A compression spring resonance frequency detection method and system

Abstract

The present application relates to the technical field of compression spring resonance frequency detection, and specifically relates to a compression spring resonance frequency detection method and system, the method comprising the following steps: collecting a mixed vibration signal of a compression spring to be measured; collecting a background noise signal of a test device as a reference noise signal; collecting a synchronous signal of a specific noise source of the test device; performing adaptive noise cancellation processing on the mixed vibration signal, the adaptive noise cancellation processing performing initial parameter training using the reference noise signal and using the noise source synchronous signal as a real-time reference input to separate a pure vibration response signal of the compression spring to be measured; performing frequency spectrum analysis on the pure vibration response signal; identifying a wideband vibration response region of the compression spring to be measured, the wideband vibration response region having a continuous amplitude higher than a preset energy threshold and having a specific width; calculating a characteristic parameter of the wideband vibration response region; and judging a degradation state of the compression spring to be measured according to the characteristic parameter. The above improves the accuracy of degradation judgment.

Description

Technical Field

[0001] The present invention relates to the technical field of compression spring resonance frequency detection, and in particular to a compression spring resonance frequency detection method and system. Background Art

[0002] Currently, vibration characteristic evaluation of key components such as compression springs in vehicle suspension systems typically aims to identify the frequency point at which they produce maximum response under specific excitation.

[0003] Take, for example, the inspection of the compression springs used in the suspension of automated guided vehicles (AGVs) in automated warehousing and logistics centers. These springs travel the exact same path day after day, subjecting them to a load with the same impact energy from a fixed direction each time they pass over a speed bump. This continuous and highly concentrated stress causes specific areas of a few coils on the spring to experience repeated stress cycles, triggering microstructural changes in the material in that area. For example, the metal lattice experiences dislocation, slippage, and pinning, resulting in work hardening. The direct physical consequence is a localized, slight increase in the elastic modulus of these specific coils compared to the rest of the spring.

[0004] After a period of service, the physical properties of these compression springs had fundamentally changed, with their internal elastic modulus exhibiting a non-uniform distribution. This non-uniformity disrupted the original energy transfer path within the springs during vibration. When these springs were sent to the quality inspection department for routine NVH performance evaluation, problems began to emerge. The quality inspection department used a standard resonant frequency testing procedure for the compression springs. The springs were vertically mounted on a dedicated test bench, a shaker applied a swept-frequency excitation, and a vibration sensor collected real-time vibration response data. However, the analysis system failed to detect any identifiable, distinct resonant peaks on the spectrum. Instead, the overall signal amplitude showed a slight increase over a relatively broad frequency range, forming a low, gentle "bulge" with a blurred shape and unclear boundaries. This non-uniform distribution of the spring's elastic modulus degraded the macroscopic vibration characteristics: the originally concentrated vibration energy was dispersed over a wider frequency band. Automated interpretation algorithms based on peak search were unable to provide any valid results for this pattern. Summary of the Invention

[0005] The purpose of the present invention is to address the above-mentioned deficiencies and provide a method and system for detecting the resonant frequency of a compression spring.

[0006] The present invention adopts the following technical solutions:

[0007] A method for detecting the resonant frequency of a compression spring, the method comprising the following steps: collecting a mixed vibration signal of the compression spring to be tested; collecting a background noise signal of a test device as a reference noise signal; collecting a synchronization signal of a specific noise source of the test device; performing adaptive noise cancellation processing on the mixed vibration signal, wherein the adaptive noise cancellation processing utilizes the reference noise signal for initial parameter training, and utilizes the noise source synchronization signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested, wherein when an electromagnetic exciter is used as an excitation source to apply excitation to the compression spring to be tested and causes the synchronization signal of the specific noise source of the test device to generate synchronous modulation, the steps of the adaptive noise cancellation processing comprise: obtaining a baseline noise template of the specific noise source of the test device when the electromagnetic exciter is in a non-working state, synchronously obtaining a driving control signal of the electromagnetic exciter, modulating the baseline noise template of the specific noise source of the test device according to the driving control signal of the electromagnetic exciter, generating a virtual noise reference signal, performing adaptive noise cancellation processing on the mixed vibration signal, wherein the adaptive noise cancellation processing utilizes the reference noise signal for initial parameter training, Practice, and use the virtual noise reference signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested; perform spectrum analysis on the pure vibration response signal; identify the broadband vibration response area of ​​the compression spring to be tested from the spectrum analysis result, the amplitude of the broadband vibration response area is continuous and higher than the preset energy threshold and has a specific width, wherein the spectrum analysis result is refined, and the steps of the refined processing include: preliminarily identifying that the amplitude of the broadband vibration response area in the spectrum analysis result is continuous and higher than the preset energy threshold and has a specific width to obtain a preliminary broadband response range, identifying local maximum points within the preliminary broadband response range, analyzing the spectrum curve shape within the preliminary broadband response range according to the distribution of the local maximum points, determining the precise starting frequency and cutoff frequency of the broadband vibration response area based on the analysis result of the spectrum curve shape, and storing the precise starting frequency and cutoff frequency of the broadband vibration response area; calculating characteristic parameters of the broadband vibration response area, the characteristic parameters including center frequency, response bandwidth and total energy of the frequency band; judging the degradation state of the compression spring to be tested according to the characteristic parameters.

[0008] The above scheme solves the problem in the prior art that it is difficult to accurately separate, identify and judge the degradation state of the compression spring when the vibration response after degradation presents a broadband "bulge" shape and overlaps with the background noise. It can effectively identify the broadband vibration response area and extract characteristic parameters, thereby realizing accurate judgment of the degradation state of the compression spring.

[0009] In order to improve the solution, the present application also proposes that the steps of determining the degradation state of the compression spring to be tested based on the characteristic parameters include: analyzing the characteristic parameters, classifying the degradation modes of the broadband vibration response area according to the center frequency, and obtaining the classified degradation mode; based on the classified degradation mode, and in combination with the response bandwidth and the total energy of the frequency band, comparing with the preset mode threshold, evaluating the severity of the classified degradation mode; based on the severity of the classified degradation mode and comparing the total energy of the frequency band, determining the dominant degradation mode; based on the severity of the dominant degradation mode and the severity of the classified degradation mode, generating maintenance recommendations, thereby determining the degradation state of the compression spring to be tested.

[0010] To improve the solution, this application also proposes that the steps of generating maintenance recommendations include: obtaining the spare parts, maintenance stations and maintenance personnel shifts available in the logistics center to obtain maintenance resource availability information; obtaining the current tasks and future scheduling plans of the AGV to be maintained to obtain the operating status of the AGV to be maintained; evaluating the maintenance urgency based on the severity of the dominant degradation mode and the classified degradation mode; prioritizing the maintenance tasks based on the degradation status of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information and the operating status of the AGV to be maintained to obtain the priority sorting results; adjusting the operating schedule of the AGV to be maintained and allocating maintenance resources based on the priority sorting results; generating maintenance recommendations based on the adjusted operating schedule of the AGV to be maintained and the allocated maintenance resources.

[0011] To improve the solution, this application also proposes that the steps of prioritizing maintenance tasks according to the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained include: setting weights for the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained; calculating the comprehensive priority score of the maintenance task based on the weight; and prioritizing the maintenance tasks according to the comprehensive priority score of the maintenance task.

[0012] To improve the solution, this application also proposes that the steps of setting weights for the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained include: obtaining the operating load of the logistics center, spare parts inventory, maintenance personnel on-the-job status and AGV task priority, and determining the operating mode of the logistics center; according to the operating mode of the logistics center, adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained.

[0013] In order to improve the solution, this application also proposes that the steps of adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained according to the operation mode of the logistics center include: presetting weight adjustment rules under different operation modes of the logistics center; selecting a corresponding weight set from the preset weight adjustment rules according to the operation mode of the logistics center; adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained according to the selected weight set.

[0014] To improve the solution, this application also proposes that the steps of obtaining the logistics center's operating load, spare parts inventory, maintenance personnel on-the-job status and AGV task priority, and determining the logistics center's operating mode include: pre-setting standard thresholds; pre-setting rule sets; determining the logistics center's operating mode based on the logistics center's operating load, spare parts inventory, maintenance personnel on-the-job status and AGV task priority, and based on preset standard thresholds and preset rule sets.

[0015] To improve the solution, the present application also proposes a compression spring resonance frequency detection system, which is applied to the above-mentioned compression spring resonance frequency detection method. The system includes: an acquisition module, which is used to acquire the mixed vibration signal of the compression spring to be tested, the background noise signal of the test equipment as a reference noise signal, and the synchronization signal of the specific noise source of the test equipment; a cancellation processing module, which is used to perform adaptive noise cancellation processing on the mixed vibration signal, and the adaptive noise cancellation processing uses the reference noise signal for initial parameter training, and uses the noise source synchronization signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested; a spectrum analysis module, which is used to perform spectrum analysis on the pure vibration response signal; an identification module, which is used to identify the broadband vibration response area of ​​the compression spring to be tested from the spectrum analysis results, the amplitude of the broadband vibration response area is continuous and higher than the preset energy threshold and has a specific width; a calculation module, which is used to calculate the characteristic parameters of the broadband vibration response area, the characteristic parameters including the center frequency, the response bandwidth and the total energy of the frequency band; a discrimination module, which discriminates the degradation state of the compression spring to be tested according to the characteristic parameters.

[0016] Through the above solution, a system for implementing the above detection method is provided. Through modular design, the method can be executed efficiently and automatically, thereby improving detection efficiency and accuracy.

[0017] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a method for detecting the resonant frequency of a compression spring according to the present invention;

[0019] Figure 2 Fig. 1 is a structural schematic diagram of a compression spring resonance frequency detection system according to the present application. DETAILED DESCRIPTION

[0020] The present application will be described in detail by way of specific embodiments, and those skilled in the art can understand the advantages and effects of the present application from the disclosure. The present application can be implemented or applied by other different embodiments, and the details in the specification can be modified and changed based on different views and applications without departing from the spirit of the present application. In addition, the drawings of the present application are only simple schematic illustrations, not actual size drawings, and it is declared in advance. The following embodiments will further illustrate the related technical content of the present application, but the disclosed content is not used to limit the protection scope of the present application.

[0021] The present embodiment provides a compression spring resonance frequency detection method and system, which combines Figure 1 and Figure 2 as shown.

[0022] Reference Figure 1, a method for detecting the resonant frequency of a compression spring, the method comprising the following steps: collecting a mixed vibration signal of the compression spring to be tested; collecting a background noise signal of a test device as a reference noise signal; collecting a synchronization signal of a specific noise source of the test device; performing adaptive noise cancellation processing on the mixed vibration signal, wherein the adaptive noise cancellation processing uses the reference noise signal for initial parameter training, and uses the noise source synchronization signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested, wherein when the electromagnetic exciter is used as an excitation source to apply excitation to the compression spring to be tested and causes the synchronization signal of the specific noise source of the test device to generate synchronous modulation, the steps of the adaptive noise cancellation processing comprise: obtaining a baseline noise template of the specific noise source of the test device when the electromagnetic exciter is in a non-working state, synchronously obtaining a driving control signal of the electromagnetic exciter, modulating the baseline noise template of the specific noise source of the test device according to the driving control signal of the electromagnetic exciter, generating a virtual noise reference signal, performing adaptive noise cancellation processing on the mixed vibration signal, and performing adaptive noise cancellation processing on the reference noise signal for initial parameter training. Practice, and use the virtual noise reference signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested; perform spectrum analysis on the pure vibration response signal; identify the broadband vibration response area of ​​the compression spring to be tested from the spectrum analysis result, the amplitude of the broadband vibration response area is continuous and higher than the preset energy threshold and has a specific width, wherein the spectrum analysis result is refined, and the steps of the refined processing include: preliminarily identifying that the amplitude of the broadband vibration response area in the spectrum analysis result is continuous and higher than the preset energy threshold and has a specific width to obtain a preliminary broadband response range, identifying local maximum points within the preliminary broadband response range, analyzing the spectrum curve shape within the preliminary broadband response range according to the distribution of the local maximum points, determining the precise starting frequency and cutoff frequency of the broadband vibration response area based on the analysis result of the spectrum curve shape, and storing the precise starting frequency and cutoff frequency of the broadband vibration response area; calculating characteristic parameters of the broadband vibration response area, the characteristic parameters including center frequency, response bandwidth and total energy of the frequency band; judging the degradation state of the compression spring to be tested according to the characteristic parameters.

[0023] The synchronization signal of the specific noise source of the test equipment refers to a signal generated synchronously with the operating status or excitation cycle of the specific noise source within the test equipment. It can be obtained in various ways, such as directly monitoring the operating frequency of the noise source through a sensor or obtaining the noise source's drive signal through a control system interface. Its main purpose is to provide a reference that is highly correlated with the interfering noise, so that this noise can be accurately tracked and eliminated in adaptive noise cancellation processing. Adaptive noise cancellation processing refers to a technology that dynamically adjusts filter parameters to separate the target signal from the mixed signal. It can be implemented using various algorithms such as the least mean square (LMS) algorithm, the recursive least squares (RLS) algorithm, or the Kalman filter. For example, adaptive filters based on the LMS algorithm and adaptive filters based on the RLS algorithm are used. Its main purpose is to effectively remove noise and preserve the integrity of the target signal when the target signal and the noise signal overlap in the frequency domain. A broadband vibration response region refers to the situation in which the vibration energy of the compression spring under test is no longer concentrated at a single frequency in the spectrum analysis results, but is instead dispersed over a continuous region with a certain frequency range. This can be manifested as a flat pattern on the spectrum graph. This region primarily characterizes changes in the vibration characteristics of the compression spring due to material degradation, namely, the diffusion of vibration energy. A preset energy threshold is the lower energy limit used to distinguish valid vibration responses from background noise or random fluctuations when identifying a broadband vibration response region. This threshold can be set based on empirical data, statistical analysis, or the requirements of a specific application scenario. It primarily ensures that the identified region has sufficient energy to avoid misidentifying weak noise or irrelevant signals as a compression spring response. A specific width refers to the frequency range occupied by the broadband vibration response region on the frequency axis. This width can be defined based on the expected degradation pattern of the compression spring, material properties, or industry standards. It primarily quantifies the degree of vibration energy diffusion and serves as a criterion for identifying valid broadband response regions. Characteristic parameters refer to numerical indicators used to quantify the characteristics of the broadband vibration response area, including the center frequency, response bandwidth and total energy of the frequency band. These parameters can be obtained by calculating the energy-weighted average frequency, frequency range and energy accumulation of all frequency components in the broadband area respectively. Their main purpose is to provide a comprehensive set of quantitative indicators to facilitate the subsequent judgment of the degradation state of the compression spring.

[0024] To achieve the above objectives, the solution of the present application accurately identifies the degradation state of the compression spring through a series of collaborative steps. First, the system collects a mixed vibration signal from the compression spring under test. This signal contains the actual vibration response of the compression spring and various noise interferences in the test environment. To effectively remove these interferences, the background noise signal of the test equipment is simultaneously collected as a reference noise signal, and the synchronization signal of the specific noise source of the test equipment is collected. These noise signals and synchronization signals are key inputs for precise noise cancellation. Subsequently, the mixed vibration signal is subjected to adaptive noise cancellation processing. This processing process uses the reference noise signal for initial parameter training, allowing the cancellation system to learn and adapt to the inherent noise characteristics of the test environment. Furthermore, by using the noise source synchronization signal as a real-time reference input, the system can dynamically track and eliminate specific noise related to the operation of the test equipment, even if the frequency of this noise partially overlaps with the broadband vibration response region of the compression spring. This adaptive processing mechanism avoids the loss of useful signals that may be caused by traditional fixed filters when removing noise, thereby effectively isolating the pure vibration response signal of the compression spring under test. After obtaining a pure vibration response signal, a spectrum analysis is performed on it, converting the time-domain signal into a frequency-domain representation to reveal the frequency components of the compression spring. Based on this, the broadband vibration response region of the compression spring under test is identified from the spectrum analysis results. This identification process is designed to target the diffusion characteristics of vibration energy after compression spring degradation. It requires that the amplitude of the identified region be continuous, above a preset energy threshold, and have a specific width, ensuring that the identified region is a true degradation response of the compression spring, rather than random noise. Once the broadband vibration response region is identified, its characteristic parameters are calculated, including the center frequency, response bandwidth, and total energy within the frequency band. These parameters provide a quantitative description of the vibration characteristics of the compression spring after degradation: the center frequency reflects the approximate location of energy concentration, the response bandwidth reveals the degree of energy diffusion, and the total energy within the frequency band indicates the overall strength of the vibration response. Ultimately, based on these quantitative characteristic parameters, the system determines the degradation state of the compression spring under test. By comprehensively analyzing the shift in center frequency, the expansion of response bandwidth, and the change in total energy within the frequency band, the degree and pattern of compression spring degradation can be accurately assessed, providing a reliable basis for subsequent maintenance decisions. The entire process forms a closed loop, from signal acquisition to final judgment, ensuring accurate assessment of the compression spring degradation state in complex noisy environments.

[0025] In some preferred embodiments, an acceleration sensor can be used, which is mounted on the top or bottom of the compression spring under test, to collect its mixed vibration signals under excitation. Meanwhile, in order to obtain the background noise signals of the test equipment, an additional acceleration sensor or microphone can be placed at a location far away from the compression spring but close to the test bench, whose output is taken as the reference noise signal. For the synchronous signal of the specific noise source of the test equipment, if the excitation source is an electromagnetic shaker, the driving current or voltage signal of the shaker can be acquired synchronously, or the rotational speed pulse signal of the motor of the shaker can be acquired through a rotational speed sensor installed on the motor, as the synchronous input of the noise source. In the data processing stage, the mixed vibration signal can be sent to a digital signal processor (DSP) or microcontroller, which runs an adaptive noise cancellation algorithm, such as an adaptive filter based on the least mean square (LMS) criterion. The filter first uses the background noise signal for initial weight training, and then uses the noise source synchronous signal as a real-time reference input to dynamically adjust the filter coefficients, so as to accurately separate the pure vibration response signal of the compression spring from the mixed signal. The separated pure vibration response signal is then subjected to frequency spectrum analysis through the fast Fourier transform (FFT) algorithm, generating its frequency amplitude spectrum. In the spectrum analysis result, a special software module can be configured to identify the broadband vibration response region. The module first scans the frequency spectrum to find the frequency band with continuous amplitude and higher than the preset energy threshold, and further verifies whether these frequency bands meet the specific width requirement. For example, an energy threshold can be set, and it is required that the frequency band continuously higher than the threshold covers at least a certain minimum frequency range to ensure the effectiveness of the identification. Once the broadband vibration response region is identified, the calculation module will automatically calculate its characteristic parameters. The center frequency can be determined by weighted average of the spectral amplitude in the region; the response bandwidth is directly calculated as the frequency range of the region; the total energy of the frequency band can be obtained by integrating the spectral energy in the region. Finally, the discrimination module can classify or evaluate the degradation state of the compression spring based on the preset rule set or trained classification model, such as decision tree or support vector machine, according to these calculated characteristic parameters, so as to give a clear degradation discrimination result.

[0026] Among them, the baseline noise template refers to the noise characteristic data of the specific noise source of the test equipment obtained when the electromagnetic exciter is not working. It can be recorded in the form of time domain waveform, frequency domain spectrum or time-frequency domain joint representation. Its purpose is to provide a noise benchmark that is not affected by the excitation; modulation refers to the process of adjusting the amplitude, frequency or phase and other characteristics of the baseline noise template according to the driving control signal of the electromagnetic exciter. It can be achieved by multiplication modulation, frequency offset or phase offset algorithms. Its purpose is to simulate the actual impact of the electromagnetic exciter on the noise source; virtual noise reference signal refers to an analog noise signal generated by modulating the baseline noise template. It can be generated by digital signal processing technology. Its purpose is to provide a reference input that can reflect the noise state affected by the electromagnetic exciter.

[0027] The solution of this application obtains a baseline noise template for the specific noise source of the test equipment when the electromagnetic exciter is in the non-operating state, thereby establishing the original characteristics of the noise source when not under excitation. Subsequently, the electromagnetic exciter's drive control signal is synchronously acquired. This signal contains information about the electromagnetic exciter's operating state, such as frequency and amplitude, which is important for understanding its impact on the noise source. Based on this, the baseline noise template is modulated according to the electromagnetic exciter's drive control signal to generate a virtual noise reference signal. This modulation process simulates the synchronous modulation effect of the electromagnetic exciter on the specific noise source of the test equipment, ensuring that the generated virtual noise reference signal reflects the true characteristics of the noise when the electromagnetic exciter is in operation. Finally, an adaptive noise cancellation process is performed on the mixed vibration signal. This process uses the reference noise signal for initial parameter training to quickly converge and adapt to environmental noise. At the same time, the generated virtual noise reference signal is used as a real-time reference input. In this way, even when the electromagnetic exciter is operating and the synchronous signal of the specific noise source is modulated, the adaptive noise cancellation algorithm can obtain a noise reference, thereby filtering out noise from the mixed vibration signal and isolating the pure vibration response signal of the compression spring under test. This enables the compression spring resonant frequency detection method to continuously acquire the target signal under complex excitation conditions, overcoming the problem of unsatisfactory cancellation effect of traditional methods when the noise source signal is distorted, and ensuring the reliability of subsequent spectrum analysis and degradation state judgment.

[0028] In some preferred embodiments, when an electromagnetic vibrator acts as an excitation source to excite a compression spring under test, causing synchronous modulation of a synchronization signal from a specific noise source in the test equipment, the adaptive noise cancellation process is implemented as follows. First, when the electromagnetic vibrator is not operating, a microphone or accelerometer can be used to capture noise signals from the specific noise source in the test equipment (e.g., a hydraulic pump, cooling fan, or motor) and store them as a baseline noise template. This template can be a time-domain waveform sequence or its corresponding frequency-domain characteristics. Next, a drive control signal for the electromagnetic vibrator is synchronously acquired. This signal can be a voltage signal, a current signal, or a digital control instruction that reflects the real-time operating frequency and amplitude of the vibrator. The pre-stored baseline noise template can then be modulated based on the acquired drive control signal. For example, if the drive control signal indicates that the vibrator is operating at a specific frequency and amplitude, a signal processor can use a frequency offset or amplitude scaling algorithm to adjust the baseline noise template to simulate the noise characteristics at that specific frequency and amplitude, thereby generating a virtual noise reference signal. Finally, the virtual noise reference signal is input into an adaptive noise canceller. This canceller can use the least mean square (LMS) algorithm or the recursive least squares (RLS) algorithm, combined with initial parameters pre-trained using the background noise signal, to process the real-time collected mixed vibration signal, thereby filtering out the noise components affected by the exciter and obtaining a pure vibration response signal of the compression spring to be tested.

[0029] Refined processing refers to the initial identification of broadband vibration response regions followed by a series of in-depth analysis steps to improve identification accuracy and boundary precision. This aims to overcome the inaccurate identification issues of traditional methods when faced with ambiguous or interfering signals. Preliminary identification involves an initial screening of spectrum analysis results based on preset criteria such as amplitude, continuity, and width to quickly locate potential broadband vibration response regions. This aims to narrow the scope of subsequent refined processing and improve processing efficiency. Local maximum points are defined as points on the spectrum curve within the preliminary broadband response range whose amplitude is higher than that of adjacent frequency points. This aims to capture energy concentrations or characteristic points within the spectrum curve, providing key reference for subsequent morphological analysis. Spectral morphology refers to the overall shape and trend of the spectrum curve within the preliminary broadband response range, as well as the number, location, relative amplitude, and spacing of local maximum points. This aims to reveal the inherent structure and energy distribution characteristics of the broadband vibration response region, thereby more accurately determining its true boundary. The precise start and cutoff frequencies refer to the actual boundary frequencies of the broadband vibration response region, determined through in-depth analysis of the spectrum curve morphology. Their purpose is to provide a highly accurate frequency range to ensure that the characteristic parameters calculated subsequently can truly reflect the degradation state of the compression spring.

[0030] The scheme of the present application can more accurately identify the wideband vibration response region of the pressure spring to be tested by fine processing the spectrum analysis result. Specifically, after the pure vibration response signal is subjected to spectrum analysis, a preliminary identification is first performed, i.e., according to the conditions of amplitude continuity, a preset energy threshold and a specific width, a preliminary wideband response range is screened out from the spectrum analysis result. This preliminary screening step can quickly locate the potential wideband vibration response region, providing a focused analysis interval for subsequent fine processing. On this basis, all local maximum points in the preliminary wideband response range are further identified. These local maximum points are energy concentration points on the spectrum curve, and their positions and relative amplitudes are crucial for understanding the internal structure of the spectrum. Subsequently, according to the distribution of these local maximum points, the spectrum curve form in the preliminary wideband response range is analyzed in depth. This form analysis is not just a simple threshold judgment, but takes into account the number of peaks, their frequency interval, relative height and overall trend of the curve, so that the internal characteristics of the wideband vibration response can be more comprehensively understood. Based on the results of this fine spectrum curve form analysis, the accurate start frequency and cutoff frequency of the wideband vibration response region can be determined. This accurate boundary determination avoids misjudging noise or non-related signals as part of the wideband response, and also avoids missing effective information of the wideband response. Finally, these accurate start frequency and cutoff frequency are stored, providing accurate data basis for subsequent calculation of characteristic parameters (such as center frequency, response bandwidth and total energy of frequency band) of the wideband vibration response region. By applying the fine processing steps to the spectrum analysis result of the pure vibration response signal after adaptive noise cancellation processing, the scheme can effectively overcome the identification difficulty mentioned in the background art due to the pressure spring degradation leading to ambiguous vibration response form and partial overlap with device noise. Adaptive noise cancellation processing ensures the purity of the input spectrum, so that fine processing can focus on the wideband response characteristics of the pressure spring itself, and is not affected by external interference. This combination makes it possible to accurately identify the true boundaries of the wideband vibration response region even in the case of weak signal, irregular form and overlap with noise, so as to ensure that the characteristic parameters calculated subsequently can accurately reflect the degradation state of the pressure spring, and improve the accuracy of pressure spring degradation state discrimination.

[0031] In some preferred embodiments, a preliminary identification is performed to determine if the amplitude of a broadband vibration response region in the spectrum analysis results is continuous, exceeds a preset energy threshold, and has a specific width. This yields a preliminary broadband response range. For example, an energy threshold can be set, such as 10% of the maximum spectrum amplitude. The spectrum data is then scanned to identify all frequency bands whose amplitudes continuously exceed this threshold. A minimum width threshold, such as 50 Hz, is also set to ensure that the identified frequency bands are at least 50 Hz wide to exclude narrowband noise or transient signals, thereby obtaining a preliminary broadband response range. Local maximum points within the preliminary broadband response range are identified. Specifically, a sliding window method or peak detection algorithm can be employed. For example, for each frequency point within the preliminary broadband response range, its amplitude is compared with the amplitudes of the N preceding and following frequency points. If the amplitude of the point is the maximum among these 2N+1 points, it is marked as a local maximum point, where N can be a preset integer, such as N=5. Based on the distribution of the local maximum points, the spectral curve shape within the preliminary broadband response range is analyzed. For example, the number of identified local maximum points can be analyzed. If there is only one local maximum, the curve shape may be close to a single peak. If there are multiple local maximums, further analysis can be performed on the frequency spacing and relative amplitudes between them. For example, if two local maximums are close together and have similar amplitudes, this may indicate a bimodal structure within the broadband response. Based on the analysis of the spectral curve shape, the precise start and cutoff frequencies of the broadband vibration response region are determined. As a specific implementation, if the analysis results indicate a single peak, the spectrum amplitude can be searched from the local maximum point in both directions until the amplitude drops to a certain percentage of the local maximum (e.g., 50% or 30%), or until the slope of the amplitude decreases significantly, thereby determining the precise start and cutoff frequencies. If the analysis results indicate multiple peaks, more complex curve fitting or energy accumulation analysis can be used to determine the precise boundaries of the overall broadband response based on the influence range and overlap of the individual peaks. The precise start and cutoff frequencies of the broadband vibration response region are stored. Specifically, these precise frequency values ​​can be stored in a data structure, such as an array, a database record, or a configuration file. This data can be directly read and used by the subsequent characteristic parameter calculation module to ensure calculation accuracy.

[0032] The present application further proposes the steps of determining the degradation state of the compression spring to be tested based on characteristic parameters, including: analyzing the characteristic parameters, classifying the degradation modes of the broadband vibration response area according to the center frequency, and obtaining the classified degradation mode; based on the classified degradation mode, and in combination with the response bandwidth and the total energy of the frequency band, comparing with the preset mode threshold, evaluating the severity of the classified degradation mode; based on the severity of the classified degradation mode and comparing the total energy of the frequency band, determining the dominant degradation mode; based on the severity of the dominant degradation mode and the severity of the classified degradation mode, generating maintenance recommendations, thereby determining the degradation state of the compression spring to be tested.

[0033] To better understand the above scheme, degradation mode classification refers to categorizing the degradation state of the compression spring under test into predefined types based on the center frequency characteristics of the compression spring's vibration response. For example, the degradation mode can be classified as mild, moderate, or severe based on the center frequency offset or the frequency range in which it is located. The purpose is to provide a preliminary structured understanding of the compression spring degradation phenomenon and provide a basis for subsequent evaluation. The preset mode threshold refers to a pre-set numerical standard or range for each degradation mode used to quantitatively assess its severity. It can be determined using empirical data, historical data analysis, or expert knowledge. For example, when the response bandwidth exceeds a certain value or the total energy in the frequency band is lower than a certain value, it can be set to indicate that the degradation severity has reached a certain level. The purpose is to provide a quantitative basis for the severity assessment of the degradation mode. The severity of a classified degradation mode refers to the quantification of the impact of a degradation mode on the performance of a compression spring by comparing the response bandwidth and total energy of the frequency band to the corresponding preset mode threshold within the identified degradation mode type. This can be achieved through a graded assessment, percentage assessment, or risk index assessment. Its purpose is to provide a description of the compression spring's degradation status to facilitate subsequent maintenance decisions. The dominant degradation mode refers to the degradation type that has the greatest impact on the overall performance of the compression spring among the various possible degradation modes. This can be determined through a comprehensive assessment of the severity of each degradation mode, the total energy of the frequency band, and the weight of its impact on the spring's function. Its purpose is to identify the degradation issues that require attention and resolution, and to guide the focus of maintenance work. Maintenance recommendations are operational guidance or decision-making plans provided to users based on the degradation status assessment results of the compression spring. These may include measures such as replacing components, adjusting operating parameters, conducting regular inspections, or scheduling maintenance plans. Their purpose is to transform technical assessment results into actionable actions to extend equipment life and ensure operational safety.

[0034] The solution of the present application performs spectral analysis on the pure vibration response signal and calculates characteristic parameters such as the center frequency, response bandwidth and total energy of the broadband vibration response area. On this basis, these characteristic parameters are further analyzed in a multi-dimensional and phased manner, thereby realizing the judgment of the degradation state of the compression spring.

[0035] Specifically, the degradation mode of the broadband vibration response region is first classified using the center frequency. As an indicator of the overall deviation in the compression spring's vibration characteristics, the center frequency provides a preliminary indication of the possible degradation type. By associating it with pre-set patterns, the degradation problem can be broken down into several sub-problems, providing a basis for subsequent assessment. Secondly, within each classified degradation mode, the response bandwidth and total energy in the frequency band are compared against pre-set mode thresholds to assess the severity of the mode. The response bandwidth reflects the spread of the vibration energy, while the total energy in the frequency band reflects the overall vibration capability of the compression spring. By combining these two parameters, the degradation mode characteristics can be more comprehensively quantified, avoiding the misjudgment that can result from relying on a single parameter, thereby improving the accuracy of the assessment. Next, after assessing the severity of different degradation modes, the dominant degradation mode of the current compression spring is determined by comparing these severities and the total energy in the frequency band. Compression springs may experience multiple degradation conditions simultaneously. Identifying the dominant mode allows maintenance resources to be focused on resolving key issues, improving maintenance efficiency. Finally, maintenance recommendations are generated based on the identified dominant degradation modes and the severity of each categorized degradation mode. This allows degradation assessment results to be directly translated into actionable maintenance measures, such as replacing components, adjusting operating parameters, or increasing inspections. This enables closed-loop management of compression spring status, extending its service life and reducing maintenance costs.

[0036] It is precisely because this scheme, on the basis of obtaining the characteristic parameters of the broadband vibration response of the compression spring, further introduces the mechanism of multi-parameter comprehensive analysis, hierarchical evaluation and dominant pattern recognition, that the judgment of the degradation state of the compression spring is no longer limited to the preliminary judgment of a single indicator, but can reveal the degradation pattern and severity of the compression spring, and ultimately provide a maintenance strategy, thus overcoming the problem of incomplete and inaccurate single parameter judgment in the existing technology and improving the practicality of the compression spring degradation state assessment.

[0037] In some preferred embodiments, the present application is specifically implemented as follows: After receiving characteristic parameters such as the center frequency, response bandwidth, and total energy of the compression spring to be tested, the system can first classify the degradation mode of the broadband vibration response area based on the center frequency. For example, if the center frequency shifts by more than a preset value A in the low-frequency direction relative to the baseline center frequency of the new compression spring, it is classified as a "material fatigue degradation mode"; if it shifts by more than a preset value B in the high-frequency direction, it is classified as a "local hardening degradation mode"; if the shift is within the preset range but the response bandwidth increases, it is classified as a "structural relaxation degradation mode." In this way, a classified degradation mode can be obtained. Then, based on the classified degradation mode, the system can combine the response bandwidth and total energy of the frequency band and compare them with the preset mode threshold to assess the severity of the classified degradation mode. For example, for the "material fatigue degradation mode," a response bandwidth threshold W1 and a total energy threshold E1 can be preset. If the current compression spring's response bandwidth is greater than W1 and its total energy is less than E1, it is assessed as "Severe"; if the response bandwidth is between W1 and W2 and its total energy is between E1 and E2, it is assessed as "Moderate"; otherwise, it is assessed as "Mild." This method allows for a quantitative assessment of the severity of each classified degradation mode. The system then determines the dominant degradation mode based on the severity of the classified degradation modes and a comparison of the total energy of the frequency bands. For example, if the "Material Fatigue Degradation Mode" is assessed as "Severe" and its total energy of the frequency band is the lowest among all modes, it is determined to be the dominant degradation mode. If multiple modes have the same severity, their total energy of the frequency bands is further compared, and the one with the lowest energy is selected as the dominant mode, as the lowest energy generally indicates performance degradation. Finally, based on the identified dominant degradation mode and the severity of the classified degradation modes, the system generates maintenance recommendations to determine the degradation state of the compression spring under test. For example, if the dominant degradation mode is "material fatigue" and the severity is "serious," the system might recommend "immediately replace the compression spring and inspect the AGV's related suspension components." If the dominant degradation mode is "localized hardening" and the severity is "moderate," the system might recommend "replace the compression spring during the next scheduled maintenance and adjust the AGV's path to reduce impact." If all modes are classified as "minor," the system recommends "continue observation and include it in the next routine inspection." These maintenance recommendations can be generated based on actual conditions and pre-set maintenance strategies.

[0038] This application further proposes that the steps for generating maintenance recommendations include: obtaining the spare parts, maintenance stations and maintenance personnel shifts available in the logistics center to obtain maintenance resource availability information; obtaining the current tasks and future scheduling plans of the AGV to be maintained to obtain the operating status of the AGV to be maintained; evaluating the maintenance urgency based on the dominant degradation mode and the severity of the classified degradation mode; prioritizing the maintenance tasks based on the degradation status of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained to obtain a priority sorting result; adjusting the operating schedule of the AGV to be maintained and allocating maintenance resources based on the priority sorting result; generating maintenance recommendations based on the adjusted operating schedule of the AGV to be maintained and the allocated maintenance resources.

[0039] The maintenance resource availability information refers to a summary of the state of various resources currently available for performing maintenance tasks in the logistics center, and can specifically include the types and quantities of spare parts, the occupancy and idle quantities of maintenance workstations, and the shift arrangement and skill distribution of maintenance personnel, and aims to provide necessary material and human resources for the execution of maintenance tasks. The to-be-maintained AGV running state refers to the real-time working condition and future planning of the AGV that needs to be maintained in the logistics system, and can specifically include the type of task currently being executed by the AGV, the task priority, the estimated completion time, and the scheduling plan of the AGV in the future period of time, and aims to evaluate the impact of maintenance operations on the overall operational efficiency of the logistics center. The maintenance urgency refers to the timeliness and importance of maintenance requirements determined according to the spring degradation mode and its severity, and can be classified according to the potential impact of the degradation mode on the safe operation and performance stability of the AGV, such as different levels of emergency, high, medium, and low, and aims to provide key input basis for the priority ranking of maintenance tasks. The priority ranking result refers to the sequence obtained by arranging the importance of multiple to-be-maintained tasks according to preset rules and evaluation standards, and can specifically be a list containing task ID and corresponding priority score, and aims to guide the rational allocation of maintenance resources and the orderly execution of maintenance operations. The adjustment of the to-be-maintained AGV running schedule refers to modifying the original task execution plan of the AGV according to the priority of the maintenance task and the availability of the AGV, and can specifically include pausing the current task, re-planning the path, or assigning the task to other AGVs, and aims to create necessary time windows for maintenance operations while minimizing the disturbance to logistics. The allocation of maintenance resources refers to assigning appropriate spare parts, maintenance workstations, and maintenance personnel to specific maintenance tasks according to the priority ranking result of the maintenance task and the maintenance resource availability information, and can specifically include allocating required spare parts from inventory, reserving idle maintenance workstations, and arranging maintenance personnel with corresponding skills, and aims to ensure that maintenance tasks can be carried out in a timely and effective manner. The maintenance suggestion refers to a specific maintenance plan proposed for the degradation state of the to-be-tested spring in combination with the actual situation of the logistics center, and can specifically include the recommended replacement parts, recommended maintenance methods, recommended maintenance time windows, and required maintenance resource list, and aims to provide executable decision guidance for the operation and management of the logistics center.

[0040] The solution of this application realizes the intelligent management of compression spring maintenance tasks by comprehensively considering multi-dimensional information. First, the system obtains the spare parts, maintenance stations and maintenance personnel shifts available in the logistics center to form maintenance resource availability information, which provides a basic resource constraint for subsequent maintenance decisions. At the same time, the current tasks and future scheduling plans of the AGV to be maintained are obtained to obtain the operating status of the AGV to be maintained, which enables maintenance decisions to fully consider the impact on the daily operations of the logistics center. On this basis, using the degradation status of the compression spring to be tested determined in the previous step, especially the severity of the dominant degradation mode and the classified degradation mode, the system can accurately assess the maintenance urgency and clarify the urgency of the maintenance needs. Subsequently, the four key information of the degradation status of the compression spring to be tested, the assessed maintenance urgency, the maintenance resource availability information and the operating status of the AGV to be maintained are integrated to prioritize the maintenance tasks, thereby obtaining the priority ranking results. This sorting process ensures that maintenance tasks can be reasonably arranged according to their importance, urgency, resources and operating conditions. Based on this prioritization result, the system intelligently adjusts the operating schedule of the AGVs requiring maintenance, reserving an appropriate time window for maintenance operations and simultaneously allocating the required maintenance resources, including spare parts, repair stations, and maintenance personnel. Ultimately, specific maintenance recommendations are generated based on the adjusted AGV operating schedule and allocated maintenance resources. The coordinated operation of this series of steps not only enables this solution to accurately identify the degradation state of the compression springs, but also further transforms the diagnostic results into highly practical and operational maintenance strategies, effectively balancing equipment maintenance needs with the operational efficiency of the logistics center, thereby improving the overall operational efficiency and reliability of the automated warehousing and logistics center.

[0041] In some preferred embodiments, the present application is specifically implemented as follows. Assume that in a certain automated warehousing and logistics center, the system detects through the previous steps that the compression spring of an AGV has a dominant degradation mode of reduced elastic modulus, and the severity is assessed as "moderate". At this time, the system first queries the maintenance resource management system of the logistics center to obtain the currently available spare parts list, for example, whether there is a corresponding model of compression spring in stock; the real-time occupancy status of the maintenance station, for example, whether there is a vacant maintenance station; and the maintenance personnel's shift schedule, for example, whether there is a maintenance personnel with the skills to replace the compression spring on duty, thereby obtaining maintenance resource availability information. At the same time, the system obtains the current task of the AGV to be maintained from the AGV scheduling management platform, for example, it is currently performing a low-priority handling task; and the scheduling plan for the next 24 hours, for example, there are no high-priority tasks in the next 4 hours, and obtains the operating status of the AGV to be maintained. Based on the "moderate" elastic modulus reduction degradation mode of the compression spring, the system assesses its maintenance urgency as "medium", indicating that although it does not need to be shut down immediately, maintenance should be arranged in the near future. The system then inputs the compression spring's degradation status (moderate elastic modulus reduction), its maintenance urgency (medium), maintenance resource availability (sufficient spare parts, idle workstations, and active personnel), and the operational status of the AGV to be maintained (low priority tasks with no future high-priority tasks) into the prioritization module. Based on pre-set priority rules (e.g., when resources are sufficient and the AGV task has a lower priority, medium-urgency tasks are prioritized), the module calculates the maintenance task's overall priority score and ranks it higher among all currently pending maintenance tasks, generating a priority ranking result. Based on this ranking result, the system sends instructions to the AGV scheduling system to adjust the AGV's operational schedule. For example, after completing its current low-priority task, it can be scheduled to return to a charging station or designated maintenance area, reserving a two-hour maintenance window. Simultaneously, the system sends instructions to the maintenance resource management system to allocate maintenance resources. For example, it can reserve an available maintenance workstation and notify a qualified maintenance personnel to arrive at the workstation at a specified time and prepare the required compression spring spare part. Finally, the system generates a detailed maintenance recommendation based on the adjusted AGV operation schedule and allocated maintenance resources. For example, it states, "It is recommended that the compression spring of AGV number XXX be replaced between 3:00 PM and 5:00 PM today. Maintenance station A has been reserved, and maintenance personnel Zhang San is responsible. The required compression spring model is YYY and has been allocated from inventory."

[0042] The present application further proposes the steps of prioritizing maintenance tasks according to the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating state of the AGV to be maintained, including: setting weights for the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating state of the AGV to be maintained; calculating the comprehensive priority score of the maintenance task based on the weight; and prioritizing the maintenance tasks according to the comprehensive priority score of the maintenance task.

[0043] Among them, setting weights refers to assigning a numerical value to each factor that affects the priority of the maintenance task, such as the degradation state of the compression spring to be tested, the urgency of maintenance, the maintenance resource availability information, and the operating status of the AGV to be maintained, to represent its relative importance in the overall decision-making. It can be determined by expert experience evaluation, historical data analysis, or training based on a machine learning model; calculating the comprehensive priority score of the maintenance task refers to integrating the weighted values ​​of each factor to obtain a single numerical value that can reflect the overall urgency or importance of the maintenance task. It can be achieved by weighted summation, weighted average, or other multi-factor fusion algorithms; prioritizing maintenance tasks according to the comprehensive priority score of the maintenance task refers to arranging all maintenance tasks according to the calculated comprehensive priority score according to the score, so as to determine the order of their execution. It can be achieved by arranging the scores in ascending or descending order.

[0044] The solution of this application addresses the problem of insufficient information integration in maintenance task prioritization by introducing a multi-dimensional information weighted fusion mechanism. Specifically, when prioritizing maintenance tasks based on the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained, weights are first assigned to these key factors that influence maintenance decisions. This weighting process enables the system to identify and quantify the relative importance of different factors in maintenance decisions. For example, in some cases, the degree of degradation of the compression spring may be more decisive than the current operating status of the AGV, while in other cases, the scarcity of maintenance resources may become the dominant factor. By assigning different weights, the system can flexibly adapt to different maintenance strategies and operational needs. Based on this, a comprehensive priority score for the maintenance task is calculated based on the set weights. This step integrates multiple dimensions of information, such as the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained, into a unified value through weighted summation or other fusion algorithms. This comprehensive scoring method avoids the undue influence of a single factor on maintenance decisions, ensuring that all relevant information is fully considered. This allows the priority score to more comprehensively and accurately reflect the true urgency and importance of the maintenance task. Ultimately, maintenance tasks are prioritized based on their comprehensive priority scores. By sorting these scores, the system generates a logically coherent and highly executable maintenance task list, ensuring that maintenance resources are prioritized for tasks with the highest scores and the most urgent need. This coordinated approach shifts maintenance task prioritization from a simple rule-based approach or single-factor consideration to a refined integration and quantitative assessment of multi-dimensional information, significantly improving the scientific and effective nature of maintenance decisions. This approach integrates closely with the previous steps of determining the degradation status of compression springs, assessing maintenance urgency, and obtaining information on maintenance resource availability and AGV operating status. Accurately determining the degradation status of compression springs provides the foundational data for maintenance decisions, while maintenance urgency, resource availability, and AGV operating status provide a real-time operational context. Based on this, this solution forms a more refined and dynamic prioritization mechanism by weighted integration of the acquired and evaluated information. This makes the generation of maintenance recommendations more in line with actual needs, thus effectively solving the problem of how to integrate multi-dimensional information and ensure that the ranking results accurately reflect the actual maintenance needs and urgency.

[0045] In some preferred embodiments, the specific process of prioritizing maintenance tasks based on the degradation state of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information, and the operating status of the AGV to be maintained can be implemented as follows. First, weights can be set for the degradation state of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information, and the operating status of the AGV to be maintained. For example, the weight of the degradation state can be set to 0.4, the weight of the maintenance urgency can be set to 0.3, the weight of the maintenance resource availability information can be set to 0.2, and the weight of the operating status of the AGV to be maintained can be set to 0.1. These weights can be adjusted according to the actual operating strategy of the logistics center, historical maintenance data analysis, or expert experience. Then, based on the set weights, the comprehensive priority score of the maintenance task is calculated. Specifically, a quantitative level or value can be set for each factor. For example, degradation status can be categorized as mild, moderate, and severe, with corresponding scores of 1, 3, and 5, respectively; maintenance urgency can be categorized as low, medium, and high, with corresponding scores of 1, 3, and 5, respectively; maintenance resource availability can be categorized as sufficient, tight, and scarce, with corresponding scores of 5, 3, and 1, respectively (the scarcer the resource, the higher the priority); and the operating status of the AGV to be maintained can be categorized as idle, low-load, and high-load, with corresponding scores of 1, 3, and 5, respectively (the higher the load, the higher the priority). The overall priority score for the maintenance task is then calculated by multiplying the quantitative scores of each factor by their corresponding weights and summing all the products. For example, for a maintenance task with a degradation status of "severe" (score 5), maintenance urgency of "high" (score 5), maintenance resource availability of "tight" (score 3), and an AGV operating status of "high-load" (score 5), the overall priority score can be calculated as: 5*0.4+5*0.3+3*0.2+5*0.1=2.0+1.5+0.6+0.5=4.6. Finally, maintenance tasks are prioritized based on their comprehensive priority scores. Once the comprehensive priority scores of all pending maintenance tasks are calculated, the system ranks them in descending order. Tasks with higher scores have higher maintenance priorities and are therefore scheduled for execution first. For example, a task with a score of 4.6 will be prioritized over a task with a score of 3.5. This comprehensive score-based ranking method ensures that maintenance resources are allocated to tasks that demonstrate greater urgency and importance across multiple dimensions.

[0046] This application further proposes the steps of setting weights for the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained, including: obtaining the operating load of the logistics center, spare parts inventory, maintenance personnel on-the-job status and AGV task priority, and determining the operating mode of the logistics center; according to the operating mode of the logistics center, adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained.

[0047] The logistics center's operating load refers to the current or projected business volume, throughput, or intensity of operations within the logistics center over a specific period of time. This can be expressed as metrics such as order processing volume, cargo inbound and outbound volume, and AGV operation density. Spare parts inventory refers to the current quantity and availability of spare parts and components used for AGV maintenance within the logistics center. This can include inventory data for components such as compression springs, motors, and batteries. Maintenance personnel availability refers to the number, skill level, current work status, and shift schedule of maintenance technicians available for AGV maintenance within the logistics center. AGV task priority refers to the importance or urgency of current or future AGV tasks within the logistics center. This can be categorized based on task type, timeliness, and impact on overall operations. The logistics center's operating mode refers to the overall operating status or strategy of the logistics center within a specific time period, based on a combination of factors such as its operating load, spare parts inventory, maintenance personnel availability, and AGV task priority. This can be achieved by mapping these metrics to predefined modes, such as peak-time high-load mode, spare parts shortage mode, maintenance personnel shortage mode, or low-load normal mode, using preset rules, machine learning models, or expert systems. Adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained means changing the relative importance or influence of various factors used to calculate the comprehensive priority score of the maintenance task according to the determined operation mode of the logistics center. This can be achieved by consulting a preset weight configuration table, applying an adjustment algorithm, or performing optimization calculations based on data. Its purpose is to make the priority sorting of maintenance tasks more in line with the actual operation needs of the current logistics center and optimize the allocation of maintenance resources.

[0048] The solution of this application addresses the lack of adaptability of traditional fixed-weight prioritization methods in dynamic operating environments by introducing a mechanism that senses and responds to the logistics center's operating mode. Specifically, it first acquires real-time or near-real-time data such as the logistics center's operating load, spare parts inventory, maintenance personnel availability, and AGV task priorities. These data are key indicators of the logistics center's current operating status. Through comprehensive analysis of these indicators, the system can determine the current logistics center operating mode. For example, when the operating load is high and AGV task priorities are generally high, the system may identify it as peak-hour emergency mode; when spare parts inventory is low or maintenance personnel are understaffed, it may identify it as resource-constrained mode. The ability to identify these different operating modes enables subsequent weight adjustments. Based on this, the system adjusts the weights of the compression spring degradation state, maintenance urgency, maintenance resource availability, and the operating status of the AGV to be maintained, according to the determined logistics center operating mode. This adjustment mechanism eliminates static maintenance task prioritization and enables it to adapt to the logistics center's ever-changing needs. For example, in peak emergency mode, the system can increase the weights of AGV task priority and maintenance urgency to ensure that maintenance of critical AGVs is prioritized, thereby reducing the impact on overall operations. In resource-constrained mode, the system can reduce the weight of maintenance resource availability while increasing the weights of degradation status and maintenance urgency to prioritize maintenance tasks that require maintenance but have relatively low resource requirements. This weight adjustment method, combined with the previous steps of setting weights for the degradation status, maintenance urgency, maintenance resource availability information of the compression spring to be tested, and calculating the overall priority score, allows the final maintenance task priority sorting results to more accurately reflect the actual operating strategy and resource constraints of the current logistics center, thereby achieving optimal allocation of maintenance resources and improving the overall operational efficiency of the logistics center.

[0049] In some embodiments, to assign weights to the degradation state of the compression spring to be tested, its maintenance urgency, maintenance resource availability, and the operating status of the AGV to be maintained, the system can first obtain logistics center operational load data, such as the number of orders processed per hour or the total AGV mileage, from the logistics center management system via a data interface. Furthermore, spare parts inventory data, such as the current number of compression spring spare parts, can be obtained from the inventory management system. Maintenance personnel availability, including the number of maintenance personnel available for the current shift and their skill levels, can be obtained from the human resources management system. Furthermore, AGV task priority information, such as the urgency classification of currently executing AGV tasks, can be obtained from the AGV scheduling system. The system can pre-set a set of rules or train a classification model, such as one based on a decision tree or neural network, using these acquired operational load, spare parts inventory, maintenance personnel availability, and AGV task priority data as input to output the current logistics center operating mode, such as normal operating mode, peak operating mode, spare parts shortage mode, or maintenance personnel shortage mode. Once the logistics center operating mode is determined, the system can select a weight set corresponding to that mode from a predefined weight configuration database. For example, during peak hours, the AGV task priority and maintenance urgency can be weighted higher, while the maintenance resource availability can be weighted relatively lower. During spare parts shortages, the maintenance urgency and degradation status can be weighted higher to prioritize AGVs requiring maintenance and with low spare parts demand. In this way, the system can adjust the weights of the degradation status of the compression springs to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGVs to be maintained, depending on the logistics center's operating model. This ensures that the prioritization of maintenance tasks can adapt to changes in the actual operating environment.

[0050] The present application further proposes the steps of adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained according to the operation mode of the logistics center, including: presetting weight adjustment rules under different operation modes of the logistics center; selecting a corresponding weight set from the preset weight adjustment rules according to the operation mode of the logistics center; adjusting the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained according to the selected weight set.

[0051] Among them, the preset weight adjustment rules under different logistics center operating modes refer to a series of pre-set strategies or algorithms for guiding the weight allocation of various maintenance factors (such as the degradation state of the compression spring, maintenance urgency, maintenance resource availability information, and AGV operating status) under different operating conditions of the logistics center. They can be implemented using a rule base based on expert experience, a machine learning model based on historical data analysis, or a weight matrix obtained through simulation optimization. Its purpose is to ensure that the priority evaluation of maintenance tasks can dynamically adapt to actual needs under different operating modes; the weight set refers to a set of quantifiable values ​​selected or calculated from the preset weight adjustment rules according to the specific logistics center operating mode. These values ​​correspond to the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained. It can be an array or vector containing multiple weight values. Its purpose is to provide a quantifiable weight allocation scheme for the current operating mode to facilitate the subsequent calculation of the comprehensive priority score of the maintenance task.

[0052] The solution of the present application solves the problem of unreasonable maintenance task priority sorting under different logistics center operating modes by introducing a dynamic weight adjustment mechanism. Specifically, first, the system presets weight adjustment rules for multiple logistics center operating modes. These rules are pre-established based on the characteristics and maintenance needs of different operating modes. For example, in peak operating modes, more emphasis may be placed on the operating status and maintenance urgency of the AGV, while in low-load or spare parts sufficient modes, more attention may be paid to the degradation state of the compression spring. Secondly, based on the logistics center operating mode information obtained in real time, the system selects a weight set that matches the current mode from these preset rules. This selection process ensures the targetedness and timeliness of the weight allocation. Finally, the system adjusts the weights of the degradation state of the compression spring, maintenance urgency, maintenance resource availability information and AGV operating status based on the selected weight set. This dynamic adjustment enables the influence of various factors in the calculation of maintenance task priorities to reflect the real needs of the current operating environment.

[0053] In this way, the solution of the present application is closely combined with the previous steps of setting weights and calculating comprehensive priority scores based on the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information and the operating status of the AGV to be maintained, so that the priority sorting of maintenance tasks is no longer static, but can be dynamically optimized according to the actual operating conditions of the logistics center. For example, when the logistics center is in a high-load operation mode, the system can automatically increase the weights of the AGV operating status and maintenance urgency, thereby giving priority to maintenance tasks that may cause AGV shutdown or affect core business, ensuring the continuity of the logistics process. On the contrary, when the load is low or resources are abundant, the system can increase the weight of the degradation state of the compression spring in order to perform preventive maintenance and extend the life of the components. This dynamic weight adjustment mechanism makes the generation of maintenance recommendations more accurate and the allocation of maintenance resources more reasonable, thereby avoiding the unreasonable priority sorting of maintenance tasks that may result from fixed weight allocation, thereby improving the overall operational efficiency of the logistics center and the availability of AGVs.

[0054] In some preferred embodiments, the present application is specifically implemented as follows. First, during the system initialization or configuration phase, weight adjustment rules for a variety of logistics center operating modes can be preset. For example, three representative operating modes can be defined: high-load operating mode, low-load operating mode, and spare parts shortage mode. For the high-load operating mode, a weight set can be preset. For example, the weight of the operating status of the AGV to be maintained is set to 0.4, the maintenance urgency is set to 0.3, the degradation state of the compression spring to be tested is set to 0.2, and the maintenance resource availability information is set to 0.1, so as to ensure that the operation continuity of the AGV is prioritized when the business volume is large. For the low-load operating mode, another weight set can be preset. For example, the weight of the degradation state of the compression spring to be tested is set to 0.4, the maintenance resource availability information is set to 0.3, the maintenance urgency is set to 0.2, and the operating status of the AGV to be maintained is set to 0.1, so as to perform more adequate preventive maintenance when the business pressure is relatively low. For spare parts shortage scenarios, a pre-set weighting set can be used. For example, weights for maintenance resource availability information are set to 0.5, maintenance urgency is set to 0.3, the degradation state of the compression spring to be tested is set to 0.1, and the operating status of the AGV to be maintained is set to 0.1. This prioritizes tasks that are complete with available resources and have high urgency. Secondly, during actual operation, the system can continuously monitor data such as the logistics center's operating load, spare parts inventory, maintenance personnel availability, and AGV task priorities. For example, if the system detects that order volume consistently exceeds a certain threshold and the AGV task queue length increases, it can be determined that the current operation mode is high-load. In this case, the system can automatically select a weighting set corresponding to the high-load operation mode from the pre-set weight adjustment rules. Finally, based on the selected weighting set, for example, the weighting set selected for the high-load operation mode, the system adjusts the weights for the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to 0.2, 0.3, 0.1, and 0.4, respectively. These adjusted weights will be directly applied to the calculation of maintenance task priority, so that under the current high-load operation mode, the influence of AGV operating status and maintenance urgency on maintenance task priority will be enhanced, thereby ensuring that maintenance tasks for critical AGVs can be handled first.

[0055] This application further proposes obtaining the logistics center's operating load, spare parts inventory, maintenance personnel on-the-job status and AGV task priority, and the steps for determining the logistics center's operating mode include: presetting standard thresholds; presetting rule sets; determining the logistics center's operating mode based on the logistics center's operating load, spare parts inventory, maintenance personnel on-the-job status and AGV task priority, and based on preset standard thresholds and preset rule sets.

[0056] The preset standard threshold refers to the critical values ​​set for various parameters such as the logistics center's operating load, spare parts inventory, maintenance personnel availability, and AGV task priority. These thresholds can be set based on historical operational data analysis, industry best practices, or expert experience. Their purpose is to provide a quantitative benchmark for the state classification of various operational parameters, thereby converting continuously changing parameters into discrete state identifiers. The preset rule set refers to a logical set that defines the operating mode of the logistics center under different combinations of operating parameter states. This can be implemented using decision trees, expert system rule bases, or inference rules based on fuzzy logic. Its purpose is to provide a systematic judgment mechanism that can comprehensively consider the influence of multiple factors to accurately identify the specific operating mode of the current logistics center.

[0057] The solution of this application introduces preset standard thresholds and preset rule sets to fine-tune key parameters such as the logistics center's operating load, spare parts inventory, maintenance personnel availability, and AGV task priority, thereby accurately determining the logistics center's operating mode. Specifically, various logistics center operating parameters are first acquired, reflecting the center's current actual operating status. These acquired parameters are then compared with preset standard thresholds to determine the specific state of each parameter, such as whether the operating load is high, medium, or low; whether the spare parts inventory is sufficient, tight, or short; whether the maintenance personnel availability is sufficient, moderate, or insufficient; and whether the AGV task priority is high, medium, or low. This state information is then input into the preset rule set. The preset rule set contains judgment logic for different parameter state combinations. For example, when the operating load is high, the spare parts inventory is tight, and the maintenance personnel are insufficient, it may be determined to be "emergency maintenance mode"; while when the operating load is low, the spare parts inventory is sufficient, and the maintenance personnel are sufficient, it may be determined to be "routine maintenance mode." Through this mechanism based on threshold judgment and rule reasoning, the system can comprehensively consider multi-dimensional information to determine the current operating model of the logistics center.

[0058] The determination of the operation mode of the logistics center provides key contextual information for the subsequent prioritization of maintenance tasks. After determining the operation mode of the logistics center, the system can dynamically adjust the weights of the degradation state of the to-be-tested compression spring, the maintenance urgency, the maintenance resource availability information, and the operating state of the to-be-maintained AGV according to the mode. For example, in the "emergency maintenance mode", the weights of the degradation state of the compression spring and the maintenance urgency can be increased to ensure that the AGV with a compression spring that is about to fail or has failed is given priority for maintenance, avoiding affecting the logistics operation; and in the "regular maintenance mode", the weights of the maintenance resource availability information and the AGV task priority can be appropriately increased to balance the maintenance demand and logistics efficiency. Through this dynamic adjustment of the weights, the comprehensive priority score of the maintenance task can more accurately reflect the actual demand and resource status of the logistics center at the moment, so that the prioritization of the maintenance task is more reasonable, and the maintenance suggestion generated finally is more meaningful, ensuring that the maintenance of the AGV can both respond to equipment degradation in a timely manner and be consistent with the overall operation goal of the logistics center.

[0059] In some preferred embodiments, the specific process of determining the operation mode of the logistics center can be implemented as follows: First, the system can preset a series of standard thresholds. For example, for the operation load of the logistics center, "low load threshold" and "high load threshold" can be set to divide the load into low, medium, and high intervals; for spare parts inventory, "safety inventory threshold" and "warning inventory threshold" can be set to divide the inventory into sufficient, tight, and short; for the on-duty status of maintenance personnel, "sufficient personnel threshold" and "insufficient personnel threshold" can be set to divide the personnel status into sufficient, moderate, and insufficient; for the AGV task priority, "emergency task threshold" and "regular task threshold" can be set to divide the task priority into high, medium, and low. Second, the system can preset a rule set, which can be composed of a series of "if…then…" logical sentences. For example, a rule can be: "If the operation load is higher than the high load threshold, the spare parts inventory is lower than the warning inventory threshold, and the on-duty status of maintenance personnel is lower than the insufficient personnel threshold, then determine the operation mode as 'emergency maintenance mode'." Another rule can be: "If the operation load is lower than the low load threshold, the spare parts inventory is higher than the safety inventory threshold, and the on-duty status of maintenance personnel is higher than the sufficient personnel threshold, then determine the operation mode as'regular maintenance mode'." These rules can cover various combinations of operation states that may occur in the logistics center.

[0060] Specifically, when the system needs to determine the logistics center's operating mode, it obtains real-time data on the current logistics center's operating load, spare parts inventory, maintenance personnel availability, and AGV task priority. The system then compares this real-time data with preset standard thresholds, quantifying each parameter into a corresponding state (for example, high load, tight inventory, insufficient staff, high-priority tasks). Finally, the system inputs these parameter states into a preset rule set, and the rule engine matches and infers based on the preset logic to output the specific operating mode of the current logistics center, such as "emergency maintenance mode," "regular maintenance mode," "peak maintenance mode," or "off-peak maintenance mode."

[0061] refer to Figure 2 , the present application further proposes a compression spring resonance frequency detection system, which is applied to a compression spring resonance frequency detection method. The system includes: an acquisition module, which is used to acquire the mixed vibration signal of the compression spring to be tested, the background noise signal of the test equipment as a reference noise signal, and the synchronization signal of the specific noise source of the test equipment; a cancellation processing module, which is used to perform adaptive noise cancellation processing on the mixed vibration signal, and the adaptive noise cancellation processing uses the reference noise signal for initial parameter training, and uses the noise source synchronization signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested; a spectrum analysis module, which is used to perform spectrum analysis on the pure vibration response signal; an identification module, which is used to identify the broadband vibration response area of ​​the compression spring to be tested from the spectrum analysis results, the amplitude of the broadband vibration response area is continuous and higher than the preset energy threshold and has a specific width; a calculation module, which is used to calculate the characteristic parameters of the broadband vibration response area, the characteristic parameters including the center frequency, the response bandwidth and the total energy of the frequency band; a discrimination module, which discriminates the degradation state of the compression spring to be tested according to the characteristic parameters.

[0062] Among them, the acquisition module refers to a unit for obtaining physical signals and converting them into processable electrical signals or digital signals, which can be realized by hardware devices such as sensors, data acquisition cards or analog-to-digital converters, and its purpose is to provide raw data for subsequent signal processing; the cancellation processing module refers to a unit for separating the target signal from the mixed signal and suppressing noise interference, which can be realized by digital signal processors (DSP), field programmable gate arrays (FPGA) or general-purpose processors cooperating with software algorithms, and its purpose is to improve the signal-to-noise ratio of the signal and ensure the accuracy of subsequent analysis; the spectrum analysis module refers to a unit for converting time-domain signals into frequency-domain signals to reveal the frequency components of the signals, which can be realized by fast Fourier transform (FFT) processors, spectrum analyzers or special spectrum analysis software, and its purpose is to exhibit the frequency distribution characteristics of the signal; the identification module refers to a unit for identifying specific patterns or features from processed data, which can be realized by pattern recognition algorithms, machine learning models or rule-based expert systems, and its purpose is to locate and extract key vibration response areas; the calculation module refers to a unit for mathematical operation and statistical analysis of data, which can be realized by microcontrollers, central processing units (CPUs) or special computing chips, and its purpose is to quantify the characteristics of the vibration response area; the discrimination module refers to a unit for classifying or evaluating input data according to pre-set rules or models, which can be realized by decision trees, neural networks or threshold-based comparators, and its purpose is to determine the degradation degree of the measured pressure spring.

[0063] The solution of this application implements the method for detecting the resonant frequency of a compression spring by formalizing each step into independent system modules, achieving systematic integration and efficient operation. Specifically, the acquisition module first acquires the mixed vibration signal of the compression spring under test, while simultaneously acquiring the background noise signal of the test equipment as a reference and the synchronization signal of a specific noise source. These raw signals form the basis for subsequent processing. Subsequently, the cancellation processing module receives the mixed vibration signal, a reference noise signal, and the synchronization signal of the noise source. Using an adaptive noise cancellation algorithm, it performs initial training using the reference noise signal and uses the synchronization signal of the noise source as a real-time reference input to effectively separate the pure compression spring vibration response signal from the mixed signal. This process significantly improves signal purity and lays the foundation for subsequent precise analysis. Next, the spectrum analysis module performs frequency domain conversion on the pure vibration response signal after cancellation processing to reveal its frequency composition. Based on this, the identification module conducts in-depth analysis of the spectrum analysis results to accurately identify the broadband vibration response region of the compression spring under test. This region has a continuous amplitude, exceeds a preset energy threshold, and has a specific width. This is crucial for capturing the vibration energy diffusion phenomenon caused by compression spring degradation. Next, the calculation module quantifies the identified broadband vibration response area and calculates characteristic parameters such as the center frequency, response bandwidth and total energy of the frequency band. Finally, the discrimination module accurately evaluates the degradation state of the compression spring based on these calculated characteristic parameters. Through this modular system design, the present application transforms the compression spring resonance frequency detection technology, which originally remained at the method level, into an operational and deployable physical system. The responsibilities of each module are clear, the data flow is smooth, and they work together to automate and standardize the complex signal processing and state discrimination processes. This systematic implementation method overcomes the limitations of pure methodology in practical applications, so that the detection of the degradation state of the compression spring no longer relies on complex manual signal analysis, but is automatically completed through an integrated system, thereby greatly improving the convenience, efficiency and accuracy of detection, and providing solid technical support for the maintenance and management of compression springs.

[0064] In some preferred embodiments, the present application is implemented as follows: The acquisition module may consist of a piezoelectric accelerometer and a multi-channel data acquisition card. The accelerometer is mounted on the compression spring to collect its vibration signal. The data acquisition card is responsible for synchronously collecting the analog signal output by the sensor, the background noise signal in the test bench environment (e.g., collected via a separate microphone), and the electrical signal output from a specific noise source such as a vibrator or hydraulic pump. The acquisition card converts these analog signals into digital signals and transmits them to the processing unit via a USB or Ethernet interface. The cancellation processing module may be implemented using a high-performance digital signal processor (DSP) chip, such as TI's TMS320 series DSP. This DSP chip internally runs an adaptive filtering algorithm, such as the least mean square (LMS) algorithm or the recursive least squares (RLS) algorithm. During system startup or calibration, the DSP uses the collected background noise signal to perform initial training of the algorithm parameters. During the actual testing process, the DSP uses the synchronous signal of the specific noise source as a reference input to subtract the noise component from the mixed vibration signal in real time, outputting a pure vibration response signal. The spectrum analysis module can be integrated within the cancellation processing module's DSP chip or implemented as a standalone microcontroller (MCU), such as the STMicroelectronics STM32 series. This module performs a fast Fourier transform (FFT) algorithm to convert the cancellation-processed time-domain vibration response signal into frequency-domain spectrum data. The FFT window size and overlap ratio can be configured to balance frequency resolution and time response. The identification module can be implemented as a software program running on an embedded processor (such as the ARM Cortex-A series). This program receives the spectrum data output by the spectrum analysis module and first smoothes it to reduce noise fluctuations. Then, using a sliding window or peak detection algorithm, it identifies regions with continuous amplitudes above a preset energy threshold and a specific width. The preset energy threshold and width can be set based on historical data or expert experience. The calculation module can be integrated with the identification module on the same embedded processor. This module further processes the identified broadband vibration response region, calculating its center frequency (e.g., using a weighted average method), response bandwidth (e.g., using a -3dB bandwidth method), and total energy in the band (e.g., by summing the squared spectral amplitudes within the region). The identification module can be implemented as another software module on the embedded processor. This module receives the characteristic parameters output by the calculation module and compares them with a pre-stored database of compression spring degradation patterns. The database contains characteristic parameter ranges or patterns corresponding to different degradation states (e.g., mild, moderate, and severe). Based on the comparison results, the identification module outputs a degradation status determination for the compression spring under test, such as "normal," "mildly fatigued," or "replacement required."

[0065] The contents disclosed above are only preferred feasible embodiments of the present invention and do not limit the scope of protection of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention description and drawings are included in the scope of protection of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A method for detecting the resonant frequency of a compression spring, characterized in that: The method comprises the following steps: Collect the mixed vibration signal of the compression spring to be tested; Collect the background noise signal of the test equipment as the reference noise signal; Acquire synchronous signals of specific noise sources of test equipment; Adaptive noise cancellation processing is performed on the mixed vibration signal. The adaptive noise cancellation processing uses a reference noise signal for initial parameter training and uses the noise source synchronization signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested. When the electromagnetic exciter is used as an excitation source to apply excitation to the compression spring to be tested and causes the synchronization signal of the specific noise source of the test equipment to generate synchronous modulation, the steps of the adaptive noise cancellation processing include: obtaining a baseline noise template of the specific noise source of the test equipment when the electromagnetic exciter is in a non-working state, synchronously obtaining a drive control signal of the electromagnetic exciter, modulating the baseline noise template of the specific noise source of the test equipment according to the drive control signal of the electromagnetic exciter to generate a virtual noise reference signal, and performing adaptive noise cancellation processing on the mixed vibration signal. The adaptive noise cancellation processing uses the reference noise signal for initial parameter training and uses the virtual noise reference signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested; Perform spectrum analysis on pure vibration response signals; Identify a broadband vibration response region of the compression spring to be tested from the spectrum analysis results, wherein the amplitude of the broadband vibration response region is continuous, higher than a preset energy threshold, and has a specific width, wherein the spectrum analysis results are refined, and the refined processing steps include: preliminarily identifying that the amplitude of the broadband vibration response region in the spectrum analysis results is continuous, higher than a preset energy threshold, and has a specific width, thereby obtaining a preliminary broadband response range; identifying local maximum points within the preliminary broadband response range; analyzing the spectrum curve shape within the preliminary broadband response range based on the distribution of the local maximum points; determining a precise starting frequency and a cutoff frequency of the broadband vibration response region based on the analysis results of the spectrum curve shape; and storing the precise starting frequency and the cutoff frequency of the broadband vibration response region; Calculate the characteristic parameters of the broadband vibration response area, including center frequency, response bandwidth and total energy of the frequency band; The degradation state of the compression spring to be tested is determined based on the characteristic parameters.

2. A method for detecting the resonant frequency of a compression spring according to claim 1, characterized in that: The steps of determining the degradation state of the compression spring to be tested according to the characteristic parameters include: Analyze the characteristic parameters and classify the degradation modes of the broadband vibration response area according to the center frequency to obtain the classified degradation modes; According to the classified degradation mode, the response bandwidth and the total energy of the frequency band are compared with the preset mode threshold to evaluate the severity of the classified degradation mode; According to the severity of the classified degradation modes and comparing the total energy of the frequency bands, the dominant degradation mode is determined; Based on the dominant degradation mode and the severity of the classified degradation mode, maintenance recommendations are generated to determine the degradation state of the compression spring under test.

3. A method for detecting the resonant frequency of a compression spring according to claim 2, characterized in that: The steps to generate maintenance recommendations include: Obtain available spare parts, maintenance stations, and maintenance personnel shifts in the logistics center to obtain maintenance resource availability information; Obtain the current tasks and future scheduling plans of the AGV to be maintained, and obtain the operating status of the AGV to be maintained; Assess maintenance urgency based on the dominant degradation pattern and the severity of the classified degradation patterns; Prioritize the maintenance tasks according to the degradation status of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information, and the operating status of the AGV to be maintained, and obtain the priority ranking result; Adjust the operation schedule of the AGVs to be maintained and allocate maintenance resources based on the priority sorting results; Generate maintenance recommendations based on the adjusted AGV operation schedule and allocated maintenance resources.

4. A method for detecting the resonant frequency of a compression spring according to claim 3, characterized in that: The steps for prioritizing maintenance tasks based on the degradation status of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained include: Set weights for the degradation status of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained; Calculate the comprehensive priority score of the maintenance task based on the weight; The maintenance tasks are prioritized according to their comprehensive priority scores.

5. A method for detecting the resonant frequency of a compression spring according to claim 4, characterized in that: The steps of setting weights for the degradation state of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information, and the operating state of the AGV to be maintained include: Obtain the logistics center's operating load, spare parts inventory, maintenance personnel availability, and AGV task priorities to determine the logistics center's operating model; According to the operation mode of the logistics center, the weights of the degradation state of the compression spring to be tested, maintenance urgency, maintenance resource availability information, and the operating status of the AGV to be maintained are adjusted.

6. A method for detecting the resonant frequency of a compression spring according to claim 5, characterized in that: According to the logistics center operation mode, the steps for adjusting the weights of the degradation state of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information, and the operating state of the AGV to be maintained include: Preset weight adjustment rules under different logistics center operation modes; According to the logistics center operation model, select the corresponding weight set from the preset weight adjustment rules; According to the selected weight set, the weights of the degradation state of the compression spring to be tested, the maintenance urgency, the maintenance resource availability information, and the operating state of the AGV to be maintained are adjusted.

7. A method for detecting the resonant frequency of a compression spring according to claim 5, characterized in that: The steps to determine the logistics center's operating model include: Preset standard threshold; Preset rule sets; The logistics center operation mode is determined based on the logistics center's operating load, spare parts inventory, maintenance personnel on-duty status and AGV task priority, and according to preset standard thresholds and preset rule sets.

8. A compression spring resonance frequency detection system, applied to the compression spring resonance frequency detection method according to claim 1, characterized in that: The system includes: An acquisition module is used to acquire the mixed vibration signal of the compression spring to be tested, the background noise signal of the test equipment as a reference noise signal, and the synchronization signal of the specific noise source of the test equipment; A cancellation processing module is used to perform adaptive noise cancellation processing on the mixed vibration signal. The adaptive noise cancellation processing uses the reference noise signal for initial parameter training and uses the noise source synchronization signal as a real-time reference input to separate the pure vibration response signal of the compression spring to be tested; Spectrum analysis module, used to perform spectrum analysis on pure vibration response signals; an identification module, configured to identify a broadband vibration response region of the compression spring to be tested from the spectrum analysis results, wherein the amplitude of the broadband vibration response region is continuous, higher than a preset energy threshold, and has a specific width; A calculation module is used to calculate characteristic parameters of the broadband vibration response area, the characteristic parameters including center frequency, response bandwidth and total energy of the frequency band; The discrimination module discriminates the degradation state of the compression spring to be tested according to the characteristic parameters.

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