1160 results about "Dynamic modelling" patented technology

The invention provides a digital factory operation virtual simulation teaching method and system, and relates to the technical field of smart factories, and the method comprises the steps: collecting factory operation data, carrying out the standardization processing and correlation analysis, constructing a multi-dimensional data model, carrying out the feature extraction and semantic mining of the hierarchical data of each dimension, and constructing a multi-level digital twin factory model. Carrying out dynamic modeling, constructing a personalized knowledge graph, generating a learning demand list, matching the learning demand list with a digital twin factory model, and generating a multi-dimensional simulation environment; in the multi-dimensional simulation environment, performing analysis and intention recognition on an operation instruction of the learner, converting the operation instruction into a virtual operation, calculating a normative score of the virtual operation, generating corresponding feedback information based on the normative score and the personalized knowledge graph, updating model parameters based on a response of the learner to the feedback information, and performing simulation on the learner according to the updated model parameters. And completing iteration of the model.

The invention belongs to the technical field of electromechanical equipment operation and maintenance, and discloses an electromechanical equipment self-adaptive intelligent early warning system based on multi-source sensing data. The system is composed of a multi-source sensing module, an edge data acquisition and preprocessing module, a data cleaning and multi-dimensional feature extraction module, an equipment state dynamic modeling module, an intelligent fault prediction and trend analysis module, a self-adaptive early warning threshold generation and dynamic adjustment module, and an intelligent decision and remote cooperation module. The system is composed of a multi-source sensing module, an intelligent low-carbon operation and maintenance management and control module and a digital twin system integration and full-period mapping module, multiple sensors are deployed through the multi-source sensing module to acquire multi-dimensional data of equipment, cleaning and calibration are performed through the edge data acquisition and preprocessing module, deep processing is performed through the data cleaning and multi-dimensional feature extraction module, and multi-dimensional feature extraction is performed through the multi-source sensing module. The data integrity and accuracy are ensured; and data are quickly transmitted among the modules, so that the monitoring system can accurately present the running state of the equipment in real time.

The invention discloses a pharmaceutical quality traceability decision-making method and system based on a dynamic knowledge graph, and relates to the technical field of pharmaceutical quality control. The method comprises the following steps: defining a dynamic knowledge graph containing quality-related entities, wherein the dynamic knowledge graph at least comprises a quality event entity, a production element entity and a time sequence causal relationship between the quality event entity and the production element entity; capturing production data related to quality in real time, and generating event data with a quality rule identifier based on a preset triggering condition; and carrying out dynamic state modeling on the production element entity to generate a state vector containing a time sequence attenuation characteristic and a quality influence weight. By constructing the dynamic knowledge graph and designing the time sequence causal reasoning algorithm, real-time capture, dynamic modeling and causal relationship analysis of production data are achieved, the defects of the traditional technology in real-time performance and relevance are overcome, and an efficient and accurate intelligent solution is provided for pharmaceutical quality traceability.

The invention discloses an energy storage battery pack equalization control method, which belongs to the field of energy storage battery packs, and comprises the following steps: S1, constructing a multi-physics field-aging coupling model; s2, based on a multi-physical field-aging coupling model, space-time-frequency domain joint state estimation and unbalanced pattern recognition are realized; s3, formulating optimal equalization topology and control parameters according to space-time-frequency domain joint state estimation and an unbalanced mode recognition result; s4, establishing an electric-thermal-fluid multi-physics field coupling control model, and optimizing equalization current regulation and control, liquid cooling flow velocity and cooling fan rotating speed based on the optimal equalization topology and control parameters; and S5, optimizing the full life cycle of the digital twin drive. By adopting the equalization control method for the energy storage battery pack, the efficient coordination of the energy storage battery pack from dynamic modeling, accurate state estimation and intelligent equalization decision to full life cycle management is realized, and the equalization efficiency and the system reliability are remarkably improved.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle parking apron dynamic landing point adjustment method and system based on environment awareness, and the method comprises the steps: obtaining environment data and flight parameters, constructing a three-dimensional terrain grid model, and analyzing the stability; loading obstacle data and path constraints to generate a comprehensive evaluation model, and simulating to obtain regulation and control data; training the model to generate initial adjustment parameters, and combining the parameters with the model to obtain dynamic adjustment simulation information; constructing a multi-path optimization model to obtain optimal parameters; and combining real-time sensing to generate a safety probability, and adjusting a landing strategy. The system comprises a data acquisition module, a terrain modeling module and the like. According to the scheme, intelligent optimization and real-time adjustment of a landing point are realized through multi-dimensional environment perception and dynamic modeling, the landing safety and reliability of the unmanned aerial vehicle in a complex environment are improved, and the method is suitable for an intelligent landing scene of the unmanned aerial vehicle.

The invention relates to a multi-defense-area intelligent linkage alarm method based on an AIoT gateway and related equipment, and the method comprises the following steps: fusing the environment data of an Internet of Things sensor of a plurality of defense areas with a video image stream to generate three-dimensional reconstruction data of a defense area scene; and monitoring and analyzing the data, and extracting an environment feature sequence and a trajectory analysis sequence. And when it is detected that the threat levels of the multiple defense areas exceed a threshold value, causal inference is carried out in combination with gateway historical alarm data, and a cross-defense-area association event chain is generated. According to the method and the system, the AIoT gateway is used as a core, then a cooperative response strategy preset by the AIoT gateway is matched, and the SP voice call module is linked through a 4G / 5G network, so that multi-channel alarm pushing is realized, and the technical problem that comprehensive understanding and dynamic modeling of a defense area scene are difficult to form due to the lack of deep fusion and cooperative analysis means between video image data and environment sensor data is solved.

The invention discloses a psychological health assessment system based on big data. The psychological health assessment system comprises a data acquisition module, a data processing module, a dynamic modeling module, a risk assessment module and an intervention generation module. The data acquisition module acquires a heterogeneous data source of a user in real time through a distributed interface and generates structured data; the data processing module receives the structured data to generate a multi-modal feature vector; the dynamic modeling module receives the multi-modal feature vector, constructs a user psychological state atlas through a graph neural network, and updates a topological structure of the atlas based on a time sliding window to generate a psychological state evolution signal; the risk assessment module receives the psychological state evolution signal and forms an early warning signal; and the intervention generation module receives the early warning signal, generates a personalized intervention instruction and transmits the personalized intervention instruction to the user terminal. The psychological health assessment system based on the big data can solve the problems of calculation redundancy, poor geometric adaptability and low knowledge distillation efficiency when a traditional attention mechanism processes a circular region.

The invention relates to the technical field of virtual simulation and human-computer interaction, in particular to a semantic association modeling and self-adaptive simulation method based on multilayer correction, which comprises the following steps of: acquiring characteristic data from a three-dimensional simulation model and an electrical schematic diagram, and generating situation dynamic model data through data cleaning and standardization processing; constructing a semantic incidence matrix based on the situation dynamic model data, generating a geometric layer model, a logic layer model and a cross-domain layer model, and generating dynamic state and logic state data through distributed cellular automaton simulation; and in combination with simulation data, optimizing a semantic incidence matrix and a dynamic modeling rule by utilizing a causal path inference algorithm and a multi-layer correction mechanism, and realizing state linkage and user interaction feedback of the three-dimensional simulation model and the electrical schematic diagram. The state synchronization accuracy and interaction efficiency of the complex system are remarkably improved, and the method is suitable for the fields of industrial design, simulation verification and teaching training.

The invention discloses a territorial space planning optimization method and system based on three-dimensional modeling, and relates to the technical field of three-dimensional modeling, and the method comprises the steps: obtaining multi-source territorial space data, constructing a three-dimensional space data set, carrying out the three-dimensional geometric modeling, and generating a territorial space three-dimensional model; carrying out multi-dimensional space analysis on the land space three-dimensional model, obtaining a space conflict feature set, carrying out planning constraint, and formulating a space optimization suggestion; executing the space optimization suggestion to perform three-dimensional dynamic modeling, generating a multi-time sequence planning simulation result to perform space planning evaluation, generating a land space planning score, performing planning compensation based on the backtracking space optimization suggestion, updating the space optimization suggestion, and obtaining a space planning optimization scheme. The technical problem that in the prior art, land space planning depends on two-dimensional data, the space conflict and the dynamic evolution process are difficult to comprehensively recognize, and the planning scheme is insufficient in scientificity is solved, and the technical effect of improving the space conflict recognition precision and the planning decision scientificity is achieved.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

The invention relates to a control method of a digital ultrasonic flaw detector calibrating device. The control method comprises the following steps: acquiring shape data and material parameters of a to-be-detected material, calling a matched standard test block, calculating adaptive ultrasonic frequency and flaw detector parameters, creating and solving a multi-objective optimization model, and planning a detection path of the to-be-detected material based on an optimal solution; emitting ultrasonic waves with matched frequency to implant the quantum dot mark array to carry out fluorescence-ultrasonic dual-mode signal correlation calibration; scanning according to a planned detection path, establishing a time-varying channel model to predict a current time delay variation, extracting a fundamental frequency and a harmonic component of a probe excitation pulse by adopting an autocorrelation algorithm to serve as a reference, constructing a state vector of a filter based on the predicted current time delay variation, and performing phase correction on a received signal to obtain a phase correction signal; and analyzing the defect of the to-be-detected material based on the corrected receiving signal. By integrating material parameter dynamic modeling, multi-modal signal collaborative calibration and adaptive path optimization, the detection precision and efficiency under complex working conditions are improved.

The invention relates to the technical field of intelligent logistics scheduling, in particular to a logistics scheduling planning method and system based on a graph neural network and reinforcement learning, and the method comprises the following steps: S1, constructing a dynamic graph structure of a logistics network; s2, carrying out embedded learning on the dynamic graph structure through a graph attention network, and extracting a multi-dimensional feature vector of each node; s3, inputting the multi-dimensional feature vector into a multi-agent reinforcement learning framework to generate an initial vehicle path planning scheme; s4, dynamically correcting the road section traffic state in the initial vehicle path planning scheme; s5, iteratively updating the vehicle path planning scheme through local reinforcement learning; and S6, outputting a final collaborative optimization cargo transportation track and a vehicle driving path. According to the method, dynamic modeling and multi-agent path collaborative optimization of a logistics network structure can be realized, and the method has adaptive adjustment capability on real-time traffic and environment change, so that the overall scheduling efficiency is improved.

The invention, which relates to the technical field of air transportation and income management, discloses a dynamic generation method and system for an air travel price, and the system comprises a user behavior fine-grained tracking module, a dynamic pricing decision engine module, a cross-channel cooperative control module, and a compliance auditing and feedback module. Through a real-time data stream fusion technology, multivariate signals such as competition dynamic signals, user behavior signals and external environment signals are integrated, and second-level strategy response is realized in combination with the online training capability of a reinforcement learning model; through dynamic state space modeling, variables such as demand popularity, user sensitivity and external risk are coded into six-dimensional vectors, and the limitation of a fixed formula is broken through in combination with the nonlinear mapping capability of a deep Q network; through three measures of dynamic modeling, elastic constraint and cross-chain cooperation, the problems of response lag, high compliance risk and split user experience of a traditional pricing technology are solved.

The invention discloses a container tracking management system and method. The method comprises the following steps: S1, carrying out dynamic encryption binding and multi-dimensional data acquisition on an RFID chip; s2, realizing real-time position data acquisition of the container under different environmental conditions through a multi-source signal fusion and error correction algorithm; s3, various sensor modules are installed inside and outside the container, and data processing is carried out through distributed edge nodes; s4, performing on-chain storage on total data generated in the transportation process through a Hash encryption algorithm, and realizing automatic data verification, state tracking and abnormity alarm in combination with an intelligent contract; s5, carrying out real-time dynamic modeling analysis on the transportation path and resource allocation, and dynamically adjusting the transportation plan by using a distributed scheduling strategy; and S6, integrating the real-time state data, the path optimization model and the transportation scheduling decision, and providing a report for a user. The method has the advantages of high positioning precision, high data security, high transportation efficiency and good real-time performance.

The invention is an advanced deep learning system that combines a latent transformer core with a latent dynamics analyzer. This system processes input data into latent space vectors, which are then analyzed in parallel for both prediction and dynamic modeling. The latent transformer generates short-term predictions, while the latent dynamics analyzer derives equations of motion describing the underlying system dynamics. By integrating spectral analysis and change detection, the system can identify significant shifts in behavior, particularly useful for complex systems like financial markets. The invention enables more accurate predictions, interpretable insights, and early detection of regime changes. Its end-to-end training approach ensures all components work harmoniously, balancing predictive accuracy with physical plausibility and interpretability.

The invention relates to the technical field of robot anomaly prediction, in particular to a robot anomaly prediction method and system based on multi-dimensional fusion and causal inference. The method comprises the steps of performing multi-scale depth state characterization based on acquired robot multi-joint sensing data, and performing dynamic causal graph fusion based on the multi-scale depth state characterization. Comprising the steps of priori knowledge graph construction based on a kinematics chain, dynamic association attention mechanism construction based on data driving, state fusion of knowledge and attention guidance and global state vector generation. Performing hierarchical space-time dependency prediction based on the fused features, wherein the hierarchical space-time dependency prediction comprises robot joint topological graph construction, spatial dependency dynamic modeling, long-range time evolution prediction and future robot health state prediction; the method shows excellent performance in a plurality of core dimensions such as prediction precision, early warning timeliness and diagnosis interpretability, and has extremely high actual deployment value and engineering popularization potential.

The invention relates to the technical field of remote sensing monitoring, in particular to an SAR satellite data driven freeze-thaw landslide deformation monitoring system which comprises a multi-band SAR data acquisition module, a data preprocessing module, a deformation detection module, a dynamic modeling module, a risk assessment early warning module and a report comprehensive display module. Wherein the multi-band SAR data acquisition module is used for acquiring multi-band radar image data; the data preprocessing module is used for carrying out denoising, correction and image registration processing; the deformation detection module is used for generating a high-precision earth surface deformation graph; the dynamic modeling module is used for constructing a dynamic geologic model of the freeze-thaw landslide; and the risk assessment early warning module is used for carrying out landslide risk assessment. According to the invention, through integration of SAR satellite data and dynamic geological modeling, the precision and real-time performance of freeze-thaw landslide monitoring are significantly improved, the accuracy and timeliness of disaster early warning are enhanced, and efficient scientific and technological support is provided for geological disaster management.

The invention provides a smart campus-oriented multi-hyper fusion platform collaborative scheduling system and method, and is applied to the technical field of data processing. Resource sensing and dynamic modeling processing is performed on multi-hyper fusion platform resource pool data to generate target resource model data, and the target resource model data is composed of a resource real-time monitoring index, load prediction model output, a resource isomerism adaptation result and a cross-platform protocol conversion adaptation parameter; the target resource model data is processed, platform collaborative scheduling strategy parameters are generated based on reinforcement learning, and a campus business scene reward and punishment mechanism is introduced in the reinforcement learning process; processing the platform collaborative scheduling strategy parameters to generate a dynamic resource allocation scheme; processing the dynamic resource allocation scheme and the campus service demand data, and generating a service and resource matching agent model based on an intelligent optimization algorithm; and processing the target campus information based on the service and resource matching agent model to generate campus resource scheduling information.

The invention belongs to the technical field of rail transit, and discloses a rail transit environment foreign matter intrusion detection method and system based on a visual model. The method comprises the following steps: firstly, extracting a suspicious region sub-graph in an original image through preprocessing, and respectively extracting a coarse-grained global feature vector and a fine-grained local feature vector by using an image encoder; meanwhile, text prompt information matched with the image content is generated, and a corresponding text feature vector is extracted; the multi-level similarity of the image feature vector and the text feature vector is calculated, and the respective weight fusion is combined to obtain a comprehensive abnormal score; and finally, dynamically modeling abnormal score distribution based on a Gaussian mixture model to realize foreign matter invasion judgment. According to the method, image and text multi-modal information is fused, the detection precision of foreign matters in a complex scene is improved through multi-scale feature association, the dynamic environment adaptability is enhanced by adopting an adaptive threshold strategy, and the problems that a traditional method is high in false detection rate and insufficient in generalization ability in a complex background of a railway are effectively solved.

The invention discloses a public institution kitchen energy data management system based on cloud data, particularly relates to the field of energy management, and comprises a multi-source heterogeneous data acquisition module, an edge intelligent preprocessing module, a cloud data lake storage module, a multi-dimensional analysis engine module, an intelligent decision center module and a visual interaction module. According to the method, space-time alignment of multi-source data is realized through protocol adaptive conversion and millisecond time synchronization technologies, a high-consistency analysis basis is constructed, real-time index calculation and adaptive model optimization are implemented by edge calculation and cloud dynamic modeling collaborative architecture, hidden energy loss is accurately identified, and the real-time performance of the system is improved. The intelligent decision center is combined with digital twinborn verification to generate a control strategy, a minute-level closed-loop response mechanism is formed, automatic adjustment and optimization of equipment parameters and intelligent distribution of maintenance work orders are achieved, manual intervention delay is eliminated, timeliness and execution accuracy of an energy strategy are guaranteed, and multi-system collaborative management efficiency is remarkably improved.

The invention discloses a knowledge graph optimization method based on large model and multi-modal data fusion. The method comprises the following steps: S1, constructing a multi-modal data set; s2, forming a preprocessed multi-modal data set; s3, generating a unified semantic vector representation; s4, inputting the unified semantic vector representation into an adaptive Poisson distribution model, performing dynamic modeling on the arrival rate and distribution characteristics of the multi-modal data, and determining adaptive parameters of data sampling and updating; s5, generating a fused semantic representation set; s6, performing entity extraction and relation identification by using the fused data representation, and constructing a preliminary knowledge graph; and S7, carrying out automatic verification, redundant information elimination and structure adaptive adjustment on nodes, edges and attributes of the knowledge graph to form an optimized knowledge graph. According to the method, the data acquisition and atlas updating frequency can be adjusted according to the real-time data semantic change, and it is ensured that the atlas construction process has semantic driving performance and time sensitivity.

The invention relates to the technical field of cloud computing application, and discloses a cloud computing-based shale fracture modeling platform, which comprises a multi-source data acquisition module, a heterogeneous data fusion module, a fracture dynamic modeling module, a parameter optimization module and a cloud verification module. The multi-source data acquisition module acquires various data and uploads the data to the cloud; the heterogeneous data fusion module processes data isomerism to generate a fusion feature matrix; the crack dynamic modeling module constructs a three-dimensional dynamic model; the parameter optimization module optimizes physical property parameters; and the cloud verification module verifies the model in multiple dimensions and manages the version. The platform solves the problems of traditional modeling in the aspects of data acquisition, fusion, modeling algorithm, parameter optimization and model verification by means of the advantages of cloud computing, can efficiently process multi-source data, accurately construct a crack model, improve the precision and reliability of the model, realize comprehensive verification and effective management of the model, and provide powerful support for efficient exploitation of shale gas.

The invention relates to the technical field of inertial assistance, in particular to a Beidou high-precision space-time reference dynamic compensation system based on inertial assistance, which is characterized in that an inertial measurement unit module is used for measuring original measurement data, and the original measurement data comprises a carrier phase and a Doppler observation value; the processing unit module is used for fusing the inertial measurement unit, executing real-time environment perception and dynamic modeling, classifying an operation environment and predicting error characteristics; performing an adaptive robust fusion filtering algorithm that dynamically adjusts filter parameters and switches a filter model to estimate the position and attitude of the carrier; and in combination with radio wave precision single-point positioning enhancement service, super-tight combination signal tracking and integrity monitoring are realized, an estimated inertial assistance system state is fed back to a tracking loop of the multi-frequency Beidou receiving module, and real-time fault detection is performed on original measurement data.

The invention relates to the technical field of fire-fighting early warning, and discloses a fire-fighting early warning system based on image data relevance. The system comprises a multi-source image acquisition module used for acquiring fire-fighting scene multi-source heterogeneous image data; the correlation feature analysis module is used for performing cross-data-source correlation analysis on the data to generate feature vectors; the spatio-temporal dynamic modeling module is used for constructing a multi-dimensional feature fusion space to generate an associated spatio-temporal feature tensor; the resource optimization scheduling library is used for constructing a double-layer collaborative library; and the intelligent early warning control module generates a real-time fire early warning instruction and an emergency response decision through a hierarchical reinforcement learning framework. The system also has the functions of building structure deformation detection, smoke diffusion prediction and the like. Through cooperation of multiple modules, accurate fire-fighting early warning and efficient emergency response are realized, the fire prevention and control capability is improved, and life and property safety is effectively guaranteed.

The invention relates to the technical field of plastic production, in particular to an intelligent processing method and system for plastic mold process parameters. The method comprises the following steps: carrying out sensor network deployment on a plastic mold to obtain a mold parameter sensing network; operating parameters of the plastic mold are collected in real time through the mold parameter sensing network, and mold operating state data flow is obtained; performing process state feature extraction on the mold operation state data stream to obtain mold process state feature data; performing spatio-temporal data hierarchical analysis on the mold process state feature data to obtain mold state multi-scale feature data; and performing layered thermal field dynamic modeling on the plastic mold based on the mold state multi-scale characteristic data to obtain a mold thermal field evolution model. The key process parameters of the mold can be identified, and the stability and reliability of the mold performance are enhanced.

The invention relates to the technical field of display screens, and discloses an anti-dazzle Micro LED display screen and an anti-dazzle adjusting method and system.The method comprises the steps that multi-dimensional spectral data collection is conducted on incident light, and a multi-dimensional spectral feature matrix is obtained; performing space-time light field reconstruction of tensor decomposition and spherical harmonic function expansion operation on the multi-dimensional spectral feature matrix to obtain four-dimensional light field feature data; inputting the four-dimensional light field feature data into a deep learning prediction network for dynamic modeling to obtain light field distribution prediction data; performing anti-dazzle display parameter optimization based on the light field distribution prediction data to obtain a display parameter target value; and executing a hierarchical control strategy according to the display parameter target value, and generating a multi-parameter cooperative control signal for light transmittance adjustment, polarization state modulation and microstructure actuation, the method realizes accurate conversion from the light field characteristics to the control parameters, ensures the stability and continuity of the display effect, effectively reduces the system energy consumption, and improves the operation efficiency.