Boiler operation management system and method based on electrical automation

Abstract

The invention discloses a boiler operation management system and method based on electrical automation, and relates to the technical field of industrial automation control, and the system comprises a data collection unit which is used for collecting operation state parameters, environment parameters and energy consumption data of a boiler; the core control unit is used for processing the real-time data provided by the data acquisition unit; the fault prediction and alarm unit is used for monitoring the operation state of the boiler in real time; the execution unit is used for receiving an instruction of the core control unit and adjusting operation parameters of the boiler; the big data analysis unit is used for analyzing energy consumption, efficiency and emission conditions of the boiler; according to the remote monitoring and operation unit, an operator can check the operation state of the boiler in real time through an SCADA system or a remote monitoring system based on a cloud platform. By the adoption of the self-adaptive control algorithm, fuel supply and the air proportion can be dynamically adjusted according to real-time environment conditions, and it is ensured that the combustion efficiency of the boiler is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, and in particular to a boiler operation management system and method based on electrical automation. Background Art

[0002] In the existing boiler operation management system, although the degree of automation has been improved, there are still many deficiencies in the system's operating efficiency, fault prediction, energy consumption management, etc. Most of these systems rely on traditional PLC (Programmable Logic Controller) control technology and simple feedback control mechanism, lacking intelligent analysis and adaptive control, and are difficult to meet the high standards of modern industrial boilers in terms of energy saving, environmental protection, emissions and safe operation.

[0003] In the existing technology, the combustion control of boilers is mostly based on fixed parameter settings, and cannot be adaptively adjusted according to changes in actual working conditions and environmental conditions (such as external temperature, humidity, fuel composition, etc.). This not only leads to low combustion efficiency, but also fails to respond in time when the boiler load changes, which easily leads to fuel waste and excessive energy consumption. In addition, the existing system usually does not combine big data analysis to optimize energy consumption management, making it difficult to identify the peak energy consumption points in boiler operation and make optimization suggestions. Summary of the invention

[0004] In view of the problems existing in the existing boiler operation management and method based on electrical automation, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that in the prior art, the combustion control of the boiler is mostly based on fixed parameter settings and cannot be adaptively adjusted according to changes in actual working conditions and environmental conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a boiler operation management system based on electrical automation, which includes:

[0008] Data acquisition unit: used to collect the operating status parameters, environmental parameters and energy consumption data of the boiler, obtain the temperature, pressure and flow information of the boiler through sensors and monitoring equipment, and transmit the collected data to the core control unit for real-time processing and analysis;

[0009] Core control unit: used to process the real-time data provided by the data acquisition unit, and generate control instructions based on intelligent algorithms and preset optimization rules, which can perform combustion optimization, energy consumption management and pressure regulation operations;

[0010] Fault prediction and alarm unit: used to monitor the operating status of the boiler in real time, combine historical data and machine learning algorithms to predict faults and issue early warning information to remind operators to perform maintenance and repairs;

[0011] Execution unit: used to receive instructions from the core control unit and adjust the operating parameters of the boiler, including fuel supply control, air volume regulation and burner control;

[0012] Big data analysis unit: Based on the boiler operation data collected over a long period of time, it analyzes the boiler's energy consumption, efficiency and emissions and generates optimization suggestions;

[0013] Remote monitoring and operation unit: Through the SCADA system or cloud-based remote monitoring system, operators can view the operating status of the boiler in real time, receive alarm information, and perform remote operations and adjustments.

[0014] As a preferred solution of the boiler operation management system based on electrical automation of the present invention, the data acquisition unit includes:

[0015] Temperature acquisition module, used to detect the temperature of key parts of the boiler, including furnace temperature, exhaust gas temperature and water temperature;

[0016] The pressure acquisition module is used to monitor the pressure status of the boiler in real time, including the steam pressure and water pressure in the boiler;

[0017] Fuel flow acquisition module, used to detect the fuel supply and ensure the balance between fuel supply and combustion demand;

[0018] Air volume collection module, used to detect the flow of primary and secondary air to ensure sufficient air supply for combustion;

[0019] The data transmission module transmits all collected data to the core control unit via a wired or wireless network for real-time analysis and processing.

[0020] As a preferred solution of the boiler operation management system based on electrical automation of the present invention, the core control unit includes:

[0021] Intelligent combustion optimization module, based on real-time data from the data acquisition unit, combined with adaptive control algorithms and machine learning models, automatically adjusts the supply of fuel and air volume;

[0022] Energy consumption management module, which analyzes boiler operation data and reduces energy consumption by optimizing fuel use and energy distribution;

[0023] The pressure control module adjusts the operating parameters of the boiler by analyzing the pressure data so that the pressure is always maintained within a safe range;

[0024] The fault diagnosis module is used to monitor the various subsystems of the boiler in real time. When abnormal data is found, it can automatically identify potential fault risks and issue early warnings.

[0025] As a preferred solution of the boiler operation management system based on electrical automation of the present invention, the fault prediction and alarm unit includes:

[0026] The fault prediction module, based on the historical operation data of the boiler and combined with machine learning algorithms, analyzes the long-term operation trend and abnormal conditions of the boiler and predicts the boiler failure in advance;

[0027] Real-time monitoring module continuously monitors the boiler's temperature, pressure, fuel supply and air volume, and issues an alarm immediately if any parameter is found to be outside the preset range;

[0028] The alarm module is used to provide real-time feedback of abnormal situations to operators, and send alarm information through local display screens, remote monitoring platforms or mobile terminals to remind them to carry out maintenance.

[0029] As a preferred solution of the boiler operation management system based on electrical automation described in the present invention, wherein: the execution unit includes:

[0030] A fuel supply control module is used to accurately control the supply amount of fuel according to the instructions of the core control unit;

[0031] Air volume adjustment module, used to automatically adjust the supply of primary and secondary air required during boiler combustion;

[0032] The burner control module is used to dynamically adjust the swing angle and combustion intensity of the burner according to the instructions of the core control unit.

[0033] As a preferred solution of the boiler operation management system based on electrical automation described in the present invention, the big data analysis unit includes:

[0034] Historical data storage module, used to store the long-term historical data of boiler operation, including multiple indicators such as temperature, pressure, fuel consumption and air volume;

[0035] Energy consumption optimization analysis module, which analyzes the energy consumption of the boiler based on the stored data, identifies energy consumption problems in operation and makes optimization suggestions;

[0036] The emission monitoring module is used to monitor the pollutant emissions of the boiler and propose optimization measures to reduce emissions by analyzing the emission data;

[0037] The trend prediction module predicts the future energy consumption trend and maintenance requirements of the boiler based on historical data and combined with big data analysis technology.

[0038] As a preferred solution of the boiler operation management system based on electrical automation of the present invention, the remote monitoring and operation unit includes:

[0039] Remote monitoring platform, through SCADA or cloud platform, operators can remotely view various operating parameters of the boiler in real time, including temperature, pressure, fuel supply, air volume and emission data;

[0040] Remote operation module, allowing operators to adjust the operating status of the boiler, fuel supply and air volume ratio through the remote platform;

[0041] The alarm receiving module is used to receive the alarm information from the fault prediction and alarm unit, and remind the operator to deal with the abnormal situation of the boiler in time.

[0042] In a second aspect, an embodiment of the present invention provides a boiler operation management method based on electrical automation, comprising the following steps:

[0043] The data acquisition unit obtains the temperature, pressure, fuel supply and air supply parameters of the boiler operation in real time and transmits the data to the core control unit;

[0044] The core control unit adjusts the fuel supply, air flow and burner swing angle through the intelligent combustion optimization module based on the collected real-time data to ensure that the boiler is in the optimal combustion state;

[0045] The real-time monitoring module monitors the key parameters of the boiler and triggers the alarm module when an abnormality is found, notifying the operator to conduct inspection and maintenance;

[0046] The execution unit receives the control instructions generated by the core control unit and adjusts the fuel supply, air flow and burner parameters in real time;

[0047] The big data analysis unit analyzes long-term operating data, identifies energy consumption issues and generates optimization suggestions;

[0048] The alarm receiving module sends the fault alarm information to the remote platform, so that the operator can receive the alarm in time and take measures.

[0049] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the above-mentioned boiler operation management system based on electrical automation is implemented.

[0050] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the above-mentioned boiler operation management system based on electrical automation is implemented.

[0051] The beneficial effects of the present invention are:

[0052] 1. The present invention adopts an adaptive control algorithm, which can dynamically adjust the fuel supply and air ratio according to real-time environmental conditions (such as temperature, humidity, fuel composition, etc.) to ensure the maximum combustion efficiency of the boiler. At the same time, the system combines big data analysis to conduct in-depth mining of the historical operation data of the boiler, generate energy consumption optimization suggestions, and help enterprises achieve breakthroughs in energy saving and consumption reduction.

[0053] 2. By integrating machine learning algorithms, the fault prediction module of the present invention can identify potential faults in boiler operation in advance, avoiding taking measures only when a major fault occurs. The system can dynamically predict the health status of the equipment based on historical data and real-time data, and issue an early warning to notify operators to perform maintenance in advance, reducing the risk of equipment downtime and extending the service life of the boiler.

[0054] 3. The present invention introduces a remote monitoring and operation unit, which enables operators to monitor the boiler operating status in real time through the SCADA system or cloud platform, and remotely adjust boiler parameters when necessary to ensure that the boiler is in the best operating state under different working conditions. Through this function, the system improves the flexibility and safety of remote operation and reduces the complexity and response time of on-site operation.

[0055] 4. The big data analysis module of the present invention is combined with the emission monitoring function, which can monitor the pollutant emission level of the boiler in real time and dynamically adjust the combustion parameters according to the emission data to ensure that the boiler emissions meet environmental protection standards. In addition, the system reduces the occurrence of incomplete combustion through intelligent optimization of the combustion process, thereby further reducing pollutant emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0057] Figure 1 This is the framework diagram of the boiler operation management system based on electrical automation.

[0058] Figure 2 This is the architecture diagram of the data acquisition unit of the boiler operation management system based on electrical automation.

[0059] Figure 3 This is the core control unit architecture diagram of the boiler operation management system based on electrical automation.

[0060] Figure 4 This is the architecture diagram of the fault prediction and alarm unit of the boiler operation management system based on electrical automation.

[0061] Figure 5 This is the execution unit architecture diagram of the boiler operation management system based on electrical automation.

[0062] Figure 6 This is the architecture diagram of the big data analysis unit of the boiler operation management system based on electrical automation.

[0063] Figure 7 This is the architecture diagram of the remote monitoring and operation unit of the boiler operation management system based on electrical automation.

[0064] Figure 8 The figure is a flow chart of the boiler operation management method based on electrical automation. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0068] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0069] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0070] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] Example 1

[0072] Reference Figure 1 to Figure 7 , which is the first embodiment of the present invention, provides a boiler operation management system based on electrical automation, involving the integration of multiple modules and functions, and adopts the combination of underlying technology and logic to realize intelligent control, energy consumption management and fault prediction of boilers, including:

[0073] Implementation of the data acquisition unit:

[0074] The data acquisition unit is the basic module of the system, which is mainly composed of multiple sensors, acquisition equipment and data transmission modules. This unit is responsible for real-time monitoring of the key operating parameters of the boiler to ensure that the system can obtain accurate operating status information under dynamic conditions.

[0075] Temperature sensor network: Multiple high-precision temperature sensors are installed in different parts of the boiler to monitor key point temperatures such as furnace temperature, exhaust gas temperature, water temperature, etc. These sensors use digital signal processing technology to convert the measured analog signals into digital signals through analog-to-digital converters (ADCs) and transmit them to the core control unit.

[0076] Pressure sensor module: Boiler pressure control is a key safety factor. The system continuously monitors the steam and water pressure inside the boiler through multiple pressure sensors. The pressure sensors used are usually piezoresistive or capacitive sensors, which can accurately capture subtle changes in boiler pressure. The pressure signal collected by the sensor is filtered to ensure data stability and accuracy.

[0077] Fuel flow meter: Fuel flow meter is used to monitor the fuel consumption of boilers, usually ultrasonic flow meter or vortex flow meter. The flow meter can calculate the total fuel consumption according to the flow rate and flow of fuel, and provide the system with real-time fuel supply information to ensure the continuous stability of the combustion process.

[0078] Air volume sensor: The air volume sensor monitors the air volume in the boiler to ensure sufficient air supply during the boiler combustion process. Through real-time monitoring of primary and secondary air volume, the sensor transmits the signal to the core control unit to adjust the power output of the fan.

[0079] Bottom-level logic: The bottom-level logic of the data acquisition unit is to monitor the various parameters of the boiler in real time through a multi-point sensor network. These sensors transmit data to the core control unit through standardized communication protocols (such as Modbus, Profibus, etc.). The system relies on fast and accurate real-time data to support subsequent analysis and control.

[0080] Implementation of the core control unit:

[0081] The core control unit is the "brain" of the entire system, responsible for processing all data transmitted from the data acquisition unit and making automated control decisions. Its core technologies include advanced adaptive control algorithms, combustion optimization algorithms and energy consumption management strategies.

[0082] Adaptive combustion optimization module: This module is based on adaptive control theory and can adjust fuel and air supply according to real-time data to ensure maximum combustion efficiency. The underlying technology uses an algorithm that combines PID controller with fuzzy logic control to adjust fuel and air volume according to the real-time status of the boiler (such as load, ambient temperature, etc.).

[0083] Energy consumption management module: This module optimizes the operating efficiency of the boiler through big data analysis based on historical data collected over a long period of time. The underlying algorithm uses regression and clustering algorithms in machine learning to identify the stage with the highest energy consumption during boiler operation and provide optimization and adjustment suggestions.

[0084] Real-time data processing and analysis: Through real-time processors and embedded control systems, the core control unit can quickly respond to data changes and dynamically adjust boiler operating parameters. Data processing uses efficient parallel computing and distributed processing technology to ensure high-frequency data processing requirements and ensure the real-time response capability of the system.

[0085] Underlying logic: The core control unit relies on advanced control algorithms and data processing technology. By analyzing real-time data and learning historical data, the system can automatically optimize combustion and adaptively adjust the operating status of the boiler under different working conditions.

[0086] Implementation of Fault Prediction and Alarm Unit:

[0087] The fault prediction and alarm unit is used to monitor the operating status of the boiler in real time, predict potential faults and issue alarms in time. The unit integrates advanced machine learning technology and historical data analysis to detect signs of faults in advance and take preventive measures.

[0088] Fault prediction module: A machine learning model is built based on the boiler's historical data, and the decision tree and random forest algorithms in supervised learning are used to predict the boiler's operating status. The model is trained on the boiler's historical fault data, learns common fault characteristics, and evaluates current data to predict potential risks.

[0089] Alarm module: When an anomaly is detected, the system notifies the operator through local and remote alarm mechanisms. The alarm system is based on a rule engine and real-time threshold detection. When the boiler's temperature, pressure or fuel flow exceeds the set safety range, an alarm is triggered immediately.

[0090] Underlying logic: Fault prediction relies on machine learning technology and the continuous accumulation of historical data, and identifies possible faults through pattern recognition technology. The alarm mechanism is based on real-time data stream analysis to ensure that early warnings can be issued before problems occur.

[0091] Implementation of the execution unit:

[0092] The execution unit is the executor of the boiler control instructions and is responsible for adjusting the boiler's fuel supply, air volume control and burner parameters according to the instructions issued by the core control unit.

[0093] Fuel supply control module: This module controls the opening of the fuel valve through a servo motor to accurately control the fuel supply. The system can automatically adjust the fuel supply according to the real-time combustion efficiency and fuel demand to ensure the stability of boiler combustion.

[0094] Air volume adjustment module: The air volume adjustment module controls the air volume through the variable frequency fan to ensure that the air supply matches the fuel supply. The fan control system adopts advanced variable frequency control technology, which can dynamically adjust the wind speed and air volume according to the boiler demand to avoid insufficient or excessive air supply.

[0095] Burner control module: The burner control adjusts the swing angle of the burner through a stepper motor or servo system to ensure uniform distribution of the combustion flame. According to the changes in boiler load, the system can accurately control the combustion intensity and flame shape to ensure combustion efficiency.

[0096] Bottom-level logic: The execution unit is based on the principle of automatic control and controls the fuel supply and air supply of the boiler through real-time data feedback. The bottom-level implementation of this unit relies on high-speed communication buses and distributed control networks to ensure fast response and accuracy of execution operations.

[0097] Implementation of Big Data Analysis Unit:

[0098] The big data analysis unit identifies energy consumption problems in boiler operation by analyzing long-term operating data and provides optimization suggestions.

[0099] Historical data storage module: stores the long-term operation data of the boiler, including indicators such as temperature, pressure, fuel consumption and air volume. These data are stored through a distributed storage system to ensure data security and availability.

[0100] Energy consumption optimization analysis module: Use big data analysis technology to analyze the energy consumption of the boiler. The system uses data mining algorithms to discover the energy consumption patterns of the boiler under different operating conditions and generate adjustment suggestions.

[0101] Underlying logic: Big data analysis units rely on distributed databases and parallel processing frameworks (such as Hadoop and Spark). These underlying technologies support efficient processing and analysis of massive data, and can quickly identify energy consumption problems and provide solutions.

[0102] Implementation of remote monitoring and operation unit:

[0103] The remote monitoring and operation unit allows operators to remotely monitor the operating status of the boiler and perform operations through the SCADA system or cloud platform.

[0104] Remote monitoring platform: Operators can view the real-time data of the boiler through the cloud platform and monitor the parameters such as temperature, pressure, fuel consumption and air volume of the boiler operation.

[0105] Remote operation module: The remote operation module allows operators to remotely adjust the boiler's fuel supply, air volume and burner swing angle according to the actual operation of the boiler. The operation instructions are transmitted through an encrypted communication channel to ensure data security and reliability.

[0106] Bottom-layer logic: The remote monitoring and operation unit is based on the Internet communication protocol, using encryption technology (such as TLS / SSL) to ensure communication security, and combines with the SCADA system to achieve real-time data display and remote control. The bottom layer of the system uses an event-driven control architecture to ensure timely response to remote operations.

[0107] This embodiment also provides a computer device, which is suitable for the boiler operation management system based on electrical automation, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the boiler operation management system based on electrical automation as proposed in the above embodiment.

[0108] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a method bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating method and a computer program. The internal memory provides an environment for the operation of the operating method and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0109] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the boiler operation management system based on electrical automation as proposed in the above embodiment is implemented.

[0110] The storage medium proposed in this embodiment and the data storage system proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0111] Example 2

[0112] Reference Figure 8 As shown, based on the first embodiment, this embodiment further provides a boiler operation management method based on electrical automation, comprising the following steps:

[0113] S1. Data collection: Data collection is the first step in boiler operation management, ensuring that the system can obtain the boiler operation status in real time and perform subsequent control.

[0114] Data collection process:

[0115] The system collects real-time operating data through sensors installed at different key locations of the boiler (including temperature, pressure, fuel flow and air volume sensors). Specifically, temperature sensors detect furnace, exhaust, and water temperatures, pressure sensors monitor steam and water pressure, fuel flow meters monitor fuel supply, and air volume sensors detect primary and secondary air flow.

[0116] The data sensor sends the collected data to the central control system through communication protocols (such as Modbus and Profibus). The data transmission adopts redundant design to ensure that the data will not be lost in the event of network fluctuations or interruptions.

[0117] Underlying technology:

[0118] The sensor network uses low-power communication technologies (such as LoRa or Zigbee) to ensure energy efficiency and signal coverage, which is particularly suitable for deployment in large-scale boiler plants.

[0119] The sensor signal in the data acquisition unit is converted into a digital signal by an analog-to-digital converter (ADC) and then enters the embedded processor for preprocessing to eliminate noise and mutations to ensure the accuracy and stability of the data.

[0120] S2. Real-time analysis and optimization: After the collected data is processed, it is analyzed by the core control unit to ensure that the operating parameters of the boiler can be adaptively adjusted according to the real-time status.

[0121] Analysis and optimization process:

[0122] The adaptive combustion optimization module in the core control unit adjusts the fuel supply, air volume and burner swing angle according to real-time data. The module uses advanced control algorithms (such as a combination of fuzzy control and PID control) to ensure that the boiler combustion efficiency is optimized.

[0123] The system will continuously adjust the combustion parameters according to external environmental parameters (such as external temperature, humidity, etc.) and internal data of the boiler to avoid fuel waste or excessive air supply, thereby maintaining optimal combustion conditions.

[0124] Underlying technology:

[0125] Real-time data is processed by multi-threading through parallel processors to ensure that a large amount of sensor data can be processed every second. The core control unit uses an embedded Linux operating system to support real-time data processing and the generation of high-precision control instructions.

[0126] The control algorithm is implemented in FPGA (field programmable gate array), which can speed up the response and is particularly suitable for high-frequency data acquisition and control scenarios.

[0127] S3. Fault prediction and alarm: The fault prediction and alarm module uses machine learning algorithms to predict potential equipment failures in real time and provide early warnings.

[0128] Fault prediction and alarm process:

[0129] The fault prediction module analyzes the boiler's historical operating data and real-time status, and combines decision trees and neural network algorithms to determine whether the equipment has potential faults. For example, if the steam pressure is in the high value area for a long time, the system will issue an early warning of excessive pressure.

[0130] When the system detects an abnormality in the boiler's operating parameters (such as temperature, pressure, fuel flow, etc.), it immediately notifies the operator through the alarm module. The alarm signal can be sent to relevant personnel through local display, remote monitoring platform or mobile device.

[0131] Underlying technology:

[0132] The fault prediction module uses machine learning frameworks such as TensorFlow or PyTorch to train historical data and generate a fault prediction model. The model parameters are updated through continuous machine learning iterations to ensure that the prediction accuracy increases as the data increases.

[0133] The real-time alarm uses a dual mechanism, which can generate hardware alarm signals on the local PLC controller and also remotely alarm through the SCADA system to ensure multiple protections.

[0134] S4. Execution adjustment: The execution unit is responsible for dynamically adjusting the key parameters of the boiler according to the instructions of the core control unit.

[0135] Execute the adjustment process:

[0136] Based on the results of real-time analysis, the system adjusts the opening of the fuel supply valve through a servo motor to ensure that the fuel supply matches the actual needs of the boiler.

[0137] The air volume control module adjusts the supply of primary and secondary air through the variable frequency fan to ensure that the air supply is coordinated with the fuel combustion. The fan control system dynamically adjusts the wind speed and air volume based on real-time monitoring data.

[0138] Underlying technology:

[0139] The servo control system uses high-speed serial communication (such as EtherCAT or CAN bus) to transmit control instructions, ensuring that the fuel supply and air supply can respond quickly according to real-time data. The frequency conversion fan is adjusted through PWM (pulse width modulation) signal, which can accurately control the air volume.

[0140] A distributed control structure is used in the system to ensure that each execution unit can respond to instructions independently and maintain synchronous execution.

[0141] S5. Big data analysis and optimization suggestion generation: The big data analysis module is responsible for processing and analyzing the long-term operation data of the boiler and providing optimization suggestions.

[0142] Big data analysis process:

[0143] The system uses big data analysis tools (such as Hadoop and Spark) to deeply mine the long-term stored historical operation data (including temperature, pressure, fuel consumption and emissions). The energy consumption optimization analysis module discovers the energy consumption characteristics of the boiler under different working conditions based on the data pattern and generates optimization adjustment suggestions.

[0144] The emission monitoring module continuously monitors the pollutant emissions of the boiler to ensure that the boiler's emissions meet environmental protection requirements, and provides suggestions for reducing pollutant emissions through analysis of emission data.

[0145] Underlying technology:

[0146] Distributed data storage systems (such as HDFS) are used to store and process large-scale historical data. Combining Hadoop and MapReduce technologies to perform parallel computing on data can quickly identify energy consumption problems and emission anomalies.

[0147] Optimization recommendations are generated based on the random forest algorithm and support vector machine model, which can extract correlations from multidimensional data and generate detailed optimization strategies.

[0148] S6. Remote monitoring and operation: The remote monitoring and operation module allows operators to monitor the boiler status in real time and perform remote operations through the SCADA system or cloud-based platform.

[0149] Remote monitoring and operation process:

[0150] Through the SCADA system, operators can remotely view the boiler's operating data, including real-time temperature, pressure, fuel supply and air volume data. The system uses TLS / SSL encrypted communication technology to ensure the security of data transmission.

[0151] Operators can remotely adjust the operating status of the boiler through the operation interface of the cloud platform. Through intelligent control algorithms, parameters such as fuel supply, air volume, and burner swing angle are adjusted. The system provides real-time feedback on the adjusted operating status, which facilitates operators to further optimize control strategies.

[0152] Underlying technology:

[0153] Remote monitoring and operation use TCP / IP-based communication, combined with VPN and firewall technology to ensure system security. The operation interface interacts with the backend server through REST API to ensure response speed and data security.

[0154] Real-time operation instructions are transmitted through encrypted communication channels, and the system uses data encryption technology (such as AES-256) to ensure the integrity and confidentiality of data transmission to prevent external attacks or data leakage.

[0155] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0156] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the present invention.

Claims

1. A boiler operation management system based on electrical automation, characterized in that: include, Data acquisition unit: used to collect the operating status parameters, environmental parameters and energy consumption data of the boiler, obtain the temperature, pressure and flow information of the boiler through sensors and monitoring equipment, and transmit the collected data to the core control unit for real-time processing and analysis; Core control unit: used to process the real-time data provided by the data acquisition unit, and generate control instructions based on intelligent algorithms and preset optimization rules, which can perform combustion optimization, energy consumption management and pressure regulation operations; Fault prediction and alarm unit: used to monitor the operating status of the boiler in real time, combine historical data and machine learning algorithms to predict faults and issue early warning information to remind operators to perform maintenance and repairs; Execution unit: used to receive instructions from the core control unit and adjust the operating parameters of the boiler, including fuel supply control, air volume regulation and burner control; Big data analysis unit: Based on the boiler operation data collected over a long period of time, it analyzes the boiler's energy consumption, efficiency and emissions and generates optimization suggestions; Remote monitoring and operation unit: Through the SCADA system or cloud-based remote monitoring system, operators can view the operating status of the boiler in real time, receive alarm information, and perform remote operations and adjustments.

2. The boiler operation management system based on electrical automation according to claim 1, characterized in that: The data acquisition unit comprises: Temperature acquisition module, used to detect the temperature of key parts of the boiler, including furnace temperature, exhaust gas temperature and water temperature; The pressure acquisition module is used to monitor the pressure status of the boiler in real time, including the steam pressure and water pressure in the boiler; Fuel flow acquisition module, used to detect the fuel supply and ensure the balance between fuel supply and combustion demand; Air volume collection module, used to detect the flow of primary and secondary air to ensure sufficient air supply for combustion; The data transmission module transmits all collected data to the core control unit via a wired or wireless network for real-time analysis and processing.

3. The boiler operation management system based on electrical automation according to claim 2, characterized in that: The core control unit comprises: Intelligent combustion optimization module, based on real-time data from the data acquisition unit, combined with adaptive control algorithms and machine learning models, automatically adjusts the supply of fuel and air volume; Energy consumption management module, which analyzes boiler operation data and reduces energy consumption by optimizing fuel use and energy distribution; The pressure control module adjusts the operating parameters of the boiler by analyzing the pressure data so that the pressure is always maintained within a safe range; The fault diagnosis module is used to monitor the various subsystems of the boiler in real time. When abnormal data is found, it can automatically identify potential fault risks and issue early warnings.

4. The boiler operation management system based on electrical automation as claimed in claim 3, characterized in that: The fault prediction and alarm unit comprises: The fault prediction module, based on the historical operation data of the boiler and combined with machine learning algorithms, analyzes the long-term operation trend and abnormal conditions of the boiler and predicts the boiler failure in advance; Real-time monitoring module continuously monitors the boiler's temperature, pressure, fuel supply and air volume, and issues an alarm immediately if any parameter is found to be outside the preset range; The alarm module is used to provide real-time feedback of abnormal situations to operators, and send alarm information through local display screens, remote monitoring platforms or mobile terminals to remind them to carry out maintenance.

5. The boiler operation management system based on electrical automation according to claim 4, characterized in that: The execution unit includes: A fuel supply control module, used to accurately control the supply amount of fuel according to the instructions of the core control unit; Air volume adjustment module, used to automatically adjust the supply of primary and secondary air required during boiler combustion; The burner control module is used to dynamically adjust the swing angle and combustion intensity of the burner according to the instructions of the core control unit.

6. The boiler operation management system based on electrical automation according to claim 5, characterized in that: The big data analysis unit includes: Historical data storage module, used to store the long-term historical data of boiler operation, including multiple indicators such as temperature, pressure, fuel consumption and air volume; Energy consumption optimization analysis module, which analyzes the energy consumption of the boiler based on the stored data, identifies energy consumption problems in operation and makes optimization suggestions; The emission monitoring module is used to monitor the pollutant emissions of the boiler and propose optimization measures to reduce emissions by analyzing the emission data; The trend prediction module predicts the future energy consumption trend and maintenance requirements of the boiler based on historical data and combined with big data analysis technology.

7. The boiler operation management system based on electrical automation according to claim 6, characterized in that: The remote monitoring and operation unit includes: Remote monitoring platform, through SCADA or cloud platform, operators can remotely view various operating parameters of the boiler in real time, including temperature, pressure, fuel supply, air volume and emission data; Remote operation module, allowing operators to adjust the operating status of the boiler, fuel supply and air volume ratio through the remote platform; The alarm receiving module is used to receive the alarm information from the fault prediction and alarm unit, and remind the operator to deal with the abnormal situation of the boiler in time.

8. A boiler operation management method based on electrical automation, based on the boiler operation management system based on electrical automation according to any one of claims 1 to 7, characterized in that: The following steps are included: The data acquisition unit obtains the temperature, pressure, fuel supply and air supply parameters of the boiler operation in real time and transmits the data to the core control unit; The core control unit adjusts the fuel supply, air flow and burner swing angle through the intelligent combustion optimization module based on the collected real-time data to ensure that the boiler is in the optimal combustion state; The real-time monitoring module monitors the key parameters of the boiler and triggers the alarm module when an abnormality is found, notifying the operator to conduct inspection and maintenance; The execution unit receives the control instructions generated by the core control unit and adjusts the fuel supply, air flow and burner parameters in real time; The big data analysis unit analyzes long-term operating data, identifies energy consumption issues and generates optimization suggestions; The alarm receiving module sends the fault alarm information to the remote platform, so that the operator can receive the alarm in time and take measures.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the boiler operation management system based on electrical automation according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the boiler operation management system based on electrical automation described in any one of claims 1 to 7 is implemented.

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