7851results about How to "Ensure reliability" patented technology

The invention discloses a crop growth prediction method based on multi-source data fusion analysis, and the method comprises the steps: carrying out the standardization processing and missing value filling processing of multi-source data, constructing a time window, calculating the correlation between data sources, and generating a current data credibility vector based on historical credibility evaluation data; extracting features by adopting multi-scale decomposition, detecting abnormal points and analyzing abnormal degrees, and constructing an abnormal conduction network to evaluate conduction influence; performing feature importance evaluation and feature fusion; initial weight distribution is calculated, a weight prediction model is constructed, and consistency optimization is carried out on a weight matrix; performing hierarchical prediction by adopting a multi-level prediction model, and performing collaborative integration based on a prediction result and an optimized weight matrix; and calculating a prediction result deviation and carrying out optimization adjustment, and outputting a final prediction result. According to the invention, accurate prediction of the crop growth state is realized, and the reliability and practicability of the prediction result are improved.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention relates to the technical field of power systems, in particular to a substation main equipment self-checking system based on a multi-source heterogeneous data fusion technology. The self-inspection system comprises a data acquisition and preprocessing module, a self-adaptive data fusion algorithm module, an equipment fault mode dynamic identification and intelligent repair module, a multi-scale time sequence prediction and emergency plan generation module, an equipment health state evaluation and residual life prediction module, and a fault diagnosis and knowledge base updating module. A user interaction and remote monitoring management module, and an intelligent prediction and optimization scheduling module; according to the substation main equipment self-checking system based on the multi-source heterogeneous data fusion technology, comprehensive monitoring and intelligent management of substation equipment are achieved by integrating various data acquisition and processing technologies.

The invention relates to the technical field of power fault diagnosis, in particular to a multi-modal data fusion-based power fault diagnosis method and system, which comprehensively collects and analyzes electrical, environmental and mechanical parameters of power equipment through a multi-modal data fusion technology, constructs a multi-modal parameter feature matrix, and performs multi-modal data fusion on the basis of the multi-modal parameter feature matrix. Dynamic anomaly detection and personalized monitoring strategy formulation are realized, the accuracy and timeliness of fault diagnosis are improved, false alarm and missing alarm are reduced, operation and maintenance resource allocation is optimized, and stable operation and efficient power supply of a power system are ensured; the method comprises the following steps: respectively acquiring multi-modal data information of all same power equipment in a transformer area; feature extraction is carried out on the multi-modal data information of each power device to obtain a multi-modal parameter set; and carrying out data integration on all the multi-modal parameter sets which are collected at the same time to obtain a multi-modal parameter characteristic matrix which can represent the operation states of all the power equipment of the same model in the transformer area.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

The invention discloses a control method and a control system of a data center liquid cooling heat dissipation system, and relates to the technical field of liquid cooling heat dissipation, the system realizes real-time state monitoring of server operation through a data acquisition and monitoring module, integrates multiple data such as temperature, flow velocity and pressure difference, forms a detailed comprehensive heat dissipation data set, and improves the heat dissipation efficiency. And a database is constructed for storage. And meanwhile, a cooling liquid flow velocity analysis control module of the system generates a server heat dissipation coefficient CN through the server operation environment parameter C1 and the server operation parameter C2, the server heat dissipation coefficient CN is compared with a preset heat dissipation threshold C for evaluation, and the cooling liquid flow velocity is adjusted in real time. Therefore, when the load of the server is increased, the cooling intensity can be automatically increased, the chip temperature is reduced, and stable operation of the server is kept; and when the load is reduced, the cooling intensity is reduced, the energy consumption is reduced, and the cooling efficiency and the energy utilization rate of the system are remarkably improved.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention relates to the technical field of laser control, in particular to a high-power fiber laser stable output control method and system. The method comprises the following steps: monitoring a multi-mode sensing signal of an optical fiber laser in real time based on an optical fiber sensor array; performing dynamic optical fiber sensing change analysis on the multi-mode sensing signal of the optical fiber laser to generate a dynamic optical fiber sensing change characteristic; laser output phase distribution analysis is carried out on the dynamic optical fiber sensing change characteristics, real-time power feedback adjustment is carried out, and a dynamic light beam power feedback adjustment strategy is constructed; overheat power unbalance analysis is carried out based on a fiber laser multi-mode sensing signal, and local temperature control power fine tuning is carried out, so that a local temperature control power fine tuning strategy is constructed; and performing sensor position calculation on the optical fiber sensor array one by one, and performing nonlinear optical effect analysis to generate nonlinear optical effect characteristics. According to the invention, efficient and stable laser output control is realized.

The invention belongs to but not limited to the technical field of emotion recognition, and discloses a multi-modal classroom emotion recognition method and system based on modal adaptive learning, and the method comprises the steps: building a multi-modal data collection system, collecting the multi-modal data, such as texts, audios and videos, in a classroom teaching process in real time, and carrying out the real-time collection of the multi-modal data; and preprocessing and feature extraction are carried out on data of different modes. For emotion expression feature differences of different classroom scenes, a multi-modal data fusion method based on adaptive modal scoring (AMS) is provided, and a time sequence modeling and emotion classification model is constructed. The multi-modal fusion and time sequence modeling mechanism simulates the multi-dimensional perception of the teacher on the emotional states of the students in the class, and enhances the interpretability of the emotion recognition system. According to the system, weighted fusion is carried out on data features from texts, audios and videos through fusion of multi-modal data, and complete emotion feature representation is formed.

The invention provides a textile defect detection method based on machine vision, and the method comprises the steps: obtaining a corresponding camera resolution parameter, an algorithm complexity parameter and a detection sensitivity parameter from a database according to a textile quality requirement, and carrying out the initialization setting of a machine vision system; performing feature extraction on the optimized textile surface image, extracting texture and morphological features related to wrinkle flaws, and constructing a feature spectrum capable of reflecting wrinkle features; identifying a suspected wrinkle flaw area in the characteristic spectrum, determining the contour of the flaw, and obtaining the position and range information of the wrinkle flaw; according to a pre-established wrinkle defect type database, performing mode matching on the quantized wrinkle characteristics, and if the matching degree exceeds a preset threshold value, determining the specific type of the wrinkle defect; and a support vector machine algorithm is adopted to classify the wrinkle degree quantitative indexes, wrinkle depth and density parameters are combined, different levels of wrinkle flaws are divided, and the wrinkle severity is evaluated.

The invention discloses a key generation and distribution system based on quantum random numbers, and relates to the technical field of quantum key distribution. The system comprises a quantum random number generation module, a light source control module, a quantum key distribution module, a detector module, a data processing module, a communication control module, a network anomaly monitoring module and an identity authentication module. According to the secret key generation and distribution system based on the quantum random number, the quantum random number generator generates a real unpredictable random number sequence by using the randomness principle in quantum mechanics, and the random number sequence is taken as the basis of the secret key, so that the unpredictability and the safety of the secret key are fundamentally improved; compared with a traditional secret key generation method based on a pseudo-random number, the quantum random number has higher randomness and lower predictable risk, various cryptographic analysis attacks can be effectively resisted, the uncrackability of the secret key is ensured, and an unbreakable safety guarantee is provided for information communication.

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

The invention provides an unmanned aerial vehicle multi-target tracking method and system based on visual and millimeter-wave radar information fusion, and the method comprises the steps: obtaining an association result through employing a space-time consistency model, constructing a self-adaptive joint probability data association frame according to the association result, so as to obtain an initial target state estimation value, and carrying out the recognition of the initial target state estimation value. A prospective path planning algorithm is adopted to obtain an optimal observation angle flight path, a Bayesian network is utilized to obtain initial observation data, based on the initial observation data, an optimal target state estimation value is generated, a distributed cooperative sensing mechanism is used to generate an initial tracking task allocation scheme, and an intelligent resource allocation strategy is implemented. Generating an initial resource allocation scheme, starting a quick response protocol to obtain an optimal tracking task allocation scheme, and generating an optimal resource allocation scheme based on the optimal tracking task allocation scheme; according to the technical scheme provided by the invention, the accuracy of target identification is improved, the environmental adaptability is enhanced, the tracking efficiency is improved, and the working time is prolonged.

The invention discloses a metering-based carbon emission accounting system, which relates to the technical field of carbon emission accounting, and comprises the steps of acquiring energy consumption data, modeling the acquired energy consumption data, predicting an energy consumption trend, analyzing the energy consumption data, generating a preliminary carbon emission factor according to different energy types and consumption, and calculating the carbon emission of the carbon emission accounting system according to the preliminary carbon emission factor. And performing iterative optimization on the initial carbon emission factor through a carbon emission factor generation and optimization module, calculating the carbon emission by using the optimized carbon emission factor, setting a threshold value for comparison, performing early warning on the abnormal condition of the carbon emission in real time, and matching an emission reduction suggestion through an emission reduction optimization suggestion module. According to the invention, through energy consumption data acquisition, energy consumption trend prediction, carbon emission factor generation and optimization, and early warning and emission reduction optimization suggestion, the automation and intelligence level of carbon emission management is improved, and powerful technical support is provided for green low-carbon transformation.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

The invention relates to the technical field of banking business marketing data processing, in particular to a multi-channel-based banking business marketing data processing method and system, and aims to guarantee the accuracy and reliability of a marketing strategy by integrating multi-channel data and acquiring and verifying a banking business marketing data source in real time. Then, a machine learning algorithm is used for generating a customer portrait, analyzing a behavior pattern and potential requirements of the customer portrait, generating a personalized marketing strategy, a natural language processing technology is combined, real-time performance and pertinence of marketing content are ensured, pushing is carried out according to customer preferences during pushing, the pushing opportunity is optimized, and the system monitors feedback behaviors in real time after pushing, so that the marketing quality is improved. A strategy effect is evaluated, marketing activities are optimized by using an A / B test, closed-loop feedback is formed, a strategy is continuously improved, and high efficiency and accuracy of a marketing scheme are ensured. According to the method, automatic strategy generation and optimization are realized through the AI technology, the marketing efficiency and customer experience of banking business are greatly improved, and the method is suitable for a multi-channel data processing scene.

The invention relates to the technical field of health event prediction, in particular to an AI-based abnormal health event prediction system, which comprises a data acquisition module, a data preprocessing module, a causal analysis and health trend deduction module, a dynamic risk assessment and hierarchical management module, a feedback and optimization module and a user interaction and suggestion module, the data acquisition module collects health data of a user in real time; the data preprocessing module is used for preprocessing the collected health data; the causal analysis and health trend deduction module deduces the health trend of the user; the dynamic risk assessment and level-to-level management module dynamically assesses the health risk of the user; and the feedback and optimization module optimizes the health trend prediction model. According to the method and the system, the user is helped to take proper actions to prevent or deal with potential health problems, refined and personalized health management is realized, and the overall health level of the user is improved.

The invention relates to the technical field of pile foundation engineering, in particular to a construction method of a long-pile wharf pile foundation lateral displacement prediction model. The method comprises the following steps that displacement, stress and load parameter monitoring is conducted on a pile foundation of the high-pile wharf, and real-time monitoring parameter data is generated; performing working condition identification on the pile foundation structure through the real-time monitoring parameter data to generate working condition data of the pile foundation structure; multi-source load characteristic analysis is conducted on the pile foundation structure working condition data, load coupling effect calculation is conducted, and load action data are generated; performing soil-pile interaction analysis according to the load action data to generate soil-pile response data; and performing material performance deterioration evaluation according to the soil-pile response data to generate material state data. The working condition identification method based on the multi-source monitoring data is established, comprehensive perception of the pile foundation structure state is achieved, and a reliable data basis is provided for subsequent displacement prediction.

The invention relates to the technical field of data processing, in particular to a pneumology department intelligent early warning system based on data fusion. The method comprises the following steps: monitoring and collecting multi-source physiological data of a patient to obtain original physiological data; preprocessing the original physiological data to obtain preprocessed physiological data; performing intelligent fusion processing on the preprocessed physiological data to obtain comprehensive physiological feature data; performing anomaly detection analysis on the comprehensive physiological feature data to obtain an analysis result of the breathing state; performing risk grade division based on the analysis result of the breathing state; and making medical suggestions based on the risk levels. The technical problems that in the implementation process of intelligent early warning of the pneumology department, a current respiration monitoring system is inaccurate in multi-source physiological data fusion and low in patient health condition judgment accuracy are solved.

The invention provides a private domain social relation mining method and system based on a graph neural network, and relates to the technical field of social relation mining, and the method comprises the steps: carrying out the entity alignment and relation mapping of historical behavior data of a private domain platform user and an external knowledge base through employing a knowledge graph technology, generating user behavior feature data, and constructing a user relation graph; processing the user data by using a federal map neural network model containing a space-time attention layer to obtain a user node representation vector; and according to the user node representation vector, calculating a social influence index and a relationship strength index between the users, determining a final social circle layer structure by adopting a spectral clustering method and causal intervention analysis, and generating a social relationship analysis report. According to the method, complex user social relations in a private domain platform can be effectively mined, the accuracy and stability of social circle layer identification are improved, and technical support is provided for precise marketing and personalized recommendation.

The invention discloses an automatic test case generation method based on a large model and a particle swarm algorithm, and the method comprises the steps: designing a large language model demand analysis module, carrying out the semantic analysis of a demand document or code annotation of a software system through a large model, and generating a preliminary test case template; designing a particle swarm optimization module, and globally optimizing an input parameter combination and an execution path of the test case through a particle swarm optimization so as to improve the test coverage rate and the defect detection capability; and an automatic test execution and feedback module is designed, the particle swarm optimization evaluates the effectiveness of the test case according to the fitness function, a strategy is iteratively optimized and generated, and the test case is adjusted through a feedback mechanism. According to the method, efficient test cases can be effectively generated, various functions and boundary conditions of the system are covered, dynamic optimization of the test cases is achieved through a feedback mechanism, and comprehensiveness and reliability of system testing are ensured.

The invention belongs to the technical field of gas leakage monitoring, and particularly relates to a multi-sensor fused gas leakage sensing, identifying and alarming method, which comprises the following steps of: firstly, monitoring the state of a gas pipeline in real time through an integrated sensor, acquiring and preprocessing multi-dimensional data, secondly, analyzing sound wave and image data by utilizing a pre-trained abnormal sensing model, and finally, analyzing the abnormal sensing model. Thirdly, determining whether the gas pipeline is abnormal or not according to data such as temperature and pressure and abnormal monitoring scores, quickly positioning a leakage point through a multi-position node sensor and a time positioning algorithm once abnormal leakage is detected, and finally, judging whether the gas pipeline is abnormal or not according to the leakage position and an abnormal determination result. Through the gas leakage emergency strategy library and the alarm device, when a corresponding emergency processing scheme is rapidly generated, abnormal fixed-point alarm is rapidly carried out, and gas safety is ensured.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention relates to the technical field of model construction, and discloses a neural network-based frozen soil unfrozen water content detection model construction method and system. The method comprises the following steps: carrying out segmented sampling test on a clay sample in a temperature interval of 0 DEG C to-20 DEG C to obtain a multi-dimensional original data set, and carrying out standardization and principal component analysis on the data set to obtain a standardized feature vector; and performing three-stage classification marking on the feature vectors to obtain a training data matrix. A deep neural network is adopted to carry out five-fold cross validation training, and a prediction model of three hidden layers is constructed. And performing correction calculation on model output according to soil mineral composition and porosity parameters, and finally identifying an abnormal value through root-mean-square error analysis and a three-time standard deviation principle to obtain a standardized prediction result. According to the method, the mapping relation between the thermal parameters and the unfrozen water content is established, correction is carried out in combination with the soil body characteristic parameters, and the technical problem that the prediction precision of the unfrozen water content is not high in the prior art can be solved.

The invention discloses an ROV positioning navigation system based on vision inertial navigation fusion and pose correction, which comprises an inertial measurement value and image preprocessing module loaded on an ROV, the inertial measurement value and image preprocessing module is connected with a vision and inertial navigation data fusion module through a wire, and the vision and inertial navigation data fusion module is connected with a pose correction module. The invention also discloses a positioning navigation method of the ROV positioning navigation system based on visual inertial navigation fusion and pose correction. According to the method, the inertial data provided by the IMU and the environment characteristic information captured by the visual sensor are fused, high-precision estimation of the position and attitude of the ROV can be realized, the sensor data are optimally fused by using the MSCKF algorithm, the influence of the error of a single sensor on the positioning result is reduced, and the positioning precision and reliability of underwater operation are remarkably improved.

The invention provides an MBB multi-channel local aggregation superposition transmission method and an MBB multi-channel local aggregation superposition transmission system. Belongs to the technical field of communication. The method comprises the following steps that: a plurality of physical links are established in a wireless communication system, and the system selects the plurality of optimal physical links for aggregation according to a current network environment, a current equipment state and a current user demand; dividing data to be transmitted into a plurality of data packets, packaging each data packet according to the characteristics of the selected physical link, and transmitting the packaged data packets in parallel through the selected physical link; in the transmission process, the system monitors the transmission state of each link in real time, and dynamically adjusts the transmission rate and power distribution of each link according to the current network load and equipment state. By transmitting a plurality of physical links in parallel, the network bandwidth can be fully utilized, the data transmission rate is improved, and the transmission delay is reduced.

The invention relates to the technical field of mineral resource reserve simulation, in particular to a geological mineral resource reserve simulation method. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition on a target geological area, and carrying out comprehensive feature extraction to obtain a comprehensive data set; constructing an initial static geologic model according to the comprehensive data set to obtain the initial static geologic model; building a multi-physics field coupling model according to the initial static geologic model to obtain a multi-physics field coupling model grid; performing metallogenic fluid migration and enrichment simulation according to the multi-physics field coupling model grid to obtain a metallogenic fluid migration simulation result; carrying out geologic body geometrical morphology updating on the initial static geologic model according to a metallogenic fluid migration simulation result to obtain a dynamic geologic model; and performing reserve prediction model training according to the dynamic geologic model to obtain a reserve prediction model. According to the method, the mineral resource reserves are evaluated more accurately and dynamically through a mineral resource reserve simulation technology.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention relates to the technical field of multimedia interaction, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment, and the method comprises the following steps: S1, carrying out the arrangement of a sensor, and carrying out the intelligent sensing and data collection; s2, generating an intelligent interaction strategy; s3, equipment cooperative control and execution; and S4, performing real-time feedback and system optimization. According to the method, user behaviors and environment data are collected in real time through the multi-modal sensor, user requirements are accurately speculated in combination with a deep learning algorithm, the optimal interaction strategy and task priority are generated, individuation and interaction efficiency of user experience are remarkably improved, multi-device cooperation is optimized through distributed task scheduling, and user experience is improved. According to the method, display content and environment parameters are dynamically adjusted, self-optimization of the system is realized by utilizing user feedback, real-time performance, smoothness and reliability of interactive experience of the exhibition hall are ensured, rapid fault response and accurate maintenance demand prediction are realized through anomaly detection and equipment log analysis, and the stability and sustainability of operation of the exhibition hall system are greatly improved.