Method, device, equipment and medium for measuring flue gas flow rate of coal-fired unit flue

Abstract

The application discloses a flue gas flow velocity measurement method, device, equipment and medium of a coal-fired unit flue, relates to the thermal power technology field, and comprises the following steps: acquiring global flow velocity distribution data corresponding to flue gas; setting different candidate measuring lines on a target monitoring section based on the global flow velocity distribution data, calculating the line-surface relative deviation and stability degree of each candidate measuring line according to the global flow velocity distribution data, and determining a target measuring line according to the line-surface relative deviation and stability degree; acquiring a flue gas flow velocity distribution curve corresponding to the target measuring line, and determining the measuring point arrangement density of each position according to the curvature of the flue gas flow velocity distribution curve; arranging measuring points on the target measuring line according to the measuring point arrangement density, and measuring the flue gas flow velocity of the target coal-fired unit flue by using the measuring points. By determining the measuring point arrangement density according to the curvature of the flue gas flow velocity distribution curve, the defect that the local high-speed jet flow area and the low-speed ash accumulation area cannot be recognized and accurately covered is solved.

Description

Technical Field

[0001] This invention relates to the field of thermal power technology, and in particular to a method, apparatus, equipment and medium for measuring flue gas velocity in the flue of a coal-fired unit. Background Technology

[0002] The tail flue of the power plant serves as the medium for SCR (Selective Catalytic Reduction) denitrification, air preheater heat exchange, and flue gas dust removal and desulfurization. The flue gas velocity distribution inside the flue directly determines the reliability of the carbon emission and total flue gas pollutant calculation results. Deviations in velocity measurement will directly cause distortion in the carbon emission calculation.

[0003] Current methods for measuring flue gas velocity rely on a triple weighting coefficient to determine the optimal cross section for measuring flue gas velocity. However, this method can only determine the overall velocity dispersion and cannot identify local high-speed jets or low-speed ash accumulation zones. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for measuring flue gas velocity in coal-fired power unit flues. This method can determine the density of measuring points based on the curvature of the flue gas velocity distribution curve, thus solving the problem of not being able to identify and accurately cover local high-speed jet zones and low-speed ash accumulation zones. The specific solution is as follows: In a first aspect, this application provides a method for measuring flue gas velocity in the flue of a coal-fired power unit, including: Mesh the three-dimensional model corresponding to the flue of the target coal-fired unit and set the boundary conditions to perform flow field simulation under different operating conditions, and obtain the global velocity distribution data of the flue gas in the flue of the target coal-fired unit under different operating conditions. Based on the global velocity distribution data, different candidate measuring lines are set on the target monitoring section. The relative deviation between the measuring line velocity and the cross-sectional average velocity of each candidate measuring line under different operating conditions is calculated according to the global velocity distribution data, as well as the stability of the velocity distribution of the same measuring line under different operating conditions. Based on the relative deviation and the stability, several target measuring lines are determined from the candidate measuring lines. The measuring line velocity represents the average value of the flue gas velocity on the candidate measuring line, the cross-sectional average velocity represents the average value of the flue gas velocity on the target monitoring section, and the relative deviation represents the deviation between the measuring line velocity and the cross-sectional average velocity. Obtain the flue gas velocity distribution curve corresponding to the target measurement line, and determine the measurement point layout density at each location based on the curvature of the flue gas velocity distribution curve. According to the density of the measurement points, measurement points are arranged on the target measurement line, and the flue gas velocity of the target coal-fired unit is measured using the measurement points.

[0005] Optionally, the step of meshing the three-dimensional model corresponding to the target coal-fired unit flue includes: The three-dimensional model is sequentially divided into several meshes with the mesh size decreasing, and flow field simulations are performed on the three-dimensional model based on the corresponding mesh division results to obtain the flow velocity distribution data corresponding to the same cross section under different mesh division results. When the deviation of the flow velocity distribution data corresponding to two adjacent sets of mesh division results is less than a preset threshold, the mesh division result with the larger mesh size in the two adjacent sets of mesh division results is determined as the target mesh division result corresponding to the three-dimensional model, so as to complete the mesh division operation of the three-dimensional model.

[0006] Optionally, before setting different candidate measurement lines on the target monitoring section based on the global velocity distribution data, the method further includes: Within the target monitoring pipe section, different candidate monitoring sections are set according to a preset interval, and the cross-sectional flue gas velocity data corresponding to each candidate monitoring section is extracted from the global velocity distribution data. The relative standard deviation of flow velocity, the flow velocity skewness coefficient, and the flow velocity range rate of each candidate monitoring section are calculated based on the flue gas velocity data of each section. The relative standard deviation of flow velocity is a data characterizing the overall dispersion of flow velocity at each point on the section. The flow velocity skewness coefficient is a data characterizing the distribution bias of the target high-speed jet zone and the target low-speed stagnation zone on the section. The flow velocity range rate is a data characterizing the degree of difference between the maximum flow velocity and the minimum flow velocity on the section. The comprehensive score corresponding to each candidate monitoring section is determined based on the relative standard deviation of the flow velocity, the flow velocity skewness coefficient, and the flow velocity range rate, and the target monitoring section is determined based on the comprehensive score.

[0007] Optionally, determining the density of measuring points at each location based on the curvature of the flue gas velocity distribution curve includes: The first and second derivatives of the flue gas velocity distribution curve are performed, and the curvature of the flue gas velocity distribution curve at each position is calculated based on the corresponding first and second derivatives; wherein, the curvature at any position represents the degree of drastic change in velocity at that position. The density of measuring points at each location is determined based on the curvature corresponding to each location; where the magnitude of curvature is positively correlated with the density of measuring points.

[0008] Optionally, after determining a number of target survey lines from the candidate survey lines based on the relative deviation between the lines and the surface and the degree of stability, the method further includes: Obtain the target measuring line flow velocity under various working conditions and the target monitoring section average flow velocity under various working conditions; The ratio of the average flow velocity at the target cross section to the flow velocity at the target measuring line corresponding to each working condition is determined as the single working condition correction coefficient for each working condition. Calculate the deviation of each single-condition correction coefficient from the average correction coefficient under multiple conditions, and determine the weight of each condition based on the magnitude of the deviation. The single-condition correction coefficients corresponding to each working condition are weighted and summed according to the weight of each working condition to obtain the target measuring line weight coefficient, so as to use the measuring line weight coefficient to correct the flow velocity of the target measuring line.

[0009] Optionally, after measuring the flue gas velocity in the target coal-fired unit flue using the measuring point, the method further includes: Obtain the measured flow velocity data of each measuring point on the target measuring line, and calculate the flow velocity of the target measuring line based on the measured flow velocity data of each measuring point; Obtain the weight coefficient of the target measurement line, multiply the flow velocity of the target measurement line by the weight coefficient to obtain the corrected flow velocity of the target measurement line, and obtain the final cross-sectional average flow velocity of the target monitoring section based on the corrected flow velocity of the target measurement line.

[0010] Optionally, after measuring the flue gas velocity in the target coal-fired unit flue using the measuring point, the method further includes: Obtain the measured flow velocity data and condition characteristic parameters of the measuring point under the test condition, and input the measured flow velocity data and condition characteristic parameters into the pre-trained target compensation amount generation model to obtain the dynamic deviation compensation amount under the test condition. The dynamic deviation compensation is superimposed on the final cross-sectional average velocity to obtain the corrected cross-sectional average velocity.

[0011] Secondly, this application provides a flue gas velocity measuring device for a coal-fired power unit flue, comprising: The flow field simulation module is used to mesh the three-dimensional model corresponding to the flue of the target coal-fired unit and set the boundary conditions to perform flow field simulation under different operating conditions, and obtain the global velocity distribution data of the flue gas in the flue of the target coal-fired unit under different operating conditions. The measuring line determination module is used to set different candidate measuring lines on the target monitoring section based on the global velocity distribution data, calculate the relative deviation between the measuring line velocity and the cross-sectional average velocity of each candidate measuring line under different operating conditions, and the stability of the velocity distribution of the same measuring line under different operating conditions, and determine several target measuring lines from the candidate measuring lines based on the relative deviation and the stability. The measuring line velocity represents the average value of the flue gas velocity on the candidate measuring line, the cross-sectional average velocity represents the average value of the flue gas velocity on the target monitoring section, and the relative deviation represents the deviation between the measuring line velocity and the cross-sectional average velocity. The density determination module is used to obtain the flue gas velocity distribution curve corresponding to the target measuring line, and determine the density of measuring points at each location based on the curvature of the flue gas velocity distribution curve. The flue gas velocity measurement module is used to arrange measurement points on the target measurement line according to the measurement point layout density, and to use the measurement points to measure the flue gas velocity of the target coal-fired unit flue.

[0012] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for measuring the flue gas velocity in the flue of a coal-fired unit.

[0013] Fourthly, this application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the aforementioned method for measuring flue gas velocity in the flue of a coal-fired unit.

[0014] This application first meshes and sets boundary conditions for the three-dimensional model corresponding to the flue gas duct of the target coal-fired unit to perform flow field simulations under different operating conditions, obtaining global velocity distribution data of the flue gas in the flue gas duct of the target coal-fired unit under different operating conditions. Then, based on the global velocity distribution data, different candidate measuring lines are set on the target monitoring section. The relative deviation between the measuring line velocity and the average velocity of the section under different operating conditions is calculated according to the global velocity distribution data, as well as the stability of the velocity distribution of the same measuring line under different operating conditions. Several target measurement lines are determined from the candidate measurement lines to a certain extent; the measurement line velocity represents the average flue gas velocity on the candidate measurement lines, the cross-sectional average velocity represents the average flue gas velocity on the target monitoring cross-section, and the line-surface relative deviation represents the deviation between the measurement line velocity and the cross-sectional average velocity. Then, the flue gas velocity distribution curve corresponding to the target measurement line is obtained, and the measurement point layout density at each position is determined according to the curvature of the flue gas velocity distribution curve. Finally, measurement points are arranged on the target measurement lines according to the measurement point layout density, and the flue gas velocity of the target coal-fired unit flue is measured using the measurement points. Therefore, this application solves the problem that local extreme regions of the flow field under a single condition are difficult to fully capture by obtaining global velocity distribution data through multi-condition flow field simulation; it solves the problem that the selected measurement lines cannot stably represent the overall velocity distribution of the cross section and are prone to missing local distortion regions by selecting target measurement lines from candidate measurement lines based on the relative deviation between the line and the surface and the stability of the multi-condition flow field; and it solves the problem that traditional uniform point layout cannot identify and accurately cover local high-speed jet areas and low-speed ash accumulation areas by determining the measurement point layout density based on the curvature of the flue gas velocity distribution curve. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of a method for measuring flue gas velocity in a coal-fired power unit flue, as disclosed in this application. Figure 2 This application discloses a flowchart for measuring the flue gas velocity in a coal-fired power unit flue. Figure 3 This is a flowchart of a target monitoring section selection method disclosed in this application; Figure 4 This application discloses a flowchart for selecting a target survey line. Figure 5This application discloses a flowchart for obtaining the line correction coefficient. Figure 6 This is a flowchart of a measurement point deployment scheme disclosed in this application; Figure 7 This application discloses a flowchart for compensation of measurement results. Figure 8 This is a schematic diagram of a lightweight deployment of a model disclosed in this application; Figure 9 This is a schematic diagram of the flue gas velocity measuring device for a coal-fired power unit flue disclosed in this application; Figure 10 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for measuring flue gas velocity in a coal-fired power unit flue, including: Step S11: Mesh the three-dimensional model corresponding to the target coal-fired unit flue and set boundary conditions to perform flow field simulation under different operating conditions, and obtain the global velocity distribution data of the flue gas in the target coal-fired unit flue under different operating conditions.

[0019] It should be noted that the flue in this embodiment is specifically designed for rectangular flues. The overall process for measuring flue gas velocity in this solution is as follows: Figure 2 As shown, the process includes steps such as constructing a three-dimensional model of the flue, mesh generation, establishing different load conditions, and setting multiple monitoring sections.

[0020] This embodiment first requires the construction of a three-dimensional model of the flue. The constructed three-dimensional model should restore the original flue structure as much as possible, especially the bends. The main focus is on restoring the upstream flue section where the pipe sections for which the points need to be laid out are located. The downstream flue section can be omitted because the structure of the upstream flue is one of the main factors affecting the flow field changes. Commonly used software includes the modeling software built into CFD (Computational Fluid Dynamics) and SolidWorks.

[0021] In this embodiment, the process of meshing the three-dimensional model corresponding to the target coal-fired unit flue includes: performing several mesh divisions on the three-dimensional model in descending order of mesh size, and performing flow field simulations on the three-dimensional model based on the corresponding mesh division results to obtain the flow velocity distribution data corresponding to the same cross section under different mesh division results; when the deviation of the flow velocity distribution data corresponding to two adjacent mesh division results is less than a preset threshold, the mesh division result with the larger mesh size in the two adjacent mesh division results is determined as the target mesh division result corresponding to the three-dimensional model, so as to complete the mesh division operation of the three-dimensional model.

[0022] In other words, after importing the model into the simulation software, the mesh generation needs to differentiate between critical and non-critical areas. For example, the downstream section of the aforementioned monitoring pipe segment has virtually no impact on flow velocity monitoring and should be non-critically meshed, while the remaining sections should be critically meshed. In addition, mesh independence verification is necessary because computer resources are limited, and the simulation accuracy cannot be improved by infinitely reducing the mesh size. Independence verification involves setting several meshes of different sizes and comparing them with the same monitoring section. If the flow field characteristics of a smaller mesh are not significantly different from those of a slightly larger mesh, the larger mesh should be used as the mesh size for subsequent simulations.

[0023] In addition, the boundary conditions in this embodiment mainly include the inlet velocity magnitude and distribution, relative pressure, flue gas density, and dynamic viscosity. Commonly used turbulence models include K-ε, K-ω, and SST K-ω models. The SST K-ω model combines the advantages of K-ε and K-ω models, providing high prediction accuracy in both near-wall regions and free shear flows, and accurately capturing complex flow phenomena such as backflow and separation in the tail flue. Furthermore, this model has low sensitivity to inlet turbulence conditions, resulting in more stable calculation results. Although its computational cost is higher, the SST K-ω model is the preferred model for simulating complex flow fields in the tail flue of thermal power plants due to its excellent overall performance. In practical turbulence model selection, different turbulence models can be used to compare accuracy. Through flow field simulation under different operating conditions, global velocity distribution data of flue gas in the target coal-fired unit flue under different operating conditions can be obtained.

[0024] Step S12: Based on the global velocity distribution data, different candidate measuring lines are set on the target monitoring section. The relative deviation between the measuring line velocity and the cross-sectional average velocity of each candidate measuring line under different operating conditions is calculated according to the global velocity distribution data, as well as the stability of the velocity distribution of the same measuring line under different operating conditions. Based on the relative deviation and the stability, several target measuring lines are determined from the candidate measuring lines. The measuring line velocity represents the average value of the flue gas velocity on the candidate measuring line, the cross-sectional average velocity represents the average value of the flue gas velocity on the target monitoring section, and the relative deviation represents the deviation between the measuring line velocity and the cross-sectional average velocity.

[0025] In this embodiment, before setting different candidate monitoring lines on the target monitoring section based on global velocity distribution data, the method further includes: setting different candidate monitoring sections within the target monitoring pipe segment according to a preset interval, and extracting the cross-sectional flue gas velocity data corresponding to each candidate monitoring section from the global velocity distribution data; calculating the relative standard deviation of velocity, velocity skewness coefficient, and velocity range rate of each candidate monitoring section based on the flue gas velocity data of each section; wherein, the relative standard deviation of velocity is data characterizing the overall dispersion of velocity at each point on the section, the velocity skewness coefficient is data characterizing the distribution bias of the target high-speed jet region and the target low-speed stagnation region on the section, and the velocity range rate is data characterizing the degree of difference between the maximum and minimum velocity on the section; determining the comprehensive score corresponding to each candidate monitoring section based on the relative standard deviation of velocity, the velocity skewness coefficient, and the velocity range rate, and determining the target monitoring section based on the comprehensive score.

[0026] The general process for selecting the target monitoring section is as follows: Figure 3 As shown, the process includes steps such as establishing inlet flow velocity inputs for different operating conditions, setting different monitoring sections within the target monitoring pipe segment, and selecting different sampling points on the interface. The process for selecting the target monitoring section in this embodiment is as follows: 1. Establish inlet flow velocities for several different load conditions: The flow field characteristics differ under each load condition, so several different scenarios need to be established to cover as many different scenarios as possible. Here, the simulation mainly reflects the difference in inlet velocity distribution; the influence of the velocity magnitude is negligible. According to the self-modeling theory, when the fluid Reynolds number is sufficiently high, its flow characteristics are independent of geometry or velocity, and are determined solely by the dimensionless parameter Reynolds number. This characteristic provides an important theoretical basis for flue gas flow field analysis, and the Reynolds number... The calculation formula is as follows: ; In the formula: The density of the fluid ( ), The characteristic velocity of the fluid (m / s) Characteristic length (m) The dynamic viscosity of the fluid is given in Pa·s.

[0027] ; In the formula: a and b are the two side lengths of the rectangle.

[0028] For most engineering applications, the Reynolds number Re of the straight pipe section in the second self-modulation zone should typically be greater than 1 × 10⁻⁶. 5 This indicates that the flow field is in a relatively stable turbulent state, and the influence of Reynolds number variation on the overall characteristics of the flow field can be ignored. Therefore, the conclusions obtained through single flow rate simulation analysis are applicable to other flow rate values ​​under the same working conditions, so in most cases, the influence of flow velocity does not need to be considered.

[0029] 2. Selection of monitoring sections and sampling points: The monitoring sections are selected at fixed intervals, starting from the upstream bend. Monitoring sections are taken at fixed intervals, with the distance selected based on the actual accuracy requirements. For higher accuracy requirements, the selected intervals can be smaller to select the optimal monitoring sections.

[0030] The selection of sampling points follows these principles: 1. Sampling points can be selected according to relevant regulations (equal area method); 2. Because this sampling point is for subsequent calculations. More sampling points can be taken here to more accurately reflect the cross-section. The selection method still follows the equal area method, but it divides the grid into more small grids.

[0031] 3. Calculate overall uniformity based on flow velocity data: Relative standard deviation (RSD) As a quantitative evaluation index, it quantifies the distribution of the flow field at different cross-sections, and serves as a criterion for judging the uniformity of the flow field. The calculation formula is as follows: ; In the formula: The average flow velocity at the sampling points (m / s) The standard deviation of the sampling points is (m / s).

[0032] It can only characterize the overall uniform velocity dispersion. Now we need to determine the skewness parameter (i.e., the velocity skewness coefficient). This is used to reflect the morphology and distortion characteristics of the flow velocity distribution, and is calculated as follows: ; In the formula: The average flow velocity at the sampling points (m / s) The standard deviation of the sampling points (m / s) The flow velocity value (m / s) at the i-th sampling point This represents the number of sampling points.

[0033] when The larger the jet, the stronger the local high-speed jet region, which easily causes wear on the measuring point. When the value is less than 0, the smaller the value, the more likely there is a local low-speed stagnation zone, which is prone to dust accumulation and blockage. Therefore, the selected cross-section... It should be as close to 0 as possible.

[0034] At the same time, the overall situation should be considered. In cases where the flow rate is small but extreme velocity points exist, the velocity range ratio is used here. To characterize the flow, the velocity range should be minimized, as shown in the following formula.

[0035] ; in, and These represent the maximum and minimum flue gas velocities on the cross-section, respectively.

[0036] Finally, the entropy weight method is used to objectively assign weights to each index, resulting in the comprehensive uniformity index UI (i.e., comprehensive score). The lower the value, the better the comprehensive uniformity of the flow field. By comparing the above comprehensive uniformity index UI, the monitoring section with the smaller value is selected as the optimal monitoring section (i.e., the target monitoring section).

[0037] In addition, in this embodiment, the general process for determining the target survey line is as follows: Figure 4 As shown, the process includes selecting a survey line on the target monitoring section, selecting sampling points on the survey line, and determining the optimal survey line height. The specific procedure for determining the target survey line is as follows: 1. Selection of candidate survey lines: The spacing between the measuring lines is selected horizontally at a fixed interval. The smaller the spacing, the more measuring lines are selected, and the final selected measuring lines can better reflect the surface velocity. The industry standard stipulates that the location of the points must be more than 0.2m away from the wall, because if the distance is too close, the wall effect will occur.

[0038] 2. Selection of sampling points: On each candidate measurement line, measurement points are selected at fixed intervals, and the distance from the wall surface must be greater than 0.2m.

[0039] 3. Determining the survey line: First, calculate the relative deviation of the line-to-surface flow velocity (i.e., the relative deviation of the line to the surface) at different heights under each working condition. The calculation formula is as follows.

[0040] ; In the formula: The cross-sectional average flow velocity (m / s) The average flow velocity (m / s) is the measured line velocity. The smaller the relative deviation between line and surface velocity, the better it reflects the surface velocity, indicating that the measuring line is good under this working condition. In order to ensure the stability of the measuring line, the standard deviation of the relative deviation between line and surface velocity under various working conditions should be calculated as follows.

[0041] ; In the formula: The standard deviation of the relative deviation of line-surface flow velocity under multiple operating conditions The line-to-surface velocity deviation (m / s) under the i-th operating condition. The average flow velocity deviation (m / s) under all operating conditions. This represents the quantity for all operating conditions.

[0042] The stability of this measuring line under multiple operating conditions can be obtained by measuring the standard deviation of the relative deviation between the line and the surface velocity; the smaller the value, the more stable it is.

[0043] At the same time, the uniformity of the flow velocity along the measuring line should be calculated to characterize the degree of velocity dispersion at each measuring point along the same height measuring line, so as to avoid the problem of local distortion where "the mean of the measuring line is accurate, but the flow velocity fluctuates greatly along the line". Here, the standard deviation of the measuring points is used as the measure, and the calculation formula is as follows.

[0044] ; In the formula: The standard deviation of the flow velocity at the measuring points on the measuring line (m / s) Let the flow velocity at the i-th measuring point be (m / s). This represents the total number of measurement points.

[0045] Finally, the standard deviation of the linear velocity for all operating conditions was calculated. The standard deviation used to calculate the standard deviation of the flow velocity in the measuring line This is used to reflect the stability of the survey line.

[0046] Finally, multiple measurement lines and The two indicators are calculated using the entropy weight method to obtain a comprehensive representative score. The higher the score, the better the effect. Taking into account factors such as cost, 2-3 survey lines are generally selected, and they are not concentrated in one area.

[0047] In this embodiment, after determining several target measurement lines from each candidate measurement line based on the relative deviation between the line and the surface and the stability degree, the method further includes: obtaining the target measurement line flow velocity under each working condition and the target monitoring section average flow velocity under each working condition; determining the ratio of the target section average flow velocity to the target measurement line flow velocity under each working condition as the single working condition correction coefficient; calculating the deviation of each single working condition correction coefficient from the average correction coefficient under multiple working conditions, and determining the weight of each working condition based on the magnitude of the deviation; and performing a weighted summation of the single working condition correction coefficients corresponding to each working condition based on the weight of each working condition to obtain the measurement line weight coefficient of the target measurement line, so as to use the measurement line weight coefficient to correct the target measurement line flow velocity.

[0048] It should be noted that the ratio of the average velocity of the measuring line to the average velocity of the cross section at a selected height will not be 1 under all operating conditions. Therefore, to further improve the accuracy of the measuring line average velocity in representing the cross section average velocity, a height weight correction coefficient (i.e., the measuring line weight coefficient) needs to be introduced for correction. The process is as follows: Figure 5 As shown, this includes calculating the correction factor for a single working condition, calculating the deviation of the correction factor, and determining the final correction factor.

[0049] The specific process for determining the weight coefficient of the survey line is as follows: Taking a single survey line as an example, firstly, establish the correction coefficient under a single working condition (i.e., the single working condition correction coefficient), and the formula is as follows.

[0050] ; In the formula: For correction coefficients, The cross-sectional average velocity (i.e., the target cross-sectional average velocity) (m / s) The average velocity of the measuring line (i.e., the velocity of the target measuring line) is (m / s).

[0051] The formula for calculating the deviation of the correction coefficient of the survey line under a single working condition is as follows.

[0052] ; In the formula: The correction factor for the deviation of the survey line under a certain working condition. The correction factor for the j-th working condition is... This is the correction factor under multiple operating conditions (i.e., the average correction factor under multiple operating conditions). The weights are adaptively assigned based on the dispersion of the operating condition coefficients themselves; the smaller the dispersion (meaning more stable flow field between operating conditions), the higher the weight. The weighting is amplified by the inverse of variance, while the influence of distorted operating conditions is reduced. The contribution ratio of each operating condition is objectively determined entirely by the data distribution, improving the robustness and long-term metrological adaptability of the height correction coefficient across the entire operating range. The weight calculation formula is as follows.

[0053] ; In the formula: This refers to the weight of the survey line correction coefficient (i.e., the survey line weight coefficient). The correction factor for the deviation of the survey line under a certain working condition. This represents the quantity for all operating conditions.

[0054] The final correction factor for the survey line is: ; In the formula: For the final survey line correction coefficient, For the correction factor of the survey line, The correction factor for the deviation of the survey line under a certain working condition. This represents the quantity for all operating conditions.

[0055] 1 / The more stable the operating condition, the higher the weight assigned. Operating conditions with long-term stability and uniform flow field morphology contribute more to the final correction coefficient. Operating conditions with severe distortions, such as start-up / shutdown and low load conditions, are automatically weighted. It is a normalization process that ensures the weights sum to 1, resulting in a more robust correction coefficient and smaller long-term variable load metering error.

[0056] It should be noted that the multi-index comprehensive evaluation algorithm and working condition weight allocation calculation logic in the line screening and weight correction stage of this embodiment can be replaced without changing the core effect of adaptive correction.

[0057] Step S13: Obtain the flue gas velocity distribution curve corresponding to the target measurement line, and determine the measurement point layout density at each location based on the curvature of the flue gas velocity distribution curve.

[0058] In this embodiment, determining the density of measuring points at each location based on the curvature of the flue gas velocity distribution curve includes: taking the first and second derivatives of the flue gas velocity distribution curve, and calculating the curvature of the flue gas velocity distribution curve at each location based on the corresponding first and second derivatives; wherein the curvature at any location characterizes the degree of drastic change in velocity at that location; determining the density of measuring points at each location based on the curvature corresponding to each location; wherein the magnitude of curvature is positively correlated with the density of measuring points.

[0059] It should be noted that most current methods for constructing measuring points along a measuring line include Gaussian interpolation and Chebyshev interpolation. However, interpolation follows a fixed mathematical distribution, is only related to coordinates, and does not accurately reflect the actual flow velocity pattern. Therefore, the method adopted in this invention is to automatically place measuring points based on the curvature of the actual flow velocity curve (i.e., the flue gas velocity distribution curve). Points with high curvature (distortion, jet, boundary layer) are denser, while points with flat flow (stable region) are sparser. The measuring point deployment process is as follows: Figure 6 As shown, the steps include calculating the flow velocity curvature of the measuring line, defining the initial number of measuring points, and determining the optimal number of measuring points.

[0060] The specific process for determining the density of measuring points is as follows: First, extract the flow velocity data from a certain height survey line and construct a one-dimensional lateral flow velocity distribution, as shown in the following formula.

[0061] ; In the formula: The continuous lateral velocity distribution (m / s) across the entire height measurement line. The continuous transverse coordinates (m) of the flue section To calculate the width of the flue (m).

[0062] Calculate the curvature, which represents the degree of drastic change in flow velocity. The calculation formula is as follows.

[0063] ; In the formula: The curvature of the velocity curve at continuous horizontal coordinate x, The first derivative of the flow velocity represents the rate of change of the flow velocity. The second derivative of the flow velocity represents the degree of flow velocity distortion.

[0064] The curvature is normalized to characterize the density of measurement points at the current location, as shown in the following formula.

[0065] ; In the formula: The curvature at the lateral position x, For curvature weights.

[0066] The non-uniform measurement point positions are generated based on the curvature weight integral, as shown in the following formula.

[0067] ; In the formula: Let the coordinates of the i-th determined transverse measuring point be... The total number of transverse measuring points for a single measuring line, The adaptive strength coefficient (taken as 0.8-1.2, a fixed constant) For curvature weights.

[0068] Then, one-dimensional Lagrange interpolation is used to reconstruct the transverse velocity of the single measuring line for error comparison, and the calculation is as follows.

[0069] ; In the formula: For the simulated flow velocity at the measurement point location, The interpolated continuous velocity distribution along the entire measurement line is used to compare the error with the actual velocity.

[0070] Finally, the optimal number of measuring points m is determined using the error evaluation formula, as follows.

[0071] ; In the formula: —Interpolation error when the number of transverse measurement points is m.

[0072] Then increase the number of measuring points to ensure If the error is less than 1%, the accuracy is considered saturated, and the smallest m that satisfies the convergence condition is taken as the optimal number of transverse measuring points. Of course, this error can be controlled according to actual costs. Relevant standards stipulate that it cannot exceed 10% when the flow velocity is greater than 10 m / s, and cannot exceed 5% when the flow velocity is less than 10 m / s.

[0073] The aforementioned curvature adaptive point placement process can also use methods such as approximate differentiation and variable step size optimization to generate non-uniform measurement points, and adjust the error threshold to control the number of measurement points.

[0074] Step S14: Arrange measuring points on the target measuring line according to the measuring point layout density, and use the measuring points to measure the flue gas velocity of the target coal-fired unit flue.

[0075] In this embodiment, after measuring the flue gas velocity of the target coal-fired unit flue using measuring points, the method further includes: acquiring the measured flow velocity data of each measuring point on the target measuring line, and calculating the flow velocity of the target measuring line based on the measured flow velocity data of each measuring point; acquiring the measuring line weight coefficient corresponding to the target measuring line, multiplying the flow velocity of the target measuring line by the measuring line weight coefficient to obtain the corrected flow velocity corresponding to the target measuring line, and acquiring the final cross-sectional average flow velocity of the target monitoring section based on the corrected flow velocity corresponding to the target measuring line.

[0076] In this embodiment, the flow velocity of the target measuring line needs to be corrected using the measuring line weight coefficient to obtain the corrected flow velocity corresponding to the target measuring line, and the final cross-sectional average flow velocity of the target monitoring section is obtained using the corrected flow velocity.

[0077] In addition, after measuring the flue gas velocity of the target coal-fired unit flue using measuring points, the method further includes: acquiring the measured flow velocity data and operating condition characteristic parameters of the measuring points under the test condition; inputting the measured flow velocity data and operating condition characteristic parameters of the measuring points into the pre-trained target compensation amount generation model to obtain the dynamic deviation compensation amount under the test condition; and superimposing the dynamic deviation compensation amount onto the final cross-sectional average flow velocity to obtain the corrected cross-sectional average flow velocity.

[0078] The process for compensating for the above results is as follows: Figure 7 As shown, the steps include model training and using the model output to compensate for dynamic bias.

[0079] This embodiment constructs a multi-condition coupled velocity deviation sample, and the extracted input features include: linear velocity calculated through measuring points, correction coefficient of the measuring line, and the unit's operating load status. , Parameters such as the flow velocity are calculated by weighting the measured points in the matrix obtained in the first six steps and then calculating the difference between the calculated flow velocity and the actual simulated cross-sectional flow velocity. This difference is used as the dynamic deviation correction amount for multiple working conditions and serves as the model training label. Simultaneously, several new input flow velocity working conditions are constructed to supplement the difference dataset.

[0080] This deep learning model (i.e., the target compensation generation model) does not solely input flow velocity characteristics, but rather incorporates the measurement line correction coefficient, load status, and... , The input serves as an auxiliary feature to accurately identify the error amplification patterns corresponding to highly distorted cross sections and highly distorted survey lines.

[0081] Commonly used models include deep learning algorithms such as BP (Back Propagation), LSTM (Long Short-Term Memory), and WOA (Whale Optimization Algorithm), but they are difficult to capture the effects of flow field distortion. Therefore, we use CNN-LSTM neural network here.

[0082] (1) Spatial feature extraction of CNN (Convolutional Neural Networks): The velocity data of each layer with adaptive horizontal point placement is used to construct a two-dimensional matrix and input into the convolutional layer. The convolutional kernel automatically captures the spatial distortion characteristics of the high-speed jet region, low-speed boundary layer, and vortex region within the matrix measurement points, restores the spatial velocity distribution law brought about by the curvature adaptive point placement, and quantifies the calculation error under different combinations of measurement point density.

[0083] (2) LSTM temporal feature extraction: Input continuous time series of unit load, UI / Flow field indicators capture the temporal drift patterns of deviations during load rises and falls and peak shaving transitions, compensating for the shortcoming that static weighting coefficients cannot be dynamically adjusted over time.

[0084] (3) Multi-feature attention fusion layer: For low load, high (High distortion flow field) The model is assigned higher feature weights for the working condition, which forces the model to focus on learning the extreme working conditions where the error of the static point layout scheme mentioned above is amplified. This solves the problem that traditional networks fit well for stable high load working conditions but poorly for low distortion working conditions.

[0085] (4) Residual output layer design: The model does not directly output the final compensated flow velocity, but only the dynamic deviation compensation amount. The original average flow velocity obtained in the previous steps is superimposed to obtain the corrected accurate flow velocity, which reduces the difficulty of network fitting.

[0086] It should be noted that in this embodiment, after training, the CNN-LSTM network undergoes lightweight processing such as pruning and quantization compression. The lightweight model can be directly embedded into existing online flue gas monitoring and DCS (Distributed Control System) systems without requiring additional high-performance computing equipment. Simultaneously, by combining existing parameters such as temperature, humidity, and carbon dioxide concentration, parameters such as carbon emissions can be output. The lightweight deployment method of the model in this embodiment is as follows: Figure 8 As shown.

[0087] The aforementioned deviation compensation step can also utilize other spatiotemporal fusion deep learning networks or traditional machine learning regression models to achieve dynamic flow velocity compensation.

[0088] Therefore, this application solves the problem that local extreme regions of the flow field under a single condition are difficult to fully capture by obtaining global velocity distribution data through multi-condition flow field simulation; it solves the problem that the selected measurement lines cannot stably represent the overall velocity distribution of the cross section and are prone to missing local distortion regions by selecting target measurement lines from candidate measurement lines based on the relative deviation between the line and the surface and the stability of the multi-condition flow field; and it solves the problem that traditional uniform point layout cannot identify and accurately cover local high-speed jet areas and low-speed ash accumulation areas by determining the measurement point layout density based on the curvature of the flue gas velocity distribution curve.

[0089] See Figure 9 As shown in the figure, an embodiment of the present invention discloses a flue gas velocity measuring device for a coal-fired power unit flue, comprising: The flow field simulation module 11 is used to mesh the three-dimensional model corresponding to the flue of the target coal-fired unit and set the boundary conditions to perform flow field simulation under different operating conditions, and obtain the global velocity distribution data of the flue gas in the flue of the target coal-fired unit under different operating conditions. The measuring line determination module 12 is used to set different candidate measuring lines on the target monitoring section based on the global velocity distribution data, calculate the relative deviation between the measuring line velocity and the cross-sectional average velocity of each candidate measuring line under different operating conditions, and the stability of the velocity distribution of the same measuring line under different operating conditions, and determine several target measuring lines from the candidate measuring lines based on the relative deviation and the stability. The measuring line velocity represents the average value of the flue gas velocity on the candidate measuring line, the cross-sectional average velocity represents the average value of the flue gas velocity on the target monitoring section, and the relative deviation represents the deviation between the measuring line velocity and the cross-sectional average velocity. The density determination module 13 is used to obtain the flue gas velocity distribution curve corresponding to the target measuring line, and determine the density of measuring points at each position according to the curvature of the flue gas velocity distribution curve. The flue gas velocity measurement module 14 is used to arrange measuring points on the target measuring line according to the measuring point layout density, and to use the measuring points to measure the flue gas velocity of the target coal-fired unit flue.

[0090] In some specific embodiments, the flow field simulation module 11 may specifically include: The mesh generation unit is used to sequentially divide the three-dimensional model into several meshes of decreasing size, and to perform flow field simulation on the three-dimensional model based on the corresponding mesh generation results, so as to obtain the flow velocity distribution data corresponding to the same cross section under different mesh generation results. The mesh division result determination unit is used to determine the mesh division result with the larger mesh size in the two adjacent mesh division results as the target mesh division result corresponding to the three-dimensional model when the deviation of the flow velocity distribution data corresponding to the two adjacent mesh division results is less than a preset threshold, so as to complete the mesh division operation of the three-dimensional model.

[0091] In some specific embodiments, the survey line determination module 12 further includes: The cross-section setting unit is used to set different candidate monitoring cross-sections within the target monitoring pipe section according to a preset interval, and extract the cross-section flue gas velocity data corresponding to each candidate monitoring cross-section from the global velocity distribution data. The cross-sectional data calculation unit is used to calculate the relative standard deviation of the flow velocity, the flow velocity skewness coefficient, and the flow velocity range rate of each candidate monitoring cross-section based on the flue gas velocity data of each cross-section; wherein, the relative standard deviation of the flow velocity is data characterizing the overall dispersion of the flow velocity at each point on the cross-section, the flow velocity skewness coefficient is data characterizing the distribution bias of the target high-speed jet zone and the target low-speed stagnation zone on the cross-section, and the flow velocity range rate is data characterizing the degree of difference between the maximum flow velocity and the minimum flow velocity on the cross-section; The target section determination unit is used to determine the comprehensive score corresponding to each of the candidate monitoring sections based on the relative standard deviation of the flow velocity, the flow velocity skewness coefficient, and the flow velocity range rate, and to determine the target monitoring section based on the comprehensive score.

[0092] In some specific embodiments, the density determination module 13 may specifically include: The curvature calculation unit is used to perform first-order and second-order differentiation on the flue gas velocity distribution curve, and calculate the curvature of the flue gas velocity distribution curve at each position based on the corresponding first-order and second-order derivatives; wherein, the curvature at any position represents the degree of drastic change in velocity at that position. The density determination unit is used to determine the density of measuring points at each location based on the curvature corresponding to each location; wherein, the magnitude of curvature is positively correlated with the density of measuring points.

[0093] In some specific embodiments, the survey line determination module 12 further includes: The flow velocity acquisition unit is used to acquire the flow velocity of the target measuring line under various working conditions and the average flow velocity of the target monitoring section under various working conditions. The correction coefficient determination unit is used to determine the ratio of the average flow velocity of the target section to the flow velocity of the target measuring line corresponding to each working condition as the single working condition correction coefficient for each working condition. The weight determination unit is used to calculate the deviation of each single-condition correction coefficient from the average correction coefficient under multiple conditions, and to determine the weight of each condition based on the magnitude of the deviation corresponding to each condition. The correction coefficient summation unit is used to perform weighted summation of the single-condition correction coefficients corresponding to each working condition according to the weight of each working condition, so as to obtain the measurement line weight coefficient of the target measurement line, so as to use the measurement line weight coefficient to correct the flow velocity of the target measurement line.

[0094] In some specific embodiments, the flue gas velocity measurement module 14 further includes: The measured data acquisition unit is used to acquire the measured flow velocity data of each measuring point on the target measuring line, and to calculate the flow velocity of the target measuring line based on the measured flow velocity data of each measuring point. The flow velocity correction unit is used to obtain the weight coefficient of the target measurement line corresponding to the target measurement line, multiply the flow velocity of the target measurement line by the weight coefficient of the measurement line to obtain the corrected flow velocity corresponding to the target measurement line, and obtain the final cross-sectional average flow velocity of the target monitoring section based on the corrected flow velocity corresponding to the target measurement line.

[0095] In some specific embodiments, the flue gas velocity measurement module 14 further includes: The compensation amount acquisition unit is used to acquire the measured flow velocity data and working condition characteristic parameters of the measuring point under the test condition, and input the measured flow velocity data and working condition characteristic parameters into the pre-trained target compensation amount generation model to obtain the dynamic deviation compensation amount under the test condition. The compensation amount superposition unit is used to superimpose the dynamic deviation compensation amount onto the final cross-sectional average flow velocity to obtain the corrected cross-sectional average flow velocity.

[0096] Furthermore, embodiments of this application also disclose an electronic device, Figure 10 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0097] Figure 10 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the flue gas velocity measurement method for coal-fired power unit flues disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0098] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0099] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0100] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the flue gas velocity measurement method of the coal-fired unit flue gas duct executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0101] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for measuring flue gas velocity in the flue gas duct of a coal-fired unit. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0103] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0105] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0106] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for measuring flue gas velocity in a coal-fired power unit flue, characterized in that, include: Mesh the three-dimensional model corresponding to the flue of the target coal-fired unit and set the boundary conditions to perform flow field simulation under different operating conditions, and obtain the global velocity distribution data of the flue gas in the flue of the target coal-fired unit under different operating conditions. Based on the global velocity distribution data, different candidate measuring lines are set on the target monitoring section. The relative deviation between the measuring line velocity and the cross-sectional average velocity of each candidate measuring line under different operating conditions is calculated according to the global velocity distribution data, as well as the stability of the velocity distribution of the same measuring line under different operating conditions. Based on the relative deviation and the stability, several target measuring lines are determined from the candidate measuring lines. The measuring line velocity represents the average value of the flue gas velocity on the candidate measuring line, the cross-sectional average velocity represents the average value of the flue gas velocity on the target monitoring section, and the relative deviation represents the deviation between the measuring line velocity and the cross-sectional average velocity. Obtain the flue gas velocity distribution curve corresponding to the target measurement line, and determine the measurement point layout density at each location based on the curvature of the flue gas velocity distribution curve. According to the density of the measurement points, measurement points are arranged on the target measurement line, and the flue gas velocity of the target coal-fired unit is measured using the measurement points.

2. The method for measuring flue gas velocity in the flue of a coal-fired unit according to claim 1, characterized in that, The mesh generation of the 3D model corresponding to the flue gas duct of the target coal-fired unit includes: The three-dimensional model is sequentially divided into several meshes with the mesh size decreasing, and flow field simulations are performed on the three-dimensional model based on the corresponding mesh division results to obtain the flow velocity distribution data corresponding to the same cross section under different mesh division results. When the deviation of the flow velocity distribution data corresponding to two adjacent sets of mesh division results is less than a preset threshold, the mesh division result with the larger mesh size in the two adjacent sets of mesh division results is determined as the target mesh division result corresponding to the three-dimensional model, so as to complete the mesh division operation of the three-dimensional model.

3. The method for measuring flue gas velocity in the flue of a coal-fired unit according to claim 1, characterized in that, Before setting different candidate measurement lines on the target monitoring section based on the global velocity distribution data, the method further includes: Within the target monitoring pipe section, different candidate monitoring sections are set according to a preset interval, and the cross-sectional flue gas velocity data corresponding to each candidate monitoring section is extracted from the global velocity distribution data. The relative standard deviation of flow velocity, the flow velocity skewness coefficient, and the flow velocity range rate of each candidate monitoring section are calculated based on the flue gas velocity data of each section. The relative standard deviation of flow velocity is a data characterizing the overall dispersion of flow velocity at each point on the section. The flow velocity skewness coefficient is a data characterizing the distribution bias of the target high-speed jet zone and the target low-speed stagnation zone on the section. The flow velocity range rate is a data characterizing the degree of difference between the maximum flow velocity and the minimum flow velocity on the section. The comprehensive score corresponding to each candidate monitoring section is determined based on the relative standard deviation of the flow velocity, the flow velocity skewness coefficient, and the flow velocity range rate, and the target monitoring section is determined based on the comprehensive score.

4. The method for measuring flue gas velocity in the flue of a coal-fired unit according to claim 1, characterized in that, The step of determining the density of measuring points at each location based on the curvature of the flue gas velocity distribution curve includes: The first and second derivatives of the flue gas velocity distribution curve are performed, and the curvature of the flue gas velocity distribution curve at each position is calculated based on the corresponding first and second derivatives; wherein, the curvature at any position represents the degree of drastic change in velocity at that position. The density of measuring points at each location is determined based on the curvature corresponding to each location; where the magnitude of curvature is positively correlated with the density of measuring points.

5. The method for measuring flue gas velocity in the flue of a coal-fired unit according to claim 1, characterized in that, After determining a number of target survey lines from the candidate survey lines based on the relative deviation between the lines and the surface and the degree of stability, the method further includes: Obtain the target measuring line flow velocity under various working conditions and the target monitoring section average flow velocity under various working conditions; The ratio of the average flow velocity at the target cross section to the flow velocity at the target measuring line corresponding to each working condition is determined as the single working condition correction coefficient for each working condition. Calculate the deviation of each single-condition correction coefficient from the average correction coefficient under multiple conditions, and determine the weight of each condition based on the magnitude of the deviation. The single-condition correction coefficients corresponding to each working condition are weighted and summed according to the weight of each working condition to obtain the target measuring line weight coefficient, so as to use the measuring line weight coefficient to correct the flow velocity of the target measuring line.

6. The method for measuring flue gas velocity in the flue of a coal-fired unit according to claim 5, characterized in that, After measuring the flue gas velocity in the target coal-fired unit flue using the measuring point, the method further includes: Obtain the measured flow velocity data of each measuring point on the target measuring line, and calculate the flow velocity of the target measuring line based on the measured flow velocity data of each measuring point; Obtain the weight coefficient of the target measurement line, multiply the flow velocity of the target measurement line by the weight coefficient to obtain the corrected flow velocity of the target measurement line, and obtain the final cross-sectional average flow velocity of the target monitoring section based on the corrected flow velocity of the target measurement line.

7. The method for measuring flue gas velocity in a coal-fired power unit flue according to claim 6, characterized in that, After measuring the flue gas velocity in the target coal-fired unit flue using the measuring point, the method further includes: Obtain the measured flow velocity data and condition characteristic parameters of the measuring point under the test condition, and input the measured flow velocity data and condition characteristic parameters into the pre-trained target compensation amount generation model to obtain the dynamic deviation compensation amount under the test condition. The dynamic deviation compensation is superimposed on the final cross-sectional average velocity to obtain the corrected cross-sectional average velocity.

8. A flue gas velocity measuring device for a coal-fired power unit flue, characterized in that, include: The flow field simulation module is used to mesh the three-dimensional model corresponding to the flue of the target coal-fired unit and set the boundary conditions to perform flow field simulation under different operating conditions, and obtain the global velocity distribution data of the flue gas in the flue of the target coal-fired unit under different operating conditions. The measuring line determination module is used to set different candidate measuring lines on the target monitoring section based on the global velocity distribution data, calculate the relative deviation between the measuring line velocity and the cross-sectional average velocity of each candidate measuring line under different operating conditions, and the stability of the velocity distribution of the same measuring line under different operating conditions, and determine several target measuring lines from the candidate measuring lines based on the relative deviation and the stability. The measuring line velocity represents the average value of the flue gas velocity on the candidate measuring line, the cross-sectional average velocity represents the average value of the flue gas velocity on the target monitoring section, and the relative deviation represents the deviation between the measuring line velocity and the cross-sectional average velocity. The density determination module is used to obtain the flue gas velocity distribution curve corresponding to the target measuring line, and determine the density of measuring points at each location based on the curvature of the flue gas velocity distribution curve. The flue gas velocity measurement module is used to arrange measurement points on the target measurement line according to the measurement point layout density, and to use the measurement points to measure the flue gas velocity of the target coal-fired unit flue.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the flue gas velocity measurement method for a coal-fired unit flue as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the flue gas velocity measurement method for a coal-fired unit flue as described in any one of claims 1 to 7.

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