Thermal power plant pipeline on-line monitoring system

Abstract

The invention provides a thermal power plant pipeline on-line monitoring system, and relates to the technical field of thermal power plant pipeline monitoring, and the system comprises a data monitoring module which is used for collecting the operation data of a fuel conveying pipeline, a steam pipeline and a cooling water pipeline of a thermal power plant at each moment in a monitoring period; the processing value determination module is used for acquiring a fire coal accumulation degree processing value, a steam pipeline leakage degree processing value and a cooling water pipeline corrosion degree processing value of a fuel conveying pipeline of the thermal power plant at each moment; the pipeline risk assessment module is used for obtaining a thermal power plant fuel conveying pipeline risk assessment value, a steam pipeline risk assessment value and a cooling water pipeline risk assessment value in the corresponding monitoring period; and the thermal power plant pipeline maintenance prediction module is used for predicting a thermal power plant pipeline maintenance necessity degree value in the next monitoring period based on the thermal power plant pipeline maintenance prediction matrix. The operation state of the thermal power plant pipeline can be accurately monitored in real time, the pipeline risk is predicted through a scientific method, and the safety and stability of the thermal power plant are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of thermal power plant pipeline monitoring, and in particular relates to an online monitoring system for thermal power plant pipelines. Background Art

[0002] The pipeline system of a thermal power plant is not only a link between major equipment, but also the basis for ensuring efficient, stable and safe operation of the entire power plant. The existing thermal power plant pipelines rely on manual regular inspections to ensure the safe operation of the thermal power plant. On the one hand, manual inspections are prone to misjudgments and missed inspections due to human factors. On the other hand, since it cannot be predicted in advance, inspections and repairs are only carried out when risk problems are found in the pipelines. The maintenance cost is high and the safety risks during the maintenance process are also high. Summary of the invention

[0003] The present invention provides an online monitoring system for pipelines in a thermal power plant, which is used to solve at least one of the technical problems mentioned above.

[0004] In order to solve the above technical problems, the present invention discloses an online monitoring system for pipelines in a thermal power plant, comprising:

[0005] The data monitoring module is used to collect the operating data of the fuel delivery pipeline, steam pipeline and cooling water pipeline of the thermal power plant at each moment during the monitoring period;

[0006] A processing value determination module is used to obtain a processing value of the coal accumulation degree of the fuel delivery pipeline of the thermal power plant at each moment, a processing value of the leakage degree of the steam pipeline, and a processing value of the corrosion degree of the cooling water pipeline based on the operation data of the fuel delivery pipeline, steam pipeline, and cooling water pipeline of the thermal power plant at each moment;

[0007] The pipeline risk assessment module is used to construct a pipeline risk assessment matrix based on the coal accumulation degree processing value of the thermal power plant fuel transmission pipeline, the steam pipeline leakage degree processing value, the cooling water pipeline corrosion degree processing value and the trained thermal power plant fuel transmission pipeline performance assessment model, steam pipeline performance assessment model, and cooling water pipeline performance assessment model at each moment in the monitoring period, and obtain the thermal power plant fuel transmission pipeline risk assessment value, steam pipeline risk assessment value, and cooling water pipeline risk assessment value in the corresponding monitoring period;

[0008] The thermal power plant pipeline maintenance prediction module is used to construct a thermal power plant pipeline maintenance prediction matrix based on the risk assessment values ​​of the thermal power plant fuel transmission pipeline, steam pipeline risk assessment values ​​and cooling water pipeline risk assessment values ​​in several monitoring cycles, and predict the necessity degree value of the thermal power plant pipeline maintenance in the next monitoring cycle based on the thermal power plant pipeline maintenance prediction matrix, and provide overall pipeline maintenance prompts based on the necessity degree value of the thermal power plant pipeline maintenance in the next monitoring cycle.

[0009] Preferably, the data monitoring module includes:

[0010] A fuel delivery pipeline acquisition subunit, used for real-time acquisition of internal image data of the fuel delivery pipeline;

[0011] A steam pipe data acquisition subunit, used to collect the sound intensity of the steam pipe in real time based on an ultrasonic leak detector;

[0012] The cooling water pipeline collection subunit is used to collect the corrosion gas detection concentration around the cooling water pipeline in real time.

[0013] Preferably, the processing value determination module includes:

[0014] A unit for determining the degree of coal accumulation in a fuel delivery pipeline, for obtaining a degree of coal accumulation in the fuel delivery pipeline corresponding to each moment based on a plurality of images of the interior of the fuel delivery pipeline collected at each moment;

[0015] A steam pipeline leakage degree processing value determination unit, used to obtain a steam pipeline leakage degree processing value corresponding to each moment based on the detection value of the ultrasonic leakage detector at each moment;

[0016] The cooling water pipeline corrosion degree processing value determination unit is used to obtain the cooling water pipeline corrosion degree processing value corresponding to each moment based on the corrosive gas detection concentration around the cooling water pipeline at each moment.

[0017] Preferably, the unit for determining the processing value of the coal accumulation degree of the fuel delivery pipeline includes:

[0018] The middle pixel acquisition subunit is used to acquire the pixel value of each pixel point in the plurality of images of the interior of the fuel delivery pipeline collected at each moment, and calculate the pixel mean value of the adjacent pixel points corresponding to each pixel point of each image of the interior of the fuel delivery pipeline, and use it as the middle pixel of the corresponding pixel point of the image of the interior of the fuel delivery pipeline;

[0019] The pipeline background acquisition subunit is used to calculate the sum of the intermediate pixels of the corresponding pixels on the several images of the interior of the fuel delivery pipeline collected at each moment, and the quotient of the sum of the intermediate pixels of the corresponding pixels on the images of the interior of the fuel delivery pipeline and the total number of the images of the interior of the fuel delivery pipeline collected at each moment is used as the pipeline background pixel value of the pixel, and the image composed of the pipeline background pixel values ​​of each pixel is used as the pipeline background image;

[0020] The coal accumulation area locking subunit calculates the absolute value of the difference between the actual pixel value of each pixel point in each image inside the fuel delivery pipeline and its pipeline background pixel value. When the absolute value of the difference between the actual pixel value of the pixel point and its pipeline background pixel value is greater than the preset pixel difference, the pixel point is marked as a coal accumulation area pixel point, and the area formed by all coal accumulation area pixels is the coal accumulation area;

[0021] The fuel pipeline coal accumulation degree processing value calculation subunit is used to calculate the current moment fuel pipeline coal accumulation degree processing value based on the coal accumulation area:

[0022] Among them, U ij is the coal accumulation degree processing value of the fuel transmission pipeline at the jth moment of the ith monitoring period, α is the error compensation coefficient of the coal accumulation area, e is a natural number with a value of 2.71, S ij1 is the area of ​​the coal accumulation region obtained at the jth moment in the ith monitoring period based on a number of internal images of the fuel delivery pipeline collected at that moment, and S0 is the inner wall area of ​​the fuel delivery pipeline.

[0023] Preferably, based on the detection value of the ultrasonic leak detector at each moment, the corresponding steam pipeline leakage degree processing value at each moment is obtained: Among them, V ij is the processed value of the steam pipeline leakage degree at the jth moment in the ith monitoring cycle, β is the error compensation coefficient of the ultrasonic leak detector detection value, e is a natural number with a value of 2.71, P ij1 is the detection value of the ultrasonic leak detector at the jth moment in the ith monitoring cycle, and P0 is the benchmark detection value of the ultrasonic leak detector when the steam pipeline is not leaking.

[0024] Preferably, based on the detected concentration of corrosive gas around the cooling water pipeline at each moment, the processed value of the corrosion degree of the cooling water pipeline corresponding to each moment is obtained: Among them, W ij is the cooling water pipeline corrosion degree treatment value at the jth moment in the ith monitoring period, γ1 is the carbon monoxide concentration detection value error compensation coefficient, γ2 is the nitrogen oxide concentration detection value error compensation coefficient, γ3 is the hydrogen sulfide concentration detection value error compensation coefficient, e is a natural number, the value is 2.71, u ij1 is the carbon monoxide concentration detection value at the jth moment in the ith monitoring cycle, u0 is the carbon monoxide concentration benchmark detection value when the corrosion degree of the cooling water pipeline is within the normal range, and v ij1 is the nitrogen oxide concentration detection value at the jth moment in the ith monitoring cycle, v0 is the nitrogen oxide concentration benchmark detection value when the corrosion degree of the cooling water pipeline is within the normal range, and w ij1 is the hydrogen sulfide concentration detection value at the jth moment in the ith monitoring cycle, and w0 is the baseline detection value of the hydrogen sulfide concentration when the corrosion degree of the cooling water pipeline is within the normal range.

[0025] Preferably, the pipeline risk assessment module includes:

[0026] The performance evaluation value acquisition submodule is used to input the processed value of the coal accumulation degree of the fuel transmission pipeline of the thermal power plant, the processed value of the leakage degree of the steam pipeline, and the processed value of the corrosion degree of the cooling water pipeline at each moment in the monitoring period into the trained thermal power plant fuel transmission pipeline performance evaluation model, steam pipeline performance evaluation model, and cooling water pipeline performance evaluation model, respectively, to obtain the fuel transmission pipeline performance evaluation value, steam pipeline performance evaluation value, and cooling water pipeline performance evaluation value at each moment in the monitoring period;

[0027] The pipeline risk assessment matrix construction submodule is used to construct a pipeline risk assessment matrix based on the coal accumulation degree processing value of the fuel transmission pipeline of the thermal power plant at each moment in the monitoring period, the steam pipeline leakage degree processing value, the cooling water pipeline corrosion degree processing value and the fuel transmission pipeline performance evaluation value, steam pipeline performance evaluation value and cooling water pipeline performance evaluation value at each moment in the monitoring period;

[0028] The risk assessment value acquisition submodule is used to obtain the risk assessment value of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in the corresponding monitoring period based on the pipeline risk assessment matrix.

[0029] Preferably, a pipeline risk assessment matrix construction submodule is used to construct a pipeline risk assessment matrix:

[0030] in, is the pipeline risk assessment matrix for the i-th monitoring period, a i1 is the fuel pipeline performance evaluation value at the first moment of the ith monitoring cycle, a i2 is the fuel pipeline performance evaluation value at the second moment of the ith monitoring cycle, a in is the fuel pipeline performance evaluation value at the nth moment of the ith monitoring cycle, U i1 is the coal accumulation degree processing value of the fuel pipeline at the first moment of the i-th monitoring cycle, U i2 is the coal accumulation degree processing value of the fuel pipeline at the second moment of the i-th monitoring cycle, U in is the coal accumulation degree processing value of the fuel pipeline at the nth moment of the ith monitoring cycle, b i1 is the steam pipeline risk assessment value at the first moment of the i-th monitoring cycle, b i2 is the steam pipeline risk assessment value at the second moment of the ith monitoring cycle, b in is the steam pipeline risk assessment value at the nth moment of the ith monitoring cycle, V i1 is the steam pipeline leakage degree processing value at the first moment of the i-th monitoring cycle, V i2 is the steam pipeline leakage processing value at the second moment of the i-th monitoring cycle, V inis the steam pipeline leakage degree processing value at the nth moment in the ith monitoring cycle, c i1 is the cooling water pipeline performance evaluation value at the first moment of the i-th monitoring cycle, c i2 is the cooling water pipeline performance evaluation value at the second moment of the i-th monitoring cycle, c in is the cooling water pipeline performance evaluation value at the nth moment of the ith monitoring cycle, W i1 is the cooling water pipeline corrosion degree treatment value at the first moment of the i-th monitoring cycle, W i2 is the cooling water pipeline corrosion degree treatment value at the second moment of the i-th monitoring cycle, W in is the cooling water pipeline corrosion degree treatment value at the nth moment in the ith monitoring cycle;

[0031] The risk assessment value acquisition submodule obtains the risk assessment value of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in the corresponding monitoring period:

[0032] Delete all elements related to the performance evaluation value of the fuel transmission pipeline and the coal accumulation degree treatment value of the fuel transmission pipeline in the pipeline risk assessment matrix of the i-th monitoring period, obtain a new matrix one, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring period and the new matrix one, and use it as the risk assessment value of the fuel transmission pipeline of the thermal power plant in the i-th monitoring period;

[0033] Delete all elements related to the steam pipeline risk assessment value and the steam pipeline leakage degree processing value of the pipeline risk assessment matrix of the i-th monitoring cycle, obtain the new matrix 2, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring cycle and the new matrix 2, and use it as the steam pipeline risk assessment value of the i-th monitoring cycle;

[0034] Delete all elements involving cooling water pipeline performance evaluation values ​​and cooling water pipeline corrosion degree treatment values ​​in the pipeline risk assessment matrix of the i-th monitoring cycle, obtain a new matrix three, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring cycle and the new matrix three, and use it as the cooling water pipeline risk assessment value of the i-th monitoring cycle.

[0035] Preferably, the thermal power plant pipeline maintenance prediction module includes:

[0036] The necessity degree value calculation submodule of thermal power plant pipeline maintenance is used to calculate the necessity degree value of thermal power plant pipeline maintenance in the corresponding monitoring period based on the risk assessment value of the thermal power plant fuel transmission pipeline, the steam pipeline risk assessment value and the cooling water pipeline risk assessment value in each monitoring period:

[0037] Necessity value of pipeline maintenance in thermal power plants in the i-th monitoring cycle:

[0038] in, is the necessity value of pipeline maintenance in thermal power plants in the ith monitoring period, ln is the logarithm with base e, D i is the risk assessment value of the fuel pipeline of the thermal power plant in the i-th monitoring period, σ1 is the weight of the risk assessment value of the fuel pipeline of the thermal power plant in the i-th monitoring period, d0 is the benchmark risk assessment value of the fuel pipeline of the thermal power plant, E i is the steam pipeline risk assessment value of the ith monitoring period, σ2 is the weight of the steam pipeline risk assessment value of the ith monitoring period, e0 is the steam pipeline benchmark risk assessment value, F i is the risk assessment value of the cooling water pipeline in the ith monitoring period, σ3 is the weight of the risk assessment value of the cooling water pipeline in the ith monitoring period, and f0 is the baseline risk assessment value of the cooling water pipeline;

[0039] A thermal power plant pipeline maintenance prediction matrix construction submodule is used to construct a thermal power plant pipeline maintenance prediction matrix based on the necessity degree values ​​of thermal power plant pipeline maintenance in several monitoring cycles;

[0040] A thermal power plant pipeline maintenance necessity degree value prediction submodule is used to predict the thermal power plant pipeline maintenance necessity degree value in the next monitoring period based on the thermal power plant pipeline maintenance prediction matrix;

[0041] The overall pipeline maintenance prompt submodule is used to provide overall pipeline maintenance prompts based on the necessity level of pipeline maintenance in the thermal power plant in the next monitoring period.

[0042] Preferably, based on the necessity degree values ​​of pipeline maintenance in thermal power plants in several monitoring cycles, a thermal power plant pipeline maintenance prediction matrix is ​​constructed:

[0043] in, is the pipeline maintenance prediction matrix of the thermal power plant corresponding to the end of the i-th monitoring period, is the necessity level of pipeline maintenance in thermal power plants in the first monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the second monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the third monitoring cycle, is the necessity value of pipeline maintenance in thermal power plants in the i-1th monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the i-th monitoring period;

[0044] Necessity value of pipeline maintenance in thermal power plants in the i+1th monitoring cycle:

[0045] The mean of all matrix elements in any row except the first row in the thermal power plant pipeline maintenance prediction matrix corresponding to the end of the i-th monitoring cycle is obtained, and the value of the last column in the thermal power plant pipeline maintenance prediction matrix corresponding to the end of the i-th monitoring cycle is set to a value equal to the mean, thereby obtaining a new matrix, and the rank of the new matrix is ​​regarded as the degree of necessity of the thermal power plant pipeline maintenance in the i+1-th monitoring cycle.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention can monitor the operating status of the pipeline of a thermal power plant in real time and accurately, and predict pipeline risks through scientific methods, which is helpful to improve the safety and stability of the thermal power plant and reduce the possibility of accidents. At the same time, the automatic collection and prediction functions of the data monitoring module can reduce the burden of manual monitoring, improve work efficiency, reduce misjudgments and missed detections caused by human factors, and ensure the long-term safe and stable operation of the thermal power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 The present invention is a schematic diagram of an online monitoring system for pipelines in a thermal power plant. DETAILED DESCRIPTION

[0050] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0051] In addition, in the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions and technical features between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] The present invention provides the following embodiments

[0053] Example 1

[0054] The embodiment of the present invention provides an online monitoring system for pipelines in a thermal power plant, such as Figure 1 As shown, including:

[0055] The data monitoring module is used to collect the operating data of the fuel delivery pipeline, steam pipeline and cooling water pipeline of the thermal power plant at each moment during the monitoring period;

[0056] A processing value determination module is used to obtain a processing value of the coal accumulation degree of the fuel delivery pipeline of the thermal power plant at each moment, a processing value of the leakage degree of the steam pipeline, and a processing value of the corrosion degree of the cooling water pipeline based on the operation data of the fuel delivery pipeline, steam pipeline, and cooling water pipeline of the thermal power plant at each moment;

[0057] The pipeline risk assessment module is used to construct a pipeline risk assessment matrix based on the coal accumulation degree processing value of the thermal power plant fuel transmission pipeline, the steam pipeline leakage degree processing value, the cooling water pipeline corrosion degree processing value and the trained thermal power plant fuel transmission pipeline performance assessment model, steam pipeline performance assessment model, and cooling water pipeline performance assessment model at each moment in the monitoring period, and obtain the thermal power plant fuel transmission pipeline risk assessment value, steam pipeline risk assessment value, and cooling water pipeline risk assessment value in the corresponding monitoring period;

[0058] The thermal power plant pipeline maintenance prediction module is used to construct a thermal power plant pipeline maintenance prediction matrix based on the risk assessment values ​​of the thermal power plant fuel transmission pipeline, steam pipeline risk assessment values ​​and cooling water pipeline risk assessment values ​​in several monitoring cycles, and predict the necessity degree value of the thermal power plant pipeline maintenance in the next monitoring cycle based on the thermal power plant pipeline maintenance prediction matrix, and provide overall pipeline maintenance prompts based on the necessity degree value of the thermal power plant pipeline maintenance in the next monitoring cycle.

[0059] In this embodiment, the operation data of the fuel delivery pipeline, steam pipeline and cooling water pipeline of the thermal power plant include internal image data of the fuel delivery pipeline, sound intensity of the steam pipeline collected by the ultrasonic leak detector, and corrosion gas detection concentration around the cooling water pipeline.

[0060] In this embodiment, the performance evaluation model of the fuel pipeline of a thermal power plant is a model obtained by training a neural network model using a large number of coal accumulation degree processing values ​​of the fuel pipeline of the thermal power plant as training input samples and the fuel pipeline performance evaluation values ​​as output.

[0061] The steam pipeline performance evaluation model is a model obtained by training a neural network model using a large number of steam pipeline leakage degree processing values ​​as training input samples and steam pipeline performance evaluation values ​​as output quantities.

[0062] The cooling water pipeline performance evaluation model is a model obtained by training a neural network model using a large number of cooling water pipeline corrosion degree treatment values ​​as training input samples and cooling water pipeline performance evaluation values ​​as output.

[0063] The working principle and beneficial effects of the above technical solution are as follows: the present invention collects the operation data of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in real time through the data monitoring module, and calculates the operation status processing values ​​(such as the coal accumulation degree processing value, the leakage degree processing value and the corrosion degree processing value) of these pipelines at different time points through the processing value determination module, and then constructs the pipeline risk assessment matrix through the pipeline risk assessment module, calculates the risk assessment value of each pipeline in each monitoring cycle, and finally, predicts the necessity of future pipeline maintenance according to the risk assessment values ​​of multiple monitoring cycles through the pipeline maintenance prediction module of the thermal power plant, and issues corresponding maintenance prompts;

[0064] The present invention can monitor the operating status of the pipeline of a thermal power plant in real time and accurately, and predict pipeline risks through scientific methods, which is helpful to improve the safety and stability of the thermal power plant and reduce the possibility of accidents. At the same time, the automatic collection and prediction functions of the data monitoring module can reduce the burden of manual monitoring, improve work efficiency, reduce misjudgments and missed detections caused by human factors, and ensure the long-term safe and stable operation of the thermal power plant.

[0065] Example 2

[0066] Based on Example 1, the data monitoring module includes:

[0067] A fuel delivery pipeline acquisition subunit, used for real-time acquisition of internal image data of the fuel delivery pipeline;

[0068] A steam pipe data acquisition subunit, used to collect the sound intensity of the steam pipe in real time based on an ultrasonic leak detector;

[0069] The cooling water pipeline collection subunit is used to collect the corrosion gas detection concentration around the cooling water pipeline in real time.

[0070] The working principle and beneficial effects of the above technical solution are: by using a variety of collection methods, comprehensive and accurate monitoring of the operating status of the thermal power plant pipeline is ensured, providing a reliable data basis for the subsequent determination of processing values.

[0071] Example 3

[0072] Based on Example 2, the processing value determination module includes:

[0073] A unit for determining the degree of coal accumulation in a fuel delivery pipeline, for obtaining a degree of coal accumulation in the fuel delivery pipeline corresponding to each moment based on a plurality of images of the interior of the fuel delivery pipeline collected at each moment;

[0074] A steam pipeline leakage degree processing value determination unit, used to obtain a steam pipeline leakage degree processing value corresponding to each moment based on the detection value of the ultrasonic leakage detector at each moment;

[0075] The cooling water pipeline corrosion degree processing value determination unit is used to obtain the cooling water pipeline corrosion degree processing value corresponding to each moment based on the corrosive gas detection concentration around the cooling water pipeline at each moment.

[0076] The working principle and beneficial effects of the above technical solution are as follows: through the calculation process of the coal accumulation degree processing value of the fuel transmission pipeline, the steam pipeline leakage degree processing value and the cooling water pipeline corrosion degree processing value, the operating status of each pipeline can be more accurately quantitatively evaluated.

[0077] Example 4

[0078] Based on Example 3, the fuel delivery pipeline coal accumulation degree processing value determination unit includes:

[0079] The middle pixel acquisition subunit is used to acquire the pixel value of each pixel point in the plurality of images of the interior of the fuel delivery pipeline collected at each moment, and calculate the pixel mean value of the adjacent pixel points corresponding to each pixel point of each image of the interior of the fuel delivery pipeline, and use it as the middle pixel of the corresponding pixel point of the image of the interior of the fuel delivery pipeline;

[0080] The pipeline background acquisition subunit is used to calculate the sum of the intermediate pixels of the corresponding pixels on the several images of the interior of the fuel delivery pipeline collected at each moment, and the quotient of the sum of the intermediate pixels of the corresponding pixels on the images of the interior of the fuel delivery pipeline and the total number of the images of the interior of the fuel delivery pipeline collected at each moment is used as the pipeline background pixel value of the pixel, and the image composed of the pipeline background pixel values ​​of each pixel is used as the pipeline background image;

[0081] The coal accumulation area locking subunit calculates the absolute value of the difference between the actual pixel value of each pixel point in each image inside the fuel delivery pipeline and its pipeline background pixel value. When the absolute value of the difference between the actual pixel value of the pixel point and its pipeline background pixel value is greater than the preset pixel difference, the pixel point is marked as a coal accumulation area pixel point, and the area formed by all coal accumulation area pixels is the coal accumulation area;

[0082] The fuel pipeline coal accumulation degree processing value calculation subunit is used to calculate the current moment fuel pipeline coal accumulation degree processing value based on the coal accumulation area:

[0083] Among them, U ijis the coal accumulation degree processing value of the fuel transmission pipeline at the jth moment of the ith monitoring period, α is the error compensation coefficient of the coal accumulation area, e is a natural number with a value of 2.71, S ij1 is the area of ​​the coal accumulation region obtained at the jth moment in the ith monitoring period based on a number of internal images of the fuel delivery pipeline collected at that moment, and S0 is the inner wall area of ​​the fuel delivery pipeline.

[0084] The working principle and beneficial effects of the above technical solution: the coal accumulation degree processing value determination unit of the fuel transmission pipeline can accurately identify the coal accumulation area, and provide precise pipeline maintenance guidance for thermal power plants by quantifying the coal accumulation degree, which helps to prevent safety accidents caused by coal accumulation. At the same time, this image analysis method has high accuracy and reliability, which can significantly improve the accuracy of pipeline monitoring and reduce the possibility of misjudgment.

[0085] Example 5

[0086] On the basis of Example 3, based on the detection value of the ultrasonic leakage detector at each moment, the steam pipeline leakage degree processing value corresponding to each moment is obtained: Among them, V ij is the processed value of the steam pipeline leakage degree at the jth moment in the ith monitoring cycle, β is the error compensation coefficient of the ultrasonic leak detector detection value, e is a natural number with a value of 2.71, P ij1 is the detection value of the ultrasonic leak detector at the jth moment in the ith monitoring cycle, and P0 is the benchmark detection value of the ultrasonic leak detector when the steam pipeline is not leaking.

[0087] The working principle and beneficial effects of the above technical solution are: through ultrasonic leakage detection technology, the leakage condition of the steam pipeline can be detected quickly and accurately, providing accurate and reliable data support for subsequent calculations.

[0088] Example 6

[0089] On the basis of Example 3, based on the detected concentration of corrosive gas around the cooling water pipeline at each moment, the processed value of the corrosion degree of the cooling water pipeline corresponding to each moment is obtained: (3); where W ij is the cooling water pipeline corrosion degree treatment value at the jth moment in the ith monitoring period, γ1 is the carbon monoxide concentration detection value error compensation coefficient, γ2 is the nitrogen oxide concentration detection value error compensation coefficient, γ3 is the hydrogen sulfide concentration detection value error compensation coefficient, e is a natural number, the value is 2.71, u ij1 is the carbon monoxide concentration detection value at the jth moment in the ith monitoring cycle, u0 is the carbon monoxide concentration benchmark detection value when the corrosion degree of the cooling water pipeline is within the normal range, and v ij1is the nitrogen oxide concentration detection value at the jth moment in the ith monitoring cycle, v0 is the nitrogen oxide concentration benchmark detection value when the corrosion degree of the cooling water pipeline is within the normal range, and w ij1 is the hydrogen sulfide concentration detection value at the jth moment in the ith monitoring cycle, and w0 is the baseline detection value of the hydrogen sulfide concentration when the corrosion degree of the cooling water pipeline is within the normal range.

[0090] The working principle and beneficial effects of the above technical solution are: by integrating the detection data of various corrosive gases, the corrosion degree of the cooling water pipeline can be evaluated more comprehensively and accurately, providing accurate and reliable data support for subsequent calculations.

[0091] Example 7

[0092] Based on Example 1, the pipeline risk assessment module includes:

[0093] The performance evaluation value acquisition submodule is used to input the processed value of the coal accumulation degree of the fuel transmission pipeline of the thermal power plant, the processed value of the leakage degree of the steam pipeline, and the processed value of the corrosion degree of the cooling water pipeline at each moment in the monitoring period into the trained thermal power plant fuel transmission pipeline performance evaluation model, steam pipeline performance evaluation model, and cooling water pipeline performance evaluation model, respectively, to obtain the fuel transmission pipeline performance evaluation value, steam pipeline performance evaluation value, and cooling water pipeline performance evaluation value at each moment in the monitoring period;

[0094] The pipeline risk assessment matrix construction submodule is used to construct a pipeline risk assessment matrix based on the coal accumulation degree processing value of the fuel transmission pipeline of the thermal power plant at each moment in the monitoring period, the steam pipeline leakage degree processing value, the cooling water pipeline corrosion degree processing value and the fuel transmission pipeline performance evaluation value, steam pipeline performance evaluation value and cooling water pipeline performance evaluation value at each moment in the monitoring period;

[0095] The risk assessment value acquisition submodule is used to obtain the risk assessment value of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in the corresponding monitoring period based on the pipeline risk assessment matrix.

[0096] The working principle and beneficial effects of the above technical solution are as follows: by establishing a pipeline risk assessment matrix, the overall risk of the pipeline can be comprehensively and systematically assessed, providing a scientific basis for formulating maintenance plans. This not only improves the accuracy of risk assessment, but also helps managers understand the risk distribution of the pipeline more clearly, take preventive and maintenance measures in a timely manner, reduce potential safety hazards, and improve the operating efficiency and safety of thermal power plants.

[0097] Example 8

[0098] Based on Example 7, a pipeline risk assessment matrix construction submodule is used to construct a pipeline risk assessment matrix:

[0099] in, is the pipeline risk assessment matrix for the i-th monitoring period, a i1 is the fuel pipeline performance evaluation value at the first moment of the ith monitoring cycle, a i2 is the fuel pipeline performance evaluation value at the second moment of the ith monitoring cycle, a in is the fuel pipeline performance evaluation value at the nth moment of the ith monitoring cycle, U i1 is the coal accumulation degree processing value of the fuel pipeline at the first moment of the i-th monitoring cycle, U i2 is the coal accumulation degree processing value of the fuel pipeline at the second moment of the i-th monitoring cycle, U in is the coal accumulation degree processing value of the fuel pipeline at the nth moment of the ith monitoring cycle, b i1 is the steam pipeline risk assessment value at the first moment of the i-th monitoring cycle, b i2 is the steam pipeline risk assessment value at the second moment of the ith monitoring cycle, b in is the steam pipeline risk assessment value at the nth moment of the ith monitoring cycle, V i1 is the steam pipeline leakage degree processing value at the first moment of the i-th monitoring cycle, V i2 is the steam pipeline leakage processing value at the second moment of the i-th monitoring cycle, V in is the steam pipeline leakage degree processing value at the nth moment in the ith monitoring cycle, c i1 is the cooling water pipeline performance evaluation value at the first moment of the i-th monitoring cycle, c i2 is the cooling water pipeline performance evaluation value at the second moment of the i-th monitoring cycle, c in is the cooling water pipeline performance evaluation value at the nth moment of the ith monitoring cycle, W i1 is the cooling water pipeline corrosion degree treatment value at the first moment of the i-th monitoring cycle, W i2 is the cooling water pipeline corrosion degree treatment value at the second moment of the i-th monitoring cycle, W in is the cooling water pipeline corrosion degree treatment value at the nth moment in the ith monitoring cycle;

[0100] The risk assessment value acquisition submodule obtains the risk assessment value of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in the corresponding monitoring period:

[0101] Delete all elements related to the performance evaluation value of the fuel transmission pipeline and the coal accumulation degree treatment value of the fuel transmission pipeline in the pipeline risk assessment matrix of the i-th monitoring period, obtain a new matrix one, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring period and the new matrix one, and use it as the risk assessment value of the fuel transmission pipeline of the thermal power plant in the i-th monitoring period;

[0102] Delete all elements related to the steam pipeline risk assessment value and the steam pipeline leakage degree processing value of the pipeline risk assessment matrix of the i-th monitoring cycle, obtain the new matrix 2, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring cycle and the new matrix 2, and use it as the steam pipeline risk assessment value of the i-th monitoring cycle;

[0103] Delete all elements involving cooling water pipeline performance evaluation values ​​and cooling water pipeline corrosion degree treatment values ​​in the pipeline risk assessment matrix of the i-th monitoring cycle, obtain a new matrix three, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring cycle and the new matrix three, and use it as the cooling water pipeline risk assessment value of the i-th monitoring cycle.

[0104] The working principle and beneficial effects of the above technical solution are as follows: the establishment of the pipeline risk assessment matrix makes the assessment results more intuitive and easy to understand, improves the efficiency of the assessment, and can also help managers identify and respond to pipeline risks more quickly, reduce errors in manual calculations, and improve the accuracy and reliability of the assessment.

[0105] Example 9

[0106] Based on Example 1, the pipeline maintenance prediction module of a thermal power plant includes:

[0107] The necessity degree value calculation submodule of thermal power plant pipeline maintenance is used to calculate the necessity degree value of thermal power plant pipeline maintenance in the corresponding monitoring period based on the risk assessment value of the thermal power plant fuel transmission pipeline, the steam pipeline risk assessment value and the cooling water pipeline risk assessment value in each monitoring period:

[0108] Necessity value of pipeline maintenance in thermal power plants in the i-th monitoring cycle:

[0109] in, is the necessity value of pipeline maintenance in thermal power plants in the ith monitoring period, ln is the logarithm with base e, D i is the risk assessment value of the fuel pipeline of the thermal power plant in the i-th monitoring period, σ1 is the weight of the risk assessment value of the fuel pipeline of the thermal power plant in the i-th monitoring period, d0 is the benchmark risk assessment value of the fuel pipeline of the thermal power plant, E i is the steam pipeline risk assessment value of the ith monitoring period, σ2 is the weight of the steam pipeline risk assessment value of the ith monitoring period, e0 is the steam pipeline benchmark risk assessment value, F iis the risk assessment value of the cooling water pipeline in the ith monitoring period, σ3 is the weight of the risk assessment value of the cooling water pipeline in the ith monitoring period, and f0 is the baseline risk assessment value of the cooling water pipeline;

[0110] A thermal power plant pipeline maintenance prediction matrix construction submodule is used to construct a thermal power plant pipeline maintenance prediction matrix based on the necessity degree values ​​of thermal power plant pipeline maintenance in several monitoring cycles;

[0111] A thermal power plant pipeline maintenance necessity degree value prediction submodule is used to predict the thermal power plant pipeline maintenance necessity degree value in the next monitoring period based on the thermal power plant pipeline maintenance prediction matrix;

[0112] The overall pipeline maintenance prompt submodule is used to provide overall pipeline maintenance prompts based on the necessity level of pipeline maintenance in the thermal power plant in the next monitoring period.

[0113] The working principle and beneficial effects of the above technical solution are: by scientifically predicting the necessity of pipeline maintenance, preventive measures can be taken before accidents occur, reducing the possibility of sudden failures, improving the operating efficiency and safety of thermal power plants, and improving the scientificity and rationality of maintenance plans.

[0114] Example 10

[0115] On the basis of Example 9, based on the necessity degree values ​​of pipeline maintenance in thermal power plants in several monitoring periods, a thermal power plant pipeline maintenance prediction matrix is ​​constructed:

[0116] in, is the pipeline maintenance prediction matrix of the thermal power plant corresponding to the end of the i-th monitoring period, is the necessity level of pipeline maintenance in thermal power plants in the first monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the second monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the third monitoring cycle, is the necessity value of pipeline maintenance in thermal power plants in the i-1th monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the i-th monitoring period;

[0117] Necessity value of pipeline maintenance in thermal power plants in the i+1th monitoring cycle:

[0118] The mean of all matrix elements in any row except the first row in the thermal power plant pipeline maintenance prediction matrix corresponding to the end of the i-th monitoring cycle is obtained, and the value of the last column in the thermal power plant pipeline maintenance prediction matrix corresponding to the end of the i-th monitoring cycle is set to a value equal to the mean, thereby obtaining a new matrix, and the rank of the new matrix is ​​regarded as the degree of necessity of the thermal power plant pipeline maintenance in the i+1-th monitoring cycle.

[0119] The working principle and beneficial effects of the above technical solution are as follows: when the necessity degree value of the thermal power plant pipeline maintenance in the i+1th monitoring cycle is greater than the preset necessity degree value of the maintenance, an overall pipeline maintenance prompt is performed, and the level of the overall pipeline maintenance prompt is determined according to the difference between the necessity degree value of the thermal power plant pipeline maintenance in the i+1th monitoring cycle and the preset necessity degree value of the maintenance, thereby achieving scientific maintenance of the pipeline under reasonable allocation of resources.

[0120] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An online monitoring system for pipelines in thermal power plants, characterized by: include: The data monitoring module is used to collect the operating data of the fuel delivery pipeline, steam pipeline and cooling water pipeline of the thermal power plant at each moment during the monitoring period; A processing value determination module is used to obtain a processing value of the coal accumulation degree of the fuel delivery pipeline of the thermal power plant at each moment, a processing value of the leakage degree of the steam pipeline, and a processing value of the corrosion degree of the cooling water pipeline based on the operation data of the fuel delivery pipeline, steam pipeline, and cooling water pipeline of the thermal power plant at each moment; The pipeline risk assessment module is used to construct a pipeline risk assessment matrix based on the coal accumulation degree processing value of the thermal power plant fuel transmission pipeline, the steam pipeline leakage degree processing value, the cooling water pipeline corrosion degree processing value and the trained thermal power plant fuel transmission pipeline performance assessment model, steam pipeline performance assessment model, and cooling water pipeline performance assessment model at each moment in the monitoring period, and obtain the thermal power plant fuel transmission pipeline risk assessment value, steam pipeline risk assessment value, and cooling water pipeline risk assessment value in the corresponding monitoring period; The thermal power plant pipeline maintenance prediction module is used to construct a thermal power plant pipeline maintenance prediction matrix based on the risk assessment values ​​of the thermal power plant fuel transmission pipeline, steam pipeline risk assessment values ​​and cooling water pipeline risk assessment values ​​in several monitoring cycles, and predict the necessity degree value of the thermal power plant pipeline maintenance in the next monitoring cycle based on the thermal power plant pipeline maintenance prediction matrix, and provide overall pipeline maintenance prompts based on the necessity degree value of the thermal power plant pipeline maintenance in the next monitoring cycle.

2. The on-line monitoring system for pipelines in thermal power plants according to claim 1 is characterized by: The data monitoring module includes: A fuel delivery pipeline acquisition subunit, used for real-time acquisition of internal image data of the fuel delivery pipeline; A steam pipe data acquisition subunit, used to collect the sound intensity of the steam pipe in real time based on an ultrasonic leak detector; The cooling water pipeline collection subunit is used to collect the corrosion gas detection concentration around the cooling water pipeline in real time.

3. The on-line monitoring system for pipelines in thermal power plants according to claim 2 is characterized by: The processing value determination module includes: A unit for determining the degree of coal accumulation in a fuel delivery pipeline, for obtaining a degree of coal accumulation in the fuel delivery pipeline corresponding to each moment based on a plurality of images of the interior of the fuel delivery pipeline collected at each moment; A steam pipeline leakage degree processing value determination unit, used to obtain a steam pipeline leakage degree processing value corresponding to each moment based on the detection value of the ultrasonic leakage detector at each moment; The cooling water pipeline corrosion degree processing value determination unit is used to obtain the cooling water pipeline corrosion degree processing value corresponding to each moment based on the corrosive gas detection concentration around the cooling water pipeline at each moment.

4. The on-line monitoring system for pipelines in thermal power plants according to claim 3 is characterized by: The unit for determining the coal accumulation degree processing value of the fuel transmission pipeline includes: The middle pixel acquisition subunit is used to acquire the pixel value of each pixel point in the plurality of images of the interior of the fuel delivery pipeline collected at each moment, and calculate the pixel mean value of the adjacent pixel points corresponding to each pixel point of each image of the interior of the fuel delivery pipeline, and use it as the middle pixel of the corresponding pixel point of the image of the interior of the fuel delivery pipeline; The pipeline background acquisition subunit is used to calculate the sum of the intermediate pixels of the corresponding pixels on the several images of the interior of the fuel delivery pipeline collected at each moment, and the quotient of the sum of the intermediate pixels of the corresponding pixels on the images of the interior of the fuel delivery pipeline and the total number of the images of the interior of the fuel delivery pipeline collected at each moment is used as the pipeline background pixel value of the pixel, and the image composed of the pipeline background pixel values ​​of each pixel is used as the pipeline background image; The coal accumulation area locking subunit calculates the absolute value of the difference between the actual pixel value of each pixel point in each image inside the fuel delivery pipeline and its pipeline background pixel value. When the absolute value of the difference between the actual pixel value of the pixel point and its pipeline background pixel value is greater than the preset pixel difference, the pixel point is marked as a coal accumulation area pixel point, and the area formed by all coal accumulation area pixels is the coal accumulation area; The fuel pipeline coal accumulation degree processing value calculation subunit is used to calculate the current moment fuel pipeline coal accumulation degree processing value based on the coal accumulation area: Among them, U ij is the coal accumulation degree processing value of the fuel transmission pipeline at the jth moment of the ith monitoring period, α is the error compensation coefficient of the coal accumulation area, e is a natural number with a value of 2.71, S ij1 is the area of ​​the coal accumulation region obtained at the jth moment in the ith monitoring period based on a number of internal images of the fuel delivery pipeline collected at that moment, and S0 is the inner wall area of ​​the fuel delivery pipeline.

5. The on-line monitoring system for pipelines in thermal power plants according to claim 3 is characterized by: Based on the detection value of the ultrasonic leak detector at each moment, obtain the corresponding steam pipeline leakage degree processing value at each moment: Among them, V ij is the processed value of the steam pipeline leakage degree at the jth moment in the ith monitoring cycle, β is the error compensation coefficient of the ultrasonic leak detector detection value, e is a natural number with a value of 2.71, P ij1 is the detection value of the ultrasonic leak detector at the jth moment in the ith monitoring cycle, and P0 is the benchmark detection value of the ultrasonic leak detector when the steam pipeline is not leaking.

6. The on-line monitoring system for pipelines in thermal power plants according to claim 3 is characterized by: Based on the corrosion gas detection concentration around the cooling water pipeline at each moment, obtain the corresponding cooling water pipeline corrosion degree processing value at each moment: Among them, W ij is the cooling water pipeline corrosion degree treatment value at the jth moment in the ith monitoring period, γ1 is the carbon monoxide concentration detection value error compensation coefficient, γ2 is the nitrogen oxide concentration detection value error compensation coefficient, γ3 is the hydrogen sulfide concentration detection value error compensation coefficient, e is a natural number, the value is 2.71, u ij1 is the carbon monoxide concentration detection value at the jth moment in the ith monitoring cycle, u0 is the carbon monoxide concentration benchmark detection value when the corrosion degree of the cooling water pipeline is within the normal range, and v ij1 is the nitrogen oxide concentration detection value at the jth moment in the ith monitoring cycle, v0 is the nitrogen oxide concentration benchmark detection value when the corrosion degree of the cooling water pipeline is within the normal range, and w ij1 is the hydrogen sulfide concentration detection value at the jth moment in the ith monitoring cycle, and w0 is the baseline detection value of the hydrogen sulfide concentration when the corrosion degree of the cooling water pipeline is within the normal range.

7. The on-line monitoring system for pipelines in thermal power plants according to claim 1, characterized in that: Pipeline risk assessment modules include: The performance evaluation value acquisition submodule is used to input the processed value of the coal accumulation degree of the fuel transmission pipeline of the thermal power plant, the processed value of the leakage degree of the steam pipeline, and the processed value of the corrosion degree of the cooling water pipeline at each moment in the monitoring period into the trained thermal power plant fuel transmission pipeline performance evaluation model, steam pipeline performance evaluation model, and cooling water pipeline performance evaluation model, respectively, to obtain the fuel transmission pipeline performance evaluation value, steam pipeline performance evaluation value, and cooling water pipeline performance evaluation value at each moment in the monitoring period; The pipeline risk assessment matrix construction submodule is used to construct a pipeline risk assessment matrix based on the coal accumulation degree processing value of the fuel transmission pipeline of the thermal power plant at each moment in the monitoring period, the steam pipeline leakage degree processing value, the cooling water pipeline corrosion degree processing value and the fuel transmission pipeline performance evaluation value, steam pipeline performance evaluation value and cooling water pipeline performance evaluation value at each moment in the monitoring period; The risk assessment value acquisition submodule is used to obtain the risk assessment value of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in the corresponding monitoring period based on the pipeline risk assessment matrix.

8. The on-line monitoring system for pipelines in thermal power plants according to claim 7, characterized in that: Pipeline risk assessment matrix construction submodule, build pipeline risk assessment matrix: in, is the pipeline risk assessment matrix for the i-th monitoring period, a i1 is the fuel pipeline performance evaluation value at the first moment of the ith monitoring cycle, a i2 is the fuel pipeline performance evaluation value at the second moment of the ith monitoring cycle, a in is the fuel pipeline performance evaluation value at the nth moment of the ith monitoring cycle, U i1 is the coal accumulation degree processing value of the fuel pipeline at the first moment of the i-th monitoring cycle, U i2 is the coal accumulation degree processing value of the fuel pipeline at the second moment of the i-th monitoring cycle, U in is the coal accumulation degree processing value of the fuel pipeline at the nth moment of the ith monitoring cycle, b i1 is the steam pipeline risk assessment value at the first moment of the i-th monitoring cycle, b i2 is the steam pipeline risk assessment value at the second moment of the ith monitoring cycle, b in is the steam pipeline risk assessment value at the nth moment of the ith monitoring cycle, V i1 is the steam pipeline leakage degree processing value at the first moment of the i-th monitoring cycle, V i2 is the steam pipeline leakage processing value at the second moment of the i-th monitoring cycle, V in is the steam pipeline leakage degree processing value at the nth moment in the ith monitoring cycle, c i1 is the cooling water pipeline performance evaluation value at the first moment of the i-th monitoring cycle, c i2 is the cooling water pipeline performance evaluation value at the second moment of the ith monitoring cycle, c in is the cooling water pipeline performance evaluation value at the nth moment of the ith monitoring cycle, W i1 is the cooling water pipeline corrosion degree treatment value at the first moment of the i-th monitoring cycle, W i2 is the cooling water pipeline corrosion degree treatment value at the second moment of the i-th monitoring cycle, W in is the cooling water pipeline corrosion degree treatment value at the nth moment in the ith monitoring cycle; The risk assessment value acquisition submodule obtains the risk assessment value of the fuel transmission pipeline, steam pipeline and cooling water pipeline of the thermal power plant in the corresponding monitoring period: Delete all elements related to the performance evaluation value of the fuel transmission pipeline and the coal accumulation degree treatment value of the fuel transmission pipeline in the pipeline risk assessment matrix of the i-th monitoring period, obtain a new matrix one, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring period and the new matrix one, and use it as the risk assessment value of the fuel transmission pipeline of the thermal power plant in the i-th monitoring period; Delete all elements related to the steam pipeline risk assessment value and the steam pipeline leakage degree processing value of the pipeline risk assessment matrix of the i-th monitoring cycle, obtain the new matrix 2, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring cycle and the new matrix 2, and use it as the steam pipeline risk assessment value of the i-th monitoring cycle; Delete all elements involving cooling water pipeline performance evaluation values ​​and cooling water pipeline corrosion degree treatment values ​​in the pipeline risk assessment matrix of the i-th monitoring cycle, obtain a new matrix three, and calculate the difference between the rank of the pipeline risk assessment matrix of the i-th monitoring cycle and the new matrix three, and use it as the cooling water pipeline risk assessment value of the i-th monitoring cycle.

9. The on-line monitoring system for pipelines in thermal power plants according to claim 1, characterized in that: The pipeline maintenance prediction module of thermal power plants includes: The necessity degree value calculation submodule of thermal power plant pipeline maintenance is used to calculate the necessity degree value of thermal power plant pipeline maintenance in the corresponding monitoring period based on the risk assessment value of the thermal power plant fuel transmission pipeline, the steam pipeline risk assessment value and the cooling water pipeline risk assessment value in each monitoring period: Necessity value of pipeline maintenance in thermal power plants in the i-th monitoring cycle: in, is the necessity value of pipeline maintenance in thermal power plants in the ith monitoring period, ln is the logarithm with base e, D i is the risk assessment value of the fuel pipeline of the thermal power plant in the i-th monitoring period, σ1 is the weight of the risk assessment value of the fuel pipeline of the thermal power plant in the i-th monitoring period, d0 is the benchmark risk assessment value of the fuel pipeline of the thermal power plant, E i is the steam pipeline risk assessment value of the ith monitoring period, σ2 is the weight of the steam pipeline risk assessment value of the ith monitoring period, e0 is the steam pipeline benchmark risk assessment value, F i is the risk assessment value of the cooling water pipeline in the ith monitoring period, σ3 is the weight of the risk assessment value of the cooling water pipeline in the ith monitoring period, and f0 is the baseline risk assessment value of the cooling water pipeline; A thermal power plant pipeline maintenance prediction matrix construction submodule is used to construct a thermal power plant pipeline maintenance prediction matrix based on the necessity degree values ​​of thermal power plant pipeline maintenance in several monitoring cycles; A thermal power plant pipeline maintenance necessity degree value prediction submodule is used to predict the thermal power plant pipeline maintenance necessity degree value in the next monitoring period based on the thermal power plant pipeline maintenance prediction matrix; The overall pipeline maintenance prompt submodule is used to provide overall pipeline maintenance prompts based on the necessity level of pipeline maintenance in the thermal power plant in the next monitoring period.

10. The on-line monitoring system for pipelines in thermal power plants according to claim 9, characterized in that: Based on the necessity degree values ​​of thermal power plant pipeline maintenance in several monitoring cycles, a thermal power plant pipeline maintenance prediction matrix is ​​constructed: in, is the pipeline maintenance prediction matrix of the thermal power plant corresponding to the end of the i-th monitoring period, is the necessity level of pipeline maintenance in thermal power plants in the first monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the second monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the third monitoring cycle, is the necessity value of pipeline maintenance in thermal power plants in the i-1th monitoring cycle, is the necessity level of pipeline maintenance in thermal power plants in the i-th monitoring period; Necessity value of pipeline maintenance in thermal power plants in the i+1th monitoring cycle: The mean of all matrix elements in any row except the first row in the thermal power plant pipeline maintenance prediction matrix corresponding to the end of the i-th monitoring cycle is obtained, and the value of the last column in the thermal power plant pipeline maintenance prediction matrix corresponding to the end of the i-th monitoring cycle is set to a value equal to the mean, thereby obtaining a new matrix, and the rank of the new matrix is ​​regarded as the degree of necessity of the thermal power plant pipeline maintenance in the i+1-th monitoring cycle.

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