Boiler expansion monitoring method and system, storage medium and electronic equipment
By combining the actual load and historical data of the boiler, and using a three-dimensional laser sensor to monitor boiler expansion, the problem of poor accuracy in expansion monitoring in existing technologies has been solved, enabling timely and accurate monitoring and early warning of boiler expansion.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-03-27
AI Technical Summary
In existing boiler expansion monitoring methods, the expansion data obtained by manual identification has large errors, resulting in poor monitoring accuracy and an inability to identify abnormal expansion risks in a timely manner.
By acquiring the actual load and historical location of the boiler to be monitored, expansion data of the target monitoring point is obtained using a three-dimensional laser sensor. Combined with historical load analysis, the expansion analysis sequence of the target monitoring point is determined, expansion early warning information is sent in a timely manner, correlation anomalies are identified, and monitoring accuracy is improved.
It enables timely and accurate monitoring of boiler expansion, accurately detects abnormal expansion and provides timely warnings, thereby reducing the risk of structural damage.
Smart Images
Figure CN121739355A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of boiler monitoring technology, specifically to a boiler expansion monitoring method, system, storage medium, and electronic equipment. Background Technology
[0002] Boiler expansion refers to the physical deformation of the boiler body (including metal components such as the boiler drum, water-cooled walls, headers, and furnace walls) due to temperature changes during ignition, heating, operation, and shutdown cooling. It is an inevitable phenomenon during boiler operation and is essentially a manifestation of the thermophysical properties of metallic materials. Boiler expansion monitoring involves deploying specialized monitoring devices and setting judgment criteria to track the amount of thermal expansion and contraction deformation of the boiler body (metal components such as the boiler drum, water-cooled walls, headers, and furnace walls) in real time throughout the entire heating, operation, and cooling process. It analyzes the uniformity and coordination of expansion and promptly identifies abnormal expansion risks. This is a core monitoring aspect ensuring the safe and stable operation of boilers.
[0003] Currently, the common method for monitoring boiler expansion is to install local three-dimensional expansion indicators at different monitoring points on the boiler, and then manually identify the expansion data from the local three-dimensional expansion indicators to obtain expansion data from different monitoring points. Based on the expansion data, it is determined whether there is any abnormality in the boiler, and an early warning is issued in time if an abnormality is found. However, since the local three-dimensional expansion indicator uses a rigid pointer to correspond to a millimeter-level scale, the expansion data obtained by manual identification has a large error, resulting in poor accuracy for monitoring boiler expansion. Summary of the Invention
[0004] To improve the accuracy of boiler expansion monitoring, this application provides a boiler expansion monitoring method, system, storage medium, and electronic device.
[0005] The first aspect of this application provides a boiler expansion monitoring method, specifically including: The current actual load of the boiler under monitoring is obtained, and multiple historical locations and multiple historical loads on the outer wall of the boiler under monitoring are obtained. The historical locations are the locations where the boiler under monitoring has experienced abnormal expansion, and the historical loads are the loads of the boiler under monitoring when abnormal expansion occurred. Based on the actual load, each of the historical locations, and each of the historical loads, determine multiple target monitoring points for expansion monitoring of the boiler under current monitoring within a preset height range; The actual expansion data of each target monitoring point is acquired by a preset three-dimensional laser sensor, and the expansion analysis order of each target monitoring point is determined. Based on the expansion analysis sequence and actual expansion data of each target monitoring point, determine whether the corresponding target monitoring point has experienced abnormal expansion. If abnormal expansion occurs, send a first expansion warning message to the terminal for the corresponding target monitoring point. If no abnormal expansion is observed at any of the target monitoring points, then it is determined whether there is any abnormal correlation between the target monitoring points. When there is an abnormal correlation among the target monitoring points, the target monitoring points with abnormal correlation are grouped into an abnormal combination, and a second expansion warning message is sent to the terminal for the abnormal combination.
[0006] By adopting the above technical solution, after obtaining the current actual load, historical location, and historical load of the boiler under monitoring, and combining the historical location and historical load, the probability of abnormal expansion at various locations of the boiler under monitoring under the current actual load is analyzed. Then, target monitoring points are specifically set within a preset height range to facilitate timely and accurate detection of abnormal expansion during subsequent expansion monitoring of these target points. Furthermore, according to the expansion analysis sequence of each target monitoring point, it is determined whether abnormal expansion has occurred at the corresponding target monitoring point, thus achieving more targeted anomaly analysis. If abnormal expansion occurs, a first expansion warning message is promptly sent to the terminal. If no abnormal expansion occurs at any target monitoring point, it indicates that the actual expansion data of each target monitoring point is within a reasonable range, but the possibility of abnormal expansion cannot be ruled out. Since abnormal expansion deviations between associated target monitoring points also constitute abnormal expansion and can cause structural damage to the boiler under monitoring, a first expansion warning message is sent to the terminal when there are correlated anomalies among the target monitoring points. This achieves a more accurate determination of abnormal expansion and a more accurate warning, thereby improving the accuracy of boiler expansion monitoring.
[0007] In one implementation, determining multiple target monitoring points for expansion monitoring of the boiler under monitoring within a preset height range based on the actual load, each of the historical locations, and each of the historical loads specifically includes: Based on the historical locations, multiple distribution areas are determined, and at least one target distribution area is determined from each of the distribution areas, wherein the target distribution area is a distribution area that is prone to abnormal expansion; Based on the historical loads of the boiler under monitoring when abnormal expansion occurs in the target distribution area, multiple load intervals are determined, and at least one target load interval corresponding to the target distribution area is determined from each load interval. The target load interval is the load interval in which the boiler under monitoring is likely to be located when abnormal expansion occurs in the target distribution area. Calculate the regional weight coefficient of the target distribution area and the interval weight coefficient of each target load interval. The regional weight coefficient represents the probability of abnormal expansion in the target distribution area, and the interval weight coefficient represents the probability that the load of the boiler to be monitored is in the corresponding target load interval when abnormal expansion occurs. Based on the actual load, the regional weighting coefficient, and the interval weighting coefficient, multiple target monitoring points that need to be monitored for expansion within the preset height range of the boiler to be monitored are determined.
[0008] In one implementation, determining multiple target monitoring points for expansion monitoring of the boiler under monitoring within a preset height range, based on the actual load, the regional weighting coefficient, and the weighting coefficients of each interval, specifically includes: The target load interval where the actual load is located is determined as the reference load interval, and when the reference load interval exists in each target load interval corresponding to at least one of the target distribution areas, the corresponding target distribution area is determined as the reference distribution area. Multiply the regional weight coefficient of the reference distribution area with the interval weight coefficient of the corresponding reference load interval to obtain the multiplication result corresponding to the reference distribution area; The product results corresponding to at least one region of interest in all the reference distribution regions are summed to obtain a first risk value. If the first risk value exceeds a preset risk threshold, the preset height range verification is determined to be passed. If the first risk value does not exceed the risk threshold, the preset height range verification is determined to be failed. The region of interest is a reference distribution region within the preset height range. When the preset height range verification is passed, multiple target monitoring points for expansion monitoring of the boiler under monitoring within the preset height range are determined.
[0009] In one embodiment, when the preset height range is verified to be correct, determining multiple target monitoring points for expansion monitoring of the boiler under monitoring within the preset height range specifically includes: When the preset height range is verified to be correct, the multiplication results corresponding to all the reference distribution areas are summed to obtain the overall risk value of abnormal expansion under the actual load. Based on the overall risk value, a correction factor is determined for the multiplication result corresponding to each of the areas of concern. The larger the overall risk value, the larger the correction factor, and the correction factor is not less than 1. The multiplication result corresponding to each region of interest is multiplied by the correction factor to obtain the correction result. Based on each correction result, the first number of target monitoring points in the corresponding region of interest is determined. The larger the correction result, the more first monitoring points are set. Determine a second number of target monitoring points within the symmetrical region of each region of interest, and determine a third number of target monitoring points within the upstream and downstream regions of each region of interest. The symmetrical region is a region symmetrical to the region of interest with the central axis of the boiler to be monitored as the axis of symmetry. The first, second, and third setting quantities corresponding to the same area of interest are summed to obtain the final setting quantity. Based on each of the final setting quantities, target monitoring points are set within the preset height range.
[0010] In one embodiment, the method further includes: When the preset height range verification fails, cluster analysis is performed on the height of each of the reference distribution areas to obtain multiple candidate height ranges; Multiply the regional weight coefficient of at least one reference distribution area within the candidate height range with the interval weight coefficient of the corresponding reference load interval to obtain at least one product result; The product results are summed to obtain a second risk value. If the second risk value exceeds a preset risk threshold, the preset height range is adjusted to the candidate height range.
[0011] In one implementation, the step of determining whether there is an abnormal correlation between the target monitoring points if none of the target monitoring points show abnormal expansion specifically includes: If no abnormal expansion is observed at any of the target monitoring points, it is determined whether there is at least one symmetrical combination among all the target monitoring points, wherein the symmetrical combination includes two associated monitoring points that are spatially symmetrical about the central axis of the boiler to be monitored. If they exist, the product results corresponding to the regions of interest containing the associated monitoring points are summed to obtain the first abnormal risk value corresponding to the symmetrical combination. Based on the first abnormal risk value, the abnormal investigation order of the symmetrical combination is determined, and based on the abnormal investigation order, the first inflation deviation between the actual inflation data of each of the associated monitoring points is determined. The larger the first abnormal risk value, the earlier the corresponding abnormal investigation order is. If the first expansion deviation exceeds a preset deviation threshold, then it is determined that there is an abnormal correlation between the associated monitoring points.
[0012] In one implementation, the step of determining whether there is an abnormal correlation between the target monitoring points if none of the target monitoring points show abnormal expansion further includes: If no abnormal expansion is observed at any of the target monitoring points, at least one adjacent combination is selected from all the target monitoring points, and the adjacent combination contains two adjacent monitoring points that are vertically adjacent. The summation of the multiplication results corresponding to each region of interest containing the adjacent monitoring points is used to obtain the second abnormal risk value corresponding to the adjacent combination. Based on the second abnormal risk value, the abnormal inspection order of the adjacent combination is determined, and based on the abnormal inspection order, the second inflation deviation between the actual inflation data of each adjacent monitoring point in the adjacent combination is determined. If the second inflation deviation exceeds a preset deviation threshold, it is determined that there is a correlation anomaly between each of the adjacent monitoring points. The larger the second abnormal risk value, the earlier the corresponding abnormal inspection order.
[0013] A second aspect of this application provides a boiler expansion monitoring system, specifically comprising: The information acquisition module is used to acquire the current actual load of the boiler under monitoring, and to acquire multiple historical locations and multiple historical loads on the outer wall of the boiler under monitoring. The historical locations are the locations where the boiler under monitoring has experienced abnormal expansion, and the historical loads are the loads of the boiler under monitoring when abnormal expansion occurred. The point determination module is used to determine multiple target monitoring points within a preset height range for expansion monitoring of the boiler to be monitored, based on the actual load, each of the historical locations, and each of the historical loads. The sequence determination module is used to acquire the actual expansion data of each target monitoring point through a preset three-dimensional laser sensor, and determine the expansion analysis sequence of each target monitoring point; The first early warning module is used to determine whether the corresponding target monitoring point has abnormal expansion according to the expansion analysis sequence and actual expansion data of the individual target monitoring points. If abnormal expansion occurs, the first expansion early warning information is sent to the terminal for the corresponding target monitoring point. An anomaly detection module is used to determine whether there is an abnormal correlation between the target monitoring points if none of the target monitoring points show abnormal expansion. The second early warning module is used to classify the target monitoring points with abnormal correlation into an abnormal combination when there is an abnormal correlation among the target monitoring points, and to send a second expansion early warning information to the terminal for the abnormal combination.
[0014] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.
[0015] A fourth aspect of this application provides an electronic device, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.
[0016] In summary, this application includes at least one of the following beneficial technical effects: By combining historical location and historical load, it analyzes the probability of abnormal expansion at various locations of the boiler under the current actual load, and then specifically sets target monitoring points within a preset height range. This facilitates timely and accurate detection of abnormal expansion during subsequent expansion monitoring of these target monitoring points. Furthermore, based on the expansion analysis sequence of each target monitoring point, it sequentially determines whether abnormal expansion has occurred at the corresponding target monitoring point, thereby achieving more targeted anomaly analysis of the target monitoring points. If abnormal expansion occurs, a first expansion warning message is promptly sent to the terminal. If no abnormal expansion occurs at any of the target monitoring points, it indicates that the actual expansion data of each target monitoring point is within a reasonable range, but the possibility of abnormal expansion cannot be ruled out. Since abnormal expansion deviations between associated target monitoring points also constitute abnormal expansion, they can also cause structural damage to the boiler under monitoring. Therefore, when there are correlational anomalies among the target monitoring points, a first expansion warning message is sent to the terminal to achieve a more accurate determination of abnormal expansion and a more accurate warning, thereby improving the accuracy of boiler expansion monitoring. Attached Figure Description
[0017] Figure 1 This is a schematic flowchart of a boiler expansion monitoring method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the relationship between a target distribution area and a target load interval, provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of a boiler expansion monitoring system provided in an embodiment of this application; Figure 4 This is a schematic diagram of another boiler expansion monitoring system provided in an embodiment of this application.
[0018] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. Location determination module; 13. Sequence determination module; 14. First early warning module; 15. Anomaly judgment module; 16. Second early warning module; 17. Range verification module. Detailed Implementation
[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.
[0020] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.
[0021] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.
[0022] See Figure 1 This application discloses a flowchart of a boiler expansion monitoring method, which can be implemented using a computer program or run on a boiler expansion monitoring system based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application, specifically including: S101: Obtain the current actual load of the boiler to be monitored, and obtain multiple historical locations and multiple historical loads on the outer wall of the boiler to be monitored.
[0023] Specifically, in this embodiment, the boiler to be monitored is the boiler currently requiring expansion monitoring to avoid structural damage, safety accidents, and efficiency losses caused by abnormal expansion, thus ensuring the safe, stable, and long-term operation of the boiler. Abnormal expansion refers to the deviation of the thermal expansion of various parts of the outer wall of the boiler from the designed expected pattern and range during operation (start-up, shutdown, load changes, stable operation). Essentially, it is the inability of the boiler body's thermal stress to be released normally as designed, leading to a state of structural stress imbalance. Boiler load refers to the effective energy output of the boiler per unit time, a core indicator for measuring the boiler's operating capacity and condition; simply put, it is the boiler's "output power." Actual load is the actual effective energy output of the boiler to be monitored at the current time.
[0024] Furthermore, a feasible method for obtaining the actual load is as follows: obtain the circulating water volume of the boiler to be monitored by an electromagnetic flow meter or an ultrasonic flow meter, then measure the supply water temperature (t1) and return water temperature (t2) by using a thermocouple or a resistance thermometer, and finally, substitute the circulation volume, supply water temperature and return water temperature into the calculation formula: Q=c×m×(t1−t2) / 3600 to determine the current actual load of the boiler to be monitored.
[0025] Furthermore, based on the historical records of abnormal expansion of the boiler under monitoring, multiple historical locations on the outer wall of the boiler under monitoring are obtained. These historical locations are where abnormal expansion has occurred. For example, location a on the water-cooled wall of the boiler under monitoring may have excessive expansion in the X-axis, potentially causing bending and deformation of the water-cooled wall tubes; location a exhibits abnormal expansion. Although the expansion in all three directions (X-axis, Y-axis, and Z-axis) is normal at two spatially symmetrical locations b and c on the boiler under monitoring, the expansion deviation at these two locations is abnormal, also constituting abnormal expansion and potentially causing abnormal damage to the boiler under monitoring. The historical load is the load on the boiler under monitoring when abnormal expansion occurs. Additionally, based on the aforementioned historical records, the historical load on the boiler under monitoring when abnormal expansion occurs is obtained. The historical records include, but are not limited to, information such as the locations on the outer wall of the boiler under monitoring where abnormal expansion has occurred and the load on the boiler under monitoring when abnormal expansion occurred.
[0026] S102: Based on the actual load, historical locations, and historical loads, determine multiple target monitoring points within a preset height range for the boiler currently under monitoring to undergo expansion monitoring.
[0027] Specifically, in this embodiment, one feasible implementation is as follows: All historical locations are clustered using a preset clustering algorithm. Specifically, the spatial coordinates of the historical locations are used as features to calculate the similarity (e.g., Euclidean distance) between each historical location. Similar historical locations are grouped into a distribution region (cluster). Each historical location is assigned to a cluster, resulting in multiple distribution regions. The clustering algorithm can be either k-means or DBSCAN. Then, the first number of historical locations contained in each distribution region is counted. If the first number exceeds a preset threshold, the distribution region is determined as a target distribution region, i.e., a distribution region prone to abnormal expansion. At least one target distribution region exists.
[0028] Furthermore, from all historical loads, multiple historical loads of the boiler to be monitored when an abnormal expansion occurs in a single target distribution area are selected. A clustering algorithm is then used to perform cluster analysis on these historical loads, dividing them into multiple load intervals that cover all selected historical loads. Next, the second number of historical loads contained in each load interval is counted. If the second number exceeds a preset threshold, then that load interval is determined as the target load interval, i.e., the load interval in which the boiler to be monitored is likely to be when an abnormal expansion occurs in the target distribution area. Then, the regional weight coefficient of a single target distribution area is calculated. The regional weight coefficient is the ratio of the first number corresponding to the single target distribution area to the sum of the first numbers corresponding to all target distribution areas, representing the probability of an abnormal expansion in that target distribution area. Then, the interval weight coefficient of each target load interval corresponding to that target distribution area is calculated. The interval weight coefficient is the ratio of the second number corresponding to the single target load interval to the sum of all second numbers corresponding to all target load intervals, representing the probability that the load of the boiler to be monitored will be in the corresponding target load interval when an abnormal expansion occurs. For example, there are three target distribution regions L, M, and N. The first number corresponding to target distribution region L is 40, the first number corresponding to target distribution region M is 50, and the first number corresponding to target distribution region N is 10. Therefore, the region weight coefficient for target distribution region L is: 40 / (40+50+10) = 0.4. Further, target distribution region L corresponds to target load intervals L1, L2, and L3; target distribution region M corresponds to target load intervals M1, M2, etc.; and target distribution region N corresponds to target load intervals N1, N2, etc. The second number corresponding to target load interval L1 is 30, the second number corresponding to target load interval L2 is 40, and the second number corresponding to target load interval L3 is 30. Therefore, the interval weight coefficient for target load interval L1 is: 30 / (30+40+30) = 0.3. See details in [link to relevant documentation]. Figure 2 .
[0029] Finally, based on the actual load of the boiler to be monitored, the regional weight coefficient of each target distribution area, and the interval weight coefficient of each target load interval corresponding to each target distribution area, multiple target monitoring points that need to be monitored for expansion within the preset height range of the boiler to be monitored are determined. One feasible method for determining these points is as follows: The target load interval where the actual load is located is determined as the reference load interval. Simultaneously, if the reference load interval exists among the various target load intervals corresponding to the target distribution area, then the target distribution area is determined as the reference distribution area; that is, the area where the location of abnormal expansion of the boiler to be monitored may exist under the current actual load. At least one reference distribution area exists. Then, the regional weight coefficient of a single reference distribution area is multiplied by the interval weight coefficient of the corresponding reference load interval to obtain the multiplication result corresponding to that reference distribution area. The multiplication result represents the probability of abnormal expansion occurring in that reference distribution area under the current actual load. The multiplication results corresponding to at least one area of concern in all reference distribution areas are summed to obtain a first risk value, where the area of concern is a reference distribution area within a preset height range. The first risk value represents the overall probability of abnormal expansion occurring within the preset height range of the boiler to be monitored under the current actual load. If the first risk value exceeds the preset risk threshold, it indicates that under actual load, the overall probability of abnormal expansion of the boiler under monitoring within the preset height range is relatively high. This suggests that using the preset height range as the monitoring height range is highly targeted and reasonable, and can accurately monitor the abnormal expansion of the boiler under monitoring. In this case, the preset height range verification is confirmed to be successful. Conversely, if the first risk value does not exceed the risk threshold, the preset height range verification is confirmed to be unsuccessful.
[0030] Furthermore, after the preset height range verification is passed, multiple target monitoring points for expansion monitoring of the boiler under monitoring within the preset height range are determined. The specific process is as follows: the multiplication results corresponding to all reference distribution areas are summed to obtain the overall risk value of abnormal expansion of the boiler under monitoring under actual load. The larger the overall risk value, the greater the risk of abnormal expansion of the boiler under monitoring under actual load. Then, based on the overall risk value, the correction factor for the multiplication results corresponding to each area of concern is determined. The correction factor is not less than 1; the larger the overall risk value, the larger the correction factor. Specifically, the correction factor can be determined through a preset mapping relationship table. The mapping relationship table includes the mapping relationship between different risk value ranges and their corresponding correction factors, all determined based on regression analysis of historical data of the overall risk value and the correction factor. For example, the mapping relationship table includes a risk value range of 0-0.2 with a corresponding correction factor of 1.1; a risk value range of 0.2-0.4 with a corresponding correction factor of 1.2, and so on. If the overall risk value is 0.3, then the correction factor is 1.2.
[0031] Next, the product result for each region of concern is multiplied by this correction factor to obtain the correction result, which more accurately characterizes the likelihood of abnormal expansion in the region of concern under the current actual load. Simultaneously, based on the correction result for each region of concern, the first number of target monitoring points within that region is determined. The larger the correction result, the greater the likelihood of abnormal expansion, requiring more focused monitoring, and thus, a larger number of target monitoring points are set up. Then, based on the first number of target monitoring points within the region of concern, a second number of target monitoring points in its symmetrical region is determined. Specifically, the first number can be directly set as the second number. The larger the correction result for the region of concern, the greater the correlation between the location in the region of concern and its location in its symmetrical region, and the greater the likelihood of abnormal expansion. Therefore, more monitoring points should be set up in the symmetrical region. The symmetrical region is defined as the area symmetrical to the region of concern, with the central axis of the boiler being monitored (a geometric reference line running vertically through the boiler) as its axis of symmetry. The symmetrical region is also distributed on the outer wall of the boiler being monitored. The first set quantity can be determined by a preset formula, which is: First set quantity = Basic set quantity + INT(correction result / 0.1) × density coefficient. Wherein, the basic set quantity is 2, the density coefficient ranges from 0.2 to 0.5 and is adjusted according to the size of the area of interest; INT() is an integer function. For example, if the correction result is 0.75, substituting it into the formula, the calculation process is: 2 + INT(0.75 / 0.1) × 0.5 = 2 + 7 × 0.5 ≈ 6, then the first set quantity is 6.
[0032] Furthermore, after determining the first and second setting quantities for a single area of interest, the first and second setting quantities are summed to obtain the final setting quantity for that area of interest. Specifically, the first setting quantity of target monitoring points is evenly distributed within the area of interest, and then symmetrical points of each target monitoring point are sequentially set within the symmetrical area of that area. Following this method, the setting of target monitoring points within the preset height range is finally completed.
[0033] It should be noted that correlation anomalies refer to logical contradictions or deviations exceeding reasonable ranges in the overall expansion data changes of symmetrical points at the same height or adjacent points above and below. Simply put, the expansion data of a single point may be normal, but there is an uneven expansion problem from the perspective of the entire boiler under monitoring. This also belongs to the abnormal expansion of the boiler.
[0034] In this embodiment, a feasible way to determine the symmetrical region is as follows: select several feature points on the boundary of the region of interest, draw radial lines from each feature point to the projection point O of the central axis, rotate each radial line 180° around point O to obtain several symmetrical feature points, and finally connect each symmetrical feature point to form a closed region, which is the symmetrical region of the region of interest.
[0035] In other embodiments, when the preset height range verification fails, a preset clustering algorithm is used to perform cluster analysis on the heights (lengths along the central axis of the boiler to be monitored) of each reference distribution area, resulting in multiple candidate height ranges to cover all heights. The clustering algorithm can be either k-means or DBSCAN. The logic of the cluster analysis is detailed in step S102, specifically the logic of the distribution area-related cluster analysis, and will not be repeated here. Then, the regional weight coefficient of at least one reference distribution area involved in or covered within a single candidate height range is multiplied by the interval weight coefficient of the corresponding reference load interval to obtain the product result corresponding to each reference distribution area. The product result represents the probability of abnormal expansion occurring within the corresponding reference distribution area under actual load. The products are summed to obtain a second risk value, representing the overall probability of abnormal expansion occurring within the candidate height range under actual load. If the second risk value exceeds the preset risk threshold, it indicates that there is a high probability of abnormal expansion within the selected height range under actual load. In this case, the preset height range is adjusted to the selected height range. Finally, multiple target monitoring points that need to be monitored for expansion within this selected height range are determined.
[0036] S103: Obtain the actual expansion data of each target monitoring point through a preset three-dimensional laser sensor, and determine the expansion analysis sequence of each target monitoring point.
[0037] Specifically, after the target monitoring points on the outer wall of the boiler are determined, three-dimensional laser sensors are installed at these points to acquire the actual expansion data of each monitoring point. The three-dimensional laser sensors are synthetic three-dimensional laser sensors, which are non-contact measurement devices that integrate laser ranging, scanning imaging, and data synthesis algorithms. Their core function is to quickly acquire the three-dimensional spatial coordinate information of the object being measured. Furthermore, the actual expansion data can be understood as the linear expansion position of the target monitoring point in the three orthogonal directions of X, Y, and Z, thereby determining whether the expansion of different parts of the boiler is uniform during the heating and pressurization process.
[0038] Furthermore, the order of expansion analysis for each target monitoring point is determined. The earlier the expansion analysis is performed, the higher the priority for judging abnormal expansion. One feasible method is to determine the expansion analysis order of the target monitoring point based on the correction result of the region of interest where the target monitoring point is located. The larger the correction result, the earlier the expansion analysis order. If the target monitoring point is located in a symmetrical region, the larger the correction result of the region of interest corresponding to the symmetrical region, the earlier the expansion analysis order. Among these, the expansion analysis order of the target monitoring point located in the region of interest is higher than that of the target monitoring point located in the symmetrical region.
[0039] S104: Based on the expansion analysis sequence of individual target monitoring points and the actual expansion data, determine whether the corresponding target monitoring point has experienced abnormal expansion. If abnormal expansion occurs, send the first expansion warning information to the terminal for the corresponding target monitoring point.
[0040] Specifically, following the expansion analysis sequence for each target monitoring point, the system sequentially determines whether abnormal expansion exists based on the actual expansion data of each point. One feasible method is to compare the expansion data along the X, Y, and Z axes with their respective normal expansion ranges. If all three axes are within their normal ranges, the target monitoring point does not exhibit abnormal expansion. Conversely, if any one axis's expansion data falls outside its normal range, the target monitoring point is deemed to have abnormal expansion, and a first expansion warning is sent to the personnel's terminal for that point. It should be noted that by sequentially analyzing the expansion data, the system determines whether abnormal expansion has occurred at each target monitoring point, thereby enabling targeted, accurate, and rapid identification of any abnormal expansion issues in the monitored boiler.
[0041] S105: If no abnormal expansion is observed at any of the target monitoring points, determine whether there is any abnormal correlation between the target monitoring points.
[0042] Specifically, if all target monitoring points show no abnormal expansion, even if the expansion data of a single target monitoring point is normal, it does not mean that the boiler under monitoring is free from abnormal expansion. There may be anomalies in the correlation between the target monitoring points. Therefore, it is necessary to determine whether there are anomalies in the correlation between the target monitoring points. One feasible method is to determine whether there is a symmetrical combination among all target monitoring points. The specific process is as follows: calculate the perpendicular distance from each of the two target monitoring points to the central axis of the boiler under monitoring, and simultaneously determine the line connecting the two target monitoring points. If the two perpendicular distances are equal, and the line connecting them is perpendicular to the central axis and bisected by the central axis, it means that the two target monitoring points are symmetrical about the central axis, and thus a symmetrical combination exists. If such a combination exists, then symmetrical combinations are selected from all target monitoring points. The symmetrical combination includes two correlated monitoring points that are spatially symmetrical about the central axis of the boiler under monitoring.
[0043] The summation of the multiplication results for each region of interest containing associated monitoring points yields the first anomaly risk value for that symmetrical combination. This first anomaly risk value characterizes the overall probability of abnormal expansion at two associated monitoring points within the symmetrical combination under actual load. A higher first anomaly risk value indicates a greater likelihood of a correlated anomaly between the two monitoring points. Based on this first anomaly risk value, the anomaly investigation order for each symmetrical combination is determined. A higher first anomaly risk value corresponds to a higher priority in the anomaly investigation order, thus prioritizing the investigation of correlated anomalies. Next, following the anomaly investigation order for each symmetrical combination, the first expansion deviation between the actual expansion data of the two associated monitoring points in the corresponding symmetrical combination is calculated sequentially; this deviation occurs along the X, Y, and Z axes. If the first expansion deviation exceeds a preset deviation threshold, it indicates a large expansion deviation between the two symmetrical associated monitoring points, posing a significant risk of uneven expansion leading to additional thermal and mechanical stresses. Therefore, a correlated anomaly is determined to exist between the corresponding monitoring points. Conversely, if the deviation is less than the threshold, no correlated anomaly is determined to exist between the corresponding monitoring points.
[0044] In other embodiments, if the actual expansion data of all target monitoring points are within the normal range and no abnormalities are observed, then at least one adjacent combination is selected from all target monitoring points. This adjacent combination contains two vertically adjacent monitoring points, which correspond vertically along the central axis of the boiler under monitoring, and the distance between the two adjacent monitoring points is less than a preset distance threshold. Further, the product results of the two regions of interest containing the adjacent monitoring points are summed to obtain a second anomaly risk value corresponding to this adjacent combination. The second anomaly risk value characterizes the overall probability of abnormal expansion at the two adjacent monitoring points in the adjacent combination under actual load. Next, based on the second anomaly risk value, the anomaly inspection order of the corresponding adjacent combinations is determined. The larger the second anomaly risk value, the greater the probability of a correlated anomaly between the two adjacent monitoring points in the corresponding adjacent combination, and the earlier the corresponding anomaly inspection order, prioritizing the inspection of correlated anomalies. This allows for more targeted inspection of correlated anomalies, thereby more accurately and quickly identifying the abnormal expansion problem of the boiler under monitoring.
[0045] Finally, according to the anomaly inspection sequence, the second expansion deviation between the actual expansion data of two adjacent monitoring points in the corresponding adjacent combination is calculated. If the second expansion deviation exceeds the preset deviation threshold, it indicates that the expansion consistency of the same vertical area of the boiler under monitoring is poor, which is likely to cause structural damage to the boiler under monitoring. In this case, it is determined that there is a correlation anomaly between these two adjacent monitoring points.
[0046] S106: When there is an abnormal correlation among the target monitoring points, the target monitoring points with abnormal correlation are classified into an abnormal combination, and a second expansion warning message is sent to the terminal for the abnormal combination.
[0047] Specifically, if there are correlational anomalies among the various target monitoring points, it indicates that the boiler under monitoring has an abnormal expansion problem, which is likely to cause structural damage. In this case, the various target monitoring points with correlational anomalies are classified into an abnormal combination, and a second expansion warning message is sent to the personnel's terminal for the abnormal combination, thereby reminding the personnel to go and check in time.
[0048] The implementation principle of the boiler expansion monitoring method in this application embodiment is as follows: Combining historical location and historical load, the probability of abnormal expansion at various locations of the boiler under the current actual load is analyzed. Target monitoring points are then specifically set within a preset height range to facilitate timely and accurate detection of abnormal expansion during subsequent expansion monitoring of these target points. Furthermore, based on the expansion analysis sequence of each target monitoring point, it is determined whether abnormal expansion has occurred at the corresponding target monitoring point, thereby achieving more targeted anomaly analysis. If abnormal expansion occurs, a first expansion warning message is promptly sent to the terminal. If no abnormal expansion occurs at any target monitoring point, it indicates that the actual expansion data of each target monitoring point is within a reasonable range, but the possibility of abnormal expansion cannot be ruled out. Abnormal expansion deviations between associated target monitoring points also constitute abnormal expansion and can cause structural damage to the boiler under monitoring. Therefore, when there is a correlation between the target monitoring points, a first expansion warning message is sent to the terminal to achieve a more accurate determination of abnormal expansion and a more accurate warning, thereby improving the accuracy of boiler expansion monitoring.
[0049] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.
[0050] Please see Figure 3 This is a schematic diagram of the boiler expansion monitoring system provided in this application embodiment. This boiler expansion monitoring system can be implemented as all or part of a system through software, hardware, or a combination of both. The system includes an information acquisition module 11, a location determination module 12, a sequence determination module 13, a first early warning module 14, an anomaly determination module 15, and a second early warning module 16.
[0051] The information acquisition module 11 is used to acquire the current actual load of the boiler under monitoring, and to acquire multiple historical locations and multiple historical loads on the outer wall of the boiler under monitoring. The historical locations are the locations where the boiler under monitoring has experienced abnormal expansion, and the historical loads are the loads of the boiler under monitoring when abnormal expansion occurred. The point determination module 12 is used to determine multiple target monitoring points for expansion monitoring of the boiler under current monitoring within a preset height range based on the actual load, historical locations, and historical loads. The sequence determination module 13 is used to acquire the actual expansion data of each target monitoring point through a preset three-dimensional laser sensor and determine the expansion analysis sequence of each target monitoring point; The first early warning module 14 is used to determine whether the corresponding target monitoring point has abnormal expansion according to the expansion analysis sequence of a single target monitoring point and the actual expansion data. If abnormal expansion occurs, the first expansion early warning information is sent to the terminal for the corresponding target monitoring point. The anomaly detection module 15 is used to determine whether there is an abnormal correlation between the target monitoring points if no abnormal expansion occurs at any of the target monitoring points. The second early warning module 16 is used to classify the target monitoring points with abnormal correlations into an abnormal combination when there are abnormal correlations among the target monitoring points, and to send a second expansion early warning information to the terminal for the abnormal combination.
[0052] Optional, the location determination module 12 is specifically used for: Based on historical locations, multiple distribution areas are identified, and at least one target distribution area is determined from each distribution area. The target distribution area is the distribution area that is prone to abnormal expansion. Based on the historical loads of the boiler to be monitored when abnormal expansion occurs in the target distribution area, multiple load intervals are determined, and at least one target load interval corresponding to the target distribution area is determined from each load interval. The target load interval is the load interval in which the boiler to be monitored is likely to be when abnormal expansion occurs in the target distribution area. Calculate the regional weight coefficient of the target distribution area and the interval weight coefficient of each target load interval. The regional weight coefficient represents the probability of abnormal expansion in the target distribution area, and the interval weight coefficient represents the probability that the load of the boiler to be monitored is in the corresponding target load interval when abnormal expansion occurs. Based on the actual load, regional weighting coefficient, and weighting coefficient of each interval, multiple target monitoring points that need to be monitored for expansion within the preset height range of the boiler to be monitored are determined.
[0053] Optional, the location determination module 12 is specifically used for: The target load interval where the actual load is located is determined as the reference load interval, and when there is a reference load interval in each target load interval corresponding to at least one target distribution area, the corresponding target distribution area is determined as the reference distribution area. Multiply the regional weight coefficient of the reference distribution area with the interval weight coefficient of the corresponding reference load interval to obtain the multiplication result corresponding to the reference distribution area; The summation of the multiplication results corresponding to at least one region of interest in all reference distribution regions is used to obtain the first risk value. If the first risk value exceeds the preset risk threshold, the preset height range verification is determined to be passed. If the first risk value does not exceed the risk threshold, the preset height range verification is determined to be failed, and the region of interest is the reference distribution region within the preset height range. When the preset height range verification is passed, multiple target monitoring points for expansion monitoring of the boiler under current monitoring within the preset height range are determined.
[0054] Optional, the location determination module 12 is specifically used for: When the preset height range verification passes, the multiplication results corresponding to all reference distribution areas are summed to obtain the overall risk value of abnormal expansion under actual load. Based on the overall risk value, determine the correction factor for the multiplication result of each area of concern. The larger the overall risk value, the larger the correction factor, and the correction factor shall not be less than 1. Multiply the result of the product corresponding to each region of interest by the correction factor to obtain the correction result. Based on each correction result, determine the first number of target monitoring points in the corresponding region of interest. The larger the correction result, the more first settings are required. Determine the second number of target monitoring points to be set within the symmetrical area of each area of interest. The symmetrical area is the area that is symmetrical to the area of interest with the central axis of the boiler to be monitored as the axis of symmetry. The first and second setting quantities corresponding to the same area of interest are summed to obtain the final setting quantity. Based on each final setting quantity, target monitoring points are set within a preset height range.
[0055] Optional, such as Figure 4 As shown, the system also includes a range verification module 17, which is specifically used for: When the preset height range verification fails, cluster analysis is performed on the height of each reference distribution area to obtain multiple candidate height ranges; Multiply the regional weight coefficient of at least one reference distribution area within the candidate height range with the interval weight coefficient of the corresponding reference load interval to obtain at least one product result; The results of each product are summed to obtain a second risk value. If the second risk value exceeds the preset risk threshold, the preset height range is adjusted to the candidate height range.
[0056] Optional, the exception detection module 15 is specifically used for: If no abnormal expansion is observed at any of the target monitoring points, it is determined whether there is at least one symmetrical combination among all the target monitoring points. The symmetrical combination contains two associated monitoring points that are spatially symmetrical about the central axis of the boiler to be monitored. If they exist, the product of the regions of interest containing the associated monitoring points will be summed to obtain the first abnormal risk value corresponding to the symmetrical combination. Based on the first abnormal risk value, the abnormal investigation order of the symmetrical combination is determined. Based on the abnormal investigation order, the first inflation deviation between the actual inflation data of each associated monitoring point is determined. The larger the first abnormal risk value, the earlier the corresponding abnormal investigation order is. If the first expansion deviation exceeds the preset deviation threshold, then it is determined that there is an abnormal correlation between the associated monitoring points.
[0057] Optional, the exception detection module 15 is specifically used for: If no abnormal expansion is observed at any of the target monitoring points, at least one adjacent combination is selected from all the target monitoring points. The adjacent combination contains two adjacent monitoring points that are vertically adjacent to each other. Summing the multiplication results of each region of interest containing adjacent monitoring points yields the second abnormal risk value corresponding to the adjacent combination. Based on the second abnormal risk value, the abnormal inspection order of adjacent combinations is determined, and based on the abnormal inspection order, the second inflation deviation between the actual inflation data of each adjacent monitoring point in the adjacent combination is determined. If the second inflation deviation exceeds the preset deviation threshold, it is determined that there is a correlation anomaly between each adjacent monitoring point. The larger the second abnormal risk value, the earlier the corresponding abnormal inspection order.
[0058] It should be noted that the boiler expansion monitoring system provided in the above embodiments is only illustrated by the division of the above functional modules when executing the boiler expansion monitoring method. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the boiler expansion monitoring system and the boiler expansion monitoring method embodiment provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.
[0059] This application also discloses a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements a boiler expansion monitoring method as described in the above embodiments.
[0060] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.
[0061] The above-described boiler expansion monitoring method is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the method.
[0062] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned boiler expansion monitoring method.
[0063] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.
[0064] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.
[0065] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.
[0066] In this electronic device, the boiler expansion monitoring method of the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.
[0067] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.
Claims
1. A method for monitoring boiler expansion, characterized in that, The method includes: The current actual load of the boiler under monitoring is obtained, and multiple historical locations and multiple historical loads on the outer wall of the boiler under monitoring are obtained. The historical locations are the locations where the boiler under monitoring has experienced abnormal expansion, and the historical loads are the loads of the boiler under monitoring when abnormal expansion occurred. Based on the actual load, each of the historical locations, and each of the historical loads, determine multiple target monitoring points for expansion monitoring of the boiler under current monitoring within a preset height range; The actual expansion data of each target monitoring point is acquired by a preset three-dimensional laser sensor, and the expansion analysis order of each target monitoring point is determined. Based on the expansion analysis sequence and actual expansion data of each target monitoring point, determine whether the corresponding target monitoring point has experienced abnormal expansion. If abnormal expansion occurs, send a first expansion warning message to the terminal for the corresponding target monitoring point. If no abnormal expansion is observed at any of the target monitoring points, then it is determined whether there is any abnormal correlation between the target monitoring points. When there is an abnormal correlation among the target monitoring points, the target monitoring points with abnormal correlation are grouped into an abnormal combination, and a second expansion warning message is sent to the terminal for the abnormal combination.
2. The boiler expansion monitoring method according to claim 1, characterized in that, The step of determining multiple target monitoring points for expansion monitoring of the boiler under monitoring within a preset height range based on the actual load, each of the historical locations, and each of the historical loads specifically includes: Based on the historical locations, multiple distribution areas are determined, and at least one target distribution area is determined from each of the distribution areas, wherein the target distribution area is a distribution area that is prone to abnormal expansion; Based on the historical loads of the boiler under monitoring when abnormal expansion occurs in the target distribution area, multiple load intervals are determined, and at least one target load interval corresponding to the target distribution area is determined from each load interval. The target load interval is the load interval in which the boiler under monitoring is likely to be located when abnormal expansion occurs in the target distribution area. Calculate the regional weight coefficient of the target distribution area and the interval weight coefficient of each target load interval. The regional weight coefficient represents the probability of abnormal expansion in the target distribution area, and the interval weight coefficient represents the probability that the load of the boiler to be monitored is in the corresponding target load interval when abnormal expansion occurs. Based on the actual load, the regional weighting coefficient, and the interval weighting coefficient, multiple target monitoring points that need to be monitored for expansion within the preset height range of the boiler to be monitored are determined.
3. The boiler expansion monitoring method according to claim 2, characterized in that, The step of determining multiple target monitoring points for expansion monitoring of the boiler under monitoring within a preset height range based on the actual load, the regional weighting coefficient, and the weighting coefficients of each interval specifically includes: The target load interval where the actual load is located is determined as the reference load interval, and when the reference load interval exists in each target load interval corresponding to at least one of the target distribution areas, the corresponding target distribution area is determined as the reference distribution area. Multiply the regional weight coefficient of the reference distribution area with the interval weight coefficient of the corresponding reference load interval to obtain the multiplication result corresponding to the reference distribution area; The product results corresponding to at least one region of interest in all the reference distribution regions are summed to obtain a first risk value. If the first risk value exceeds a preset risk threshold, the preset height range verification is determined to be passed. If the first risk value does not exceed the risk threshold, the preset height range verification is determined to be failed. The region of interest is a reference distribution region within the preset height range. When the preset height range verification is passed, multiple target monitoring points for expansion monitoring of the boiler under monitoring within the preset height range are determined.
4. The boiler expansion monitoring method according to claim 3, characterized in that, When the verification within the preset height range is passed, multiple target monitoring points for expansion monitoring of the boiler under monitoring within the preset height range are determined, specifically including: When the preset height range verification is passed, the multiplication results corresponding to all the reference distribution areas are summed to obtain the overall risk value of abnormal expansion under the actual load. Based on the overall risk value, a correction factor is determined for the multiplication result corresponding to each of the areas of concern. The larger the overall risk value, the larger the correction factor, and the correction factor is not less than 1. The multiplication result corresponding to each region of interest is multiplied by the correction factor to obtain the correction result. Based on each correction result, the first number of target monitoring points in the corresponding region of interest is determined. The larger the correction result, the more first monitoring points are set. Determine a second number of target monitoring points within the symmetrical region of each region of interest, wherein the symmetrical region is the region symmetrical to the region of interest with the central axis of the boiler to be monitored as the axis of symmetry. The first and second setting quantities corresponding to the same area of interest are summed to obtain the final setting quantity, and target monitoring points are set within the preset height range according to each of the final setting quantities.
5. The boiler expansion monitoring method according to claim 3, characterized in that, The method further includes: When the preset height range verification fails, cluster analysis is performed on the height of each of the reference distribution areas to obtain multiple candidate height ranges; Multiply the regional weight coefficient of at least one reference distribution area within the candidate height range with the interval weight coefficient of the corresponding reference load interval to obtain at least one product result; The product results are summed to obtain a second risk value. If the second risk value exceeds a preset risk threshold, the preset height range is adjusted to the candidate height range.
6. The boiler expansion monitoring method according to claim 3, characterized in that, If none of the target monitoring points show abnormal expansion, then it is determined whether there is an abnormal correlation between the target monitoring points, specifically including: If no abnormal expansion is observed at any of the target monitoring points, it is determined whether there is at least one symmetrical combination among all the target monitoring points, wherein the symmetrical combination includes two associated monitoring points that are spatially symmetrical about the central axis of the boiler to be monitored. If they exist, the product results corresponding to the regions of interest containing the associated monitoring points are summed to obtain the first abnormal risk value corresponding to the symmetrical combination. Based on the first abnormal risk value, the abnormal investigation order of the symmetrical combination is determined, and based on the abnormal investigation order, the first inflation deviation between the actual inflation data of each of the associated monitoring points is determined. The larger the first abnormal risk value, the earlier the corresponding abnormal investigation order is. If the first expansion deviation exceeds a preset deviation threshold, then it is determined that there is an abnormal correlation between the associated monitoring points.
7. The boiler expansion monitoring method according to claim 3, characterized in that, If none of the target monitoring points show abnormal expansion, then determining whether there is an abnormal correlation between the target monitoring points further includes: If no abnormal expansion is observed at any of the target monitoring points, at least one adjacent combination is selected from all the target monitoring points, and the adjacent combination contains two adjacent monitoring points that are vertically adjacent. The summation of the multiplication results corresponding to each region of interest containing the adjacent monitoring points is used to obtain the second abnormal risk value corresponding to the adjacent combination. Based on the second abnormal risk value, the abnormal inspection order of the adjacent combination is determined, and based on the abnormal inspection order, the second inflation deviation between the actual inflation data of each adjacent monitoring point in the adjacent combination is determined. If the second inflation deviation exceeds a preset deviation threshold, it is determined that there is a correlation anomaly between each of the adjacent monitoring points. The larger the second abnormal risk value, the earlier the corresponding abnormal inspection order.
8. A boiler expansion monitoring system, characterized in that, include: The information acquisition module (11) is used to acquire the current actual load of the boiler to be monitored, and to acquire multiple historical locations and multiple historical loads on the outer wall of the boiler to be monitored. The historical locations are the locations where the boiler to be monitored has experienced abnormal expansion, and the historical loads are the loads of the boiler to be monitored when abnormal expansion occurred. The point determination module (12) is used to determine multiple target monitoring points for expansion monitoring of the current boiler under monitoring within a preset height range based on the actual load, each of the historical locations and each of the historical loads; The sequence determination module (13) is used to acquire the actual expansion data of each target monitoring point through a preset three-dimensional laser sensor and determine the expansion analysis sequence of each target monitoring point; The first early warning module (14) is used to determine whether the corresponding target monitoring point has abnormal expansion according to the expansion analysis sequence and actual expansion data of the individual target monitoring points. If abnormal expansion occurs, the first expansion early warning information is sent to the terminal for the corresponding target monitoring point. Anomaly determination module (15) is used to determine whether there is an abnormal correlation between the target monitoring points if no abnormal expansion occurs at any of the target monitoring points. The second early warning module (16) is used to classify the target monitoring points with abnormal correlation into an abnormal combination when there is an abnormal correlation between the target monitoring points, and to send a second expansion early warning information to the terminal for the abnormal combination.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-7.
10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-7.