1152results about How to "Reduce uncertainty" patented technology

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention relates to the technical field of data processing, and discloses a quality control method and system based on buoy observation data. The method comprises the following steps: performing multi-dimensional anomaly detection processing on preprocessed standard format data to obtain an anomaly detection report marked with abnormal and suspicious data points; performing multi-source data fusion and cross validation processing on the data in the anomaly detection report to obtain an initial data quality score; performing intelligent correction processing on the anomaly detection report to obtain a corrected data set and uncertainty estimation data corresponding to the corrected data set; performing consistency analysis processing on the corrected data set in a space-time dimension to obtain a space-time consistency score and abnormal region marking data; and carrying out comprehensive evaluation processing on the abnormal region mark data to obtain a target data quality grade identifier and a target quality report, and carrying out quality control strategy analysis to obtain a target quality control strategy. According to the invention, the accuracy of quality control based on buoy observation data is improved.

The invention relates to the technical field of computers, and discloses a carbon emission calculation method based on deep learning, which comprises the following steps: S1, collecting multi-source data through a sensor, satellite remote sensing, an intelligent electric meter and a monitoring system, carrying out cleaning, denoising and missing value filling on the collected data, and in addition, carrying out data processing on the collected data; the structured data and the unstructured data are integrated through a multi-source data fusion technology; s2, performing feature extraction on the original data by adopting a deep feature learning model, and screening out key features contributing to carbon emission prediction; and S3, performing real-time decomposition and anomaly detection on the data by combining an autoregressive integral moving average model and an online learning algorithm. By combining an autoregressive integral moving average model and an online Bayesian learning method, carbon emission calculation can dynamically adjust parameters according to real-time data to adapt to environmental changes, seasonal fluctuations and emergencies, so that the flexibility and real-time performance of a prediction model are remarkably improved.

The invention relates to the technical field of intelligent power grid dispatching, in particular to a power grid dispatching method and system, and the method comprises the steps: collecting environment data, equipment health data and economic and market data, and generating fusion data through edge computing equipment; a multi-model integrated deep learning model is adopted to predict power demand and supply, a reinforcement learning algorithm is combined to simulate a power grid dispatching scene, and an optimal dispatching strategy is generated; and dynamically adjusting the output power of the power station and the power grid node load distribution, and updating the scheduling strategy in real time. According to the method, the prediction precision is improved through multi-model integration, a scene simulation strategy is optimized by utilizing reinforcement learning, and coordinated optimization of global and local states is realized in combination with multi-level feedback analysis, so that the stability and economy of power grid operation are remarkably improved, meanwhile, carbon emission is reduced, and the method is suitable for complex scenes of modern intelligent power grid dispatching.

According to the goaf detection method based on transient electromagnetic and seismic wave field joint inversion, data acquisition and joint inversion are carried out on the goaf through the combination of the transient electromagnetic detection system and the reflection seismic detection system, the complementary advantages of the two detection methods are played, the bottlenecks of a single method in the aspects of resolution and signal-to-noise ratio are broken through, and the goaf detection method based on transient electromagnetic and seismic wave field joint inversion is realized. Fine characterization of the structure, the physical property and the stability of the goaf is realized; a machine learning classification algorithm is introduced to analyze a goaf multi-attribute feature data set, and intelligent discrimination of the type, scale and stability of the goaf under complex geological conditions is realized; establishing a quantitative response relationship between goaf physical property parameters and goaf electrical and elastic difference characteristic parameters, constructing a goaf transient electromagnetic and seismic wave field joint forward modeling numerical model, and constructing an electromagnetic-seismic joint inversion objective function; and calculating and solving to obtain goaf resistivity, seismic wave velocity three-dimensional distribution and uncertainty quantitative evaluation results, and reducing blind areas of goaf judgment.

The invention discloses a multi-target intelligent optimization air conditioner control method and system based on multi-dimensional machine learning, and aims to acquire outdoor environment data by acquiring personnel flow and environment data inside and outside a building in real time and combining weather forecast information and the like, and predict future personnel flow trend and environment change conditions by utilizing a machine learning algorithm, so as to realize intelligent optimization of the air conditioner. And data support is provided for an artificial intelligence control strategy of the air conditioning system. Based on the prediction results, a multi-target optimization control algorithm is used for conducting multi-dimensional parameter regression on the air conditioner operation effect, and operation parameters such as the temperature, the humidity and the wind speed of the air conditioner system are dynamically adjusted. The air conditioning system is suitable for various large buildings such as office buildings, shopping malls and airports, the energy utilization efficiency of the air conditioning system is remarkably improved and the energy consumption is greatly reduced on the premise that the indoor environment comfort is ensured, and the air conditioning system has remarkable energy-saving and carbon-reducing effects, wide application prospects and remarkable economic and environmental protection benefits.

The invention relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

The invention provides a landslide space prediction method based on a knowledge graph and representation learning. The landslide space prediction method specifically comprises the following steps: S1, constructing a landslide susceptibility knowledge graph considering mechanism knowledge and spatial-temporal characteristics; a knowledge graph ontology mode is designed, landslide disaster field knowledge modeling considering spatial-temporal characteristics and mechanism knowledge is completed, and on this basis, entities are extracted from multi-source data, relationships are constructed, and a knowledge graph data layer is constructed; and S2, through map semantic feature learning based on a ComplEx model, directed weighted graph construction based on a landslide susceptibility knowledge map, equivalent positive and negative landslide sampling based on a Louvain algorithm and map structural feature learning based on a GraphSAGE model, realizing landslide space prediction based on the knowledge map and representation learning. According to the method, diversified feature inductive learning under the constraint of fewer landslide samples in a complex heterogeneous environment can be realized, and the landslide space distribution probability is predicted through a node classification task.

The invention provides an urban underground space high-precision detection method, system and device and a storage medium, and relates to the technical field of geological exploration. The method comprises the following steps: acquiring reflection seismic data, high-resolution electromagnetic response data, environmental noise data, well-side seismic data and logging data of a target area, and performing refined processing on the reflection seismic data, the high-resolution electromagnetic response data, the environmental noise data, the well-side seismic data and the logging data to obtain multi-source multi-scale detection data; performing constraint condition extraction based on the multi-source multi-scale detection data to obtain a multi-source constraint condition, and constructing an urban underground space three-dimensional information model based on the multi-source constraint condition and the multi-source multi-scale detection data; and performing data mining based on the urban underground space three-dimensional information model to obtain three-dimensional geological data. According to the scheme, the accuracy of urban underground space detection can be improved.

The invention discloses a flood discharge gate opening degree real-time automatic distribution method for a centralized control center, and relates to the technical field of water conservancy gate control, and the method comprises the steps: setting a monitoring point in each sub-region, and collecting the mechanical state, the vibration stress parameter and the surrounding geological parameter of a flood discharge gate, so as to construct a gate wear index Msz; therefore, the state of the gate can be monitored in real time, and abrasion and fault problems can be found and early warned in time. And establishing a sub-region prediction model, extracting water flow characteristics and river volume characteristics, calculating an average water flow index Slzs and a regional river clogging index Ydzs of each sub-region, correlating the obtained predicted river load index Fzz, and performing evaluation. And setting a first difference threshold value C1 and a second difference threshold value C2, calculating and obtaining a load difference value FCz according to the predicted river load index Fzz and the load threshold value R, generating an opening instruction, and setting a priority distribution mechanism. The opening degree of the gate is optimized, and the accuracy and response speed of flood discharge control are ensured.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a monitoring and early warning system and method for countering unmanned aerial vehicle detection interference, and in the monitoring and early warning system, a signal receiving module independently receives electric signals of multiple unmanned aerial vehicles through array space stations; the abnormal signal detection module performs feature extraction, and detects abnormal signals by using frequency feature change and a spectrum analysis technology; the time difference of arrival detection module calculates the time difference of arrival of each space station to the abnormal signal, and the angle of arrival detection module analyzes and determines the angle of arrival through a space covariance matrix; the cross positioning module synthesizes the time difference and the angle information to determine the position of the abnormal unmanned aerial vehicle; and the adaptive interference suppression module identifies the type of an abnormal unmanned aerial vehicle by using a feature extraction result and a machine learning model, selects a suppression strategy, controls an array space station to align to the position of the abnormal unmanned aerial vehicle, realizes efficient suppression, and protects normal operation of a legal unmanned aerial vehicle and other aerial equipment at the same time.

The invention provides a heart multi-mode digital twinning simulation method and system, electronic equipment and a medium, and belongs to the technical field of digital twinning. The method comprises the following steps: S1, collecting multi-modal data of a heart; s2, preprocessing the acquired multi-modal data to obtain heart digital data; s3, based on the heart digital data, constructing a multi-modal digital twinborn model of the heart and optimizing the multi-modal digital twinborn model; s4, performing real-time simulation on the electrophysiological activity of the heart by using the optimized multi-modal digital twin model and using a Runge-Kutta method in combination with a PSO (Particle Swarm Optimization) algorithm; s5, based on the optimized multi-mode digital twinborn model and the simulation result, simulation and planning of virtual cardiac surgical treatment are carried out; and S6, dynamically evaluating the multi-modal digital twin model, and monitoring the state change of the heart in real time. According to the heart multi-mode digital twinborn simulation method and system, the electronic equipment and the medium, efficient simulation and real-time monitoring of the heart state can be achieved.

The invention provides a database adaptive migration method and system based on a credential environment, and relates to the technical field of data processing.The method comprises the steps that an intelligent migration model is used for analyzing the structure, including a table structure, index configuration, constraint conditions and data types, of a source database, data size distribution is calculated, and a source database structure feature result is generated; according to a source database structure feature result, automatically selecting and configuring a target database type, adjusting environment parameters of a target database, and generating target database configuration information; according to the structure of a source database and configuration information of a target database, a data mapping rule is automatically established by utilizing an intelligent migration model, and the data mapping rule comprises automatic correspondence of table names and field names, automatic conversion of data types, and an automatic migration scheme of indexes and constraints. According to the method, automatic and intelligent migration from the source database to the target database is realized through the intelligent migration model, the performance is automatically optimized, the migration report is generated, and the migration efficiency and quality are improved.

The invention discloses an electroporation ablation voltage control method and system based on a pulse sequence, and relates to key technologies such as intelligent target area identification, electric field modeling, dose control, real-time monitoring and feedback. Tumors are accurately identified and treatment is planned through the AI technology, electric field distribution is optimized to ensure the accurate effect of electroporation pulses, pulse parameters are dynamically adjusted through closed-loop control, and treatment effect evaluation is provided through the sensor fusion technology. The system integrates data analysis and machine learning, intelligently adjusts treatment strategies, realizes personalized treatment and complication prediction, and improves the treatment effect and patient safety. The system is suitable for precise tumor treatment, and healthy tissues are protected while the curative effect is ensured.

The invention discloses a 3D printing process intelligent control method and system based on real-time feedback, and relates to the technical field of 3D printing, a data acquisition module collects a printing layer surface image and printing data in real time, constructs an image data set and a printing data set, and performs cleaning and standardization processing on the data in a preprocessing module; the image analysis module analyzes surface defects of a printing layer by using a convolutional neural network, generates a defect score D, compares the defect score D with a preset defect score threshold D1, evaluates the state of the printing layer and triggers early warning, and the comprehensive evaluation module calculates a crack generation probability Z by combining printing data and the defect score; and the intelligent control module compares the crack generation probability Z with a preset crack generation probability threshold value Zl, evaluates the printing state and feeds back and controls printing parameters according to a result, so that crack and structure defects in the printing process are effectively prevented and reduced, and the printing precision and the finished product quality are improved.

The invention relates to the technical field of power grid monitoring, in particular to a power grid abnormal state monitoring and early warning system which comprises a historical reference establishment module, an operation deviation analysis module, an abnormal threshold adjustment module and an abnormal early warning module. According to the invention, through real-time data acquisition and historical data analysis, the monitoring and early warning efficiency of power grid operation is improved, and through collection of power grid operation records, extraction of data of differential time periods, evaluation and analysis of the average load and volatility of the data, and construction of a historical power grid operation reference, the operation trend and potential abnormity can be captured more accurately. The method reduces the uncertainty of judgment depending on experience, can recognize a deviation mode and an adjusted load detection threshold value by comparing current data with a historical reference, enables the abnormal early warning to be more accurate, avoids excessive false alarms and missed alarms, optimizes the evaluation of early warning levels and the implementation of early warning notification, guarantees the safety and stability of a power grid, and improves the safety and reliability of the power grid. And the economic efficiency and the reliability of power grid operation are also improved.

The invention discloses a crane path planning and obstacle avoidance early warning system, relates to the technical field of engineering machinery, and solves the problem that the spatial position of an obstacle in a vehicle body coordinate system is determined by acquiring a coordinate conversion relation between a radar coordinate system where an obstacle point cloud point is located and the vehicle body coordinate system in the prior art. The technical problem that the risk of collision between the vehicle body and the obstacle is increased due to the fact that the position of the obstacle is changed is not considered; according to the invention, the real-time environment map is constructed through the real-time environment information; screening out a plurality of alternative paths from the plurality of paths according to the static obstacle; calculating roadblock evaluation coefficients of the plurality of alternative paths according to the dynamic obstacles; selecting a target path from the plurality of alternative paths according to the roadblock evaluation coefficient; the method comprises the following steps: acquiring a real-time image of a crane within a preset range through image acquisition equipment, judging whether avoidance is needed or not according to the real-time image, and continuously detecting the real-time image; and the collision risk between the vehicle body and the obstacle is reduced to a great extent.

The invention discloses an in-tunnel construction vehicle meeting scheduling system based on positioning equipment. The system comprises a basic service module which is used for storing basic data for business service analysis in an acquisition and collection mode; the business service module is used for carrying out detailed analysis of the corresponding service through the data acquired by the basic service, and providing an analysis result to the display end for display; and the display end module is used for displaying the analysis result of the business service module, wherein the display modes comprise a mobile phone APP, a webpage large screen, an LED panel, a tunnel loudspeaker and a tunnel traffic light. The system carries out weight grading on vehicle and personnel positioning labels by standardizing a tunnel construction process, and integrates data of avoidance points of a transverse tunnel. And in combination with an intelligent technology, efficient vehicle meeting scheduling, intelligent construction path planning and avoidance point path planning of the construction area are realized.

The invention discloses an automatic welding method and system based on a collaborative robot, and the method comprises the steps: collecting and preprocessing three-dimensional point cloud data of a to-be-welded steel part and the collaborative robot, and collecting thermal physical property data of to-be-welded steel; based on the three-dimensional point cloud data and the thermophysical property data of the steel, a welding simulation model is constructed through the digital twin technology; constructing a welding quality evaluation model based on a deep learning technology, and performing a virtual welding test by using the welding simulation model and the welding quality evaluation model to obtain an optimal welding route and optimal welding parameters; and based on the optimal welding route and the optimal welding parameters, the collaborative robot is started to conduct automatic welding. The invention relates to the technical field of automatic welding, and solves the technical problem that an existing automatic welding technology lacks welding parameter self-adaptive optimization and welding route intelligent planning through combination of a digital twinning technology and a deep learning technology.

The invention relates to the field of image detection, in particular to a food nondestructive testing model construction method based on machine vision. The method comprises the following steps: performing multi-view image shooting on food to obtain a multi-view image of the food; constructing a three-dimensional model of the food based on the multi-view image; performing texture identification on the multi-view image, and mapping the texture to the surface of the three-dimensional model to obtain the surface texture of the three-dimensional model; positioning a three-dimensional model deformation area of the three-dimensional model based on the three-dimensional model surface texture; performing internal image acquisition on the deformation area, and performing three-dimensional modeling on the deformation area based on the images; fitting the internal deformation three-dimensional model to the food three-dimensional model to obtain a food deformation three-dimensional model; and performing infrared spectrum imaging processing on the food, and performing nondestructive marking on the food based on the deformation three-dimensional model. According to the invention, the obtained image data is analyzed through machine vision, and accurate determination of food damage deformation is facilitated.

The invention relates to an automatic driving forced lane changing decision-making method based on a dynamic continuous game, which belongs to the technical field of intelligent connected automobiles and comprises the following steps: acquiring motion state information of a self-vehicle and surrounding vehicles in a forced lane changing scene through a vehicle-mounted sensor and vehicle-to-vehicle communication; calculating a collision boundary; generating an initial decision based on non-cooperative non-zero and dynamic games; optimizing the initial strategy by adopting a game strategy based on information continuity; and outputting a final forced lane change decision under the condition that the strategy after game decision optimization based on information continuity is continuously kept for three periods. According to the automatic driving forced lane changing dynamic game decision-making method considering decision-making continuity, the decision-making model is constructed by adopting non-cooperative non-zero and dynamic games, so that the driving safety, comfort and efficiency are improved; through a dynamic game algorithm based on decision continuity, decision adaptability and predictability are enhanced, stability and consistency of decision output are ensured, and uncertainty and risk are reduced.

The invention discloses a crack defect detection method based on an improved YOLOv8 model, and belongs to the technical field of target detection. The method comprises the steps of obtaining a crack common data set; performing data enhancement on the crack common data set, and dividing the crack common data set into a training set, a verification set and a test set according to a proportion; constructing a crack detection model of the improved YOLOv8 model; training and verifying the constructed crack detection model; and evaluating the final performance of the model through the four indexes. According to the invention, by modifying the Bottleneck structure of the C2f module, no extra FLOPs is brought; by adopting the HWD module, the relevance between the features is fully utilized, and more context information of the target object is reserved, so that other interference information mingled when the features are extracted by a convolution kernel is avoided, and the uncertainty of the information is effectively reduced; a Dyhead detection head is adopted to replace an original YOLOv8 detection head, a target detection head and attention are unified, the expression ability of the target detection head of the model is remarkably improved under the condition that the calculation amount is not increased, and the precision of the model is improved.

The invention discloses a medical ultrasonic image intelligent processing method and system, and relates to the technical field of medical image.According to the main scheme, normal feature coordinates of different organs and tissues are calculated by obtaining normal ultrasonic image data, to-be-processed image data and historical medical record information of a patient; extracting coordinates of a to-be-detected image and comparing the coordinates with normal coordinates for preliminary judgment; performing secondary judgment by combining ovality and circularity; setting a lesion gray threshold to judge whether a lesion exists or not; calculating the size of a lesion area when the lesion exists; according to the method and the system, the comprehensive blood flow evaluation index is calculated in combination with color Doppler blood flow imaging data, the disease evaluation value is calculated according to known medical record data and the like, the disease level is judged, the diagnosis report is automatically generated, the accuracy and the efficiency of medical ultrasonic diagnosis are improved, and powerful support is provided for clinical diagnosis.

The invention relates to the technical field of path planning, in particular to an emergency evacuation path planning method and system and a storage medium. The method comprises the following steps: obtaining building layout data; performing building structure topology analysis on the building layout data to generate building structure layout data; arranging monitoring equipment according to the building structure layout data to obtain monitoring equipment deployment data; performing environment perception on the monitoring equipment based on the monitoring equipment deployment data so as to generate environment perception data; performing static image acquisition on the environment sensing data to obtain a static environment image; performing channel connection on the static environment image to generate building channel distribution data; and performing potential dangerous channel area identification on the building channel distribution data through the static environment image to generate potential dangerous channel area data. According to the invention, by integrating environment perception, dynamic video stream analysis and intelligent path scheduling, the flexibility and accuracy of emergency evacuation path planning are improved.

The invention discloses an intelligent control method and system for photovoltaic power generation and energy storage, and relates to the technical field of deep learning, a charging load index Cfzs is calculated and evaluated through a charging load prediction module, and the load demand in a fixed period can be predicted more accurately; the discharge load index Ffzs is calculated and evaluated, so that the discharge load of the battery is accurately predicted, and the uncertainty caused by load fluctuation is reduced; the power generation prediction module calculates a power generation index Fdzs according to the charging load index Cfzs and the discharging load index Ffzs and evaluates the power generation index Fdzs, so that a more accurate power generation prediction result is obtained; and the battery management control module performs secondary correction according to the prediction results and the environmental data, and dynamically adjusts a charging priority control strategy, a discharging priority control strategy and a charging and discharging balance control strategy of the battery, so that the system can reasonably distribute charging and discharging loads when the fluctuation of the generating capacity is relatively large, and the stability of power supply is ensured.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to the testis size character of Hu sheep as well as a screening method and application of the SNP molecular marker, and belongs to the fields of genetic breeding and molecular biology. The marker is located in an intron region of a Hu sheep chromosome 3 LRRIQ1 gene and is remarkably related to the Hu sheep testis size character, and the accuracy and reliability of the SNP molecular marker are ensured through a scientific and rigorous screening method; according to the marker, Hu sheep individuals related to testis size characters can be rapidly and accurately screened out, the breeding efficiency and accuracy are improved, the breeding process is accelerated, the breeding cost is reduced, and the economic benefits are improved; the genetic diversity is protected, the accuracy of seminal fluid quality evaluation is improved, a powerful tool is provided for Hu sheep breeding work, and important basic data is also provided for researching a genetic mechanism of testis development and functions; the method can also be used as an auxiliary diagnosis tool to assist in identifying potential genetic defects or disease risks and promote progress and development of the breeding technology.

The invention discloses a rotary excavating pile construction method for a karst cave dense area capable of preventing water and soil loss. According to the real-time monitoring system, the sensor is installed on the drill bit, various data such as the drill bit position, the drilling speed and the hole wall stability in the drilling process are collected in real time, and real-time feedback is provided for construction. By means of the system, constructors can know the drilling state in time, drilling parameters are adjusted in real time through data analysis, and the drilling process is optimized. By means of real-time monitoring and adjusting, the construction efficiency is improved, risks caused by geological condition changes are greatly reduced, and the safety of the construction process and the drilling accuracy are guaranteed. According to the self-adaptive slurry injection system, the flow and pressure of slurry can be automatically adjusted according to real-time monitoring data, especially when a karst cave or an easily-collapsed hole section is encountered, the system can automatically increase the slurry injection amount so as to maintain the stability of a hole wall, the construction efficiency is improved, and the construction cost is reduced.

The invention discloses a depth space-time super-resolution mapping method fusing historical high-resolution land coverage data, and the method comprises the steps: constructing a depth space-time super-resolution mapping model structure which is formed by connecting a super-resolution adversarial network SRGAN structure and a MUNet network structure in series; mapping from low-resolution images to high-resolution land coverage category probabilities in combination with super-resolution reconstruction and semantic segmentation is carried out, a historical high-resolution land coverage category map of a target area is used as auxiliary information, and changed categories and unchanged categories are distinguished by comparing category information of the low-resolution images in a historical period and a target period; and a change detection-based sub-pixel category allocation optimization model fusing a historical high-resolution land cover classification map and a predicted high-resolution category probability map is constructed, so that the depth space-time super-resolution mapping of the low-resolution remote sensing image is completed. The method has the advantages of high simulation precision, stability and the like, and is suitable for earth surface information extraction work such as classification, land cover / utilization, change detection and the like when remote sensing images are insufficient.

The invention belongs to the technical field of geological disaster monitoring and early warning, and particularly relates to a landslide catastrophe risk probability intelligent prediction method based on a two-phase substance point method. A two-phase substance point method is used for achieving rainfall-induced landslide catastrophe whole-process numerical simulation, then probability distribution of response quantities such as the sliding distance, the slump range and the slip mass volume is obtained, and the risk probability is predicted in combination with disaster-bearing body characteristics; according to the prior distribution of each parameter variable, a random sample is extracted as an input data set, a response quantity sample obtained through analog calculation is used as an output data set, an XGBoost model is adopted for training, and an agent model of a two-phase substance point method is constructed; and carrying out parameter inversion on slope monitoring data by utilizing Bayesian probability inversion, dynamically updating probability distribution of response quantity of each model, and predicting risk probability in real time according to the response quantity distribution. The physical significance is clear, and the prediction efficiency and the prediction precision are improved.

The invention discloses a real-time cultural tourism path optimization and adjustment system based on big data analysis, and relates to the technical field of cultural tourism path optimizing.The system comprises an acquisition and analysis module, the acquisition and analysis module is connected with a combination and matching module, the combination and matching module is connected with a path optimization module, and the path optimization module is connected with a data processing module. The path optimization module is connected with an interaction feedback module, and the collection and analysis module is used for collecting tourism behavior characteristic information of tourists in real time by using a big data analysis technology. According to the real-time cultural tourism path optimization and adjustment system based on big data analysis, a personalized model is established, tourism paths can be customized for tourists, unique tourism requirements and preferences of the tourists are met, the satisfaction degree and participation degree of the tourists in the tourism process are improved, the tourism paths can be adjusted in time according to real-time external data, and the tourism efficiency is improved. The tourists are prevented from being caught in unfavorable conditions such as congestion and severe weather, and smooth and efficient travel itinerary is ensured.