CN117371607A - Boiler steam-water flow reconstruction monitoring system based on Internet of things technology - Google Patents

Boiler steam-water flow reconstruction monitoring system based on Internet of things technology Download PDF

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CN117371607A
CN117371607A CN202311423818.XA CN202311423818A CN117371607A CN 117371607 A CN117371607 A CN 117371607A CN 202311423818 A CN202311423818 A CN 202311423818A CN 117371607 A CN117371607 A CN 117371607A
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data
module
sensor
boiler
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胡曦
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Shanghai Boiler Works Co Ltd
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Shanghai Boiler Works Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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    • F22B37/02—Component parts or details of steam boilers applicable to more than one kind or type of steam boiler
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Abstract

The invention discloses a boiler steam-water flow reconstruction monitoring system based on the technology of the Internet of things, which belongs to the technical field of industrial automation and comprises a sensor module, a data acquisition module, a data transmission module, a processing center module, an intelligent maintenance module, a map fusion module, a remote monitoring terminal, an alarm notification module, a report module and a system optimization module; the invention can process more data and meet service requirements without reconstructing the whole system, improves the reliability and the availability of the data, better meets different service requirements, utilizes the related knowledge in the knowledge graph, enhances the understanding and judging ability of the sensor data, improves the accuracy and the interpretability of predictive maintenance, improves the compression efficiency, effectively improves the data access efficiency, reduces the frequent access to data sources such as a database and the like, and further improves the system performance.

Description

Boiler steam-water flow reconstruction monitoring system based on Internet of things technology
Technical Field
The invention relates to the technical field of industrial automation, in particular to a boiler steam-water flow reconstruction monitoring system based on the technology of the Internet of things.
Background
Boilers are a key element of energy conversion and have an irreplaceable position in industrial production and energy supply. However, boiler operation and safety management have been an important issue in the industry, and conventional manual monitoring and control methods face challenges of low operation efficiency, energy waste and safety risks. Along with the rapid development of industrial automation and digitization, the technology of the Internet of things is gradually integrated into various fields, and brings brand new reform and opportunity for the traditional industrial process. In the energy field, boilers are important energy conversion devices, and safe operation and energy efficiency optimization of the boilers are important. However, the traditional boiler monitoring and control methods have some limitations, and cannot meet the requirements of modern industry on efficient and intelligent monitoring. Based on the above, the boiler steam-water flow reconstruction monitoring system based on the internet of things technology has been developed, and aims to realize accurate monitoring, optimal control and safety management of the boiler steam-water flow by means of comprehensive sensor network, real-time data processing, remote monitoring terminal and other technical means, so that support is provided for sustainable development of the industrial field.
The existing boiler steam-water flow reconstruction monitoring system cannot process more data, has poor data reliability and usability, and has weak understanding and judging capability on sensor data; in addition, the existing boiler steam-water flow reconstruction monitoring system is low in data access efficiency, frequent access times of data sources such as databases are more, system performance is reduced, and therefore the boiler steam-water flow reconstruction monitoring system based on the Internet of things technology is provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a boiler steam-water flow reconstruction monitoring system based on the technology of the Internet of things.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the boiler steam-water flow reconstruction monitoring system based on the internet of things comprises a sensor module, a data acquisition module, a data transmission module, a processing center module, an intelligent maintenance module, a map fusion module, a remote monitoring terminal, an alarm notification module, a report module and a system optimization module;
the sensor module is used for being arranged at each key position of the boiler and monitoring key parameters inside and outside the boiler in real time;
the data acquisition module is used for receiving data from the sensor and carrying out integrated preprocessing on the sensor data;
the data transmission module is used for establishing a data transmission channel and transmitting the data to the processing center module in a wired or wireless mode;
the processing center module is used for receiving, processing and analyzing all the uploaded groups of data and carrying out distributed storage on all the groups of data;
the map fusion module is used for fusing the boiler knowledge map with the acquired data;
the intelligent maintenance module is used for intelligently predicting a maintenance period and optimizing a maintenance plan according to the sensor data and the history record;
the remote monitoring terminal is used for providing a user interface and allowing a user to remotely monitor and control the running state of the boiler, the parameter trend and the alarm information;
the report module is used for carrying out deep analysis on the acquired data, generating a trend chart, a report and operation analysis, and providing basis for decision making;
the system optimization module is used for optimizing and adjusting the system operation performance.
As a further scheme of the invention, the sensor module specifically comprises a temperature sensor, a pressure sensor, a flow sensor, a liquid level sensor, a gas sensor, a humidity sensor, a vibration sensor, a current sensor, an oxygen sensor, a PH sensor, a C0 sensor, a C02 sensor and an infrared sensor; the remote monitoring terminal is specifically a remote control console, a smart phone, a tablet personal computer, a desktop personal computer and a notebook personal computer.
As a further scheme of the invention, the integrated pretreatment of the data acquisition module comprises the following specific steps:
step one: the data acquisition module detects missing values in each group of data, marks the positions of the missing values in the corresponding data, performs statistics and visual analysis on the missing values in each group of data to obtain the distribution condition and the influence range of the missing values, and fills or deletes the missing values;
step two: classifying the processed data of each group according to different sensors, respectively integrating and summarizing the data into a sample data set, calculating the standard deviation of each group of data, and then respectively detecting and screening out abnormal data according to the calculated standard deviation;
step three: noise and fluctuation are removed through exponential smoothing to obtain trend and periodic information, meanwhile, standardization processing is carried out on each group of data to form a unified format, whether repeated data records exist or not is detected, and if repeated data exist, the repeated data are deleted.
As a further scheme of the invention, the distributed storage of the processing center module comprises the following specific steps:
step I: the processing center module divides each group of data according to a preset time interval to obtain a plurality of groups of data blocks, and then automatically distributes a unique ID for each group of data blocks or generates the identification of each group of data blocks through a hash algorithm;
step II: collecting information of each group of nodes, selecting proper nodes to store each group of data blocks according to a data block dividing rule and node load conditions through a load balancing algorithm, and after the data blocks are stored, configuring and copying a specified number of data blocks to a plurality of groups of nodes according to the requirements of a system and available resources;
step III: when the data stored by the nodes changes, the data update is transmitted from one node to other nodes through a data synchronization algorithm, then the node operation condition is automatically detected, and the data migration or repair is carried out on the fault node.
As a further scheme of the invention, the data fusion of the map fusion module comprises the following specific steps:
step (1): the map fusion module collects various knowledge and information related to the steam-water flow of the boiler from expert knowledge, literature data, the Internet and a boiler resource database, and classifies, de-duplicates and screens the collected boiler knowledge;
step (2): identifying and extracting entities in the processed boiler knowledge through an NLP technology, extracting corresponding attributes of each entity from related knowledge information, and establishing a relation between the entities to form a connection of boiler knowledge graphs;
step (3): processing the entity, attribute and relation into a corresponding graph structure in a triplet mode, selecting a proper graph database to store and manage the boiler knowledge graph, continuously updating and maintaining the boiler knowledge graph, and then matching the entity in the preprocessed historical data with the corresponding entity in the knowledge graph.
As a further scheme of the invention, the intelligent maintenance module predicts the specific steps of maintenance as follows:
step (1): the intelligent maintenance module integrates and generalizes the historical sensor data into a verification data set, then divides the data set into a training set and a testing set, initializes the weight and parameters of the convolutional neural network, then introduces the training set into the convolutional neural network, calculates the corresponding output of the convolutional neural network, and then measures the loss value between the prediction result of the model and the real label through a loss function;
step (2): if the loss value does not meet the preset condition, retraining the convolutional neural network, updating the parameters of the convolutional neural network, and when the loss value meets the preset condition, evaluating the trained convolutional neural network by using a test set, calculating the performance index of the convolutional neural network on the test set, and outputting a prediction maintenance model;
step (3): preprocessing new sensor data, taking the preprocessed data as input data, enabling the node number and the data dimension of an input layer of a predictive maintenance model to be consistent with those of the input layer of the model defined in a training stage, and then enabling the input data to pass through all hidden layers of the model from the input layer of the predictive maintenance model;
step (4): each hidden layer respectively carries out linear transformation and nonlinear activation on input data, the processed data is transferred layer by layer through weights and activation functions among layers, then an output layer outputs a final prediction result, a proper threshold is set, probability values in the prediction result are converted into final classification labels, and real values in the prediction result can be mapped into corresponding water environment levels and displayed in a chart or visual form.
As a further scheme of the invention, the system optimization module performance optimization comprises the following specific steps:
the first step: the system optimization module determines accessed data and data with larger calculation cost in the system according to user preset information, determines pointers pointing to a next node and a last node, and determines a linked list node structure according to the determined data objects and the pointers;
and a second step of: creating an empty linked list, setting the maximum capacity of the linked list according to the memory resource and performance requirements of the system, searching the data in the cache linked list when the data is required to be accessed, moving the data to the head of the linked list if the data exists in the linked list, indicating that the data is used recently, acquiring the data from a database or other data sources if the data is not in the linked list, and adding the data to the head of the linked list;
and a third step of: the method comprises the steps of periodically monitoring the length, cache hit rate and performance index of a linked list, judging the data which are not accessed for the longest time in the linked list based on the latest access time when the cache capacity reaches the upper limit, removing the corresponding data node from the tail of the linked list and releasing resources, updating the head pointer of the linked list to a new head node, recording the cache hit rate and the times of elimination operation, and periodically monitoring the system performance.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, each group of data is divided according to a preset time interval to obtain a plurality of groups of data blocks, then a unique ID is automatically allocated to each group of data blocks or an identification of each group of data blocks is generated through a hash algorithm, each group of data blocks is stored through a proper node selected through a load balancing algorithm, after the data blocks are stored, a specified number of data blocks are configured and copied to a plurality of groups of nodes according to the requirements and available resources of a system, when the data stored by the nodes are changed, the data are updated and transmitted from one node to other nodes through a data synchronization algorithm, then the running condition of the nodes is automatically detected, data migration or restoration is carried out on fault nodes, various knowledge and information related to a boiler steam-water flow are collected, the collected boiler knowledge is preprocessed, then the entity in the knowledge is identified and extracted, the entity, the attribute and the relation are processed into a corresponding graph structure through a ternary group mode, the boiler knowledge graph is stored and managed, the history graph is continuously updated and maintained according to the requirements and available resources of the system, and the corresponding knowledge graph in the preprocessed entity and the entity in the boiler knowledge graph are processed, the preprocessed data are processed, the corresponding graph can be better than the knowledge graph can be better understood and better, the service can be better understood and the service can be better understood, and the service can be better satisfied, and the service can be better well need to be better matched and can be better stored, and the service can be better understood, and better required and well understood.
2. The invention determines the accessed data and the data with larger calculation cost in the system according to the preset information of the user, then determines the pointers pointing to the next node and the last node, determines the node structure of the linked list according to the determined data object and the pointers, creates an empty linked list, simultaneously removes and releases the corresponding data node from the tail of the linked list according to the memory resource and the performance requirement of the system, and sets the maximum capacity of the linked list.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
FIG. 1 is a system block diagram of a boiler steam-water flow reconstruction monitoring system based on the Internet of things technology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, the boiler steam-water flow reconstruction monitoring system based on the internet of things technology comprises a sensor module, a data acquisition module, a data transmission module, a processing center module, an intelligent maintenance module, a map fusion module, a remote monitoring terminal, an alarm notification module, a report module and a system optimization module.
The sensor module is used for being arranged at each key position of the boiler and monitoring key parameters inside and outside the boiler in real time.
In this embodiment, the sensor module specifically includes a temperature sensor, a pressure sensor, a flow sensor, a liquid level sensor, a gas sensor, a humidity sensor, a vibration sensor, a current sensor, an oxygen sensor, a PH sensor, a C0 sensor, a C02 sensor, and an infrared sensor.
The data acquisition module is used for receiving data from the sensor and carrying out integrated preprocessing on the sensor data.
Specifically, the data acquisition module detects missing values in each group of data, marks the positions of the missing values in the corresponding data, performs statistics and visual analysis on the missing values in each group of data to obtain distribution conditions and influence ranges of the missing values, fills or deletes the missing values, classifies the processed groups of data according to different sensors, respectively integrates and generalizes the processed groups of data into a sample data set, calculates standard deviation of each group of data, respectively detects and screens out abnormal data according to the calculated standard deviation, removes noise and fluctuation through exponential smoothing to obtain trend and periodic information, simultaneously performs standardization processing on each group of data to obtain unified format, detects whether repeated data records exist, and deletes the repeated data if the repeated data exist.
The data transmission module is used for establishing a data transmission channel and transmitting the data to the processing center module in a wired or wireless mode; the processing center module is used for receiving, processing and analyzing the uploaded data of each group and carrying out distributed storage on the data of each group.
Specifically, the processing center module divides each group of data according to a preset time interval to obtain a plurality of groups of data blocks, then automatically distributes a unique ID for each group of data blocks or generates the identification of each group of data blocks through a hash algorithm, collects each group of node information, selects proper nodes to store each group of data blocks according to a data block dividing rule and a node load condition through a load balancing algorithm, after the data block storage is completed, configures and copies a specified number of data blocks to a plurality of groups of nodes according to the requirement of a system and available resources, when the data stored by the nodes changes, transmits the data update from one node to other nodes through a data synchronization algorithm, then automatically detects the node operation condition, and performs data migration or repair on the fault nodes.
The map fusion module is used for fusing the boiler knowledge map with the acquired data.
Specifically, the map fusion module collects various kinds of knowledge and information related to the boiler steam-water flow from expert knowledge, literature data, the Internet and a boiler resource database, classifies, de-weights and screens the collected boiler knowledge, identifies and extracts entities in the processed boiler knowledge through NLP technology, extracts corresponding attributes of each entity from the related knowledge information, establishes a relation among the entities to form a connection of boiler knowledge maps, processes the entities, the attributes and the relation into a corresponding map-like structure in a triplet form, selects a proper map database to store and manage the boiler knowledge maps, continuously updates and maintains the boiler knowledge maps, and then matches the entities in the preprocessed historical data with the corresponding entities in the knowledge maps.
Example 2
Referring to fig. 1, the boiler steam-water flow reconstruction monitoring system based on the internet of things technology comprises a sensor module, a data acquisition module, a data transmission module, a processing center module, an intelligent maintenance module, a map fusion module, a remote monitoring terminal, an alarm notification module, a report module and a system optimization module.
The intelligent maintenance module is used for intelligently predicting maintenance period and optimizing maintenance plan according to the sensor data and the history record.
Specifically, the intelligent maintenance module integrates and generalizes historical sensor data into a verification data set, then divides the data set into a training set and a testing set, initializes weights and parameters of a convolutional neural network, then guides the training set into the convolutional neural network, calculates corresponding output of the model, then measures a loss value between a predicted result of the model and a real label through a loss function, retrains the convolutional neural network and updates the parameters of the convolutional neural network if the loss value does not meet a preset condition, evaluates the trained convolutional neural network by using the testing set when the loss value meets the preset condition, calculates performance indexes of the convolutional neural network on the testing set, outputs a predicted maintenance model, preprocesses new sensor data, takes the preprocessed data as input data, enables the node number and the data dimension of an input layer of the predicted maintenance model to be consistent with the model input layer defined in a training stage, then starts to pass through all hidden layers of the model from the input layer of the predicted maintenance model, and then carries out linear transformation and nonlinear activation on the input data respectively, and carries out mapping on the processed weight and the corresponding output of the predicted result to a real label in a final water environment form or can be set to a predicted result in a corresponding probability level map form.
The remote monitoring terminal is used for providing a user interface and allowing a user to remotely monitor and control the running state of the boiler, the parameter trend and the alarm information; the report module is used for carrying out deep analysis on the acquired data, generating a trend chart, a report and operation analysis, and providing a basis for decision making; the system optimization module is used for optimizing and adjusting the system operation performance.
Specifically, the system optimization module determines accessed data and data with larger calculation cost in the system according to preset information of a user, then determines pointers pointing to a next node and a last node, determines a link table node structure according to determined data objects and the pointers, creates an empty link table, simultaneously sets the maximum capacity of the link table according to system memory resources and performance requirements, searches the data in a cache link table when the data needs to be accessed, moves the data to the head of the link table if the data exists in the link table, indicates that the data is recently used, acquires the data from a database or other data sources and adds the data to the head of the link table if the data is not in the link table, periodically monitors the length, the cache hit rate and the performance index of the link table, judges the data which is not accessed for the longest time in the link table based on the recently accessed time when the cache capacity reaches the upper limit, removes and releases resources from the tail of the link table, simultaneously updates the head pointer of the link table to the new head node, records the cache hit rate and the number of elimination operations, and periodically monitors the system performance.
It should be further noted that the remote monitoring terminal is specifically a remote console, a smart phone, a tablet computer, a desktop computer, and a notebook computer.

Claims (7)

1. The boiler steam-water flow reconstruction monitoring system based on the internet of things is characterized by comprising a sensor module, a data acquisition module, a data transmission module, a processing center module, an intelligent maintenance module, a map fusion module, a remote monitoring terminal, an alarm notification module, a report module and a system optimization module;
the sensor module is used for being arranged at each key position of the boiler and monitoring key parameters inside and outside the boiler in real time;
the data acquisition module is used for receiving data from the sensor and carrying out integrated preprocessing on the sensor data;
the data transmission module is used for establishing a data transmission channel and transmitting the data to the processing center module in a wired or wireless mode;
the processing center module is used for receiving, processing and analyzing all the uploaded groups of data and carrying out distributed storage on all the groups of data;
the map fusion module is used for fusing the boiler knowledge map with the acquired data;
the intelligent maintenance module is used for intelligently predicting a maintenance period and optimizing a maintenance plan according to the sensor data and the history record;
the remote monitoring terminal is used for providing a user interface and allowing a user to remotely monitor and control the running state of the boiler, the parameter trend and the alarm information;
the report module is used for carrying out deep analysis on the acquired data, generating a trend chart, a report and operation analysis, and providing basis for decision making;
the system optimization module is used for optimizing and adjusting the system operation performance.
2. The internet of things-based boiler steam flow reconstruction monitoring system of claim 1, wherein the sensor module specifically comprises a temperature sensor, a pressure sensor, a flow sensor, a liquid level sensor, a gas sensor, a humidity sensor, a vibration sensor, a current sensor, an oxygen sensor, a PH sensor, a C0 sensor, a C02 sensor, and an infrared sensor; the remote monitoring terminal is specifically a remote control console, a smart phone, a tablet personal computer, a desktop personal computer and a notebook personal computer.
3. The boiler steam-water flow reconstruction monitoring system based on the internet of things technology according to claim 2, wherein the data acquisition module integrates the following specific steps of:
step one: the data acquisition module detects missing values in each group of data, marks the positions of the missing values in the corresponding data, performs statistics and visual analysis on the missing values in each group of data to obtain the distribution condition and the influence range of the missing values, and fills or deletes the missing values;
step two: classifying the processed data of each group according to different sensors, respectively integrating and summarizing the data into a sample data set, calculating the standard deviation of each group of data, and then respectively detecting and screening out abnormal data according to the calculated standard deviation;
step three: noise and fluctuation are removed through exponential smoothing to obtain trend and periodic information, meanwhile, standardization processing is carried out on each group of data to form a unified format, whether repeated data records exist or not is detected, and if repeated data exist, the repeated data are deleted.
4. The boiler steam-water flow reconstruction monitoring system based on the internet of things technology according to claim 3, wherein the processing center module performs the following specific steps of:
step I: the processing center module divides each group of data according to a preset time interval to obtain a plurality of groups of data blocks, and then automatically distributes a unique ID for each group of data blocks or generates the identification of each group of data blocks through a hash algorithm;
step II: collecting information of each group of nodes, selecting proper nodes to store each group of data blocks according to a data block dividing rule and node load conditions through a load balancing algorithm, and after the data blocks are stored, configuring and copying a specified number of data blocks to a plurality of groups of nodes according to the requirements of a system and available resources;
step III: when the data stored by the nodes changes, the data update is transmitted from one node to other nodes through a data synchronization algorithm, then the node operation condition is automatically detected, and the data migration or repair is carried out on the fault node.
5. The boiler steam-water flow reconstruction monitoring system based on the internet of things technology according to claim 4, wherein the map fusion module data fusion specifically comprises the following steps:
step (1): the map fusion module collects various knowledge and information related to the steam-water flow of the boiler from expert knowledge, literature data, the Internet and a boiler resource database, and classifies, de-duplicates and screens the collected boiler knowledge;
step (2): identifying and extracting entities in the processed boiler knowledge through an NLP technology, extracting corresponding attributes of each entity from related knowledge information, and establishing a relation between the entities to form a connection of boiler knowledge graphs;
step (3): processing the entity, attribute and relation into a corresponding graph structure in a triplet mode, selecting a proper graph database to store and manage the boiler knowledge graph, continuously updating and maintaining the boiler knowledge graph, and then matching the entity in the preprocessed historical data with the corresponding entity in the knowledge graph.
6. The internet of things-based boiler steam-water flow reconstruction monitoring system of claim 5, wherein the intelligent maintenance module predicts the specific steps of:
step (1): the intelligent maintenance module integrates and generalizes the historical sensor data into a verification data set, then divides the data set into a training set and a testing set, initializes the weight and parameters of the convolutional neural network, then introduces the training set into the convolutional neural network, calculates the corresponding output of the convolutional neural network, and then measures the loss value between the prediction result of the model and the real label through a loss function;
step (2): if the loss value does not meet the preset condition, retraining the convolutional neural network, updating the parameters of the convolutional neural network, and when the loss value meets the preset condition, evaluating the trained convolutional neural network by using a test set, calculating the performance index of the convolutional neural network on the test set, and outputting a prediction maintenance model;
step (3): preprocessing new sensor data, taking the preprocessed data as input data, enabling the node number and the data dimension of an input layer of a predictive maintenance model to be consistent with those of the input layer of the model defined in a training stage, and then enabling the input data to pass through all hidden layers of the model from the input layer of the predictive maintenance model;
step (4): each hidden layer respectively carries out linear transformation and nonlinear activation on input data, the processed data is transferred layer by layer through weights and activation functions among layers, then an output layer outputs a final prediction result, a proper threshold is set, probability values in the prediction result are converted into final classification labels, and real values in the prediction result can be mapped into corresponding water environment levels and displayed in a chart or visual form.
7. The boiler steam-water flow reconstruction monitoring system based on the internet of things technology according to claim 1, wherein the system optimization module performance optimization specifically comprises the following steps:
the first step: the system optimization module determines accessed data and data with larger calculation cost in the system according to user preset information, determines pointers pointing to a next node and a last node, and determines a linked list node structure according to the determined data objects and the pointers;
and a second step of: creating an empty linked list, setting the maximum capacity of the linked list according to the memory resource and performance requirements of the system, searching the data in the cache linked list when the data is required to be accessed, moving the data to the head of the linked list if the data exists in the linked list, indicating that the data is used recently, acquiring the data from a database or other data sources if the data is not in the linked list, and adding the data to the head of the linked list;
and a third step of: the method comprises the steps of periodically monitoring the length, cache hit rate and performance index of a linked list, judging the data which are not accessed for the longest time in the linked list based on the latest access time when the cache capacity reaches the upper limit, removing the corresponding data node from the tail of the linked list and releasing resources, updating the head pointer of the linked list to a new head node, recording the cache hit rate and the times of elimination operation, and periodically monitoring the system performance.
CN202311423818.XA 2023-10-30 2023-10-30 Boiler steam-water flow reconstruction monitoring system based on Internet of things technology Pending CN117371607A (en)

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