Aluminum plate processing online monitoring and analyzing system based on machine learning

Abstract

The invention provides an aluminum plate processing online monitoring analysis system based on machine learning, relates to the technical field of aluminum material processing, and adopts a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm and a remote monitoring and control system to form the whole system. According to the system, various data parameters in the aluminum alloy material machining process are monitored in real time through a multi-sensor data acquisition system, and rapid and accurate processing and analysis are carried out through a real-time data processing and analysis algorithm; the abnormity detection and early warning system can give an alarm in time and provide corresponding processing suggestions to help operators to quickly take measures, and the system can automatically adjust processing parameters through analysis and learning of historical processing data and real-time monitoring data by using an intelligent optimization algorithm, so that comprehensive monitoring and analysis of the processing process can be realized, and the processing efficiency is improved. Product quality and production efficiency are improved, and production cost and manual intervention are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of aluminum material processing, and in particular to an online monitoring and analysis system for aluminum plate processing based on machine learning. Background Art

[0002] According to a defect detection method and system for aluminum alloy casting products disclosed in Chinese patent number "CN117233179A", the method includes: placing the aluminum alloy casting product to be detected on an X-ray detection device; collecting an X-Ray detection image of the aluminum alloy casting product to be detected by the X-ray detection device; and analyzing the X-Ray detection image to obtain a defect detection result. In this way, defect detection of aluminum alloy casting products is achieved.

[0003] The above patent documents and prior art have the following technical problems when used:

[0004] Problem 1: It only issues warnings for abnormal situations during aluminum alloy processing, but cannot provide corresponding countermeasures and suggestions, and the level of intelligence is not high;

[0005] Question 2: During the aluminum alloy processing, it is impossible to adaptively adjust the processing parameters based on the processing monitoring, and manual intervention is required, which is not conducive to fully automated monitoring and analysis and processing. Summary of the invention

[0006] Technical issues solved

[0007] In view of the shortcomings of the prior art, the present invention provides an online monitoring and analysis system for aluminum plate processing based on machine learning, which solves the following problems:

[0008] 1. During the aluminum alloy processing process, warnings cannot be given while processing suggestions are given, and the degree of intelligence is not high;

[0009] 2. During the aluminum alloy processing process, the system can only be adjusted through manual intervention and cannot be adjusted according to the situation.

[0010] Technical Solution

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: an online monitoring and analysis system for aluminum plate processing based on machine learning, the system consists of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm and a remote monitoring and control system, wherein:

[0012] Multi-sensor data acquisition system: composed of multiple sensors, used to collect various data parameters during the processing of aluminum alloy materials;

[0013] Real-time data processing and analysis algorithm: Utilize the machine algorithm steps within the system to quickly and accurately process and analyze the data collected by the multi-sensor data acquisition system, and monitor data anomalies during the processing of aluminum alloy materials;

[0014] Abnormality detection and early warning system: monitors the entire aluminum alloy material processing process, organizes and analyzes it through internal machine learning algorithms, and issues an alarm and provides corresponding processing suggestions when the system detects abnormalities in the processing process;

[0015] Intelligent optimization algorithm: Through the analysis and learning of historical processing data and real-time monitoring data, the processing parameters are automatically adjusted to optimize the processing process. The intelligent optimization algorithm has a built-in machine learning algorithm;

[0016] Remote monitoring and control system: Provides remote monitoring and control functions, allowing operators to remotely monitor the processing process through the network, remotely control and adjust the processing equipment, and perform remote management and production scheduling.

[0017] Preferably, the multi-sensor data acquisition system includes a laser measurement sensor, an image recognition sensor, a pressure sensor and a temperature sensor.

[0018] Preferably, the detection types of the multi-sensor data acquisition system mainly include size detection, surface quality detection, pressure monitoring and temperature monitoring.

[0019] Preferably, the real-time data processing and analysis algorithm mainly includes data preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization, and each step is performed in the order of preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization.

[0020] Preferably, the real-time data processing and analysis algorithm is internally provided with at least a rule-based algorithm, a statistical method, a machine learning algorithm and an adaptive control algorithm.

[0021] Preferably, the anomaly detection and early warning system includes an anomaly detection algorithm, threshold setting, early warning mechanism and abnormal record analysis.

[0022] Preferably, the intelligent optimization algorithm includes three steps: parameter adjustment strategy, optimization objective function and adaptive learning.

[0023] Preferably, the anomaly detection and early warning system further includes the following contents:

[0024] Anomaly detection algorithms: used to monitor the real-time collected data, identify and analyze abnormal conditions during processing. These algorithms are designed based on rules, statistical methods, and machine learning principles;

[0025] Threshold setting: For each monitored parameter, set the corresponding abnormal threshold. When the monitored data exceeds the set threshold range, the system will identify it as an abnormal situation.

[0026] Preferably, the anomaly detection and early warning system further includes the following contents:

[0027] Early warning mechanism: When an abnormal situation is detected, the system will immediately issue an alarm to notify relevant personnel to handle it. The early warning is carried out through sound, vision, and mobile phone text messages;

[0028] Abnormal recording and analysis: For each abnormal situation, the system will record detailed information, including the time of abnormal occurrence and specific parameter changes, for subsequent analysis and processing

[0029] Beneficial Effects

[0030] The present invention provides an online monitoring and analysis system for aluminum plate processing based on machine learning. It has the following beneficial effects:

[0031] 1. The present invention adopts a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm and a remote monitoring and control system to form the entire system. The system monitors various data parameters in the aluminum alloy material processing process in real time through the multi-sensor data acquisition system, and uses the real-time data processing and analysis algorithm to quickly and accurately process and analyze. Once the system detects an abnormal situation in the processing process, the anomaly detection and early warning system will promptly issue an alarm and provide corresponding processing suggestions to help operators take measures quickly to avoid production accidents or quality problems and ensure the safety and smooth progress of production.

[0032] 2. The present invention adopts an intelligent optimization algorithm to automatically adjust processing parameters and optimize the processing process by analyzing and learning historical processing data and real-time monitoring data. This enables the system to adaptively adjust processing parameters according to different processing conditions and requirements, improve production efficiency and product quality, and achieve comprehensive monitoring and analysis of the processing process, improve product quality and production efficiency, and reduce production costs and manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0036] like Figure 1 As shown in the figure, the online monitoring and analysis system for aluminum plate processing based on machine learning is composed of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm, and a remote monitoring and control system, among which:

[0037] Multi-sensor data acquisition system: It is composed of multiple sensors and is used to collect various data parameters in the processing of aluminum alloy materials. The system is composed of multiple sensors and is mainly used to collect various parameters in the processing of aluminum alloy materials, such as temperature, pressure, vibration, displacement, stress, strain, etc. The preprocessing of sensor data includes filtering, normalization, and noise reduction steps. For the fusion of multiple sensor data, the algorithm used is weighted average method and Kalman filter (Ka lmanFilter);

[0038] Weighted average method in data fusion:

[0039]

[0040] x i is the reading of the ith sensor, w i is the corresponding weight,

[0041] The state formula of the Karpin filter is:

[0042]

[0043] is the state estimate at the kth moment, K k is the gain of Karl Suga, z k is the measurement value, and H is the measurement matrix.

[0044] Real-time data processing and analysis algorithm: Utilize the machine algorithm steps within the system to quickly and accurately process and analyze the data collected by the multi-sensor data acquisition system, monitor the data anomalies during the processing of aluminum alloy materials, and identify potential anomalies during the processing by processing the data from the sensor. Data cleaning and outlier detection: Use the statistical z-score method anomaly detection algorithm, and use the machine learning algorithm for real-time data classification and regression analysis. The z-score calculation formula is:

[0045]

[0046] x is the data point, μ is the data mean, and σ is the data standard deviation.

[0047] Linear Regression Model:

[0048] y=β0+β1x1+β2x2+…+β n x n +∈

[0049] y is the target variable, x i is the input variable, β i is the regression coefficient and v is the error term.

[0050] Abnormal detection and early warning system: monitors the entire aluminum alloy material processing process, organizes and analyzes it through internal machine learning algorithms. When the system detects abnormalities in the processing process, it promptly issues an alarm and provides corresponding processing suggestions. The system issues an alarm and provides processing suggestions when abnormalities are detected through real-time analysis of data. The classification algorithm based on supervised learning uses support vector machines (SVM) and decision trees for abnormality detection, and the clustering algorithms based on unsupervised learning, such as k-means and DBSCAN, are used for abnormal behavior pattern recognition;

[0051] SVM classification decision function:

[0052]

[0053] α i is the Lagrange multiplier, y i is the class label, K(x i ,x) is the kernel function.

[0054] k-means clustering algorithm objective function:

[0055]

[0056] C i is the i-th cluster, μ iis the centroid of the ith cluster, J: objective function value, which represents the sum of the squares of the Euclidean distances of all data points to the centers of their respective clusters, k is the number of clusters, and i represents the ordinal number of the cluster.

[0057] Intelligent optimization algorithm: automatically adjusts processing parameters and optimizes the processing process by analyzing and learning historical processing data and real-time monitoring data. The intelligent optimization algorithm has a built-in machine learning algorithm. The intelligent optimization algorithm automatically adjusts processing parameters to optimize the processing process by analyzing historical data and real-time data. Reinforcement learning algorithm in machine learning: parameter optimization is achieved through Q-learning and deep Q network (DQN). Prediction model based on historical data: used for prediction and adjustment of processing parameters, Ql earning update formula;

[0058]

[0059] Q(s,a) is the value function of selecting action a in state s, α is the learning rate, and γ is the discount factor.

[0060] LSTM calculation formula:

[0061] h t =o t ⊙tanh(C t )

[0062]

[0063] h t is the output vector, C t is a memory unit, i t 、f t , o t They are input, forget and output gates, W c 、b c are the parameter matrix and bias vector associated with the candidate memory unit (or candidate state), respectively, t Represents the external data or features input to the LSTM unit at time t.

[0064] Remote monitoring and control system: Provide remote monitoring and control functions, allowing operators to remotely monitor the processing process through the network, remotely control and adjust the processing equipment, perform remote management and production scheduling, realize remote monitoring through H.264 and other video encoding technologies, and combine SSL / TLS encryption technology to ensure the security of remote communication. The motion compensation formula in H.264 encoding is:

[0065]

[0066] P, P ′are the pixel values ​​of the current frame and the reference frame respectively, D(s,t) is the distortion measure for motion compensation, s represents the index or identifier of the reference frame, indicating from which reference frame the block data is extracted for motion compensation, t represents the time point or frame number of the current frame, and j represents the index of a specific position or block in the frame.

[0067] Public key encryption formula in SSL / TLS handshake protocol:

[0068]

[0069] C is the ciphertext, M is the plaintext, K pub is the public key, E is the encryption algorithm, and the E encryption algorithm uses RSA-AES combined encryption, combining RSA asymmetric encryption and AES symmetric encryption. The specific contents are as follows:

[0070] 1. Generate a key pair: Generate a pair of RSA keys: public key K pub and private key K privo , generate a random symmetric AES key K aes ;

[0071] 2. Message encryption: Use the symmetric AES encryption algorithm to encrypt the message M and obtain the ciphertext C aes

[0072]

[0073] Use the asymmetric RSA encryption algorithm to encrypt the symmetric key K aes Encrypt and get the encrypted key C rsa

[0074]

[0075] C aes and C rsa Combined into the final ciphertext C:

[0076] C=(C rsa ,C aes )

[0077] 3. Message decryption:-After receiving the ciphertext C, first use the private key K priv C rsa Decrypt and restore the original symmetric AES key K aes :

[0078]

[0079] Use the recovered symmetric AES key K aes C aes Decrypt and restore the original message M:

[0080]

[0081] The algorithm combines the security of RSA and the efficiency of AES. Even if the attacker obtains the ciphertext C aes , it is also impossible to decrypt message M because the symmetric key K aes It is encrypted using RSA, and only the private key K priv Only those who can decrypt it can do so. The symmetric encryption speed of AES is faster than that of RSA, which is suitable for encrypting large amounts of data. RSA is used to encrypt small amounts of data (such as keys), avoiding the inefficiency of using RSA directly for encryption of large amounts of data. Without changing the algorithm structure, AES can be replaced with other symmetric encryption algorithms (such as ChaCha20) or RSA with other asymmetric encryption algorithms (such as ECC), thereby enhancing the adaptability and security of the algorithm.

[0082] The whole system is composed of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm and a remote monitoring and control system. The system monitors various data parameters in the aluminum alloy material processing process in real time through a multi-sensor data acquisition system, and uses real-time data processing and analysis algorithms to quickly and accurately process and analyze. Once the system detects an abnormal situation in the processing process, the anomaly detection and early warning system will promptly issue an alarm and provide corresponding processing suggestions to help operators take measures quickly to avoid production accidents or quality problems and ensure the safety and smooth progress of production. The system uses an intelligent optimization algorithm to automatically adjust processing parameters and optimize the processing process through analysis and learning of historical processing data and real-time monitoring data. This enables the system to realize adaptive adjustment of processing parameters according to different processing conditions and requirements, improve production efficiency and product quality, and realize comprehensive monitoring and analysis of the processing process, improve product quality and production efficiency, and reduce production costs and manual intervention. Specific embodiment 2:

[0084] like Figure 1 As shown, the multi-sensor data acquisition system includes laser measurement sensors, image recognition sensors, pressure sensors and temperature sensors. The specific contents are as follows:

[0085] Laser measurement sensor: used to measure the size and shape of aluminum alloy materials, including length, width, and thickness parameters. Through the laser measurement sensor, the size of aluminum alloy materials during processing can be monitored in real time;

[0086] Image recognition sensor: used to detect and analyze the surface quality of aluminum alloy materials, including surface defects, foreign matter, and bubbles. The image recognition sensor captures the surface image of the aluminum alloy material and analyzes it through image processing technology to achieve real-time monitoring of the surface quality.

[0087] Pressure sensor: used to monitor the pressure applied to the aluminum alloy material during processing to ensure the stability and quality of the processing. The pressure sensor collects the pressure changes during the processing in real time and transmits the data to the data acquisition system for processing and analysis;

[0088] Temperature sensor: used to monitor the temperature of processing equipment and aluminum alloy materials to prevent excessively high or low temperatures from affecting the processing process. The temperature sensor monitors the temperature changes of processing equipment and aluminum alloy materials in real time and issues alarms in time to ensure the stability of the processing process.

[0089] The detection types of multi-sensor data acquisition systems mainly include dimension detection, surface quality detection, pressure monitoring and temperature monitoring.

[0090] Dimension detection: Use laser measurement sensors to monitor the dimensional changes of aluminum alloy materials in real time, including length, width, and thickness parameters, to ensure dimensional accuracy and consistency during processing;

[0091] Surface quality inspection: Use image recognition sensors to inspect and analyze the surface quality of aluminum alloy materials, including surface defects, foreign matter, and bubbles, to ensure that the product surface quality meets the requirements;

[0092] Pressure monitoring: Use pressure sensors to monitor the changes in pressure applied to aluminum alloy materials during processing to ensure the stability and quality of the processing;

[0093] Temperature monitoring: Use temperature sensors to monitor the temperature changes of processing equipment and aluminum alloy materials to prevent excessively high or low temperatures from having adverse effects on the processing process;

[0094] Through the setting of multi-sensor data acquisition system, comprehensive monitoring and real-time analysis of the aluminum alloy material processing process can be achieved to ensure the improvement of product quality and production efficiency. Specific embodiment three:

[0096] like Figure 1 As shown in the figure, the real-time data processing and analysis algorithm mainly includes data preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization, and each step is carried out in the order of preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization. The specific contents of each step are as follows:

[0097] Data preprocessing: Preprocess the raw data collected from multiple sensors, including noise removal, smoothing, and data normalization to ensure data quality and reliability;

[0098] Feature extraction: Extract meaningful features from the preprocessed data to describe the characteristics and laws of the aluminum alloy material processing process. Common features include mean, variance, and gradient.

[0099] Real-time monitoring and analysis: Based on the extracted features, design real-time monitoring and analysis algorithms to detect abnormal conditions and problems in the processing process and make timely responses. Common algorithms include rule-based algorithms, statistical methods, and machine learning methods;

[0100] Adaptive optimization: Adaptively adjust processing parameters or control strategies based on real-time monitoring and analysis results to optimize the processing and improve product quality and production efficiency.

[0101] The real-time data processing and analysis algorithm contains at least rule-based algorithms, statistical methods, machine learning algorithms, and adaptive control algorithms. The specific contents are as follows:

[0102] Rule-based algorithms: judgments and decisions are made based on pre-set rules or conditions.

[0103] Statistical methods: Use statistical principles to analyze data and identify patterns. Common methods include mean, variance, covariance, and correlation coefficient, such as:

[0104]

[0105] Machine learning methods: Use machine learning algorithms to learn patterns and regularities from data and use them for prediction, classification or clustering tasks. Common machine learning algorithms include support vector machines (SVM), neural networks, and decision trees.

[0106] y=f(x)

[0107] Adaptive control algorithm: Adaptively adjust control parameters based on real-time monitoring data and feedback information to achieve optimal system performance.

[0108] In practical applications, a variety of algorithms and technologies will be combined and selected according to specific needs and situations to achieve real-time monitoring and analysis of the aluminum alloy material processing process. Specific embodiment four:

[0110] like Figure 1 As shown in the figure, the anomaly detection and early warning system includes anomaly detection algorithm, threshold setting, early warning mechanism and abnormal record analysis. The specific contents are as follows:

[0111] Anomaly detection algorithms: used to monitor the real-time collected data, identify and analyze abnormal conditions during processing. These algorithms are designed based on rules, statistical methods, and machine learning principles;

[0112] Threshold setting: For each monitored parameter, set the corresponding abnormal threshold. When the monitored data exceeds the set threshold range, the system will identify it as an abnormal situation;

[0113] Early warning mechanism: When an abnormal situation is detected, the system will immediately issue an alarm to notify relevant personnel to handle it. The early warning is carried out through sound, vision, and mobile phone text messages;

[0114] Abnormal recording and analysis: For each abnormal situation, the system will record detailed information, including the time when the abnormality occurred and the specific parameter changes, for subsequent analysis and processing.

[0115] The specific method is as follows:

[0116] Rule setting: Based on the experience and expertise of the processing process, a set of abnormality detection rules is set, for example, setting the temperature exceeding a certain range, the pressure increasing abnormally, and the size deviating from the standard rules;

[0117] Statistical methods: Use statistical methods to analyze the data collected in real time and identify abnormal situations. For example, use mean, variance, and standard deviation indicators to determine whether the data is abnormal;

[0118] Machine learning methods: Use machine learning algorithms to learn normal patterns from historical data and compare newly collected data with the learned patterns to identify abnormal situations. Commonly used algorithms include support vector machines, neural networks, and random forests.

[0119] Real-time monitoring: The system monitors various parameters during the processing in real time and continuously analyzes the data. Once an abnormal situation is found, the early warning mechanism is triggered immediately.

[0120] Through the above methods, the anomaly detection and early warning system can timely discover and handle abnormal situations in the processing process to ensure product quality and production efficiency. Specific embodiment five:

[0122] like Figure 1 As shown in the figure, the intelligent optimization algorithm includes three steps: parameter adjustment strategy, optimization objective function and adaptive learning. The specific contents are as follows:

[0123] Parameter adjustment strategy: Automatically adjust key parameters in the processing, such as temperature, pressure, and speed, based on real-time monitoring data and feedback information to optimize the processing process;

[0124] Optimization objective function: Design an optimization objective function to measure the efficiency and quality of the processing process. The optimization goal is to maximize production speed, minimize processing costs, and maximize product quality.

[0125] Adaptive learning: The system has adaptive learning capabilities and can continuously adjust the parameters and strategies of the optimization algorithm based on historical data and real-time monitoring data to adapt to different processing scenarios and workpiece requirements.

[0126] The specific method is as follows:

[0127] Rule-based optimization: Set a set of rule-based optimization strategies and select appropriate parameter adjustment solutions according to different processing conditions and product requirements;

[0128] Statistical methods: Use statistical methods to analyze and model the data in the processing process, and adjust the processing parameters according to the modeling results to achieve the optimization goal;

[0129] Machine learning method: Use machine learning algorithms to learn the patterns and rules of the processing process from historical data, and automatically adjust the processing parameters according to the learned patterns. Commonly used algorithms include genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms.

[0130] When the intelligent optimization algorithm is actually used, one or more of the following algorithms can be selected:

[0131] Genetic algorithm: Genetic algorithm is a bionic optimization algorithm that searches for the optimal solution by simulating the evolutionary process in nature. Its basic formula includes selection, crossover and mutation operations;

[0132] Particle Swarm Optimization Algorithm: Particle swarm optimization algorithm simulates the behavior of a flock of birds or a school of fish, and searches for the optimal solution by continuously adjusting the position and speed of particles. Its basic formula includes the formula for updating the position and speed of particles;

[0133] Simulated annealing algorithm: The simulated annealing algorithm simulates the temperature change during metal annealing and searches for the optimal solution through random perturbations and acceptance probabilities. Its basic formula includes the calculation formula for acceptance probability and the rules for state transition.

[0134] State transfer: current state is x, new state is x new Determined by the following formula:

[0135] x new =x+δ

[0136] δ is a random perturbation.

[0137] x new The acceptance probability is calculated by the following formula:

[0138]

[0139] ΔE is the energy difference between the new and old states, that is:

[0140] ΔE=E(x new )-E(x)

[0141] T is the current temperature, which decreases gradually as the number of iterations increases.

[0142] As the algorithm proceeds, the temperature T decreases gradually according to the following formula:

[0143] T new =αT

[0144] α is the temperature drop coefficient, which ranges from (0,1) and is selected based on the actual temperature change of the material.

[0145] Through the above methods and algorithms, the intelligent optimization algorithm realizes real-time optimization of the processing process, improves product quality and production efficiency, and reduces production costs and energy consumption.

[0146] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0147] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, the scope of the invention being defined by the appended claims and their equivalents.

Claims

1. Aluminum plate processing online monitoring and analysis system based on machine learning, characterized by: The system consists of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm, and a remote monitoring and control system, among which: Multi-sensor data acquisition system: composed of multiple sensors, used to collect various data parameters during the processing of aluminum alloy materials; Real-time data processing and analysis algorithm: Utilize the machine algorithm steps within the system to quickly and accurately process and analyze the data collected by the multi-sensor data acquisition system, and monitor data anomalies during the processing of aluminum alloy materials; Abnormality detection and early warning system: monitors the entire aluminum alloy material processing process, organizes and analyzes it through internal machine learning algorithms, and issues an alarm and provides corresponding processing suggestions when the system detects abnormalities in the processing process; Intelligent optimization algorithm: Through the analysis and learning of historical processing data and real-time monitoring data, the processing parameters are automatically adjusted to optimize the processing process. The intelligent optimization algorithm has a built-in machine learning algorithm; Remote monitoring and control system: Provides remote monitoring and control functions, allowing operators to remotely monitor the processing process through the network, remotely control and adjust the processing equipment, and perform remote management and production scheduling.

2. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 1 is characterized in that: The multi-sensor data acquisition system includes a laser measurement sensor, an image recognition sensor, a pressure sensor and a temperature sensor.

3. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 1 is characterized in that: The detection types of the multi-sensor data acquisition system mainly include size detection, surface quality detection, pressure monitoring and temperature monitoring.

4. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 1 is characterized in that: The real-time data processing and analysis algorithm mainly includes data preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization, and each step is performed in the order of preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization.

5. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 1 is characterized in that: The real-time data processing and analysis algorithm is internally provided with rule-based algorithms, statistical methods, machine learning algorithms and adaptive control algorithms.

6. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 1 is characterized in that: The anomaly detection and early warning system includes an anomaly detection algorithm, threshold setting, early warning mechanism and abnormal record analysis.

7. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 1 is characterized in that: The intelligent optimization algorithm includes three steps: parameter adjustment strategy, optimization objective function and adaptive learning.

8. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 6 is characterized in that: The anomaly detection and early warning system further includes the following contents: Anomaly detection algorithms: used to monitor the real-time collected data, identify and analyze abnormal conditions during processing. These algorithms are designed based on rules, statistical methods, and machine learning principles; Threshold setting: For each monitored parameter, set the corresponding abnormal threshold. When the monitored data exceeds the set threshold range, the system will identify it as an abnormal situation.

9. The online monitoring and analysis system for aluminum plate processing based on machine learning according to claim 6 is characterized in that: The anomaly detection and early warning system further includes the following contents: Early warning mechanism: When an abnormal situation is detected, the system will immediately issue an alarm to notify relevant personnel to handle it. The early warning is carried out through sound, vision, and mobile phone text messages; Abnormal recording and analysis: For each abnormal situation, the system will record detailed information, including the time when the abnormality occurred and the specific parameter changes, for subsequent analysis and processing.

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