2262results about How to "Accurate calculation" patented technology

The invention discloses a patient rehabilitation training data acquisition method and system based on visual identification, and relates to the technical field of rehabilitation training data acquisition, and the method comprises the steps: carrying out the image segmentation of a patient rehabilitation training video through a deep learning model, and separating a patient motion feature region from the patient rehabilitation training video; marking key skeleton nodes in the patient motion feature region, and establishing a three-dimensional coordinate system of the key skeleton nodes; calculating a motion angle and displacement data between adjacent skeleton nodes according to the three-dimensional coordinates of the key skeleton nodes; and performing matching degree analysis on the motion angle and displacement data and a preset rehabilitation standard to generate a rehabilitation training score. According to the method, the deep learning model is adopted to perform image segmentation on the rehabilitation training video, the motion feature region of the patient is accurately extracted from the image, and key skeleton nodes are marked and analyzed in combination with the three-dimensional coordinate system, so that accurate acquisition of rehabilitation training data of the patient is realized.

The invention relates to the technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention relates to a structural parameter construction method for reducing vibration of a transformer. The method comprises the following steps: acquiring vibration characteristics and working condition data of the transformer; constructing a genetic algorithm model based on the objective function; candidate solution vibration performance verification based on finite element simulation; constructing a hybrid optimization strategy based on a genetic algorithm and finite element simulation; verifying and optimizing the scheme under a multi-dimensional working condition; outputting a vibration calculation result and evaluating a scheme. According to the structural parameter construction method for reducing the vibration of the transformer, the preliminary schemes are screened by applying a fast convergence genetic algorithm, and the candidate schemes are subjected to high-precision verification in combination with finite element simulation, so that the defects of a traditional vibration calculation method in the aspects of efficiency and accuracy are overcome; and the design change can be quickly responded, and the vibration performance is optimized, so that the high-standard requirements of modern power equipment on the aspects of safety and reliability are met, and powerful technical support is provided for the design and improvement of the transformer.

The invention discloses a three-dimensional point cloud reconstruction volume calculation method, and relates to the technical field of data processing, and the method comprises the steps: S1, obtaining point cloud data in a stock bin; s2, generating an initialized grid M according to the point cloud data; s3, generating an Alpha shape of the point cloud according to the point cloud data; s4, building the grid M, and deleting the point cloud data outside the Alpha shape; s5, performing Laplace smoothing processing on the point cloud data processed in the step S4; s6, performing hole filling processing based on a minimum curved surface on the point cloud data processed in the step S5 to obtain a three-dimensional reconstruction model; and S7, calculating the volume of the three-dimensional reconstruction model according to the calculus principle. The point cloud can be accurately calculated through a calculus method after the point cloud model is reconstructed.

The invention discloses a low earth orbit satellite Internet of Things communication method and system based on a dynamic block chain, and the method specifically comprises the steps: taking at least one ground station as a consensus node in each network partition, forming a consensus network, and selecting a main node from a plurality of consensus nodes of each consensus network through employing an RBFT algorithm; a satellite transit algorithm is adopted to predict transit information of each low-orbit satellite, and the low-orbit satellites passing through any consensus network are set as accounting nodes of the consensus network according to the transit information; the method comprises the following steps: acquiring a service request of a satellite communication terminal, forwarding the service request to a main node of a consensus network to which the service request belongs through a low-orbit satellite of a transit satellite communication terminal, performing verification and priority ranking on the service request according to the main node, and if the verification is passed, performing broadcast and consensus verification on all nodes of the current consensus network. According to the invention, the communication efficiency and safety of the low-orbit satellite Internet of Things are improved, and reliable technical support is provided for satellite Internet application.

The invention relates to the technical field of PCB defect detection, and discloses a PCB defect detection method based on a coordinate attention mechanism, and the method comprises the steps: obtaining a positive sample data set; inputting the positive sample data set into a defect detection model, and outputting a plurality of local neighborhood feature sets; repeated items are screened from the multiple local neighborhood feature sets, and the screened local neighborhood feature sets are obtained and stored in a memory bank; inputting a to-be-detected PCB picture into the defect detection model, and outputting a plurality of local feature sets; acquiring a first local neighborhood feature set closest to each local feature set from a memory bank; calculating a local anomaly score according to each local feature set and the corresponding first local neighborhood feature set; acquiring a global abnormal score graph based on all the local abnormal scores; and determining an abnormal region according to the global abnormal score graph and an abnormal threshold value, and segmenting the abnormal region to complete detection. According to the invention, the PCB defect detection accuracy and the defect segmentation precision can be improved.

The invention relates to a pier damage early warning analysis method under the action of flood, and solves the technical problems of high disaster recognition cost, low bridge damage recognition precision, untimely early warning and alarming and the like based on monitoring equipment in the existing concrete bridge flood-disaster-resistant technology. According to the method, by means of an established plastic damage simulation model, expensive sensors do not need to be arranged on a bridge structure, only basic information of the bridge structure and flood information such as the flow speed, the water level and the scouring depth are obtained, and damage and failure analysis of the bridge pier structure under the action of different influence factors by the flood action can be studied; structural damage state qualitative analysis and modal integrity damage index quantitative analysis are combined to determine bridge pier structure damage grades and damage failure judgment criteria, and early warning values corresponding to different damage grades of a bridge pier are inverted, so that accurate damage identification and rapid grading early warning of the bridge pier structure under the flood effect are realized. The flood disaster resistance of the bridge structure is improved, safe operation of the bridge is guaranteed, and the service life of the bridge is prolonged.

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention provides a forest fire prediction method and system using a remote sensing image, and relates to the technical field of data processing, and the method comprises the steps: defining a fitness function used for evaluating the advantages and disadvantages of each individual, and each individual represents a group of weight parameters; randomly generating an initial population meeting a coding mode, wherein each individual represents a group of weight parameters; the fitness of each individual is calculated according to a fitness function, and a new generation of population is generated through selection, crossover and mutation operation; judging whether a preset termination condition is met or not, if the termination condition is met, stopping iteration, and outputting a current final weight parameter combination; according to the final weight parameter combination, combined with land surface temperature, vegetation coverage rate and vegetation water content data, calculating a forest fire danger dynamic danger index; and classifying the fire danger grades according to the forest fire danger dynamic danger index. The accuracy of the prediction result can be improved.

The invention relates to a method and a system for realizing channel characteristic simulation of a wireless channel model of an unmanned ship based on an unmanned aerial vehicle. The method comprises the following steps: constructing the wireless channel model of the unmanned ship by the unmanned aerial vehicle; the unmanned ship wireless channel model comprises the steps of generating a channel impulse response, generating an unmanned aerial vehicle random track, generating an unmanned ship random track, generating an evaporation waveguide model, generating an evaporation waveguide model, generating small-scale parameters and updating the small-scale parameters; calculating channel statistical characteristics; the channel statistical characteristics comprise time delay power spectrum density, a space-time correlation function, a stationary interval and root-mean-square time delay extension. The method can better correspond to a track in a real environment, and the randomness of the track is realized.

The invention discloses a high-voltage cable distributed continuous monitoring big data analysis processing method, and relates to the technical field of high-voltage cable operation monitoring and fault early warning. Calculating a partial discharge pulse average value, a current harmonic component and a voltage fluctuation amplitude in sequence by combining a temperature change rate and the like; calculating a predicted temperature influence index according to the temperature change rate, the temperature data and the operation state data, sequentially calculating a predicted partial discharge influence index, a predicted current influence index and a predicted voltage influence index in combination with the predicted temperature influence index, and calculating an operation basic index according to the basic parameter data; and finally, calculating a cable operation risk index according to the operation basic index and the predicted voltage influence index, setting a risk index threshold set, judging whether to trigger early warning or not according to a comparison result, and generating an adjustment instruction and a risk assessment report so as to guarantee safe operation of the high-voltage cable.

The invention discloses an ultra wide band (UWB) real-time positioning method and system for an obstacle shielding scene. The UWB real-time positioning method comprises the steps of initializing a positioning system, collecting UWB ranging and IMU data, executing multilateral positioning and preliminary positioning calculation, constructing a Jacobian matrix, iteratively optimizing position parameters, updating the posture of an unmanned vehicle, performing state prediction and error correction, performing robustness fusion on UWB and IMU data, and comprehensively processing a result to output an accurate position. According to the method, IMU data and UWB ranging data are fused, position parameters are optimized by using an error state Kalman filter and a Levenberg-Marquardt algorithm, high-precision positioning is realized, and meanwhile, the method has robustness to UWB ranging errors and IMU measurement noise.

The invention discloses a structure parameter library construction method, a super-lens design method and an optical system. The super-structure grating primitive structure parameter library construction method comprises the steps of obtaining a first grating primitive structure and determining a first periodic distribution function according to the first grating primitive structure; according to the characteristics of the grating primitive structure, full-analytic Fourier transform expansion is carried out on the first periodic distribution function, and a first Fourier analytic function corresponding to the first grating primitive structure is obtained; performing strict coupling wave analysis on the first Fourier analytic function in a preset polarization direction to obtain first complex amplitude information; and according to the first complex amplitude information, determining first matrix information used for describing a polarization behavior of a first grating primitive structure, and associating the first matrix information and the first grating primitive structure as first design information in a parameter library. The structural parameter library construction method provided by the invention is suitable for an application scene for processing polarization characteristics and phase information, and avoids segmentation or discretization.

The invention discloses an industrial internet security situation assessment system based on deep learning, and relates to the technical field of industrial internet security, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a deep learning model training module and a data storage module, the system has the advantages that the deep learning model training module adopts a combined architecture and an optimization algorithm to train an accurate model, the real-time monitoring module monitors real-time data in the industrial internet according to received data, abnormal conditions are found in time, and the real-time monitoring module monitors the real-time data in the industrial internet according to the real-time data. The method comprises the following steps of: acquiring the security situation value data of the current industrial internet, transmitting the security situation value data of the current industrial internet to a situation awareness module, and performing deep quantitative evaluation and trend prediction by the situation awareness module by using a fuzzy logic and evidence theory fusion algorithm and integrating historical and real-time data to accurately calculate a comprehensive security situation value so as to calculate a more accurate comprehensive security situation value. And accurate judgment and timely and effective response to the security situation are ensured.

The invention discloses a spaceborne hyperspectral detection laser radar atmospheric particle inversion method. The method comprises the following steps of solving a laser ranging value and an altitude corresponding to a spaceborne hyperspectral resolution laser radar (HSRL) echo signal sampling point; reducing the noise of the echo signal by using a noise removal method; carrying out distance and system constant correction on the de-noised signal; a forecasting model is used for reanalyzing the data set, and S6, a molecular scattering signal transmittance lookup table is established through an atmosphere model and an iodine molecule hyperspectral filter absorption spectrum; calculating back scattering coefficients and scattering ratios of the cloud and the aerosol; performing hierarchical identification by using a scattering ratio and voltage signal-to-noise ratio threshold method; determining an effective inversion region through hierarchical region processing; calculating an initial extinction coefficient to a radar ratio; and performing Gaussian smoothing filtering on the radar ratio to obtain a final radar ratio, and calculating a final extinction coefficient profile. The method is characterized in that cloud and aerosol level identification is carried out on spaceborne HSRL echo signals, a molecular transmittance lookup table of a hyperspectral channel is established, and cloud and aerosol optical parameters are accurately and efficiently solved.

The invention provides a dynamic load electric energy metering optimization method and system. Belongs to the technical field of data processing, and comprises the following steps: acquiring voltage and current signals in a dynamic load environment in real time by using a high-precision sensor, and discretizing the acquired signals to obtain a voltage value and a current value of a sampling point; and performing statistical analysis on the voltage and current values of the sampling points, extracting key parameters, and constructing a dynamic load feature vector based on the extracted key parameters. By collecting voltage and current signals in real time and carrying out accurate statistical analysis and feature extraction, the electric energy error value can be calculated more accurately, so that the accuracy of electric energy metering is improved.

The invention discloses a monitoring method and device for a cable-supported bridge, a storage medium and a program product, and relates to the technical field of bridge engineering, and the method comprises the steps: obtaining a vibration signal of each bridge cable of a to-be-detected bridge in a current monitoring period; for any target inhaul cable in the bridge inhaul cables, fast Fourier transform is carried out on the target vibration signal to obtain a target spectrogram, and the target vibration signal is the vibration signal of the target inhaul cable; identifying a main frequency peak of each amplitude value in the target spectrogram, determining a respective frequency order of each main frequency peak based on a main peak frequency value of each main frequency peak, and determining an inhaul cable force value of the target inhaul cable based on each frequency order; and performing anomaly detection on the to-be-detected bridge based on the cable force value of each inhaul cable to obtain a monitoring result. The vibration frequency order of the inhaul cable is identified, so that the cable force value of the inhaul cable is calculated based on the frequency order, the accuracy of the cable force value is improved, and the accuracy of bridge vibration monitoring is improved.

The invention discloses an electric power system inertia online evaluation method, device and equipment and a readable storage medium, and relates to the technical field of novel electric power systems, a unified inertia analysis model comprising network construction type equipment, network following type equipment and a synchronous generator is established through theoretical derivation, then dynamic modeling is performed based on extended Kalman filtering, and the inertia of the electric power system is evaluated. Inertia solution is converted into a parameter identification problem, so that inertia levels of various generators in the system are obtained, continuous tracking of equivalent inertia of the system is realized by adopting a recursive least square algorithm with a forgetting factor, and accurate calculation of inertia of each unit can be realized. The local and system equivalent inertia of the new energy power system under various control strategies is effectively evaluated, and an important reference basis is provided for power grid operation.

The invention provides an electron microscope automatic focusing method and system based on a deep learning model, and relates to the technical field of electron microscope automatic focusing, and the method comprises the steps: collecting a plurality of images at different focusing distances through an electron microscope, and recording the focusing distance of each image; calculating the characteristic value of each collected image, combining the focusing distance and the characteristic value of the image into a characteristic vector, and generating a characteristic sequence according to the characteristic vectors obtained under different focusing distances; training a pre-constructed neural network model by using the feature sequence to obtain a trained neural network model; and inputting a to-be-tested image into the trained neural network model, calculating a difference value between the predicted optimal focusing distance and the focusing distance of the collected image, and judging whether the difference value is within a set range or not. According to the invention, the microscopic image acquisition efficiency and quality are obviously improved.

The invention discloses a melon and fruit planting water and fertilizer consumption data collection and analysis system and method, and particularly relates to the technical field of melon and fruit planting. The method comprises the following steps: acquiring readings of a plurality of sensors in real time, identifying and marking data conflicts, dynamically adjusting the credibility weight of each sensor according to the correlation and precision change of the readings of the sensors, and calculating an abnormal coefficient of water and fertilizer consumption data; the abnormal coefficient and the set gradient standard threshold value are compared and analyzed, the sensor reading is corrected, and the irrigation plan is dynamically adjusted, so that the problem of wrong decision caused by data conflicts of different sensors is effectively solved, the irrigation system is ensured to operate more accurately and stably, and the scientificity of crop growth and the resource utilization efficiency are improved.

The invention discloses an automatic control method and system for water-based gravure ink production, and relates to the technical field of production control. An automatic control system for water-based gravure ink production comprises a formula acquisition module, a raw material configuration module, a dynamic adjustment module, a performance detection module, a finished product detection module and a record optimization module. The production batches in the production schedule are automatically obtained, the corresponding ink formula is matched, it is guaranteed that the raw material demand quantity of each production batch is accurately calculated, human errors are reduced through automatic configuration and accurate metering of the raw materials, and high efficiency and consistency of production are guaranteed; the temperature and the viscosity are monitored in real time in the mixing process, and the stirring time and the stirring power are dynamically adjusted according to the standard curve, so that the mixing process is optimized, the stability and the consistency of product performance are ensured, and meanwhile, the production period is shortened.

The invention relates to the technical field of semiconductor material preparation, and provides a method for controlling the resistivity of a fifth main group element doped crystal bar, crystal pulling time is distinguished in different crystal pulling stages, the volatilization coefficient of doped elements in different time periods is adjusted, the volatilization amount of the doped elements in each time interval is calculated more accurately, and the resistivity of the doped crystal bar is controlled. Therefore, the amount of doping elements required for controlling the resistance can be calculated more accurately, the resistance of the crystal bar can be adjusted more effectively and more accurately, the resistance difference between the head and the tail of the single-crystal crystal bar can be better reduced, meanwhile, through timely adjustment of the control process of the grade and the ending volatilization amount, the supplementary doping condition in the crystal pulling process is defined, and the crystal pulling efficiency is improved. And the resistance hit rate of the crystal bar is improved, and the head and tail resistance difference is reduced, so that the resistivity of each part of the crystal bar is accurately controlled, and the consistency of subsequent silicon wafer product quality is ensured.

The invention relates to the technical field of carbon emission accounting, and particularly discloses a carbon emission calculation method and system for railway infrastructure construction, and the method comprises the steps: firstly, calling basic data of a railway infrastructure from a background database and corresponding carbon emission which is verified to be real to construct a reference sample set; and carrying out embedded coding and dynamic semantic search on the basic data of the to-be-verified railway infrastructure uploaded by the user and each reference sample by adopting a data processing technology based on deep learning, so as to query response information based on the semantic query between the to-be-verified basic data and each reference sample. And the reasonable carbon emission amount of the railway infrastructure to be verified is intelligently estimated, and whether the total carbon emission amount of the railway infrastructure to be verified uploaded by the user is true or not is judged based on the reasonable carbon emission amount of the railway infrastructure to be verified. According to the method, rapid and accurate calculation and verification of the carbon emission in the railway infrastructure construction process can be realized, false declaration is avoided, and the authenticity and reliability of the carbon emission data are improved.

The invention relates to a high-precision variable air volume valve and an air volume control method thereof. The variable air volume valve is composed of a valve body, a front valve block set, a rear valve block, two actuators and a controller, linear adjustment of the air volume can be achieved, the air volume adjustment precision is improved, and adjustment of high and low air volume intervals is supported. The controller collects pressure difference data of an inlet and an outlet through the pressure difference sensor, and the real-time air volume is calculated through a built-in Kolmogorov-Arnold (KAN) neural network model. By comparing the actual air volume with the target air volume in real time, the controller adjusts the actuator to change the opening degree of the front valve plate set and the opening degree of the rear valve plate set, and accurate air volume control is achieved. According to the technical scheme, the accuracy and the adjusting range of air volume measurement are improved, additional resistance in the air pipe is reduced, and the performance and the operation efficiency of an air conditioning system are remarkably optimized.

The invention provides a method for calculating migratory parent fish quantity by using fish early-stage resource quantity, which comprises the following steps: collecting early-stage resource quantity data, migratory parent fish quantity data and various parameters, and processing the collected data to obtain a comprehensive data set; constructing a basic model for representing the relationship among the number of roes, the number of larva fishes and the number of migratory parent fishes in combination with various parameters; designing a model architecture of the basic model by using a long-short-term memory network to obtain an initial prediction model, and quantifying the initial prediction model by using a Bayesian method to obtain a calculation prediction model; in combination with an MCMC method, performing parameter estimation and model training on the reckoning prediction model to realize optimization of the reckoning prediction model; and calculating the number of migratory parent fishes by using the optimized calculation and prediction model. According to the method, multiple data sources can be combined, the ecological environment complexity, the time sequence dynamics and the prediction uncertainty are fully considered, and accurate calculation of the migratory parent fish quantity is achieved.

The invention discloses a muck volume measurement method, system and device for shield tunnel construction, and the method comprises the steps: obtaining first data corresponding to muck to be measured in the shield tunnel construction in real time based on a self-adaptive window; in combination with a triangle cumulative sectional area method and an AE-FPFH-ICP algorithm, performing parallel processing to generate second data and third data corresponding to the to-be-measured muck; according to the first data, in combination with the second data and the third data, fourth data corresponding to the muck to be measured is generated in real time; the invention discloses a muck volume measuring system, a corresponding system and a corresponding device, the muck volume can be accurately calculated in real time under the condition of high-frequency data acquisition, the calculation process is simplified, the section measurement precision is improved, and the high automation of the measurement process is realized by automatically calculating the running speed of a conveyor belt; and numerical integration is carried out in combination with a Simpson method, so that the accuracy of volume calculation is ensured, and a more reliable and efficient solution is provided for muck volume measurement in tunnel construction.

The invention provides an electric energy meter correction electric quantity calculation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining metering historical data of a target electric energy meter; analyzing the metering historical data by adopting a phase angle feature portrait algorithm, and identifying a wiring error type corresponding to the target electric energy meter; determining a corresponding correction transformation matrix based on the wiring error type; and calculating the corrected electric quantity of the target electric energy meter based on the corrected transformation matrix. The invention provides a novel calculation scheme for the corrected electric quantity of the electric energy meter, the corrected electric quantity of the electric energy meter is calculated more accurately by adopting an advanced algorithm and a mathematical model, the interference of human factors is reduced, various complex wiring error conditions can be adapted, and the calculation efficiency is improved. Therefore, the accuracy and reliability of the correction electric quantity calculation method of the electric energy meter of the power system are improved.

The invention discloses a coal conveying belt deviation detection method, system and device based on a YOLOv8 algorithm. The method comprises the steps that an image of a coal conveying belt is collected in real time and preprocessed; performing edge detection on the coal conveying belt in the coal conveying belt image by using a YOLOv8 algorithm; performing deviation calculation according to a first deviation calculation step in combination with the edge coordinates of the coal conveying belt to obtain a first deviation parameter; working condition data and environment data of the coal conveying belt are collected in real time; by means of a first deviation parameter self-adaptive threshold value adjusting mechanism, a first deviation parameter threshold value adjusting value corresponding to the working condition data and the environment data collected in real time is obtained, a first deviation parameter preset threshold value is adjusted, and whether the calculated first deviation parameter exceeds the first deviation parameter preset threshold value obtained after threshold value adjustment or not is judged; and obtaining a first result of whether deviation occurs or not in real time and correspondingly generating deviation prompt information of the coal conveying belt. The coal conveyor belt deviation detection device can complete more accurate coal conveyor belt deviation detection.

The invention discloses a three-dimensional magnetotelluric inversion method, a three-dimensional magnetotelluric inversion device and three-dimensional magnetotelluric inversion equipment, and relates to the technical field of three-dimensional magnetotelluric inversion processing. A network model suitable for three-dimensional magnetotelluric inversion is constructed based on a deep learning technology, so that the input of the model is apparent resistivity and phase; the output is a three-dimensional resistivity distribution parameter of the to-be-measured area; a data-driven and physical-driven combined weighted loss function is designed and introduced into the network model, so that three-dimensional forward modeling operation of magnetotelluric is integrated into forward propagation of the network model, the network model can follow magnetotelluric physical constraints in the training process, and the inversion precision of the anomalous body is integrally improved.

The invention relates to the technical field of power-assisted robots, in particular to a self-adaptive control lower limb hydraulic power-assisted exoskeleton robot system which comprises a waist supporting device, a leg supporting device, a foot supporting device, a hydraulic power-assisted assembly, an attitude sensor, a lower limb force sensor, a wearable electromyogram sensor, a step obtaining module and a data analysis module. The system can intelligently adjust the output power of the hydraulic power assisting assembly according to the motion posture of the exoskeleton, the pressure data of the foot supporting device and the wearing target muscle state so as to provide proper walking assisting power and bearing support, and the system can further conduct secondary adjustment and fatigue alarm by monitoring the pace frequency and the muscle fatigue degree in real time. The exoskeleton assisting power output power in the carrying process is gradually adjusted according to the comparison result between the weight of the wearing target and the weight of the to-be-moved object, so that assisting power provided by the exoskeleton robot is effectively matched with the weight of the to-be-moved object when the wearing target carries the object.