A method and system for detecting and evaluating linearity of a steam turbine flow characteristic curve

Abstract

The application discloses a kind of linearity detection and evaluation method and system of steam turbine flow characteristic curve, belong to steam turbine control and performance diagnosis technical field.This method obtains steam turbine relative flow characteristic curve in advance as input data, uses adaptive Kalman filter to carry out smoothing processing to curve data, to eliminate noise;Then, based on the smooth data, construct piecewise linear interpolation function, set step length to carry out dense sampling and calculate difference quotient sequence, by calculating the point-by-point deviation of the difference quotient sequence and actual growth rate, the number of less than deviation threshold is counted, to calculate linearity evaluation score;Finally, according to the threshold interval to which the score belongs, automatically output linearity evaluation conclusion.The application realizes objective, fine quantitative evaluation of the linearity of flow characteristic curve, replaces the traditional subjective experience judgment, and provides a reliable automated diagnostic tool for unit performance optimization.

Description

Technical Field

[0001] This invention relates to the field of steam turbine control and performance diagnosis technology, specifically to a method and system for detecting and evaluating the linearity of steam turbine flow characteristic curves. Background Technology

[0002] The flow characteristics of a steam turbine are one of the most important characteristics for the safe and stable operation of a thermal power unit. They are manifested as a functional relationship between the unit's load command and the actual steam flow rate. The quality of these characteristics affects the primary frequency regulation performance, AGC performance, and overall operational safety and economy of the thermal power unit. The steam turbine flow characteristics exhibit some differences across different load ranges, manifested in varying linearities of the function curve. A key indicator for evaluating the quality of steam turbine flow characteristics is the linearity of the function curve. Good linearity reflects the stability of the steam flow control as the load changes. The smaller the difference in linearity of the flow characteristics across different load ranges, the higher the overall linearity. This contributes to improved coordinated control, AGC, and primary frequency regulation performance, as well as energy saving, consumption reduction, and flexible operation, ultimately enhancing the overall operational efficiency of the unit.

[0003] Existing methods for detecting the linearity of flow characteristics mainly rely on technicians' observation and estimation of curve trends. This approach is highly subjective and has low accuracy, making it unable to accurately assess the linearity of the turbine flow characteristic curve. The article "Online Diagnosis and Optimization System for Flow Characteristics of Coal-fired Unit Steam Turbines" uses an ideal linear straight line as the target curve. It evaluates the linearity of flow characteristics by calculating the deviation between the actually measured turbine flow characteristics and this curve and comparing it to a set threshold. However, this method only uses a single ideal linear curve as the target curve and does not adapt to the multi-segmented characteristics of turbine flow characteristics, making it difficult to accurately and effectively assess local deviations in each interval. Summary of the Invention

[0004] The technical problem to be solved by this invention is how to address the issue that traditional methods rely on human experience to judge the linearity of the steam turbine flow characteristic curve, and lack objective and unified quantitative evaluation standards.

[0005] This invention solves the above-mentioned technical problems through the following technical means: a method for detecting and evaluating the linearity of a steam turbine flow characteristic curve, comprising:

[0006] S1. Input the discrete data point sequence of the turbine flow characteristic curve to be detected into the adaptive Kalman filter for filtering, and output the smoothed curve data point sequence. S2. Calculate the difference quotient sequence corresponding to the smoothed curve data point sequence, and calculate the linearity evaluation score based on the difference quotient sequence; S3. Based on the linearity evaluation score, output the linearity evaluation result of the flow characteristic curve.

[0007] This invention smooths the input flow characteristic curve using an adaptive Kalman filter, fundamentally eliminating random noise and interference in the original data and providing a reliable data foundation for subsequent analysis. By calculating the difference quotient sequence of the smoothed curve and performing quantitative analysis, the evaluation of curve linearity is transformed into an objective mathematical score, achieving a fundamental shift from subjective qualitative to objective quantitative assessment. Based on a preset scoring threshold, this score is automatically judged, outputting a clear evaluation and corresponding suggestions, directly transforming the quantitative results into standardized and actionable operation and maintenance guidance. The entire process achieves fully automated and objective evaluation from noisy data input to clear operation and maintenance decisions, significantly improving detection efficiency and decision-making accuracy.

[0008] Furthermore, the adaptive Kalman filter in S1 is specifically as follows: An adaptive Kalman filter is used to filter the flow characteristic curve to be detected. The adaptive Kalman filtering algorithm is as follows. The turbine flow characteristic system satisfies the state-space equation:

[0009] in, for The state vector of the system at any given time. Here is the state transition matrix. for The state vector of the system at any given time. For the input matrix, for The input vector of the system at any given time, The process error follows a covariance matrix of... The system follows a normal distribution, and the measurements of the system satisfy:

[0010] in, This is the transformation matrix between the measured value and the target value. The measurement error follows a covariance matrix of... The normal distribution The adaptive weights are calculated using the following formula:

[0011] in, , and The first , , The measured value at time, For hyperparameters; exist At time t, a priori estimation of the flow characteristic curve is performed, and its calculation formula is as follows:

[0012] in, This is the prior estimate in the Kalman filtering algorithm; Calculate the Kalman estimate based on the prior estimate:

[0013] in, For Kalman gain, The prior covariance matrix, The posterior covariance matrix is... This is the adaptively adjusted covariance matrix.

[0014] Furthermore, S2 includes: S2.1 Connect the smoothed adjacent data points to construct a piecewise linear interpolation function; S2.2. Divide the domain of the flow characteristic curve into equal parts by setting a step size to obtain the interpolation point sequence, and use the piecewise linear interpolation function to calculate the corresponding function value sequence. S2.3 Calculate the difference quotient sequence of the function value sequence; S2.4 Calculate the point-by-point deviation between the difference quotient sequence and the actual growth rate, and calculate the linearity evaluation score based on the point-by-point deviation. The calculation of the linearity evaluation score further includes: setting a deviation threshold; counting the number of deviation points in the point-by-point deviation that are less than the deviation threshold; multiplying the ratio of the number of deviation points to the total number of deviation points by 100, and using the result as the linearity evaluation score under the current step size. The step size is decreased sequentially, and steps S2.2 to S2.4 are repeated. When the fluctuation of the linearity evaluation score calculated successively is less than the preset convergence threshold, the score is determined to be stable, and the score at this time is taken as the final linearity evaluation score.

[0015] Furthermore, the piecewise linear interpolation function in S2.1 is:

[0016] in, For the first The coordinates of the scattered points after smoothing.

[0017] Furthermore, S3 specifically refers to: Preset a first scoring threshold and a second scoring threshold; The linearity evaluation score is compared with the first scoring threshold and the second scoring threshold. When the score is greater than the first scoring threshold, the linearity of the flow characteristic curve is determined to be good. When the score is between the first scoring threshold and the second scoring threshold, the linearity of the flow characteristic curve is determined to be average and an inflection point is indicated. When the score is less than the second scoring threshold, the linearity of the flow characteristic curve is determined to be poor and an optimization prompt is generated.

[0018] This invention provides a system for detecting and evaluating the linearity of a steam turbine flow characteristic curve, comprising: The data filtering module is used to input the discrete data point sequence of the turbine flow characteristic curve to be detected into the adaptive Kalman filter for filtering and output a smoothed curve data point sequence. The linearity quantification calculation module is used to calculate the difference quotient sequence corresponding to the smoothed curve data point sequence, and to calculate the linearity evaluation score based on the difference quotient sequence. The characteristic health status evaluation module is used to output the linearity evaluation result of the flow characteristic curve based on the linearity evaluation score.

[0019] Furthermore, the adaptive Kalman filter in the data filtering module is specifically as follows: An adaptive Kalman filter is used to filter the flow characteristic curve to be detected. The adaptive Kalman filtering algorithm is as follows. The turbine flow characteristic system satisfies the state-space equation:

[0020] in, for The state vector of the system at any given time. Here is the state transition matrix. for The state vector of the system at any given time. For the input matrix, for The input vector of the system at any given time, The process error follows a covariance matrix of... The system follows a normal distribution, and the measurements of the system satisfy:

[0021] in, This is the transformation matrix between the measured value and the target value. The measurement error follows a covariance matrix of... The normal distribution The adaptive weights are calculated using the following formula:

[0022] in, , and The first , , The measured value at time, For hyperparameters; exist At time t, a priori estimation of the flow characteristic curve is performed, and its calculation formula is as follows:

[0023] in, This is the prior estimate in the Kalman filtering algorithm; Calculate the Kalman estimate based on the prior estimate:

[0024] in, For Kalman gain, The prior covariance matrix, The posterior covariance matrix is... This is the adaptively adjusted covariance matrix.

[0025] Furthermore, the linear metric calculation module includes: An interpolation unit is used to connect the smoothed adjacent data points to construct a piecewise linear interpolation function; The sampling and calculation unit is used to set a step size to evenly divide the domain of the flow characteristic curve to obtain an interpolation point sequence, and to calculate the corresponding function value sequence using the piecewise linear interpolation function; The difference quotient calculation unit is used to calculate the difference quotient sequence of the function value sequence; The scoring calculation unit is used to calculate the point-by-point deviation between the difference quotient sequence and the actual growth rate, and to calculate the linearity evaluation score based on the point-by-point deviation. The calculation of the linearity evaluation score further includes: setting a deviation threshold; counting the number of deviation points in the point-by-point deviation that are less than the deviation threshold; multiplying the ratio of the number of deviation points to the total number of deviation points by 100, and using the result as the linearity evaluation score at the current step size. The step size set in the sampling and calculation unit decreases sequentially, triggering the sampling and calculation unit, the difference quotient calculation unit, and the scoring calculation unit to re-execute the calculation; when the linearity evaluation score calculated by the scoring calculation unit successively fluctuates less than the preset convergence threshold, the score is determined to be stable, and the score at this time is output as the final linearity evaluation score to the characteristic health status evaluation module.

[0026] Furthermore, the piecewise linear interpolation function in the sampling and calculation unit is:

[0027] in, For the first The coordinates of the scattered points after smoothing.

[0028] Furthermore, the characteristic health status evaluation module is used to perform the following judgment operations: Preset a first scoring threshold and a second scoring threshold; The linearity evaluation score is compared with the first scoring threshold and the second scoring threshold. When the score is greater than the first scoring threshold, the linearity of the flow characteristic curve is determined to be good. When the score is between the first scoring threshold and the second scoring threshold, the linearity of the flow characteristic curve is determined to be average and an inflection point is indicated. When the score is less than the second scoring threshold, the linearity of the flow characteristic curve is determined to be poor and an optimization prompt is generated.

[0029] The advantages of this invention are: (1) This invention uses an adaptive Kalman filter to filter the turbine flow characteristic curve to be tested, effectively removing measurement noise and random disturbances from the original flow characteristic curve. This solves the fundamental problem of subsequent analysis distortion caused by poor data quality, and provides a stable and reliable smooth curve data basis for linearity evaluation. (2) This invention calculates the difference quotient sequence based on smoothed curve data, and calculates a quantitative linearity evaluation score by comparing the difference quotient with the actual growth rate. By using mathematical methods to transform the linearity of the curve into an objective numerical score, it solves the problems of strong subjectivity and inconsistent conclusions caused by the traditional reliance on manual visual judgment, and can accurately locate the local nonlinear interval with a low score.

[0030] (3) This invention compares the calculated linearity evaluation score with a preset scoring threshold, determines the linearity health status of the turbine flow characteristic curve based on the interval to which the score belongs, and generates corresponding prompt information. This solves the problem that the evaluation results rely on expert interpretation and cannot directly drive standardized operation and maintenance actions, and realizes an automated closed loop from data to decision-making. Attached Figure Description

[0031] Figure 1 This is a flowchart of a method for detecting and evaluating the linearity of a steam turbine flow characteristic curve according to Embodiment 1 of the present invention; Figure 2This is a comparison curve of the original measurement data and the filtered effective data in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the scoring results of linearity detection of a flow characteristic curve in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the scoring results of linearity detection for another flow characteristic curve in Embodiment 1 of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0033] Example 1 like Figure 1 As shown, a method for detecting and evaluating the linearity of a steam turbine flow characteristic curve includes: S1. Input the discrete data point sequence of the turbine flow characteristic curve to be detected into the adaptive Kalman filter for filtering, and output the smoothed curve data point sequence.

[0034] Specifically, the turbine flow characteristic data is continuously collected dynamic time-series data, which is affected by factors such as equipment vibration, measuring instrument error, and operating condition fluctuations, resulting in random interference. Conventional Kalman filters, due to their fixed filtering parameters, have poor adaptability to the non-stationary fluctuations of turbine flow, and are prone to over-filtering or under-filtering. Therefore, this embodiment adaptively adjusts the Kalman filter to adapt it to the characteristics of the turbine flow data.

[0035] Obtain the discrete data point sequence of the relative flow characteristic curve of the steam turbine to be evaluated. ,in, This is a relative flow command. This corresponds to the actual relative flow rate.

[0036] An adaptive Kalman filter is used to filter the flow characteristic curve to be detected. The adaptive Kalman filtering algorithm is as follows. The turbine flow characteristic system satisfies the state-space equation:

[0037] in, for The state vector of the system at any given time, with core state parameters such as flow rate characterizing the turbine's flow characteristics. The state transition matrix is ​​determined by the dynamic operating characteristics of the steam turbine. for The state vector of the system at any given time. The input matrix is ​​determined by the influence characteristics of the input parameters on the state parameters. for The input vector of the timing system represents the operating input parameters of the steam turbine, such as the opening degree of the control valve. The process error follows a covariance matrix of... It follows a normal distribution.

[0038] The system's measurements satisfy the test equation:

[0039] in, This is the transformation matrix between the measured value and the target value. The measurement error follows a covariance matrix of... The normal distribution For adaptive weights, the sum of discrete integrals of the second-order difference quotients of the measured values ​​and the hyperparameters are used. The determined calculation formula is as follows:

[0040] in, , and The first , , The measured value at time, This is a hyperparameter.

[0041] exist At time t, a priori estimation of the flow characteristic curve is performed, and its calculation formula is as follows:

[0042] in, This is the prior estimate in the Kalman filtering algorithm.

[0043] Calculate the Kalman estimate based on the prior estimate:

[0044] in For Kalman gain, The prior covariance matrix, The posterior covariance matrix is... This is the adaptively adjusted covariance matrix. After traversing all data points, a smoothed data point sequence is obtained. .like Figure 2As shown, the filtered data curve becomes smoother compared to the original data curve.

[0045] S2. Calculate the difference quotient sequence corresponding to the smoothed curve data point sequence, and calculate the linearity evaluation score based on the difference quotient sequence.

[0046] Specifically, the linearity of the turbine flow characteristic curve directly reflects the stability of the unit operation. Conventional linearity detection uses overall linear fitting, which can easily mask local slope abrupt changes (such as flow inflection points). However, piecewise linear interpolation can achieve local fitting of data and accurately identify local inflection points. Therefore, this embodiment uses piecewise linear interpolation combined with small step size to detect linearity, thereby improving detection accuracy.

[0047] After filtering with an adaptive Kalman filter, the linearity of the turbine flow characteristic curve can be calculated using a piecewise linear interpolation function. The specific procedure is as follows: S2.1, will Connecting adjacent points in the function with straight lines forms a piecewise linear function. For any Construct piecewise linear interpolation functions according to the following rules:

[0048] in, It is a piecewise linear interpolation function. As the interpolation independent variable, This represents the smoothed actual relative flow rate.

[0049] S2.2, Set an initial step size Within the relative instruction range An equally spaced sequence of interpolation points is generated within the interval, and the result is calculated using a piecewise linear interpolation function. The actual relative flow value corresponding to each point is used to obtain a sequence of function values. ,calculate The difference quotient sequence corresponding to the sequence , where the j-th difference quotient The calculation formula is: The D-sequence characterizes the actual relative flow rate change of the curve in a small segment.

[0050] S2.3 Calculate the actual growth rate of the entire smooth curve. The formula is as follows: ; S2.4, Calculate the difference quotient sequence D and absolute deviation sequence , where the j-th absolute deviation The calculation formula is: Set an allowable deviation threshold. ,statistics medium to small The number of elements Calculate the current step size. Linearity score below: 100, of which, is the total number of difference quotient sequences.

[0051] Gradually decrease the step size (For example, take values ​​of 1.0, 0.5, 0.1, and 0.05 in sequence), repeat steps S2.2-S2.4 to obtain a series of scores. .along with As the score decreases, it will tend to stabilize. When this condition is met... At that time, it was considered that the scoring had converged, where, To set the preset convergence threshold, take The final linearity evaluation score .

[0052] S3. Based on the linearity evaluation score, output the linearity evaluation result of the flow characteristic curve.

[0053] Specifically, based on the final score, a diagnostic conclusion and recommendations are output.

[0054] Set evaluation threshold: Preset first rating threshold Second scoring threshold .

[0055] Logical judgment and output: like The linearity of the flow characteristic curve was determined to be good.

[0056] like The linearity of the flow characteristic curve is generally considered to be average, and an inflection point is indicated.

[0057] like It determines the poor linearity of the flow characteristic curve and generates optimization suggestions.

[0058] Linearity test results are as follows Figure 3 , Figure 4 As shown, Figure 3 and Figure 4 The results of applying this method to two different characteristic curves are shown.

[0059] Figure 3 The curve shown has areas of poor linearity, such as abrupt slope changes and obvious bends, which, according to rough manual observation, account for about 30% of the entire curve. After calculation using the method of this invention, the final linearity score obtained is about 70.5 points, which is highly consistent with the judgment that "about 70% of the interval has good linearity," proving the consistency between the algorithm's quantitative results and the expert's qualitative observation.

[0060] Figure 4 The nonlinear characteristics of the curve shown are more obvious, with poor linearity in about 50% of the region. After calculation using the method of this invention, the final linearity score is about 55.8 points, which accurately reflects the objective situation that the curve has "poor linearity in half of the intervals".

[0061] The comparison between these two figures clearly demonstrates that the scoring mechanism provided by this invention can effectively and objectively quantify the linearity of the curve, and its scoring results match the visual judgment expectations of professionals, achieving a leap from subjective experience judgment to objective quantitative evaluation.

[0062] Example 2 Based on Embodiment 1, Embodiment 2 of the present invention also provides a system for detecting and evaluating the linearity of a steam turbine flow characteristic curve, comprising: The data filtering module is used to input the discrete data point sequence of the turbine flow characteristic curve to be detected into an adaptive Kalman filter for filtering, and output a smoothed curve data point sequence.

[0063] Specifically, adaptive Kalman filters include: An adaptive Kalman filter is used to filter the flow characteristic curve to be detected. The adaptive Kalman filtering algorithm is as follows. The turbine flow characteristic system satisfies the state-space equation:

[0064] in, for The state vector of the system at any given time. Here is the state transition matrix. for The state vector of the system at any given time. For the input matrix, for The input vector of the system at any given time, The process error follows a covariance matrix of... The system follows a normal distribution, and the measurements of the system satisfy:

[0065] in, This is the transformation matrix between the measured value and the target value. The measurement error follows a covariance matrix of... The normal distribution The adaptive weights are calculated using the following formula:

[0066] in, , and The first , , The measured value at time, For hyperparameters; No. During each iteration, a priori estimation of the flow characteristic curve is performed, and its calculation formula is as follows:

[0067] in, This is the prior estimate in the Kalman filtering algorithm; Calculate the Kalman estimate based on the prior estimate:

[0068] in, For Kalman gain, The prior covariance matrix, The posterior covariance matrix is... This is the adaptively adjusted covariance matrix.

[0069] The linearity quantification calculation module is used to calculate the difference quotient sequence corresponding to the smoothed curve data point sequence, and to calculate the linearity evaluation score based on the difference quotient sequence.

[0070] Specifically, the interpolation unit is used to connect smoothed adjacent data points to construct a piecewise linear interpolation function; The sampling and calculation unit is used to set the step size to evenly divide the domain of the flow characteristic curve, obtain the interpolation point sequence, and calculate the corresponding function value sequence using the piecewise linear interpolation function.

[0071] The piecewise linear interpolation function is:

[0072] in, For the first The coordinates of the scattered points after smoothing.

[0073] The difference quotient calculation unit is used to calculate the difference quotient sequence of the function value sequence.

[0074] The scoring calculation unit is used to calculate the point-by-point deviation between the difference quotient sequence and the actual growth rate, and to calculate the linearity evaluation score based on the point-by-point deviation. The calculation of the linearity evaluation score further includes: setting a deviation threshold; counting the number of deviation points that are less than the deviation threshold in the point-by-point deviation; multiplying the ratio of the number of deviation points to the total number of points by 100, and using the result as the linearity evaluation score under the current step size.

[0075] The step size in the sampling and calculation unit decreases sequentially, triggering the sampling and calculation unit, the difference quotient calculation unit, and the scoring calculation unit to re-execute the calculation. When the linearity evaluation score calculated by the scoring calculation unit fluctuates less than the preset convergence threshold, the score is determined to be stable, and the score at this time is output as the final linearity evaluation score to the characteristic health status evaluation module.

[0076] The characteristic health status evaluation module is used to output the linearity evaluation results of the flow characteristic curve based on the linearity evaluation score.

[0077] Specifically, the characteristic health status evaluation module is used to perform the following judgment operations: Preset a first scoring threshold and a second scoring threshold; The linearity evaluation score is compared with the first and second scoring thresholds; When the score is greater than the first scoring threshold, the linearity of the flow characteristic curve is considered to be good. When the score is between the first and second scoring thresholds, the linearity of the flow characteristic curve is judged to be average and an inflection point is indicated. When the score is less than the second scoring threshold, the linearity of the flow characteristic curve is judged to be poor and an optimization suggestion is generated.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting and evaluating the linearity of a steam turbine flow characteristic curve, characterized in that, include: S1. Input the discrete data point sequence of the turbine flow characteristic curve to be detected into the adaptive Kalman filter for filtering, and output the smoothed curve data point sequence. S2. Calculate the difference quotient sequence corresponding to the smoothed curve data point sequence, and calculate the linearity evaluation score based on the difference quotient sequence; S3. Based on the linearity evaluation score, output the linearity evaluation result of the flow characteristic curve.

2. The method for detecting and evaluating the linearity of a steam turbine flow characteristic curve according to claim 1, characterized in that, The adaptive Kalman filter in S1 is specifically: An adaptive Kalman filter is used to filter the flow characteristic curve to be detected. The adaptive Kalman filtering algorithm is as follows. The turbine flow characteristic system satisfies the state-space equation: in, for The state vector of the system at any given time. Here is the state transition matrix. for The state vector of the system at any given time. For the input matrix, for The input vector of the system at any given time, The process error follows a covariance matrix of... The system follows a normal distribution, and the measurements of the system satisfy: in, This is the transformation matrix between the measured value and the target value. The measurement error follows a covariance matrix of... The normal distribution The adaptive weights are calculated using the following formula: in, , and The first , , The measured value at time, For hyperparameters; exist At time t, a priori estimation of the flow characteristic curve is performed, and its calculation formula is as follows: in, This is the prior estimate in the Kalman filtering algorithm; Calculate the Kalman estimate based on the prior estimate: in, For Kalman gain, The prior covariance matrix, The posterior covariance matrix is... This is the adaptively adjusted covariance matrix.

3. The method for detecting and evaluating the linearity of a steam turbine flow characteristic curve according to claim 1, characterized in that, S2 includes: S2.1 Connect the smoothed adjacent data points to construct a piecewise linear interpolation function; S2.

2. Divide the domain of the flow characteristic curve into equal parts by setting a step size to obtain the interpolation point sequence, and use the piecewise linear interpolation function to calculate the corresponding function value sequence. S2.3 Calculate the difference quotient sequence of the function value sequence; S2.4 Calculate the point-by-point deviation between the difference quotient sequence and the actual growth rate, and calculate the linearity evaluation score based on the point-by-point deviation. The calculation of the linearity evaluation score further includes: setting a deviation threshold; counting the number of deviation points in the point-by-point deviation that are less than the deviation threshold; multiplying the ratio of the number of deviation points to the total number of deviation points by 100, and using the result as the linearity evaluation score under the current step size. The step size is decreased sequentially, and steps S2.2 to S2.4 are repeated. When the fluctuation of the linearity evaluation score calculated successively is less than the preset convergence threshold, the score is determined to be stable, and the score at this time is taken as the final linearity evaluation score.

4. The method for detecting and evaluating the linearity of a steam turbine flow characteristic curve according to claim 3, characterized in that, The piecewise linear interpolation function in S2.1 is: in, For the first The coordinates of the scattered points after smoothing.

5. The method for detecting and evaluating the linearity of a steam turbine flow characteristic curve according to claim 1, characterized in that, Specifically, S3 is: Preset a first scoring threshold and a second scoring threshold; The linearity evaluation score is compared with the first scoring threshold and the second scoring threshold. When the score is greater than the first scoring threshold, the linearity of the flow characteristic curve is determined to be good. When the score is between the first scoring threshold and the second scoring threshold, the linearity of the flow characteristic curve is determined to be average and an inflection point is indicated. When the score is less than the second scoring threshold, the linearity of the flow characteristic curve is determined to be poor and an optimization prompt is generated.

6. A system for detecting and evaluating the linearity of a steam turbine flow characteristic curve, characterized in that, include: The data filtering module is used to input the discrete data point sequence of the turbine flow characteristic curve to be detected into the adaptive Kalman filter for filtering and output a smoothed curve data point sequence. The linearity quantification calculation module is used to calculate the difference quotient sequence corresponding to the smoothed curve data point sequence, and to calculate the linearity evaluation score based on the difference quotient sequence. The characteristic health status evaluation module is used to output the linearity evaluation result of the flow characteristic curve based on the linearity evaluation score.

7. The linearity detection and evaluation system for the flow characteristic curve of a steam turbine according to claim 6, characterized in that, The adaptive Kalman filter in the data filtering module is specifically: An adaptive Kalman filter is used to filter the flow characteristic curve to be detected. The adaptive Kalman filtering algorithm is as follows. The turbine flow characteristic system satisfies the state-space equation: in, for The state vector of the system at any given time. Here is the state transition matrix. for The state vector of the system at any given time. For the input matrix, for The input vector of the system at any given time, The process error follows a covariance matrix of... The system follows a normal distribution, and the measurements of the system satisfy: in, This is the transformation matrix between the measured value and the target value. The measurement error follows a covariance matrix of... The normal distribution The adaptive weights are calculated using the following formula: in, , and The first , , The measured value at time, For hyperparameters; exist At time t, a priori estimation of the flow characteristic curve is performed, and its calculation formula is as follows: in, This is the prior estimate in the Kalman filtering algorithm; Calculate the Kalman estimate based on the prior estimate: in, For Kalman gain, The prior covariance matrix, The posterior covariance matrix is... This is the adaptively adjusted covariance matrix.

8. The linearity detection and evaluation system for the flow characteristic curve of a steam turbine according to claim 6, characterized in that, The linear metric calculation module includes: An interpolation unit is used to connect the smoothed adjacent data points to construct a piecewise linear interpolation function; The sampling and calculation unit is used to set a step size to evenly divide the domain of the flow characteristic curve to obtain an interpolation point sequence, and to calculate the corresponding function value sequence using the piecewise linear interpolation function; The difference quotient calculation unit is used to calculate the difference quotient sequence of the function value sequence; The scoring calculation unit is used to calculate the point-by-point deviation between the difference quotient sequence and the actual growth rate, and to calculate the linearity evaluation score based on the point-by-point deviation. The calculation of the linearity evaluation score further includes: setting a deviation threshold; counting the number of deviation points in the point-by-point deviation that are less than the deviation threshold; multiplying the ratio of the number of deviation points to the total number of deviation points by 100, and using the result as the linearity evaluation score at the current step size. The step size set in the sampling and calculation unit decreases sequentially, triggering the sampling and calculation unit, the difference quotient calculation unit, and the scoring calculation unit to re-execute the calculation; when the linearity evaluation score calculated by the scoring calculation unit fluctuates less than the preset threshold, the score is determined to be stable, and the score at this time is output as the final linearity evaluation score to the characteristic health status evaluation module.

9. The linearity detection and evaluation system for the flow characteristic curve of a steam turbine according to claim 8, characterized in that, The piecewise linear interpolation function in the sampling and calculation unit is: in, For the first The coordinates of the scattered points after smoothing.

10. The linearity detection and evaluation system for the flow characteristic curve of a steam turbine according to claim 6, characterized in that, The characteristic health status evaluation module is used to perform the following judgment operations: Preset a first scoring threshold and a second scoring threshold; The linearity evaluation score is compared with the first scoring threshold and the second scoring threshold. When the score is greater than the first scoring threshold, the linearity of the flow characteristic curve is determined to be good. When the score is between the first scoring threshold and the second scoring threshold, the linearity of the flow characteristic curve is determined to be average and an inflection point is indicated. When the score is less than the second scoring threshold, the linearity of the flow characteristic curve is determined to be poor and an optimization prompt is generated.

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