CN114387124A - 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 tool on each power plant interface machine respectively sends the time sequence data to an MQTT server at the center side of the nuclear power industry Internet platform through a corresponding MQTT client and respectively uploads the time sequence data to different topics according to the attribute of the time sequence data; setting a first consumer module, subscribing and consuming data in the MQTT server by the first consumer module, and then processing the data; the Kafka system receives time sequence data sent by a first consumer module and stores different types of time sequence data in different topics; setting a second consumer module, subscribing and consuming data in the Kafka message system by the second consumer module, and then processing the data; and the time sequence database is used for separately storing the low-frequency time sequence data and the high-frequency time sequence data according to the attribute of the time sequence data. The method provided by the invention can manage the 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 operation phase of a nuclear power plant, a large amount of measurement data is generated. The measured data is data for sensing the running state of the nuclear power plant, promoting the digitization and the intelligent management of the power plant. The measurement data includes online and offline measurement data. The on-line measurement data mainly comprises production process control system data and data acquired through sensors, intelligent terminals and the like of the industrial Internet of things. Offline measurement data is typically obtained by measuring records during operations such as operation and maintenance of the power plant. The time series data is further divided into high frequency data and low frequency data according to the difference of the acquisition frequency and the data source.
At present, China Nuclear Power Large Nuclear Power Source platform (DHP) is established by the Wuhan Nuclear Power operation technology corporation in Zhonghe, and is used as a supporting platform and a nerve center of digital nuclear power, data of China nuclear power massive industrial systems and equipment are integrated, an extensible open nuclear power industry Internet platform is established, an ecological system is developed by synchronously developing nuclear power industry application and development oriented to various scenes and reusable data, so that the use efficiency and the sharing range of hardware, service and data of a nuclear power plant are improved, intelligent management and operation optimization of China nuclear power business and resources are realized, and a series of innovative nuclear power industry application oriented to a nuclear power whole industry chain is driven.
The effective acquisition and storage of the time sequence data are one of the bases for the effective expansion of the whole China nuclear power large nuclear power source platform, and in order to more reasonably and efficiently access and store the time sequence data, a time sequence data storage method of the nuclear power industry internet platform is needed to be provided so as to carry out standard and efficient management on the time sequence data storage mode of the nuclear power industry internet platform.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides the time sequence data storage method of the nuclear power industry internet platform.
In order to achieve the above purpose, the invention provides the following technical scheme:
a time sequence data storage method of a nuclear power industry internet platform comprises the following steps:
step S1: the method comprises the steps that time sequence data are respectively sent to an MQTT server at the center side of a nuclear power industry Internet platform through corresponding MQTT clients by a time sequence data acquisition and uploading tool on each power plant interface machine, and are respectively uploaded to different topics according to the attributes of the time sequence data;
step S2: setting a first consumer module, subscribing and consuming data in the MQTT server by the first consumer module, and then processing the data;
step S3: the Kafka system receives time sequence data sent by a 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 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: according to the high-low frequency attributes of the acquired 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 attributes of the original data.
Further, the step S42 for invoking the 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 as follows:
if the data type numerical value is 0, converting the time sequence data measuring point value into a double-precision floating point value;
if the data type numerical value is 1, converting the time sequence data measuring point value into a single-precision floating point value;
if the data type numerical value is 2, converting the time sequence data measuring point value into an integer value;
if the data type numerical value is 3, converting the time sequence data measuring point value into a character string value;
and if the data type numerical value is 4, converting the time sequence data measuring point value into a Boolean value.
Further, 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, the low-frequency time sequence data of the same measuring point in different time periods are stored in one area according to the codes of the measuring points corresponding to 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 streaming framework system Flink.
Furthermore, the 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 an 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 industry internet platform can effectively carry out large-scale effective acquisition and standard storage on the time sequence data of a plurality of power plants, and can meet the requirement of processing speed. Specifically, through the data processing system architecture disclosed by the invention, when a large amount of data from a plurality of power plants are transmitted simultaneously, peak clipping processing can be carried out, so that the phenomenon 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 data types with different frequencies in the data acquisition and transmission processes so as to ensure that the storage and access efficiency of the time sequence data of different types is optimized; the invention stores the data from different power plants and different units in different areas, and can effectively improve the retrieval efficiency of the data when the time sequence data is called for inquiry or analysis subsequently; in addition, the time sequence database deployed in the invention supports the mounting of data to a plurality of SSD solid state disks, and supports the adoption of a disk array RAID, so that the storage and access efficiency of the data can be further improved, and the data storage safety is ensured.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block 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 time-series data consumption process according to an embodiment of the present invention.
Detailed Description
Although the time series data storage method of the nuclear power industry internet platform can be implemented in various different ways, the protection scope of the invention is not limited to the exemplary embodiment. Accordingly, the drawings and description of the specific embodiments are to be regarded as illustrative in nature, and not as restrictive.
The following is a more detailed description of the present invention by way of 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 message system Kafka, and a time sequence database. Correspondingly, the invention 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 tool on each power plant interface machine respectively sends the time sequence data to an MQTT server on the center side of a nuclear power industry Internet platform through a corresponding MQTT client, and uploads the time sequence data to different topics according to the attributes of the time sequence data, wherein the low-frequency time sequence data are sent to a designated low-frequency Topic: Low-Topic, high frequency timing data sent to the specified high frequency Topic: High-Topic;
in the embodiment, each power plant sends device data and PI data of each power plant to an MQTT server at a center side through a corresponding MQTT client program, and because the spatial distance between the DHP center side and each power plant is far away and often works by kilometers, the MQTT protocol is used for transmitting the time sequence data in the embodiment, is an instant communication protocol based on a 'publish/subscribe' mode, can effectively realize one-to-many or many-to-many remote communication, can ensure that the data can be effectively transmitted to the center side, and simultaneously, in the transmission process of the MQTT server, high-frequency and low-frequency time sequence data are distinguished from a source;
step S2: setting a first consumer module, wherein the first consumer module is in a distributed structure. Subscribing to 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 can be used as a producer of a subsequent Kafka distributed message system, converts the monitored time sequence data into a format corresponding to the subsequent Kafka system, sends the format to the Kafka system in real time, and sends the low-frequency time sequence data to a corresponding low-frequency Topic: Low-Topic, high frequency data is sent to the corresponding high frequency Topic: High-Topic.
Although the MQTT server solves the problem of long-distance transmission of time-series data packets, the storage space is small, and each power plant side simultaneously transmits data, and the amount of the transmitted data is very large, so a first consumer module is provided. The first consumer module subscribes to data of the MQTT server, the Flink can realize initial peak clipping processing of the data, respectively processes high-frequency and low-frequency time sequence data, converts the high-frequency and low-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 time sequence data sent by a first consumer module, and stores different types of time sequence data in different topics, wherein the low-frequency Topic: Low-Topic is used to store Low frequency time series 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 publish/subscribe message processing mode. Kafka operates in a cluster, and is formed by multiple brokers together. The producer sends the message to a specific topic, which is then consumed by the consumers subscribing to the topic in poll. Each topic is divided into one or more partitions, each partition is composed of a series of ordered and immutable messages and is an ordered queue. In particular, Kafka writes to a disk in a sequential write manner, and thus at a much faster rate than writes to a disk randomly.
Step S4: setting a second consumer module for subscribing 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 processing flow diagram of the received time series data, which is simultaneously used as the data producer in the time series database, by the second consumer module is shown in fig. 2, and includes the following steps:
step S41: the received time sequence data is subjected to preliminary processing to obtain original data information;
step S42: acquiring high-frequency and low-frequency attributes 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 data in a database according to the measuring point attributes of the original data.
Further, the preliminary processing in step S41 includes one or more combinations of unpacking, decrypting, and decompressing, and specifically, it is determined according to the acquired data that 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 for invoking the 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.
Wherein, the preset rule is shown in table 1:
if the data type numerical value is 0, converting the time sequence data measuring point value into a double-precision floating point value;
if the data type numerical value is 1, converting the time sequence data measuring point value into a single-precision floating point value;
if the data type numerical value is 2, converting the time sequence data measuring point value into an integer value;
if the data type numerical value is 3, converting the time sequence data measuring point value into a character string value;
and if the data type numerical value is 4, converting the time sequence data measuring point value into a Boolean value.
TABLE 1
| Data type value | Name of type | 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 partitioned mode and storing the low-frequency time sequence data and the high-frequency time sequence data in different storage structures according to the attributes of time sequence data measuring points.
Preferably, in this embodiment, for the high-frequency time series data, when storing, all data in one data packet in each high-frequency measurement point are stored as a whole in a package. Such as: a high frequency data packet may contain 20000 float type values, and during storage, the 20000 float type values are all converted into binary and stored together with the measurement point attribute information in the form of binary string. The formula high-frequency time sequence data is generally equipment data, and when data is inquired and analyzed subsequently, the data does not need to be accurate to a certain specified time, and all high-frequency data acquired at a certain time only needs to be taken out at one time. Therefore, the method disclosed by the invention can be used for packaging and storing all the 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 the high-frequency data required by a user can be quickly obtained during subsequent calling.
Preferably, in this embodiment, when the database stores the low-frequency time series data, the data of the measuring points belonging to the same power plant or unit are stored in the same logic area according to the codes of the measuring points corresponding to the low-frequency time series data. The low-frequency time sequence data are generally PI data and low-frequency equipment data, and when the low-frequency time sequence data are needed to be used for follow-up query and analysis of the data, the data in a time period are often needed to be analyzed, so that the data of the same power plant and unit measuring point data are stored in the same logic area by the database, and the needed data can be quickly retrieved when the time point data query and the time period historical data query are carried out subsequently.
In this embodiment, in the time sequence database, each unit of each power plant corresponds to a special storage area, and time sequence data of the measurement points belonging to the power plant and the unit are stored. Such as: if a certain measuring point data is a number 2 unit from Fuqing nuclear power, the data needs to be stored into a storage area of FQ.02 (the 'FQ' represents the Fuqing nuclear power and the '02' represents the number 2 unit), and if a certain measuring point data is a number 1 unit from Sanmen nuclear power, the data needs to be stored into a storage area of ZS.01 (the 'ZS' represents the Sanmen nuclear power and the '01' represents the number 1 unit). And when the Flink program of the second consumer module stores the time sequence data, reading the measuring point codes, analyzing the power plant and the unit to which the measuring point codes belong according to the measuring point codes, and storing the data into the corresponding storage area. Each piece of data stores a millisecond time stamp, a data quality, and a point value for the low frequency time series data.
Further, in this embodiment, data in the time sequence database may be mounted to a plurality of SSD solid state disks, and the bottom storage supports redundant storage in a RAID manner. On the one hand, the system IO can be improved, the storage and access efficiency of time sequence data is improved, and on the other hand, fault redundancy can be supported, and the data storage safety is guaranteed.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are 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 appended claims.
Claims (8)
1. A time sequence data storage method of a nuclear power industry internet platform is characterized by comprising the following steps:
step S1: the method comprises the steps that time sequence data are respectively sent to an MQTT server at the center side of a nuclear power industry Internet platform through corresponding MQTT clients by a time sequence data acquisition and uploading tool on each power plant interface machine, and are respectively uploaded to different topics according to the attributes of the time sequence data;
step S2: setting a first consumer module, subscribing and consuming data in the MQTT server by the first consumer module, and then processing the data;
step S3: the Kafka system receives time sequence data sent by a 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 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.
2. The method of claim 1, wherein 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: according to the high-low frequency attributes of the acquired 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 attributes of the original data.
3. The method of claim 2, wherein the step S42 of calling a preset rule to convert the raw 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.
4. The time series data storage method of the nuclear power industry internet platform according to claim 2, wherein the preset rule is that:
if the data type numerical value is 0, converting the time sequence data measuring point value into a double-precision floating point value;
if the data type numerical value is 1, converting the time sequence data measuring point value into a single-precision floating point value;
if the data type numerical value is 2, converting the time sequence data measuring point value into an integer value;
if the data type numerical value is 3, converting the time sequence data measuring point value into a character string value;
and if the data type numerical value is 4, converting the time sequence data measuring point value into a Boolean value.
5. The time series data storage method of the nuclear power industry internet platform as claimed in claim 1, wherein when the database stores the high frequency time series data, all data in one data packet of each high frequency measurement point is packaged and stored as a whole.
6. The time sequence data storage method of the nuclear power industry internet platform as claimed in claim 1, wherein the database stores the low frequency time sequence data, the low frequency time sequence data of the same measuring point in different time periods are stored in one area according to the code of the measuring point corresponding to 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.
7. The method for storing time series data of the nuclear power industry internet platform according to claim 1, wherein the first consumer module and the second consumer module are both a streaming processing framework system Flink.
8. The time sequence data storage method of the nuclear power industry internet platform as claimed in claim 1, wherein the data of the time sequence database can be mounted on a plurality of SSD solid state disks, and the bottom storage support is performed in RAID mode.
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