3182results about "Forecasting" patented technology

The invention provides an underwater tunnel shield construction excavation face stability evaluation method, system and equipment, and the method comprises the steps: determining an excavation face stability evaluation index based on a shield construction excavation face instability mechanism; dividing the stability of the excavation surface into a plurality of grades, establishing each grade space, and determining the quantitative interval of each evaluation index in each grade; calculating a combined weight of each stability evaluation index by adopting a combined weighting method, and taking the combined weight as a judgment basis of the influence of the evaluation indexes on an evaluation result; and constructing an ideal point evaluation function by adopting an ideal point method so as to represent the membership degree of the to-be-evaluated object to each grade, calculating the grade membership degree of the to-be-evaluated section in each grade of the evaluation system, and determining the stability grade of the excavation surface in the construction process of the evaluation section.

The invention discloses a disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method. When a minimum electricity generating cost incremental quantity model only containing one balance node tidal current model is solved, the total demand unit change of a system is fixed on a balance node, and an obtained lambda, namely electricity generating cost minimum incremental quantity cannot reflect actual operation of the system in most cases during total demand unit change of a system. The disperse relaxation factor tidal current model based minimum electricity generating cost incremental quantity obtaining method adopts solves the provided model by means of an original antithesis interior point method, and the lambda of the system is controllable according to selected disperse relaxing factors.

The application discloses a kind of unmanned vehicle accurate parking methods of port crane scene, through the setting of desired accurate parking position, adjust the distance and direction of vehicle, guide vehicle accurate parking, through the position that template map has saved desired accurate parking, and the plane parameter of crane, improve the accuracy of vehicle stop, can improve the efficiency of loading and unloading box operation, increase the economic benefit of single vehicle unit time, simultaneously, through laser radar, the point cloud data of crane can be directly scanned and obtained, realize the accurate parking of port crane scene, do not need to make any modification to crane equipment and install any equipment, there is no special requirement to crane structure, size etc., with good universality, the characteristics such as low introduction cost.

A method of controlling operation of a fleet of vehicles comprising a plurality of vehicles is provided. The method acquires first sensor data from a plurality of first sensors of a first vehicle, and second sensor data from a plurality of second sensors of a second vehicle; processes the first sensor data using a first vehicle dynamics model to estimate at least one first vehicle operating cost parameter value related to operation of the first vehicle; processes the second sensor data using a second vehicle dynamics model to estimate at least one second vehicle operating cost parameter value related to operation of the second vehicle; processes the first and second vehicle operating cost parameter values using a reinforcement learning model to estimate a fleet operating cost parameter value; and determines a recommended component redistribution of the first vehicle to the second vehicle to optimize the estimated fleet operating cost parameter value.

The system described herein can automatically match, link, or otherwise associate electronic activities with one or more record objects. For an electronic activity that is eligible or qualifies to be matched with one or more record objects, the system can identify one or more set of rules or rule sets. Using the rule sets, the system can identify candidate record objects. The system can then rank the identified candidate record objects to select one or more record objects with which to associate the electronic activity. The system can then store an association between the electronic activity and the selected one or more record objects.

Systems and methods for providing failover readiness prediction, comprise: during a training stage: selecting a first set of data from a database of historical failover data based at least in part on feature importance within the historical failover data; label encoding the first set of data; tokenizing the first set of data; converting the first set of data into a set of dense vector representations; and training a classification model using the set of dense vector representations; during a predication stage: receiving a request to predict a failover outcome for a given failover process; assessing a status of concurrent system conditions; and predicting at least one of a success probability of the failover process, or an expected time for at least one of a plurality of steps in the failover process, based at least in part on the classification model and the status of the concurrent system conditions.

An aerosol provision system generates aerosol from an aerosol generating material for user inhalation. The system includes a computer configured to estimate user demand for an active ingredient within the generated aerosol, based on one or more inhalation actions of the user during an immediately preceding inhalation session, where an inhalation session being no more than one hour long. The computer adjusts one or more operational parameters of the aerosol provision system responsive to the estimated user demand, and adapts aerosol provision for the next inhalation action.

The application relates to the technical field of deep learning, and particularly discloses a heat supply load prediction method of an LSTM+transformer model, which comprises the following steps: S1: obtaining water supply data changing with time in historical water supply records of a shopping mall, and constructing a differential equation based on the water supply data; S2: pre-setting a sub-period, obtaining short-term data of the sub-period, obtaining long-term data of the past 24 hours, respectively obtaining a short-term vector and a long-term vector, and generating an encoding state based on the short-term vector and the long-term vector; S3: collecting outdoor air temperature and generating an outdoor air temperature set, adding disturbance to the outdoor air temperature to obtain outdoor air temperature disturbance, recalculating the encoding state, and performing strategy adjustment based on the encoding state s and the encoding state st. The scheme can output optimal heat supply time and recommend water supply temperature in advance, avoid excessive heat supply, reduce energy consumption, and simultaneously enhance the self-adaptive capacity to outdoor temperature sharp fluctuation by combining a differential equation with an air temperature disturbance mechanism.

We provide the system. [Solution] A means for receiving requests in natural language from users and performing semantic analysis on those requests, A means for searching a spatial information database based on generated search conditions and generating search results, A means for transmitting the aforementioned search results to the user's terminal, suggesting them in a visually displayable format, and making them interactively operable, A means of guiding users to their destination using visualized spatial information based on their choices, A system that includes this.

The application discloses a source-load interval prediction method based on physical orientation, a storage medium and equipment thereof, according to the nonlinear dependence of the quantitative influence factors and the source load analyzed by the maximum mean difference, the key influence factor screening and the extraction of the physical constraint relationship are completed by using the random forest and the Spearman rank correlation analysis; the PGA-LSTM source-load interval prediction model based on the physical variable tracking and the conditional monotonicity constraint mechanism is established. A composite loss function integrating point prediction, physical constraint and interval coverage is designed, and the optimal hyperparameters of the model are sought through the particle swarm optimization algorithm; finally, the source-load prediction interval containing the upper bound and the lower bound is output. The prediction method based on the physical orientation effectively solves the problems of the lack of physical consistency and the weak generalization ability of the pure data-driven model in building energy prediction. Through the embedded physical mechanism constraint, the coverage and compactness of the prediction interval are significantly improved, and reliable quantitative basis is provided for the robust scheduling and risk management of the building energy system.

A non-transitory computer-readable recording medium stores therein an information processing program that causes a computer to execute a process including generating a set of a plurality of routes for a plurality of moving bodies that depart from a specific node and return to the specific node via a plurality of nodes, the plurality of routes representing an order in which the plurality of moving bodies visit a predetermined number of nodes among the plurality of nodes, solving a route selection problem generated using the generated set of routes and provided with constraints on a shipment volume of the specific node or a node serving as a delivery source of packages among the plurality of nodes, and outputting a result of solving the route selection problem.

The application relates to a marine nuclear pollution diffusion prediction method based on multi-modal data fusion, which comprises the following steps: constructing a marine nuclear pollution data set, wherein the marine nuclear pollution data set comprises marine environment data and marine nuclear pollution data; introducing graph structure modeling based on the marine nuclear pollution data set, aggregating node features through graph convolution, and distributing cross-modal attention weight to the features; inputting the distributed features into a ConvNeXt V2 for feature extraction to generate a feature map, and constructing a diffusion initialization network based on nuclear pollution features; constructing a diffusion evolution network, combining continuously acquired marine environment data and marine nuclear pollution data on the basis of the diffusion initialization network, obtaining nuclear pollution diffusion prediction results at different times through three diffusion evolutions, generating a prediction graph, and calculating the centroid trajectory of the nuclear pollution concentration distribution after each diffusion prediction; and realizing automatic and high-precision prediction of marine nuclear pollution diffusion, and significantly improving the prediction efficiency and reliability.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The application relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraints, and specifically comprises the following steps: step 1: synchronously collecting data such as spectrum information, DO, COD, temperature and pH at key monitoring sites, constructing a water dynamics-water quality coupling equation, and simulating the space-time dynamic distribution of water quality parameters; step 2: outputting a water quality sensitive area through a water dynamics-water quality model, screening sensor layout points by combining information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution map by adopting a spatial interpolation method; step 3: analyzing main pollution sources according to the synchronous collection of spectrum information at key monitoring sites, and adopting principal component analysis and an attention mechanism neural network; step 4: based on real-time optical characteristic value-DO data, combining a spatial topological network, a water quality gradient and a cross-region covariance to capture the spatial correlation of water quality parameters between different points, and constructing an optical characteristic value-DO-COD dynamic prediction model; combining pollution tracing and spectrum characteristic correction to correct the COD prediction value, adopting ensemble Kalman filtering to assimilate multi-source data, and improving the model accuracy.

Provided are a method and system for predicting an activation potency of agonist molecules on G Protein-Coupled Receptors (GPCRs). The method includes: blindly speculating complex structures formed by binding of a ligand to an activated receptor structure and an inactivated receptor structure, respectively, via global molecular docking; extracting an initial path enabling an inactivated complex structure to be activated to an activated complex structure based on an enhanced sampling algorithm; searching for a minimum free energy path closest to the initial path by applying an automatic path optimization algorithm; calculating a free energy distribution curve along the minimum free energy path by employing umbrella sampling and determining an energy barrier height and a free energy difference before and after activation, thereby determining the activation potency of the ligand structure on the GPCRs.

A power operation system according to the present invention comprises: one or more acquisition units selected from a state acquisition unit that acquires various states of a storage battery such as a charging rate, a capacity deterioration state, a battery temperature of a storage battery and the like, a power trade-price acquisition unit that acquires a future power trade-price prediction value in a power transaction market, an imbalance-fee acquisition unit that acquires a future imbalance-fee prediction value, a power-generation amount acquisition unit that acquires a future power-generation amount prediction value, and a power-demand amount acquisition unit that acquires a future power-demand amount prediction value; a plan creation unit that creates one or more selected from a charge / discharge plan of the storage battery having a maximum power operation profit, a power-generation sales plan, and a demand procurement plan on the basis of some or all of the states of the acquired storage battery and a power generation facility or a power consumption facility, the power trade price, the imbalance fee, the power generation amount, and the power-demand amount prediction value; and a storage unit that stores a constraint condition for restricting an operation of the storage battery and an avoidance measure of the restriction.

The application discloses a kind of wind farm overhead power collection line ice thickness prediction method, comprising: extracting the same point position icing on-line monitoring terminal icing period historical monitoring data;Identify all sample air temperature minimum value, and divide air temperature into several first small intervals;According to the air temperature interval divided, air temperature, relative humidity, icing thickness are classified;Identify the relative humidity minimum value in each air temperature interval, and divide the relative humidity in each air temperature first small interval into several second small intervals, extract the icing thickness that satisfies the constraint of each relative humidity interval;Kernel density estimation is carried out to the icing thickness of each interval, extract the icing thickness array when the maximum cumulative kernel density of each interval is equal to 0.85, and calculate its average value;Establish wind farm overhead power collection line single-point icing thickness prediction model.The present application can overcome the technical problems that the existing icing thickness prediction mode is difficult to operate, difficult to apply in practice and low in accuracy.

To provide a hydrogen production plant profit calculation device, an expected profit calculation device, a facility capacity calculation device and a hydrogen production amount calculation device which calculate further optimal profit calculation and facility capacity when there are a future sales price and subsidy.SOLUTION: A hydrogen production plant profit calculation device 1 includes: an input unit for acquiring a hydrogen production sales plan in a hydrogen production plant, an operation parameter of the hydrogen production plant, a facility capacity plan of the hydrogen production plant and estimated model construction data of hydrogen production; a sales income calculation unit for predicting a sales income of hydrogen on the basis of the hydrogen production sales plan; an operation cost calculation unit for predicting an operation cost required for hydrogen production on the basis of the operation parameter; a facility cost calculation unit for predicting a facility cost required for the facility of the hydrogen production on the basis of the facility capacity plan; an auxiliary calculation unit for predicting auxiliary amounts for at least one or more of the sales income, the operation cost and the facility cost; and a profit calculation unit for predicting a profit of the hydrogen production plant on the basis of the sales income, the operation cost, the facility cost and the auxiliary amount.SELECTED DRAWING: Figure 1

The application belongs to the field of computer software and engineering information technology, and particularly relates to a compliance risk control and payment management system and method for the engineering construction industry, which comprises: an engineering document intelligent analysis module, which adopts a constructed multi-modal AI model to identify and structure four-tuple information; an engineering project structured modeling module, which constructs a multi-level regional tree model, and associates each leaf node in the tree model with a unit price, a weight and a payment trigger condition agreed in a project contract; a mapping engine, which stores and manages payment rules, and supports parsing natural language clauses into executable logic rules; a dynamic progress payment calculation module, which traverses the multi-level regional tree model, and automatically accumulates corresponding unit prices and engineering quantities; an intelligent early warning and risk control module, which collects multi-dimensional process data of each approval link in real time, and dynamically determines an overdue probability through a pre-constructed risk scoring model. The application can realize high-precision semantic analysis, accurate mapping, dynamic pricing and intelligent risk control.

We provide the system. [Solution] A means of collecting product data from multiple commercial facilities, A method for predicting demand using machine learning techniques, A means for optimally allocating goods among multiple commercial facilities based on predicted demand, A means of monitoring and updating information on changes in product inventory in real time, A means of analyzing user purchasing behavior and formulating sales strategies, A means of providing information on product inventory status within commercial facilities via a smartphone app, A system that includes means for instructing commercial facilities to replenish their stock based on demand forecasts that take weather information and event information into account.

We provide the system. [Solution] Collect fish finder data, By integrating past weather conditions, seasons, and fishing records, and analyzing them with artificial intelligence, A means for notifying an information processing device of information indicating the location of the fishing grounds in coordinates, A means to function as a platform for information recipients to act efficiently, with the aim of managing urban resources. Based on the analyzed data, this information will be provided to fishermen as a means to optimize resource utilization within urban areas. A system that includes this.

A system and method are disclosed including a computer that receives a formulation of a multi-objective linear programming planning problem, the formulation including at least one variable fixed at an upper bound or a lower bound. The computer also solves the formulation for a higher-order objective and fixes the upper bound or the lower bound of at least one variable to preserve a solution of the formulation for the higher-order objective. The computer also replaces at least one variable in the formulation with a value of the upper bound or the lower bound, when the upper bound of at least one variable is fixed at the lower bound or the lower bound of at least one variable is fixed at the upper bound, and checks whether replacing at least one variable with the value of the upper bound or the lower bound divides the formulation into two independent components.

This invention discloses a method and system for real-time monitoring and prediction of carbon intensity, belonging to the field of carbon emission monitoring technology. The method acquires and processes electricity-related data such as installed capacity and real-time load of each province hourly through an ETL data channel; it draws a map of China based on ECharts, dividing it into five color-level intervals according to carbon intensity values: 0-300, 301-500, 501-700, 701-900, and 901-1200 gCO2 / kWh; responding to user clicks on provinces, it displays detailed data for each province through indicator cards, circular progress charts, and one-sided bar charts; it calls a pre-trained STFCI hybrid model to predict carbon intensity hourly for the next 24 hours. This model integrates a 2-layer 128-neuron LSTM unit with an XGBoost unit with 100 trees and a learning rate of 0.1, outputting predicted values ​​after feature concatenation and a fully connected layer; the prediction results are displayed as a bar chart, marking customizable low-carbon threshold lines, highlighting low-carbon periods, and generating electricity consumption suggestions. The corresponding system includes a data interface, database, visualization, prediction, and WeChat service modules. The database adopts an InfluxDB+MySQL combined architecture, and the WeChat service module supports mobile text and image queries. The STFCI model of this invention has an average absolute error of only 12.3 gCO2 / kWh, significantly improving prediction accuracy, realizing hourly real-time monitoring and multi-dimensional visualization of carbon intensity, and providing precise decision support for power dispatching and energy conservation and emission reduction when combined with mobile services.

Provided is a control device that calculates load distribution to a generator having excellent operation efficiency and determining whether to make a bid in response to a request for power supply from a power supply and demand adjustment market. A supply / demand adjustment control device includes: an acquisition unit that acquires first power requested for a power plant including a plurality of generators and second power requested from a power supply and demand adjustment market; a load distribution unit that distributes a total load that is a sum of the first power and the second power to the plurality of generators in descending order of operation efficiency, to distribute a load close to a rated load as much as possible to the generators; and a bidding determination unit that determines whether to make a bid for a request from the power supply and demand adjustment market, on the basis of whether the total load is able to be supplied.

The application discloses a building energy efficiency prediction and scheduling method and system based on an open source Hongmeng, belongs to the technical field of artificial intelligence, and is used for solving the technical problems of the architecture fragmentation, data incompatibility and control lag of a traditional building energy efficiency management system, which leads to incomplete energy consumption perception and the absence of prediction and scheduling capability. The method comprises the following steps: based on an open source distributed operating system, the physical devices in a target building are managed, and an energy efficiency service resource pool is constructed based on the physical devices; based on the real-time data flow of the energy efficiency service resource pool, an energy efficiency digital twin of the target building is constructed; according to the energy efficiency service resource pool, an energy efficiency relationship diagram of the target building is constructed, and the energy efficiency relationship diagram is dynamically modulated; based on the modulated energy efficiency relationship diagram, an optimal energy-saving service path meeting the constraint condition is solved; the optimal energy-saving service path is input into the energy efficiency digital twin for simulation verification; after the verification, the optimal energy-saving service path is dispatched to each physical device for distributed execution.

The application discloses a comprehensive management and control platform and method based on an ore dressing process optimization model, relates to the technical field of intelligent ore dressing, and comprises the following steps: acquiring ore genomics attribute parameters, analyzing implicit association rules between the ore genomics attribute parameters and floatability through a graph neural network model trained by historical ore dressing production data, and generating grinding ore proportioning combination parameters; constructing a dynamic coupling model of a buffer storage location change rate and a grinding unit power consumption, and generating a stockpile strategy instruction; fusing the grinding ore proportioning combination parameters and the stockpile strategy instruction, forming a grinding parameter setting result; performing a grinding avoidance control operation, outputting an ore dressing whole-process decision graph, and generating a grinding stockpile strategy execution route. The application analyzes implicit association rules between ore genomics attribute parameters and floatability through a graph neural network model, and realizes accurate capture and quantitative analysis of complex structure characteristics of ore particles.

The invention relates to a system and method designed to monitor and analyze human, financial and material resources within industrial environments, providing real-time data collection and advanced analytics to assess the current state of the project, project future scenarios and simulate interactions between various parameters. The goal of the application is to provide a centralized, comprehensive view of the current status of the project, monitoring the total use of resources (workers, materials, tools), financial status, warehouse status and simulating the future state of the project based on current dynamics, thereby eliminating data fragnentation and contributing to greater reliability. These simulations help determine whether the project will be completed within the planned time frame and with the planned resources, enabling informed decision-making and the detection of problem areas and possible anomalies, which leads to more efficient time and resource management during project implementation.

This invention provides a wind power plant scheduling method and system based on fatigue distribution. [Solution] The solution includes establishing multiple wind turbine points based on the parameters of the wind turbine unit, generating predicted power for each wind turbine point within the current adjustment cycle based on a predetermined power prediction model based on historical environmental parameters, generating an initial scheduling policy based on the power grid scheduling command and the predicted power for all wind turbine points within the current adjustment cycle, obtaining fatigue evaluation values ​​for each wind turbine point, determining whether or not to modify the initial scheduling policy based on all fatigue evaluation values, generating an initial scheduling policy based on the operating parameters of each wind turbine unit, optimizing and adjusting the initial scheduling policy, and optimizing the uniformity of the fatigue distribution of the wind power plant while assuming no loss of power generation.

The application discloses a kind of electric vehicle charging pile order exception clearing settlement method and device, comprising: obtaining the real-time order of vehicle networking platform;Based on the inference engine of preset detection real-time order whether it is abnormal order;Response to real-time order is normal order, then directly upload real-time order to vehicle networking platform;Response to real-time order is abnormal order, then based on correction algorithm or preset prediction model carries out numerical correction or data prediction to real-time order, obtains revised abnormal order and uploads to vehicle networking platform.One aspect, according to the mass historical abnormal data obtained by vehicle networking platform, mining clearing screening rule, combined with the experience rule of power grid expert custom clearing screening rule, the inference engine obtained can accurately detect abnormal order, on the other hand, it can be to electric vehicle charging pile abnormal order numerical correction or data prediction, reduce the economic loss caused by order exception.

To create an operation plan capable of setting power to be provided to a power receiving device to a desired aspect and achieving a target requested by a power system.SOLUTION: An operation plan creation device 1 is applied to a power system consuming first power obtained from a first energy source (photovoltaic power generation device) and second power obtained from a second energy source (external system). The operation plan creation device comprises: a predicted value designation unit 11 designating a predicted value of power to be supplied as the first power from the first energy source to the power system; a constraint condition designation unit 12 designating a constraint condition which includes a term indicating the first power and a term indicating the second power, and separately treats the first power and the second power; a target function designation unit 13 for designating a target function formulating a target requested by the power system; and an optimization computation unit 15 obtaining an operation plan for the power system by solving a problem defined by the predicted value of the power to be supplied, the constraint condition and the target function.SELECTED DRAWING: Figure 2