303 results about "Adaptive system" patented technology

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

This invention introduces an adaptive system for multi-modal media processing and delivery, addressing challenges in modern digital content distribution. The technology dynamically analyzes and processes media content in real-time, optimizing delivery across diverse devices, networks, and content types. Key features include adaptive processing that adjusts compression, encoding, and delivery protocols based on content characteristics and delivery constraints. The system incorporates artificial intelligence for continuous improvement, learning from historical data and user feedback. It addresses network variability and device diversity, adapting to changing conditions and optimizing content for different platforms. Security and personalization features enable protected content distribution and tailored user experiences. The invention's cross-media optimization approach allows efficient handling of various media formats within a unified framework. Its scalable, modular design suits applications from consumer streaming to enterprise-level distribution. This comprehensive solution aims to enhance content distribution efficiency and user experience in the complex, evolving digital media landscape.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention discloses an intelligent photovoltaic grid-connected adaptive system based on multi-modal sensing and predictive analysis, and the system comprises a multi-modal sensing unit which is used for collecting the multi-source information of a photovoltaic system; the predictive fault analysis module is used for predicting potential system faults based on the multi-source data; the self-adaptive phase-frequency synchronous control unit is used for dynamically adjusting the output parameters of the photovoltaic inverter, so that the output of the photovoltaic inverter is matched with the parameters of the power grid; the comprehensive decision engine is used for generating a grid-connected or off-grid instruction; the self-repairing execution unit is used for generating a specific response scheme and controlling related equipment to execute based on the fault early warning and the strategy instruction of the comprehensive decision engine; and the remote monitoring and manual intervention interface is used for providing a monitoring interface. Through cooperative work of multi-modal data perception, predictive fault analysis and adaptive control, safe, efficient and intelligent grid connection of the photovoltaic system and the power grid is realized, and the reliability, safety and economical efficiency of the photovoltaic power generation system are improved.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.

The invention relates to the technical field of agent systems, and provides an MCP agent-oriented dynamic tool discovery and adaptive system construction method, which comprises the following steps of: designing a uniform interface to register tools, and storing tool data through a dictionary structure; environment parameters are collected, external input and context information are received, three types of data are fused, task types are judged according to priorities, and task requirements are analyzed; according to the capability description during tool registration, the suitability of task execution is judged, a correlation score is calculated, and tools with the score lower than a preset score are screened; comprehensive calculation is carried out by combining the correlation score of the tool, the cost and the delay value, and the tool for executing the task is selected according to the score; according to the selected tool and the current operation environment, resources needed by tasks and an execution sequence are judged, and an execution chain is automatically constructed; and recording and collecting feedback data, evaluating an execution result, and iterating tool parameters.

The invention relates to the technical field of intelligent forming, in particular to an intelligent adjusting type medical cotton ball forming method and system.The intelligent adjusting type medical cotton ball forming method comprises the following steps that pressure data are collected through a distributed pressure sensor array, pressure feature vectors are generated through Gaussian filtering denoising, a pressure change trend is analyzed based on the feature vectors, and a gradient adjusting instruction is output; and controlling the valve to adjust air pressure to obtain compensation parameters, driving the servo motor to adjust a mold gap, collecting offset data to generate a feedback signal, and inputting a neural network to evaluate a forming result in combination with feedback and adjustment instructions. According to the method, a distributed sensor array collects data, multi-dimensional denoising improves feature extraction, a dynamic function analyzes a pressure trend, an offset adjustment parameter is identified, closed-loop compensation is realized through cooperative control, a displacement sensor feeds back gap change, parameter modeling optimizes precision consistency, a convolutional network extracts time sequence features, and a self-adaptive system is constructed through fusion analysis. And intelligent prediction and optimization of the forming quality are realized.

The invention relates to the technical field of data processing, in particular to an artificial intelligence-assisted password security policy dynamic adaptive adjustment method, which comprises the following steps of: acquiring login operation data to generate an encryption feature gradient; the aggregation server generates a dynamically updated federal reference model through a double-channel model updating mechanism, wherein a long-term model updating channel is fused with historical parameters to maintain system stability, and a short-term behavior time window channel dynamically adjusts recent gradient weight based on a time decay function. A graph computation engine generates a spatio-temporal composite risk score. And the risk decision engine generates a third-level response instruction. A risk misjudgment event is verified based on a response result, high-value training samples are screened to drive incremental training, space-time sensitive parameters are finely adjusted by a meta-learning optimizer, and noise is injected into a quantum gradient jammer to defend attacks. Optimized parameters are synchronized to a system module, and a closed-loop adaptive system is formed by combining homomorphic encryption transmission, model digital watermarking, quantum source hardware binding and anti-quantum signature.

The invention relates to the technical field of thermal management control, and particularly discloses a thermal management controller regulation and control method and system based on dynamic thermal inertia prediction. According to the method, the dynamic thermal inertia model is constructed through system identification, and the future temperature field evolution and thermal accumulation risks of the system are accurately predicted. On the basis, a multi-layer nested compensation strategy composed of pre-compensation, main compensation and residual compensation is generated in three dimensions of a compensation angle, a compensation order and a compensation domain, and prospective accurate intervention is achieved. In order to evaluate the control effect, an artifact intensity entropy value is introduced as a quantitative index, and a model and a strategy are corrected in real time through an outer ring optimization mechanism to form self-correction. When continuous thermal unbalance caused by hardware design defects is detected, the system can further start hardware cooperative regulation and control, local heat capacity and a heat dissipation path are optimized fundamentally, cross-level cooperative optimization from a control algorithm to a physical structure is achieved, and finally high-precision, low-energy-consumption and self-adaptive system-level thermal management is achieved.

The invention relates to the technical field of computer data processing, and particularly provides an MCU low-power-consumption firmware upgrading method based on energy self-adaption, which comprises the following steps of: establishing a communication link between an upgrading initiating end and MCU equipment, and acquiring energy state parameters in real time through an energy monitoring terminal arranged in the MCU equipment; according to a preset energy state threshold interval, the comprehensive value of the energy state parameters is divided into a low energy state, a medium energy state and a high energy state; dynamically adjusting an upgrading strategy parameter of the current firmware according to a preset adjustment rule based on the current energy state level; and transmitting target firmware data to the MCU equipment through the communication link based on the dynamically adjusted upgrading strategy parameters, and executing firmware upgrading operation until upgrading is completed. According to the scheme, an adaptive system of energy state perception-strategy dynamic adjustment-hardware collaborative optimization is constructed, and the problems of high power consumption and unbalanced efficiency and reliability caused by the fact that the energy state is not combined in the prior art are effectively solved.

The invention discloses an electric power steering fault diagnosis and self-adaption system based on torque offset, which relates to the technical field of fault diagnosis and comprises a data acquisition unit, a model establishment unit, a torque offset evaluation unit, a fault judgment unit and a power output adjustment unit. According to the method, the return-to-power torque model is constructed based on the vehicle dynamics law and experience adjustment, the model parameters are optimized to obtain the return-to-power torque model, meanwhile, the real-time output torque of the EPS system is calculated according to the steering wheel torque signal, the deviation coefficient of the EPS system is comprehensively calculated, and the accuracy of the EPS system is improved. The deviation coefficient is evaluated according to a preset fault grading interval, a multi-grade fault judgment mechanism is established according to the change rule of the deviation coefficient, dynamic health evaluation of the EPS system motor performance is achieved, the evaluation serves as the basis of control strategy adjustment, the output assistance value can be dynamically adjusted, return control can be enhanced or a safety mode can be switched, and the reliability of the EPS system motor performance is improved. Therefore, the redundancy control capability and the driving safety are improved.

The invention discloses a self-adaptive system and method for end-side cloud dynamic task scheduling of a power distribution internet of things, and relates to the technical field of cloud computing regulation and control, and the method comprises the steps: collecting power distribution network environment data and worker operation data through a power distribution network equipment sensor connected through a gateway; carrying out preprocessing and feature extraction on the collected data to generate a standardized data stream; dynamically distributing calculation tasks to a local gateway or a cloud server according to a worker request type and an equipment load state; dynamically adjusting a resource allocation strategy based on the network bandwidth of the power distribution network, the computing power of the equipment and the task priority; monitoring data transmission safety and equipment state abnormity of the cloud computing environment of the power distribution network in real time, and triggering a protection mechanism; and displaying the data processing result, the task state and the security alarm information through a gateway interface. The method has the advantages that by dynamically distributing local and cloud computing resources, the task processing efficiency and the system adaptability under the power distribution network multi-device cooperation scene are remarkably improved.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications — such as capabilities, objects, mappings, policies, and workflows — as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention discloses a multi-sensor fusion sensing precision self-adaptive system, and relates to the technical field of radio positioning and multi-sensor fusion sensing, the system integrates multi-source sensing units such as a laser radar and a GPS, establishes a unified reference by means of a space-time synchronization module to complete data alignment, and optimizes data quality through preprocessing means such as filtering and denoising; a dynamic weight fusion module allocates weights according to the real-time quality scores, and a unified sensing result is output by fusing the positioning and speed measuring results; through multi-dimensional index evaluation precision grade, self-adaptive adjustment of sensor parameters and fusion strategies, and balance of precision and energy consumption, a result is output in a standardized format, an operation state is fed back, and an external system is linked. According to the invention, through multi-sensing fusion and dynamic adaptive adjustment, the sensing precision and stability are significantly improved; the method can intelligently adapt to multiple scenes, resists interference, has fault-tolerant capability, adapts to new requirements through self-learning evolution, and provides reliable perception support for automatic driving, unmanned aerial vehicle surveying and mapping and the like.

The invention discloses an IMU online adaptive calibration method and calibration device and an automatic driving system, and relates to the technical field of deep learning, automatic driving and navigation. Comprising the following steps: acquiring original measurement data from an IMU in real time; inputting the original measurement data into an error prediction model to output predicted IMU error parameters, wherein the error prediction model adopts a hybrid neural network architecture, is trained and updated based on an end-to-end framework containing a differentiable physical model, and is configured to execute offline training in an offline stage and execute adaptive updating in an online application stage; and correcting the original measurement data by using the IMU error parameter and executing dead reckoning to generate a calibrated motion state track. According to the invention, a static off-line calibration system is improved into a dynamic on-line adaptive system, so that the long-term robustness and reliability are remarkably improved. A differentiable physical model is introduced as a training bridge to realize an end-to-end physical model training normal form, so that the training precision can be improved and the training convergence can be accelerated.

The invention provides an intelligent leveling and ground pressure self-adaption system of a modularized movable laboratory, and relates to the technical field of laboratory leveling, the system realizes high-precision leveling through six-degree-of-freedom hydraulic support legs, is equipped with pressure and displacement sensors, and is combined with spherical hinge universal joints to compensate ground inclination. And the reliability and the safety of the system are improved through the backup design of the double hydraulic pumps and the energy accumulator. The system further has a ground pressure self-adaption function, the pressure of the supporting legs is dynamically adjusted based on the ground type and a bearing capacity model, local overload is prevented, and an anti-sedimentation strategy is set. In addition, the system integrates technologies of Beidou / GNSS positioning assistance, multi-modal data fusion, block chain traceability and the like, and the leveling efficiency, stability and traceability are enhanced. The system is suitable for mobile laboratory deployment under complex terrains, and has the characteristics of high efficiency, accuracy and reliability.

Adaptive system probe action to minimize input / output dirty data transfers is described. In one or more implementations, a system includes a processor, a memory configured to store data, and a cache configured to store a portion of the data stored in the memory for execution by the processor. The system also includes a cache coherence controller including a cache line history. The cache coherence controller is configured detect a direct memory access request from an input / output device. The direct memory access request is associated with an input / output operation involving the data. The cache coherence controller is further configured to identify a cache line associated with the direct memory access request, and, in response to the cache line history including a dirty data transfer record corresponding to the cache line, selectively send a probe to the cache based on a state of the cache line.

The invention discloses an active congestion avoiding method and system based on flow prediction, and relates to the technical field of network communication, and the method comprises the steps: collecting the network state information of a physical network in real time; dynamically synchronizing and mapping network state information of a physical network to a virtual mirror image by using a digital twinning technology; under the virtual mirror image, traffic prediction is carried out on the network state information based on a time domain convolutional network model, and a traffic prediction result is obtained; the traffic prediction result is transmitted into a scheduling strategy engine for dynamic analysis and evaluation, and an active traffic scheduling strategy for traffic prediction is determined; issuing the active flow scheduling strategy to a physical network for execution; and according to the latest network state information, an active flow scheduling strategy is dynamically adjusted to form a closed-loop adaptive system. According to the method, the virtual mirroring of the network state information is realized by using the digital twin technology, the traffic prediction result is obtained by using the time domain convolutional network model, and the traffic scheduling strategy is actively adjusted before traffic congestion occurs, so that congestion is effectively avoided.

The invention discloses an upper limb rehabilitation intelligent self-adaptive system based on multi-modal fusion and MRD-FES cooperation. The system comprises a multi-modal signal acquisition module, a dynamic intention analysis module, a variable damping precise execution module, a neural remodeling strengthening module and a quantitative effect evaluation module. The tDCS-MRD-FES time sequence cooperation protocol is provided, the cortical excitability is improved through tDCS preprocessing, MRD drives training based on intention, FES closed loop strengthens muscle contraction, and vibration tactile feedback is assisted to strengthen sensory movement integration. And the quantitative effect evaluation module further combines a neuroplasticity index and clinical scale digitalization to realize dynamic optimization of training parameters.

The present invention provides a computer system and a computer-implemented method for adaptive quality management in artificial intelligence (AI) processing. The system comprises a memory unit storing machine-readable instructions and a processor configured to execute the instructions to store, in a persistent correction memory, records of prior corrections to AI outputs; retrieve a correction for a current input based on similarity between the input and stored records; generate, by an audit-trail module, a tamper-evident record of the correction and its application; schedule, by a predictive resource manager, AI workloads in dependence on service-level constraints, carbon-intensity data, and quality metrics; and update, by a feedback controller, retrieval and scheduling parameters based on effectiveness information. The processor thereby enables the persistent correction memory, audit-trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system that reduces recurrence of AI errors, ensures verifiable auditability, and optimizes computational efficiency.

The invention provides a big data advertisement putting method and system based on a cloud platform. The method comprises the following steps of performing multi-source data collection and preprocessing; dynamically constructing a user portrait; performing multi-dimensional analysis on the behavior pattern; target audience subdivision is based on a Gaussian mixture model; dynamically generating personalized advertisement creativity; a step of optimizing a multi-target delivery strategy; a step of real-time bidding and bidding optimization; a step of putting execution and real-time monitoring; a step of effect evaluation and multi-dimensional feedback; and carrying out adaptive system adjustment and iterative optimization. The big data advertisement putting method based on the cloud platform has the advantages that putting precision and efficiency are improved, audiences are subdivided through complex statistics, dynamic creativity is generated, advertisement correlation is enhanced, the predicted click rate is increased by 15-20%, and budget waste is reduced. And the cloud platform supports stream processing and automatic adjustment, so that the system can quickly respond to market changes, and the delay is reduced to a millisecond level.

The invention discloses a self-adaptive system data inspection and anomaly repair method for an intelligent computing center, and relates to the technical field of intelligent computing system operation and maintenance and data processing, and the method comprises the steps: collecting system operation data of a plurality of computing nodes of the intelligent computing center, and carrying out the dynamic weight standardization processing of the system operation data based on a resource consumption proportion; obtaining a weight standardization data matrix; constructing a node correlation degree matrix according to the weight standardized data matrix, and generating a system state expected value through a bidirectional recurrent neural network model; based on the deviation distribution of the system state expected value and the current weight standardized data, an adaptive interval estimation algorithm is adopted to determine an anomaly recognition boundary; and performing data reconstruction on the data exceeding the abnormal recognition boundary by adopting a multi-source cooperative compensation mechanism, and outputting repaired system operation data through inverse weight standardization processing. According to the invention, full-process intelligent inspection and accurate abnormity repair of the operation data of the intelligent calculation center system are realized.

The invention relates to the technical field of motor control, in particular to a dead-beat predictive current control method for a permanent magnet synchronous motor and a motor system.The method comprises the steps that a permanent magnet synchronous motor discretization mathematical model considering control system delay under the d-q axis of a synchronous rotating coordinate system is constructed, parameter mismatch is considered, modeling is conducted on introduced disturbance, and a dead-beat predictive current is obtained; an improved generalized extended state observer is constructed, an observation value of d-q axis disturbance is further obtained, real-time accurate identification of inductance parameter changes is achieved through an inductance parameter identification method based on a model reference adaptive system, and inductance parameters in a controller and the observer are dynamically corrected based on lumped disturbance and an inductance parameter identification result. Therefore, the robust dead-beat predictive current control method of the motor is formed. According to the invention, current harmonics are effectively reduced, and the robustness and control performance of the system are greatly improved.

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent sound field adaptive system of digital professional sound equipment, and aims to solve the problems of inaccurate sound field regulation and control, response lag, dependence on manual tuning and the like in a complex acoustic environment. The system comprises a sound field sensing module, an acoustic modeling and analysis module, a self-adaptive sound field regulation and control engine, a multi-channel digital audio processing unit and a feedback optimization module, and dynamic sound field modeling, real-time audio processing and environment self-adaptive regulation and control are realized through distributed sensing, hybrid modeling, multi-target optimization and closed-loop feedback. The system supports rapid re-calibration, multi-scene memory and user preference learning, ensures voice clarity and music fidelity, balances full-field hearing consistency, and significantly reduces manual intervention requirements.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.

The invention discloses a self-adaptive intelligent irrigation robot for hilly terrains. The self-adaptive intelligent irrigation robot comprises a crawler-type mobile chassis, a multi-mode irrigation device, a visual navigation module and an energy management system. The robot does not depend on a high-cost GPS, but utilizes an OpenCV visual module to carry out field path identification and hydrant positioning. Through a deep learning model, the system can automatically identify the types of crops such as tea trees and fruit trees, and automatically switch between two irrigation modes of drip irrigation and micro-spraying according to an identification result, so that precise operation is realized. The crawler-type chassis is integrated with a dip angle self-adaptive system, the differential speed of the crawler belts on the two sides can be dynamically adjusted, and it is ensured that the crawler-type chassis stably advances on a 35-degree abrupt slope. After the irrigation task is completed, the pneumatic recovery device can efficiently wind the water pipe, and the automation efficiency is improved. The problems that a traditional irrigation robot is high in positioning cost, poor in terrain adaptability and serious in water resource waste are effectively solved.

The invention relates to the field of natural language processing, in particular to a low-rank adaptation system capable of differential polarization rank search, which comprises an initialization module, a warm-up training module, a joint optimization module, a sparse pruning module and a final fine tuning module. According to the method, dynamic rank allocation and continuous search are realized by introducing trainable rank architecture parameters, and model parameter waste and calculation overhead are remarkably reduced by combining polarization regularization and L2 regularization strategies. According to the method, the performance of the large model in the downstream task can be effectively improved, meanwhile, the method is suitable for scenes with multi-task concurrence, resource-constrained device deployment and high real-time requirements, and a technical solution is provided for efficient adaptation of the large-scale pre-training model.

The invention belongs to the technical field of vacuum pump control, and relates to a production line vacuum pump energy-saving control method based on vacuum degree feedback and a PI algorithm. A stability index is constructed based on the vacuum change rate, the effective deviation and the vibration intensity to divide the system state, and the proportion and integral gain of the PI algorithm are dynamically adjusted according to the system state, the effective deviation and the temperature influence coefficient, so that the control parameters can adapt to different states and temperature interference of the system. The problem of response lag or overshoot of a traditional fixed parameter PI algorithm during working condition fluctuation is solved, and the working condition adaptability is improved; and finally, overshoot is avoided by adjusting an integral term, energy efficiency optimization constraints are superposed, and on the premise that surge and temperature safety threshold values are met, unnecessary high-frequency operation is limited, and redundant energy consumption under a stable working condition is reduced.

The utility model provides an airing height self-adaptive system and a clothes airing machine, and relates to the technical field of airing. The airing height self-adaption system is applied to the clothes airing machine and comprises a main control module arranged in a main machine of the clothes airing machine and a distance detection module arranged on an airing rod of the clothes airing machine. The distance detection module is in communication connection with the main control module, and is used for detecting existence of a detection object within a preset distance from the airing rod to a preset direction and sending a detection result to the main control module; and the main control module is used for controlling the airing rod to stop lifting through a power system of the clothes airing machine according to the received detection result. According to the system, the distance detection module connected with the main control module is arranged, the airing rod of the clothes airing machine can stay at the airing height matched with the height of a detected object on the basis of the structure and line structure, airing height self-adaption of a user of the clothes airing machine is achieved, and the intelligent degree of the clothes airing machine is remarkably improved.