7results about How to "Predict in advance" patented technology

The invention relates to the technical field of digital operation and maintenance systems, in particular to a digital operation and maintenance management and control system and method for electromechanical facilities, and the system comprises a vibration data collection module, a data processing module, a vibration characteristic analysis module, a data processing module, a data processing module and a data processing module, receiving the vibration state parameters sent by the data processing module; based on the vibration state parameters, executing a vibration data topological characteristic analysis and prediction system algorithm; generating a prediction result; the operation and maintenance decision module is in communication connection with the vibration feature analysis module and receives the prediction result sent by the vibration feature analysis module; generating operation and maintenance decision information based on the prediction result; the execution control module is in communication connection with the operation and maintenance decision-making module and receives operation and maintenance decision-making information sent by the operation and maintenance decision-making module; the corresponding operation and maintenance control operation is executed based on the operation and maintenance decision information, so that tiny anomalies possibly neglected by a traditional method can be captured, and the accuracy and advance time of fault prediction are improved.

The invention discloses a drainage basin ecological hydrological process simulation and prediction system based on a mechanism hydrological model and AI coupling, and relates to the technical field of drainage basin ecological hydrological simulation and prediction. Multi-source data integration and preprocessing are carried out through a data input layer, and a standardized data set is output and provided for a model coupling layer; the model coupling layer directly receives the standardized data set of the data input layer and outputs an optimized simulation result and a predicted value to the simulation and prediction layer; the simulation prediction layer receives a simulation result and a prediction value of the model coupling layer, performs multi-dimensional ecological hydrological element collaborative simulation and short-term-medium and long-term scene adaptive prediction, and outputs a simulation prediction result to the result output layer; and the result output layer receives the simulation prediction result of the simulation prediction layer and provides visual interaction and decision suggestion generation functions. The invention provides an innovative scheme of deep fusion of a mechanism model and an AI technology, and comprehensive requirements of physical rationality, data fitting precision, multi-element coverage and efficient response are covered.

This invention provides a method, device, equipment, and medium for ensuring the motion safety of a full-motion flight simulator. The method includes: acquiring human position information within the monitoring area of ​​the full-motion simulator; determining a target risk state based on the positional relationship between the human position information and a first or second risk area; controlling the full-motion flight simulator based on a first control command when the target risk state is a first risk state; and controlling the simulator based on a second control command when the target risk state is a second risk state. This invention automatically determines the target risk state based on the positional relationship between human position information and preset first and second risk areas. For different risk states, it performs differentiated automatic control on the full-motion flight simulator based on first or second control commands with different intervention levels, improving the ability to predict and intervene in dangerous situations, realizing a shift from passive response to proactive prevention, and ensuring the safety and response efficiency of the full-motion simulator operation.

The present invention belongs to the technical field of straw fly identification, and specifically relates to a LAMP primer set, a kit and an application thereof for detecting straw flies. The LAMP primer set includes an outer primer F3 shown in SEQ ID NO.1, an outer primer B3 shown in SEQ ID NO.2, an inner primer FIP shown in SEQ ID NO.3 and an inner primer BIP shown in SEQ ID NO.4. The present invention also provides a kit for detecting straw flies, an application of the LAMP primer set and the kit in identifying and / or detecting straw flies, and a method for detecting straw flies using the LAMP primer set. The method has the advantages of strong specificity, high sensitivity, simple operation process, low detection cost, etc., solves the problem of distinguishing straw flies, and lays a foundation for quarantine inspection, epidemic monitoring and prevention and control of straw flies in the future.

The invention discloses a financial risk control prediction model construction method, and relates to the technical field of financial risk control prediction, a data collection module is utilized to collect and store features related to an evaluation period of a current customer, and a customer feature evaluation module is utilized to construct a prediction model. Sequentially calculating and outputting a risk score F, a risk early warning index FY and a dynamic risk adjustment coefficient DT, and evaluating and updating an interest rate adjustment coefficient V of the current customer in the next evaluation period by using a customer feature evaluation module to obtain a risk evaluation result; according to the invention, multi-dimensional risk factors are comprehensively considered, root operation is introduced to enhance a non-linear relationship, an interest rate adjustment coefficient V is flexibly adjusted, historical and current risk information is combined, overdue severity is quantified, comprehensive influence of a credit score P and repayment capability HN is considered, a latest credit score P is introduced to carry out dynamic adjustment, and a cyclic influence mechanism is formed. The innovations aim at improving the accuracy and flexibility of risk prediction and providing more effective risk control support for financial institutions.

This utility model discloses a highway slope displacement monitoring and early warning device, relating to the field of slope monitoring technology. It includes: a base, anchored to a stable slope on one side of a crack via a first fixing column; a fixing block, anchored to the other side of the crack via a second fixing column; an arc-shaped sensing rod, with its two ends connected to the base and the fixing block respectively, and its middle section bent away from the slope; the arc-shaped sensing rod is hollow inside, and contains several elastic deformation force sensors arranged sequentially along its length; and it also has a visual warning unit. Using this solution, the device can receive and transmit data on changes in local stress based on the displacement of the arc-shaped sensing rod's two ends. After data analysis and processing, the device is linked to the visual warning unit, quickly alerting pedestrians or vehicles while also facilitating the location of monitoring points for professionals.

The present invention discloses a system for predicting a health condition based on deep learning by using a plurality of electrocardiograms according to an embodiment of the present invention includes: an electrocardiogram measurement unit (110) configured to acquire N pieces of electrocardiogram data by measuring electrocardiograms of the same examinee at different times; a prediction unit (120) configured to generate disease information predicting the presence / absence, progress and degree of a disease by integrating semantic feature values or output values from the N pieces of electrocardiogram data provided from the electrocardiogram measurement unit (110) through a diagnostic algorithm previously constructed by being trained on datasets of N pieces of electrocardiogram data at different times and diseases corresponding to the electrocardiogram data; and an input unit (130) configured to convert the N pieces of electrocardiogram data input from the electrocardiogram measurement unit (110) and input them to the prediction unit (120).