43 results about "Self adjustment" patented technology

The invention relates to an intelligent valve control method based on a double-delay depth deterministic strategy gradient. The method comprises the steps that firstly, a regulating valve mathematical model is established, a valve transfer function is determined, and a valve simulation environment is established; then, on the basis of the valve simulation environment, parameters of the regulating valve mathematical model are optimized and adjusted through a double-delay depth deterministic strategy gradient algorithm; and finally, PID valve control is realized based on the optimized and adjusted parameters. Compared with the traditional method, the PID controller optimized and set by using the TD3 algorithm has the advantages that the steady-state error of the system is remarkably reduced, and the target tracking precision is remarkably improved. Through self-adaptive adjustment of reinforcement learning, the system can be matched with a reference target more accurately, error accumulation is reduced, and the stability of precision control is improved. Compared with manual adjustment, the TD3 algorithm can continuously optimize controller parameters according to real-time feedback of the system, so that the control system has higher adaptability and self-adjustment capability.

The invention relates to the technical field of intelligent remote control, and discloses a multi-machine collaborative management and control method for coal mine fully-mechanized excavation, fully-mechanized mining, main transportation, auxiliary transportation, production assistance, power supply and distribution and other fixed scenes, and the method comprises the following steps: S1, system architecture design: constructing a multi-machine collaborative management and control system, by means of a special multi-element sensing and control module configured for each equipment unit in a fixed scene, a central control system serves as a coordination center, and autonomous operation is achieved by means of an edge computing center control module of the central control system under the condition of communication interruption or delay; s2, real-time sensing and data fusion: integrating deployed sensors in existing equipment, and collecting equipment operation condition states and environment information; a self-learning function and a distributed data storage mechanism are introduced into the edge computing device, so that self-adjustment and optimization of each device in a complex coal mine environment are realized, local data sharing and self-adaptive control can still be realized even under the condition of communication interruption or delay, and continuity and safety of operation are ensured.

The invention relates to the technical field of voice analysis, and particularly discloses a vocal music training method and system based on artificial intelligence, and the method comprises the steps: collecting and analyzing the body posture parameters of a target vocal music training person, carrying out the preprocessing, detecting the posture abnormality, forming a first abnormal training set, capturing and analyzing the pronunciation feature parameters, and recognizing the pronunciation abnormality, thereby obtaining a vocal music training result; and integrating the first abnormal training set and the second abnormal training set to generate a total abnormal parameter training set, matching a correction set, and visually displaying the body posture abnormal degree and the abnormal reason and the pronunciation abnormal degree and the reason, so that the target vocal music training personnel can visually see own posture and pronunciation problems, and self-adjustment and correction are facilitated.

The invention provides a log data-oriented system fault mining method, which belongs to the technical field of information, and comprises the following steps: collecting original log data of a system, and preprocessing original system logs, including analysis, key information extraction, vectorization and generation of a structured log event sequence; processing the log event sequence by adopting a two-stage model fusing anomaly detection and mode clustering so as to distinguish accidental anomaly and a real fault mode; and for each candidate fault mode cluster, analyzing a time sequence causal relationship between internal events, and positioning a root cause event according to the causal flow score of the node. The method has the advantages that dependence on artificial experience is reduced through full-process automatic modeling and analysis; according to the method, log analysis, feature extraction, anomaly detection and root cause positioning are all automatically completed by adopting a preset algorithm process, and the detection effect is maintained through self-adjustment of the model, so that the flexibility and sustainability of fault mining are improved.

The invention belongs to the field of flight personnel state monitoring, and discloses a meta-consciousness ability training method and system based on a neural biofeedback technology. The system can obtain neural activity data directly related to the distraction state in a real and continuous task situation, hysteresis and unreliability caused by dependence on subjective reports or behavioral expressions are avoided, and the accuracy and objectivity of distraction monitoring are improved from the source. The decoding model is embedded into a closed-loop neural feedback training system, so that an acoustic feedback signal can be triggered in time when a distraction state occurs, a trainee is prompted to perceive attention deviation, and the self consciousness state is autonomously regulated and controlled without external guidance. The closed-loop feedback can strengthen a conditioned reflex mechanism paying attention to a regression task, and the self-monitoring and self-adjusting capability of meta-awareness is remarkably improved.

A semi-automatic system (1) for processing surfaces (6) is described, comprising a work unit (3) and a control device (4) for the work unit (3). The work unit (3) is movable, preferably self-propelled and / or self-adjusting, and has at least one movable processing head (5). The processing head (5) has at least one rotatable processing unit (50) for contact processing of a surface (6), in particular of material (M) on a surface (6), and a drive (51) which drive (51) is assigned to the rotatable processing unit (50) and is configured to actively move the rotatable processing unit (50) along the surface (6), preferably in an automated process. The invention further relates to a method for controlling such a semi-automatic system (1).

The utility model discloses a shoe pressure clamping device which comprises a roller shell and an I-shaped shoe beam arranged in the roller shell, a shoe pressure plate is arranged on the side, close to an upper pressure roller, of the shoe beam, the shoe pressure clamping device further comprises a hydraulic oil cylinder arranged on the side, close to the shoe pressure plate, of the shoe beam, and the output end of the hydraulic oil cylinder is connected with an output rod. The end, away from the hydraulic oil cylinder, of the output rod is connected with two first mounting plates through a connecting assembly, one ends of the two first mounting plates are connected with the shoe pressing plate, and the shoe beam is provided with an adapting assembly used for conducting self-adaption on the shoe pressing plate. According to the utility model, the pair shoe pressing plate is rotationally connected with the output rod of the hydraulic oil cylinder, so that the pair shoe pressing plate can properly rotate and deviate under the extrusion action of pressure, the pair shoe pressing plate is aligned with the center of the pair roller, the pressure deviation is prevented, and the pair shoe pressing plate can be stably pressed under the relative swinging action between the hydraulic oil cylinder and the pair shoe pressing plate. And the shoe pressing plate can be self-adjusted according to the dynamic condition of the pressing area.

The invention discloses a precise neurology department nursing thousand-pre-nursing method. The method comprises the following steps of 1, collecting and analyzing patient data; 2, making a personalized nursing plan; 3, implementing dynamic monitoring and feedback; 4, implementing virtual reality psychological intervention; and 5, effect evaluation and continuous improvement are carried out. According to the technical scheme, by effectively integrating the multi-dimensional patient data, the personalized level of the nursing plan can be remarkably improved. The change not only enables the nursing scheme to be more targeted, but also can perform dynamic adjustment according to the real-time feedback and physiological change of the patient to improve the treatment effect, and under the support of precise nursing, introduction of the virtual reality technology provides a brand new solution for psychological intervention. Patients are subjected to psychological training in an immersive environment, anxiety and pressure can be effectively reduced, and the sense of participation and interactivity of treatment are enhanced. By means of the mode, the limitation of time and space is broken through, and the patient can conduct self-adjustment in a more relaxed state.

The invention discloses a self-adaptive gas extraction accurate regulation and control system based on multi-source information fusion, and the system improves the extraction efficiency: by responding to the trend change, the extraction is increased in advance before the gas emission is increased, the average extraction concentration can be improved by 10-25%, and the equipment safety is guaranteed; equipment overload and damage can be avoided from a control source, the service life is prolonged, energy is saved, consumption is reduced, the system can be automatically and smoothly switched between an efficient mode and an energy-saving mode, comprehensive energy consumption can be reduced by 15-30%, the adaptability is high, parameters of the control system can be automatically adjusted along with changes of working conditions, manual intervention is reduced, and the adaptability to different mine conditions is higher.

The invention relates to a multi-satellite distributed cooperative task planning method and system based on a multi-agent game, and belongs to the technical field of autonomous task management and control of spacecrafts. The method comprises the following steps: constructing a multi-satellite peer-to-peer network without a primary satellite; task whole network broadcast synchronization is carried out; each satellite generates an initial planning scheme based on self constraints; an inter-satellite communication exchange scheme is adopted; each satellite calculates an optimal self-adjustment scheme and a corresponding regret value under the current other-satellite scheme; selecting a part of satellites to update the scheme based on a preset game learning rule; iteration is carried out until the'regret values' of all satellites are zero, that is, the system reaches Nash equilibrium, and a final collaborative planning scheme is output. According to the method, the game model is established, the global optimization target is decomposed into individual local benefits consistent with the global optimization target, so that the satellite can be self-organized to converge to a high-quality collaborative solution only by means of local information interaction, the dependence on a central node is reduced, and the autonomy, robustness, response speed and expansibility of the system are remarkably improved.

The present invention relates to the field of model development technology, and discloses an intelligent agent orchestration method and system, the method comprising the following steps: extracting key content by receiving and processing information from project management tools and technical sources, and organizing it into a structured change request list; predicting the specific impact of each change request on the CI / CD process based on the structured change request list and existing configuration information; and outputting a set of reasonable configuration modification plans based on the prediction results. The present invention can automatically process each link in the CI / CD process, and can continuously optimize in a data-driven manner. At the same time, it can quickly respond to changes in demand and technology, minimize manual configuration changes, avoid introducing human errors, continuously optimize the CI / CD flow through data analysis and feedback loops, and use AI intelligent agents for self-adjustment and optimization to reduce dependence on human operators.

A sailboat support includes a keel support platform, a mechanical linkage assembly, and rotating support arms to proportionally distribute the weight of the sailboat between the keel and the hull. The support is designed so that the keel bears the majority of the load while the hull receives proportional support through a flexible cradle. The system is self-adjusting, thus reducing the need for manual adjustments and improving ease of use and safety.

The invention discloses a Tri-training-based adaptive rotating machine fault diagnosis method in the passive data field. The method comprises the following steps: S1, carrying out source domain data input and initial three-classifier training; s2, target domain data input and pseudo label generation are carried out; s3, result scoring and model fusion are carried out; s4, performing cooperative training of the three classifiers; s5, carrying out iterative optimization and self-adjustment; and S6, outputting a target domain data fault diagnosis result. By introducing mutual supervision and self-training of the three classifiers, model optimization can be carried out by effectively utilizing unlabeled data. Compared with other adaptive methods in the passive data field, the method utilizes a feedback mechanism of a plurality of models to generate pseudo labels through continuous iteration, so that the utilization efficiency and stability of label-free target domain data are improved, the fault diagnosis precision of the models under the target domain working condition is gradually improved, and the fault diagnosis efficiency is improved. And an efficient and reliable solution is provided for fault diagnosis of the rotating machinery of the power system.

The invention relates to the field of hearing aid self-inspection, in particular to a hearing aid operation self-inspection method, which performs fault diagnosis by setting a filtering module and an intelligent algorithm, realizes real-time monitoring and automatic diagnosis of a hearing aid in an operation process, effectively improves the effect that a user can find potential problems of the hearing aid in time through self-inspection, and improves the user experience. The self-checking result can enable a user to intuitively know the working state of the hearing aid and increase confidence and satisfaction during use, and through regular self-checking and timely maintenance, the failure rate of the hearing aid can be reduced, the service life of the hearing aid can be prolonged, and the user experience can be improved. And self-inspection can help a user to perform self-adjustment when a problem is slight, so that the maintenance cost caused by a fault is reduced.

The invention relates to the technical field of model development, and discloses an agent arrangement method and system based on a large model, and the method comprises the following steps: receiving and processing information from a project management tool and a technology source, extracting key content, arranging the key content into a structured change request list, and storing the change request list in a database; based on the structured change request list and the existing configuration information, the specific influence of each change request on the CI / CD process is predicted, and a group of reasonable configuration modification schemes is output according to the prediction result. According to the method, each link in the CI / CD process can be automatically processed, continuous optimization can be realized through a data driving mode, meanwhile, the change of requirements and technologies can be quickly responded, manual configuration change is minimized, introduction of human errors is avoided, CI / CD streams are continuously optimized through data analysis and feedback circulation, self-adjustment and optimization are carried out by utilizing an AI intelligent agent, and the CI / CD flow optimization efficiency is improved. And the dependence on human operators is reduced.

The invention relates to the technical field of numerical control engraving equipment servo control, and discloses an engraving machine servo parameter self-tuning method, device and equipment and a medium, and the method comprises the steps: determining a contour error which is matched with a standard test track and is used for representing the machining precision; determining a vibration index which is matched with the standard test track and is used for representing the machining stability; and based on a fitness function constructed by the contour error and the vibration index, determining a target servo parameter combination adapted to the current processing working condition, so that a servo controller of the carving machine carries out carving processing according to the target servo parameter combination, the full-process autonomous decision-making from state perception, performance evaluation to parameter self-optimization is realized, so that the technical problem of how to carry out self-adjustment according to specific working conditions in the actual machining process of the engraving machine is solved.

The invention discloses a fish habitat water flow structure restoration method, and belongs to the technical field of water conservancy and hydropower ecological protection and fishery resource protection. The method comprises the following steps: S01, analyzing the climate change trend of a target water area; s02, establishing a fish demand model; s03, predicting possible flow velocity change, depth change and area expansion and reduction conditions of the target water area by adopting hydrodynamic simulation software; s04, a set of self-adjustable water flow structure repairing scheme is designed; s05, the water flow structure repairing scheme is visually displayed; s06, carrying out water flow structure repair scheme trial points in the target water area, and adjusting and perfecting a water flow structure repair scheme; and S07, evaluating the effect of the repair measures, and adjusting and optimizing the water flow structure repair scheme according to the evaluation result. Climate change, fish ecological requirements and hydrodynamic simulation are deeply coupled, a self-adjustment optimization mechanism is integrated in the restoration method, and an innovative solution is provided for long-term stability of the fish habitat.

This invention relates to the field of central air conditioning duct cleaning technology, and in particular to an automatic cleaning device for central air conditioning ventilation ducts. The device includes a front panel, a middle cleaning panel, and a rear panel. One end of the middle cleaning panel is fixedly connected to the front panel, and the other end is fixedly connected to the rear panel. A steering mechanism and a climbing mechanism are installed on the front panel. A middle cleaning mechanism and a dust collection mechanism are installed on the middle cleaning panel. A tail cleaning mechanism and a drive mechanism are installed on the rear panel. This invention utilizes a linkage and a servo motor to achieve left and right steering of the front wheels, allowing the device to perform horizontal steering and self-adjustment of its direction of travel within the duct, thus improving cleaning efficiency. The climbing mechanism enables the device to climb vertically within rectangular ducts, expanding its application scenarios for duct cleaning.

The application discloses a self-adaptive hierarchical decision method and device, system and storage medium, comprising the following steps: modeling a teaching process as a long-term sequence decision process; adopting a hierarchical decision structure to divide the long-term sequence decision process into a macro stage decision layer and a micro execution decision layer; taking the long-term teaching effect as an optimization target to evaluate the decision effect in the whole teaching cycle; continuously updating the belief state according to the student learning feedback and dynamically adjusting the macro and micro decision strategies, so that the teaching decision has a self-adjusting capability. The technical scheme of the application can optimize the teaching decision in a long term under the condition that the student state is not completely observable.

The invention relates to the technical field of building energy-saving control, and discloses a building energy consumption management method and system. The core lies in that an energy consumption preset constraint condition and a data stream analysis method are invented, energy consumption characteristic analysis is performed on energy consumption data streams, building energy consumption distribution characteristics are identified, the characteristics are combined with environmental data, similarity calibration is performed on a preset energy-saving decision space, a decision trigger interval is determined, and candidate strategies are generated; and the fitness value of each candidate strategy is calculated and screened, so that the feasibility of the scheme is ensured. And further, on the basis of future energy consumption characteristics and environment data obtained through prediction, feedforward compensation optimization is carried out on the screened strategy, and a final optimization scheme is generated and executed. According to the method, through dynamic calibration and prediction optimization, active generation and self-adjustment of an energy-saving strategy are realized, and the intelligent level and energy efficiency of building energy management are improved.

A method for applying material (M) to a work surface (5) using a semi-automatic material application system (1) is described. The semi-automatic material application system (1) comprises a material supply unit (2), a material application device (3), and a control unit (4) at least for the material application device (3). The material application device (3) is movable, preferably self-propelled and / or self-adjusting. An information acquisition device (44) of the material application system (1) acquires at least one piece of information (I) concerning at least one criterion of a material application to be carried out, which is physically arranged on the work surface (5). Using the information (I), the material application device (3) is controlled such that material (M) is applied to a target area (6) of the work surface (5).The invention further relates to an adhesive tape (7) for use in a method for applying material (M) to a work surface (5) by means of a semi-automatic material application system (1) and a corresponding use of an adhesive tape (7) as well as a set (10) comprising an adhesive tape (7) and a semi-automatic material application system (1).

The utility model discloses a color frame for green plants in landscape gardens, which belongs to the technical field of landscape gardens, and comprises a main frame body, two telescopic frames are arranged in the main frame body, the two telescopic frames are adjustably and fixedly connected with the main frame body through bolt mechanisms, four grooves are formed in the outer parts of the two telescopic frames, and the two telescopic frames are fixedly connected with the main frame body through bolts. The unfolding length of the main frame body and the two telescopic frames can be adjusted by disassembling and fixing the plug pin mechanism, the installation foot plate at the bottom can be unfolded, then installation bolts are driven into ground installation holes, the installation foot plate is fixed to the ground, and installation of the device is achieved. The mounting plate of the hanging frame mechanism is fixed on a wall surface, then the main frame body is inserted into the connecting rod, and the fastening nut is used for fixing, so that self adjustment and mounting mode adjustment can be carried out according to different space and ground requirements, the use convenience is improved, and the cost is prevented from being increased.

The invention relates to the technical field of thread feeding devices, and discloses a thread feeding device with a protection function for bed knitwear processing, which comprises a back plate, a plurality of conveying rollers are arranged on the left side of the back plate, a support frame is arranged on the left side of the back plate, vertical grooves are formed in the left wall and the right wall of the support frame, and lifting blocks are movably connected in the two vertical grooves; knitting threads are wound around the lower portion of the rotating column, so that the knitting threads can be tensioned through the weight of the rotating column in the knitting thread conveying process, winding caused by looseness is avoided, meanwhile, the rotating column can float up and down along with the tightness of the knitting threads to conduct self-adjustment, the knitting threads are prevented from being broken, and the conveying efficiency is improved. And meanwhile, when the knitting threads are broken or a bundle of knitting threads are conveyed, the rotating column loses support and moves downwards, so that the lifting plate can move downwards to trigger the two triggers, the conveying device can be closed, and a worker is reminded of going to check.

The invention belongs to the technical field of Internet of Things. The invention provides the Internet of Things transmission dynamic tuning method and system, a control protocol covering a full transmission chain of an Internet of Things scene is established for server-side equipment, transmission equipment and acquisition equipment, self-adjustment of a transmission framework is realized, the problems of low resource utilization efficiency and poor service expansibility caused by solidification of the transmission framework are effectively solved, and the transmission efficiency of the Internet of Things scene is improved. The transmission framework can flexibly adapt to different application scene requirements; according to the invention, a self-adaptive transmission protocol selection and switching mechanism is designed, and the optimal transmission protocol is dynamically selected according to actual network conditions and data characteristics, so that the transmission efficiency is remarkably improved, and meanwhile, the transmission reliability and stability are ensured.

The invention relates to the technical field of autonomous planning navigation, in particular to an anti-interference multi-route autonomous planning navigation method in a polar region environment, which comprises the following steps: environment perception and data acquisition: collecting polar region environment data by using a millimeter wave radar, a laser radar, a meteorological sensor, an infrared night vision device and a high-definition camera. According to the method, a dynamically updated environment situation map is constructed through a multi-source data fusion technology, an accurate and reliable basis is provided for planning, a multi-route parallel generation and multi-target optimization mechanism is adopted, the diversity and robustness of a scheme set are ensured, the anti-interference capability of a decision-making system is enhanced, and the decision-making efficiency is improved. And a whole-course linkage mechanism from real-time interference evaluation to dynamic rolling optimization to closed-loop verification is also established, and self-adjustment and continuous optimization can be carried out according to environmental feedback, so that the autonomy, safety and adaptability of polar navigation are remarkably improved.

The utility model relates to the technical field of coating, in particular to a device for improving coating precision and uniformity. Comprising a positioning calibration module, a coating module and a detection feedback module, the detection feedback module scans and monitors the surface of a thin film on the whole coating substrate and feeds back data to the structure and the starting method of the PLC controller in real time according to the thickness of the thin film, so that data interconnection among the positioning calibration module and the coating module is achieved through a detection feedback scheme of the device for improving the coating precision and uniformity; a set of circulating system is formed, finally, high stability and automatic production of the tool bit are achieved through self-adjustment, coating of other types of linear planes except a plane substrate can be achieved, the using scene is not limited by most substrates, and then a battery module of a tile structure is manufactured. And the method has the effects of extremely high commercial value and technical value.

The present application relates to the technical field of voice analysis, and particularly discloses a vocal music training method and system based on artificial intelligence, which collects and analyzes body posture parameters of a target vocal music training person, detects posture abnormalities after preprocessing, forms a first abnormal training set, simultaneously captures and analyzes pronunciation feature parameters, identifies pronunciation abnormalities, forms a second abnormal training set, comprehensively generates an abnormal parameter training total set, and matches a correction set, and the abnormal degree and causes of body posture and the abnormal degree and causes of pronunciation are visually displayed, so that the target vocal music training person can directly see his / her own posture and pronunciation problems, and is facilitated to make self-adjustment and correction.

The invention discloses a behavior analysis control method for safe operation of a fume hood, and relates to the technical field of behavior analysis control, and the method comprises the following steps: defining a safe operation reference and a real-time monitoring system of the fume hood; performing behavior pattern analysis according to the reference parameters and the real-time monitoring data; performing safety early warning according to a behavior analysis result; automatic control measures are taken according to the early warning signal; performing energy efficiency optimization according to a control result; performing long-term operation data feedback according to the optimization scheme; further optimizing the behavior mode and the energy efficiency according to the operation data; and carrying out automatic upgrading and self-adjustment on the system according to an optimization result. The operation safety, the energy efficiency and the operation efficiency of the fume hood are effectively improved, the stability and the high efficiency of a system in a complex environment are ensured, the flexibility of the system and the adaptability of the fume hood in different environments are improved, and efficient and safe operation can be kept under frequently-changing operation conditions in a laboratory or an industrial environment.

The present invention relates to the field of industrial automation and intelligent manufacturing technologies, and particularly to a real-time monitoring method, system and storage medium for the electroplating production process. By combining visual and traditional sensor data and using advanced feature engineering techniques to monitor the electroplating production process in real time, the dimension and quality of the monitoring data are improved. The multi-level machine learning models applied in the solution include deep learning, time series analysis and unsupervised learning, which effectively analyze complex production data and improve the comprehensiveness and depth of anomaly detection. By integrating the outputs of each model through decision fusion technology, a comprehensive anomaly detection report is generated, thereby improving the reliability and accuracy of diagnosis. In addition, the present invention adopts a dynamic feedback and self-adjustment mechanism to automatically adjust the production line parameters according to the anomaly detection results, optimize the process flow in real time, and significantly improve the production efficiency and product quality.

The present invention relates to a resonator (1) comprising at least one first region (10) and at least one second region (20) arranged opposite to each other, at least one first hinge (11) connected to the said first region (10), at least one second hinge (21) connected to the said second region (20), and at least one lever (40) connected to the said first hinge (11) and the said second hinge (21) so as to be at least partially movable along a first axis (I). The novelty resides in that the resonator comprises at least one adjustment mechanism (50) positioned between the first region (10) and the second region (20) to maintain continuous resonance by automatically tracking the varying excitation vibration frequency, wherein the said adjustment mechanism (50) is capable of being positioned in the vicinity of the first hinge (11) so as to align the instantaneous center of velocity (II) of the lever (40) at a predetermined position in response to the varying excitation vibration frequency.