A method for data acquisition and intelligent monitoring of industrial PLC equipment

Abstract

This invention discloses a method for data acquisition and intelligent monitoring of industrial PLC equipment. It connects to multiple types of PLC equipment via a standardized industrial communication protocol to collect raw data. This data is then parsed and transformed using a point attribute configuration table, and combined with a dynamic dictionary mapping table to generate readable information. Real-time anomaly monitoring and tiered alarms are implemented, and dynamic association with a work order management system enables production monitoring and analysis. A layered modular architecture is adopted, supporting cross-platform deployment and effectively improving the efficiency, accuracy, and intelligence of data acquisition and monitoring, meeting the needs of complex industrial production scenarios.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, specifically to a method for data acquisition and intelligent monitoring of industrial PLC equipment. Background Technology

[0002] In the process of industrial automation, PLC devices, as core components of industrial control systems, are widely used in various production scenarios. Significant differences exist in the communication protocols and data storage structures of PLC devices from different manufacturers and models, posing numerous challenges when building a unified data acquisition and monitoring system. Traditional data acquisition methods are often designed for specific types of PLC devices, lacking versatility and scalability, making it difficult to achieve unified management and monitoring of multiple types of PLC devices. Furthermore, raw PLC data is mostly stored in byte format, lacking intuitive business semantic information, requiring complex parsing and conversion before it can be applied to actual production management. Moreover, during data acquisition and processing, there is a lack of effective monitoring and response mechanisms for communication link anomalies and data parsing errors, easily leading to data loss and the inability to promptly detect and handle production anomalies, affecting production efficiency and product quality. Therefore, there is an urgent need for a method that is compatible with multiple types of PLC devices and enables efficient data acquisition and intelligent monitoring. Summary of the Invention

[0003] The purpose of this invention is to provide a method for data acquisition and intelligent monitoring of industrial PLC equipment, which solves the problems of difficulty in data acquisition of various types of PLC equipment, lack of intelligence in data processing, and inadequate response to abnormal situations in the existing technology, and realizes efficient acquisition, intelligent processing and real-time monitoring of PLC equipment data in industrial production process.

[0004] The industrial PLC equipment data acquisition and intelligent monitoring method of the present invention specifically includes the following steps: establishing a real-time connection with multiple types of PLC equipment through a standardized industrial communication protocol, and acquiring raw byte data based on a preset acquisition cycle or event triggering mechanism; parsing and converting the raw byte data according to a predefined point attribute configuration table, wherein the point attributes include data type identifier, register address format, proportional conversion coefficient, and unit mapping rules; matching the parsed values ​​with a dynamic dictionary mapping table, and generating readable information with business logic association through semantic conversion; monitoring abnormal events in the data acquisition link and parsing process in real time, generating abnormal logs and triggering a hierarchical alarm mechanism; and dynamically associating the processed data with the work order management system to realize real-time monitoring of the production process, abnormal event backtracking, and performance indicator analysis.

[0005] In one embodiment, the point attribute configuration table is divided into status monitoring points, process parameter points, equipment alarm points and operation control points according to the PLC point function type, and each type of point is configured with an independent data parsing strategy and mapping rules.

[0006] Furthermore, the point attribute configuration table is the core basis for the entire data parsing process. It categorizes PLC point attributes into status monitoring points, process parameter points, equipment alarm points, and operation control points based on their function. Taking a status monitoring point as an example, if it monitors the operating status of equipment, the configuration table will explicitly identify its data type as bit data, the register address format will correspond to the specific address area where the equipment stores this status information, and the unit mapping rule will map different bit combinations to specific status descriptions. For process parameter points, the data type may be floating-point, the register address will point to the register storing temperature data, and the scaling factor will be used to convert the original register value into the actual temperature value. Through this detailed configuration, the system can accurately parse and convert data from different types of points.

[0007] In one embodiment, the parsing and conversion steps include: selecting the corresponding parsing algorithm according to the data type identifier, including: bit operation algorithm, integer / floating-point byte sequence parsing algorithm and ASCII string decoding algorithm; extracting independent state bits from the bit data using bit masking technology and dynamically matching them with a preset state dictionary; performing linear or nonlinear mathematical transformations on the original numerical values ​​based on the proportional conversion coefficient and unit mapping rules to generate standardized engineering data.

[0008] In one embodiment, the dynamic dictionary mapping table supports an online update mechanism, allowing the mapping relationship between numerical values ​​and business semantics to be modified in real time through a configuration interface, including equipment status descriptions, alarm level definitions, and process parameter thresholds.

[0009] In one embodiment, the register address format parsing adopts a unified address translation model, including: standardizing the encoding of register addresses for multi-brand PLCs, constructing a mapping relationship between address type identifiers and data block offsets; automatically calculating the physical storage location and byte length of data blocks based on the register address format to adapt to the register structure of heterogeneous PLC devices.

[0010] Furthermore, the register address formats of PLCs from different brands vary, and the unified address translation model acts as a translator. It standardizes the register addresses of different PLC brands, establishing a mapping relationship between address type identifiers and data block offsets. For example, Siemens PLC register addresses are represented in the form of "DBX.Y" (where X is the data block number and Y is the offset address), while Mitsubishi PLC register addresses begin with "D" followed by numbers. The unified address translation model converts these different address formats into standardized codes that the system can recognize, such as assigning specific identifiers to each brand's address type, and then determining the specific location of the data in the storage area based on the offset. Based on this, the system can automatically calculate the physical storage location and byte length of data blocks, accurately acquiring and processing data regardless of the type of PLC device, adapting to the register structures of heterogeneous PLC devices, and ensuring the accuracy of data acquisition and parsing.

[0011] In one embodiment, the abnormal event monitoring includes: enabling a data caching queue when the communication link is interrupted, and reconstructing data continuity based on timestamps; performing real-time diagnosis of parsing errors, data overflows, and logical conflicts through an abnormal capture engine, and generating abnormal event logs with error codes; and triggering corresponding alarm strategies based on the severity level of the abnormality, including interface alerts, SMS notifications, and automatic generation of work orders.

[0012] Furthermore, in the section "Enabling a data cache queue and reconstructing data continuity based on timestamps when a communication link is interrupted," communication link interruptions can be caused by various reasons such as network failures and equipment failures. Enabling a data cache queue temporarily stores the data to be transmitted. Reconstructing data continuity based on timestamps means that after communication is restored, the system integrates the cached data with the data collected after recovery according to the timestamps recorded when the data was cached, ensuring that the data is not out of order or has time discrepancies, maintaining data integrity and continuity, and guaranteeing the accuracy of subsequent analysis and processing. The anomaly capture engine continuously monitors various anomalies during the data parsing process. For example, when the parsing algorithm cannot correctly process the data type and parsing errors occur; when the data value exceeds the preset range and data overflow occurs; or when there are logical contradictions between the data, such as two status bits of the same device being in mutually exclusive states simultaneously, the anomaly capture engine can quickly detect these problems and generate anomaly event logs with error codes, facilitating technicians to quickly locate and resolve the problems.

[0013] In one embodiment, the performance index analysis includes: automatically calculating the overall equipment efficiency based on time-series data streams and performing root cause analysis of production efficiency by associating work order data; and generating a multi-dimensional performance analysis map through a visualization engine, including heat maps of equipment utilization rate, quality pass rate, and process stability.

[0014] In one embodiment, the method adopts a layered modular architecture, including: a data acquisition layer, which implements multi-PLC brand communication driver encapsulation through a protocol adapter; a data processing layer, which includes a data parsing engine, a semantic conversion module, and an exception handling unit; and a business integration layer, which provides standardized API interfaces for data interaction with external systems, including MES systems, alarm platforms, and databases.

[0015] Furthermore, the data acquisition layer focuses on establishing connections with various types of PLC devices and collecting data. The protocol adapter encapsulates the communication driver, hiding the communication differences between different PLC devices and providing a unified interface to the upper layer. The data processing layer is responsible for parsing, transforming, and handling anomalies in the collected data. Each module has a clear division of labor: the data parsing engine is responsible for parsing the data, the semantic conversion module gives the data business meaning, and the anomaly handling unit ensures the correctness of data processing. The business integration layer realizes data interaction with external systems such as the MES system and alarm platform through standardized API interfaces.

[0016] In one embodiment, the method supports cross-platform deployment, including: implementing high-throughput communication between the data acquisition layer and the business integration layer via TCP / IP protocol in a PC architecture; and employing a lightweight communication protocol stack in an embedded architecture to optimize real-time data transmission efficiency through a memory sharing mechanism.

[0017] In one embodiment, the dynamic associated work order management system includes: binding real-time collected data with work order identifiers to construct a full lifecycle data chain for production batches; automatically generating maintenance work orders based on abnormal event logs, and associating historical data to perform fault mode analysis and push process optimization suggestions.

[0018] Beneficial effects

[0019] This invention proposes a method for data acquisition and intelligent monitoring of industrial PLC equipment. By using a standardized communication protocol, it achieves unified access for multiple types of PLC devices, solving the compatibility problems of traditional methods. Utilizing a predefined point attribute configuration table and a dynamic dictionary mapping table, it accurately parses and semantically transforms raw data, giving the data clear business logic relationships, facilitating understanding and application by production management personnel. A real-time anomaly monitoring and hierarchical alarm mechanism can promptly detect and handle various anomalies during data acquisition and processing. Data integrity is ensured through data caching and continuous reconstruction. Combined with anomaly event logs and automatic work order generation, it improves fault handling efficiency and reduces production downtime. Dynamic association with the work order management system enables the integration and analysis of data throughout the entire production lifecycle. It not only monitors production status in real time but also allows for fault mode analysis and process optimization by tracing historical data, effectively improving production efficiency and product quality. The layered modular architecture and cross-platform deployment characteristics give the system excellent scalability and adaptability, allowing flexible application to industrial production scenarios of different scales and types, reducing system development and maintenance costs, and providing strong technical support for the intelligent and digital transformation of industrial production. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the working steps of the industrial PLC equipment data acquisition and intelligent monitoring method provided in this embodiment of the invention;

[0022] Figure 2 A diagram illustrating the parsing and conversion steps provided in this embodiment of the invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0025] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0026] Example 1

[0027] To address the aforementioned technical problems, the present invention aims to provide a method for data acquisition and intelligent monitoring of industrial PLC equipment, thereby resolving issues such as difficulties in data acquisition from various types of PLC equipment, lack of intelligence in data processing, and inadequate response to abnormal situations in the prior art, and realizing efficient acquisition, intelligent processing, and real-time monitoring of PLC equipment data during industrial production.

[0028] The industrial PLC equipment data acquisition and intelligent monitoring method of the present invention specifically includes the following steps:

[0029] First, a standardized industrial communication protocol is used to establish real-time connections with various types of PLC devices. This standardized protocol is compatible with mainstream PLC device communication protocols on the market, providing a unified data access channel for PLC devices of different brands and models. Raw byte data is acquired based on a preset acquisition cycle or an event-triggered mechanism. The preset acquisition cycle can be flexibly set according to the needs of the production scenario to ensure the timeliness and effectiveness of data acquisition; the event-triggered mechanism triggers data acquisition in a timely manner when specific events occur, such as changes in equipment status or abnormal process parameters, ensuring that critical data is not missed.

[0030] Furthermore, the raw byte data is parsed and transformed according to a predefined point attribute configuration table. The point attribute configuration table is categorized by PLC point function type into status monitoring points, process parameter points, equipment alarm points, and operation control points, with each category having its own independently configured data parsing strategy and mapping rules. During the parsing and transformation process, the corresponding parsing algorithm is selected based on the data type identifier. The data type identifier clarifies the data storage format and processing method. For example, for bit data, bit masking technology is used to extract independent status bits, which are then dynamically matched with a preset status dictionary; for integer and floating-point data, corresponding byte sequence parsing algorithms are used; and for ASCII string data, decoding algorithms are applied. Simultaneously, based on proportional conversion coefficients and unit mapping rules, linear or nonlinear mathematical transformations are performed on the raw numerical values, converting the raw physical quantity data into standardized data that meets engineering application requirements, giving the data practical business significance and readability.

[0031] Furthermore, the parsed values ​​are matched with a dynamic dictionary mapping table, and readable information with business logic relevance is generated through semantic transformation. The dynamic dictionary mapping table supports an online update mechanism, allowing the mapping relationship between values ​​and business semantics to be modified in real time through a configuration interface, including equipment status descriptions, alarm level definitions, and process parameter thresholds. This flexibility enables the system to adapt to changes in different production scenarios and business needs, and to update the business semantic interpretation of data in a timely manner.

[0032] During data acquisition and processing, abnormal events in the data acquisition and parsing process are monitored in real time. When the communication link is interrupted, a data caching queue is activated to reconstruct data continuity based on timestamps, ensuring data integrity. An anomaly capture engine performs real-time diagnosis of parsing errors, data overflows, and logical conflicts, generating anomaly event logs with error codes to facilitate subsequent troubleshooting and analysis. Corresponding alarm strategies are triggered based on the severity of the anomaly, including interface alerts, SMS notifications, and automatic work order generation, enabling relevant personnel to promptly understand the anomaly and take appropriate measures.

[0033] Finally, the processed data is dynamically linked with the work order management system to achieve real-time monitoring of the production process, traceability of abnormal events, and analysis of performance indicators. Real-time collected data is bound to work order identifiers to construct a full lifecycle data chain for production batches. Analysis of this data chain allows for tracing each stage of the production process, providing comprehensive data support for production management. Maintenance work orders are automatically generated based on abnormal event logs, and historical data is linked for fault mode analysis and process optimization suggestions, achieving intelligent management of the production process. Simultaneously, the Overall Equipment Effectiveness (OEE) is automatically calculated based on time-series data streams, and work order data is linked for root cause analysis of production efficiency. A multi-dimensional performance analysis graph is generated through a visualization engine, including heatmaps of equipment utilization, quality pass rate, and process stability, intuitively displaying production efficiency and providing strong support for production decisions. In some embodiments, such as in an automotive manufacturing production line, there are multiple brands and models of PLC equipment responsible for controlling different production stages, such as welding, painting, and assembly. To achieve unified data acquisition and monitoring of these PLC equipment, a layered modular architecture system based on this invention is first built. The data acquisition layer deploys multiple protocol adapters, configuring corresponding communication drivers for different brands of PLC devices. For example, the S7 protocol driver is used for Siemens S7 series PLCs; the MC protocol driver is used for Mitsubishi FX series PLCs; and the FINS protocol driver is used for Omron PLCs, etc. These protocol adapters encapsulate the communication protocols of different PLC devices, providing a unified data acquisition interface to the upper layer.

[0034] The data processing layer is deployed on a high-performance server and includes a data parsing engine, a semantic conversion module, and an exception handling unit. The data parsing engine is responsible for parsing and converting the collected raw byte data according to a predefined point attribute configuration table; the semantic conversion module matches the parsed values ​​with a dynamic dictionary mapping table to generate readable information with business logic relationships; and the exception handling unit monitors abnormal events in the data acquisition and parsing process in real time. The business integration layer provides standardized API interfaces for data interaction with the automotive manufacturing company's MES system, alarm platform, and production database. Through these interfaces, processed data is transmitted to various business systems, while control commands and configuration information are received from these systems.

[0035] This method adopts a layered modular architecture, specifically including a data acquisition layer, a data processing layer, and a business integration layer. The data acquisition layer encapsulates communication drivers for multiple PLC brands through a protocol adapter, shielding the differences in communication protocols between different PLC devices and providing a unified data acquisition interface for the upper layers. The data processing layer includes a data parsing engine, a semantic conversion module, and an exception handling unit, responsible for parsing, converting, and handling exceptions in the acquired data. The business integration layer provides standardized API interfaces for data interaction with external systems, including MES systems, alarm platforms, and databases, achieving seamless integration with other industrial systems and improving the system's versatility and scalability.

[0036] In one embodiment, the point attribute configuration table is divided into status monitoring points, process parameter points, equipment alarm points and operation control points according to the PLC point function type, and each type of point is configured with an independent data parsing strategy and mapping rules.

[0037] Furthermore, the point attribute configuration table is the core basis for the entire data parsing process. It categorizes PLC point attributes into status monitoring points, process parameter points, equipment alarm points, and operation control points based on their function. Taking a status monitoring point as an example, if the monitored data is the operating status of equipment, the configuration table will explicitly identify its data type as bit data. The register address format corresponds to the specific address area where the equipment stores this status information; for example, the M0.0-M0.7 area of ​​a PLC device is used to store different status bits. The proportional conversion factor is usually not required for this type of point (because bit data mainly reflects status rather than physical quantities), and the unit mapping rule maps different bit combinations to specific status descriptions, such as "00000001" indicating that the equipment is in "startup state". For process parameter points, such as temperature monitoring points, the data type may be floating-point, the register address points to the register storing temperature data, and the proportional conversion factor is used to convert the original register value into the actual temperature value. Through this detailed configuration, the system can accurately parse and convert data from different types of points.

[0038] In one embodiment, the parsing and conversion steps include: selecting the corresponding parsing algorithm according to the data type identifier, including: bit operation algorithm, integer / floating-point byte sequence parsing algorithm and ASCII string decoding algorithm; extracting independent state bits from the bit data using bit masking technology and dynamically matching them with a preset state dictionary; performing linear or nonlinear mathematical transformations on the original numerical values ​​based on the proportional conversion coefficient and unit mapping rules to generate standardized engineering data.

[0039] It should be noted that the parsing engine employs a multi-level processing strategy: first, it locates the data block based on the register address type identifier, and then selects the parsing algorithm according to the data type identifier. For bit data, bitmasking technology is used to separate independent status bits and match them with a preset status dictionary (such as "run / stop"); for analog data, linear / nonlinear formulas are applied to transform the original values, and engineering units are appended. The dynamic dictionary mapping table has a built-in version control mechanism, which updates business rules such as thresholds and alarm descriptions in real time through the configuration interface, ensuring that the semantic mapping iterates synchronously with production requirements.

[0040] In one embodiment, the dynamic dictionary mapping table supports an online update mechanism, allowing the mapping relationship between numerical values ​​and business semantics to be modified in real time through a configuration interface, including equipment status descriptions, alarm level definitions, and process parameter thresholds.

[0041] Furthermore, in some embodiments, the anomaly handling unit deploys a multi-dimensional monitoring strategy: when communication is interrupted, a cache queue is activated and the data stream is reconstructed according to timestamps; parsing errors, numerical exceedances, and other events trigger the diagnostic engine, generating logs with error codes and executing tiered alarms (interface alerts, SMS notifications). Anomalies are dynamically associated with the work order system, and maintenance solutions are pushed based on the fault mode library, while feedback is also sent to the performance analysis module to optimize process parameters.

[0042] In one embodiment, the register address format parsing adopts a unified address translation model, including: standardizing the encoding of register addresses for multi-brand PLCs, constructing a mapping relationship between address type identifiers and data block offsets; automatically calculating the physical storage location and byte length of data blocks based on the register address format to adapt to the register structure of heterogeneous PLC devices.

[0043] In one embodiment, the abnormal event monitoring includes: enabling a data caching queue when the communication link is interrupted, and reconstructing data continuity based on timestamps; performing real-time diagnosis of parsing errors, data overflows, and logical conflicts through an abnormal capture engine, and generating abnormal event logs with error codes; and triggering corresponding alarm strategies based on the severity level of the abnormality, including interface alerts, SMS notifications, and automatic generation of work orders.

[0044] In one embodiment, the performance index analysis includes: automatically calculating the overall equipment efficiency based on time-series data streams and performing root cause analysis of production efficiency by associating work order data; and generating a multi-dimensional performance analysis map through a visualization engine, including heat maps of equipment utilization rate, quality pass rate, and process stability.

[0045] It should be noted that when abnormal events occur during production, this method automatically generates maintenance work orders based on abnormal event logs. For example, when the torque value of a certain assembly station is detected to exceed the process requirements, the system automatically generates a maintenance work order containing information on the abnormal location, fault description, and historical similar fault handling solutions, and pushes it to the maintenance personnel. After the maintenance personnel handle the fault, they feed the processing results back to the work order management system. The system automatically updates the work order status and associates the processing results with historical data for subsequent fault mode analysis. The system automatically calculates the Overall Equipment Effectiveness (OEE) based on time-series data streams and performs root cause analysis of production efficiency by associating it with work order data. For example, by analyzing data such as equipment downtime, production speed loss, and product defect rate during the production process of a certain work order, the system calculates the corresponding OEE value for that work order. If the OEE value is lower than the expected target, the system further analyzes whether it is caused by equipment failure, unreasonable process parameters, or operational errors, providing targeted improvement suggestions for production managers. A multi-dimensional performance analysis graph is generated through a visualization engine. For example, heatmaps of equipment utilization can be generated to visually display the usage of each piece of equipment over different time periods; heatmaps of quality pass rates can be generated to reflect changes in product quality during the production process; and heatmaps of process stability can be generated to show the fluctuation range of key process parameters. These visualizations provide strong support for production decisions.

[0046] In one embodiment, the method adopts a layered modular architecture, including: a data acquisition layer, which implements multi-PLC brand communication driver encapsulation through a protocol adapter; a data processing layer, which includes a data parsing engine, a semantic conversion module, and an exception handling unit; and a business integration layer, which provides standardized API interfaces for data interaction with external systems, including MES systems, alarm platforms, and databases.

[0047] In one embodiment, the method supports cross-platform deployment, including: implementing high-throughput communication between the data acquisition layer and the business integration layer via TCP / IP protocol in a PC architecture; and employing a lightweight communication protocol stack in an embedded architecture to optimize real-time data transmission efficiency through a memory sharing mechanism.

[0048] In one embodiment, the dynamic associated work order management system includes: binding real-time collected data with work order identifiers to construct a full lifecycle data chain for production batches; automatically generating maintenance work orders based on abnormal event logs and associating them with historical data for fault mode analysis and process optimization suggestion push. When deployed on a PC, the data acquisition layer transmits data through a high-throughput TCP / IP channel; in embedded scenarios, a lightweight protocol stack is used, utilizing memory sharing to reduce latency. During implementation, protocol drivers need to be configured according to device type, point attribute tables need to be imported and dynamic dictionaries initialized, and API interfaces need to be used to connect to business systems. During the testing phase, protocol compatibility, parsing accuracy, and anomaly response time need to be verified.

[0049] Furthermore, this method supports cross-platform deployment. In a PC architecture, it achieves high-throughput communication between the data acquisition layer and the business integration layer via TCP / IP protocol, meeting the requirements of data transmission speed and stability in large-scale industrial production scenarios. In an embedded architecture, it employs a lightweight communication protocol stack and optimizes real-time data transmission efficiency through a memory-sharing mechanism, making it suitable for resource-constrained industrial control scenarios and further expanding the application scope of this method. The industrial PLC equipment data acquisition and intelligent monitoring method of this invention has achieved significant results after being applied to an automobile manufacturing production line. By standardizing the communication protocol, it enables unified access for multiple brands and models of PLC equipment, greatly improving system compatibility and reducing development and maintenance costs caused by equipment differences.

[0050] Predefined point attribute configuration tables and dynamic dictionary mapping tables enable more accurate and efficient data parsing and semantic transformation, allowing production managers to quickly obtain production data with clear business logic and improving decision-making efficiency. Real-time anomaly monitoring and tiered alarm mechanisms promptly detect and handle numerous potential production anomalies. Data integrity is ensured through data caching and continuous reconstruction. Combined with automated work order generation and fault mode analysis, fault handling time is significantly shortened, reducing production line downtime losses. Dynamic integration with the work order management system enables full lifecycle management of the production process. OEE calculation and multi-dimensional performance analysis graphs provide data support for production optimization, effectively improving equipment utilization and product quality pass rates. Cross-platform deployment capabilities allow the system to flexibly adapt to the needs of different production stages in automotive manufacturing enterprises. Whether on large servers or small embedded devices, the system can run efficiently, improving overall system performance and applicability.

[0051] As can be seen from the above specific embodiments, the industrial PLC equipment data acquisition and intelligent monitoring method of the present invention has strong practicality and operability, can effectively solve the problem of PLC equipment data acquisition and monitoring in industrial production, and provide strong support for the intelligent and digital transformation of industrial production.

[0052] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for data acquisition and intelligent monitoring of industrial PLC equipment, characterized in that, Includes the following steps: Establish real-time connections with various types of PLC devices through standardized industrial communication protocols, and acquire raw byte data based on preset acquisition cycles or event triggering mechanisms; The original byte data is parsed and converted according to a predefined point attribute configuration table. The point attributes include data type identifier, register address format, scaling conversion coefficient and unit mapping rule. The parsed values ​​are matched with a dynamic dictionary mapping table, and readable information with business logic is generated through semantic transformation. Real-time monitoring of abnormal events in the data acquisition and parsing process, generating abnormal logs and triggering a tiered alarm mechanism; The processed data is dynamically linked to the work order management system to achieve real-time monitoring of the production process, backtracking of abnormal events, and analysis of performance indicators. It also includes parsing and conversion steps: selecting the corresponding parsing algorithm according to the data type identifier; extracting independent state bits from the bit data using bit masking technology and dynamically matching them with a preset state dictionary; performing linear or nonlinear mathematical transformations on the original values ​​based on the proportional conversion coefficient and unit mapping rules to generate standardized engineering data; The register address format parsing adopts a unified address translation model, including: standardizing the encoding of register addresses for multi-brand PLCs and constructing a mapping relationship between address type identifiers and data block offsets; Automatically calculates the physical storage location and byte length of data blocks based on register address format, adapting to the register structure of heterogeneous PLC devices.

2. The industrial PLC equipment data acquisition and intelligent monitoring method according to claim 1, characterized in that, The point attribute configuration table is divided into status monitoring points, process parameter points, equipment alarm points and operation control points according to the PLC point function type. Each type of point is configured with independent data parsing strategies and mapping rules.

3. The industrial PLC equipment data acquisition and intelligent monitoring method according to claim 1, characterized in that, The dynamic dictionary mapping table supports an online update mechanism, allowing the mapping relationship between numerical values ​​and business semantics to be modified in real time through the configuration interface, including equipment status descriptions, alarm level definitions, and process parameter thresholds.

4. The industrial PLC equipment data acquisition and intelligent monitoring method according to claim 1, characterized in that, The abnormal event monitoring includes: enabling a data caching queue when the communication link is interrupted, and reconstructing data continuity based on timestamps; performing real-time diagnosis of parsing errors, data overflows, and logical conflicts through an abnormal capture engine, and generating abnormal event logs with error codes; and triggering corresponding alarm strategies based on the severity level of the abnormality, including interface alerts, SMS notifications, and automatic generation of work orders.

5. The industrial PLC equipment data acquisition and intelligent monitoring method according to claim 1, characterized in that, The performance index analysis includes: automatically calculating the overall equipment efficiency based on time-series data streams and performing root cause analysis of production efficiency by linking work order data; and generating multi-dimensional performance analysis maps through a visualization engine, including heat maps of equipment utilization rate, quality pass rate, and process stability.

6. The industrial PLC equipment data acquisition and intelligent monitoring method according to claim 1, characterized in that, The method adopts a layered modular architecture, including: a data acquisition layer, which implements multi-PLC brand communication driver encapsulation through a protocol adapter; a data processing layer, which includes a data parsing engine, a semantic conversion module, and an exception handling unit; and a business integration layer, which provides standardized API interfaces for data interaction with external systems, including MES systems, alarm platforms, and databases.

7. The industrial PLC equipment data acquisition and intelligent monitoring method according to claim 1, characterized in that, The method supports cross-platform deployment, including: implementing high-throughput communication between the data acquisition layer and the business integration layer through the TCP / IP protocol in a PC architecture; and using a lightweight communication protocol stack in an embedded architecture to optimize real-time data transmission efficiency through a memory sharing mechanism.

8. The method for data acquisition and intelligent monitoring of industrial PLC equipment according to claim 1, characterized in that, The dynamic association work order management system includes: binding real-time collected data with work order identifiers to build a full lifecycle data chain for production batches; automatically generating maintenance work orders based on abnormal event logs, and associating historical data to perform fault mode analysis and push process optimization suggestions.

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