41 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.

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 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 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 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 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.

This invention discloses a thermal resistance testing system and method for a thermistor, relating to the field of double-sided thermal resistance testing technology. The system includes an upper cavity and a lower cavity. The upper cavity includes an upper outer plate assembly and an upper inner plate assembly. An upper drive motor is mounted on the upper outer plate assembly, and an upper water-cooling assembly is installed inside the upper inner plate assembly. An upper cooling element and an upper heat-conducting component are sequentially mounted at the lower end of the upper water-cooling assembly. Both the upper outer plate assembly and the upper heat-conducting component are mounted on an upper pressure plate. The lower cavity includes a lower outer plate assembly and a lower inner plate assembly. A lower drive motor is mounted on the lower outer plate assembly, and a lower water-cooling assembly is installed inside the lower inner plate assembly. A lower cooling element and a lower heat-conducting component are sequentially mounted at the upper end of the lower water-cooling assembly. Both the lower outer plate assembly and the lower heat-conducting component are mounted on a download plate. The closed-loop system of this invention is a double-layer closed-loop system, enabling automatic calibration before testing, and continuous feedback results for more accurate testing.

The invention discloses a power grid intelligent supply chain operation efficiency evaluation and intelligent decision-making system fusing multi-source data, and relates to the technical field of power grid supply chain management, in particular to the power grid intelligent supply chain operation efficiency evaluation and intelligent decision-making system fusing the multi-source data. Comprising a heterogeneous data access gateway, a data quality governance engine, a dynamic semantic association module, a supply chain operation efficiency multi-dimensional evaluation model, an intelligent decision generation module and a closed-loop feedback and self-learning optimization module. The system uniformly accesses and manages multi-source heterogeneous data, constructs a dynamic association data network, carries out efficiency evaluation and deviation diagnosis based on a multi-dimensional index system and a dynamic datum line, generates decision suggestions in combination with a rule engine and multi-objective optimization simulation, supports hierarchical autonomous execution, and continuously feeds back optimization evaluation weights and decision rules, so that the decision suggestions of the multi-dimensional index system and the dynamic datum line are optimized. Real-time sensing, intelligent diagnosis, predictive decision making and self-adaptive optimization of the operation state of the supply chain are achieved, and the operation efficiency, toughness and decision making scientificity of the supply chain are remarkably improved.

Systems and methods translate and govern directives across heterogeneous platforms. In some embodiments, directives are normalized into a common intermediate representation (OR) 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 OR 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), loT / 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.

The invention provides a heat supply station energy-saving control method and device based on load feedback, relates to the technical field of automatic control, and solves the technical problems that in the prior art, core control parameters cannot be dynamically and cooperatively adjusted based on the actual load of a user, so that the energy consumption of a heat supply station is high, and the heat supply quality stability is insufficient. The method specifically comprises the steps that core load parameters of a heat supply station and the average indoor temperature of a user are obtained; the core load parameters and the average indoor temperature of the user are preprocessed, and correlation indexes are calculated; inputting the associated indexes into a heat supply station load feedback control model, and adjusting the boiler combustion intensity and the circulating water pump frequency; and based on the heat supply station load feedback control model, continuously feeding back and adjusting the core load parameter and the average indoor temperature of the user according to a period, and then performing closed-loop control on the heat supply station based on the continuously adjusted core load parameter and the average indoor temperature of the user. The method is used for energy-saving control of the heat supply station.

A remote switch actuator includes an actuator housing having a push plate coupled to a pair of actuator pins, the push plate having a first state where the push plate is disposed about an end of an actuation path and a second state where the push plate is disposed about an opposed end of the actuation path. The remote switch actuator may further include at least one sensor positioned within the actuator housing and configured to continuously detect the position of the push plate along the actuation path. The remote switch actuator may further include a switch controller communicatively coupled to the actuator housing and the at least one sensor and having a user interface configured to display the position of the push plate sensed by the at least one sensor.

The invention provides a laboratory equipment group power supply and heat dissipation resource allocation method based on reinforcement learning, and the method comprises the steps: carrying out the real-time collection and fusion of multi-dimensional state data, and achieving the high-quality data preprocessing through the combination of signal processing and anomaly detection; a multi-dimensional sensitivity analysis and orthogonal decoupling coding technology is adopted, and potential influences of different optimization targets are effectively separated; a layered reward shaping and dynamic adjustment mechanism is constructed, and the stability and the multi-target trade-off capability of reinforcement learning training are improved; experience playback sampling optimization based on strategy diversity is introduced to improve strategy generalization, power supply and heat dissipation control instructions can be dynamically generated and efficiently optimized, and the energy efficiency ratio, task completion efficiency and temperature control safety of laboratory equipment operation are remarkably improved through continuous feedback self-adaptive iteration.

The invention discloses an unmanned engineering machinery cluster collaborative operation method. The method comprises the following steps: establishing an earthwork construction expert knowledge base; collecting topographic data of the target construction area; based on the construction task and the collected topographic data, solving an optimal construction operation scheme through an earthwork construction expert knowledge base; according to the construction operation scheme and the construction state and operation data of the unmanned engineering machinery monitored in real time, coordinating and distributing machinery resources and issuing construction tasks; and the unmanned engineering machinery executes the distributed construction task and continuously feeds back the construction state and the operation data to form a closed-loop feedback cycle. According to the method, the multi-modal fusion, whole-course unmanned and multi-population linkage three-dimensional operation matrix is constructed, so that intelligent optimization of mechanical scheduling in the construction process is realized, various types of unmanned engineering machinery including excavation, transportation, leveling and the like can be subjected to clustered collaborative operation, and the construction operation efficiency is improved.

The present application relates to the technical field of electric leakage detection, in particular to a coal mine underground low-voltage remote electric leakage test system and method, the system comprises: a task allocation module, a break state discrimination module, a signal transmission module, a state recognition module, a path receipt module. In the present application, the load level and channel response of the circuit are accurately evaluated, the task channel is dynamically matched, the task allocation blind area and error are reduced, the impedance difference structure and continuous feedback trend are combined to identify abnormal branches, the signal amplitude and interference proportion relationship are used to intelligently adjust the driving and decoding mode, the signal transmission stability and data restoration accuracy are improved, the waveform drift trend and load state are calculated in real time to accurately detect the electric leakage position, the task initiation and response nodes are compared to ensure the path synchronization legality, the efficient linkage between the load state change and signal drift is realized, the electric leakage detection response speed and accuracy are improved, and the line abnormal state discrimination accuracy is strengthened.

The invention discloses an intelligent substation logic principle automatic matching method. According to the method, links such as voltage level layering, interval type division, equipment parameter configuration and the like are associated with historical data in real time, and each step is not an independent design module any more, but forms a mutually matched organic whole. The subsequent matching threshold setting can be directly influenced through the early-stage voltage grade layering result, the interval type division can provide the basis for the locking logic priority ranking, the repeated labor of manual intervention is greatly reduced through the cross-step data flow, and the parameter conflict problem caused by disconnection of all links in the traditional design is avoided. Meanwhile, generated configuration results can be continuously fed back to a historical database, matching models and logic rules are continuously optimized, and configuration precision and efficiency can be higher and higher after long-term use.