Method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis

Abstract

The invention relates to the technical field of industrial automation, and discloses a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis, which comprises the following steps of: 1, acquiring operation data of the industrial control equipment through at least one industrial control protocol which is a Modbus protocol, an OPC UA protocol or a PROFIBUS protocol, the operation data comprises sensor data, register data and equipment state data of the equipment; and 2, analyzing the collected data packet by using a protocol analyzer, extracting key information related to the equipment operation state, the key information comprising parameters such as equipment working frequency, load, vibration, temperature and pressure, and converting the key information into a uniform data format. By collecting, analyzing and analyzing the operation data of the industrial control equipment in real time, abnormal conditions in operation of the equipment can be found in time, spreading of equipment faults is avoided, and stable operation of the equipment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis. Background Art

[0002] With the continuous improvement of industrial automation, more and more industrial control equipment is widely used in various production and energy facilities. Industrial control equipment plays a vital role in modern industrial production, and its stability and safety directly affect production efficiency and safety level. Especially in key industries such as energy, chemical industry, and electricity, the normal operation of industrial control equipment is of great significance to ensuring the economic benefits and production safety of enterprises. However, the complexity and diversity of industrial control equipment make it increasingly difficult to monitor its operating status and diagnose faults. Therefore, how to timely detect and predict the failure of industrial control equipment has become a key technical problem to ensure the stable operation of equipment.

[0003] At present, the operation monitoring of industrial control equipment mainly relies on the traditional method based on manual inspection and regular maintenance, supplemented by basic monitoring systems for data collection. Existing monitoring systems mostly rely on simple alarm mechanisms. When the equipment is abnormal, the system prompts the operation and maintenance personnel to check and repair through the preset threshold alarm. In addition, with the gradual development of the Internet of Things, big data and artificial intelligence technologies, more and more industrial control equipment monitoring systems have begun to introduce data analysis and predictive maintenance technologies, using equipment operation data for fault diagnosis and early warning. These technologies can improve the monitoring efficiency of equipment to a certain extent and reduce the probability of failure. However, the existing technologies still have many limitations in practical applications.

[0004] The main problem of the existing technology is the lack of multi-protocol support for industrial control equipment, as well as the limited ability to detect equipment anomalies and predict faults. Due to the different industrial control protocols used by different manufacturers and different types of equipment, the existing monitoring system can often only monitor equipment with a single protocol, and lacks compatibility with multiple industrial control protocols (such as Modbus, IEC61850, DNP3, etc.), resulting in the inability to uniformly collect and analyze equipment data in complex industrial environments, affecting the effectiveness of the overall monitoring system. In addition, most of the existing fault detection and prediction models rely on manually set rules and simple statistical analysis methods, which makes it difficult to effectively identify potential equipment failures and lacks data-driven real-time analysis and optimization capabilities. Therefore, the existing technology cannot comprehensively and accurately detect equipment anomalies and predict faults, and cannot provide timely and effective decision support for equipment maintenance. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis, thereby solving the problems in the prior art of insufficient compatibility with multiple industrial control protocols and limited equipment abnormality detection and fault prediction capabilities.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is implemented, comprising the following steps: Step 1: Collecting operation data of industrial control equipment through at least one industrial control protocol, wherein the industrial control protocol is Modbus protocol, OPC UA protocol or PROFIBUS protocol, and the operation data includes sensor data, register data and equipment status data of the equipment; Step 2: Use a protocol parser to parse the collected data packets, extract key information related to the equipment operation status, including parameters such as equipment operating frequency, load, vibration, temperature and pressure, and convert the key information into a unified data format; Step 3: De-noising the parsed data, using a Kalman filter or a wavelet transform method; Step 4: Extract the trend characteristics, periodic characteristics, and amplitude characteristics of equipment operation from the time series data through the sliding window technology, and standardize the extracted characteristics to generate feature vectors; Step 5: Use an autoencoder or long short-term memory network model to detect abnormalities during equipment operation through the extracted feature vectors and identify potential faults; Step 6: Based on the regression analysis model, support vector regression model or long short-term memory network model, the remaining service life of the equipment is predicted according to the historical data and real-time data of the equipment, and the probability of equipment failure is calculated; Step 7: When an anomaly is detected or the fault prediction value reaches the set threshold, an early warning signal is automatically generated, and an optimization algorithm is used to generate a scheduling plan for maintenance tasks based on the equipment health status, fault priority, and availability of maintenance resources to reduce equipment downtime.

[0007] Preferably, the protocol parsing step parses register data, device status data and sensor data in the Modbus protocol, OPC UA protocol or PROFIBUS protocol, and the data includes the working status, load, temperature and vibration parameters of the device.

[0008] Preferably, the data preprocessing step uses a Kalman filter or a wavelet transform method to denoise the collected data, supplement missing values, and correct outliers, and the processed data is used for subsequent feature extraction and model training.

[0009] Preferably, the feature extraction step uses a sliding window technique to segment the equipment time series data, extract the trend, periodicity, and amplitude features during the equipment operation process, and standardize the extracted features to form a feature vector for training the model.

[0010] Preferably, the anomaly detection step uses an autoencoder or a long short-term memory network model to perform anomaly detection on the device based on the historical operation data and real-time data of the device and output an abnormal signal.

[0011] Preferably, the fault prediction step uses a support vector regression model, a regression analysis model or a long short-term memory network model to predict the remaining service life of the equipment based on historical data and real-time data, and calculate the probability of equipment failure.

[0012] Preferably, the early warning and dispatching steps include: Step 7.1: When the operating state or fault prediction value of the equipment exceeds a preset threshold, a warning signal is generated; Step 7.2: Based on the health status of the equipment, fault priority, and availability of maintenance resources, a heuristic algorithm is used to generate a scheduling plan for maintenance tasks and determine the priority and implementation time of maintenance.

[0013] Preferably, the maintenance task scheduling step uses a genetic algorithm, a particle swarm optimization algorithm or other heuristic algorithms to schedule the maintenance tasks to ensure that the maintenance tasks of the equipment are performed in order of priority and to reduce equipment downtime.

[0014] Preferably, the data collection step is implemented through an edge computing gateway, which performs preliminary processing on the collected device data, including denoising, compression and caching, and the processed data is uploaded to the cloud platform via the network for subsequent analysis.

[0015] Preferably, the method further includes the step of archiving equipment maintenance records, wherein the equipment maintenance records include historical fault records, maintenance records and detection data of the equipment and are used to optimize the anomaly detection and fault prediction model.

[0016] The present invention provides a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis. It has the following beneficial effects: 1. The present invention can timely discover abnormal conditions in the operation of equipment by real-time collection, parsing and analysis of the operation data of industrial control equipment, avoid the spread of equipment failures, and ensure the stable operation of equipment. In particular, in the real-time monitoring process of equipment, through the protocol parsing technology based on the industrial control protocol, various operation data of the equipment can be obtained comprehensively and accurately, thereby ensuring the early discovery of potential failure hazards of the equipment and improving the safety and reliability of the equipment.

[0017] 2. The present invention adopts industrial control protocol parsing technology, which is compatible with a variety of common industrial control protocols, so that the method can be applied to industrial control equipment of different brands and models, and realize unified monitoring and abnormal detection of multiple types of equipment. Regardless of the protocol used by the equipment, the system can automatically convert the data format by parsing the industrial control protocol, avoiding the data incompatibility problem caused by the diversification of equipment and improving the versatility of the monitoring system.

[0018] 3. The present invention performs in-depth analysis and feature extraction on equipment data. The present invention can not only detect abnormal conditions in equipment operation in real time, but also predict future equipment failures based on machine learning and deep learning algorithms. This method not only identifies the current fault state of the equipment, but also predicts the potential failure risk of the equipment by establishing an effective fault diagnosis model and prediction model, thereby improving the accuracy of fault diagnosis and prediction, and avoiding production downtime and economic losses caused by equipment failure.

[0019] 4. The present invention can provide a scientific decision-making basis for the operation and maintenance of equipment through a real-time early warning and fault prediction mechanism. When an abnormality occurs in the equipment, the system will automatically issue an early warning signal according to the equipment status, and provide detailed fault information to the operation and maintenance personnel to help them quickly locate the source of the fault and formulate a treatment plan. At the same time, the system will also adjust the operating load of the equipment according to the predicted fault risk, automatically dispatch the equipment, and ensure the normal operation of the power, production and other systems, thereby improving the efficiency of equipment operation and maintenance and reducing the frequency and cost of manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the specification 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.

[0022] Please see attached Figure 1 The embodiment of the present invention provides a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis, including the following steps: 1. Data collection and industrial control protocol analysis For the anomaly detection method in the present invention, data collection and industrial control protocol parsing are key steps to achieve equipment status monitoring. Industrial control equipment (such as PLC, sensors, actuators, etc.) will generate a large amount of real-time data during operation. Collecting and transmitting through appropriate protocols can provide basic data support for subsequent anomaly detection and fault prediction. In this process, the industrial control protocol, as a communication protocol between the device and the control system, plays an important role in data transmission and exchange. Different industrial control protocols have different data formats and encoding methods, so targeted data parsing technology is needed to ensure that the key information extracted from the device can effectively support subsequent analysis and processing.

[0023] In this embodiment, the process of data collection and protocol analysis mainly includes the following steps: 1.1 Data Collection In this embodiment, the operation data of the device is collected in real time through different types of industrial control protocols. Common industrial control protocols include Modbus protocol, OPC UA protocol and PROFIBUS protocol, which are suitable for different devices and scenarios. Specifically, the data collected by the device through these protocols mainly includes: Equipment sensor data: such as temperature, pressure, flow, vibration and other parameters; Equipment status data: including the switch status, alarm information, fault code, etc. of the equipment; Register data: including the register data of the controller (such as PLC, DCS), which usually contains the operating status of the equipment, setting parameters and real-time control instructions. The specific method of data collection depends on the protocol used by the equipment. For example, the Modbus protocol transmits data through RTU or TCP / IP protocol, and the OPC UA protocol communicates through XML and SOAP-based methods. The equipment regularly sends real-time data packets to the data acquisition system through the network interface of the industrial control protocol (such as Modbus TCP, RS-485, etc.).

[0024] 1.2 Analysis of Industrial Control Protocol After data collection, the next task is to parse the collected data packets through the protocol parser to extract key information related to the device operation status. Since different industrial control protocols use different data formats, a dedicated parsing module needs to be designed for each protocol during the parsing process. In this embodiment, the steps of the data parsing process are as follows: 1.2.1 The role of the protocol analyzer The protocol parser is responsible for decoding the original data packets, extracting valid data and converting it into a unified format. Each industrial control protocol (such as Modbus, OPC UA or PROFIBUS protocol) contains different data structures. For example, the data of the Modbus protocol is usually in the form of registers, while the OPC UA protocol is organized in the form of objects and attributes. The parser needs to perform corresponding parsing according to the format of different protocols to ensure that the correct device status information is extracted from the original data.

[0025] 1.2.2 Modbus protocol analysis In this embodiment, for the analysis of the Modbus protocol, the protocol parser will extract key data such as the working status, load, sensor data, etc. of the device by parsing the function code, data address, data value, etc. in the Modbus RTU or TCP packet. For example, the data registers in the Modbus protocol are usually used to store the status data of the device, such as temperature, pressure, running time, etc. By reading these registers, the parser can convert the digital values ​​in these registers into actual physical parameters and provide them to the subsequent analysis module.

[0026] 1.2.3 OPC UA Protocol Analysis For the OPC UA protocol, the parser will extract the objects, variables, and properties of the device from the OPC UA data stream. The OPC UA protocol is a service-based protocol, and its communication content usually includes the real-time data, set values, warning information, etc. of the device. The parser maps the objects and variables in the OPC UA message and converts them into a unified data format for subsequent processing. For example, extract the temperature value from the device's temperature sensor and the set value from the controller.

[0027] 1.2.4 PROFIBUS protocol analysis For the PROFIBUS protocol, the protocol parser needs to parse the frame structure of the transmitted data and extract the operating status information of the device from the data frame. The PROFIBUS protocol is suitable for industrial automation equipment with high real-time requirements, such as PLCs and drives. In the PROFIBUS protocol, data transmission adopts a message passing mechanism based on the device address. The protocol parser will extract the operating parameters of the device from the frame, such as vibration, load, etc., and convert them into a standardized format for subsequent use.

[0028] 1.3 Data format conversion and standardization In order to ensure that the data extracted from different industrial control protocols can be uniformly managed and processed, the data format conversion technology is adopted in this embodiment. The protocol parser will convert the extracted device data into a standardized data format according to the characteristics of different protocols. Common standardized formats include JSON, XML or CSV formats, etc. The following operations need to be performed during the data conversion process: 1.3.1 Data field mapping and conversion Data field mapping is to map data items in different protocols to standardized data structures. For example, a register data in the Modbus protocol may correspond to a temperature value, while in the OPC UA protocol, the same data item may be the value of a temperature sensor. The protocol parser needs to map fields between different protocols to ensure data consistency.

[0029] 1.3.2 Data Verification and Cleansing During the conversion process, the protocol parser also needs to perform data verification to ensure the integrity and accuracy of the data. If abnormal data or data that does not conform to the expected format is encountered during the parsing process, the parser will clean it or discard it. This step helps to remove outliers and noise in the data and ensure the accuracy of subsequent analysis.

[0030] 1.3.3 Data Standardization In order to eliminate the differences in data formats between different devices and different protocols, all data will be uniformly standardized after being converted to a standardized format. This includes normalizing values, uniformly formatting timestamps, etc. Standardized data formats can ensure consistency and effectiveness in subsequent processing steps (such as feature extraction, anomaly detection, etc.).

[0031] 1.4 Data Storage and Transmission The data after protocol parsing and standardization will be stored in the local database or cloud platform. The specific storage method is as follows: 1.4.1 Local Storage For scenarios with small data volumes or high real-time requirements, the parsed data can be stored in a local database. Common local databases include MySQL, SQLite, etc., which manage and store data through database tables.

[0032] 1.4.2 Cloud Platform Storage For the collection and processing of large-scale data, the data will be uploaded to the cloud platform through the network for centralized storage and management. The cloud platform can provide stronger data processing and analysis capabilities, facilitating unified monitoring and management of large-scale equipment. Common cloud platforms include AWS, Azure, etc., which provide high-availability and high-reliability storage through cloud storage services. Through the above steps, the data collection and industrial control protocol parsing module in this embodiment can effectively collect the real-time operation data of the equipment and convert it into a standardized data format, providing reliable data support for subsequent anomaly detection and fault prediction. 2. Data preprocessing and denoising In the process of collecting operating data of industrial control equipment, due to the complexity of the equipment operating environment and external interference factors (such as electrical noise, sensor errors, etc.), the collected raw data often contains noise and outliers. These unstable factors may affect the accuracy of subsequent analysis and anomaly detection. Therefore, data preprocessing and denoising are key steps to ensure data quality and improve the accuracy of subsequent models. The present invention processes the collected data by using denoising techniques such as Kalman filtering and wavelet transform to ensure the smoothness and accuracy of the data, thereby improving the effectiveness of anomaly detection.

[0033] In this embodiment, the process of data preprocessing and denoising mainly includes the following steps: 2.1 Kalman filter denoising Kalman filtering is a filtering algorithm based on recursive estimation, which is suitable for noise suppression in dynamic systems. Since the operating status of industrial control equipment has time series characteristics and is often affected by noise and interference, Kalman filtering can effectively use the historical data and current measurement values ​​of the equipment to estimate the true status of the equipment, thereby eliminating the impact of noise on the data.

[0034] Step 2.1.1: In the application of Kalman filtering, the state model and measurement model of the device are first defined. The state model of the device describes the actual operating state of the device, such as the process of temperature, pressure, etc. changing over time, while the measurement model reflects the relationship between the collected sensor data and the device state. Based on these models, the Kalman filter can combine the measured data at each moment with the predicted value at the previous moment through a recursive algorithm to obtain the optimal estimate of the device state.

[0035] Step 2.1.2: In specific applications, the state of the device is updated by setting the Kalman Gain. The size of the Kalman Gain is dynamically adjusted according to the ratio of the measurement error to the prediction error, thereby balancing the model's prediction and the actual measurement data. This process can significantly reduce the noise component in the collected sensor data and improve the smoothness and stability of the data.

[0036] 2.2 Wavelet transform denoising Wavelet transform is a time-frequency analysis method suitable for denoising non-stationary signals (such as equipment operation data). Since the data of industrial control equipment may show characteristics such as periodicity and mutation, wavelet transform can effectively separate the noise components in the signal through multi-scale analysis. Therefore, the present invention combines the wavelet transform method to denoise the collected equipment operation data.

[0037] Step 2.2.1: Wavelet transform separates the high-frequency noise of the signal from the low-frequency effective signal by decomposing the signal into sub-signals of different frequency bands. When processing device data, first use discrete wavelet transform (DWT) to decompose the original data and extract the low-frequency component (i.e. the stable part of the signal) and high-frequency component (i.e. the noise part) of the signal.

[0038] Step 2.2.2: In the processing stage after wavelet transform, high-frequency noise components are deleted and low-frequency components are reconstructed. The noise caused by external interference or sensor inaccuracy is removed by filtering and smoothing the low-frequency signal.

[0039] Step 2.2.3: Finally, the processed signal is reconstructed into a time series through inverse wavelet transform to obtain the denoised equipment operation data. This process can effectively eliminate the impact of high-frequency noise and interference factors on the data, thereby improving the accuracy of subsequent anomaly detection algorithms.

[0040] 2.3 Outlier Detection and Correction In addition to using filtering technology to remove noise, data preprocessing also includes the detection and correction of outliers. Outliers that significantly deviate from the normal range may appear in the device data due to sensor failure or communication errors. If these outliers are not processed, they will affect the results of subsequent analysis. Therefore, in the process of data preprocessing, it is necessary to detect outliers and take corresponding correction measures.

[0041] Step 2.3.1: The outlier detection method can be based on statistical models, such as the z-score method, box plot method, etc., to detect whether there are outliers in the collected data. For each data point, by calculating its relative difference with other data points, if the difference exceeds the set threshold, it is determined to be an outlier.

[0042] Step 2.3.2: For the detected outliers, this embodiment uses interpolation methods for correction. For example, in time series data, if the sensor data at a certain moment is abnormal, it can be repaired by using the effective data of the previous and next time periods through linear interpolation, spline interpolation, etc. If there are obviously unreasonable outliers, they will be directly eliminated to ensure that the data in subsequent analysis is not affected.

[0043] 2.4 Missing Value Handling In actual data collection, some parameters of the equipment may have missing data due to signal loss or sensor failure. If such missing values ​​are not processed, it will directly affect the subsequent data analysis. To this end, this embodiment uses interpolation and model prediction methods to process missing values.

[0044] Step 2.4.1: For short-term missing values, linear interpolation or polynomial interpolation can be used to fill the data gap. Linear interpolation is based on the linear relationship between the previous and next data points, while polynomial interpolation is suitable for situations where the data changes are more complex.

[0045] Step 2.4.2: For missing data over a long period of time, this embodiment uses a prediction method based on a machine learning model (such as a KNN algorithm, a regression model, etc.) to predict missing values. These models are based on known equipment operation data and predict missing values ​​by learning the historical changes in equipment status. Through the above data preprocessing and denoising steps, the noise, outliers, and missing values ​​in the original data are effectively removed and corrected, ensuring the data quality in the subsequent analysis and anomaly detection process. This process provides stable and accurate basic data for anomaly detection and fault prediction of industrial control equipment, and improves the credibility and accuracy of the detection results.

[0046] 3. Feature extraction and data standardization For the task of detecting anomalies in the operation of industrial control equipment, feature extraction and data standardization are important steps in data analysis and model building. By extracting features from the collected equipment operation data, complex raw data can be converted into numerical features that are easy to analyze. These features can effectively reflect the operating status and potential problems of the equipment. Feature extraction requires not only in-depth analysis of the data, but also the selection of appropriate features based on the operating characteristics of the equipment, while maintaining data consistency under different equipment and different working conditions. Data standardization is to eliminate the impact of different feature scales and dimensional differences, so that the features can have equal weight and effectiveness in subsequent machine learning models. Feature extraction and standardization are key links to ensure the stability and accuracy of subsequent anomaly detection models.

[0047] In this embodiment, the specific steps of feature extraction and data standardization include the following aspects: 3.1 Feature Extraction Feature extraction aims to extract key information that can effectively describe the status and abnormalities of equipment from the original equipment data. Since the operation data of industrial control equipment usually contains time series information, the status of the equipment often shows characteristics such as periodicity, trend and mutation, so it is necessary to extract features from the data from multiple dimensions. The process of feature extraction can be divided into the following categories: 3.1.1 Statistical feature extraction Statistical features are one of the most common feature extraction methods, which are mainly obtained by descriptive statistical analysis of equipment operation data. Common statistical features include: mean, variance, standard deviation, maximum value, minimum value, kurtosis, skewness, etc. Through these statistics, we can understand the operation fluctuation of the equipment over a period of time, thereby providing basic data for subsequent anomaly detection. For example, in vibration sensor data, the standard deviation can reflect the change of vibration amplitude, and the kurtosis can reflect the occurrence of abnormal shock.

[0048] 3.1.2 Time Domain Feature Extraction Time domain features are mainly used to describe the trend of data changes over time. For the operation data of the equipment, time domain features can be extracted in the following ways: Time window sliding method: Divide the equipment data into multiple time windows, and extract features in each window, such as mean, variance, maximum value, minimum value, etc. Trend change analysis: Analyze the trend change part in the data to capture whether there is a sudden change or gradual change trend in the operation status of the equipment, which helps to detect potential failures of the equipment.

[0049] 3.1.3 Frequency Domain Feature Extraction Frequency domain analysis is an effective feature extraction method for data with periodic or frequency characteristics. By performing frequency domain analysis such as Fourier transform or wavelet transform on equipment data, frequency components can be extracted from the spectrum to identify the operating mode of the equipment. For example, the frequency domain characteristics of vibration signals can reveal problems such as wear and failure of mechanical parts, and the energy distribution of different frequency bands in the spectrum can reflect the health status of the equipment.

[0050] 3.1.4 Time-frequency domain feature extraction Combining the advantages of time domain and frequency domain, time-frequency domain analysis can consider both the time information and frequency information of data, and is suitable for the analysis of non-stationary signals. By extracting time-frequency features through methods such as wavelet transform or Hilbert-Huang transform (HHT), changes and anomalies in equipment operation can be captured more comprehensively. This method is particularly effective in equipment data with alternating high and low frequencies (such as mechanical vibration signals).

[0051] 3.2 Data Standardization The purpose of data standardization is to eliminate the differences in scale and unit between features so that different features have the same dimension and weight, which is crucial for subsequent machine learning modeling and anomaly detection. Since different sensors and device parameters may use different dimensions and value ranges during the actual acquisition process, standardization can make these features comparable on the same scale, thereby improving the accuracy of anomaly detection and the convergence speed of the model.

[0052] 3.2.1 Selection of Standardization Method This embodiment uses common standardization methods, namely Z-score standardization and Min-Max standardization, which are selected according to the requirements of different data features: Z-score standardization: For features with normal distribution, the Z-score standardization method is used to transform the data. This method transforms the data into a distribution form with a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation. Z-score standardization is suitable for most device data, such as physical quantities such as temperature and pressure.

[0053] Min-Max Normalization: For features with large fluctuations or different ranges, the Min-Max normalization method is used to map the data to the interval [0, 1]. This method is often used for features with large data fluctuations or no obvious normal distribution, such as control parameters of equipment.

[0054] 3.2.2 Standardization Implementation Steps In the process of data standardization, each feature is first standardized independently. Specifically, for each device data feature, its mean and standard deviation (Z-score standardization) or minimum and maximum values ​​(Min-Max standardization) are first calculated, and then the data is transformed according to the standardization formula. The transformed data will have the same scale, which is convenient for subsequent analysis.

[0055] 3.2.3 Notes on data standardization During the training and testing phases, it is necessary to ensure that the same normalization parameters are used. That is, the mean, standard deviation and other parameters calculated for the training data set must be applied to the normalization of the test data set.

[0056] When dealing with missing values ​​or outliers, it is necessary to ensure that they have been effectively filled or corrected before standardization, otherwise the results of standardization may be affected.

[0057] 3.3 Feature Selection and Dimensionality Reduction In addition to feature extraction and standardization, feature selection and dimensionality reduction are important means to improve model efficiency and accuracy. Since the data collected by the equipment may contain redundant features and noise, feature selection can help reduce the impact of irrelevant features, and dimensionality reduction can help reduce the complexity of the data and improve computational efficiency.

[0058] 3.3.1 Feature Selection In the feature extraction stage, features of multiple dimensions are extracted, but not all features contribute to anomaly detection. Therefore, feature selection technology is used to screen out the most representative features. Common feature selection methods include: chi-square test, mutual information method, correlation coefficient analysis, etc.

[0059] 3.3.2 Principal Component Analysis (PCA) Principal component analysis (PCA) is a commonly used dimensionality reduction technique that extracts the principal components of the data by performing a linear transformation on the feature space, thereby achieving data dimensionality reduction. Through PCA, the dimension of the data can be reduced while retaining most of the data variability, thereby improving the training efficiency of subsequent models. Through the above feature extraction and data standardization steps, this embodiment successfully converts the original device data into meaningful features, ensuring that the subsequent anomaly detection model can accurately identify the abnormal behavior of the device in data analysis. At the same time, data standardization ensures that different features are processed at the same scale, avoiding deviations in model results caused by inconsistent feature scales. These processing steps provide stable and reliable input data for subsequent model training and anomaly detection.

[0060] 4. Anomaly detection and fault prediction The purpose of anomaly detection and fault prediction for industrial control equipment is to timely discover potential equipment failures through analysis of equipment operation data, prevent equipment damage or shutdown, and ensure the continuity and safety of production. Anomaly detection focuses on identifying whether the equipment has deviated from the normal working state during operation, while fault prediction further analyzes the trend of anomalies and predicts the occurrence of faults, so as to take measures in advance. In order to accurately and efficiently realize anomaly detection and fault prediction, the present invention adopts advanced algorithms such as machine learning and deep learning, combines the time series characteristics of data and the historical operation mode of the equipment, and constructs anomaly detection and fault prediction models suitable for industrial control equipment.

[0061] In this embodiment, the specific steps of anomaly detection and fault prediction mainly include the following aspects: 4.1 Anomaly Detection Methods The core purpose of anomaly detection is to identify unexpected abnormal behaviors during equipment operation, which may be early signs of equipment failure. Common anomaly detection methods include statistical-based methods, model-based methods, and data-driven machine learning methods.

[0062] 4.1.1 Anomaly Detection Based on Statistical Methods The statistical method mainly uses the historical behavior patterns of equipment operation data to determine whether the current data is abnormal. For example, by calculating the mean and standard deviation of various equipment parameters, a threshold can be set to determine whether the data deviates from the normal range. If the data exceeds the set threshold range, it is judged to be abnormal. This method is simple and efficient, and is suitable for equipment states with clear and stable change patterns.

[0063] 4.1.2 Model-based anomaly detection The model-based anomaly detection method determines whether the current operating status of the device meets expectations by establishing a mathematical model of the device. Common models include regression models and time series models. In the model training phase, historical data is used to fit the normal operating rules of the device; in the detection phase, the currently collected data is input into the model, the predicted value is calculated, and compared with the actual value. If the deviation exceeds a certain threshold, it is judged as an anomaly. This type of method is suitable for situations where the device status has a strong regularity.

[0064] 4.1.3 Anomaly Detection Based on Machine Learning Machine learning methods can automatically discover abnormal patterns in data by building classification or regression models based on historical data. Common machine learning algorithms include support vector machines (SVM), decision trees, K-nearest neighbors (KNN), etc. By learning the normal operating mode of the equipment, the model can automatically identify and distinguish between normal and abnormal states. Compared with traditional statistical methods, machine learning methods are more flexible and adaptable and can handle more complex equipment behavior patterns.

[0065] 4.1.4 Anomaly Detection Based on Deep Learning Deep learning algorithms, especially autoencoders and recurrent neural networks (RNNs), can learn complex features in device operation data and capture potential abnormal patterns in multi-dimensional data. Autoencoders learn the normal mode of a device by compressing input data and reconstructing its original data. When the reconstruction error exceeds a certain threshold, it indicates that the input data deviates greatly from the normal mode and is judged to be abnormal. RNNs are suitable for time series data and can learn the time series features of device operation through historical data, and detect mutations or abnormal fluctuations in the data in a timely manner.

[0066] 4.2 Fault prediction method Fault prediction is to further analyze the equipment's operating data based on anomaly detection to predict the possible time and type of faults, so as to take measures in advance for maintenance or repair, reduce production downtime, and extend equipment life. Fault prediction methods usually rely on historical fault data, the equipment's working status and its operating mode, and judge the possibility of equipment failure through modeling and trend analysis.

[0067] 4.2.1 Fault prediction based on time series analysis Time series analysis methods analyze the historical operation data of equipment, extract information such as trends and periodicity, and predict the operation status of equipment in the future. Common time series prediction methods include ARIMA (autoregressive integrated moving average) model, LSTM (long short-term memory network), etc. These methods can effectively capture the time dependency in equipment operation, so as to accurately predict the future status of equipment and determine whether failure will occur.

[0068] 4.2.2 Fault prediction based on regression model The regression model predicts the occurrence of equipment failure by establishing a mapping relationship between the input and output of the equipment. For example, a regression model between equipment sensor data and the occurrence of failure can be established through algorithms such as linear regression and support vector regression (SVR) in machine learning. When the model predicts an abnormal change in the equipment state and the change trend is consistent with the precursor of failure, it can be determined as a potential failure.

[0069] 4.2.3 Fault prediction based on deep learning Through deep network structures, deep learning methods can automatically extract features from the historical operation data of equipment and perform fault prediction. In particular, long short-term memory networks (LSTM) and convolutional neural networks (CNN) show extremely high accuracy when processing time series data and multi-dimensional data. LSTM is suitable for time series data with a long span and can capture potential fault signals of equipment during long-term operation; while CNN is suitable for processing equipment data with local features and can extract spatial or time-related fault modes from it.

[0070] 4.2.4 Fault Trend Analysis Fault trend analysis conducts multi-dimensional analysis on equipment operation data, and combines equipment health index, vibration signal, temperature change and other data to establish equipment health assessment model. By monitoring the gradual change trend of equipment status, it is possible to determine whether a fault will occur in the future and estimate the probability of a fault. The health assessment model can provide early warning of potential equipment failures and help maintenance personnel intervene in a timely manner.

[0071] 4.3 Comprehensive application of anomaly detection and fault prediction The anomaly detection and fault prediction method in this embodiment combines the above-mentioned various algorithms, and improves the accuracy of anomaly detection and the advance amount of fault prediction through the integration of multiple technologies. By combining machine learning with deep learning algorithms, it is not only possible to monitor the operating status of the equipment in real time, but also to analyze the future operating trends of the equipment and predict possible fault times and types. This comprehensive method provides more scientific and accurate support for fault prevention and maintenance decisions of industrial control equipment, reduces the occurrence of sudden equipment failures, reduces maintenance costs, and improves production efficiency.

[0072] 4.3.1 Data Fusion and Multimodal Learning In practical applications, industrial control equipment is usually equipped with multiple different types of sensors, such as temperature sensors, pressure sensors, vibration sensors, etc. The data generated by these sensors often have different characteristics. In order to make full use of these multimodal data, data fusion technology is used to integrate and analyze data from different sources to improve the accuracy of anomaly detection and fault prediction. For example, multimodal learning methods based on deep learning can learn more comprehensive equipment operation characteristics by fusing signals from different sensors, thereby improving detection and prediction capabilities.

[0073] 4.3.2 Adaptive Threshold and Model Update Since the operating status of industrial control equipment may change over time, the original model and threshold may become invalid. In order to cope with such changes, this embodiment introduces an adaptive threshold mechanism to maintain the accuracy and robustness of the model by adjusting the threshold or updating the model in real time. For example, through online learning methods, the model can perform incremental learning based on new data and continuously optimize the ability of anomaly detection and fault prediction. Through the above-mentioned technical implementation of anomaly detection and fault prediction, the present invention can monitor the status of the equipment in real time during the operation of industrial control equipment, detect anomalies in time, and predict potential faults, provide effective early warning and decision support, ensure the normal operation of production equipment, and extend the service life of equipment.

[0074] 5. Early warning and dispatch The purpose of early warning and dispatching of industrial control equipment is to timely detect potential faults or anomalies during the operation of the equipment, inform relevant personnel through the early warning mechanism, and take measures to intervene to avoid equipment failure or reduce its impact on production. At the same time, through the intelligent support of the dispatching system, the maintenance and overhaul of the equipment can be reasonably arranged to ensure the continuity of production and the normal operation of the equipment. The combination of the early warning system and the dispatching system not only helps to improve production efficiency, but also effectively reduces maintenance costs and downtime, and further improves the reliability of equipment and production safety.

[0075] In this embodiment, the specific steps of early warning and dispatch include the following aspects: 5.1 Establishment and implementation of early warning mechanism The core of the early warning mechanism is to judge the changing trend of the equipment status through model algorithms based on the real-time monitoring data of the equipment, and set the early warning threshold. Once the equipment status exceeds the threshold, the system will trigger the early warning. The early warning mechanism includes not only simple alarms triggered by a single threshold, but also multi-dimensional early warnings that integrate multiple data features, intelligent algorithms and historical experience.

[0076] 5.1.1 Threshold-based warning The threshold-based early warning method is the most basic and common early warning mechanism. The system monitors the normal operation status of the equipment and sets certain thresholds (such as the maximum and minimum values ​​of physical quantities such as temperature, pressure, and vibration). When the operating data of the equipment exceeds these thresholds, an alarm can be triggered. This method is suitable for situations where there are clear standards and normal ranges for the key parameters of the equipment. The setting of thresholds is usually based on the technical specifications of the equipment, historical data analysis, or empirical values.

[0077] 5.1.2 Model-based early warning The model-based early warning method predicts the future state of the equipment by establishing a mathematical model of the equipment. When the deviation between the actual operating state of the equipment and the model prediction value exceeds the set range, the system will issue an early warning. This method can adapt to the dynamic changes of the equipment and promptly detect anomalies caused by changes in the external environment or operational errors. Through real-time monitoring and model calibration, the system can continuously adjust the prediction model to ensure the accuracy of the early warning.

[0078] 5.1.3 Intelligent early warning based on machine learning and deep learning With the development of artificial intelligence technology, intelligent early warning methods based on machine learning and deep learning are gradually applied to the monitoring and management of industrial control equipment. By training historical data, machine learning models can automatically learn the operating rules of equipment and identify abnormal patterns. The emergence of abnormal data will trigger the early warning mechanism of the model to predict potential failures or abnormal operations in a timely manner. Commonly used algorithms include decision trees, random forests, neural networks, etc. These algorithms can achieve accurate early warning in complex environments through self-learning and continuous optimization.

[0079] 5.1.4 Early warning mechanism based on multi-dimensional data fusion For the operating status of industrial control equipment, early warnings that rely only on a single data source are often prone to false alarms or missed alarms. Therefore, the use of multi-dimensional data fusion methods to integrate data from different sensors (such as temperature, pressure, vibration, current, etc.) can improve the accuracy of early warnings. Through the fusion of multi-dimensional data, the status of the equipment can be more comprehensively reflected, ensuring that the early warning system can detect potential faults or abnormalities in a timely manner.

[0080] 5.2 Warning Notification and Response After the warning is triggered, timely notification of relevant personnel and taking appropriate response measures are the key functions of the early warning system. The early warning notification not only needs to convey the warning information to the maintenance personnel, but also needs to automatically generate processing procedures or instructions through the system for rapid response.

[0081] 5.2.1 Warning Information Transmission and Notification Mechanism The warning system will promptly transmit warning information to equipment maintenance personnel or management personnel through SMS, email, APP push and other methods. Warning information usually contains key content such as fault type, fault location, occurrence time, abnormal parameter value, etc. In order to ensure the efficiency and accuracy of information transmission, the system can also adopt different notification strategies according to different levels of warning. For example, warning information of serious faults can be notified by phone, while minor abnormalities can be sent by SMS or email.

[0082] 5.2.2 Automatic response and disposal suggestions After receiving the early warning information, the system can automatically generate response suggestions or disposal processes based on historical experience and the type of equipment failure. For example, if the temperature of the equipment is abnormal, the system will automatically remind the relevant personnel to conduct temperature detection and guide them to check whether the cooling system is working properly. If the equipment failure is more serious, the system will automatically dispatch maintenance personnel to handle it on site. Through automated response and disposal suggestions, human intervention can be reduced, and response speed and processing efficiency can be improved.

[0083] 5.3 Implementation of the Scheduling System The dispatching system is an important part of the early warning system. Its purpose is to reasonably arrange the inspection, maintenance and upkeep tasks of the equipment according to the equipment operation status and fault early warning information. The core of the dispatching system is to intelligently optimize the allocation of maintenance resources according to multiple factors such as production plan, equipment status, fault urgency, etc., to ensure that equipment repair and maintenance work can be carried out efficiently and orderly.

[0084] 5.3.1 Task Priority and Resource Optimization Scheduling During the scheduling process, the task priority must first be set based on the equipment's fault level, production requirements, and resource availability. For equipment on critical production lines, fault priority is obviously higher than that of non-critical equipment. The scheduling system intelligently assigns priority to each task by analyzing the urgency of each task and the availability of maintenance resources (such as personnel, equipment, tools, etc.), ensuring that the most urgent tasks can be handled in a timely manner.

[0085] 5.3.2 Fault repair and preventive maintenance scheduling The scheduling system should not only handle sudden failures, but also consider the arrangement of preventive maintenance tasks. Preventive maintenance can reduce the failure rate of equipment through regular inspection and cleaning. The scheduling system arranges preventive repair and maintenance of equipment in advance according to the equipment's operating cycle, the manufacturer's recommended maintenance plan and historical data to avoid premature or late failure of equipment. Through reasonable maintenance scheduling, the overall downtime of equipment can be effectively reduced and the operating efficiency of equipment can be improved.

[0086] 5.3.3 Personnel Scheduling and Working Time Management The scheduling system also needs to coordinate the scheduling and working time management of maintenance personnel. By matching the skills, work experience and current task requirements of maintenance personnel, the scheduling system can ensure that the right personnel participate in the maintenance and repair of equipment at the right time. The system can also intelligently arrange the work plan of maintenance personnel according to the working time requirements of the task, thereby improving the overall resource utilization.

[0087] 5.4 Closed-loop feedback mechanism for early warning and dispatch In order to ensure the accuracy and effectiveness of the early warning and dispatching system, a closed-loop feedback mechanism should be established in the early warning and dispatching process. By tracking the results of fault handling, the operating status of the repaired equipment, and the data changes during the maintenance process, the system can optimize and adjust the early warning model and dispatching strategy.

[0088] 5.4.1 Feedback Data Collection and Analysis After the fault handling is completed, the system will continue to collect the equipment's operating data and analyze the status of the repaired equipment. By comparing the equipment parameters before and after the repair, the system can evaluate the repair effect and optimize the subsequent fault prediction and early warning strategy based on the results.

[0089] 5.4.2 Early Warning and Scheduling Strategy Optimization Based on historical data and feedback information, the system can gradually optimize the early warning strategy and scheduling decision model. For example, according to the frequency of occurrence and processing time of different types of faults, the system can adjust the early warning threshold and response time, and optimize the scheduling process to achieve more efficient equipment management and fault handling.

[0090] Through the implementation of the above-mentioned early warning and scheduling process, it can be ensured that the operation anomalies of industrial control equipment can be responded to and handled in a timely manner, ensuring the efficient and stable operation of the equipment, reducing downtime and improving production efficiency.

[0091] 6. Data Archiving and Model Optimization In view of the large amount of data generated during the operation of industrial control equipment, data archiving and model optimization are important components for achieving long-term monitoring, continuous improvement and system adaptation. By archiving and managing historical data, rich basic data can be provided for subsequent data analysis, model training and optimization. At the same time, model optimization ensures that in the process of continuous data accumulation, the system can continuously adjust and improve the prediction accuracy, thereby improving the monitoring accuracy of equipment operation, reducing the failure rate, and enhancing the adaptability of the overall system. In this embodiment, the specific contents of data archiving and model optimization include data storage management, data archiving and retrieval mechanism, and the process of continuously optimizing machine learning and deep learning models based on historical data. By archiving a large amount of equipment operation data and combining it with regular model optimization, it not only helps to improve the analysis accuracy of the equipment operation status, but also further enhances the intelligence level of fault prediction and anomaly detection.

[0092] 6.1 Data Archiving and Management Data archiving is the basis for long-term fault prediction and anomaly detection. Its purpose is to effectively store various types of data collected by industrial control equipment during operation for subsequent analysis and processing. Equipment operation data is diverse, including but not limited to sensor data such as temperature, pressure, vibration, current, voltage, and equipment operation status information (such as start and stop status, fault records, etc.).

[0093] 6.1.1 Data Storage Structure In this embodiment, data archiving adopts a hierarchical storage architecture, and different storage media are selected for storage based on factors such as the importance of the data, the need for frequent access, and the storage duration. Common data storage methods include relational databases (such as MySQL, PostgreSQL), time series databases (such as InfluxDB, Prometheus), and distributed storage (such as HDFS, AWS S3, etc.). Time series data (such as sensor data) is stored in a time series database to ensure fast data writing and retrieval, while other types of device status data are stored in a relational database to ensure efficient management of structured data.

[0094] 6.1.2 Data Archiving and Cleaning As time goes by, the data of equipment operation continues to increase. In order to save storage resources and improve storage efficiency, the system needs to archive and clean up data regularly. Data archiving transfers long-term stored data to efficient storage media, such as cloud storage or cold storage, so that it can be accessed and analyzed when needed. For data that no longer needs to be accessed frequently, the system will clean it up regularly and retain necessary historical data to avoid affecting the performance of the storage system.

[0095] 6.1.3 Data Retrieval and Query Efficient retrieval of archived data is an important foundation for subsequent model training and analysis. In order to ensure that historical data can quickly respond to retrieval needs, this embodiment uses efficient query engines such as Elasticsearch, ClickHouse, etc. These engines can quickly locate and retrieve relevant information in massive data. At the same time, the data retrieval system supports flexible query syntax and can perform multi-dimensional retrieval based on time range, device ID, parameter category, etc.

[0096] 6.2 Model Optimization As equipment operation data accumulates and changes, the initial machine learning and deep learning models may not be able to maintain high accuracy for a long time. Therefore, model optimization has become a key part of continuous system improvement. By continuously optimizing model parameters and algorithm structure, equipment fault prediction and anomaly detection can be made more accurate, while improving the adaptability and stability of the system.

[0097] 6.2.1 Model Retraining As time goes by, the operating status and working conditions of the equipment may change, so the machine learning model must be retrained regularly. Each time the model is retrained, new equipment operation data is included in the training set to help the model adapt to new equipment behavior characteristics or changes in the external environment. The retraining process usually includes steps such as data preprocessing, feature extraction, model training and evaluation. By continuously updating the training data, the model can capture the latest dynamics of the equipment's operating status and improve its prediction performance.

[0098] 6.2.2 Adaptive Adjustment and Online Learning In some application scenarios, the operating state of the device may change rapidly, so traditional batch training may not be able to adapt to these changes in time. In this embodiment, an online learning method is introduced so that the model can be adaptively adjusted according to the newly collected data. Through online learning, the model can be continuously updated in the real-time data stream, thereby maintaining high accuracy and real-time performance. Common online learning algorithms include incremental learning, online gradient descent, etc., which can fine-tune the model according to the new data without retraining the entire model.

[0099] 6.2.3 Model Evaluation and Performance Optimization Each model optimization requires evaluation and verification to ensure the effectiveness of the optimization. Evaluation methods usually include calculations of cross-validation, accuracy, recall, F1 value and other indicators. By regularly evaluating the performance of the model, it can be found whether the model is overfitting or underfitting, ensuring that it can maintain good performance in various data scenarios. Based on the evaluation results, the model parameters are further adjusted or the algorithm is optimized. For example, for a deep learning model, the model can be optimized by adjusting the network structure, increasing the number of layers, or using different activation functions.

[0100] 6.2.4 Model Fusion and Ensemble Learning In practical applications, a single model may be difficult to cover all anomaly detection and fault prediction scenarios. Therefore, the model fusion and ensemble learning methods are adopted in this embodiment. By combining multiple different types of models (such as decision trees, support vector machines, neural networks, etc.), the robustness and accuracy of the system can be effectively improved. Common ensemble learning methods include random forests, XGBoost, AdaBoost, etc. These methods improve the overall performance by integrating the prediction results of multiple models, thereby reducing the misjudgment or missed judgment that may be caused by a single model.

[0101] 6.2.5 Model Interpretability and Visualization In the process of equipment failure prediction and anomaly detection, the interpretability of the model is particularly important. For industrial control systems, it is often necessary to guide maintenance decisions through the output results of the model, and the decisions need to have a certain degree of interpretability. In order to improve the transparency and credibility of the model, this embodiment adopts model interpretability technology, such as SHAP (SHapleyAdditive exPlanations) value, LIME (Local Interpretable Model-agnosticExplanations) and other methods. These methods can help analyze the decision-making basis of the model and explain the contribution of each feature to the prediction results, thereby providing valuable reference for the diagnosis and maintenance of equipment failures.

[0102] 6.3 Feedback Mechanism for Data Archiving and Model Optimization Data archiving and model optimization are not simply independent processes, but need to form a closed-loop feedback mechanism. By comparing each prediction result with the actual fault, the system can further accumulate experience and continuously improve the early warning and fault prediction model. The data, maintenance records and actual results after each fault handling will become the basis for the next optimization and provide data support for subsequent model optimization.

[0103] 6.3.1 Establishment of feedback mechanism Every time a failure occurs, the system records the relevant failure information, repair process, and repair effect. During the model optimization process, this data is used as feedback information to input into the model training to help the system identify new failure modes or operating anomalies. Through this feedback mechanism, the model can be continuously optimized as the equipment operation process evolves, so that the equipment monitoring system always maintains a high degree of accuracy and real-time performance.

[0104] Through data archiving and model optimization, this embodiment can not only effectively manage and store a large amount of equipment operation data, but also ensure that the equipment's fault prediction and anomaly detection models are continuously optimized and improved with the support of continuously accumulated new data. This process greatly improves the long-term adaptability of the system, allowing industrial control equipment to maintain stable operation in a constantly changing environment, reduce equipment failure rates, and improve production efficiency.

[0105] Embodiment 1: This embodiment takes the blast furnace equipment of a steel plant as an example, and specifically describes the method for detecting the operation anomaly of the blast furnace equipment based on industrial control protocol analysis. As an important production equipment, the stability of the operation state of the blast furnace is crucial to steel production. In this scenario, this paper collects real-time data of the equipment and combines industrial control protocol analysis technology, data preprocessing, feature extraction and standardization, anomaly detection, fault prediction, early warning and scheduling and other technical steps to build a complete blast furnace equipment operation anomaly detection and fault prediction system.

[0106] 1. Data collection and industrial control protocol analysis For blast furnace equipment, the system collects data in real time through sensors installed on key parts of the equipment (such as furnace temperature sensors, gas flow meters, current sensors, etc.). These data include but are not limited to physical quantities such as temperature, pressure, vibration, current, voltage, as well as the switch status and fault records of the blast furnace.

[0107] In this embodiment, the industrial control protocol parsing module is responsible for decoding PLC control systems, SCADA systems, Modbus and other protocols, and extracting equipment control signals and sensor data. Through industrial control protocol parsing technology, data transmitted in binary or hexadecimal format in the industrial control system can be converted into parameters that are easy to understand and process. These parameters include not only the key operating data of the blast furnace, but also information such as equipment status and control commands, which is convenient for subsequent abnormal detection and analysis.

[0108] 2. Data preprocessing and denoising After data collection, the monitoring data of the blast furnace equipment is preprocessed to remove irrelevant information and noise and enhance the signal of the data. In this embodiment, the system first fills in missing values ​​and interpolates the collected data through a time series analysis method to ensure the integrity of the data.

[0109] The wavelet transform denoising algorithm was applied to the vibration signals and temperature sensor data of blast furnace equipment. Wavelet transform has good time-frequency localization characteristics and can effectively filter out high-frequency noise while retaining important fault feature signals. After data cleaning, the data becomes more stable and the noise interference is reduced, providing a reliable foundation for subsequent feature extraction and model training.

[0110] 3. Feature extraction and data standardization After data preprocessing, the system extracts features from the operating data of the blast furnace equipment. In this embodiment, based on the operating characteristics of the blast furnace, multiple features such as temperature, pressure, vibration, and gas component concentration in the furnace are selected. The frequency domain features are extracted by Fourier transform, and the time domain features are extracted by combining traditional statistical methods (such as mean, standard deviation, maximum value, minimum value, etc.). In this way, the extracted features can fully reflect the operating status of the equipment and the potential failure risks.

[0111] In order to eliminate the influence of different dimensions, the system standardizes the extracted features. The Z-Score standardization method is used to convert the mean of each feature to 0 and the standard deviation to 1, ensuring that each feature is compared at the same scale. Data standardization can eliminate the dimensional differences between features and improve the training effect of subsequent models.

[0112] 4. Anomaly detection and fault prediction After feature extraction and data standardization, the system uses a combination of machine learning and deep learning to perform anomaly detection and fault prediction. Through the integration of multiple algorithms, the system can accurately identify potential faults in different working environments of blast furnace equipment.

[0113] In this embodiment, the system first uses a support vector machine (SVM) for preliminary anomaly detection. SVM can effectively separate normal and abnormal operating states and identify possible minor changes in the operation of the equipment. When abnormal data is identified, the system will further use a deep learning model based on long short-term memory (LSTM) to combine the equipment's time series data to predict future equipment failure trends. LSTM networks are particularly suitable for processing time-related sequence data and can predict future failures by memorizing long-term historical data.

[0114] 5. Early warning and dispatch Based on the abnormality detection and fault prediction module of blast furnace equipment, the system has established an automated early warning mechanism and dispatching system. Through real-time monitoring and analysis of the equipment operation status, when the system identifies equipment abnormalities, it will trigger an early warning according to the set threshold.

[0115] In this embodiment, when the temperature, pressure, vibration and other key parameters of the blast furnace equipment exceed the set threshold, the system will automatically issue an early warning. The early warning information will be notified to the relevant equipment management personnel through SMS, email, etc., reminding them to check the equipment. For more serious faults, the system will also arrange corresponding maintenance personnel for emergency treatment through the dispatch system.

[0116] During the early warning and fault handling process, the system will also conduct intelligent scheduling based on production plans and equipment conditions. If a blast furnace equipment fails and needs to be shut down for maintenance, the system will optimize production scheduling and automatically adjust the task arrangements of other production lines to ensure the continuity of the production process.

[0117] 6. Data Archiving and Model Optimization During the operation of the blast furnace equipment, all collected data and its processing results will be archived in real time. The system uses a time series database (such as InfluxDB) for efficient data storage and regularly archives historical data. These data not only provide a basis for subsequent fault analysis, but also provide valuable training data for model optimization.

[0118] In order to improve the system's fault prediction accuracy, the system regularly retrains the machine learning model. As the operating characteristics of blast furnace equipment continue to change, the original model may lose some accuracy. By continuously inputting new historical data, the system can dynamically adjust and optimize the prediction model to ensure that it can provide high-precision fault prediction in actual applications.

[0119] This embodiment realizes real-time monitoring, anomaly detection and fault prediction of blast furnace equipment through technical means based on industrial control protocol analysis. Through a series of technical means such as data acquisition, protocol analysis, data preprocessing, feature extraction, anomaly detection, early warning and scheduling, as well as data archiving and model optimization, a complete intelligent equipment operation management system is formed. The system can effectively reduce equipment failure rate, improve production efficiency, and provide strong technical support for equipment maintenance and management.

[0120] 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 variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is implemented, characterized in that: The following steps are involved: Step 1: Collecting operation data of industrial control equipment through at least one industrial control protocol, wherein the industrial control protocol is Modbus protocol, OPC UA protocol or PROFIBUS protocol, and the operation data includes sensor data, register data and equipment status data of the equipment; Step 2: Use a protocol parser to parse the collected data packets, extract key information related to the equipment operation status, including equipment operating frequency, load, vibration, temperature and pressure parameters, and convert the key information into a unified data format; Step 3: De-noising the parsed data, using a Kalman filter or a wavelet transform method; Step 4: Extract the trend characteristics, periodic characteristics, and amplitude characteristics of equipment operation from the time series data through the sliding window technology, and standardize the extracted characteristics to generate feature vectors; Step 5: Use an autoencoder or long short-term memory network model to detect abnormalities during equipment operation through the extracted feature vectors and identify potential faults; Step 6: Based on the regression analysis model, support vector regression model or long short-term memory network model, the remaining service life of the equipment is predicted according to the historical data and real-time data of the equipment, and the probability of equipment failure is calculated; Step 7: When an anomaly is detected or the fault prediction value reaches the set threshold, an early warning signal is automatically generated, and an optimization algorithm is used to generate a scheduling plan for maintenance tasks based on the equipment health status, fault priority, and availability of maintenance resources to reduce equipment downtime.

2. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is implemented, characterized in that: The protocol parsing step parses register data, device status data and sensor data in the Modbus protocol, OPC UA protocol or PROFIBUS protocol, wherein the data includes the working status, load, temperature and vibration parameters of the device.

3. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is implemented, characterized in that: The data preprocessing step uses a Kalman filter or a wavelet transform method to denoise the collected data, supplement missing values, and correct outliers. The processed data is used for subsequent feature extraction and model training.

4. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is implemented, characterized in that: The feature extraction in step 4 uses a sliding window technique to segment the device time series data, extract the trend, periodicity, and amplitude features during the operation of the device, and standardize the extracted features to form a feature vector for training the model.

5. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is implemented, characterized in that: The anomaly detection step uses an autoencoder or a long short-term memory network model to perform anomaly detection on the device based on the historical operation data and real-time data of the device and output an abnormal signal.

6. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is characterized in that: The fault prediction step uses a support vector regression model, a regression analysis model or a long short-term memory network model to predict the remaining service life of the equipment based on historical data and real-time data, and calculates the probability of equipment failure.

7. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is characterized in that: The early warning and dispatching steps include: Step 7.1: When the operating state or fault prediction value of the equipment exceeds a preset threshold, a warning signal is generated; Step 7.2: Based on the health status of the equipment, fault priority, and availability of maintenance resources, a heuristic algorithm is used to generate a scheduling plan for maintenance tasks and determine the priority and implementation time of maintenance.

8. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is characterized in that: The maintenance task scheduling step uses a genetic algorithm, a particle swarm optimization algorithm or other heuristic algorithms to schedule the maintenance tasks to ensure that the maintenance tasks of the equipment are performed in order of priority and to reduce equipment downtime.

9. According to claim 1, a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis is characterized in that: The data collection step is implemented through an edge computing gateway, which performs preliminary processing on the collected device data, including denoising, compression and caching, and the processed data is uploaded to the cloud platform via the network for subsequent analysis.

10. The method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis according to claim 1 is characterized in that: The method also includes the step of archiving equipment maintenance records, wherein the equipment maintenance records include historical fault records, maintenance records and detection data of the equipment and are used to optimize the anomaly detection and fault prediction model.

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