1937 results about "Behavioral data" patented technology

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention discloses a behavior-driven twinborn prediction method, and relates to the technical field of intelligent information processing and prediction modeling, and the method comprises the following steps: building a unified event time baseline, collecting nanosecond clock offset information of each data source, building a cross time sequence suspicion chart, recognizing time synchronization abnormal nodes, and forming a credible time anchor point set; and based on the trusted time anchor point set, executing anti-fact playback, reconstructing a historical evolution process of behavior data, generating a time offset vector set, and constructing a corresponding time sequence offset spectrum. According to the method, through construction of a unified time baseline, trusted anchor points, anti-fact replay, causal topology and time reversal control, time sequence dislocation identification, calibration and false trajectory elimination of multi-source behavior data are realized, a dynamic self-healing closed-loop prediction mechanism is established, and the twin system prediction accuracy and stability are improved.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of the Internet of Things, and discloses an animal physiological feature data acquisition system and analysis method based on the Internet of Things, and the method comprises the following steps: cleaning and standardizing original sensor data, including filling missing data, eliminating noise interference through a dynamic filtering technology, unifying the dimension of multi-source data, and analyzing the data; the method comprises the following steps of: acquiring a Beidou time service time reference, eliminating redundant features, interpolating and aligning asynchronous sampling data to the Beidou time service time reference, correcting a time sequence phase difference of physiological and behavior data through a dynamic path matching technology, and correcting GPS / Beidou positioning drift in combination with Kalman filtering. Through multi-source sensing fusion and an edge-fog-cloud cooperative computing architecture, the effective collection rate of animal physiological data is achieved, abnormal response is delayed and compressed, the flexible energy supply technology and dynamic communication optimization are combined, the endurance and communication success rate is maintained in a pasture, and the false alarm rate is reduced compared with a traditional method; common diseases such as abnormal body temperature, digestive system diseases and the like are accurately warned and covered.

The invention provides a user behavior tracking and portrait generation system, which is characterized in that multi-source original behavior data is acquired through a data acquisition module, and a standard behavior event stream is generated through cleaning and standardization; the behavior modeling module is used for carrying out cross-event association and self-defined time window combination modeling on a standard event flow based on a configurable rule engine to generate a complex event flow; the real-time processing and calculation module performs real-time aggregation calculation on the complex event stream, generates a real-time behavior index, a trigger signal and an incremental portrait snapshot in combination with a preset rule, and pushes a behavior trigger signal to a downstream service system; the user portrait management module dynamically updates user tag weights and values according to the real-time indexes, and generates a target user portrait tag library and a lightweight tag change event stream; the data storage and query module stores data of each link; and the visual configuration and operation and maintenance module issues a configuration instruction through a visual interface, and monitors full-link operation and task scheduling. And real-time accurate portrait construction and instant service response are realized.

The invention discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention discloses a distributed adjustable resource layered dynamic aggregation method and system for a virtual power plant. The method comprises the steps of collecting operation data, environment data, user behavior data and market data of a distributed power supply, a flexible load and an energy storage facility, extracting core features, extracting feature parameters by adopting a quantification method, and constructing an adjustable resource feature database; constructing an operation capability index model, and obtaining a resource comprehensive operation capability value based on the index and the weight thereof; scheduling the resources in a classified, partitioned and graded manner; establishing a three-layer structure of a bottom-layer resource model, a middle-layer aggregation model and an upper-layer optimization model, and constructing a multi-objective optimization model; by executing a marketization transaction strategy, a power grid dispatching response strategy and an income distribution strategy, collaborative optimization operation of the virtual power plant, the power market and power grid dispatching is achieved, and finally multi-scene simulation verification and model parameter optimization are conducted. According to the scheme of the invention, efficient aggregation and collaborative optimization scheduling of adjustable resources are realized.

The invention relates to the technical field of agent testing, and particularly provides an agent dynamic behavior safety testing method and system based on a risk conduction quantification model, and the method comprises the steps: collecting agent behavior data which comprises a plurality of pieces of risk operation information and a risk association relationship; constructing a behavior risk association map by taking the risk operation as a node and the association relationship as an edge, labeling a risk attribute and an aging factor for the node, and labeling risk conduction intensity for the edge; constructing a risk rule base based on the risk labeling result and a preset risk judgment logic; inputting the behavior data, the behavior risk association map and the rule base into a trained quantitative model, and calculating a risk conduction probability, an influence range and a critical node; monitoring a behavior track in real time, suspending a task when a multi-dimensional risk threshold is triggered, backtracking an inference chain and generating an intervention record; and fusing the multi-dimensional data to generate a test report. According to the method, the problems of correlation risk missed judgment and response lag in the traditional test are solved, and the pertinence and reliability of the safety test are improved.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention relates to the technical field of blood glucose prediction, in particular to a blood glucose fluctuation prediction method based on historical data. The method comprises the following steps: acquiring corresponding historical blood glucose monitoring data, daily behavior data, physiological status data and medical intervention data of a user, performing time axis alignment and abnormal value cleaning treatment, and extracting basic features and associated influence features corresponding to blood glucose fluctuation; performing space-time dimension fusion on the basic features and the associated influence features, and generating a blood glucose fluctuation prediction curve of the user in a future preset time period; performing comparative analysis on the blood glucose fluctuation prediction curve and actual blood glucose monitoring data of the user, calculating a prediction deviation degree and a trend goodness of fit, and generating a corresponding deviation analysis result; feature weight distribution of the dynamic prediction model is optimized based on the deviation analysis result, and a personalized blood glucose fluctuation prediction report is generated and comprises a risk early warning threshold value and an intervention suggestion triggering condition. According to the method, prediction efficiency and personalized management of blood glucose fluctuation can be realized.

The invention discloses a child autism adaptive brain-computer fusion intervention system based on a large model, and the system comprises a multi-modal data collection module which is used for collecting the neural data, behavior data and clinical scale data of a user; the feature extraction and user portrait construction module is used for performing feature extraction and subtype recognition on the multi-modal data to generate a personalized portrait; the multi-defect intervention normal form generation module is used for dynamically generating a personalized intervention task on the basis of a large language model in combination with the personalized portrait and the historical state of the task; the self-adaptive regulation and control module is used for dynamically adjusting an intervention strategy and task difficulty according to the feedback of the real-time neural data and the dynamic change of the behavior data; the user interaction interface module is used for providing a multi-modal human-computer interaction interface; and the effect evaluation and long-term tracking module is used for generating an individual and group intervention effect report. By utilizing the system and the method, accurate, personalized, multi-dimensional and long-term intervention on the autism children can be realized.

The invention discloses an anomaly detection method and system for transaction flow data, and relates to the technical field of big data analysis. The method comprises the following steps: based on transaction flow data, acquiring operation behavior data of a target user in a transaction process and interaction behavior data after the transaction is completed, and sorting according to timestamps to form a user operation behavior sequence and a user interaction behavior sequence; inputting the sequence into a pre-trained abnormal risk prediction model, and outputting an initial risk coefficient, wherein the model integrates an individual behavior baseline and an adaptive weight module; acquiring a group behavior baseline of the similar user group, and correcting the initial risk coefficient in combination with a matching result of the sequence and the group baseline to obtain a final risk coefficient; and performing classification abnormity early warning operation on the final risk coefficient based on a preset risk threshold. According to the method, through whole-process behavior sequence analysis, individual and group baseline dual calibration and dynamic weight adjustment, the accuracy and timeliness of transaction flow data anomaly detection are effectively improved.

The invention provides an e-commerce platform data processing system and method. The method comprises the steps that browsing behavior data and commodity selection behavior data of a user in an e-commerce platform are collected, the browsing behavior data comprise page staying duration, rolling browsing rate and page switching frequency, and the commodity selection behavior data comprise carbon emission reduction attributes and green consumption identifiers of commodities selected by the user; and calculating a cognitive fatigue index of the user in a preset time window based on the browsing behavior data, comparing the cognitive fatigue index with a personalized threshold value, and when the cognitive fatigue index exceeds the threshold value, triggering a push self-suppression mechanism so as to reduce the push frequency, simplify the content complexity or delay the push time. By introducing a cognitive fatigue index and green consumption point dual-drive mechanism, green consumption is promoted while user experience is ensured, humanization and sustainability of e-commerce platform pushing strategies are realized, and the method has the beneficial effects of improving user satisfaction and optimizing a consumption structure.

The invention discloses a wind-solar-water-storage complementary system short-term risk scheduling method considering uncertainty, and the method comprises the steps: converging historical physical operation data and multi-subject behavior data, and constructing a training data set and a system parameter set; based on the training data set, constructing a combined robust radius and wind-solar combined scene containing behavior risk quantification; based on the system parameter set and the training data set, constructing a dynamic risk scheduling unit fusing multi-dimensional risks; solving a candidate short-term scheduling scheme by combining a joint robust radius and a dynamic risk scheduling unit and adopting a behavior risk-oriented Bayesian optimization method; and performing multi-subject consensus evaluation on the candidate short-term scheduling scheme, determining a final execution short-term scheduling scheme, and performing uplink execution. According to the method, the problem of separation of physical risks and behavior risks is solved, and the behavior acceptability of the scheme is improved.

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

The invention provides a driving authority management method and device based on biological feature verification and a medium, and belongs to the technical field of vehicles. The method comprises the steps of collecting facial features, voiceprint data and driving behavior data by detecting a starting operation of a current driver; the identity of the current driver is recognized by using a multi-modal biological recognition algorithm, and the accuracy and anti-counterfeiting capability of identity verification are improved by adopting a bimodal fusion scheme of face recognition and voiceprint recognition; when it is determined that identity recognition of the current driver succeeds, a driving account is determined to judge whether the driver has the use permission or not, a binding relation of driver identity-account-function permission is established, and accurate matching of the intelligent driving permission and the driver qualification is achieved; the open state of the intelligent driving function is dynamically controlled based on a permission verification result, if the permission exists, the intelligent driving function is automatically prepared, if the permission does not exist, the function is locked, a clear prompt is given, and the safety risk that the intelligent driving function is used before being learned is avoided from the source.

The invention discloses a multi-modal stimulation regulation and control system combining interactive feedback and AI self-adaption. The system comprises an AI main control and synchronization module which generates a unified rhythm time sequence; the acoustic stimulation output module outputs a rhythm acoustic signal; the visual LED stimulation module outputs rhythm visual signals; the game / animation interaction module provides an interaction task synchronized with the acousto-optic rhythm; the sensing acquisition and feedback module is used for acquiring multi-modal physiological and behavior data in real time; the amplifier output module drives the execution module or equipment; the power management module realizes voltage stabilization and low power consumption switching; the behavior habit forming mechanism module binds a stimulation signal and an interaction task; and the human-assisted management interface module supports remote parameter setting and real-time monitoring. The interactive feedback and AI self-adaptive multi-mode stimulation regulation and control system is combined, microsecond-level synchronization and multi-source closed-loop self-adaption of sound and light and animation can be achieved, a guidance scheme for personalized customization of treatment and behavioral habit cultivation can be achieved, and the efficiency, accuracy and effectiveness of system regulation and control are remarkably improved.

The invention relates to a marketing method and device based on user behavior data analysis, equipment and a medium, and the method comprises the steps: collecting the browsing, clicking and purchasing behavior data of a user in real time, and generating an initial behavior sequence in real time through a streaming processing engine; on the basis of a preset time window, activity levels are divided by means of a user behavior density algorithm, and high-intention potential users are dynamically locked; adjusting a behavior weight according to the activeness level, generating a real-time behavior score, and extracting key nodes which are not purchased completely in combination with a historical behavior link; dynamically optimizing a trigger threshold through a behavior frequency, and capturing a user conversion intention peak; personalized recommendation content is generated based on the priority labels, and the historical behavior database is continuously optimized through user feedback data. According to the method, the problems that in traditional marketing, a static threshold value is difficult to adapt to behavior fluctuation, the historical data utilization rate is low, and response is delayed are solved, real-time dynamic adjustment and accurate intervention of marketing decisions are achieved, the false triggering rate is reduced, and the user conversion efficiency is improved.

The invention relates to a lightweight low-delay continuous authentication method and system based on a linear attention hybrid network, and belongs to the technical field of information. The method specifically comprises the following steps: S1, a registration stage: collecting behavior data of a legal user and preprocessing the behavior data; training a lightweight hybrid feature extraction model, and establishing and storing a user behavior contour; s2, an authentication stage: collecting user behavior data in real time and preprocessing the user behavior data, extracting behavior characteristics and matching the behavior characteristics with stored behavior contours, and executing access control operation according to a matching result. According to the method, a global attention mechanism and a space-time attention mechanism are introduced, the long-term dependency relationship in a behavior biological feature sequence is fully captured, and the robustness of the model to noise and abnormal values is enhanced; through effective fusion of multi-sensor data and global features of different time points, high-precision legality verification of a user identity in a small authentication window is finally realized.

The invention relates to the technical field of intelligent wharfs, in particular to a wharf container truck dynamic optimization scheduling method and system combining machine learning and path planning. Comprising a behavior data acquisition and feature coupling unit; a learnable incentive and behavior guide unit; a scheduling demand prediction unit; and a path planning and scheduling unit. According to the method, on the basis of the coupling characteristics, the excitation coefficient is optimized through reinforcement learning, the excitation instruction is dynamically pushed, and targeted guidance of the non-operation staying behavior of the container truck is achieved; according to the method, based on standardized time series data, an association rule of a historical staying period and a working condition is learned through an LSTM model, a prediction result is optimized in combination with real-time data, a prospective constraint basis is provided for scheduling, and meanwhile, a time, space, resource and priority multi-dimensional path constraint system is constructed through a structural causal model; container truck-berth matching and dynamic path planning are completed by matching with an improved A * algorithm fused with dynamic weights, and scheduling conflicts are effectively avoided.

The invention provides an automobile cabin end side model multi-modal perception enhancement method, which comprises the following steps: S1) deploying an RGB camera in a cabin, and collecting facial biological characteristic data, hand interaction behavior data and cabin overall environment data of a driver; s2) adjusting image illumination and contrast by adopting an algorithm, dynamically focusing a key area through a target detection frame to process a shielding problem, and eliminating static redundant information; s3) constructing a neural network, extracting facial biological features and the like in parallel, and outputting low-dimensional feature vectors; s4) time sequence association is established, key area feature weights are enhanced through a space attention mechanism, and a logic relationship among different features is explicitly modeled; s5) aggregating the weighted feature maps by using feature attention pooling, and retaining a core feature channel in combination with a channel pruning technology to realize model compression; s6) dynamically adjusting the calculation priority and weight distribution of each feature extraction branch; and S7) the end side uploads the sample to the cloud side, and the cloud side generates an update package and pushes the update package to the end side to complete model iteration.

The invention relates to the technical field of data processing, and discloses a pharmaceutical enterprise financial risk identification method, which comprises the following steps: constructing a dynamic panoramic financial behavior database through real-time fusion of multi-source heterogeneous data; using an intelligent algorithm to automatically detect an abnormal cash flow sequence significantly deviating from the health model; performing deep association mining on the same-period financial events and constructing a causal association map; dynamically quantifying a risk open value and a conduction path based on Monte Carlo simulation; generating a structured early warning report real-time push decision node; according to the invention, full-process dynamic management and control from data fusion, risk identification, cause tracing to quantitative early warning are realized, and the problems of low efficiency, narrow coverage, easy omission and difficulty in capturing complex and modeled anomalies due to the fact that financial cash flow anomalies are identified mainly depending on manual report checking or simple threshold alarm in traditional financial risk management are solved. And the active prevention and control capability of an enterprise on financial service risks such as cash flow breakage, debt paying crisis and operation interruption is obviously improved.

The invention provides an intelligent cattle farm feeding management system, which belongs to the technical field of intelligent agriculture and animal husbandry, and comprises a data acquisition module, a data processing module, a control module, a user interface module, a communication module and a storage module. The system obtains cattle farm environment and cattle physiological behavior data in real time through a multi-source sensor and image acquisition equipment, intelligent analysis and decision generation are carried out through algorithms such as machine learning, reinforcement learning and fuzzy logic, and automatic management of functions such as feed feeding, environment regulation and control and health early warning is achieved. The system has the characteristics of comprehensive perception, intelligent decision making and accurate execution, the cattle farm feeding efficiency can be effectively improved, cattle health is guaranteed, and the operation cost is reduced.

The invention discloses a coal storage and transportation intelligent management method and system, relates to the technical field of intelligent management systems, and aims to solve the problems of lack of environment monitoring, scheduling decision lag, extensive safety response and serious data island in the prior art. The method comprises the following steps: collecting the physical state, environmental parameters and operation behavior data of a coal storage yard in real time through a distributed sensor network; constructing a three-dimensional dynamic digital twinborn model and inputting the model into a time sequence neural network to predict the risk of spontaneous combustion, collapse or dust explosion in the future; and generating a dynamic scheduling instruction in combination with the task priority and the equipment state, and triggering a multi-level security response mechanism. The system comprises a sensor network module, a data fusion modeling module, a risk prediction module, a scheduling optimization module, and a closed-loop control and safety response module. According to the scheme, safety early warning preposition, scheduling decision intelligent optimization and resource energy efficiency collaborative improvement in the storage and transportation process are achieved, and the response capability and operation reliability of the coal supply chain are remarkably enhanced.

The invention discloses an intelligent communication content adaptation system and method based on user intention recognition. The system integrates and processes texts, voices, emoticons and unstructured behavior data through a multi-modal input analysis module; the deep learning intention recognition engine adopts a triple attention mechanism, current input and weighted historical interaction features are fused, and multi-level intention classification including basic operation, semantic targets and emotion driving is output; the real-time emotion state analysis module fuses acoustics, semantics and physiological indexes to generate a dynamic emotion matrix; the content adaptation decision engine is based on the intention confidence and the emotional state; and the multi-channel output optimization module performs cooperative adjustment on the speech synthesis rhythm, the text abstract and the visual interface according to the speech synthesis rhythm and the text abstract. According to the method, the perception precision of the deep intention and emotion of the user in a complex scene is remarkably improved, the individuation and multi-channel adaptive optimization of the response content are realized, and the communication efficiency and the user experience are effectively improved.

The invention discloses an injection molding production optimization method based on multi-agent cooperation, and the method comprises the following steps: decomposing a plurality of targets in an injection molding production process, and constructing a layered multi-agent structure; collecting data in the injection molding production process in real time, and constructing a global expert behavior track and a local expert behavior track; based on the global target and each local target, obtaining initial parameters of a global reward function and a local reward function; adopting an inverse reinforcement learning method to obtain an optimal global reward function and an optimal local reward function; obtaining an optimal decision strategy of each agent through a hierarchical collaboration mechanism by each hierarchical agent; when detecting that a conflict exists between the targets, dynamically adjusting the reward weight of the reward function by adopting a conflict coordination mechanism; and continuously collecting new expert behavior data, and updating the optimal decision strategy of each agent. According to the invention, a layered multi-agent inverse reinforcement learning method is adopted, and multi-target dynamic collaborative optimization of injection molding production is realized.

The invention belongs to the technical field of curative effect evaluation of teenager depression drugs, and particularly relates to a curative effect evaluation method of a drug for treating teenager depression. According to the method, through dynamic fusion analysis of multi-dimensional physiological and behavior data, the dynamic effect of drug intervention can be reflected more comprehensively and accurately, meanwhile, the data dimension is effectively reduced through dimension reduction processing and key feature vector extraction, capture of key information is ensured, and the accuracy of drug intervention is improved by constructing a drug use window and conducting quantitative scoring on the drug curative effect in the window. According to the method, evaluation of the drug curative effect and introduction of an interpolation compensation technology are realized, the problem of data missing is further solved, a continuous dynamic curative effect evaluation sequence is formed, and a time sequence evolution rule of the drug curative effect can be intuitively reflected according to a curative effect change trend identified by the dynamic curative effect evaluation sequence; the comprehensive curative effect stability index is calculated by combining the duration and the alternating frequency of the curative effect change trend in the medication cycle, and a scientific basis is provided for determining the drug curative effect grade.

The invention provides an opencast coal mine VR safety training dynamic difficulty regulation and control method, which comprises the steps of collecting multi-dimensional interaction behavior data of a target student in a VR training scene in real time, and encoding the multi-dimensional interaction behavior data into a behavior vector sequence; inputting the behavior vector sequence into a pre-trained prediction model, and outputting a potential risk behavior and a risk occurrence probability; when the risk occurrence probability is higher than a preset threshold value, generating a risk event continuously caused by the potential risk behavior through a risk deduction model; dynamically generating a negative guide plot based on the risk event, and implanting the negative guide plot into the VR training scene; and dynamically adjusting a subsequent generation strategy of the negative guide plot according to a response result of the target student to the negative guide plot. The method has the technical effects of deeply understanding the behavior intention of the student, early predicting the potential risk and performing dynamic intervention.

The invention provides a machine learning-based mine post personnel behavior data fusion processing method and system, and the method comprises the steps: building a dynamic association rule of mine post personnel behavior data, combing the association relation of data from different sources, and combining with a historical result to optimize a weight. Obtaining a multi-source behavior data set, and performing hierarchical matching with the association rule to screen out a to-be-fused behavior data subset; and calling a pre-trained machine learning fusion model to carry out bidirectional fusion adaptation processing on the to-be-fused behavior data subset and the association rule to obtain dynamically optimized fusion adaptation parameters. And cross-source feature fusion processing is carried out based on the fusion adaptation parameters, and a fusion feature set with post behavior logic consistency is generated. And carrying out multi-dimensional fusion validity verification on the fusion feature set, and outputting a processing result or carrying out iterative optimization according to a verification result. Mine multi-source behavior data can be effectively integrated, implicit association is mined, the accuracy and reliability of data fusion are improved, and powerful support is provided for mine operation management.

The invention relates to the technical field of risk identification, and provides a risk identification system and a risk identification method. According to the risk identification system, by introducing the scene perception module, the data alignment module and the multi-modal analysis module, automatic acquisition and fusion of multi-source heterogeneous data, including images, videos, voices, sensor data, operation information and the like, of a construction site are realized, and the comprehensive perception ability of the system to the site state is effectively improved. Through a data alignment mechanism guided by structured data, multi-dimensional alignment of time, space and task semantics can be realized based on a historical schedule, B I M parameters, a construction plan and the like, the problem that multi-source data is difficult to fuse in a traditional method is solved, and the accuracy and consistency of data modeling are improved. A pre-trained multi-modal risk identification model is adopted, the cross-modal analysis capability is achieved, images, texts and behavior data of a construction site can be comprehensively understood, and then risk identification is accurately carried out.