102 results about "Real time modeling" patented technology

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention provides a carbon emission accounting method and system based on an artificial intelligence technology, and belongs to the field of carbon emission. According to the technical scheme, the method comprises the following steps: constructing a sensor network of an equipment operation state, obtaining real-time operation data, and preprocessing the real-time operation data through an edge computing node; establishing a dynamic prediction model of emission factor boundary parameters based on the real-time operation data; according to the dynamic prediction model, constructing a carbon emission propagation map, analyzing the spatial and temporal distribution trend of carbon emission intensity, and identifying the position of an abnormal emission source; according to the abnormal emission source position identification result, carbon emission trend prediction is executed, and emission early warning response is triggered in combination with a preset threshold value. The method has the beneficial effects that dynamic prediction of carbon emission factors, identification of emission abnormal sources and trend early warning response are realized, the real-time modeling and space-time propagation analysis capabilities of the system on carbon emission behaviors are enhanced, and the intelligent level and response efficiency of emission management are effectively improved.

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, device and equipment based on field monitoring and real-time modelling, and the method comprises the steps: collecting a topographic data file and an earth surface data text of a target area to construct a sub-meter DEM digital elevation model; radar real-time base data, radar and ground rainfall historical observation data, brightness temperature data and geographical coordinate information of a target area are obtained to solve actual radar real-time base data, rain short-term forecast data and soil moisture data, and then the data are utilized to calculate a preset hydrological model to obtain a mountain torrent gully basin distributed hydrological model result; and establishing a hydrological-hydrodynamic coupling model according to the sub-meter DEM digital elevation model and the torrential flood gully basin distributed hydrological model so as to forecast the current flood state and the future flood state. Therefore, the problems that an existing mountainous area hydrological model cannot completely reflect the current state of a drainage basin, and the simulation error of a hydrodynamic model is large are solved.

The invention discloses an intelligent optimization and resource dynamic redistribution method and system based on a graph neural network. The method comprises the steps of data acquisition and modeling, network topology feature extraction, congestion detection and prediction, data flow redistribution and intelligent optimization, and real-time monitoring and feedback mechanism. According to the invention, real-time modeling and intelligent optimization of a network topology structure and a data flow are realized through the graph neural network model, so that a potential congestion area can be accurately identified and positioned; in combination with a flow redistribution strategy of a maximum entropy principle, the system can dynamically adjust the distribution of data streams and resources, relieve congestion and improve the overall transmission efficiency; by introducing an improvement rule of cascade failure, secondary congestion caused by failure of the congested nodes is effectively prevented, and the stability of the network is enhanced.

The invention relates to the technical field of laser deposition control, in particular to an unmanned aerial vehicle metal part ultrasonic field auxiliary laser deposition system which comprises the following modules: a data acquisition module for acquiring a molten pool thermal field signal, a sound field signal and a laser scattering signal, and performing sampling, filtering and synchronous processing to obtain molten pool original data; the micro-flow digital twinning module is used for establishing a molten pool micro-flow digital twinning model according to the molten pool original data and simulating molten pool micro-flow behavior to obtain micro-flow prediction data; and the microflow ultrasonic regulation and control module is used for generating an ultrasonic pulse sequence according to the microflow prediction data and applying the ultrasonic pulse sequence to the molten pool to regulate and control the flow field. According to the invention, through the molten pool microflow digital twinning module and the microflow ultrasonic regulation and control module, real-time modeling and regulation and control of the molten pool microflow structure in the complex geometric part deposition process are realized, the problem that local vortexes, shear layer differences and flow field anomalies cannot be identified in real time in the traditional technology is solved, and the molten pool microflow evolution can be actively reconstructed.

The invention provides a chronic disease risk early warning system and method based on artificial intelligence. The method comprises the following steps: acquiring historical monitoring information of a diabetic patient; determining development trends of different disease course stages through historical monitoring information, and performing trend evolution based on all the development trends to obtain evolution characteristics of each disease course stage; determining the risk contribution degree of each health index to the chronic disease risk according to the linear correlation among different health indexes, and determining a risk monitoring model of the target patient through all the risk contribution degrees; performing confidence adjustment on the chronic disease risk in the risk monitoring model according to each evolution feature, and further obtaining a confidence risk value of the current disease course stage of the target patient; and carrying out risk prompting on the target patient based on the confidence risk value. By adopting the scheme of the invention, real-time modeling can be carried out on the dynamic change of the course of disease of the patient under the background of massive health data.

The invention relates to a radar point cloud real-time modeling water conservancy earthwork measurement method and device, equipment and a medium. According to the method, a vegetation root system interference area is scanned and recognized through a dual-frequency polarization radar, a coordinate set of the vegetation root system interference area is obtained, an elevation compensation model is constructed based on dielectric constant difference and fractal dimension to generate a compensation parameter set, and original point cloud is processed by combining a bare soil reference curved surface and adopting a dynamic constraint particle filtering algorithm to obtain real ground point cloud. Then, a corrected terrain curved surface is generated through parasitic energy field constraint optimization, and finally, compensation parameters are fused to realize high-precision earth volume calculation in a root system interference environment, so that point cloud distortion and a terrain curved surface systematic lifting effect caused by a vegetation root system are greatly reduced, and an earth measurement result of a complex vegetation coverage area meets an engineering acceptance standard.

The invention discloses a sea-air target trajectory prediction method and device based on dynamic graph learning and a generative adversarial network, and a medium, and belongs to the technical field of sea-air target trajectory prediction, and the method comprises the steps: S1, data preprocessing; s2, dynamic graph learning and generative adversarial network (GAN) training are carried out, feature dimension relevance is modeled in real time through a dynamic graph structure, and a GAN module is utilized to generate adversarial trajectory features; s3, CNN-Transform hybrid model prediction is carried out, local feature extraction is carried out through a CNN module, global dependency modeling is carried out through a Transform module, and a decoder part is associated with a historical trajectory and future prediction through a cross attention mechanism; and S4, performing visualization and evaluation based on dimension reduction. According to the invention, the precision, robustness and interpretability of trajectory prediction in a complex scene are significantly improved.

The invention relates to the technical field of logistics storage management, and discloses a logistics storage system, which comprises an environment sensing module used for monitoring task points, path states, obstacle positions and dynamic order changes in a storage environment in real time, and generating path weight data; the master control module is connected with the environment sensing module and is used for receiving the order demand, generating a task point set, obtaining path weight data and carrying out global scheduling on the transfer robot according to the path weight data; and the path optimization module is connected with the master control module and is used for calculating the optimal path of the transfer robot based on the multi-agent game model. According to the technical scheme, the environment sensing module is adopted to perform real-time modeling on the storage environment, and the path weight data is generated, so that the technical effect of dynamically updating the path information is achieved, and the defect of path planning lag in the complex storage environment is overcome.

The invention discloses a stockyard inventory making method and system, and relates to the technical field of stockyard inventory making. The method comprises the steps of obtaining original point cloud data of a stockyard; filtering the original point cloud data to obtain filtered point cloud data; segmenting the filtering point cloud data to obtain target modeling point data and non-modeling point data; constructing a three-dimensional model of the material pile in the storage yard based on the target modeling point data; and the volume of the material pile is calculated according to the three-dimensional model, and stocktaking is completed. Based on the scanner and the server, the influence of environmental factors and flying dust interference on the point cloud data can be removed, and the optimal point cloud denoising effect is realized, so that a more accurate three-dimensional model can be established, and the stocktaking precision is improved. Besides, the scanner is installed on a riding track at the top of the greenhouse, the problem that the stocktaking function of an existing remote control system of the bucket-wheel stacker-reclaimer occupies the operation time of the bucket-wheel stacker-reclaimer can be solved, real-time modeling stocktaking is achieved, the refreshing rate is higher, and the stocktaking speed is increased.

The invention discloses a deep hole presplitting intelligent visiting and evaluating system and a method thereof, and belongs to the field of drilling peeping, and the system comprises a deep hole presplitting visiting bionic robot which is provided with a hydraulic dynamic reducing mechanism; the multi-modal fusion detection module is used for realizing omnibearing acquisition of monitoring data by adopting optical acoustic mechanics three-source sensing; the embedded real-time modeling module is internally provided with a large model and performs real-time three-dimensional modeling by programming a crack three-dimensional reconstruction algorithm; the edge calculation prediction module carries a heterogeneous calculation platform and is internally provided with a laser radar, and obstacle avoidance and accurate depth setting are carried out; and the communication and energy module adopts a dual-redundancy communication link, transmits high-broadband data by using an optical fiber composite cable, and transmits a key early warning signal. By adopting the deep hole presplitting intelligent visiting and evaluating system and the method thereof, the problems of low detection efficiency, single aperture adaptability and data dimension, insufficient analysis function and no prediction function of a traditional drilling peeping instrument are solved.

The invention relates to the technical field of unmanned aerial vehicle surveying and mapping, and discloses a pressure pipeline axonometric mapping system based on an unmanned aerial vehicle, and the system uses a pipeline anchoring relative navigation module to guide the unmanned aerial vehicle to fly along a line with a target pipeline as a reference; the outer diameter and three-dimensional center point coordinates of the pipeline are obtained in real time through an orthogonal contour scanning and parameter resolving module; a self-adaptive flight and measurement control module is used for adjusting the flight distance according to the measured outer diameter feedback, and adjusting sensor parameters according to the imaging quality feedback; meanwhile, the coordinates of the center point are incrementally constructed into a topological graph through an airborne real-time topological graph construction and node processing module, pipe fitting nodes are autonomously detected and recognized, and then a preset strategy is called to conduct refined scanning on the nodes. According to the invention, autonomous navigation independent of global positioning, self-adaptive closed-loop control of measurement and flight, and airborne integrated real-time modeling of pipe network topology and geometric information are realized, and the surveying and mapping precision, the automation level and the operation efficiency are remarkably improved.

The invention provides an intelligent control method and system for the wind speed of an electric fan self-adaptive to the environment temperature, and relates to the technical field of intelligent household appliance control. The method comprises the steps that the average environment temperature is calculated based on a first temperature value, a second temperature value and a third temperature value, and meanwhile the wind speed correction value is combined; generating an initial wind speed gear parameter according to a preset temperature-wind speed mapping rule; superposing the wind speed correction value to the initial wind speed gear parameter to generate a final wind speed gear control parameter; converting the final wind speed gear control parameter into a motor rotating speed control instruction, and adjusting the rotating speed of an electric fan motor in real time; wherein the temperature-wind speed mapping rule meets the requirements that the wind speed gear is increased when the temperature is increased and the wind speed gear is reduced when the temperature is reduced. Through a closed-loop process of synchronous acquisition, real-time modeling and dynamic correction, temperature change is quickly converted into a motor rotating speed instruction, and intelligent control over the wind speed of the electric fan is achieved.

The invention discloses an interactive behavior feature extraction method and device, equipment, a medium and a product, and relates to the technical field of computers, the method comprises the following steps: obtaining original live broadcast data generated in a live broadcast room, the live broadcast room comprising at least one interactive object; determining multi-modal live broadcast behavior data of the original live broadcast data in the target time window based on the multi-modal behavior data of the interactive object; dividing the multi-modal live broadcast behavior data into a plurality of behavior subsequences; based on the behavior data corresponding to the behavior subsequences, determining behavior cooperation features between the behavior subsequences, behavior interaction features between the interaction objects and time evolution features; and determining a target feature of the live broadcast room based on a fusion result of the behavior cooperation feature, the behavior interaction feature and the time evolution feature. By implementing the technical scheme of the invention, real-time modeling for the multi-behavior sub-sequence of the live broadcast room is realized, and the global feature of the live broadcast room level can be accurately extracted.

The invention provides an industrial robot anti-collision early warning method and system. The method comprises the following steps: environment perception and multi-source data fusion; predicting a dynamic obstacle track; robot posture real-time modeling is carried out; carrying out collision risk joint assessment; dynamically dividing early warning levels; intelligent planning of an obstacle avoidance path; dynamically compensating joint torque; multi-stage early warning collaborative response is carried out; performing iterative evaluation on the early warning effect; and self-evolving the anti-collision knowledge base. The three key technical problems in the industrial robot anti-collision field are solved, dependence on high-cost physical sensors (such as a torque sensor and a laser radar) is eliminated, collision detection of any position of the whole robot is achieved, and the cost is reduced by 60% or above. The limitation of a static obstacle model is overcome, and real-time track prediction and risk grading of dynamic targets such as moving personnel and conveying belt materials are achieved. Through multi-source data fusion and a risk probability model, the false alarm rate is reduced to 5% or below, and meanwhile it is guaranteed that the missing report rate is 0.

The invention belongs to the field of power transmission line tree obstacle detection, and particularly relates to a power transmission line tree obstacle detection method based on thunder-vision fusion, and the method comprises a data collection module which comprehensively collects the original data of the surrounding environment of a power transmission line through a laser radar, a camera and POS equipment. The advantages are obvious. Laser radar and camera data are fused, data registration and feature extraction technologies are applied, tree obstacles are accurately identified and positioned, the danger degree is accurately judged, and the detection precision is far higher than that of a single sensor. Quasi-real-time data transmission is achieved through the point cloud technology, and hidden dangers can be found in time in cooperation with real-time modeling and rapid distance measurement. The intelligent device integrates multiple sensors, automatically analyzes tree barrier risks, reduces manual intervention and reduces cost. In addition, detailed detection reports can be generated, support is provided for operation and maintenance, multi-element perception fusion enhances environmental adaptability, and stable operation of the power transmission line is guaranteed.

The invention provides a multi-source fusion heterogeneous data extraction method and system and a storage medium, and the method comprises the steps: constructing a dynamic hypergraph neural network and a dynamic hypergraph structure, carrying out the feature aggregation, and obtaining a space-time enhancement feature representation; performing causal reasoning and meta-learning optimization to generate robust feature representation; and by executing data reconstruction and fusion, outputting a heterogeneous data extraction result after multi-source fusion. According to the method, a dynamic hypergraph neural network, a sliding window and an attention mechanism are combined, data entity spatio-temporal evolution is modeled in real time, and the problem of correlation lag of a traditional static hypergraph is solved; then, a causal element learning confrontation module integrates causal reasoning, element learning and confrontation training to improve model robustness; finally, data reconstruction, meta-learning and adversarial loss are subjected to weighted fusion through a joint optimization strategy, the feature expression ability is improved, and the generalization performance of the model in a small sample scene is ensured.

The invention discloses a lithium battery recycling and disassembling method based on AI + 3D vision, and relates to the technical field of battery recycling, and the method comprises the steps of S1, multispectral polarization image acquisition, S2, polarization parameter dynamic calibration, S3, image fusion, S4, deep learning denoising optimization, S5, 3D point cloud reconstruction and feature extraction, and S6, robot motion planning and execution. According to the method, a dynamic situation field model is combined with an impedance control principle, so that the robot can sense obstacle distribution in real time in a dynamic environment and autonomously adjust a motion strategy, and meanwhile, the contact force between an end effector and a target object is accurately controlled; a rejection and attraction field is modeled in real time, so that the sudden mechanical interference risk in the disassembling process is effectively avoided; the impedance coupling mechanism ensures that the robot maintains stable interaction force in contact operation through force feedback closed-loop adjustment, and battery damage or tool failure caused by sudden change of external force is avoided.

The invention relates to the technical field of computers, discloses a panoramic situation intelligent emergency decision management and control method and system based on cloud computing, and aims to solve the problems of serious information island, response lag, weak intelligent decision capability, disjunction of resource scheduling and emergency strategies, man-machine interaction separation and the like in an existing emergency management system. The method comprises the steps that multi-source heterogeneous data are collected through a global data access engine, and a unified data lake is formed through data fusion and cleaning; constructing a dynamic three-dimensional digital twinborn model to realize panoramic situation real-time modeling; and carrying out risk identification and graded early warning by using a deep neural network. According to the technical scheme, global situation second-level perception, risk active prediction, scientific auxiliary decision making and integrated collaborative management and control are realized, and the emergency response efficiency and the decision making intelligence level are remarkably improved.

The invention discloses an AI-driven vehicle-mounted sound field real-time modeling and voice separation method, and relates to the technical field of voice signal processing. A main control unit comprising a time sequence synchronizer, a resource scheduler and a health monitor is constructed. 3D sound field modeling is carried out by adopting a lightweight STCN + bidirectional LSTM network, adaptive updating of the model is realized through EWC incremental learning, and a CNN-LSTM noise classification network and targeted suppression algorithms such as ANF / spectral subtraction are developed. The voice separation module adopts an improved Conv-TasNet architecture, 3D spatial constraint and a multi-task loss function are fused, and low delay is realized under INT8 quantization and pipeline processing. The system dynamically optimizes parameters through a real-time regulation and control unit, supports scene self-adaption, finally achieves a separation effect in a mixed noise scene, reduces the delay of the whole system, and effectively improves the definition and stability of vehicle-mounted voice interaction.

This invention provides a method and system for intelligent mixing of metallurgical dust and sludge based on deep machine learning. By mining data from metallurgical dust and sludge strong mixers, preprocessing data acquisition, real-time modeling of strong mixers, multi-step predictive modeling of strong mixers, intelligent control of water addition, intelligent control of binder, and intelligent control of return material, this invention solves problems such as uneven mixing and unsatisfactory mixing effects in the metallurgical dust and sludge field. It improves the processing capacity of the mixing system, enhances production quality and capacity, and paves the way for the digital and intelligent transformation of metallurgical dust and sludge.

This invention discloses a parameter estimation method and system for the forward and inverse models of an automotive electromagnetic valve-controlled shock absorber. The method includes the following steps: driving the shock absorber to perform simple harmonic motion at a set stroke and frequency under different currents; constructing a parameterized model of the shock absorber based on the changes in output force during the compression and recovery strokes; constructing a parameter estimation objective function to estimate the parameters of the parameterized model; fitting the estimated parameters as a function of the current to construct the forward model of the shock absorber, which calculates the damping force based on the current state of the shock absorber; selecting key variable parameters in the forward model that change with the current to calculate the sensitivity coefficient; constructing the inverse model of the shock absorber, generating the desired damping force using the forward model, and solving for the current corresponding to the desired damping force based on the inverse model. This invention can adapt to the dynamic characteristics of CDC shock absorbers under different road conditions, accurately meet the real-time modeling needs of complex road conditions, and significantly improve the practicality and response efficiency of the model.

A vacuum sweeper self-adaptive dust collection control system and the use, the system comprising: a, an environment mapping module which comprises a multi-dimensional sensing technology and a real-time modeling algorithm, and is configured to analyze the dust concentration and the particle size, and construct a three-dimensional model of a working environment; b, a dynamic programming module which is connected to the environment mapping module, and is configured to calculate a dust collection path and suction parameters, and generate a dust collection strategy; and c, a particle control module which is connected to the dynamic programming module and is configured to perform localized attraction or repulsion of dust particles in a specific area.

The invention discloses a rain clutter modeling method based on an electromagnetic scattering principle. Firstly, a radar emission signal model and a rain field model are constructed, and the rain field model comprises raindrop size, raindrop last speed and rainfall distribution; then, uniformly dividing the detection space into a plurality of scattering units; calculating the backscattering sectional areas of the raindrops, and further superposing the backscattering sectional areas of all the raindrops in the unit volume to obtain the scattering rate of the unit volume; according to the scattering rate per unit volume and the volume of the scattering unit, calculating the backscattering sectional area of the scattering unit; and finally, calculating the rain clutter echo signal of a single scattering unit, and superposing the rain clutter echo signals of all scattering units in the distance ring to obtain the rain clutter echo signal of the distance ring. And superposing the rain clutter echo signals of all distance rings to obtain a rain clutter echo signal in the detection space. According to the method, the modeling accuracy is improved, the calculation complexity is greatly reduced, and the method is suitable for large-scale real-time modeling.

The invention relates to the technical field of risk assessment, and provides a multi-dimensional risk real-time assessment method and system for dynamic confrontation. The method comprises the following steps: accessing a data source to execute data acquisition, and establishing a multi-source dynamic data set; carrying out attack intention and behavior attribution analysis on the multi-source dynamic data set, and outputting an attacker strategy intention graph; modeling an attacker strategy by using a random game based on the strategy intention graph, and configuring a dynamic attack tree; constructing a conditional dependency graph according to the dynamic attack tree path dependency relationship, and mapping the conditional dependency graph into a Bayesian network structure; according to a Bayesian network, a risk propagation model is constructed, a node state is updated, and risk probability mapping is established, so that the technical problem that a traditional static risk assessment method cannot reflect risk evolution in a dynamic confrontation process in real time is solved, and the aim of improving the risk evolution in a dynamic confrontation process is achieved through collection and analysis of a multi-dimensional dynamic data source. And real-time modeling and risk propagation evaluation of attacker behaviors are realized, so that the technical effects of improving the adaptive capability and the real-time response capability of network security protection are achieved.

This invention relates to the field of Building Information Modeling (BIM) technology, specifically to a collaborative control system for high-fill open-cut tunnel construction based on BIM collaboration. The system includes a multi-source sensing and acquisition module, a BIM digital twin coordination module, an intelligent decision-making module, an execution control and feedback module, and a signal execution module. This invention collects various data from the construction site in real time and performs dynamic real-time modeling using a BIM digital twin model. The system generates control signals based on real-time data analysis, promptly identifying and locating hazardous areas on the construction site, effectively reducing safety risks during construction and ensuring the stability and safety of the construction process. This invention performs in-depth analysis of construction site data, calculates key construction indicators in real time, generates machinery scheduling schemes, and generates specific control signals for filling machinery, trigger signals for the support system, and adjustment signals for drainage equipment, enabling precise scheduling of construction machinery according to actual needs.

Medical systems are described, including a medical system including a display having a user interface that includes graphical elements corresponding to a first set of medical parameters and a type of intraluminal state, and a processor configured to receive user input selecting a status or value for at least one medical parameter of the first set of medical parameters and the type of intraluminal state, determine the intraluminal state over time based on the user input, and display on the user interface a graphical representation of the intraluminal state over time.