54 results about "Continuous feedback" patented technology

The invention discloses a bridge management and maintenance decision-making system and method based on multi-agent collaborative optimization, and the system comprises a monitoring agent which is deployed in a cloud server and is used for obtaining abnormal data in a bridge structure and environment data; the diagnosis agent is used for evaluating the health state of the bridge by utilizing a built-in knowledge base, a built-in machine learning model and a built-in physical model based on the abnormal data; the decision-making agent is used for generating a plurality of candidate maintenance schemes based on an evaluation result, and screening out an optimal scheme by integrating the comprehensive utility of each scheme and the resource matching degree score of the resource scheduling agent on each scheme; the resource scheduling agent generates a construction plan according to the optimal scheme; the coordination / communication agent is used for ensuring efficient cooperation among the agents through a communication protocol and a negotiation mechanism among the agents; the diagnosis report, the optimal scheme and the construction plan are integrated and presented to a bridge manager through a human-computer interaction interface; and collaborative optimization of bridge management and maintenance decisions is realized through continuous feedback and self-learning.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The invention relates to the technical field of electric leakage detection, in particular to an underground coal mine low-voltage remote electric leakage test system and method, and the system comprises a task allocation module, an off-state discrimination module, a signal transmission module, a state recognition module and a path receipt module. According to the method, the loop load level and the channel response condition are accurately evaluated, the task channel is dynamically matched, the task distribution blind area and error are reduced, the impedance difference structure and the continuous feedback trend are integrated to recognize the abnormal branch segment, and the driving and decoding mode is intelligently adjusted by using the signal amplitude and the interference proportion relation; signal transmission stability and data reduction accuracy are improved, a waveform drift trend and a load state combination are calculated in real time to accurately detect an electric leakage position, task initiation and response nodes are compared to ensure path synchronization legality, efficient linkage between load state change and signal drift is realized, electric leakage detection response speed and precision are improved, and reliability of electric leakage detection is improved. And the line abnormal state discrimination accuracy is enhanced.

According to the method for regulating and controlling the stability amplitude through the ultrasonic power source, the output of the ultrasonic power source is dynamically adjusted through intelligent control and a feedback mechanism, the amplitude of the transducer is stable under different loads, and the machining precision and efficiency are improved. An equivalent model is constructed through a power supply output characteristic equation and a dynamic response relationship, voltage, current and mechanical information are monitored in real time by combining a high-precision sensor, expected power output is predicted by applying a neural network after deep preprocessing, a power error is calculated by using a PID algorithm, and power supply output is dynamically adjusted. According to the method, through continuous feedback and correction, it is ensured that the amplitude of the system is reasonable and stable under the complex working condition, the stability and applicability of the ultrasonic transducer system are remarkably enhanced, and the method is particularly suitable for ultrasonic machining equipment in the complex load environment.

The invention discloses a cooking robot control system based on voice processing, and relates to the technical field of intelligent robotics.The cooking robot control system comprises a steady-state locking subsystem, a dynamic detection subsystem, a change analysis subsystem and a feedback subsystem, firstly, a cooking robot sends a rotating speed control instruction to a motor corresponding to a stirring paddle by recognizing a voice command of a user, and the rotating speed control instruction is sent to the motor corresponding to the stirring paddle; the method comprises the following steps: collecting pot wall temperature data in a pot cavity, monitoring operation data of a motor of a stirring paddle in the pot cavity to determine a steady-state control mode, identifying the time for performing centrifugal inrush flow on food in the pot cavity based on the steady-state control mode and the push resistance rate monitored in real time to form centrifugal inrush flow and sauce redistribution operation, and then acquiring the pot wall temperature data in real time to obtain a sauce redistribution result. The temperature difference change before and after the short-time pulse acceleration is executed is analyzed to obtain an execution change temperature difference, an adjustment mechanism is triggered based on the execution change temperature difference, and finally, after the adjustment mechanism is executed, the temperature difference change is continuously fed back until the cooking operation is completed.

The invention discloses a Go question type recommendation system and method based on user ability evaluation, and relates to the technical field of smart learning. The system is composed of a data acquisition module, a data processing module, a learning recommendation module and a feedback evaluation module. The data acquisition module covers quantification of five-force dimensions, actual combat data analysis and training behavior records of users; and the data processing module generates an accurate three-dimensional feature vector through knowledge graph filtering, recent development area principle sorting and feature extraction. The learning recommendation module intelligently matches the most suitable question type library content according to the feature vector of the user, and ensures that the recommended question can not only fit the current capability level of the user, but also meet the specific learning target of the user; and the feedback evaluation module dynamically updates the capability value of the user by using an IRT model, optimizes recommendation algorithm parameters in combination with user feedback and strengthens error exercise to form a closed-loop feedback mechanism. And according to accurate question type recommendation, the learning effect is further enhanced through continuous feedback and optimization.

Systems and methods translate and govern directives across heterogeneous platforms. In some embodiments, directives are normalized into a common intermediate representation (CIR) for validation, conflict handling, and deployment. In other embodiments, a direct semantic mapping engine converts source directives directly into target-native, machine-executable directives without a CIR and without normalization, consulting a platform capability database and synthesizing target-compatible scripts, profiles, workflows, or rules where native primitives are absent. Ephemeral triggers (e.g., time window, incident, risk, posture, geofence) cause generation and atomic activation of temporary directives that supersede baseline directives for a defined scope and interval, with withdrawal on expiry to restore the baseline. Adapters actuate across families including cloud control planes, operating systems (Windows / macOS / Linux), IoT / OT, Kubernetes, identity, email / DLP, browser, and SaaS / social platforms. Both approaches share validation, simulation, and governance / deployment engines supporting approvals, audit logging, staged rollout, rollback, and continuous feedback.

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and / or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and / or a generalized linear model. Machine learning models also incorporate ongoing feedback from the users who can either accept or reject suggested matches and hence the models undergo an evolution process and constantly update from user patterns.

The invention provides an EEG (electroencephalogram)-based self-sensitivity quantitative evaluation method, and belongs to the field of physiological signal processing and mode recognition. The method comprises the following steps: collecting resting state and task state electroencephalogram signals of a subject, and obtaining a self-efficacy feeling score of the subject in combination with a scale; the electroencephalogram signals are preprocessed, window segmentation is carried out on the preprocessed signals, and multi-dimensional features such as a time domain, a frequency domain, a power spectrum entropy and a micro state are extracted; and inputting the features and the performance feeling score into a deep learning model based on LSTM-Attention for training, and constructing an evaluation model. The model can output a quantitative score according to the electroencephalogram signal, and the evaluation result continuously iterates and optimizes the model through continuous feedback data, so that the evaluation precision is improved. According to the method, objective and accurate quantification of self-effectiveness is achieved by combining the electroencephalogram signal features and the deep learning technology, the internal association between the self-effectiveness and electroencephalogram activity is revealed, and high practicability is achieved.

The invention relates to the technical field of computers, and discloses an intelligent monitoring method and system for fumigation liquid production, and the method comprises the steps: collecting energy consumption data in a fumigation liquid production process in real time through a sensor, and uploading the energy consumption data to a block chain; verifying the legality of the uploaded data, and when the data is verified to be legal, triggering an intelligent contract to execute an optimization decision, and automatically adjusting the operation parameters of the fumigation liquid; and an adjustment result is fed back through the block chain, and the intelligent contract is iterated according to real-time data, so that continuous improvement of the system is ensured. Through combination of the Lagrange programming neural network and the simulated annealing algorithm, multi-stage constraints can be effectively processed, it is ensured that key parameters in the production process are accurately controlled, and the production efficiency is improved. Through a continuous feedback mechanism and optimization iteration, it is ensured that energy consumption in the fumigation liquid production process is always in an optimal state, and dual optimization of energy saving and production benefits is achieved.

The application discloses a moving direction correction method of a bionic robot, and relates to the technical field of bionic robots.The method collects positioning parameters and attitude parameters of the bionic robot and sends the parameters to a control module.An error compensation module performs error self-checking, calculates an error factor, modifies the control law of the bionic robot, and sends the control law to the control module.The control module controls the movement of the bionic robot according to the error-compensated control law.A feedback module judges whether the bionic robot deviates from a target position according to the positioning parameters and the attitude parameters, sends feedback signals to the control module, adjusts the control law according to the feedback signals, and continues to control the movement of the bionic robot, and continuously feeds back and adjusts until the bionic robot reaches the target position and stops the movement control.The application can reduce system errors, improve stability and precision, has real-time feedback control and yaw detection capabilities, and improves the moving accuracy of the bionic robot.

The invention relates to the technical field of ship turbine cooling, in particular to a ship turbine water circulation system which is provided with a variable pipeline assembly and a pressure detection valve assembly, and when the pressure detection valve assembly detects that the circulating water pressure does not reach the standard circulating water pressure, including lower or higher than the standard circulating water pressure, the variable pipeline assembly is started; and the pressure detection valve assembly feeds back a signal to the variable pipeline assembly. The variable pipeline assembly controls the flowing diameter of circulating water, and therefore the purpose of regulating and controlling the circulating water pressure is achieved. The circulating water pressure can be gradually adjusted to the standard circulating water pressure along with continuous feedback and regulation between the pressure detection valve assembly and the variable pipeline assembly, and therefore the reliability and stability of the ship turbine running under any working condition can be guaranteed. In addition, impact on pipelines and equipment of the water circulation system due to overlarge pressure can be avoided, and the service life of the pipelines and the equipment is prolonged. The pressure detection valve assembly can achieve the purpose of rapidly stabilizing circulating water pressure by adjusting the opening degree of a valve of the pressure detection valve assembly.

The invention relates to the technical field of remote control, in particular to an automatic contraction hanging basket remote control system which comprises a main control unit recognition module, an automatic control module, a remote control terminal module, a data communication optimization module and an action instruction distribution module. In the invention, by combining a hanging basket contraction program with action triggering of a mechanical execution mechanism, stress monitoring of a hanging basket connecting part and action feedback, deep fusion of automatic control and real-time monitoring is realized, dynamic control and an execution path of the hanging basket are optimized, accurate action execution and efficient resource allocation are ensured, and the safety of the hanging basket is improved. The system can perform real-time control according to different working environments, errors and potential safety hazards caused by manual operation are reduced, and through continuous feedback and data updating, the system can automatically adjust a control strategy, enhance adaptability and self-repairing capability, ensure efficient and safe operation of hanging basket operation under different conditions, improve the intelligent and automatic level, and improve the working efficiency. And the overall operation efficiency and safety are obviously improved.

The invention discloses a dynamic personal archive optimization method based on continuous feedback, relates to the technical field of personal archive optimization, is used for solving the problems that strategy evolution based on feedback cannot be supported and real-time adjustment means are lacked, and provides a dynamic personal archive optimization method based on continuous feedback by uploading a plurality of candidate archive variants by a user and completing format, size and repeatability verification by a system. The dynamic experiment and optimization module establishes a dobby machine model based on the set, dynamically selects and displays variants by adopting an epsilon-greedy algorithm, collects feedback, calculates and displays a reward value, updates a weight in combination with a smoothing mechanism, avoids local optimum caused by early fluctuation, and improves the dynamic experiment and optimization efficiency. And the personalized archive consultant module constructs a random forest regression model based on long-term data and a tiger machine result, analyzes feature contributions in combination with a Shapley value, generates optimization suggestions, and improves archive attraction and user experience.

The invention provides a battery feedback adjusting method and device, electronic equipment and a readable storage medium. The battery feedback adjusting method comprises the following steps: monitoring the highest monomer voltage of a vehicle battery when the vehicle battery permits continuous feedback current; judging whether a pre-current-reducing strategy is triggered or not when the highest single voltage reaches a voltage difference of a pre-overvoltage voltage threshold value; the pre-overvoltage voltage threshold value is lower than an overvoltage voltage threshold value; if not, returning to continuously execute the monitoring of the maximum monomer voltage of the vehicle battery when the continuous feedback current allowed by the vehicle battery is allowed; and if yes, executing the pre-flow-reducing strategy. Therefore, the kinetic energy is effectively recovered by triggering the current reduction strategy in advance, slowing down the voltage rising speed and reducing the triggering frequency of the current reduction strategy in advance.

The present disclosure describes a multi-vehicle control system that enables remote operation of one or more vehicles. The system may process different inputs to generate instructions for diverse operational settings. The vehicles, equipped with sensors, may provide continuous feedback, including haptic, to enhance real-time adaptability and efficiency. This multi-vehicle control system may offer a practical solution for remote control of multiple vehicles, optimized via constant feedback.

The present invention pertains to the technical field of managing queues for theme parks, amusement parks, museums, exhibitions and the like. The invention relates to a dynamic pricing system for express queues that employs proportional control to adjust the price of passes based on the current, predicted and desired waiting time. Using an automated observer that simulates and predicts waiting times on the basis of real-time visitor flow, it dynamically adjusts the prices of express queue passes in order to keep express queue waiting times close to a target value predetermined by the operator. It differs from existing techniques that use fixed prices or daily adjustments without continuous feedback, because it applies a proportional gain, derived from empirical data, to calculate real-time price adjustments that directly respond to changes in demand conditions in the express queue.

The application discloses a kind of vertical field large model training method and system based on building regulations, it is related to large model training technical field, constructs dynamic regulation knowledge base including clause semantic atlas, constraint relationship matrix, and generates structured knowledge atlas based on clause semantic atlas and constraint relationship matrix;While constructing multiple type databases to store building data, setting up matching unit and incremental knowledge pool to connect multiple type databases;Training data is provided through structured knowledge atlas and incremental knowledge pool, large model is trained based on training data, and building vertical field large model is generated.The application significantly improves the intelligent capacity of model by constructing structured knowledge atlas and incremental knowledge pool, and using continuous feedback mechanism, effectively cope with the complexity of building regulations, realize the high compliance and practicality of building vertical field large model.

The invention provides a standardized process digital twinning system and method for aviation equipment quality optimization, and relates to the technical field of digital twinning and information physical system auxiliary aviation equipment, and the system comprises an extraction module L1, a support module L2, an application verification module L3, an analysis module L4 and an optimization application module L5. By constructing the standardized process digital twin system for aviation equipment quality optimization, data-driven closed-loop optimization is realized, and a data closed loop from standard application to standard revision is constructed, so that the standard is based on aviation equipment development data, and the scientificity and timeliness of the aviation equipment standard are improved; through feedforward simulation verification and continuous feedback optimization based on operation data, standard defects can be found and repaired, and a solid foundation is provided for the whole life cycle of aviation equipment; by means of practical application and verification of the aviation equipment welding standard and feedback and optimization of the application process of the aviation equipment welding standard, the aviation equipment welding standard defects are successfully repaired.

The application relates to the technical field of computers and discloses an intelligent monitoring method and system for fumigation liquid production, which comprises the following steps: collecting energy consumption data in a fumigation liquid production process in real time through a sensor and uploading the data to a block chain; verifying the legality of the uploaded data; when the data is verified as legal, triggering an intelligent contract to execute an optimization decision and automatically adjusting operation parameters of the fumigation liquid; feeding back adjustment results through the block chain and iteratively updating the intelligent contract according to real-time data to ensure continuous improvement of the system. Through the combination of Lagrange programming neural networks and simulated annealing algorithms, multistage constraints can be effectively processed, key parameters in the production process can be accurately controlled, and production efficiency can be improved. Through a continuous feedback mechanism and optimization iteration, the energy consumption in the fumigation liquid production process can always be in an optimal state, and dual optimization of energy saving and production benefit is realized.

A multi-target signal self-tracking method based on feedback judges and decides a tracking mode of a target signal in real time through continuous feedback excitation in a scanning process, controls an antenna to switch back and forth between two pointing modes of airspace scanning and fixed-point tracking, obviously improves tracking efficiency and tracking sensitivity of the multi-target signal, and improves tracking accuracy of the multi-target signal. Tracking is carried out if signals exist, scanning is carried out if no signals exist, and the signal tracking requirement in a multi-target scene can be better met.

A website building system (WBS) hook code optimization system includes a website session repository, an event receiver, a hook code bundler, an optimization determiner, a code creator, and a hook code executor. The website session repository stores historical performance data from a plurality of hook code executions. The event receiver receives an event associated with hook code, and the hook code bundler creates a hook code bundle based on that event. The optimization determiner analyzes the historical data to generate optimization instructions for the bundle. Following these instructions, the code creator generates execution code for an optimized hook code bundle. The hook code executor runs the optimized bundle, generating current performance results for the event. Finally, the executor logs these results back into the repository, updating the historical data to enable subsequent optimizations in a continuous feedback loop.

The present application belongs to the technical field of waste plastic precipitation, and discloses a cleaning separation and precipitation tank for a waste plastic recycling system and a precipitation method; the method comprises the following steps: S3: based on feature data sets and real-time basic data sets, the precipitation effect in the next time period is predicted to obtain a predicted precipitation effect; S4: the predicted precipitation effect is compared, and a precipitation adjustment measure report is calculated based on the real-time basic data sets; S5: based on the precipitation adjustment measure report, a real-time control adjustment device is controlled, and the separation of waste plastics is adjusted in real time based on plastic density data; S6: actual turbidity data is obtained, error calculation is performed on the actual turbidity data and predicted turbidity data, and the prediction accuracy is adjusted in real time based on the turbidity error value; in general, the present application has the significant advantages of strong precipitation effect and energy consumption optimization balance ability, large operation cost saving effect, and good continuous feedback updating system effect.

Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and / or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and / or a generalized linear model. Machine learning models also incorporate ongoing feedback from the users who can either accept or reject suggested matches and hence the models undergo an evolution process and constantly update from user patterns.

The application discloses a blade regulation system based on damage monitoring and an adaptive surface strengthening process, and belongs to the technical field of steam turbine blade life management. The system is based on the settings of a damage monitoring unit, a data processing and decision unit, an adaptive strengthening execution unit, a system control and interaction unit and a feedback optimization unit. Through multi-dimensional damage monitoring, intelligent evaluation driven by machine learning, dynamic parameter generation, closed-loop regulation and system iterative optimization, the system realizes intelligent regulation and control of the whole process from damage perception, strategy generation, accurate execution to effect verification. The system can not only capture the damage state of the blade in all directions and dynamically generate optimal strengthening parameters, but also can improve the system model and knowledge base through continuous feedback iteration, effectively improve the efficiency and reliability of blade strengthening repair, effectively prolong the service life of the blade, and reduce the maintenance cost and downtime risk of industrial equipment.

A website building system (WBS) hook code optimization system includes a website session repository, an event receiver, a hook code bundler, an optimization determiner, a code creator, and a hook code executor. The website session repository stores historical performance data from a plurality of hook code executions. The event receiver receives an event associated with hook code, and the hook code bundler creates a hook code bundle based on that event. The optimization determiner analyzes the historical data to generate optimization instructions for the bundle. Following these instructions, the code creator generates execution code for an optimized hook code bundle. The hook code executor runs the optimized bundle, generating current performance results for the event. Finally, the executor logs these results back into the repository, updating the historical data to enable subsequent optimizations in a continuous feedback loop.

The invention provides an agricultural product supply and marketing management system based on big data analysis, and the system comprises a digital label module which marks the basic information, extended information and unique identification of each single agricultural product; the dynamic adjustment module is used for obtaining an optimal distribution scheme by using multi-objective optimization and graph network path planning based on the result of the digital label module and the logistics information; the dynamic adjustment module updates the logistics information in real time in the transportation process of the agricultural products, analyzes the potential risk of the logistics information through an abnormity early warning model, and adjusts the optimal distribution scheme when the potential risk exceeds a threshold value; and the traceability management module is used for generating an A / B test scheme according to the optimal distribution scheme, implementing better optimization suggestions in the test scheme into the distribution scheme which is being executed, and completing continuous feedback and iteration of the distribution scheme. The method has the beneficial effects that distribution paths and inventory configuration are more reasonable, and the logistics cost and loss are remarkably reduced; the A / B test closed loop promotes continuous upgrading of the operation level.

The application discloses a method for waste heat recovery based on machine learning optimization. It belongs to the field of industrial energy saving and automatic control technology. By constructing a standardized state vector, multi-source operating parameters and physical knowledge are fused. A high-fidelity digital twin environment model that conforms to physical laws is established using a graph attention-physical information fusion network. In this environment, a robust strategy network is trained with curriculum learning and a dynamic multi-objective reward mechanism. Finally, the deployed strategy network can achieve real-time rolling optimization control. Through continuous feedback of online interaction data, the digital twin environment model and the strategy network are jointly fine-tuned. This scheme significantly improves the overall operating energy efficiency and steam quality guarantee capability of the waste heat recovery process under varying operating conditions, while ensuring the stability of the control instructions and the safety of the main steam turbine operation, achieving closed-loop intelligent optimization and lifelong adaptive evolution from perception, decision-making to execution.

The invention relates to the field of constructional engineering, and discloses a sound barrier gradual change type photovoltaic facade intelligent optimization layout method based on photo-thermal coupling, and the method comprises the following steps: S1, collecting illumination intensity, temperature, humidity and wind speed data; s2, calculating the illumination intensity of the current photovoltaic panel based on a dynamic optimization algorithm of a photo-thermal coupling model, and adjusting the inclination angle and the spacing of the photovoltaic panel according to the illumination intensity; s3, establishing a coupling model between the sound insulation performance of the sound barrier and the photovoltaic layout, evaluating the influence of the photovoltaic layout on the sound insulation effect of the sound barrier through acoustic analysis, and synchronously optimizing the layout design; and S4, adjusting the layout of the photovoltaic panel in real time and performing continuous feedback optimization. According to the invention, by introducing the photo-thermal coupling dynamic optimization algorithm, the inclination angle, the spacing and the arrangement mode of the photovoltaic panels can be adjusted in real time, so that the photovoltaic panels can dynamically adapt according to factors such as environment illumination intensity and temperature, thereby ensuring that the photovoltaic panels are always in the optimal illumination condition.