A port equipment vibration signal filtering method and system

Abstract

The present application relates to the technical field of port equipment vibration signal filtering, and specifically to a port equipment vibration signal filtering method and system, the method comprising: collecting the vibration sequence, wind force sequence and wind direction sequence of port crane equipment; obtaining each wind force change node and wind direction change node; calculating the compact characteristic value of each wind direction change node; obtaining the chaos factor of each wind force change node; and then obtaining the wind load change factor; calculating the degree of disorder of wind field characteristics; based on the wind load change factor and the degree of disorder of wind field characteristics, obtaining the amplification factor of the baseline drift of the vibration signal; obtaining the optimized decomposition layer number of the modal decomposition algorithm based on the amplification factor, and then filtering the vibration signal of the port equipment. The present application solves the problem of decreased accuracy of vibration signals caused by baseline drift.

Description

Technical Field

[0001] The present application relates to the technical field of port equipment vibration signal filtering, and in particular to a port equipment vibration signal filtering method and system. Background Art

[0002] With the rapid development of port logistics, the requirements for equipment operation efficiency and safety are getting higher and higher. Port equipment such as cranes and conveyor belts are an indispensable part of port logistics operations. These equipment will produce vibrations during long-term operation due to mechanical wear, load changes, improper operation and other factors. Vibration signals, as an important means of equipment status monitoring, can reflect the real-time operating status of the equipment. By analyzing vibration signals, abnormal conditions of the equipment can be discovered in time to prevent potential failures.

[0003] Since ports are usually close to the ocean and are affected by the marine climate, coupled with the thermal difference between land and ocean, local air currents are easily formed. At the same time, the port area is densely populated with buildings, which affects the flow path of wind, causing rapid changes in wind force and direction, resulting in complex and changeable wind field conditions.

[0004] Early vibration signal processing mainly relied on simple filters and manual experience. With the advancement of technology, modern technical means such as digital signal processing technology and artificial intelligence algorithms have been introduced into the analysis of vibration signals, which has improved the accuracy and intelligence level of signal processing. Vibration signal filtering can separate useful signals from noise, thereby more accurately judging the working status of the equipment. The development of filtering technology, such as low-pass filtering, high-pass filtering, band-pass filtering, etc., provides more options for the analysis of vibration signals.

[0005] The crane rod will vibrate under the action of wind. Changes in wind speed and direction directly affect the amplitude and characteristics of the vibration, causing vortex-induced vibration of the rod, resulting in a large change in the vibration signal, which in turn causes baseline drift, causing the original signal to slowly float up and down. The impact of baseline drift is multifaceted, and it will have a negative impact on the accuracy and stability of the measurement results during long-term measurements. If the baseline drift is not eliminated, the trend item may be mistaken for the real original signal, which will affect the accuracy of the signal and subsequent data processing results, thereby amplifying the baseline drift effect. The existing filtering technology has not fully solved the problem of decreased accuracy of vibration signals caused by baseline drift. Summary of the invention

[0006] In order to solve the above technical problems, the purpose of this application is to provide a port equipment vibration signal filtering method and system, and the technical solutions adopted are as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for filtering a vibration signal of a port equipment, the method comprising the following steps:

[0008] Collect the vibration sequence of the port equipment and the wind force sequence and wind direction sequence of the wind force sensor and wind direction sensor installed on the port equipment; obtain each wind force change node and wind direction change node based on the numerical difference in the differential sequence of the wind force sequence;

[0009] The compact characteristic value of each wind direction change node is obtained based on the distance between the wind direction change nodes; the confusion factor of each wind force change node is obtained based on the average discrete degree of the corresponding values ​​of the elements in the neighborhood window where the wind force change node is located;

[0010] The wind load variation factor is obtained based on the compact eigenvalue and the chaos factor; the degree of disorder of wind field characteristics is obtained based on the discrete degree of elements in the wind force sequence and wind direction sequence and the correlation between the wind force sequence and the wind direction sequence;

[0011] Based on the wind load variation factor and the degree of wind field characteristic disturbance, the amplification factor of the vibration signal baseline drift is obtained;

[0012] The optimized decomposition layer number of the modal decomposition algorithm is obtained based on the amplification factor; and the vibration signal of the port equipment is filtered based on the optimized decomposition layer number.

[0013] Furthermore, the process of acquiring the wind direction change node and the wind force change node includes:

[0014] Obtain the first-order difference sequence of the wind direction sequence, recorded as the wind direction difference sequence;

[0015] Calculate the mean of all elements in the wind direction difference sequence, and take all elements in the wind direction difference sequence that are greater than the mean as wind direction change nodes in the wind direction difference sequence;

[0016] The elements in the wind force difference sequence that have the same position as the wind direction change node in the wind direction difference sequence are taken as wind force change nodes.

[0017] Furthermore, the method for obtaining the compact eigenvalue is:

[0018] For each wind direction change node, the minimum value of the distance between the wind direction change node and all other wind direction change nodes is calculated as the compact eigenvalue of each wind direction change node.

[0019] Further, the method for obtaining the confusion factor is:

[0020] For each wind change node, a neighborhood window of a preset length adjacent to and before the wind change node is selected, and the standard deviation of all elements in the neighborhood window is calculated as the confusion factor of each wind change node.

[0021] Furthermore, the formula of the wind load variation factor is: ; Where A is the wind load variation factor, is the compact eigenvalue of the i-th wind direction change node, is the confusion factor of the wind force change node in the wind force difference sequence that has the same position as the i-th wind direction change node in the wind direction difference sequence, and X is the total number of wind direction change nodes.

[0022] Furthermore, the method for obtaining the degree of turbulence of the wind field characteristics is:

[0023] For all wind direction change nodes in the wind direction difference sequence, the smallest collection time of the two adjacent wind direction data corresponding to the wind direction change node in the wind direction sequence is taken as the wind direction change time, and the time period between each wind direction change time is taken as each wind direction change time period. The wind force sequence and the wind direction sequence are divided according to the wind direction change time to obtain each wind force subsequence and wind direction subsequence;

[0024] Calculate the coefficient of variation of all wind force subsequences and wind direction subsequences, calculate the absolute value of the correlation coefficient between the wind force subsequences and wind direction subsequences in all the same wind direction change time periods, calculate the absolute value of the difference between the coefficients of variation of the wind force subsequences and wind direction subsequences divided in all the same wind direction change time periods, calculate the ratio of the absolute value of the difference to the absolute value, and take the average of the ratios in all wind direction change time periods as the degree of disorder of the wind field characteristics.

[0025] Furthermore, the method for obtaining the amplification factor is:

[0026] The product of the wind load variation factor and the degree of turbulence of the wind field characteristics is calculated as the amplification factor of the vibration signal baseline drift.

[0027] Furthermore, the formula for optimizing the number of decomposition layers is: ; In the formula, To optimize the number of decomposition levels, is the preset initial value of the decomposition layer, B is the amplification factor of the vibration signal baseline drift, is the normalization function, Adjust parameters for the preset.

[0028] Further, filtering the vibration signal of the port equipment based on the optimized decomposition layer number includes:

[0029] The vibration sequence is used as the input of the modal decomposition algorithm, the IMF components are output, the lowest frequency IMF components are extracted, the lowest frequency IMF components are removed using the wavelet threshold denoising method, and all the remaining IMF components after removal are reconstructed to obtain the denoised vibration signal.

[0030] In a second aspect, an embodiment of the present application further provides a port equipment vibration signal filtering system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0031] This application has at least the following beneficial effects:

[0032] The present application proposes a method and system for filtering vibration signals of port equipment. In view of the vibration load problem of crane tie rods under wind force, differential method and node analysis are used to reflect the compactness of wind direction changes and the volatility of wind intensity changes, so as to solve the complexity and uncertainty of baseline drift in vibration signals. In view of the stability problem of wind field changes, Pearson correlation coefficient and coefficient of variation are used to reflect the unevenness of wind field and the inconsistency of wind force and wind direction changes, so as to eliminate the influence of unstable wind field characteristics on vibration signal analysis. In view of the amplification effect of wind load changes and wind field characteristic disturbances on vibration signals, the amplification effect of baseline drift of vibration signals is quantified, so as to eliminate the negative influence of wind field changes on the stability of vibration signals. By improving the empirical mode decomposition algorithm, the number of decomposition layers is dynamically adjusted according to the amplification factor of the baseline drift of vibration signals by the wind field, so as to realize accurate filtering of the low-frequency signal of baseline drift after amplification by the wind field, effectively remove the amplified baseline drift noise, and improve the analysis quality of crane vibration signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flowchart of a method for filtering vibration signals of port equipment provided by one embodiment of the present application;

[0035] Figure 2 An image of a vibration signal affected by noise provided by an embodiment of the present application;

[0036] Figure 3 A vibration signal image after denoising provided by an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of a port equipment vibration signal filtering method and system proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0039] The specific scheme of a port equipment vibration signal filtering method and system provided by the present application is described in detail below with reference to the accompanying drawings.

[0040] See also Figure 1 , which shows a flowchart of a method for filtering vibration signals of port equipment provided by an embodiment of the present application, the method comprising the following steps:

[0041] Step S1, collecting the vibration sequence, wind force sequence and wind direction sequence of the port crane equipment.

[0042] Port cranes are large mechanical equipment commonly found in coastal areas. They often face the challenge of strong winds. As a key component of port cranes, tie rods are particularly sensitive to vortex-induced vibrations caused by wind due to their slender proportions and relatively low stiffness.

[0043] A vibration acceleration sensor is arranged at the bearing seat of the lifting equipment's tie rod to collect vibration signals to reduce the loss of vibration information in the transmission path and capture the vibration signal more accurately. Wind force and wind direction sensors are installed at the bearing seat of the lifting equipment's tie rod to monitor the impact of wind on the tie rod. The collection interval of the vibration acceleration sensor and the wind force and direction sensor are set to 0.1s, and the collection frequency of the vibration acceleration sensor is set to 1kHz. The vibration sequence, wind force sequence and wind direction sequence are obtained, and the three sequences obtained are transmitted to the data processing module for subsequent analysis.

[0044] Step S2, based on the numerical differences in the differential sequence of the wind force sequence, obtaining each wind force change node and wind direction change node; obtaining the compact characteristic value of each wind direction change node based on the distance between the wind direction change nodes; obtaining the confusion factor of each wind force change node based on the average discrete degree of the corresponding values ​​of the elements in the neighborhood window where the wind force change node is located;

[0045] The crane rod will vibrate under the action of wind. Changes in wind speed and direction directly affect the amplitude and characteristics of the vibration. In particular, strong winds or gusts will cause vortex-induced vibration of the rod, resulting in a large change in the vibration signal, which in turn causes the influence of baseline drift.

[0046] In the collected wind force and wind direction sequences, the continuous changes in wind speed and direction will directly affect the amplitude and characteristics of the vibration. The greater the rate of change of wind force and wind direction, the greater the change in the vibration amplitude and characteristics of the crane pull rod. The first-order difference sequences of the wind force sequence and wind direction sequence are calculated as the wind force difference sequence and wind direction difference sequence. Each element in the wind force difference sequence and the wind direction difference sequence represents the change amplitude of wind force and wind direction during a collection interval.

[0047] The mean of all elements in the wind direction difference sequence is calculated, and all elements in the wind direction difference sequence that are greater than the mean are regarded as wind direction change nodes. The wind direction change nodes in the wind direction difference sequence indicate that the wind direction change angles of these nodes exceed the average level, and they are nodes with more significant wind direction changes.

[0048] Furthermore, for each wind direction change node, the minimum value of the distance between the wind direction change node and all other wind direction change nodes is calculated as the compact characteristic value of each wind direction change node. This embodiment uses Euclidean distance as the distance measurement, and the implementer can select other measurement methods according to actual conditions.

[0049] Wind direction and wind force are usually not completely independent. A sudden change in wind direction may be accompanied by a change in wind force. Conversely, by analyzing the wind direction change nodes, we can identify the period when wind force may change, thereby analyzing the local change trend of wind force. According to the above analysis, all elements in the wind force difference sequence that have the same position as all wind direction change nodes in the wind direction difference sequence are selected as wind force change nodes; with each wind force change node as the center, two adjacent elements are selected before and after in the wind force difference sequence to form the neighborhood window of each wind force change node, and the standard deviation of all elements in the neighborhood window of each wind force change node is calculated as the confusion factor of each wind force change node.

[0050] Step S3, obtaining the wind load variation factor based on the compact eigenvalue and the confusion factor; obtaining the degree of disorder of the wind field characteristics based on the discrete degree of the elements in the wind force sequence and the wind direction sequence and the correlation between the wind force sequence and the wind direction sequence; obtaining the amplification factor of the baseline drift of the vibration signal based on the wind load variation factor and the degree of disorder of the wind field characteristics.

[0051] Furthermore, in order to reflect the change of wind load, the wind load change factor is calculated, and the formula is: ; Where A is the wind load variation factor, is the compact eigenvalue of the i-th wind direction change node, is the confusion factor of the wind force change node in the wind force difference sequence that has the same position as the i-th wind direction change node in the wind direction difference sequence, and X is the total number of wind direction change nodes, that is, the total number of wind force change nodes.

[0052] In the formula, the compact eigenvalue It reflects the concentration of wind direction changes near the i-th wind change node, that is, the compactness of wind direction changes. If the compact eigenvalue The smaller the value, the more concentrated the wind direction change is, and vice versa, the more dispersed it is, which means that the closer the wind direction change is in time, the greater the change in wind load caused, and A increases accordingly; the chaos factor It measures the volatility or chaos of wind intensity changes near the i-th wind direction change node, reflecting the instability of wind intensity when wind direction changes. The chaos factor The larger the value, the greater the change in wind intensity, the greater the change in wind load, and the stronger the vibration. At this time, the larger the value of the wind load change factor A; conversely, the smaller the value of the wind load change factor A.

[0053] The wind load variation factor A combines the compactness of wind direction changes and the volatility of wind intensity changes, and describes the complexity and uncertainty of the wind load borne by the crane rod under specific wind conditions. The larger the value of the wind load variation factor A, the more complex and unstable the vibration of the crane rod under the action of wind, and the stronger the impact on the baseline drift generated by the vibration signal.

[0054] The non-uniformity of wind characteristics means that the change of wind force over time is not smooth and continuous, but shows the characteristics of rapid change or mutation. This non-uniformity further aggravates the baseline drift of the vibration signal of the crane pull rod on the basis of wind load. The non-uniformity of wind force leads to rapid changes in wind load. These rapidly changing loads cause the vibration signal to fluctuate, resulting in nonlinear changes in the amplitude and frequency of the vibration signal, thereby aggravating the impact of baseline drift on the vibration signal.

[0055] In addition, the wind load on the crane's tie rod interacts with the wind's variation characteristics, resulting in inconsistent wind pressure height variation coefficients at different wind directions and exhibiting an amplification effect. For example, the tower body and boom, as important components of the crane, have a spatial truss structure. When affected by wind, due to their aerodynamic shape, the wind pressure height variation coefficient will show inconsistency at different wind directions, which means that under a specific wind direction, the wind load borne by the crane's tie rod may be greater than expected. The uncertainty of this wind load directly exacerbates the problem of baseline drift of the vibration signal, posing a challenge to the stability and safety of the crane.

[0056] Furthermore, for all wind direction change nodes in the wind direction difference sequence, the smallest collection time of the two collection times of the two adjacent wind direction data corresponding to the wind direction change node in the wind direction sequence is taken as the wind direction change time, and the time period between each wind direction change time is taken as each wind direction change time period. The wind force sequence and the wind direction sequence are divided according to the wind direction change time to obtain each wind force sub-sequence and wind direction sub-sequence.

[0057] Further, the coefficient of variation of all wind force sub-sequences and wind direction sub-sequences is calculated, and the absolute value of the correlation coefficient between the wind force sub-sequences and wind direction sub-sequences in all the same wind direction change time periods is calculated, the absolute value of the difference between the coefficient of variation of the wind force sub-sequences and wind direction sub-sequences in all the same wind direction change time periods is calculated, the ratio of the absolute value of the difference to the absolute value is calculated, and the average of the ratios in all wind direction change time periods is taken as the degree of disorder c of the wind field characteristics; in this embodiment, the Pearson correlation coefficient is selected as the measurement method of the correlation coefficient, and the implementer may select other correlation coefficients according to actual conditions.

[0058] Based on the wind load variation factor A of the port crane tie rod and the degree of turbulence c of the wind field characteristics of the port crane tie rod, the amplification factor B of the wind field on the baseline drift of the vibration signal is calculated. The formula is: ; In the formula, B is the amplification factor, A is the wind load variation factor, and c is the degree of turbulence of the wind field characteristics.

[0059] It should be noted that the wind load variation factor A reflects the complexity and uncertainty of the wind load borne by the crane rod under specific wind conditions. The larger the value of the wind load variation factor A, the more complex and unstable the vibration of the crane rod under the action of wind, the stronger the influence on the baseline drift of the vibration signal, and the stronger the generated baseline drift amplification effect, that is, the larger the value of the amplification factor B of the wind field on the baseline drift of the vibration signal; conversely, the smaller the value of the amplification factor B of the wind field on the baseline drift of the vibration signal; the smaller the degree of turbulence c of the wind field characteristics, the lower the connection between the changes in wind force and wind direction, the more unstable the wind field characteristics, and the smaller the value of the amplification factor B of the wind field on the baseline drift of the vibration signal; the larger the degree of turbulence c of the wind field characteristics, reflects the unevenness of the wind field, that is, the changes in wind force and wind direction in time are not smooth and continuous, but show the characteristics of rapid changes or mutations, which aggravates the baseline drift of the vibration signal of the crane rod. The greater the amplification effect on the baseline drift, the larger the value of the amplification factor B of the wind field on the baseline drift of the vibration signal.

[0060] The amplification factor B describes the degree to which the wind field characteristics amplify the baseline drift of the crane's tie rod vibration signal. The larger the amplification factor B value is, the more significant the baseline drift of the vibration signal caused by the wind field is, and the greater the challenge it poses to the stability and safety of the crane.

[0061] Step S4, obtaining the optimized decomposition layer number of the modal decomposition algorithm based on the amplification factor; filtering the vibration signal of the port equipment based on the optimized decomposition layer number.

[0062] In port equipment, the wind field changes such as wind force and wind direction amplify the baseline drift of the equipment vibration signal, which may make it more difficult to extract fault features from the vibration signal. Therefore, the empirical mode decomposition (EMD) algorithm is used to denoise the low-frequency signal of the baseline drift in the vibration signal after the wind field amplification. The vibration signal image affected by noise is shown in Figure 1. Figure 2 shown.

[0063] Empirical mode decomposition can decompose nonlinear and non-stationary signals into a series of intrinsic mode functions (IMFs). Baseline drift mainly exists in the low-frequency IMF components. By removing the low-frequency components and then reconstructing the remaining IMFs, a signal without baseline drift can be obtained.

[0064] However, due to the amplification effect of wind field changes on baseline drift, the EMD algorithm is difficult to determine the low-frequency range of baseline drift, resulting in modal aliasing and poor decomposition effect.

[0065] Set the initial value of the number of decomposition levels of the EMD algorithm is 5. In the EMD algorithm, the more decomposition layers there are, the lower the frequency of the decomposed signal components tend to be. Here, the decomposition layers of the EMD algorithm are improved according to the amplification factor of the baseline drift of the vibration signal in the wind field. The specific improved relationship is: ,in To optimize the number of decomposition levels, is the initial value of the decomposition layer, B is the amplification factor of the baseline drift of the vibration signal, is the normalization function, is a preset adjustment parameter. In this embodiment, the value is 10, which is used to control the adjustment range of the decomposition layer number, reduce mode aliasing, and improve the accuracy of decomposition. For the floor rounding function, it should be noted that the adjustment parameters can be improved according to the actual wind conditions, crane size and other factors, and the implementer can choose other values ​​according to the actual situation.

[0066] Wind field changes have an amplifying effect on the baseline drift of vibration signals. By considering the amplification factor of the wind field, the number of decomposition layers can be adjusted more accurately to adapt to the actual changes in the signal. The improved calculation formula can dynamically adjust the number of decomposition layers according to the specific conditions of the wind field. When the wind field has a stronger amplification effect on the baseline drift, the frequency of the baseline drift after amplification is higher. The algorithm sets a lower optimized decomposition layer by improving the calculation formula, so that the frequency range of the low-frequency IMF component obtained by the EMD algorithm is higher, which can effectively remove the amplified baseline drift noise, and vice versa. The denoised vibration signal image is as follows: Figure 3 shown.

[0067] The number of optimized decomposition layers can be automatically adjusted according to the actual wind field conditions, making the algorithm more adaptable and able to process signals under different wind conditions. Among all the IMF components output by the EMD algorithm, the lowest frequency IMF component is extracted, and the wavelet threshold denoising method is used to remove the lowest frequency IMF component, and the IMF component after noise removal is reconstructed to obtain the denoised vibration signal. In this way, the baseline drift noise in the vibration signal is filtered.

[0068] Based on the same inventive concept as the above method, an embodiment of the present application also provides a port equipment vibration signal filtering system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned port equipment vibration signal filtering methods when executing the computer program.

[0069] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A port equipment vibration signal filtering method, characterized in that: The method comprises the following steps: Collect the vibration sequence of the port equipment and the wind force sequence and wind direction sequence of the wind force sensor and wind direction sensor installed on the port equipment; obtain each wind force change node and wind direction change node based on the numerical difference in the differential sequence of the wind force sequence; The compact characteristic value of each wind direction change node is obtained based on the distance between the wind direction change nodes; the confusion factor of each wind force change node is obtained based on the average discrete degree of the corresponding values ​​of the elements in the neighborhood window where the wind force change node is located; The wind load variation factor is obtained based on the compact eigenvalue and the chaos factor; the degree of disorder of wind field characteristics is obtained based on the discrete degree of elements in the wind force sequence and wind direction sequence and the correlation between the wind force sequence and the wind direction sequence; Based on the wind load variation factor and the degree of wind field characteristic disturbance, the amplification factor of the vibration signal baseline drift is obtained; Obtain the optimal decomposition level of the modal decomposition algorithm based on the amplification factor; filter the vibration signal of the port equipment based on the optimized decomposition level; The method for obtaining the compact eigenvalue is: For each wind direction change node, the minimum value of the distance between the wind direction change node and all other wind direction change nodes is calculated as the compact eigenvalue of each wind direction change node; The method for obtaining the confusion factor is: For each wind change node, select a neighborhood window of preset length adjacent to the wind change node, and calculate the standard deviation of all elements in the neighborhood window as the confusion factor of each wind change node; The formula for the wind load variation factor is: ; Where A is the wind load variation factor, is the compact eigenvalue of the i-th wind direction change node, is the confusion factor of the wind force change node in the wind force difference sequence that has the same position as the i-th wind direction change node in the wind direction difference sequence, and X is the total number of wind direction change nodes; The method for obtaining the degree of turbulence of the wind field characteristics is: For all wind direction change nodes in the wind direction difference sequence, the smallest collection time of the two adjacent wind direction data corresponding to the wind direction change node in the wind direction sequence is taken as the wind direction change time, and the time period between each wind direction change time is taken as each wind direction change time period. The wind force sequence and the wind direction sequence are divided according to the wind direction change time to obtain each wind force subsequence and wind direction subsequence; Calculate the coefficient of variation of all wind force subsequences and wind direction subsequences, and calculate the absolute value of the correlation coefficient between the wind force subsequences and wind direction subsequences in all the same wind direction change time periods, calculate the absolute value of the difference between the coefficients of variation of the wind force subsequences and wind direction subsequences divided in all the same wind direction change time periods, calculate the ratio of the absolute value of the difference to the absolute value, and take the average of the ratios in all wind direction change time periods as the degree of turbulence of wind field characteristics; The method for obtaining the amplification factor is: The product of the wind load variation factor and the degree of turbulence of the wind field characteristics is calculated as the amplification factor of the vibration signal baseline drift.

2. A port equipment vibration signal filtering method as claimed in claim 1, characterized in that: The process of acquiring the wind direction change node and the wind force change node includes: Obtain the first-order difference sequence of the wind direction sequence, recorded as the wind direction difference sequence; Calculate the mean of all elements in the wind direction difference sequence, and take all elements in the wind direction difference sequence that are greater than the mean as wind direction change nodes in the wind direction difference sequence; The elements in the wind force difference sequence that have the same position as the wind direction change node in the wind direction difference sequence are taken as wind force change nodes.

3. A port equipment vibration signal filtering method as claimed in claim 1, characterized in that: The formula for optimizing the number of decomposition layers is: ; In the formula, To optimize the number of decomposition levels, is the preset initial value of the decomposition layer, B is the amplification factor of the vibration signal baseline drift, is the normalization function, Adjust parameters for the preset.

4. A port equipment vibration signal filtering method as claimed in claim 1, characterized in that: The filtering of the port equipment vibration signal based on the optimized decomposition layer number includes: The vibration sequence is used as the input of the modal decomposition algorithm, the IMF components are output, the lowest frequency IMF components are extracted, the lowest frequency IMF components are removed using the wavelet threshold denoising method, and all the remaining IMF components after removal are reconstructed to obtain the denoised vibration signal.

5. A port equipment vibration signal filtering system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the port equipment vibration signal filtering method as described in any one of claims 1 to 4 are implemented.

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