CN118859887A - Automated monitoring platform and method compatible with multiple DCS systems - Google Patents

Abstract

The present application discloses an automated monitoring platform and method compatible with multiple DCS systems, the platform comprising: multiple DCS systems, industrial software and manufacturing equipment; the industrial software is used to adapt the preset standard communication protocol for the communication software of each DCS system; the DCS system collects the real-time data of the manufacturing equipment, performs communication verification processing on the real-time data, obtains the equipment operation data, performs real-time early warning and function removal on the manufacturing equipment according to the equipment operation data, and sends the equipment operation data to the industrial software through the adapted preset standard communication protocol; the industrial software receives the equipment operation data sent by each DCS system through the adapted preset standard communication protocol, and monitors the operating status of the manufacturing equipment according to the equipment operation data. Therefore, by adopting the embodiments of the present application, obstacles to system integration and data exchange can be avoided, and potential risks of manufacturing equipment can be discovered in a timely manner, thereby improving production efficiency and production safety.

Description

Automated monitoring platform and method compatible with multiple DCS systems

Technical Field

The present application relates to the field of computer technology, and in particular to an automated monitoring platform and method compatible with multiple DCS systems, which can be used for a data security exchange and sharing platform.

Background Art

In modern industrial production, DCS (Distributed Control System) is widely used to improve production efficiency, ensure production safety and optimize resource allocation. These systems are responsible for monitoring and controlling various links in the industrial process, including but not limited to chemical, petroleum, electricity, metallurgy and pharmaceutical industries. With the development of Industry 4.0 and intelligent manufacturing, higher requirements are placed on the integration, data processing capabilities and real-time response speed of DCS systems.

At present, DCS systems from different manufacturers may use different communication protocols and data formats, resulting in obstacles in system integration and data exchange. At the same time, the current DCS system cannot detect potential risks of manufacturing equipment in a timely manner, thus reducing production efficiency and production safety.

Summary of the invention

The embodiments of the present application provide an automated monitoring platform compatible with multiple DCS systems. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key/important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

In a first aspect, an embodiment of the present application provides an automated monitoring platform compatible with multiple DCS systems, the platform comprising:

Multiple DCS systems, industrial software and manufacturing equipment, the industrial software includes a software basic configuration center; wherein, multiple DCS systems are respectively connected to the industrial software and the manufacturing equipment in communication; wherein,

Industrial software is used to adapt the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center to eliminate the communication differences between the communication software of different DCS systems;

The DCS system is used to collect real-time data of manufacturing equipment, perform communication verification on the real-time data, obtain equipment operation data, and provide real-time early warning and function removal for the manufacturing equipment based on the equipment operation data, and send the equipment operation data to the industrial software through the adapted preset standard communication protocol;

The industrial software is also used to receive the equipment operation data sent by each DCS system through an adapted preset standard communication protocol, and monitor the operating status of the manufacturing equipment based on the equipment operation data.

Optionally, based on the equipment operation data, real-time early warning and function removal of manufacturing equipment can be carried out, including:

Preprocess equipment operation data to obtain a standardized equipment operation parameter sequence;

According to the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence, the manufacturing equipment is early-warning processed to obtain the early-warning result. The preset equipment operation parameter sequence is determined based on the historical operation data.

Based on the early warning results, standardized equipment operating parameters with early warning levels greater than or equal to a preset threshold in the standardized equipment operating parameter sequence are screened out to obtain multiple target parameters;

From the manufacturing equipment, identify the equipment function node corresponding to each target parameter;

The function of the equipment function node corresponding to each target parameter is cut off.

Optionally, each standardized device operating parameter in the standardized device operating parameter sequence carries attribute information, and each preset device operating parameter in the preset device operating parameter sequence carries attribute information;

According to the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence, the manufacturing equipment is early-warning processed to obtain early-warning results, including:

According to the attribute information, the standardized device operating parameter sequence is paired with the preset device operating parameter sequence to obtain the standardized device operating parameters and the preset device operating parameters under the same attribute information;

Compare the standardized device operating parameters and the preset device operating parameters under the same attribute information of each group of successful pairing to determine the absolute value of the difference between the standardized device operating parameters and the preset device operating parameters under the same attribute information;

Based on the size of each absolute value, the corresponding warning level is obtained from the pre-established mapping relationship between the absolute value and the warning level, and the warning level of each standardized equipment operating parameter is obtained;

The warning level of each standardized equipment operating parameter is used as the warning result for the manufacturing equipment.

Optionally, according to the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence, early warning processing is performed on the manufacturing equipment to obtain early warning results, including:

The standardized equipment operation parameter sequence and the preset equipment operation parameter sequence are input into the pre-trained early warning model to perform early warning processing on the manufacturing equipment; the pre-trained early warning model is obtained by machine learning based on the historical equipment operation parameter set and the network parameters to be processed, and the network parameters to be processed are obtained by analyzing the correlation between all network parameters in the early warning model and the feature sequence, and the feature sequence is obtained based on the historical equipment operation parameter set;

Output the warning level corresponding to each standardized equipment operating parameter in the standardized equipment operating parameter sequence;

The warning level corresponding to each standardized equipment operating parameter is used as the warning result for the manufacturing equipment.

Optionally, generate a pre-trained warning model by following these steps, including:

Collect equipment operating parameters of manufacturing equipment under normal operation and abnormal operation within a preset time period to obtain a historical equipment operating parameter set;

Based on the preset equipment operation parameter sequence, each parameter in the historical equipment operation parameter set is labeled with a level label used to characterize the warning level, and a historical equipment operation parameter set carrying the label is obtained;

Use neural networks to create early warning models;

Perform feature conversion on the historical equipment operation parameter set carrying the tag to obtain a feature sequence;

Traverse all network parameters in the early warning model;

Analyze the correlation between the characteristic sequence and all network parameters to determine the network parameters to be processed that are related to the characteristic sequence among all network parameters;

According to the historical equipment operation parameter set and the network parameters to be processed, the early warning model is trained to obtain a pre-trained early warning model.

Optionally, each historical device operating parameter in the historical device operating parameter set carries attribute information, and each preset device operating parameter in the preset device operating parameter sequence carries attribute information;

Based on the preset equipment operation parameter sequence, each parameter in the historical equipment operation parameter set is marked with a level label used to characterize the warning level, including:

According to the attribute information, the historical device operating parameter set is paired with the preset device operating parameter sequence to obtain the historical device operating parameters and the preset device operating parameters under the same attribute information;

Compare each set of successfully paired historical device operating parameters and preset device operating parameters under the same attribute information to determine the absolute value of the difference between the historical device operating parameters and the preset device operating parameters under the same attribute information;

Based on the size of each absolute value, the corresponding warning level is obtained from the pre-established mapping relationship between the absolute value and the warning level, and the warning level label of each historical equipment operation parameter is obtained;

The warning level label of each historical equipment operation parameter is adopted, and each parameter in the historical equipment operation parameter set is marked with a level label used to characterize the warning level.

Optionally, an early warning model is trained according to the historical device operation parameter set and the network parameters to be processed to obtain a pre-trained early warning model, including:

According to the network parameters to be processed, a first matrix and a second matrix are constructed, wherein the first matrix includes parameters related to columns of the original weight matrix in the neural network, and the second matrix includes parameters related to rows of the original weight matrix in the neural network;

According to the historical equipment operation parameter set, the matrix parameters of the first matrix and the second matrix are trained to obtain the loss value of the early warning model;

When the loss value of the early warning model reaches the minimum, a pre-trained early warning model is generated; alternatively, when the loss value of the early warning model does not reach the minimum, the loss value is propagated to update the model parameters of the early warning model, and the steps of training the matrix parameters of the first matrix and the second matrix according to the historical device operation parameter set are continued.

Optionally, the loss value calculation function is:

Where L is the loss value, is the number of samples of the historical device running parameter set, is the loss value of a single historical device operating parameter, , is the forward propagation function, For each historical device operating parameter in the historical device operating parameter set, is the first matrix, is the second matrix, is the original weight matrix of the neural network, is the predicted value, It is a level label used to characterize the warning level of each historical device operating parameter in the historical device operating parameter set.

Optionally, the operating status of the manufacturing equipment is monitored based on the equipment operating data, including:

Pre-process equipment operation data;

Aggregate the preprocessed device operation data to obtain aggregated device operation data;

Use preset chart visualization components to visualize the aggregated equipment operation data to obtain a status visualization trend chart of the equipment status of the manufacturing equipment;

Display status visualization trend charts to monitor the operating status of manufacturing equipment.

In a second aspect, an automated monitoring method compatible with multiple DCS systems includes:

Industrial software uses the software basic configuration center to adapt the preset standard communication protocol for the communication software of each DCS system to eliminate the communication differences between the communication software of different DCS systems;

The DCS system collects real-time data of manufacturing equipment, performs communication verification on the real-time data, obtains equipment operation data, and performs real-time early warning and function removal on the manufacturing equipment based on the equipment operation data, and sends the equipment operation data to the industrial software through the adapted preset standard communication protocol;

The industrial software receives the equipment operation data sent by each DCS system through an adapted preset standard communication protocol, and monitors the operating status of the manufacturing equipment based on the equipment operation data.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

FIG1 is a schematic diagram of the system structure of an automated monitoring platform compatible with multiple DCS systems provided in an embodiment of the present application;

FIG. 2 is a protocol data representation diagram of a preset standard communication protocol adapted for each DCS system configuration provided by an embodiment of the present application;

FIG3 is a schematic diagram of data representation of real-time data collected by a DCS system through sensors and instruments controlled by the DCS system according to an embodiment of the present application;

FIG4 is a schematic diagram of log data representation of a real-time early warning for manufacturing equipment and after action removal provided by an embodiment of the present application;

FIG5 is a schematic diagram of an early warning message received by a relevant person provided in an embodiment of the present application;

FIG6 is a schematic diagram of a method flow of a warning model training method provided in an embodiment of the present application;

FIG7 is a flow chart of an automated monitoring method compatible with multiple DCS systems provided in an embodiment of the present application;

FIG8 is a flow chart of an automated monitoring method compatible with multiple DCS systems provided in an embodiment of the present application;

FIG9 is a schematic diagram of the structure of an automatic monitoring device compatible with multiple DCS systems provided in an embodiment of the present application;

FIG. 10 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

DETAILED DESCRIPTION

The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of systems and methods consistent with some aspects of the present application as detailed in the attached claims.

In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And/or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character "/" generally indicates that the objects associated before and after are in an "or" relationship.

The present application provides an automated monitoring platform and method compatible with multiple DCS systems to solve the problems existing in the above-mentioned related technical issues. In the embodiments of the present application, on the one hand, the industrial software adapts the preset standard communication protocol to the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that the data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects the real-time data of the manufacturing equipment, performs communication verification processing, obtains the equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and early warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety. The following uses an exemplary embodiment for detailed description.

Please refer to Figure 1, which is a system structure diagram of an automation monitoring platform compatible with multiple DCS systems provided by an embodiment of the present application, the system including: multiple DCS systems, industrial software and manufacturing equipment, the industrial software including a software basic configuration center; wherein the multiple DCS systems are respectively connected to the industrial software and the manufacturing equipment for communication; wherein the industrial software is used to adapt the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center to eliminate the communication differences between the communication software of different DCS systems; the DCS system is used to collect real-time data of the manufacturing equipment, perform communication verification processing on the real-time data, obtain equipment operation data, and perform real-time early warning and action removal on the manufacturing equipment based on the equipment operation data, and send the equipment operation data to the industrial software through the adapted preset standard communication protocol; the industrial software is also used to receive the equipment operation data sent by each DCS system through the adapted preset standard communication protocol, and monitor the operation status of the manufacturing equipment based on the equipment operation data.

Among them, industrial software refers to software systems specially designed for monitoring and managing industrial processes, which usually include data acquisition, analysis, visualization and alarm functions. The software basic configuration center is used to manage and configure the settings of other software components, especially the adaptation and parameter configuration of communication protocols. The DCS system is a computer control system used to control factories or production processes. It consists of multiple control nodes, each of which is responsible for monitoring and controlling specific equipment or processes. The preset standard communication protocol is a set of predefined rules and standards for transmitting data between devices and systems. These protocols ensure that devices from different manufacturers can communicate with each other. The preset standard communication protocols include Modbus protocol, Profibus protocol, and OPC protocol.

The Modbus protocol is a serial communication protocol that is widely used in the field of industrial control. It is based on a master-slave structure, where the master device can initiate requests and the slave device responds to these requests. It supports multiple communication methods such as ASCII, RTU and TCP/IP. Modbus has been widely used in industrial automation due to its simplicity, openness and easy implementation.

Profibus protocol is a fieldbus communication protocol used in the field of industrial automation. It supports high-speed data transmission and real-time communication, and is suitable for scenarios with high requirements for communication speed and real-time performance. As an open standard, Profibus allows devices from different manufacturers to achieve interoperability.

The OPC protocol, whose full name is Object Linking and Embedding (OLE) for Process Control, is an important communication protocol in the field of industrial automation. Since its birth, it has provided great convenience for the integration of industrial control systems.

Among them, real-time data refers to data collected instantly when an event occurs, which is essential for monitoring and controlling the manufacturing process. Communication verification processing is a data verification process that ensures that the transmitted data is accurate and has not been tampered with or damaged during transmission. Real-time early warning is an automatic monitoring system that can immediately issue a warning when a potential problem or anomaly is detected. Action cutoff is a safety measure that automatically cuts off or adjusts the control signal when a serious anomaly is detected to prevent accidents. Equipment operation data describes the operating status and performance of the equipment within a specific time.

In some embodiments, the factory has multiple production units, each of which is controlled by a different DCS system, which comes from different suppliers, so there are communication differences between them. The factory IT department works with the production department to determine the key equipment parameters that need to be monitored, such as temperature, pressure, flow, etc. A SCADA system that is compatible with multiple DCS systems is selected as industrial software, and the system has the function of a software basic configuration center. In the software basic configuration center of the SCADA system, an adaptive preset standard communication protocol, such as OPC UA or Modbus TCP, is configured for each DCS system. Engineers of each DCS system update their communication settings according to the configuration of the SCADA system to ensure that they can communicate with the SCADA system. The DCS system collects real-time data through the sensors and instruments it controls, and performs communication verification processing on the data, such as CRC verification, to ensure the integrity and accuracy of the data. Real-time warning logic is set in the DCS system, such as triggering an early warning when the temperature exceeds the set threshold. At the same time, the action removal logic is configured, such as automatically closing the relevant valves when a serious abnormality is detected. The data that has been verified by communication is sent to the SCADA system through the adaptive preset standard communication protocol. The SCADA system receives the equipment operation data from each DCS system for further processing and analysis for visual display. When the SCADA system detects any warning signal, the system will automatically notify relevant personnel, such as sending text messages or emails.

Specifically, the preset standard communication protocol adapted for each DCS system configuration is shown in Figure 2, and the DCS system identifier is a unique identifier for each DCS system. The DCS system name is the name or description of the DCS system. The vendor is the name of the vendor that provides the DCS system. The communication protocol type is the communication protocol used by the DCS system, such as OPC UA, Modbus TCP, PROFINET, etc. The communication port is the network port used by the communication protocol. The IP address is the IP address of the DCS system, which is used for network communication. The device identification prefix is the identification prefix of the device in the DCS system, which helps to distinguish devices in different DCS systems. Communication parameters are additional parameters required for specific communication protocols, such as timeouts, connection strings, etc. Remarks are any special configuration requirements or precautions.

Specifically, the DCS system collects real-time data through the sensors and instruments it controls. For example, as shown in Figure 3, the measurement parameter is the specific physical quantity measured by the sensor or instrument, such as temperature, pressure, flow, etc. The data type is the type of data, usually a real number, representing a continuous measurement value. The unit is the unit of the measurement parameter. The normal range is the normal working range of the parameter, and exceeding this range may indicate an abnormality. The data update frequency is the frequency of sensor or instrument data update, usually in seconds or milliseconds. The DCS system identifier is the unique identifier of the DCS system that controls the sensor or instrument.

Specifically, the log data table after real-time early warning and action removal of manufacturing equipment is shown in Figure 4, which is a detailed description of the early warning event. The early warning condition is the specific condition that triggers the early warning, such as temperature, pressure, flow or vibration exceeding or falling below the preset threshold. The early warning action is the response action of the system after detecting the early warning condition, such as sending an alarm signal, sound and light alarm, displaying a warning message, etc. The action removal action is a further measure taken to prevent the problem from expanding, such as shutting down the heater, starting the pressure reducing valve, reducing the feed rate, etc. The trigger time is the specific time when the early warning is triggered. The DCS system identifier is the unique identifier of the DCS system that triggers the early warning.

Specifically, when the SCADA system detects any warning signal, the warning information received by the relevant personnel is shown in Figure 5. Figure 5 includes the warning information of reactor overheating, high pressure and low flow. After the user clicks to confirm, the warning information can be cleared.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

In some embodiments of the present application, the specific process of real-time early warning and action removal of manufacturing equipment based on equipment operation data includes: preprocessing equipment operation data to obtain a standardized equipment operation parameter sequence; performing early warning processing on the manufacturing equipment according to the standardized equipment operation parameter sequence and a preset equipment operation parameter sequence to obtain an early warning result, wherein the preset equipment operation parameter sequence is determined based on historical operation data; based on the early warning result, screening out standardized equipment operation parameters in the standardized equipment operation parameter sequence whose warning level is greater than or equal to a preset threshold value to obtain multiple target parameters; identifying the equipment function node corresponding to each target parameter from the manufacturing equipment; and performing action removal on the equipment function node corresponding to each target parameter.

Among them, preprocessing refers to the steps of processing the original equipment operation data, such as cleaning, denoising, normalization, etc., to facilitate subsequent analysis. Standardized equipment operation parameter sequence refers to the set of equipment operation parameters converted into a unified format or unit after preprocessing, which is helpful for cross-equipment or cross-system comparison and analysis. Preset equipment operation parameter sequence refers to the standard or expected value sequence of equipment operation parameters predefined based on historical operation data and experience. Early warning processing refers to the use of analytical methods (such as statistical analysis, machine learning, etc.) to evaluate equipment operation parameters to predict potential equipment problems or abnormalities. Early warning results refer to the conclusions obtained after early warning processing, usually including early warning levels or abnormal indicators, which are used to guide subsequent decisions and actions. Early warning levels are classifications of the severity of early warnings, and different levels are usually set based on risk assessment. The preset threshold is a standard or limit value used to determine whether the early warning level has reached the level where action needs to be taken. Target parameters refer to those parameters whose early warning levels are greater than or equal to the preset threshold, which indicate possible problems or risks. Equipment function nodes refer to components or parts with specific functions in manufacturing equipment, such as sensors, actuators, or control units. Action removal means taking automatic or manual measures to stop or change the operation of a specific equipment function node when a potential risk or failure is detected to prevent the problem from expanding.

In an embodiment of the present application, by preprocessing the equipment operation data and standardizing it, it can be more accurately compared with the preset normal operating parameters, so as to perform effective early warning processing. This method can quickly identify those target parameters with higher warning levels based on thresholds determined by historical data. Furthermore, by identifying the equipment function nodes corresponding to these target parameters and timely removing the effects, potential equipment failures can be prevented, unexpected downtime can be reduced, and production efficiency and safety can be improved. This proactive prevention and rapid response mechanism significantly improves the reliability of manufacturing equipment and the timeliness of maintenance, ensuring the continuity and stability of the production process.

Among them, each standardized device operating parameter in the standardized device operating parameter sequence carries attribute information, and each preset device operating parameter in the preset device operating parameter sequence carries attribute information.

In some embodiments of the present application, early warning processing is performed on manufacturing equipment based on a standardized equipment operating parameter sequence and a preset equipment operating parameter sequence, and the specific process of obtaining the early warning result includes: pairing the standardized equipment operating parameter sequence with the preset equipment operating parameter sequence according to the attribute information to obtain the standardized equipment operating parameters and the preset equipment operating parameters under the same attribute information; comparing each group of successfully paired standardized equipment operating parameters and the preset equipment operating parameters under the same attribute information to determine the absolute value of the difference between the standardized equipment operating parameters and the preset equipment operating parameters under the same attribute information; based on the size of each absolute value, obtaining the corresponding early warning level from the pre-established mapping relationship between the absolute value and the early warning level to obtain the early warning level of each standardized equipment operating parameter; using the early warning level of each standardized equipment operating parameter as the early warning result for early warning of the manufacturing equipment.

Among them, attribute information refers to specific characteristics of equipment operating parameters, such as the unique tag of a temperature sensor. A standardized equipment operating parameter sequence refers to a set of equipment operating parameters that have been preprocessed and standardized and have been converted into a unified format or unit. A preset equipment operating parameter sequence refers to a set of expected equipment operating parameters set based on historical data, design specifications or experience. Pairing refers to the process of matching standardized equipment operating parameters with preset equipment operating parameters that have the same attribute information. The absolute value of the difference refers to the difference between the standardized equipment operating parameters and the preset equipment operating parameters, taking the absolute value to ignore the direction of the difference. The warning level is the risk or warning level determined based on the difference between the actual value of the equipment operating parameter and the preset value. A mapping relationship refers to a pre-established rule or standard for converting the absolute value of the difference into a corresponding warning level.

In an embodiment of the present application, by pairing and comparing the standardized equipment operating parameters with the preset parameters, the deviation between the equipment operating status and the expectation can be accurately identified. By calculating the absolute value of the difference and referring to the predefined mapping relationship, a warning level can be assigned to each parameter, which provides a quantitative risk assessment for the operating status of the manufacturing equipment. This method makes the warning more refined and personalized, and can provide customized warning results for different equipment parameters and operating conditions. Such an early warning system not only improves the ability to identify potential equipment problems, but also enhances the real-time monitoring and response capabilities of the equipment operating status, thereby effectively preventing equipment failures, reducing production interruptions, ensuring the continuity and stability of the production process, and ultimately improving overall production efficiency and safety.

In some embodiments of the present application, early warning processing is performed on manufacturing equipment based on a standardized equipment operating parameter sequence and a preset equipment operating parameter sequence, and the specific process of obtaining the early warning result includes: inputting the standardized equipment operating parameter sequence and the preset equipment operating parameter sequence into a pre-trained early warning model to perform early warning processing on the manufacturing equipment; the pre-trained early warning model is obtained by machine learning based on a historical equipment operating parameter set and network parameters to be processed, and the network parameters to be processed are obtained by analyzing the correlation between all network parameters in the early warning model and the feature sequence, and the feature sequence is obtained based on the historical equipment operating parameter set; outputting the early warning level corresponding to each standardized equipment operating parameter in the standardized equipment operating parameter sequence; and using the early warning level corresponding to each standardized equipment operating parameter as the early warning result for the manufacturing equipment.

Among them, the early warning model refers to a mathematical model used to predict and identify potential problems or anomalies, which is usually trained by analyzing historical data. Machine learning is a data analysis technology that uses algorithms to enable computer systems to use data to continuously improve performance without explicit programming. The historical equipment operation parameter set refers to the data set collected from the past operation of the equipment, which is used to train the early warning model. The pending network parameters refer to the network parameters that need to be analyzed and selected when building the early warning model. The correlation analysis of these parameters with the feature sequence is helpful for model training and optimization. The feature sequence refers to the key information sequence extracted from the historical equipment operation parameter set for training the model. The early warning level refers to the level of classification of the risk or abnormality of the equipment operation status according to the output of the early warning model.

In an embodiment of the present application, by inputting a standardized sequence of equipment operating parameters and preset expected values into an early warning model obtained through machine learning training based on historical data, early warning processing can be effectively performed on manufacturing equipment. This method utilizes the correlation analysis between the network parameters to be processed and the feature sequence to optimize the predictive ability of the model. The early warning level corresponding to each parameter output provides an intuitive risk assessment for the equipment operating status, allowing operators to quickly identify and respond to potential abnormal situations. Such an early warning system not only improves the accuracy and response speed of equipment monitoring, but also helps prevent equipment failures and production interruptions, thereby ensuring the continuity and stability of the production process, and ultimately improving production efficiency and safety.

In some embodiments of the present application, a pre-trained early warning model is generated according to the following steps, including: collecting equipment operating parameters of manufacturing equipment under normal operation and abnormal operation within a preset time period to obtain a historical equipment operating parameter set; based on a preset equipment operating parameter sequence, labeling each parameter in the historical equipment operating parameter set with a level label used to characterize the warning level to obtain a historical equipment operating parameter set carrying the label; using a neural network to create an early warning model; performing feature conversion on the historical equipment operating parameter set carrying the label to obtain a feature sequence; traversing all network parameters in the early warning model; analyzing the correlation between the feature sequence and all network parameters to determine the network parameters to be processed that are related to the feature sequence among all network parameters; training the early warning model according to the historical equipment operating parameter set and the network parameters to be processed to obtain a pre-trained early warning model.

Among them, manufacturing equipment refers to machinery or systems used to produce products in an industrial environment. Equipment operating parameters refer to variables that describe the operating status of equipment, such as temperature, pressure, speed, etc. The historical equipment operating parameter set refers to a data set of equipment operating parameters collected over a period of time, including data during normal operation and abnormal operation. Level labels refer to tags used to indicate warning levels, which are usually used in the training of machine learning models to indicate the risk level of data points. The historical equipment operating parameter set with labels refers to a historical equipment operating parameter set with warning level labels attached to each data point, which is used to train models to identify warning signals of different levels. Feature conversion is part of data preprocessing, which converts raw data into a form that can be processed more efficiently by the model, such as converting non-numerical data into numerical data. Feature sequence refers to an ordered set of features obtained after feature conversion, which is used for model training and prediction. Network parameters refer to parameters in a neural network model, including weights and biases, which are optimized during the training process to improve model performance. To-be-processed network parameters refer to network parameters that need special attention and processing during model training, and these parameters have a strong correlation with feature sequences.

In an embodiment of the present application, by collecting the operating parameters of manufacturing equipment under normal and abnormal operating conditions and constructing a historical equipment operating parameter set, warning level labels can be labeled for these parameters, thereby creating a labeled data set. Using these data, a neural network early warning model can be trained, which can learn the difference between normal and abnormal operating modes through feature conversion and optimization of network parameters. By analyzing the correlation between feature sequences and network parameters, the model can identify key network parameters to be processed, which are crucial for early warning analysis. Finally, a pre-trained early warning model is obtained, which can accurately predict possible abnormalities of the equipment, thereby issuing early warnings, helping the maintenance team take preventive measures, reduce equipment failures and production interruptions, and improve production efficiency and safety. This method makes equipment maintenance more proactive and efficient, greatly improving the reliability and economic benefits of industrial production.

Among them, each historical device operating parameter in the historical device operating parameter set carries attribute information, and each preset device operating parameter in the preset device operating parameter sequence carries attribute information.

In some embodiments of the present application, based on a preset device operating parameter sequence, a specific process of marking each parameter in a historical device operating parameter set with a level label used to characterize a warning level includes: pairing the historical device operating parameter set with the preset device operating parameter sequence according to attribute information to obtain historical device operating parameters and preset device operating parameters under the same attribute information; comparing each group of successfully paired historical device operating parameters and preset device operating parameters under the same attribute information to determine the absolute value of the difference between the historical device operating parameters and the preset device operating parameters under the same attribute information; based on the size of each absolute value, obtaining the corresponding warning level from a pre-established mapping relationship between the absolute value and the warning level, and obtaining a warning level label for each historical device operating parameter; using the warning level label of each historical device operating parameter, marking each parameter in the historical device operating parameter set with a level label used to characterize the warning level.

In some embodiments of the present application, the early warning model is trained according to the historical device operating parameter set and the network parameters to be processed, and the specific process of obtaining the pre-trained early warning model includes: constructing a first matrix and a second matrix according to the network parameters to be processed, the first matrix including column-related parameters of the original weight matrix in the neural network, and the second matrix including row-related parameters of the original weight matrix in the neural network; training the matrix parameters of the first matrix and the second matrix according to the historical device operating parameter set to obtain the loss value of the early warning model; when the loss value of the early warning model reaches the minimum, generating the pre-trained early warning model; or, when the loss value of the early warning model does not reach the minimum, propagating the loss value to update the model parameters of the early warning model, and continuing to execute the step of training the matrix parameters of the first matrix and the second matrix according to the historical device operating parameter set.

Specifically, the calculation function of the loss value is:

Where L is the loss value, is the number of samples of the historical device running parameter set, is the loss value of a single historical device operating parameter, , is the forward propagation function, For each historical device operating parameter in the historical device operating parameter set, is the first matrix, is the second matrix, is the original weight matrix of the neural network, is the predicted value, It is a level label used to characterize the warning level of each historical device operating parameter in the historical device operating parameter set.

In some embodiments of the present application, the specific process of monitoring the operating status of manufacturing equipment based on equipment operating data includes: preprocessing equipment operating data; aggregating the preprocessed equipment operating data to obtain aggregated equipment operating data; using a preset chart visualization component to visualize the aggregated equipment operating data to obtain a state visualization trend chart of the equipment state of the manufacturing equipment; and displaying the state visualization trend chart to monitor the operating status of the manufacturing equipment.

Aggregation refers to organizing and merging pre-processed data according to specific rules or standards, such as by time series, equipment type, etc. Aggregated equipment operation data refers to a data set that has been aggregated, which integrates multiple data points together for overall analysis and trend observation. Preset chart visualization components refer to pre-defined chart templates or tools for displaying data in a graphical manner, such as line charts, bar charts, pie charts, etc. Visualization processing refers to the use of charts, images or other visual elements to represent data, making the data easier to understand and analyze. Status visualization trend charts refer to charts that show the changing trend of equipment operation status, such as the temperature change curve over time, which is used to intuitively display the equipment status. Display refers to presenting the processed and analyzed data or charts to users through the user interface for easy viewing and analysis.

In the embodiment of the present application, by pre-processing the equipment operation data, the quality and consistency of the data can be ensured, laying a solid foundation for subsequent analysis. Furthermore, aggregating these data allows us to observe the overall situation of equipment operation from a macro perspective. By visualizing the aggregated data using a preset chart visualization component, an intuitive state visualization trend chart can be obtained, which can clearly show the changing trend of key indicators of equipment operation over time. By displaying these trend charts, operators can monitor the operating status of manufacturing equipment in real time, detect abnormalities or potential problems in a timely manner, and take corresponding measures to prevent equipment failures, reduce production losses, and improve production efficiency and equipment utilization.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

Please refer to Figure 6, which is a schematic diagram of a method flow of a warning model training method provided in an embodiment of the present application. As shown in Figure 6, the detection method in the embodiment of the present application may include the following steps:

S101, collecting equipment operating parameters of manufacturing equipment under normal operation and abnormal operation within a preset time period to obtain a historical equipment operating parameter set;

S102, based on a preset equipment operation parameter sequence, marking each parameter in a historical equipment operation parameter set with a level label used to characterize a warning level, and obtaining a historical equipment operation parameter set carrying the label;

S103, using neural network to create an early warning model;

S104, performing feature conversion on the historical equipment operation parameter set carrying the tag to obtain a feature sequence;

S105, traversing all network parameters in the early warning model;

S106, analyzing the correlation between the characteristic sequence and all network parameters to determine the network parameters to be processed that are related to the characteristic sequence among all network parameters;

S107, training an early warning model according to the historical device operation parameter set and the network parameters to be processed to obtain a pre-trained early warning model.

In an embodiment of the present application, by collecting the operating parameters of manufacturing equipment under normal and abnormal operating conditions and constructing a historical equipment operating parameter set, warning level labels can be labeled for these parameters, thereby creating a labeled data set. Using these data, a neural network early warning model can be trained, which can learn the difference between normal and abnormal operating modes through feature conversion and optimization of network parameters. By analyzing the correlation between feature sequences and network parameters, the model can identify key network parameters to be processed, which are crucial for early warning analysis. Finally, a pre-trained early warning model is obtained, which can accurately predict possible abnormalities of the equipment, thereby issuing early warnings, helping the maintenance team take preventive measures, reduce equipment failures and production interruptions, and improve production efficiency and safety. This method makes equipment maintenance more proactive and efficient, greatly improving the reliability and economic benefits of industrial production.

Please refer to Figure 7, which is a flow chart of an automated monitoring method compatible with multiple DCS systems provided in an embodiment of the present application. As shown in Figure 7, the detection method in the embodiment of the present application may include the following steps:

S201, the industrial software adapts the preset standard communication protocol to the communication software of each DCS system through the software basic configuration center to eliminate the communication differences between the communication software of different DCS systems;

S202, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing on the real-time data, obtains equipment operation data, and performs real-time early warning and function removal on the manufacturing equipment according to the equipment operation data, and sends the equipment operation data to the industrial software through an adapted preset standard communication protocol;

S203, the industrial software receives the equipment operation data sent by each DCS system through an adapted preset standard communication protocol, and monitors the operation status of the manufacturing equipment based on the equipment operation data.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

Please refer to Figure 8, which is a flow chart of an automated monitoring method compatible with multiple DCS systems for an embodiment of the present application, which is applied to a server. As shown in Figure 8, the detection method of the embodiment of the present application may include the following steps:

S301, collecting real-time data of manufacturing equipment;

S302, performing communication verification processing on real-time data to obtain equipment operation data;

S303, based on the equipment operation data, provide real-time warning and function removal for the manufacturing equipment, and send the equipment operation data to the industrial software through an adapted preset standard communication protocol.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

Please refer to FIG9, which shows a schematic diagram of the structure of an automatic monitoring device compatible with multiple DCS systems provided by an exemplary embodiment of the present application. The automatic monitoring device compatible with multiple DCS systems can be implemented as all or part of an electronic device through software, hardware or a combination of both. The device 1 includes a collection module 10, a verification processing module 20, and an early warning removal module 30.

A collection module 10, used to collect real-time data of manufacturing equipment;

The verification processing module 20 is used to perform communication verification processing on real-time data to obtain equipment operation data;

The early warning and removal module 30 is used to provide real-time early warning and function removal for manufacturing equipment according to equipment operation data, and to send the equipment operation data to the industrial software through an adapted preset standard communication protocol.

It should be noted that the automatic monitoring device compatible with multiple DCS systems provided in the above embodiment only uses the division of the above functional modules as an example when executing the automatic monitoring method compatible with multiple DCS systems. In actual applications, the above functional allocation can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the automatic monitoring device compatible with multiple DCS systems provided in the above embodiment and the automatic monitoring method compatible with multiple DCS systems belong to the same concept, and the embodiment and implementation process thereof are detailed in the method embodiment, which will not be repeated here.

The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

The present application also provides a computer-readable medium having program instructions stored thereon, and when the program instructions are executed by a processor, the automated monitoring method compatible with multiple DCS systems provided by the above-mentioned various method embodiments is implemented.

The present application also provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the automated monitoring method compatible with multiple DCS systems of the above-mentioned various method embodiments.

Please refer to FIG10 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in FIG10 , the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

The communication bus 1002 is used to realize the connection and communication between these components.

The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts of the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be at least one storage system located away from the aforementioned processor 1001. As shown in Figure 10, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an automation monitoring application compatible with multiple DCS systems.

In the electronic device 1000 shown in FIG10 , the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call the automatic monitoring application compatible with multiple DCS systems stored in the memory 1005, and specifically perform the following operations:

Collect real-time data from manufacturing equipment;

Perform communication verification on real-time data to obtain equipment operation data;

Based on the equipment operation data, real-time early warning and function removal are carried out on the manufacturing equipment, and the equipment operation data is sent to the industrial software through the adapted preset standard communication protocol.

In the embodiment of the present application, on the one hand, the industrial software adapts the preset standard communication protocol for the communication software of each DCS system through the software basic configuration center. By adapting the preset standard communication protocol, the communication differences between different DCS systems are eliminated, and the seamless integration and exchange of data are realized, so that data can flow smoothly between systems, and the automation and information level of the entire manufacturing process is improved. On the other hand, the DCS system collects real-time data of manufacturing equipment, performs communication verification processing, obtains equipment operation data, and performs real-time warning and action removal based on these data. Through the real-time monitoring and warning system, the abnormal state of the equipment can be discovered in time, and the action removal mechanism can be used to prevent the risk from further expanding, reducing downtime and production interruption, thereby improving production efficiency and production safety.

Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program compatible with the automatic monitoring of multiple DCS systems can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program compatible with the automatic monitoring of multiple DCS systems can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program compatible with the automatic monitoring of multiple DCS systems can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium compatible with the automatic monitoring of multiple DCS systems can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims (10)

1. An automated monitoring platform compatible with multiple DCS systems, characterized in that the platform comprises: Multiple DCS systems, industrial software and manufacturing equipment, wherein the industrial software includes a software basic configuration center; wherein the multiple DCS systems are respectively connected to the industrial software and the manufacturing equipment in communication; wherein, The industrial software is used to adapt the preset standard communication protocol to the communication software of each DCS system through the software basic configuration center to eliminate the communication differences between the communication software of different DCS systems; The DCS system is used to collect real-time data of the manufacturing equipment, perform communication verification processing on the real-time data to obtain equipment operation data, and perform real-time early warning and function removal on the manufacturing equipment according to the equipment operation data, and send the equipment operation data to the industrial software through the adapted preset standard communication protocol; The industrial software is also used to receive the equipment operation data sent by each DCS system through the adapted preset standard communication protocol, and monitor the operation status of the manufacturing equipment based on the equipment operation data. 2. The platform according to claim 1, characterized in that the real-time early warning and function removal of the manufacturing equipment according to the equipment operation data comprises: Preprocessing the equipment operation data to obtain a standardized equipment operation parameter sequence; According to the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence, the manufacturing equipment is subjected to early warning processing to obtain an early warning result, wherein the preset equipment operation parameter sequence is determined based on historical operation data; Based on the warning result, the standardized equipment operation parameters with a warning level greater than or equal to a preset threshold in the standardized equipment operation parameter sequence are screened out to obtain a plurality of target parameters; From the manufacturing equipment, identifying an equipment function node corresponding to each target parameter; The function of the device function node corresponding to each target parameter is removed. 3. The platform according to claim 2, characterized in that each standardized device operating parameter in the standardized device operating parameter sequence carries attribute information, and each preset device operating parameter in the preset device operating parameter sequence carries attribute information; The step of performing early warning processing on the manufacturing equipment according to the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence to obtain an early warning result includes: According to the attribute information, the standardized device operating parameter sequence is paired with the preset device operating parameter sequence to obtain the standardized device operating parameters and the preset device operating parameters under the same attribute information; Compare the standardized device operating parameters and the preset device operating parameters under the same attribute information of each group of successful pairing to determine the absolute value of the difference between the standardized device operating parameters and the preset device operating parameters under the same attribute information; Based on the size of each absolute value, the corresponding warning level is obtained from the pre-established mapping relationship between the absolute value and the warning level, and the warning level of each standardized equipment operating parameter is obtained; The warning level of each standardized equipment operating parameter is used as the warning result for the manufacturing equipment. 4. The platform according to claim 2, characterized in that the early warning processing of the manufacturing equipment is performed according to the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence to obtain the early warning result, comprising: Inputting the standardized equipment operation parameter sequence and the preset equipment operation parameter sequence into a pre-trained early warning model to perform early warning processing on the manufacturing equipment; the pre-trained early warning model is obtained by machine learning based on a historical equipment operation parameter set and a network parameter to be processed, the network parameter to be processed is obtained by analyzing the correlation between all network parameters in the early warning model and a feature sequence, and the feature sequence is obtained based on the historical equipment operation parameter set; Outputting the warning level corresponding to each standardized equipment operating parameter in the standardized equipment operating parameter sequence; The warning level corresponding to each standardized equipment operating parameter is used as the warning result for the manufacturing equipment. 5. The platform according to claim 4, characterized in that the pre-trained early warning model is generated according to the following steps, including: Collecting equipment operating parameters of the manufacturing equipment under normal operation and abnormal operation within a preset time period to obtain a historical equipment operating parameter set; Based on a preset equipment operation parameter sequence, each parameter in the historical equipment operation parameter set is labeled with a level label for representing the warning level, so as to obtain a historical equipment operation parameter set carrying the label; Use neural networks to create early warning models; Perform feature conversion on the historical equipment operation parameter set carrying the tag to obtain a feature sequence; Traversing all network parameters in the early warning model; Analyzing the correlation between the characteristic sequence and all the network parameters to determine the network parameters to be processed that are related to the characteristic sequence among all the network parameters; The early warning model is trained according to the historical device operation parameter set and the network parameters to be processed to obtain a pre-trained early warning model. 6. The platform according to claim 5, characterized in that each historical device operation parameter in the historical device operation parameter set carries attribute information, and each preset device operation parameter in the preset device operation parameter sequence carries attribute information; The step of marking each parameter in the historical equipment operation parameter set with a level label for representing the warning level based on the preset equipment operation parameter sequence includes: According to the attribute information, the historical device operating parameter set is paired with the preset device operating parameter sequence to obtain the historical device operating parameters and the preset device operating parameters under the same attribute information; Compare each set of successfully paired historical device operating parameters and preset device operating parameters under the same attribute information to determine the absolute value of the difference between the historical device operating parameters and the preset device operating parameters under the same attribute information; Based on the size of each absolute value, the corresponding warning level is obtained from the pre-established mapping relationship between the absolute value and the warning level, and the warning level label of each historical equipment operation parameter is obtained; The warning level label of each historical device operating parameter is used to mark each parameter in the historical device operating parameter set with a level label used to characterize the warning level. 7. The platform according to claim 5, characterized in that the training of the early warning model according to the historical device operation parameter set and the to-be-processed network parameters to obtain the pre-trained early warning model comprises: According to the network parameters to be processed, construct a first matrix and a second matrix, wherein the first matrix includes column-related parameters of an original weight matrix in the neural network, and the second matrix includes row-related parameters of the original weight matrix in the neural network; According to the historical equipment operation parameter set, matrix parameters of the first matrix and the second matrix are trained to obtain a loss value of the early warning model; When the loss value of the early warning model reaches the minimum, a pre-trained early warning model is generated; or, when the loss value of the early warning model does not reach the minimum, the loss value is propagated to update the model parameters of the early warning model, and the step of training the matrix parameters of the first matrix and the second matrix according to the historical equipment operation parameter set is continued. 8. The platform according to claim 7, characterized in that the calculation function of the loss value is: Where L is the loss value, is the number of samples of the historical device running parameter set, is the loss value of a single historical device operating parameter, , is the forward propagation function, For each historical device operating parameter in the historical device operating parameter set, is the first matrix, is the second matrix, is the original weight matrix of the neural network, is the predicted value, It is a level label used to characterize the warning level of each historical device operating parameter in the historical device operating parameter set. 9. The platform according to claim 1, wherein monitoring the operating status of the manufacturing equipment according to the equipment operating data comprises: Preprocessing the equipment operation data; Aggregating the preprocessed device operation data to obtain aggregated device operation data; Using a preset chart visualization component to visualize the aggregated equipment operation data, to obtain a state visualization trend chart of the equipment state of the manufacturing equipment; The status visualization trend graph is displayed to monitor the operating status of the manufacturing equipment. 10. An automated monitoring method compatible with multiple DCS systems, characterized in that the method comprises: The industrial software adapts the preset standard communication protocol to the communication software of each DCS system through the software basic configuration center to eliminate the communication differences between the communication software of different DCS systems; The DCS system collects the real-time data of the manufacturing equipment, performs communication verification processing on the real-time data to obtain equipment operation data, and performs real-time early warning and function removal on the manufacturing equipment according to the equipment operation data, and sends the equipment operation data to the industrial software through the adapted preset standard communication protocol; The industrial software receives the equipment operation data sent by each DCS system through the adapted preset standard communication protocol, and monitors the operation status of the manufacturing equipment according to the equipment operation data.