Structured management system for intelligent supervision database of coal-fired power plant

Abstract

The invention provides a structured management system for an intelligent supervision database of a coal-fired power plant. The system comprises a disk monitoring system database acquisition module, a disk monitoring system database management module, a disk monitoring system database browsing module and a disk monitoring system database background module, and data compression, data storage and data backup recovery are performed on acquired data, so that centralized management of disk monitoring system data is realized; meanwhile, data information sharing with other monitoring systems of the coal-fired power plant is realized through related communication interfaces; the data compression adopts an LZW lossless data compression algorithm; the data storage is based on an SDN network architecture, an optimal cloud storage strategy combination is selected according to different data characteristics of a coal-fired power plant, an optimal cloud storage strategy combination is selected according to different data characteristics of a power enterprise, the purpose of improving the storage performance is achieved, and unified centralized management and maintenance are formed for a database.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and in particular to an intelligent monitoring panel database management system for a coal-fired power plant. Background Art

[0002] With the advent of the big data era, the Internet of Things and cloud technology have penetrated into all aspects of life. In particular, power companies are generating massive amounts of data information every moment. Centralized management of data information can not only improve the work efficiency of power companies, but also enhance their core competitiveness.

[0003] As the core of the information system of power enterprises, database plays a very important role in the intelligent transformation of power enterprises. The advantage of database system is that it can not only complete the storage and maintenance of data, but also complete the distributed service and concurrent operation control of data. However, the storage strategy and storage method of traditional database system have problems such as high data redundancy, difficult data query, and inability to realize data sharing, and no unified centralized management and protection have been formed. Therefore, it is particularly important to manage the database system. Summary of the invention

[0004] The present application provides a structured management system for an intelligent monitoring database of a coal-fired power plant, which is intended to adapt to the storage and simple query of massive and complex monitoring data, and form a unified centralized management system.

[0005] A structured management system for an intelligent monitoring database of a coal-fired power plant, comprising:

[0006] The monitoring system database acquisition module is used to collect data from the electrical system, steam-water system and boiler system related to the operating status of the coal-fired unit, and complete the control system data acquisition through the relevant data acquisition interface;

[0007] The monitoring system database management module is used to compress, store and back up the data of the monitoring system database acquisition module, realize the centralized management of the monitoring system data, and realize the sharing of data information with other monitoring systems of the coal-fired power plant through relevant communication interfaces; wherein, the data compression adopts the LZW lossless data compression algorithm; the data storage is based on the SDN network architecture, and the optimal cloud storage strategy combination is selected according to the different data characteristics of the coal-fired power plant;

[0008] The monitoring system database browsing module is used to output the data of the monitoring system database acquisition module, query the results, generate reports and conduct human-computer interaction, providing intuitive real-time monitoring images for power plant operation and maintenance personnel, and providing a reference for macro decision-making for power plant managers;

[0009] The backend module of the monitoring system database is used to manage user rights, configure data configuration and database expansion of the monitoring system database, and regularly maintain and manage the database to ensure the security of the database.

[0010] Furthermore, the main steps of the LZW lossless data compression algorithm are as follows:

[0011] 1) Read byte p and determine whether compression is complete;

[0012] 2) Calculate the hash address and read the dictionary based on the hash address to determine whether the value in the address is empty;

[0013] 3) Determine whether the dictionary conflicts and whether the dictionary is full, and assign the value of w to p;

[0014] 4) Complete the compression.

[0015] Furthermore, the monitoring system database browsing module includes a result query unit, a report generation unit and a human-computer interaction unit; wherein:

[0016] The result query unit provides users with quick query and compound query buttons, and displays the query results in a list format;

[0017] The report generation unit automatically generates daily operation reports according to the set time, reflects the alarm status of the measuring point through the background color, and provides electronic signature function;

[0018] The human-computer interaction unit uses the main viewing area, monitoring alarm list area and graphic data selection area to provide a full-scale display of the database system.

[0019] Furthermore, the database backend module of the monitoring system includes a user authority management unit, a data configuration unit and a database expansion unit; wherein:

[0020] The user authority management unit is used to assign certain operation authority to different power plant users, and divide the power plant users into login query users, resource integration users and database management users according to different authority levels;

[0021] The data configuration unit supports users to create, modify and update data in the database one by one or in batches to complete the configuration of the database configuration;

[0022] The database expansion unit is used to modify the database form through the database expansion function to meet the needs of different power plant users.

[0023] Furthermore, the different data characteristics of the coal-fired power plant are classified according to the timeliness, access frequency, structure type, scale and security of the data.

[0024] Specifically, according to the timeliness and access frequency of data, the following are possible:

[0025] For real-time data, the storage strategy is: use streaming data processing services with in-memory databases to quickly read, write, and process data;

[0026] For hot data, the storage strategy is: use high-performance block storage, file system, or use the high-performance tier of the object storage service;

[0027] For warm data, the storage strategy is: use OSS’s intelligent tiered storage, which automatically moves infrequently accessed data to a lower-cost storage tier;

[0028] For cold data, the storage strategy is to use OSS archive storage or glacier storage.

[0029] Specifically, according to the structural type of the data, the following are possible:

[0030] For structured data, use a relational database, cloud-native database, or data warehouse service;

[0031] For semi-structured / unstructured data, use cloud implementations of object storage services or big data storage services.

[0032] Furthermore, the monitoring system database management module uses adaptive load balancing technology to monitor the performance indicators of each server node in real time and dynamically allocate resources to cope with constantly changing requests and load patterns.

[0033] Compared with the prior art, this application has at least the following beneficial effects:

[0034] The present application performs data compression, data storage and data backup and recovery on the data of the acquisition module of the monitoring system database, realizes the centralized management of the monitoring system data, and realizes the sharing of data information between other monitoring systems of the coal-fired power plant through relevant communication interfaces; among them, the LZW lossless compression algorithm is applied to the management of the coal-fired unit database for the first time, which can further improve the compression rate without losing the reconstruction accuracy, making up for the limitation of the reconstruction error of the traditional lossy compression algorithm, and has the advantage of high compression rate; in addition, if the existing technology adopts a lossless compression algorithm, Huffman coding is usually adopted, which is a variable-length coding method based on frequency statistics (prefix coding), which is only encoded according to the frequency of characters, while the LZW coding adopted in the present application is an adaptive coding technology, which can dynamically update the dictionary according to the actual data, so that it is more suitable for the structured management of the intelligent monitoring database; at the same time, based on the SDN network architecture and according to the different data characteristics of the coal-fired power plant, the optimal cloud storage strategy combination is selected, so as to be more efficient, economical and meet business needs, realize the rapid query of data and data sharing, and form a unified centralized management and maintenance of the database. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a structural block diagram of a structured management system for an intelligent monitoring database of a coal-fired power plant described in this application;

[0036] Figure 2 This is the flow chart of the LZW lossless compression algorithm.

[0037] The numbers in the figure show:

[0038] 1. Monitoring system database acquisition module; 2. Monitoring system database management module; 3. Monitoring system database browsing module; 4. Monitoring system database background module;

[0039] 21. Data compression unit; 22. Data storage unit; 23. Data backup and recovery unit;

[0040] 31. Result query unit; 32. Report generation unit; 33. Human-computer interaction unit;

[0041] 41. User authority management unit; 42. Data configuration unit; 43. Database expansion unit. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] In the description of the present application: unless otherwise specified, expressions such as “include”, “comprising”, “having”, etc. also mean “not limited to” (certain units, components, devices, steps, etc.).

[0044] The data in the existing monitoring system is stored in a messy manner, the data volume is huge, and there is a lack of unified management and maintenance. It is impossible to achieve information sharing and mutual interaction with the existing SIS, DCS and other systems of the coal-fired power plant, and it is even more impossible to provide complete decision-making support for power companies. For this reason, an embodiment of the present application is based on the monitoring system database to provide a structured management system for the intelligent monitoring database of a coal-fired power plant.

[0045] like Figure 1 As shown, the structured management system of the intelligent monitoring database of the coal-fired power plant includes a monitoring system database acquisition module 1, a monitoring system database management module 2, a monitoring system database browsing module 3 and a monitoring system database background module 4.

[0046] A) The monitoring system database acquisition module 1 is used to collect data from the electrical system, steam-water system and boiler system related to the operating status of the coal-fired unit, and complete the control system data acquisition through the relevant data acquisition interface.

[0047] B) Monitoring system database management module 2 is used to compress, store and back up the data of the monitoring system database acquisition module, realize centralized management of monitoring system data, and share data information with other monitoring systems of the coal-fired power plant through relevant communication interfaces. The data compression adopts the LZW (Lempel-Ziv-Welch) lossless data compression algorithm; the data storage is based on the SDN network architecture, and the optimal cloud storage strategy combination is selected according to the different data characteristics of the coal-fired power plant.

[0048] The monitoring system database management module 2 can be divided into a data compression unit 21, a data storage unit 22 and a data backup and recovery unit 23, as follows:

[0049] b1) Data compression unit 21: This unit uses the LZW lossless data compression algorithm to achieve fast data processing and transmission, which not only reduces the data bandwidth but also greatly reduces the occupied storage space;

[0050] Here, the main steps of the LZW lossless data compression algorithm are as follows:

[0051] 1) Read byte p and determine whether compression is complete;

[0052] 2) Calculate the hash address and read the dictionary based on the hash address to determine whether the value in the address is empty;

[0053] 3) Determine whether the dictionary conflicts and whether the dictionary is full, and assign the value of w to p;

[0054] 4) Complete the compression.

[0055] b2) Data storage unit 22: This unit adopts an efficient cloud storage strategy and selects the optimal cloud storage strategy combination according to different data characteristics of power enterprises to achieve the purpose of improving storage performance;

[0056] b3) Data backup and recovery unit 23: This unit realizes the replication of database structure and data by automatic backup, and can automatically restore the database when a database failure occurs.

[0057] The monitoring system database management module 2 uses SDN technology (Software Defined Networking) to achieve a flexible and dynamic network management mode, optimize network performance in real time, and make network configuration and management simpler and more intuitive. SDN technology separates the control plane from the data forwarding plane of network devices, and manages the traffic and routing policies of the entire network through a centralized controller. This architecture enables network administrators to manage the network in a more flexible and dynamic way, which is mainly reflected in the following aspects:

[0058] Centralized control: The controller in the SDN architecture can see the status of the network globally and adjust network resource allocation according to real-time needs. This means that network management is no longer scattered across various network devices, but is uniformly controlled by a central point, simplifying network configuration and management.

[0059] Automation and programming: Network administrators can interact with SDN controllers through programming interfaces (APIs) to automatically perform network configuration and policy adjustments, which greatly reduces the workload of manual configuration and improves efficiency and accuracy.

[0060] Real-time optimization: SDN can monitor network performance in real time and respond. For example, when network congestion is detected, it can automatically reroute traffic or quickly restore services when certain links fail, thereby keeping the network in optimal condition.

[0061] Policy-driven networking: SDN allows for the flexible formulation and immediate application of network policies, for example, dynamically adjusting network behavior based on business needs, security policies, or quality of service (QoS) requirements.

[0062] Resource pooling and virtualization: SDN supports the pooling and virtualization of network resources, which means that physical network resources can be abstracted into virtual resources and dynamically allocated as needed to improve resource utilization.

[0063] Enhanced security: Because SDN provides comprehensive visibility and control over the network, security policies can be implemented more effectively, such as isolating specific traffic and blocking malicious attacks.

[0064] The monitoring data of coal-fired power plants may have the characteristics of high frequency, large volume, unstructured, and time series. These data may need to be processed in real time, or they may be used for long-term analysis and historical trend research. In order to effectively handle the above data characteristics, this embodiment classifies the data according to the dimensions of timeliness, access frequency, structure type, scale, and security, and implements the corresponding optimal cloud storage strategy based on different data categories with these characteristics:

[0065] The characteristics of coal-fired power plant intelligent monitoring data are diverse and can be classified according to the dimensions of data timeliness, access frequency, structure type, scale and security. The following are different data categories based on these characteristics and the corresponding optimal cloud storage strategies.

[0066] Classification by data timeliness and access frequency:

[0067] 1. Real-time data

[0068] - Characteristics: High-speed generation, requiring immediate processing and response.

[0069] -Storage strategy: Use streaming data processing services such as Kinesis or MQTT, combined with in-memory databases such as Redis or Memcached to quickly read, write, and process data.

[0070] 2. Hot Data

[0071] - Characteristics: Recently generated, frequently accessed.

[0072] -Storage strategy: Use high-performance block storage such as EBS (Elastic Block Store) or file systems such as NFS, or use the high-performance tier of object storage services, such as the standard storage type of Alibaba Cloud OSS.

[0073] 3. Warm data

[0074] - Characteristics: moderate access frequency, possibly used for periodic analysis.

[0075] -Storage strategy: Use lower-cost object storage services, such as OSS's intelligent tiered storage type, which automatically moves infrequently accessed data to lower-cost storage tiers.

[0076] 4. Cold Data

[0077] - Features: low access frequency and long-term preservation.

[0078] - Storage strategy: Use archival storage services, such as OSS's archive storage type, or Glacier Storage, which offer extremely low costs and long access latency.

[0079] Classification by data structure type:

[0080] 1. Structured Data

[0081] - Features: has fixed format and mode.

[0082] -Storage strategy: Use relational databases such as RDS (Relational Database Service) or cloud-native databases such as PolarDB, or data warehouse services such as MaxCompute.

[0083] 2. Semi-structured / unstructured data

[0084] - Features: no fixed format, such as log files, images, videos, etc.

[0085] -Storage strategy: Use object storage services such as OSS, or big data storage services such as HDFS (Hadoop Distributed File System) in the cloud.

[0086] Classification by data size:

[0087] 1. Small-scale data

[0088] -Features: The data volume is small and may be monitoring data in the initial deployment phase or certain specific functional modules.

[0089] -Storage strategy: Use relational database services (such as Amazon RDS, Google Cloud SQL, Alibaba Cloud RDS) to store structured data for easy query and analysis. For unstructured data, you can use small object storage services (such as AWS S3, Google Cloud Storage, Alibaba Cloud OSS) and enable version control to maintain data integrity.

[0090] 2. Medium-scale data

[0091] -Features: The data volume is moderate, which may involve real-time monitoring data of multiple devices and requires frequent read and write operations.

[0092] -Storage strategy: Structured data can continue to use relational database services, but consider adding read-write replicas or using cloud-native databases (such as Google Cloud Spanner and Alibaba Cloud PolarDB) to improve performance and reliability. Unstructured data is suitable for using standard-tier object storage services to ensure high-speed reading and writing while controlling costs.

[0093] 3. Large-scale data

[0094] - Features: PB-level data volume, involving a mixture of historical data and real-time data with a long span of time.

[0095] -Storage strategy: Use distributed databases or big data storage services (such as Google Bigtable, Alibaba Cloud Table Store) to process massive structured data and support high-concurrency read and write. Unstructured data should use cost-optimized object storage (such as AWS S3 GlacierDeepArchive, Alibaba Cloud OSS archive storage), combined with intelligent tiered storage, to automatically transfer infrequently used data to a lower-cost storage tier.

[0096] According to data security classification:

[0097] 1. Public Data

[0098] - Features: The data does not contain sensitive information and will not pose a security risk when published or shared.

[0099] - Storage strategy: You can use cost-effective storage services, such as standard object storage services, without additional security measures.

[0100] 2. Sensitive Data

[0101] -Characteristics: The data contains business secrets, personal privacy or other sensitive information, which requires strict security and access control.

[0102] -Storage strategy: Use encrypted storage services, such as object storage with server-side encryption enabled (SSE-S3, SSE-KMS), to ensure that data is protected both in transit and in storage. Implement fine-grained access control and auditing, and use IAM (Identity and Access Management) services to manage user permissions. Consider using private or hybrid cloud deployments to enhance the physical isolation and security of data.

[0103] 3. Regulatory Compliance Data

[0104] - Characteristics: Data must comply with specific industry regulations or standards, such as GDPR, HIPAA, SOX, etc.

[0105] -Storage strategy: Choose cloud storage providers that meet specific regulatory requirements. These services usually provide data residency options, data encryption, compliance reporting, etc. Use logging and auditing services to track data access and modification to ensure traceability.

[0106] In actual applications, the intelligent monitoring data of coal-fired power plants may have multiple characteristics at the same time, so the optimal cloud storage strategy may be a combination of the above strategies to balance cost, performance and security requirements.

[0107] In this embodiment, adaptive load balancing technology is also used to monitor the performance indicators of each server node in real time and dynamically allocate resources to cope with the ever-changing requests and load patterns, further improving reliability and stability. Specifically, it includes:

[0108] 1. Software-defined load balancing

[0109] The load balancing services of cloud platforms (such as AWS's ELB and Azure's Application Gateway) can automatically adjust traffic distribution according to real-time load, and can even trigger automatic expansion and contraction events to cope with changes in request volume.

[0110] 2. Containerization and microservice architecture + container orchestration tools

[0111] Container orchestration tools such as Kubernetes can automatically adjust the number of container instances based on resource usage to achieve automatic expansion and contraction. For example, when CPU or memory usage exceeds a certain threshold, instances are automatically increased; conversely, when the load decreases, instances are automatically reduced to save resources.

[0112] 3. Intelligent Algorithm

[0113] AI or machine learning-based algorithms can learn historical load patterns, predict future load demands, and adjust resource allocation in advance to cope with upcoming peaks or troughs.

[0114] 4. Elastic Scaling Strategy

[0115] Elastic scaling groups (such as AWS's Auto Scaling Group) can automatically increase or decrease server instances based on preset indicators (such as CPU usage and network traffic) to maintain a stable performance level.

[0116] 5. Failover and redundancy

[0117] The heartbeat detection and health check mechanism can monitor the server status in real time. When a failure is detected, it automatically redirects the traffic to the healthy server to achieve seamless switching.

[0118] 6. Database-level load balancing

[0119] Database proxies and connection pools can intelligently allocate database connections based on current load and resource usage, optimize read and write operations, and reduce response time.

[0120] These adaptive features allow the system to dynamically adjust resource allocation based on real-time monitored performance indicators and load patterns in the face of changing requests and load patterns to maintain optimal performance and resource utilization, thereby achieving more efficient and reliable operation.

[0121] In addition, the network protocols and technologies of the cloud storage system can be optimized by adopting faster Ethernet technology and fiber optic communication technology to further improve data transmission efficiency.

[0122] C) Monitoring system database browsing module 3, used for outputting data of monitoring system database acquisition module, querying results, generating reports and human-computer interaction, providing intuitive real-time monitoring screen for power plant operation and maintenance personnel, and providing macro decision-making reference for power plant management personnel, including result query unit 31, report generation unit 32 and human-computer interaction unit 33, as follows:

[0123] c1) Result query unit 31: This unit provides users with quick query and compound query buttons, and displays the query results in the form of a list;

[0124] c2) Report generation unit 32: This unit automatically generates a daily operation report according to the set time, reflects the alarm status of the measuring point through the background color, and provides an electronic signature function;

[0125] c3) Human-computer interaction unit 33: This unit uses the main viewing area, the monitoring alarm list area and the graphic data selection area to display the database system in an all-round way;

[0126] The main viewing area is used to display the overall operation status of the coal-fired unit equipment. The alarm can be suspended and restored for the equipment, system or monitoring group that does not need to be alarmed temporarily.

[0127] Among them, the monitoring alarm list area can display alarm signals. By clicking, you can enter the detailed processing interface, which has the functions of sorting and classifying the alarm signal list, as well as single and batch confirmation of alarm signals;

[0128] Wherein, the graphic data selection area is used to display the graphic data information of the selected recording signal;

[0129] D) The monitoring system database backend module 4 is used to manage user rights, configure data configuration and expand the database of the monitoring system database, regularly maintain and manage the database to ensure the security of the database, including a user rights management unit 41, a data configuration unit 42 and a database expansion unit 43, as follows:

[0130] d1) User authority management unit 41, which specifies certain operation authority for different power plant users, and divides the power plant users into login query users, resource integration users and database management users according to different authority levels;

[0131] d2) Data configuration unit 42, which can create, modify and update data in the database one by one or in batches to complete the configuration of the database configuration;

[0132] d3) Database expansion unit 43: This unit completes the modification of database forms through the database expansion function to meet the needs of different power plant users.

[0133] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A structured management system for intelligent monitoring database of coal-fired power plants. It is characterized in that include: The monitoring system database acquisition module is used to collect data from the electrical system, steam-water system and boiler system related to the operating status of the coal-fired unit, and complete the control system data acquisition through the relevant data acquisition interface; The monitoring system database management module is used to compress, store and back up the data of the monitoring system database acquisition module, realize the centralized management of the monitoring system data, and realize the sharing of data information with other monitoring systems of the coal-fired power plant through relevant communication interfaces; wherein the data compression adopts the LZW lossless data compression algorithm; The data storage is based on the SDN network architecture and selects the optimal cloud storage strategy combination according to the different data characteristics of the coal-fired power plant; The monitoring system database browsing module is used to output the data of the monitoring system database acquisition module, query the results, generate reports and conduct human-computer interaction, providing intuitive real-time monitoring images for power plant operation and maintenance personnel, and providing a reference for macro decision-making for power plant managers; The backend module of the monitoring system database is used to manage user rights, configure data configuration and database expansion of the monitoring system database, and regularly maintain and manage the database to ensure the security of the database.

2. According to claim 1, the structured management system of the intelligent monitoring database of the coal-fired power plant, It is characterized in that The main steps of the LZW lossless data compression algorithm are as follows: 1) Read byte p and determine whether compression is complete; 2) Calculate the hash address and read the dictionary based on the hash address to determine whether the value in the address is empty; 3) Determine whether the dictionary conflicts and whether the dictionary is full, and assign the value of w to p; 4) Complete the compression.

3. According to claim 1, the structured management system of the intelligent monitoring database of the coal-fired power plant, It is characterized in that The monitoring system database browsing module includes a result query unit, a report generation unit and a human-computer interaction unit; wherein: The result query unit provides users with quick query and compound query buttons, and displays the query results in a list format; The report generation unit automatically generates daily operation reports according to the set time, reflects the alarm status of the measuring point through the background color, and provides electronic signature function; The human-computer interaction unit uses the main viewing area, monitoring alarm list area and graphic data selection area to provide a full-scale display of the database system.

4. According to claim 1, the structured management system of the intelligent monitoring database of the coal-fired power plant, It is characterized in that The monitoring system database backend module includes a user authority management unit, a data configuration unit and a database expansion unit; wherein: The user authority management unit is used to assign certain operation authority to different power plant users, and divide the power plant users into login query users, resource integration users and database management users according to different authority levels; The data configuration unit supports users to create, modify and update data in the database one by one or in batches to complete the configuration of the database configuration; The database expansion unit is used to modify the database form through the database expansion function to meet the needs of different power plant users.

5. According to claim 1, the structured management system of the intelligent monitoring database of the coal-fired power plant, It is characterized in that The different data characteristics of the coal-fired power plant are classified according to the timeliness, access frequency, structure type, scale and security of the data.

6. The structured management system of the intelligent monitoring database of a coal-fired power plant according to claim 5, It is characterized in that According to the timeliness and access frequency of data, the following are classified: For real-time data, the storage strategy is: use streaming data processing services with in-memory databases to quickly read, write, and process data; For hot data, the storage strategy is: use high-performance block storage, file system, or use the high-performance tier of the object storage service; For warm data, the storage strategy is: use OSS’s intelligent tiered storage, which automatically moves infrequently accessed data to a lower-cost storage tier; For cold data, the storage strategy is to use OSS archive storage or glacier storage.

7. The structured management system of the intelligent monitoring database of a coal-fired power plant according to claim 5, It is characterized in that According to the structural type of data, the following are classified: For structured data, use a relational database, cloud-native database, or data warehouse service; For semi-structured / unstructured data, use cloud implementations of object storage services or big data storage services.

8. The structured management system of the intelligent monitoring database of a coal-fired power plant according to claim 5, It is characterized in that The monitoring system database management module adopts adaptive load balancing technology to monitor the performance indicators of each server node in real time and dynamically allocate resources to cope with the constantly changing requests and load patterns.

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