Online monitoring and early warning method for water cooling system based on Internet of Things

Abstract

The invention relates to the technical field of data processing, in particular to a water cooling system online monitoring and early warning method based on the Internet of Things. The method comprises the following steps: collecting the outlet water temperature of the water cooling system during operation to obtain a time sequence temperature data sequence; acquiring initial abnormal data in the time sequence temperature data sequence, and utilizing an ARIMA prediction model to predict a difference coefficient and an autoregressive coefficient when the outlet water temperature of the water cooling system in the future time; constructing an initial window, and obtaining an optimal moving average coefficient of the ARIMA prediction model according to the degree of interference and data fluctuation characteristics of the initial window; according to the autoregression coefficient, the difference coefficient and the optimal moving average coefficient, the outlet water temperature of the water cooling system in the future time is predicted, a predicted value is obtained, and abnormity early warning is carried out according to the predicted value. According to the method, the outlet water temperature of the water cooling system in the future time is accurately predicted, and then effective early warning is carried out on the outlet water temperature of the water cooling system.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an online monitoring and early warning method for a water cooling system based on the Internet of Things. Background Art

[0002] With the continuous advancement of industrial technology and the rapid development of Internet of Things technology, water cooling systems are increasingly being used in multiple fields, such as petrochemical, electric power, steel, metallurgy and other industries. Water cooling systems are essential to ensure the stable operation of high-temperature equipment. However, if the heat is not removed in time, it may cause serious safety hazards. The fault detection and handling methods of traditional water cooling systems mainly rely on manual inspection and manual intervention, with slow response speed, unable to prevent and handle system failures in time, and easily causing equipment downtime and production interruptions. Therefore, it is necessary to monitor and predict the outlet water temperature of the water cooling system in real time to prevent equipment operation problems.

[0003] The traditional method uses the ARIMA prediction model to determine the sliding average coefficient by analyzing the ACF diagram and PACF diagram of the time series data, and predicts future data based on the sliding average coefficient. However, since the outlet water temperature of the water cooling system is affected by the operating status of the equipment, heat exchange efficiency, ambient temperature, etc., the outlet water temperature is a fluctuating data. In addition, when using NTC thermistors to collect the outlet water temperature of the water cooling system, there may be various external interference factors in the system operating environment, such as temperature changes, mechanical vibrations, electromagnetic interference, etc. These factors may affect the measurement of the NTC thermistor, causing the collected outlet water temperature to be disturbed and produce abnormal performance. Therefore, the abnormal data caused by external interference may show characteristics similar to the real abnormal data in the ACF diagram and PACF diagram, making the sliding average coefficient not the optimal sliding average coefficient, resulting in inaccurate prediction of future data, and thus difficult to effectively warn the outlet water temperature of the water cooling system.

[0004] Therefore, how to accurately predict the outlet water temperature of the water cooling system in the future and then provide effective early warning for the outlet water temperature of the water cooling system has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an online monitoring and early warning method for a water cooling system based on the Internet of Things to solve the problem of how to accurately predict the outlet water temperature of the water cooling system in the future, and then effectively warn the outlet water temperature of the water cooling system.

[0006] An embodiment of the present invention provides an online monitoring and early warning method for a water cooling system based on the Internet of Things, the method comprising the following steps: Collect the outlet water temperature of the water cooling system during operation within a preset time range to obtain a time series temperature data sequence; Performing time series analysis on the time series temperature data sequence, obtaining the differential coefficient and the autoregressive coefficient when predicting the outlet water temperature of the water cooling system at a future time using the ARIMA prediction model, and constructing an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model in the time series temperature data sequence according to the autoregressive coefficient of the ARIMA prediction model; According to the data fluctuation characteristics in the time series temperature data sequence, initial abnormal data in the time series temperature data sequence is obtained, according to the recoverability and continuity of the initial abnormal data in the initial window in the time series temperature data sequence, the interference degree of the initial window is obtained, and according to the interference degree of the initial window and the data fluctuation characteristics in the initial window, the optimal sliding average coefficient of the ARIMA forecasting model is obtained; According to the autoregressive coefficient, differential coefficient, and optimal sliding average coefficient of the ARIMA prediction model, the outlet water temperature of the water cooling system in the future is predicted to obtain a predicted value, and according to the predicted value, an abnormal warning is issued for the outlet water temperature of the water cooling system in the future.

[0007] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention collects the outlet water temperature of the water cooling system when it is running within a preset time range to obtain a time series temperature data sequence; performs time series analysis on the time series temperature data sequence to obtain the differential coefficient and the autoregressive coefficient when the outlet water temperature of the water cooling system in the future is predicted using the ARIMA prediction model, and constructs an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model in the time series temperature data sequence according to the autoregressive coefficient of the ARIMA prediction model; obtains the initial abnormal data in the time series temperature data sequence according to the data fluctuation characteristics in the time series temperature data sequence, obtains the interference degree of the initial window according to the recoverability and continuity of the initial abnormal data in the initial window in the time series temperature data sequence, and obtains the optimal sliding average coefficient of the ARIMA prediction model according to the interference degree of the initial window and the data fluctuation characteristics in the initial window; predicts the outlet water temperature of the water cooling system in the future according to the autoregressive coefficient, the differential coefficient, and the optimal sliding average coefficient of the ARIMA prediction model to obtain a predicted value, and performs an abnormal warning on the outlet water temperature of the water cooling system in the future according to the predicted value. The present invention constructs an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model by acquiring the autoregressive coefficient of the ARIMA prediction model, and obtains the optimal sliding average coefficient of the ARIMA prediction model according to the interference degree and data fluctuation characteristics of the initial window. The optimal sliding average coefficient of the ARIMA prediction model can make the prediction of the outlet water temperature of the water cooling system in the future more accurate, and thus provide effective early warning for the outlet water temperature of the water cooling system in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0009] Figure 1 This is a method flow chart of an online monitoring and early warning method for a water cooling system based on the Internet of Things provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0010] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0011] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0012] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0013] See also Figure 1 , is a method flow chart of an online monitoring and early warning method for a water cooling system based on the Internet of Things provided by the first embodiment of the present invention, such as Figure 1 As shown, the method may include: Step S101, collecting the outlet water temperature when the water cooling system is running within a preset time range to obtain a time series temperature data sequence.

[0014] This embodiment is aimed at industrial equipment with a water cooling system and operating in a fixed mode. An NTC thermistor is added to the water cooling system. When the industrial equipment is working normally, the NTC thermistor is used to collect the outlet water temperature of the water cooling system in real time. The outlet water temperature of the water cooling system in industrial equipment is generally -5℃-0℃.

[0015] When collecting the outlet water temperature of the water cooling system during operation, factors that affect the collection frequency include: (1) the scale of the water cooling system: large-scale cold water systems may require more frequent collection to ensure the stability and efficiency of the system; (2) the complexity of the water cooling system: complex cold water systems may require more frequent collection to monitor and control multiple parameters; (3) the control accuracy of the water cooling system: high-precision control requirements may require more frequent collection to achieve precise temperature regulation. Since this embodiment is for industrial water cooling systems, which require a higher collection frequency, the collection frequency is set to once per minute. There is no restriction here and it can be set according to the specific implementation scenario.

[0016] In this embodiment, the outlet water temperature when the water cooling system is running within 24 hours is collected at a collection frequency of once per minute. There is no limitation here. According to the specific implementation scenario setting, the outlet water temperature when the water cooling system is running within 24 hours can be composed into a time series temperature data sequence.

[0017] Step S102, performing time series analysis on the time series temperature data sequence, obtaining the differential coefficient and autoregressive coefficient when using the ARIMA prediction model to predict the outlet water temperature of the water cooling system at a future time, and constructing an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model in the time series temperature data sequence according to the autoregressive coefficient of the ARIMA prediction model.

[0018] In order to predict the outlet water temperature of the water cooling system in the future, it is necessary to perform time series analysis on the time series temperature data sequence to obtain the differential coefficient, autoregressive coefficient and sliding average coefficient when using the ARIMA prediction model to predict the outlet water temperature of the water cooling system in the future. However, since the outlet water temperature of the water cooling system is affected by the operating status of the equipment, heat exchange efficiency, ambient temperature, etc., the outlet water temperature is a fluctuating data. In addition, when using NTC thermistors to collect the outlet water temperature of the water cooling system, there may be various external interference factors in the system operating environment, such as temperature changes, mechanical vibrations, electromagnetic interference, etc. These factors may affect the measurement of the NTC thermistor, causing the collected outlet water temperature to be disturbed and produce abnormal performance. Therefore, the abnormal data caused by external interference may show characteristics similar to the real abnormal data in the ACF diagram and PACF diagram, making the sliding average coefficient not the optimal sliding average coefficient, resulting in inaccurate prediction of future data.

[0019] Therefore, it is necessary to obtain the optimal sliding average coefficient of the ARIMA prediction model so as to accurately predict the outlet water temperature of the water cooling system in the future.

[0020] In order to solve the above problem, it is first necessary to obtain the differential coefficient and autoregressive coefficient when using the ARIMA prediction model to predict the outlet water temperature of the water cooling system in the future. Then, according to the autoregressive coefficient of the ARIMA prediction model, an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model is constructed in the time series temperature data sequence. Then, according to the degree of interference in the initial window and the data fluctuation characteristics in the initial window, the optimal sliding average coefficient of the ARIMA prediction model is obtained.

[0021] Among them, the differential coefficient and autoregressive coefficient are obtained when the ARIMA prediction model is used to predict the outlet water temperature of the water cooling system in the future, including: In the time series temperature data sequence, the difference coefficient of the ARIMA prediction model is obtained by the difference method and the ADF test method; the autocorrelation coefficient and the partial autocorrelation coefficient of the time series temperature data sequence are obtained, and the autoregressive coefficient of the ARIMA prediction model is obtained by analyzing the ACF (autocorrelation coefficient) graph and the PACF (partial autocorrelation coefficient) graph. Among them, the difference method, the ADF test method, the autocorrelation coefficient and the partial autocorrelation coefficient belong to the existing technology and will not be repeated here.

[0022] Among them, according to the autoregressive coefficient of the ARIMA forecast model, the initial window corresponding to the initial sliding average coefficient of the ARIMA forecast model is constructed in the time series temperature data sequence, including: Taking the autoregressive coefficient of the ARIMA prediction model as the window length, an initial window including the last outlet water temperature data in the time series temperature data sequence is constructed as the initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model.

[0023] In one embodiment, taking the autoregressive coefficient p of the ARIMA prediction model as an example, the last water outlet temperature data in the time series temperature data sequence is used as the last water outlet temperature data in the initial window, and an initial window with a window length of p is constructed as the initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model.

[0024] At this point, the initial window corresponding to the initial moving average coefficient of the ARIMA forecasting model is obtained.

[0025] Step S103, according to the data fluctuation characteristics in the time series temperature data sequence, obtain the initial abnormal data in the time series temperature data sequence, obtain the interference degree of the initial window according to the recoverability and continuity of the initial abnormal data in the time series temperature data sequence, and obtain the optimal sliding average coefficient of the ARIMA prediction model according to the interference degree of the initial window and the data fluctuation characteristics in the initial window.

[0026] After obtaining the initial window corresponding to the initial sliding average coefficient of the ARIMA forecasting model, it is necessary to first obtain the initial abnormal data in the time series temperature data sequence according to the data fluctuation characteristics in the time series temperature data sequence, and then obtain the degree of interference of the initial window according to the recoverability and continuity of the initial abnormal data in the time series temperature data sequence in the initial window, and then obtain the optimal sliding average coefficient of the ARIMA forecasting model according to the degree of interference of the initial window and the data fluctuation characteristics in the initial window.

[0027] Among them, according to the data fluctuation characteristics in the time series temperature data sequence, the method for obtaining the initial abnormal data in the time series temperature data sequence is as follows: The upper edge value in the time series temperature data sequence is obtained through the box plot of the time series temperature data sequence, and the outlet water temperature data in the time series temperature data sequence that is greater than the upper edge value is used as the initial abnormal data in the time series temperature data sequence.

[0028] In one embodiment, a box plot of a time series temperature data sequence is obtained, an upper edge value of the box plot of the time series temperature data sequence is obtained, the upper edge value of the box plot of the time series temperature data sequence is used as an abnormal threshold for determining initial abnormal data, and outlet water temperature data in the time series temperature data sequence that is greater than the upper edge value of the box plot of the time series temperature data sequence is used as initial abnormal data in the time series temperature data sequence, to obtain all initial abnormal data in the time series temperature data sequence.

[0029] Since the interference is characterized by contingency, recoverability, and discontinuity, the initial abnormal data caused by external interference is relatively isolated, and the difference between the initial abnormal data and the normal outlet water temperature data is obvious, and the outlet water temperature data will be restored to the normal outlet water temperature data after the external interference ends, which leads to the trend change relationship between the initial abnormal data caused by external interference and the outlet water temperature data around it conforming to the characteristics of a quadratic curve. Since the recoverability of the real abnormal data is low, and the real abnormal data often has a certain continuity after it appears, the initial abnormal data caused by external interference is often relatively isolated, and there is a large distance between it and the rest of the initial abnormal data, while the real abnormal data is relatively dense and may have a small distance with other initial abnormal data. Therefore, according to the distance between the initial abnormal data and other initial abnormal data and the number of initial abnormal data in the initial window, the continuity frequency and recoverability probability of the initial abnormal data in the initial window can be obtained, so that the interference value of each initial abnormal data in the initial window can be obtained according to the recoverability probability and continuity frequency of the initial abnormal data, and then according to the interference value of each initial abnormal data in the initial window, the interference degree of the initial window can be obtained.

[0030] Among them, according to the recoverability and continuity of the initial abnormal data in the initial window in the time series temperature data sequence, the specific method of obtaining the interference degree of the initial window includes: (1) Any initial abnormal data in the initial window is recorded as data to be processed, and in the time series temperature data sequence, two water outlet temperature data adjacent to the data to be processed are detected to see whether they are initial abnormal data.

[0031] In one embodiment, taking any initial abnormal data A in the initial window as an example, the initial abnormal data A is the i-th water outlet temperature data in the time series temperature data sequence, and any initial abnormal data A in the initial window is recorded as the data to be processed A. In the time series temperature data sequence, the i-1th water outlet temperature data B and the i+1th water outlet temperature data C adjacent to the data to be processed A are obtained, and according to the method for obtaining the initial abnormal data in the time series temperature data sequence, it is detected whether the water outlet temperature data B and the water outlet temperature data C are the initial abnormal data.

[0032] (2) If the two water outlet temperature data adjacent to the data to be processed are not initial abnormal data, then with the data to be processed as the center, the continuous non-initial abnormal data adjacent to the data to be processed and the data to be processed are combined into a sub-window of the data to be processed, and the disturbed value of the data to be processed is obtained according to the data fluctuation characteristics in the sub-window.

[0033] In one embodiment, if the outlet water temperature data B and the outlet water temperature data C adjacent to the data to be processed A are not initial abnormal data, then with the data to be processed A as the center, in the time series temperature data sequence, non-initial abnormal data continuous with the outlet water temperature data B and non-initial abnormal data continuous with the outlet water temperature data C are respectively obtained, and the data to be processed A, the outlet water temperature data B, the outlet water temperature data C, and the non-initial abnormal data continuous with the outlet water temperature data B and the non-initial abnormal data continuous with the outlet water temperature data C form a subwindow of the data to be processed A. According to the data fluctuation characteristics in the subwindow, the interference value of the data to be processed is obtained.

[0034] For example: Assume that the time series temperature data sequence is (1, 2, 3, D, 4, 5, 6, 7, 8, B, A, C, 9, 10), and the initial window is (7, 8, B, A, C, 9, 10), where numbers 1-10 are non-initial abnormal data, and letters A and D are initial abnormal data. If the outlet water temperature data B and the outlet water temperature data C are not initial abnormal data, then the non-initial abnormal data continuous with the outlet water temperature data B are 4-8, and the non-initial abnormal data continuous with the outlet water temperature data C are 9-10. At this time, the sub-window is (4, 5, 6, 7, 8, B, A, C, 9, 10).

[0035] Among them, according to the data fluctuation characteristics in the sub-window, the method for obtaining the disturbed value of the data to be processed is as follows: a. Taking the data to be processed as the last water outlet temperature data, a small window is established in the subwindow, the water outlet temperature data in the small window is fitted to obtain a fitting straight line for the data to be processed, and the fitting straight line of each water outlet temperature data except the first water outlet temperature data in the subwindow is obtained respectively to obtain a corresponding fitting straight line sequence, and the correlation coefficient between each fitting straight line except the first fitting straight line in the fitting straight line sequence and the previous fitting straight line is calculated respectively to form a correlation coefficient sequence, and the correlation coefficient sequence is fitted to obtain a fitting curve of the correlation coefficient.

[0036] In one embodiment, taking the data A to be processed as an example, the data A to be processed is taken as the last water outlet temperature data of the small window, and the first water outlet temperature data of the sub-window is taken as the first water outlet temperature data of the small window. A small window is established in the sub-window, and the water outlet temperature data in the small window are fitted using the least squares method to obtain a fitting straight line of the data A to be processed. According to the method for obtaining the fitting straight line of the data A to be processed, a fitting straight line of each water outlet temperature data starting from the second water outlet temperature in the sub-window is obtained, and the fitting straight line of each water outlet temperature data starting from the second water outlet temperature in the sub-window is formed into a fitting straight line sequence, and the Spearman correlation coefficient between each two adjacent fitting straight lines in the fitting straight line sequence is calculated respectively. The Spearman correlation coefficient belongs to the prior art and will not be described in detail here. A correlation coefficient sequence is formed, and the correlation coefficient sequence is fitted using the least squares method to obtain a fitting curve of the correlation coefficient, wherein the least squares method belongs to the prior art and will not be described in detail here.

[0037] b. Obtain the derivative of the correlation coefficient corresponding to the data to be processed on the fitting curve, calculate the addition result of the derivative and constant 1, record it as the first addition result, obtain the number of extreme points on the fitting curve, calculate the absolute value of the difference between constant 1 and the number of extreme points on the fitting curve, record it as the first absolute value of the difference, calculate the addition result of the first absolute value of the difference and a second preset value, record it as the second addition result, obtain the average of the inverse of the first addition result and the inverse of the second addition result as the recoverability probability of the data to be processed.

[0038] In one embodiment, taking the data A to be processed as an example, the data A to be processed is the i-th water outlet temperature data in the time series temperature data sequence. First, the extreme value points on the fitting curve are obtained by using the derivative method, and the number of extreme value points on the fitting curve is obtained. The derivative method belongs to the prior art and will not be described here. Then, in the sequence of fitted straight lines, starting from the second fitted straight line, the Spearman correlation coefficient between each fitted straight line and the previous fitted straight line is recorded as the Spearman correlation coefficient corresponding to each fitted straight line, and the derivative of the Spearman correlation coefficient corresponding to the data A to be processed on the fitting curve is obtained. According to the number of extreme value points on the fitting curve and the derivative of the Spearman correlation coefficient corresponding to the data A to be processed on the fitting curve, the recoverability probability of the data A to be processed is calculated: Where X is the recoverability probability of the data A to be processed; is the derivative of the Spearman correlation coefficient of the data to be processed A on the fitting curve; b is the number of extreme points on the fitting curve; c is the second preset value; i is the sequence number of the outlet water temperature data in the time series temperature data sequence; | | is the absolute value symbol.

[0039] It should be noted that the second preset value c is a constant 2, which is used to prevent the formula calculation error caused by the denominator being 0. There is no limitation here and it can be set according to the specific implementation scenario; The closer b is to 0, the greater the probability that the Spearman correlation coefficient corresponding to the data A to be processed is an extreme point, and the greater the probability of recoverability of the data A to be processed; the closer b is to 1, the more the fitting curve conforms to the characteristics of the quadratic curve, and the greater the probability of recoverability of the data A to be processed; the greater the probability of recoverability of the data A to be processed, the greater the possibility that the data A to be processed is disturbed data, and the greater the interference value of the data A to be processed.

[0040] c On the fitting curve, obtain the correlation coefficient corresponding to the data to be processed, obtain the mean between the correlation coefficients corresponding to the previous water outlet temperature data and the next water outlet temperature data of the data to be processed, record it as the first mean, calculate the absolute value of the difference between the correlation coefficient corresponding to the data to be processed and the first mean, record it as the second absolute value of the difference, calculate the difference between the constant 1 and the reciprocal of the second absolute value of the difference, as the influence degree of the data to be processed.

[0041] In one embodiment, taking the data A to be processed as an example, the data A to be processed is the i-th water outlet temperature data in the time series temperature data sequence, the Spearman correlation coefficient corresponding to the data A to be processed on the fitting curve, and the Spearman correlation coefficient corresponding to the previous water outlet temperature data and the next water outlet temperature data of the data A to be processed are obtained, and the influence degree of the data A to be processed is calculated: Among them, Y is the influence degree of the data A to be processed; is the Spearman correlation coefficient of the data A to be processed on the fitting curve; is the Spearman correlation coefficient of the next outlet water temperature data of the data A to be processed on the fitting curve; is the Spearman correlation coefficient of the previous water outlet temperature data of the data A to be processed on the fitting curve; i is the sequence number of the water outlet temperature data in the time series temperature data sequence; | | is the absolute value symbol.

[0042] It should be noted that Indicates the influence of the data to be processed A on the trend of the previous outlet water temperature data and the next outlet water temperature data. The larger it is, the greater the impact of the data A to be processed is; the greater the impact of the data A to be processed is, the greater the interference impact on the data A to be processed is, and the greater the interference value of the data A to be processed is.

[0043] d. Obtain the number of initial abnormal data in the initial window, obtain the number of outlet water temperature data between each initial abnormal data and the data to be processed in the initial window, form a number sequence, obtain the minimum number in the number sequence, calculate the ratio of the number of initial abnormal data in the initial window to the minimum number, record it as a first ratio, normalize the first ratio, and obtain the continuity frequency of the data to be processed.

[0044] In one embodiment, taking the data A to be processed as an example, the data A to be processed is the i-th outlet water temperature data in the time series temperature data sequence. In the initial window, the number of all initial abnormal data is obtained, and the number of outlet water temperature data between each initial abnormal data and the data A to be processed is obtained to form a number sequence, the minimum number in the number sequence is obtained, and the continuity frequency of the data A to be processed is calculated: Where Z is the continuity frequency of the data A to be processed; M is the number of initial abnormal data in the initial window; is a sequence of numbers; is the minimum number in the number sequence; norm() is the normalization function; i is the sequence number of the outlet water temperature data in the time series temperature data sequence.

[0045] It should be noted that The larger it is, the larger the number of intervals between the data to be processed A and the other initial abnormal data in the initial window is, and the smaller the continuity frequency of the data to be processed A is; the smaller the continuity frequency of the data to be processed A is, the greater the possibility that the data to be processed A is interfered with is, and the greater the interference value of the data to be processed A is.

[0046] e. Calculate the average values ​​of the recoverability probability, impact degree and continuity frequency of the data to be processed as the interference value of the data to be processed.

[0047] In one implementation, taking the data to be processed A as an example, the interference value of the data to be processed A is calculated: in, is the interference value of the data A to be processed; X is the recoverability probability of the data A to be processed; Y is the influence degree of the data A to be processed; Z is the continuity frequency of the data A to be processed.

[0048] It should be noted that the greater the recoverability probability of the data A to be processed, the greater the possibility that the data A to be processed is the interfered data, and the greater the interference value of the data A to be processed; the greater the influence of the data A to be processed, the greater the interference impact of the data A to be processed, and the greater the interference value of the data A to be processed; the smaller the continuity frequency of the data A to be processed, the greater the possibility that the data A to be processed is interfered, and the greater the interference value of the data A to be processed.

[0049] (3) If there is an initial abnormal data in the two water outlet temperature data adjacent to the data to be processed, the initial abnormal data adjacent to the data to be processed is recorded as adjacent abnormal data, and the non-to-be-processed data adjacent to the adjacent abnormal data is recorded as data to be detected, and it is detected whether the data to be detected is the initial abnormal data. When the data to be detected is not the initial abnormal data, with the data to be processed as the center, the continuous non-initial abnormal data adjacent to the data to be processed, the continuous non-initial abnormal data adjacent to the adjacent abnormal data, and the data to be processed are used to form a sub-window of the data to be processed, and the interference value of the data to be processed is obtained according to the data fluctuation characteristics in the sub-window.

[0050] In one embodiment, taking the data to be processed A as an example, if there is an initial abnormal data in the outlet water temperature data B and the outlet water temperature data C adjacent to the data to be processed A, the initial abnormal data in the outlet water temperature data B and the outlet water temperature data C are recorded as adjacent abnormal data, and in the two outlet water temperature data adjacent to the adjacent abnormal data, the non-to-be-processed data are recorded as the to-be-detected data, and the to-be-detected data are detected. When the to-be-detected data is not the initial abnormal data, with the to-be-processed data as the center, in the time series temperature data sequence, the non-initial abnormal data continuous with the to-be-detected data and the continuous non-initial abnormal data adjacent to the to-be-processed data are respectively obtained, and the non-initial abnormal data continuous with the to-be-detected data, the continuous non-initial abnormal data adjacent to the to-be-processed data, the to-be-processed data and the to-be-detected data are used to form a subwindow of the data to be processed A, and the disturbed value of the data to be processed is obtained according to the above-mentioned method for obtaining the disturbed value of the data to be processed.

[0051] For example: Assume that the time series temperature data sequence is (1, 2, 3, D, 4, 5, 6, 7, 8, B, A, C, 9, 10), and the initial window is (7, 8, B, A, C, 9, 10), where numbers 1-10 are non-initial abnormal data, and letters A and D are initial abnormal data. If the outlet water temperature data B is the initial abnormal data and the outlet water temperature data C is not the initial abnormal data, then the outlet water temperature data B is recorded as the adjacent abnormal data B, and the outlet water temperature data 8 is recorded as the data to be detected. When the data to be detected 8 is not the initial abnormal data, with the data to be processed A as the center, in the time series temperature data sequence, the non-initial abnormal data 4-7 that are continuous with the data to be detected 8, and the continuous non-initial abnormal data C-10 that are adjacent to the data to be processed A, the sub-window formed at this time is (4, 5, 6, 7, 8, A, C, 9, 10).

[0052] (4) When the data to be detected is initial abnormal data, the interference value of the data to be processed is set to a first preset value.

[0053] In one implementation, taking the data A to be processed as an example, if there is an initial abnormal data in the outlet water temperature data B and the outlet water temperature data C adjacent to the data A to be processed, the initial abnormal data in the outlet water temperature data B and the outlet water temperature data C are recorded as adjacent abnormal data, and in the two outlet water temperature data adjacent to the adjacent abnormal data, the non-to-be-processed data are recorded as the data to be detected, and the data to be detected is detected. When the data to be detected is the initial abnormal data, the data A to be processed is directly recorded as the abnormal data, and the interference value of the data A to be processed is set to 0.1. There is no limitation here, and it can be set according to the specific implementation scenario.

[0054] (5) If the two water outlet temperature data adjacent to the data to be processed are both initial abnormal data, the interference value of the data to be processed is set to the first preset value.

[0055] In one embodiment, taking the data A to be processed as an example, if the outlet water temperature data B and the outlet water temperature data C adjacent to the data A to be processed are both initial abnormal data, the data A to be processed is directly recorded as abnormal data, and the interference value of the data A to be processed is set to 0.1. There is no restriction here and it can be set according to the specific implementation scenario.

[0056] (6) Obtaining an interference value of each to-be-processed data in the initial window, and obtaining an interference degree of the initial window according to the interference value of each to-be-processed data in the initial window.

[0057] Specifically, the sum of the interference values ​​of all the data to be processed in the initial window is obtained, recorded as the third addition result, the ratio of the number of data to be processed in the initial window to the number of water outlet temperature data in the initial window is calculated, recorded as the second ratio, and the product of the second ratio and the third addition result is calculated as the interference degree of the initial window.

[0058] In one embodiment, according to the above method for obtaining the interference value of the data to be processed A, the interference values ​​of all the data to be processed in the initial window are obtained, and the interference degree of the initial window is calculated: in, is the interference degree of the initial window; M is the number of data to be processed in the initial window; n is the number of water outlet temperature data in the initial window; is the disturbed value of the jth data to be processed in the initial window; j is the sequence number of the data to be processed in the initial window.

[0059] It should be noted that, the more the number of data to be processed in the initial window is, the greater the interference value of each data to be processed in the initial window is, and the greater the interference degree of the initial window is.

[0060] After obtaining the interference degree of the initial window, the optimal sliding average coefficient of the ARIMA forecasting model can be obtained according to the interference degree of the initial window and the data fluctuation characteristics in the initial window.

[0061] The method to obtain the optimal moving average coefficient of the ARIMA forecast model is as follows: (1) Calculate the difference between the constant 1 and the disturbed value of each data to be processed as the outlier value of each data to be processed, set the outlier threshold, and record the data to be processed whose outlier value in the initial window is greater than or equal to the outlier threshold as outlier data.

[0062] In one implementation, taking the data to be processed A as an example, the abnormal value of the data to be processed A is calculated: in, is the outlier value of the data A to be processed; is the disturbed value of the data A to be processed.

[0063] It should be noted that the larger the interference value of the data A to be processed is, the more interference affects the data A to be processed, that is, the abnormal situation of the data A to be processed is more likely to be caused by external interference rather than the actual abnormal water outlet temperature, so the abnormal value of the data A to be processed is smaller.

[0064] According to the method for obtaining the outlier value of the data A to be processed, the outlier value of each data to be processed in the initial window is obtained, and the outlier value threshold is set to 0.6, without any restriction. It can be set according to the specific implementation scenario, and the data to be processed whose outlier value in the initial window is greater than or equal to 0.6 is recorded as outlier data, and all outlier data in the initial window are obtained.

[0065] (2) The non-initial abnormal data and the abnormal data in the initial window are combined into a reference data sequence, and the fluctuation value of the reference data sequence is obtained according to the data fluctuation characteristics in the reference data sequence.

[0066] After obtaining all abnormal data in the initial window, all non-initial abnormal data in the initial window are obtained, and all non-initial abnormal data and all abnormal data in the initial window are combined into a reference data sequence. According to the data fluctuation characteristics in the reference data sequence, the fluctuation value of the reference data sequence is obtained.

[0067] Among them, according to the data fluctuation characteristics in the reference data sequence, the method for obtaining the fluctuation value of the reference data sequence is: Respectively calculating the absolute value of the difference between every two adjacent reference data in the reference data sequence, normalizing the absolute value of the difference between every two adjacent reference data in the reference data sequence to obtain a normalized difference absolute value sequence, and obtaining a mean of the normalized difference absolute value sequence, which is recorded as a third mean; The abnormal data in the initial window are combined into an abnormal data sequence, the variance of the abnormal data sequence is obtained, the sum of the variance and a constant 1 is calculated to obtain a fourth sum result, and the difference between the constant 1 and the reciprocal of the fourth sum result is calculated and recorded as a first difference; A weighted summation process is performed on the third mean and the first difference to obtain a fluctuation value of the reference data sequence.

[0068] In one embodiment, all abnormal data in the initial window are combined into an abnormal data sequence, the variance of the abnormal data sequence is obtained, and the fluctuation value of the reference data sequence is calculated: in, is the fluctuation value of the reference data series; is the kth reference data in the reference data sequence; is the k-1th reference data in the reference data sequence; k is the sequence number of the reference data in the reference data sequence; m is the number of reference data in the reference data sequence; is the variance of the abnormal data sequence; norm() is the normalization function; | | is the absolute value symbol; for The weight of for The weight of is 0.4, It is 0.6. There is no limit here and it can be set according to the specific implementation scenario.

[0069] It should be noted that the greater the difference between every two adjacent reference data in the reference data sequence, the greater the fluctuation of the reference data in the reference data sequence, and the greater the fluctuation value of the reference data sequence; the greater the variance of the abnormal data sequence, the greater the fluctuation of the abnormal data in the reference data sequence, and the greater the fluctuation value of the reference data sequence.

[0070] (3) According to the fluctuation value of the reference data sequence and the interference degree of the initial window, the initial window is adjusted to obtain an adjusted initial window, and the length of the adjusted initial window is used as the optimal sliding average coefficient of the ARIMA forecasting model.

[0071] Since the window can smooth the noise data, expanding the initial window can reduce the interference degree of the data in the initial window. However, the abnormal data in the initial window has large volatility, that is, the short-term changes are more obvious. Expanding the initial window is likely to cause partial loss of short-term fluctuation details of the abnormal data. Therefore, the initial window can be adjusted according to the fluctuation value of the reference data sequence and the interference degree of the initial window. The window can be expanded when the interference degree of the initial window is large, and the window can be reduced when the fluctuation value of the reference data sequence in the initial window is large, so as to obtain the adjusted initial window.

[0072] Among them, the initial window is adjusted according to the fluctuation value of the reference data sequence and the interference degree of the initial window. The specific method of obtaining the adjusted initial window is as follows: a Calculate the difference between constant 1 and the fluctuation value of the reference data sequence, record it as the second difference, obtain the average of the interference degree of the initial window and the second difference, and use it as the adjustment coefficient of the initial window.

[0073] In one embodiment, the adjustment factor of the initial window is calculated: in, is the adjustment coefficient of the initial window; is the degree of interference of the initial window; is the fluctuation value of the reference data series.

[0074] It should be noted that the greater the degree of interference of the initial window, the more the initial window needs to be expanded, and the larger the adjustment coefficient of the initial window; the greater the fluctuation value of the reference data sequence, the more the initial window needs to be reduced, and the smaller the adjustment coefficient of the initial window.

[0075] b. Obtaining an adjustment coefficient threshold interval. If the adjustment coefficient of the initial window is within the adjustment coefficient threshold interval, there is no need to adjust the initial window.

[0076] In one implementation, the adjustment coefficient threshold interval is [0.5, 0.6], which is not limited here and can be set according to the specific implementation scenario. If the adjustment coefficient of the initial window is within [0.5, 0.6], the initial window does not need to be adjusted.

[0077] cIf the adjustment coefficient of the initial window is less than the minimum value of the adjustment coefficient threshold interval, then calculate the difference between the minimum value of the adjustment coefficient threshold interval and the adjustment coefficient of the initial window, record it as the third difference, calculate the product of the number of outlet water temperature data in the initial window and the third difference, record it as the first product, calculate the difference between the number of outlet water temperature data in the initial window and the first product, as the first window length after the initial window is reduced, reduce the length of the initial window to the first window length, and obtain the adjusted initial window.

[0078] In one embodiment, if the adjustment coefficient of the initial window is less than 0.5, it means that the fluctuation value of the reference data sequence in the initial window is too large. In this case, the initial window needs to be reduced, and the length of the first window after the initial window is reduced is calculated: Where Is is the length of the first window after the initial window is reduced; n is the number of outlet water temperature data in the initial window; is the adjustment factor of the initial window.

[0079] It should be noted that the smaller the adjustment coefficient of the initial window is, the greater the degree to which the initial window needs to be reduced is, and the smaller the length of the first window after the initial window is reduced is.

[0080] After obtaining the first window length after the initial window is reduced, the length of the initial window is reduced to the first window length to obtain an adjusted initial window.

[0081] dIf the adjustment coefficient of the initial window is greater than the maximum value of the adjustment coefficient threshold interval, then calculate the difference between the adjustment coefficient of the initial window and the maximum value of the adjustment coefficient threshold interval, record it as the fourth difference, calculate the product of the number of outlet water temperature data in the initial window and the fourth difference, record it as the second product, calculate the sum of the number of outlet water temperature data in the initial window and the first product as the second window length after the initial window is expanded, expand the length of the initial window to the second window length, and obtain the adjusted initial window.

[0082] In one implementation, if the adjustment coefficient of the initial window is greater than 0.6, it means that the interference degree of the initial window is too large. In this case, the initial window needs to be expanded, and the length of the second window after the initial window is expanded is calculated: Where Ik is the length of the second window after the initial window is enlarged; n is the number of outlet water temperature data in the initial window; is the adjustment factor of the initial window.

[0083] It should be noted that, the larger the adjustment coefficient of the initial window is, the greater the degree to which the initial window needs to be enlarged is, and the larger the length of the second window after the initial window is enlarged is.

[0084] After obtaining the second window length after the initial window is enlarged, the length of the initial window is expanded to the second window length to obtain an adjusted initial window.

[0085] After obtaining the adjusted initial window, the length of the adjusted initial window is used as the optimal sliding average coefficient of the ARIMA forecasting model.

[0086] So far, the optimal moving average coefficient of the ARIMA forecasting model is obtained.

[0087] Step S104, predicting the outlet water temperature of the water cooling system at a future time according to the autoregressive coefficient, differential coefficient, and optimal sliding average coefficient of the ARIMA prediction model, obtaining a predicted value, and issuing an abnormal warning for the outlet water temperature of the water cooling system at a future time according to the predicted value.

[0088] After obtaining the optimal sliding average coefficient of the ARIMA prediction model, the outlet water temperature of the water cooling system in the future is predicted according to the differential coefficient, autoregressive coefficient and optimal sliding average coefficient of the ARIMA prediction model, and a predicted value is obtained. An outlet water temperature threshold is set, and according to the predicted value of the outlet water temperature of the water cooling system in the future and the outlet water temperature threshold, an abnormal warning is issued for the outlet water temperature of the water cooling system in the future. The ARIMA prediction model belongs to the prior art and will not be described in detail here.

[0089] Among them, according to the predicted value of the outlet water temperature of the water cooling system at a future time and the outlet water temperature threshold, the specific method of providing an abnormal warning for the outlet water temperature of the water cooling system at a future time is as follows: A water outlet temperature threshold is set, and if the predicted value is greater than or equal to the water outlet temperature threshold, an early warning process is performed on the water outlet temperature of the water cooling system at a future time.

[0090] In one embodiment, the outlet water temperature threshold is set to 1°C. If the predicted value of the outlet water temperature of the water cooling system at a future time is greater than or equal to 1°C, an abnormal warning is issued for the outlet water temperature of the water cooling system at a future time. If the predicted value of the outlet water temperature of the water cooling system at a future time is less than 1°C, it means that the outlet water temperature of the water cooling system at a future time is normal, and no abnormal warning is issued.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. The online monitoring and early warning method of water cooling system based on Internet of Things is characterized by: The method comprises: Collect the outlet water temperature of the water cooling system during operation within a preset time range to obtain a time series temperature data sequence; Performing time series analysis on the time series temperature data sequence, obtaining the differential coefficient and the autoregressive coefficient when predicting the outlet water temperature of the water cooling system at a future time using the ARIMA prediction model, and constructing an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model in the time series temperature data sequence according to the autoregressive coefficient of the ARIMA prediction model; According to the data fluctuation characteristics in the time series temperature data sequence, initial abnormal data in the time series temperature data sequence is obtained, according to the recoverability and continuity of the initial abnormal data in the initial window in the time series temperature data sequence, the interference degree of the initial window is obtained, and according to the interference degree of the initial window and the data fluctuation characteristics in the initial window, the optimal sliding average coefficient of the ARIMA forecasting model is obtained; According to the autoregressive coefficient, differential coefficient, and optimal sliding average coefficient of the ARIMA prediction model, the outlet water temperature of the water cooling system in the future is predicted to obtain a predicted value, and according to the predicted value, an abnormal warning is issued for the outlet water temperature of the water cooling system in the future.

2. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 1 is characterized in that: The step of constructing an initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model in the time series temperature data sequence according to the autoregressive coefficient of the ARIMA prediction model includes: Taking the autoregressive coefficient of the ARIMA prediction model as the window length, an initial window including the last outlet water temperature data in the time series temperature data sequence is constructed as the initial window corresponding to the initial sliding average coefficient of the ARIMA prediction model.

3. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 1 is characterized in that: The step of acquiring initial abnormal data in the time series temperature data sequence according to the data fluctuation characteristics in the time series temperature data sequence comprises: The upper edge value in the time series temperature data sequence is obtained through the box plot of the time series temperature data sequence, and the outlet water temperature data in the time series temperature data sequence that is greater than the upper edge value is used as the initial abnormal data in the time series temperature data sequence.

4. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 1 is characterized in that: The obtaining the interference degree of the initial window according to the recoverability and continuity of the initial abnormal data in the initial window in the time series temperature data sequence includes: Record any initial abnormal data in the initial window as data to be processed, and detect whether two water outlet temperature data adjacent to the data to be processed are initial abnormal data in the time series temperature data sequence; If the two water outlet temperature data adjacent to the data to be processed are not initial abnormal data, then the data to be processed is taken as the center, and the continuous non-initial abnormal data adjacent to the data to be processed and the data to be processed are used to form a subwindow of the data to be processed, and the disturbed value of the data to be processed is obtained according to the data fluctuation characteristics in the subwindow; If there is an initial abnormal data in the two water outlet temperature data adjacent to the data to be processed, the initial abnormal data adjacent to the data to be processed is recorded as adjacent abnormal data, and the non-to-be-processed data adjacent to the adjacent abnormal data is recorded as the data to be detected, and it is detected whether the data to be detected is the initial abnormal data. When the data to be detected is not the initial abnormal data, with the data to be processed as the center, the continuous non-initial abnormal data adjacent to the data to be processed, the continuous non-initial abnormal data adjacent to the adjacent abnormal data, and the data to be processed are used to form a subwindow of the data to be processed, and according to the data fluctuation characteristics in the subwindow, the disturbed value of the data to be processed is obtained; When the data to be detected is initial abnormal data, setting the interference value of the data to be processed to a first preset value; If two water outlet temperature data adjacent to the data to be processed are both initial abnormal data, setting the interference value of the data to be processed to a first preset value; The interference value of each to-be-processed data in the initial window is obtained, and the interference degree of the initial window is obtained according to the interference value of each to-be-processed data in the initial window.

5. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 4 is characterized in that: The obtaining, according to the data fluctuation characteristics in the sub-window, the disturbed value of the data to be processed comprises: Taking the data to be processed as the last water outlet temperature data, a small window is established in the subwindow, the water outlet temperature data in the small window is fitted to obtain a fitting straight line for the data to be processed, and the fitting straight line of each water outlet temperature data except the first water outlet temperature data in the subwindow is obtained respectively, and a corresponding fitting straight line sequence is obtained, and the correlation coefficient between each fitting straight line except the first fitting straight line in the fitting straight line sequence and the previous fitting straight line is calculated respectively to form a correlation coefficient sequence, and the correlation coefficient sequence is fitted to obtain a fitting curve of the correlation coefficient; Obtaining the derivative of the correlation coefficient corresponding to the data to be processed on the fitting curve, calculating the addition result of the derivative and constant 1, recording it as a first addition result, obtaining the number of extreme points on the fitting curve, calculating the absolute value of the difference between constant 1 and the number of extreme points on the fitting curve, recording it as a first absolute value of the difference, calculating the addition result of the first absolute value of the difference and a second preset value, recording it as a second addition result, obtaining the average of the reciprocal of the first addition result and the reciprocal of the second addition result as the recoverability probability of the data to be processed; On the fitting curve, obtain the correlation coefficient corresponding to the data to be processed, obtain the mean between the correlation coefficients corresponding to the previous water outlet temperature data and the next water outlet temperature data of the data to be processed, record it as a first mean, calculate the absolute value of the difference between the correlation coefficient corresponding to the data to be processed and the first mean, record it as a second absolute value of the difference, calculate the difference between a constant 1 and the reciprocal of the second absolute value of the difference, as the influence degree of the data to be processed; Obtaining the number of initial abnormal data in the initial window, respectively obtaining the number of outlet water temperature data between each initial abnormal data and the data to be processed in the initial window to form a number sequence, obtaining the minimum number in the number sequence, calculating the ratio of the number of initial abnormal data in the initial window to the minimum number, recording it as a first ratio, normalizing the first ratio, and obtaining the continuity frequency of the data to be processed; The average values ​​of the recoverability probability, the impact degree and the continuity frequency of the data to be processed are calculated as the interference value of the data to be processed.

6. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 4 is characterized in that: The obtaining the interference degree of the initial window according to the interference degree of each to-be-processed data in the initial window comprises: Obtain the sum of the interference values ​​of all the data to be processed in the initial window, recorded as the third addition result, calculate the ratio of the number of data to be processed in the initial window to the number of water outlet temperature data in the initial window, recorded as the second ratio, and calculate the product of the second ratio and the third addition result as the interference degree of the initial window.

7. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 4 is characterized in that: The step of obtaining the optimal sliding average coefficient of the ARIMA forecasting model according to the interference degree of the initial window and the data fluctuation characteristics in the initial window includes: Calculate the difference between the constant 1 and the disturbed value of each to-be-processed data respectively as the abnormal value of each to-be-processed data, set the abnormal value threshold, and record the to-be-processed data whose abnormal value in the initial window is greater than or equal to the abnormal value threshold as abnormal data; The non-initial abnormal data and the abnormal data in the initial window form a reference data sequence, and according to the data fluctuation characteristics in the reference data sequence, obtain the fluctuation value of the reference data sequence; According to the fluctuation value of the reference data sequence and the interference degree of the initial window, the initial window is adjusted to obtain an adjusted initial window, and the length of the adjusted initial window is used as the optimal sliding average coefficient of the ARIMA prediction model.

8. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 7 is characterized in that: The obtaining the fluctuation value of the reference data sequence according to the data fluctuation feature in the reference data sequence includes: Respectively calculating the absolute value of the difference between every two adjacent reference data in the reference data sequence, normalizing the absolute value of the difference between every two adjacent reference data in the reference data sequence to obtain a normalized difference absolute value sequence, and obtaining a mean of the normalized difference absolute value sequence, which is recorded as a third mean; The abnormal data in the initial window are combined into an abnormal data sequence, the variance of the abnormal data sequence is obtained, the sum of the variance and a constant 1 is calculated to obtain a fourth sum result, and the difference between the constant 1 and the reciprocal of the fourth sum result is calculated and recorded as a first difference; A weighted summation process is performed on the third mean and the first difference to obtain a fluctuation value of the reference data sequence.

9. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 7 is characterized in that: The adjusting the initial window according to the fluctuation value of the reference data sequence and the interference degree of the initial window to obtain the adjusted initial window includes: Calculate the difference between the constant 1 and the fluctuation value of the reference data sequence, record it as a second difference, and obtain the average of the interference degree of the initial window and the second difference as the adjustment coefficient of the initial window; Acquire an adjustment coefficient threshold interval, and if the adjustment coefficient of the initial window is within the adjustment coefficient threshold interval, there is no need to adjust the initial window; If the adjustment coefficient of the initial window is less than the minimum value of the adjustment coefficient threshold interval, the difference between the minimum value of the adjustment coefficient threshold interval and the adjustment coefficient of the initial window is calculated, recorded as the third difference, the product of the number of outlet water temperature data in the initial window and the third difference is calculated, recorded as the first product, the difference between the number of outlet water temperature data in the initial window and the first product is calculated as the first window length after the initial window is reduced, the length of the initial window is reduced to the first window length, and the adjusted initial window is obtained; If the adjustment coefficient of the initial window is greater than the maximum value of the adjustment coefficient threshold interval, the difference between the adjustment coefficient of the initial window and the maximum value of the adjustment coefficient threshold interval is calculated, recorded as the fourth difference, the product of the number of outlet water temperature data in the initial window and the fourth difference is calculated, recorded as the second product, the sum of the number of outlet water temperature data in the initial window and the first product is calculated as the second window length after the initial window is expanded, the length of the initial window is expanded to the second window length, and the adjusted initial window is obtained.

10. The online monitoring and early warning method for a water cooling system based on the Internet of Things according to claim 9 is characterized in that: The step of providing an abnormal warning for the outlet water temperature of the water cooling system at a future time according to the predicted value includes: A water outlet temperature threshold is set, and if the predicted value is greater than or equal to the water outlet temperature threshold, an early warning process is performed on the water outlet temperature of the water cooling system at a future time.

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