884 results about "Closed loop" patented technology

This invention relates to the field of hydraulic engineering, and more particularly to a method for safety assessment and optimization design of large-flow aqueduct structures. The method includes: collecting displacement, strain, and temperature monitoring sequences based on a triple probe, completing grouping, coordinate and time registration, and establishing a monitoring baseline; constructing a spatial model including support nonlinearity and fluid-structure boundary conditions, applying loading conditions, and obtaining response predictions; aligning monitoring and prediction in time and location, performing hierarchical Bayesian filtering inversion, and obtaining parameter posteriors and structural health; under the integration of in-service monitoring and accelerated degradation characteristics, using multi-task learning to generate material evolution and lifespan intervals, and automatically generating and simulating optimization schemes under reliability and maintainability constraints. This application forms a closed loop from assessment to design, effectively enhancing data comparability, traceability of uncertain information, and adaptability of the scheme to engineering projects.

A heel backer shaper comprises a case, a last-compressing ironing base, a last rotation mechanism, a shaping wheel, an automatic selector switch, and a footboard. One side of the upper half of the case is not closed. The last rotation mechanism is disposed on the upper half of the case. The last rotation mechanism comprises a cylinder and a rotary table; the rotary table is disposed at the top of the cylinder; the last-compressing ironing base is disposed on the top inner wall of the case and right faces the rotary table. During heel shaping, the heel backer shaper can adjust the operating performances, such as the position of the shaping wheel on a backer, knocking force, ironing temperature and delay time, according to the quality conditions of a shoe so as to acquire optimal shaping effect; during normal working, the heel backer shaper can operate in an automatic manner; an electrothermal plate on the last-compressing ironing base is controlled via a three-compartment closed-loop controlled silicon voltage regulator, temperature control precision is greatly improved, a temperature difference can be controlled within a 10 DEG C range, and the plane of the shaped backer is flat, with clear edge and smooth wrinkle-free surface.

An adaptive air intake control system and method for cooling towers, coupled with airflow diversion for energy saving, belongs to the field of adaptive control system technology. The system includes an information acquisition unit, an execution unit, and an adaptive control unit. The information acquisition unit collects environmental wind parameters and key operating parameters in real time. The execution unit includes multiple independent fan blade groups with circumferential partitioning and vertical layering for precise execution of commands. The adaptive control unit executes operational decisions, selecting the dominant operating mode from airflow diversion energy saving, anti-freezing energy saving, and anti-drip energy saving modes; it then identifies and outputs wind condition characteristics and thermal anomaly indicators through key parameter status identification, and generates control commands based on the adaptive control strategy and command generation. The system forms a closed loop, capable of adaptively adjusting the identification model and control strategy based on feedback. A corresponding control method is also disclosed, enabling intelligent and precise control of air intake in cooling towers under complex environments, improving cooling efficiency and energy saving while ensuring anti-freezing safety.

The application discloses a kind of robot autonomous exploration mapping system and method, system includes perception module, decision module, planning module and control module, the output of perception module is connected with the input of decision module, and perception module transmits front point candidate set to decision module, the output of decision module is connected with the input of planning module, and decision module transmits optimal target point to planning module, the output of planning module is connected with the input of control module, and planning module transmits motion instruction to control module, and the output of control module is connected with the input of perception module, and control module state information is fed back to perception module, and forms closed loop control system. It can be adapted to diversified industrial scene, with the advantages of efficient completion of autonomous exploration, balancing decision quality and real-time, self-adapting different structure environment, improving decision stability and the like.

The application relates to the cross field of water conservancy and hydropower engineering and emergency management information technology, and discloses a watershed emergency decision-making method based on a space-time graph and multiple agents, which comprises the following steps: constructing and real-time updating a space-time knowledge graph; the space-time knowledge graph is expressed through a quadruple; monitoring a disaster event; driving an agent to cooperatively schedule a task according to the disaster event; generating multiple optimal schemes based on the space-time knowledge graph; visualizing simulation deduction of the optimal schemes to determine an optimal scheme, and real-time monitoring and feedback during execution to form a decision-making closed loop; in the process of visualizing simulation deduction, a scheduling instruction of the optimal scheme and hydrological boundary conditions are received to perform flood numerical simulation; the evolution process of the flood is reproduced in a three-dimensional scene to evaluate the effect of the optimal scheme, and the optimal scheme is determined with the aid of the evaluation. According to the above technical scheme, the accuracy of spatial influence, the economy of resource allocation and the timeliness of response action can be considered in generating an emergency scheme.

This invention discloses a method and device for processing dew point meter measurement data, relating to the fields of industrial automation measurement and control and fault diagnosis technology. It constructs a dynamically sensing window with variable shape to preprocess time-series data from the dew point meter, calculates fault probability entropy in real time, detects instantaneous anomalies, and combines a fault-window shape mapping library to determine the window shape and adaptively switch it to obtain anomaly identification results. A corresponding algorithm is selected from a correction strategy library to correct the anomaly. This invention constructs a dynamically sensing window with variable shape, driven by real-time calculated fault probability entropy, enabling adaptive switching between panoramic, sniper, and multi-focus shapes. Utilizing a built-in fault-window shape mapping library, it associates anomaly symptoms with fault physical mechanisms, separates multiple concurrent faults, infers root causes, and avoids decision space explosion. Based on the diagnostic results, it calls targeted algorithms from the correction strategy library to correct anomalies, forming an intelligent closed loop of identification-diagnosis-correction.

The application discloses a kind of multi-level quantitative evaluation methods for electronic product information clearing, verification and traceable credible closed-loop system, comprising S100, evaluation framework establishment and test environment preparation step: S110, define evaluation model, the evaluation model includes clearing effect layer (L1), verification credible layer (L2), trace and authentication layer (L3), system behavior and security layer (L4) and performance and efficiency layer (L5).The application has the advantages that: from qualitative to quantitative: a large number of quantitative indicators such as KL divergence, bit recovery rate, throughput are introduced, and the evaluation results are objective, accurate and comparable.The method carries out deep security testing: not only test function, but also take the system itself as attack target, carry out penetration testing and process behavior monitoring, can find deeply hidden design defects and security vulnerabilities, which cannot be achieved by traditional function testing.At the same time, the method can guide technology development, and provide decision basis for procurement and supervision;And high automation and repeatability.

The application discloses a pose-deformation joint estimation method and system for micro contact assembly process, a workpiece is scanned by a mobile device from multiple perspectives, and a global point cloud model of the workpiece is reconstructed. Next, the overall pose parameters and the local surface topography of the workpiece are solved synchronously, and then a displacement field and a strain field are constructed to quantitatively represent the deformation state. Subsequently, the image, point cloud, pose and deformation parameters are fused, and a pre-trained joint estimation model is used to realize rapid prediction of the current coupling state of the workpiece. Through the built-in consistency verification and online updating mechanism, the reliability of the prediction result is ensured, and the final reliable state estimation is converted into a robot assembly correction instruction in real time, thereby forming a closed loop of perception, estimation and control. The application can realize sub-millimeter level pose measurement and high-precision deformation prediction, significantly improve the dynamic adaptability, robustness and automation level of the assembly process, and is suitable for automatic assembly of high-precision components in the fields of aviation, aerospace and the like.

This invention discloses a method, system, and storage medium for assessing the adjustable load of a virtual power plant in public buildings. The method includes: acquiring raw influencing factor data, including at least air conditioning system operating parameters reflecting equipment aging and building thermal parameters reflecting building thermophysical characteristics; constructing hysteresis features characterizing thermal inertia based on the air conditioning system operating parameters, and constructing multi-parameter coupling features based on their coupling relationship with building thermal parameters; inputting the obtained data into a pre-trained adjustable load assessment model to obtain the adjustable load assessment result; wherein the model is a multi-model fusion model and integrates a rolling mixed-integer linear programming solver for synchronously generating a full-chain control scheme for the air conditioning system. This invention, by introducing a quantitative expression of the thermal inertia of the air conditioning system and multi-parameter coupling features, achieves an integrated closed loop of assessment and control, significantly improving the response accuracy and overall benefits of public buildings participating in virtual power plants.

The invention discloses an intelligent automobile active obstacle avoidance control system and method suitable for complex road conditions. The system comprises a multi-source road condition sensing fusion module, a dynamic risk assessment and decision module, an obstacle avoidance path planning module, a vehicle dynamics control module, a man-machine interaction and emergency takeover module and a system state monitoring and fault diagnosis module. According to the invention, the problem of unreliability of a single sensor in rainy days, congestion and other scenes is solved through multi-source sensor fusion, and the sensing precision is ensured; a multi-factor risk assessment model avoids misjudgment of single distance judgment, and the decision more conforms to actual safety requirements; path planning is combined with vehicle dynamics constraint, closed-loop control ensures accurate path tracking, and the vehicle is prevented from being out of control.

The present application realizes full closed loop voltage regulation without manual intervention, speed improvement, adaptation to long distance low voltage power supply scene, and ensures that the load is always in the rated working voltage range by implementing effective voltage value collection, threshold intelligent judgment and gear automatic switching. The present application discards the carbon brush regulating coil structure of the traditional voltage stabilizer, adopts the scheme of switching transformer fixed gear by AC contactor, improves the service life of the equipment, and greatly simplifies the equipment structure.

The application discloses a kind of wind light storage micro-grid optimization operation method and system.The method adopts three-layer architecture of top optimization, middle layer cooperation and bottom execution, and forms the intelligent closed loop of " prediction-planning-coordination-execution-feedback ".Top is based on LSTM prediction and traffic jam algorithm to carry out global power flow congestion identification and dredging, and utilizes improved particle swarm algorithm in feasible interval to carry out multi-objective fine optimization, and generates optimal operation instruction;Middle layer dynamically adjusts optimization target weight according to operation scene, and decomposes instruction into multiple device cooperation strategy;Bottom execution customized control algorithm, realize device accurate regulation and millisecond level fast protection.The application effectively solves the problems of micro-grid multi-objective cooperative optimization, prediction control disconnection and operation risk foresight protection, and significantly improves the voltage stability, economy, dynamic response speed and safety and reliability of system operation.

This application discloses a natural language-driven edge-cloud collaborative intelligent operation and maintenance system and its control method, belonging to the field of intelligent operation and maintenance technology. The system includes a management plane, a control plane, an edge agent, an intelligent operation and maintenance center, a task center, and a skill center. The intelligent operation and maintenance center uses a directed graph state machine engine to convert natural language operation and maintenance goals into executable steps. The task center atomically distributes tasks to the edge agent and streams execution fragments. The skill center implements version management and AI-assisted generation of operation and maintenance skills. This invention achieves a fully automated closed loop from understanding operation and maintenance intentions to edge execution and knowledge accumulation, significantly shortening fault repair time, improving operation and maintenance security and knowledge reuse capabilities, and has outstanding substantive features and significant progress.

The application discloses a kind of new energy power system small disturbance stability evaluation methods based on geometric separation.Method includes: obtaining the geometric characteristics of the equivalent admittance matrix of the admittance matrix transfer function of the network side and the converter side of new energy power system, and then determining whether new energy power system and each converter meet closed loop stability condition by geometric separation method, if meet, then evaluate new energy power system stability.The method of the application supports the stability verification of massive heterogeneous power electronic equipment in a decentralized and modular manner, effectively avoiding the computational complexity caused by the growth of system dimension;At the same time, the proposed geometric separation condition provides a clear and operable guidance for the external characteristic specification of grid-connected equipment and controller design, which helps to achieve "plug and play" and high proportion of safe access of new energy power system.

The present application relates to a kind of vehicle control method, device, electronic equipment and storage medium, vehicle control method includes: obtaining the driving state data of vehicle and user-selected soothing level information;According to driving state data, determine road surface water category, according to driving state data and road surface water category, determine road wet grade;If front road condition data of road in front of vehicle can be obtained from cloud, according to road wet grade, road surface water category and driving state data, determine the confidence of front road condition data;According to front road condition data, confidence and soothing level information, determine the set of anti-drowsy control parameters, according to the set of anti-drowsy control parameters Control vehicle operation.This application embodiment can realize the complete closed loop from initiative prediction to dynamic check to hierarchical execution, so that vehicle can obtain optimal drowsy soothing effect under different road conditions and different user needs, significantly reduce the sensory conflict caused by road condition prediction deviation or water burst.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration for closed loop power control. The UE may transmit a sounding reference signal (SRS) using a value of an SRS power control adjustment state that is the same or different from a value of a physical uplink shared channel power control adjustment state based at least in part on the configuration comprising a closed loop indication or being a radio resource control configuration. Numerous other aspects are described.

The present application relates to the technical field of energy consumption control, and particularly relates to an energy consumption prediction and energy saving control system. The technical scheme comprises a data acquisition and communication module, an energy consumption prediction and optimization module, an intelligent control execution module, an energy management and visualization module, and a system coordination mechanism. The data acquisition and communication module is used for collecting operation parameters, environmental parameters and equipment states of building mechanical and electrical equipment in real time, and realizing data aggregation and transmission through a multi-protocol bus. The present application realizes the cooperative optimization of building energy consumption through dynamic prediction and intelligent control, responds to environmental changes in real time, and accurately coordinates the operation of multiple devices, thereby breaking through the limitations of traditional model hysteresis and coupled interference. In combination with multi-source data fusion diagnosis, hidden dangers are actively identified, operation and maintenance costs are reduced, standardized integrated design simplifies construction management, intuitive visual control is provided, and a full-cycle closed loop of energy efficiency improvement and intelligent operation and maintenance is realized.

The application relates to the technical field of road engineering machinery, in particular to a separating agent spraying device for a rubber-tired road roller and a spraying control method. The device comprises an information acquisition unit, a spraying device and a controller. The controller dynamically determines a target spraying amount and controls the spraying device to execute based on the rubber wheel surface adhesion state information acquired by the information acquisition unit in real time, forming a real-time feedback control closed loop. The controller has a built-in mapping relationship model, and the model is iteratively updated after each spraying according to the state information collected again as effect data, realizing self-learning and continuous optimization of the system. The scheme realizes the leap of separating agent spraying from a fixed mode to dynamic and accurate regulation and control through the construction of an intelligent closed loop of "perception-decision-execution-optimization", solves the problem of material waste; at the same time, by introducing the model online updating mechanism, the system has self-adaptive ability and can continuously optimize the control strategy according to the working conditions, realizing long-term optimal control performance and material utilization.

The application discloses a laser interference detection parameter adaptive control method based on reinforcement learning, comprising the following steps: obtaining relevant data to form an interference state vector, and constructing an interference optical physics model and a safety constraint model; constructing a sub-target generation unit to generate a target optimization direction set; constructing a hierarchical reinforcement learning structure to output an adjustment control quantity sequence of a detection parameter; constructing an external reward generation unit and an internal reward generation unit, introducing a reward consistency mapping unit to correct the internal reward, and generating an updated hierarchical reinforcement learning structure; periodically updating the interference state vector to realize a laser interference detection parameter adaptive control closed loop. The application utilizes hierarchical reinforcement learning to combine optical physics and safety constraint modeling, realizes adaptive optimization control of a laser interference detection parameter, and has the advantages of high measurement precision, high intelligent degree of parameter adjustment, and safe and stable operation.

This invention belongs to the field of fan control technology. It provides a laboratory exhaust gas-induced high-altitude jet fan control system, comprising: a multi-source data acquisition module for real-time acquisition of multi-dimensional time-series data related to laboratory exhaust gas emission safety, including fan operating parameters, building environment parameters, exhaust gas source equipment status parameters, and meteorological environment parameters; and a predictive maintenance and early warning module based on the multi-dimensional time-series data. By integrating multi-source data perception, chronic risk trend prediction, system vulnerability assessment based on digital twins, and graded resilience control strategies, a closed loop of "perception-prediction-assessment-decision-execution" is constructed. This achieves early identification, proactive early warning, and adaptive control of coupled risks between "equipment-building-environment-personnel," thereby enhancing the proactive safety capability to prevent toxic exhaust gases from entering the breathing zone of personnel.

This invention provides a fault identification method for test equipment based on diagnostic semantic adaptation, belonging to the field of test equipment operation status monitoring technology. It includes: S1, collecting multi-measurement channel sensor signals and image information generated during the test and performing preprocessing; S2, performing measurement channel mapping, unit conversion, range matching, and normalization processing on multi-source asynchronous data; S3, determining the fault probability of the test equipment, obtaining the fused fault confidence level, and generating a structured test equipment fault diagnosis record package; S4, analyzing the fault types of the test equipment and executing the test equipment fault identification alarm program. This invention uses an edge-side temporal fault discrimination network to perform real-time fault screening on a unified state vector, and employs a multimodal transfer diagnostic network for fault verification and incremental learning, forming a parameter optimization closed loop to improve the accuracy and robustness of fault identification.

The invention discloses an XGBoost-based power generation quotation rationality evaluation method, system and equipment and a medium, and relates to the technical field of power market operation and supervision, and the method comprises the steps: collecting historical operation and market environment data, obtaining five evaluation indexes through a prediction model, elastic analysis, matching degree calculation and clustering analysis, fusing the indexes to construct a feature vector, and obtaining a power generation quotation rationality evaluation result. And training a classification model to evaluate the rationality of quotation, and generating a monitoring early warning signal when the matching degree is too low, the price response is too strong, the quotation deviation is too large and the behavior mode is abnormal by continuously tracking the change trend of key indexes. According to the invention, by fusing the multi-dimensional dynamic features and adopting the elastic time sequence matching and interpretable machine learning model, a power generation quotation behavior intelligent supervision closed loop integrating comprehensive evaluation, accurate quantification and active early warning is constructed, and the comprehensiveness, accuracy, objectivity and timeliness of market supervision are significantly improved.

This invention discloses a precise control system for constant-pressure water return flow in spherical tanks of a compressed air energy storage power station, belonging to the field of energy storage technology. It includes independent constant-pressure water return branches corresponding to each spherical tank and a central control unit. Each independent constant-pressure water return branch is equipped with a constant-pressure water return on / off valve, a flow detection device, and a constant-pressure water return regulating valve. The central control unit has a calculation module that calculates the theoretical total drainage volume based on the initial liquid level during tank transfer. It can compare the cumulative return flow with the theoretical total drainage volume in real time, adjust the regulating valve opening in a closed loop, and interlock the valve to close when the flow rate reaches the target. This invention can adapt to parameter changes under different operating conditions and during tank transfer periods, improve the accuracy of return flow control, stabilize the operating pressure of the spherical tanks, enhance the safety and stability of the energy storage system, extend the service life of the spherical tanks, and reduce system operating losses.

The application relates to the technical field of underground structure engineering, and discloses a model test device and a test method for active and passive combined anti-floating of an underground structure, which comprises a sealed pressure-bearing model box, a servo water pressure loading system, a buried soil / structure system and a multi-element data acquisition and control system. The application provides a test space through the sealed pressure-bearing model box and simulates complex multilayer hydrogeological conditions; the servo water pressure loading system is used for accurately controlling water flow and pressure injected into the model box; the buried soil / structure system is used for simulating real soil-structure interaction and structure deformation conditions; and the multi-element data acquisition and control system is used for collecting test data and driving an actuator to form a closed loop control verification.

This invention discloses an intelligent self-healing system and method for operation and maintenance based on an LLM Agent, belonging to the fields of information technology and operation and maintenance automation technology. The system includes a data acquisition and preprocessing module, a rule scheduling module, a large language model intelligent agent module, a work order generation module, and an approval and execution module. It collects and standardizes multi-source monitoring data through dual channels, and the rule scheduling module intelligently distributes data according to single alarm or multi-alarm aggregation conditions: simple scenarios directly trigger the automatic operation and maintenance mechanism; complex scenarios submit the alarm context and predefined operation types to the LLM Agent to output optimal execution parameters. The work order generation module automatically generates work orders; the execution engine automatically completes the repair operation. This invention solves the problems of rigidity in traditional rule engines and the uncontrollability and difficulty in integration of pure AI models, achieving an end-to-end self-healing closed loop that balances security and intelligence, significantly improving the automation coverage and decision-making accuracy in complex scenarios.

The application discloses an underwater dam defect self-adaptive repair method, which solves the problems of existing repair methods, such as solidification of a repair method process, dependence on experience for parameter setting, and lack of intelligent closed-loop feedback. The method comprises the following steps: acquiring multi-source detection data of a target defect area of an underwater dam, and extracting defect characteristic parameters; generating a customized repair scheme containing a repair process sequence and process parameters based on the defect characteristic parameters; performing a repair operation according to the customized repair scheme, and synchronously collecting process monitoring data; assessing the repair quality in real time based on the process monitoring data, and obtaining a repair quality conclusion; when the repair quality conclusion is unqualified, updating the defect characteristic parameters and the process parameters according to the unqualified degree and dimension, and re-performing the repair operation and quality assessment based on the updated parameters. The application improves the intelligent level and resource efficiency of underwater repair operation by constructing an adaptive closed loop of perception-planning-execution-evaluation-feedback.

The application provides a transformer substation multi-environment factor intelligent management and control system and method based on edge cloud cooperation, wherein the system combines edge side high frequency real-time processing and cloud complex analysis capability through edge cloud cooperation architecture, realizes optimal resource configuration, and significantly reduces response delay and bandwidth pressure. The system integrally monitors multiple environmental factors such as noise, electromagnetic field, wastewater and solid waste, uses cloud AI for trend prediction and deep correlation analysis, realizes the leap from single threshold alarm to intelligent forward-looking early warning. A whole-process closed loop of'monitoring-analysis-early warning-management and control-verification' is constructed, edge rapid execution is cooperated with cloud strategy decision, and the change from passive management to active and accurate prevention and control is realized. By deeply binding the monitoring data and the pollution discharge permission requirements, the account and compliance report are automatically generated, the automation level and work efficiency of environmental protection management are greatly improved, and the enterprise compliance risk is effectively reduced.

This invention discloses an automated gradient optimization method for liquid chromatography based on real-time peak feedback, applicable to liquid chromatography systems including detectors, chromatographic pumps, and data acquisition and control units. The method includes: executing an initial gradient; acquiring chromatograms and extracting the retention time and half-peak width of each peak; calculating the resolution of adjacent peaks; if there are substandard peak pairs, reducing the rate of change of the mobile phase gradient within the corresponding elution window to improve resolution; if all peaks meet the standard, identifying and compressing the inter-peak blank time period to shorten analysis time; and outputting the optimal gradient through closed-loop iteration when both resolution and blank periods are simultaneously satisfied. This invention is fully automated, using deterministic feedback based on measured peak parameters. It uniquely incorporates resolution correction and time compression into the same closed loop and automatically handles their contradictions, ultimately converging to the optimal balance between resolution and analysis time, significantly reducing solvent consumption and manual intervention.

The application discloses a closed-loop control system for defect map generation and quality grading of a focus-adjustable lens, relates to the technical field of focus-adjustable lens quality control, and comprises a test station management module, a defect identification module, a quality grading module, a trace storage module and a closed-loop feedback module; wherein the test station management module uniformly collects optical, electrical and mechanical detection data, the defect identification module organizes, compares and merges the detection data according to position coordinates, test conditions and detection time to generate a defect map, the quality grading module completes grade determination, and the trace storage module and the closed-loop feedback module complete result storage and adjustment feedback; thereby relatively improving the problems that detection data of multiple stations is difficult to be uniformly positioned and merged, and stable support for defect map generation, quality grading and closed-loop feedback is difficult.