235results about How to "Improve data processing efficiency" patented technology

The invention relates to the technical field of marine environment prediction, in particular to a marine environment multi-modal fusion prediction method and system based on digital twinning. The method comprises the following steps: capturing time-space correlation characteristics of multi-modal data of a marine environment based on a dynamic multi-modal graph neural network; performing multi-scale feature fusion on the space-time correlation features by using a multi-scale gating unit to obtain comprehensive feature representation; predicting the comprehensive feature representation by using a mixed time sequence prediction framework to obtain preliminary marine environment prediction data, including short-term dynamic modeling and long-term trend modeling; and performing noise fitting on the preliminary marine environment prediction data by using a generative adversarial network to generate final marine environment prediction data. According to the invention, through innovative combination of dynamic multi-modal data fusion, a mixed time sequence prediction framework and a multi-scale gating Tanh unit, the problems of insufficient data fusion, limited prediction precision and poor dynamic response capability in the traditional marine environment monitoring and prediction technology are effectively solved.

The invention provides an integrated logistics management system for cargo transportation. The integrated logistics management system comprises a multi-mode collaborative perception module, a semantic reasoning module, a scene adaptation decision module, an intelligent execution module and a logistics trust chain module. The multi-mode collaborative sensing module collects logistics full-scene state data through a sensor network; the semantic reasoning module is used for constructing a knowledge graph based on a graph attention network and realizing unified modeling of multi-source information; the scene adaptation decision module is combined with a pulse coupled neural network (PCNN) and a swarm intelligence collaboration mechanism to complete routing optimization; the intelligent execution module adopts a hierarchical task management and dynamic adjustment mechanism, and adjusts a task scheme according to execution abnormity and credit evaluation; the logistics trust chain module constructs a logistics trust system through a hierarchical consensus mechanism, a credit scoring model and a trust propagation and early warning mechanism, and realizes risk prevention and control of the whole process. The system solves the technical problems of a traditional logistics system in the aspects of data integration, dynamic supervision, decision support, collaborative scheduling, safety traceability and the like.

The invention belongs to the field of video monitoring systems, and particularly relates to a cascade-based multi-platform data synchronization method and system, and the method comprises the steps: designing an interface security mechanism of cross-platform data exchange; constructing a data synchronization service architecture, namely deploying corresponding data synchronization services at different levels of platforms to realize resource data synchronization, and supporting data synchronization of multi-level cascade or platform interconnection; performing data initialization synchronization; real-time synchronization of data change is realized; establishing a service heartbeat keep-alive mechanism; service multi-instance deployment is carried out, and efficient data processing and flexible load balancing are achieved. According to the invention, many defects of a data synchronization mechanism of an existing video monitoring system are effectively solved, and the stability, expansibility and data processing efficiency of the system are improved.

The invention relates to the technical field of artificial intelligence, in particular to a model construction method for a GUI agent, a program product and a storage medium, and the model construction method for the GUI agent provided by the invention comprises the following steps: performing pre-training based on a general data set in the GUI field to obtain a base model; a user demonstration operation track is collected online, and a personalized task execution track is obtained; automatically labeling the task execution track to obtain a labeled track; performing task planning training on the base model based on the marked track to obtain a scene planner, and combining the base model and the scene planner to obtain a personalized model; based on the plug-in scene knowledge base, corresponding scene knowledge is retrieved as context supplement during personalized task action planning. The task execution track is automatically labeled, a large model is guided to actively label and understand thinking in human execution operation, the next behavior is further planned, and the adaptive capacity of a GUI intelligent agent to the personalized use environment of a user is greatly improved.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention discloses a loom data monitoring method and system, computer equipment and a storage medium. The method comprises the following steps of S10, collecting operation data of a loom through a distributed sensor network; s20, optimizing the operation data of the weaving machine by utilizing edge computing equipment to obtain optimized operation data of the weaving machine; s30, the edge computing device transmits the optimized operation data of the weaving machine to a cloud server; s40, the cloud server analyzes the optimized operation data of the weaving machine based on a weaving machine operation state evaluation model to obtain an operation state evaluation result of the weaving machine; and S50, the cloud server performs predictive maintenance on the weaving machine based on the operation state evaluation result of the weaving machine. The technical problems of poor real-time performance, low maintenance efficiency and weak prediction performance of loom monitoring in the prior art can be solved.

The invention provides a smart power grid data communication optimization method based on edge calculation, and relates to the technical field of smart power grids, and the method comprises the steps: obtaining an inspection topology network of a target power grid; performing multi-angle communication analysis to obtain K power transmission equipment communication feature tuple sets and L inspection site communication feature tuples; carrying out edge step identification to obtain M edge topology sub-networks; performing balanced configuration of sub-network service resources to obtain M edge computing servers; performing routing inspection by using a routing inspection unmanned aerial vehicle and a routing inspection robot to obtain routing inspection video data of L sites; and distributing the data to M edge computing servers for data preprocessing, and uploading the data to a cloud computing center for routing inspection data analysis to obtain a routing inspection analysis result. According to the invention, the technical problem of low data processing efficiency caused by data transmission delay and overweight network load due to the fact that power grid monitoring generally depends on centralized data processing in the prior art is solved.

The invention discloses an intelligent interactive thematic map generation system and method based on a large language model. Comprising a large language model module, a thematic map template library, an intelligent data source management module, an intelligent cartographic engine, an interactive optimization module, a user interaction configuration module, a configuration library, an external service interface module, a model data quality verification module, a thematic map personalized correction module and the like. A user interactively selects a template, specifies a data source and describes drawing requirements through a natural language, LLM drives an intelligent drawing engine to generate a thematic map, fine adjustment and real-time optimization of the user are supported, and user-friendly, efficient and flexible thematic map making is achieved. Meanwhile, the system has the functions of data quality verification, personalized configuration, interactive history learning and the like, a powerful tool is provided for non-professional users to quickly customize and finely adjust thematic maps, and the intelligent level and adaptability of thematic map making are greatly improved.

The invention provides a static var generator fault diagnosis method and system based on a neural network. The method comprises the following steps: collecting operation parameter data of a static var generator SVG in a fault state and a normal state during operation; and preprocessing operation parameter data in the fault diagnosis database when the SVG operates in a fault state and a normal state. And constructing and training a neural network model of dominant faults and hidden faults based on the preprocessed data. And inputting operation parameter data in a test set into the trained LSTM model to predict dominant faults. And assigning the hidden fault characteristics of the SVG in the test set under the normal operation condition, inputting the hidden fault characteristics into the trained BP neural network model, and predicting the hidden fault of the SVG. According to the SVG fault diagnosis method, fault diagnosis is carried out on SVG operation parameter data through the constructed and trained neural network model, the SVG fault diagnosis efficiency is improved, dominant and hidden fault treatment strategies can be given, and the method is beneficial to assisting in rapidly removing faults.

The invention belongs to the technical field of spectral analysis, and discloses a spectral analysis and image recognition combined intelligent stoma evaluation system and method.A three-dimensional scanning module captures the three-dimensional form of a stoma and the three-dimensional form of surrounding tissue of the stoma through the high-precision laser or structured light scanning technology; the spectral analysis module is used for accurately analyzing the color change of the stoma; the tiny color difference of the stoma area is detected to help to identify potential tissue abnormality; the image recognition and processing module is used for analyzing the collected stoma image by using a deep learning algorithm and automatically recognizing morphological characteristics of the stoma; the data analysis and feedback system is used for automatically generating a stoma health assessment report according to the collected multi-dimensional data; through a set standard reference value, an actual measurement value is compared, and a corresponding suggestion is given, for example, whether a nursing scheme needs to be adjusted or further medical intervention needs to be carried out. According to the method, the real-time state of the stoma is accurately evaluated through non-contact scanning and spectrum detection.

The invention belongs to the technical field of microorganisms, and particularly relates to a microorganism detection culture device and method.The system comprises an intelligent environment control system, and a sensor monitors the temperature, humidity and gas concentration of a culture environment in real time to ensure that environment parameters needed by microorganism growth are kept in the optimal state; the automatic sample processing and operating system avoids pollution or errors caused by manual intervention through a mechanical arm or an automatic transmission device; the real-time image monitoring and analyzing system is used for monitoring the growth state of the microorganisms in the culture dish; the camera can continuously shoot images in the culture process; the rapid detection module rapidly obtains activity information of microorganisms, rapid detection is achieved, and time needed by traditional culture is shortened. According to the invention, accurate control, automatic operation and rapid detection are realized, and an efficient, safe and intelligent solution is provided for microbial culture and detection.

The invention provides a big language model-based intelligent analysis system for a newly created enterprise, and the system comprises a data collection module which is used for collecting data of the newly created enterprise; the data preprocessing module is used for preprocessing the data of the initially created enterprise; the vector database and knowledge graph construction module is used for constructing a vector database and a knowledge graph based on the preprocessed initially created enterprise data; the context information construction module is used for carrying out deep semantic analysis on related information, input by a user, of the to-be-analyzed enterprise, and fusing a semantic analysis result with data of the vector database and the knowledge graph to obtain context information; and the intelligent analysis module is used for carrying out intelligent analysis on the context information based on the initially created enterprise research analysis auxiliary large model, obtaining development state data of the to-be-analyzed enterprise, and completing initially created enterprise intelligent analysis based on the large language model. And comprehensive and efficient retrieval and processing of related data of the initially created enterprise are realized.

The invention provides a highland cooperative control method fusing a graph convolutional neural network and reinforcement learning, through controlling urban road traffic signal lamps, an expressway ramp signal machine and a variable speed limit unit, expressway traffic congestion is relieved, the urban traffic passing efficiency is improved, road driving vehicles are controlled by means of the signal lamps and the speed limit unit, and the traffic efficiency is improved. Traffic flow scheduling optimization in a global road network is achieved; the whole structure adopts a distributed cooperative control framework, is controlled by a decision-making agent, and performs autonomous decision-making and adjustment according to real-time traffic conditions, so that the adaptability and robustness of the system are enhanced, and the defects of large data calculation amount, difficulty in processing, control strategy lag and the like caused by traditional centralized control are effectively avoided; the graph convolutional neural network model is constructed to aggregate the feature information of the peripheral decision intelligent agent, and the control weight is dynamically allocated, so that the information sharing capability is enhanced, the suboptimal decision caused by lack of comprehensive perception of the surrounding environment is prevented, and the cooperation efficiency of the whole system is improved.

The invention relates to the technical field of organic electroluminescence adjustment, and provides a power equipment abnormity early warning system for a mobile terminal, which integrates a real-time monitoring module, an early warning identification module and a data detection module. By monitoring operation data of the power equipment in real time, combining with a random forest model to carry out abnormity early warning, sending early warning information to a mobile terminal in real time and receiving user feedback to optimize an early warning strategy, a data detection module carries out grouping processing on the power equipment based on a time period, so that the data processing efficiency is improved; the system achieves the comprehensive monitoring of the operation state of the power equipment, remarkably improves the early warning accuracy, response speed and user interaction experience, effectively guarantees the safe and stable operation of a power system, promotes the scientific maintenance and management of the power equipment, is suitable for various types of power equipment, and improves the safety and reliability of the power equipment. The method has a wide application prospect and an important practical value, and provides powerful support for intelligent and safe operation of an electric power system.

The invention discloses a project cost consultation method based on artificial intelligence, and the method mainly comprises the following steps: S1, providing an intelligent system, and obtaining a project quantity list, a construction plan and a material price; s2, receiving and analyzing project requirements through a natural language processing algorithm, and generating a cost consultation task; s3, analyzing the project data by using a machine learning algorithm, and generating a budget scheme and a pre-settlement report; s4, executing risk identification, and providing risk control suggestions; s5, dynamically adjusting a budget scheme based on project progress conditions, and providing cost optimization suggestions; and S6, generating a report including cost prediction, risk control and optimization schemes. The method optimizes the analysis and processing of the cost data through an intelligent algorithm, improves the budgeting accuracy and risk recognition capability, reduces the manual intervention, and improves the working efficiency and decision scientificity.

The invention relates to the field of aviation flight tests, and discloses a flight test airborne data unified recording and processing method, system and device and a computer readable storage medium. The method comprises the following steps: receiving an external time code time service signal, and generating a synchronization timestamp for performing time synchronization on each module in a system; collecting multi-source data streams from a network interface, an analog quantity PCM interface, a video interface and a 1553B interface; performing protocol conversion and format standardization processing on the multi-source data stream to obtain a data stream with a uniform format; based on a data fusion algorithm, carrying out fusion processing on the standardized data streams to generate a fusion data sequence in a unified format; performing grading processing on the fused data sequence according to the importance of the data; performing adaptive compression processing on the graded data sequence to generate a compressed storage data sequence; and transmitting the compressed data storage sequence to a ground station through a wireless transmission protocol. According to the invention, the flight test efficiency is effectively improved.

The invention relates to the technical field of agricultural machinery, in particular to an irrigation area measurement and control integrated gate health monitoring method and system, and the method comprises the steps of data entry, model establishment, equipment planning and installation, data collection, data analysis and health monitoring. Compared with the defects of low efficiency, unreasonable planning, inaccurate installation position and the like of manual planning of an equipment installation scheme in the prior art, the scheme of the invention adopts an intelligent algorithm to carry out installation planning and simulation of the data acquisition equipment in the 3D model, and carries out optimization according to actual conditions and historical experience to obtain a final installation scheme, so that the installation efficiency of the data acquisition equipment is improved. The method for automatically planning the equipment installation scheme not only improves the efficiency of equipment installation, but also ensures the scientificity and accuracy of planning, so that the data acquisition equipment can be more accurately installed at a key position, thereby improving the accuracy and reliability of gate health monitoring.

The invention discloses an AI data processing adaptive acceleration system based on a Bayesian algorithm, which belongs to the technical field of data processing and computing, and specifically comprises the steps of positioning a target operator, creating a data processing pipeline monitoring thread, collecting target data and hardware resource information and storing the target data and hardware resource information as current configuration information, and starting an automatic optimization function. The method comprises the following steps: collecting operator information, marking bottleneck operators, setting iteration times to carry out hyper-parameter search on the operator information, updating current configuration information into new configuration information by utilizing a result of each hyper-parameter search, guiding to adjust operators on a data processing pipeline by utilizing the new configuration information, and after executing the iteration times, carrying out data processing on the operators on the data processing pipeline. And the optimal hyper-parameter is screened out and stored in a local file, and is directly called and the operator is adjusted when an assembly line is started next time, so that the data processing speed during AI model training can be effectively improved.

The invention provides an analysis and management system for telecontrol information data, and relates to the technical field of electric power and data analysis, and the system comprises a configuration inspection module which carries out the differential analysis of a master station telecontrol information point table and a substation telecontrol information point table, and generates a telecontrol configuration file; the data acquisition module acquires test telecontrol information data and a telecontrol configuration file, and performs communication link matching to obtain a communication link check code; the data preprocessing module preprocesses the test telecontrol information data to obtain test telecontrol information preprocessing data; the data verification and analysis module performs exception verification and analysis on the test telecontrol information preprocessing data to obtain an integrity index; the abnormal data prediction module constructs a telecontrol information data abnormal prediction model and performs abnormal prediction on test telecontrol information data collected in real time; and the real-time management module manages the test telecontrol information data in real time according to the prediction result. According to the scheme, the operation efficiency of the system and the accuracy of telecontrol information data analysis can be improved.

The invention discloses a Zynq-based convolutional neural network hardware acceleration system, which is realized based on a Zynq heterogeneous chip and comprises a PL end module, a PS end module and a storage module. The PL end module comprises a convolution acceleration module, a full connection layer acceleration module and a Softmax activation module and is used for completing convolution calculation, full connection operation and activation function processing; the PS end module comprises an input data processing module and a post-processing module and is responsible for loading and preprocessing input data and sorting and screening detection results. The storage module is composed of an on-chip memory and an off-chip memory, wherein the on-chip memory stores an intermediate processing result of input data, a convolution kernel and intermediate feature map data; and the off-chip memory stores the original image data, the weight parameter of the full connection layer and the final detection result. All the modules are connected through a 64-bit AXI bus, the PS end module is responsible for coordinating data flow, and the PL end module executes efficient parallel computing. The system makes full use of the high-performance characteristic of the Zynq heterogeneous architecture, is suitable for an efficient reasoning acceleration scene of the convolutional neural network, and particularly has a wide application prospect in embedded AI equipment.

The invention relates to the field of big data processing, in particular to a big data processing system based on AI cloud computing training and a computer device. The system comprises a data acquisition module, a preprocessing module, a data storage optimization module, a machine learning processing module, a deep learning classification module, a real-time data processing module, a data quality guarantee module and a user interface and configuration management module. The output end of the data acquisition module is connected with the input ends of the preprocessing module and the machine learning processing module, and the input end of the deep learning classification module is connected with the output end of the machine learning processing module. The output end of the deep learning classification module is connected with the input end of the real-time data processing module and the input end of the user interface and configuration management module, the input end of the data storage optimization module is connected with the output end of the preprocessing module, and the output end of the preprocessing module is connected with the input end of the data quality guarantee module. The method is suitable for big data processing.

The invention discloses an echo segmented pulse compression method under a high sampling rate based on an FPGA (Field Programmable Gate Array). According to the method, down-sampling preprocessing is firstly carried out on the signals sampled by the ADC with the sampling rate of 124 MHz, the sampling rate is reduced to 62 MHz on the premise that spectrum aliasing does not happen to the signals, then segmentation range pulse compression is carried out on the 1PRT echo signals, the total processing amount is reduced by 40%, the data calculation amount can be remarkably reduced, the operation speed and the data processing efficiency can be improved, and the method is suitable for large-scale popularization and application. And FPGA logic resources are saved. And short-time 1s echo pulse compression preprocessing is realized by adopting a method of synthesizing 1000 PRTs, so that the communication data rate is greatly reduced. And in cooperation with operation of other components of the surface scene deformation monitoring system, the system reliability is improved, and the scene deformation monitoring task can be smoothly carried out.

The invention discloses an insurance data analysis method and system based on a multi-modal pre-training large model, and relates to the field of insurance data analysis, and the key point of the technical scheme comprises the steps: collecting comprehensive insurance data, carrying out data quality improvement processing on the comprehensive insurance data, obtaining a quality improvement data set, and carrying out cross-modal integration processing on the quality improvement data set. Obtaining an associated integrated chart; constructing a multi-task driven model, performing adaptive updating on the model, inputting the associated integrated chart into the pre-trained multi-task driven model, and generating an insurance analysis report; an insurance analysis report is displayed on a computer end page, interface interaction with a user is carried out, high-quality analysis and processing of insurance data and self-adaptive updating of a model are achieved, and high-quality and high-efficiency services are provided.

The invention relates to a data processing method and device based on a large language model, equipment and a storage medium. The method comprises the following steps: acquiring input data, matching the input data with a plurality of pre-constructed data matrixes to obtain a data matrix matched with the data type of the input data, and determining the matched data matrix as a current data matrix; constructing an inference tree model based on the input data and the current data matrix; determining a plurality of candidate sequences based on the inference tree model; and performing parallel verification on the plurality of candidate sequences, determining the output probability distribution of each candidate sequence, and obtaining an output sequence matched with the data content of the input data according to the output probability distribution. Compared with the prior art, the data processing efficiency can be improved, and the data output quality and accuracy can also be improved.

The invention discloses an ANS-based aquaculture dynamic water quality monitoring method and system. The method comprises the following steps: data acquisition: firstly, carrying out standardization processing on acquired water quality data; model construction: an ANS water quality prediction model is constructed, an ANN network architecture is adopted, an LSTM layer is added in an ANN network to be connected between an input layer and a hidden layer, and the added LSTM hidden layer is used for extracting water quality time sequence correlation features; model training; and water quality prediction: inputting water quality data collected in real time into the trained ANS water quality prediction model, and performing water quality prediction to obtain predicted values of various water quality indexes in a future set time period. And related water quality parameters are predicted through the ANS water quality prediction model, so that the accuracy of water quality prediction is improved. In addition, the dynamic monitoring equipment polls that the fish can realize autonomous cruise in the culture lake, images and water quality information are transmitted to the intelligent terminal in real time, and accurate water quality parameters of the culture lake are provided in real time.

The invention provides a target identification method and system based on multi-source information fusion, and relates to the technical field of low-altitude target detection. The method comprises the following steps: acquiring target radar track data, interception equipment track data and position area information data; based on target radar track data, extracting a first feature in an RCS form dimension, and extracting a second feature in a motion dimension; based on the target radar track data and the track data of the monitoring equipment, determining a frequency spectrum monitoring correlation factor and regional position information features; performing feature fusion on the first feature, the second feature, the spectrum interception correlation factor and the regional position information feature to obtain a target feature, and identifying a target type; and identifying a target threat level based on the target type and the target radar track data. The method and the device are used in a target identification process based on multi-source information fusion, and the technical problem that the target threat degree cannot be accurately identified in a complex environment in the prior art is solved.

The invention discloses a security check system for roll-on and roll-off vehicles, belongs to the technical field of security check systems, and provides a flexible and reliable solution by fully considering complexity and diversity of port and navigation environments in the design of the system. The unique innovation points and functions should serve as key protection technical schemes, uniqueness and wide application prospects of the patent are ensured, mobile operation scene support is achieved, and efficient application of ports and shipping parties in the mobile operation scene is supported. A user is not limited by fixed ticket checking equipment any more, and operation can be carried out at any place such as a wharf and a cabin. Due to the flexibility, related business operation is more convenient and efficient, the dynamic requirements of modern port and navigation business are greatly met, the security check efficiency and accuracy are improved, and a user can quickly enter a working state to perform identification and data acquisition on various certificates and bills by innovatively using a handheld terminal authorization and login mechanism.

The invention provides a language resource library system based on data security control, and the system comprises an acquisition module which collects language resources in a manner of manual input and crawler program directional capture, obtains a language data set, and stores the language data set in a database; the processing module develops a software program according to a preset metadata specification, and processes and manually marks the language data set to obtain a standard data set; the management module is used for performing data management, user management and log management in combination with the data access control model and the network security monitoring model; the presentation module is used for providing data retrieval and statistical analysis functions according to the data type and carrying out visualized display of the data; and the product library module is used for developing a language resource platform. By establishing a perfect security mechanism, unauthorized access and data leakage can be effectively prevented, the security of language resources in cross-border transmission and use is improved, and efficient and stable operation of the system is ensured.

The invention relates to a hybrid accelerator card management method and device, an electronic device and a storage medium, and is applied to the field of computer hardware acceleration and data storage and transmission, and the hybrid accelerator card management method comprises the following steps: determining a runtime library interface of a hybrid accelerator card in response to a received server data transmission request; registering a memory address of the hybrid acceleration card to a preset hardware abstraction layer based on a remote direct memory access technology; a unified hardware abstraction interface is preset in the hardware abstraction layer; the runtime library interface is bound with the unified hardware abstract interface; and calling the runtime library interface to execute data transmission operation between the hybrid acceleration cards through a preset hardware abstraction layer. According to the method and the device, the GPU acceleration cards of different manufacturers are compatible, and meanwhile, the effect of improving the data processing and transmission efficiency is achieved.

The invention relates to a system precision detection method and system of a surgical mechanical arm and an electronic device.The precision detection method comprises the steps that the spatial transformation relation between an execution assembly coordinate system where an execution assembly is located and an image coordinate system where image acquisition equipment is located is determined; controlling the tail end of the surgical mechanical arm to move to an actual pose based on the position of the target feature object, and acquiring feature object data which is acquired by the image acquisition equipment for the target feature object and is in the image coordinate system under the actual pose; and on the basis of the spatial transformation relation, the feature object data under the image coordinate system is converted to be under the execution assembly coordinate system, and a system precision result aiming at the surgical mechanical arm is obtained. By means of the system precision detection method, the problems that the system precision detection efficiency of the surgical mechanical arm is low and the manual error of contact type measurement is large are solved, and the system precision detection method of the surgical mechanical arm is efficient, precise and high in anti-interference capacity.