36results about How to "Good data base" patented technology

The invention discloses a novel CAD model feature recognition method based on a graph and a point cloud neural network, and the method comprises the steps: creating a data set containing heterogeneous machining features, converting a B-Rep model into a surface adjacent graph with surface-edge attributes, a surface UV grid, and a surface point cloud input neural network model; the neural network model integrates a graph encoder, a point encoder and a multi-task head, and the graph encoder and the point encoder learn classifiable embedded vectors from shape primitives of different scales of points, edges and surfaces respectively; and the multi-task head converts the embedded vector into corresponding semantic segmentation, instance segmentation and bottom surface segmentation results through three meta-learners. According to the method, insights can be extracted from different representation forms of a CAD model, semantic segmentation, instance segmentation and bottom surface segmentation tasks of heterogeneous processing features are processed in parallel, and experiments prove that Gamp is a Gamp; the PCNet has effectiveness and superiority in non-heterogeneous and heterogeneous processing feature recognition, and has application potential in real industrial scenes.

The invention provides an electrical equipment fault diagnosis early warning system, and relates to the technical field of equipment fault diagnosis, and the system comprises a multi-sensor module which is used for obtaining electrical, thermal, mechanical and electromagnetic parameters of electrical equipment; the acquisition processing module is used for summarizing the acquired various parameters and carrying out preprocessing and feature extraction; the fault diagnosis module is used for analyzing indexes of electrical characteristics, thermal characteristics, mechanical characteristics and electromagnetic characteristics of the electrical equipment, fusing the indexes based on the type of the equipment and diagnosing the fault condition of the equipment; the early warning module is used for generating a fault trend image of the electrical equipment based on the fusion result; the man-machine interaction module is used for generating a corresponding signal based on the fault condition and the trend image; through the reasonable design and the processing mode based on the equipment working type, the method has good adaptability and universality to different electrical equipment and complex working conditions, overcomes the limitation of the prior art, improves the diagnosis accuracy, and provides time for maintenance personnel to avoid fault deterioration.

The invention relates to a wind turbine generator set anomaly detection method considering multi-unit state similarity. Firstly, an evaluation index system is constructed, and design parameters and historical operation data are analyzed. Then data acquisition and preprocessing are carried out, an SCADA system is used for acquiring data, and denoising is carried out in combination with a local outlier factor algorithm and wavelet transform; and then qualitative evaluation is carried out, a cloud model is constructed to describe data uncertainty, a membership degree is calculated, and a cluster is dynamically adjusted. And then comprehensive evaluation is carried out, index weights are determined through an analytic hierarchy process, and scores are calculated in combination with membership degrees. And finally, performing anomaly detection, setting a threshold value, combining a dynamic clustering method, and taking a quantile of a sliding time window and a similar unit prediction residual error as a real-time early warning threshold value. According to the method, the accuracy and reliability of wind turbine generator anomaly detection are improved.

The invention relates to the technical field of water area public safety, in particular to a cross-modal intelligent perception fusion early warning method and system, and the method comprises the steps: respectively constructing a dam leakage recognition model, a dam deformation recognition model and a dam water pressure recognition model through collecting the sound wave data, image data and water pressure data of a dam in real time; and fast Fourier transform is carried out on the sound wave data through the dam leakage identification model, frequency domain features are extracted, and dam leakage abnormal coefficients are obtained. Recognizing the image data according to the dam deformation recognition model, obtaining dam image contour features and dam image region features, and obtaining a dam deformation abnormal coefficient. And recognizing the water pressure data according to the dam water pressure recognition model to obtain a dam water pressure abnormal coefficient. And obtaining a comprehensive dam abnormal coefficient according to the three abnormal coefficients, comparing the comprehensive dam abnormal coefficient with a preset dam abnormal threshold value, and performing real-time early warning. Through cross-modal data fusion analysis, the intelligent sensing and real-time management and control capabilities of dam public safety are improved.

The invention belongs to the field of autism spectrum disorder recognition, and particularly discloses an autism spectrum disorder recognition method based on a multi-mode graph convolutional network, and the method comprises the steps: collecting patient data, preprocessing the data, and constructing the graph convolutional network. The MPPCA method based on the threshold value is designed, prior information related to noise variance and the PCA denoising threshold value are combined, signals with dominant noise can be better distinguished, the function of the prior information related to the noise variance and the like is fully played, and a better data basis is provided for follow-up phenotype prediction; according to the method, a multi-modal graph convolutional network is used, phenotypes are predicted according to brain imaging data, behavior data and gene data of a patient, advantage complementation among different data types is utilized, and specific parameterization design is performed on the weight of a filter, so that the recognition accuracy of the autism spectrum disorder is improved.

The invention provides a model training method and device, a face emotion recognition method and device, equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring an image sequence, a voice sequence and corresponding text information of a sample face video; emotion information is marked in the sample face video in advance, and the emotion information comprises at least two emotion categories and corresponding intensity information; performing feature extraction on the image sequence to obtain image features of the sample face video; performing feature extraction on the voice sequence to obtain voice features of the sample face video; performing feature extraction on the text information to obtain text features of the sample face video; and performing model training according to the image features, the voice features, the text features and the emotion information to obtain a face emotion recognition model. Compared with the prior art, the problems that emotion recognition has limitation and accuracy cannot be guaranteed are solved.

The invention belongs to the technical field of spacecraft navigation, and provides a pulsar TOA extraction method based on periodic coincidence optimal estimation. The method comprises five steps of dual-channel detection photon simulation, periodic coincidence analysis, coincidence time window optimization, TOA sequence generation and optimal TOA extraction analysis. The method comprises the following steps: constructing a threshold function by utilizing the periodic characteristics of pulsar signal photons, periodically sliding a time window to carry out coincidence processing, eliminating noise photons, and reserving the signal photons. And optimizing the coincidence time window to improve the noise rejection ratio to more than 90%, generating a TOA measurement sequence, and selecting the minimum TOA measurement value as the optimal estimation from the TOA measurement sequence. According to the method, the pulsar TOA measurement precision can be effectively improved, and especially in a short time and under a small detector area, the method has important significance for an X-ray pulsar navigation system to realize high-precision spacecraft autonomous navigation and positioning.

The invention discloses a network frame project evaluation method and device for a power distribution network, and the method comprises the steps: obtaining a power distribution network data set based on power grid equipment data and a power grid topological graph, and the power grid equipment data is the data of all equipment in the power distribution network; analyzing the power distribution network data set, and determining a network frame project; giving a plurality of evaluation dimensions of the net rack project, and configuring a corresponding evaluation index for each evaluation dimension; evaluating the network frame project in combination with the power distribution network data set to obtain an evaluation result; wherein the evaluation dimension comprises a green development dimension, and the green development dimension is used for evaluating the sustainability of the net rack project. Through integration of various data in the power distribution network and extraction of the network frame project, a good data basis is provided for evaluation of various network frame projects in the power distribution network, on the basis, multiple dimensions including the green development dimension of the network frame project are evaluated, and the comprehensiveness and accuracy of evaluation of the power distribution network and the network frame project are improved.

The present invention discloses a photovoltaic microgrid energy management and optimization scheduling system, relates to the technical field of smart grids, and solves the problems of unstable output power, complex control strategy and poor real-time response capability of the existing system; the present invention predicts future photovoltaic power generation and load curves through an intelligent prediction and analysis module, provides a reliable data basis for the optimal scheduling of the system, adjusts the energy distribution between the photovoltaic system, energy storage device and controllable load in real time through a dynamic scheduling optimization module, realizes efficient energy utilization and stable operation of the system, optimizes the output of distributed energy resources and the charging and discharging strategy of the energy storage device through a distributed energy resource control module and an energy storage management and optimization module, and further improves the real-time response capability and operation efficiency of the system; the present invention greatly improves the energy management, prediction accuracy, real-time response capability and safety control capability of the existing photovoltaic microgrid.

The present invention relates to the field of drone control technology, and specifically to a drone swarm collaborative control system and method in a complex environment based on image recognition. The system preprocesses collected real-time environment image data of the drone swarm to generate real-time environment image feature data of the drone swarm, builds real-time environment three-dimensional model data of the drone swarm based on the real-time environment image feature data, and matches the data with the collected preset flight route data of the drone swarm. According to the matching result, preset flight route adjustment data of the drone swarm is generated. If the preset flight route adjustment data of the drone swarm needs to be adjusted, the flight route of the drone swarm is planned by using a constructed drone swarm flight route planning model to generate drone swarm flight route planning data. Finally, drone swarm collaborative control data is built, and drone swarm flight route adjustment operations are performed to achieve collaborative control of the drone swarm and scientific adjustment of the flight route.

The invention relates to a low-slow small target identification method based on radar and visual data fusion, and the method comprises the steps: S1, setting a radar system for collecting radar data, and a camera system for collecting visual data; s2, synchronizing data acquisition of a radar system and a camera system, and respectively obtaining corresponding radar point cloud data and image data; s3, after radar point cloud data obtained by the radar system and image data of the camera system are preprocessed, data fusion is carried out to obtain fused data; and S4, inputting the fusion data into a pre-trained neural network model, and carrying out detection and classification of low-slow small targets.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle group cooperative control system and method in a complex environment based on image recognition, and the system comprises the steps: carrying out the data preprocessing of the collected real-time environment image data of an unmanned aerial vehicle group, and generating the real-time environment image feature data of the unmanned aerial vehicle group; and constructing unmanned aerial vehicle group real-time environment three-dimensional model data based on the unmanned aerial vehicle group real-time environment image feature data, matching the unmanned aerial vehicle group real-time environment three-dimensional model data with the collected unmanned aerial vehicle group preset flight route data, generating unmanned aerial vehicle group preset flight route adjustment data according to a matching result, and if the unmanned aerial vehicle group preset flight route adjustment data needs to be adjusted, determining that the unmanned aerial vehicle group needs to be adjusted. And if yes, performing flight route planning on the unmanned aerial vehicle group through the constructed unmanned aerial vehicle group flight route planning model, generating unmanned aerial vehicle group flight route planning data, finally constructing unmanned aerial vehicle group cooperative control data, and executing unmanned aerial vehicle group flight route adjustment operation, thereby realizing cooperative control of the unmanned aerial vehicle group and scientific adjustment of the flight route.

The invention belongs to the technical field of computers, and provides a financial dispute case civil litigation evidence intelligent verification and first copy method, which comprises the following steps of: 1, formulating an evidence material and evidence element data standardization template; step 2, intelligent verification of evidence: after the system receives the evidence material and evidence element data submitted based on the standardized template, automatically extracting the element data in the evidence material by means of an AI technology, and comparing the extracted element data with the evidence element data; 3, evidence quantitative judgment; step 4, processing the case; and step 5, outbound calling of the first link. According to the invention, the intelligent level and reliability of evidence verification and first copy of financial dispute cases are improved.

The invention discloses a digital twinning monitoring system for a building environment. The digital twinning monitoring system comprises an environment sensing physical layer, a communication processing twinning layer, an analysis modeling prediction layer and a building environment monitoring application layer. The invention belongs to the technical field of building environment monitoring systems, and particularly relates to a building environment digital twinning monitoring system, which adopts a U-shaped network combined with a convolutional long and short term memory module, performs data classification and hierarchical storage by a multi-task learning data classification method, and improves the overall operation efficiency of the system. A multi-task space-time convolutional network model is adopted to carry out three-way building environment monitoring prediction, so that the overall availability of the system is improved; the building environment digital twinning comprehensive monitoring is adopted, a building environment digital twinning monitoring comprehensive scheme is obtained, an application layer of a digital twinning system is perfected, unification of environment management, space management and personnel safety management is achieved, and feasible technical reference is provided for further optimization of the building environment digital twinning monitoring system.

The invention discloses a computer data management system and method, and relates to the technical field of data management, and the method comprises the steps: reading a heterogeneous data set, dividing the heterogeneous data set into text, time sequence and binary data groups according to data types, extracting a core field of each data group, and generating a deduplication feature and an index feature, setting a sliding window based on the data type and generating a window index feature abstract; calculating a comprehensive similarity score between the data blocks according to the sliding window, screening a similar data block set, determining a reference data block and a redundant data block, clustering the reference data block to generate an initial cluster, and if the global deduplication rate and the clustering preprocessing validity do not meet preset conditions, re-initializing the sliding window; according to the method, the cluster structure is optimized and the cluster feature abstract is generated based on the retrieval popularity predicted value of the initial clustering cluster, the three-layer index structure comprising the global index, the intra-cluster index and the feature index is constructed, the retrieval efficiency is tested, the index hierarchy is optimized, and the storage efficiency and the retrieval performance of heterogeneous data management are remarkably improved.

This application relates to the field of industrial data processing and intelligent control technology, and discloses a method for dynamic modeling and prediction of industrial big data combined with digital twins. The method includes: acquiring operating status data, process execution data, control command records and prediction results, and constructing a sequence of control action events; segmenting the operating status data according to the sequence of control action events to form natural operating data segments and intervention operating data segments, extracting distribution characteristics, fluctuation characteristics and response characteristics, and generating a segmented behavioral feature set; calculating the control reflexive influence quantity and generating a reflexive drift marker, dividing the natural evolution dataset and the control feedback dataset, and predicting them separately; generating control risk constraint quantity, screening, delaying or limiting the control commands to be executed, and iteratively correcting the dataset according to the execution results; this method can distinguish between control feedback changes and natural evolution changes, reduce drift misjudgments, and suppress multi-round oscillations and parameter fluctuations.

The present invention discloses a building environment digital twin monitoring system, including an environment perception physical layer, a communication processing twin layer, an analysis modeling prediction layer, and a building environment monitoring application layer. The present invention belongs to the technical field of building environment monitoring systems, specifically a building environment digital twin monitoring system, the present invention adopts a U-shaped network combined with a convolutional long short-term memory module, and performs data classification and hierarchical storage by a multi-task learning data classification method, thereby improving the overall operation efficiency of the system; adopts a multi-task spatiotemporal convolutional network model to perform three-way building environment monitoring prediction, thereby improving the overall availability of the system; adopts a building environment digital twin comprehensive monitoring, obtains a building environment digital twin monitoring comprehensive solution, improves the application layer of the digital twin system, realizes the unification of environmental management, space management, and personnel safety management, and provides a feasible technical reference for further optimization of the building environment digital twin monitoring system.

The invention discloses a remote energy storage operation and maintenance control system for a photovoltaic thermal power station, which relates to the technical field of energy storage control. The power station operation data of a plurality of photovoltaic thermal power stations are collected, a communication path of each photovoltaic thermal power station is established for uploading the power station operation data, a corresponding task to be operated is generated according to the power station operation data, the task to be operated is disassembled into a plurality of sub-tasks and their complexity is calculated, local processing or cloud processing is determined according to the complexity of the sub-tasks, all sub-tasks of the same task to be operated after completing local processing and cloud processing are subjected to feature analysis to obtain the operation characteristics and energy storage characteristics of each photovoltaic thermal power station, a power station operation fault diagnosis node and a power station energy storage status monitoring node are constructed to obtain the operation fault and energy storage change trend of each photovoltaic thermal power station, remote operation and maintenance and repair of photovoltaic thermal power stations with operation faults are carried out, and corresponding management measures are taken according to the energy storage change trend.

The present invention relates to the technical field of machine learning, and specifically to a method and system for automatic optimization of durenyl parameters based on deep learning. The first historical parameter data is obtained by collecting historical production data of durenyl gas-phase oxidation to prepare pyromellitic anhydride, and the first pre-processed historical parameter data is obtained by pre-processing. It is divided into first positive example data and first negative example data according to by-product data. The first positive example data is analyzed to calculate the first parameter range of each parameter. The first real-time parameter data is obtained to determine whether it is within the first parameter range. If not, an early warning is given; if so, the first real-time parameter data is input into a first pyromellitic anhydride regression model to obtain first predicted output data, and the data is input into a first pyromellitic anhydride classification model to obtain a first predicted category. The reaction parameters are automatically adjusted according to the first predicted category to achieve intelligent optimization and effective control of preparing pyromellitic anhydride from durenyl.

The application discloses a chloride-based multi-parameter humidity and conductivity comprehensive calculation method, comprising the following steps: S1, collecting basic parameter information of chloride materials, and performing relevant data arrangement, and comparing after entering a system; S2, then setting an environmental humidity change range, and obtaining moisture absorption and surface water layer thickness under different relative humidity conditions; S3, establishing a relevant coupling model, and considering ion mobility, charge transmission channel number and interface polarization effect of the chloride materials under different humidity; S4, introducing a multi-parameter regulation factor, correcting environmental temperature, pressure and material surface state, and obtaining correct data; the method has high application prospect and popularization value.

The application relates to a chemical short-range order measurement method and system based on an atomic pair distribution function, and the method comprises the following steps: performing a total scattering experiment on a polycrystalline powder sample to obtain total scattering data, a scattering function and an atomic pair distribution function; based on the total scattering data, an initial three-dimensional atomic structure model is constructed; further, the initial three-dimensional atomic structure model is fitted and reconstructed by taking minimization of a fitting goodness factor as a target, so that a three-dimensional atomic structure model after fitting and reconstruction is obtained; according to the three-dimensional atomic structure model after fitting and reconstruction, a radial distribution function and a bulk average distribution concentration are calculated, and a chemical short-range order parameter calculation cutoff is set according to a near-neighbor atomic cutoff distance; further, the probability of an atom finding other types of atoms under the calculation cutoff is calculated; and the chemical short-range order parameter is calculated based on the probability and the bulk average distribution concentration. Compared with the prior art, the application has the advantage of wide application scenarios.

The invention discloses an audio classification method, system and device based on deep learning and a medium, and the method comprises the steps: obtaining an audio sequence, carrying out the preprocessing of the audio sequence, and obtaining an audio data set; segmenting an audio frame according to the audio data set, and extracting a frequency spectrum sequence, a wavelet phase diagram, a wavelet intensity diagram and a Mel spectrum of the audio data set; respectively extracting feature vectors corresponding to the audio frames, the spectrum sequences, the wavelet phase diagrams, the wavelet intensity diagrams and the Mel spectrograms by adopting a plurality of groups of independent multi-scale feature extraction modules to obtain feature vectors extracted by each group of multi-scale feature extraction modules; performing feature fusion on the feature vectors extracted by each group of multi-scale feature extraction modules to obtain a feature fusion vector; and performing audio classification according to the feature fusion vector to obtain an audio classification result. According to the invention, the accuracy and anti-interference performance of audio classification can be improved.

This invention discloses a method for constructing a road cavity detection model based on 3D ground-penetrating radar (GPR). The method includes constructing a hybrid spatiotemporal detection model, which comprises a feature preprocessing module, a soft clipping module, a spatiotemporal feature extraction module, and an output module. The feature preprocessing module performs feature pre-extraction on preprocessed and labeled data, outputting a pre-extracted feature map. The soft clipping module performs spatiotemporal perception on the pre-extracted feature map, outputting a clipped feature map. The spatiotemporal feature extraction module analyzes the clipped feature map, outputting feature information with global spatiotemporal information. The output module integrates the feature information and predicts the existence of cavities and the range of their occurrence channels. By combining the correlations between data in the spatiotemporal dimension, it accurately detects underground cavities, solving the problems of high computational load and difficulty in adjusting model parameters in existing 3D GPR high-resolution data interpretation. This provides an efficient and reliable technical solution for intelligent road cavity detection and infrastructure safety maintenance.

The invention discloses a three-dimensional modeling method and system for port whole-process management and control. The three-dimensional modeling method comprises the following steps: acquiring initial laser point cloud data, initial millimeter wave point cloud data, dust concentration and a visibility estimation value; performing dust point cloud elimination on the initial laser point cloud data and the initial millimeter wave point cloud data to obtain laser point cloud data and millimeter wave point cloud data; determining a laser point cloud weight and a millimeter wave point cloud weight; based on the laser point cloud weight and the millimeter wave point cloud weight, performing weighted fusion on the laser point cloud data and the millimeter wave point cloud data to obtain final point cloud data; and performing three-dimensional modeling based on the final point cloud data. According to the method, the point cloud belonging to the dust in the initial point cloud data is removed, only the required point cloud data is reserved, the influence caused by the dust is avoided, and a good data basis is provided for modeling from unloading of a car dumper and transportation of a belt conveyor to stockpiling of a storage yard in the whole-process operation.

The present invention relates to a method and system for separating irregular multiple waves. The separation method and system break through the assumption of regular interference in traditional methods. Based on focusing transformation, by analyzing the energy distribution of different types of reflected waves in the focusing domain, high-resolution τ‑p transformation and regularized non-steady-state regression adaptive matching filtering are used to simultaneously separate primary waves, free surface multiple waves and interlayer multiple waves. The method specifically includes a data acquisition step, a focusing operator construction step, a focusing transformation step, a τ‑p transformation step, a primary wave separation step, a free surface multiple wave separation step, and an interlayer multiple wave separation step. The free surface multiple waves and interlayer multiple waves accurately separated by the present invention can lay a good data foundation for subsequent multiple wave suppression and imaging applications.

The invention discloses a steel-concrete nondestructive testing method and device based on time-varying analysis, and relates to the field of steel-concrete structure detection.The steel-concrete nondestructive testing device comprises an acquisition module, a data processing module, a data processing module and a data processing module, collecting a stress wave signal, a vibration response signal, a sound wave propagation signal and an elastic wave signal of the structure at a preset interval continuous moment and a temporary moment of stress change; the processing module is used for receiving the signals acquired by the acquisition module, performing noise reduction and time sequence alignment processing on the signals, and synchronously establishing association mapping between the signals and corresponding acquisition moments; when the system is applied to steel-concrete structure detection, multi-dimensional signals of key stress positions can be accurately collected, signal time-varying laws are mined after processing, health state parameters are matched to generate visual health assessment, data can be dynamically updated, structural abnormity can be early warned in time, core influence factors can be marked, potential damage can be effectively prevented and controlled, and the system is high in practicability. Long-term safety of the structure is guaranteed, and service life is prolonged.

A method for unsupervised enhancement of hot-rolled coil surface defect image data belongs to the technical field of metallurgical hot-rolled coil defect detection. The generative adversarial network consists of two deep neural networks that compete with each other. It is a probabilistic generation model. The way it generates samples is to forward propagate through the generator, and then use gradient backpropagation to optimize the calculation after the discriminator discriminates. It does not rely on any prior assumptions. In the process of training the generative adversarial network, the two networks continuously iterate and optimize in the mutual game. The generator learns to generate more realistic samples, while the discriminator network tries to discriminate whether the data samples are real samples or generated fake samples. The two sides continue to compete until neither side can get better. Finally, the two networks reach a dynamic equilibrium, that is, the image distribution generated by the generator is close to the real image distribution, and the discriminator cannot recognize true and false images. The advantage is that it solves the problems of small and difficult to obtain image data of surface defects of hot-rolled coils in the field of metallurgical steel rolling, and low recognition accuracy.

The invention relates to the technical field of electric digital data processing, in particular to a model training data processing method and system and electronic equipment. Firstly, MIME types are determined through an HTTP response header for preliminary classification, it is ensured that data are classified in first-level classification directories of texts, pictures, audios and videos, and for data with missing MIME types, a type weighted scoring method is used for auxiliary classification, so that the classification accuracy is improved; thirdly, extracting a second-level classification label of the first-level classification data through an existing data set, automatically generating a second-level classification result in combination with a zero sample learning algorithm, storing the second-level classification result into a corresponding second-level directory, and further refining data categories; and finally, checking whether the data volume is lower than a generation threshold value or not by traversing a secondary classification directory, automatically triggering data amplification, balancing the number of various types of samples, and ensuring the balance and representativeness of the data. According to the method, multi-level automatic classification and data amplification are effectively realized, and the model training data processing efficiency and processing quality are improved.

The invention relates to the technical field of steel wire damage diagnosis, in particular to a steel wire damage diagnosis method and system.The method comprises the steps that a surface image of a to-be-diagnosed steel wire is obtained and preprocessed to obtain a first steel wire diagnosis image, a steel wire abrasion diagnosis model is constructed to recognize the first steel wire diagnosis image, and steel wire abrasion characteristics are obtained; obtaining a steel wire wear coefficient according to the steel wire wear characteristics; constructing a steel wire deformation diagnosis model to identify the first steel wire diagnosis image to obtain steel wire deformation characteristics, and obtaining a steel wire deformation coefficient according to the steel wire deformation characteristics; and according to the steel wire wear coefficient and the steel wire deformation coefficient, a steel wire damage coefficient is obtained, and according to the steel wire damage coefficient and a preset steel wire damage coefficient threshold value set, steel wire damage categories are obtained, and the steel wire damage categories comprise intact damage, slight damage, moderate damage and serious damage. By comprehensively analyzing the abrasion and deformation of the steel wire, the accuracy of steel wire damage diagnosis is remarkably improved.

The invention relates to the technical field of medical instruments, in particular to a multifunctional operation arm suitable for hysteromyoma resection under a laparoscope, which comprises an intelligent operation system and a support, a plurality of mechanical arms are mounted on the support, and end effectors with different functions are mounted at the tail ends of the mechanical arms. The mechanical arm operation system comprises a patient analysis module, an operation planning module, a real-time feedback module and a mechanical arm control module. The operation planning module is used for constructing an operation planning engine through a CNN (Convolutional Neural Network); the operation planning engine is used for inputting an operation three-dimensional model and generating an operation suggestion scheme. The operation planning engine is used for generating corresponding operation suggestion schemes for different operation three-dimensional models, a doctor is effectively assisted in constructing and adjusting the operation schemes, then through the design of the mechanical arm control module, the doctor is assisted in performing operation, and the operation effect is effectively improved.