112 results about "Predictive systems" patented technology

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention discloses a modularized intelligent tubular column conveying system based on digital twinning and self-adaptive control, and belongs to the technical field of digital twinning. The modularized intelligent tubular column conveying system is characterized in that a digital twinning model integrating a mechanical structure, electrical control and hydraulic power attributes is constructed; acquiring multi-source sensing data including an image sequence, a three-dimensional point cloud and vibration acceleration in real time, driving the digital twin model to perform synchronous simulation, and calculating a deviation value between the digital twin model and internal simulation data; on the basis of the deviation value, a control logic parameter adjusting instruction is generated through a predefined mapping rule, then updated control logic parameters are executed, and a complete pipe column conveying process is simulated to generate a predictive system state sequence; and finally, the safety-related state subsets are issued to a field controller to guide and execute preventive actions. According to the invention, deep fusion and closed-loop control of digital twinning and physical entities are realized, and the debugging efficiency, the environmental adaptability and the operation safety of a tubular column conveying system are effectively improved.

The invention relates to the technical field of geothermal energy utilization and underground heat exchange, in particular to a medium-deep layer buried pipe heat exchanger seepage thermal response rapid prediction method based on artificial intelligence, and the method comprises the steps: obtaining temperature field snapshot data of a medium-deep layer buried pipe heat exchanger system under different operation conditions; performing orthogonal decomposition on the temperature field snapshot data to obtain main modal characteristics and corresponding amplitudes; the parameters of all the operation conditions serve as input variables, all the main modal features and the corresponding amplitudes serve as output variables, a BP neural network is trained, and an artificial intelligence agent model is constructed; and obtaining parameters of a target operation condition, inputting the parameters into the artificial intelligence agent model, outputting the amplitude of each main mode under the target operation condition, reconstructing a temperature field, and completing rapid prediction of the seepage thermal response of the medium-deep layer buried pipe heat exchanger under the target operation condition. According to the method, the temperature field distribution of the DBHE system under the action of underground water seepage can be quickly predicted.

The invention relates to a data-driven criminal period prediction system, belongs to the technical field of artificial intelligence, solves the problems of insufficient accuracy, poor regional adaptability and the like of prisoner classification and criminal period prediction in the prior art, and provides a multi-model fusion provincial prisoner classification and criminal period prediction system based on real judicial sentencing logic. The system comprises an input display module, a legal judgment element extraction module, a sentency and insignment prediction module, a sentency judgment prediction module and a judgment output display module, and can effectively improve the prediction precision and optimize judicial decision support by fusing multiple advanced algorithms and combining judicial practices of different provinces. The method has a wide application prospect and a strong regional adaptation capability.

Systems, methods, and computer-readable storage media for aggregating media (and commentary on that media) into a topology. To do so, the system receives first content (and associated metadata) as well as second content (and associated metadata). The system then generates a topology based on a relationship between the first content and the second content, where the topology has a number of dimensions based on the metadata of the different pieces of content. The system then compares the topology and the metadata to previously stored topologies and / or metadata and, based on that comparison, executes a machine learning algorithm. The output of that machine learning algorithm includes predicted future changes to the topology, which the system uses to reduce the number of dimensions within the topology.

The invention relates to the technical field of electric power management, and particularly discloses a multi-element load short-term prediction system for an electric power system, and the system obtains an actual operation data source and a meteorological data source of a power grid through a multi-source data collection platform, and carries out the preprocessing of the data sources. The method comprises the steps of extracting local fluctuation characteristics according to an actual operation data source and a meteorological data source of a power grid, fusing to generate a composite data sequence, designing a load trend sensing model based on the operation data sequence and the composite data sequence to analyze a power load recovery trend, responding to power grid dispatching according to a prediction result, and forming a feedback closed loop. The prediction and scheduling strategy is continuously corrected and optimized, internal and external multi-dimensional information is fused, and the system structure of short-term fluctuation and long-term trend is considered, so that the prediction precision is improved, and the robustness and the adaptive capacity of the system are remarkably enhanced.

The disclosed system provides advanced mission control predictive systems for low earth orbit semi-autonomous satellites. In operation, the system may include a satellite transit behavior prediction module configured to train a neural network based on current and past satellite orbital transit paths so as to predict future transit paths. Knowing precise future satellite transit paths enables a ground-based satellite control system to more efficiently (and with greater accuracy) control certain operations of the satellites. For example, a ground-based satellite control system can prepare for communications to commence at a particular time. Legacy systems can only predict one or two days in advance However, using a neural network that not only takes into consideration historical transit data, but also learned details such as solar wind, cloud patterns, etc., the neural network predicts future satellite transit paths with statistically-certain accuracy leading to a much narrower cone of uncertainty.

The present application relates to the field of safety science and technology, and provides a dissipative analysis method of system failure process, the system failure evolution process is a changing process influenced by the outside world, and the method is provided for determining the dissipative property thereof; the characteristics of the system failure evolution process are discussed, the dissipative property of the evolution is studied, and a calculation method of key parameters in the dissipative structure is proposed; the evolution process satisfies four conditions of the dissipative structure; the calculation method of the key parameters can be constructed with the aid of the physical meaning of the evolution process and the mathematical method of the spatial failure network, so as to quantitatively calculate and judge the dissipative property of the evolution process; and the method can be used to judge and predict the influence of the final result of the system failure evolution process on subsequent events. The present application can quantitatively calculate and judge the dissipative property of the system failure evolution process, and according to the result of the dissipative property, whether the system failure evolution process is stable and whether there is a failure overflow risk can be judged.

The application relates to a message middleware domestic substitution adaptation method for power dispatching, which comprises the following steps: step 1: the system is split into independent services, and each service is responsible for a specific function; a lightweight communication protocol is used for communication between the services; step 2: each service instance is independently expanded according to the load condition; step 3: a Docker image is created for each microservice, local development and testing are carried out by using Docker Compose, and automatic deployment, expansion and management are carried out by using Kubernetes; step 4: a local scheduler is deployed on each node, communication of local tasks is realized based on a message middleware, AI technology is used to predict system load and performance bottlenecks, and a message middleware domestic substitution adaptation scheme is optimized; and step 5: according to evaluation indexes, compatibility and stability tests are carried out on the message middleware domestic substitution adaptation scheme. The application can effectively ensure the compatibility and stability of the power dispatching system when the message middleware is domestically substituted.

The invention relates to the technical field of social network information spreading prediction, and discloses an information spreading prediction method fusing public opinion field effect and a heterogeneous hypergraph. In order to solve the problems that in the prior art, only pairwise user relations are relied on, multi-user group influences cannot be described, different information is subjected to cascade independent processing, and multi-topic competition is not considered, the invention provides a prediction scheme fusing public opinion field effects and heterogeneous hypergraph learning. A heterogeneous hypergraph is constructed to obtain user multivariate relation representation, then a public opinion field effect is utilized to quantify attraction energy of different information topics to a user, attention competition among multiple topics is modeled, and a more real user propagation tendency is obtained; and finally, realizing joint prediction of user interest features and social influence features through an interactive fusion mechanism. The method can be used for scenes of information propagation trend analysis, public opinion monitoring, marketing recommendation, false information early warning and the like.

A machine learning based (ML-based) method and system for forecasting cash flow, is disclosed. Initially, the data associated with business units are obtained from data sources. The data are pre-processed to generate pre-processed data. Features associated with financial information are determined for horizons based on the pre-processed data using a plurality of input AI models. Feature combinations are generated by integrating the features associated with the financial information, for each horizon of the horizons. Forecasts are generated for each horizon for a pre-determined time interval, using a stacked AI model including forecasting models. The generated forecasts for the cash flow of the business units, are provided as an output, to the users on user interfaces associated with electronic devices associated with the users.

The invention relates to the field of manufacturing execution systems and intelligent manufacturing, in particular to a manufacturing execution system management method oriented to production data, which comprises the following steps: a multi-source data acquisition step: acquiring physical sensing state data, manual interaction time data and system scheduling data of a manufacturing execution system, and integrating to generate multi-source heterogeneous production data; a trust quantitative evaluation step of quantifying the distortion degree of the execution state based on the multi-source heterogeneous production data to obtain an execution trust entropy; a cross validation prediction step: comparing the physical sensing state data with the man-machine interaction time data to calculate execution trust entropy, and predicting system failure risk; a dynamic scheduling decision step: setting a scheduling degradation adjustment mechanism based on execution trust entropy, and outputting a target scheduling instruction to adjust the running state of the system; according to the method, the dynamic balance between pursuit of efficiency and maintenance of authenticity of underlying data is realized, and the vulnerability resistance of the complex manufacturing network is effectively improved.

The application provides a productivity middle platform micro-service granularity determination method, medium and system, and belongs to the technical field of electric digital data processing. Firstly, stable load indexes and fluctuation load indexes are obtained through productivity middle platform runtime parameter collection and multidimensional decomposition. Then, a business domain interaction matrix is constructed and singular value decomposition is performed to generate an initial micro-service division scheme. Next, an evaluation equation set containing data consistency, interface calling, business cohesion and service dependency is established. On this basis, a dependency complexity coefficient matrix is constructed, and an L-shaped domino covering algorithm is used to determine a service merging scheme. Finally, the system complexity is predicted through a service evaluation neural network model, and a genetic algorithm is used for iterative optimization until the optimal micro-service granularity scheme is obtained, thereby solving the technical problem that the productivity middle platform micro-service granularity cannot be dynamically and adaptively adjusted in the prior art.

Systems and methods are disclosed for training a model to predict a cybersecurity risk based on entity firmographics. A breach dataset comprising a number of breach indicator values for a number of entities is generated, wherein each respective breach indicator value is (i) mapped to a respective entity of the entities and (ii) an evaluation of at least one of the first security incidents being associated with the respective entity during a time period. A number of aggregated risk feature values for a plurality of geographic locations are determined based on a plurality of second security observations. The aggregated risk feature values are joined to the breach indicator values and firmographic parameter values to form a training dataset. A model is trained using the training dataset to generate a predictive risk assessment for an entity of the entities based on the firmographic parameter values associated with the entity.

The invention provides a dynamic space-time cross fusion economic prediction system and method based on graph attention and event response LSTM, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining flight flow data, economic index data and external event labeling data of a target region; constructing an aviation network diagram structure with economic regions as nodes and air routes as edges, and endowing the edges with economic prior weights calculated based on economic association strength indexes among the regions; an economic priori weight is fused through an economic-driven graph attention network model, and spatial feature representation is extracted; dynamic fusion of space-time features is realized by using a space-time cross attention network; and responding to the impact of the emergency through the long-short-term memory model regulated and controlled by the event signal, and carrying out time sequence modeling and prediction. According to the method, the problems of spatial modeling distortion, insufficient dynamic adaptability and rigid spatio-temporal feature fusion in the prior art are solved, the prediction precision and robustness are remarkably improved, and a scientific basis can be provided for aviation policies and economic decisions.

A patient monitoring system includes a camera that selectively delivers a video feed to a monitoring station. A processor evaluates the video feed, and converts the video feed into a plurality of data points for an elopement detection system. The plurality of data points correspond at least to a combination of facial features and components of clothing for each person within the video feed. The processor is further configured to associate the combination of facial features and the components of clothing for each person to define a confirmed association, to identify whether the person is a patient or a non-patient, to verify the confirmed association of the patient by comparing updated data points from the video feed with the confirmed association, and to activate an alert when the confirmed association of the patient is unverified based upon a comparison with the updated data points.

This invention provides a system and method for verifying system interfaces based on artificial intelligence, relating to the field of interface verification technology. The method includes: acquiring historical concurrent user counts, historical system performance data, and historical load impact data; acquiring system interface architecture data and historical system interface architecture data; acquiring real-time load impact data; determining a concurrent user count relationship function based on historical concurrent user counts and historical load impact data; determining the predicted concurrent user count for a prediction period based on the real-time load impact data and the concurrent user count relationship function; obtaining a trained system performance prediction model; processing the predicted concurrent user counts and system interface architecture data according to the trained system performance prediction model to obtain predicted system performance coefficients; and generating a system interface verification report based on the predicted system performance coefficients. According to this invention, the accuracy of system interface verification can be improved.

A system and method for measuring, forecasting, and modulating aqueous outflow is described here. The system and method operate on an ocular microphysiological system that reproduces the trabecular-meshwork-membrane-Schlemm's canal interface under a defined hydrodynamic program. The signals may include TEER resistance, pressure-flow measurements, OCT / OCTA images, and Raman spectra. The Ocular MPS system may include encoding the multimodal signals into a device-agnostic feature representation that includes descriptors of junction continuity, belt thickness, tortuosity, pathway activity, and outflow-resistance proxies. The Ocular MPS system may execute a physics-informed graph state-space model that fuses the device-agnostic feature representation into inferred parameters. The inferred parameters may be inferred from barrier integrity, permeability, and outflow facility, subject to monotonic constraints between structural and hydraulic variables. The Ocular MPS system may quantify uncertainty of inferred parameters, generating calibrated forecasts of aqueous outflow performance. The Ocular MPS system may control a drive actuator according to the calibrated forecasts.

The invention provides an electric power spot market price prediction system and method based on artificial intelligence. The system comprises a market boundary prediction analysis module, a spot market price prediction module, an agent electricity purchase decision optimization module and a prediction result redisk analysis module. According to the system, a deep learning model fused with a CNN-MLP-Attention algorithm is adopted, kernel density estimation is combined to carry out electricity price interval prediction, meteorological data weighting processing, feature engineering, rolling training and multi-day prediction functions are integrated, and 1-7-day high-precision determinacy and uncertainty prediction of the electricity price of the electric power spot market is achieved. The method solves the problems of low electricity price prediction precision, large uncertainty, lack of scientific support of electricity purchasing strategies and the like in the existing electric power spot market, can effectively improve the decision scientificity of power grid enterprises in the spot market, reduces the transaction risk, and improves the efficiency of the power grid enterprises. And full-process data support and strategy simulation capability are provided for a power grid enterprise to participate in an agent power purchase transaction mode.

An intelligent dosing platform incorporating comprehensive predictive analytics and forecasting capabilities for injectable medication administration. The platform includes predictive analytics modules configured to generate forecasts for financial costs, medication demand patterns, and adverse event probabilities using machine learning algorithms and statistical modeling techniques. The system features financial modeling modules that analyze cost patterns associated with medication procurement, administration infrastructure, and insurance reimbursements, while demand forecasting modules predict medication needs based on seasonal trends, patient population changes, and treatment protocol modifications. Adverse event prediction modules identify risk factors using patient risk profiles and clinical outcome histories. The platform incorporates machine learning capabilities that continuously improve prediction accuracy using patient outcome data and administration patterns. Comprehensive reporting modules generate customized predictive reports for healthcare administrators, pharmaceutical suppliers, and insurance providers, while optimization modules recommend actions for cost reduction and patient safety improvement based on predictive insights.

The application relates to the technical field of carbon emission metering, and discloses a power system carbon metering method and system. Based on an abnormal noise model, system state prediction is performed according to real-time measurement data of a target power grid, and a predicted system state is obtained. Node carbon emission factors of each power grid node and uncertain sensitivity coefficients thereof are acquired, carbon emission metering is performed according to the predicted system state and the node carbon emission factors, and carbon metering results of each power grid node are obtained. The uncertain sensitivity coefficients are obtained according to the sensitivity of the node carbon emission factors to input uncertainties. Uncertainties of the input uncertainties are synthesized according to the uncertain sensitivity coefficients, and carbon emission factor uncertainties are obtained. The beneficial effects are that the sensitivity of the node carbon emission factors to input uncertainties is deduced according to an analytical expression of the node carbon emission factors, the carbon emission factor uncertainties are quantitatively synthesized, the accurate positioning of key error sources is realized, and the carbon metering accuracy and traceability of the power system are improved.

The invention belongs to the technical field of power dispatching and operation, and particularly provides a negative electricity price scene-oriented source-grid-load-storage collaborative interaction and marginal cost optimization system and method, and the method comprises the steps: monitoring the state of a power market through a data collection module, predicting the supply and demand conditions of the system, and judging that there is a negative electricity price risk; calculating real-time marginal cost of the schedulable resources, wherein the real-time marginal cost comprises power generation resource marginal cost, energy storage resource marginal cost and load regulation marginal cost; establishing an optimization model by taking the minimization of the total marginal cost of the system as a target, and solving to obtain an optimal scheduling scheme of each resource; converting the optimal scheduling scheme into a specific control instruction, issuing the specific control instruction to each resource main body of a source, a network, a load and a storage, and executing power generation adjustment, charging and discharging or load interruption operation; and monitoring the execution effect of each resource instruction and the actual clearing electricity price of the market in real time, calculating the execution deviation, and carrying out rolling correction in the next optimization period. According to the invention, early warning of the negative electricity price risk can be realized, and global optimization scheduling is carried out on the source network load storage resources.

The invention relates to a parking lot pricing prediction system and method based on artificial intelligence and big data, and belongs to the technical field of big data analysis. The system comprises a three-layer state sensing and coding module, wherein a graph loop fusion network, an attention scoring model and a user clustering model are arranged in the three-layer state sensing and coding module; the graph loop fusion network is used for encoding the network game layer data into graph embedding vectors; the attention scoring model is used for carrying out dynamic weight distribution and fusion according to the importance of the influence of the current moment on the parking demand; the user clustering model is used for clustering individual heterogeneous layer data into a plurality of typical user groups, and extracting group features to obtain user heterogeneous state vectors. According to the method, a'three-layer perception-collaborative game 'framework is constructed, a regional Pareto improved pricing strategy is generated by utilizing a multi-agent dynamic game, meanwhile, the method has real-time response and decision interpretation capabilities, and social benefits and collaborative commercial and public benefits are embedded in a reward function of the method.

This invention belongs to the field of intelligent diagnosis technology for mental illnesses, specifically relating to a system for predicting the social function of patients with depression. The system includes: an input module for inputting patient indicators; a feature extraction module for extracting gray matter features (IC_SA_GMV) of attention-related brain networks from the input indicators; a prediction module integrating a social function prediction model for patients with depression, which calculates the predicted social function of the patient by analyzing the indicators; and an output module for outputting the prediction results. The system provided by this invention can accurately predict the social function of patients with depression and has promising application prospects.

The present application relates to a kind of salt cavern gas storage group-oriented gas production capacity dynamic prediction method, the salt cavern gas storage group-oriented gas production capacity dynamic prediction method includes basic data collection and real-time update, single cavity dynamic gas production capacity prediction, system and multiple constraint integration and coupling analysis, based on multiple constraint's gas production capacity prediction and result visualization and instruction issue.Compared with prior art, the salt cavern gas storage group-oriented gas production capacity dynamic prediction method of the present application has the advantages of integrating geological, engineering, equipment and market data, constructing multiple constraint coupling model, realizing dynamic and fine prediction of salt cavern gas storage group gas production capacity.

The invention provides a method and device for creating an intelligent decision data set based on log data, and relates to the technical field of cloud computing, cluster management, big data processing and artificial intelligence, and the method comprises the steps: collecting original log data from a server and a cluster, and achieving the collection and preprocessing of the log data; key information is extracted from the preprocessed log data, and log event analysis and structured processing are realized based on a self-attention mechanism; high-quality features are extracted and constructed from structured log data, and feature engineering and data enhancement are achieved; and combining the enhanced feature set with a predefined decision target to generate an intelligent decision data set for model training and evaluation, thereby realizing construction and evaluation of the intelligent decision data set. According to the method, high-quality data support is provided for the intelligent decision model, the system resource demand, the task execution time and the failure risk can be effectively predicted, the resource allocation efficiency is greatly optimized, and a solid foundation is provided for large-scale computing system resource management and scheduling.

This invention discloses a port communication system and method for a power router, relating to the field of power communication technology. The system includes a data acquisition module, an intelligent communication decision engine, a communication execution module, and a storage module. This invention utilizes an intelligent communication decision engine that integrates artificial intelligence technology to perform deep analysis and learning of data, predicting system operation trends and communication needs. This solves the problem that traditional fixed-rule decision-making cannot adapt to dynamic system changes, achieving accurate prediction of communication needs. When an anomaly is detected, the intelligent communication decision engine automatically generates the optimal communication decision scheme, which is then executed by the communication execution module. This addresses the problems of delayed response and unreasonable allocation of communication resources in existing technologies when dealing with faults or anomalies, enabling rapid handling of abnormal situations. Ultimately, this improves the intelligence and adaptability of the power router port communication system, ensures timely transmission and processing of critical data, and improves the operating efficiency of the power system.

The disclosed system provides advanced mission control predictive systems for low earth orbit semi-autonomous satellites. In operation, the system may include a satellite transit behavior prediction module configured to train a neural network based on current and past satellite orbital transit paths so as to predict future transit paths. Knowing precise future satellite transit paths enables a ground-based satellite control system to more efficiently (and with greater accuracy) control certain operations of the satellites. For example, a ground-based satellite control system can prepare for communications to commence at a particular time. Legacy systems can only predict one or two days in advance However, using a neural network that not only takes into consideration historical transit data, but also learned details such as solar wind, cloud patterns, etc., the neural network predicts future satellite transit paths with statistically-certain accuracy leading to a much narrower cone of uncertainty.

This shipment amount prediction system includes a prediction unit and an output unit. The prediction unit predicts the future shipment amount of a product on the basis of one or more shipment amount prediction models generated from time-series data which corresponds to the sales period of the product and which includes an explanatory variable related to the distribution of the product and a target variable which is the shipment amount of the product. The output unit outputs the predicted future shipment amount.