A method and device for reducing the noise of a diesel generator set in a computer center

Abstract

The application provides a noise reduction method and device for a diesel generator set of a wisdom center, first determines a variable-diameter airflow rate sequence, and performs mean value processing on an even variable-diameter airflow rate sequence and an odd variable-diameter airflow rate sequence obtained by conversion of the variable-diameter airflow rate sequence, to obtain a stable even variable-diameter airflow rate sequence and a stable odd variable-diameter airflow rate sequence; performs cross-odd-point traversal on the stable even variable-diameter airflow rate sequence and the stable odd variable-diameter airflow rate sequence respectively, to obtain an even odd-point variable-diameter airflow rate sequence and an odd odd-point variable-diameter airflow rate sequence; performs excess data screening on the even odd-point variable-diameter airflow rate sequence and the odd odd-point variable-diameter airflow rate sequence, to obtain a first excess variable-diameter airflow value and a second excess variable-diameter airflow value and determine an excess noise value; and finally, according to comparison of the excess noise value and a preset excess noise value, performs humidification and flow reduction on a variable-diameter position of the exhaust pipeline, to reduce noise.

Description

Technical Field

[0001] This application relates to the field of intelligent computing center technology, and more specifically, to a method and apparatus for reducing noise in a diesel generator set for an intelligent computing center. Background Technology

[0002] Intelligent computing centers are the most important centers for producing computing power in the intelligent era. They use converged architecture computing systems as platforms and data as resources. They can drive AI models to deeply process data with powerful computing power, continuously generate various intelligent computing services, and supply them to organizations and individuals in the form of cloud services through the network.

[0003] With the development of modern industry and life, noise pollution has become an issue that cannot be ignored. In many application fields, especially in the process of energy production and use, noise generation has become increasingly prominent. As a common energy supply equipment, diesel generator sets are equipped with intelligent control, monitoring and management systems. They have advanced intelligent functions such as automated control, remote monitoring and fault diagnosis. These generator sets are usually used in scenarios such as power supply, emergency power supply and remote regional power supply. During operation, they generate a lot of noise, which affects the surrounding environment and human health. Therefore, noise reduction of diesel generator sets in intelligent computing centers has become an important research and engineering problem.

[0004] In existing technologies, firstly, the exhaust system is optimized by considering the streamline of the airflow to reduce the generation of flow noise. By changing parameters such as the diameter, length, and curvature of the exhaust pipe, the airflow within the exhaust system is made smoother, thereby reducing flow noise. Secondly, a soundproof enclosure is used to enclose the diesel generator set, isolating noise transmission. The inner wall of the enclosure uses sound-absorbing materials to absorb some sound wave energy, thus reducing noise radiation. At the same time, a ventilation system is designed inside the soundproof enclosure to ensure both normal operation of the generator set and sound insulation. In addition, vibration reduction measures are adopted in the internal structure of the diesel generator set to reduce vibration-transmitted noise. By installing vibration damping devices in key parts of the generator set, the transmission path of vibration noise is effectively blocked. However, at the diameter change point of the exhaust pipe, the airflow may be subject to certain interference and pressure changes. Turbulence may occur at the diameter change point of the exhaust pipe, leading to refraction and reflection of sound waves, ultimately resulting in increased noise. Summary of the Invention

[0005] This application provides a noise reduction method and device for a diesel generator set in a smart computing center, in order to solve the technical problem that turbulence may occur at the diameter change of the exhaust pipe, leading to the refraction and reflection of sound waves, which ultimately increases noise.

[0006] To solve the above-mentioned technical problems, this application adopts the following technical solution:

[0007] In a first aspect, this application provides a method for reducing the noise of a diesel generator set in a smart computing center, including:

[0008] Start the diesel generator set in the intelligent computing center, collect the variable-diameter airflow rate at the variable-diameter point of the exhaust pipe, and determine the variable-diameter airflow rate sequence by timestamp order;

[0009] The variable-diameter airflow rate sequence is converted into an even variable-diameter airflow rate sequence and an odd variable-diameter airflow rate sequence. The even variable-diameter airflow rate sequence and the odd variable-diameter airflow rate sequence are mean-valued respectively to obtain a stationary even variable-diameter airflow rate sequence and a stationary odd variable-diameter airflow rate sequence.

[0010] By performing cross-anomaly point traversal on the stationary even-path variable airflow rate sequence and the stationary odd-path variable airflow rate sequence respectively, the corresponding even-anomaly variable airflow rate sequence and odd-anomaly variable airflow rate sequence are obtained.

[0011] Excess data screening is performed on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excess flow velocity value and the second excess flow velocity value, and then the excess noise value is determined by the first excess flow velocity value and the second excess flow velocity value.

[0012] The excessive noise value is compared with the preset excessive noise value. When the excessive noise value is greater than the preset excessive noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

[0013] In some embodiments, the variable-diameter airflow rate at the variable-diameter section of the exhaust pipe is acquired by an airflow rate sensor.

[0014] In some embodiments, the variable-diameter airflow rate is sampled at equal time intervals.

[0015] In some embodiments, converting the variable-diameter airflow rate sequence into an even-variable-diameter airflow rate sequence and an odd-variable-diameter airflow rate sequence specifically includes:

[0016] Obtain all even-numbered variable-diameter airflow velocities in the variable-diameter airflow velocity sequence to obtain multiple even-numbered variable-diameter airflow velocities.

[0017] Arrange the multiple even-diameter airflow velocities in chronological order to obtain an even-diameter airflow velocity sequence.

[0018] Obtain all odd-numbered variable-diameter airflow velocities in the variable-diameter airflow velocity sequence to obtain multiple odd-variable-diameter airflow velocities;

[0019] The multiple odd-path airflow velocities are arranged in chronological order to obtain an odd-path airflow velocity sequence.

[0020] In some embodiments, averaging the even-path flow rate sequence and the odd-path flow rate sequence respectively to obtain a stationary even-path flow rate sequence and a stationary odd-path flow rate sequence specifically includes:

[0021] The mean value of the even-diameter airflow rate sequence is determined to obtain the even-diameter airflow rate mean value, wherein the even-diameter airflow rate sequence includes multiple even-diameter airflow rates;

[0022] The residual between each even-diameter airflow rate and the mean even-diameter airflow rate is calculated to obtain all the stationary even-diameter airflow rates.

[0023] All stationary even-diameter airflow velocities are non-negative and arranged in chronological order to obtain a sequence of stationary even-diameter airflow velocities.

[0024] The mean value of the odd-path flow rate sequence is determined to obtain the odd-path flow rate mean value, wherein the odd-path flow rate sequence includes multiple odd-path flow rates;

[0025] The residual between each odd-path airflow rate and the mean odd-path airflow rate is calculated to obtain all the stationary odd-path airflow rates.

[0026] All the stationary odd-path airflow velocities are processed to be non-negative and arranged in chronological order to obtain the stationary odd-path airflow velocity sequence.

[0027] In some embodiments, performing excess data filtering on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excess flow velocity value and the second excess flow velocity value specifically includes:

[0028] Determine the harmonic values ​​of the even-moving variable-diameter gas velocity sequence and the singular-moving variable-diameter gas velocity sequence;

[0029] Determine the volatility of the even-moving variable-diameter gas velocity sequence and the volatility of the singular-moving variable-diameter gas velocity sequence;

[0030] Set the even-anomaly multiple threshold for the even-anomaly variable flow rate sequence and the odd-anomaly multiple threshold for the odd-anomaly variable flow rate sequence;

[0031] The even excess data screening conditions for even abnormal variable flow rate in the even abnormal variable flow rate sequence are determined based on the harmonic value of the even abnormal variable flow rate sequence, the volatility of the even abnormal variable flow rate sequence, and the even abnormal multiple threshold.

[0032] The criteria for filtering singular excess data of singular dynamic variable flow rate in the singular dynamic variable flow rate sequence are determined based on the harmonic value of the singular dynamic variable flow rate sequence, the volatility of the singular dynamic variable flow rate sequence, and the singular dynamic multiple threshold.

[0033] The even-abnormal variable-diameter airflow intensity in the even-abnormal variable-diameter airflow intensity sequence is filtered according to the even-abnormal data filtering conditions, and the filtered even-abnormal variable-diameter airflow intensity is centralized to obtain the first excess variable-diameter airflow value.

[0034] The singular dynamic variable flow rate in the singular dynamic variable flow rate sequence is filtered according to the singular excess data filtering conditions, and the filtered singular dynamic variable flow rate is centralized to obtain the second excess variable flow rate value.

[0035] In some embodiments, the even excess data filtering criteria are determined by the following formula:

[0036]

[0037] in, The harmonic value representing the velocity sequence of even-moving variable-radius airflow. This represents the volatility of an even-moving variable-path flow velocity sequence. Indicates the threshold for even-numbered multiples of change. The first of the even-moving variable-diameter flow rate sequence Individual variable flow rate.

[0038] Secondly, this application provides a noise reduction device for a diesel generator set in a smart computing center, comprising:

[0039] The variable-diameter airflow rate acquisition module is used to start the diesel generator set of the intelligent computing center, acquire the variable-diameter airflow rate at the variable-diameter point of the exhaust pipe, and determine the variable-diameter airflow rate sequence by timestamp order.

[0040] A steady-state even-odd-variable-diameter airflow rate determination module is used to convert the variable-diameter airflow rate sequence into an even-variable-diameter airflow rate sequence and an odd-variable-diameter airflow rate sequence, and to average the even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence respectively, thereby obtaining a steady-state even-variable-diameter airflow rate sequence and a steady-state odd-variable-diameter airflow rate sequence.

[0041] The odd-even variable-path airflow speed determination module is used to perform cross-variation point traversal on the stationary even-variable-path airflow speed sequence and the stationary odd-variable-path airflow speed sequence respectively, and obtain the even-variable-path airflow speed sequence and the odd-variable-path airflow speed sequence accordingly.

[0042] The excessive noise value determination module is used to perform excessive data filtering on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excessive flow velocity value and the second excessive flow velocity value, and then determine the excessive noise value from the first excessive flow velocity value and the second excessive flow velocity value.

[0043] The noise reduction module is used to compare the excess noise value with a preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

[0044] Thirdly, this application provides a computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described noise reduction method for a smart computing center diesel generator set.

[0045] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for reducing the noise of a diesel generator set in a smart computing center.

[0046] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0047] The noise reduction method and apparatus for a diesel generator set in a smart computing center provided in this application first starts the diesel generator set in the smart computing center, collects the variable-diameter airflow rate at the diameter change point of the exhaust pipe, and determines the variable-diameter airflow rate sequence by timestamp order; the variable-diameter airflow rate sequence is converted into an even-diameter airflow rate sequence and an odd-diameter airflow rate sequence, and the even-diameter airflow rate sequence and the odd-diameter airflow rate sequence are mean-averaged respectively to obtain a stationary even-diameter airflow rate sequence and a stationary odd-diameter airflow rate sequence; the stationary even-diameter airflow rate sequence and the stationary odd-diameter airflow rate sequence are then mean-averaged respectively. The flow rate sequence is traversed through cross-anomaly points to obtain even-anomaly variable diameter flow rate sequences and odd-anomaly variable diameter flow rate sequences. Excess data is filtered from the even-anomaly variable diameter flow rate sequences and the odd-anomaly variable diameter flow rate sequences to obtain a first excess flow rate value and a second excess flow rate value. Then, an excess noise value is determined from the first excess flow rate value and the second excess flow rate value. The excess noise value is compared with a preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

[0048] In this application, the variable-diameter airflow rate is first obtained to determine whether the airflow is normal and whether there are any abnormalities that cause an increase in noise. By obtaining the variable-diameter airflow rate, the relationship between noise and the variable-diameter airflow rate is further analyzed. Secondly, the even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence are averaged to eliminate the overall trend changes in the even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence, so as to obtain the stationary even-variable-diameter airflow rate sequence and the stationary odd-variable-diameter airflow rate sequence. This is to highlight the short-term fluctuations in the variable-diameter airflow rate data, and to more accurately analyze and understand the data change patterns, thereby better detecting noise. Then, the stationary even-variable-diameter airflow rate sequence and the stationary odd-variable-diameter airflow rate sequence are respectively traversed for cross-anomaly points to find abnormal points or abrupt changes in noise caused by changes in the variable-diameter airflow rate. Finally, by determining the excess noise value, the excess noise value is compared with the preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are performed at the diameter change of the exhaust pipe to reduce the generation of noise. Attached Figure Description

[0049] Figure 1 This is an exemplary flowchart of a noise reduction method for a diesel generator set in a smart computing center, according to some embodiments of this application.

[0050] Figure 2 These are schematic diagrams of exemplary hardware and / or software of a noise reduction device for a smart computing center diesel generator set, as shown in some embodiments of this application.

[0051] Figure 3 This is a schematic diagram of the structure of a computer device that implements a noise reduction method for a diesel generator set in a smart computing center, according to some embodiments of this application. Detailed Implementation

[0052] The core of this application is to first start the diesel generator set of the intelligent computing center, collect the variable-diameter airflow rate at the diameter change point of the exhaust pipe, and determine the variable-diameter airflow rate sequence by timestamp sequence; convert the variable-diameter airflow rate sequence into an even-diameter airflow rate sequence and an odd-diameter airflow rate sequence, and then average the even-diameter airflow rate sequence and the odd-diameter airflow rate sequence respectively to obtain a stationary even-diameter airflow rate sequence and a stationary odd-diameter airflow rate sequence; and then perform cross-anomalies on the stationary even-diameter airflow rate sequence and the stationary odd-diameter airflow rate sequence respectively. The process involves point traversal to obtain even-variable diameter flow rate sequences and odd-variable diameter flow rate sequences. Excess data filtering is performed on these sequences to obtain a first excessive flow rate value and a second excessive flow rate value. The excessive noise value is then determined using these two values. The excessive noise value is compared with a preset excessive noise value. If the excessive noise value is greater than the preset value, humidification and flow reduction are applied to the diameter change section of the exhaust pipe to reduce noise.

[0053] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a noise reduction method for a diesel generator set in a smart computing center according to some embodiments of this application. The noise reduction method 100 for the diesel generator set in a smart computing center mainly includes the following steps:

[0054] In step 101, the diesel generator set of the intelligent computing center is started, the variable diameter airflow rate at the variable diameter point of the exhaust pipe is collected, and the variable diameter airflow rate sequence is determined by the timestamp sequence.

[0055] Intelligent computing center diesel generator sets are diesel generator sets that can achieve intelligent control, monitoring and management. They have advanced intelligent functions such as automatic control, remote monitoring and fault diagnosis. These diesel generator sets are usually used in scenarios such as power supply, emergency power supply and remote regional power supply. During operation, they will generate a lot of noise, which will affect the surrounding environment and human health. Therefore, noise reduction of intelligent computing center diesel generator sets has become an urgent problem to be solved.

[0056] In some embodiments, the intelligent computing center's diesel generator set is started to obtain the variable-diameter airflow rate at the variable-diameter point of the exhaust pipe. The variable-diameter airflow rate sequence is determined by the timestamp order. Specifically, this can be done in the following way:

[0057] Start the diesel generator set in the intelligent computing center and collect the variable airflow rate at the diameter change point of the exhaust pipe through the airflow rate sensor;

[0058] The collected variable-diameter airflow velocities are arranged in order of timestamps to obtain a variable-diameter airflow velocity sequence.

[0059] In practice, an airflow rate sensor is installed at the diameter change point of the exhaust pipe to obtain the airflow rate at the diameter change point. The airflow rate is sampled at equal time intervals, and the collected airflow rates are arranged in time stamp order to obtain a flow rate sequence. For example, the flow rates obtained from timestamps 08:00:00, 08:15:00, 08:30:00, 08:45:00, and 09:00:00 are 10.5 m / s, 11.2 m / s, 10.8 m / s, 10.3 m / s, and 11.0 m / s, respectively. The flow rate sequence obtained by arranging the flow rates in time stamp order is [10.5, 11.2, 10.8, 10.3, 11.0]. This will not be elaborated further here.

[0060] It should be noted that the variable-diameter airflow rate can also be obtained by other airflow measurement devices, such as hot-wire anemometers and ultrasonic anemometers. These devices can sense the movement of airflow at the variable-diameter section of the ventilation duct during the operation of the diesel generator set in the intelligent computing center through sensors or probes, thereby realizing real-time acquisition of the airflow rate. In addition, it should be noted that obtaining the variable-diameter airflow rate may involve some fluid dynamics calculations, including parameters such as airflow cross-sectional area, airflow density, and flow velocity. Specifically, in the embodiments of this application, obtaining the variable-diameter airflow rate can help monitor the performance of the exhaust system, detect anomalies, and analyze the noise generation at the exhaust duct.

[0061] It should also be noted that the variable-diameter airflow rate in this application refers to the value obtained by measuring the airflow velocity at the variable-diameter point of the exhaust pipe during the operation of the diesel generator set. The airflow velocity value at the variable-diameter point of the exhaust pipe is an important working parameter that can be used to evaluate the operating status of the exhaust system and determine whether the airflow is normal or whether there are any abnormalities that lead to an increase in noise.

[0062] In this application, the timestamp order is the chronological order, which refers to arranging data according to the order of time. In the dataset, each data entry is accompanied by a timestamp, indicating the time when the data was recorded. By arranging the data according to the timestamp order, a chronological order can be obtained, thereby enabling better analysis and understanding of the development trend and changes of events.

[0063] In step 102, the variable-diameter airflow rate sequence is converted into an even-variable-diameter airflow rate sequence and an odd-variable-diameter airflow rate sequence. The even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence are then averaged to obtain a stationary even-variable-diameter airflow rate sequence and a stationary odd-variable-diameter airflow rate sequence, respectively.

[0064] In some embodiments, converting the variable-diameter airflow rate sequence into an even-diameter airflow rate sequence and an odd-diameter airflow rate sequence can be achieved in the following ways:

[0065] Obtain all even-numbered variable-diameter airflow velocities in the variable-diameter airflow velocity sequence to obtain multiple even-numbered variable-diameter airflow velocities.

[0066] Arrange the multiple even-diameter airflow velocities in chronological order to obtain an even-diameter airflow velocity sequence.

[0067] Obtain all odd-numbered variable-diameter airflow velocities in the variable-diameter airflow velocity sequence to obtain multiple odd-variable-diameter airflow velocities;

[0068] The multiple odd-path airflow velocities are arranged in chronological order to obtain an odd-path airflow velocity sequence.

[0069] In specific implementation, the even-numbered variable-diameter airflow rate mentioned above is called the even variable-diameter airflow rate, and the odd-numbered variable-diameter airflow rate mentioned above is called the odd variable-diameter airflow rate. The even variable-diameter airflow rate sequence is obtained by arranging multiple even variable-diameter airflow rates in chronological order, and the odd variable-diameter airflow rate sequence is obtained by arranging multiple odd variable-diameter airflow rates in chronological order. These details will not be elaborated here.

[0070] It should be noted that obtaining the variable-diameter airflow velocity sequence at the diameter change point of the exhaust pipe may be affected by a variety of factors. At the diameter change point, the airflow may be subject to certain disturbances and pressure changes. Turbulence may occur in the airflow at the diameter change point, leading to the refraction and reflection of sound waves, which ultimately increases noise. Some of these factors may have periodic variations, while others may have random variations. Dividing the variable-diameter airflow velocity sequence into even-diameter and odd-diameter airflow velocity sequences helps to separate these different types of variations and better understand noise and periodic variations.

[0071] In some embodiments, the even-path flow rate sequence and the odd-path flow rate sequence are averaged to obtain the stationary even-path flow rate sequence and the stationary odd-path flow rate sequence, respectively. Specifically, this can be achieved in the following manner:

[0072] The mean value of the even-diameter airflow rate sequence is determined to obtain the even-diameter airflow rate mean value, wherein the even-diameter airflow rate sequence includes multiple even-diameter airflow rates;

[0073] The residual between each even-diameter airflow rate and the mean even-diameter airflow rate is calculated to obtain all the stationary even-diameter airflow rates.

[0074] All stationary even-diameter airflow velocities are processed to be non-negative and combined in chronological order to obtain a sequence of stationary even-diameter airflow velocities.

[0075] The mean value of the odd-path flow rate sequence is determined to obtain the odd-path flow rate mean value, wherein the odd-path flow rate sequence includes multiple odd-path flow rates;

[0076] The residual between each odd-path airflow rate and the mean odd-path airflow rate is calculated to obtain all the stationary odd-path airflow rates.

[0077] All the stationary odd-path airflow velocities are processed to be non-negative and combined in chronological order to obtain the stationary odd-path airflow velocity sequence.

[0078] In practice, the stationary even-path variable airflow rate and the stationary odd-path variable airflow rate are processed to be non-negative, that is, the absolute values ​​of the stationary even-path variable airflow rate and the stationary odd-path variable airflow rate are taken to make all data non-negative, so as to improve the distribution characteristics of the data and make subsequent processing more effective.

[0079] It should be noted that the purpose of residual calculation is to eliminate the overall trend change of variable diameter airflow rate in the even and odd variable diameter airflow rate sequences, so as to obtain stationary even and odd variable diameter airflow rate sequences. This highlights the short-term fluctuations in the variable diameter airflow rate data, allowing for a more accurate analysis and understanding of the data's variation patterns, and thus better noise detection. In addition, the stationary even and odd variable diameter airflow rates are sequences obtained by averaging and residual calculation of the even and odd variable diameter airflow rates in the original variable diameter airflow rate sequence. Their purpose is to highlight the periodic changes and short-term fluctuations of the variable diameter airflow rate data, and reduce the influence of sound wave refraction and reflection on the variable diameter airflow rate.

[0080] In step 103, the stationary even-path variable airflow rate sequence and the stationary odd-path variable airflow rate sequence are respectively subjected to cross-anomaly point traversal to obtain the even-path variable airflow rate sequence and the odd-path variable airflow rate sequence.

[0081] In some embodiments, the cross-anomaly point traversal of the stationary even-path variable airflow velocity sequence in this application to obtain the even-path variable airflow velocity sequence can be specifically performed in the following manner:

[0082] Determine the number of positions of the stationary even-diameter airflow rate traversed in the stationary even-diameter airflow rate sequence. ;

[0083] Obtain the total number of stationary even-diameter airflow velocities in the stationary even-diameter airflow velocity sequence. ;

[0084] Determine the first in the stationary even-diameter flow rate sequence a steady-state even-diameter airflow rate ;

[0085] Determine the lumped quantity of the stationary even-diameter airflow rate in the stationary even-diameter airflow rate sequence. ;

[0086] Determine the crossover ergodic factor for a stationary even-path flow rate sequence ;

[0087] Based on the number of positions of the stationary even-diameter airflow rates traversed in the stationary even-diameter airflow rate sequence The total number of stationary even-diameter airflow velocities in the stationary even-diameter airflow velocity sequence The first in the stationary even-diameter airflow velocity sequence a steady-state even-diameter airflow rate The lumped quantity of the stationary even-diameter airflow rate in the stationary even-diameter airflow rate sequence and the crossover point ergodic factor of the stationary even-diameter airflow rate sequence The even-variable-diameter flow rate of the steady-state flow rate at each location is determined by the following formula:

[0088]

[0089] in, In the sequence of stationary even-diameter flow rates, the first... The even-anomaly variable airflow velocity corresponding to the position of a stationary even-diameter variable airflow velocity. This represents the position number of the stationary even-diameter airflow rate traversed in the stationary even-diameter airflow rate sequence. This represents the total number of stationary even-diameter airflow rates in a stationary even-diameter airflow rate sequence. The first in the steady-state variable diameter airflow velocity sequence The stationary even-diameter airflow rate is represented by the lumped quantity of the stationary even-diameter airflow rate in the sequence of stationary even-diameter airflow rates. The crossover point ergodic factor represents the rate sequence of stationary even-path flow.

[0090] The even-variable flow rate of the even-variable flow rate at each location is combined to obtain the even-variable flow rate sequence.

[0091] Similarly, in this application, the cross-anomaly point traversal of the stationary odd-path airflow velocity sequence can be used to obtain the corresponding odd-path airflow velocity sequence in the following manner:

[0092] Determine the number of positions of the stationary odd-path flow rates traversed in the stationary odd-path flow rate sequence. ;

[0093] Obtain the total number of stationary odd-path airflow velocities in the stationary odd-path airflow velocity sequence. ;

[0094] Determine the first in the stationary odd-path flow rate sequence A steady, odd-variable airflow rate ;

[0095] Determine the lumped quantity of stationary odd-path flow rates in a stationary odd-path flow rate sequence. ;

[0096] Determine the cross-anomaly ergodic factor for a stationary odd-path airflow rate sequence ;

[0097] Based on the number of positions of the stationary odd-path airflow rates traversed in the stationary odd-path airflow rate sequence The total number of stationary odd-path airflow velocities in the stationary odd-path airflow velocity sequence The first in the steady odd-path airflow velocity sequence A steady, odd-variable airflow rate The lumped quantity of the stationary odd-path airflow rate in the stationary odd-path airflow rate sequence and the cross-anomaly ergodic factor of the steady odd-path airflow rate sequence Determine the singular dynamic variable flow rate of the stable odd variable flow rate at each location;

[0098] In practice, the singular dynamic variable-diameter airflow speed is determined by the following formula:

[0099]

[0100] in, In the sequence of stationary odd-path airflow rates, the first... The singular dynamic variable flow rate corresponding to the position of a stationary odd variable flow rate This represents the number of positions of the stationary odd-path flow rate traversed in the stationary odd-path flow rate sequence. This represents the total number of stationary odd-path flow rates in a stationary odd-path flow rate sequence. The first in the steady odd-path flow rate sequence A steady, odd-variable airflow rate The lumped quantity represents the stationary odd-path flow rate in a stationary odd-path flow rate sequence. The cross-anomaly ergodic factor represents the rate sequence of stationary odd-path airflow.

[0101] The singular dynamic variable flow rate sequence is obtained by combining the stable singular variable flow rate at each location.

[0102] In practice, the determination of the position number of the stationary even-path airflow rate traversed in the above stationary even-path airflow rate sequence and the position number of the stationary odd-path airflow rate traversed in the stationary odd-path airflow rate sequence is to start counting from the starting element in the sequence, one by one, until the target element is reached. For example, in a sequence: [10, 25, 15, 30, 20], to determine the position number of element 30, we start counting from the starting element of the sequence, and after passing through 3 elements, we reach the target element 30, so the position number is 4.

[0103] In specific implementation, in this embodiment, the central tendency of the stationary even-path airflow rate in the aforementioned stationary even-path airflow rate sequence is determined by calculating the average value of the stationary even-path airflow rate in the sequence. That is, the average value of the stationary even-path airflow rate is used as the central tendency. In this application, the central tendency of the stationary even-path airflow rate in the stationary even-path airflow rate sequence is used to characterize the central trend of the data. Furthermore, the central tendency of the stationary even-path airflow rate in the stationary even-path airflow rate sequence can also be calculated using the median, mode, weighted average, geometric mean, or harmonic mean. Without limitation, the central tendency of the steady-state odd-path airflow rate in the above-mentioned steady-state odd-path airflow rate sequence is determined by calculating the average value of the steady-state odd-path airflow rate in the steady-state odd-path airflow rate sequence. That is, the average value of the steady-state odd-path airflow rate is used as the central tendency of the steady-state odd-path airflow rate. In this application, the central tendency of the steady-state odd-path airflow rate in the steady-state odd-path airflow rate sequence is used to characterize the central trend of the data. In addition, the central tendency of the steady-state odd-path airflow rate in the steady-state odd-path airflow rate sequence can also be obtained by the median, mode, weighted average, geometric mean, harmonic mean, etc., without limitation.

[0104] Furthermore, it should be noted that the cross-anomaly traversal factor for both the stationary even-path variable airflow rate sequence and the stationary odd-path variable airflow rate sequence in this application is a constant value between 0 and 1. The specific value can be selected based on the characteristics of the stationary even-path variable airflow rate data in the stationary even-path variable airflow rate sequence and the stationary odd-path variable airflow rate data in the stationary odd-path variable airflow rate sequence, such as data distribution and trends. If the data is relatively stable within a certain range, a lower cross-anomaly traversal factor can be given; conversely, a higher cross-anomaly traversal factor should be given. A larger cross-anomaly traversal factor will be more sensitive in identifying anomalies, while a smaller cross-anomaly traversal factor may be more stable. The selection of an appropriate cross-anomaly traversal factor needs to be adjusted and verified in practical applications to achieve the goal of accurately identifying anomalies. Through the cross-anomaly traversal factor, anomalies in the stationary even-path variable airflow rate sequence and the stationary odd-path variable airflow rate sequence in the diesel generator set can be effectively identified, providing valuable information for subsequent analysis and processing.

[0105] In this application, the even-anomaly variable flow rate sequence refers to a sequence obtained by combining each even-anomaly variable flow rate obtained after traversing the cross-anomaly point of each stable even-anomaly variable flow rate in the stable even-anomaly variable flow rate sequence. The even-anomaly variable flow rate is a measure used to characterize the anomaly of the stable even-anomaly variable flow rate in the stable even-anomaly variable flow rate sequence. The odd-anomaly variable flow rate sequence refers to a sequence obtained by combining each odd-anomaly variable flow rate obtained after traversing the cross-anomaly point of each stable odd-anomaly variable flow rate in the stable odd-anomaly variable flow rate sequence. The odd-anomaly variable flow rate is a measure used to characterize the anomaly of the stable odd-anomaly variable flow rate in the stable odd-anomaly variable flow rate sequence.

[0106] In this application, the cross-anomaly point traversal refers to simultaneously traversing the stationary even-diameter variable-diameter airflow rate sequence and the stationary odd-diameter variable-diameter airflow rate sequence, obtaining the anomaly metric value for each data point, and extracting possible anomaly points based on the metric value. In specific implementation, the above formula is used for traversal to find possible anomalies or abrupt changes. Cross-anomaly point traversal plays an important role in detecting and analyzing anomalies in variable-diameter airflow rate sequences, as well as in realizing generator set condition monitoring and fault prediction. These anomalies may represent abrupt changes or abnormal conditions in the generator set's operating state, leading to an increase in noise.

[0107] In step 104, excess data is filtered out from the even-variable-diameter airflow velocity sequence and the odd-variable-diameter airflow velocity sequence to obtain the first excess variable-diameter airflow value and the second excess variable-diameter airflow value. Then, the excess noise value is determined from the first excess variable-diameter airflow value and the second excess variable-diameter airflow value.

[0108] In some embodiments, the excess data filtering of the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excess flow velocity value and the second excess flow velocity value can be performed in the following manner:

[0109] Determine the harmonic values ​​of the even-moving variable-diameter gas velocity sequence and the singular-moving variable-diameter gas velocity sequence;

[0110] Determine the volatility of the even-moving variable-diameter gas velocity sequence and the volatility of the singular-moving variable-diameter gas velocity sequence;

[0111] Set the even-anomaly multiple threshold for the even-anomaly variable flow rate sequence and the odd-anomaly multiple threshold for the odd-anomaly variable flow rate sequence;

[0112] The even excess data screening conditions for even abnormal variable flow rate in the even abnormal variable flow rate sequence are determined based on the harmonic value of the even abnormal variable flow rate sequence, the volatility of the even abnormal variable flow rate sequence, and the even abnormal multiple threshold.

[0113] The criteria for filtering singular excess data of singular dynamic variable flow rate in the singular dynamic variable flow rate sequence are determined based on the harmonic value of the singular dynamic variable flow rate sequence, the volatility of the singular dynamic variable flow rate sequence, and the singular dynamic multiple threshold.

[0114] The even-abnormal variable-diameter airflow intensity in the even-abnormal variable-diameter airflow intensity sequence is filtered according to the even-abnormal data filtering conditions, and the filtered even-abnormal variable-diameter airflow intensity is centralized to obtain the first excess variable-diameter airflow value.

[0115] The singular dynamic variable flow rate in the singular dynamic variable flow rate sequence is filtered according to the singular excess data filtering conditions, and the filtered singular dynamic variable flow rate is centralized to obtain the second excess variable flow rate value.

[0116] In practice, the even excess data filtering conditions can be determined by the following formula:

[0117]

[0118] in, The harmonic value representing the velocity sequence of even-moving variable-radius airflow. This represents the volatility of an even-moving variable-path flow velocity sequence. Indicates the threshold for even-numbered multiples of change. The first of the even-moving variable-diameter flow rate sequence Individual variable flow rate.

[0119] In practice, the criteria for filtering the odd excess data can be determined by the following formula:

[0120]

[0121] in, The harmonic value representing the velocity sequence of singular variable-path flow. This represents the volatility of a singular dynamic variable-path flow velocity sequence. Indicates the threshold for singular motion multiples. The first of the singular dynamic variable-path flow rate sequences A unique dynamic variable diameter airflow.

[0122] In specific implementation, this application determines the harmonic value of the even-variable flow velocity sequence and the harmonic value of the odd-variable flow velocity sequence. Taking the harmonic value of the even-variable flow velocity sequence as an example, it is obtained by taking the reciprocal of each even-variable flow velocity in the even-variable flow velocity sequence, averaging the reciprocals of each even-variable flow velocity, and then differentiating the result. The harmonic value of the even-variable flow velocity sequence can be determined by the following formula:

[0123]

[0124] in, The harmonic value representing the velocity sequence of even-moving variable-radius airflow. This represents the total number of even-moving variable-diameter gas speeds in the even-moving variable-diameter gas speed sequence. In the even-moving variable-diameter flow rate sequence, the first... Individual variable flow rate.

[0125] It should be noted that the harmonic values ​​of the singular variable-diameter flow rate sequence are determined in the same way as those of the even variable-diameter flow rate sequence, and will not be repeated here. The harmonic values ​​are used to provide a more accurate average value to solve the problem of uneven data distribution.

[0126] In practice, the volatility of the even-variable-diameter flow rate sequence is determined by calculating the standard deviation of the even-variable-diameter flow rate in the even-variable-diameter flow rate sequence. That is, the standard deviation of the even-variable-diameter flow rate is used as the volatility of the even-variable-diameter flow rate sequence. The volatility of the odd-variable-diameter flow rate sequence is determined by calculating the standard deviation of the odd-variable-diameter flow rate in the odd-variable-diameter flow rate sequence. That is, the standard deviation of the odd-variable-diameter flow rate is used as the volatility of the odd-variable-diameter flow rate sequence. This will not be elaborated further here.

[0127] It should be noted that in this embodiment, the value 3 is selected as the even / odd multiple threshold. The set multiple threshold is a parameter used to determine whether a data point is an outlier. It means that when determining whether a data point is outlier, the data point is compared with the mean of the dataset. If the difference between the data point and the mean exceeds the set multiple threshold, then the data point is considered an outlier. Specifically, the set multiple threshold is a multiplier, which is usually used to amplify or reduce the difference between the data point and the mean to adapt to different situations and needs. The specific value of the threshold can be determined according to the requirements of the actual application. It is usually selected based on experience or domain knowledge. For example, a multiple threshold of 3 means that if the difference between a data point and the mean exceeds 3 times the standard deviation, then it will be considered an outlier. The choice of this threshold can be adjusted according to the characteristics of the dataset and the sensitivity of outliers. If you want to identify outliers more strictly, you can choose a larger multiple threshold, and vice versa.

[0128] It should be noted that in this application, excessive data filtering refers to filtering out outliers or noise from the dataset to ensure the quality and reliability of the data. The purpose of excessive data filtering is to remove noise and outliers. In the embodiments of this application, excessive data filtering is mainly used to filter out values ​​with more prominent changes in even-variable flow velocity in the even-variable flow velocity sequence and in the odd-variable flow velocity sequence, so as to better perform subsequent analysis.

[0129] In some embodiments, the average value between the first excess flow rate and the second excess flow rate is determined, thereby obtaining the excess noise value. It should be noted that the excess noise value is obtained by performing the above series of operations based on the flow rate at the diameter change point of the exhaust pipe, and is used to quantify the noise at the diameter change point of the exhaust pipe.

[0130] In step 105, the excessive noise value is compared with the preset excessive noise value. When the excessive noise value is greater than the preset excessive noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

[0131] In some embodiments, the excessive noise value is compared with a preset excessive noise value. When the excessive noise value is greater than the preset excessive noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

[0132] In practice, a threshold for excessive noise is preset. This threshold is set based on factors such as environmental requirements and noise standards and is used for judgment. The obtained excessive noise value is compared with the preset excessive noise value. If the excessive noise value is greater than the preset value, it means that the noise generated at the diameter change of the exhaust pipe exceeds the expectation and may have an adverse effect on the environment or equipment. When it is determined that the excessive noise value is greater than the preset value, measures such as humidification and flow reduction can be taken. This can be achieved by increasing the air humidity and adjusting the airflow velocity in the exhaust pipe. Specifically, a heat exchanger is installed at the diameter change of the exhaust pipe. The heat from the exhaust is transferred to the medium flowing through the heat exchanger (such as water or air) to achieve the purpose of humidifying and cooling the exhaust airflow. Humidification can change the acoustic characteristics of the airflow, thereby reducing the propagation and generation of noise.

[0133] In another aspect, in some embodiments, this application provides a noise reduction device for a diesel generator set in a smart computing center, with reference to... Figure 2 The figure is a schematic diagram of exemplary hardware and / or software of a noise reduction device for a diesel generator set in a smart computing center, according to some embodiments of this application. The noise reduction device 200 for the diesel generator set includes: a variable-diameter airflow rate acquisition module 201, a steady-state odd-even variable-diameter airflow rate determination module 202, an odd-even abnormal variable-diameter airflow rate determination module 203, an excess noise value determination module 204, and a noise reduction module 205, which are described below:

[0134] The variable diameter airflow rate acquisition module 201 in this application mainly starts the diesel generator set of the intelligent computing center to collect the variable diameter airflow rate at the variable diameter point of the exhaust pipe and determines the variable diameter airflow rate sequence by the timestamp order.

[0135] The steady odd-even variable diameter airflow rate determination module 202 in this application is mainly used to convert the variable diameter airflow rate sequence into an even variable diameter airflow rate sequence and an odd variable diameter airflow rate sequence, and to average the even variable diameter airflow rate sequence and the odd variable diameter airflow rate sequence respectively to obtain a steady even variable diameter airflow rate sequence and a steady odd variable diameter airflow rate sequence.

[0136] The odd-even variable-path airflow speed determination module 203 in this application is mainly used to perform cross-variation point traversal on the steady even-variation airflow speed sequence and the steady odd-variation airflow speed sequence respectively, so as to obtain the even-variation airflow speed sequence and the odd-variation airflow speed sequence.

[0137] The excessive noise value determination module 204 in this application is mainly used to perform excessive data filtering on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excessive flow velocity value and the second excessive flow velocity value, and then determine the excessive noise value from the first excessive flow velocity value and the second excessive flow velocity value.

[0138] The noise reduction module 205 in this application is mainly used to compare the excess noise value with the preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are performed at the diameter change of the exhaust pipe to reduce noise.

[0139] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to acquire the code and execute the above-described noise reduction method for a smart computing center diesel generator set.

[0140] In some embodiments, reference Figure 3 The figure is a schematic diagram of the structure of a computer device applying a noise reduction method for a smart computing center diesel generator set, according to some embodiments of this application. The noise reduction method for the smart computing center diesel generator set in the above embodiments can... Figure 3 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0141] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (AIC), or one or more for controlling the execution of the noise reduction method of the intelligent computing center diesel generator set in this application.

[0142] The communication bus 302 may include a path for transmitting information between the aforementioned components.

[0143] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or it may be an electrically erasable programmable read-only memory (EEPROM), a compact dic read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via a communication bus 302. The memory 303 may also be integrated with the processor 301.

[0144] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the excessive noise value can be achieved by the processor 301 and one or more software modules in the program code in the memory 303.

[0145] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0146] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (independent CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0147] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital device (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0148] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for reducing the noise of a diesel generator set in a smart computing center.

[0149] In summary, the noise reduction method and apparatus for a diesel generator set in a smart computing center disclosed in this application first starts the diesel generator set in the smart computing center, collects the variable-diameter airflow rate at the variable-diameter section of the exhaust pipe, and determines the variable-diameter airflow rate sequence by timestamp order; the variable-diameter airflow rate sequence is converted into an even variable-diameter airflow rate sequence and an odd variable-diameter airflow rate sequence, and the even variable-diameter airflow rate sequence and the odd variable-diameter airflow rate sequence are mean-averaged respectively to obtain a stationary even variable-diameter airflow rate sequence and a stationary odd variable-diameter airflow rate sequence; the stationary even variable-diameter airflow rate sequence and the stationary odd variable-diameter airflow rate sequence are mean-averaged respectively. The variable-diameter airflow velocity sequence is traversed through cross-anomaly points to obtain even-anomaly variable-diameter airflow velocity sequences and odd-anomaly variable-diameter airflow velocity sequences. Excess data is filtered from the even-anomaly variable-diameter airflow velocity sequences and the odd-anomaly variable-diameter airflow velocity sequences to obtain a first excess variable-diameter airflow value and a second excess variable-diameter airflow value. Then, an excess noise value is determined from the first excess variable-diameter airflow value and the second excess variable-diameter airflow value. The excess noise value is compared with a preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are applied to the variable-diameter section of the exhaust pipe to reduce noise.

[0150] In this application, the variable-diameter airflow rate is first obtained to determine whether the airflow is normal and whether there are any abnormalities that cause an increase in noise. By obtaining the variable-diameter airflow rate, the relationship between noise and the variable-diameter airflow rate is further analyzed. Secondly, the even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence are averaged to eliminate the overall trend changes in the even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence, so as to obtain the stationary even-variable-diameter airflow rate sequence and the stationary odd-variable-diameter airflow rate sequence. This is to highlight the short-term fluctuations in the variable-diameter airflow rate data, and to more accurately analyze and understand the data change patterns, thereby better detecting noise. Then, the stationary even-variable-diameter airflow rate sequence and the stationary odd-variable-diameter airflow rate sequence are respectively traversed for cross-anomaly points to find abnormal points or abrupt changes in noise caused by changes in the variable-diameter airflow rate. Finally, by determining the excess noise value, the excess noise value is compared with the preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are performed at the diameter change of the exhaust pipe to reduce the generation of noise.

[0151] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0152] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for reducing noise from a diesel generator set in a smart computing center, characterized in that, Includes the following steps: Start the diesel generator set in the intelligent computing center, collect the variable-diameter airflow rate at the variable-diameter point of the exhaust pipe, and determine the variable-diameter airflow rate sequence by timestamp order; The variable-diameter airflow rate sequence is converted into an even variable-diameter airflow rate sequence and an odd variable-diameter airflow rate sequence. The even variable-diameter airflow rate sequence and the odd variable-diameter airflow rate sequence are mean-valued respectively to obtain a stationary even variable-diameter airflow rate sequence and a stationary odd variable-diameter airflow rate sequence. By performing cross-anomaly point traversal on the stationary even-path variable airflow rate sequence and the stationary odd-path variable airflow rate sequence respectively, the corresponding even-anomaly variable airflow rate sequence and odd-anomaly variable airflow rate sequence are obtained. Excess data screening is performed on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excess flow velocity value and the second excess flow velocity value, and then the excess noise value is determined by the first excess flow velocity value and the second excess flow velocity value. The excessive noise value is compared with the preset excessive noise value. When the excessive noise value is greater than the preset excessive noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

2. The method as described in claim 1, characterized in that, The airflow rate at the diameter change point of the exhaust pipe is collected by an airflow rate sensor.

3. The method as described in claim 1, characterized in that, The variable-diameter airflow rate is sampled at equal time intervals.

4. The method as described in claim 1, characterized in that, Converting the variable-diameter airflow rate sequence into an even-variable-diameter airflow rate sequence and an odd-variable-diameter airflow rate sequence specifically includes: Obtain all even-numbered variable-diameter airflow velocities in the variable-diameter airflow velocity sequence to obtain multiple even-numbered variable-diameter airflow velocities. Arrange the multiple even-diameter airflow velocities in chronological order to obtain an even-diameter airflow velocity sequence. Obtain all odd-numbered variable-diameter airflow velocities in the variable-diameter airflow velocity sequence to obtain multiple odd-variable-diameter airflow velocities; The multiple odd-path airflow velocities are arranged in chronological order to obtain an odd-path airflow velocity sequence.

5. The method as described in claim 1, characterized in that, The mean values ​​for the even-path flow rate sequences and the odd-path flow rate sequences are respectively obtained by averaging them to obtain the stationary even-path flow rate sequences and the stationary odd-path flow rate sequences, specifically including: The mean value of the even-diameter airflow rate sequence is determined to obtain the even-diameter airflow rate mean value, wherein the even-diameter airflow rate sequence includes multiple even-diameter airflow rates; The residual between each even-diameter airflow rate and the mean even-diameter airflow rate is calculated to obtain all the stationary even-diameter airflow rates. All stationary even-diameter airflow velocities are non-negative and arranged in chronological order to obtain a sequence of stationary even-diameter airflow velocities. The mean value of the odd-path flow rate sequence is determined to obtain the odd-path flow rate mean value, wherein the odd-path flow rate sequence includes multiple odd-path flow rates; The residual between each odd-path airflow rate and the mean odd-path airflow rate is calculated to obtain all the stationary odd-path airflow rates. All the stationary odd-path airflow velocities are processed to be non-negative and arranged in chronological order to obtain the stationary odd-path airflow velocity sequence.

6. The method as described in claim 1, characterized in that, The excess data filtering performed on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excess flow velocity value and the second excess flow velocity value specifically includes: Determine the harmonic values ​​of the even-moving variable-diameter gas velocity sequence and the singular-moving variable-diameter gas velocity sequence; Determine the volatility of the even-moving variable-diameter gas velocity sequence and the volatility of the singular-moving variable-diameter gas velocity sequence; Set the even-anomaly multiple threshold for the even-anomaly variable flow rate sequence and the odd-anomaly multiple threshold for the odd-anomaly variable flow rate sequence; The even excess data screening conditions for even abnormal variable flow rate in the even abnormal variable flow rate sequence are determined based on the harmonic value of the even abnormal variable flow rate sequence, the volatility of the even abnormal variable flow rate sequence, and the even abnormal multiple threshold. The criteria for filtering singular excess data of singular dynamic variable flow rate in the singular dynamic variable flow rate sequence are determined based on the harmonic value of the singular dynamic variable flow rate sequence, the volatility of the singular dynamic variable flow rate sequence, and the singular dynamic multiple threshold. The even-abnormal variable-diameter airflow intensity in the even-abnormal variable-diameter airflow intensity sequence is filtered according to the even-abnormal data filtering conditions, and the filtered even-abnormal variable-diameter airflow intensity is centralized to obtain the first excess variable-diameter airflow value. The singular dynamic variable flow rate in the singular dynamic variable flow rate sequence is filtered according to the singular excess data filtering conditions, and the filtered singular dynamic variable flow rate is centralized to obtain the second excess variable flow rate value.

7. The method as described in claim 6, characterized in that, The screening criteria for even excess data are determined by the following formula: in, The harmonic value representing the velocity sequence of even-moving variable-radius airflow. This represents the volatility of an even-moving variable-path flow velocity sequence. Indicates the threshold for even-numbered multiples of change. The first of the even-moving variable-diameter flow rate sequence Individual variable flow rate.

8. A noise reduction device for a diesel generator set in a smart computing center, characterized in that, include: The variable-diameter airflow rate acquisition module is used to start the diesel generator set of the intelligent computing center, acquire the variable-diameter airflow rate at the variable-diameter point of the exhaust pipe, and determine the variable-diameter airflow rate sequence by timestamp order. A steady-state even-odd-variable-diameter airflow rate determination module is used to convert the variable-diameter airflow rate sequence into an even-variable-diameter airflow rate sequence and an odd-variable-diameter airflow rate sequence, and to average the even-variable-diameter airflow rate sequence and the odd-variable-diameter airflow rate sequence respectively, thereby obtaining a steady-state even-variable-diameter airflow rate sequence and a steady-state odd-variable-diameter airflow rate sequence. The odd-even variable-path airflow speed determination module is used to perform cross-variation point traversal on the stationary even-variable-path airflow speed sequence and the stationary odd-variable-path airflow speed sequence respectively, and obtain the even-variable-path airflow speed sequence and the odd-variable-path airflow speed sequence accordingly. The excessive noise value determination module is used to perform excessive data filtering on the even-variable flow velocity sequence and the odd-variable flow velocity sequence to obtain the first excessive flow velocity value and the second excessive flow velocity value, and then determine the excessive noise value from the first excessive flow velocity value and the second excessive flow velocity value. The noise reduction module is used to compare the excess noise value with a preset excess noise value. When the excess noise value is greater than the preset excess noise value, humidification and flow reduction are applied to the diameter change point of the exhaust pipe to reduce noise.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the noise reduction method for a diesel generator set in a smart computing center as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the noise reduction method for the diesel generator set of the intelligent computing center as described in any one of claims 1 to 7.

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