Nuclear power plant operator station time sequence data access method
By adopting a data access method based on an IoT time-series database, the problems of easy loss of historical data and low query efficiency of nuclear power plant operator stations have been solved, achieving efficient storage and fast query, and ensuring the real-time performance and accuracy of the data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NUCLEAR POWER OPERATION TECH CORP
- Filing Date
- 2025-11-25
- Publication Date
- 2026-04-28
AI Technical Summary
The historical data storage methods used in nuclear power plant operator stations suffer from problems such as easy data loss, low query efficiency, and high operation and maintenance costs, making it difficult to meet the demands of high-intensity operation.
A data access method based on IoT time-series database is adopted, which temporarily stores data through hash table, inserts data into IoT time-series database in batches, and uses multimodal coding and standardized query interface to achieve efficient storage and query.
It improves data reliability and integrity, reduces server load, saves storage space, supports high-frequency writing and fast querying, and ensures data real-time performance and accuracy.
Smart Images
Figure CN121935271A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of industrial time-series database technology, and in particular to a method, apparatus, computer equipment and storage medium for accessing time-series data at nuclear power plant operator stations based on an Internet of Things (IoT) time-series database. It is applicable to scenarios involving high-reliability historical data access and trend analysis at nuclear power plant operator stations. Background Technology
[0002] In nuclear power plant operator training and actual operation, trend functionality, as a crucial component of the operator station, is primarily used to display historical and real-time data trends of key parameters such as temperature, pressure, and flow rate. Currently, historical data is typically accessed through traditional file storage methods, but this approach suffers from the following problems:
[0003] 1. Data reliability defects: File overwriting makes data versions untraceable; server failures lead to the loss of historical data; problems are basically impossible to troubleshoot and locate.
[0004] 2. Business continuity defects: Low retrieval efficiency; as the data volume continues to increase, the file-based method cannot meet the high-intensity operation requirements of nuclear power plants in terms of storage and retrieval efficiency; occasional failures in storing and retrieving values based on the window platform.
[0005] 3. Operation and maintenance deficiencies: There is no standardized query interface, and custom file parsing tools need to be developed, making it difficult to easily browse stored historical values; there is a lack of data compression mechanism, and actual tests show that the number of files stored for seven days is as high as terabytes.
[0006] Therefore, there is an urgent need for an efficient, stable, and easy-to-maintain historical data access solution to ensure the reliable operation of nuclear power plant operator trend functions and the integrity of data. Summary of the Invention
[0007] The purpose of this invention is to solve the problems of easy data loss, low query efficiency, and high operation and maintenance costs in the storage of historical data files of nuclear power plant operator stations. It provides a method, device, computer equipment and storage medium for storing and retrieving time-series data of nuclear power plant operator stations based on IoT time-series database, so as to achieve efficient storage, efficient compression and efficient reading of time-series data, thereby ensuring the real-time and accuracy of key parameter data access.
[0008] To achieve the above objectives, on the one hand, the present invention provides a method for accessing time-series data at a nuclear power plant operator station based on an Internet of Things (IoT) time-series database, comprising the following steps:
[0009] S1. Data Acquisition: Obtain time-series data from the data server and temporarily store it in shared memory using a hash table;
[0010] S2. Storage Optimization: Time-series data is retrieved from shared memory and written to the IoT time-series database using a batch insertion method; the IoT time-series database storage format includes identifiers, simulated time, real time, and names of each point;
[0011] S3. Query Service: At the operator station, a query interface is provided to respond to query requests, retrieve time series data from the IoT time series database and return it.
[0012] As one possible approach, S1 retrieves time-series data from a data server and temporarily stores it in shared memory using a hash table, including the following steps:
[0013] Time-series data is retrieved from the data server, stored in a hash table through memory operations such as memory copying, and then placed in shared memory.
[0014] One possible approach is bulk insertion, which includes intelligent bulk write strategies and multimodal storage encoding.
[0015] One possible approach is the intelligent batch write strategy, which includes: collecting time-series data from shared memory at a certain sampling frequency and temporarily storing it in a vector container; using an insertion-aligned flat interface to batch fill the time-series data temporarily stored in the vector container through a flat structure; and inserting the time-series data in the vector container into the IoT time-series database at a certain insertion frequency; with the insertion frequency being lower than the sampling frequency.
[0016] One possible approach is multimodal storage encoding, which includes: selecting an encoding scheme based on the data type of the time-series data; using plaintext encoding for floating-point types to ensure precision; using run-length encoding for short integer types to improve compression ratio; and using run-length encoding for Boolean types to optimize storage.
[0017] One possible approach is to use a data storage format that includes identifiers, simulated time, real time, and values for multiple points. The identifiers are stacked sequentially, and the identifier is incremented by 1 for each batch of points inserted to improve read efficiency.
[0018] As one possible approach, the query interface supports SQL syntax and various query intervals, with query times for thousands of data points as low as milliseconds.
[0019] To achieve the above objectives, in another aspect, the present invention also provides a nuclear power plant operator station time-series data access device based on an Internet of Things time-series database, comprising:
[0020] The data acquisition system, deployed on the data server, is used to obtain time-series data from the data server and temporarily store it in shared memory using a hash table;
[0021] The storage optimization layer, deployed on the data server, is used to retrieve time-series data from shared memory and write the data into the IoT time-series database using a batch insertion method.
[0022] The service interface layer, deployed on the operator station, provides a query interface, responds to query requests, retrieves time-series data from the IoT time-series database, and returns the data.
[0023] To achieve the above objectives, in a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the above-described method for accessing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database.
[0024] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing computer-readable instructions, which, when executed, implement the steps of the aforementioned method for accessing and retrieving time-series data at a nuclear power plant operator station based on an Internet of Things time-series database.
[0025] Beneficial technical effects of the present invention:
[0026] This invention relates to a method, apparatus, computer equipment, and storage medium for accessing time-series data at nuclear power plant operator stations based on an IoT time-series database. Through batch insertion and memory optimization, the insertion frequency can reach 200 milliseconds, supporting high-frequency writing of over 50,000 points. Employing multimodal encoding, the compression rate reaches 65% to 85%, saving over 75% of space when storing seven days' worth of data. It provides a standardized query interface for easy data browsing and troubleshooting, supporting cross-platform queries. Data reliability is high; data lost can be retrieved from the server, improving data integrity to over 99.99%. Server load is reduced by over 90%, effectively avoiding server overload through batch insertion and frequency optimization. Attached Figure Description
[0027] Figure 1 This is a flowchart illustrating an embodiment of the nuclear power plant operator station time-series data access method based on an Internet of Things time-series database according to the present invention. Detailed Implementation
[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0030] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments.
[0031] See Figure 1 The flowchart illustrates an embodiment of a method for accessing time-series data at a nuclear power plant operator station based on an Internet of Things (IoT) time-series database. The method includes the following steps:
[0032] S1. Setup and Configuration of IoT Time Series Database Service Environment
[0033] S11. Deploy the IoT time-series database service on the data server, including installing JDK, configuring the Java environment, and setting IoT time-series database service parameters; IoT time-series database service parameters include memory parameters and the number of threads;
[0034] S2, Data Acquisition and IoT Time-Series Database Storage Design
[0035] S21. On the data server, by creating new types, the memory addresses of different data types are assigned to the new types and stored in the container;
[0036] S22. The measurement point information database adopts an Internet of Things time series database, and the storage format is shown in Table 1, including identifier (ID), simulated time, real time and name of each point;
[0037] S23. Add an ID to each batch of data, stacking them sequentially to improve reading efficiency; the ID is an integer, occupying 4 bytes, to avoid dynamic length processing;
[0038] S24. When storing real time and simulation time, use 500ms system time as a scale. Simulation time may have a 10ms error with real time, but by storing it in the IoT time series database, the operator station can select the query point and then limit it with system time and simulation time to obtain the value to be queried.
[0039] S3, Data Acquisition and Storage Process
[0040] S31. Adopt a multi-batch caching batch insertion strategy: temporarily store data in memory, accumulate it in batches, and then call the insertion alignment tablet interface to uniformly insert it into the IoT time series database, solving the server overload problem caused by high-frequency writing; specifically, in the early stage of deployment, the accumulation caused "write rejection", which was adjusted to multiple columns with the same timestamp aligned and submitted at once in batches of 10 frames. Compared with writing frame by frame, this strategy can reduce the overall burden on the server side; although it reduces the insertion frequency to 2 seconds / time, it is imperceptible for trend viewing and does not affect real-time data judgment.
[0041] S32: During the insertion process, exceptions such as out of range or unsupported data types are captured to avoid single points of failure affecting subsequent data insertion.
[0042] S33: The operator station starts the transmission control protocol server, listens for query requests from clients, and supports cross-platform data querying;
[0043] S4. Data Reading and Transmission
[0044] S41. The operator station client sends a query request, including information such as the measurement point name, measurement point length, start time, end time, and number of points;
[0045] S42. After the data server hears the query request, it queries the IoT time series database service for relevant data and returns the data to the client.
[0046] S43. Utilize the built-in cost-saving interface of the IoT time-series database to handle query and data transmission mechanisms, simplifying code development.
[0047] S5. Compared with traditional file storage methods, this invention has strong debuggability, can accurately locate insertion problems, and avoid data loss. Specifically, through the logs and exception capture mechanism of the IoT time-series database, the cause of insertion failure can be quickly located. To facilitate troubleshooting, the write path records "batch write size, time taken, return code, and number of retries". When encountering "write refused" or timeout, "window start and end, number of frames, batch size, and failure code" will be written into the log. In this way, the problem can be quickly located back to "which batch, which points, and what time".
[0048] S6. The operator station uses linear fitting to draw trend curves with a sampling frequency of 200ms to ensure trend accuracy. The number of sampling points is increased from more than 20,000 to more than 50,000, while maintaining an insertion frequency of 200 milliseconds (ms). It should be noted that when the minimum step size selected by the front end (such as 200ms) is less than the actual average collection interval, the front end only uses least squares fitting at the drawing layer to perform display-level interpolation on the empty spaces to ensure visual continuity. This interpolation is not used for statistics and export.
[0049] S7. Actual testing showed that storing 7 days of data traditionally requires TB-level space. This invention, through compression and optimization, reduces the storage space for 7 days of data from TB-level to GB-level, saving more than 75% of storage space.
[0050] S8. Through overall optimization, the data server load was reduced by more than 90%, and data integrity was improved to more than 99.99%. In this embodiment, the IoT time-series database is IoTDB.
[0051] The IoTDB table structure, designed to facilitate querying and writing and to be compatible with educational control operations such as reset, freeze, and rollback, is shown in Table 1.
[0052] Table 1 IoTDB Table Structure
[0053]
[0054] This invention employs a dual-time system synchronous storage mechanism, including the simulator's running time (SimTime) and the system's real time (SysTime). SimTime is generated by the internal clock of the nuclear power simulator and changes synchronously with the simulator's operating state (running, paused, frozen, rewind). SysTime is provided by the computer operating system but is also controlled by the control system; when the simulator performs a freeze, pause, or rewind operation, SysTime remains synchronously frozen or rewinding. This invention simultaneously writes SimTime and SysTime into each sampling record, enabling trend queries to analyze based on both virtual running time and real time dimensions. This solves the problem that special behaviors such as time reversal and freeze during nuclear power simulator operation are difficult to record using traditional single-timestamp models.
[0055] This invention uses the AlignedTablet structure of IoTDB to achieve aligned writing of two time fields and multi-measurement data, ensuring the synchronization of the dual-time system and the consistency of data, which can meet the unique timing consistency requirements in the nuclear power plant simulation environment.
[0056] As an implementation of the above method, the present invention provides an embodiment of a nuclear power plant operator station time-series data access device based on an Internet of Things time-series database. This device embodiment corresponds to the embodiment of the above-described nuclear power plant operator station time-series data access method based on an Internet of Things time-series database. This device can be specifically applied to various electronic devices.
[0057] The nuclear power plant operator station time-series data access device based on an IoT time-series database described in this embodiment includes:
[0058] The data acquisition system, deployed on the data server, is used to obtain time-series data from the data server and temporarily store it in shared memory using a hash table;
[0059] The storage optimization layer, deployed on the data server, is used to retrieve time-series data from shared memory and write the data into the IoT time-series database using a batch insertion method.
[0060] The service interface layer, deployed on the operator station, provides a query interface, responds to query requests, retrieves time-series data from the IoT time-series database, and returns the data.
[0061] As an implementation of the above method, the present invention provides an embodiment of a computer device, which corresponds to the embodiment of the above method.
[0062] The computer device described in this embodiment includes a memory and a processor. The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the steps of the above-described method for accessing and retrieving time-series data at a nuclear power plant operator station based on an Internet of Things time-series database.
[0063] As an implementation of the above method, the present invention provides an embodiment of a computer-readable storage medium, which corresponds to the embodiment of the above method.
[0064] The computer-readable storage medium described in this embodiment stores computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the above-described method for accessing time-series data at a nuclear power plant operator station based on an IoT time-series database.
[0065] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.
Claims
1. A method for accessing time-series data at a nuclear power plant operator station based on an Internet of Things (IoT) time-series database, characterized in that, Includes the following steps: S1. Data Acquisition: Obtain time-series data from the data server and temporarily store it in shared memory using a hash table; S2. Storage Optimization: Time-series data is retrieved from shared memory and written to the IoT time-series database using a batch insertion method; the IoT time-series database storage format includes identifiers, simulated time, real time, and names of each point; S3. Query Service: At the operator station, a query interface is provided to respond to query requests, retrieve time series data from the IoT time series database and return it.
2. The method for accessing and storing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database according to claim 1, characterized in that, S1, retrieve time-series data from the data server and temporarily store it in shared memory using a hash table, including the following steps: Time-series data is retrieved from the data server, stored in a hash table through memory operations, and then placed in shared memory.
3. The method for accessing and storing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database according to claim 1, characterized in that, Batch insertion includes intelligent batch write strategies and multimodal storage encoding.
4. The method for accessing and storing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database according to claim 3, characterized in that, The intelligent batch write strategy includes: collecting time-series data from shared memory at a certain sampling frequency and temporarily storing it in a vector container; using an insertion-aligned flat interface, batch filling the time-series data temporarily stored in the vector container through a flat structure, and inserting the time-series data in the vector container into the IoT time-series database at a certain insertion frequency; the insertion frequency is lower than the sampling frequency.
5. The method for accessing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database according to claim 3, characterized in that, Multimodal storage encoding includes: selecting an encoding scheme based on the data type of time-series data; using plaintext encoding for floating-point types to ensure precision; using run-length encoding for short integer types to improve compression ratio; and using run-length encoding for Boolean types to optimize storage.
6. The method for accessing and storing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database according to claim 1, characterized in that, The data storage format includes identifiers, simulated time, real time, and values of multiple points. The identifiers are sequentially stacked, and the identifier is incremented by 1 for each batch of points inserted.
7. The method for accessing and storing time-series data at a nuclear power plant operator station based on an Internet of Things time-series database according to claim 1, characterized in that, The query interface supports SQL syntax and multiple query intervals, with query time for thousands of data points as low as milliseconds.
8. A time-series data storage and retrieval device for a nuclear power plant operator station based on an Internet of Things (IoT) time-series database, characterized in that, include: The data acquisition system, deployed on the data server, is used to obtain time-series data from the data server and temporarily store it in shared memory using a hash table; The storage optimization layer, deployed on the data server, is used to retrieve time-series data from shared memory and write the data into the IoT time-series database using a batch insertion method. The service interface layer, deployed on the operator station, provides a query interface, responds to query requests, retrieves time-series data from the IoT time-series database, and returns the data.
9. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, characterized in that, When the processor executes the computer-readable instructions, it implements the steps of the nuclear power plant operator station time-series data access method based on an Internet of Things time-series database as described in any one of claims 1-7.
10. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, When the computer-readable instructions are executed, they implement the steps of the nuclear power plant operator station time-series data access method based on an Internet of Things time-series database as described in any one of claims 1-7.