106 results about "Real time model" patented technology

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention discloses a traffic image labeling method and device based on a D-S evidence theory and a medium, and relates to the technical field of image labeling. According to the method, two different types of real-time models are simultaneously utilized to process the same image in parallel, and two uncertainty detection results of the same image are obtained; a KM algorithm is utilized to match targets in two uncertainty detection results, then a prior credibility score is utilized to reduce a conflict coefficient, and two different types of small model prediction results are combined through an uncertainty theory. According to the traffic image labeling method and device based on the D-S evidence theory and the medium, the strong generalization ability of the open set model is utilized to make up for the defects of a closed set model, the use of a large model to improve the hardware dependence is avoided, the precision problem of a single small model is made up under the condition that the efficiency is ensured, and the iteration duration of the model is shortened.

The invention relates to a method and device for determining the fatigue loss of an offshore photovoltaic pile foundation stand column and a medium, and relates to the technical field of fatigue analysis. The method comprises the steps that pile foundation load dynamic data of the pile foundation stand column are determined according to historical stand column state data; determining a target residual life of the three-dimensional digital twin model under an extreme service condition based on the pile foundation load dynamic data; based on the target residual life, the column material parameters of the pile foundation column, the pile foundation load dynamic data and historical environment variable data corresponding to the historical column state data, training a to-be-trained neural network model to obtain a fatigue life prediction model; and inputting real-time model state data of the three-dimensional digital twinborn model into the fatigue life prediction model to obtain real-time residual life probability distribution of the three-dimensional digital twinborn model, and determining real-time fatigue loss of the pile foundation stand column according to the real-time residual life probability distribution. The accuracy of the fatigue loss analysis result is improved.

The invention discloses a dynamic scene incremental reconstruction and rendering method based on 3DGS, and belongs to the field of specific computer models, and the method comprises the steps: constructing a 3DGS model at an initial moment based on an original image set; obtaining any visual angle image at the moment t in the dynamic scene, and determining a first updating area through semantic segmentation and target recognition; generating an increment updating region based on the luminosity error, the local similarity and the global semantic feature; generating a second update region, modeling in the second update region, and minimizing region reconstruction errors to generate an optimized Gaussian point set; and fusing and optimizing the real-time model at the previous moment to obtain a real-time model Gt at the moment t for real-time rendering. According to the method, geometric and texture information of a scene can be effectively coded, accurate detection and local increment updating of a dynamic region are supported, Gaussian point parameters are optimized to improve the continuity and visual quality of a model, low-delay real-time rendering is realized, and the efficiency and quality of three-dimensional scene processing in a dynamic environment are effectively improved.

The invention discloses a power system distribution robust scheduling optimization method based on optimal configuration, and relates to the field of power system robust scheduling, and the method comprises the steps: carrying out the coupling modeling of a power transmission network extension plan, a DPFC and an energy storage device, and solving a model to generate an optimal configuration scheme; constructing an uncertainty set capable of being dynamically updated, and obtaining a day-ahead scheduling scheme by adopting a dual-stage distribution robust optimization method in combination with the optimal configuration scheme; on the basis of a day-ahead scheduling scheme, a hybrid robust and adaptive model prediction control framework is utilized, and a real-time control parameter of the DPFC and a rapid power adjustment instruction of energy storage are generated through rolling optimization; according to the invention, by combining optimal configuration, dual-stage distribution robust optimization and hybrid robust adaptive model prediction control, collaborative optimization of long-term planning and short-term scheduling is realized, and through real-time model parameter updating and dynamic uncertainty processing, real-time scheduling is realized. The problems that a scheduling scheme is insufficient in robustness, and economical efficiency and real-time adaptability are difficult to consider are solved.

The invention relates to the technical field of new energy automobile batteries, in particular to a new energy automobile battery state prediction method, optimization control method and system, and the method comprises the steps: collecting the real-time operation data of a new energy automobile battery; uploading the real-time operation data to a cloud processing platform in which historical operation data of the battery is stored, and dynamically predicting the state of the battery; fusing the charge state data driven by the real-time model and the charge state data driven by the historical data to carry out charge state estimation prediction, and outputting charge state estimation parameters; a circulation attenuation model is combined with reinforcement learning correction, historical circulation data is used for fitting a capacity attenuation curve, and a dynamic battery health state estimation value is output; a fusion thermal model and an internal resistance model are adopted, a safety voltage / temperature boundary is predicted through digital twin simulation, and a dynamic battery power state estimation value is output; and generating a battery control strategy based on the battery state parameters and issuing the battery control strategy to an onboard BMS module for execution.

The invention relates to an accurate feeding control method, device and system for a solid waste-based material 3D printer, and the method comprises the following steps: S1, collecting the viscosity and shear stress data of a solid waste-based material in real time based on a miniature rheological sensor array at an inlet of a spiral feeder by adopting a wavelet transform signal analysis method, and synchronously obtaining the material temperature through a temperature sensor, and constructing a material rheological characteristic dynamic model, and generating a dynamic rheological characteristic matrix. The method has the advantages that the real-time model is constructed by dynamically sensing the rheological characteristics of the material and fusing temperature data through a wavelet transform signal analysis method, and the capture precision of the heterogeneity of the solid waste material is remarkably improved; multi-source heterogeneous sensor data is subjected to fusion analysis through a graph neural network, the fluidity index and the blockage risk probability are accurately quantified, and the effect of early warning of abnormal working conditions is achieved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and decision-making method, device, equipment and medium, and the method comprises the steps: obtaining multi-dimensional source data, carrying out the standardization processing, screening the standardization data based on a decision-making scene, and obtaining a core index set, the method comprises the following steps: establishing and optimizing indexes, mining association characteristics among the indexes, constructing and optimizing a dynamic statistical model, carrying out standardization processing on newly added multi-dimensional source data, obtaining a key decision index value by utilizing the dynamic statistical model, and carrying out comparison judgment on the key decision index value and a preset decision threshold value, and generating a decision scheme by combining the comparison judgment result with a preset scheme generation mechanism. According to the method, multi-scene adaptive support and real-time decision optimization are realized through unified multi-dimensional data standardization, dynamic scene-driven core index screening and association characteristic mining, real-time model updating and closed-loop decision scheme generation.

System for training an interventionalist to perform an invasive percutaneous intervention or an endoscopic intervention on an organ, by using a tool in this organ, where a merging unit is arranged for merging in real-time in a common environment a real-time 3D model of an end portion of a tool and a pre-computed 3D model of at least a portion of the organ. A display shows to the interventionalist the common environment, so that the interventionalist can see in real-time on the display where the real-time 3D model of this end portion of the tool is located with respect to the pre-computed 3D model of the portion of the organ, thus making the training of the interventionalist possible.

The invention belongs to the field of power grid dispatching, and particularly relates to a power grid dispatching method, device and equipment based on a BiTrackGCT model and a medium, and the method comprises the steps: collecting power grid data; the power grid data are input into a preset BiTrackGCT model, the BiTrackGCT model comprises a day-ahead BiTrackGCT model, an intra-day BiTrackGCT model and a real-time BiTrackGCT model, the BiTrackGCT model extracts space and time sequence characteristics of the power grid data, and an ideal state prediction value and an actual state prediction value of a future power grid under multiple time scales are output based on the space and time sequence characteristics; and carrying out advanced scheduling according to the actual state prediction value and the ideal state prediction value of the future power grid under multiple time scales. A convolutional neural network and a three-layer weight sharing gating circulation unit are combined, a complex time sequence relation between photovoltaic power generation and load requirements is accurately captured, a tree structure algorithm is used for optimizing model hyper-parameters, and the influence of prediction errors on scheduling is reduced.

The invention discloses a vibration temperature control cooperative control method and system for concrete forming, and the method comprises the steps: building an initial prediction model of a vibration temperature control bidirectional coupling effect through concrete mix proportion parameters, historical vibration control parameters and initial temperature field data; the model is dynamically corrected through vibration time sequence data and temperature field space data which are collected in real time to obtain a real-time coupling effect prediction model, and then the real-time model is used for predicting the compactness deviation and the temperature gradient of the next control period; then vibration parameter adjustment amount and temperature control measure intervention intensity are calculated based on the predicted value through a multi-target collaborative optimization algorithm to generate a collaborative control instruction, and finally collaborative optimization of vibration and temperature control in the concrete pouring process is achieved according to the instruction. According to the invention, precise collaborative optimization of vibration and temperature control in the concrete pouring process can be realized, and the stability of concrete forming quality is effectively guaranteed.

The application relates to the technical field of virtual reality, in particular to a simulation mine digital twin teaching system and method based on virtual reality technology. The method comprises the following steps: generating a virtual mine space on a cloud server; obtaining a virtual model of a coal mining device as a digital twin of the coal mining device; obtaining real-time model parameters of a physical model of the coal mining device operated by a student; performing online update processing on the digital twin of the coal mining device according to the real-time model parameters to obtain an updated digital twin; the cloud server maps a corresponding virtual three-dimensional model of the coal mining device in the virtual mine space according to the updated digital twin; and a machine learning algorithm evaluates the coal mining operation of the student according to the real-time model parameters. The application can enhance the real experience of the student during training.

A structure inspection system includes an image conversion device located in a far edge of the structure inspection system that is proximate to a physical structure, and a near edge processor located in a near edge of the structure inspection system that is remote from the physical structure. The image conversion device receives an image of the physical structure, and renders a real time 3D model of the physical structure based on the first images. The near edge processor is configured to provide a baseline 3D model of the physical structure to the image conversion device. The image conversion device further compares the first real time 3D model with the baseline 3D model, and provides an indication when the first real time 3D model deviates from the baseline 3D model.

The invention relates to a BIM-based construction site robot real-time monitoring method and system, and relates to the technical field of building engineering monitoring, and the method comprises the steps: obtaining a real-time BIM model of a target construction body, planning monitoring points, and obtaining a monitoring point sequence and an abnormal rate sequence; monitoring and controlling a robot according to the monitoring point sequence, extracting monitoring point BIM data and inspection data, and carrying out consistency verification to obtain an initial consistency rate; predicting a movement anomaly rate according to the BIM data, configuring a re-check parameter by combining the initial consistency rate and the anomaly rate, and obtaining a re-check consistency rate through re-check; and according to the anomaly rate, the movement anomaly rate and the re-check consistency rate of the monitoring points, calculating to obtain a fusion anomaly rate, marking the monitoring points and obtaining a monitoring result. The method solves the problems that traditional construction site monitoring only adopts a single monitoring mode and does not consider related errors and probability problems, so that judgment on construction progress management reasonability is not accurate enough, result credibility is not enough, and efficient management and control requirements of a construction site cannot be met.

This invention relates to the field of meteorological services and provides a method and system for correcting subseasonal model precipitation based on climate zoning and sea surface temperature (SST) background constraints. The method includes: climate zoning, adding SST background constraints, reanalysis data modeling, model historical return data modeling, real-time prediction model matching, and prediction result correction. The system includes: a data preprocessing unit, a climate zoning unit, an SST background constraint unit, a reanalysis data modeling unit, a model historical return data modeling unit, and a real-time model matching and prediction result correction unit. This invention considers the importance of regional differences in precipitation and SST background constraints, solving the problems of significant regional differences in daily summer precipitation in my country, the tendency for overfitting in single-point modeling, and the lack of physical constraints that make correction methods unsuitable. It effectively utilizes physically meaningful predictability sources to modify the cumulative probability density correction method based on percentile mapping, thereby improving my country's summer subseasonal precipitation prediction capabilities.

The application belongs to the technical field of vehicle software development, and particularly relates to a method for realizing hard real-time of a non-real-time system in hardware-in-the-loop simulation and electronic equipment, wherein the method for realizing hard real-time of the non-real-time system in the hardware-in-the-loop simulation comprises the following steps: creating a real-time model task and forming a task program; the task program automatically reads computer configuration and judges the number Z of processor cores of a current computer device; when the number Z of the processor cores does not exceed X, the number n of threads is set to 1 to execute the real-time model task, otherwise the number n of threads is set to (Z-X) / Y to execute the real-time model task, wherein Y represents the number of threads of one physical core of the processor.

The invention discloses a carbon emission evaluation method, system, equipment and medium based on a multi-energy electric power system, and relates to the field of carbon emission evaluation of electric power systems, comprising the steps of dividing the operation cycle of the electric power system into three time scales of day-ahead, day-in and real-time, and performing data acquisition at each time scale; constructing a day-ahead model, an intra-day model and a real-time model based on the collected data; obtaining a predicted unit plan and a predicted carbon potential based on the day-ahead model; inputting the predicted unit plan and the predicted carbon potential into the intra-day model, and optimizing and correcting the predicted carbon potential; real-time data is collected and compared with the predicted carbon potential, real-time carbon potential is obtained through real-time model calculation, and corrected parameters are fed back to a day-ahead model. According to the method, the day-ahead, intra-day and real-time three-level dynamic carbon flow model is constructed, and the hysteresis problem of a traditional method in a new energy fluctuation scene is solved. Seamless connection of data in different time dimensions is realized through a cross-scale carbon potential transfer function and a coordination controller.

The application discloses a kind of digital twin and stress prediction method and device of large component additive manufacturing process, and time sequence image is obtained by scanning in time sequence by slidable line laser head, corresponding three-dimensional real-time model is constructed and surface displacement difference in molten pool formation to cooling solidification process is obtained;It is input to three-dimensional real-time model as surface displacement load and is carried out numerical simulation, temperature field, stress field are obtained by numerical simulation, and the temperature measured by infrared thermal imager, the stress measured by stress sensor and the temperature, stress obtained by numerical simulation are compared, the data obtained are input into three-dimensional real-time model, and the digital twin of real-time molten pool and deposition layer is formed.The application can well reduce simulation time, improve the efficiency of printing process numerical simulation, and effectively monitor and predict the deformation and cracks generated in large component printing process.

To achieve hardware real-time by a non-real-time system of a hardware-in-loop simulation (HILS).SOLUTION: A method includes: enabling a task program to automatically read computer configuration and determine the number Z of processor cores of a current computer device; when the number Z of the cores is not greater than X, set a thread number to be allocated to a real-time model task, to n=1 to execute the real-time model task, and when the number Z of the cores is greater than X, set the thread number to be allocated to the real-time model task to n=(Z-X) / Y to execute the real-time model task, Y representing the maximum number of threads that one physical core of the processor supports, (Z-X) / Y being a natural number, and X being set so that the number of own cores not involved in a hardware-in-loop simulation (HILS) is X / Y.SELECTED DRAWING: Figure 1

A simulation modeling method and system for a real-time model of a silicon carbide IGBT module, which relate to the technical field of power electronics and are used for a silicon carbide IGBT module. On the basis of acquired module characteristic parameters and oscillation characteristic parameters, by means of a targeted data acquisition method, key information can be quickly and accurately provided for modeling, thereby greatly reducing unnecessary computational amount, and significantly improving the modeling efficiency. On the basis of a constructed initial real-time model, real-time oscillation simulation is performed by using the acquired oscillation characteristic parameters and module characteristic parameters, such that the oscillation characteristics at the moment of turning on / off a silicon carbide IGBT module in actual operation can be simulated more accurately; oscillation simulation data is compared to preset standard working condition data, if the two are consistent, the initial real-time model can serve as a target real-time model, thereby ensuring the accuracy of the model.

The invention relates to a QEC detection method and system based on AR. The method comprises the steps that a pre-constructed engine standard model is disassembled into part units, a three-dimensional model set in the installation process of all the part units is obtained, and the three-dimensional model obtained after installation of each part unit is finished is a part unit standard model; obtaining an engine real-time model based on AR equipment; when the difference degree between the engine real-time model and the standard model is higher than a first threshold value, a first part unit with the largest difference degree is obtained, whether the corresponding real-time model is matched with the three-dimensional model set or not is judged, if yes, the first part unit is maintained towards the corresponding standard model state, and if not, the first part unit is maintained; and when all the part units in contact with the first part unit are synchronously changed to the corresponding standard model states, the engine real-time model is obtained again to continue execution, and otherwise, a QEC request is sent out. Compared with the prior art, the method has the advantages that the safety and the efficiency of the whole QEC operation inspection and verification process are improved, and the like.

This invention relates to a lightweight adaptive method for testing non-stationary data streams. Its core lies in constructing a two-branch adaptive architecture that decouples observation and execution statistics. A shadow observation branch, independent of the main inference branch, maintains a statistical reference baseline in real time, unaffected by adaptive calibration actions, thus suppressing the closed-loop coupling problem between drift perception and statistical updates at the mechanism level. Based on this decoupled architecture, the shadow branch calculates a composite drift surrogate metric to map and generate dynamic momentum parameters, and introduces relaxed cumulative variables to perform three-level state determination and spatiotemporal misalignment mask generation. Finally, during inference forward propagation, dynamic online calibration of statistics is performed only for the key levels selected by the mask. This invention alleviates the adaptation lag and statistical oscillation risks caused by fixed momentum, suppresses the statistical degradation risk caused by single-sample noise, and is suitable for real-time model optimization in single-sample, non-stationary streaming scenarios.

The invention relates to the technical field of BIM-based construction management, in particular to a BIM-based construction progress control management method and system, and solves the technical problem of relatively large prediction deviation in BIM-based construction progress management establishment in the prior art. Factors occurring in the construction process are selected through field investigation, the influence degree is analyzed, the actual progress is extracted according to a 4D real-time model established through BIM, the influence coefficient is calculated through a regression model, and the predicted construction period and the actual construction period are compared to verify the effectiveness of the model; in addition, according to the technical scheme, the BIM technology is applied to the construction process, project actual conditions can be visually inquired through progress visual management based on a real-time 3D model, and progress deviation is analyzed; and meanwhile, the predicted construction period is used as a time parameter to be linked with the BIM three-dimensional model, so that the dynamic management of the subsequent project progress is realized.

The application discloses a kind of based on neural network and genetic algorithm's thermal power unit working condition dynamic optimization method, system, equipment and storage medium, it is related to thermal power unit intelligent dynamic optimization technical field, including determining fitness function parameter, constructs BP neural network model;Establish the boundary line equation of parameter to be optimized, the upper and lower boundary lines of parameter to be optimized under different loads are fitted out;Using genetic algorithm receives BP neural network model and boundary line equation, publishes working condition optimization AI model, configures non-optimized parameter;Boiler efficiency working condition optimization real-time model is constructed, and based on non-optimized parameter real-time optimization is carried out to parameter to be optimized, obtains efficiency deviation and corresponding parameter to be optimized deviation;The method described in the application realizes thermal power unit working condition dynamic optimization, parameter to be optimized reasonable boundary constraint and efficiency deviation and parameter deviation linkage output, improves the pertinence, coherence and executability of optimization result under complex load condition.

The invention belongs to the technical field of vehicle software development, and particularly relates to a system and method for a non-real-time system to achieve hard real-time in hardware-in-loop simulation, and the system comprises a computer device, a hardware-in-loop simulation device and a hardware-in-loop simulation device. The computer device is configured to execute a task creating module and a task thread setting module; wherein the task creating module is configured to create a real-time model task and form a task program; the task thread setting module is configured to enable a task program to automatically read computer configuration and judge the number Z of processor cores of the current computer device, when the number Z of the cores does not exceed X, the thread number n is set to be equal to 1 so as to execute a real-time model task, otherwise, the thread number n is set to be equal to (Z-X) / Y so as to execute the real-time model task, wherein Y represents the number of threads of one physical core of the processor.

The invention discloses a microgrid voltage and frequency stable adjustment method and system based on a distributed integral model, a medium and a processor, and relates to the technical field of microgrid control. The method comprises the following steps: independently identifying system parameters and states on line through each distributed energy node, and constructing a linear time-varying model; an integral effect is introduced and converted into an incremental state space model containing an integral item, an IIMPC framework is formed by combining control variable constraints, a control target is converted into a secondary optimization problem with constraints, a distributed energy reference power signal is solved, and a master controller and a slave controller cooperatively control dynamic load distribution according to the distributed energy reference power signal; and finally, comparing the real-time model in-loop simulation with the optimal PI controller to evaluate the performance. The method improves the control precision and robustness, and solves the problems of low response speed and poor flexibility in the prior art.

The invention relates to a QEC (Quality Error Correction) maintenance safety supervision method and system based on AR (Augmented Reality). According to the method, after a maintenance general task is generated, the maintenance general task is decomposed into a plurality of sub-tasks and sent to pre-obtained online AR equipment, and a maintenance piece where each sub-task is located and a target model are obtained; obtaining an associated sub-task set according to the maintenance piece associated sub-tasks; when the current sub-task is executed, acquiring a marked sub-task from the associated sub-task set of the current sub-task; acquiring a real-time model of a maintenance piece where the marked sub-task is located, and executing the next sub-task if the real-time model meets a first preset condition before and after the current sub-task is executed; if the real-time models of the maintenance pieces where all the sub-tasks are located in the current associated sub-task set meet a second preset condition, the associated sub-task set is qualified; and when the completion time of all the sub-tasks and the total task real-time model meet a third preset condition, the maintenance total task is qualified. Compared with the prior art, the method has the advantages of improving data analysis efficiency and accuracy in QEC maintenance work and the like.

The invention belongs to the technical field of carbon assets, and particularly relates to a carbon energy consumption management analysis method and system, a terminal and a storage medium. The carbon energy consumption management analysis method comprises the following steps of performing configuration management and control on project equipment needing to be managed in a management area so as to form a management control network; summarizing the previous data of the database to form a preliminary budget model of each project in the management control network; generating a real-time acquisition instruction according to the management control network, and generating a verification management acquisition instruction within a preset time; comparing real-time acquisition data obtained by the real-time acquisition instruction with verification management data obtained by the verification management acquisition instruction to generate actual real-time data; generating a real-time model according to the actual real-time data; and generating a management analysis report according to the real-time model and the preliminary budget model. According to the invention, false data transmission caused by attack of a transmission path can be avoided; and the accuracy and authenticity of carbon asset management analysis are improved.

This specification discloses a fragmented intelligent agent system and its processing method based on multi-model tentacle-like collaboration. The intelligent agent system, by setting a control central module, decomposes the original task context into multiple context fragments, and distributes these fragments to multiple isolated real-time model modules for processing, ensuring that no single real-time model module can obtain the complete task context during processing. The system further uses a fragment reconstruction module to verify and reconstruct the consistency of the processing results from multiple real-time model modules, thereby generating the final decision result. This specification also provides a detailed description of the intelligent agent system's structural composition, context fragmentation method, multi-model collaboration mechanism, processing result reconstruction process, and compatible operating modes, aiming to improve security, reliability, and scalability while ensuring processing power.