492 results about "Predictability" patented technology

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention relates to the technical field of fault detection of the Internet of Things, in particular to a valve path detection method and system based on the Internet of Things, and the method comprises the following steps: based on real-time data of a valve path, removing high-frequency noise and high-frequency components in temperature fluctuation through a low-pass filter, carrying out the mean value smoothing operation of pressure data, and carrying out the mean value smoothing operation; and performing data standardization conversion on the flow data to obtain valve operation state data. According to the method, through real-time collection and optimization processing of valve operation data, it is ensured that the data is accurate and stable, weighted calculation is carried out in combination with the data influence degree, the representativeness of state feature extraction and the perception ability of working condition changes are enhanced, and based on state feature trend analysis, the sensitivity of fault detection is improved; and health state evaluation is carried out in combination with the operation duration and the load change, so that health state scoring is realized, the predictability of the long-term operation state is improved, false alarms and missing alarms are reduced, and the accuracy of fault early warning and the equipment health monitoring capability are enhanced.

The invention relates to the technical field of electronic equipment detection, and discloses an FPC connector quality detection method and system based on multi-modal fusion, and the method comprises the steps: obtaining an original multi-modal data set containing electrical stress, thermal stress and mechanical stress, and carrying out the filtering and time sequence alignment preprocessing, and obtaining a standardized multi-physical field sequence; constructing a three-dimensional space field intensity distribution diagram according to the sequence, and identifying field intensity anomalies by adopting density clustering so as to accurately position potential defects; learning a time sequence evolution mode based on the multi-moment observation value of the defect position to predict the future diffusion trend of the defect; and integrating the diffusion trend, the real-time field intensity and the environment variable to carry out comprehensive modeling and evaluation, and generating a dynamically updated and final quality detection report. According to the method, root cause diagnosis of connector defects, accurate three-dimensional space fault positioning and accurate prediction of future evolution trends can be realized, and the detection precision and predictability are remarkably improved.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention discloses an intelligent mold part machining cutting tool control method and system. The method comprises the following steps that S1, a multi-source sensing information collection layer is established; s2, constructing a cutter health knowledge graph; s3, generating a dynamic decision control instruction; s4, executing a bimodal control response; and S5, realizing closed-loop control optimization. Through quadruple mechanism coupling of global coverage of a multi-mode sensing layer, dynamic deduction of a knowledge graph decision-making layer, risk isolation of a double-track execution layer and intelligent evolution of a closed-loop optimization layer, the method is realized in an industrial control domain for the first time: raising a sensing dimension, and converting a physical world fragmentation signal into a cutter full life cycle digital twinborn body; reconstructing decision logic, replacing traditional threshold judgment with topological correlation, and foreseeably inhibiting ill-conditioned failure; the system is ecological and self-consistent, and the control strategy continuously evolves in operation to form the anti-interference capability; and finally, normal form transition of tool wear control from passive remediation to active immunity is achieved.

The invention discloses a task scheduling method based on predictable resource state atlas modeling, a platform is oriented to a heterogeneous computing environment, a unified resource state atlas is constructed by collecting multi-dimensional resource state parameters of computing nodes, and performance characteristics and communication topological relations among the nodes are comprehensively described. On the basis, a bidirectional time sequence model and an attention mechanism are fused, and the load trend of each node in a future short time is predicted. The platform constructs a multi-factor scheduling scoring function based on a task feature vector and resource state prediction map, integrates parameters such as resource matching degree, prediction load, communication delay and energy consumption cost, dynamically evaluates the adaptability of tasks and resources, and realizes adaptive scheduling and optimal resource allocation of the tasks. Compared with the prior art, the method has the advantages of being high in resource state predictability, high in task allocation intelligence degree, outstanding in platform evolution capability and the like, and is suitable for intelligent task scheduling application in a large-scale heterogeneous resource environment.

The invention relates to the technical field of construction control, in particular to an underground super-long thick and large concrete structure construction control method and system.The method comprises the following steps that through vertical displacement and strain data, trend extraction nodes are analyzed, difference sections are divided, load recognition influence areas are positioned, and structural sections are sequenced according to fluctuation increments; and adjusting cooling, supporting and loading parameters to form an intervention arrangement, and performing year-on-year adjustment based on construction data to generate feedback to complete regulation and control. According to the method, load-deformation dynamic mapping is established through time sequence association of displacement and strain data, a difference section identification mechanism is constructed based on time difference comparison, natural deformation and load surge response are distinguished, fluctuation characteristics of a load surge section are extracted to form response sensitivity ranking, and cooling parameters and a loading sequence are dynamically adjusted in combination with increment comparison. Graded regulation and control are realized, space-time dimensions of construction records and monitoring data are coupled, the crack positioning precision is improved, the predictability of deformation control is enhanced, and the regulation and control redundancy cost is reduced.

The invention relates to the technical field of data security, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device and equipment based on dynamic association analysis and a medium, and the method comprises the steps: obtaining original data, and converting the original data into initial state information; performing dynamic evolution based on the initial state information to generate a continuous state matrix; calculating correlation information among internal elements according to the continuous state matrix; determining a target hash mapping function based on the correlation information; processing the continuous state matrix by using a target Hash mapping function to generate an intermediate encryption representation; constructing an orthogonally distributed Hash center set; and comparing the intermediate encrypted representation with the Hash center set, and discretizing a comparison result to obtain a final output code stream. According to the method, the Hash mapping function which can be adaptively adjusted along with state evolution is constructed, so that the predictability is reduced, and meanwhile, the resistance to disturbance and statistical attack is enhanced.

The invention discloses a continuous charging production line intelligent management and control strategy and framework, and the framework further comprises a multi-physics field coupling modeling module which carries out the physical mechanism modeling and state prediction of a production process through a heat-fluid-solid three-field coupling model on the basis of a conventional data collection and control module; the intelligent twin module adopts a mode of dynamically fusing a mechanism model and a data-driven residual model to improve the precision and robustness of system state estimation; the scheduling optimization module is used for realizing dynamic adaptive scheduling of AGV paths and production tasks based on a multi-target deep reinforcement learning strategy; and the multi-layer safety causal reasoning module realizes advanced prediction and active prevention and control of safety risks through a causal map, fuzzy logic and a linkage response mechanism. According to the method, the problem that safety, quality and efficiency are difficult to collaboratively optimize in the prior art is solved, and the predictability, safety, scheduling efficiency and adaptive capacity of the production line are remarkably improved.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

The invention discloses a digital station building equipment state intelligent monitoring system and method. The system comprises a data acquisition module, a multi-dimensional data processing module, an LSTM neural network analysis module and a state evaluation module. The data acquisition module acquires multi-modal time series data through a multi-type sensor group; the multi-dimensional data processing module performs space-time alignment and feature fusion on the data to construct a composite feature vector; the LSTM neural network analysis module adopts an attention gating mechanism to fuse dual-scale features, and outputs equipment state probability distribution and a residual life prediction value; and the state evaluation module updates the posterior probability of the health state of the equipment based on the dynamic Bayesian network so as to realize graded early warning and maintenance decision. According to the invention, the accuracy and predictability of equipment state monitoring are improved, the requirements of the digital station building for accurate monitoring and predictive maintenance of the equipment state are met, and the intelligent level of operation and maintenance management is improved.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

The invention belongs to the technical field of livestock and poultry breeding environment intelligent control, and particularly relates to a meat duck breeding environment temperature regulation and control method and system integrating quantile regression prediction and reinforcement learning. The method comprises the following steps: collecting multi-dimensional data of a breeding environment and operation state information of environment regulation and control equipment, and performing feature construction to obtain a feature vector; using the multi-source data set to predict in-house temperature based on quantile regression to obtain predicted temperatures under different quantiles; modeling a breeding environment temperature regulation problem into a Markov decision process, and dynamically adjusting fan power and a wet curtain equipment state; and setting an online updating model step threshold, and performing actual deployment and application on the reinforcement learning decision model obtained by training and fusing quantile regression information. According to the method, the problems that the meat duck breeding environment temperature regulation and control technology still faces insufficient predictability, regulation and control lag, control strategy static performance, multi-target optimization deficiency and the like are solved.

The invention relates to the technical field of digital twinning, in particular to a digital twinning monitoring system for a precision servo press production line, which comprises a physical production line module, an edge calculation module, a cloud digital twinning module and a visual monitoring module, wherein the physical production line module is used for monitoring and collecting production line data in real time; the edge calculation module preprocesses and analyzes the collected original data, judges product defects, equipment faults and employee abnormal states in real time to trigger edge early warning, and outputs a multi-dimensional data set; the cloud digital twinborn module is used for constructing a digital twinborn body, establishing a real-time coupling simulation model fusing electromagnetism, heat, force and sound to simulate the internal physical process of equipment in real time, predicting the performance change and potential fault of the equipment, positioning the fault root cause and predicting the residual life; and the visual monitoring module is used for carrying out multi-dimensional visual presentation and providing decision support. Therefore, the problems of insufficient production line staff monitoring, single physical field monitoring, lack of fault predictability and the like are solved.

Techniques of updating machine learning models in a network include combining global and local models at each electronic entity of a network. For example, a first electronic entity (e.g., a user device) may train a local machine learning (ML) model based on data collected by the first electronic entity or other entities (e.g., other user devices, servers) and make predictions based on that model. Nevertheless, other electronic entities may also train or store their own local ML models. Accordingly, for more insight about the network and better predictability of the ML models, the first electronic entity may obtain a ML model from a second electronic entity, i.e., a global ML model. Upon receipt of the global ML model, the first electronic entity may aggregate the local ML model and the global ML model to produce an updated global ML model.

The invention discloses a rock fracture prediction method and system based on a resistivity data assimilation algorithm, and the method comprises the steps: building a numerical model for simulating the asymptotic fracture process of a rock mass under the load effect according to the boundary conditions and initial conditions of the rock mass; simulating the stress damage process of the rock mass by using a numerical calculation method to obtain prediction data of stress field and fracture damage field distribution of the rock mass; monitoring the damage process of the rock mass sample in real time by using a high-density electrical method to obtain observation data; rock mass fracture damage distribution is obtained based on observation data; an assimilation algorithm is adopted, information of observation data and information of prediction data are combined, and initial conditions or parameters of the numerical model are corrected; and then corrected rock fracture prediction data is obtained. Through the data assimilation algorithm, different data and model simulation results are fused in the power frame of rock fracture, the model track is automatically adjusted continuously depending on observation, the analysis result with higher precision and more consistency is obtained, and the prediction precision and predictability of the model are improved.

The invention discloses a traffic signal global collaborative prediction method based on a quantum entanglement state, and the method comprises the steps: building a quantization model of a dynamic evolution path of a target traffic network in a time window in the future, and constructing a space-time diagram model; mapping the signal phase state of the intersection into a time-space integrated quantum state by adopting a layered quantum coding scheme; on the basis of a preset traffic optimization target, constructing Hamiltonian including spatial coupling, time evolution and a cost item, solving a ground state of the Hamiltonian through a mixed quantum-classical calculation method, and determining an optimal dynamic evolution path; coding the actual state of the current traffic network into an initial state, carrying out sequential measurement through an optimal evolution operator, decoding a cooperative signal control strategy of each time point in the future, and executing the cooperative signal control strategy through a rolling time domain control framework; according to the invention, global optimization is carried out by using quantum parallelism, the optimal cooperation strategy in the whole space-time range can be obtained at one time, and the overall operation efficiency, predictability and robustness of the traffic network are improved.

The invention provides a vehicle-mounted controller memory monitoring method based on differential redundancy, and relates to the technical field of electric monitoring, and the method comprises the following steps: constructing a multi-dimensional graph representation and feature dictionary of a memory region; utilizing reinforcement learning to adaptively optimize a monitoring strategy according to a real-time state and a prediction risk; executing multi-level differential verification of context awareness, and taking a verification result as an observation evidence; performing deep fault probability prediction and interpretability analysis by adopting a graph neural network; through Bayesian or fuzzy logic intelligent fusion verification observation and model prediction, high-confidence risk assessment is realized; and active protection instructions such as predictive error correction and fault isolation are generated and triggered based on an evaluation result and closed-loop feedback. According to the scheme, prediction, adaptation, efficient verification and intelligent decision making are integrated, the foreseeability, adaptability, efficiency and robustness of vehicle-mounted memory monitoring are remarkably improved, and a comprehensive and advanced guarantee is provided for function safety and information safety of a vehicle.

The invention discloses a wind power plant intelligent simulation acquisition test method and system, and relates to the technical field of wind power plant intelligent acquisition and state prediction control, and the method comprises the steps: obtaining the operation state parameters of a wind turbine generator, and constructing a twinborn prediction model based on a time sequence. And comparing a model prediction value with a measured value to calculate an operation state error, and estimating prediction uncertainty. And dynamically adjusting the sampling frequency of the sampling channel according to the prediction uncertainty and controlling test execution. According to the method disclosed by the invention, a smart wind power plant simulation acquisition test system from data driving to state modeling to risk perception to dynamic regulation and control to multi-node collaborative integration is realized. According to the technical scheme, independent technical contributions are provided, link-by-link logic closed loops are constructed, a breakthrough is made from a traditional static acquisition mode, a digital wind field acquisition test system with predictability, self-adaptability and linkage response capability is formed, and the monitoring efficiency, the risk control granularity and the system resource utilization rate are improved.

Systems and methods iteratively train, using training data, a natural language processing (NLP) model to interpret conversational input during a conversation between an agent and a user by predicting key statements to be used in the prediction of a user intent, the training comparing outputs to a target variable during each iteration and adjusting parameters of the NLP model during each iteration to improve predictability of the user intent from the conversational input. Real-time conversational data is transmitted to the NLP model and the trained NLP algorithm derives key statements predicted to indicate intents and predicts one or more user intents based on the data from the conversation. One or more pre-filled forms predicted to effectuate the one or more user intents is generated, the pre-filled forms including generated text derived from information from the data of the conversation, and the form is transmitted to an agent device.

The invention discloses a three-dimensional dynamic safety management and control method and system for power transmission and transformation hoisting operation, and relates to the technical field of construction safety, and the method comprises the steps: obtaining point cloud data of a target operation area through ground three-dimensional laser scanning; according to the point cloud data, a three-dimensional grid model is constructed through an oblique photography technology and a Beidou real-time dynamic carrier phase difference technology, and a semantic electronic fence is generated; according to a pure vision technology, through target detection, three-dimensional reconstruction and time sequence tracking, a hanging object 4D digital branch is constructed, and a hanging object prediction track is obtained; the shortest three-dimensional distance is calculated through an efficient collision detection algorithm according to the hanging object prediction track and the semantic electronic fence, multi-dimensional risk assessment is carried out, and graded early warning signals are generated; and carrying out comprehensive alarm prompting according to the graded early warning signal. According to the invention, a traditional passive protection mode depending on single threshold alarm is changed, and quantitative evaluation and predictive management and control of dynamic risks are realized.

According to the invisible watermark embedding method based on the wavelet domain statistical characteristics, the high-frequency sub-band in the wavelet domain is selected as the embedding area, and the robustness of the watermark to conventional attacks such as compression and noise is improved while invisibility is guaranteed. Based on a self-adaptive embedding weight mechanism of a covariance matrix eigenvalue, the embedding strength of different texture regions can be dynamically adjusted, the robustness is enhanced by a smooth region, and distortion is suppressed by an edge region. Nonlinear disturbance is generated by adopting a deep neural network and chaotic encryption preprocessing is combined, so that the predictability of a linear statistical rule is broken, and the method has a relatively good application prospect.

The invention discloses a cold-chain logistics temperature control supervision method and system based on GPS linkage, and relates to the related technical field of cold-chain logistics, and the method comprises the steps: installing a vehicle-mounted terminal on a cold-chain logistics transport vehicle, and collecting the vehicle position information and carriage temperature data in real time; encrypting and uploading to a monitoring processing center, and calling cold-chain logistics temperature control analysis dual channels of the target cold-chain goods; obtaining a temperature comparison result with a preset safe temperature threshold value, and matching and determining a target temperature control analysis channel; and performing temperature control analysis, determining a temperature control adjustment strategy parameter, and performing cold-chain logistics temperature control supervision. The technical problems that in the prior art, temperature control supervision lags behind, deep fusion analysis with the vehicle position and the transportation state is not carried out, specific reasons of temperature abnormity cannot be distinguished, and the regulation and control strategy is lack of accuracy and predictability are solved, and real-time, accurate and intelligent cold chain transportation temperature control abnormity recognition and supervision are achieved. And the reliability, the safety and the efficiency of cold-chain logistics are improved.

The invention discloses a virtual simulation and security evaluation method and system based on a network security target range, and the method comprises the steps: constructing a network asset knowledge graph for describing a virtualized target range environment through information collection; secondly, based on an attack strategy grammar rule base, adopting a Monte Carlo Tree Search (MCTS) algorithm to carry out intelligent attack simulation on the knowledge graph so as to discover a nonlinear and multi-stage attack path; thirdly, constructing a Bayesian attack graph (BAG) based on the knowledge graph, and performing probabilistic and systematic quantitative evaluation on the security risk of each asset node in the network through Bayesian reasoning; and finally, integrating the attack path and the quantitative risk value, and generating a comprehensive assessment report containing a visual path, a risk sequence and a reinforcement suggestion. According to the target range simulation method and device, the problems that existing target range simulation is insufficient in confrontation authenticity, one-sided in safety evaluation and lack of predictability are solved by combining the strategy simulation of the MCTS and the global quantitative analysis of the BAG.

The invention relates to the technical field of supply chain management, in particular to a risk control method and system for a supply chain. According to the method, the limitation of a traditional method in processing complex data can be overcome, the inventory-production data of each link of the supply chain is acquired in real time, the risk influence index is dynamically calculated in combination with internal and external influence factors, potential risks can be comprehensively identified and dynamically assessed, and the risk assessment efficiency is improved. According to the method, coping strategies can be adjusted in time according to market demand fluctuation, policy change and other external factors, the response capability and adaptability of the supply chain are improved, the effectiveness of supply chain risk control is ensured through real-time monitoring of the risk control deviation degree, the defect that in-time early warning and adjustment are lacked in a traditional method is overcome, and the risk control efficiency is improved. Serious consequences of supply chain interruption, cost increase and the like are effectively prevented, the accuracy, the flexibility and the predictability of supply chain risk management are improved, and the supply chain risk management method has remarkable practical application value.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention relates to the technical field of geotechnical material local modeling, in particular to a geotechnical material data driving local modeling method and device based on a knowledge migration strategy, and the method comprises the steps: building a first mapping data pair based on the stress-strain physical quantity of a test curve sampling point, and building a high-fidelity data set of a geotechnical material; constructing a second mapping data pair based on the stress-strain state of the simulation curve sampling point and the intermediate physical quantity label, and establishing a low-fidelity data set of the geotechnical material; pre-training the target data-driven constitutive model by using the low-fidelity data set to generate an initial geotechnical material data-driven constitutive model; and driving the model to be fine-tuned by using the high-fidelity data set for the initial geotechnical material data, and generating final geotechnical material data to drive the model. Therefore, the problem of insufficient generalization ability and prediction precision of a data-driven model due to limited training data and unpredictability of a stress path in the prior art is solved.

The invention discloses a network topic hot spot extraction method based on bullet screen semantic recognition, and aims to solve the problems of bullet screen data semantic sparsity, semantic offset, noise interference and the like. The method is characterized by comprising the following steps: carrying out semantic coding on a bullet screen by utilizing a Transform structure; a dynamic semantic evolution perception model is constructed, semantic offset is measured through KL divergence, and topological correlation analysis is carried out through GCN; constructing a space-time density field in combination with a video time axis to realize space-time coupling feature fusion; automatically extracting a hot spot cluster by adopting an improved density peak clustering algorithm; and predicting a hotspot evolution trend by using an LSTM model. By means of the technical scheme, topic hotspots can be accurately captured, semantic evolution logic can be recognized, and the purity and predictability of hotspot extraction are improved.