1351results about How to "Reduce human error" patented technology

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention discloses an Internet of Things equipment access protocol component development method, which is used for quickly developing an Internet of Things equipment access protocol analysis and processing component at an Internet of Things platform side, and comprises the following steps: S1, constructing a protocol analysis and processing general framework according to the protocol analysis and processing component of an Internet of Things platform; s2, abstracting a specific logic processing part of a protocol in the general framework into a method / function interface; s3, constructing a protocol expert AI knowledge base for data learning; s4, constructing an AI large model and a multi-agent processing flow, selecting a specified large model, configuring related parameters, extracting required parameter extraction / coding logic, and generating an interface implementation code; s5, integrating each generated interface implementation code with the universal framework; and S6, testing, adjusting and optimizing the integrated access protocol analysis and processing component. According to the method, the unified development template can be provided, so that the development technical threshold is reduced, and the development efficiency is greatly improved.

The invention discloses a full-automatic laser welding forming system, which relates to the technical field of laser welding, and comprises a laser generation and light beam modulation module, a material conveying and positioning module, a welding execution module, a visual inspection and monitoring module, an intelligent self-adaptive control module and an energy recovery and reutilization module, the material module accurately conveys and positions materials; the welding module robot is accurate in operation; the visual module detects welding seams; the intelligent module performs self-adaptive control; the energy module recycles energy. The welding quality is improved, and defects are reduced; the production efficiency is improved and the cost is reduced; operation is simple, and manpower requirements are reduced; energy is saved, environment is protected, and green concept is met; real-time monitoring is achieved, and the product quality and the management level are guaranteed.

The invention provides a construction method, a control method and a system of a flight control network, and the method comprises the steps: employing a depth deterministic strategy gradient algorithm, building an attitude control network of an unmanned aerial vehicle in a flight process based on the state information and a target flight value of the unmanned aerial vehicle in the flight process, employing a depth reinforcement learning algorithm, and carrying out the attitude control of the unmanned aerial vehicle in the flight process. Based on the state information and the information of the environment where the unmanned aerial vehicle is located, a path planning network and an intelligent avoidance network of the unmanned aerial vehicle in the flight process are built respectively; based on the attitude control network, the path planning network and the intelligent avoidance network, constructing a hierarchical deep reinforcement learning network of the unmanned aerial vehicle in the flight process; and training the hierarchical deep reinforcement learning network by adopting historical flight data of the unmanned aerial vehicle in different flight environments and a preset flight reward mechanism to obtain a flight control network of the unmanned aerial vehicle. According to the invention, the adaptability and flexibility of the unmanned aerial vehicle in the flight process can be improved.

The invention discloses an intelligent bid evaluation method and system based on a large model, and a related product, relates to the technical field of purchase bid evaluation systems, and solves the problems that an existing intelligent bid evaluation system is difficult to understand a complex logical relationship and semantic connotation and cannot provide a comprehensive and accurate scoring result. According to the technical scheme, the method comprises the steps that S1, bidding and tendering files are obtained; s2, performing disassembly processing based on the bidding and tendering file to obtain a plurality of bidding and tendering data pairs; s3, performing instruction construction on the bidding and tendering data pair based on a historical bidding and tendering database to obtain a prompt instruction of the bidding and tendering data pair; s4, processing the prompt instruction of the bidding and tendering data pair based on the large model after training and fine tuning to obtain the score of the bidding and tendering data pair, and accumulating the scores to obtain the score of the bidding file; through the method based on the large model, the scoring effect is effectively improved in the intelligent bid evaluation work, the efficiency is improved, manpower and material resources are saved, and meanwhile interference caused by human factors is avoided.

The invention discloses an automatic quality-supplementing correction method and system for the unbalance amount of a fan, and the method comprises the following steps: collecting geometric parameters and material characteristics in a static state of the fan, and constructing a mechanical distribution model of the fan; the method comprises the following steps: collecting vibration signals and airflow characteristics in real time in a dynamic operation state, calculating the amount of unbalance in operation and decomposing the amount of unbalance into a primary component and a secondary component; on the basis of the unbalance prediction trend, determining a quality supplementing range, carrying out multi-point quality supplementing operation through a dispensing technology, and monitoring a quality supplementing effect by using an optical fiber sensor; and if the residual unbalance amount after quality supplementation still exceeds the standard, real-time compensation adjustment is carried out through the electromagnetic torque regulation and control module. Full-process automatic correction from static state to dynamic state and from quality supplement to fine adjustment is achieved, the method has the advantages of being real-time, accurate and intelligent, vibration and noise in the fan operation process can be effectively reduced, the operation stability of equipment is improved, and the service life of the equipment is prolonged.

The invention relates to the technical field of plant extraction, in particular to an intelligent plant extraction control method and system, and the method comprises the following steps: based on historic plant extraction data, including temperature, pressure and pH value during extraction, analyzing the long-term trend of parameters, predicting the requirements of plant extraction conditions by simulating parameter reactions under various conditions, and obtaining a plant extraction result; and obtaining a parameter prediction result. According to the method, long-term trend prediction of parameters such as temperature, pressure and pH value is realized through analysis of historically recorded plant extraction data, so that the parameters of extraction equipment are adjusted in advance, changes of different plant materials and environmental conditions are effectively adapted, the extraction efficiency is optimized, and by dynamically simulating the expression of the extraction efficiency in various time periods, the extraction efficiency is improved. According to the innovative scheme, an optimal extraction time window is mined, resource consumption and extraction time in the extraction process are reduced, and meanwhile the quality and consistency of extracts are kept.

The invention relates to a concrete surface crack detection method and system based on a machine learning algorithm, and the method comprises the steps: obtaining concrete wave velocity, frequency, amplitude, strength, elastic modulus, temperature and linear expansion coefficient, analyzing characteristic parameters, building a mathematical relation model, fitting data through a machine learning algorithm, determining model parameters, and calculating the concrete surface crack detection result. Relevant parameters of to-be-detected concrete are input into the multi-parameter digital quantity plate model, the model calculates potential risks and predicted values of cracks, the multi-parameter digital quantity plate model is applied to real-time detection of the concrete structure to achieve quality evaluation and crack prediction, the concrete structure is monitored regularly, and model data are updated; the system comprises a data acquisition module, a data preprocessing module, a feature extraction module, a crack measurement module, a multi-parameter digital quantity board model establishment module, a multi-parameter comprehensive analysis module, a real-time monitoring and early warning module, a database management module and a user interface and interaction module.

The invention provides a data flow-based runtime text context transmission and task instance dynamic scheduling method, which relates to the technical field of data processing, and comprises the following steps of: receiving a task scheduling request, constructing a data flow dependency graph containing task nodes and a data flow direction relationship, and obtaining runtime context information of task instances corresponding to the task nodes, comprising task instance identification, state information and input and output parameter information. And establishing a mapping relationship between task instance input and output parameters by analyzing a data flow dependency graph and runtime context information, and generating a parameter transfer rule. According to the method, task instance state information is monitored, when the state of an upstream task changes, an upstream output parameter is transmitted to a downstream input parameter according to a parameter transmission rule, and when the downstream input parameter is complete, dynamic scheduling execution of a downstream task instance is triggered, so that data flow driven task scheduling is achieved, and the scheduling efficiency and flexibility are improved.

The invention discloses a tunnel crack tiny defect detection method based on a generative adversarial network. The method comprises the following steps: S1, acquiring a space-time crack data set containing tunnel surface tiny crack information; s2, generating a preprocessed space-time fracture data set; s3, generating a crack expansion image data set; s4, designing a generative adversarial network model which comprises a generator and a discriminator; s5, inputting the preprocessed space-time crack data set into the generative adversarial network model; s6, deploying the trained generative adversarial network model into a tunnel monitoring system; s7, performing classification according to the form, size and expansion trend of the crack, and dividing the crack into different risk grades; and S8, according to the crack detection result and the expansion trend thereof. The tunnel crack detection accuracy and the crack propagation prediction capability are improved, the automation level of the system is remarkably improved, and efficient and accurate crack detection and monitoring can be realized in a complex environment.

The invention discloses an official document generation system and method based on a large model, belongs to the technical field of artificial intelligence, and aims to solve the technical problems of low official document format and content accuracy, poor material failure and poor content innovation in official document generation. Comprising a model training module used for performing model training on an official document generation model based on official document samples in an official document sample library; the model fine tuning module is used for performing layered fine tuning on the trained official document generation model; the cue word optimization module is used for carrying out cue word matching on user input based on a built-in cue word set; the outline generation module is used for calling a final official document generation model to generate an official document outline based on the cue word combination and user input; and the official document generation module is used for calling the final official document generation model to generate and output an official document body based on the user input and the final official document outline, and calling the privacy data shielding module to shield the privacy data in the official document body.

The invention relates to the technical field of data management, in particular to a data asset sorting method, which comprises the following steps: preliminarily classifying all data assets in an enterprise, performing quality evaluation on the preliminarily classified data assets, generating a quality evaluation report, and performing data redundancy detection and optimization based on the quality evaluation result. Cleaning redundant data by adopting a deduplication algorithm and an optimization algorithm, carrying out data correlation analysis, generating a data correlation graph through a graph model and a statistical model, and determining the relationship between data sets; and finally, according to a correlation analysis result, integrating the data sets with high correlation into a unified data set by adopting a data fusion algorithm, and reducing a storage space through a data compression technology. According to the method, the accuracy of data classification and management can be improved, the data redundancy is reduced, the data storage and access efficiency is optimized, and the data security and privacy protection are enhanced through security evaluation and protection measures.

The invention belongs to the technical field of shot blasting processes, and provides a self-adaptive aero-engine blade surface shot blasting control system and a shot blasting process. The system comprises a measurement module used for obtaining the three-dimensional geometrical shape of the complex surface of the blade and generating a digital three-dimensional model; the sensor module is used for monitoring and feeding back the shot blasting process condition in real time; the control module is used for dynamically adjusting shot blasting parameters by adopting a self-adaptive algorithm; the shot blasting execution module is used for executing a shot blasting process; the real-time monitoring feedback module is used for dynamically adjusting shot blasting parameters; the process comprises the steps that after the blade surface is cleaned, a three-dimensional model generated by a measuring module and surface data measured by a sensor module are fed back to a control module, a shot blasting injection parameter instruction is obtained through a fuzzy control algorithm, and a shot blasting machine execution module executes the shot blasting process. And the real-time monitoring feedback module dynamically adjusts shot blasting parameters until the shot blasting process requirements are met. According to the invention, accurate and uniform shot blasting treatment on the surface of the blade in a complex shape can be realized.

The invention relates to the technical field of power industry, and discloses a distribution network three-dimensional visualization dynamic operation and maintenance system development method and system, and the method comprises the steps: scanning power distribution equipment and environment, and obtaining three-dimensional point cloud data; preprocessing and feature extraction are carried out on the point cloud data, and a three-dimensional model of the power distribution network equipment is constructed. And importing the three-dimensional model and the real-time monitoring data of the power distribution network into a visual platform, and evaluating the running state of the power distribution network. Different early warning mechanisms are set, and abnormity can be found in time. According to the invention, the fault detection and response speed is improved. And the accuracy of state evaluation and fault positioning is improved. In combination with machine learning and multi-level state evaluation, the intelligent operation and maintenance of the power distribution network are realized, risks can be automatically identified, early warning can be generated, manual operation errors are reduced, and the stability and safety of the system are improved. Through the three-dimensional visualization platform, operation and maintenance personnel can understand the state and the fault condition of the power distribution network more visually, the decision process is optimized, and the working efficiency is improved.

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

The invention discloses a novel noise HV spectrum ratio imaging method based on polarization. The method comprises the following steps: collecting background noise data in a predetermined monitoring area; preprocessing the acquired background noise data, wherein the preprocessing comprises data format conversion, data splicing, mean value removal, trend removal, waveform pinching and band-pass filtering processing; preprocessed data are processed by adopting a polarization analysis technology, a wave field is separated by setting a threshold value and analyzing a power spectrum, and a background noise area is divided into a linear body wave component and a nonlinear surface wave component. According to the polarization-based noise HV spectrum ratio imaging method, the precision and efficiency of underground structure analysis are remarkably improved through the precise separation of the wave field and the automatic processing flow of the HV spectrum ratio, and by means of polarization analysis optimized data processing, the automatically generated frequency and depth profile and the automatically picked excellent frequency and peak value, the imaging precision and efficiency of the underground structure are greatly improved. And the analysis of the underground velocity structure and the wave impedance interface is more accurate.

The invention discloses an automatic stereoscopic warehouse goods sorting system and method, and relates to the technical field of intelligent sorting, the system comprises an input layer, a storage layer, an execution layer, a control layer and a management layer, the input layer comprises a bar code RFID scanning system, a machine vision identification system, a sensor and an input terminal, the storage layer comprises a three-dimensional goods shelf, a tray goods shelf system and a storage management system, the execution layer comprises a stacking machine, a conveying belt, a sorting robot and a carrying vehicle, and the control layer comprises a programmable logic controller, a monitoring and data acquisition system and a communication network. According to the automatic stereoscopic warehouse goods sorting system and method, efficient cooperative work among equipment can be achieved, and the goods processing speed and efficiency can be improved.

The invention discloses a sliding bearing-rotor system stability test system and method under an impact load, and relates to the field of rotating machinery test in mechanical engineering. According to the technical scheme, different types of impact loads are applied through a vibration acceleration platform, and starting, stopping or external impact force in actual working conditions is simulated. In the testing process, a high-precision sensor and a multi-channel acquisition board card are used for monitoring the vibration characteristic and the rotating speed of the system in real time, it is guaranteed that signals of different positions and types can be synchronously acquired, and comprehensive system state information is provided. Then, in combination with time domain and frequency domain analysis technologies, key features are extracted from mass data by applying an advanced Fourier transform algorithm, the resonant frequency, modal characteristics and potential fault modes of the system are identified, and accurate evaluation of the stability of the system is realized. Experimental results show that the real-time monitoring and analysis method can effectively improve the stability test precision of the sliding bearing-rotor system under the impact load, and provides powerful technical support for engineering application. The method is used for evaluating the stability of the sliding bearing-rotor system under the impact load.

The invention discloses a method and device for providing insurance consultation service, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: performing semantic analysis on a consultation question text to obtain key text information; mapping the key text information to a pre-constructed insurance domain knowledge graph to obtain an initial query node set; searching associated knowledge nodes directly or indirectly connected with each initial query node in an insurance domain knowledge graph; calculating relevancy scores among the associated knowledge nodes, obtaining the knowledge nodes with scores ranked in the first N positions according to the relevancy scores, and constructing a knowledge sub-graph based on the first N positions of knowledge nodes; the knowledge sub-graph is expanded and supplemented; and converting the extended knowledge sub-graph into an answer text in a natural language form. The invention further relates to the technical field of block chains, and the problem text is stored on a block chain node. According to the invention, comprehensive knowledge related to the question can be provided, and the accuracy and efficiency of consultation service are improved.

The invention discloses a method, a system and equipment for optimizing batching parameters in an industrial silicon smelting process. The method comprises the following steps: firstly, acquiring process parameters in an industrial silicon smelting process, standardizing the process parameters, then converting the process parameters into weight factors influencing a smelting result, and mapping a relation between the smelting result and the weight factors into a parameter equation based on a preset multiple linear regression model, obtaining a parameter set containing a weight factor and a linear regression coefficient; then, based on the parameter set, the initial raw material parameters and the reaction process parameters, an equivalent circuit model is constructed and simulated to generate smelting result data under different parameter combinations, and therefore parameter adjustment variables are obtained; according to the numerical range of the parameter adjustment variables and the initial raw material parameters, an optimal batching parameter combination is obtained; and finally, performing optimization treatment on batching parameters in the industrial silicon smelting process. According to the method, accurate control and rapid optimization adjustment of batching parameters can be stably achieved in the industrial silicon smelting process.

The invention provides an efficient article sorting method and system of an intelligent warehousing system, and relates to the technical field of intelligent warehousing systems.The method comprises the steps that warehousing real-time environment data and a target image set for target objects to be sorted are obtained; inputting the target image set into a preset object classification model to determine a target classification corresponding to the target object; determining at least one candidate free goods allocation matched with the target classification, and determining a target transportation path of the target object according to each candidate free goods allocation, the storage real-time environment data and the target classification; determining a target clamping strategy matched with the target classification; and the target path and the target clamping strategy are sent to the AGV and the mechanical arm correspondingly, so that the AGV and the mechanical arm cooperatively sort the target objects. Therefore, the intelligent level of the intelligent warehousing system is remarkably improved, and automatic sorting tasks for various objects can be completed more intelligently.

The invention discloses a network security attack and defense confrontation scene generation system for a novel power system. The scene editor module receives user interaction and constructs or modifies an attack and defense scene; the network simulation module generates real network environment data based on a network space surveying and mapping technology, constructs a simulation network environment and manages simulation data through a simulation database; the AI automatic control module integrates an AI algorithm to simulate an attack behavior, intelligently generates and adjusts a defense strategy, and manages an attack and defense rule through a rule engine; the security isolation module deploys security equipment to realize sensitive data isolation; the data flow direction is controlled through strategy configuration, and information leakage is prevented; the evaluation and feedback module is used for collecting data in the drilling process, carrying out multi-evaluation on the drilling effect and generating reports and suggestions; and the business module adopts a plug-in architecture, dynamically loads or unloads each functional module, constructs a multi-layer defense system, and realizes generation, evaluation and feedback of an intelligent confrontation strategy. According to the invention, the authenticity and effectiveness of attack and defense drilling are improved.

The invention relates to the technical field of data processing, and discloses a multi-point synchronous accurate hoisting positioning method and device for a bridge precast beam. The method comprises the following steps: optimizing three-dimensional scene data to obtain target environment data, performing path planning to obtain multi-point hoisting path data, and performing optimization processing on the path data to obtain an optimized hoisting path and attitude change data; performing real-time visual tracking processing on the precast beam to obtain real-time position and attitude data, and performing filtering processing on the real-time position and attitude data to obtain filtered pose data; and performing comparative analysis on the pose data, the optimized hoisting path data and the optimized pose change data to obtain control instruction data, and performing cooperative control on the multi-point hoisting system according to the control instruction data. The posture of the large precast beam can be accurately and efficiently controlled, the positioning precision is improved, and the requirement for rapid and efficient hoisting is met.

The invention relates to the technical field of circuit board assembly, and discloses an automatic circuit board feeding device and method.The automatic circuit board feeding device comprises a machining platform, a feeding mechanism is installed at the bottom of the machining platform and used for controlling a plurality of supporting plates to vertically move, a material suction arm is slidably connected to the rear side of the top of the machining platform, and a mounting mold is arranged on the left side of the machining platform; a deviation rectifying mechanism is arranged in the material suction arm, a material suction plate is installed at the bottom of the material suction arm, a detection lamp is installed in the middle of the machining platform and used for visually detecting a circuit board, and a recycling mechanism is arranged on the side, close to the material suction arm, of the rear end of the machining platform. Related gears, threaded rods and other parts are driven by the motor, accurate positioning and efficient conveying of the tray are achieved, the material suction arm can automatically suck circuit boards, all the parts are in close cooperation, the automation degree is high, manual operation links are greatly reduced, and the feeding time is effectively shortened.

The invention relates to a wall column perpendicularity detection device and method. The wall column perpendicularity detection device comprises a supporting frame, a lifting base, a driving assembly, a rolling assembly and a marking assembly. The lifting base is installed on the supporting frame in a sliding mode in the vertical direction and can slide in a lifting mode along the supporting frame under the action of the driving assembly. The rolling assembly comprises a connecting frame and a measuring roller; the measuring roller is rotationally mounted on the connecting frame; the connecting frame is installed on the inner side of the lifting base through a telescopic assembly, and the telescopic assembly can enable the measuring roller to adaptively adjust the distance between the connecting frame and the lifting base in the lifting process along the wall face. And a fixed measuring track can be formed on the rolling assembly through the marking assembly in the process that the measuring roller ascends and descends along the wall surface. The method effectively solves the problems that a traditional detection method is low in efficiency, poor in precision, weak in applicability and the like, and greatly improves the efficiency and accuracy of wall column perpendicularity detection.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention belongs to the technical field of image segmentation, and discloses a multi-modal medical image automatic segmentation method and system based on a three-dimensional reconstruction model.The multi-modal medical image automatic segmentation method comprises the steps that multi-modal image data are acquired, and local features of the multi-modal image data are extracted; identifying a patient tissue structure in the multi-modal image data based on the local features; segmenting the tissue structure of the patient in the multi-modal image data by using a segmentation technology, and reconstructing a three-dimensional virtual model based on the tissue structure of the patient; and fusing a three-dimensional virtual model body drawing result and a three-dimensional virtual model surface drawing result to realize three-dimensional display of a segmentation result. According to the method, the influence of human errors and subjective judgment is reduced by identifying and segmenting the tissue structure, so that boundaries of various tissues are identified more accurately, automatic image segmentation can be completed in a short time, and subsequent diagnosis and analysis can be performed more quickly.

The invention relates to the field of robot technology, machine vision, deep learning, computer graphics and digital twinning, in particular to a mechanical arm action recognition and control method based on the machine vision and the digital twinning technology. The motion state and environment information of the mechanical arm are captured in real time through the machine vision technology, high-precision recognition of the motion of the mechanical arm can be achieved in combination with an image processing algorithm, and a control strategy is adjusted in real time according to the recognition result; based on the digital twin model, the state change of the mechanical arm can be monitored in real time, and the mechanical arm and the virtual model are synchronously driven, so that the action of the mechanical arm is ensured to be consistent with the expected action; through intelligent identification and automatic adjustment, manual operation errors are reduced, the working efficiency and the production precision are improved, and the complexity of system maintenance is reduced; real-time optimization of the operation process of the mechanical arm can be achieved through combination of digital twinning and machine vision, potential faults are avoided through simulation and prediction, and stable operation of a production line is ensured.

The invention discloses a water absorption rate detection method and system for a macromolecular water absorption material, and relates to the technical field of macromolecular water absorption materials.The water absorption rate detection method for the macromolecular water absorption material comprises the following steps that the initial weight of a to-be-tested macromolecular water absorption material sample is weighed; dropwise adding test liquid to the surface of the polymer water-absorbing material sample by using a burette; obtaining test feature data; factors influencing the water absorption performance of the polymer water absorption material are obtained; predicting key factors influencing the water absorption rate of the polymer water absorption material; and judging whether the water absorbing material meets a preset performance standard or not. Conditions of each experiment can be ensured to be consistent, so that repeatability and reliability of results are improved, the water absorption process can be accurately controlled by dropwise adding test liquid through a burette, the water absorption behavior of the material can be better understood, key factors are adjusted through an optimization algorithm, then the performance of the material can be improved, and the test accuracy is improved. And the preset performance standard is met.