Multi-dimensional perception and intelligent cooperation industrial robot manufacturing system
Through a multi-dimensional perception and intelligence-coordinated industrial robot manufacturing system, combined with multiple sensors and deep learning algorithms, the precise identification and capture of electronic components is achieved, and the welding and assembly quality is monitored in real time, the defect detection capability of the production process is improved, and the overall efficiency and quality of electronic manufacturing is improved.
Patent Information
- Application Number
- CN202510189145.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-20
- Publication Date
- 2025-06-27
AI Technical Summary
The existing industrial robot electronic manufacturing systems have low accuracy in identification and grabbing of electronic components, unstable welding and assembly quality, and low defect detection efficiency, which cannot meet the requirements of modern electronic manufacturing industry for high-quality products.
The industrial robot manufacturing system that integrates multi-dimensional perception and intelligence, including high-resolution vision sensors, laser ranging sensors, etc., combines deep learning and adaptive control algorithms to realize accurate identification and capture of electronic components, monitor welding and assembly quality in real time, and detect defects through multi-modal data fusion and artificial intelligence, and complete tasks in a coordinated manner by multi-robots.
It improves the accuracy of identification and grabbing of electronic components, ensures welding and assembly quality, enhances the defect detection capability of the production process, improves production efficiency and management level, and ensures product quality.
Smart Images
Figure CN120217279A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of information technology, and particularly relates to an industrial robot manufacturing system with multi-dimensional perception and intelligent collaboration. Background Art
[0002] The electronics manufacturing industry is an important part of modern manufacturing. With the development trend of miniaturization, integration, and intelligence of electronic products, higher requirements are put forward for the precision, efficiency, and quality of electronics manufacturing. Industrial robots, with their characteristics of automation, high precision, and repeatability, have been widely used in the process of electronics manufacturing, such as in the processes of picking and placing electronic components, welding, and assembling. However, there are still many problems with industrial robots in the field of electronics manufacturing, which limit the further development of the electronics manufacturing industry and the improvement of product quality.
[0003] There are a wide variety of electronic components, with small sizes and various shapes, and some components also have characteristics such as reflectivity and easy deformation on their surfaces. Traditional industrial robot electronics manufacturing systems mostly rely on single vision recognition technology to identify and locate electronic components. This method is easily affected by factors such as lighting conditions, component surface characteristics, and small deviations in installation positions, resulting in low recognition and grasping accuracy. In the actual production process, situations such as incorrect component grasping and position deviation often occur, which not only reduces production efficiency but also may cause component damage and product quality problems.
[0004] The welding and assembling processes in electronics manufacturing have extremely high requirements for precision and stability. Different electronic components and welding processes require different welding parameters and assembling forces. Moreover, during the welding and assembling processes, due to factors such as changes in environmental temperature and humidity and small vibrations of the equipment, the welding and assembling quality is easily affected. Most of the existing industrial robot welding and assembling systems adopt preset fixed parameters and operation procedures and cannot be adjusted and optimized in real time according to the actual situation, resulting in unstable welding and assembling quality and difficult to guarantee the yield rate of products. The quality of electronics manufacturing products is directly related to their performance and service life. Therefore, it is crucial to detect product defects in a timely manner during the production process. Currently, industrial robot electronics manufacturing systems mainly rely on manual sampling inspection or simple vision inspection equipment for defect detection. This method is inefficient and prone to missed inspections and false inspections. Moreover, for some internal defects and potential quality problems, traditional detection methods are difficult to detect and cannot meet the requirements of the modern electronics manufacturing industry for high-quality products. Summary of the Invention
[0005] The present invention provides an industrial robot manufacturing system with multi-dimensional perception and intelligent collaboration, which includes a perception layer, a data processing layer, a decision-making layer, an execution layer, and an application layer; the perception layer includes high-resolution vision sensors, laser range sensors, capacitance sensors, infrared sensors, temperature sensors, pressure sensors, current sensors, displacement sensors installed on industrial robots, as well as high-resolution vision detection devices, X-ray detection devices, and ultrasonic detection devices installed on production lines, which are used to obtain information of electronic components, parameter information during the production process, and detection data of products in real time, and transmit the data to the data processing layer; the data processing layer includes a multi-dimensional data fusion module, a multi-dimensional feature extraction module, a deep learning model training module, an adaptive control parameter adjustment module, a multi-modal data fusion module, and an artificial intelligence defect detection model training module, which are used to fuse, extract features, train models, and adjust parameters for the collected data; the decision-making layer includes an electronic component precise identification and grasping module, a welding and assembly quality intelligent control module, a production process defect detection module, a multi-robot collaboration control module, and an intelligent management and traceability module, which are used to generate control instructions according to the results of the data processing layer; the execution layer is an industrial robot, which executes electronic component identification, grasping, welding, assembly, defect detection, and collaboration tasks according to the control instructions generated by the decision-making layer, and transmits the feedback information during the execution process back to the data processing layer and the decision-making layer; the application layer is an intelligent management and traceability platform for the electronic manufacturing process, which provides an interactive interface for managers to realize functions such as real-time monitoring, data analysis, decision support, and traceability of the electronic manufacturing process.
[0006] Furthermore, the system adopts an electronic component precise identification and grasping algorithm based on multi-dimensional perception and deep learning. The algorithm includes: a sensor deployment and data collection step, reasonably deploying high-resolution vision sensors, laser range sensors, capacitance sensors, and infrared sensors, and collecting multi-dimensional information of electronic components in real time according to a set frequency and transmitting it to the multi-dimensional data fusion module; a multi-dimensional data fusion and feature extraction step, the multi-dimensional data fusion module uses a fusion network based on an attention mechanism to fuse different types of sensor data, and the multi-dimensional feature extraction module uses a convolutional neural network (CNN) to analyze and process the fused data to extract detailed features of the electronic components; a deep learning model training and application step, collecting a large amount of different types of electronic component data to construct a training dataset, using a convolutional neural network (CNN) or a recurrent neural network (RNN) to learn and train the training dataset, establishing an electronic component identification and grasping model, and in actual production, inputting the extracted features into the trained model to achieve precise identification and grasping.
[0007] Furthermore, the system adopts an intelligent regulation algorithm for welding and assembly quality based on adaptive control and real-time monitoring. This algorithm includes: sensor installation and data acquisition steps, where temperature sensors, pressure sensors, current sensors, and displacement sensors are installed on the welding and assembly tools of industrial robots, and a solder joint strength detector and an assembled part dimension measuring instrument are installed on the production line to monitor the parameters and product quality information during the welding and assembly processes in real time and transmit them to the adaptive control parameter adjustment module; the implementation step of the adaptive control algorithm, where the adaptive control parameter adjustment module uses the adaptive control algorithm to calculate the adjustment amount of the welding and assembly parameters of industrial robots in real time based on the collected data; the execution step of intelligent regulation of welding and assembly quality, where the intelligent regulation module for welding and assembly quality automatically adjusts the welding and assembly actions and parameters of industrial robots according to the adjustment amount, and at the same time optimizes the parameters of the adaptive control algorithm based on the feedback information during the production process.
[0008] Furthermore, the system adopts a production process defect detection system based on multi-modal data fusion and artificial intelligence. This system includes: detection equipment deployment and data acquisition steps, where high-resolution vision detection equipment, X-ray detection equipment, and ultrasonic detection equipment are deployed on the industrial robot production line to collect multi-modal data during the production process at a set frequency and transmit them to the multi-modal data fusion module; multi-modal data fusion and feature extraction steps, where the multi-modal data fusion module uses a fusion network based on the attention mechanism to fuse the data collected by different types of detection equipment, and uses a convolutional neural network (CNN) to analyze and process the fused data to extract the key information of product defect features; the training and application steps of the artificial intelligence defect detection model, where a large number of product detection data of different types are collected to construct a training data set, and deep learning convolutional neural networks and recurrent neural networks are used to learn and train the training data set to establish an association model between product defect features and defect types. In actual production, the extracted features are input into the trained model to achieve fast and accurate detection of product defects.
[0009] Furthermore, the system adopts an efficient production system for electronics manufacturing based on multi-robot cooperation and task assignment optimization. This system includes: task analysis and assignment steps, where the multi-robot cooperation control module decomposes the task into multiple subtasks according to the technological process and production tasks of electronics manufacturing and reasonably assigns them to multiple industrial robots, considering factors such as the working ability, working range, and current state of the robots during the assignment; communication and cooperation mechanism establishment steps, where a communication network between multiple robots is established, and the movement and operation of multiple robots are coordinated through a cooperation control algorithm to make them cooperate with each other to complete different production processes, and the cooperation strategy is adjusted according to the real-time task situation and production progress; the implementation step of intelligent task assignment optimization, where the multi-robot cooperation control module adjusts the task assignment plan in real time according to the changes in production tasks and the working status of the robots.
[0010] Furthermore, the implementation of the intelligent management and traceability platform for the electronic manufacturing process includes: a data collection and transmission step, where the intelligent management and traceability module collects various data during the electronic manufacturing process in real time through the data interface with the industrial robot electronic manufacturing system, and after formatting, transmits it to the platform through the network; a data analysis and visualization step, where big data analysis technology is used to analyze and mine the data transmitted to the platform, and the analysis results are displayed on the platform interface in the form of charts, reports, etc. through visualization technology; a traceability function implementation step, where a unique identifier is assigned to each batch of products, and managers can query all relevant information of the product during the production process by entering the product identifier on the platform.
[0011] Furthermore, the multi-dimensional data fusion module performs fusion processing on the data collected by multi-dimensional sensors to construct a multi-dimensional feature model of electronic components.
[0012] Furthermore, the multi-modal data fusion module performs fusion processing on the data collected by multi-modal detection devices to construct a comprehensive detection data model of products.
[0013] Furthermore, the deep learning model training module uses deep learning algorithms to learn and train a large amount of electronic component data to establish an electronic component identification and grasping model.
[0014] Furthermore, the artificial intelligence defect detection model training module uses artificial intelligence algorithms to learn and train a large amount of product detection data to establish an association model between product defect features and defect types.
[0015] Beneficial effects:
[0016] Improve the accuracy of electronic component identification and grasping: The precise identification and grasping algorithm of electronic components based on multi-dimensional perception and deep learning can comprehensively utilize the information of multiple sensors to achieve precise identification and grasping of electronic components, improve the accuracy and stability of identification and grasping, reduce grasping errors and position offsets, and improve production efficiency and component utilization rate.
[0017] Ensure the quality of welding and assembly: The intelligent control algorithm for welding and assembly quality based on adaptive control and real-time monitoring can automatically adjust the welding and assembly actions and parameters of the industrial robot according to the parameters and product quality information monitored in real time, realize the intelligent control of welding and assembly quality, ensure the stability and consistency of welding and assembly quality, and improve the yield rate of products.
[0018] Enhance the defect detection ability in the production process: The production process defect detection system based on multi-modal data fusion and artificial intelligence can quickly and accurately detect various defects of products, including appearance defects, internal defects, and potential quality problems, improve the efficiency and accuracy of defect detection, reduce the outflow of defective products, and ensure product quality.
[0019] Improve production efficiency: The high-efficiency production system for electronic manufacturing based on multi-robot cooperation and task assignment optimization can reasonably allocate tasks to multiple robots to complete collaboratively, realize information sharing and collaborative operation among robots, improve production efficiency, shorten the production cycle, and meet the needs of large-scale electronic manufacturing.
[0020] Facilitate production management and traceability: The intelligent management and traceability platform for the electronic manufacturing process provides real-time production process monitoring and decision-making support for managers, has a traceability function, facilitates quality traceability and problem troubleshooting, improves production management level, and ensures product quality. Description of the Drawings
[0021] Figure 1 Schematic diagram of the algorithm flow. Detailed Implementation Modes
[0022] Example 1:
[0023] System architecture implementation
[0024] Perception layer: Install various types of sensors such as high-resolution vision sensors, laser rangefinder sensors, capacitance sensors, infrared sensors, temperature sensors, pressure sensors, current sensors, displacement sensors on industrial robots, and install detection equipment such as high-resolution vision detection equipment, X-ray detection equipment, and ultrasonic detection equipment on the production line to obtain information of electronic components, parameter information in the production process, and detection data of products in real time. These sensors and detection equipment collect data in real time and transmit the data to the data processing layer.
[0025] Data processing layer: It includes a multi-dimensional data fusion module, a multi-dimensional feature extraction module, a deep learning model training module, an adaptive control parameter adjustment module, a multi-modal data fusion module, and an artificial intelligence defect detection model training module. The multi-dimensional data fusion module performs fusion processing on the data collected by multi-dimensional sensors to construct a multi-dimensional feature model of electronic components; the multi-dimensional feature extraction module uses deep learning and data mining techniques to extract key information that can reflect the characteristics of electronic components from the fused data; the deep learning model training module uses deep learning algorithms to learn and train a large amount of electronic component data to establish an electronic component recognition and grasping model; the adaptive control parameter adjustment module uses adaptive control algorithms to adjust the welding and assembly parameters of industrial robots according to the data collected by sensors; the multi-modal data fusion module performs fusion processing on the data collected by multi-modal detection devices to construct a comprehensive detection data model of products; the artificial intelligence defect detection model training module uses artificial intelligence algorithms to learn and train a large amount of product detection data to establish a product defect detection model.
[0026] Decision-making layer: It includes an electronic component precise recognition and grasping module, a welding and assembly quality intelligent control module, a production process defect detection module, a multi-robot cooperation control module, and an intelligent management and traceability module. The electronic component precise recognition and grasping module uses the electronic component precise recognition and grasping algorithm to achieve precise recognition and grasping of electronic components according to the results of the multi-dimensional data fusion module and the multi-dimensional feature extraction module; the welding and assembly quality intelligent control module realizes intelligent control of welding and assembly quality according to the results of the adaptive control parameter adjustment module and combined with real-time monitoring data; the production process defect detection module uses the production process defect detection system to quickly and accurately detect product defects according to the results of the multi-modal data fusion module and the artificial intelligence defect detection model training module; the multi-robot cooperation control module reasonably distributes tasks to multiple robots according to the technological process and production tasks of electronic manufacturing and realizes cooperation control between robots; the intelligent management and traceability module is responsible for collecting, storing, analyzing, and displaying data in the electronic manufacturing process to realize intelligent management and traceability of the electronic manufacturing process.
[0027] Execution layer: Industrial robots execute tasks such as electronic component recognition, grasping, welding, assembly, defect detection, and cooperation tasks according to the control instructions generated by the decision-making layer. At the same time, the feedback information in the execution process is transmitted back to the data processing layer and the decision-making layer for system adjustment and optimization.
[0028] Application layer: It is mainly an intelligent management and traceability platform for the electronic manufacturing process, providing a friendly interaction interface for managers to realize functions such as real-time monitoring, data analysis, decision support, and traceability of the electronic manufacturing process.
[0029] Precision Recognition and Grasping Algorithm for Electronic Components Based on Multi-Dimensional Perception and Deep Learning:
[0030] Sensor Deployment and Data Acquisition: According to the requirements and characteristics of electronic manufacturing, deploy multi-dimensional sensors such as high-resolution vision sensors, laser ranging sensors, capacitance sensors, and infrared sensors reasonably to ensure comprehensive and accurate acquisition of multi-dimensional information of electronic components. The sensors collect data in real time at a set frequency and transmit it to the multi-dimensional data fusion module in the data processing layer through the network.
[0031] Multi-Dimensional Data Fusion and Feature Extraction: The multi-dimensional data fusion module uses a multi-dimensional data fusion algorithm based on deep learning, such as a fusion network based on the attention mechanism, to fuse different types of sensor data. The multi-dimensional feature extraction module uses deep learning algorithms, such as convolutional neural networks (CNNs), to analyze and process the fused data and extract detailed features such as the type, size, shape, material, and surface temperature of the electronic components.
[0032] Deep Learning Model Training and Application: Collect a large amount of data on different types of electronic components to construct a training dataset. Use deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), to learn and train the training dataset to establish an electronic component recognition and grasping model. During the actual production process, the precision recognition and grasping module of electronic components inputs the extracted features into the trained model to achieve precise recognition and grasping of electronic components.
[0033] Intelligent Regulation Algorithm for Welding and Assembly Quality Based on Adaptive Control and Real-Time Monitoring:
[0034] Sensor Installation and Data Acquisition: Install various high-precision sensors such as temperature sensors, pressure sensors, current sensors, and displacement sensors on the welding and assembly tools of industrial robots to monitor various parameters during the welding and assembly processes in real time, such as welding temperature, welding pressure, welding current, and assembly displacement. At the same time, install high-precision detection equipment on the production line, such as solder joint strength detectors and assembly part dimension measuring instruments, to detect the quality of the welded and assembled products in real time and obtain quality information such as the shape, size, strength of the solder joints and the fitting accuracy of the assembly parts. The sensors and detection equipment collect data in real time at a set frequency and transmit it to the adaptive control parameter adjustment module in the data processing layer through the network.
[0035] Implementation of Adaptive Control Algorithm: The adaptive control parameter adjustment module calculates the adjustment amounts of the welding and assembly parameters of the industrial robot in real time using the adaptive control algorithm based on the processing parameters collected by the sensors and the product quality information obtained by the detection equipment. The intelligent regulation module for welding and assembly quality automatically adjusts the welding and assembly actions and parameters of the industrial robot according to the adjustment amounts to achieve intelligent regulation of the welding and assembly quality.
[0036] Execution of Intelligent Regulation for Welding and Assembly Quality: During the electronic manufacturing process, the industrial robot adjusts the welding and assembly actions and parameters in real time according to the control instructions generated by the intelligent regulation module for welding and assembly quality to ensure the stability and consistency of the welding and assembly quality. At the same time, according to the feedback information in the production process, the parameters of the adaptive control algorithm are continuously optimized to improve the regulation effect of the welding and assembly quality.
[0037] Production Process Defect Detection System Based on Multimodal Data Fusion and Artificial Intelligence:
[0038] Deployment of Detection Equipment and Data Acquisition: Reasonably deploy multimodal detection equipment such as high-resolution vision detection equipment, X-ray detection equipment, and ultrasonic detection equipment on the industrial robot production line to ensure that multimodal data in the production process can be comprehensively and accurately collected. The detection equipment collects data in real time according to the set frequency and transmits it to the multimodal data fusion module in the data processing layer through the network.
[0039] Multimodal Data Fusion and Feature Extraction: The multimodal data fusion module uses a multimodal data fusion algorithm based on deep learning, such as a fusion network based on the attention mechanism, to fuse the data collected by different types of detection equipment. Artificial intelligence algorithms, such as convolutional neural networks (CNNs), are used to analyze and process the fused data to extract key information that can reflect product defect features.
[0040] Training and Application of Artificial Intelligence Defect Detection Model: Collect a large amount of product detection data of different types, including data of normal products and defective products, to construct a training data set. Use artificial intelligence algorithms, such as convolutional neural networks and recurrent neural networks in deep learning, to learn and train the training data set to establish an association model between product defect features and defect types. During the actual production process, the production process defect detection module inputs the extracted features into the trained model to achieve fast and accurate detection of product defects.
[0041] Efficient Production System for Electronic Manufacturing Based on Multi-Robot Collaboration and Task Allocation Optimization:
[0042] Task analysis and allocation: The multi-robot cooperation control module conducts a detailed analysis of the tasks according to the technological process and production tasks of electronic manufacturing, decomposes the tasks into multiple subtasks, and reasonably allocates them to multiple industrial robots according to the characteristics and requirements of each subtask. When allocating tasks, factors such as the working ability, working range, and current status of the robots are considered to ensure the rationality and efficiency of task allocation.
[0043] Establishment of communication and cooperation mechanism: Establish a communication network among multiple robots to achieve real-time information interaction among the robots. Through cooperation control algorithms, coordinate the movements and operations of multiple robots to ensure that they can cooperate with each other and complete different production processes simultaneously, improving production efficiency. During the cooperation process, the robots can adjust the cooperation strategy in a timely manner according to the real-time task situation and production progress to ensure the efficient and stable operation of the production process.
[0044] Realization of intelligent task allocation optimization: The multi-robot cooperation control module has the function of intelligent task allocation optimization, which can adjust the task allocation plan in real time according to the changes in production tasks and the working status of the robots. For example, when a certain robot fails or the task volume suddenly increases, the intelligent task allocation optimization function can automatically re-allocate the tasks to other robots to ensure the smooth completion of production tasks.
[0045] Intelligent management and traceability platform for the electronic manufacturing process:
[0046] Data collection and transmission: The intelligent management and traceability module collects various data in the electronic manufacturing process in real time through the data interface with the electronic manufacturing system of industrial robots, such as electronic component information, production process parameters, product quality data, production time, robot operation status, etc. After formatting the collected data, it is transmitted to the intelligent management and traceability platform through the network.
[0047] Data analysis and visualization: Using big data analysis technology, analyze and mine the data transmitted to the platform, extract valuable information, such as production efficiency analysis, quality problem prediction, equipment failure warning, etc. Through visualization technology, display the analysis results in the form of charts, reports, etc. on the interface of the intelligent management and traceability platform, providing intuitive and clear production process monitoring and decision-making support for managers.
[0048] Realization of traceability function: In the electronic manufacturing process, assign a unique identifier to each batch of products. When quality traceability is required, managers can quickly query all relevant information of the product during the production process, including raw material sources, production processes, production personnel, inspection results, etc., by entering the identifier of the product on the intelligent management and traceability platform, which is convenient for quality traceability and problem investigation to ensure product quality.
[0049] Improve the recognition and grasping accuracy of electronic components: The precise recognition and grasping algorithm of electronic components based on multi-dimensional perception and deep learning can comprehensively utilize the information of multiple sensors to achieve the precise recognition and grasping of electronic components, improve the accuracy and stability of recognition and grasping, reduce grasping errors and position offsets, and improve production efficiency and component utilization rate.
[0050] Ensure the quality of welding and assembly: The intelligent control algorithm for welding and assembly quality based on adaptive control and real-time monitoring can automatically adjust the welding and assembly actions and parameters of industrial robots according to the parameters and product quality information monitored in real time, realize the intelligent control of welding and assembly quality, ensure the stability and consistency of welding and assembly quality, and improve the yield rate of products.
[0051] Enhance the defect detection ability in the production process: The production process defect detection system based on multi-modal data fusion and artificial intelligence can quickly and accurately detect various defects of products, including appearance defects, internal defects and potential quality problems, improve the efficiency and accuracy of defect detection, reduce the outflow of defective products, and ensure product quality.
[0052] Improve production efficiency: The efficient production system for electronic manufacturing based on multi-robot cooperation and task assignment optimization can reasonably allocate tasks to multiple robots to complete collaboratively, realize information sharing and collaborative operation among robots, improve production efficiency, shorten the production cycle, and meet the needs of large-scale electronic manufacturing.
[0053] Facilitate production management and traceability: The intelligent management and traceability platform for the electronic manufacturing process provides real-time production process monitoring and decision-making support for managers, has a traceability function, is convenient for quality traceability and problem troubleshooting, improves the production management level, and ensures product quality.
Claims
1. An industrial robot manufacturing system with multi-dimensional perception and intelligent collaboration, characterized in that: It includes a perception layer, a data processing layer, a decision layer, an execution layer and an application layer; the perception layer includes high-resolution visual sensors, laser ranging sensors, capacitive sensors, infrared sensors, temperature sensors, pressure sensors, current sensors, displacement sensors installed on industrial robots, and high-resolution visual inspection equipment, X-ray inspection equipment, and ultrasonic inspection equipment installed on the production line, which are used to obtain information on electronic components, parameter information in the production process, and product inspection data in real time, and transmit the data to the data processing layer; the data processing layer includes a multi-dimensional data fusion module, a multi-dimensional feature extraction module, a deep learning model training module, an adaptive control parameter adjustment module, a multi-modal data fusion module, and an artificial intelligence defect detection model training module, which are used to The collected data are fused, feature extracted, model trained and parameter adjusted; the decision layer includes an electronic component accurate identification and grasping module, a welding and assembly quality intelligent control module, a production process defect detection module, a multi-robot collaborative control module, and an intelligent management and traceability module, which are used to generate control instructions according to the results of the data processing layer; the execution layer is an industrial robot, which performs electronic component identification, grasping, welding, assembly, defect detection and collaborative tasks according to the control instructions generated by the decision layer, and transmits feedback information during the execution process back to the data processing layer and the decision layer; the application layer is an intelligent management and traceability platform for the electronic manufacturing process, which provides an interactive interface for managers to realize real-time monitoring, data analysis, decision support and traceability functions of the electronic manufacturing process.
2. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1 is characterized in that: The system adopts an accurate identification and grasping algorithm of electronic components based on multi-dimensional perception and deep learning. The algorithm includes: sensor deployment and data collection steps, which reasonably deploy high-resolution visual sensors, laser ranging sensors, capacitive sensors, and infrared sensors to collect multi-dimensional information of electronic components in real time according to a set frequency and transmit it to a multi-dimensional data fusion module; multi-dimensional data fusion and feature extraction steps, in which the multi-dimensional data fusion module adopts a fusion network based on an attention mechanism to fuse different types of sensor data, and the multi-dimensional feature extraction module uses a convolutional neural network (CNN) to analyze and process the fused data to extract detailed features of electronic components; deep learning model training and application steps, which collects a large amount of different types of electronic component data to construct a training data set, uses a convolutional neural network (CNN) or a recurrent neural network (RNN) to learn and train the training data set, and establishes an electronic component identification and grasping model. In actual production, the extracted features are input into the trained model to achieve accurate identification and grasping.
3. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1 is characterized in that: The system adopts an intelligent control algorithm for welding and assembly quality based on adaptive control and real-time monitoring, and the algorithm includes: sensor installation and data collection steps, in which temperature sensors, pressure sensors, current sensors, and displacement sensors are installed on the welding and assembly tools of the industrial robot, and solder joint strength detectors and assembly size measuring instruments are installed on the production line to monitor parameters and product quality information in the welding and assembly process in real time, and transmit them to the adaptive control parameter adjustment module; adaptive control algorithm implementation step, in which the adaptive control parameter adjustment module calculates the adjustment amount of the welding and assembly parameters of the industrial robot in real time based on the collected data using the adaptive control algorithm; welding and assembly quality intelligent control execution step, in which the welding and assembly quality intelligent control module automatically adjusts the welding and assembly actions and parameters of the industrial robot based on the adjustment amount, and optimizes the parameters of the adaptive control algorithm based on feedback information in the production process.
4. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1 is characterized in that: The system adopts a production process defect detection system based on multimodal data fusion and artificial intelligence, which includes: a detection equipment deployment and data collection step, in which high-resolution visual detection equipment, X-ray detection equipment, and ultrasonic detection equipment are deployed on the industrial robot production line to collect multimodal data in the production process in real time according to a set frequency and transmit it to a multimodal data fusion module; a multimodal data fusion and feature extraction step, in which the multimodal data fusion module adopts a fusion network based on an attention mechanism to fuse data collected by different types of detection equipment, uses a convolutional neural network (CNN) to analyze and process the fused data, and extracts key information of product defect features; an artificial intelligence defect detection model training and application step, in which a large amount of different types of product detection data are collected to construct a training data set, and the training data set is learned and trained using a deep learning convolutional neural network and a recurrent neural network, etc., to establish a correlation model between product defect features and defect types. In actual production, the extracted features are input into the trained model to achieve rapid and accurate detection of product defects.
5. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1, characterized in that: The system adopts an efficient production system for electronic manufacturing based on multi-robot collaboration and task allocation optimization, and the system includes: a task analysis and allocation step, in which a multi-robot collaborative control module decomposes the task into multiple sub-tasks and reasonably allocates them to multiple industrial robots according to the process flow and production tasks of electronic manufacturing, and considers factors such as the robot's working ability, working range, and current status during allocation; a communication and collaboration mechanism establishment step, in which a communication network is established among multiple robots, and the movement and operation of multiple robots are coordinated through a collaborative control algorithm so that they cooperate with each other to complete different production processes, and the collaboration strategy is adjusted according to the real-time task situation and production progress; an intelligent task allocation optimization implementation step, in which the multi-robot collaborative control module adjusts the task allocation plan in real time according to changes in production tasks and the working status of the robots.
6. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1, characterized in that: The implementation of the intelligent management and traceability platform for the electronic manufacturing process includes: a data collection and transmission step, in which the intelligent management and traceability module collects various data in the electronic manufacturing process in real time through the data interface with the industrial robot electronic manufacturing system, and transmits the data to the platform through the network after formatting; a data analysis and visualization step, in which big data analysis technology is used to analyze and mine the data transmitted to the platform, and the analysis results are displayed on the platform interface in the form of charts, reports, etc. through visualization technology; a traceability function implementation step, in which a unique identifier is assigned to each batch of products, and the management personnel enter the product identifier on the platform to query all relevant information of the product in the production process.
7. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1, characterized in that: The multi-dimensional data fusion module performs fusion processing on the data collected by the multi-dimensional sensor to construct a multi-dimensional feature model of the electronic components.
8. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1, characterized in that: The multimodal data fusion module performs fusion processing on the data collected by the multimodal detection equipment to construct a comprehensive detection data model for the product.
9. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1, characterized in that: The deep learning model training module uses a deep learning algorithm to learn and train a large amount of electronic component data to establish an electronic component recognition and grasping model.
10. The multi-dimensional perception and intelligent collaborative industrial robot manufacturing system according to claim 1, characterized in that: The artificial intelligence defect detection model training module uses artificial intelligence algorithms to learn and train a large amount of product inspection data to establish an association model between product defect features and defect types.