Power equipment system real-time dynamic monitoring method and system based on Internet of Things

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a power equipment system real-time dynamic monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring different data types; forming vibration sequences for the vibration signals, and obtaining fitting curve functions of all the vibration sequences; determining a trend sequence based on the variation coefficient of the vibration sequence; combining the trend sequence and the vibration sequence with a fitting curve function to determine a vibration trend coefficient; clustering the period based on the vibration trend coefficient, respectively obtaining a local change trend and an overall change trend based on cluster data and all data, and obtaining an equipment disturbance coefficient based on the local change trend and the overall change trend; and determining a prediction function based on the equipment disturbance coefficient, and obtaining a corrected vibration signal in combination with the vibration trend coefficient difference of adjacent periods, thereby completing dynamic monitoring. According to the invention, the monitoring accuracy of the power equipment fault is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of electric variable measurement, and specifically to a real-time dynamic monitoring method and system for electric power equipment systems based on the Internet of Things. Background Art

[0002] With the rapid development of smart grids and the energy internet, power equipment, as critical infrastructure of the power system, faces a significant impact on grid security, stability, and power supply reliability. In recent years, IoT technology has significantly empowered the field of power equipment monitoring. By deploying multiple types of sensors to collect real-time operating parameters such as temperature, vibration, and current, combined with edge computing and cloud-based intelligent analysis, this technology is driving the transformation and upgrade of power equipment monitoring from "periodic maintenance" to "real-time dynamic prediction." Sensor monitoring data is analyzed to determine whether power equipment is experiencing anomalies. Therefore, accurate sensor data analysis and mining is crucial to ensuring the quality of dynamic monitoring of power equipment.

[0003] However, existing methods for using sensor data often rely on setting thresholds or performing simple trend analysis. This involves detecting faults based on the relationship between sensor data and set thresholds, or predicting power equipment faults based on changes in historical sensor data. These methods are highly dependent on the accuracy of sensor data. In actual sensor monitoring, there is a high probability of sensor drift due to factors such as environmental changes, aging, or power fluctuations. This can lead to errors in the analysis results of the monitoring data, resulting in significant errors in the monitoring of power equipment. Therefore, a more accurate method for dynamic monitoring of power equipment based on the Internet of Things is urgently needed. Summary of the Invention

[0004] In order to solve the technical problem of low monitoring accuracy, this application provides a real-time dynamic monitoring method and system for power equipment systems based on the Internet of Things. The technical solutions adopted are as follows: In a first aspect, the present application proposes a method for real-time dynamic monitoring of power equipment systems based on the Internet of Things, the method comprising the following steps: Obtain vibration signals, voltage data, current data, and device temperature over time for each monitoring point in each cycle; The vibration signal within a cycle is formed into a vibration sequence, and the vibration sequence is converted into a frequency spectrum and then fitted to obtain a fitting curve function; for each monitoring point, the coefficient of variation of the vibration sequence in all cycles before each cycle is sorted to obtain the trend sequence of the cycle; the correlation of the vibration sequence and the correlation of the trend sequence between monitoring points in the same cycle are recorded as the horizontal correlation measure and the vertical correlation measure, respectively; the amplitude change trend is determined by the fitting curve function; the vibration trend coefficient is determined by the horizontal correlation measure and the vertical correlation measure between all monitoring points in each cycle and the amplitude, fluctuation and amplitude change trend of the vibration sequence of all monitoring points in each cycle; Cluster the cycles based on the vibration trend coefficient; record the voltage data, current data, and device temperature as data types; for each data type, record the data changes in the cluster and the changes in all data as local change trends and overall change trends, respectively; obtain the device disturbance coefficient of each cycle based on the local change trends and overall change trends; The equipment disturbance coefficient is fitted to determine the fluctuation prediction function. Based on the fluctuation prediction function and the difference in vibration trend coefficients of adjacent periods, the vibration signal is corrected using an optimization algorithm to obtain the corrected vibration signal, and dynamic monitoring is completed.

[0005] In the above scheme, the present application first calculates the vibration trend coefficient based on the change of the vibration signal in the monitoring of the power equipment. This indicator quantifies the change trend of the vibration data from the two aspects of the lateral change and longitudinal change of the vibration data, thereby enhancing the accuracy of measuring the performance change of the power equipment; then, the equipment disturbance coefficient is calculated in combination with the electrical parameters and equipment temperature of the power equipment. This indicator quantifies the change of the global parameters of the power equipment, improves the distinction between the fault factors and non-fault factors that affect the monitoring parameters of the power equipment, and measures the degree of disturbance, which helps to enhance the detection accuracy of power equipment failures. Through this method, the lateral and longitudinal changes of the vibration data are taken into account, and the degree of influence of the fault factors is quantified based on the changes in the global parameters, thereby enhancing the removal of the influence of non-fault factors and realizing the correction of the vibration data. The corrected vibration data can more accurately reflect the fault condition of the power equipment, thereby realizing a more accurate dynamic monitoring method of power equipment based on the Internet of Things and enhancing the monitoring accuracy of power equipment failures.

[0006] In one embodiment, the method of converting the vibration sequence into a frequency spectrum and then fitting to obtain a fitting curve function is: The vibration sequence of each periodic monitoring point is used as input, and the corresponding spectrum is obtained by Fourier transform. In the spectrum, the frequency is used as the horizontal axis and the corresponding amplitude is used as the vertical axis to perform polynomial fitting and output the fitting curve function.

[0007] In one embodiment, the vibration trend coefficient is positively correlated with the transverse correlation measure and the longitudinal correlation measure; and negatively correlated with the amplitude, fluctuation, and amplitude variation trend of the vibration sequence.

[0008] In one embodiment, the amplitude of the vibration sequence is the mean of all peak values ​​in the vibration sequence, the fluctuation of the vibration sequence is the variance of all amplitudes in the vibration sequence, and the amplitude change trend of the vibration sequence is the proportion of data points with positive derivatives in the fitting curve function.

[0009] In one embodiment, the device temperature of each cycle is the device temperature within the time interval of the cycle.

[0010] In one embodiment, the method of recording the data changes in the cluster and the changes of all data as local change trends and overall change trends respectively is: Sort the periods of each data type in each cluster in time sequence, use its data value as the horizontal axis and the corresponding time sequence as the vertical axis to perform polynomial fitting to obtain the polynomial function of each data type, which is recorded as the first polynomial function; Sort all cycles of each data type in time sequence, use its data value as the horizontal axis and the corresponding time sequence as the vertical axis to perform polynomial fitting to obtain the polynomial function of each data type, which is recorded as the second polynomial function; The first polynomial function and the second polynomial function are respectively integrated to obtain the overall change trend and the local change trend.

[0011] In one embodiment, the method of integrating the first polynomial function and the second polynomial function to obtain the overall change trend and the local change trend is: The overall trend of change is the integral area of ​​the second polynomial function; The expression of local change trend is: , represents the sum of the vibration trend coefficients in the jth cluster, represents the sum of all vibration tendency coefficients, represents the integral area of ​​the first polynomial function of the mth data type in the jth cluster, Indicates the local change trend of the mth data type in the i-th period.

[0012] In one embodiment, the equipment disturbance coefficient is positively correlated with the difference between the overall change trend and the local change trend.

[0013] In one embodiment, the objective function of the optimization algorithm is: , is the vibration trend coefficient of the vth iteration of the xth period, is the vibration trend coefficient of the x-1th period, represents the volatility forecast value of the xth period, represents the target value of the vth iteration of the xth cycle.

[0014] In the second aspect, an embodiment of the present application also provides a real-time dynamic monitoring system for an electric power equipment system based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for real-time dynamic monitoring of an electric power equipment system based on the Internet of Things.

[0015] The beneficial effects of this application are: This application first calculates the vibration trend coefficient based on the change of the vibration signal in the monitoring of the power equipment. This indicator quantifies the change trend of the vibration data from the two aspects of the lateral change and longitudinal change of the vibration data, thereby enhancing the accuracy of measuring the performance change of the power equipment; then, in combination with the electrical parameters and equipment temperature of the power equipment, the equipment disturbance coefficient is calculated. This indicator quantifies the global parameter changes of the power equipment, improves the distinction between fault factors and non-fault factors that affect the monitoring parameters of the power equipment, and measures the degree of disturbance, which helps to enhance the detection accuracy of power equipment failures. Through this method, the lateral and longitudinal changes of the vibration data are taken into account, and the degree of influence of the fault factors is quantified based on the changes in the global parameters, thereby enhancing the removal of the influence of non-fault factors and realizing the correction of the vibration data. The corrected vibration data can more accurately reflect the fault condition of the power equipment, thereby realizing a more accurate dynamic monitoring method of power equipment based on the Internet of Things and enhancing the monitoring accuracy of power equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 A flowchart of a method for real-time dynamic monitoring of power equipment systems based on the Internet of Things is provided as an embodiment of the present application. DETAILED DESCRIPTION

[0018] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a real-time dynamic monitoring method and system for an IoT-based power equipment system proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0020] A real-time dynamic monitoring method and system embodiment of an electric power equipment system based on the Internet of Things: The following describes in detail a method and system for real-time dynamic monitoring of power equipment systems based on the Internet of Things provided by this application with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of a method for real-time dynamic monitoring of an electric power equipment system based on the Internet of Things provided by an embodiment of the present application, the method comprising the following steps: Step S001: collecting vibration signals, voltage data, current data and device temperature.

[0022] Set on the front, bottom and outer surface of the high-voltage side of the power equipment Monitoring points are set up at each monitoring point; a piezoelectric accelerometer is used to collect the vibration signal of the power equipment. In this embodiment, the vibration signal collection frequency is 20kHz. Voltage and current data are then collected at the input and output terminals of the power equipment using voltage transformers and current transformers, respectively. In this embodiment, the collection frequency is 10kHz. The collection period for the vibration signal, voltage data, and current data is determined. In this embodiment, the collection period is 1s. Thermocouple sensors are used at key components of the power equipment to collect the device temperature of the power equipment. The device temperature is collected once every preset time interval, and the time interval between the two adjacent device temperature collections is obtained. The average of the two collected device temperatures is used as the device temperature during the time interval. In this embodiment, the time interval is 30s.

[0023] At the perception layer of the Internet of Things, monitoring data of power equipment is collected through various sensors in the above manner, and then the collected data is transmitted to the application layer through the power Internet of Things communication network at the network layer. The application layer analyzes these monitoring data to achieve dynamic monitoring of power equipment.

[0024] At this point, the vibration signal, voltage data, current data and equipment temperature in each time interval of each monitoring point are collected.

[0025] Step S002: The vibration signals constitute a vibration sequence, and a fitting curve function of all vibration sequences is obtained; a trend sequence is determined based on the coefficient of variation of the vibration sequence; and a vibration trend coefficient is determined by combining the trend sequence and the vibration sequence with the fitting curve function.

[0026] Various types of power equipment are responsible for ensuring the stable operation and monitoring protection of the power system. Therefore, monitoring of power equipment is crucial to the power system. The development of Internet of Things technology has enhanced the real-time monitoring capabilities of power equipment, and has achieved comprehensive monitoring of the operating status of power equipment through a variety of sensors in the perception layer. In the analysis of these sensor monitoring data, existing technologies mostly rely on the accuracy of sensor data. However, in actual applications, due to the complexity of the environment in which the power equipment is located, the detection accuracy of the sensor will inevitably be affected. These errors will lead to misjudgments or omissions in the judgment of power equipment fault conditions. Therefore, it is necessary to enhance the ability to analyze and utilize sensor data, further eliminate errors as much as possible, and enhance the accuracy of power equipment monitoring.

[0027] As a highly sensitive mechanical device, changes in operating conditions or equipment performance can affect its operation, whether by altering its response speed, affecting its motion patterns, or affecting its electromagnetic field distribution. These factors, in turn, can cause changes in the equipment's vibration. Under normal circumstances, due to good performance, the vibration amplitude and fluctuations are relatively small, with larger amplitudes near the fundamental wave and smaller or barely noticeable amplitudes of high-frequency components. Furthermore, changes in vibration are primarily affected by operating conditions, which represent overall fluctuations for different monitoring points on the equipment. Therefore, there should be a high degree of correlation between the vibration conditions and variations across monitoring points. When equipment performance degrades, it affects its motion, increasing vibration fluctuations during operation. This performance degradation also increases the energy of high-frequency components in the vibration signal. Furthermore, since equipment performance degradation is often due to the performance degradation of certain components, the vibration signal near these components is significantly affected. The farther away from these components, the smaller the impact. Furthermore, the more affected a monitoring point is, the greater the difference in vibration signal variation from other points.

[0028] For each monitoring point, the vibration signal of each cycle is combined into a vibration sequence. Using the vibration sequence of each monitoring point as input, a Fourier transform is used to obtain the corresponding spectrum. Within the spectrum, a polynomial fit is performed with frequency as the horizontal axis and the corresponding amplitude as the vertical axis, outputting the fitted curve function. Fourier transform and polynomial fitting are well-known techniques and will not be further described in detail.

[0029] Calculate the coefficient of variation of the vibration sequence of each monitoring point in each cycle; for each monitoring point, sort the coefficients of variation of all cycles before each cycle in chronological order to obtain the trend sequence of the monitoring point in each cycle.

[0030] When the performance of the power equipment is good, its vibration is relatively stable, so that the amplitude and fluctuation of the vibration are small, the low-frequency component has a higher amplitude, and the high-frequency component is less, that is, the amplitude of the high-frequency component is basically not obvious, so the amplitude change from low frequency to high frequency shows an obvious decreasing trend feature; secondly, the vibration conditions between different monitoring points on the power equipment have a high similarity, and the longitudinal time series changes of the vibration data between different monitoring points also have a high similarity, so the vibration trend coefficient is relatively large.

[0031] The correlation of the vibration sequence between the monitoring points in the same period is used as the horizontal correlation measurement, and the correlation of the trend sequence between the monitoring points in the same period is used as the vertical correlation measurement; in this embodiment, the correlation calculation method is the Pearson correlation coefficient, and the implementer can use other correlation calculation methods.

[0032] The vibration trend coefficient is determined by the lateral correlation measurement and longitudinal correlation measurement between all monitoring points in each cycle and the amplitude, fluctuation and amplitude change trend of the vibration sequence of all monitoring points in each cycle.

[0033] The vibration trend coefficient is positively correlated with the horizontal correlation measure and the vertical correlation measure; and is negatively correlated with the amplitude, fluctuation and amplitude change trend of the vibration sequence.

[0034] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large; the specific relationship is determined by actual application and this application does not impose any special restrictions.

[0035] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by actual application and this application does not impose any special restrictions.

[0036] Preferably, the expression of the vibration tendency coefficient is: , represents the mean of the horizontal correlation measure between all monitoring points in the i-th period, represents the mean of the longitudinal correlation measure among all monitoring points in the i-th period; represents the mean value of all peak values ​​in the vibration sequence of the nth monitoring point in the i-th cycle, represents the variance of the amplitude in the vibration sequence of the nth monitoring point in the ith period, It represents the proportion of positive derivatives of each point in the fitting curve function corresponding to the nth monitoring point in the i-th cycle, represents the number of monitoring points, Represents the vibration trend coefficient of the i-th cycle.

[0037] The amplitude of the vibration sequence is the mean of all peak values ​​in the vibration sequence; the larger the mean, the larger the amplitude; the fluctuation of the vibration sequence is the variance of all amplitudes in the vibration sequence; and if the derivative in the fitting curve function is a positive number, it means that the point is an increasing point, and the proportion of positive derivatives can be regarded as the number of increasing points, that is, the amplitude change trend. The more increasing points there are, the lower the decreasing trend is, the more unstable the vibration is, and the smaller the vibration trend coefficient is.

[0038] At this point, the vibration trend coefficient of each cycle is obtained.

[0039] Step S003: clustering the cycles based on the vibration trend coefficient, obtaining the local change trend and the overall change trend based on the clustered cluster data and all the data respectively, and obtaining the equipment disturbance coefficient based on the two.

[0040] The vibration trend coefficient measures the performance changes of power equipment from the perspective of vibration signal changes. However, this performance measurement can only measure the performance change trend from the perspective of the vibration signal. However, due to the different vibration signal change trends during the operation of power equipment, the vibration signals at different monitoring points have different changes. At the same time, due to the different change trends of the vibration signals, the degree of influence of the disturbance is also different. For example, when the power quality of the power input of the power equipment fluctuates, the power equipment fault point may be more affected by the power fluctuation. Therefore, only through the vibration signal analysis of the vibration trend coefficient, it is impossible to determine which monitoring point vibration signal is similar to the disturbance of the global monitoring data of the power equipment. It is also impossible to correct the monitoring data of the power equipment, which will affect the subsequent power equipment fault detection. Therefore, it is necessary to further combine the global parameter changes of the power equipment such as electrical parameters and equipment temperature to conduct disturbance analysis of the power equipment monitoring data, and then determine the degree of disturbance of the power equipment.

[0041] For each cycle's vibration trend coefficient, the vibration trend coefficients of all cycles preceding it are used as input, and a K-means clustering algorithm is used to classify all cycles. The K value in the clustering algorithm is determined using the elbow method. K-means clustering and the elbow method are well-known techniques and will not be described in detail here.

[0042] Record voltage data, current data, and device temperature as data types; For device temperature, the device temperature of the time interval is regarded as the device temperature of the period within the time interval; The periods of each data type in each cluster are sorted in time series order, and the data values ​​are used as the horizontal axis and the corresponding time series as the vertical axis to perform polynomial fitting to obtain the polynomial function of each data type, which is recorded as the first polynomial function.

[0043] All cycles of each data type are sorted in time series order, and the data values ​​are used as the horizontal axis and the corresponding time series as the vertical axis to perform polynomial fitting to obtain the polynomial function of each data type, which is recorded as the second polynomial function. The horizontal axis lengths of the first polynomial function and the second polynomial function are the same.

[0044] Because global data types such as device temperature, voltage, and current data are caused by non-fault-related factors, clustering cycles using a clustering algorithm results in relatively uniform characteristics within each cycle. Therefore, the device disturbance coefficient can be determined by comparing the clusters with the overall data. Therefore, the initial device disturbance coefficient is adjusted based on the difference between the overall and local trends to determine the device disturbance coefficient for each cycle. The overall trend is the integral area of ​​the second polynomial function, and the trend of the locally integrated data type is the weighted sum of the integral areas of all clusters.

[0045] The equipment disturbance coefficient is positively correlated with the difference between the overall change trend and the local change trend.

[0046] Firstly, the ratio of the sum of the vibration trend coefficients in each cluster to the sum of all vibration trends is used as the weight to weight the first polynomial function to obtain the local change trend.

[0047] Preferably, the expression of the local change trend is: , represents the sum of the vibration trend coefficients in the jth cluster, represents the sum of all vibration tendency coefficients, represents the integral area of ​​the first polynomial function of the mth data type in the jth cluster, Indicates the local change trend of the mth data type in the i-th period.

[0048] Preferably, in this embodiment, the expression of the device disturbance coefficient is: , Indicates the overall change trend of the mth data type in the i-th period, Indicates the local change trend of the mth data type in the i-th period, Indicates the number of data types, Indicates the initial value of the device disturbance coefficient. In this embodiment, the initial value is 1. represents the equipment disturbance coefficient of the i-th cycle.

[0049] It can be understood that the more similar the vibration changes are, the more similar the corresponding power equipment performance is, so that the global parameter differences such as the corresponding equipment temperature, voltage, and current are mainly caused by non-fault reasons. Therefore, by performing curve fitting on each cluster separately, the change trend of the power equipment parameters under non-fault reasons can be determined; and the actual change trend of the equipment temperature, voltage, and current data obtained is the coupling result of fault and non-fault reasons, so the change trend under non-fault reasons can be used to measure the impact of fault causes; secondly, since the data change trends in each cluster may be different, when the impact of the fault cause is greater, it will cause the data change to be difficult to completely eliminate the impact of the fault cause, so the results of different clusters are integrated by weighted averaging, so the obtained equipment disturbance coefficient is the data fluctuation caused by the fault cause.

[0050] At this point, the equipment disturbance coefficient of the i-th cycle is obtained.

[0051] Step S004: determining a prediction function based on the equipment disturbance coefficient, and obtaining a corrected vibration signal in combination with the difference in vibration trend coefficients of adjacent cycles to complete dynamic monitoring.

[0052] When monitoring power equipment, the equipment disturbance coefficient of each cycle is obtained, and the equipment disturbance coefficient is used as the horizontal coordinate, and the difference in vibration trend coefficients of two adjacent cycles is used as the vertical coordinate. Polynomial fitting is performed to obtain a data fluctuation prediction function.

[0053] For each cycle of the vibration signal, obtain the equipment disturbance coefficient and vibration trend coefficient for that cycle, and then obtain the vibration trend coefficient for the cycle before that cycle. Substitute the equipment disturbance coefficient for that cycle into the fluctuation prediction function to obtain the fluctuation prediction value for that cycle.

[0054] The objective function is determined based on the difference between the fluctuation prediction value and the vibration trend coefficient difference between two adjacent cycles. The objective function is continuously iterated through an optimization algorithm to obtain the minimum value of the objective function. The vibration signal corresponding to the minimum value is used as the corrected vibration signal for that cycle.

[0055] The objective function of the optimization algorithm is: , is the vibration trend coefficient of the vth iteration of the xth period, is the vibration trend coefficient of the x-1th period, represents the volatility forecast value of the xth period, represents the target value of the vth iteration of the xth cycle.

[0056] Under the preset number of iterations, the vibration signal corresponding to the iteration corresponding to the minimum value of the target value of the objective function is taken as the corrected vibration signal. In this embodiment, the number of iterations is 100.

[0057] The corrected vibration signal is used to dynamically monitor the fault status of the power equipment using a vibration test method. The vibration test method is a well-known technology in the field of non-electrical quantity detection of power equipment and will not be described in detail.

[0058] At this point, the dynamic monitoring of power equipment is completed.

[0059] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a real-time dynamic monitoring system for an electric power equipment system based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for real-time dynamic monitoring of an electric power equipment system based on the Internet of Things.

[0060] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

[0061] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A real-time dynamic monitoring method for power equipment system based on the Internet of Things, characterized in that: The method comprises the following steps: Obtain vibration signals, voltage data, current data, and device temperature over time for each monitoring point in each cycle; The vibration signal within a cycle is formed into a vibration sequence, and the vibration sequence is converted into a frequency spectrum and then fitted to obtain a fitting curve function; for each monitoring point, the coefficient of variation of the vibration sequence in all cycles before each cycle is sorted to obtain the trend sequence of the cycle; the correlation of the vibration sequence and the correlation of the trend sequence between monitoring points in the same cycle are recorded as the horizontal correlation measure and the vertical correlation measure, respectively; the amplitude change trend is determined by the fitting curve function; the vibration trend coefficient is determined by the horizontal correlation measure and the vertical correlation measure between all monitoring points in each cycle and the amplitude, fluctuation and amplitude change trend of the vibration sequence of all monitoring points in each cycle; Cluster the cycles based on the vibration trend coefficient; record the voltage data, current data, and device temperature as data types; for each data type, record the data changes in the cluster and the changes in all data as local change trends and overall change trends, respectively; obtain the device disturbance coefficient of each cycle based on the local change trends and overall change trends; The equipment disturbance coefficient is fitted to determine the fluctuation prediction function. Based on the fluctuation prediction function and the difference in vibration trend coefficients of adjacent periods, the vibration signal is corrected using an optimization algorithm to obtain the corrected vibration signal, and dynamic monitoring is completed.

2. A method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 1, characterized in that: The method of converting the vibration sequence into a frequency spectrum and then fitting to obtain a fitting curve function is: The vibration sequence of each periodic monitoring point is used as input, and the corresponding spectrum is obtained by Fourier transform. In the spectrum, the frequency is used as the horizontal axis and the corresponding amplitude is used as the vertical axis to perform polynomial fitting and output the fitting curve function.

3. The method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 1, characterized in that: The vibration trend coefficient is positively correlated with the horizontal correlation measure and the vertical correlation measure; and is negatively correlated with the amplitude, fluctuation and amplitude change trend of the vibration sequence.

4. A method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 3, characterized in that: The amplitude of the vibration sequence is the mean of all peak values ​​in the vibration sequence, the fluctuation of the vibration sequence is the variance of all amplitudes in the vibration sequence, and the amplitude change trend of the vibration sequence is the proportion of data points with positive derivatives in the fitting curve function.

5. The method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 1, characterized in that: The device temperature of each cycle is the device temperature within the time interval of the cycle.

6. The method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 1, characterized in that: The method of recording the data changes in the cluster and the changes of all data as local change trends and overall change trends respectively is: Sort the periods of each data type in each cluster in time sequence, use its data value as the horizontal axis and the corresponding time sequence as the vertical axis to perform polynomial fitting to obtain the polynomial function of each data type, which is recorded as the first polynomial function; Sort all cycles of each data type in time sequence, use its data value as the horizontal axis and the corresponding time sequence as the vertical axis to perform polynomial fitting to obtain the polynomial function of each data type, which is recorded as the second polynomial function; The first polynomial function and the second polynomial function are respectively integrated to obtain the overall change trend and the local change trend.

7. A method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 6, characterized in that: The method of integrating the first polynomial function and the second polynomial function to obtain the overall change trend and the local change trend is: The overall trend of change is the integral area of ​​the second polynomial function; The expression of local change trend is: , represents the sum of the vibration trend coefficients in the jth cluster, represents the sum of all vibration tendency coefficients, represents the integral area of ​​the first polynomial function of the mth data type in the jth cluster, Indicates the local change trend of the mth data type in the i-th period.

8. The method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 1, characterized in that: The equipment disturbance coefficient is positively correlated with the difference between the overall change trend and the local change trend.

9. The method for real-time dynamic monitoring of power equipment system based on Internet of Things according to claim 1, characterized in that: The objective function of the optimization algorithm is: , is the vibration trend coefficient of the vth iteration of the xth period, is the vibration trend coefficient of the x-1th period, represents the volatility forecast value of the xth period, represents the target value of the vth iteration of the xth cycle.

10. A real-time dynamic monitoring system for power equipment systems based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the real-time dynamic monitoring method of the power equipment system based on the Internet of Things as described in any one of claims 1 to 9 are implemented.

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