CN114387124B - Time sequence data storage method of nuclear power industry internet platform - Google Patents
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Abstract
The invention provides a time sequence data storage method of a nuclear power industry internet platform, which comprises the following steps: the time sequence data acquisition and uploading tools on the power plant interface machines respectively send the time sequence data to the MQTT server at the center side of the nuclear power industrial Internet platform through the corresponding MQTT client, and respectively upload the time sequence data to different Topic according to the attribute of the time sequence data; setting a first consumer module, subscribing and consuming the data in the MQTT server by the first consumer module and then processing the data; the Kafka system receives the time sequence data sent by the first consumer module and stores different types of time sequence data in different topics; setting a second consumer module, subscribing and consuming the data in the Kafka message system by the second consumer module and then processing the data; the time sequence database is used for storing the low-frequency time sequence data and the high-frequency time sequence data separately according to the attribute of the time sequence data. The method provided by the invention can manage time sequence data more reasonably and efficiently.
Description
Technical Field
The invention relates to the technical field of time sequence data storage, in particular to a time sequence data storage method of a nuclear power industry internet platform.
Background
During the operating phase of a nuclear power plant, a large amount of measurement data is generated. The measured data are data for sensing the running state of the nuclear power plant and advancing the digital and intelligent management of the power plant. The measurement data includes online and offline measurement data. The online measurement data mainly comprises production process control system data and data collected by sensors, intelligent terminals and the like of the industrial Internet of things. Offline measurement data is typically obtained from measurement records during operations such as running, servicing, etc. on power plant equipment. The time sequence data is further divided into high-frequency data and low-frequency data according to the difference of acquisition frequency and data sources.
At present, china's nuclear power large nuclear source platform (DHP) is established by China's Wuhan nuclear power operation technology stock, serves as a supporting platform and a neural center of digital nuclear power, integrates data of China's nuclear power massive industrial systems and equipment, constructs an extensible open type nuclear power industrial Internet platform, synchronously develops a nuclear power industrial application development ecological system oriented to various scenes and reusability, improves the utilization efficiency and sharing range of nuclear power plant hardware, service and data, realizes intelligent management and operation optimization of China's nuclear power business and resources, and drives a series of innovative nuclear power industrial applications oriented to nuclear power full-industry chains.
The effective acquisition and storage of the time sequence data are one of the bases for effective development of the work of the whole China nuclear power large nuclear source platform, and in order to access and store the time sequence data more reasonably and efficiently, it is necessary to provide a time sequence data storage method of the nuclear power industrial Internet platform so as to conduct standard and efficient management on the time sequence data storage mode of the nuclear power industrial Internet platform.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, thereby providing the time sequence data storage method of the nuclear power industrial Internet platform, which can realize the effective acquisition and standardized storage of the time sequence data of the nuclear power industrial Internet platform, and further manage the time sequence data more reasonably and efficiently.
In order to achieve the above object, the present invention provides the following technical solutions:
a time sequence data storage method of a nuclear power industry internet platform comprises the following steps:
Step S1: the time sequence data acquisition and uploading tools on the power plant interface machines respectively send the time sequence data to the MQTT server at the center side of the nuclear power industrial Internet platform through the corresponding MQTT client, and respectively upload the time sequence data to different Topic according to the attribute of the time sequence data;
step S2: setting a first consumer module, wherein the first consumer module subscribes and consumes data in the MQTT server and then processes the data;
step S3: the Kafka system receives the time sequence data sent by the first consumer module and stores different types of time sequence data in different topics;
step S4: setting a second consumer module, wherein the second consumer module subscribes to and consumes data in the Kafka message system and then processes the data;
Step S5: the time sequence database is used for storing the low-frequency time sequence data and the high-frequency time sequence data separately according to the attribute of the time sequence data.
Further, the processing of the received time series data packet by the second consumer module comprises the following steps:
step S41: unpacking, decrypting and decompressing the received time sequence data respectively to obtain original data information;
Step S42: and according to the obtained high-low frequency attribute of the original data, calling a preset rule to convert the original data into a specified data format, and determining a storage area of the original time sequence data in a database according to the measuring point attribute of the original data.
Further, the step S42 of calling a preset rule to convert the original data into the specified data format includes: and acquiring the data type of the time sequence data, and converting the original data into a specified data format according to the data type and a preset rule.
Further, the preset rule is:
If the data type value is 0, converting the time sequence data measuring point value into a double-precision floating point value;
If the data type value is 1, converting the time sequence data measuring point value into a single-precision floating point value;
if the data type value is 2, converting the time sequence data measuring point value into an integer value;
if the data type value is 3, converting the time sequence data measuring point value into a character string value;
and if the data type value is 4, converting the time sequence data measuring point value into a Boolean value.
Furthermore, when the database stores the high-frequency time sequence data, all data in one data packet of each high-frequency measuring point can be packaged and stored as a whole.
Further, the database stores the low-frequency time sequence data, and the low-frequency time sequence data of the same measuring point in different time periods is stored in one area according to the codes of the corresponding measuring points of the low-frequency time sequence data, and the data of different measuring points from the same power plant and the same unit are also stored in the same area.
Further, the first consumer module and the second consumer module are both stream processing framework systems Flink.
Furthermore, the data of the time sequence database can be mounted on a plurality of SSD solid state disks, and the bottom storage support is carried out in a RAID mode.
Compared with the prior art, the time sequence data storage method of the nuclear power industry internet platform has the following beneficial effects:
The time sequence data storage method of the nuclear power industrial Internet platform provided by the invention can effectively collect and store the time sequence data of a plurality of power plants in a standard manner, and can meet the requirement of processing speed. Specifically, through the data processing system architecture, when a large amount of data from a plurality of power plants are transmitted simultaneously, peak clipping processing can be performed, so that the occurrence of packet loss or packet leakage in the data transmission process is avoided; meanwhile, the storage method disclosed by the invention is used for distinguishing low-frequency time sequence data from high-frequency time sequence data, and different processing methods are adopted for different frequency data types in the data acquisition and transmission processes so as to ensure the optimization of storage and access efficiency of different types of time sequence data; according to the invention, data from different power plants and different units are stored in different areas, and when time sequence data is subsequently called for inquiry or analysis, the retrieval efficiency of the data can be effectively improved; in addition, the time sequence database arranged in the invention supports the mounting of data to a plurality of SSD solid state disks, supports the adoption of RAID, can further improve the storage and access efficiency of the data, and ensures the storage safety of the data.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a data processing and storage system architecture according to an embodiment of the present invention;
fig. 2 is a flowchart of a second consumer module timing data consumption process according to an embodiment of the present invention.
Detailed Description
Although the temporal data storage method of the present invention for a nuclear power industry internet platform may be implemented in a variety of different ways, it is not intended to limit the scope of the present invention to the exemplary embodiments. Accordingly, the drawings and description of the embodiments are to be regarded as illustrative in nature, and not as restrictive.
Further details are provided below with reference to the specific embodiments.
As shown in FIG. 1, the data processing system architecture of the present invention includes an MQTT server, a streaming framework system Flink, a distributed publish-subscribe messaging system Kafka, and a time-ordered database. The invention correspondingly provides a time sequence data storage method of a nuclear power industry internet platform, which comprises the following overall working procedures:
Step S1: the time sequence data acquisition and uploading tools on the power plant interface machines respectively send the time sequence data to the MQTT server at the central side of the nuclear power industrial Internet platform through the corresponding MQTT client, and respectively upload the time sequence data to different topics according to the attribute of the time sequence data, wherein the low-frequency time sequence data is sent to the appointed low-frequency Topic: low-Topic, high frequency timing data is sent to the specified high frequency Topic: high-Topic;
In this embodiment, each power plant sends device data and PI data of each power plant to an MQTT server on a central side through a corresponding MQTT client program, and because the spatial distance between the DHP central side and each power plant is far apart, usually calculated in kilometers, the MQTT protocol is used for transmitting the time sequence data, and is an instant messaging protocol based on a "publish/subscribe" mode, so that one-to-many or many-to-many remote communication can be effectively realized, data can be ensured to be effectively transmitted to the central side, and meanwhile, in the transmission process of the MQTT server, high-frequency and low-frequency time sequence data are distinguished from the source;
step S2: a first consumer module is provided, the first consumer module being of a distributed architecture. Subscribing the low-frequency Topic and the high-frequency Topic in the MQTT server; after receiving the low-frequency time sequence data packet and the high-frequency time sequence data packet, the consumer module is used as a producer of a subsequent Kafka distributed message system to convert the monitored time sequence data into a corresponding format of the subsequent Kafka system, and sends the format of the subsequent Kafka system to the Kafka system in real time, and the low-frequency time sequence data is sent to a corresponding low-frequency Topic in the Kafka system: low-Topic, high frequency data are sent to the corresponding high frequency Topic: in High-Topic.
The MQTT server solves the problem of long-distance transmission of time-series data packets, but has small storage space, and each power plant side simultaneously transmits data, and the amount of the transmitted data is very large, so that a first consumer module is set, and preferably, in this embodiment, the first consumer module is a link, which is a distributed processing engine for streaming data and batch data, and can stably, reliably and rapidly consume and process messages in the MQTT server. The first consumer module subscribes to the data of the MQTT server, the Flink can realize the primary peak clipping processing of the data, respectively processes the high-frequency time sequence data and the low-frequency time sequence data, converts the high-frequency time sequence data into a format required by the Kafka system and then sends the format to the Kafka system.
Step S3: the Kafka system receives the time sequence data sent by the first consumer module and stores different types of time sequence data in different topics, wherein the low-frequency topics in Kafka: low-Topic is used to hold Low frequency timing data, high frequency Topic: the High-Topic is used for storing High-frequency time sequence data;
Kafka is a distributed message system supporting partition storage and multiple copies, and can effectively solve the problem of data processing after agent downtime by adopting a message processing mode of publish/subscribe. Kafka operates in clusters, consisting of multiple brookers together. The producer sends the message to a specific topic and then consumers subscribed to the topic consume it in a poll manner. Wherein each topic is in turn divided into one or more partitions, each partition consisting of a sequence of ordered, immutable messages, an ordered queue. In particular, kafka writes to disk in a sequential write fashion, and thus at a much faster rate than randomly writes to disk.
Step S4: setting a second consumer module, and subscribing to Low-Topic and High-Topic in the Kafka system; the consumer module receives the low-frequency time sequence data packet and the high-frequency time sequence data packet; processing the data packet, and writing the time sequence data after the standardized processing into a time sequence database;
preferably, in this embodiment, the second consumer module is used as a data producer in the timing database to process the received timing data, as shown in fig. 2, and includes the following steps:
step S41: the received time sequence data is initially processed to obtain original data information;
Step S42: and acquiring the high-low frequency attribute of the original data, calling a preset rule to convert the original data into a specified data format, and determining the storage area of the original data in a database according to the measuring point attribute of the original data.
Further, the preliminary processing in step S41 includes one or more of unpacking, decrypting and decompressing, and specifically, according to the obtained data, the unpacking, decrypting and decompressing operations are performed if the time-series data packet is encrypted and compressed, and the unpacking and decrypting operations are performed if the time-series data packet is only encrypted.
Further, the step S42 of invoking a preset rule to convert the original data into a specified data format includes: and acquiring the data type of the time sequence data, and converting the original data into a specified data format according to the data type and a preset rule.
Wherein, the preset rules are shown in table 1:
If the data type value is 0, converting the time sequence data measuring point value into a double-precision floating point value;
If the data type value is 1, converting the time sequence data measuring point value into a single-precision floating point value;
if the data type value is 2, converting the time sequence data measuring point value into an integer value;
if the data type value is 3, converting the time sequence data measuring point value into a character string value;
and if the data type value is 4, converting the time sequence data measuring point value into a Boolean value.
TABLE 1
| Data type value | Type name | Data type | Description of the invention |
| 0 | Double precision floating point value | DOUBLE | For low frequency data storage |
| 1 | Single precision floating point value | FLOAT | For low frequency data storage |
| 2 | Integer value | INT | For low frequency data storage |
| 3 | String value | STRING | For high frequency data storage |
| 4 | Boolean value | BOOLEAN | For low frequency data storage |
Step S5: the time sequence database is used for storing the low-frequency time sequence data and the high-frequency time sequence data in a region according to the attribute of the time sequence data measuring point, and storing the low-frequency time sequence data and the high-frequency time sequence data by adopting different storage structures.
Preferably, in this embodiment, for the high-frequency time-series data, all data in a data packet in each high-frequency measurement point is packaged and stored as a whole when the high-frequency time-series data is stored. Such as: a high frequency data packet may contain 20000 float type values, and when storing, the 20000 float type values are converted into binary values and then stored together with the measurement point attribute information in the form of binary strings. The high-frequency time sequence data is generally equipment data, and when the data is queried and analyzed later, the data is not required to be accurate to a certain appointed moment, and all the high-frequency data acquired at a time are required to be completely taken out at one time. Therefore, the method disclosed by the invention can pack and store all data in one data packet of each high-frequency measuring point as a whole, so that the storage space of a database can be effectively saved on one hand, and on the other hand, all high-frequency data required by a user can be rapidly obtained during subsequent calling.
Preferably, in this embodiment, when the database stores the low-frequency time sequence data, the data of the measuring points belonging to the same power plant and unit are stored in the same logic area according to the codes of the corresponding measuring points of the low-frequency time sequence data. The low-frequency time sequence data is usually PI data and low-frequency equipment data, and when the low-frequency time sequence data is needed for subsequent data inquiry, analysis and the like, the data in a time period is needed to be analyzed, so that the database disclosed by the invention stores the measured point data belonging to the same power plant and unit into the same logic area, and the needed data can be quickly retrieved when the time point data inquiry and the time period historical data inquiry are performed subsequently.
In this embodiment, in the time sequence database, each unit of each power plant corresponds to a specific storage area, and the time sequence data of the measuring points belonging to the power plant and the unit are stored. Such as: if the data of a certain measuring point is a No. 2 unit from Fuqing nuclear power, the data needs to be stored in a storage area of FQ.02 (FQ represents Fuqing nuclear power and 02 represents No. 2 unit), and if the data of a certain measuring point is a No. 1 unit from three nuclear power, the data needs to be stored in a storage area of ZS.01 (ZS represents three nuclear power and 01 represents No. 1 unit). And when the Flink program of the second consumer module stores the time sequence data, reading the measuring point code, analyzing the power plant and the unit to which the measuring point code belongs according to the measuring point code, and then storing the data into a corresponding storage area. Corresponding to the low-frequency time sequence data, each piece of data stores a millisecond-level time stamp, data quality and a measuring point value.
Furthermore, in this embodiment, data of the time sequence database may be mounted on multiple SSD solid state disks, and the underlying storage support performs redundant storage in a RAID manner. On one hand, the system IO can be improved, the storage and access efficiency of time sequence data are improved, and on the other hand, fault redundancy can be supported, and the data storage safety is ensured.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. The time sequence data storage method of the nuclear power industry internet platform is characterized by comprising the following steps of:
Step S1: the time sequence data acquisition and uploading tools on the power plant interface machines respectively send the time sequence data to the MQTT server at the central side of the nuclear power industrial Internet platform through the corresponding MQTT client, and respectively upload the time sequence data to different topics according to the attribute of the time sequence data, wherein the low-frequency time sequence data is sent to the appointed low-frequency Topic, and the high-frequency time sequence data is sent to the appointed high-frequency Topic; each power plant transmits equipment data and PI data of each power plant to an MQTT server at the center side through a corresponding MQTT client program, and high-frequency time sequence data and low-frequency time sequence data are distinguished from the source in the transmission process of the MQTT server;
Step S2: setting a first consumer module, wherein the first consumer module subscribes and consumes data in an MQTT server and then processes the data, subscribes to a low-frequency Topic and a high-frequency Topic in the MQTT server, and after receiving a low-frequency time sequence data packet and a high-frequency time sequence data packet, the first consumer module can be used as a producer of a subsequent Kafka distributed message system at the same time, converts the monitored time sequence data into a corresponding format of the subsequent Kafka system, sends the format to the Kafka system in real time, and sends the low-frequency time sequence data to the corresponding low-frequency Topic in the Kafka system and the high-frequency data to the corresponding high-frequency Topic;
Step S3: the Kafka system receives the time sequence data sent by the first consumer module and stores different types of time sequence data in different topics, wherein the low-frequency topics in the Kafka are used for storing the low-frequency time sequence data, and the high-frequency topics are used for storing the high-frequency time sequence data;
Step S4: setting a second consumer module, wherein the second consumer module subscribes to and consumes data in the Kafka message system and then processes the data; the second consumer module processing the received time series data packet comprises:
Step S41: unpacking, decrypting and decompressing the received time sequence data respectively to obtain original time sequence data information;
Step S42: according to the obtained high-low frequency attribute of the original time sequence data, calling a preset rule to convert the original time sequence data into a specified data format and determining a storage area of the original time sequence data in a time sequence database according to the measuring point attribute of the original time sequence data;
Step S5: the time sequence database is used for storing the low-frequency time sequence data and the high-frequency time sequence data separately according to the attribute of the time sequence data.
2. The method for storing time series data of a nuclear power industry internet platform according to claim 1, wherein the step S42 of calling a preset rule to convert the original data into a specified data format comprises: and acquiring the data type of the time sequence data, and converting the original data into a specified data format according to the data type and a preset rule.
3. The method for storing time series data of a nuclear power industry internet platform according to claim 1, wherein the preset rule is:
if the data type value is 0, converting the time sequence data measuring point value into a double-precision floating point value;
If the data type value is 1, converting the time sequence data measuring point value into a single-precision floating point value;
if the data type value is 2, converting the time sequence data measuring point value into an integer value;
if the data type value is 3, converting the time sequence data measuring point value into a character string value;
and if the data type value is 4, converting the time sequence data measuring point value into a Boolean value.
4. The method for storing time series data of a nuclear power industry internet platform according to claim 1, wherein when the database stores the high frequency time series data, all data in one data packet of each high frequency measuring point are packaged and stored as a whole.
5. The method for storing time series data of a nuclear power industrial internet platform according to claim 1, wherein the database stores the low frequency time series data, the low frequency time series data of the same measuring point in different time periods are stored in one area according to the codes of the corresponding measuring points of the low frequency time series data, and the data of different measuring points from the same power plant and the same unit are also stored in the same area.
6. The method for storing time series data of a nuclear power industry internet platform according to claim 1, wherein the first consumer module and the second consumer module are both stream processing frame systems Flink.
7. The time sequence data storage method of the nuclear power industry internet platform according to claim 1, wherein data of the time sequence database can be mounted on a plurality of SSD solid state disks, and the bottom layer storage support is carried out in a RAID mode.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111784170A (en) * | 2020-07-03 | 2020-10-16 | 中冶赛迪重庆信息技术有限公司 | Metallurgical industry data management system and method |
| CN112529036A (en) * | 2020-11-06 | 2021-03-19 | 上海发电设备成套设计研究院有限责任公司 | Fault early warning method, device, equipment and storage medium |
| CN113157449A (en) * | 2021-04-16 | 2021-07-23 | 上海寰果信息科技有限公司 | Real-time stream data analysis processing method based on MQTT |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111784170A (en) * | 2020-07-03 | 2020-10-16 | 中冶赛迪重庆信息技术有限公司 | Metallurgical industry data management system and method |
| CN112529036A (en) * | 2020-11-06 | 2021-03-19 | 上海发电设备成套设计研究院有限责任公司 | Fault early warning method, device, equipment and storage medium |
| CN113157449A (en) * | 2021-04-16 | 2021-07-23 | 上海寰果信息科技有限公司 | Real-time stream data analysis processing method based on MQTT |
Non-Patent Citations (1)
| Title |
|---|
| 基于核电工业互联网平台的时序数据实时接入研究;方华建 等;《电子技术应用》;20211106(2021 年11 月 增刊);132-137 * |
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