119 results about "Spatiotemporal Analysis" patented technology

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses an intelligent-based abnormal carbon emission data automatic identification early warning system, which belongs to the technical field of intellectualization and comprises a data acquisition preprocessing module, a high-precision space-time analysis module, an intelligent analysis module, an automatic abnormal identification module, a carbon footprint tracing module, a self-adaptive adjustment module and an intelligent emission prediction module. A carbon emission source and time-space distribution characteristics are accurately positioned through spatial positioning and time sequence analysis of the high-precision time-space analysis module, a spatial distribution diagram is more detailed and accurate through the optimized sensor position and an interpolation algorithm, and time sequence analysis is more accurate through timestamp correction and power spectrum density analysis. The method effectively evaluates the periodic intensity of the signal, analyzes a hidden mode and an association rule in the data through the intelligent analysis module integrating the spatial-temporal characteristics and the related information of the data acquisition and preprocessing module, analyzes the spatial-temporal association between variables through the calculation of a clustering center and the association intensity, and facilitates the discovery of a potential abnormal mode.

The invention relates to a tartary buckwheat pest and disease damage dynamic monitoring method, system and device and a medium, and belongs to the technical field of agricultural intelligent monitoring, the dynamic monitoring method comprises the following steps: periodically collecting environmental parameter data through fixed sensor nodes deployed in a farmland, and obtaining leaf vibration signals and multispectral image data at the same time; performing space-time alignment on the blade vibration signal and the multispectral image data, correcting radiation distortion in the multispectral image data, and outputting a registration data set; according to the registration data set, fusing to generate a multi-modal feature vector, inputting the multi-modal feature vector into a pre-trained space-time analysis model, and outputting a risk level distribution diagram with a geographic coordinate mark; generating a control instruction set according to the risk level distribution map, and triggering execution equipment to execute pest control operation; and optimizing weight parameters of the space-time analysis model through the generative adversarial network based on the execution log of the control instruction set and the historical multi-modal feature vector. The scientificity and timeliness of pest control can be improved.

The invention discloses a digital intelligent operation monitoring system for an expressway. The system comprises a data acquisition module, a feature extraction and fusion module, a space-time analysis and prediction module, an anomaly detection module and an intelligent decision module. Wherein the feature extraction and fusion module fuses multi-source heterogeneous data by using a collaborative mechanism of a graph neural network and an auto-encoder; the space-time analysis and prediction module generates a prediction result containing a congestion diffusion probability by using a Fourier neural operator, and provides reference data for anomaly detection; and the intelligent decision-making module inputs the prediction result as an environment model of a reinforcement learning agent, and dynamically adjusts a reward function according to the predicted congestion diffusion probability, so as to realize prospective autonomous decision-making. According to the invention, through deep cooperation and feedback closed loop among the modules, the traffic operation efficiency and the safety emergency capability are significantly improved.

The invention relates to the technical field of cadastral data dynamic management, and discloses a cadastral data dynamic updating and storage method, device and equipment and a storage medium. By receiving a land law enforcement task set of a target law enforcement vehicle, a law enforcement driving route map for a plurality of law enforcement tasks is constructed; issuing cadastral data of each law enforcement task to a cadastral data edge library at different driving road sections is taken as a decision variable, and the storage capacity of a target law enforcement vehicle, the maximum communication capability of each driving road section, the cadastral data advanced issuing characteristic and the cadastral data real-time performance are considered; and controlling cadastral data scheduling of the cadastral data edge library and the cadastral data core library, and assisting law enforcement officers in land law enforcement. Therefore, by adopting the task-oriented accurate data issuing and dynamic and the space-time analysis-based bandwidth prediction and optimization strategy, organic unification of the real-time performance, the security and the access efficiency of the cadastral data is realized, and efficient and reliable data support is provided for land law enforcement.

The invention discloses an online anomaly monitoring method and system for a linear movement cutting ore pulp sampler, and relates to the technical field of industrial automation, and the method comprises the steps: executing frequency band energy separation and sliding window statistical analysis on a working condition data set of the ore pulp sampler, and obtaining a multi-dimensional feature matrix; performing weight distribution and dynamic weighted aggregation on the multi-dimensional feature matrix to form a space-time analysis data packet, performing collaborative analysis on the space-time analysis data packet, and outputting a trend collaborative interaction matrix; and performing risk quantification and contribution degree distribution on the trend collaborative interaction matrix by using an entropy weight method to generate an abnormal quantification parameter, and performing confidence coefficient weighted calculation on the abnormal quantification parameter to form an abnormal probability value. According to the method, the working condition data set of the ore pulp sampler is fully fused through the sliding window statistical analysis and the entropy weight method, and meanwhile, deep feature mining and spatial relation fusion are performed through the dynamic causal atlas and the space-time convolutional neural network model, so that the reliability of anomaly monitoring is improved.

The invention provides a tobacco marketing hotspot event real-time analysis method based on big data and AI, and relates to the technical field of big data and artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data through a distributed crawler, and achieving the structured processing of unstructured data through the semantic analysis and multi-modal fusion technology; hot event identification and early warning are carried out by combining deep learning and a propagation dynamics model; further fusing the knowledge graph, NLP and space-time analysis to generate brand specification popularity ranking and trend prediction; and finally, an evaluation model is constructed based on historical and real-time data, new product research and development, brand promotion and supply chain optimization strategies are output, and full-process intelligent decision support is realized.

The invention provides an unmanned aerial vehicle cluster forest fire scene three-dimensional situation construction and updating method and system, and the method comprises the steps: receiving real-time fire scene data from an unmanned aerial vehicle cluster, and building an initial fire scene three-dimensional situation model based on a UNet network and a digital elevation model; the fire scene three-dimensional situation model comprises a fire scene three-dimensional situation map and a pyramid hierarchical structure; when a preset updating period is reached, constructing a pyramid hierarchical structure of the key updating area based on the real-time fire scene data; according to the pyramid hierarchical structure of the key updating area, the pyramid hierarchical structure in the to-be-updated fire scene three-dimensional situation model is compressed based on space-time analysis driving of information entropy, then robust registration fusion is carried out, and the fire scene three-dimensional situation model is updated.

The invention discloses a network loss data cleaning method, system and device based on multi-dimensional space-time analysis and a medium, and belongs to the technical field of data processing, and the method comprises the steps: collecting power grid operation data and topological structure information, and constructing a time sequence data set; identifying nodes with abnormal features in the time series data set as edge nodes based on an anomaly identification mechanism of topology constraints, and constructing edge data packets for different topology types; performing anomaly judgment according to the feature change of each type of data packets, and outputting a corresponding topological structure anomaly result; performing dynamic cleaning according to a topological structure abnormal result; and after the dynamic cleaning is completed, executing load flow calculation, and carrying out typed statistics and display on a network loss result in combination with a topology category. According to the method, a network loss data cleaning mechanism fusing a topological structure, a time sequence and an operation state is constructed, so that abnormal nodes and abnormal parameters are accurately identified, and data cleaning and statistical optimization facing network loss calculation requirements are realized.

The invention relates to the field of medical image processing and artificial intelligence, in particular to a pulmonary nodule treatment effect AI evaluation system which comprises an image acquisition module, an image registration module, a feature representation module, a multi-scale analysis module, a trajectory analysis module, a response prediction module and a decision support module. According to the system, accurate alignment of CT images before and after treatment is realized through a 4D registration technology, manifold representation of a pulmonary nodule state is constructed based on a differential geometry theory, and nodule features are mapped into points on a high-dimensional manifold; extracting features of different time and space scales by adopting multi-scale space-time analysis, and constructing a manifold trajectory representing a treatment response process; geodesic prediction is realized by using a Riemann geometric framework, and long-term curative effect is predicted from early treatment response; the system not only evaluates the current treatment effect, but also can provide personalized treatment suggestions and optimal follow-up visit plans.

A time-lapse image classification device and method is disclosed that uses a diffractive optical network to classify an optical input, significantly advancing classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and / or the diffractive optical network relative to each other. The design space and performance limits of time-lapse diffractive optical networks were numerically tested, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive optical network on the CIFAR-10 dataset. Time-lapse diffractive optical networks will be broadly useful for the spatio-temporal analysis of input signals using all-optical processors.

The invention discloses an EEG intelligent agent automatic analysis method based on a large language model, which takes the large language model as a strategy engine, autonomously understands user analysis intentions and intelligently decomposes tasks, and further dynamically schedules and collaboratively integrates analysis resources including traditional feature engineering, diversified deep learning models and an external knowledge base. According to the method, end-to-end automatic coordination and execution of complex EEG analysis tasks such as signal preprocessing, feature extraction, event positioning, classification diagnosis, emotion recognition, sleep staging and the like can be realized, the limitation of a single detection or classification task is broken through, and through context perception and flexible space-time analysis capability, the accuracy of the EEG analysis is improved. And multitask and continuous deep reasoning and interpretation can be carried out on the complex EEG data. According to the method, the general planning and reasoning capability of a large language model is deeply fused with a special analysis model in the EEG field, so that the automation level, flexibility and clinical application potential of EEG analysis are remarkably improved.

Generating a semantic synopsis is provided. A dataset comprising structured data is received and semantic analysis of the dataset is performed to determine categories of information included in the datasets. A spatio-temporal analysis of the dataset is also performed to determine time periods and locations to which the dataset applies. A static synopsis of the dataset is then generated in natural language sentences that describes semantic information in the dataset and time periods and locations covered by the dataset. The synopsis can be customized according to a user role and search query data.

The invention discloses a mountainous area fog forecasting method, which comprises the following steps that: through the cooperative work of a satellite sub-module, a ground sub-module and a forecasting sub-module, the accurate forecasting of a foggy weather condition is realized, and the satellite sub-module records the complete cycle change of a cloud layer and an air temperature, extracts parameters and carries out space-time analysis; the ground submodule records fog visibility, aerosol concentration and air temperature data in real time; the prediction sub-module receives and processes the data, establishes a foggy day prediction model by using cloud computing and machine learning technologies, sets thresholds of different foggy day types, and predicts and judges the foggy day types by comparing the real-time monitoring data with the thresholds; by combining a satellite remote sensing image processing technology and ground real-time monitoring data, the system can obtain more comprehensive and accurate meteorological information, thereby improving the accuracy and reliability of foggy day prediction. A machine learning algorithm is utilized to train a large amount of historical data, so that the prediction model can learn rules and features generated by foggy days, and the prediction precision is improved.

The invention relates to the technical field of data visualization, in particular to a fluid data visualization method and system based on space-time analysis, and the method comprises the following steps: obtaining the number, coordinate and deployment duration of each sampling point, collecting rainfall, temperature, humidity and pressure, calculating credibility, eliminating low-credibility data, extracting a parameter sequence analysis trend, and generating an abnormal grade label. And constructing a trend offset factor group, identifying a high-offset variation grid, and generating a variation dynamic map sequence. According to the method, invalid or interference data is eliminated by using calculation of sampling credibility, an anomaly level label set is constructed through comparison and classification of trend change indexes and adjacent space partitions, the anomaly description capability of space-time trend change is enhanced, and an abnormal region state is calibrated through double standards of trend inversion and anomaly density, so that the accuracy of the abnormal region state is improved. Potential abnormal variation areas are effectively captured, identification codes are given, and the abnormal recognition precision and the area positioning efficiency in the fluid data visualization process are integrally improved.

The invention discloses a traffic transportation government affair hotline mining method based on natural language processing, and belongs to the technical field of artificial intelligence and machine learning. According to the method, for structured and unstructured data in a traffic transportation government affair hotline, firstly, a five-class word segmentation dictionary containing cleaning words, noise words, synonyms, additive words and stop words is constructed; converting the unstructured text into structured data through improved data cleaning, text word segmentation and feature representation; carrying out clustering analysis by utilizing an LDA topic model, and extracting public demand topics and high-frequency keywords; hot appeals and trend changes are mined in combination with space-time analysis and association analysis, and finally a visual analysis report is generated. According to the method, the problems of poor model interpretability and insufficient field adaptability in the prior art are solved, the accuracy of hotline data processing and the effectiveness of theme recognition are remarkably improved through multi-dictionary collaborative optimization and field knowledge fusion, and accurate decision support is provided for a traffic transportation management department. A real taxi field case in a certain city is used as an example for research, an experiment proves that the method has an accurate theme identification function, and the complaint and report work order amount in the traffic transportation government affair hotline taxi field in the city is reduced by 20% on year-on-year basis in 2024.

The invention discloses a real-time storage management method for a large amount of dynamic data, and relates to the technical field of data management, and the method comprises the steps: carrying out the real-time analysis of a dynamic data flow through a storage configuration matrix by using a pulse neural network, generating a data feature vector, and dividing the cold and hot categories of the data through an intelligent classifier, meanwhile, optimization is carried out according to a compression strategy in the storage configuration matrix, a preprocessed data packet is output, intelligent routing distribution is carried out according to a coding label of the preprocessed data packet, hot data is routed to a high-speed storage layer, and cold data is routed to a capacity storage layer; executing data storage and index construction in each storage layer through a plastic queue management mechanism, and generating a storage position mapping table; according to the method, the pulse distribution rate is calculated through the pulse neural network to generate the spatio-temporal characteristics, and the spatio-temporal characteristics are input into the gradient boosting tree model to execute weighted voting to divide cold and hot categories, so that deep spatio-temporal analysis and intelligent classification of dynamic data streams are realized.

The invention discloses a self-adaptive precision polishing control system and method based on multi-modal perception and artificial intelligence decision. According to the system, a multi-mode sensing network is constructed through an acoustic emission sensor, a force / torque sensor, a vibration sensor, a visual sensor and a temperature sensor, and physical signals in the polishing process are collected in real time. The system adopts a deep learning model to carry out feature fusion and space-time analysis on multi-source sensing data, realizes accurate estimation of the material removal rate and comprehensive scoring of the surface quality, further integrates a reinforcement learning optimization engine, takes a process state as input, takes control parameter adjustment as action, and takes a multi-target reward function as guidance, and realizes comprehensive evaluation of the surface quality. And the autonomous optimization decision of the polishing parameters is realized. The invention further comprises a digital twinning system for offline training and online verification, and a transfer learning-based small sample rapid adaptation mechanism, so that the polishing quality consistency, the processing efficiency and the process autonomy can be remarkably improved.

The invention discloses an aquatic organism diversity analysis method based on hydropower station construction influence, and is applied to the technical field of biological diversity analysis. Comprising the following steps: dividing a target water body into a plurality of monitoring areas according to a hydropower station construction scheme; hydrological data and biodiversity data are collected in different periods; performing space-time analysis on the hydrological data and the biodiversity data of different monitoring areas, identifying change conditions and calculating quantitative indexes; matching the hydrological data with the biodiversity data, and analyzing the influence capability of different parameters in the hydrological data on the biodiversity data; and evaluating the influence degree of the hydropower station construction on the aquatic organism diversity. Multi-source parameters before hydropower station construction, in the construction process and in the operation process are monitored, the influence of hydropower station construction on aquatic organism diversity of a target water body is analyzed through hydrological data, and data reference is provided for protecting species diversity.

The invention discloses an efficient production scheduling optimization method and an intelligent scheduling system, and belongs to the technical field of production system comprehensive control, and the method comprises the steps: obtaining production data in real time, inputting a time-space analysis model of a fusion graph neural network and a time sequence analysis algorithm, and obtaining an equipment fault probability in a future preset time window; when the fault probability of the equipment is greater than a preset fault threshold value, generating a plurality of candidate scheduling schemes by using a preset quantum heuristic algorithm according to a preset process constraint condition and the production data set, and evaluating a comprehensive performance index; and selecting a candidate scheduling scheme according to an evaluation result, and obtaining a final scheduling scheme to generate an equipment control instruction. According to the method, dynamic fault prediction fusing a graph neural network and time sequence analysis, multi-target optimization scheduling of a quantum heuristic algorithm, adaptive processing parameter adjustment and a high-reliability digital twinning technology are adopted, efficient and intelligent production scheduling can be achieved, and the production efficiency and the equipment utilization rate are improved.

The invention relates to the technical field of data monitoring management, in particular to a commodity monitoring system based on big data, which comprises a data acquisition module for forming a multi-dimensional data set of each commodity; the space-time analysis module is used for analyzing the circulation data of each commodity and determining the space-time attribute of each commodity; the propagation identification module is used for determining time sensitivity coefficients and circulation sensitivity coefficients of propagation information and commodity categories; the association identification module is used for determining an associated commodity set of each commodity based on the circulation data of each commodity after space-time division; and the circulation monitoring module is used for determining a circulation change coefficient of the corresponding circulation change commodity based on an analysis result of the propagation information, determining circulation change parameters of the circulation change commodity and the corresponding associated commodity according to the circulation change coefficient, and forming a purchase / inventory adjustment message. According to the invention, the inventory turnover rate and the matching rate of commodity sales, consumption and demand can be improved.

This invention provides a spatiotemporal embodied intelligent system integrating BeiDou and remote sensing, belonging to the interdisciplinary research field of Earth spatiotemporal analysis and decision-making and artificial intelligence technologies. It includes an intelligent sensing module, a data fusion processing module, an intelligent decision-making module, and an execution and feedback module. The intelligent sensing module acquires positioning and remote sensing data; the data fusion processing module fuses the positioning and remote sensing data acquired by the intelligent sensing module to obtain spatiotemporal data of the target area; the intelligent decision-making module generates decision instructions for the target area based on the spatiotemporal data; and the execution and feedback module executes corresponding operations according to the decision instructions and provides feedback results. Furthermore, it enables intelligent equipment terminals such as unmanned aerial vehicles, unmanned agricultural machinery, and unmanned ships to execute corresponding operations based on the decision instructions, and continuously learns, evolves, and optimizes the feedback results.

The invention relates to the technical field of power distribution network management, and particularly discloses an energy storage node configuration method and system based on space-time analysis, and the method comprises the steps: obtaining a distribution transformer of a target region and a power line based on the distribution transformer, obtaining an output point location of the power line, and determining a monitoring point location in the power line according to the output point location; acquiring line data, and performing two-dimensional processing on the line data; based on the line data after two-dimensional processing, determining the spatial anomaly degree of each output point location; based on the line data of each output point location, determining the time anomaly of each output point location; and carrying out statistics on space anomalies and time anomalies of all output point locations, and determining energy storage nodes. According to the method, space-time analysis is performed on the data of all the output point locations, some unstable output point locations are selected, some energy storage nodes are additionally arranged based on the output point locations and are used for supplying energy to the output point locations, and the stability of the whole power supply network is improved.

This invention discloses an online anomaly monitoring method and system for a linear moving cutting slurry sampler, relating to the field of industrial automation technology. The method includes performing frequency band energy separation and sliding window statistical analysis on the slurry sampler's operating condition dataset to obtain a multi-dimensional feature matrix; performing weight allocation and dynamic weighted aggregation on the multi-dimensional feature matrix to form a spatiotemporal analysis data package; performing synergy analysis on the spatiotemporal analysis data package to output a trend synergy interaction matrix; and using the entropy weight method to perform risk quantification and contribution allocation on the trend synergy interaction matrix to generate anomaly quantification parameters. The anomaly quantification parameters are then weighted with confidence to form anomaly probability values. This invention fully integrates the slurry sampler's operating condition dataset through sliding window statistical analysis and the entropy weight method, while simultaneously improving the reliability of anomaly monitoring through deep feature mining and spatial relationship fusion using dynamic causal graphs and spatiotemporal convolutional neural network models.

The application discloses a kind of war destruction global evaluation method based on multi-source remote sensing data, can realize the spatio-temporal continuous monitoring and evaluation of large-scale regional war destruction.It includes the following steps: obtaining and preprocessing land use, NPP-VIIRS night light, multispectral image and surface temperature and other multi-source remote sensing data;Using local optimal threshold method to extract built-up area boundary;Based on total value of night light and light ratio index, using spatio-temporal cumulative calculation method to represent the spatio-temporal evolution characteristics of armed conflict in built-up area;Through principal component analysis method, build non-built-up area war damage index, and reveal the evolution trend and spatial differentiation of non-built-up area armed conflict based on spatio-temporal analysis method.The method of the application can effectively depict the spatio-temporal characteristics of war destruction, has the advantages of global coverage, time sequence integrity, high resolution, and can provide scientific decision support for humanitarian aid and post-war reconstruction planning in conflict area.

The invention relates to a medical image AI auxiliary diagnosis method based on dynamic feature extraction. The method comprises the following steps: carrying out protocol / equipment domain conditioned space-time analysis on medical image data corresponding to a target area to obtain aligned sequence image data; performing dynamic representation construction on the aligned sequence image data to obtain a modeling feature representation sequence; performing hidden state dynamic modeling on the modeling feature representation sequence to obtain hidden state posterior distribution; and carrying out probability diagnosis reasoning analysis on the hidden state posterior distribution to obtain medical image auxiliary diagnosis data. By adopting the method, the diagnosis accuracy and interpretability can be improved.

The invention relates to the technical field of tailing pond safety and geotechnical engineering, and discloses a tailing pond dam slope anti-sliding stability analysis method based on a Sweden segmentation method. The method comprises the steps of fusing dam slope form point cloud, a stratum lithology profile and a multi-stage pore water pressure monitoring sequence through a field domain conversion technology, and constructing a unified three-dimensional space-time analysis model. And intelligently generating a candidate slip trend surface based on the high-precision point cloud data, and carrying out strip division. Rock and soil parameters and pore water pressure which change along with time and space are given to each strip block, and a dynamic strip state set is formed. And carrying out loop iterative calculation by utilizing a Sweden slice method principle, simulating stability evolution under different working conditions, and outputting a stability result of each sliding surface. Finally, the dominant slip plane and the most unfavorable working condition are identified, and an accurate basis is provided for reinforcement decision. According to the method, deep fusion of multi-source data and dynamic simulation of a stability state are realized, and the accuracy of an analysis result and engineering applicability are improved.

This invention relates to the field of power transmission line monitoring technology, and particularly to a method and system for remote monitoring of dense power transmission channels based on spatiotemporal analysis. The method includes the following steps: analyzing the spatiotemporal correlation characteristics of the operating states of construction equipment and transmission lines based on construction equipment operation data and transmission line operation data, and constructing a set of interference factors of construction equipment on the transmission lines; evaluating the anti-interference capability of the transmission lines against each interference factor in the interference factor set based on transmission line operation data; extracting the interference intensity of each interference factor in the interference factor set and determining the interference risk of each interference factor based on the anti-interference capability evaluation results; and determining the safe operating area of ​​construction equipment within the monitoring area based on the spatial distribution characteristics of the interference risk. This invention effectively improves the operational safety and intelligent management level of dense power transmission channels by combining the evaluation of the transmission line's anti-interference capability with its operating state.

The application discloses a tunnel disaster prevention safety risk prediction system and method and a storage medium, and relates to the technical field of tunnel disaster prevention safety risk prediction, and specifically discloses a tunnel disaster prevention safety risk prediction system, which comprises a space-time analysis module, a time analysis module and a risk prediction module.