4294 results about "Data transport" patented technology

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention discloses a solid state disk management system and a data processing method, and relates to the technical field of solid state disk management. Flexible task scheduling and resource management are provided through a lightweight operating system module, and a computing task program defined by a user is supported to be dynamically loaded; edge computing or machine learning operators are efficiently executed in combination with a programmable hardware processing unit and a DMA channel of the computing acceleration engine module, the data preloading module is utilized to predict and preload data to DDR based on LBA access history, access delay is reduced, an NVMe protocol is expanded by means of the task unloading interface module, host task issuing and result returning are achieved, and the data processing efficiency is improved. And hardware-level memory protection is ensured through the security isolation module, so that the data calculation processing capacity of the solid state disk is remarkably improved, localized calculation tasks such as edge calculation and machine learning are supported, mass data transmission is effectively reduced, and bus and network loads are relieved.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention relates to the field of crane management, and discloses an intelligent agent control method and system.The method comprises the steps that operation parameters and operation environment data are collected in real time through multiple sensors arranged on a crane and subjected to standardization processing; transmitting the processed data to an operation state analysis model, and outputting an operation state evaluation value so as to analyze the current operation state of the crane and predict potential risks; based on the analysis result, an optimization control instruction is generated and issued to each execution mechanism controller, and corresponding mechanisms are driven to execute precise actions; through multi-source data fusion and intelligent analysis, real-time monitoring and risk assessment of the running state of the crane are achieved, the self-adaptive capacity and safety of the control system are improved, the manual intervention requirement is effectively reduced, and the method is suitable for intelligent crane control in the complex working environment.

The invention provides a software supply chain risk analysis method. A data acquisition and preprocessing module acquires component information from a plurality of software warehouses and performs format unified processing; the components are modeled as nodes through a hypergraph construction module, the dependency relationship is modeled as hyperedges, and a hypergraph structure is dynamically constructed and updated so as to express many-to-many dependency relationship more accurately; performing representation learning on the hypergraph structure through a hypergraph neural network, generating embedded vectors of the components, and fusing context features of the components; based on an embedded vector, risk scoring, anomaly detection and classification are carried out by adopting multi-task learning and a graph attention mechanism, so that the generalization ability of the model and the risk identification precision are improved; online incremental updating of a hypergraph structure and model parameters is achieved through a dynamic updating and feedback mechanism, and the real-time performance and stability of the model are ensured. According to the method, the data transmission security, the transmission efficiency and the compatibility between devices are improved, and the method has higher risk analysis capability and system adaptability.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention relates to the technical field of mine production operation management, in particular to a mine production operation intelligent decision-making system based on multi-source heterogeneous data fusion. A data preprocessing module; a data fusion modeling module; an intelligent decision module; and a decision execution feedback module. In the scheme, an edge computing terminal is deployed through a data acquisition unit, a multimode communication technology is integrated, data of an underground sensor, electromechanical equipment and the like are accessed in real time, and the data transmission reliability is ensured through a communication quality evaluation formula; the business system interconnection unit maps heterogeneous fields of systems such as ERP, WMS and the like into global unified business keys by means of semantic middleware, and semantic ambiguity is eliminated. The data preprocessing module integrates the dispersed data into a standardized data set through intelligent cleaning, format conversion and three-dimensional association, and provides data support for decision making.

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

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 intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention provides a construction progress dynamic regulation and control system and method for a building machine, relates to the technical field of special equipment for building construction, and solves the problem of limitation of an existing building machine regulation and control system in the aspects of dynamics, collaboration and the like. In the system, a sensing execution layer is used for collecting construction data in real time and correspondingly executing a regulation and control instruction, and transmitting the data to an edge calculation layer for processing; the edge computing layer is used for carrying out localized real-time preprocessing and decision generation on the data, and synchronizing key information to the cloud analysis layer to obtain a regulation and control strategy; the cloud analysis layer is used for constructing virtual mapping of a construction process, optimizing a regulation and control strategy and driving a dynamic regulation and control module to perform multi-dimensional decision making; and the dynamic regulation and control module is used for comprehensively predicting the construction progress, coordinating and optimizing the target, handling sudden anomalies, generating regulation and control instructions and feeding back the regulation and control instructions to the sensing execution layer to form a closed loop. According to the method, the construction resource utilization rate and the progress prediction precision of the building machine can be effectively improved.

A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.

The invention relates to an OpenGL (Open Graphics Library)-based large-scale three-dimensional model object quick pickup method. The method comprises the following steps: converting an interactive operation of a user into a three-dimensional space structure; a CPU constructs octree space acceleration structures based on a bounding box of an object in a large-scale three-dimensional model, and leaf nodes, intersecting with a pickup ray or a pickup view pyramid, of an axis alignment bounding box in each octree space acceleration structure are screened out to serve as candidate leaf nodes; collecting triangles contained in all candidate leaf nodes, and constructing a candidate triangle set; dynamically allocating thread groups and threads of the GPU based on the number of candidate triangles in the candidate triangle set; and performing highlight display on the candidate triangles intersected with the pickup ray or the pickup view cone, and feeding back IDs of the corresponding candidate triangles. According to the method, the data transmission quantity and the video memory access pressure are remarkably reduced, the pickup response time is greatly shortened in a large-scale three-dimensional model scene, and the requirements of real-time interaction and visual operation can be met.

A computer-implemented method for simulating an aircraft system in a closed-loop functional simulation environment is disclosed. The computer-implemented method may include: receiving, at a computer system, one or more control inputs from an interface; processing, by a control system model, the one or more control inputs to generate one or more system command signals; applying the one or more system command signals to one or more virtual system models; simulating a physical response of the aircraft based on output generated by the one or more virtual system models in response to application of the one or more system command signals; generating, responsive to monitoring the output generated by the one or more virtual system models, sensor data; transmitting the sensor data to the control system model; and outputting simulation results, wherein the simulation results at least comprise an indication of the physical response of the aircraft.

The invention relates to the technical field of water conservancy project structure health monitoring and intelligent sensing, in particular to a water conservancy project online monitoring and abnormity diagnosis system based on edge calculation, which comprises a data acquisition module used for acquiring current water level data, current temperature data and actually measured structure response data; the reference reconstruction module is used for generating an ideal state vector; the fault simulation module is used for generating a theoretical damaged simulation state vector; the topology judgment module is used for calculating a first difference value between the actual measurement structure response data and the ideal state vector, calculating a second difference value between the theoretical damaged simulation state vector and the ideal state vector, and calculating the topology similarity based on the first difference value and the second difference value; the risk assessment module is used for generating a structure safety risk index in combination with the fault strength scalar and the topological similarity; according to the method, the problems of data transmission lag and low online calculation efficiency in a traditional centralized processing mode are effectively solved, and the real-time requirement of an engineering site is met.

Examples of the present disclosure describe systems and methods for determining exploit prevention software settings using machine learning. In aspects, exploit prevention software may be used to identify processes executing on a computing device. Metadata for the identified processes may be determined and transmitted to a machine learning system. The machine learning system may use an exploit prevention model to determine exploit prevention configuration settings for each of the processes, and may transmit the configuration setting to the computing device. The computing device may implement the configuration settings to protect the processes and monitor the stability of the protected processes as they execute. The computing device may transmit the stability data to the machine-learning system. The machine-learning system may then modify the exploit prevention model based on the stability data.

A system for classifying apple leaf diseases using deep learning and feature fusion, consisting of: A data input module, which includes a data storage module, is configured to store a data set created using various data sources, with the data set containing apple leaf images being derived from the data sets "Apple Leaf 9", "Kashmiri Apple Plant Disease" and "Plant Village Apple Leaf"; a data preprocessing module configured to perform preprocessing of the newly prepared dataset of apple leaves, wherein the data preprocessing module is configured to perform data decoding, expansion, resizing, segmentation, scaling and color conversion of the input image data; a feature extraction module that is operationally connected to the data processing module and is configured to receive preprocessed data and transfer the preprocessed data to one or more convolutional neural network models for feature extraction; a feature fusion module configured to combine the extracted features from the Convolutional Neural Networks to develop a fused feature vector; a hyperparameter optimization module configured to optimize the hyperparameters of the feature extraction models by implementing a particle swarm optimization algorithm; a classification module configured to classify the fused trait vector into 13 apple leaf disease classes using a random forest classifier; and a user interface connected to the classification module, configured to display the classification results.

Provided in the embodiments of the present disclosure are a data query method for sensitive data, and an electronic device, a storage medium and a computer program product. In the embodiments of the present disclosure, when an access terminal queries sensitive data in a database system, the database system sequentially performs desensitization processing and encryption processing on the sensitive data. Thus, provided is a data protection solution in which encryption is combined with desensitization, what flows out of the database system is encrypted desensitized data, and what the access terminal obtains after decryption is desensitized data rather than sensitive data. Sensitive data is protected by means of encryption during data transmission. In addition, what an access terminal obtains is desensitized data rather than sensitive data, thereby effectively reducing the risk of leakage of the sensitive data.

The invention relates to a distributed storage access method initiated by a GPU (Graphics Processing Unit) in an AI (Artificial Intelligence) scene. The method comprises the following steps: S100: constructing a software cache module and an I / O (Input / Output) queue module in a GPU memory; s200, the GPU thread queries the target data through the software cache module, reads the target data if the target data is hit, and enters the next step if the target data is not hit; s300, when the I / O request is not hit, the repeated I / O requests initiated by multiple threads are automatically combined to generate a single effective I / O request, and the request is written into an I / O queue module; s400, the user mode distributed storage client monitors the written submission queue, reads the target data and then transmits the target data to the software cache module, and a completion queue of the I / O queue module is updated; and S500, the GPU thread polls the updated completion queue, and updates the reference count of the software cache module after reading the data. The main purpose of the invention is to realize high-throughput and low-delay data transmission without the participation of a CPU (Central Processing Unit).

The invention discloses an engineering vehicle safety simulation and prediction system based on digital twinning, and the system comprises a data collection layer which collects multi-source heterogeneous data in real time; in the knowledge graph layer, a streaming inference engine is constructed based on an Apache Jena graph database, and an entity-relationship-attribute triple dynamic graph structure is adopted; according to the AI model layer, a physical rule serves as a loss function constraint term to be embedded into a neural network through a physical information neural network, a digital organ model concept is combined to split a vehicle into key organs for heterogeneous modeling, a simplified physical model is adopted in the core physical process, and an LSTM-AI model is adopted in external behaviors; the explanatory analysis layer is used for integrating an SHAP / LIME explanatory tool to output a visual evidence chain during fault prediction, and deploying an online incremental learning framework to allow the model to learn from new data and dynamically adjust normal range definition; and the visualization and application layer is used for performing three-dimensional visualization rendering based on WebGL or Three.js, and ensuring data transmission security through block chain evidence storage and end-to-end encryption.

The invention relates to the technical field of high-performance computing resource management, and discloses a computing resource allocation method for a distributed supercomputing center. The method comprises the following steps: on the basis of obtaining real-time computing task and supercomputing center resource data and uniformly quantifying, integrally predicting resource requirements of future tasks; constructing a mixed integer linear programming model with the minimization of the total operation cost as a single target, wherein the total operation cost is the sum of the energy cost, the carbon emission cost, the data transmission cost and the SLA default penalty cost; solving the model by taking the time-varying electricity price, the green energy ratio, the resource capacity and the network parameters of each center as constraint conditions to generate an optimal resource allocation scheme; and then, by dynamically monitoring the resource state and the task progress, the model is triggered to resolve when the resource utilization rate is detected to be unbalanced or default risks, so that self-adaptive adjustment is realized. According to the invention, global collaborative resource allocation across super computing centers is realized, and operation economy, environmental sustainability and service reliability are considered.

The invention discloses a boiler leakage prevention on-line monitoring method and system, and relates to the technical field of boiler leakage monitoring, and the method comprises the following steps: S100, collecting an infrared image and a visible light image of a monitoring area in real time through an image collection module, and transmitting the data to a preprocessing module; s200, a preprocessing module carries out denoising and edge strengthening processing, conventional edge features are reserved, and tiny temperature difference features are focused; s300, the control module rejects interference factors based on the similarity of the infrared image and the visible light image to obtain a suspected leakage image; s400, carrying out data acquisition on the suspected leakage region by using an acoustic imaging module, analyzing and recording the region data by a deep learning module based on historical detection data, and constructing a time sequence model; and S500, the control module analyzes and judges the overlapping state of the pipeline at the leakage point according to the visible light image and the information collected by the sound wave imaging module, and locates a leakage source.

It is provided an apparatus comprising interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions. The machine-readable instructions include instructions to receive data from a remote entity for handling by a computing system. The machine-readable instructions further include instructions to instantiate a first TEE and a second TEE. The machine-readable instructions further include instructions to generate log data corresponding to predefined activities of the computing system and to generate system record data of a system log of the computing system at predetermined times. The machine-readable instructions further include instructions to generate first audit data by the first TEE and second audit data by the second TEE. The machine-readable instructions further include instructions to transmit the first and the second audit data to a detection system for data aggregation and anomaly detection.

The invention belongs to the technical field of cloud side-end collaborative data processing, and discloses a cloud side-end collaborative heterogeneous data fusion processing system, which establishes dynamic association of data features, resource states and service priorities through a scheduling module, and adopts a reinforcement learning algorithm combining DQN and an attention mechanism to perform fusion processing on heterogeneous data. Task allocation weights are dynamically adjusted with the minimum delay, the highest precision and the maximum business value as targets; the sensing module and the preprocessing module complete lightweight processing such as feature extraction and noise reduction redundancy elimination at the edge end, and the cross-node data transmission amount is reduced. The complex fusion task dispatches cloud computing power support, and reserves resources in advance by means of an LSTM-based task urgency predictor; the cross-modal fusion module designs a double-stage fusion algorithm, in the first stage, cross-modal semantic association is enhanced through multi-head self-attention, and in the second stage, the fusion weight is adjusted according to the reliability coefficient of the sensing module.

Techniques are discussed herein for detecting and resolving disparities between sensor data perceived by sensor-based systems operating in an environment and corresponding map data of the environment. Sensor data may be captured by a vehicle or other sensor system operating in an environment, including representations of objects at various locations in the environment. The object representations may be used to determine disparities between the sensor data and map data associated with the same locations. Such disparities may be caused by, for example, physical changes in the environment, map data changes, and / or localization errors of the sensor system. The techniques discussed herein further include analyzing the map data and sensor data to determine causes associated with the disparities, and resolving the disparities by updating the map data and / or transmitting updated sensor configuration data to sensor systems in the environment.

The invention discloses a self-adaptive sensing anti-shake regulation and control method and system for a spinal surgery auxiliary mechanical arm, and relates to the technical field of spinal surgery, and the method comprises the steps: collecting multi-dimensional data of a mechanical arm tail end actuator through a multi-dimensional sensor group, and transmitting the multi-dimensional data to an AeroStead space-level stability calculation platform; the platform carries out filtering processing on the data, and vibration of the mechanical arm and disturbance components caused by external interference are separated; constructing a spine mechanical arm rigid-flexible coupling dynamic disturbance observation and compensation model by using the disturbance component to observe disturbance and generate a compensation amount; calling a spinal operation area three-dimensional reconstruction and dynamic obstacle avoidance mixed density network model, and fusing different images to generate an operation area three-dimensional dynamic model and an obstacle avoidance adjustment signal; a mechanical arm motion control instruction is generated through a force sense position cooperation self-adaptive control algorithm; and the mechanical arm is driven to move for closed-loop regulation and control. According to the method, through fusion of multiple models and algorithms, the disturbance compensation precision, the operation area modeling accuracy and the control collaboration are improved, and the operation safety is guaranteed.