Boiler anti-abrasion and anti-explosion intelligent early warning method based on quantitative risk analysis

Abstract

The invention discloses a boiler anti-abrasion and anti-explosion intelligent early warning method based on quantitative risk analysis, and relates to the technical field of boiler anti-abrasion and anti-explosion intelligent early warning. Quantifying the data, and determining a pipe wall thickness loss risk based on a pipe wall thickness loss rate algorithm; and determining the overall explosion risk, and carrying out wear-proof and explosion-proof intelligent early warning based on quantitative data. According to the boiler anti-abrasion and anti-explosion intelligent early warning method based on quantitative risk analysis, the three-dimensional model is introduced, so that the overall structure of the boiler and the state of each part of the boiler are visually displayed, and operation and maintenance personnel can be assisted to perform more accurate and efficient operation on boiler equipment. And based on the early warning data, the equipment is prompted in a fine manner, so that the operation and maintenance personnel can notice the fault area. The method has better effects in the aspects of efficiency, accuracy and auxiliary effect.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent early warning technology for boiler wear and explosion prevention, and in particular to an intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis. Background Art

[0002] As one of the core equipment in the power and thermal systems, the safety and stability of boiler operation directly affect the efficiency and safety of power generation. In recent years, with the increase in boiler operating load and the aggravation of equipment aging, boiler anti-wear and explosion-proof technology has received widespread attention. Modern boiler anti-wear and explosion-proof technology relies on the integration of multiple technologies such as real-time monitoring, data collection, risk analysis and early warning systems. The advancement of sensor technology, Internet of Things (IoT) and data analysis technology has gradually enabled boiler anti-wear and explosion-proof management to enter the intelligent stage. Through real-time monitoring and quantitative analysis of key data such as temperature, pressure, wall thickness, etc. of boiler equipment, potential wear, corrosion and pipe burst risks can be effectively identified, and effective preventive measures can be taken in advance. In addition, combined with three-dimensional modeling and virtual simulation technology, it also provides a more intuitive and dynamic display method for boiler health status monitoring.

[0003] Although the current boiler anti-wear and explosion-proof technology has made significant progress in the construction of real-time monitoring and early warning systems, the existing technology still has some shortcomings. First, most of the existing monitoring systems rely on single sensor data collection, such as temperature, pressure, flow and wall thickness. Although these data can provide basic equipment status information, they do not take into account the combined effects of multiple risk factors. For example, the interaction between temperature fluctuations, pressure fluctuations and tube wall loss has an important impact on the safety of boiler equipment, and the existing system cannot effectively integrate and analyze these multi-dimensional risk factors, resulting in an inability to comprehensively assess the overall risk level of the boiler. Therefore, existing technologies often cannot accurately identify the potential risks of equipment in complex operating environments, especially under the interactive effects of temperature, pressure and wall thickness loss, and traditional monitoring methods may miss some hidden dangers.

[0004] Secondly, most of the existing boiler anti-wear and explosion-proof systems focus on the identification of a single fault mode, such as pipe burst or wall thickness loss, but lack the ability to intelligently analyze the overall health status of the boiler. Most of the warning mechanisms of existing systems rely on fixed thresholds for triggering, and fail to take into account the dynamic changes of the boiler under different loads and operating conditions. Therefore, when faced with complex factors such as temperature and pressure fluctuations and wall thickness loss during the long-term operation of the boiler, traditional methods may give early or late warnings, resulting in low warning accuracy. In addition, the existing technology has poor system integration and lacks a digital management platform that can comprehensively and real-time reflect the overall status of the boiler, making it impossible to synchronize data transmission, analysis, and decision support in real time. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing boiler anti-wear and explosion-proof monitoring method has the problem of single data monitoring, inability to comprehensively consider multi-dimensional risk factors, low accuracy of the early warning system, and how to analyze the overall risk of the boiler in real time and comprehensively.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent early warning of boiler wear and explosion prevention based on quantitative risk analysis, including collecting key data and processing the data. The data is quantified, and the risk of tube wall thickness loss is determined based on the tube wall thickness loss rate algorithm. The overall explosion risk is determined, and intelligent early warning of wear and explosion prevention is performed based on the quantitative data.

[0008] As a preferred solution of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in the present invention, the key data collection and data preprocessing include installing sensors on the heating surface pipes, headers, and pipe connectors of the boiler to collect real-time data.

[0009] The sensor comprises: Temperature sensors monitor the surface and internal temperatures of various boiler components.

[0010] The pressure sensor monitors the pressure changes in the boiler.

[0011] The ultrasonic thickness gauge detects the wall thickness changes of boiler pipes according to the preset cycle.

[0012] Flow sensors detect flue gas, steam and water flow.

[0013] Upload all data collected by sensors to the digital ledger of boiler anti-wear and explosion management.

[0014] Extract historical maintenance data, equipment installation information, and historical fault records from the power plant's equipment management system. The historical data includes: The inspection scope, repair parts and defect types of each maintenance.

[0015] The material, design temperature range, design pressure range and installation drawings of the equipment.

[0016] Historical failure modes and frequencies.

[0017] Upload the management system data to the digital ledger of boiler anti-wear and explosion management.

[0018] A three-dimensional model is constructed based on the digital ledger of boiler anti-wear and explosion-proof management. The model presents the overall shape of the heating surface of the boiler body, the spatial structure of the furnace tubes, and the layout of the tube screens.

[0019] As a preferred solution of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in the present invention, the quantification of data includes performing risk quantitative assessment on the difference between temperature and pressure, and constructing a temperature change rate risk assessment model, which is expressed as: , in, is the current device temperature, is the device temperature at the previous moment, is the temperature change adjustment coefficient, and are the maximum and minimum design temperatures of the boiler respectively.

[0020] The pressure change rate risk assessment model is constructed and expressed as: , in, is the boiler pressure at the current moment, is the boiler pressure at the previous moment, is the adjustment coefficient of pressure fluctuation, It is the maximum design pressure of the boiler.

[0021] As a preferred solution of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in the present invention, the method for determining the risk of tube wall thickness loss based on the tube wall thickness loss rate algorithm includes marking the corresponding pipeline in yellow in the three-dimensional model when a change in wall thickness is detected, and constructing a tube wall loss change rate risk assessment model to assess the tube wall loss risk, which is expressed as: , in, is the wall thickness change between the current moment and the previous moment, is the initial wall thickness of the boiler tube, is the sensitivity coefficient of wall thickness change, is the historical wall thickness change rate, is the smoothing coefficient of wall thickness variation, is the acceleration factor of boiler operation time on wall thickness loss, The cumulative operating time of the boiler.

[0022] As a preferred solution of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in the present invention, wherein: the determination of the overall explosion risk includes the system identifying the temperature change rate risk , Pressure change rate risk and the risk of pipe wall loss change rate , when the three indicators do not exceed the preset first risk threshold, a comprehensive risk assessment of the boiler equipment is performed, expressed as: , in, Represents each single risk score, is the weighting coefficient for each risk factor, For each risk weight, is the total number of risk assessment items.

[0023] As a preferred solution of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in the present invention, the intelligent early warning for wear and explosion prevention based on quantitative data includes issuing a boiler status early warning when the three indicators exceed a preset first risk threshold.

[0024] when When the first risk threshold is exceeded, cracks in the heating surface pipes, unstable boiler load, or overheated steam leakage are identified, posing a risk of pipe burst. The 3D model issues a high-temperature risk warning, and locates the abnormal area through the boiler 3D model and temperature monitoring sensors. The 3D model marks the furnace tubes, welds, and inlet and outlet pipes in red.

[0025] when When the first risk threshold is exceeded, cracks in welding points or valve failures are identified, posing a risk of pipe burst. The 3D model issues a pressure risk warning, and locates abnormal areas through the 3D model of the boiler and the pressure monitoring sensor. The 3D model marks the headers, manifolds and welding points in red.

[0026] when When the first risk threshold is exceeded, the risk of pipeline burst or steam leakage is identified, and the 3D model issues an early warning of boiler pipeline flattening and corrosion. The 3D model marks the tube screens, headers and welds in red.

[0027] As a preferred solution of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in the present invention, wherein: the intelligent early warning for wear and explosion prevention based on quantitative data also includes when the three indicators do not exceed the preset first risk threshold, but and At the same time, it is greater than the second risk threshold, identifying the presence of superheated steam leakage, issuing a prompt to reduce the boiler load, and the 3D model marks the welding points and four-pipe connection parts in red.

[0028] when and At the same time, it is greater than the second risk threshold, identifying the presence of valve failure, issuing a prompt to reduce the boiler load, and the 3D model marking the valves and headers in red.

[0029] when and At the same time, it is greater than the second risk threshold, identifying the presence of gas and liquid leakage, and issuing a prompt to reduce the boiler load. The three-dimensional model marks the header, four-pipe connections and welding points in red.

[0030] Another object of the present invention is to provide an intelligent early warning system for boiler wear and explosion prevention based on quantitative risk analysis, which can more accurately assess the overall risk level of boiler equipment by comprehensively quantifying multiple key risk factors such as temperature change, pressure fluctuation and wall thickness loss rate. This solves the problem that the current boiler wear and explosion prevention monitoring method contains single data monitoring and cannot comprehensively consider multi-dimensional risk factors.

[0031] As a preferred solution of the boiler anti-wear and explosion-proof intelligent early warning system based on quantitative risk analysis described in the present invention, it includes: a data acquisition module, a data quantification module, and an explosion-proof early warning module. The data acquisition module is used to collect key data and process the data. The data quantification module is used to quantify the data and determine the risk of tube wall thickness loss based on the tube wall thickness loss rate algorithm. The explosion-proof early warning module is used to determine the overall explosion risk and perform anti-wear and explosion-proof intelligent early warning based on the quantitative data.

[0032] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of an intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis.

[0033] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis.

[0034] Beneficial effects of the present invention: The intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis provided by the present invention introduces a three-dimensional model, which enables the overall structure of the boiler and the status of its various components to be visualized, and assists operation and maintenance personnel to operate the boiler equipment more accurately and efficiently. Based on the early warning data, the equipment is prompted in a detailed manner to ensure that the operation and maintenance personnel can pay attention to the fault area. The present invention achieves better results in terms of efficiency, accuracy and auxiliary effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0036] Figure 1An overall flow chart of an intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0037] 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.

[0038] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a boiler wear and explosion prevention intelligent early warning method based on quantitative risk analysis, including: S1: Collect key data and process the data.

[0039] Furthermore, key data collection and data preprocessing include installing sensors on the boiler's heating surface pipes, headers, and pipe connectors to collect real-time data.

[0040] Sensors include: Temperature sensors monitor the surface and internal temperatures of various boiler components.

[0041] The pressure sensor monitors the pressure changes in the boiler.

[0042] The ultrasonic thickness gauge detects the wall thickness changes of boiler pipes according to the preset cycle.

[0043] Flow sensors detect flue gas, steam and water flow.

[0044] Upload all data collected by sensors to the digital ledger of boiler anti-wear and explosion management.

[0045] Extract historical maintenance data, equipment installation information, and historical fault records from the power plant's equipment management system. The historical data includes: The inspection scope, repair parts and defect types of each maintenance.

[0046] The material, design temperature range, design pressure range and installation drawings of the equipment.

[0047] Historical failure modes and frequency of occurrence (such as burst pipes, leaks, corrosion, etc.).

[0048] Upload the management system data to the digital ledger of boiler anti-wear and explosion management.

[0049] The sliding window method is used for data preprocessing. A three-dimensional model is constructed based on the digital record of boiler wear and explosion prevention management. The model presents the overall shape of the boiler body heating surface, the spatial structure of the furnace tube, and the layout of the tube screen. All the subtle parts including the four tubes, headers, headers, inlet and outlet pipes, welds, combustion holes, fire viewing holes, manholes, soot blowing holes, etc. are dynamically presented in three-dimensional virtual digital form by computers.

[0050] S2: Quantify the data and determine the risk of pipe wall thickness loss based on the pipe wall thickness loss rate algorithm.

[0051] Furthermore, the data is quantified, including the risk quantification assessment of the difference between temperature and pressure, and the temperature change rate risk assessment model is constructed, which is expressed as: , in, is the current device temperature, is the device temperature at the previous moment, is the temperature change adjustment coefficient, and are the maximum and minimum design temperatures of the boiler respectively.

[0052] The pressure change rate risk assessment model is constructed and expressed as: , in, is the boiler pressure at the current moment, is the boiler pressure at the previous moment, is the adjustment coefficient of pressure fluctuation, It is the maximum design pressure of the boiler.

[0053] It should be noted that the determination of the pipe wall thickness loss risk based on the pipe wall thickness loss rate algorithm includes marking the corresponding pipe in yellow in the three-dimensional model when a change in wall thickness is detected, and constructing a pipe wall loss change rate risk assessment model to assess the pipe wall loss risk, which is expressed as: , in, is the wall thickness change between the current moment and the previous moment, is the initial wall thickness of the boiler tube, is the sensitivity coefficient of wall thickness change, is the historical wall thickness change rate, is the smoothing coefficient of wall thickness variation, is the acceleration factor of boiler operation time on wall thickness loss, The cumulative operating time of the boiler.

[0054] It should also be noted that determining the overall explosion risk includes systematic identification of the risk of temperature change rate , Pressure change rate risk and the risk of pipe wall loss change rate , when the three indicators do not exceed the preset first risk threshold, a comprehensive risk assessment of the boiler equipment is performed, expressed as: , in, Represents each single risk score, is the weighting coefficient for each risk factor, For each risk weight, is the total number of risk assessment items.

[0055] S3: Determine the overall explosion risk and conduct intelligent early warning for wear and explosion prevention based on quantitative data.

[0056] Furthermore, intelligent early warning for wear and explosion prevention based on quantitative data includes boiler status early warning when the three indicators exceed the preset first risk threshold.

[0057] when When the first risk threshold is exceeded, cracks in the heating surface pipes, unstable boiler load, or overheated steam leakage are identified, posing a risk of pipe burst. The 3D model issues a high-temperature risk warning, and locates the abnormal area through the boiler 3D model and temperature monitoring sensors. The 3D model marks the furnace tubes, welds, and inlet and outlet pipes in red.

[0058] Heating surface pipe cracks: The heating surface of the boiler is the most prone to failure. When the temperature is too high, the pipe is prone to thermal fatigue and cracks. Therefore, the temperature monitoring sensor can detect temperature fluctuations or local overheating in time by monitoring the boiler temperature in real time, and then identify the risk of potential cracks.

[0059] Unstable boiler load: When the boiler load fluctuates too much, the temperature and pressure change dramatically, which can easily lead to excessive temperature differences on the heating surface, thereby increasing the risk of cracks and pipe bursts.

[0060] Superheated steam leakage: Steam leakage is usually accompanied by an increase in boiler temperature. When the temperature is too high, the pressure-bearing capacity of the boiler pipes is reduced, which can easily cause pipe bursts.

[0061] when When the first risk threshold is exceeded, cracks in welding points or valve failures are identified, posing a risk of pipe burst. The 3D model issues a pressure risk warning, and locates abnormal areas through the 3D model of the boiler and the pressure monitoring sensor. The 3D model marks the headers, manifolds and welding points in red.

[0062] Welding point cracking: The welding points of the boiler are subjected to huge alternating temperatures and pressures, and are prone to fatigue cracking. When pressure fluctuations intensify, the welding points may crack due to repeated stress, leading to pipe burst accidents.

[0063] Valve failure: The valve is an important safety device in the boiler system. Once the valve fails, the pressure in the boiler cannot be effectively regulated, which may cause the boiler to explode. By monitoring the pressure changes of the boiler through the pressure sensor, the system can identify valve failure in time.

[0064] when When the first risk threshold is exceeded, the risk of pipeline burst or steam leakage is identified, and the 3D model issues an early warning of boiler pipeline flattening and corrosion. The 3D model marks the tube screens, headers and welds in red.

[0065] Pipeline wear and flattening: During long-term operation, boiler pipelines may suffer wear or flattening due to corrosion and wear. These damages will significantly reduce the pressure-bearing capacity of the pipeline.

[0066] It should be noted that the intelligent early warning for wear and explosion prevention based on quantitative data also includes the situation where none of the three indicators exceeds the preset first risk threshold, but and At the same time, it is greater than the second risk threshold, identifying the presence of superheated steam leakage, issuing a prompt to reduce the boiler load, and the 3D model marks the welding points and four-pipe connection parts in red.

[0067] when and At the same time, it is greater than the second risk threshold, identifying the presence of valve failure, issuing a prompt to reduce the boiler load, and the 3D model marking the valves and headers in red.

[0068] when and At the same time, it is greater than the second risk threshold, identifying the presence of gas and liquid leakage, and issuing a prompt to reduce the boiler load. The three-dimensional model marks the header, four-pipe connections and welding points in red.

[0069] The first risk threshold and the second risk threshold need to be set so that the first risk threshold is higher than the second risk threshold, that is, there are three intervals.

[0070] Example 2, an embodiment of the present invention, provides an intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0071] First, in order to verify the effectiveness and innovation of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis, the following experimental steps and processes were designed. The experiment was set up on the boiler system of a power plant, and the core technologies of the method were used, mainly including real-time data acquisition, calculation of pipe wall thickness loss rate, quantitative evaluation of temperature and pressure change rate, and intelligent early warning function of overall explosion risk.

[0072] The experiment first installed a variety of sensors on the boiler's heating surface pipes, headers, and pipe connections, including temperature sensors, pressure sensors, ultrasonic thickness gauges, and flow sensors, to collect the following data: Temperature sensor: monitors the surface and internal temperature of boiler components (such as heating surface pipes, headers, etc.); Pressure sensor: monitors pressure changes inside the boiler, especially during load fluctuations and transient load fluctuations; Ultrasonic thickness gauge: Regularly detect changes in boiler pipe wall thickness, focusing on heating surface pipes; Flow sensor: monitors the flow of flue gas, steam and water to evaluate the working status of the boiler.

[0073] Table 1 Experimental data comparison table

[0074] From the experimental data table, we can see that as the operation time of the boiler equipment increases, the change trend of parameters such as temperature, pressure, wall thickness and flow rate becomes more obvious. For example, from equipment 1 to equipment 6, the temperature gradually rises from 540°C to 600°C, while the pressure and flue gas flow rate also show a corresponding increase trend. At the same time, the wall thickness of the pipe gradually decreases from 14.5mm to 12.5mm, which means that during the long-term operation of the boiler, the wear and corrosion problems of the pipe gradually appear.

[0075] Based on these data, the temperature change rate risk assessment model and pressure change rate risk assessment model of the present invention can accurately quantify the impact of temperature and pressure fluctuations on boiler equipment. Especially when the temperature and pressure changes of equipment 5 and 6 are large (590°C and 6.0MPa, 600°C and 6.2MPa, respectively), the system can deduce a higher risk score based on the data, and further evaluate the loss of pipeline wall thickness through the pipe wall loss change rate risk assessment model.

[0076] It can be seen from the table data that with the increase of temperature and pressure, the loss of pipe wall thickness gradually increases, which provides data support for the risk of pipe burst and leakage in the boiler. Compared with the existing early warning method that only considers temperature or pressure, the present invention can accurately predict the potential failure of the boiler under complex working conditions by comprehensively evaluating the temperature, pressure and wall thickness change rate.

[0077] Embodiment 3, an embodiment of the present invention, provides a boiler anti-wear and explosion-proof intelligent early warning system based on quantitative risk analysis, including a data acquisition module, a data quantification module, and an explosion-proof early warning module.

[0078] The data acquisition module is used to collect key data and process the data. The data quantification module is used to quantify the data and determine the risk of pipe wall thickness loss based on the pipe wall thickness loss rate algorithm. The explosion-proof warning module is used to determine the overall explosion risk and provide intelligent warning for wear and explosion prevention based on the quantitative data.

[0079] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0080] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0081] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0082] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logical function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0083] 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 may 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 claims of the present invention.

Claims

1. An intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis, characterized in that: include: Collect key data and process the data; Quantify the data and determine the risk of pipe wall thickness loss based on the pipe wall thickness loss rate algorithm; Determine the overall explosion risk and conduct intelligent early warning for wear and explosion prevention based on quantitative data; The key data collection and data preprocessing includes installing sensors on the heating surface pipes, headers, and pipe connectors of the boiler to collect real-time data; The sensor comprises: Temperature sensors monitor the surface and internal temperatures of boiler components; The pressure sensor monitors the pressure changes of the boiler; The ultrasonic thickness gauge detects the wall thickness changes of boiler pipes according to the preset cycle; Flow sensors detect flue gas, steam and water flow; Upload the data collected by all sensors to the digital ledger of boiler anti-wear and explosion management; Extract historical maintenance data, equipment installation information, and historical fault records from the power plant's equipment management system. The historical data includes: The inspection scope, repair parts and defect types of each maintenance; Equipment material, design temperature range, design pressure range and installation drawings; Historical failure modes and frequencies; Upload the management system data to the digital ledger of boiler anti-wear and explosion management; A three-dimensional model is constructed based on the digital ledger of boiler anti-wear and explosion-proof management. The model presents the overall shape of the heating surface of the boiler body, the spatial structure of the furnace tubes, and the layout of the tube screens.

2. The intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis according to claim 1 is characterized by: The quantification of data includes performing risk quantification assessment on the difference between temperature and pressure, and constructing a temperature change rate risk assessment model, which is expressed as: ， in, is the current device temperature, is the device temperature at the previous moment, is the temperature change adjustment coefficient, and are the maximum and minimum design temperatures of the boiler respectively; The pressure change rate risk assessment model is constructed and expressed as: ， in, is the boiler pressure at the current moment, is the boiler pressure at the previous moment, is the adjustment coefficient of pressure fluctuation, It is the maximum design pressure of the boiler.

3. The intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis according to claim 2 is characterized in that: The method for determining the pipe wall thickness loss risk based on the pipe wall thickness loss rate algorithm includes marking the corresponding pipe in yellow in the three-dimensional model when a change in the wall thickness is detected, and constructing a pipe wall loss change rate risk assessment model to assess the pipe wall loss risk, which is expressed as: ， in, is the wall thickness change between the current moment and the previous moment, is the initial wall thickness of the boiler tube, is the sensitivity coefficient of wall thickness change, is the historical wall thickness change rate, is the smoothing coefficient of wall thickness variation, is the acceleration factor of boiler operation time on wall thickness loss, The cumulative operating time of the boiler.

4. The intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis according to claim 3 is characterized by: Determining the overall explosion risk includes systematic identification of the temperature change rate risk , Pressure change rate risk and the risk of pipe wall loss change rate , when the three indicators do not exceed the preset first risk threshold, a comprehensive risk assessment of the boiler equipment is performed, expressed as: ， in, Represents each single risk score, is the weighting coefficient for each risk factor, For each risk weight, is the total number of risk assessment items.

5. The intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis according to claim 4 is characterized in that: The intelligent early warning for wear and explosion prevention based on quantitative data includes early warning of boiler status when the three indicators exceed a preset first risk threshold; when When the first risk threshold is exceeded, cracks in the heating surface pipes, unstable boiler load, or overheated steam leakage are identified, posing a risk of pipe burst. The 3D model issues a high-temperature risk warning, locates the abnormal area through the 3D model of the boiler and the temperature monitoring sensor, and marks the furnace tubes, welds, and inlet and outlet pipes in red on the 3D model. when When the first risk threshold is exceeded, the risk of pipe burst due to cracked welds or valve failure is identified, and the 3D model issues a pressure risk warning. The abnormal area is located through the 3D model of the boiler and the pressure monitoring sensor. The 3D model marks the header, header and weld points in red. when When the first risk threshold is exceeded, the risk of pipeline burst or steam leakage is identified, and the 3D model issues an early warning of boiler pipeline flattening and corrosion. The 3D model marks the tube screens, headers and welds in red.

6. The intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis according to claim 5 is characterized by: The intelligent early warning for wear and explosion prevention based on quantitative data also includes when the three indicators do not exceed the preset first risk threshold, but and If the risk is greater than the second risk threshold, the system will identify the existence of superheated steam leakage, issue a reminder to reduce boiler load, and mark the welding points and four-pipe connection parts in red on the 3D model; when and If the risk is greater than the second risk threshold, valve failure is detected and a reminder to reduce boiler load is issued. The 3D model marks the valve and header in red. when and At the same time, it is greater than the second risk threshold, identifying the presence of gas and liquid leakage, and issuing a prompt to reduce the boiler load. The three-dimensional model marks the header, four-pipe connections and welding points in red.

7. A system using the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis as claimed in any one of claims 1 to 6, characterized in that: Including data acquisition module, data quantification module, explosion-proof warning module; The data acquisition module is used to collect key data and process the data; The data quantification module is used to quantify the data and determine the pipe wall thickness loss risk based on the pipe wall thickness loss rate algorithm; The explosion-proof warning module is used to determine the overall explosion risk and to perform intelligent warning for wear and explosion prevention based on quantitative data.

8. 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 steps of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent early warning method for boiler wear and explosion prevention based on quantitative risk analysis described in any one of claims 1 to 6 are implemented.

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