Trend inspection method based on thermal power plant protection measuring point data acquisition python programming

Abstract

The invention discloses a trend routing inspection method based on thermal power plant protection measuring point data acquisition python programming, and relates to the technical field of thermal power plant trend routing inspection, and the method comprises the steps: collecting thermal power plant protection measuring point data in real time, preprocessing the measuring point data, employing a Python programming environment, carrying out the Pearson correlation coefficient calculation of the preprocessed data, and obtaining the thermal power plant trend routing inspection result. Optimizing calculation of the Pearson's correlation coefficient by using a quantum calculation technology to obtain the correlation level between the measuring points; a correlation judgment threshold is set, when correlation abnormity is monitored, abnormity early warning is carried out, a monitoring result is output to a user interface, and a user carries out monitoring, equipment maintenance and equipment parameter adjustment on the thermal power plant according to the output result. According to the method, the problems of low intelligent level and high equipment maintenance cost of traditional thermal power plant monitoring can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of trend inspection of thermal power plants, and in particular to a trend inspection method based on Python programming for data collection of protection measuring points of thermal power plants. Background Art

[0002] In today's power system, the safe and stable operation of thermal power plants is crucial, and real-time monitoring and trend inspections are key technologies for achieving this goal. Data-driven monitoring methods are increasingly being used in thermal power plants. Traditional trend inspection methods rely primarily on manual experience, periodically checking data from key protection points to identify potential problems. This approach has limitations in data processing speed, accuracy, and real-time performance. The Python programming language, due to its simplicity, readability, and powerful functionality, has been widely used in data processing and analysis. However, traditional technologies use relatively simple preprocessing methods when processing large amounts of data from measurement points, making it difficult to effectively extract useful information from the data. Methods for calculating correlation levels between measurement points are relatively simple and lack efficiency and accuracy. In particular, when calculating the Pearson correlation coefficient, no technology has yet been developed that can be optimized in conjunction with quantum computing. Existing technologies often rely too heavily on experience and lack scientific evidence when setting correlation thresholds, resulting in inaccurate anomaly warnings. In response to these problems, our invention proposes a trend inspection method based on Python programming for data collection of thermal power plant protection measurement points. By collecting data from thermal power plant protection measurement points in real time and preprocessing it, and then using quantum computing technology to optimize the calculation of the Pearson correlation coefficient, the real-time, accuracy and effectiveness of trend inspections are improved. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a trend inspection method based on Python programming for data collection of thermal power plant protection measurement points, which can solve the problems of low intelligence level and high equipment maintenance cost of traditional thermal power plant monitoring.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a trend inspection method based on Python programming for data collection of thermal power plant protection measurement points, comprising:

[0006] Data from thermal power plant protection measurement points is collected in real time and preprocessed. The Pearson correlation coefficient of the preprocessed data is calculated using the Python programming environment. Quantum computing technology is used to optimize the calculation of the Pearson correlation coefficient to obtain the correlation level between measurement points. A correlation judgment threshold is set, and when a correlation anomaly is detected, an anomaly warning is issued. The monitoring results are output to the user interface, and the user can monitor the thermal power plant, perform equipment maintenance, and adjust parameters based on the output results.

[0007] As a preferred solution of the trend inspection method based on Python programming for data collection of thermal power plant protection measurement points described in the present invention, wherein: the data collected from the thermal power plant protection measurement points includes real-time collection of data from the thermal power plant temperature measurement points, pressure measurement points, flow measurement points, liquid level measurement points, vibration measurement points, flue gas composition measurement points, electrical parameter measurement points, air volume measurement points, and ash carbon content measurement points;

[0008] The temperature measurement points include monitoring of the turbine inlet steam temperature, turbine exhaust temperature, boiler wall temperature, boiler heating surface temperature and lubricating oil temperature; the pressure measurement points include monitoring of boiler steam pressure, turbine inlet steam pressure and feed water pressure; the flow measurement points include monitoring of feed water flow, steam flow and fuel flow; the liquid level measurement points include monitoring of boiler drum water level and turbine condensate level; the vibration measurement points include monitoring of turbine shaft vibration and generator bearing vibration; the flue gas composition measurement points include monitoring of flue gas oxygen content and flue gas harmful components; the electrical parameter measurement points include monitoring of generator and transformer current, voltage and power factor; the air volume measurement points include monitoring of supply air volume and induced air volume.

[0009] As a preferred solution of the trend inspection method based on Python programming for thermal power plant protection measurement point data acquisition according to the present invention, the preprocessing includes cleaning, denoising, standardization and normalization of the measurement point data;

[0010] The cleaning includes removing data irrelevant to the measurement points and filling the missing data with the mean or median; the denoising includes removing outliers with the Z-score algorithm. The algorithm formula is as follows:

[0011]

[0012] Where μ is the mean, σ is the standard value, x is the current input data, and z is the denoised data. When |z|>3, the data is considered abnormal and replaced with normal data. When 0<|z|≤3, the data is considered normal.

[0013] The standardization includes scaling the data between [0, 1] using the minimum-maximum standardization, as follows:

[0014]

[0015] Among them, x max is the maximum value of the data, x min is the minimum value of the data, x is the current input data, x norm The data are standardized.

[0016] As a preferred solution of the trend inspection method based on Python programming for data collection of thermal power plant protection measurement points described in the present invention, the quantum computing technology includes converting the processed data into a quantum bit superposition state, using quantum parallelism to simultaneously calculate the covariance of the data points, and using quantum operations to calculate the covariance;

[0017] The method of calculating the covariance using quantum operations includes calculating the frequency components of data using Fourier transform, quickly accessing and operating data points using QRAM, calculating the mean and standard deviation, obtaining data in the data set using a quantum search algorithm, and calculating the covariance.

[0018] As a preferred solution of the trend inspection method based on Python programming for data collection of thermal power plant protection measurement points described in the present invention, the calculation of the optimized Pearson correlation coefficient includes inputting the preprocessed data into the optimized Pearson calculation formula to calculate the correlation between the data. The calculation formula is as follows:

[0019]

[0020] Among them, X i is the i-th observation value of variable X, Y i is the i-th observation value of variable Y, is the mean of variable X, is the mean of variable Y, n is the number of observations, and λ i is the exponential decay parameter of the ith observation, t is the time, e is the base of the natural logarithm, R Q (X, Y) is the Pearson correlation coefficient, and its value range is [-1, 1].

[0021] As a preferred solution of the trend inspection method based on the data acquisition of thermal power plant protection measuring point by Python programming, the setting of correlation judgment threshold includes setting R according to historical data. Q (X, Y) = 0.5 is the correlation limit value, when 0≤|R Q When (X, Y)|<0.5, it indicates that there is no correlation between the calculated data, and an abnormal correlation warning is issued; when 0.5≤|R Q When (X, Y)|≤1, it means that the correlation between the calculated data is normal, and the correlation between the remaining data continues to be calculated.

[0022] As a preferred solution of the trend inspection method based on Python programming for data collection of thermal power plant protection measuring points described in the present invention, the outputting of monitoring results to the user interface includes creating a chart by selecting the Python data visualization library Matplotlib to display the correlation coefficient value of each measuring point pair, integrating the chart into the user interface, regularly updating the analysis results, and maintaining the chart function and appearance.

[0023] The equipment parameter adjustment includes the user monitoring the thermal power plant, equipment maintenance and equipment parameter adjustment according to the output monitoring results. Q When (X, Y)|<0.5, the system will issue a correlation abnormality warning and display the result on the user interface. The user can perform equipment maintenance and equipment parameter adjustment based on the result and recalculate and optimize the Pearson correlation coefficient until the correlation reaches a normal level; when 0.5≤|R Q When (X, Y)|≤1, the system does not issue a correlation anomaly warning and continues to calculate the correlation between the remaining data. The device continues to maintain the current operating status and device parameters.

[0024] As a preferred solution of the trend inspection method based on Python programming for data collection of thermal power plant protection measurement points described in the present invention, the prediction of potential equipment failures includes inspecting the operation trends of thermal power plant equipment based on the correlation level of monitoring points and historical data analysis, predicting potential equipment failures and optimizing equipment operating parameters;

[0025] When 0<Trend Inspection Index<0.5, the Trend Inspection Index is in the normal area, the equipment is in normal operation, there is no operating risk, and the current operation and equipment parameters continue to be maintained; when 0.5≤Trend Inspection Index<2, the Trend Inspection Index is in the warning area, the equipment is in a warning state, and continuous attention should be paid to the equipment status, and further monitoring and inspection are required; when the Trend Inspection Index ≥2, the Trend Inspection Index is in the fault area, the equipment is in a fault state, and the equipment should be repaired immediately and the equipment operating parameters should be adjusted.

[0026] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of a trend inspection method based on Python programming for data collection of thermal power plant protection measurement points.

[0027] A computer-readable storage medium stores a computer program thereon, characterized in that when the computer program is executed by a processor, the steps of a trend inspection method based on Python programming for data collection of protection measurement points in a thermal power plant are implemented.

[0028] Beneficial effects of the present invention: The method of the present invention is of great significance to the normal operation of the unit for accurate observation of important protection or adjustment measuring points. Once the transmitter is offset or out of tolerance, it must be discovered, repaired or replaced as soon as possible, so trend inspection is required; the use of trend inspection technology based on python programming for data acquisition of thermal power plant protection measuring points can effectively improve the quality and efficiency of inspection, avoid the situation where deviations in measuring points are not discovered in time, and by adopting this trend inspection technology, the correlation between measuring points can be intuitively seen, the efficiency of trend inspection is improved, manpower and time are saved, and it has a good promotion prospect for practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 A flowchart of a trend inspection method based on Python programming for data collection of thermal power plant protection measurement points is provided in accordance with an embodiment of the present invention.

[0031] Figure 2 A schematic diagram of the Pearson correlation coefficient of a trend inspection method based on Python programming for data collection at thermal power plant protection measurement points, provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0035] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0036] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0037] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0038] Example 1, with reference to Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a trend inspection method based on Python programming for data collection of thermal power plant protection measurement points, including:

[0039] S1: Real-time data collection of thermal power plant protection measurement points, pre-processing of the measurement point data, and calculation of the Pearson correlation coefficient of the pre-processed data using the Python programming environment;

[0040] Furthermore, the data collected from the thermal power plant protection measurement points include real-time collection of data from the thermal power plant temperature measurement points, pressure measurement points, flow measurement points, liquid level measurement points, vibration measurement points, flue gas composition measurement points, electrical parameter measurement points, air volume measurement points, and ash carbon content measurement points;

[0041] The temperature measurement points include monitoring of the turbine inlet steam temperature, turbine exhaust temperature, boiler wall temperature, boiler heating surface temperature and lubricating oil temperature; the pressure measurement points include monitoring of boiler steam pressure, turbine inlet steam pressure and feed water pressure; the flow measurement points include monitoring of feed water flow, steam flow and fuel flow; the liquid level measurement points include monitoring of boiler drum water level and turbine condensate level; the vibration measurement points include monitoring of turbine shaft vibration and generator bearing vibration; the flue gas composition measurement points include monitoring of flue gas oxygen content and flue gas harmful components; the electrical parameter measurement points include monitoring of generator and transformer current, voltage and power factor; the air volume measurement points include monitoring of supply air volume and induced air volume.

[0042] Furthermore, the preprocessing includes cleaning, denoising, standardizing and normalizing the measurement point data;

[0043] The cleaning includes removing data irrelevant to the measurement points and filling the missing data with the mean or median; the denoising includes removing outliers with the Z-score algorithm. The algorithm formula is as follows:

[0044]

[0045] Where μ is the mean, σ is the standard value, x is the current input data, and z is the denoised data. When |z|>3, the data is considered abnormal and replaced with normal data. When 0<|z|≤3, the data is considered normal.

[0046] The standardization includes scaling the data between [0, 1] using the minimum-maximum standardization, as follows:

[0047]

[0048] Among them, x max is the maximum value of the data, x min is the minimum value of the data, x is the current input data, x norm The data are standardized.

[0049] S2: Use quantum computing technology to optimize the calculation of the Pearson correlation coefficient, obtain the correlation level between measurement points, set the correlation judgment threshold, and issue an abnormal warning when the correlation is abnormal;

[0050] Furthermore, the quantum computing technology includes converting the processed data into a superposition state of quantum bits, utilizing quantum parallelism to simultaneously calculate the covariance of the data points, and calculating the covariance using quantum operations;

[0051] The method of calculating the covariance using quantum operations includes calculating the frequency components of data using Fourier transform, quickly accessing and operating data points using QRAM, calculating the mean and standard deviation, obtaining data in the data set using a quantum search algorithm, and calculating the covariance.

[0052] Furthermore, the calculation of the optimized Pearson correlation coefficient includes inputting the preprocessed data into the optimized Pearson calculation formula to calculate the correlation between the data. The calculation formula is as follows:

[0053]

[0054] Among them, X i is the i-th observation value of variable X, Y i is the i-th observation value of variable Y, is the mean of variable X, is the mean of variable Y, n is the number of observations, and λ i is the exponential decay parameter of the ith observation, t is the time, e is the base of the natural logarithm, R Q (X, Y) is the Pearson correlation coefficient, the range is [-1, 1];

[0055] in, Is to calculate the covariance of X and Y;

[0056] in, is to calculate the standardized covariance of X;

[0057] in, is to calculate the standardized covariance of Y;

[0058] Integrating the covariance can improve computational efficiency and accuracy;

[0059] in, After integrating and multiplying, we get a quantum-optimized version of the covariance, which improves the accuracy of the calculation.

[0060] in, It is a quantum optimized version of the standard deviation, which improves the calculation accuracy;

[0061] S3: Output the monitoring results to the user interface, and the user can monitor the thermal power plant, perform equipment maintenance, and adjust equipment parameters based on the output results;

[0062] The setting of correlation judgment threshold includes setting R according to historical data. Q (X, Y) = 0.5 is the correlation limit value, when 0≤|R Q When (X, Y)|<0.5, it indicates that there is no correlation between the calculated data, and an abnormal correlation warning is issued; when 0.5≤|R QWhen (X, Y)|≤1, it means that the correlation between the calculated data is normal, and the correlation between the remaining data continues to be calculated;

[0063] Outputting the monitoring results to the user interface includes creating a chart by selecting the Python data visualization library Matplotlib to display the correlation coefficient value of each measurement point pair, integrating the chart into the user interface, regularly updating the analysis results, and maintaining the chart function and appearance;

[0064] The equipment parameter adjustment includes the user monitoring the thermal power plant, equipment maintenance and equipment parameter adjustment according to the output monitoring results. Q When (X, Y)|<0.5, the system will issue a correlation abnormality warning and display the result on the user interface. The user can perform equipment maintenance and equipment parameter adjustment based on the result and recalculate and optimize the Pearson correlation coefficient until the correlation reaches a normal level; when 0.5≤|R Q When (X, Y)|≤1, the system does not issue a correlation anomaly warning and continues to calculate the correlation between the remaining data. The device continues to maintain the current operating status and device parameters.

[0065] S4: Based on the correlation level of monitoring points and historical data analysis, conduct inspections on the operating trends of thermal power plant equipment, predict potential equipment failures and optimize equipment operating parameters.

[0066] The prediction of potential equipment failures includes conducting inspections of the operating trends of thermal power plant equipment based on the correlation level of monitoring points and historical data analysis, predicting potential equipment failures and optimizing equipment operating parameters. The trend inspection index calculation formula is as follows:

[0067]

[0068] Among them, T(c,a) is the trend inspection index of thermal power plant equipment at time a, σ b,j (c,τ) is the correlation level between monitoring points b and j at time τ, Φ is the cumulative distribution function of the standard normal distribution, I k (c,τ) is the data value of the kth monitoring point at time τ, is the average data value of all monitoring points at time τ, m is the total number of monitoring points, λ is the attenuation factor, G(f(ΔP(c,τ)),Δa) is the complex information filtering function, f(ΔP(c,τ)) is the equipment parameter change function, Δa is the time interval, τ is the inspection time, and ΔP(c,τ) is the change of the equipment parameter P at time τ;

[0069] The cumulative distribution function of the Φ standard normal distribution is expressed as:

[0070]

[0071] where \(l\) is the upper limit of integration, \(s\) is the integration variable, and \(e\) is the base of the natural logarithm;

[0072] The complex information filtering function is expressed as:

[0073]

[0074] where \(f(\Delta P(c,\tau))\) is the device parameter change function, \(\tau\) is the inspection time, \(\alpha\) and \(\gamma\) are amplitude adjustment constants, \(\beta\) and \(\delta\) are attenuation parameters, \(\tau\) max is the maximum value of the inspection time, and \(\Delta a\) is the time interval;

[0075] The device parameter change function is expressed as:

[0076]

[0077] where \(\Delta P(c,\tau)\) is the change in the device parameter \(P\) at time \(\tau\), is the average value of the device parameter \(P\) at time \(\tau\), \(c\) is the identifier of the device, and \(\tau\) is the inspection time;

[0078] When \(0 < T(c,a)<0.5\), the trend inspection index is in the normal region, the device is in the normal operating state, there is no operating risk, and the current operation and device parameters are continued. When \(0.5\leq T(c,a)<2\), the trend inspection index is in the warning region, the device is in the warning state, the device status should be continuously monitored, and further monitoring and inspection are required. When \(T(c,a)\geq2\), the trend inspection index is in the failure region, the device is in the failure state, and the device should be immediately repaired and the device operation parameters should be adjusted.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limitations. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention. <>

[0080] Embodiment 2, the second embodiment of the present invention, which is different from the previous embodiment in that:

[0081] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0085] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0086] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A trend inspection method based on Python programming for data collection of thermal power plant protection measurement points, characterized by: include, Collect data from thermal power plant protection measurement points in real time, pre-process the data, and calculate the Pearson correlation coefficient of the pre-processed data using the Python programming environment; Quantum computing technology is used to optimize the calculation of the Pearson correlation coefficient, obtain the correlation level between measurement points, set the correlation judgment threshold, and issue an abnormal warning when the correlation is abnormal; The monitoring results are output to the user interface, and users can monitor the thermal power plant, perform equipment maintenance, and adjust parameters based on the output results; Based on the correlation level of monitoring points and historical data analysis, the operating trends of thermal power plant equipment are inspected to predict potential equipment failures and optimize equipment operating parameters.

2. A trend inspection method based on Python programming for data collection of thermal power plant protection measuring points according to claim 1, characterized in that: The data collected from the thermal power plant protection measurement points include real-time collection of data from the thermal power plant temperature measurement points, pressure measurement points, flow measurement points, liquid level measurement points, vibration measurement points, flue gas composition measurement points, electrical parameter measurement points, air volume measurement points, and ash carbon content measurement points; The temperature measurement points include monitoring of the turbine inlet steam temperature, turbine exhaust temperature, boiler wall temperature, boiler heating surface temperature and lubricating oil temperature; the pressure measurement points include monitoring of boiler steam pressure, turbine inlet steam pressure and feed water pressure; the flow measurement points include monitoring of feed water flow, steam flow and fuel flow; the liquid level measurement points include monitoring of boiler drum water level and turbine condensate level; the vibration measurement points include monitoring of turbine shaft vibration and generator bearing vibration; the flue gas composition measurement points include monitoring of flue gas oxygen content and flue gas harmful components; the electrical parameter measurement points include monitoring of generator and transformer current, voltage and power factor; the air volume measurement points include monitoring of supply air volume and induced air volume.

3. A trend inspection method based on Python programming for data collection of thermal power plant protection measuring points according to claim 2, characterized in that: The preprocessing includes cleaning, denoising, standardizing and normalizing the measurement point data; The cleaning includes removing data irrelevant to the measurement points and filling the missing data with the mean or median; the denoising includes removing outliers with the Z-score algorithm. The algorithm formula is as follows: Where μ is the mean, σ is the standard value, x is the current input data, and z is the denoised data. When |z|>3, the data is considered abnormal and replaced with normal data. When 0<|z|≤3, the data is considered normal. The standardization includes scaling the data between [0, 1] using the minimum-maximum standardization, as follows: Among them, x max is the maximum value of the data, x min is the minimum value of the data, x is the current input data, x norm The data are standardized.

4. A trend inspection method based on Python programming for data collection of thermal power plant protection measuring points according to claim 3, characterized in that: The quantum computing technology includes converting processed data into a quantum bit superposition state, using quantum parallelism to simultaneously calculate the covariance of data points, and using quantum operations to calculate the covariance; The method of calculating the covariance using quantum operations includes calculating the frequency components of data using Fourier transform, quickly accessing and operating data points using QRAM, calculating the mean and standard deviation, obtaining data in the data set using a quantum search algorithm, and calculating the covariance.

5. A trend inspection method based on Python programming for data collection of thermal power plant protection measurement points according to claim 4, characterized in that: The calculation of the optimized Pearson correlation coefficient includes inputting the preprocessed data into the optimized Pearson calculation formula to calculate the correlation between the data. The calculation formula is as follows: Among them, X i is the i-th observation value of variable X, Y i is the i-th observation value of variable Y, is the mean of variable X, is the mean of variable Y, n is the number of observations, and λ i is the exponential decay parameter of the ith observation, t is the time, e is the base of the natural logarithm, R Q (X, Y) is the Pearson correlation coefficient, and its value range is [-1, 1].

6. A trend inspection method based on Python programming for data collection of thermal power plant protection measuring points according to claim 5, characterized in that: The setting of correlation judgment threshold includes setting R according to historical data. Q (X, Y) = 0.5 is the correlation limit value, when 0≤|R Q When (X, Y)|<0.5, it indicates that there is no correlation between the calculated data, and an abnormal correlation warning is issued; when 0.5≤|R Q When (X, Y)|≤1, it means that the correlation between the calculated data is normal, and the correlation between the remaining data continues to be calculated.

7. A trend inspection method based on Python programming for data collection of thermal power plant protection measuring points according to claim 6, characterized in that: Outputting the monitoring results to the user interface includes creating a chart by selecting the Python data visualization library Matplotlib to display the correlation coefficient value of each measurement point pair, integrating the chart into the user interface, regularly updating the analysis results, and maintaining the chart function and appearance; The equipment parameter adjustment includes the user monitoring the thermal power plant, equipment maintenance and equipment parameter adjustment according to the output monitoring results. Q When (X, Y)|<0.5, the system will issue a correlation abnormality warning and display the result on the user interface. The user can perform equipment maintenance and equipment parameter adjustment based on the result and recalculate and optimize the Pearson correlation coefficient until the correlation reaches a normal level; when 0.5≤|R Q When (X, Y)|≤1, the system does not issue a correlation anomaly warning and continues to calculate the correlation between the remaining data. The device continues to maintain the current operating status and device parameters.

8. A trend inspection method based on Python programming for data collection of thermal power plant protection measuring points according to claim 7, characterized in that: The prediction of potential equipment failures includes inspecting the operating trends of thermal power plant equipment based on the correlation level of monitoring points and historical data analysis, predicting potential equipment failures and optimizing equipment operating parameters; When 0 < Trend Inspection Index < 0.5, the Trend Inspection Index is in the normal range, the equipment is in normal operation, there is no operating risk, and the current operation and equipment parameters continue to be maintained; when 0.5 ≤ Trend Inspection Index < 2, the Trend Inspection Index is in the warning range, the equipment is in a warning state, and the equipment status should be continuously monitored and further monitoring and inspection are required; When the trend inspection index is ≥2, the trend inspection index is in the fault area and the equipment is in a fault state. The equipment should be repaired immediately and the equipment operating parameters should be adjusted.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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