CN113944923A - Method for detecting boiler wall temperature overrun alarm in real time based on Spark Streaming - Google Patents
Method for detecting boiler wall temperature overrun alarm in real time based on Spark Streaming Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F22—STEAM GENERATION
- F22B—METHODS OF STEAM GENERATION; STEAM BOILERS
- F22B37/00—Component parts or details of steam boilers
- F22B37/02—Component parts or details of steam boilers applicable to more than one kind or type of steam boiler
- F22B37/38—Determining or indicating operating conditions in steam boilers, e.g. monitoring direction or rate of water flow through water tubes
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F22—STEAM GENERATION
- F22B—METHODS OF STEAM GENERATION; STEAM BOILERS
- F22B37/00—Component parts or details of steam boilers
- F22B37/02—Component parts or details of steam boilers applicable to more than one kind or type of steam boiler
- F22B37/42—Applications, arrangements, or dispositions of alarm or automatic safety devices
- F22B37/47—Applications, arrangements, or dispositions of alarm or automatic safety devices responsive to abnormal temperature, e.g. actuated by fusible plugs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2471—Distributed queries
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Abstract
The invention relates to a Spark Streaming based real-time detection boiler wall temperature overrun alarm method, which is characterized in that based on Spark big data processing and calculation framework, Kafka is subscribed by Spark Streaming driver program, SIS real-time database is run through, discrete stream DSstream is used for realizing uninterrupted 7 × 24 work, all measuring point data of boiler wall heating surface metal monitoring form a continuous RDD sequence according to batch interval, overrun measuring points are filtered through Map conversion operation, and then aggregation is carried out through Reduce sliding window operation, so that real-time detection boiler wall temperature overrun alarm statistics is realized, and remarkable effects are achieved for preventing shutdown of a unit caused by overrun, improving reliability of power generation equipment and indirectly reducing power generation cost.
Description
Technical Field
The invention relates to the field of computer application and industrial power station production and operation, in particular to a Spark Streaming-based method for detecting boiler wall temperature overrun alarm in real time.
Background
At present, the generating set of a thermal power plant enters a development stage with large capacity and high parameters, and supercritical and ultra-supercritical generating sets of 600MW and 1000MW grades are put into operation in succession. Along with the continuous improvement of unit parameters, particularly the rise of the temperature of main steam, the overtemperature problem caused by oxidation scale in the boiler pipe wall is more prominent; statistics have shown that boiler failures cause generator set outages of about 80%, with 70% of failures being caused by heating surface damage. Damage to the heating surface piping is directly or indirectly related to the over-temperature of the heating surface during operation. When the metal temperature of the pipe wall exceeds the allowable temperature of the steel material under the bearing force level, high-temperature creep deformation of the pipe can be caused, pipe explosion accidents can be caused seriously, not only can huge direct economic loss be caused, but also the hidden danger of continuous pipe explosion is buried because a large number of pipes near the pipe explosion area are damaged, the safe operation of a boiler is seriously influenced, the availability ratio of equipment is reduced, the generator set is caused to be shut down by accidents, and the power generation cost is directly increased.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention aims to provide a method for detecting the over-limit alarm of the boiler wall temperature in real time based on Spark Streaming, which realizes uninterrupted 7 × 24 work by using discretization stream DSstream, forms measuring point data of metal monitoring on the heated surface of the boiler wall into each RDD according to batch intervals (60 seconds), filters out the over-limit measuring points through Map conversion operation, and then performs aggregation output to a message system through Reduce action operation for real-time alarm, thereby having remarkable effects on preventing the shutdown of a unit caused by over-temperature, improving the reliability of power generation equipment and indirectly reducing the power generation cost.
First, the terms appearing in the present invention will be explained as follows:
RDD: the flexible Distributed data sets (RDDs for short) are an immutable Distributed object set. RDD supports two operations: a conversion operation and an action operation.
DStream: the RDD is a sequence of data received over time, the data received in each time interval exists as RDD, and a sequence composed of these discretized RDDs is called a discretized stream (discretized stream).
And (3) conversion operation: a new RDD is returned through Map () or Filter () etc., and the conversion operation is directed to each element in the RDD dataset.
And (4) action operation: the new data type is returned through Reduce () or count () and the result is finally obtained to generate the actual output.
Spark, a large data processing and computing framework, is a platform for implementing fast and versatile cluster computing. The widely used MapReduce computing model is expanded, and more computing modes including interactive query and stream processing are efficiently supported. Speed is very important when processing large-scale data sets. An important feature of Spark is the ability to compute in memory and thus be faster.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for detecting boiler wall temperature overrun alarm in real time based on Spark Streaming comprises the following steps:
step 2, subscribing a 'boiler wall temperature overrun' theme from a distributed streaming media platform Kafka, constructing a streamingContext context environment, and serializing a mapping table of receiver thread data aiming at the theme;
and 6, according to the real-time statistical result of the final window, performing DStream output operation, arranging the content in Kafka to create an over-limit alarm theme, and issuing a message to a mobile phone or a service system to achieve the aim of alarm prompting.
Advantageous effects
By adopting the invention, the alarm for detecting the wall temperature overrun of the boiler has the following remarkable advantages: 1. real-time performance; the streaming big data is generated in real time and can also give feedback results in real time. The system has quick response capability, shows the value of data alarm in a short time, and reduces the value of data extremely rapidly after the effective time is exceeded. 2. Throughput; the incoming rate and order of the data is not certain, even with large differences. The Spark distributed computing framework has higher throughput and can quickly process large data traffic. 3. The method is unlimited; data can be continuously generated and flowed in, and the operation can be continuously and stably carried out without interruption by 7 x 24.
Drawings
FIG. 1 is a schematic diagram of a method for detecting alarm of boiler wall temperature overrun in real time based on Spark Streaming.
FIG. 2 is a flow chart of the transformation operation.
FIG. 3 is a flow chart of window operations.
Detailed Description
The technical solution of the present invention is further described in detail below with reference to the accompanying drawings.
As shown in FIG. 1, a method for detecting boiler wall temperature overrun alarm in real time based on Spark Streaming comprises the following steps:
firstly, creating a 'boiler wall temperature overrun' theme (named as 'N1-OLM'), a copy coefficient (number of copies of the theme), a partition number (number of partitions of the theme) in a distributed streaming media platform Kafka, wherein the distributed streaming media platform Kafka cluster creates a specified number of copies for each partition according to a theme name Key; the subject content is provided with a data source by an SIS operation real-time database, and message transmission is carried out by converting all measuring point objects (including measuring point names, measuring point descriptions, units, time, quality, numerical values, over-temperature limit values and over-temperature levels) of metal monitoring of a heating surface of a boiler wall into text information of line changing symbols separated by commas. As follows: n1.gwzrq — 01, right side of high temperature reheater 01 point, deg.c, 1633421667,0,526.6,550, L1
N1.gwzrq — 02, high temperature reheater right side 02 point, deg.c, 1633421667,0,511.6,550, L1
……
And secondly, subscribing a 'boiler wall temperature overrun' theme from the distributed streaming media platform Kafka, constructing a streamingContext context environment, and serializing a mapping table of receiver thread data aiming at the theme. The main code for creating a receiver line from the "boiler wall temperature overrun" theme through Spark API is as follows:
Map<String,Integer>topics＝new HashMap<String,Integer>()；
topics.put(“N1-OLM”,1)；
JavaStreamingContext jssc＝new JavaStreamingContext(conf,Second(60))；
JavaPariDStream<String,String>input＝KafkaUtils.creatStream(jssc,topics)
wherein: second (60) indicates that the batch interval for new data is 60 seconds, and data is collected from Kafka; creatstream (jssc, topics) implements a link for Kafka and Spark data transmission.
And thirdly, reading data at batch intervals (60s) to form a continuous RDD sequence, namely a discretization stream, namely DStream. Spark Streaming uses the micro-batch architecture to treat Streaming as a series of continuous small-scale batch processes, each time a new batch is created at uniform time intervals, the size of the time interval being determined by the parameter of the batch interval (60S is used in this embodiment). Each batch forms an RDD, each RDD represents data within a time slice in the data stream, and the continuous RDD sequences are finally abstracted into a discretization stream DStream. As shown in fig. 2 as "raw data DStream".
And fourthly, filtering out the measuring points with the boiler wall temperature exceeding limit through conversion operation to form a new continuous RDD sequence, namely a new DStream. As shown in the flowchart of fig. 2, when Map/Filter conversion is performed at a working node of a driver program of Spark, the real-time value of each measurement point in the RDD is compared with the overrun limit, the measurement point larger than the overrun limit is returned to a new continuous RDD sequence, i.e. a new DStream, composed of elements having overrun records at each measurement point, by the new RDD contents consisting of the name, description, unit, time, overrun limit, actual value and level of the measurement point, and the overrun judgment operation for each small batch of data conversion is performed continuously over time.
And fifthly, defining the state detection points, starting a Checkpoint mechanism, integrating results of a plurality of batches through stateful action operation to form a window containing duration and step length, and carrying out reduction operation on each window more efficiently to count out the over-limit result of the detection points.
Step 1: defining a state detection point, aiming to allow the DStream to maintain any state and continuously update the DStream with new information; for stateful action operations, the current and historical RDD of the time slice are continuously calculated cumulatively, as a function: the updateStateBykey is defined as: accumulating the over-limit information of the measuring points with the over-limit boiler wall temperature according to the over-limit time, and updating the over-limit ending time in real time; the function is mainly used for ensuring the main mechanism of the fault tolerance of the working nodes, and on one hand, the function is used for controlling the state number which needs to be recalculated when the failure occurs. On the other hand, when the streaming Spark driver crashes, the driver recovers from the detection point and continues to run.
Step 2: performing a sliding window operation, wherein the Spark Streaming uses a stateful transformation operation, which is an operation of tracking data across time intervals, i.e. some data of previous batches are also used to calculate results in a new batch; mainly by sliding windows. The sliding window operation requires two parameters, the window duration and the sliding step size, both of which must be integer multiples of the batch interval (60 s). As shown in the window operation flow diagram of fig. 3. The window duration is 3 batches, and the sliding step length is 2 batches; the calculation was performed every 2 batches for the first 3 batches of data. The concrete implementation is as follows:
JavaDStream<ApachAccessLog>accessLogWindow＝accesslogStream.window(Seconds(180),Second(120))；
accessLogWindow.countByWindow(new updateRunOLM(),Seconds(180),Second(120))；
wherein updateRunOLM () is a business method of determining whether a point is overrun and accumulating the overrun time length.
And sixthly, according to the real-time statistical result of the final window, performing DStream output operation, arranging the content in a distributed streaming media platform Kafka to create an over-limit alarm (N1-OLM-Report) theme, and issuing a message to a mobile phone or a service system to achieve the aim of alarm prompt.
Step 1: summarizing the overrun results, defining a data structure: the subject content is arranged according to the calculation result to include two objects of over-limit detail object (measuring point name, description, unit, time, over-temperature limit value, actual value, level and duration) and over-limit summary object (measuring point name, description, unit, over-temperature times and over-temperature duration) to be converted into text information separated by commas and line feed character lines for message transmission.
Step 2: an alarm theme is created and output, a DStream output operation arranges an ultralimit summary object (measuring point name, description, unit, overtemperature times and overtemperature duration) in a distributed streaming media platform Kafka, creates an ultralimit alarm (N1-OLM-Report) message theme by the theme name of ultralimit alarm (N1-OLM-Report), copy coefficients (theme copy number) and partition number (partition number of the theme), and issues a message to a mobile phone or a service system to achieve the purpose of alarm prompt.
Claims (5)
1. A method for detecting boiler wall temperature overrun alarm in real time based on Spark Streaming is characterized by comprising the following steps:
step 1, creating a theme of 'boiler wall temperature overrun' in a distributed streaming media platform Kafka;
step 2, subscribing a 'boiler wall temperature overrun' theme from a distributed streaming media platform Kafka, constructing a streamingContext context environment, and serializing a mapping table of receiver thread data aiming at the theme;
step 3, reading data according to batch intervals to form a continuous RDD sequence, namely a discretization stream, namely DStream;
step 4, filtering out the measuring points with the boiler wall temperature exceeding the limit through conversion operation to form a new continuous RDD sequence, namely a new DStream;
step 5, defining state detection points, starting a Checkpoint mechanism, integrating results of a plurality of batches through stateful action operation to form a window containing duration and step length, and carrying out reduction operation on each window more efficiently to count out a detection point overrun result;
and 6, according to the real-time statistical result of the final window, performing DStream output operation, arranging the content in a distributed streaming media platform Kafka to create an over-limit alarm theme, and issuing a message to a mobile phone or a service system to achieve the aim of alarm prompt.
2. The method for detecting the boiler wall temperature overrun alarm in real time based on Spark Streaming as claimed in claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1, creating a message theme, wherein the theme comprises three parts of contents, namely a theme name, a copy coefficient, namely the number of copies of the theme, and a partition number, namely the number of partitions of the theme, and a distributed streaming media platform Kafka cluster can create a specified number of copies for each partition according to the theme name;
step 1.2 standardize the subject content format: the method comprises the following steps of measuring point name, measuring point description, unit, time, quality, numerical value, over-temperature limit value and over-temperature level, and converts the subject content into text information which is separated by commas and is divided into line-changing symbols for message transmission, and the method is as follows:
n1.gwzrq — 01, right side of high temperature reheater 01 point, deg.c, 1633421667,0,526.6,550, L1
N1.gwzrq — 02, high temperature reheater right side 02 point, c, 1633421667,0,511.6,550, L1.
3. The method for detecting the boiler wall temperature overrun alarm in real time based on Spark Streaming as claimed in claim 1, wherein the step 4 is as follows:
performing RDD conversion operation: transform operation, namely conversion operation allows DStream to execute a Map method or a Filter method on data streams of each batch, data set RDD is converted into a measure point real-time value of boiler wall temperature overrun, the measure point real-time value corresponds to the overrun limit value and is compared, the measure point which is larger than the limit value is recorded with real-time temperature and limit temperature, and a new RDD sequence is formed by measure point names, measure point description, units, time, the overrun limit value, actual values and levels.
4. The method for detecting the boiler wall temperature overrun alarm in real time based on Spark Streaming as claimed in claim 1, wherein the step 5 specifically comprises the following steps:
step 5.1, defining a state detection point, starting a Checkpoint mechanism, allowing the DStream to maintain any state, and continuously updating the DStream with new information; for stateful action operations, the current and historical RDD of the time slice are continuously calculated cumulatively, as a function: the updateStateBykey is defined as: accumulating the over-limit information of the measuring points with the over-limit boiler wall temperature according to the over-limit time, and updating the over-limit ending time in real time; the method mainly serves as a main mechanism for guaranteeing the fault tolerance of the working nodes, and on one hand, the method is used for controlling the state number to be recalculated when failure occurs; on the other hand, when the streaming computing Spark driver crashes, the driver can be recovered by using the detection point and continues to run;
step 5.2, performing a sliding window operation, wherein the Spark Streaming adopts a state conversion operation, namely, an operation of tracking data across time intervals, that is, data of some previous batches are also used for calculating results in a new batch; mainly realized by a sliding window; sliding window operation requires two parameters, window duration and sliding step length, both of which must be integer multiples of batch intervals; the window duration is 3 batches, and the sliding step length is 2 batches; the calculation was performed every 2 batches for the first 3 batches of data.
5. The method for detecting the boiler wall temperature overrun alarm in real time based on Spark Streaming as claimed in claim 1, wherein the step 6 specifically comprises the following steps:
step 6.1, summarizing overrun results, defining a data structure body: the subject content is arranged according to the calculation result to include an overlimit detail object and an overlimit summary object, the two objects are converted into text information of line changing symbol lines separated by commas for message transmission, wherein the overlimit detail object comprises a measuring point name, a measuring point description, a unit, time, an overtemperature limit value, an actual value, a level and time length, and the overlimit summary object comprises a measuring point name, a measuring point description, a unit, overtemperature times and overtemperature time length.
Step 6.2, an alarm subject is created and output, DStream output operation arranges the ultralimit summary object in a distributed streaming media platform Kafka, creates an ultralimit alarm N1-OLM-Report message subject by a subject name of ultralimit alarm N1-OLM-Report, a copy coefficient, namely the number of copies of the subject, and a partition number, namely the number of partitions of the subject, and issues a message to a mobile phone or a service system to achieve the purpose of alarm prompt.
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