A method and device for processing response signals of a pull cable based on empirical mode decomposition, a medium and equipment

Abstract

The application discloses a kind of based on empirical mode decomposition's pull sling response signal processing method, device, medium and equipment, belong to bridge operation and maintenance technical field.Its method includes: according to the real-time data of target signal of pre-set moving window length acquisition, obtain current moving window data;Current moving window data is decomposed based on empirical mode decomposition method, and current moving window decomposition signal is obtained;Using overlapping area linear weighted fusion method eliminates the data breakpoint of overlapping part, for the non-overlapping part, the decomposition result obtained in respective moving window is taken, and the smooth continuous correction decomposition signal is combined.The present application can avoid the calculation amount pressure of long time sequence signal loading and processing, eliminate the boundary data distortion of segmented processing, guarantee the continuity of each signal component after decomposition, guarantee the integrity of signal feature, and retain the reconstruction of signal.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for processing cable response signals based on empirical mode decomposition, belonging to the field of bridge operation and maintenance technology. Background Technology

[0002] In bridge health monitoring, the effective processing of cable-stayed structure response data is a crucial prerequisite for online assessment of structural condition. However, structural response data collected directly on-site is often coupled data signals influenced by multiple factors such as vehicle load, structural deterioration and changes, temperature, wind, and noise. The structural responses caused by different factors can merge, leading to ambiguity in the data characteristics of single-factor structural responses and affecting the actual judgment of the structure.

[0003] The existing data processing methods for processing such mixed data signals mainly include the following methods: (1) Blind source separation method. This method directly uses the different characteristics of different components in the structural response signal to separate the response. The accuracy of the method is usually high. The decomposed data can be interpreted and can be reconstructed into the original signal. However, the computational complexity is high and it takes a long time. It cannot be adapted to the situation in some engineering projects where the structural state needs to be judged in real time. (2) Based on the blind source separation algorithm, the original long signal is divided into multiple segments for processing to speed up the calculation and achieve a near real-time processing effect. However, the processing results of each segment of the signal are not continuous, so the recombined signal loses the value of feature analysis. (3) Online filtering algorithm, such as Kalman filtering method. This method filters out noise online through existing measured data. It has good real-time performance and the processed data is continuous. However, it can only decompose some high-frequency noise and cannot interpret the characteristics of the decomposed noise.

[0004] The methods described above are insufficient to meet the needs of real-time data decomposition and processing in practical engineering. Therefore, there is an urgent need for a processing method that can achieve real-time decomposition of mixed signals and has signal reconstruction capabilities. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, apparatus, medium and equipment for reconstructing cable response signals based on empirical mode decomposition, so as to solve the problem that cable response signals are difficult to decompose and process in real time and the data cannot be continuously connected in existing bridge health monitoring.

[0006] To achieve the above objectives, the present invention employs the following technical solution: the present invention provides a method for processing cable response signals based on empirical mode decomposition, comprising:

[0007] The timing signal of the cable response is received according to the preset moving window length p, wherein the cross length of adjacent moving windows is m;

[0008] Based on the empirical mode decomposition method, the time-series signals of two adjacent moving windows are decomposed to obtain the decomposed signals of the two moving windows respectively;

[0009] The overlapping region linear weighted fusion method is used to eliminate the data breakpoints in the overlapping part of the decomposition signal of the two moving windows to obtain the overlapping decomposition result. For the non-overlapping part of the decomposition signal of the two moving windows, the decomposition result obtained in each moving window is taken to obtain the non-overlapping decomposition result. The overlapping decomposition result and the non-overlapping decomposition result are combined to obtain the smooth and continuous corrected decomposition signal.

[0010] Further, the step of receiving the timing signal of the sling response according to the preset moving window length p includes:

[0011] Starting from a certain moment, when the length of the real-time data signal accumulates to p, the initial signal... θ 1 is:

[0012] ;

[0013] In the formula, Represents timing signals The first data point, the second data point, and the p-th data point; T represents the matrix transpose.

[0014] When the data updates continuously over time and reaches p+pm, the window moves pm steps, and the signal is processed at this time. θ 2 is:

[0015] ;

[0016] In the formula, This represents the timing signals of the (p-m+1)th data point, the (p-m+2)th data point, and the 2p-mth data point.

[0017] Further, the signal decomposition includes:

[0018] Using the empirical mode decomposition method to... θ 1. Perform signal decomposition processing to obtain:

[0019]

[0020] In the formula, To θ The i-th sub-signal vector obtained by decomposition, i=1,2,…,n, where n is the total number of sub-signal vectors, and the data length of each sub-signal vector is p;

[0021] ;

[0022] In the formula, Indicates to θThe first data point, the second data point, and the p-th data point in the i-th sub-signal vector after decomposition;

[0023] Using the empirical mode decomposition method to... θ 2. Perform signal decomposition processing to obtain:

[0024]

[0025] In the formula, Let θ2 be the i-th sub-signal vector obtained by decomposing θ2;

[0026] ;

[0027] In the formula, Indicates to θ The first data point, the second data point, and the p-th data point in the i-th sub-signal vector after decomposition.

[0028] Furthermore, θ 1 and θ The formula for calculating the overlap decomposition results of each signal component in the overlap region of 2 is as follows:

[0029] ;

[0030] In the formula, is the overlap decomposition result of the i-th sub-signal vector; k is the coefficient vector. ; Represents the sub-signal vector The Middle The set of vectors from the p-th data point to the p-th data point Represents the sub-signal vector The set of vectors from the first data point to the mth data point.

[0031] Furthermore, θ 1 and θ The formula for calculating the non-overlapping decomposition result of 2 is:

[0032] ;

[0033] In the formula, This is a non-overlapping decomposition result; Represents the sub-signal vector The first data point to the second A vector set of data points.

[0034] Furthermore, it also includes a refactoring verification process, specifically:

[0035] For non-overlapping regions, based on the characteristics of the empirical mode decomposition method, the sum of all sub-signals equals the original signal, thus enabling reconstruction.

[0036] For any position in the overlapping region All the component signal data are reconstructed from the components of the decomposition result of the empirical mode decomposition method, and expressed as:

[0037] ;

[0038] In the formula, Indicates to θ The data point at position l in the overlapping region of the first, second, and nth sub-signal vectors after decomposition. Indicates to θ The data point at position l in the overlapping region of the first, second, and nth sub-signal vectors after decomposition. This indicates that the component at position l in the overlapping region reconstructs the original signal;

[0039] For the signal after a smooth transition, the sum of its components is:

[0040] ;

[0041] In the formula, This represents the overlapping decomposition result of the first, second, and nth sub-signals. The coefficient vector at position l in the overlapping region;

[0042] The calculation results of the sum of each component show that the signal after the smooth transition can still be reconstructed into the original signal.

[0043] Secondly, the present invention also discloses a cable response signal processing device based on empirical mode decomposition, comprising:

[0044] The acquisition module is used to receive the timing signal of the cable response according to the preset moving window length p, wherein the cross length of adjacent moving windows is m;

[0045] The decomposition module is used to decompose the time-series signals of two adjacent moving windows based on the empirical mode decomposition method, so as to obtain the decomposed signals of the two moving windows respectively.

[0046] The fusion module is used to eliminate data breakpoints in the overlapping parts of the decomposed signals of the two moving windows using the linear weighted fusion method of the overlapping region, so as to obtain the overlapping decomposition result. For the non-overlapping parts of the decomposed signals of the two moving windows, the decomposition result obtained in their respective moving windows is taken to obtain the non-overlapping decomposition result. The overlapping decomposition result and the non-overlapping decomposition result are combined to obtain a smooth and continuous corrected decomposition signal.

[0047] Thirdly, the present invention also discloses a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the method described in the first aspect.

[0048] Fourthly, the present invention also discloses a computer device, comprising,

[0049] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the method of the first aspect.

[0050] The beneficial effects achieved by this invention are as follows:

[0051] The method provided by this invention is based on empirical mode decomposition (EMD) and introduces the concept of a moving cross window to achieve real-time streaming processing of segmented data, thus avoiding the computational burden of loading and processing long-sequence signals. Addressing the endpoint oscillation defect of traditional EMD methods, a weighted fusion mechanism with overlapping areas of the moving window effectively eliminates boundary data distortion in segmented processing, ensuring the continuity of each signal component after decomposition and achieving online decomposition of the structural response signal. Simultaneously, the method of this invention possesses signal reconstruction characteristics, ensuring the integrity of signal features. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a method for processing cable response signals based on empirical mode decomposition, provided by the present invention.

[0053] Figure 2 A comparison chart of the data processed by the method provided by this invention and the original data.

[0054] Figure 3 A partial comparison diagram of the data processed by the method provided by this invention and the data processed by traditional methods.

[0055] Figure 4 A comparison diagram of the data reconstruction result after processing by the method provided by the present invention and the original data. Detailed Implementation

[0056] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0057] Example 1, as Figure 1 As shown, this embodiment provides a method for processing cable response signals based on empirical mode decomposition, including:

[0058] The timing signal of the cable response is received according to the preset moving window length p, wherein the cross length of adjacent moving windows is m;

[0059] Based on the empirical mode decomposition method, the time-series signals of two adjacent moving windows are decomposed to obtain the decomposed signals of the two moving windows respectively;

[0060] The overlapping region linear weighted fusion method is used to eliminate the data breakpoints in the overlapping part of the decomposition signal of the two moving windows to obtain the overlapping decomposition result. For the non-overlapping part of the decomposition signal of the two moving windows, the decomposition result obtained in each moving window is taken to obtain the non-overlapping decomposition result. The overlapping decomposition result and the non-overlapping decomposition result are combined to obtain the smooth and continuous corrected decomposition signal.

[0061] The timing signal for receiving the suspension cable response according to the preset moving window length p includes:

[0062] Starting from a certain moment, when the length of the real-time data signal accumulates to p, the initial signal... θ 1 is:

[0063] ;

[0064] In the formula, Represents timing signals The first data point, the second data point, and the p-th data point; T represents the matrix transpose.

[0065] When the data updates continuously over time and reaches p+pm, the window moves pm steps, and the signal is processed at this time. θ 2 is:

[0066] ;

[0067] In the formula, This represents the timing signals of the (p-m+1)th data point, the (p-m+2)th data point, and the 2p-mth data point.

[0068] The signal decomposition includes:

[0069] Using the empirical mode decomposition method to... θ 1. Perform signal decomposition processing to obtain:

[0070] ;

[0071] In the formula, To θ The i-th sub-signal vector obtained by decomposition, i=1,2,…,n, where n is the total number of sub-signal vectors, and the data length of each sub-signal vector is p;

[0072] ;

[0073] In the formula, Indicates to θ The first data point, the second data point, and the p-th data point in the i-th sub-signal vector after decomposition;

[0074] Using the empirical mode decomposition method to... θ 2. Perform signal decomposition processing to obtain:

[0075] ;

[0076] In the formula, To θ The i-th sub-signal vector obtained by decomposition 2;

[0077] ;

[0078] In the formula, Indicates to θ The first data point, the second data point, and the p-th data point in the i-th sub-signal vector after decomposition.

[0079] θ 1 and θ The formula for calculating the overlap decomposition results of each signal component in the overlap region of 2 is as follows:

[0080] ;

[0081] In the formula, is the overlap decomposition result of the i-th sub-signal vector; k is the coefficient vector. ; Represents the sub-signal vector The Middle The set of vectors from the p-th data point to the p-th data point Represents the sub-signal vector The set of vectors from the first data point to the mth data point.

[0082] θ 1 and θ The formula for calculating the non-overlapping decomposition result of 2 is:

[0083] ;

[0084] In the formula, This is a non-overlapping decomposition result; Represents the sub-signal vector The first data point to the second A vector set of data points.

[0085] It also includes the reconstruction verification process, specifically:

[0086] For non-overlapping regions, based on the characteristics of the empirical mode decomposition method, the sum of all sub-signals equals the original signal, thus enabling reconstruction.

[0087] For any position in the overlapping region All the component signal data are reconstructed from the components of the decomposition result of the empirical mode decomposition method, and expressed as:

[0088] ;

[0089] In the formula, Indicates to θ The data point at position l in the overlapping region of the first, second, and nth sub-signal vectors after decomposition. Indicates to θ The data point at position l in the overlapping region of the first, second, and nth sub-signal vectors after decomposition. This indicates that the component at position l in the overlapping region reconstructs the original signal;

[0090] For the signal after a smooth transition, the sum of its components is:

[0091] ;

[0092] In the formula, This represents the overlapping decomposition result of the first, second, and nth sub-signals. The coefficient vector at position l in the overlapping region;

[0093] The calculation results of the sum of each component show that the signal after the smooth transition can still be reconstructed into the original signal.

[0094] Example 2 provides a method for reconstructing the response signal of a suspension cable based on Empirical Mode Decomposition (EMD). It is based on EMD and also applicable to its improved methods (such as EEMD, CEEMD, and CEEMDAN). The real-time decomposition method of this invention will be specifically described below in conjunction with the processing procedure of the main beam deflection monitoring data:

[0095] (1) This method is based on EMD (or its improved version) and combines it with a moving cross window for data streaming decomposition. The EMD method will not be described in detail here. First, a moving window segmentation model is established. The specific steps are as follows:

[0096] Assume the monitoring data is a time-series signal that is continuously updated over time. ( (For data point index), define the length of data processed in a single operation as... Then the window length is The cross length of the moving window is Then the window movement step size after each data processing is Starting from a certain moment, when the length of the real-time data signal accumulates to... After that, the signal is Then, the EMD method can be used for the initial signal decomposition process. The decomposed signal is:

[0097] (1);

[0098] … For decomposition There are 3 sub-signal vectors, and the data length of each sub-signal is also 1. ,Right now When new data is continuously updated over time, it reaches... When the window moves Step, the signal being processed at this time is For this data, the EMD method was also used for decomposition, and the decomposed signal is as follows:

[0099] (2);

[0100] Similarly, the data length of each sub-signal remains the same. ,Right now .

[0101] even though After Data and forward Although the data are identical, due to the endpoint effect of EMD, the signal components obtained by decomposing the identical data are not the same at the corresponding positions, resulting in significant breakpoints when splicing the data.

[0102] The dynamic segmentation method of moving window proposed in this invention uses a preset fixed window length and moving step size to split the long time-series structural response signal into continuously overlapping small windows for segment processing, thereby avoiding the computational pressure of full loading and processing of long time-series signals and realizing real-time signal processing.

[0103] (2) After obtaining the decomposed data within different windows, to address the data inconsistency caused by the endpoint effect of the EMD method, a linear weighted fusion method for overlapping areas is used to smooth the data transition and eliminate data breakpoints. For non-overlapping parts, the solutions obtained within their respective windows are still taken. The final decomposed result is then obtained as follows. It consists of the following parts:

[0104] Non-overlapping parts: ;

[0105] Overlapping parts: ;

[0106] Here This is the coefficient vector.

[0107] When there is again When new data arrives, a real-time data connection method combining window-by-window progressive connection and dynamic temporary storage-fusion-output is adopted according to the above steps to generate smooth and continuous decomposition results segment by segment.

[0108] The proposed linear weighted fusion method for overlapping regions, based on dynamic segmentation of moving windows, sets up overlapping regions of windows to weightedly fuse data in the overlapping regions of adjacent windows, so that the data at the window boundary can be smoothly transitioned, thus solving the signal breakpoints caused by the endpoint oscillation effect of the traditional EMD method.

[0109] (3) The final decomposition results can be reconstructed by adding them together, which shows that this method will not destroy the original data structure.

[0110] For non-overlapping regions, based on the characteristics of the EMD method, the sum of all sub-signals equals the original signal, and reconstruction can be performed.

[0111] For any position in the overlapping region Given all the component data from the EMD results, the original signal can be reconstructed from the known components:

[0112] (3);

[0113] (4);

[0114] For the signal after a smooth transition, the sum of its components is:

[0115] (5);

[0116] This indicates that the signal after the smooth transition can still be reconstructed into the original signal, and the proposed moving cross window and linear weighted fusion method does not destroy the information in the signal components.

[0117] like Figure 2 As shown, the unit of time, d, represents days. The blue scatter plots represent the original data, which contains significant high-frequency noise. The lowest frequency signal data value related to temperature is obscured by the high-frequency signal and is therefore unclear. The red curve represents the lowest frequency signal component result after processing by this invention. Through signal decomposition, high-frequency noise is effectively filtered out, and the cable rotation angle exhibits a clear daily cycle variation over time, consistent with the actual operating conditions of the bridge.

[0118] like Figure 3 As shown, Figure 3(a) in the diagram represents the traditional data processing method. Due to the lack of handling of edge effects, significant signal abrupt changes occur at the junctions of adjacent data segments, as indicated by the breakpoints marked by the yellow circles. In contrast, the processing result of this invention, through a linear weighted fusion algorithm for overlapping areas, achieves a smooth transition between segments without breakpoints. The final curve shape highly matches the actual rotation trend of the sling, as shown in the diagram. Figure 3 (b) in the middle.

[0119] like Figure 4 As shown, the unit of time, 'd', represents days. The blue scatter points in the graph represent the original data, while the red scatter points represent the reconstructed data after processing using the method of this invention. It is clearly observed from the graph that the changing trends and key fluctuation characteristics of the two types of data are highly consistent, with no significant deviations. This indicates that after processing using the method of this invention, the reconstructed data can accurately reproduce the core information of the original data, preserving both the true motion law of the cable rotation angle and maintaining the integrity of the original data. This further verifies that the present invention, in the process of signal decomposition and reconstruction, can effectively separate noise interference in the original data without losing key feature information, possessing reliable data reconstruction capabilities and providing real and usable data support for subsequent bridge structural condition assessment.

[0120] In summary, in practical applications of bridge engineering, the response of cable-stayed structures is generated by the coupling of multiple factors, resulting in a response signal containing multiple signal features. In real-time structural state analysis, these signal features interfere with each other, making it difficult to accurately analyze the key signal features and affecting the reliability of structural state assessment. Currently, there is a lack of methods that can decompose continuous response signals in real time and possess reconstruction capabilities. The method described in this invention, based on the EMD method, introduces the concept of a moving cross-window to achieve real-time streaming processing of segmented data, avoiding the computational burden of loading and processing long-sequence signals. Addressing the endpoint oscillation defect of the traditional EMD method, a weighted fusion mechanism with overlapping areas of the moving window effectively eliminates boundary data distortion in segmented processing, ensuring the continuity of each signal component after decomposition and achieving online decomposition of the structural response signal. Simultaneously, this invention's method possesses signal reconstruction characteristics, ensuring the integrity of signal features. The method proposed in this invention can not only be used for the decomposition of cable-stayed structural response signals but also for other structural response signals such as main beam vibration, deflection, and main tower offset. The EMD method and the proposed moving window dynamic segmentation processing method can achieve real-time decomposition and output of response signals, while also having reconfigurable characteristics. They can completely restore the original data information from the decomposition results, effectively ensuring the integrity and reliability of long-term structural responses and avoiding the loss of structural feature information after decomposition.

[0121] Example 3, based on the same inventive concept as Example 1, introduces a device for reconstructing the response signal of a suspension cable based on empirical mode decomposition, comprising:

[0122] The acquisition module is used to receive the timing signal of the cable response according to the preset moving window length p, wherein the cross length of adjacent moving windows is m;

[0123] The decomposition module is used to decompose the time-series signals of two adjacent moving windows based on the empirical mode decomposition method, so as to obtain the decomposed signals of the two moving windows respectively.

[0124] The fusion module is used to eliminate data breakpoints in the overlapping parts of the decomposed signals of the two moving windows using the linear weighted fusion method of the overlapping region, so as to obtain the overlapping decomposition result. For the non-overlapping parts of the decomposed signals of the two moving windows, the decomposition result obtained in their respective moving windows is taken to obtain the non-overlapping decomposition result. The overlapping decomposition result and the non-overlapping decomposition result are combined to obtain a smooth and continuous reconstructed signal.

[0125] Example 4, based on the same inventive concept as Example 1, describes a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the method of Example 1.

[0126] Example 5, based on the same inventive concept as Example 1, describes a computer device, including,

[0127] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing the method described in Embodiment 2.

[0128] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart...Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for processing cable response signals based on empirical mode decomposition, characterized in that, include: According to the preset moving window length p Receive the timing signal of the suspension cable response, where the crossover length of adjacent moving windows is... m ; Based on the empirical mode decomposition method, the time-series signals of two adjacent moving windows are decomposed to obtain the decomposed signals of the two moving windows respectively; The overlapping region linear weighted fusion method is used to eliminate the data breakpoints in the overlapping part of the decomposition signal of the two moving windows to obtain the overlapping decomposition result. For the non-overlapping part of the decomposition signal of the two moving windows, the decomposition result obtained in each moving window is taken to obtain the non-overlapping decomposition result. The overlapping decomposition result and the non-overlapping decomposition result are combined to obtain a smooth and continuous corrected decomposition signal. The moving window length is set in advance p The timing signals received from the sling response include: Starting from a certain moment, when the length of the real-time data signal accumulates to... p After that, the initial signal θ 1 is: ； In the formula, Represents timing signals The first data point, the second data point, the third p Data points, T Indicates matrix transpose; When the data is continuously updated over time, it reaches... p + p - m When the window moves p - m Step, the signal being processed at this time θ 2 is: ； In the formula, Indicates the first p - m +1 data point, the first p - m +2 data points, the 2nd p - m The timing signal of each data point.

2. The method for processing cable response signals based on empirical mode decomposition according to claim 1, characterized in that, The signal decomposition includes: Using the empirical mode decomposition method to... θ 1. Perform signal decomposition processing to obtain: ； In the formula, To θ The first decomposition yielded the... i Individual signal vectors i =1,2,…, n , n The total number of sub-signal vectors is , and the data length of each sub-signal vector is . p ; ； In the formula, Indicates to θ The first decomposition i The first data point, the second data point, and the third data point in the sub-signal vector. p One data point; Using the empirical mode decomposition method to... θ 2. Perform signal decomposition processing to obtain: ； In the formula, To θ The second decomposition yielded the first i Individual signal vectors; ； In the formula, Indicates to θ The 2nd decomposition i The first data point, the second data point, and the third data point in the sub-signal vector. p Data points.

3. The method for processing cable response signals based on empirical mode decomposition according to claim 2, characterized in that, θ 1 and θ The formula for calculating the overlap decomposition results of each signal component in the overlap region of 2 is as follows: ； In the formula, For the first i The overlapping decomposition results of individual signal vectors; k For the coefficient vector, ; Represents the sub-signal vector The Middle p - m +1 data point to the p A set of vectors for data points Represents the sub-signal vector The first data point to the second m A vector set of data points.

4. The method for processing cable response signals based on empirical mode decomposition according to claim 2, characterized in that, θ 1 and θ The formula for calculating the non-overlapping decomposition result of 2 is: ； In the formula, This is a non-overlapping decomposition result; Represents the sub-signal vector The first data point to the second p - m A vector set of data points.

5. The method for processing cable response signals based on empirical mode decomposition according to claim 2, characterized in that, It also includes the refactoring and verification process, specifically: For non-overlapping regions, based on the characteristics of the empirical mode decomposition method, the sum of all sub-signals equals the original signal, thus enabling reconstruction. For any position in the overlapping region l All the component signal data are reconstructed from the components of the decomposition result of the empirical mode decomposition method, and expressed as: ； ； In the formula, Indicates to θ The first component signal after decomposition, the second component signal, and the third component signal. n Location of the overlapping region in each sub-signal vector l Data points at that location, Indicates to θ The first component signal after decomposition, the second component signal, and the third component signal. n Location of the overlapping region in each sub-signal vector l Data points at that location, Indicates the location of the overlapping area l The original signal is reconstructed from the components at that point; For the signal after a smooth transition, the sum of its components is: ； In the formula, For the first sub-signal, the second sub-signal, and the... n The overlapping decomposition results of individual signals, Location of overlapping area l The coefficient vector at that location; The calculation results of the sum of each component show that the signal after the smooth transition can still be reconstructed into the original signal.

6. A cable response signal processing device based on empirical mode decomposition, characterized in that, include: The acquisition module is used to move the window according to a preset length. p Receive the timing signal of the suspension cable response, where the crossover length of adjacent moving windows is... m ; The decomposition module is used to decompose the time-series signals of two adjacent moving windows based on the empirical mode decomposition method, so as to obtain the decomposed signals of the two moving windows respectively. The fusion module is used to eliminate data breakpoints in the overlapping part of the decomposition signals of the two moving windows by using the linear weighted fusion method of the overlapping area to obtain the overlapping decomposition result. For the non-overlapping part of the decomposition signals of the two moving windows, the decomposition result obtained in each moving window is taken to obtain the non-overlapping decomposition result. The overlapping decomposition result and the non-overlapping decomposition result are combined to obtain a smooth and continuous corrected decomposition signal. The moving window length is set in advance p The timing signals received from the sling response include: Starting from a certain moment, when the length of the real-time data signal accumulates to... p After that, the initial signal θ 1 is: ； In the formula, Represents timing signals The first data point, the second data point, the third p Data points, T Indicates matrix transpose; When the data is continuously updated over time, it reaches... p + p - m When the window moves p - m Step, the signal being processed at this time θ 2 is: ； In the formula, Indicates the first p - m +1 data point, the first p - m +2 data points, the 2nd p - m The timing signal of each data point.

7. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1 to 5.

8. A computer device, characterized in that, include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the method of any of claims 1 to 5.

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