CN115776378A - Real-time access method for time sequence data of nuclear power industry Internet platform - Google Patents
Real-time access method for time sequence data of nuclear power industry Internet platform Download PDFInfo
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Abstract
The invention discloses a real-time access method of time sequence data of a nuclear power industry internet platform, which comprises the following steps: collecting sensor data or PI system data, and transmitting the sensor data or PI system data to an entering edge server; the edge server carries out caching and data cleaning, data conversion, data frequency reduction, dimension reduction and data compression processing on the time sequence data; the processed data is accessed to an MQTT server or Kafka of a nuclear power industry Internet platform by an MQTT protocol or a Kafka protocol, data is transmitted in a binary system or JSON data packet mode; and the nuclear power industry internet platform side receives the time sequence data and writes the analyzed time sequence data information into the time sequence database center. The beneficial effects are that: (1) The sensor data need pass through the security gatekeeper before entering the edge server, so that the security of data transmission is ensured.
Description
Technical Field
The invention belongs to a time sequence data real-time access method, and particularly relates to a time sequence data real-time access method for a nuclear power industry internet platform.
Background
In the nuclear power field, time series data (hereinafter referred to as time series data) mainly refers to data collected and generated by various types of real-time monitoring, checking and analyzing equipment in a nuclear power plant, and generally, the time series data mainly comes from a PI database, a field sensor and the like.
The nuclear power industry internet platform is characterized in that a sensing system of all elements is built in a unified nuclear power data standard system framework, an information isolated island is opened, a service platform of nuclear power industry big data is built, on the basis of accessing structured data, unstructured data and time sequence data, data of a nuclear power business system can be integrated and processed, a unified data standard is built, multiple services such as equipment monitoring, fault diagnosis and fault prediction are supported, and real-time access of the time sequence data is an important content in the aspect of data access of the nuclear power industry internet platform.
When the edge data is accessed in real time, the existing nuclear power industry internet platform supports the access by adopting the MQTT and the Kafka, the time sequence data comprises common time sequence data and high-frequency time sequence data, and how to use the MQTT and the Kafka to perform high-efficiency access on the two time sequence data has important influence on the construction of the nuclear power industry internet platform.
In order to effectively support real-time access of time sequence data, a reasonable real-time access mechanism and method for time sequence data need to be provided, and the high efficiency, safety and reliability of data access are ensured.
Disclosure of Invention
The invention aims to provide a real-time access method of time sequence data of a nuclear power industry internet platform, which can effectively improve the access efficiency of the time sequence data and ensure the safety and reliability of data access.
The technical scheme of the invention is as follows: a real-time access method for time sequence data of a nuclear power industry Internet platform comprises the following steps:
step 1: collecting sensor data or PI system data, and transmitting the sensor data or PI system data to an entering edge server;
step 2: the edge server performs caching and data cleaning, data conversion, data frequency reduction, dimension reduction and data compression processing on the time sequence data;
and step 3: the processed data is accessed to an MQTT server or Kafka of a nuclear power industry Internet platform through an MQTT protocol or a Kafka protocol, and the data is transmitted in a binary system or JSON data packet mode;
and 4, step 4: and the nuclear power industry Internet platform side receives the time sequence data and writes the analyzed time sequence data information into a time sequence database center.
The step 1 comprises the following steps:
step 11: the method comprises the steps that a sensor collects equipment time sequence data from field equipment, firstly, the data are transmitted to an edge gateway, and the edge gateway converts an industrial field protocol into an MQTT protocol;
step 12: the edge gateway transmits the data after protocol conversion to a security gateway;
step 13: the safety network gate transmits the data to the edge server;
step 14: and if the time sequence data is collected from the PI system, directly transmitting the collected data to the edge server.
The industrial field protocol comprises ModBus, RS-232 and HART.
The step 2 comprises the following steps:
step 21: after receiving the time sequence data sent by the edge gateway, the edge server stores the original data in a local time sequence database;
step 22: cleaning the time sequence data, and designing a cleaning rule according to the storage requirement of the time sequence data at the center side;
step 23: converting the measuring point names, and converting the power plant side measuring point names into measuring point names on a nuclear power industry internet platform according to a power plant side measuring point name code and nuclear power industry internet platform measuring point name code mapping rule;
step 24: performing frequency reduction and dimension reduction processing on the time sequence data;
step 25: and compressing the time sequence data.
The step 22 comprises the following steps:
step 221: determining the measuring point type of the time sequence data according to the measuring point name in the time sequence data;
step 222: checking whether the measured point data meet the requirements or not according to the reasonable value range of each type of measured point; if the requirements are not met, the step 224 is entered, otherwise, the subsequent check is continued;
step 223: checking whether the tested point value is an invalid value or a null value, and if the tested point value is the invalid value or the null value, entering step 224;
step 224: processing abnormal conditions of the measuring point values, and if finding out data which exceeds a normal range and is logically unreasonable, correcting or discarding the data; if the point-of-measure value is found to be an invalid value or null value, the piece of data is discarded for the invalid value, and if a null value occurs, the default value is used for padding.
The step 24 comprises the following steps:
step 241: for high-frequency time sequence data, firstly, performing plant-side calculation analysis processing in an edge server at a power plant side, and after the analysis processing is completed, transmitting a characteristic value or a result after the analysis processing to a nuclear power industry Internet platform through MQTT;
step 242: and for the common time sequence data, the down sampling is carried out and then the data is transmitted.
The step 25 comprises the following steps:
according to the data scale and the actual conditions of the commercial network bandwidth between the power plant side and the data center side of the nuclear power industry internet platform, whether data compression processing needs to be carried out on data sent to the nuclear power industry internet platform or not is judged, if the bandwidth occupied by the sent data exceeds 10% of the commercial network bandwidth, the data compression processing needs to be carried out, and a specific compression algorithm can be adopted for compression, for example: gzip, snappy, zlib.
The step 3 comprises the following steps:
step 31: the edge server carries out cleaning, conversion, dimension reduction and compression processing on the time sequence data, and evaluates the bandwidth occupied by data transmission according to the data scale after the processing is finished;
step 32: if the occupied bandwidth is less than 10% of the commercial network bandwidth when the JSON data packet is adopted for transmission, the JSON data packet mode is adopted for real-time data transmission;
step 33: if the bandwidth occupied by the JSON data packet is more than or equal to 10% of the bandwidth of the commercial network when the JSON data packet is used for transmission, the real-time data transmission needs to be carried out in a binary data packet mode.
The step 4 comprises the following steps:
step 41: the time sequence data enters an MQTT server at the platform side from the edge side;
step 42: if the data directly enter the MQTT server, the MQTT server is used as a message producer, and the data are written into the Kafka cluster;
step 43: the Flink program is used as a message consumer to receive the message in Kafka, and after receiving the message, unpacking, decompressing, decoding and data standardization processing are carried out on the message;
step 44: and if the processed data are high-frequency time sequence data, writing the processed data into the HBase database by using a Flink program, otherwise, writing the processed data into the platform side time sequence database.
The invention has the beneficial effects that:
(1) The sensor data needs to pass through a security gateway before entering the edge server, so that the security of data transmission is ensured;
(2) The edge server is adopted to carry out caching, cleaning, conversion, frequency reduction, dimension reduction and compression processing on the time sequence data, so that the quality of the accessed data is ensured, and the time sequence data access pressure of the platform side is reduced;
(3) When the message is accessed in real time, the MQTT and Kafka message can adopt a JSON format message structure and a binary message structure, and a user can select the message according to the development efficiency and the bandwidth condition from the edge side to the platform side.
Drawings
FIG. 1 is a flow of time series data acquisition and transmission to an edge server;
FIG. 2 is a flow chart of an edge server processing time series data;
FIG. 3 is a data cleaning process of the power plant side time sequence data;
FIG. 4 is a data frequency reduction processing process of the power plant side timing sequence data;
FIG. 5 is a flow chart of time data transmitted to a platform in binary or JSON data packets at a power plant side;
FIG. 6 shows a process of receiving timing data and writing the timing data into a database at the platform side.
Detailed Description
The invention is described in further detail below with reference to the figures and the embodiments.
A large amount of time sequence data can be generated in the operation process of equipment in a nuclear power plant, special sensors can be installed on some equipment, the sensors can acquire the time sequence data of the equipment in real time, and finally the time sequence data generated by the sensors can enter a nuclear power industry internet platform.
For high-frequency time sequence data, before the high-frequency time sequence data are uploaded to a nuclear power industry internet platform, cleaning, conversion, dimension reduction and compression processing are required, and for common time sequence data, such as PI system data, MQTT or Kafka can be directly accessed into the platform.
The invention provides a real-time access method of time sequence data of a nuclear power industry internet platform, which comprises the following steps:
step 1: collecting sensor data or PI system data, and transmitting the sensor data or PI system data to an entering edge server;
as shown in fig. 1, the method mainly comprises the following steps:
step 11: the method comprises the steps that a sensor collects device time sequence data from field devices, firstly, the data are transmitted to an edge gateway, the edge gateway converts an industrial field protocol into an MQTT protocol, and the industrial field protocol comprises but is not limited to ModBus, RS-232, HART and the like;
step 12: the edge gateway transmits the data after protocol conversion to a security gateway;
step 13: the safety network gate transmits the data to the edge server;
step 14: and if the time sequence data is collected from the PI system, directly transmitting the collected data to the edge server.
Step 2: the edge server carries out caching and data cleaning, data conversion, data frequency reduction, dimension reduction and data compression processing on the time sequence data;
as shown in fig. 2, the method mainly comprises the following steps:
step 21: after receiving the time sequence data sent by the edge gateway, the edge server stores the original data in a local time sequence database;
step 22: and (3) cleaning the time sequence data, and designing a reasonable cleaning rule according to the time sequence data storage requirement of the center side, such as: null processing, noise data processing, and the like, the data cleansing process is as shown in fig. 3, and the cleansing process is as follows:
step 221: and determining the measuring point type according to the measuring point name in the time sequence data. Such as: the measuring point names comprise a temperature measuring point generally comprising a TMP letter, a pressure measuring point generally comprising a PT letter, and the like;
step 222: and checking whether the measured point data meet the requirements or not according to the reasonable value range of each type of measured point. Such as: for temperature measuring points, the measuring point value cannot exceed the normal temperature range, for pressure and flow measuring points, the measuring point value cannot be a negative number, if the measuring point value does not meet the requirement, the step d) is carried out, and if the measuring point value does not meet the requirement, the subsequent inspection is continued;
step 223: it is checked whether the test point value is an invalid value or a null value. During the process of uploading the measured point data, some invalid values or null values exist, such as: if the measured point value is an invalid value or a null value, entering d), otherwise, carrying out a subsequent processing flow;
step 224: and (5) processing abnormal conditions of the measured point values. If the logically unreasonable data beyond the normal range is found, correcting or discarding the data; if the point-of-measure value is found to be an invalid value or null value, the piece of data is discarded for the invalid value, and if null value occurs, default values are used for filling, such as: if the quality value of the measuring point is empty, if the measuring point value is effective, the quality value is set to be 0 (meeting the quality requirement).
Step 23: converting the measuring point names, converting the power plant side measuring point names into measuring point names on a nuclear power industry internet platform according to a power plant side measuring point name code and a nuclear power industry internet platform measuring point name code mapping rule, wherein the measuring point name conversion rule is as follows:
The character length limitation of each part in the nuclear power industry Internet platform time sequence data measuring point codes is shown in the table 1:
TABLE 1 limitation of length of character strings of each part of time sequence data measuring point codes of nuclear power industry internet platform
| Time sequence data measuring point coding component | Maximum length of character string |
| Power plant code | 2 |
| Power plant side measuring point code | 32 |
The information corresponding to the plant name and the plant code is shown in table 2.
TABLE 2 Power plant name and Power plant code correspondence Table
An example of conversion between a side measuring point of a power plant and a measuring point of a nuclear power industry internet platform is as follows:
power plant: fuqing nuclear power
The unit: no. 5 unit
Side measuring point coding of a power plant: 5RCV002MI (u WH01)
Then, the corresponding measuring point codes in the nuclear power industry internet platform should be: FQ — 5rcv002mi _wh01.
Step 24: and performing frequency reduction and dimension reduction on the time sequence data. In the aspect of data dimension reduction, a specific data dimension reduction algorithm is used, such as: the principal component analysis algorithm, the local linear embedding algorithm and the like map data points in the original high-dimensional space to a low-dimensional space, so that redundant information and noise information are reduced, and the scale of data is reduced. In the aspect of data frequency reduction, high-frequency time sequence data and common time sequence data are respectively processed, the data frequency reduction processing mode is as shown in fig. 4, and the processing mode is as follows:
step 241: for high-frequency time sequence data, firstly, plant side calculation analysis processing is carried out in an edge server at a power plant side, and after the analysis processing is finished, a characteristic value or a result after the analysis processing is transmitted to a nuclear power industry internet platform through MQTT. For example, only the characteristic value and the frequency spectrum analysis result obtained by calculation are transmitted for the equipment vibration data;
step 242: and for the common time sequence data, the down sampling is carried out and then the data is transmitted. For example, for data in the PI system, the data may be down-sampled to minute level for transmission according to the service requirement.
Step 25: and compressing the time sequence data. According to the data scale and the actual condition of the commercial network bandwidth between the power plant side and the data center side of the nuclear power industry internet platform, whether data compression processing needs to be carried out on data sent to the nuclear power industry internet platform or not is judged, and if the bandwidth occupied by the sent data exceeds 10% of the commercial network bandwidth, the data compression processing needs to be carried out. Compression may be performed using a particular compression algorithm, such as: gzip, snappy, zlib, etc.
And step 3: the processed data is accessed to an MQTT server or Kafka of a nuclear power industry Internet platform through an MQTT protocol or a Kafka protocol, and the data is transmitted in a binary system or JSON data packet mode;
as shown in fig. 5, the method mainly comprises the following steps:
step 31: the edge server carries out cleaning, conversion, dimension reduction and compression processing on the time sequence data, and evaluates the bandwidth occupation condition of data transmission according to the data scale after the processing is finished;
step 32: if the occupied bandwidth is less than 10% of the commercial network bandwidth when the JSON data packet is adopted for transmission, the JSON data packet mode can be adopted for real-time data transmission;
step 33: if the bandwidth occupied by the JSON data packet is more than or equal to 10% of the bandwidth of the commercial network when the JSON data packet is used for transmission, the real-time data transmission needs to be carried out in a binary data packet mode.
And 4, step 4: and the nuclear power industry Internet platform side receives the time sequence data and writes the analyzed time sequence data information into a time sequence database center.
As shown in fig. 6, the method mainly comprises the following steps:
step 41: the time sequence data enters an MQTT server at the platform side from the edge side;
step 42: if the data directly enter the MQTT server, the MQTT server is used as a message producer, and the data are written into the Kafka cluster;
step 43: the Flink program is used as a message consumer to receive the message in Kafka, and after receiving the message, unpacking, decompressing, decoding and data standardization processing are carried out on the message;
step 44: if the processed data is high-frequency time sequence data, writing the data into an HBase database by using a Flink program, otherwise, writing the data into a platform side time sequence database.
The main advantages of the invention are as follows:
the invention considers two scenes of high-frequency time sequence data access and common time sequence data access in the time sequence data, and formulates corresponding access modes aiming at different time sequence data types;
the data of the edge gateway needs to pass through a one-way gateway before entering the edge server, so that the safety of data transmission can be effectively ensured;
aiming at the characteristic of large scale of high-frequency time sequence data, the calculation, processing and analysis of the high-frequency time sequence data are carried out in a side edge server of a power plant, and only characteristic values and analysis results are uploaded to a nuclear power industry internet platform, so that the occupation of bandwidth is further reduced;
when time sequence data is accessed, JSON and binary mode data packets can be adopted, so that a user can select a proper data packet format;
various data compression algorithms are provided for a user to select, and on one hand, the user can select a proper compression algorithm according to actual service requirements; on the other hand, the occupancy rate of the network bandwidth during the time sequence data transmission can be further reduced;
after the time sequence data is accessed to the nuclear power industry internet platform, the operations of decompressing, unpacking and writing the data packet into the database are automatically completed by the platform without related development work.
Claims (9)
1. A real-time access method for time sequence data of a nuclear power industry Internet platform is characterized by comprising the following steps:
step 1: collecting sensor data or PI system data, and transmitting the sensor data or PI system data to an entering edge server;
step 2: the edge server carries out caching and data cleaning, data conversion, data frequency reduction, dimension reduction and data compression processing on the time sequence data;
and step 3: the processed data is accessed to an MQTT server or Kafka of a nuclear power industry Internet platform through an MQTT protocol or a Kafka protocol, and the data is transmitted in a binary system or JSON data packet mode;
and 4, step 4: and the nuclear power industry Internet platform side receives the time sequence data and writes the analyzed time sequence data information into a time sequence database center.
2. The real-time access method of the nuclear power industry internet platform time series data as claimed in claim 1, wherein the step 1 comprises the following steps:
step 11: the method comprises the steps that a sensor collects equipment time sequence data from field equipment, firstly, the data are transmitted to an edge gateway, and the edge gateway converts an industrial field protocol into an MQTT protocol;
step 12: the edge gateway transmits the data after the protocol conversion to a security gateway;
step 13: the safety network gate transmits the data to the edge server;
step 14: and if the time sequence data is collected from the PI system, directly transmitting the collected data to the edge server.
3. The real-time access method of the nuclear power industry internet platform time series data of claim 2, characterized in that: the industrial field protocol comprises ModBus, RS-232 and HART.
4. The real-time access method of nuclear power industry internet platform time series data as claimed in claim 1, wherein said step 2 includes the steps of:
step 21: after receiving the time sequence data sent by the edge gateway, the edge server stores the original data in a local time sequence database;
step 22: cleaning the time sequence data, and designing a cleaning rule according to the storage requirement of the time sequence data at the center side;
step 23: converting the measuring point names, and converting the power plant side measuring point names into measuring point names on a nuclear power industry internet platform according to a power plant side measuring point name code and nuclear power industry internet platform measuring point name code mapping rule;
step 24: performing frequency reduction and dimension reduction processing on the time sequence data;
step 25: and compressing the time sequence data.
5. The real-time access method of nuclear power industry internet platform time series data as claimed in claim 4, wherein said step 22 includes the steps of:
step 221: determining the measuring point type of the time sequence data according to the measuring point name in the time sequence data;
step 222: checking whether the measured point data meet the requirements or not according to the reasonable value range of each type of measured point; if the requirements are not met, the step 224 is entered, otherwise, the subsequent check is continued;
step 223: checking whether the tested point value is an invalid value or a null value, and if the tested point value is the invalid value or the null value, entering step 224;
step 224: processing abnormal conditions of the measuring point values, and if data which exceed a normal range and are logically unreasonable are found, correcting or discarding the data; if the point-of-measure value is found to be an invalid value or null value, the piece of data is discarded for the invalid value, and if a null value occurs, the default value is used for padding.
6. The real-time access method of time series data of the nuclear power industry internet platform as claimed in claim 4, wherein the step 24 comprises the following steps:
step 241: for high-frequency time sequence data, firstly, performing plant-side calculation analysis processing in an edge server at a power plant side, and after the analysis processing is completed, transmitting a characteristic value or a result after the analysis processing to a nuclear power industry Internet platform through MQTT;
step 242: and for the common time sequence data, the down sampling is carried out and then the data is transmitted.
7. The real-time access method of nuclear power industry internet platform time series data as claimed in claim 4, wherein said step 25 includes the following:
according to the data scale and the actual conditions of the commercial network bandwidth between the power plant side and the data center side of the nuclear power industry internet platform, whether data compression processing needs to be carried out on data sent to the nuclear power industry internet platform or not is judged, if the bandwidth occupied by the sent data exceeds 10% of the commercial network bandwidth, the data compression processing needs to be carried out, and a specific compression algorithm can be adopted for compression, for example: gzip, snappy, zlib.
8. The real-time access method of the nuclear power industry internet platform time series data as claimed in claim 1, wherein the step 3 comprises the following steps:
step 31: the edge server carries out cleaning, conversion, dimension reduction and compression processing on the time sequence data, and evaluates the bandwidth occupation condition of data transmission according to the data scale after the processing is finished;
step 32: if the occupied bandwidth is less than 10% of the commercial network bandwidth when the JSON data packet is adopted for transmission, the JSON data packet mode is adopted for real-time data transmission;
step 33: if the bandwidth occupied by the JSON data packet is more than or equal to 10% of the bandwidth of the commercial network when the JSON data packet is used for transmission, the real-time data transmission needs to be carried out in a binary data packet mode.
9. The real-time access method of the nuclear power industry internet platform time series data as claimed in claim 1, wherein the step 4 comprises the following steps:
step 41: the time sequence data enters an MQTT server at the platform side from the edge side;
step 42: if the data directly enter the MQTT server, the MQTT server is used as a message producer, and the data is written into the Kafka cluster;
step 43: the Flink program is used as a message consumer to receive the message in Kafka, and after receiving the message, unpacking, decompressing, decoding and data standardization processing are carried out on the message;
step 44: if the processed data is high-frequency time sequence data, writing the data into an HBase database by using a Flink program, otherwise, writing the data into a platform side time sequence database.
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