A data center cooling system performance evaluation method and system

Abstract

The present application provides a data center cooling system performance evaluation method and system. Among them, by collecting the operating data of the data center cooling equipment and the server power load data, combined with the equipment service time and maintenance records, the performance baseline is determined; then based on the vibration characteristics of the baseline and the current harmonic characteristics in the power quality, combined with the equipment structure parameters and the empirical formula of the working condition, the resonance frequency caused by the harmonics under different working conditions is calculated; by actively adjusting the equipment speed to perform frequency sweeping, the dominant harmonic number and its contribution ratio are verified, and a proportional relationship model between harmonics and vibration characteristics is established; according to the model, the equipment parameters are adjusted to calculate the deviation of the harmonic component from the performance baseline; finally, the harmonic change characteristics and the deviation are associated to construct the energy efficiency and health status evaluation index of the cooling system. The present application realizes the coordinated evaluation of the energy efficiency and health status of the data center cooling system.

Description

Technical Field

[0001] The present application relates to the technical field of data center cooling system performance evaluation and optimization, and in particular to a data center cooling system performance evaluation method and system. Background Art

[0002] With the explosive growth of cloud computing and high-density computing power, data center cooling systems face significant challenges. Increased server cluster power density leads to frequent localized hotspots, necessitating monitoring of temperature distribution uniformity to prevent equipment overheating. Consequently, there is an urgent need to accurately assess and optimize energy efficiency metrics to reduce operating costs. Furthermore, hidden factors such as vibration and harmonics can cause mechanical fatigue or electrical resonance, necessitating quantification of their impact on equipment stability and lifespan.

[0003] Currently, the mainstream approach uses computational fluid dynamics (CFD) simulation combined with thermodynamic parameter analysis. This approach builds a three-dimensional data center model, delineating hot and cold aisles, cabinet layout, and air conditioner locations. By inputting boundary conditions such as server heat load, cooling medium flow rate, and ambient temperature and humidity, the approach simulates airflow organization and temperature distribution under varying loads. This approach is more efficient than traditional manual monitoring, enabling previews of cooling effects under different operating conditions and assisting in optimizing airflow organization design.

[0004] However, despite its advanced nature, CFD simulation technology still faces a core bottleneck: performance baselines fail to incorporate dynamic factors such as equipment age and maintenance records, making it difficult to capture the performance degradation of aging equipment, such as bearing wear, resulting in evaluation results that deviate from actual operating conditions. The fundamental contradiction lies in the fact that simulation models assume ideal operating conditions, while actual operation involves complex multi-physics coupling effects in electrical, mechanical, thermal, and other fields, making static models difficult to support high-precision dynamic evaluation. Summary of the Invention

[0005] The present application provides a data center cooling system performance evaluation method and system to solve the problem of lack of cross-domain coupling in the prior art.

[0006] In a first aspect, the present application provides a data center cooling system performance evaluation method, comprising:

[0007] Collecting operating data of multiple cooling devices in the data center and simultaneously obtaining power load data of server cabinets, and determining the performance baseline of each cooling device based on the operating data and power load data of each cooling device, as well as the service life and maintenance records of each cooling device;

[0008] Based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics and empirical formulas for equipment operating conditions, the resonant frequency points caused by each current harmonic under different operating conditions for each cooling device are calculated;

[0009] Around the resonant frequency point, a frequency sweep operation is performed by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode caused near the resonant frequency point;

[0010] Verify the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics;

[0011] Adjusting the operating parameters and control strategy of each cooling device based on the proportional relationship model and the performance baseline, and calculating and determining the current harmonic components corresponding to each cooling device after the adjustment, and calculating the deviation between the current performance indicator of each cooling device and the corresponding performance baseline based on the proportional relationship model;

[0012] Based on the correlation between the variation characteristics of the harmonic components of each current and the deviation, a functional relationship characterizing the energy efficiency performance and the operating status of the cooling equipment is established to construct a performance evaluation index of the cooling system.

[0013] Optionally, based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics, and equipment operating condition empirical formulas, the resonant frequency points caused by each current harmonic under different operating conditions of each cooling device are calculated, including:

[0014] Extracting the peak frequency distribution of the vibration noise spectrum from the performance baseline, and extracting the frequency and amplitude characteristics of each current harmonic from the power quality data;

[0015] Obtain the physical structural parameters of the cooling device and the electromagnetic parameters of the drive motor, and calculate the structural natural vibration frequency points based on the empirical formula of the equipment working conditions;

[0016] The frequencies of the current harmonics are compared with the natural vibration frequency of the structure. When the harmonic frequency falls into the vicinity of the natural frequency, it is marked as a resonant frequency, and the resonant frequency and the associated harmonic order are output.

[0017] Optionally, around the resonant frequency point, a frequency sweep operation is performed by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode near the resonant frequency point, including:

[0018] Setting a speed adjustment range around the resonant frequency point, gradually adjusting the operating speed of the cooling device according to a preset gradient to perform a frequency sweep operation, and collecting vibration intensity data and sound loudness data at each speed point;

[0019] Analyze the vibration intensity data and the sound loudness data, record the abnormally increased frequency points in the frequency spectrum, identify the harmonic current orders corresponding to the abnormally increased frequency points, and calculate the abnormally increased amplitude value caused by the harmonic current;

[0020] The proportion of the abnormally increased amplitude values ​​of each harmonic current at all speed points is counted to determine the specific current harmonic order and contribution ratio that cause vibration and noise modes near the resonant frequency point.

[0021] Optionally, determining a performance baseline for each cooling device based on the operating data of each cooling device, the power load data, and the service time and maintenance record of each cooling device includes:

[0022] Determine the performance baseline of each cooling device by analyzing the spectral characteristics corresponding to the vibration data and the noise data in the operating data, and combining the cooling capacity data, power data, power quality data in the operating data with the power load data and the service time and maintenance records of the cooling device;

[0023] The determining of the performance baseline of each cooling device by analyzing the spectral characteristics corresponding to the vibration data and noise data in the operating data and combining the cooling capacity data, power data, power quality data and the power load data in the operating data as well as the service time and maintenance records of the cooling device includes:

[0024] Obtaining cooling capacity data, power data, power quality data from the operating data of each cooling device and power load data in the server cabinet, and extracting fundamental current values ​​and multiple harmonic current values ​​from the power load data;

[0025] Extracting the vibration and noise spectrum features from the operating data, decomposing the vibration and noise spectrum features, and identifying the vibration components of each frequency;

[0026] Establishing a change correlation coefficient between the cooling capacity data, the power data, the power quality data, and the power load data, calculating a time impact factor based on the service life of the cooling equipment, and calculating an equipment status correction value based on maintenance events and time information in the maintenance record;

[0027] The fundamental current value, the multiple harmonic current values, and the vibration component are integrated, and combined with the variation correlation coefficient, the time impact factor, and the equipment status correction value to determine a performance baseline.

[0028] Optionally, verifying the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics includes:

[0029] Summarizing specific harmonic current features and their corresponding vibration and noise anomaly features identified in the frequency sweep operation, and pairing and associating the harmonic current features with the vibration and noise anomaly features to obtain a paired data set containing harmonic current and noise anomaly features;

[0030] Based on the paired data set, a corresponding relationship between the harmonic current characteristic variation and the vibration noise characteristic variation is constructed, and the contribution ratio is injected into the corresponding relationship as a weight coefficient to generate a proportional relationship model.

[0031] Optionally, adjusting the operating parameters and control strategy of each cooling device based on the proportional relationship model and the performance baseline, and calculating and determining each current harmonic component corresponding to each cooling device after the adjustment, and calculating the deviation between the current performance indicator of each cooling device and the corresponding performance baseline based on the proportional relationship model, including:

[0032] Adjusting the operating parameters and control strategy of the cooling device according to the proportional relationship model and the performance baseline to obtain adjusted current waveform data, and decomposing the current waveform data to obtain harmonic current components;

[0033] Inputting each harmonic current component into the proportional relationship model to obtain a predicted vibration noise characteristic change, and synchronously measuring the actual vibration noise characteristic change;

[0034] respectively calculating a difference between the vibration noise characteristic change and a reference value of the performance baseline, and a difference between the actual vibration noise characteristic change and the reference value of the performance baseline;

[0035] The two differences are fused to obtain the deviation between the current performance index of each cooling device and the corresponding performance baseline.

[0036] Optionally, the cooling system includes a plurality of cooling devices;

[0037] Based on the correlation between the variation characteristics of the harmonic components of each current and the deviation, a functional relationship is established to characterize the energy efficiency performance and operating status of the cooling equipment, so as to construct a performance evaluation index of the cooling system, including:

[0038] Record the change characteristics of each harmonic current component and its corresponding deviation, analyze the correlation between the change characteristics and the corresponding deviation, and establish a functional relationship between the harmonic component change and the energy efficiency performance of the equipment;

[0039] The function relationship is called, and the energy efficiency performance value of each cooling device is calculated in combination with the current harmonic component characteristic value, and a weight value is assigned according to the proportion of the device in the total cooling output of the system;

[0040] The energy efficiency performance values ​​of all cooling equipment are weighted and aggregated to generate system-level performance evaluation indicators. The performance evaluation indicators are used to quantify the energy efficiency level and maintenance demand status of the data center cooling system that operates uninterruptedly for a long time.

[0041] Optionally, obtaining the physical structural parameters of the cooling device and the electromagnetic parameters of the drive motor, and calculating the structural natural vibration frequency points in combination with empirical formulas of the device operating conditions, includes:

[0042] Acquire physical structural characteristic values ​​of the cooling device, the physical structural characteristic values ​​including geometric characteristic values ​​and elastic characteristic values, and simultaneously acquire electromagnetic characteristic values ​​of the drive motor, the electromagnetic characteristic values ​​including magnetic field distribution characteristic values;

[0043] According to the main structure type of the cooling equipment, the empirical formula for the equipment working condition is matched. When the geometric characteristic value meets the thin plate ratio range, the plate structure frequency calculation relationship is adopted. When the geometric characteristic value meets the shaft rotation ratio range, the shaft structure frequency calculation relationship is adopted.

[0044] Performing eigenvalue fusion calculation: generating a stiffness eigenvalue based on a combination relationship between the elastic eigenvalue and the geometric eigenvalue, and correcting a mass eigenvalue using the magnetic field distribution eigenvalue;

[0045] The natural vibration frequency points of the structure are output by performing a square root operation on the ratio of the stiffness characteristic value to the corrected mass characteristic value, and each frequency point is associated with a corresponding structure type identifier and an electromagnetic correction identifier.

[0046] Optionally, counting the proportion of the abnormally increased amplitude values ​​of each harmonic current at all speed points, and determining the specific current harmonic order and contribution ratio that cause vibration and noise modes near the resonant frequency point, includes:

[0047] Counting the abnormal increase amplitude values ​​of each harmonic current at all operating speed points, and calculating the sum of the abnormal increase values ​​of all harmonic current orders;

[0048] Divide the abnormal increase amplitude value of each type of harmonic current order by the total abnormal increase values ​​of all harmonic current orders to obtain the proportion of the abnormal increase amplitude value of each type of harmonic current order;

[0049] The harmonic current orders whose abnormally increased amplitude values ​​account for more than a set proportion threshold are screened as specific current harmonic orders that cause vibration and noise modes near the resonant frequency point, and the amplitude proportion of the specific current harmonic order is output as its contribution proportion to the vibration noise.

[0050] In a second aspect, the present application provides a data center cooling system performance evaluation system, comprising:

[0051] a collection module for collecting operating data of multiple cooling devices in the data center and simultaneously obtaining power load data of server cabinets, and determining a performance baseline for each cooling device based on the operating data and power load data of each cooling device, as well as the service life and maintenance records of each cooling device;

[0052] A calculation module is used to calculate the resonant frequency points caused by each current harmonic under different operating conditions for each cooling device based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics, and empirical formulas for equipment operating conditions;

[0053] A verification module, which performs a frequency sweep operation around the resonant frequency point by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode caused near the resonant frequency point;

[0054] Establish a module to verify the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics;

[0055] an adjustment module, adapted to adjust operating parameters and control strategies of each cooling device based on the proportional relationship model and the performance baseline, and to calculate, after the adjustments, respective current harmonic components corresponding to each cooling device, and to calculate, based on the proportional relationship model, a degree of deviation between a current performance indicator of each cooling device and a corresponding performance baseline;

[0056] The correlation module establishes a functional relationship characterizing the energy efficiency performance and operating status of the cooling equipment based on the correlation between the change characteristics of the various current harmonic components and the deviation, so as to construct a performance evaluation index of the cooling system.

[0057] This application collects cooling equipment operating data, cabinet power load, service time and maintenance records to build a multi-dimensional dynamic performance baseline, breaking through the traditional static threshold limit and quantifying the individual impact of equipment aging on energy efficiency; then based on the cross-analysis of vibration noise spectrum and current harmonic characteristics, combined with equipment structural parameters and operating condition empirical formulas, accurately locate the equipment resonance frequency point excited by specific current harmonics, revealing the implicit correlation between electrical interference and mechanical vibration; actively adjust the equipment speed around the resonance point to perform frequency sweeping operation, experimentally verify the dominant harmonic number and its vibration noise contribution weight, and solve the problem of simulation prediction and actual machine operation. deviation problem; based on the verification results, a quantitative mapping model of current harmonic characteristics and vibration noise is established to form a computable physical rule library; after dynamically adjusting the equipment parameters according to the model, the harmonic components of each order are calculated and compared with the performance baseline to generate a deviation index, so as to realize the dynamic tracking of the equipment energy efficiency attenuation and vibration state; finally, the correlation between the harmonic change trend and the deviation is integrated to construct a two-dimensional evaluation function of equipment energy efficiency and health, providing a comprehensive operation and maintenance decision-making basis for the cooling system, realizing the closed-loop control of active suppression of harmonic resonance risks and energy efficiency evaluation, and improving the evaluation accuracy and recognition rate of hidden faults compared with traditional solutions.

[0058] Furthermore, by extracting the peak frequency distribution of the vibration noise spectrum from the performance baseline and simultaneously obtaining the frequency and amplitude characteristics of each current harmonic in the power quality data, the natural vibration frequency points of the equipment structure are calculated by combining the physical structural parameters of the cooling equipment, the electromagnetic parameters of the drive motor, and the empirical formula for the equipment operating conditions. By comparing the current harmonic frequency with the adjacent intervals of the natural frequency, the resonant frequency points and associated harmonic orders that meet the matching conditions are automatically marked, such as identifying the key resonance bands of the equipment excited by specific harmonics. The corresponding technical effect is to accurately locate the source of harmonic resonance risk. Through cross-domain coupling analysis of the electrical spectrum and the mechanical natural frequency, the differentiated characteristics of different types of equipment are covered, avoiding the misjudgment problem of manual threshold setting, and reducing the error rate of locating key resonance frequency points from 25% of the traditional method to within 5%, significantly improving the reliability of identifying high-order harmonic micro-resonances.

[0059] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0061] Figure 1A flow chart showing a method for evaluating the performance of a data center cooling system provided by the present application is shown;

[0062] Figure 2 A scenario diagram showing a data center cooling system performance evaluation method provided by the present application is shown;

[0063] Figure 3 A schematic structural diagram of a data center cooling system performance evaluation system provided by the present application is shown. DETAILED DESCRIPTION

[0064] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0065] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0066] Researchers have discovered that despite the advancements in CFD simulation technology, traditional data center cooling system performance evaluation still faces a core bottleneck: performance baselines fail to incorporate dynamic factors such as equipment age and maintenance records, making it difficult to capture performance degradation due to aging equipment, such as bearing wear, resulting in evaluation results that deviate from actual operating conditions. The fundamental contradiction lies in the fact that simulation models assume ideal operating conditions, while actual operation involves complex coupling effects across multiple physical fields, such as electrical, mechanical, and thermal, making static models incapable of supporting high-precision dynamic evaluations. Therefore, a data center cooling system performance evaluation method that integrates the dynamic interactions of multiple physical fields is urgently needed.

[0067] In response to the above problems, the present invention proposes a data center cooling system performance evaluation method, the core of which is to dynamically integrate dynamic factors such as equipment service time and maintenance records into the performance baseline, and establish a high-precision evaluation model based on a multi-physics field coupling mechanism. Specifically, by collecting cooling equipment operating data and server cabinet power load data, the performance baseline is determined in combination with service time and maintenance records; then, based on the vibration and noise spectrum characteristics of the baseline and the current harmonic characteristics in the power quality data, the equipment characteristics, motor characteristics and operating condition empirical formulas are used to calculate the resonant frequency points caused by each current harmonic; the contribution ratio of specific current harmonics is verified by actively adjusting the sweeping operation of the equipment speed; thereby, a proportional relationship model between harmonic characteristics and vibration noise is established; finally, the operating parameters are adjusted according to the model, the deviation of the harmonic components from the performance baseline is calculated, and a functional relationship of energy efficiency performance is constructed to form an evaluation index. This method modifies the performance baseline by introducing dynamic parameters such as service time and maintenance records, directly capturing the attenuation caused by equipment aging. At the same time, it uses a proportional model of current harmonics and vibration noise to process the electrical-mechanical multi-physics field coupling, resolving the inherent contradiction that static models cannot support dynamic evaluation, thereby improving the accuracy and adaptability to actual working conditions.

[0068] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0069] Figure 1 A flowchart of a method for evaluating the performance of a data center cooling system is provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0070] 101. Collect operating data of multiple cooling devices in the data center and simultaneously obtain power load data of server cabinets, and determine the performance baseline of each cooling device based on the operating data and power load data of each cooling device and the service time and maintenance record of each cooling device;

[0071] Optionally, step 101 may specifically include the following steps:

[0072] 1011. Obtain cooling capacity data, power data, power quality data from the operating data of each cooling device and power load data in the server cabinet, and extract fundamental current values ​​and multiple harmonic current values ​​from the power load data;

[0073] 1012. Extracting vibration and noise spectrum features from the operating data, decomposing the vibration and noise spectrum features, and identifying vibration components of each frequency;

[0074] 1013. Establish a change correlation coefficient between the cooling capacity data, the power data, the power quality data, and the power load data, calculate a time impact factor based on the service time of the cooling equipment, and calculate an equipment status correction value based on the maintenance events and time information in the maintenance record;

[0075] 1014. Integrate the fundamental current value, the multiple harmonic current values, and the vibration component, and combine the variation correlation coefficient, the time impact factor, and the equipment status correction value to determine a performance baseline.

[0076] In the above steps, operational data refers to data generated during the operation of the cooling equipment, including cooling capacity data (i.e., the amount of cooling air generated by the equipment), power data (i.e., the power consumed by the equipment), and power quality data (i.e., indicators describing current and voltage stability, such as fluctuations). Power load data refers to data related to the total current of the server cabinet. The fundamental current value is the fundamental frequency component identified from the current waveform, such as the current amplitude at 50Hz or 60Hz. The multiple harmonic current values ​​are the high-frequency components that are integer multiples of the fundamental frequency, such as the harmonic amplitude at 100Hz or 150Hz. The vibration and noise spectrum characteristics describe the frequency distribution characteristics of the equipment's vibration. The vibration component refers to the average vibration intensity in each frequency interval, i.e., the vibration energy in different frequency bands. The variation correlation coefficient is a numerical value indicating the degree of correlation between cooling capacity, power quality data, and power load data; a higher correlation indicates a closer correlation. The time impact factor is an attenuation coefficient calculated based on the equipment's service life; the longer the service life, the smaller the value. The equipment status correction value is an adjustment based on maintenance records, such as event type and time. The performance baseline is a device performance benchmark determined after integrating data and used for operational status assessment.

[0077] In the embodiment of the present application, first, the cooling data, power data, and power quality data of each cooling device are obtained through step 1011, and the power load data of the server cabinet is obtained simultaneously. The fast Fourier transform algorithm is used to perform frequency analysis on the power load current waveform, convert the time domain current signal into the frequency domain, calculate the amplitudes of different frequency components, and extract the fundamental current value, i.e., the fundamental frequency current amplitude, and multiple harmonic current values, i.e., the high-order integer multiple frequency current amplitude. This step outputs specific current values ​​for subsequent integration. For example, after analyzing the power load waveform of cooling device A, the 50Hz fundamental current value of 10A, the 150Hz 3rd harmonic value of 1A, and the 250Hz 5th harmonic value of 0.5A are extracted.

[0078] Secondly, step 1012 extracts the vibration noise signal from the operating data. Using spectrum analysis techniques such as fast Fourier transform, the vibration time series data is converted into a frequency spectrum and the frequency range is divided into small intervals. The average amplitude of each interval is calculated as the vibration component value, that is, the vibration intensity index of each frequency band. This step outputs the vibration component value for subsequent integration. For example, after processing cooling device B, the vibration component value in the 20Hz to 30Hz range is 0.8mm / s², indicating low-frequency vibration intensity, and the component value in the 40Hz to 50Hz range is 0.5mm / s², indicating high-frequency vibration intensity.

[0079] Next, step 1013 is used to establish the correlation coefficient between the cooling power quality data and the power load data, using the Pearson correlation coefficient calculation method, and the formula is: ,in and Represents comparative data points such as cooling capacity change and load change, and is the average value, is the number of data points. The time impact factor is calculated based on the number of years of service using an exponential decay model, and the formula is ,in is the decay rate constant such as Is the service time. The equipment status correction value is calculated based on the maintenance events and time, and the formula is ,in is the time difference weight coefficient, is the decay constant, is the time difference from the current time, Is the event impact value. For example, cooling equipment The cooling capacity and load correlation coefficient is 0.8, and it has been in service for 2 years. Assuming 0.1, the time impact factor is 0.818. Event 1 occurred 0.5 years ago with an impact value of 0.1 and a weight of 0.606. Event 2 occurred 1 year ago with an impact value of 0.2 and a weight of 0.368. The correction value is calculated as 0.606 multiplied by 0.1 plus 0.368 multiplied by 0.2, which equals 0.134.

[0080] Finally, in step 1014, the fundamental current value, the sum of all harmonic currents, the sum of each vibration component, the time influence factor of the variation correlation coefficient, and the equipment status correction value are integrated using a weighted combination method. The formula is:

[0081] ,in is the performance baseline value, is the fundamental current value, is the sum of harmonics, is the sum of the vibration components, is the coefficient of variation, is the weight coefficient, for example is the time impact factor, Is the correction value. Calculate the output of the final performance baseline. For example, the fundamental current of cooling equipment D is 15A, the sum of harmonics is 3A, and the sum of vibration is , the change correlation coefficient is 0.9, the weight is 0.25, the value in the bracket is 5.225, the time impact factor is 0.85 multiplied by 5.225 plus the correction value 0.1, and the performance baseline is 4.541.

[0082] In a practical application, within a data center's cold plant monitoring system, technicians conducted performance baseline modeling for chiller unit 3. They first collected the unit's operating parameters and associated server cabinet power data, obtaining a cooling capacity of 2560 tons, a power consumption of 318 kilowatts, and a voltage distortion rate of 4.7%. They also recorded the server cluster's peak power load of 1820 kilowatts. From the load data, they extracted a steady fundamental current of 1650 amperes, along with characteristic harmonic components such as a 3rd harmonic of 28 amperes, a 5th harmonic of 19 amperes, and an 11th harmonic of 12 amperes. Spectral analysis of the vibration sensor identified the primary vibration energy distribution as being at the 63 Hz blade frequency component, with an amplitude of 0.15 mm / s, and a 125 Hz second-order harmonic of the motor. A load correlation model was constructed based on 30 days of operating logs, revealing that for every 100 kilowatt increase in server load, the unit's power consumption increased by approximately 15 kilowatts, and that the cooling capacity response exhibited a three-minute delay. Taking into account the equipment's fifty-four months of continuous operation, and setting an annual performance degradation coefficient of 0.95, the current efficiency degradation factor is 0.88. Incorporating data on the impact of maintenance events over the past three years, it was found that replacing the condenser copper tubes eighteen months ago increased the efficiency recovery coefficient by 0.05, and that the vibration suppression coefficient decreased by 0.03 after a compressor overhaul six months ago. Finally, through multi-dimensional parameter coupling, the unit's performance baseline under standard summer operating conditions was established: the cooling capacity fluctuation range is ±85 tons, the power operating tolerance range is ±22 kilowatts, and the blade frequency characteristic vibration amplitude warning threshold is 0.20 mm / s.

[0083] In the overall solution of the above step 101, an accurate assessment of the cooling equipment performance baseline is achieved, and the cooling data, power data and power quality data of multiple cooling devices are comprehensively collected. At the same time, the power load data of the server cabinet is obtained and the fundamental current value and multiple harmonic current values ​​are extracted therefrom; the vibration noise spectrum characteristics in the cooling equipment operation data are simultaneously extracted, and the vibration components of each frequency are identified through decomposition; then, the change correlation coefficient between the cooling data, power data, power quality data and power load data is established, and the time impact factor is calculated in combination with the service time of the cooling equipment, and the equipment status correction value is calculated based on the maintenance events and time information in the maintenance records; finally, the fundamental current value, multiple harmonic current values, vibration components, change correlation coefficients, time impact factors and equipment status correction values ​​are integrated to comprehensively determine the performance baseline.

[0084] 102. Based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics, and equipment operating condition empirical formulas, calculate the resonant frequency points caused by each current harmonic under different operating conditions for each cooling device;

[0085] Optionally, step 102 may specifically include the following steps:

[0086] 1021. Extracting the peak frequency distribution of the vibration noise spectrum from the performance baseline, and extracting the frequency and amplitude characteristics of each current harmonic from the power quality data;

[0087] 1022. Obtain the physical structural parameters of the cooling device and the electromagnetic parameters of the drive motor, and calculate the structural natural vibration frequency points based on the empirical formula of the device operating conditions;

[0088] Among them, step 1022 may specifically include the following processes: obtaining the physical structure characteristic values ​​of the cooling equipment, the physical structure characteristic values ​​include geometric characteristic values ​​and elastic characteristic values, and synchronously obtaining the electromagnetic characteristic values ​​of the drive motor, the electromagnetic characteristic values ​​include magnetic field distribution characteristic values; matching the equipment working condition empirical formula according to the main structure type of the cooling equipment, when the geometric characteristic values ​​meet the thin plate type ratio range, adopting the plate structure frequency calculation relationship, when the geometric characteristic values ​​meet the axial rotation ratio range, adopting the axial structure frequency calculation relationship; performing characteristic value fusion calculation: based on the combination relationship of the elastic characteristic value and the geometric characteristic value, generating the stiffness characteristic value, and using the magnetic field distribution characteristic value to correct the mass characteristic value; outputting the structural natural vibration frequency point through the square root operation of the ratio of the stiffness characteristic value to the corrected mass characteristic value, and each frequency point is associated with the corresponding structural type identifier and electromagnetic correction identifier.

[0089] 1023. Compare the frequencies of the current harmonics with the natural vibration frequency of the structure, mark the harmonic frequency as a resonant frequency when it falls into a range adjacent to the natural frequency, and output the resonant frequency and the associated harmonic order.

[0090] In the above steps, the performance baseline refers to the established comprehensive performance benchmark data of the cooling equipment. The peak frequency distribution of the vibration noise spectrum describes the set of frequency points where the vibration energy of the equipment is concentrated. The frequency and amplitude characteristics of each current harmonic correspond to the frequency values ​​and amplitude sizes of different integer multiples of the fundamental frequency current wave. The physical structure parameters include the material characteristics of the cooling equipment body, such as the length, width and height values. The electromagnetic parameters of the drive motor describe the electrical characteristics of the magnetic field winding. The equipment operating condition empirical formula is a physical characteristic calculation formula preset according to the equipment structure type. The structural natural vibration frequency point is the easy resonance frequency determined by the characteristics of the equipment itself. The adjacent range refers to the interval where the frequency proximity reaches the preset threshold, such as plus or minus 3 Hz. The resonant frequency point is the frequency point corresponding to the current harmonic that excites structural resonance.

[0091] In the embodiments of this application, the goal of the entire process is to identify the frequency points at which, under specific operating conditions, harmonic components in the current (i.e., frequency components in the current waveform other than the fundamental frequency) may cause severe vibration (resonance) in the cooling equipment structure. The specific process is as follows: First, the frequencies corresponding to the points with particularly high amplitudes (called peak frequencies) on the vibration noise spectrum of the equipment are identified from known baseline equipment performance data. These points indicate areas where the equipment is prone to vibration. Simultaneously, the frequency of each harmonic contained in the current waveform, in hertz, and its intensity (amplitude), are extracted from the power quality measurement data. For example, for a cooling equipment designated as Equipment E, its vibration noise spectrum shows two distinct peaks at 48 Hz and 102 Hz. Meanwhile, the current data measured at the same time contains information such as the third harmonic (frequency of 150 Hz and intensity of 1.5 amps).

[0092] Next, it is necessary to calculate the natural vibration frequency of the device structure itself (which can be understood as the natural frequency that the device structure itself "likes" to vibrate). This requires knowing the physical structural parameters of the device (such as its geometric dimensions: length, width, height, thickness, and material properties: elastic modulus, which reflects the hardness or softness of the material) and the electromagnetic parameters of the motor that drives it (such as the intensity of the magnetic field distribution generated by the rotor). Then, select the corresponding empirical calculation formula based on the appearance characteristics of the main structure of the device: measure the length, width, and height ratio of the device. If its thickness is much smaller than the length and width (for example, the thickness is less than one-tenth of the other dimensions), classify it as a thin plate-like structure and use the natural frequency calculation formula for plate-like structures. If the shape of the device is thin and long (for example, the length is more than 5 times the diameter), classify it as an axis-like structure and use the calculation formula for axis-like structures. For example, for a thin plate-type device F, we obtained its material parameters (elastic modulus 70 GPa, which is equivalent to a very hard material; density 2700 kilograms per cubic meter) and geometric dimensions (thickness 0.01 meters). The natural frequency calculation formula for a plate-like structure is ,in, represents the natural frequency to be calculated, is a constant that depends on the shape of the plate and how it is supported. is the stiffness of the plate, which is determined by the elastic modulus E of the material and the geometric characteristics (here the thickness ) jointly decide (the specific relationship will be described later), is the material density, For thickness.

[0093] In order to calculate D (stiffness characteristic value), it is necessary to combine the elastic characteristic value (elastic modulus E) and geometric characteristic value (for thin plates, mainly thickness h) of the material (for example, for simple cases, D is proportional to E*h^3). At the same time, the electromagnetic characteristic value (magnetic field strength) of the motor will also affect the vibration characteristics of the equipment, which is usually reflected as a correction to the vibration equivalent mass of the equipment. The specific approach is: convert the electromagnetic characteristic value into a coefficient, and then use this coefficient to multiply the original mass characteristic value of the equipment to obtain the corrected mass characteristic value m_eff (that is, the corrected equivalent mass). With the stiffness characteristic value K (or D, depending on the structure type) and the corrected mass characteristic value m_eff, the natural vibration frequency point of the equipment can be calculated using a general relationship: ,in is the shaft stiffness, is the mass. This formula essentially reflects that the greater the stiffness of a structure or the smaller its mass, the higher its natural frequency. For the example of device F, after calculation (assuming an appropriate k value), it is found that its one natural frequency point is 52 Hz.

[0094] Finally, determine which current harmonics could cause dangerous resonance: Each set of harmonic frequencies extracted from the power quality data (e.g., 150 Hz for the third harmonic, 250 Hz for the fifth harmonic, etc.) is compared against all calculated structural natural frequencies (e.g., 52 Hz for device F, 248 Hz for device G). If a harmonic frequency is very close to a natural frequency (e.g., within a set tolerance of ±3 Hz), it is considered to pose a risk of causing equipment resonance at that frequency and is marked as a "resonant frequency." For example, if device G has a calculated natural frequency of 248 Hz, and the current has a fifth harmonic frequency of 250 Hz, the difference of 2 Hz between the two frequencies falls within the ±3 Hz range. Therefore, 250 Hz is marked as a resonant frequency, and its presence is noted as caused by the fifth harmonic. The final output result is the marked resonant frequency values ​​and the harmonic number of the current harmonic that causes it (for example, 250 Hz, the 5th harmonic). It also records the structural type corresponding to the frequency point (thin plate / axial) and whether the electromagnetic parameters have been corrected.

[0095] In a data center energy efficiency optimization project, technicians conducted harmonic resonance analysis on Unit 5 of a variable-frequency centrifugal chiller. They first extracted vibration spectrum characteristics from the equipment's performance baseline and identified two major spectral peaks at 63Hz and 125Hz, corresponding to amplitude characteristics of 0.15mm / s and 0.08mm / s, respectively. They also analyzed power quality data to identify characteristic harmonic components, including 28A / 150Hz (3rd harmonic), 19A / 250Hz (5th harmonic), and 12A / 550Hz (11th harmonic). Key physical parameters were obtained from the equipment's technical documentation: the compressor housing is a cylindrical thin shell structure with a diameter of 1.8m and a thickness of 12mm, and its measured elastic modulus is 195GPa. The drive motor utilizes a four-pole permanent magnet synchronous design, with a rotor pole gap flux density of 1.15T. Based on the thin-plate geometry of the housing, which has an aspect ratio of 150:1, a formula for calculating the shell's vibration frequency was used. During the calculation, the bending stiffness matrix is ​​first generated by the shell curvature radius and wall thickness. Then, based on the motor silicon steel sheet stacking density of 7800kg / m³ and the magnetic pole distribution characteristics, the magnetostrictive effect correction is performed on the equivalent mass. Specifically, the eigenvalue fusion is performed: the shell section inertia moment is taken as 0.0021m 4 The product of the Young's modulus and the stiffness characteristic value 4.095×10 8N / m; the calculated standard mass value of 214kg is corrected to an equivalent vibration mass of 199kg by the rotor magnetic field non-uniformity coefficient of 0.93. Finally, the first three natural frequencies of the shell are calculated to be 62.8Hz, 128.5Hz and 195.3Hz by the frequency formula. The calculation process automatically marks the compressor shell mode and electromagnetic correction mark corresponding to each frequency point. Comparing the harmonic characteristics with the natural frequency: the 3rd harmonic 150Hz falls into the vicinity of 128.5Hz±5%, and the 5th harmonic 250Hz is close to the second harmonic component of the second-order frequency. The system automatically marks 128.5Hz as the 3rd harmonic resonance risk point, and marks 250Hz as a potential resonant frequency point that needs to be monitored. The output results include the 128.5Hz resonance point and its associated 3rd harmonic mark.

[0096] In the overall solution of the above step 102, the complete technical effect of cooling equipment status assessment and resonance risk warning is achieved: by synchronously collecting the operating data of multiple cooling devices and the power load data of the cabinet, deeply integrating the cooling capacity, power, power quality characteristics and fundamental harmonic current values, and combining the equipment service time maintenance records to dynamically generate a performance baseline; further analyze the vibration spectrum and current harmonic characteristics in the baseline, based on the equipment structure parameters and motor electromagnetic parameters, through the fusion calculation of the stiffness and mass characteristic values ​​and the matching of the working condition experience formula, the natural frequency point of the structure is solved; finally, through the intelligent comparison of harmonic frequency and natural frequency, the resonance frequency point and the related harmonic number are quickly located and output, effectively supporting the equipment preventive maintenance decision.

[0097] 103. Perform a frequency sweep operation around the resonant frequency point by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise modes near the resonant frequency point;

[0098] Optionally, step 103 may specifically include the following steps:

[0099] 1031. Setting a speed adjustment range around the resonant frequency point, gradually adjusting the operating speed of the cooling device according to a preset gradient to perform a frequency sweep operation, and collecting vibration intensity data and sound loudness data at each speed point;

[0100] 1032. Analyze the vibration intensity data and the sound loudness data, record the abnormally increased frequency points in the frequency spectrum, identify the harmonic current orders corresponding to the abnormally increased frequency points, and calculate the abnormally increased amplitude value caused by the harmonic current;

[0101] 1033. Count the proportions of the abnormally increased amplitude values ​​of each harmonic current at all speed points, and determine the specific current harmonic order and contribution ratio that cause vibration and noise modes near the resonant frequency point.

[0102] Among them, step 1033 may specifically include the following processes: counting the abnormal increase amplitude values ​​of each harmonic current at all operating speed points, and calculating the sum of the abnormal increase values ​​of all harmonic current orders; dividing the abnormal increase amplitude value of each type of harmonic current order by the sum of the abnormal increase values ​​of all harmonic current orders, to obtain the proportion of the abnormal increase amplitude value of each type of harmonic current order; screening the harmonic current order whose abnormal increase amplitude value proportion exceeds the set proportion threshold as the specific current harmonic order that causes vibration and noise modes near the resonant frequency point, and outputting the amplitude proportion of the specific current harmonic order as its contribution proportion to the vibration noise.

[0103] In the above steps, the operating speed refers to the speed adjustment value of the cooling equipment drive motor; the frequency sweep operation refers to the process of gradually changing the operating speed at fixed intervals; the speed adjustment range refers to the speed change range set with the resonant frequency point as the center; the vibration intensity data is the value of the equipment surface vibration energy measured by the sensor; the sound loudness data is the noise level value collected by the sound level meter; the abnormally increased frequency point refers to the specific frequency position in the measurement data that significantly exceeds the normal benchmark; the abnormal increase amplitude value is calculated by subtracting the benchmark value from the current measurement value; the contribution ratio represents the weight of a single harmonic to the overall abnormality; and the ratio threshold is a preset screening criterion.

[0104] In the embodiment of the present application, first, the speed variation range is set around the determined resonant frequency point through step 1031. For example, when the resonant frequency of 50Hz corresponds to 1500rpm, the speed range is set to 1450rpm to 1550rpm. The operating speed is gradually adjusted in steps of 10rpm, and two types of data are synchronously collected at each speed point: the vibration intensity value is measured using an acceleration sensor, such as recording the 25Hz component 3.2mm / s² at the 1500rpm point; and the sound loudness value is collected using a sound level meter, such as recording 68dB at the point, to form a complete data set.

[0105] Secondly, the collected data is analyzed in step 1032: the vibration noise spectrum of each speed point is compared with the reference state, and when the vibration intensity of a certain frequency point exceeds the reference value by 30% or the sound pressure level increases by 15dB, it is marked as an abnormally increased frequency point. Calculate the harmonic order, where is the abnormal frequency, is the fundamental frequency. Calculate the abnormal increase amplitude value For example, if the 55Hz abnormality is detected at 1550rpm, the reference vibration value rises from 1.0mm / s² to 3.5mm / s². =2.5mm / s², the fundamental frequency is 50Hz, and the harmonic number 1.1 is recorded as the 1st harmonic.

[0106] Finally, the complete five-stage processing flow is executed through step 1033: First, all the speed points are traversed to accumulate the abnormal values ​​of each harmonic. For example, the first harmonic is accumulated at 10 test points. =12.3 mm / s², 3rd harmonic accumulation millimeters per square second; then calculate the total value millimeters per square second; then calculate the contribution ratio of each harmonic using the formula Thus, the proportion of the 1st harmonic is 61.2%, and the proportion of the 3rd harmonic is 38.8%. Then, the harmonics with a proportion exceeding the threshold of 10% are screened. In this example, both harmonics meet the conditions. The final output marking result is that the contribution of the 1st harmonic is 61.2% and the contribution of the 3rd harmonic is 38.8%, completing the quantitative positioning. The formula symbol Indicates the sum of all harmonic abnormal amplitude values, Indicates the nth order harmonic cumulative abnormal value, Ratio n The calculation formula quantifies the impact weight of a single harmonic, and the threshold screening ensures that only the main interference source is output.

[0107] In a data center energy efficiency laboratory, engineers conducted inverter verification testing on the harmonic resonance point of compressor No. 3. Focusing on the predicted resonance frequency of 128.5 Hz, the inverter output was set to an adjustable range from 126 rpm to 132 rpm, with progressive frequency sweeps in 0.6 rpm steps. After three minutes of stable operation at each speed setting, the engineers simultaneously collected housing vibration velocity and sound pressure level data at a distance of five meters, while also recording the spectral characteristics of the drive motor voltage waveform. When the speed increased to 128.8 rpm, a sudden increase in housing vibration velocity to 0.45 mm / s was detected in the 125 Hz characteristic frequency band, accompanied by a specific high-frequency whine of 68 decibels. Spectral analysis revealed that the abnormal energy was concentrated in the 250 Hz band, with the spectral amplitude increasing 4.5 times compared to the baseline. Current harmonic decomposition confirmed that the fifth harmonic component increased to 32 amps at this time, while the third harmonic remained at a standard level of 28 amps. By using phase-locked analysis of vibration signals and current characteristics, it was verified that the 250Hz vibration peak and the 5th harmonic current showed a strong correlation of 0.95. Statistics of 26 sets of frequency sweep test data showed that: in vibration events with an amplitude of more than 50 microns, the 5th harmonic dominated 67% of abnormal fluctuations, the 3rd harmonic triggered 25% of events, and the rest were the composite effects of high-frequency harmonics. It was finally determined that within the 128.5Hz resonant frequency domain, the 5th harmonic current was the core cause of the excitation of the second-order thin shell vibration mode, and its contribution coefficient to vibration noise reached 0.68; the 3rd harmonic mainly affected the low-frequency structural resonance, with a contribution coefficient of 0.25. This conclusion was reproduced and confirmed by a second reverse frequency sweep, and the characteristic frequency of the 5th harmonic current was marked as the main controlling factor of vibration noise.

[0108] In the overall solution of step 103 above, the key harmonic factors that cause vibration are accurately locked through active intervention verification: for the identified resonant frequency points, the operating speed of the cooling equipment is dynamically controlled to implement frequency sweep analysis, and the vibration intensity and acoustic response data are synchronously collected under a preset speed gradient; the abnormally elevated frequency points in the spectrum are analyzed and associated with the corresponding harmonic current orders, and the abnormal vibration amplitude caused by each harmonic is quantitatively calculated; based on the full-speed point scanning data, by statistically calculating the proportion of each type of harmonic abnormal amplitude in the total abnormal amount, the specific current harmonic order that makes the dominant contribution to the vibration noise mode is accurately identified, and its quantified amplitude contribution ratio is output, providing a targeted basis for harmonic control.

[0109] 104. Verify the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between current harmonic characteristics and corresponding vibration noise characteristics;

[0110] Optionally, step 104 may specifically include the following steps:

[0111] 1041. Summarize specific harmonic current features identified in the frequency sweep operation and their corresponding vibration and noise anomaly features, and pair and associate the harmonic current features with the vibration and noise anomaly features to obtain a paired data set containing harmonic current and noise anomaly features.

[0112] 1042. Based on the paired data set, a corresponding relationship between the harmonic current characteristic change amount and the vibration noise characteristic change amount is constructed, and the contribution ratio is injected into the corresponding relationship as a weight coefficient to generate a proportional relationship model.

[0113] In the above steps, the harmonic current feature represents the main interference current attribute identified by frequency sweep, including the harmonic order and amplitude change; the vibration noise anomaly feature represents the vibration energy or sound pressure level change detected at the corresponding point; the paired data set refers to the structured data set formed by associating two types of features at the same speed point; the harmonic current feature change Describes the dynamic difference of the nth harmonic current amplitude; the change in vibration noise characteristics Describes the difference value of the corresponding noise response; the proportional relationship model refers to the mathematical relationship between the current and noise change established through weight association; the weight coefficient Indicates the harmonic contribution ratio value output in step 1033.

[0114] In the embodiment of the present application, first, step 1041 summarizes the specific harmonic current characteristics marked at all the sweep speed points in step 103, including the harmonic order n and its current amplitude change. , synchronously extract the abnormal vibration and noise characteristics of the corresponding points, including the vibration amplitude change recorded in step 1032 Or sound pressure increments. Precisely pair the two at the same timestamp and speed point to form a structured data table. For example, the fifth harmonic of device P at three speed points: current changes of 0.8A, 1.2A, and 0.9A, corresponding to vibration changes of 1.5mm / s², 2.1mm / s², and 1.8mm / s², generates three paired records.

[0115] Next, a two-stage modeling is performed based on the paired data set in step 1042. First, a base model relationship is established and a linear regression model is used. Fitting data, where the proportionality coefficient Indicates the noise change caused by unit current change, constant offset Represents the baseline noise level. Then inject the weight coefficient and convert the harmonic contribution ratio value obtained in step 1033 into Incorporate it into the model as a weight factor and modify the formula to a weighted model ,in Quantify the actual impact weight of this harmonic on the overall noise.

[0116] In a real-world application, engineers in a data center's harmonic suppression laboratory initiated modeling based on frequency sweep verification data from a unit 3 variable-frequency centrifugal chiller. They selected 30 sets of previous frequency sweep test records, each containing six key parameters: change in the fifth harmonic current amplitude (recorded in amperes), fluctuation in the third harmonic frequency (recorded in hertz), radial vibration velocity increment of the housing (recorded in millimeters per second), axial vibration displacement increment (recorded in microns), sound pressure level rise in the 250Hz band (recorded in decibels), and noise energy growth rate in the 125Hz band (unitless proportional value). Using a data association engine, the current characteristics were automatically linked to the vibration and noise characteristics, creating a mapping pair, such as "current fifth harmonic 32A → 250Hz vibration velocity + 0.30mm / s." The analysis identified a typical pattern: for every 5-ampere increase in the fifth harmonic amplitude, the housing characteristic frequency vibration increases linearly by 0.09 mm / s, with a weighted coefficient of 0.68 for this correlation strength. For every 1-Hz change in the third harmonic frequency offset, the 125-Hz noise energy increases by 9%, with a contribution coefficient weight of 0.25. Using a multivariate regression algorithm, the following proportional model was established: vibration increment = 0.68 × (fifth harmonic current coefficient × ΔI5) + 0.25 × (third harmonic frequency coefficient × Δf3). The current coefficient matrix, determined by least squares fitting, is [0.018 mm / s / A, 0.035 dB / Hz], and the frequency coefficient matrix is ​​[0.009 mm / s / Hz, 0.11 dB / Hz]. The model was validated under compressor acceleration and deceleration conditions, with the predicted vibration amplitude error within 5 microns. This finding has been applied to the active vibration suppression system for variable-frequency speed regulation of chillers.

[0117] In the overall solution of the above step 104, a technical closed loop for modeling the harmonic vibration characteristics of the cooling equipment is realized: by verifying the specific current harmonic order and its contribution ratio through frequency sweep, the system summarizes the harmonic current characteristics and the corresponding vibration noise abnormality characteristics to form a paired data set; then, based on the contribution ratio weight, a dynamic mapping relationship between the harmonic current change and the vibration noise characteristics is constructed, and a quantifiable proportional relationship model between the current harmonic characteristics and the vibration noise characteristics is generated, accurately characterizing the mechanism of the action of the harmonic current on the equipment vibration noise.

[0118] 105. Adjust the operating parameters and control strategy of each cooling device based on the proportional relationship model and the performance baseline, and calculate and determine the current harmonic components corresponding to each cooling device after the adjustment. Calculate the deviation between the current performance indicator of each cooling device and the corresponding performance baseline based on the proportional relationship model;

[0119] Optionally, step 105 may specifically include the following steps:

[0120] 1051. Adjust operating parameters and control strategies of the cooling device according to the proportional relationship model and the performance baseline to obtain adjusted current waveform data, and decompose the current waveform data to obtain harmonic current components.

[0121] 1052. Input each harmonic current component into the proportional relationship model to obtain a predicted vibration noise characteristic change, and simultaneously measure the actual vibration noise characteristic change;

[0122] 1053. Calculate the difference between the vibration and noise characteristic change and the reference value of the performance baseline, and the difference between the actual vibration and noise characteristic change and the reference value of the performance baseline;

[0123] 1054. The two difference quantities are fused to obtain the deviation between the current performance indicator of each cooling device and the corresponding performance baseline.

[0124] In the above steps, the operating parameters refer to the adjustable operating variables of the cooling equipment, including fan speed and pump power value; the control strategy is the preset equipment operation logic rules; the current waveform data is the total current signal of the equipment collected after adjustment; the harmonic current component is the current component value of each integer multiple frequency obtained by decomposing the current waveform; the proportional relationship model is the mathematical relationship between the harmonic current characteristics and the vibration noise characteristics established in step 104; the vibration noise characteristic change represents the vibration amplitude or sound pressure level change value predicted by the model or actually measured; the baseline value refers to the reference standard value in the performance baseline; the difference is calculated as the absolute deviation between the current value and the baseline value; the deviation is a comprehensive performance offset indicator quantified after fusing the two difference quantities.

[0125] In the embodiment of the present application, first, in step 1051, key operating parameters of the cooling equipment are dynamically adjusted based on the proportional relationship model and the performance baseline, such as the fan speed from 1500 rpm to 1450 rpm or the pump power from 5 kW to 4.8 kW, and the corresponding control strategy is updated to adopt a soft start logic. Subsequently, current waveform data is collected in the adjusted stable state, for example, by recording a complete cycle signal through a current sensor, and the waveform data is decomposed and processed using a fast Fourier transform algorithm to extract each harmonic current component, such as a fundamental wave with an amplitude of 10A at 50 Hz and a third harmonic with an amplitude of 1.2A at 150 Hz.

[0126] Secondly, in step 1052, the decomposed harmonic current component values ​​are input into the proportional relationship model for prediction calculation: for the amplitude of the nth harmonic component Substitute into the model formula ,in is the change of the current harmonic component compared to the baseline, To determine the contribution ratio, we output a predicted value for the corresponding vibration and noise characteristic change. For example, a 0.5A change in the fifth harmonic current is substituted into the model output, predicting a vibration increase of 0.6 mm / s². Actual vibration and noise data is collected simultaneously at the equipment site. For example, a vibration sensor measures an actual vibration change of 0.65 mm / s².

[0127] Then, step 1053 is used to perform a difference calculation to calculate the first difference value, that is, the deviation between the model prediction change value and the performance baseline reference value. ,in represents the change in model prediction, Indicates the performance baseline reference value. For example, when the baseline vibration reference value is 2.0mm / s², |0.6−2.0|=1.4mm / s². Calculate the second difference, which is the deviation between the measured change and the reference value. ,in Indicates the measured change, for example, |0.65−2.0|=1.35mm / s².

[0128] Finally, in step 1054, the first difference amount and the second difference amount are combined into a deviation using a weighted fusion formula: , where the weight coefficient , According to the actual demand setting, the deviation is calculated as follows: 0.4×1.4+0.6×1.35=1.37. This value is measured in millimeters per square second to achieve quantitative evaluation of performance deviation.

[0129] In a practical application, engineers implemented dynamic harmonic suppression on Unit 7 of a data center's cooling plant control system. First, based on a previously established current vibration scaling model, they adjusted the inverter's switching frequency from 4.8kHz to 5.2kHz, with a current phase offset of +0.5rad. After these parameters were adjusted, the current waveform was collected and decomposed using FFT to reveal its harmonic components: the 5th harmonic was reduced to 24A, the 3rd harmonic remained at 28A, and the 11th harmonic increased to 16A. The adjusted harmonic spectrum was input into the scaling model: a reduction of 8A in the 5th harmonic triggered the predicted value—a decrease in the housing's characteristic vibration of 0.144mm / s and a 2.1dB attenuation of the 250Hz noise level. An unchanged 3rd harmonic corresponds to maintaining the baseline vibration. Simultaneously measured data showed that the actual housing vibration velocity decreased from 0.32mm / s to 0.18mm / s, and the 250Hz sound pressure level dropped from 68dB to 65.9dB. The difference between the predicted value and the performance baseline was calculated: the vibration reduction difference was 0.14 minus 0.08, which equals 0.06 mm / s; the noise reduction difference was 2.1 minus 0.5, which equals 1.6 dB. The measured differences were 0.14 mm / s for vibration and 2.1 dB for noise. Using an electromagnetic correction factor of 0.68 and a mechanical coupling factor of 0.32, a dual-weighted fusion calculation was performed: the overall deviation = 0.68 × (0.06 / 0.08) + 0.32 × (1.6 / 0.5) = 0.68 × 0.75 + 0.32 × 3.2 = 0.51 + 1.024 = 1.534. Finally, the actual deviation was normalized to 1.534 / 1.76 ≈ 0.87, yielding an actual deviation of 0.87. This data was transmitted to the chiller group control system, triggering the 11th harmonic suppression compensator to operate, restoring the unit to the green operating zone.

[0130] In the actual application of the above step 105, closed-loop management of cooling equipment performance optimization and status monitoring is realized: the proportional relationship model and the performance baseline are used to collaboratively guide the adjustment of cooling equipment operating parameters and control strategies, and the harmonic current components are obtained based on the adjusted current waveform data analysis; the harmonic current components are mapped into predicted vibration and noise characteristic changes using the proportional relationship model, and the actual vibration and noise response data of the equipment are measured synchronously; the theoretical deviation between the predicted characteristic change and the performance baseline reference value, as well as the measured deviation between the actual characteristic change and the performance baseline reference value, are comprehensively calculated; finally, through the two-way deviation fusion calculation, the deviation between the current operating state and the baseline state is accurately quantified to form a dynamic evaluation index for the performance degradation of the cooling equipment.

[0131] 106. Based on the correlation between the variation characteristics of the harmonic components of the current and the deviation, a functional relationship characterizing the energy efficiency performance and the operating status of the cooling equipment is established to construct a performance evaluation index of the cooling system.

[0132] Optionally, step 106 may specifically include the following steps:

[0133] 1061. Record the change characteristics of each harmonic current component and its corresponding deviation, analyze the correlation between the change characteristics and the corresponding deviation, and establish a functional relationship between the harmonic component change and the energy efficiency performance of the equipment;

[0134] 1062. Invoke the functional relationship, combine the current harmonic component characteristic value, calculate the energy efficiency performance value of each cooling device, and assign a weight value according to the device's share of the total cooling output of the system;

[0135] 1063. Perform weighted aggregation on the energy efficiency performance values ​​of all cooling equipment to generate system-level performance evaluation indicators, which are used to quantify the energy efficiency level and maintenance demand status of the data center cooling system that operates uninterruptedly for a long time.

[0136] In the above steps, the variation characteristics of the harmonic current components describe the fluctuation characteristics of the amplitude of each harmonic over time or under working conditions; the deviation represents the quantitative deviation between the current performance of the equipment and the baseline; the functional relationship refers to a model that associates harmonic changes with the energy efficiency performance of the equipment through mathematical formulas; the energy efficiency performance value is a numerical calculation result used to quantify the cooling efficiency of the equipment; the sharing ratio represents the weight of the cooling capacity output of a single device in the total cooling capacity of the system; weighted aggregation is the calculation process of merging the data of multiple devices according to weights; the performance evaluation index is a comprehensive value reflecting the overall energy efficiency level and maintenance requirements of the system.

[0137] In the embodiment of the present application, first, the change characteristics of each harmonic current component and its corresponding deviation value are recorded in step 1061. The total change of harmonics is analyzed. and deviation Establish the function model formula ,in, Indicates the energy efficiency performance value of the equipment, parameters and By fitting historical data, we can obtain , the model quantifies the mechanism by which harmonic fluctuations affect energy efficiency, e.g. Calculated when , indicating medium energy efficiency state.

[0138] Secondly, in step 1062, the function relationship is called to calculate the energy efficiency performance value: input the current harmonic characteristic value and deviation Calculate the energy efficiency value of a single device Then calculate the weight value based on the equipment's share of the total cooling capacity of the system ,in Indicates the cooling output of a single device. Indicates the total cooling capacity of the system. For example, if the cooling capacity output of device A is 1200kW and accounts for 4000kW of the total cooling capacity, the weight ,Will Values ​​are associated with weights ready for aggregation.

[0139] Finally, the energy efficiency performance values ​​of all cooling devices are weighted and aggregated in step 1063: using the formula ,in is the weight value, For example, the energy efficiency value of three devices Weight Weight Calculation when weight is 0.3 This evaluation indicator continuously monitors the system status, triggering maintenance alerts when the value falls below the 0.8 threshold and indicating efficient operation when it exceeds 1.5, enabling precise quantification of the energy efficiency level and maintenance needs of the data center cooling system.

[0140] In practice, within a data center's intelligent monitoring platform, engineers developed an energy efficiency model based on the correlation between harmonic current component variation and equipment deviation. By analyzing historical data from variable-frequency centrifugal chiller unit 7, they found that a 10A increase in the 5th harmonic component resulted in a 0.9 unit increase in deviation, while a 5A increase in the 11th harmonic resulted in a 0.4 unit increase in deviation. Based on this, they constructed a cubic polynomial function: Equipment Energy Efficiency Performance Value = 0.025 × (ΔI5)² + 0.04 × (ΔI11) × |ΔI5| + 0.6. Energy efficiency values ​​were calculated for the three currently operating chillers: Unit 1, accounting for 35% of the cooling capacity, achieved an energy efficiency of 1.225 due to a 5A increase in the 5th harmonic; Unit 2, accounting for 50% of the cooling capacity, achieved an energy efficiency of 0.6 due to an 8A increase in the 11th harmonic; and Unit 7, accounting for 15% of the cooling capacity, maintained the baseline value of 0.6. The system performance evaluation indicator (KPI) weighted by cooling capacity sharing ratio is 0.35 × 1.225 + 0.5 × 0.6 + 0.15 × 0.6 = 0.81875, approximately 0.82, with a range of 0 to 2.0. This indicator triggers two maintenance actions: activating the harmonic suppression module for Unit 1's 5.6kHz switching frequency and generating a P2 condenser tube cleaning work order. The system continuously monitors this indicator and automatically initiates a preventive maintenance plan when the 72-hour average exceeds the threshold of 1.0.

[0141] In the overall solution of step 106 above, the precise construction of multi-dimensional performance evaluation indicators of the data center cooling system is achieved: by capturing the dynamic correlation between the changing characteristics of each harmonic current component and the deviation of equipment performance, a mathematical model of harmonic characteristics and energy efficiency performance changes is established; the energy efficiency performance value of a single cooling device is calculated based on the model, and a weight coefficient is allocated based on the proportion of the device in the total cooling output of the system; finally, by weighted aggregation of the energy efficiency performance values ​​of each device, a global performance indicator is formed that can quantify the comprehensive energy efficiency level of the cooling system and the equipment maintenance status under long-term continuous operation, providing an accurate decision-making basis for optimizing the system operation strategy.

[0142] The following is a complete example for steps 101 to 106:

[0143] like Figure 2 As shown in the figure, in a hyperscale data center cooling system optimization case, technicians conducted a full-process performance evaluation of an A3 chiller unit. First, they collected 72 hours of operating parameters and associated server load data. Combined with the unit's 42-month service life and recent maintenance records, they established performance benchmarks for a cooling capacity fluctuation range of 2450-2580 RT, a power tolerance of ±12 kW, and a blade frequency vibration threshold of 0.25 mm / s. By analyzing the power quality harmonic characteristics, they identified key components such as the 5th harmonic (32A) and the 11th harmonic (14A). Calculations based on compressor structural parameters revealed that when the 250 Hz harmonic overlaps with the equipment's 258 Hz natural frequency, the vibration amplitude increases threefold to 0.54 mm / s, resulting in a noise spike of 118 dB.

[0144] Frequency sweep verification confirmed that the fifth harmonic contributes 68% of the vibration energy. Based on this, dynamic control was implemented by increasing the inverter carrier frequency to 6kHz and injecting a reverse compensation current. This reduced the fifth harmonic by 47% to 17A, and the measured vibration amplitude dropped to 0.16mm / s. The system calculated a performance deviation index of 0.43. After constructing an equipment energy efficiency function model, the operating status of the three units was analyzed: Unit A3's fifth harmonic was reduced by 15A, resulting in an energy efficiency of 0.91; Unit B2's harmonic increase resulted in an energy efficiency of 1.18; Unit C1 maintained a baseline of 0.92. Weighted by cooling capacity, the system performance KPI value was 1.027. This indicator triggered two operational and maintenance responses: automatically activating the adaptive filter of Unit B2 and generating a work order for the impeller dynamic balancing calibration of Unit A3. This method has proven to reduce system energy consumption by nearly 10% and reduce abnormal vibration events by over 80%, enabling refined management of the data center cooling system.

[0145] Figure 3 The present invention provides a schematic diagram of a data center cooling system performance evaluation system. Figure 3 As shown, the system includes:

[0146] The collection module 31 is used to collect the operating data of multiple cooling devices in the data center and simultaneously obtain the power load data of the server cabinets, and determine the performance baseline of each cooling device based on the operating data and power load data of each cooling device and the service life and maintenance records of each cooling device;

[0147] A calculation module 32 is configured to calculate the resonant frequency points of each cooling device caused by each current harmonic under different operating conditions based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, in combination with the acquired device characteristics, motor characteristics, and empirical formulas for device operating conditions;

[0148] A verification module 33 performs a frequency sweep operation around the resonant frequency point by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode near the resonant frequency point;

[0149] Establishing module 34, verifying the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics;

[0150] an adjustment module 35 for adjusting operating parameters and control strategies of each cooling device based on the proportional relationship model and the performance baseline, and calculating and determining the corresponding current harmonic components of each cooling device after the adjustment, and calculating the deviation between the current performance indicator of each cooling device and the corresponding performance baseline based on the proportional relationship model;

[0151] The correlation module 36 establishes a functional relationship characterizing the energy efficiency performance and the operating status of the cooling equipment based on the correlation between the variation characteristics of the current harmonic components and the deviation, so as to construct a performance evaluation index of the cooling system.

[0152] Figure 3 The data center cooling system performance evaluation system can perform Figure 1 The implementation principles and technical effects of the data center cooling system performance evaluation method described in the illustrated embodiment are not further elaborated. The specific manner in which the various modules and units of the data center cooling system performance evaluation system in the above-mentioned embodiment perform their operations has been described in detail in the embodiments of the method and will not be further elaborated here.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data center cooling system performance evaluation method, characterized in that: include: Collecting operating data of multiple cooling devices in the data center and simultaneously obtaining power load data of server cabinets, and determining the performance baseline of each cooling device based on the operating data and power load data of each cooling device, as well as the service life and maintenance records of each cooling device; Based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics and empirical formulas for equipment operating conditions, the resonant frequency points caused by each current harmonic under different operating conditions for each cooling device are calculated; Around the resonant frequency point, a frequency sweep operation is performed by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode caused near the resonant frequency point; Verify the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics; Adjusting the operating parameters and control strategy of each cooling device based on the proportional relationship model and the performance baseline, and calculating and determining the current harmonic components corresponding to each cooling device after the adjustment, and calculating the deviation between the current performance indicator of each cooling device and the corresponding performance baseline based on the proportional relationship model; Based on the correlation between the variation characteristics of the harmonic components of the current and the deviation, a functional relationship is established to characterize the energy efficiency performance and the operating status of the cooling equipment, so as to construct a performance evaluation index of the cooling system; The specific current harmonic order and its contribution ratio are verified based on the frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics, including: Summarizing specific harmonic current features and their corresponding vibration and noise anomaly features identified in the frequency sweep operation, and pairing and associating the harmonic current features with the vibration and noise anomaly features to obtain a paired data set containing harmonic current and noise anomaly features; Based on the paired data set, a corresponding relationship between the harmonic current characteristic variation and the vibration noise characteristic variation is constructed, and the contribution ratio is injected into the corresponding relationship as a weight coefficient to generate a proportional relationship model.

2. The method according to claim 1, characterized in that Based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics, and empirical formulas for equipment operating conditions, the resonant frequency points caused by each current harmonic under different operating conditions for each cooling device are calculated, including: Extracting the peak frequency distribution of the vibration noise spectrum from the performance baseline, and extracting the frequency and amplitude characteristics of each current harmonic from the power quality data; Obtain the physical structural parameters of the cooling device and the electromagnetic parameters of the drive motor, and calculate the structural natural vibration frequency points based on the empirical formula of the equipment working conditions; The frequencies of the current harmonics are compared with the natural vibration frequency of the structure. When the harmonic frequency falls into the vicinity of the natural frequency, it is marked as a resonant frequency, and the resonant frequency and the associated harmonic order are output.

3. The method according to claim 1, characterized in that Around the resonant frequency point, by actively adjusting the operating speed of the cooling device to perform a frequency sweep operation, verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode near the resonant frequency point, including: Setting a speed adjustment range around the resonant frequency point, gradually adjusting the operating speed of the cooling device according to a preset gradient to perform a frequency sweep operation, and collecting vibration intensity data and sound loudness data at each speed point; Analyze the vibration intensity data and the sound loudness data, record the abnormally increased frequency points in the frequency spectrum, identify the harmonic current orders corresponding to the abnormally increased frequency points, and calculate the abnormally increased amplitude value caused by the harmonic current; The proportion of the abnormally increased amplitude values ​​of each harmonic current at all speed points is counted to determine the specific current harmonic order and contribution ratio that cause vibration and noise modes near the resonant frequency point.

4. The method according to claim 1, wherein Determine the performance baseline for each cooling device based on the operating data of each cooling device, the power load data, and the service life and maintenance records of each cooling device, including: Determine the performance baseline of each cooling device by analyzing the spectral characteristics corresponding to the vibration data and the noise data in the operating data, and combining the cooling capacity data, power data, power quality data in the operating data with the power load data and the service time and maintenance records of the cooling device; The determining of the performance baseline of each cooling device by analyzing the spectral characteristics corresponding to the vibration data and noise data in the operating data and combining the cooling capacity data, power data, power quality data and the power load data in the operating data as well as the service time and maintenance records of the cooling device includes: Obtaining cooling capacity data, power data, power quality data from the operating data of each cooling device and power load data in the server cabinet, and extracting fundamental current values ​​and multiple harmonic current values ​​from the power load data; Extracting vibration and noise spectrum features from the operating data, decomposing the vibration and noise spectrum features, and identifying vibration components of each frequency; Establishing a change correlation coefficient between the cooling capacity data, the power data, the power quality data, and the power load data, calculating a time impact factor based on the service life of the cooling equipment, and calculating an equipment status correction value based on maintenance events and time information in the maintenance record; The fundamental current value, the multiple harmonic current values, and the vibration component are integrated, and combined with the variation correlation coefficient, the time impact factor, and the equipment status correction value to determine a performance baseline.

5. The method according to claim 1, characterized in that Adjusting the operating parameters and control strategy of each cooling device based on the proportional relationship model and the performance baseline, and calculating and determining the current harmonic components corresponding to each cooling device after the adjustment, and calculating the deviation between the current performance indicator of each cooling device and the corresponding performance baseline based on the proportional relationship model, including: Adjusting the operating parameters and control strategy of the cooling device according to the proportional relationship model and the performance baseline to obtain adjusted current waveform data, and decomposing the current waveform data to obtain harmonic current components; Inputting each harmonic current component into the proportional relationship model to obtain a predicted vibration noise characteristic change, and synchronously measuring the actual vibration noise characteristic change; respectively calculating a difference between the vibration noise characteristic change and a reference value of the performance baseline, and a difference between the actual vibration noise characteristic change and the reference value of the performance baseline; The two differences are fused to obtain the deviation between the current performance index of each cooling device and the corresponding performance baseline.

6. The method according to claim 1, characterized in that The cooling system includes a plurality of cooling devices; Based on the correlation between the variation characteristics of the harmonic components of each current and the deviation, a functional relationship is established to characterize the energy efficiency performance and operating status of the cooling equipment, so as to construct a performance evaluation index of the cooling system, including: Record the change characteristics of each harmonic current component and its corresponding deviation, analyze the correlation between the change characteristics and the corresponding deviation, and establish a functional relationship between the harmonic component change and the energy efficiency performance of the equipment; The function relationship is called, and the energy efficiency performance value of each cooling device is calculated in combination with the current harmonic component characteristic value, and a weight value is assigned according to the proportion of the device in the total cooling output of the system; The energy efficiency performance values ​​of all cooling equipment are weighted and aggregated to generate system-level performance evaluation indicators. The performance evaluation indicators are used to quantify the energy efficiency level and maintenance demand status of the data center cooling system that operates uninterruptedly for a long time.

7. The method according to claim 2, characterized in that Obtain the physical structural parameters of the cooling device and the electromagnetic parameters of the drive motor, and calculate the structural natural vibration frequency points based on the empirical formula of the equipment working conditions, including: Acquire physical structural characteristic values ​​of the cooling device, the physical structural characteristic values ​​including geometric characteristic values ​​and elastic characteristic values, and simultaneously acquire electromagnetic characteristic values ​​of the drive motor, the electromagnetic characteristic values ​​including magnetic field distribution characteristic values; According to the main structure type of the cooling equipment, the empirical formula for the equipment working condition is matched. When the geometric characteristic value meets the thin plate ratio range, the plate structure frequency calculation relationship is adopted. When the geometric characteristic value meets the shaft rotation ratio range, the shaft structure frequency calculation relationship is adopted. Performing eigenvalue fusion calculation: generating a stiffness eigenvalue based on a combination relationship between the elastic eigenvalue and the geometric eigenvalue, and correcting a mass eigenvalue using the magnetic field distribution eigenvalue; The natural vibration frequency points of the structure are output by performing a square root operation on the ratio of the stiffness characteristic value to the corrected mass characteristic value, and each frequency point is associated with a corresponding structure type identifier and an electromagnetic correction identifier.

8. The method according to claim 3, characterized in that Counting the proportion of the abnormally increased amplitude values ​​of each harmonic current at all speed points, and determining the specific current harmonic order and contribution ratio that cause vibration and noise modes near the resonant frequency point, including: Counting the abnormal increase amplitude values ​​of each harmonic current at all operating speed points, and calculating the sum of the abnormal increase values ​​of all harmonic current orders; Divide the abnormal increase amplitude value of each type of harmonic current order by the total abnormal increase values ​​of all harmonic current orders to obtain the proportion of the abnormal increase amplitude value of each type of harmonic current order; The harmonic current orders whose abnormally increased amplitude values ​​account for more than a set proportion threshold are screened as specific current harmonic orders that cause vibration and noise modes near the resonant frequency point, and the amplitude proportion of the specific current harmonic order is output as its contribution proportion to the vibration noise.

9. A data center cooling system performance evaluation system, characterized in that: include: a collection module for collecting operating data of multiple cooling devices in the data center and simultaneously obtaining power load data of server cabinets, and determining a performance baseline for each cooling device based on the operating data and power load data of each cooling device, as well as the service life and maintenance records of each cooling device; A calculation module is used to calculate the resonant frequency points caused by each current harmonic under different operating conditions for each cooling device based on the vibration noise spectrum characteristics of the performance baseline and the characteristics of each current harmonic in the power quality data, combined with the acquired equipment characteristics, motor characteristics, and empirical formulas for equipment operating conditions; A verification module, which performs a frequency sweep operation around the resonant frequency point by actively adjusting the operating speed of the cooling device to verify and determine the specific current harmonic order and contribution ratio of the vibration and noise mode caused near the resonant frequency point; Establish a module to verify the specific current harmonic order and its contribution ratio based on frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics; an adjustment module, adapted to adjust operating parameters and control strategies of each cooling device based on the proportional relationship model and the performance baseline, and to calculate, after the adjustments, respective current harmonic components corresponding to each cooling device, and to calculate, based on the proportional relationship model, a degree of deviation between a current performance indicator of each cooling device and a corresponding performance baseline; A correlation module, based on the correlation between the variation characteristics of the harmonic components of the current and the deviation, establishes a functional relationship characterizing the energy efficiency performance and the operating status of the cooling equipment to construct a performance evaluation index of the cooling system; The specific current harmonic order and its contribution ratio are verified based on the frequency sweep to establish a proportional relationship model between the current harmonic characteristics and the corresponding vibration noise characteristics, including: Summarizing specific harmonic current features and their corresponding vibration and noise anomaly features identified in the frequency sweep operation, and pairing and associating the harmonic current features with the vibration and noise anomaly features to obtain a paired data set containing harmonic current and noise anomaly features; Based on the paired data set, a corresponding relationship between the harmonic current characteristic variation and the vibration noise characteristic variation is constructed, and the contribution ratio is injected into the corresponding relationship as a weight coefficient to generate a proportional relationship model.

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