62results about How to "Achieving Adaptiveness" patented technology

The invention provides an unmanned aerial vehicle intelligent charging control method and system based on multi-scene adaptation, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: collecting environment data through multiple types of sensors, carrying out the scene recognition and classification through a deep learning algorithm, and generating a scene feature vector and a parameter data set; a basic strategy is called from a preset charging strategy library according to the scene type, a reinforcement learning algorithm is adopted to optimize a charging strategy, a charging power adjustment curve is generated in combination with the environmental parameters, and a charging control instruction is output; and when the charging process is executed, monitoring battery state parameters in real time, performing safety threshold comparison and protection control, calculating a battery health index to evaluate a performance state, and feeding back an evaluation result to the charging optimization model to realize closed-loop control of the charging process.

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention provides a non-woven fabric processing technology control method and system, and relates to the technical field of technology control, and the method comprises the steps: obtaining the application scene information of a non-woven fabric, carrying out the multi-dimensional demand analysis, and obtaining the multi-dimensional processing demand information of the non-woven fabric; determining a non-woven fabric processing control scheme; preparing raw materials to obtain a raw material preparation result; performing fibrosis treatment to obtain a fibrosis treatment result; arranging, laying and forming a net to obtain a net layer processing result; carrying out consolidation treatment to obtain a non-woven fabric product; performing quality detection to obtain a non-woven fabric product quality detection result; and performing scheme optimization, and performing non-woven fabric processing technology control according to a scheme optimization result. The technical problems that a non-woven fabric processing technology in the prior art is often based on a fixed scheme, changes of market demands are difficult to respond quickly, accurate demand analysis means are lacked, and the intelligent level of the non-woven fabric processing technology is insufficient are solved.

The invention discloses a control method and system based on self-adaption MPC large structure dynamic sliding shipment, the system comprises a sensor module, a computer system and a central controller, a pressure sensor is arranged between the upper end of a hydraulic sliding shoe and a base plate of an offshore oil and gas platform module and used for collecting the pressure value of a structure supporting point; the total station is arranged around a ship deck and used for monitoring the height change of each sliding shoe relative to a wharf; the liquid level sensors are arranged at four corners of the hull and used for collecting draft data of the four corners of the hull. The inclinometers are mounted on a bow and two sides of the barge and are used for collecting six-degree-of-freedom inclination angles of the barge; the computer system carries out calculation through a self-adaptive MPC control algorithm to obtain an optimal control strategy; and the central controller dynamically adjusts the ballast water distribution and the hydraulic sliding shoe height of the ship based on the optimal control strategy. Therefore, the ship loading safety, continuity and efficiency can be remarkably improved, the ship loading time is shortened, and the damage risk of the structure is reduced.

The invention discloses a curved surface self-adaptive gluing device and a use method, and belongs to the technical field of gluing. In the device, a glue barrel penetrates through a fixed base plate in an up-down moving mode, the lower end of the glue barrel is provided with a glue opening enabling high-viscosity glue liquid to flow out smoothly, and a piston capable of moving up and down is arranged in the glue barrel; the guide wheels are arranged at the lower end of the glue barrel, distributed on the left and right sides of the glue opening and used for being in contact with a coated surface; the floating mechanism is fixed with the rubber sleeve and can be connected with the fixed substrate in an up-and-down floating manner; the glue scraping mechanism is fixed to the fixed base plate, located right behind the glue opening and used for scraping glue in constant-force fit contact with the coated surface. The gluing device is good in gluing quality, high in efficiency, free of damage to the coated surface of a coated component, convenient to use and particularly suitable for curved surface coating and plane coating.

The invention relates to a decision-making method combining a big language model and a rule engine, and the method comprises the following steps: S1, extracting key information and a user intention from a text in a business scene based on the big language model, and converting the extracted information into structured data; s2, performing business logic processing on the structured data by using a rule engine module to generate a preliminary decision result; s3, converting the preliminary decision result into a natural language text, and feeding back the natural language text to the user; and S4, storing a natural language text corresponding to the preliminary decision result, obtaining feedback data of the user, analyzing the feedback data to obtain a rule adjustment value, adjusting the weight and priority of each rule in the rule set of the rule engine module based on the rule adjustment value to obtain an optimized rule engine module, and outputting the optimized rule engine module. And the optimized rule engine module is used for outputting a next decision result. Compared with the prior art, the method has the advantage of improving the efficiency of the existing decision-making scene.

The invention discloses an optical diffraction element defect detection method and system based on deep learning and a medium. The method comprises the following steps: acquiring a first to-be-detected image, preprocessing the first to-be-detected image to obtain a first ROI sub-image of the first to-be-detected image, and establishing a geometric rectangular coordinate system; a first deep learning model deployed in advance is loaded to perform semantic segmentation on the first ROI subgraph, the first deep learning model is used for identifying the first dispensing area and outputting a first feature graph, the first deep learning model is based on a Unit + + network structure, and a multi-scale large-kernel convolution module is adopted by both an encoder and a decoder of the first deep learning model; detecting a first defect in the first ROI sub-graph by adopting a first 2D traditional algorithm; the first feature map detects a second defect and a third defect by using a second 2D traditional algorithm. According to the method, a deep learning algorithm is combined with a 2D traditional algorithm, so that the detection speed and accuracy of the optical diffraction element can be improved.

The invention relates to an intelligent conference interaction method and device based on a large language model and a medium, and the method comprises the steps: converting a conference voice into a text through a first large language model, and carrying out the slicing of the text; vectorizing conference text slices based on a natural language processing technology; storing the vectorized conference text slice into a conference domain of the corresponding conference; using the second large language model to receive a user question, and combining with a question and answer historical record to generate a composite question; searching related conference texts in the conference domain through similarity calculation and returning answers; through similarity calculation and networking query, relevant content is retrieved and returned for recommendation; and adjusting a similarity calculation function according to the feedback of the user for the answer and the recommendation. Compared with the prior art, the conference voice is recognized in real time and the content is retrieved in real time by using the large language model, so that a user can crossly understand and learn similar conference content, and the conference efficiency and the intelligent degree are improved.

The invention relates to the technical field of image processing application, and particularly discloses an artificial intelligence image processing method based on intelligent traffic, which comprises the steps of image acquisition and preprocessing, feature extraction, artificial intelligence model construction and training, intelligent analysis and decision support, intelligent image processing and feedback, result output and application and the like. According to the artificial intelligence image processing method based on the intelligent traffic, information extracted from features is used for establishing an artificial intelligence analysis model, the intelligent analysis capability of an intelligent traffic management system is remarkably enhanced, and in the third step, the extracted features and historical data are subjected to mapping calculation, so that the intelligent analysis capability of the intelligent traffic management system is improved. The constructed artificial intelligence analysis model can learn and identify the mode in the traffic image so as to predict the optimal processing method, and the establishment of the model enables the intelligent traffic management system not only to process the current image data, but also to perform learning and prediction based on historical data, thereby improving the intelligent level of the system.

The application relates to the technical field of equipment health management, and discloses a battery replacement cabinet fault prediction and health management method and system. The method comprises the following steps: acquiring an original temperature sequence, an original humidity sequence, a battery module number and a battery replacement cabinet position number, performing abnormal value correction to obtain a corrected temperature sequence and a corrected humidity sequence; performing abnormal fluctuation point screening according to the corrected temperature sequence and the corrected humidity sequence to generate an abnormality judgment record; removing interference according to the abnormality judgment record to obtain a refined feature set; performing abnormal risk analysis according to the refined feature set to obtain an overheating risk report; generating an intervention instruction according to the overheating risk report to obtain an initial execution scheme; driving operation is performed according to the initial execution scheme to obtain an equipment operation log; and performing reliability evaluation according to the equipment operation log to obtain a reliability evaluation conclusion. The method can realize early warning and health management of battery replacement cabinet faults.

The invention discloses an intelligent control system and method for intelligent equipment and a storage medium, and relates to the technical field of intelligent control. The intelligent control system comprises an analysis object determination module, a regulation and control sequence generation module, a target regulation and control sequence determination module, an equipment updating analysis module and a real-time monitoring response module; the analysis object determination module is used for taking an intelligent home decoration system database constructed by the intelligent Internet of Things corresponding to each local area network as an analysis object, and the regulation and control sequence generation module is used for generating a regulation and control sequence of corresponding intelligent equipment constructed by instruction trigger factors; the target regulation and control sequence determination module is used for screening a regulation and control sequence with a unique instruction trigger factor associated with a plurality of intelligent devices as a target regulation and control sequence; the equipment updating analysis module is used for judging whether the direct association event needs to update the target regulation and control sequence; and the real-time monitoring response module is used for responding to the updated target regulation and control sequence when the intelligent regulation and control event formed by the instruction trigger factor is monitored in real time.

This invention belongs to the technical field of power equipment operation and maintenance, power distribution line construction, and high-altitude operation machinery. A pole-mounted transformer replacement device based on a truck-mounted crane is characterized by comprising a main frame and support system, a translational load-bearing system, a chain drive system, and a vertical lifting and centering system. The main frame and support system includes a main load-bearing frame, stabilizing legs, and lifting lugs. The translational load-bearing system includes a translational load-bearing platform and a linear guide rail slider assembly. The chain drive system mainly consists of a geared motor, a drive gear, a driven gear, a short transmission chain, a drive sprocket shaft, a bearing support seat, a long transmission chain, and a reducer mounting support. The vertical lifting and centering system includes a centering mechanism, a vertical lifting mechanism, and a vertical lifting support plate. The centering mechanism includes a horizontal drive motor, a horizontal push rod mechanism, and a guide bracket. This device can be transported by truck, deployed quickly, and is suitable for operation in confined and electrified environments.

The invention provides an enterprise space appeal matching method based on an AI model, and the method comprises the following steps: collecting the multi-source sensing data of an enterprise space, and building a structured time-space information matrix through an access control, energy consumption, position and personnel label sensing terminal; constructing a causal relationship graph by using a structural causal modeling algorithm, and identifying key causal factors directly related to the spatial appeal; causal invariant characterization is extracted based on a multi-environment invariant risk minimization method, and an intervention effect model is established in combination with controllable intervention variables such as partition adjustment, capacity limitation and use strategies; generating an anti-fact sample of the candidate space unit by using an anti-fact reasoning mechanism, and calculating a matching adaptation score; establishing a matching optimization model according to constraint conditions such as topology, capacity, safety and moving lines; according to the method, high-precision and interpretable matching distribution under the conditions of complex space resources and multi-dimensional appeals is realized, and the method has the effects of flexible deployment, high matching stability and sustainable optimization.

The invention discloses a multi-terminal parallel authentication authorization and equipment perception method for a key task communication system, which belongs to the technical field of key task communication, and specifically comprises the following steps: S1, configuration preparation: a server pre-configures a terminal initial configuration file for a terminal; the initial configuration file of the terminal comprises lt; m < cpt >-UE-idgt; element, lt; m < cpt >-UE-idgt; the elements comprise the next-level lt; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; lt; the method comprises the following steps: carrying out Instance-ID-URNgt; the elements are instantiated to be unique identifiers by the configuration terminal; s2, configuration issuing and effectiveness: the server issues the initial configuration file of the terminal to the terminal, and the initial configuration file of the terminal is restarted to take effect through the terminal; s3, authentication and information reporting: the terminal initiates an authentication and service authorization request, wherein the request carries the result; the method comprises the following steps: carrying out Instance-ID-URNgt; an element; s4, service authorization and configuration issuing: the server receives the request, analyzes the terminal instantiation unique identifier, records analysis information, and determines whether to issue matched user configuration file data to the terminal; according to the invention, the terminal registration authentication process is deeply coupled with the terminal type and capability evaluation, and intelligent decision-making can be carried out based on terminal difference.

The present invention discloses a three-terminal flexible adaptive tactile sensor and a manufacturing method, comprising a first end (1), a second end (2) and a third end (3) from top to bottom; the first end (1) is a surface electrode, used to contact with the outside world to generate electrons; the second end (2) is an intermediate electrode, used to conduct electrons; the third end (3) is a bottom electrode, one end of the first end (1) is connected to the positive electrode of a power supply through a wire, and the other end of the first end (1) is connected to a host computer through a wire; one end of the second end (2) is connected to the host computer; one end of the third end (3) is connected to the negative electrode of the power supply through a wire, and the other end of the third end (3) is connected to the host computer through a wire. The present invention can self-adjust the corresponding modulus according to different pressure conditions, that is, the response is completed in the first stage under different moduli, thereby achieving an adaptive effect.

The application relates to the field of battery life prediction, in particular to a multi-feature fuel cell life prediction method based on a long short-term memory network; the method comprises the following steps: collecting voltage attenuation data and collecting running state data to construct an original input data set; analyzing the running state data, calculating a correlation coefficient according to the voltage attenuation data and each running state data, analyzing the correlation coefficient, and obtaining an input feature set; processing the input feature set and the voltage attenuation data to construct a neural network training data structure; dividing the training data structure into a training set and a test set; constructing a network architecture solution space to be searched, analyzing the network architecture solution space, constructing a fitness function, and determining an optimal network architecture parameter combination based on the fitness function; and inputting the training set into the optimal network architecture parameter combination for training to generate a target detection prediction result. The application can improve the prediction accuracy of the voltage attenuation trend.

The application relates to a power distribution network voltage control method based on a reactive virtual power plant, which comprises the following steps: constructing a double-layer collaborative regulation model based on discrete and continuous reactive regulation equipment in the power distribution network under the management of the reactive virtual power plant, and establishing power distribution network physical constraints; under the power distribution network physical constraint condition, day-ahead scheduling converts the optimization control of the discrete reactive regulation equipment into a first multi-agent reinforcement learning of a discrete action space strategy for solving, and generates day-ahead scheduling planning to be issued to an intra-day correction layer; under the conditions that the power distribution network physical constraints and the day-ahead scheduling plan are taken as boundary constraints, intra-day correction converts the control of the continuous reactive regulation equipment into a second multi-agent reinforcement learning of a continuous action space strategy for solving, and generates intra-day correction instructions; the continuous reactive regulation equipment is adjusted based on the intra-day correction instructions to obtain node voltage in the power distribution network; the node voltage in the power distribution network is fed back to the day-ahead scheduling layer to realize closed-loop collaboration of voltage optimization regulation.

The application discloses a kind of vehicle-mounted Ethernet dynamic flow scheduling method and system, it is related to vehicle communication field, comprising: T1, the data stream feature registration and global view management of the equipment of access vehicle-mounted network are carried out;T2, time window is taken as scheduling unit, multiple time slots are divided in time window, and hierarchical time slot scheduling model is constructed;T3, based on flow quota, network data is monitored and flow shaping in real time, and burst flow is inhibited;T4, according to the trigger of high priority emergency data stream, time slot preemption and time window dynamic reconstruction are executed;T5, according to network transmission quality feedback, the bandwidth quota of each data stream is adaptively adjusted.Through the above scheme, time window and three-level time slot physically isolate burst flow, ensure that safety data bandwidth such as braking, steering is monopolized, significantly reduce the packet loss rate, realize key business strong guarantee.

The invention discloses a device cooperative control method and system based on a distributed system, a terminal and a storage medium, and the method comprises the steps: obtaining the operation state data and communication link information of a plurality of physical devices, mapping each physical device into a node in a dynamic topological graph, and carrying out the cooperative control of the plurality of physical devices based on the operation state data and the communication link information; constructing feature representation of the distributed system; inputting the feature representation into a collaborative decision-making model for topological adaptive processing and inter-device long-range dependency relationship modeling, and outputting collaborative decision-making instructions corresponding to the physical devices; distributing the collaborative decision instruction to corresponding physical equipment for execution, and collecting system-level efficiency feedback generated when the collaborative decision instruction is executed; and dynamically optimizing the collaborative decision model according to the efficiency feedback and a preset system-level resource constraint so as to continuously adapt to the topology and state change of the distributed system. According to the method, the self-adaption to the dynamic change network topology is realized, and accurate collaborative decision and efficient resource control can be carried out.

The utility model provides a nursing bracket for neurosurgery, which relates to the technical field of mechanical engineering and comprises a bracket, two supporting mechanisms are assembled at the top of the bracket, two rotation limiting mechanisms are assembled in the bracket, each supporting mechanism comprises a first supporting column, two supporting blocks and two arc-shaped plates, a first column rod is fixedly connected to the top of the first supporting column, a connecting rod is fixedly connected to the side end of each second supporting column, two column grooves are symmetrically formed in each second supporting column, and the side end of each column block is slidably connected into the corresponding column groove. According to the nursing bracket for the neurosurgery department, the traditional nursing bracket used for the neurosurgery department is improved, the adaptability of the traditional nursing bracket used for the neurosurgery department to arms of doctors with different body types is improved through an additional structure, and when the arms of the doctors are placed, the self-adaption effect on the arms of the doctors can be achieved simply through the supporting mechanism.

The invention discloses an intelligent floating type electrochemical water treatment system which comprises electrochemical water treatment equipment, a power supply cabinet, a control cabinet and a remote client, side buoys are arranged on two side plates of the electrochemical water treatment equipment, two wiring electrodes are arranged at the top of the electrochemical water treatment equipment, and a scale depositing bag is arranged at the bottom of the electrochemical water treatment equipment; a negative plate and a positive plate are arranged in a box body of the electrochemical water treatment equipment and are respectively connected with two wiring electrodes; the power supply cabinet is connected with the two wiring electrodes through a control cable; a regulator is arranged on the power supply cabinet; the control cabinet is connected with the power supply cabinet through a signal cable; and the remote client is connected with the control cabinet, receives the current data and the voltage data transmitted by the control cabinet in real time, and adjusts the output current or the output voltage of the power supply cabinet through the control cabinet. According to the invention, the phenomena of equipment submerging and electrode burning loss caused by too high water level of the pool can be avoided, and the running current or voltage of the electrochemical water treatment equipment can be remotely monitored and adjusted to meet the requirement of water treatment.

The invention relates to an automobile seat posture self-adaption system and method based on TOF sensors. The automobile seat posture self-adaption system comprises the front-row seat TOF sensors installed above front-row seats, rear-row seat TOF sensors installed above rear-row seats and a seat posture position self-adaption adjusting system integrated in an automobile controller. The front-row seat TOF sensor and the rear-row seat TOF sensor scan seat three-dimensional position data in real time through infrared light reflection, and the seat posture and position self-adaptive adjusting system is used for recognizing and distinguishing passengers and recording the posture of the passengers according to infrared light scanning and storing the posture and position of the seats of the passengers. According to the passenger seat posture position memory, the corresponding position is automatically adjusted after the corresponding passenger sits on each seat again; the system has the advantages that by introducing a self-learning and self-adaptive mechanism, the self-learning and self-adaptive capabilities of the system are realized, and the requirements of different passengers on seats can be better met.

The invention discloses a parameter-adaptive excitation PID (Proportion Integration Differentiation) control method, system and equipment and a readable storage medium, and the method comprises two control links: one control link is a main control link, namely, when the terminal voltage of a synchronous motor is changed, a variable parameter compensation link is set before conventional series / parallel PID control, and a variable parameter compensation link is set before conventional series / parallel PID control; according to the compensation link, the series / parallel PID parameters can be dynamically compensated according to the working points of the synchronous motor, parameter self-adaption and optimal control are achieved, and the reactive response speeds of the synchronous motor at all the working points can tend to be consistent under the condition that the zero pole of the system is not changed; and the second step is a parameter limiting link, that is, when the deviation is smaller than a deviation given value, PID parameters are attenuated in time, so that the terminal voltage of the synchronous motor is fast and stable, and the running stability of the synchronous motor can be ensured while the dynamic reactive response speed is effectively improved.

This invention relates to the fields of unmanned aerial vehicles (UAVs) and computer vision technology, and particularly to a UAV-based method for classifying bridge components. The technical problem is that existing point cloud-based automatic classification methods for bridge components suffer from issues such as scarce samples, high annotation costs, insufficient model generalization ability, and low processing efficiency. The technical solution is a UAV-based method for classifying bridge components, comprising a data acquisition step, a data augmentation step, a data preprocessing step, a model training step, and a component classification step. In the model training step, preprocessed 3D point cloud data and its corresponding component category labels are used to train a deep learning-based point cloud semantic segmentation model, enabling the model to learn the feature representations of different bridge components. This invention uses a systematic data augmentation strategy to simulate real-world acquisition interference, combined with an efficient point cloud deep learning model, to achieve accurate, robust, and efficient classification of various bridge components.

The invention discloses an accompanying cloud workload prediction algorithm based on deep learning, and relates to the technical field of network resource prediction. The non-ground mobile network is specifically a flexible and efficient communication and calculation network constructed by an unmanned aerial vehicle carrying a server and a 5G mobile base station in a maneuvering process, and can meet the communication and calculation requirements of resource-limited Internet of Things equipment in a presence scene; according to the adjoint cloud workload prediction algorithm based on deep learning, both computing resources and network resources are provided through a non-ground mobile network, and the algorithm is mainly applied to providing resource guarantee for service requirements of highly-imminent scenes. The scene generally has no preset infrastructure, and the nature, the social and the information environment show stronger uncertainty. The intelligent service also embodies the obvious characteristic of rapid change of time, space and resource demands.

The invention discloses an intelligent software test data generation method and system based on Internet public information. The method comprises the following steps: acquiring data from the Internet based on software requirements and a test scene and preprocessing the data; enhancing the data by adopting a multi-target region Bayesian optimization technology; processing the time sequence data by using a time enhancement and variational Bayesian method; reading the software development document and extracting key information; semantic understanding and expansion are carried out based on a hierarchical classification Gaussian expansion semantic model; generating preliminary test data conforming to business logic; applying test data and collecting results; and dynamically adjusting the generation strategy by using a reinforcement learning algorithm. The invention further provides a corresponding system. According to the method and system, high-quality test data can be automatically generated, and the software test efficiency and quality are improved.

The embodiment of the invention provides a vehicle control method, a vehicle and a computer readable storage medium, and the method comprises the steps: obtaining state perception data of the vehicle, the state perception data comprising physiological signal data and operation behavior data of a vital sign object in the vehicle; performing feature extraction on the state sensing data to obtain multi-modal feature data; state identification is carried out based on the multi-mode characteristic data, a state prediction result is obtained, the state prediction result is used for determining an auxiliary control mode corresponding to the vehicle, and the auxiliary control mode is used for adjusting driving auxiliary function parameters and cabin environment response of the vehicle; and controlling the vehicle by using the state prediction result. According to the invention, the technical problems of single monitoring means, insufficient sensing accuracy and delayed safety response in the prior art are solved.

The application belongs to the cross field of mobile communication technology and artificial intelligence predictive maintenance, and discloses a maintenance method and device of a relay communication equipment of a mobile communication system, the device comprises a cross-domain data sensing module, which is used for synchronously collecting physical state data and communication service quality data; a context self-adaptive analysis module, which is used for generating an environmental interference context factor based on the communication service quality data, and dynamically adjusting the analysis algorithm parameters of the physical state data by using the factor; a fusion prediction and decision module, which is used for combining the context self-adaptive physical state characteristics and the communication service quality data to perform fault prediction; and a closed-loop self-optimization module, which is used for quantifying the feedback of the execution effect of the maintenance task to automatically optimize the prediction model. The application helps to significantly improve the signal-to-noise ratio and accuracy of fault prediction under complex environments, realizes the evolution from passive prediction to intelligent self-adaptation, and guarantees the high reliability of the communication system under extreme working conditions.

The application discloses a gymnastics training auxiliary protection device and a use method thereof, and belongs to the field of gymnastics training devices.The device comprises a pad cover, a sponge pad, a support frame, a connecting frame, a fixing frame, a buffer spring and a buffer piece; one end of each of the connecting frames is connected with a connecting slot; one end of each of the fixing frames is connected with a fixing slot; one end of each of the buffer springs is fixedly connected with one end of each of the connecting frames away from the sponge pad, and the other end of each of the buffer springs is fixedly connected with one end of each of the fixing frames away from the support frame; when an athlete jumps and somersaults from a high altitude and lands, a high-intensity impact is generated, the buffer springs in the corresponding stress areas of the sponge pad are compressed and deformed, the instantaneous impact force is absorbed, the buffer springs in the non-stress areas remain unchanged and do not deform as a whole, the self-adapting effect of heavy pressure and large buffer and light pressure and stable support is realized.