Thermal power generating unit state monitoring method, device, equipment and medium

Abstract

The invention provides a thermal power generating unit state monitoring method, device and equipment and a medium, and relates to the technical field of thermal power generation, and the method comprises the steps: obtaining the monitoring values of a plurality of preset monitoring parameters of a thermal power generating unit in a period of time in real time; wherein the preset monitoring parameters are monitored parameters of equipment or a system in the thermal power generating unit in the operation process; generating a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter in a period of time; according to a preset data analysis model corresponding to each preset monitoring parameter, analyzing the monitoring value sequence of each preset monitoring parameter to obtain a data analysis result corresponding to each preset monitoring parameter; and determining a fault evaluation result corresponding to each preset monitoring parameter according to a preset fault evaluation standard corresponding to each preset monitoring parameter and the data analysis result. According to the invention, the problems of low monitoring efficiency and low fault evaluation accuracy of the existing traditional thermal power generating unit state monitoring method can be effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of thermal power generation, and in particular to a method for monitoring the status of a thermal power unit, a device for monitoring the status of a thermal power unit, an electronic device, and a machine-readable storage medium. Background Art

[0002] As the capacity and operating parameters of thermal power generating units (hereinafter referred to as thermal power units) continue to increase, their structures and systems have become more complex. Modern thermal power units include multiple interrelated subsystems and complex equipment, including steam cycle systems, combustion systems, generators, turbines, etc.

[0003] Traditional methods of monitoring the condition of thermal power units mainly rely on manual inspections and regular testing. However, this method has limitations such as delayed response, low efficiency, and low accuracy, and it is difficult to meet the needs of high reliability and efficient operation and maintenance of modern thermal power units.

[0004] Specifically, manual inspections and regular tests often fail to provide immediate feedback on the status of the unit, which may result in potential faults not being detected in time, thus delaying the best time to take countermeasures. With the increasing complexity of the structure and size of thermal power units, manual inspections and regular tests are not only time-consuming and labor-intensive, but also difficult to ensure the comprehensiveness and systematicness of monitoring, which in turn affects the overall efficiency of operation and maintenance. In addition, due to differences in the experience and judgment of inspectors and the limitations of detection tools, traditional methods are difficult to accurately capture subtle changes in the status of the unit, which may lead to the omission of key precursors to faults. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method for monitoring the state of a thermal power unit, a device, equipment and medium for monitoring the state of a thermal power unit, so as to solve the problems of low monitoring efficiency and low fault assessment accuracy in the traditional method for monitoring the state of a thermal power unit in the prior art.

[0006] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for monitoring the state of a thermal power unit, the method comprising:

[0007] Acquire monitoring values ​​of multiple preset monitoring parameters of the thermal power unit in real time over a period of time; wherein the preset monitoring parameters are parameters monitored during the operation of the equipment or system in the thermal power unit;

[0008] Generate a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter within a period of time;

[0009] According to the preset data analysis model corresponding to each preset monitoring parameter, the monitoring value sequence of each preset monitoring parameter is analyzed respectively to obtain the data analysis result corresponding to each preset monitoring parameter;

[0010] According to the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter, the fault assessment results corresponding to each preset monitoring parameter are determined; wherein the preset fault assessment standards corresponding to the preset monitoring parameters are obtained by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameters.

[0011] In the embodiment of the present application, before analyzing the monitoring value sequence of the preset monitoring parameter, the method further includes:

[0012] The monitoring value sequence of the preset monitoring parameters is subjected to data cleaning, denoising and normalization processing in turn.

[0013] In an embodiment of the present application, the preset data analysis models corresponding to the preset monitoring parameters include a time domain analysis model, a frequency domain analysis model, and a time-frequency domain analysis model.

[0014] In the embodiment of the present application, according to the preset data analysis model corresponding to each preset monitoring parameter, the monitoring value sequence of each preset monitoring parameter is analyzed respectively to obtain the data analysis result corresponding to each preset monitoring parameter, including:

[0015] According to the monitoring value sequence of the preset monitoring parameter, the time domain characteristic value, the frequency domain characteristic value and the time-frequency domain characteristic value of the preset monitoring parameter are determined through the time domain analysis model, the frequency domain analysis model and the time-frequency domain analysis model corresponding to the preset monitoring parameter;

[0016] Among them, the data analysis results corresponding to the preset monitoring parameters include the time domain characteristic values, frequency domain characteristic values ​​and time-frequency domain characteristic values ​​of the preset monitoring parameters.

[0017] In an embodiment of the present application, the preset fault assessment criteria corresponding to the preset monitoring parameters include a first assessment criteria, a second assessment criteria, and a third assessment criteria, and the first assessment criteria, the second assessment criteria, and the third assessment criteria of the preset monitoring parameters correspond one-to-one to the time domain analysis model, the frequency domain analysis model, and the time-frequency domain analysis model corresponding to the preset monitoring parameters, respectively;

[0018] According to the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter, the fault assessment results corresponding to each preset monitoring parameter are determined, including:

[0019] If the time domain characteristic value of the preset monitoring parameter meets the first evaluation standard corresponding to the preset monitoring parameter, the frequency domain characteristic value of the preset monitoring parameter meets the second evaluation standard corresponding to the preset monitoring parameter, and the time-frequency domain characteristic value of the preset monitoring parameter meets the third evaluation standard corresponding to the preset monitoring parameter, then it is determined that the fault evaluation result corresponding to the preset monitoring parameter is no fault;

[0020] If the time domain characteristic value of the preset monitoring parameter does not meet the first evaluation standard corresponding to the preset monitoring parameter, and / or the frequency domain characteristic value of the preset monitoring parameter does not meet the second evaluation standard corresponding to the preset monitoring parameter, and / or the time-frequency domain characteristic value of the preset monitoring parameter does not meet the third evaluation standard corresponding to the preset monitoring parameter, then the fault assessment result corresponding to the preset monitoring parameter is determined to be faulty.

[0021] In an embodiment of the present application, the method further includes:

[0022] If the fault assessment result corresponding to the preset monitoring parameter is a fault, fault warning information is generated according to the preset fault type set corresponding to the preset monitoring parameter, and the fault warning information is displayed through the display terminal of the thermal power unit, and / or the fault warning information is played through the voice module of the thermal power unit.

[0023] In an embodiment of the present application, the method further includes:

[0024] The monitoring value sequence of the preset monitoring parameters and the corresponding fault assessment results are stored in the cloud.

[0025] A second aspect of the present application provides a thermal power unit state monitoring device, the device comprising:

[0026] A monitoring value acquisition module, used for acquiring in real time the monitoring values ​​of a plurality of preset monitoring parameters of the thermal power unit within a period of time; wherein the preset monitoring parameters are parameters monitored during the operation of the equipment or system in the thermal power unit;

[0027] A sequence generation module, used to generate a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter within a period of time;

[0028] A data analysis module is used to analyze the monitoring value sequence of each preset monitoring parameter according to the preset data analysis model corresponding to each preset monitoring parameter, and obtain the data analysis result corresponding to each preset monitoring parameter;

[0029] The fault assessment module is used to determine the fault assessment results corresponding to each preset monitoring parameter based on the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter; wherein the preset fault assessment standards corresponding to the preset monitoring parameters are obtained by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameters.

[0030] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for monitoring the status of a thermal power unit as described in the first aspect above when executing the computer program.

[0031] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configures the processor to execute the thermal power unit status monitoring method described in the first aspect above.

[0032] The thermal power unit status monitoring method, device, equipment and medium provided in the present application obtain the monitoring values ​​of the preset monitoring parameters of the thermal power unit in real time, then generate a monitoring value sequence according to the monitoring values, and then analyze the monitoring value sequence of the preset monitoring parameters according to the preset data analysis model corresponding to the preset monitoring parameters to obtain the data analysis results corresponding to the preset monitoring parameters. Finally, according to the preset fault assessment standards and data analysis results corresponding to the preset monitoring parameters, the fault assessment results corresponding to the preset monitoring parameters are determined. The method, device, equipment and medium can not only monitor the operating status of the thermal power unit in real time, but also determine whether there is a fault in the thermal power unit, realize abnormal early warning, provide guarantee for the safe and stable operation of the thermal power unit, and effectively solve the problems of low monitoring efficiency and low fault assessment accuracy in traditional thermal power unit status monitoring methods.

[0033] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0035] Figure 1 The flowchart of the method for monitoring the state of a thermal power unit according to an embodiment of the present application is schematically shown;

[0036] Figure 2 A schematic diagram of a structure of a thermal power unit state monitoring device according to an embodiment of the present application is shown;

[0037] Figure 3 The internal structure diagram of the computer device according to the embodiment of the present application is schematically shown.

[0038] Description of Reference Numerals

[0039] A01-processor; A02-network interface; A03-internal memory; A04-display screen; A05-input device; A06-non-volatile storage medium; B01-operating system; B02-computer program. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0041] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0042] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting 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 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 deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0043] In view of the problems of low monitoring efficiency and low fault assessment accuracy in traditional thermal power unit status monitoring methods in related technologies, the present application provides a thermal power unit status monitoring method, device, equipment and medium. The thermal power unit status monitoring method, device, equipment and medium provided in the present application are described in detail below in conjunction with the accompanying drawings through specific examples and implementation methods.

[0044] Figure 1 The flowchart of the method for monitoring the state of a thermal power unit according to an embodiment of the present application is schematically shown. Figure 1 As shown, in one embodiment of the present application, a method for monitoring the status of a thermal power unit is provided, and the method may include the following steps.

[0045] Step 200, obtaining in real time monitoring values ​​of a plurality of preset monitoring parameters of the thermal power unit within a period of time.

[0046] The preset monitoring parameters are parameters monitored during the operation of the equipment or system in the thermal power unit.

[0047] In the embodiment of the present application, the thermal power unit includes a sensor data acquisition module and a system data acquisition module, wherein the sensor data acquisition module is connected to sensors arranged at key parts such as the steam turbine, generator, boiler, etc. of the thermal power unit, and is used to obtain physical quantity data such as temperature, pressure, vibration, current, voltage, etc. The system data acquisition module is connected to the control system of the thermal power unit through a communication interface (such as OPC, Modbus), and is used to obtain operating parameter data such as power, speed, flow, etc. It is worth mentioning that in the embodiment of the present application, the acquisition frequency of physical quantity data and operating parameter data can be set according to actual needs, such as selecting the acquisition frequency between milliseconds and seconds.

[0048] That is to say, in the embodiment of the present application, the preset monitoring parameters are specific physical quantities and operating parameters. In the specific application process, which physical quantities and operating parameters are determined as preset monitoring parameters can be set by relevant technical personnel. It should be noted that in the embodiment of the present application, the same type of physical quantity or operating parameter of different equipment corresponds to different monitoring parameters, such as generator temperature and boiler internal temperature.

[0049] Step 400: generating a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter within a period of time.

[0050] In the embodiment of the present application, the thermal power unit includes a data storage module, and the monitoring values ​​of different preset monitoring parameters are stored separately. The size of the monitoring value sequence of the preset monitoring parameter (ie, how many continuously collected monitoring values ​​are included in the monitoring value sequence) is determined by relevant technical personnel.

[0051] In a specific example, the implementation method of step 400 is: for each preset monitoring parameter, the N monitoring values ​​collected continuously are sorted in the order of collection time to obtain the monitoring value sequence of the preset monitoring parameter. Among them, in any two monitoring value sequences of the preset monitoring parameter, there are no monitoring values ​​with the same collection time.

[0052] In another specific example, for each preset monitoring parameter, in two monitoring value sequences generated successively, the collection time of some monitoring values ​​is the same.

[0053] The embodiment of the present application generates a monitoring value sequence based on the collected monitoring values, thereby providing a data basis for analyzing the fault conditions of the current thermal power unit.

[0054] Optionally, in the embodiment of the present application, after step 400 generates a monitoring value sequence for each preset monitoring parameter, the method may further include the following steps.

[0055] Step 500, sequentially performing data cleaning, denoising and normalization processing on the monitoring value sequence of the preset monitoring parameters.

[0056] In the embodiment of the present application, step 500 specifically includes the following steps.

[0057] Step 510: Perform data cleaning on the monitoring value sequence of the preset monitoring parameter to obtain a first monitoring value sequence.

[0058] In a specific example, the operation of performing data cleaning on the monitoring value sequence in step 510 includes removing abnormal values ​​(such as erroneous data caused by sensor failure), deduplicating data, and filling missing values.

[0059] Step 520: denoising the first monitoring value sequence to obtain a second monitoring value sequence.

[0060] In a specific example, the operation of performing denoising on the first monitoring value sequence in step 520 includes removing noise data generated by communication interference.

[0061] Step 530: normalize the second monitoring value sequence to obtain a target monitoring value sequence.

[0062] The embodiment of the present application effectively improves the data quality by cleaning, denoising and normalizing the collected data, which facilitates subsequent analysis.

[0063] Step 600 , according to the preset data analysis model corresponding to each preset monitoring parameter, respectively analyze the monitoring value sequence of each preset monitoring parameter to obtain the data analysis result corresponding to each preset monitoring parameter.

[0064] In an embodiment of the present application, different preset monitoring parameters have their own corresponding preset data analysis models. The preset data analysis models corresponding to the preset monitoring parameters include a time domain analysis model, a frequency domain analysis model, and a time-frequency domain analysis model. The time domain analysis model, the frequency domain analysis model, and the time-frequency domain analysis model are used to extract time domain features, frequency domain features, and time-frequency domain features, respectively.

[0065] It can be understood that in an embodiment of the present application, if step 500 is executed, the object to be analyzed in step 600 is the target monitoring value sequence corresponding to the preset monitoring parameters, that is, step 600 is: according to the preset data analysis model corresponding to each preset monitoring parameter, the target monitoring value sequence of each preset monitoring parameter is analyzed respectively to obtain the data analysis results corresponding to each preset monitoring parameter.

[0066] In the embodiment of the present application, step 600 includes:

[0067] Step 610, according to the monitoring value sequence of the preset monitoring parameter, the time domain eigenvalue, frequency domain eigenvalue and time-frequency domain eigenvalue of the preset monitoring parameter are determined through the time domain analysis model, frequency domain analysis model and time-frequency domain analysis model corresponding to the preset monitoring parameter.

[0068] Among them, the data analysis results corresponding to the preset monitoring parameters include the time domain characteristic values, frequency domain characteristic values ​​and time-frequency domain characteristic values ​​of the preset monitoring parameters.

[0069] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is temperature data, the time domain characteristic value of the preset monitoring parameter can be selected to be represented by at least one of the variance, mean, and the difference between the maximum value and the minimum value.

[0070] The variance is used to measure the degree of dispersion of temperature data relative to the mean. For example, in one hour, the temperature value of a component of a thermal power unit is collected every 10 seconds, which are 500℃, 495℃, 501℃, 503℃, etc. The variance of all collected temperature values ​​is calculated. The larger the variance, the more dispersed the temperature data is and the more drastic the fluctuation is.

[0071] The mean is used to calculate the average value of temperature data within a period of time, reflecting the overall temperature level. For example, in one hour, the temperature value is collected every 10 seconds. The sum of all collected temperature values ​​is divided by the number of collections to obtain the mean temperature value during this period.

[0072] The difference between the maximum and minimum values ​​is used to reflect the temperature fluctuation range within a period of time. For example, the maximum temperature of a component of a thermal power unit is monitored to be 500°C, the minimum is 480°C, and the range is 20°C. This shows that the temperature of the component has a certain range of variation.

[0073] In a specific example, if the monitoring value of the preset monitoring parameter is temperature data, when the time domain characteristic value of the preset monitoring parameter is calculated using variance, the expression of its time-frequency analysis model is:

[0074]

[0075] Among them, Var represents the time domain characteristic value, n represents the size of the monitoring value sequence, and X i represents the i-th monitoring value in the monitoring value sequence, Represents the average value of all monitoring values ​​in the monitoring value sequence, It represents the sum of the squares of all Xi-X.

[0076] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is temperature data, the frequency domain characteristic value of the preset monitoring parameter can be calculated by power spectrum analysis, and the expression of the frequency domain analysis model is:

[0077]

[0078] Among them, S(f) represents the power spectrum (ie, the frequency domain eigenvalue), T represents the data length, and X(f) represents the Fourier transform of the signal.

[0079] Specifically, by performing a fast Fourier transform (FFT) on the temperature signal, the spectrum of the temperature signal is obtained, and the frequency corresponding to the peak in the spectrum represents the main frequency component of the temperature fluctuation. For example, if a certain frequency component is found to have a high amplitude in the spectrum, it may mean that there is a periodic thermal interference source at this frequency.

[0080] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is temperature data, the time-frequency domain characteristic value of the preset monitoring parameter can be calculated using short-time Fourier transform, and the expression of its time-frequency domain analysis model is the calculation formula of short-time Fourier transform.

[0081] Specifically, the short-time Fourier transform (STFT) can simultaneously observe the distribution of the temperature signal in time and frequency. By selecting an appropriate window function (such as a Hamming window), the STFT can analyze the changes in the frequency components of the signal in different time periods. For example, during the startup phase of a thermal power unit, STFT analysis can reveal significant fluctuations in the temperature signal in the low-frequency band, and observe that this fluctuation gradually stabilizes over time.

[0082] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is pressure data, the time domain characteristic value of the preset monitoring parameter can be selected to be represented by at least one of pulse width, slope change, rise time and fall time.

[0083] If the pressure signal is in the form of a pulse, the pulse width is the duration of the pulse. In the case of intermittent pressure fluctuations, the pulse width can help determine the periodicity, duration or intensity of the fluctuation.

[0084] By calculating the slope of the pressure curve at different points, the rate of pressure change can be reflected. For example, a sudden increase in the slope may indicate that a new pressure source has been connected or an abnormal pressure shock has occurred.

[0085] For pressure change signals, the time from the initial pressure rising to the peak pressure (i.e., the rise time) and the time from the peak pressure falling to a certain set pressure (i.e., the fall time) can be measured to analyze the dynamic process of pressure change. For example, during the opening or closing process of a valve, the rise time and fall time of the pressure can reflect the action speed of the valve.

[0086] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is pressure data, the frequency domain characteristic value of the preset monitoring parameter can be represented by power spectrum density.

[0087] Specifically, power spectral density (PSD) can obtain the power distribution of pressure signals at different frequencies by performing power spectrum analysis on pressure signals. PSD can help identify the main frequency components of pressure fluctuations and the energy of each frequency component. For example, when analyzing pressure pulsations in a pipeline, PSD can be used to determine the main frequency of the pulsation, thereby finding possible vibration sources or fluid resonance frequencies.

[0088] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is pressure data, the time-frequency domain characteristic value of the preset monitoring parameter can be calculated by wavelet transform, and the expression of the time-frequency domain analysis model is the calculation formula of wavelet transform, that is:

[0089]

[0090] Among them, W f (a,b) represents the wavelet transform coefficient (i.e., the eigenvalue in the instantaneous frequency domain), a represents the scale factor, b represents the translation factor, x(t) represents the signal, and t represents the acquisition time. represents the wavelet function.

[0091] Specifically, wavelet transform can adaptively analyze the local time-frequency characteristics of the signal, and has a good analytical effect on non-stationary pressure signals (such as sudden pressure rises and falls, intermittent fluctuations, etc.). For example, when analyzing the pressure changes of thermal power units under complex working conditions, wavelet transform can decompose the pressure signal into sub-signals of different scales and frequencies, so as to better understand the time-frequency characteristics of pressure changes.

[0092] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is current data, the time domain characteristic value of the preset monitoring parameter can be selected to be represented by at least one of the current effective value, the current peak value, and the current zero crossing point.

[0093] Among them, the effective value of current (RMS) is an important indicator to measure the actual work ability of AC current. For sinusoidal AC current, its effective value is equal to the peak value divided by the square root of 2. In thermal power units, by monitoring the effective value of current, the load conditions of equipment such as motors can be understood. For example, when the motor load increases, the effective value of current will increase accordingly.

[0094] The current peak is the maximum value reached by the current signal in one cycle. The magnitude of the current peak may be affected by the inductance, capacitance and other components in the circuit, and is also related to the characteristics of the load. In some cases where current overload needs to be limited, monitoring the current peak is also very necessary.

[0095] The AC current signal will pass through the zero point when alternating between the positive and negative half cycles. The time interval of the current passing through the zero point can be used to calculate the frequency of the current. At the same time, the stability of the zero point can also reflect the symmetry and stability of the circuit.

[0096] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is current data, the frequency domain characteristic value of the preset monitoring parameter can be represented by harmonic content.

[0097] Specifically, due to factors such as nonlinear loads in the power system, the current signal contains harmonic frequency components in addition to the fundamental frequency (50Hz or 60Hz). The power quality can be evaluated by performing spectrum analysis on the current signal and calculating the ratio of the amplitude of each harmonic frequency component to the amplitude of the fundamental wave (harmonic distortion rate). For example, in situations where a large number of electronic devices are used, more high-order harmonics may be generated, resulting in an increase in the harmonic distortion rate.

[0098] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is current data, the time-frequency domain characteristic value of the preset monitoring parameter can be calculated using S transform, and the expression of its time-frequency domain analysis model is the calculation formula of S transform.

[0099] Specifically, the S transform combines the advantages of short-time Fourier transform and wavelet transform, and can clearly display the frequency change of the current signal over time in the time-frequency domain. When analyzing the current change of thermal power units during startup, shutdown or load mutation, the S transform can effectively extract the time-frequency characteristics of the current signal and help determine the dynamic characteristics of equipment such as motors.

[0100] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is voltage data, the time domain characteristic value of the preset monitoring parameter can be selected to be represented by at least one of voltage amplitude, voltage fluctuation value, and voltage transient data.

[0101] The voltage amplitude includes peak voltage and effective voltage. Peak voltage is the maximum value reached by the voltage signal in one cycle, and effective voltage is used to measure the actual working capacity of the voltage. In the power transmission and distribution process of thermal power units, the stability of voltage amplitude is one of the key factors to ensure the normal operation of power equipment.

[0102] By calculating the standard deviation or root mean square deviation of the voltage signal, the degree of voltage fluctuation (i.e., voltage fluctuation value) can be measured. Voltage fluctuation may affect the performance and life of electrical equipment. For example, lighting equipment may flicker when the voltage fluctuation is large.

[0103] Voltage transient data includes the magnitude and duration of a sudden drop (sag) or rise (swell) in voltage in a short period of time. These transient phenomena may be caused by grid failure, startup of large equipment or lightning strikes, which may cause damage to sensitive electronic equipment.

[0104] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is voltage data, the frequency domain characteristic value of the preset monitoring parameter can be determined by spectrum analysis.

[0105] Specifically, frequency components contained in the voltage signal can be determined by performing spectrum analysis on the voltage signal. In the power system, in addition to the fundamental frequency, there may also be frequency components such as harmonics and interharmonics. By analyzing the spectrum, the frequency characteristics and power quality of the power grid can be evaluated.

[0106] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is voltage data, the time-frequency domain characteristic value of the preset monitoring parameter can be calculated using wavelet transform, and the expression of its time-frequency domain analysis model is the calculation formula of wavelet transform.

[0107] Specifically, wavelet packet transform can perform more detailed time-frequency decomposition of voltage signals. When analyzing complex voltage transient processes (such as voltage flicker, pulse interference, etc.), wavelet packet transform can provide more detailed time-frequency information to help identify the type and occurrence time of transient events.

[0108] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is power data, the time domain characteristic value of the preset monitoring parameter can be selected to be represented by at least one of average power, power fluctuation range, and power change rate.

[0109] Among them, average power refers to the average value of power in a period of time, which reflects the overall energy output level of the thermal power unit in that period of time. For example, in a day, the average power is calculated every hour. By observing the changes in these average power values, the daily load change pattern of the thermal power unit can be understood.

[0110] By determining the range between the maximum and minimum power values, the degree of power fluctuation can be measured. Power fluctuations may be caused by factors such as load changes and unit adjustments. Large power fluctuations may affect the stability of the power grid.

[0111] The power change rate indicates the rate of change of power over time, that is, the derivative of power with respect to time. The power change rate can reflect the response speed and regulation performance of the thermal power unit. For example, when the power grid requires the thermal power unit to increase its output power, the power change rate can reflect how fast the thermal power unit responds to the instruction.

[0112] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is power data, the frequency domain characteristic value of the preset monitoring parameter can be calculated using power spectrum analysis, and the expression of its frequency domain analysis model is the calculation formula of the power spectrum analysis.

[0113] Specifically, by performing spectrum analysis on the power signal, the distribution of power at different frequencies can be determined. In some cases with periodic load changes, power spectrum analysis can help find the main frequency components of the load changes, thereby providing a basis for the optimal scheduling of thermal power units.

[0114] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is power data, the time-frequency domain characteristic value of the preset monitoring parameter can be calculated using Gabor transform, and the expression of its time-frequency domain analysis model is the calculation formula of Gabor transform.

[0115] Specifically, Gabor transform is a time-frequency analysis method used to analyze the energy distribution of power signals on the time-frequency plane. When analyzing the power changes of thermal power units under variable operating conditions, Gabor transform can provide detailed information on power signals in different time and frequency intervals, which helps to deeply understand the dynamic characteristics of power changes.

[0116] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is rotational speed data, the time domain characteristic value of the preset monitoring parameter can be selectively represented by at least one of average rotational speed, rotational speed fluctuation value, acceleration and deceleration.

[0117] Among them, calculating the average speed over a period of time is a basic indicator for measuring the operating speed of rotating parts. For example, for the steam turbine of a thermal power unit, it is an important condition to ensure the normal power generation of the unit if the average speed is stable near the rated speed.

[0118] By calculating the standard deviation or range of the speed, the speed fluctuation can be measured. Speed ​​fluctuation may be caused by load changes, mechanical imbalance or control system failure. Smaller speed fluctuation is conducive to the stable operation of the unit.

[0119] Calculate the rate of change of the speed to get the acceleration and deceleration. During the startup and shutdown of the thermal power unit, the magnitude and change process of the acceleration and deceleration need to be strictly controlled to avoid excessive mechanical stress on the rotating parts.

[0120] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is rotation speed data, the frequency domain characteristic value of the preset monitoring parameter can be represented by the rotation frequency and its harmonics.

[0121] Specifically, for rotating parts, the rotation frequency is an important frequency component. The rotation frequency and its harmonic components can be obtained by performing spectrum analysis on the speed signal. For example, when analyzing shaft vibration, the rotation frequency and its harmonic components are closely related to the imbalance and misalignment of the shaft.

[0122] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is rotation speed data, the time-frequency domain characteristic value of the preset monitoring parameter can be represented by Wigner-Ville distribution.

[0123] Specifically, Wigner-Ville distribution is a time-frequency analysis method used to analyze the energy distribution of the speed signal on the time-frequency plane. When analyzing the speed changes of thermal power units under variable operating conditions or under interference, Wigner-Ville distribution can provide detailed information on the speed signal in different time and frequency intervals, helping to diagnose the cause of abnormal speed changes.

[0124] In an embodiment of the present application, if the monitoring value of the preset monitoring parameter is flow data, the time domain characteristic value of the preset monitoring parameter can be represented by at least one of the average flow, flow change rate, flow peak value and valley value.

[0125] The average flow rate is the average value of the fluid flow rate over a period of time, which reflects the overall flow level of the fluid during that period of time. For example, when monitoring the cooling water flow rate, the average flow rate can help determine whether the cooling system is working properly.

[0126] The flow rate is the rate of change of flow over time, that is, the derivative of flow with respect to time. The flow rate can reflect the dynamic characteristics of the fluid system. For example, when adjusting the valve opening, the flow rate can reflect the speed of flow response.

[0127] The difference between the flow peak and valley values ​​can reflect the fluctuation range of the flow. The flow peak and valley values ​​may be related to the operating conditions of the system (such as the start and stop of the pump) or external interference (such as pipeline leakage).

[0128] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is flow data, the frequency domain characteristic value of the preset monitoring parameter can be determined by spectrum analysis.

[0129] Specifically, the flow signal is subjected to spectrum analysis to determine the frequency components contained in the flow signal. In a fluid system, flow fluctuations may contain periodic components, and the frequencies of these periodic components can be found through spectrum analysis, thereby analyzing the causes of their generation.

[0130] In the embodiment of the present application, if the monitoring value of the preset monitoring parameter is flow data, the time-frequency domain eigenvalue of the preset monitoring parameter can be determined by combining empirical mode decomposition (EMD) with Hilbert transform.

[0131] Specifically, the flow signal is first decomposed into multiple intrinsic mode functions (IMFs) using EMD, and then each IMF is Hilbert transformed to obtain the representation of the flow signal in the time-frequency domain. This method can be used to analyze non-stationary and nonlinear flow changes. For example, when analyzing complex fluid flow conditions in pipelines, it can effectively extract the time-frequency characteristics of the flow signal.

[0132] The embodiment of the present application establishes corresponding preset data analysis models for different preset monitoring parameters according to the operating characteristics of the thermal power unit, and analyzes the collected data through the model, which helps to more accurately assess the possibility of faults.

[0133] Step 800, determining the fault assessment result corresponding to each preset monitoring parameter according to the preset fault assessment standard corresponding to each preset monitoring parameter and the data analysis result.

[0134] Among them, the preset fault assessment standard corresponding to the preset monitoring parameter is obtained by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameter.

[0135] Specifically, the historical monitoring value of the preset monitoring parameter is the parameter value of the preset monitoring parameter when the thermal power unit operates normally.

[0136] For each preset monitoring parameter, by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameter when the thermal power unit is operating normally, the reference value or normal range of the preset monitoring parameter when the thermal power unit is operating normally can be determined, and then the preset fault assessment standard corresponding to the preset monitoring parameter can be obtained.

[0137] In an embodiment of the present application, the preset fault assessment criteria corresponding to the preset monitoring parameters include a first assessment criteria, a second assessment criteria, and a third assessment criteria, and the first assessment criteria, the second assessment criteria, and the third assessment criteria of the preset monitoring parameters respectively correspond one-to-one to the time domain analysis model, the frequency domain analysis model, and the time-frequency domain analysis model corresponding to the preset monitoring parameters.

[0138] In the embodiment of the present application, step 800 includes the following steps.

[0139] Step 810, if the time domain characteristic value of the preset monitoring parameter meets the first evaluation standard corresponding to the preset monitoring parameter, the frequency domain characteristic value of the preset monitoring parameter meets the second evaluation standard corresponding to the preset monitoring parameter, and the time-frequency domain characteristic value of the preset monitoring parameter meets the third evaluation standard corresponding to the preset monitoring parameter, then it is determined that the fault assessment result corresponding to the preset monitoring parameter is no fault.

[0140] If the time domain characteristic value of the preset monitoring parameter does not meet the first evaluation standard corresponding to the preset monitoring parameter, and / or the frequency domain characteristic value of the preset monitoring parameter does not meet the second evaluation standard corresponding to the preset monitoring parameter, and / or the time-frequency domain characteristic value of the preset monitoring parameter does not meet the third evaluation standard corresponding to the preset monitoring parameter, then the fault assessment result corresponding to the preset monitoring parameter is determined to be faulty.

[0141] For example, for the generator temperature, the corresponding fault assessment result is determined by the threshold judgment method. The calculation formula of the threshold judgment method is:

[0142]

[0143] Wherein, f(x) represents the time domain analysis model corresponding to the generator temperature, and T represents the preset generator temperature threshold. When the time domain characteristic value of the generator temperature is less than or equal to the preset generator temperature threshold, the fault assessment result corresponding to the generator temperature is no fault; when the time domain characteristic value of the generator temperature is greater than the preset generator temperature threshold, the fault assessment result corresponding to the generator temperature is faulty. In other words, the first assessment criterion corresponding to the generator temperature is that the time domain characteristic value of the generator temperature is less than or equal to the preset generator temperature threshold.

[0144] In an embodiment of the present application, after the fault assessment results corresponding to the preset monitoring parameters are determined, they can be displayed to the user in the form of charts, reports, etc., that is, visualization technology is used to display the operating status of the thermal power unit to the user, helping the user to timely understand and supervise whether the current thermal power unit is operating stably.

[0145] Optionally, in an embodiment of the present application, the method further includes the following steps.

[0146] Step 910: If the fault assessment result corresponding to the preset monitoring parameter is a fault, fault warning information is generated according to the preset fault type set corresponding to the preset monitoring parameter, and the fault warning information is displayed through the display terminal of the thermal power unit, and / or the fault warning information is played through the voice module of the thermal power unit.

[0147] Among them, for each preset monitoring parameter, its corresponding preset fault type set includes a set of fault types that may be caused when the monitoring value of the preset monitoring parameter does not meet its corresponding preset fault assessment standard. The preset fault type set can be determined through relevant data or based on experience.

[0148] In a specific example, the generated fault warning information includes preset monitoring parameters and their corresponding preset fault type sets, so that relevant technical personnel can understand the cause of the warning and the object to be checked.

[0149] When the fault assessment result corresponding to the preset monitoring parameter is a fault, the embodiment of the present application sends out a warning signal by displaying the corresponding fault warning information or sound and light alarm on the display terminal of the thermal power unit, so that the relevant technical personnel can perform corresponding processing, thereby ensuring the timely communication of the fault warning information. Of course, it is understandable that the generated fault warning information can also be sent to the relevant technical personnel through SMS, email, etc., to achieve the timely communication of the fault warning information. The present application does not limit the method of communicating the fault warning information to the relevant technical personnel.

[0150] Optionally, in an embodiment of the present application, the method further includes the following steps.

[0151] Step 920: Cloud-store the monitoring value sequence of the preset monitoring parameters and the corresponding fault assessment results.

[0152] The embodiment of the present application effectively reduces the storage space required for the thermal power unit by sending the monitoring value sequence of the preset monitoring parameters and the corresponding fault assessment results to a cloud storage platform connected to the thermal power unit for storage.

[0153] The following is an application example to illustrate the method for monitoring the state of a thermal power unit provided in an embodiment of the present application.

[0154] The sensor data acquisition module of the thermal power unit adopts a distributed architecture, connecting various sensors deployed in the thermal power unit through the network to realize all-round data collection of the thermal power unit. The system data acquisition module of the thermal power unit is connected to its control system through OPC to obtain operating parameter data such as power, speed, and load. The data storage module of the thermal power unit adopts a distributed file system or database system to realize efficient storage and management of large-scale data (including monitoring values ​​and fault assessment results).

[0155] The thermal power unit obtains the parameter values ​​of each preset monitoring parameter during normal operation of the thermal power unit and performs big data analysis based on the preset data analysis model corresponding to each preset monitoring parameter to determine the preset fault assessment standard corresponding to each preset monitoring parameter.

[0156] In the actual operation of the thermal power unit, for each preset monitoring parameter, a corresponding monitoring value sequence is first generated according to the monitoring value of the preset monitoring parameter, and then the generated monitoring value sequence is analyzed based on the preset data analysis model corresponding to the preset monitoring parameter to obtain the data analysis result corresponding to the preset monitoring parameter, and then the fault assessment result corresponding to the preset monitoring parameter is determined according to the preset fault assessment standard and data analysis result corresponding to the preset monitoring parameter. If the fault assessment result is a fault, fault warning information is generated according to the preset fault type set corresponding to the preset monitoring parameter and displayed or sent to relevant technical personnel so that the relevant technical personnel can take timely measures.

[0157] It can be seen that the thermal power unit status monitoring method provided in the embodiment of the present application has the advantages of strong real-time performance, high accuracy, and high reliability, and can effectively improve the operating efficiency and safety of the thermal power unit. Specifically, the embodiment of the present application realizes real-time monitoring and fault warning of the operating status of the thermal power unit by real-time acquisition of multi-source data (including sensor data and operating parameter data) of the thermal power unit and rapid analysis and processing. The embodiment of the present application adopts big data analysis technology and specific preset data analysis models to accurately monitor the operating status of the thermal power unit and warn of faults, thereby improving the reliability and safety of the thermal power unit. The embodiment of the present application improves the monitoring efficiency and intelligence level by automatically acquiring data, data preprocessing, feature extraction, data analysis, and fault assessment without manual intervention.

[0158] In addition, the embodiments of the present application can also continuously optimize the preset fault assessment standards corresponding to the preset monitoring parameters by adjusting the samples for big data analysis, thereby adapting to the actual needs of thermal power units of different sizes and types, and has strong scalability.

[0159] Figure 1 FIG. 1 is a flow chart of a method for monitoring the state of a thermal power unit in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0160] Figure 2 The structural block diagram of the thermal power unit state monitoring device according to the embodiment of the present application is schematically shown. Figure 2 As shown, in one embodiment of the present application, a thermal power unit status monitoring device is provided, and the thermal power unit status monitoring device may include the following functional modules.

[0161] The monitoring value acquisition module is used to obtain the monitoring values ​​of multiple preset monitoring parameters of the thermal power unit in real time within a period of time. The preset monitoring parameters are the parameters monitored during the operation of the equipment or system in the thermal power unit.

[0162] The sequence generation module is used to generate a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter within a period of time.

[0163] The data analysis module is used to analyze the monitoring value sequence of each preset monitoring parameter according to the preset data analysis model corresponding to each preset monitoring parameter, and obtain the data analysis result corresponding to each preset monitoring parameter.

[0164] The fault assessment module is used to determine the fault assessment results corresponding to each preset monitoring parameter according to the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter. The preset fault assessment standards corresponding to the preset monitoring parameters are obtained by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameters.

[0165] In the embodiment of the present application, the device further includes a data preprocessing module, and the data preprocessing module is used to:

[0166] The monitoring value sequence of the preset monitoring parameters is subjected to data cleaning, denoising and normalization processing in turn.

[0167] In an embodiment of the present application, the preset data analysis models corresponding to the preset monitoring parameters include a time domain analysis model, a frequency domain analysis model, and a time-frequency domain analysis model.

[0168] In the embodiment of the present application, the data analysis module is specifically used for:

[0169] According to the monitoring value sequence of the preset monitoring parameter, the time domain eigenvalue, frequency domain eigenvalue and time-frequency domain eigenvalue of the preset monitoring parameter are determined through the time domain analysis model, frequency domain analysis model and time-frequency domain analysis model corresponding to the preset monitoring parameter.

[0170] In an embodiment of the present application, the data analysis results corresponding to the preset monitoring parameters include the time domain characteristic values, frequency domain characteristic values ​​and time-frequency domain characteristic values ​​of the preset monitoring parameters.

[0171] In an embodiment of the present application, the preset fault assessment criteria corresponding to the preset monitoring parameters include a first assessment criteria, a second assessment criteria, and a third assessment criteria, and the first assessment criteria, the second assessment criteria, and the third assessment criteria of the preset monitoring parameters respectively correspond one-to-one to the time domain analysis model, the frequency domain analysis model, and the time-frequency domain analysis model corresponding to the preset monitoring parameters.

[0172] In the embodiment of the present application, the fault assessment module is specifically used for:

[0173] If the time domain characteristic value of the preset monitoring parameter meets the first evaluation standard corresponding to the preset monitoring parameter, the frequency domain characteristic value of the preset monitoring parameter meets the second evaluation standard corresponding to the preset monitoring parameter, and the time-frequency domain characteristic value of the preset monitoring parameter meets the third evaluation standard corresponding to the preset monitoring parameter, then it is determined that the fault evaluation result corresponding to the preset monitoring parameter is no fault;

[0174] If the time domain characteristic value of the preset monitoring parameter does not meet the first evaluation standard corresponding to the preset monitoring parameter, and / or the frequency domain characteristic value of the preset monitoring parameter does not meet the second evaluation standard corresponding to the preset monitoring parameter, and / or the time-frequency domain characteristic value of the preset monitoring parameter does not meet the third evaluation standard corresponding to the preset monitoring parameter, then the fault assessment result corresponding to the preset monitoring parameter is determined to be faulty.

[0175] In the embodiment of the present application, the device further includes a fault reminder module, and the fault reminder module is used to:

[0176] If the fault assessment result corresponding to the preset monitoring parameter is a fault, fault warning information is generated according to the preset fault type set corresponding to the preset monitoring parameter, and the fault warning information is displayed through the display terminal of the thermal power unit, and / or the fault warning information is played through the voice module of the thermal power unit.

[0177] In the embodiment of the present application, the device further includes a data uploading module, and the data uploading module is used to:

[0178] The monitoring value sequence of the preset monitoring parameters and the corresponding fault assessment results are stored in the cloud.

[0179] Since the thermal power unit status monitoring device provided in the embodiment of the present application is a virtual device corresponding to the thermal power unit status monitoring method of the above embodiment, it can also solve the problems of low monitoring efficiency and low fault assessment accuracy in the traditional thermal power unit status monitoring method in the prior art.

[0180] An embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for monitoring the state of a thermal power unit described in the above embodiment when executing the computer program.

[0181] The electronic device provided in the embodiment of the present application includes a processor capable of running the thermal power unit status monitoring method of the aforementioned embodiment, and can therefore also solve the problems of low monitoring efficiency and low fault assessment accuracy in the traditional thermal power unit status monitoring method in the prior art.

[0182] An embodiment of the present application provides a machine-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the processor is configured to execute the thermal power unit status monitoring method described in the above embodiment.

[0183] The machine-readable storage medium provided in the embodiment of the present application stores instructions for enabling the machine to execute the thermal power unit status monitoring method of the above embodiment, and can therefore also solve the problems of low monitoring efficiency and low fault assessment accuracy in the traditional thermal power unit status monitoring method in the prior art.

[0184] Figure 3 The internal structure diagram of the computer device of the embodiment of the present application is schematically shown. Figure 3 As shown, in one embodiment of the present application, a computer device is provided, which may be a terminal. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a method for monitoring the state of a thermal power unit is implemented. The display screen A04 of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device A05 of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0185] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0186] In one embodiment, the thermal power unit state monitoring device provided in the present application can be implemented in the form of a computer program. The computer program can be Figure 3 The computer device is run on the computer device shown. The memory of the computer device can store various program modules constituting the thermal power unit state monitoring device, and the computer program composed of various program modules enables the processor to execute the steps of the thermal power unit state monitoring method of each embodiment of the present application described in this specification.

[0187] Figure 3 The computer device shown can be Figure 2 In the thermal power unit status monitoring device shown, the monitoring value acquisition module executes step 200 , the sequence generation module executes step 400 , the data analysis module executes step 600 , and the fault assessment module executes step 800 .

[0188] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0189] 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. 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.

[0190] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0192] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0193] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0194] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0195] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0196] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for monitoring the state of a thermal power unit, characterized in that: The method comprises: Acquire monitoring values ​​of multiple preset monitoring parameters of the thermal power unit in real time over a period of time; wherein the preset monitoring parameters are parameters monitored during the operation of the equipment or system in the thermal power unit; Generate a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter within a period of time; According to the preset data analysis model corresponding to each preset monitoring parameter, the monitoring value sequence of each preset monitoring parameter is analyzed respectively to obtain the data analysis result corresponding to each preset monitoring parameter; According to the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter, the fault assessment results corresponding to each preset monitoring parameter are determined; wherein the preset fault assessment standards corresponding to the preset monitoring parameters are obtained by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameters.

2. The method according to claim 1, characterized in that: Before analyzing the monitoring value sequence of the preset monitoring parameter, the method further includes: The monitoring value sequence of the preset monitoring parameters is subjected to data cleaning, denoising and normalization processing in turn.

3. The method according to claim 1, characterized in that: The preset data analysis models corresponding to the preset monitoring parameters include a time domain analysis model, a frequency domain analysis model and a time-frequency domain analysis model.

4. The method according to claim 3, characterized in that According to the preset data analysis model corresponding to each preset monitoring parameter, the monitoring value sequence of each preset monitoring parameter is analyzed respectively to obtain the data analysis results corresponding to each preset monitoring parameter, including: According to the monitoring value sequence of the preset monitoring parameter, the time domain characteristic value, the frequency domain characteristic value and the time-frequency domain characteristic value of the preset monitoring parameter are determined through the time domain analysis model, the frequency domain analysis model and the time-frequency domain analysis model corresponding to the preset monitoring parameter; Among them, the data analysis results corresponding to the preset monitoring parameters include the time domain characteristic values, frequency domain characteristic values ​​and time-frequency domain characteristic values ​​of the preset monitoring parameters.

5. The method according to claim 3, characterized in that: The preset fault assessment criteria corresponding to the preset monitoring parameters include a first assessment criteria, a second assessment criteria and a third assessment criteria, and the first assessment criteria, the second assessment criteria and the third assessment criteria of the preset monitoring parameters correspond one-to-one to the time domain analysis model, the frequency domain analysis model and the time-frequency domain analysis model corresponding to the preset monitoring parameters respectively; According to the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter, the fault assessment results corresponding to each preset monitoring parameter are determined, including: If the time domain characteristic value of the preset monitoring parameter meets the first evaluation standard corresponding to the preset monitoring parameter, the frequency domain characteristic value of the preset monitoring parameter meets the second evaluation standard corresponding to the preset monitoring parameter, and the time-frequency domain characteristic value of the preset monitoring parameter meets the third evaluation standard corresponding to the preset monitoring parameter, then it is determined that the fault evaluation result corresponding to the preset monitoring parameter is no fault; If the time domain characteristic value of the preset monitoring parameter does not meet the first evaluation standard corresponding to the preset monitoring parameter, and / or the frequency domain characteristic value of the preset monitoring parameter does not meet the second evaluation standard corresponding to the preset monitoring parameter, and / or the time-frequency domain characteristic value of the preset monitoring parameter does not meet the third evaluation standard corresponding to the preset monitoring parameter, then the fault assessment result corresponding to the preset monitoring parameter is determined to be faulty.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: If the fault assessment result corresponding to the preset monitoring parameter is a fault, fault warning information is generated according to the preset fault type set corresponding to the preset monitoring parameter, and the fault warning information is displayed through the display terminal of the thermal power unit, and / or the fault warning information is played through the voice module of the thermal power unit.

7. The method according to any one of claims 1 to 5, characterized in that The method further comprises: The monitoring value sequence of the preset monitoring parameters and the corresponding fault assessment results are stored in the cloud.

8. A thermal power unit status monitoring device, characterized in that: The device comprises: A monitoring value acquisition module, used for acquiring in real time the monitoring values ​​of a plurality of preset monitoring parameters of the thermal power unit within a period of time; wherein the preset monitoring parameters are parameters monitored during the operation of the equipment or system in the thermal power unit; A sequence generation module, used to generate a monitoring value sequence of each preset monitoring parameter according to the monitoring value of each preset monitoring parameter within a period of time; A data analysis module is used to analyze the monitoring value sequence of each preset monitoring parameter according to the preset data analysis model corresponding to each preset monitoring parameter, and obtain the data analysis result corresponding to each preset monitoring parameter; The fault assessment module is used to determine the fault assessment results corresponding to each preset monitoring parameter based on the preset fault assessment standards and data analysis results corresponding to each preset monitoring parameter; wherein the preset fault assessment standards corresponding to the preset monitoring parameters are obtained by performing big data analysis on the historical monitoring values ​​of the preset monitoring parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for monitoring the state of a thermal power unit according to any one of claims 1 to 7 is implemented.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the method for monitoring the state of a thermal power unit according to any one of claims 1 to 7.

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