508 results about "Adaptive algorithm" patented technology

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention provides an insulating oil gas mass spectrum analysis method and system. The method comprises the following steps: firstly, circulating vacuum degassing for one time, then deoiling and dehydrating the removed gas, introducing the deoiled and dehydrated gas into a mass spectrometry detection unit, carrying out primary detection, calling a self-adaptive algorithm and selecting proper circulating vacuum degassing times based on a primary detection result in combination with degassing temperature and degassing pressure of circulating vacuum degassing, and entering second-stage vacuum degassing; and deoiling and dehydrating the deoiled gas, and introducing the deoiled gas into a mass spectrometric detection unit for secondary detection to obtain a final detection result. According to the method, effective fusion of efficient degassing and rapid detection is realized, and reliable technical support is provided for rapid and accurate online monitoring and real-time fault diagnosis of the insulating oil dissolved gas.

The invention relates to the field of computing power scheduling, and discloses an artificial intelligence-based computing power scheduling method, which comprises the following steps of S1, full-dimensional data acquisition: acquiring hardware resource data, task operation data, user demand data and heterogeneous computing power characteristic data through distributed sensing nodes; s2, data processing and feature construction: preprocessing the data collected in the S1, extracting multi-dimensional features through feature engineering, and constructing a feature matrix adapted to an AI model; s3, computing power demand intelligent prediction: learning the feature data preprocessed in the S2 by using a federal deep learning model, and outputting time-phased and type-divided computing power demand prediction results; and S4, scheduling strategy optimization generation. Hardware resources, task operation, user requirements and heterogeneous computing power characteristic data are comprehensively collected by relying on edge-core two-level distributed sensing nodes, the collection frequency can be dynamically adjusted between 50ms and 1mi n through a self-adaptive algorithm, and real-time performance and resource economy are both considered.

The invention relates to the technical field of attention deficit hyperactivity disorder detection, and discloses a device for detecting cognitive and developmental conditions of children with hyperactivity disorder, which comprises a handle, a head-mounted device, a bracelet, a hierarchical adaptive algorithm module, a multi-dimensional execution module and a data fusion and analysis module, a multi-mode sensing module is arranged in each of the handle, the head-mounted device and the bracelet; a motion capture unit, a high-sensitivity key array, a tactile feedback module and a wireless transmission module are integrated in the handle, and a wireless electroencephalogram acquisition module, a miniature near infrared spectrum module and an eye movement tracking module are integrated in the head-mounted device. According to the invention, through deep fusion of a multi-dimensional cognitive evaluation system and gamepad hardware, the device not only breaks through the limitation of the traditional neuropsychological test in methodology, but also realizes refined capture of ADHD heterogeneity characteristics in the technical level, and provides a hardware basis for establishing an objective and quantitative diagnostic tool with high ecological efficiency.

An image blur correction device acquires a blur detection signal, and separates it into a high frequency band signal component and a low frequency band signal component using an HPF and an LPF. A high frequency side prediction processing unit acquires an output of the HPF, and a low frequency side prediction processing unit acquires an output of the LPF via a down-sampler. A prediction processing unit updates a filter coefficient of a prediction filter using an adaptive algorithm, and performs linear prediction on the blur detection signal. An adder adds an output of the high frequency side prediction processing unit to an output of the low frequency side prediction processing unit via a up-sampler, and outputs a superimposed output signal. Image blur correction control is performed on the basis of a superimposed output signal.

The invention discloses a magnetron sputtering coating method with uniform coating and high target material utilization rate, and belongs to the technical field of film preparation, and the method realizes high-precision coating through whole-process collaborative optimization of pretreatment, sputtering, deposition and post-treatment. The substrate is cleaned in cooperation with radio-frequency pulse focusing bombardment; an electromagnetic coil group containing a temperature compensation coil is used for target sputtering, a magnetic field is dynamically adjusted in combination with laser profile sensing and a PID self-adaptive algorithm, and a direct current pulse-radio frequency composite power supply is matched to solve the target problem; in the deposition stage, a quartz crystal instrument is used for closed-loop film thickness control, metal film pulse gas supply and compound film reaction gas phase adjustment; gradient temperature rise is carried out according to data of an X-ray stress meter in post-treatment, and argon is used for dynamic protection and impurity prevention. According to the magnetron sputtering coating method with uniform coating and high target material utilization rate, the uniformity of coating and the target material utilization rate are improved, and the technological adaptability and stability of coating are improved.

The invention provides an operation and maintenance alarm intelligent filtering and grading processing method based on a self-adaptive algorithm, and relates to the technical field of computer operation and maintenance management, and the method comprises the steps: obtaining an alarm event, extracting semantic and time sequence characteristics, carrying out the dynamic clustering through employing a self-adaptive similarity measurement mechanism, calculating a priority score based on an influence range and an emergency degree, and carrying out the calculation of an alarm result. And a self-adaptive filtering strategy is implemented in combination with the operation and maintenance resource state, and finally, filtered alarms are distributed to corresponding operation and maintenance units for processing, collection and feedback. According to the method, redundant alarms can be effectively reduced, resource allocation is optimized, and the operation and maintenance efficiency and the alarm processing accuracy are improved.

The invention provides a micro-grid black start recovery optimization method and device based on adaptive Benders decomposition, and the method comprises the steps: providing a Benders two-stage solving algorithm fused with an adaptive penalty factor for the hierarchy and constraint complexity of a micro-grid black start two-stage optimization model. In the first optimization stage, a micro-source recovery sequence and a recovery path are optimized based on a Benders decomposition method of an adaptive penalty factor, and in the second optimization stage, load recovery is optimized in combination with mixed integer programming. According to the method, the precision of the optical storage model is improved through dynamic characteristic correction, the recovery efficiency and stability are balanced through a two-stage framework, the solving performance is improved through an adaptive algorithm, and efficient and safe recovery of black start of the micro-grid is effectively achieved.

The invention discloses a time-varying and time-lag multi-CSTR system adaptive control method and system based on a neural network, and the method comprises the steps: collecting key operation parameters of a multi-CSTR system in real time, constructing a time-varying and time-lag dynamic model through a recurrent neural network, and updating a time-lag parameter estimation value. And designing a hierarchical neural network structure comprising an upper global coupling model and a lower local compensation model, designing an adaptive algorithm based on a stability theory to adjust the weight of the neural network, and generating a control instruction to drive an execution mechanism. The system correspondingly comprises a data acquisition layer, a time-varying time-delay estimation module and the like. According to the scheme, the problems of time varying, time lag and coupling of the multi-CSTR system are solved, and stable operation of the system is guaranteed.

An intelligent heat pump system autonomously manages thermal regulation in heating and cooling modes using real-time sensor data and adaptive control algorithms. The system comprises a heat pump integrated with components including a supply valve, regulating valve, return valve, and compressor expander, along with multiple sensors configured to monitor temperature and pressure conditions at various points, including the reservoir, cold side, and hot side. A removable memory stores a directive file containing operational parameters, including target temperatures, valve timing, and pressure control data. A control unit with a processor executes firmware modules that include sensor calibration routines, motor and valve control logic, and an adaptive algorithm that continuously compares real-time sensor input against target parameters. Based on deviations, the system dynamically adjusts component operations to regulate refrigerant flow, internal energy, temperature, and pressure, thereby driving the system toward a steady-state thermal condition. This autonomous system ensures efficient performance, enhanced temperature stability, and optimal energy usage across a range of varying environmental conditions.

The invention discloses an electromagnetic wire self-adhesion performance data acquisition and intelligent analysis method, and relates to the technical field of data analysis. Multi-source data such as turn-to-turn gap, paint layer residual thickness, tension fluctuation and conduction state are synchronously acquired in the winding process; and analysis mechanisms such as a gap reduction rate, paint layer thinning distribution, a synchronous evolution track and a gap strength response curve are introduced, so that continuous modeling and quantitative characterization of the autohension performance evolution process are realized. Compared with the prior art which only depends on a single gap threshold value or a post mechanical test to judge the bonding performance, the method can be started from a complete physical chain from gap change to paint layer extrusion to metal contact to bonding failure, and the change trend of the bonding performance under different gap levels can be accurately revealed; and tension output is corrected in real time through a self-adaptive algorithm, closed-loop optimization of the bonding quality is achieved, and the product percent of pass and the production efficiency are improved.

The invention provides an optimization method and system of a distributed battery management system. The method comprises the following steps: acquiring battery cell parameters, scene parameters and historical charging and discharging data of a battery cell, and evaluating a battery aging stage; calling a matched battery cell state calculation algorithm according to the scene parameters and the battery aging stage; calculating battery cell state information of the battery cell according to the battery cell parameters, the scene parameters and a battery cell state calculation algorithm; according to the cell state information, the scene parameters and the battery aging stage, dynamically adjusting an equalization threshold and an equalization rate, and executing an equalization strategy to obtain equalization state information; periodically uploading the operation data to a cloud server, receiving an algorithm update package, and updating an adaptive algorithm library; and calculating the overall state information of the battery pack according to the cell state information and the equilibrium state information. The calculation precision of the battery cell state can be effectively improved, the cycle life of the battery is prolonged, and the use safety of the battery is improved.

The invention discloses an intelligent self-adaptive learning method and system based on a course knowledge graph, and aims to construct a comprehensive knowledge graph through deep analysis of course contents, and the comprehensive knowledge graph comprises knowledge points, dependency relationships among the knowledge points, difficulty levels and learning sequences. By collecting and analyzing the learning behavior data of the students, the system can evaluate the mastering condition of the students on each knowledge point in real time, and further dynamically adjust learning paths and recommendation resources according to the individual requirements of the students. The system adopts an adaptive algorithm, optimizes a learning path and provides personalized learning resources according to the real-time learning progress and feedback of the students, so as to help the students to efficiently master knowledge in the shortest time. Meanwhile, the system further integrates a learning effect evaluation and feedback mechanism, and the accuracy and the intelligent degree of the learning scheme are gradually improved by continuously monitoring the learning process of the students.

The invention relates to the technical field of polishing pressure control, in particular to a polishing machine polishing head pressure control method and system.The polishing machine polishing head pressure control method comprises the steps that the contact area is calculated through a curvature self-adaption algorithm, and a basic pressure sequence is generated; estimating a real-time friction coefficient by monitoring motor current, stripping friction interference in combination with a geometric coupling factor, and reversely correcting a pressure sequence; and multi-step residual prediction control is introduced, an autoregression model is constructed to predict future pressure deviation, and a suppression sequence is generated to control feeding of the grinding head. Dynamic mapping between the contact area and the target pressure is established through the curvature self-adaption algorithm, and it is ensured that the pressure intensity of the complex curved surface is uniform; a non-linear friction interference algorithm is used for eliminating coupling errors, and misjudgment of the controller is prevented; residual prediction control is introduced to overcome system lag, advanced suppression of pressure fluctuation is achieved, and grinding robustness and surface quality are remarkably improved.

The invention discloses a lake water quality abnormity monitoring method and system based on multi-source sensing data fusion, and the method comprises the steps: collecting water quality data through a multi-source sensor, and obtaining high-quality fusion data through weighted fusion and Kalman filtering processing; and an abnormal threshold is dynamically adjusted in combination with a seasonal baseline, and a polluted area is accurately identified. Incremental sensor layout is optimized based on hydrological characteristics, a fusion network is expanded through an adaptive algorithm, and monitoring coverage and data consistency are improved. A comprehensive abnormal index is constructed by utilizing principal component analysis and multi-parameter coupling, so that time-space accurate positioning and visual display of lake water quality abnormity are realized, and intelligence, real-time performance and early warning capability of water quality monitoring are remarkably enhanced.

The invention relates to the field of digital certificates and certificate application, and discloses an anti-quantum cryptography agile migration method capable of dynamically adapting to an algorithm, which is cooperatively executed by a strategy server, a certificate server and an application terminal: the strategy server monitors a migration progress and dynamically generates and issues a migration strategy; the application terminal analyzes the strategy and applies for a specified type of certificate from the certificate server; the certificate server carries out processing and issues a certificate; and the application terminal dynamically selects an algorithm compatible with the target application to establish secure connection according to the strategy during communication. According to the method, through automatic certificate management driven by a strategy and terminal self-adaptive algorithm selection, agile and smooth migration of the cryptographic algorithm is realized, and the problems of compatibility and safety management in the migration process are effectively solved.

The invention discloses a sea route network construction method based on multistage grid clustering, and the method comprises the steps: setting a navigation region, collecting navigation data, carrying out the noise reduction, preprocessing and segmenting, and obtaining processed data; a space grid structure and a KD-tree structure are introduced on the basis of the DBSCAN algorithm to form a GBSC algorithm; a dynamic threshold mechanism is introduced on the basis of a Douglas-Peucker algorithm, and an adaptive DP algorithm is obtained; screening, clustering and correcting the trajectory segments and the trajectory points by using a GBSC algorithm and an adaptive DP algorithm to obtain a feature point set; a feature point closest to the track point is found in the feature point set through a KD-tree structure, a feature point sequence is obtained, repeated nodes are compressed to obtain a node sequence, adjacent nodes are connected, and an edge set is obtained to construct an adjacent matrix and an edge weight matrix to form an air route network; the method can truly reflect the actual navigation structure of the ship in the sea area, has a high coincidence degree with the original AIS trajectory, and significantly improves the accuracy and reliability of air route extraction.

The invention discloses an automatic driving method based on an improved Astar algorithm and a hierarchical state machine, and the method consists of three parts: road topology abstract graph construction, a lane adaptive Astar algorithm and the hierarchical state machine, and forms an efficient and cooperative'planning-decision 'integrated frame. Firstly, a road topology abstract graph is constructed. Secondly, path planning is carried out by adopting an improved lane adaptive Astar algorithm, and two stages of search strategies are included: in the first stage, road section-level global planning is carried out on a road topology abstract graph, and a macroscopic feasible path is rapidly determined; in the second stage, lane-level refined path generation is carried out in the selected road section, road curvature, width and static and dynamic obstacle distribution are comprehensively considered, and a smooth and safe accurate driving track meeting vehicle dynamics constraints is generated. Finally, a hierarchical state machine is designed, and the top layer is responsible for managing a driving mode and ensuring the global consistency of decisions; and the bottom layer executes fine speed control and lane changing control according to the top layer instruction.

The invention relates to the technical field of industrial automation control, and discloses a multi-station cooperative control method and system of a distributed PLC. The method comprises the steps that independent PLCs are arranged at feeding stations of an annular conveying line, and PLC control units are synchronously formed through an industrial Ethernet and a global clock; the order parameters and the target process information are written into a code carrier on the tray trolley through an RFID radio frequency read-write device, and the code carrier records the process progress, the detection result, the abnormal code and the beat deviation; when the code carrier enters each feeding station along with the tray trolley in sequence, the PLC control unit reads information, monitors the position of the trolley, executes a corresponding task and updates process information in real time; when the detection result is abnormal or the beat deviation exceeds a threshold value, the system triggers a beat adaptive algorithm to generate an optimization instruction set to guide the production beat adjustment; and the warehousing station combines the optimization instruction to execute finished product warehousing or repair processing. The automation level and the production efficiency of a production line are improved.

The invention discloses an electronic archive management method for a business document generation layout file, and belongs to the technical field of electronic archive management. Comprising the following steps: constructing a multi-dimensional feature vector by collecting business document scene features, screening candidate templates from a template pool by adopting a multi-level similarity matching algorithm, and adjusting and determining a target template in combination with a dynamic structure; the basic metadata and the associated system data are integrated, and qualified metadata is output through multi-level rule verification and abnormal data correction; based on the target template, executing data filling typesetting and format resource preprocessing by adopting a double-thread asynchronous processing mechanism, and generating a layout file conforming to PDF / OFD double standards through an adaptive algorithm; calculating a hash value of the layout file and writing the hash value into a block chain to realize tamper-proof evidence storage, and realizing secure storage and quick retrieval by adopting a distributed architecture in which a main file is separated from an index file. According to the method, the problems of low template matching precision, incomplete data verification, low processing efficiency, insufficient file safety guarantee and the like in the prior art are solved.

The invention discloses an oil cylinder sealing optimization system based on electromagnetic induction regulation and control, and the system comprises an electromagnetic induction unit, a coil of which generates an alternating magnetic field in a sealing area, and acts on a magnetic sensitive composite material sealing element; the sensor unit monitors the leakage rate, the temperature and the pressure in real time; the control unit executes a self-adaptive PID control algorithm, dynamically adjusts PID parameters according to sensor data, and outputs a PWM signal to control the electromagnetic induction unit so as to optimize the sealing performance in real time; the controllable alternating magnetic field is applied to the magnetic-sensitive sealing element through the electromagnetic induction unit, the elastic modulus and the contact pressure of the magnetic-sensitive sealing element are regulated in real time through the magnetocaloric or magnetostrictive effect, meanwhile, key parameters such as leakage, temperature and pressure are monitored in real time in combination with a sensor, and the sealing state is rapidly responded and dynamically adjusted through a self-adaptive PID algorithm in the control unit. Oil cylinder sealing can be self-optimized according to working condition changes, and the problem that traditional static sealing cannot adapt to dynamic working conditions is solved.

The invention discloses a drainage pipe network multi-modal data analysis method for urban inland inundation prevention and control, and belongs to the technical field of urban inland inundation prevention and control. According to the method, satellite remote sensing surface ponding images, drainage pipe network liquid levels, regional meteorology, public ponding crowdsourcing information and terrain elevation and pipe network attribute data are collected and integrated into a multi-dimensional original data set; after cleaning and standardization processing, mining waterlogging associated features by using an adaptation algorithm, and establishing a coupling relation model; waterlogging risk reasoning is carried out based on the model, and graded early warning information and pump station scheduling, pipe network dredging and emergency dredging path schemes are generated; and performing visual analysis on a result, and dynamically optimizing a model and a scheme according to real-time updated data. According to the scheme, waterlogging influence factors are comprehensively covered, the accuracy of risk assessment and prevention and control decision is improved, dynamic changes of waterlogging are adapted, and the initiative and systematicness of urban waterlogging prevention and control are enhanced.

The invention belongs to the technical field of hydroelectric power generation equipment and electromagnetic control, and particularly relates to an active magnetic suspension bearing vibration reduction device and method for vibration reduction of a hydroelectric generating set. The device comprises a displacement sensor, a control system, a power amplifier, a radial magnetic bearing, an axial magnetic bearing and a high-speed motor, the rotor offset and the vibration signal are detected in real time through the sensor, the controller calculates output based on PID, fuzzy control or a self-adaptive algorithm, dynamic adjustable magnetic force is formed by adjusting the current of the excitation winding, rigidity and damping regulation and control are achieved, and therefore vibration of the hydroelectric generating set is restrained.

The invention discloses a power distribution device parameter setting system and method based on online learning and digital twinning, which are used for solving the problem of multi-parameter collaborative setting of a direct test loop. The system acquires operation data such as voltage, current and temperature of an actual test loop in real time through the data acquisition module; the real-time data is transmitted to the digital twinning module through the Internet of Things cloud module, the real-time data is compared with the output of the digital twinning module, and parameters are dynamically corrected by adopting a self-adaptive algorithm; the test loop parameters subjected to dimensionless processing serve as optimization variables, differential equations output by a digital twin module serve as constraint conditions, an optimization function with the purpose of minimizing the difference between the TRV characteristic parameters and the standard values is constructed, parameter optimization and simulation verification are carried out, output is carried out through a control terminal, and accurate setting of the test parameters is achieved. According to the invention, electrical, thermodynamic and electromagnetic characteristics under different test conditions can be comprehensively simulated, and the repeated debugging times and time required by a traditional method are greatly reduced.

The invention discloses a color consistency calibration system for AMOLED flexible display, which relates to the field of display and comprises a deformation sensing module, a color acquisition module, an aging state sensing and dynamic compensation module, a user personalized color gamut adaptation module, a data fusion processing module, a self-adaptive calibration algorithm module, a calibration execution module and a real-time feedback verification module. According to the AMOLED flexible display color consistency calibration system, screen body stress and deformation data are captured through deformation perception, the pixel attenuation state is monitored through aging perception, a personalized module adapts to user color preference, partition integrated calibration parameters are generated through data fusion and a self-adaptive algorithm, a closed loop is formed through calibration execution and real-time feedback, and the color consistency of the AMOLED flexible display is calibrated. According to the AMOLED flexible screen color consistency calibration method, the defects that deformation-aging composite drift is ignored and personalized adaptation is lacked in the prior art are overcome, full-scene and full-life-cycle color consistency calibration of the AMOLED flexible screen is achieved, accuracy and user experience are considered, and the AMOLED flexible screen color consistency calibration method is suitable for various flexible display terminals such as folding screens and curled screens.

The invention discloses an unmanned aerial vehicle inspection maintenance planning method and device based on natural language man-machine interaction, and relates to the technical field of unmanned aerial vehicle multi-task cooperative scheduling. According to the scheme, natural language tasks are understood through semantic analysis, and maintenance procedures and resource requirements are automatically reasoned by using a knowledge graph; then, a multi-objective optimization model fusing inspection time efficiency, cost, preventive benefits and maintenance technician task load balance is constructed; and adopting an adaptive algorithm to solve and generate a candidate maintenance plan set, determining a target maintenance plan based on multi-dimensional evaluation, and finally synchronously outputting a maintenance scheduling scheme and an updated inspection task allocation scheme. According to the scheme, dependence on data entry of artificial experience and scheme planning is reduced through natural language analysis and knowledge graph reasoning, automation of maintenance decision is achieved, meanwhile, maintenance and inspection are uniformly optimized under a multi-target framework, a preventive maintenance mechanism is introduced, conversion from passive response to active prevention is achieved, and the maintenance efficiency is improved. And the overall utilization efficiency of resources is improved.

The invention relates to the technical field of agricultural engineering, in particular to a self-adaptive greenhouse environment control system which comprises a sensing layer, a decision-making layer, an execution layer and an optimization layer, and the sensing layer, the decision-making layer, the execution layer and the optimization layer achieve inter-layer data penetrating interaction and self-adaptive resource scheduling through a cross-layer dynamic cooperation mechanism. The sensing layer is used for collecting greenhouse environment parameters, crop physiological parameters and greenhouse physical structure parameters; the decision-making layer is used for carrying out collaborative decision-making through a physical model module and a self-adaptive MPC algorithm module based on the data collected by the sensing layer, and generating a control instruction; the execution layer is used for operating greenhouse environment regulation and control equipment according to the control instruction of the decision layer; and the optimization layer is used for dynamically adjusting the optimal combination of the equipment based on a preset regulation and control target. Through coupling of the cross-layer collaborative architecture, the physical model and the self-adaptive MPC algorithm, the greenhouse environment regulation and control precision is improved, the energy consumption is reduced, the per unit yield of crops is increased, and the labor and equipment cost is reduced.

The invention discloses an internet of things simulation method, device and equipment, a storage medium and a product, and relates to the technical field of simulation, and the internet of things simulation method comprises the following steps: responding to a simulation execution request, running a communication process in a function agent equipment node and a simulation virtual environment, and obtaining a communication result, the function proxy equipment nodes comprise static behavior nodes and dynamic behavior nodes, and the dynamic behavior nodes can adaptively adjust communication parameters; and visually displaying the communication result on a graphical interface. According to the method and the device, after the simulation execution request is received, the communication process is operated in the function agent equipment node and the simulation virtual environment to obtain the communication result, and the communication result is visually displayed on the graphical interface; and the dynamic behavior nodes can adaptively adjust communication parameters, so that the real effect of the adaptive algorithm can be verified in real time.

The application discloses a modulation and coding adaptive selection method for a mobile ad hoc network based on separation of a control plane and a data plane. In the mobile ad hoc network based on separation of the control plane and the data plane, a carrier sense multiple access (CSMA) mechanism is adopted in a control time slot to compete for sending fused situation information of itself, so as to complete survey and perception of neighbor nodes. The situation information includes link transmission quality and node queue length. In the data plane, the CSMA mechanism is also adopted, but the situation information of the control plane needs to be utilized to form certain 'network order', so as to reduce the decline of throughput caused by error packets and collision. On the basis of a traditional rate adaptive algorithm based on a signal-to-noise ratio (SNR), the algorithm is improved and applied to a mobile ad hoc network device based on separation of the control plane and the data plane.

The present application relates to tower crane anti-swing motion control technical field, especially a kind of six-degree-of-freedom tower crane anti-swing control method and system based on sliding mode control.The dynamic model of tower crane is constructed based on Lagrange method;The dynamic model of tower crane is linearized in equilibrium position using the constructed tower crane;According to the state information of tower crane, the error signal is selected to design the sub-sliding surface, and the initial control law is designed using the principle of negative rate of Lyapunov function change;Unknown disturbance and model uncertainty are estimated using adaptive algorithm to obtain updated control law;Design total sliding surface and introduce auxiliary function to select appropriate approach law to obtain auxiliary control law;The final control law is obtained by combining the updated control law with the auxiliary control law.The problem that tower crane is easily disturbed in complex environment is effectively solved by constructing hierarchical sliding surface and introducing adaptive algorithm, and the working efficiency and performance of tower crane are significantly improved.