Method for detecting viscosity of regulating valve in control loop based on phase-space reconstruction

Abstract

The invention discloses a method for detecting viscosity of a regulating valve in a control loop based on phase-space reconstruction, and relates to the field of valve fault diagnosis. The method provided by the invention is an unsupervised nonlinear signal analysis method, combines various technologies such as phase-space reconstruction, recursive analysis and power spectrum analysis, and proposes a viscosity statistical characteristic index and a viscosity distribution characteristic index, so as to characterize nonlinear and periodic laws caused by viscosity. And unsupervised adjusting valve viscosity fault detection is realized only through loop parameters. According to the method, the viscosity characteristics in the recursion plot are effectively extracted, calculation is simple and convenient, meanwhile, the method has high interpretability and accuracy, the huge potential of recursion analysis in valve viscosity detection is shown, and a method basis is provided for online monitoring and performance evaluation of an industrial loop.

Description

Technical Field

[0001] The invention belongs to the field of valve fault diagnosis, and in particular relates to a method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction. Background Art

[0002] As one of the most critical components in the control loop, the control valve ensures product quality and personnel safety. During the long-term operation of the valve, the nonlinear motion caused by valve sticking often leads to control loop oscillation, thereby increasing the economic and safety risks in the production process. Among the various factors that lead to control loop oscillation, valve sticking accounts for 20-30% of the loop oscillation. Therefore, valve sticking detection has always been an important issue in the performance evaluation of the control loop. In recent years, nonlinear dynamics theory has provided new ideas for signal analysis methods. Recursive analysis based on phase space reconstruction is an analytical method for dealing with complex nonlinear systems. It is considered to be a powerful tool for detecting subtle changes in signals and systems. However, most methods have not widely discussed how to effectively extract features from recursive graphs for valve sticking detection. In this regard, a highly interpretable and accurate sticking detection method has not been established. In addition, most existing neural network methods belong to black box models, which lack interpretability and require a large amount of labeled data, which poses challenges to both practical applications and model optimization. Summary of the invention

[0003] The purpose of the present invention is to solve the problems existing in the prior art and provide a method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction.

[0004] In order to achieve the above-mentioned invention object, the present invention specifically adopts the following technical solutions:

[0005] A method for detecting sticking of a regulating valve in a control loop based on phase space reconstruction comprises the following steps:

[0006] S1: Acquire the signal in the detected control loop, the signal in the control loop includes a setting signal, a control signal and a process variable signal, take the difference between the process variable signal and the setting signal as an error signal, and perform standardization processing on the error signal and the control signal;

[0007] S2: Reconstruct the control signal after the normalization process in phase space to obtain a high-dimensional first time series after the signal is reconstructed, and reconstruct the error signal after the normalization process in phase space to obtain a high-dimensional second time series after the signal is reconstructed;

[0008] S3: constructing a first recursive matrix from the high-dimensional first time series, and visually drawing a first recursive graph of the control signal using the first recursive matrix; constructing a second recursive matrix from the high-dimensional second time series, and visually drawing a second recursive graph of the error signal using the second recursive matrix;

[0009] S4: calculating a first viscosity statistical characteristic index by the shape of the geometric pattern on the first recursive graph, calculating a second viscosity statistical characteristic index by the shape of the geometric pattern on the second recursive graph, and calculating a viscosity distribution characteristic index by the distribution of the geometric pattern on the second recursive graph;

[0010] S5: taking the difference between the first viscosity statistical characteristic index and the second viscosity statistical characteristic index as the viscosity statistical characteristic index increment; if the viscosity statistical characteristic index increment is greater than a preset viscosity statistical threshold and the viscosity distribution characteristic index is greater than a preset viscosity distribution threshold, it indicates that a viscosity fault occurs in the control valve in the control loop.

[0011] Based on the above scheme, each step can be implemented in the following preferred specific manner.

[0012] Preferably, in step S1, the standardization process adopts a Z-score standardization method.

[0013] Preferably, in step S2, the phase space reconstruction uses a coordinate delay method based on Takens embedding theorem to calculate the i-th high-dimensional vector X in the high-dimensional first time series or the high-dimensional second time series. i :

[0014] X i ={x(i),x(i+τ),…x(i+(m-1)τ)}i=1,2,…N-(m-1)τ

[0015] Wherein, m represents the embedding dimension; τ represents the delay time; N represents the length of the standardized control signal or the standardized error signal; x(i), x(i+τ),…x(i+(m-1)τ represent the sampling values ​​of the standardized control signal or the standardized error signal at the i-th time point, respectively.

[0016] Preferably, the mutual information method is used to calculate the delay time reference value of the error signal and the delay time reference value of the control signal respectively, and the maximum value of the delay time reference value of the error signal and the delay time reference value of the control signal is used as the final reference value of the delay time for phase space reconstruction.

[0017] Preferably, the false neighbor method is used to calculate the embedding dimension reference value of the error signal and the embedding dimension reference value of the control signal respectively, and the maximum value of the embedding dimension reference value of the error signal and the embedding dimension reference value of the control signal is used as the final reference value of the embedding dimension for phase space reconstruction.

[0018] Preferably, in step S3, the element R in the i-th row and j-th column of the first recursive matrix or the second recursive matrix i,j The calculation is as follows:

[0019] R i,j = Θ(ε-S i,j )

[0020]

[0021] Among them, S i,j Indicates the calculation of the i-th high-dimensional vector X i and the jth high-dimensional vector X j θ represents the Heaviside function; ε represents the threshold distance.

[0022] Preferably, in step S3, when R i,j = 1, the elements in the i-th row and j-th column of the first recursive matrix or the second recursive matrix are set as black dots. i,j When =0, the elements in the i-th row and j-th column of the first recursive matrix or the second recursive matrix are set as white points to obtain the first recursive graph and the second recursive graph.

[0023] Preferably, in step S4, the first viscosity statistical characteristic index SFI_ OP And the second viscosity statistical characteristic index SFI_ PV-SP The specific steps are as follows:

[0024] S411: Obtain positions of black dots in the first recursive graph and the second recursive graph;

[0025] S412: traverse the black points in the first recursive graph and score each black point. If the scored black point has adjacent black points in the horizontal or vertical direction, preset scores for the adjacent black points, add up the scores of all adjacent black points as the score of the scored black point, and add up the scores of all scored black points in the first recursive graph to obtain a first total score score1;

[0026] S413: traverse the black points in the second recursive graph and score each black point. If the scored black point has adjacent black points in the horizontal or vertical direction, preset scores for the adjacent black points, add up the scores of all adjacent black points as the score of the scored black point, and add up the scores of all scored black points in the second recursive graph to obtain a second total score score2;

[0027] S414: traverse the black points in the first recursive graph and score each black point. If the scored black point has an adjacent black point on the diagonal line, preset a new score for the adjacent black point on the diagonal line, and satisfy that the new score is twice the preset score, and add the scores of all the adjacent black points on the diagonal line as the score of the scored black point, and add the scores of all the scored black points in the first recursive graph to obtain a third total score score3;

[0028] S415: traverse the black points in the second recursive graph and score each black point. If the scored black point has adjacent black points on the diagonal line, preset new scores for the adjacent black points on the diagonal line, and add up the scores of all the adjacent black points on the diagonal line as the score of the scored black point. Add up the scores of all the scored black points in the second recursive graph to obtain a fourth total score score4.

[0029] S416: The ratio of the first total score score1 to the third total score score3 is used as the first viscosity statistical feature index SFI_ OP :

[0030] SFI_ OP =score1 / score3

[0031] The ratio of the second total score score2 to the fourth total score score4 is used as the second viscosity statistical feature index SFI_PV -SP :

[0032] SFI_ PV-SP =score2 / score4.

[0033] Preferably, in step S4, the viscosity distribution characteristic index DFI_ PV-SP The specific steps are as follows:

[0034] S421: taking the number of black dots in each row of the second recursive graph as an element in the time series, and traversing the second recursive graph to obtain the time series;

[0035] S422: Calculate the power spectrum density of the time series, and use the power spectrum density within a preset frequency range as the final power spectrum density;

[0036] S423: Calculate the sparsity of the final power spectrum density to characterize the viscosity distribution characteristic index DFI_ PV-SP :

[0037]

[0038] Among them, N f is the total number of frequencies within the preset frequency range; px x(f) is the power spectral density of the time series L; |·| 2 represents the square of the modulus value; f represents the frequency.

[0039] Preferably, in step S5, the reference value of the viscosity statistical threshold is 0, and the reference value of the viscosity distribution threshold is 0.58.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention combines multiple methods such as phase space reconstruction, recursive analysis and power spectrum analysis, and belongs to an unsupervised nonlinear signal analysis method. It proposes a viscosity statistical characteristic index SFI and a viscosity distribution characteristic index DFI to characterize the nonlinearity and periodicity caused by viscosity, and realizes unsupervised control valve viscosity fault detection only through loop parameters. The method proposed by the present invention effectively extracts the viscosity characteristics in the recursive graph, which is not only simple and convenient to calculate, but also highly interpretable and accurate, providing a methodological basis for online monitoring and performance evaluation of industrial loops. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention;

[0043] Figure 2 A schematic diagram of the cross-shaped basic structure and score distribution in the recursive graph of the present invention;

[0044] Figure 3 It is a schematic diagram of the basic structure of the diagonal line and the distribution of scores in the recursive graph of the present invention;

[0045] Figure 4 It is a schematic diagram showing the principle of time series in the recursive graph of the present invention;

[0046] Figure 5 Schematic diagram for comparing different sticking detection methods in the international sticking fault database according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation mode of the present invention is described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. The technical features in each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0048] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for the purpose of distinguishing descriptions, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0049] like Figure 1 As shown, in a preferred implementation of the present invention, the above-mentioned method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction includes the following steps S1 to S5. The specific implementation process is described in detail below.

[0050] S1: Acquire the signals in the detected control loop, which include the setting signal SP, the control signal OP, and the process variable signals PV such as flow and pressure. The difference between the process variable signal PV and the setting signal SP is used as the error signal (PV-SP), and the error signal and the control signal OP are standardized.

[0051] It should be noted that in step S1 of the present invention, the signal sampling interval in the control loop is t s .

[0052] It should also be noted that in step S1 of the present invention, the standardization process adopts the Z-score standardization method, and the above standardization process can be expressed as:

[0053]

[0054] Where x and x′ represent the unscaled and scaled variables, respectively; represents the mean value of the variable; σ x Represents the standard deviation of the variable.

[0055] S2: Reconstruct the control signal after the standardization processing in phase space to obtain a high-dimensional first time series after the signal is reconstructed, and reconstruct the error signal after the standardization processing in phase space to obtain a high-dimensional second time series after the signal is reconstructed.

[0056] It should be noted that in step S2 of the present invention, the signals for phase space reconstruction are the error signal and the control signal after the normalization process. The phase space reconstruction uses the coordinate delay method based on Takens embedding theorem to calculate the i-th high-dimensional vector X in the high-dimensional first time series or the high-dimensional second time series. i :

[0057] X i ={x(i),x(i+τ),…x(i+(m-1)τ)}i=1,2,…N-(m-1)τ

[0058] Wherein, m represents the embedding dimension; τ represents the delay time; N represents the length of the standardized control signal or the standardized error signal; x(i), x(i+τ),…x(i+(m-1)τ represent the sampling values ​​of the standardized control signal or the standardized error signal at the i-th time point, respectively.

[0059] Furthermore, the present invention uses the Average Mutual Information (AMI) method to respectively calculate the delay time reference value τ1 of the error signal and the delay time reference value τ2 of the control signal, and takes the maximum value of the delay time reference value of the error signal and the delay time reference value of the control signal as the final reference value τ of the delay time for phase space reconstruction:

[0060] τ=max(τ1,τ2)

[0061] Furthermore, the present invention uses a false neighbor method (False Nearest Neighbor, FNN) to respectively calculate the embedding dimension reference value m1 of the error signal and the embedding dimension reference value m2 of the control signal, and takes the maximum value of the embedding dimension reference value of the error signal and the embedding dimension reference value of the control signal as the final reference value m of the embedding dimension for phase space reconstruction:

[0062] m=max(m1,m2)

[0063] S3: construct a first recursive matrix from the high-dimensional first time series, and visualize the first recursive matrix to draw a first recursive graph of the control signal; construct a second recursive matrix from the high-dimensional second time series, and visualize the second recursive matrix to draw a second recursive graph of the error signal.

[0064] It should be noted that in step S3, the recursive matrix is ​​constructed by comparing the angular distance between two high-dimensional vectors in the phase space with a threshold. Specifically, the element R in the i-th row and j-th column of the first recursive matrix or the second recursive matrix i,j The calculation is as follows:

[0065] R i,j = Θ(ε-S i,j )

[0066]

[0067] Among them, S i,j Indicates the calculation of the i-th high-dimensional vector X i and the jth high-dimensional vector X j θ represents the Heaviside function; ε represents the threshold distance.

[0068] In this embodiment, when calculating the first recursive matrix, Xi , X j Substitute the i-th and j-th high-dimensional vectors in the high-dimensional first time series; when calculating the second recursive matrix, X i , X j Substitute the i-th and j-th high-dimensional vectors in the high-dimensional second time series accordingly. In addition, the value of the threshold distance can be selected by those skilled in the art according to actual conditions. In this embodiment, it is recommended to be 10% of the maximum phase space diameter.

[0069] It should be noted that in step S3, when R i,j = 1, the elements in the i-th row and j-th column of the first recursive matrix or the second recursive matrix are set as black dots. i,j When =0, the elements in the i-th row and j-th column of the first recursive matrix or the second recursive matrix are set as white dots, thereby drawing the first recursive graph and the second recursive graph.

[0070] S4: Calculate the first viscosity statistical feature index SFI_ by the shape of the geometric pattern on the first recursive graph OP , the second viscosity statistical feature index SFI_ is calculated by the shape of the geometric pattern on the second recursive graph PV-SP , the viscosity distribution characteristic index DFI_ is calculated by the distribution of geometric patterns on the second recursive graph PV-SP .

[0071] It should be noted that in step S4 of the present invention, the viscosity statistical characteristic index is related to the cross basic structure and the diagonal basic structure in the recursive graph. Figure 2 As shown in , the cross-shaped basic structure is composed of five adjacent black dots in the middle, left, right, top, and bottom on the recursive graph. Figure 3 As shown in FIG. 1 , the diagonal basic structure is composed of three adjacent black dots in the middle, upper left, and lower right of the recursive graph. The proportion of the two basic structures in the first and second recursive graphs is evaluated by a statistical method based on structural similarity to characterize the first and second viscosity statistical characteristic indicators.

[0072] In the present invention, the specific calculation steps of the first and second viscosity statistical characteristic indicators are as follows:

[0073] S411: Obtain the positions of the black dots in the first recursive graph and the second recursive graph, that is, the positions of the black dots in the first recursive matrix or the second recursive matrix that satisfy R i,j =1 position coordinates.

[0074] S412: traverse the black points in the first recursive graph and score each black point. If the scored black point has adjacent black points in the horizontal or vertical direction, preset scores for the adjacent black points, add up the scores of all adjacent black points as the score of the scored black point, and add up the scores of all scored black points in the first recursive graph to obtain a first total score score1.

[0075] S413: traverse the black points in the second recursive graph and score each black point. If the scored black point has adjacent black points in the horizontal or vertical direction, preset scores for the adjacent black points, add up the scores of all adjacent black points as the score of the scored black point, and add up the scores of all scored black points in the second recursive graph to obtain a second total score score2.

[0076] It should be noted that in S412 to S413 of the present embodiment, the preset score for adjacent black dots in the horizontal or vertical direction is 25 points. If the scored black dots have adjacent black dots in the horizontal or vertical direction, the score of the scored black dots is 25*4=100 points. That is, when there are adjacent black dots in the horizontal or vertical direction, for each missing adjacent black dot, 25 points are correspondingly reduced from 100 points as the score of the scored black dots.

[0077] S414: traverse the black points in the first recursive graph and score each black point. If the scored black point has an adjacent black point on the diagonal line, preset new scores for the adjacent black points on the diagonal line, and satisfy that the new score is twice the preset score. Add the scores of all the adjacent black points on the diagonal line as the score of the scored black point. Add the scores of all the scored black points in the first recursive graph to obtain a third total score score3.

[0078] S415: traverse the black points in the second recursive graph and score each black point. If the scored black point has adjacent black points on the diagonal line, preset new scores for the adjacent black points on the diagonal line, and add up the scores of all the adjacent black points on the diagonal line as the score of the scored black point. Add up the scores of all the scored black points in the second recursive graph to obtain a fourth total score score4.

[0079] It should be noted that in S414 to S415 of this embodiment, the preset score for adjacent black dots on the diagonal line is 25*2=50 points. If the scored black dots all have adjacent black dots on the diagonal line, the score of the scored black dots is 50*2=100 points. That is, when there are adjacent black dots on the diagonal line, each time an adjacent black dot is missing, 50 points are correspondingly reduced from 100 points as the score of the scored black dots.

[0080] S416: The ratio of the first total score score1 to the third total score score3 is used as the first viscosity statistical feature index SFI_ OP :

[0081] SFI_ OP =score1 / score3

[0082] The ratio of the second total score score2 to the fourth total score score4 is used as the second viscosity statistical characteristic index SFI_ PV-SP :

[0083] SFI_ PV-SP =score2 / score4

[0084] It should be noted that, in step S4 of the present invention, Figure 4 As shown in FIG. 1 , the viscosity distribution characteristic index DFI performs power spectrum analysis by counting the number of black dots in each row on the recursive graph, and uses the sparsity of the power spectrum density to characterize the viscosity distribution characteristic index.

[0085] In the present invention, the specific calculation steps of the viscosity distribution characteristic index are as follows:

[0086] S421: Taking the number of black dots in each row of the second recursive graph as an element in the time series, traversing the second recursive graph to obtain the time series L.

[0087] It should be noted that the sampling interval of the time series is the same as the signal sampling interval in the control loop, which is t s .

[0088] S422: Calculate the power spectrum density of the time series L, and use the power spectrum density within a preset frequency range as the final power spectrum density.

[0089] In the present invention, the calculation method of power spectrum density belongs to the prior art. In this embodiment, the range of [0.002, 1] Hz is taken as the preset frequency range, and on this basis, the power spectrum density within the frequency range is extracted. The specific process of calculating the power spectrum density of the time series L is as follows:

[0090]

[0091] Among them, p xx (f) is the Fourier transform of the autocorrelation coefficient, i.e., the power spectral density of the time series L; r xx (k) is the autocorrelation coefficient; k is the lag number; f is the frequency; j′ is the imaginary unit; L(i) and L(i+k) represent the i-th and i+k-th elements in the time series, respectively; is the mean of the time series.

[0092] S423: Calculate the sparsity of the final power spectrum density to characterize the viscosity distribution characteristic index DFI_ PV-SP :

[0093]

[0094] Among them, N fis the total number of frequencies within the preset frequency range.

[0095] S5: Taking the difference between the first viscosity statistical characteristic index and the second viscosity statistical characteristic index as the viscosity statistical characteristic index increment ΔSFI, if both the viscosity statistical characteristic index increment ΔSFI is greater than the preset viscosity statistical threshold e1 and the viscosity distribution characteristic index DFI_ PV-SP If it is greater than the preset viscosity distribution threshold value e2, it means that the control valve in the control loop has a viscosity fault:

[0096] ΔSFI=SFI_ PV-SP -SFI_ OP >e1

[0097] DFI_ PV-SP >e2

[0098] It should be noted that, in step S5 of the present invention, the reference value of the viscosity statistical threshold e1 is 0, and the reference value of the viscosity distribution threshold e2 is 0.58.

[0099] In order to better demonstrate the specific implementation and technical effect of the present invention, a method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction shown in steps S1 to S5 in the above preferred implementation is applied to a specific example.

[0100] Example

[0101] The specific implementation process of the method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction adopted in this embodiment is as described above and will not be repeated here.

[0102] This example uses the International Stiction Data Base to verify the method. The database can be obtained through the website https: / / sites.ualberta.ca / ~bhuang / Stiction-Book.htm. It is a comprehensive process control data set, including self-regulating and integrated control loops. Most loops are flow, temperature, level and pressure loops. Most of them are affected by viscosity. The data set includes 108 types of industrial data in 6 fields such as construction, chemical industry, papermaking, electricity, mining and metallurgy, and a total of 78 data can be analyzed.

[0103] The OP, SP, and PV signals of 78 different industrial circuits in the data set are calculated and judged according to steps S1 to S5 to obtain corresponding diagnostic results. The method of the present invention is compared with the existing viscosity detection algorithms in the world. The diagnostic results of each method are as follows: Figure 5 shown.

[0104] The diagnosis results show that the method of the present invention correctly diagnosed 60 out of 78 industrial circuits, ranking at the forefront of existing international sticking detection methods. At present, the algorithms with higher accuracy are all supervised neural network models, which require large resource costs, have weak interpretability, and are limited in practical application. This example demonstrates the advancement and effectiveness of the method of the present invention.

[0105] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for detecting the viscosity of a regulating valve in a control loop based on phase space reconstruction, characterized in that: The following steps are involved: S1: Acquire the signal in the detected control loop, the signal in the control loop includes a setting signal, a control signal and a process variable signal, take the difference between the process variable signal and the setting signal as an error signal, and perform standardization processing on the error signal and the control signal; S2: Reconstruct the control signal after the normalization process in phase space to obtain a high-dimensional first time series after the signal is reconstructed, and reconstruct the error signal after the normalization process in phase space to obtain a high-dimensional second time series after the signal is reconstructed; S3: constructing a first recursive matrix from the high-dimensional first time series, and visually drawing a first recursive graph of the control signal using the first recursive matrix; constructing a second recursive matrix from the high-dimensional second time series, and visually drawing a second recursive graph of the error signal using the second recursive matrix; S4: calculating a first viscosity statistical characteristic index by the shape of the geometric pattern on the first recursive graph, calculating a second viscosity statistical characteristic index by the shape of the geometric pattern on the second recursive graph, and calculating a viscosity distribution characteristic index by the distribution of the geometric pattern on the second recursive graph; S5: taking the difference between the first viscosity statistical characteristic index and the second viscosity statistical characteristic index as the viscosity statistical characteristic index increment; if the viscosity statistical characteristic index increment is greater than a preset viscosity statistical threshold and the viscosity distribution characteristic index is greater than a preset viscosity distribution threshold, it indicates that a viscosity fault occurs in the control valve in the control loop.

2. A method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction as claimed in claim 1, characterized in that: In step S1, the standardization process adopts the Z-score standardization method.

3. The method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction according to claim 1, characterized in that: In step S2, the phase space reconstruction uses the coordinate delay method based on Takens embedding theorem to calculate the i-th high-dimensional vector X in the high-dimensional first time series or the high-dimensional second time series. i : X i ={x(i),x(i+τ),…x(i+(m-1)τ)}i=1,2,…N-(m-1)τ Wherein, m represents the embedding dimension; τ represents the delay time; N represents the length of the standardized control signal or the standardized error signal; x(i), x(i+τ),…x(i+(m-1)τ represent the sampling values ​​of the standardized control signal or the standardized error signal at the i-th time point, respectively.

4. A method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction as claimed in claim 3, characterized in that: The mutual information method is used to calculate the delay time reference value of the error signal and the delay time reference value of the control signal respectively, and the maximum value of the delay time reference value of the error signal and the delay time reference value of the control signal is used as the final reference value of the delay time for phase space reconstruction.

5. The method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction according to claim 3, characterized in that: The false neighbor method is used to calculate the embedding dimension reference value of the error signal and the embedding dimension reference value of the control signal respectively, and the maximum value of the embedding dimension reference value of the error signal and the embedding dimension reference value of the control signal is used as the final reference value of the embedding dimension for phase space reconstruction.

6. The method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction according to claim 3, characterized in that: In step S3, the element R in the i-th row and j-th column of the first recursive matrix or the second recursive matrix i,j The calculation is as follows: R i,j =Θ(ε-S i,j ) Among them, S i,j Indicates the calculation of the i-th high-dimensional vector X i and the jth high-dimensional vector X j θ represents the Heaviside function; ε represents the threshold distance.

7. A method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction as claimed in claim 6, characterized in that: In step S3, when R i,j = 1, the elements in the i-th row and j-th column of the first recursive matrix or the second recursive matrix are set as black dots. i,j When =0, the elements in the i-th row and j-th column of the first recursive matrix or the second recursive matrix are set as white points to obtain the first recursive graph and the second recursive graph.

8. A method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction as claimed in claim 7, characterized in that: In step S4, the first viscosity statistical characteristic index SFI_ OP And the second viscosity statistical characteristic index SFI_ PV-SP The specific steps are as follows: S411: Obtain positions of black dots in the first recursive graph and the second recursive graph; S412: traverse the black points in the first recursive graph and score each black point. If the scored black point has adjacent black points in the horizontal or vertical direction, preset scores for the adjacent black points, add up the scores of all adjacent black points as the score of the scored black point, and add up the scores of all scored black points in the first recursive graph to obtain a first total score score1; S413: traverse the black points in the second recursive graph and score each black point. If the scored black point has adjacent black points in the horizontal or vertical direction, preset scores for the adjacent black points, add up the scores of all adjacent black points as the score of the scored black point, and add up the scores of all scored black points in the second recursive graph to obtain a second total score score2; S414: traverse the black points in the first recursive graph and score each black point. If the scored black point has an adjacent black point on the diagonal line, preset a new score for the adjacent black point on the diagonal line, and satisfy that the new score is twice the preset score, and add the scores of all the adjacent black points on the diagonal line as the score of the scored black point, and add the scores of all the scored black points in the first recursive graph to obtain a third total score score3; S415: traverse the black points in the second recursive graph and score each black point. If the scored black point has adjacent black points on the diagonal line, preset new scores for the adjacent black points on the diagonal line, and add up the scores of all the adjacent black points on the diagonal line as the score of the scored black point. Add up the scores of all the scored black points in the second recursive graph to obtain a fourth total score score4. S416: The ratio of the first total score score1 to the third total score score3 is used as the first viscosity statistical feature index SFI_ OP : SFI_ OP =score1 / score3 The ratio of the second total score score2 to the fourth total score score4 is used as the second viscosity statistical characteristic index SFI_ PV-SP : SFI_ PV-SP =score2 / score4。 9. The method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction according to claim 1, characterized in that: In step S4, the viscosity distribution characteristic index DFI_ PV-SP The specific steps are as follows: S421: taking the number of black dots in each row of the second recursive graph as an element in the time series, and traversing the second recursive graph to obtain the time series; S422: Calculate the power spectrum density of the time series, and use the power spectrum density within a preset frequency range as the final power spectrum density; S423: Calculate the sparsity of the final power spectrum density to characterize the viscosity distribution characteristic index DFI_ PV-SP : Among them, N f is the total number of frequencies within the preset frequency range; p xx (f) is the power spectral density of the time series L; |·| 2 represents the square of the modulus value; f represents the frequency.

10. The method for detecting viscosity of a regulating valve in a control loop based on phase space reconstruction according to claim 1, characterized in that: In step S5, the reference value of the viscosity statistical threshold is 0, and the reference value of the viscosity distribution threshold is 0.58.

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