Industrial database storage system and method based on data change

Abstract

The invention discloses an industrial database storage system based on data change, and the system comprises a PLC data collection module which is used for collecting data from industrial equipment in real time; the data change detection module is used for monitoring the data acquired by the PLC data acquisition module and judging whether the data is changed or not; the storage control and optimization module is used for receiving a signal of the data change detection module and judging whether the data is stored in the industrial database module or not according to the detected data change condition; the industrial database module is used for storing the optimized data; and the management and monitoring module is used for monitoring and managing the industrial database storage system in real time. The invention further discloses an industrial database storage method based on data change. According to the method, the problems of serious storage redundancy, relatively high system load, complex system architecture and huge cost consumption in the existing industrial database are solved, and the storage efficiency and system performance of industrial data are optimized.

Description

Technical Field

[0001] This invention relates to the basic technologies for automation of steel rolling production lines in the steel industry, and more specifically, to an industrial database storage system and method based on data changes. Background Technology

[0002] In modern industrial automation systems, real-time databases are widely used to monitor and record the operating status and production data of industrial equipment. These databases are typically integrated with PLCs (Programmable Logic Controllers) and SCADA (Supervisory Control and Data Acquisition) systems to collect and store various types of data from the industrial field in real time, facilitating real-time monitoring and subsequent analysis. However, traditional industrial databases often employ a timed data collection and storage strategy; that is, regardless of whether the data has changed, the system periodically reads data from the PLC and stores it in the database at set time intervals.

[0003] While this traditional storage method ensures data continuity, it also introduces significant storage redundancy. Since much industrial data remains unchanged over short periods, timed data acquisition and storage strategies result in large amounts of duplicate data being written to the database, wasting valuable storage space and system resources. Furthermore, frequent write operations increase the database load, impacting overall system performance, especially in large-scale industrial applications.

[0004] To alleviate the problem of wasted storage space, some existing technical solutions introduce methods such as data compression and archiving to compress and store historical data or archive it periodically. However, these methods only work after data storage and cannot fundamentally reduce redundant data generated during real-time storage. Furthermore, these methods often involve complex compression algorithms or require additional storage management strategies, further increasing system complexity and management costs. Another common technical solution is storage expansion and distributed storage technology. This approach dynamically adjusts storage resources to adapt to data growth by adding extra storage nodes or expanding storage devices in the database system. This method of expanding storage can indeed solve the problem of insufficient storage space in the short term and support larger-scale data storage needs. However, while expanding storage devices to cope with data growth solves the problem of insufficient storage space, it also significantly increases the consumption of storage resources, especially when processing large amounts of unchanging data, where the resource waste caused by storage expansion is particularly noticeable. As storage devices expand, the system architecture becomes more complex, requiring the introduction of mechanisms such as distributed storage management and data consistency guarantees, increasing system management and maintenance costs.

[0005] When facing industrial control and monitoring scenarios, such as the industrial control and monitoring scenarios of steel industry production lines, the equipment in the steel rolling production line operates intermittently according to a certain rhythm. The equipment data does not change in the short period of time between production operations. The timed collection and storage strategy will lead to a large amount of duplicate data being written to the database, wasting valuable storage space and system resources.

[0006] Existing industry solutions include using distributed storage databases, which expand storage space and avoid resource bottlenecks by distributing data across different nodes. For example, patent publications CN110877961A and CN107750513A disclose a distributed time-series database and its storage method, and a distributed resource synchronization method, system, and device, respectively, which propose using sharded storage to manage time-series data, aiming to optimize system storage performance and processing efficiency. The main technical approach involves sharding data and having a control node manage the distribution and storage operations of these shards across different storage nodes. However, this method has limitations in addressing data redundancy. Since data sharding strategies are typically based on preset rules and do not directly consider real-time data changes, they cannot effectively prevent the generation of redundant data. Furthermore, sharded storage requires additional control and management resources during cross-node synchronization, increasing system complexity and operating costs, especially in large-scale data scenarios where data synchronization overhead will significantly increase.

[0007] Another approach is to employ a publish-subscribe mechanism to reduce system resource consumption through asynchronous queries. For example, in the time-series data query method, system, and computer medium based on publish-subscribe disclosed in patent publication CN109482013A, the system uses a subscription API to asynchronously query data of interest, and pushes updated content to subscribers via a publish API when the data changes. The advantage of this method is that it reduces the system overhead caused by invalid queries, thereby optimizing the utilization of system resources. However, its effectiveness is limited when dealing with data storage redundancy issues. Because it primarily addresses data query efficiency rather than directly controlling redundancy during data storage, the data storage strategy may still result in duplicate storage even when the data has not changed.

[0008] Another solution is to incrementally synchronize data from the MySQL database to MongoDB. For example, patent publication number CN106045667A discloses a method, apparatus, terminal device, and storage medium for synchronizing MySQL data to MongoDB. This method monitors changes in MySQL data and synchronizes changed data to MongoDB in real time to ensure data consistency. While this incremental synchronization strategy is effective in reducing data transfer volume and storage space waste, its application is mainly limited to data synchronization needs between relational and NoSQL databases, making its applicability somewhat limited. It may not be well-suited for industrial databases.

[0009] In summary, although these technical solutions have shown certain advantages in their respective application scenarios, they still have obvious technical limitations when facing the complex data management needs of industrial databases, especially in the industrial control scenario of steel rolling production lines. In particular, they cannot provide effective solutions for problems such as redundant data processing, efficient management of storage systems, and dynamic adaptation of storage strategies. Summary of the Invention

[0010] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an industrial database storage system and method based on data changes, which solves the problems of serious storage redundancy, high system load, complex system architecture and huge cost in existing industrial databases, and fundamentally optimizes the storage efficiency and system performance of industrial data.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] The first aspect of this invention provides an industrial database storage system based on data changes, comprising:

[0013] PLC data acquisition module is used to acquire data from industrial equipment in real time;

[0014] The data change detection module is used to monitor the data collected by the PLC data acquisition module and determine whether the data has changed.

[0015] The storage control and optimization module is used to receive signals from the data change detection module and determine whether to store the data in the industrial database module based on the detected data changes.

[0016] The industrial database module is used to store optimized data;

[0017] The management and monitoring module is used for real-time monitoring and management of the industrial database storage system.

[0018] A second aspect of the present invention provides an industrial database storage method based on data changes;

[0019] The industrial database storage system based on data change provided by the first aspect of the present invention comprises a PLC data acquisition module that collects data from industrial equipment in real time, a data change detection module that monitors the data collected by the PLC data acquisition module and determines whether the data has changed, a storage control and optimization module that receives the signal from the data change detection module and determines whether to store the data in the industrial database module based on the detected data change, the industrial database module that stores the optimized data and supports data querying and management, and a management and monitoring module that performs real-time monitoring and management of the industrial database storage system.

[0020] Preferably, the data collected in real time from the industrial equipment by the PLC data acquisition module includes temperature, pressure, flow rate, and vibration.

[0021] The data acquired by the PLC data acquisition module is transmitted to the data change detection module through the I / O interface.

[0022] Preferably, the data change detection module is configured with monitored variables and event triggering conditions are set for each variable;

[0023] Whenever the data change detection module detects a data change event, it immediately generates a change signal and transmits it to the storage control and optimization module.

[0024] Preferably, the data change detection module generates a fixed-length hash value through hash operation and compares the hash value of the current data with the hash value of the previously stored data to determine whether the data has changed.

[0025] Preferably, the storage strategy in the storage control and optimization module includes on-demand storage and delayed batch storage.

[0026] Preferably, the on-demand storage specifically includes:

[0027] For data that has undergone significant changes, a storage operation is immediately triggered to write the changed data into the industrial database module;

[0028] The delayed batch storage specifically includes:

[0029] For data that changes frequently but with small individual changes, a delayed storage method is selected. After accumulating the data changes within a set time period, the data is then stored in batches into the industrial database module.

[0030] Preferably, the implementation of the industrial database module specifically includes:

[0031] Data storage: According to the instructions of the storage control and optimization module, the industrial database module stores the changed data into the corresponding tables. Each data record includes a timestamp, device identifier, parameter type, and change value information.

[0032] Data indexing and retrieval: The industrial database module establishes multi-dimensional indexes for the stored data, including indexing by time, equipment, and parameter type.

[0033] Data backup and recovery: Regularly back up data to cloud storage or local redundant storage devices.

[0034] Preferably, the implementation of the management and monitoring module specifically includes:

[0035] Real-time monitoring is performed on the status of the PLC data acquisition module, the data change detection module, the storage control and optimization module, the industrial database module, and the management and monitoring module.

[0036] Strategy adjustment: dynamically adjust the threshold for data change detection and the priority parameters of the storage strategy;

[0037] Data analysis and reporting provides a variety of data analysis tools and generates various reports based on actual needs.

[0038] This invention provides an industrial database storage system and method based on data change, particularly for industrial control scenarios in steel rolling production lines. Through a multi-layered modular design encompassing data change detection, storage control and optimization, real-time industrial database management, and management and monitoring, it aims to optimize industrial data storage efficiency, reduce storage redundancy, lower system load, and improve the flexibility and accuracy of data management. This system is particularly suitable for large-scale data acquisition and management in industrial environments. Based on an estimated 70% effective utilization rate of steel rolling production equipment, this solution can reduce redundant data storage by approximately 30% and reduce the execution of database storage instructions by 30%, thereby reducing the energy consumption of the database system. This invention has wide applicability, is easy to use, requires minimal equipment investment, and can be widely adopted in automated steel rolling production lines, demonstrating broad prospects for technological promotion. Furthermore, it also offers the following beneficial effects:

[0039] 1) Severe storage redundancy problem

[0040] Existing industrial database systems typically use periodic storage, writing data to the database periodically even if it hasn't changed for a period of time. This results in the storage of a large amount of duplicate data, wasting storage resources. This invention introduces a data change detection mechanism to monitor the data changes in industrial equipment in real time. The system only triggers data storage when a change is detected; for data that remains unchanged, the storage process is skipped. In this way, the system only stores meaningful data changes, significantly reducing the amount of duplicate data stored and effectively solving the storage redundancy problem.

[0041] 2) High system load problem

[0042] Traditional industrial database systems store all data periodically, leading to frequent data write operations. This increases the database's I / O load and computational resource consumption, especially in large-scale industrial applications where frequent storage operations severely impact system performance. This invention addresses this by employing an on-demand storage strategy, reducing unnecessary write operations and storing data only when changes occur. This strategy significantly reduces the number of database I / O operations, decreasing system load and thus improving overall system performance and responsiveness. Particularly when processing large-scale data, this invention effectively alleviates system pressure.

[0043] 3) Complex system architecture

[0044] In existing technologies, the challenge of ever-increasing data volumes is typically addressed by expanding storage devices or introducing distributed storage architectures. However, this approach increases system complexity and requires additional management and maintenance costs. This invention optimizes the storage mechanism, reducing storage requirements and simplifying the system architecture through intelligent data management and storage strategies, without relying on expanded storage devices. Because storage redundancy is significantly reduced, the system no longer requires complex storage expansion solutions, simplifying system design and maintenance and lowering overall system complexity.

[0045] 4) Solving the problem of huge costs

[0046] In traditional systems, handling the storage demands of massive amounts of data typically requires continuous expansion of storage devices and increased energy consumption, especially in data centers and large-scale industrial environments where the cost and energy consumption of storage expansion are enormous. This invention reduces storage redundancy, lowering the demand for storage devices and thus reducing the procurement, installation, and maintenance costs. Simultaneously, due to the simplified system architecture and optimized storage resources, overall energy consumption is significantly controlled. This not only reduces initial investment and long-term operating costs but also improves the system's economic efficiency and sustainability. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the structural framework of the industrial database storage system of the present invention;

[0048] Figure 2 This is a schematic diagram illustrating the principle of the industrial database storage method of the present invention;

[0049] Figure 3 This is a schematic diagram of the storage strategy of the storage control and optimization module in the industrial database storage method of the present invention. Detailed Implementation

[0050] To better understand the above-mentioned technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0051] Combination Figure 1 As shown, the present invention provides an industrial database storage system based on data changes, comprising:

[0052] PLC data acquisition module 1 is used to collect data from industrial equipment in real time, such as key parameters like temperature, pressure, and vibration, and transmit this data to the data change detection module. A PLC (Programmable Logic Controller) is a commonly used device in industrial control, responsible for monitoring and acquiring data from various types of equipment in the industrial field.

[0053] The data change detection module 2 monitors the data acquired by the PLC data acquisition module 1 and determines whether the data has changed. Using a set threshold and algorithm, the data change detection module 2 identifies significant changes in the data and generates corresponding change signals. If the data has not changed, subsequent storage operations will not be triggered, thus reducing unnecessary data writes.

[0054] The storage control and optimization module 3 receives signals from the data change detection module 2 and determines whether to store the data in the industrial database module 4 based on the detected data changes. The storage control and optimization module 3 is responsible for implementing an on-demand storage strategy, meaning that storage operations are triggered only when data changes significantly. For frequently changing data, the storage control and optimization module 3 can also optimize the storage strategy, selecting batch storage or delayed storage to further improve storage efficiency.

[0055] Industrial database module 4 stores optimized data, supporting rapid data retrieval and management. This invention's industrial database storage system effectively indexes and manages the stored data, ensuring data integrity, traceability, and efficient query performance. By reducing the storage of redundant data, industrial database module 4 can better adapt to the management needs of large-scale data.

[0056] The management and monitoring module 5 provides system operators with an intuitive user interface, supporting real-time monitoring and management of the entire storage system. Users can view the system's operating status, data changes, storage space utilization, and more through this module. Furthermore, the management and monitoring module 5 allows users to dynamically adjust storage strategies to adapt to specific industrial needs.

[0057] Combination Figure 2 As shown, the present invention also provides an industrial database storage method based on data changes.

[0058] The industrial database storage system based on data change provided in the first aspect of this invention comprises a PLC data acquisition module 1 that collects data from industrial equipment in real time, a data change detection module 2 that monitors the data collected by the PLC data acquisition module 1 and determines whether the data has changed, a storage control and optimization module 3 that receives the signal from the data change detection module 2 and determines whether to store the data in the industrial database module 4 based on the detected data change, the industrial database module 4 that stores the optimized data and supports data query and management, and a management and monitoring module 5 that performs real-time monitoring and management of the industrial database storage system of this invention.

[0059] In an industrial environment, PLC data acquisition module 1 serves as the system's front end, responsible for acquiring various data from industrial equipment in real time. PLC data acquisition module 1 is connected to the industrial equipment, collecting key operational data such as temperature, pressure, flow rate, and vibration in real time. This data is then transmitted to data change detection module 2 via the I / O interface of PLC data acquisition module 1.

[0060] The data change detection module 2 is the core module of the industrial database storage system of this invention. It is responsible for real-time monitoring of the transmitted industrial data. The data change detection module 2 identifies data changes by monitoring data changes in industrial equipment at the data acquisition terminal. When data changes, a corresponding change signal is generated and transmitted to the storage control and optimization module 3. Specifically, the data change detection module 2 needs to configure the monitored variables and set event triggering conditions for each variable. Whenever a data change event is detected, a change signal is immediately generated and transmitted to the storage control and optimization module 3. This signal includes not only the changed data but also the timestamp of the data change and other necessary metadata.

[0061] The data change detection module 2 generates a fixed-length hash value through hash operation and compares the hash value of the current data with the hash value of the previously stored data to determine whether the data has changed.

[0062] Hash-based change detection is an efficient and universal method for detecting data changes, applicable to various data types, whether boolean, string, or numeric.

[0063] Specifically, when new data is received, a hash operation is first performed on the data to generate a hash value for the current data. If this hash value differs from the hash value of previously stored data, the data is considered to have changed, and a change signal is generated. Subsequently, this signal triggers a storage operation, writing the new data into the industrial database module 4. Simultaneously, the hash value of the old data is updated for use in the next change detection.

[0064] The core of this detection mechanism lies in the fact that hash values, as unique identifiers of data content, can quickly and accurately reflect data changes. When the hash value of data changes, whether due to a change in Boolean state (such as a device changing from on to off), a change in string (such as switching the operating mode from "automatic" to "manual"), or a fluctuation in numerical data (such as a temperature rising from 75℃ to 80℃), the system can immediately capture these changes. In this way, hash detection does not require independent processing of different data types, but uses a unified hash algorithm to generate and compare hash values, thus achieving universal change detection across data types.

[0065] Furthermore, another major advantage of hash detection lies in its efficiency. Compared to traditional methods that directly compare data content, generating and comparing hash values ​​requires far fewer computational resources and has lower computational time complexity, making it particularly suitable for industrial environments that handle large-scale data. By triggering storage operations only when the data hash value changes, this method effectively avoids repeatedly storing immutable data, greatly reducing system storage pressure and resource waste.

[0066] The storage control and optimization module 3 is responsible for receiving change signals generated by the data change detection module and determining when and how to store data in the industrial real-time database. The design focus of the storage control and optimization module 3 is to reduce the system's storage load and improve the efficiency of storage operations by optimizing storage strategies.

[0067] Combination Figure 3 As shown, the storage strategies include on-demand storage and delayed batch storage.

[0068] On-demand storage specifically includes:

[0069] For data that has changed significantly, a storage operation is immediately triggered to write the changed data to the industrial database module 4.

[0070] Delayed batch storage specifically includes:

[0071] For data that changes frequently but with small individual fluctuations, delayed storage can be chosen. After accumulating data changes over a certain period, the data can be stored in batches into the industrial database module 4. This strategy can further reduce the frequency of storage operations and lower the system load.

[0072] The implementation of Industrial Database Module 4 aims to efficiently store and manage data from the industrial field, ensuring rapid data access and accurate management. Its implementation includes the following:

[0073] Data storage: According to the instructions of the storage control and optimization module 3, the industrial database module 4 stores the changed data into the corresponding tables. Each data record includes information such as timestamp, device identifier, parameter type, and change value to ensure data integrity.

[0074] Data indexing and retrieval: Industrial database module 4 establishes multi-dimensional indexes for the stored data, including indexes by time, device, parameter type, and other dimensions. This indexing mechanism ensures that users can quickly locate the data they need when performing data queries and supports complex combinations of query conditions.

[0075] Data backup and recovery: To ensure data security, automatic data backup and fault recovery mechanisms are supported. Industrial database module 4 can periodically back up data to cloud storage or local redundant storage devices, ensuring rapid data recovery in the event of system failure.

[0076] The management and monitoring module 5 provides operators with a platform for real-time monitoring and adjustment of system configurations. Its specific implementation includes:

[0077] Real-time monitoring provides real-time monitoring capabilities for all components of the industrial database storage system of this invention, including the status of the PLC data acquisition module 1, data change detection module 2, storage control and optimization module 3, industrial database module 4, and management and monitoring module 5. When an abnormality or malfunction occurs in the industrial database storage system of this invention, the management and monitoring module 5 will trigger an alarm and provide detailed fault information to help operators quickly locate and resolve the problem.

[0078] The system allows users to adjust parameters such as data change detection thresholds and storage policy priorities through the management and monitoring modules to optimize system performance. The system also supports customized policy schemes to adapt to different industrial production requirements.

[0079] Data analysis and reporting provides a variety of data analysis tools and generates various reports based on actual needs, such as equipment operation status reports and storage space usage reports. These reports provide reliable data support for production management and decision-making.

[0080] In summary, this invention constructs a highly efficient industrial database storage system by combining multiple modules, including PLC data acquisition, data change detection, storage control and optimization, and industrial database management and monitoring. This system optimizes the data storage process, reduces storage redundancy, lowers system load, simplifies the system architecture, and provides flexible management and monitoring functions, making it suitable for large-scale data acquisition and management in industrial environments.

[0081] Existing technologies for handling large amounts of industrial data, such as data sharding and publish-subscribe mechanisms to reduce invalid queries, are all based on the premise that databases periodically receive and store real-time data such as production line sensor data. On this basis, data sharding is used to improve data storage efficiency, and publish-subscribe mechanisms are used to improve data retrieval efficiency to cope with the storage of large amounts of real-time industrial process data. However, neither considers the integration with the production characteristics of the production line. For a steel rolling production line, it fails to consider that due to production rhythm intervals, the actual effective utilization rate of equipment is far less than 100%. During equipment downtime, a large amount of data remains unchanged, is redundant and worthless, and can be ignored. This invention, however, uses a front-end data monitoring module to monitor data changes in real time, optimizes data storage strategies, and only stores data records when changes occur, thereby reducing database storage redundancy and improving data storage efficiency.

[0082] In existing technologies, distributed storage systems are widely used in the management of industrial real-time databases to address the challenges of large data volumes and high storage requirements. While distributed storage can effectively expand storage capacity and support parallel processing of large-scale data, it still has certain limitations and shortcomings. This invention, compared to distributed storage technology, exhibits significant technical advantages in several aspects, as follows:

[0083] 1) Accuracy of data storage

[0084] Distributed storage systems typically rely on periodic data acquisition and storage strategies, writing data to various distributed nodes even when it hasn't changed. While this ensures data integrity and continuity, it leads to the storage of a large amount of redundant data, increasing the system's storage pressure. This invention achieves precise storage based on actual data changes through a data change detection module. The system only triggers storage operations when data changes, avoiding unnecessary duplicate storage and significantly improving storage efficiency and effectiveness.

[0085] 2) System load optimization

[0086] Distributed storage systems, while expanding storage capacity, also increase system complexity and load, especially when handling frequent data writes, which can easily lead to system bottlenecks and performance degradation. This invention employs an on-demand storage and delayed batch storage strategy, which effectively reduces the system's I / O load by decreasing frequent write operations. This optimization strategy not only alleviates the system's storage pressure but also improves the system's operating efficiency and stability under high load conditions, avoiding system performance problems caused by over-storage.

[0087] 3) Simplified system architecture and ease of maintenance

[0088] Distributed storage systems have relatively complex architectures, requiring data synchronization and consistency maintenance among multiple nodes to ensure data integrity and availability. This complexity increases system maintenance difficulty and management costs. This invention simplifies data storage and management processes by introducing a modular system design. Functional modules such as the data change detection module, storage control and optimization module, and industrial database module work independently yet collaboratively, greatly simplifying the system architecture, reducing maintenance difficulty, and improving system manageability.

[0089] 4) Flexibility and adaptability

[0090] Distributed storage systems typically rely on fixed storage strategies and node configurations, making dynamic adjustments difficult to adapt to actual needs. This invention provides a flexible management and monitoring module, enabling users to dynamically adjust storage strategies and system configurations based on actual production requirements and data changes. This flexibility and adaptability allow the system to better suit different industrial environments and needs, performing particularly well in dynamic production environments where frequent adjustments to storage strategies are required.

[0091] In summary, compared with existing technologies, this invention offers significant technical advantages in terms of data storage accuracy, system load optimization, simplified architecture and ease of maintenance, as well as system flexibility and adaptability. These advantages make this invention more efficient and reliable in industrial environments, and it can meet the high demands of modern industrial production for large-scale data management. Through these innovations, this invention provides a superior solution for industrial real-time database storage systems.

[0092] This invention aims to optimize the storage process of industrial data, reduce storage redundancy, lower system load, improve storage efficiency and data management flexibility, and is particularly suitable for large-scale data acquisition and management in industrial environments.

[0093] 1) Multi-level modular design

[0094] This invention constructs a complete industrial data management system through a multi-layered modular design, including a PLC data acquisition module, a data change detection module, a storage control and optimization module, an industrial database module, and a management and monitoring module. Each module is responsible for different functions, and they cooperate to optimize the overall storage and management process. The overall architecture of this multi-layered modular design is protected, particularly the data flow and collaboration methods between modules, as well as the specific functional implementation of each module.

[0095] 2) Data change detection and signal generation mechanism

[0096] The data change detection module is the core of the system. It monitors the data transmitted by the PLC data acquisition module in real time to determine if significant changes have occurred. This module uses a pre-defined algorithm to identify data changes and generate corresponding change signals. Storage operations are only triggered when data changes, thus reducing unnecessary data storage and optimizing system performance. The design and implementation of the data change detection module are crucial, especially the mechanisms for data change detection and signal generation, including the selection of monitoring variables, the configuration of event triggering conditions, and the signal generation and transmission process.

[0097] 3) On-demand storage and delayed batch storage strategies

[0098] The storage control and optimization module flexibly determines the timing and method of data storage based on change signals generated by the data change detection module. Through an on-demand storage strategy, the system can immediately store data that has changed significantly; through a delayed batch storage strategy, the system can batch store data with minor changes at appropriate times, thereby effectively reducing system load. The protection of the on-demand storage and delayed batch storage strategies of the storage control and optimization module includes the selection criteria for storage strategies, the execution flow of storage operations, and the methods for batch storage management.

[0099] 4) Efficient management of industrial databases

[0100] The industrial database module is responsible for efficiently storing and managing data from the industrial field. It provides system operators with an intuitive user interface, supports real-time monitoring, strategy adjustment, and data analysis of the entire system. Through optimized data storage structures and multi-dimensional indexing mechanisms, the database enables rapid data retrieval and supports complex combinations of query conditions. Furthermore, it allows users to dynamically adjust parameters such as data change detection thresholds and storage strategy priorities according to actual needs to optimize system performance.

[0101] When system load continues to increase, the automated resource expansion module plays a crucial role. This module automatically determines whether additional underlying OPC UA servers are needed through real-time load monitoring, and expands server resources as necessary. The newly added server resources are automatically integrated into the existing server cluster, immediately sharing the load and ensuring system scalability and high availability. This dynamic expansion mechanism not only improves the system's processing capacity under high load conditions but also ensures continuous and stable system operation without manual intervention.

[0102] In summary, this invention constructs a highly efficient industrial data storage and management system through an innovative multi-layered modular design. The data change detection module in the system monitors data changes in real time, accurately judges and generates storage signals, avoiding the storage of large amounts of invalid data and significantly optimizing storage efficiency. The storage control and optimization module, based on the data change signals, adopts on-demand storage and delayed batch storage strategies, effectively reducing system load and ensuring the timeliness and flexibility of data storage. Through these technological innovations, this invention achieves efficient data storage management, simplifies and optimizes the system architecture, and significantly improves the application efficiency of industrial real-time databases in large-scale data acquisition and management. The aforementioned key points and points to be protected constitute the core technology of this invention and are an important component of its technical protection.

[0103] Example

[0104] This embodiment provides an industrial database storage system and method based on data change. The data change detection module is responsible for real-time monitoring of transmitted industrial data. This module identifies data changes by monitoring data changes in industrial equipment at the data acquisition end. For example, when the HMD (Hot Metal Detector) data of a steel rolling production line changes from 0 to 1, it means the steel plate has reached the location of the hot metal detector. The system generates a corresponding HMD change signal and transmits it to the storage control module. The storage control and optimization module receives the change signal generated by the data change detection module, adopts an on-demand storage strategy, immediately triggers the storage operation, and writes the changed data HMD=1 and the corresponding time of occurrence into the industrial real-time database.

[0105] When the Hot Metal Detector (HMD) data from the hot metal detector on the steel rolling production line changes from 1 to 0, it means that the steel plate has left the location of the hot metal detector. The system will generate a corresponding HMD change signal and transmit it to the storage control module. The storage control and optimization module is responsible for receiving the change signal generated by the data change detection module, and using an on-demand storage strategy, immediately triggers the storage operation, writing the changed data HMD=0 and the corresponding time of occurrence into the industrial real-time database.

[0106] While the steel plate is under the hot metal detector, HMD remains equal to 1, and the data is not saved. After the steel plate leaves the hot metal detector, HMD remains equal to 0, and the data is not saved. This avoids saving useless, unchanging, and redundant information to the database, thereby improving the database's operating efficiency.

[0107] In other words, the database storage process using the solution of this invention has the ability to judge and perceive the data value of the steel rolling production process.

[0108] Those skilled in the art should recognize that the above embodiments are merely illustrative of the present invention and are not intended to limit the present invention. Any variations or modifications to the above embodiments that are within the spirit and essence of the present invention will fall within the scope of the claims of the present invention.

Claims

1. An industrial database storage system based on data changes, characterized in that, include: PLC data acquisition module is used to acquire data from industrial equipment in real time; The data change detection module is used to monitor the data collected by the PLC data acquisition module and determine whether the data has changed. The storage control and optimization module is used to receive signals from the data change detection module and determine whether to store the data in the industrial database module based on the detected data changes. The industrial database module is used to store optimized data; The management and monitoring module is used for real-time monitoring and management of the industrial database storage system.

2. An industrial database storage method based on data changes, characterized in that: The industrial database storage system based on data change as described in claim 1 is used, wherein the PLC data acquisition module collects data from industrial equipment in real time, the data change detection module monitors the data collected by the PLC data acquisition module and determines whether the data has changed, the storage control and optimization module receives the signal from the data change detection module and determines whether to store the data in the industrial database module based on the detected data change, the industrial database module stores the optimized data and supports data query and management, and the management and monitoring module performs real-time monitoring and management of the industrial database storage system.

3. The industrial database storage method based on data changes according to claim 2, characterized in that: The PLC data acquisition module collects data from industrial equipment in real time, including temperature, pressure, flow rate, and vibration. The data acquired by the PLC data acquisition module is transmitted to the data change detection module through the I / O interface.

4. The industrial database storage method based on data changes according to claim 2, characterized in that: The data change detection module configures the monitored variables and sets event triggering conditions for each variable; Whenever the data change detection module detects a data change event, it immediately generates a change signal and transmits it to the storage control and optimization module.

5. The industrial database storage method based on data changes according to claim 4, characterized in that: The data change detection module generates a fixed-length hash value through hash operation and compares the hash value of the current data with the hash value of the previously stored data to determine whether the data has changed.

6. The industrial database storage method based on data changes according to claim 2, characterized in that: The storage strategies in the storage control and optimization module include on-demand storage and delayed batch storage.

7. The industrial database storage method based on data changes according to claim 6, characterized in that, The on-demand storage specifically includes: For data that has undergone significant changes, a storage operation is immediately triggered to write the changed data into the industrial database module; The delayed batch storage specifically includes: For data that changes frequently but with small individual changes, a delayed storage method is selected. After accumulating the data changes within a set time period, the data is then stored in batches into the industrial database module.

8. The industrial database storage method based on data changes according to claim 2, characterized in that, The implementation of the industrial database module specifically includes: Data storage: According to the instructions of the storage control and optimization module, the industrial database module stores the changed data into the corresponding tables. Each data record includes a timestamp, device identifier, parameter type, and change value information. Data indexing and retrieval: The industrial database module establishes multi-dimensional indexes for the stored data, including indexing by time, equipment, and parameter type. Data backup and recovery: Regularly back up data to cloud storage or local redundant storage devices.

9. The industrial database storage method based on data changes according to claim 2, characterized in that, The implementation of the management and monitoring module specifically includes: Real-time monitoring is performed on the status of the PLC data acquisition module, the data change detection module, the storage control and optimization module, the industrial database module, and the management and monitoring module. Strategy adjustments include dynamically adjusting the threshold for data change detection and the priority parameters of the storage strategy. Data analysis and reporting provides a variety of data analysis tools and generates various reports based on actual needs.

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