizing fine tuning of the large model. According to the method, causal reasoning and causality are embedded into a reinforcement learning feedback process of large model fine tuning, so that the large model can learn a basic causal reasoning rule, and the logicality, the interpretability and the robustness of thinking chain reasoning can be improved.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention discloses an acute abdominal pain cause differential diagnosis system based on deep learning, and relates to the technical field of medical artificial intelligence, comprising: a data acquisition module acquires initial self-described text data of a patient; the standardization processing module generates a standardized symptom set through standardization processing; the cause reasoning module performs reasoning according to the standardized symptom set and the medical knowledge graph to obtain a suspected disease set; the contradiction detection module is used for detecting the contradiction between the symptom set and the self-contained text and calculating the contradiction intensity and disease cause relevancy; the priority calculation module ranks contradiction priorities according to contradiction intensity and disease cause relevancy; the contradiction processing module judges the contradiction significance score, if the contradiction significance score is lower than a threshold value, a standardized symptom set is output, and otherwise, an optimal clarification action is generated to solve the contradiction; an iteration updating module updates a symptom set according to clarification action feedback, and causes are inferred again until a termination condition is met; the method can ensure that the pathogenesis reasoning process gradually approaches the real pathogenesis, and improves the diagnosis reliability.

The invention relates to a multi-channel marketing effect evaluation method and system based on causal reasoning and anti-fact Shapley, and belongs to the technical field of digital marketing analysis, and the method comprises the steps: collecting the original behavior data of a user in a multi-channel marketing environment, and constructing a behavior sequence according to the original behavior data; performing feature extraction and engineering processing based on the behavior sequence to obtain structured features; respectively training a tendency scoring model and a result model by adopting a dual machine learning method based on the structured features, and obtaining an unbiased causal effect estimated value of each channel contact on the conversion target through cross fitting; calculating a causal contribution value and a causal contribution confidence interval of each channel contact through an anti-fact Shapley value calculation method; and performing budget optimization distribution according to the causal contribution value, and outputting a marketing strategy suggestion. The method has the effects of accurately quantifying the causal contribution of each marketing channel, eliminating the self-selection deviation of the user and providing a reliable statistical basis.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention provides a circuit board electroplating process quality monitoring and early warning method, which comprises the following steps: carrying out real-time acquisition and standardization processing on process parameters (such as current density, bath solution temperature and pH value) of procedure nodes of acid pickling, activation, electroplating and the like by utilizing a distributed data acquisition system, and fusing historical causal relationships based on a knowledge graph structure to obtain a circuit board electroplating process quality monitoring and early warning result; according to the method, causal strength dynamic evaluation is realized by combining improved Granger test and a dynamic Pearson coefficient, long-range dependence between multi-hop graph attention network modeling parameters is adopted, an optimal abnormal attribution path is further screened through path credibility attenuation and a trend semantic encoder, and finally, an optimization suggestion template is automatically matched to output a process adjustment scheme, so that the accuracy of the abnormal attribution path is improved. According to the invention, the causal reasoning accuracy and the automatic optimization capability of the electroplating process anomaly analysis are improved, and the operation reliability and the intelligent level are improved.

The invention discloses a dance costume prop retrieval method based on causal reasoning and cross-modal matching, which comprises the following steps: in a matching task, calculating the similarity between costume prop images and text matching features by a model, and judging whether the costume prop images and the text matching features are matched or not according to a threshold value; and when the task is retrieved, calculating the similarity between the query text and the image feature set, and sorting to obtain a result. The process of calculating the features and the similarity comprises the following steps: firstly, extracting multi-scale visual features of an image by using Faster R-CNN, and extracting multi-granularity semantic features of a text by using BERT; then, the two types of features are subjected to self-attention and gating fusion respectively and then are input into a Transform layer, and final visual and text representation is obtained; then, multi-head attention calculation is carried out on the two representations, self-attention calculation is carried out after sequences are combined, and matching features are obtained; and finally, dividing the dot product of the two feature vectors by the modular length product to calculate the similarity. According to the invention, the matching accuracy of dance costume props can be improved.

The invention discloses a knowledge-enhanced multi-granularity causal fault diagnosis method, and the method comprises the steps: constructing a hierarchical bearing fault domain knowledge graph which comprises a structural layer, a fault layer and a feature layer, and systematically integrating multi-dimensional domain knowledge; designing a three-level causal chain structure covering a microscopic layer, a mesoscopic layer and a macroscopic layer, and realizing cross-scale reasoning from a microscopic signal feature to a macroscopic fault decision; a knowledge-causal dynamic fusion mechanism based on working condition perception is provided, and adaptive coupling of domain knowledge and causal reasoning is realized through dynamic weight adjustment; and finally, a diagnosis result containing fault types, positions and severity is output, so that the defects of an existing method in the aspects of knowledge fusion, cross-granularity reasoning and interpretability are effectively overcome, and the precision and credibility of bearing fault diagnosis under complex working conditions are improved.

The invention relates to the technical field of supply chain risk prediction based on causal reasoning, and discloses a supply chain risk prediction method and device based on causal reasoning, equipment and a medium. The method comprises the following steps: constructing a double-layer knowledge graph of a regulation layer knowledge graph and a logistics physical layer knowledge graph; real-time fusion and characterization of geographical regulation and logistics operation multi-source heterogeneous data are realized, the perception real-time performance and coverage dimension of risk events are improved, then a multi-level causal chain is automatically generated by introducing a large language model, a complex conduction path of risks in a supply chain network can be deeply inferred, and the risk prediction accuracy is improved. According to the method, the defect that hidden association mining is insufficient in a traditional method is overcome, reasoning of risk traceability is improved, and finally a risk prediction result is obtained by mapping causal chain nodes to a logistics physical layer knowledge graph and performing probabilistic calculation. The method has the beneficial effects that the risk prediction of the specified supply chain is realized, and the accuracy of the risk prediction result is improved.

The invention discloses a causal reasoning-based satellite-ground collaborative remote sensing image interpretation method and device, relates to the technical field of remote sensing image processing, and mainly aims to solve the problem that the inference requirement of environmental causes in a remote sensing image cannot be met in the prior art. Comprising the following steps: a ground end obtains initial interpretation features obtained by performing initial interpretation on multi-modal remote sensing data by a satellite-borne end, and interprets the initial interpretation features based on a first interpretation model of which model training is completed to obtain core interpretation features; determining causal variables based on the core interpretation features, and constructing a causal variable graph based on the causal variables; determining a cause label and an interpretation result of the label area according to the causal variable graph, and obtaining a verification result corresponding to the cause label and the interpretation result; and based on the knowledge distillation and the verification result, determining learning core features of the first interpretation model for updating training, so that a second interpretation model in the satellite-borne end coordinates the learning core features fed back based on the ground end.

The invention discloses a user demand prediction method and system based on multi-modal causal reasoning, and the method comprises the steps: carrying out the potential causal relation extraction of a multi-modal data set, and obtaining the causal effect data; constructing an initial long-term demand prediction model based on the causal effect data; training the initial long-term demand prediction model according to the multi-modal data set to obtain a long-term demand prediction model, and decomposing the contribution degree of each user behavior variable to demand intensity based on an SHAP value method in the training process to obtain a causal relationship weight; constructing an initial short-term demand prediction model based on a causal convolutional network based on the causal relationship weight, and inputting the historical short-term user behavior sequence into the initial short-term demand prediction model for training to obtain a short-term demand prediction model; and performing real-time prediction on the user demand according to the short-term demand prediction model and the long-term demand prediction model. According to the method provided by the invention, accurate prediction of user demands is realized.

The invention relates to the technical field of data analysis, can be applied to agricultural disaster monitoring, financial science and technology and other business scenes, and discloses a multi-source monitoring analysis and decision-making method, device, equipment and medium, and the method comprises the steps: obtaining multi-source monitoring data, and carrying out the time-space multi-scale fusion to generate time-space fusion data, extracting difference features of the target object before and after the event based on the fusion data to identify a first type of events, predicting a second type of events based on dynamic time sequence features by using a space-time diagram network model, and constructing a knowledge graph to perform causal reasoning on two types of event results to generate a reasoning enhancement result; and generating comprehensive abnormal analysis information and a response decision according to a reasoning enhancement result. According to the method, spatial-temporal features of multi-source data are fused, a causal reasoning mechanism is introduced, the identification and prediction results are subjected to correlation analysis, dynamic interpretation and response generation of complex events are achieved, and therefore the anomaly identification precision and the real-time performance and interpretability of risk decision making are improved.

The invention belongs to the technical field of electronic government affair big data processing, and discloses an electronic government affair big data processing system and method. The system comprises a traffic data acquisition module, a depth data acquisition module, a data interaction extraction module, a traffic quantitative prediction module, an auxiliary decision generation module, a causal reasoning interpretation module, a vulnerable group inclination module and a data distinguishing processing module, and is used for analyzing feature vectors to obtain a congestion prediction value, analyzing the congestion prediction value, and obtaining a traffic prediction result. The method comprises the following steps: analyzing a congestion prediction value, obtaining an auxiliary decision report according to an analysis result, performing causal inference on the auxiliary decision report, combining an inference result with the auxiliary decision report to obtain a causal inference report, performing calculation based on region type data and complaint evaluation data, and correcting the congestion prediction value according to a calculation result to obtain a prediction correction value. The method has the remarkable advantages of being high in traffic condition prediction accuracy, high in data mining capacity and large in management balance effect.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention discloses a multi-modal rumor detection method fusing texts, images and social propagation structures, and belongs to the technical field of natural language processing, computer vision and causal reasoning. Specifically, the invention provides a unified causal inference framework, and hybrid deviation in multi-modal data is effectively stripped by integrating text anti-fact causal inference and an image dot product causal intervention mechanism. Under the framework, social propagation structure features are further fused, and a multi-head collaborative attention mechanism is adopted, so that deep alignment and semantic enhancement in cross-modal features are realized. Adversarial samples are generated through projection gradient descent for adversarial training, and model parameters are optimized in combination with anti-fact loss, so that the classification accuracy and generalization ability of the model are improved. According to the rumor detection method, a causal reasoning normal form is introduced into a rumor detection task, the effectiveness of an anti-fact and intervention mechanism in a complex information scene is verified, and a new theoretical support and method path are provided for constructing a credible multi-modal information system.

The invention discloses a multi-modal quantifiable security threat reasoning method and system based on causal reasoning, and relates to the technical field of network security, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, carrying out multi-modal fusion; s4, carrying out cross-modal causal association; s5, a causal inference engine; s6, real-time monitoring and response are carried out; and S7, performing CSA security proxy. According to the multi-modal quantifiable security threat inference method and system based on causal inference, comprehensive monitoring and response to network security threats are realized by fusing causal inference and a multi-modal dynamic sensing technology; the core problems that in traditional network security threat detection, the false alarm rate is high, attack root cause positioning is fuzzy, and cross-modal data relevance is weak are solved.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention relates to a self-interpretation multi-modal personality assessment method based on a multi-agent hierarchical reasoning chain of a large language model, which comprises the following steps of: firstly, unifying multi-modal features such as voice and vision to a text space through modal self-interpretation personality traits representation of a unified space, and generating text description codes related to personality traits; constructing a causal graph model by using a Bayesian causal reasoning theory, analyzing specific contribution of each modal data to personality traits evaluation, and generating a clear reasoning chain and explanation; a two-stage hierarchical reasoning framework is utilized, coarse granularity personality classification is completed in the first stage, psychological measurement norm data is introduced in the second stage, a'classification-scoring 'dynamic mapping mechanism is established, and fine granularity scoring calibration is achieved. Meanwhile, the interpretability of the model is deepened through a hierarchical confidence transfer mechanism, and it is ensured that each scoring result can be traced to the original feature, the classification credibility and the adjustment rule; according to the method, a multi-modal personality assessment special large language model is constructed, and a more transparent, explainable and credible technical basis is provided for personality assessment.

The invention provides a multi-modal fusion causal analysis method and platform for industrial safety production, and the method comprises the steps: carrying out the preprocessing of multi-modal key data from an industrial site, and generating a standardized time series data matrix; based on the standardized time sequence data matrix, combining industrial physical rules and operation process semantics to construct an industrial safety causal atlas; based on a self-supervised learning mechanism, optimizing the structure and edge weight of the industrial safety causal atlas by using the standardized time sequence data matrix to obtain an optimized causal reasoning model; and performing path-level risk activation judgment and accident causal traceability by using a real-time standardized time sequence data matrix constructed by the causal reasoning model and real-time inflow multi-modal key data to generate a risk alarm and traceability report. According to the method, the perspectiveness, the accuracy and the interpretability of industrial safety monitoring are remarkably improved.