Silencing scheme generation system for aeration fan of power plant

Abstract

The invention discloses a noise elimination scheme generation system for an aeration fan of a power plant. The noise elimination scheme generation system comprises an input module, a processing module, an output module and a control module. When the method is implemented, fan parameters, environmental data and an acoustic model can be automatically integrated, a customized silencing scheme can be quickly generated, the system optimizes a design process by using an intelligent algorithm, the scheme accuracy and the generation efficiency are remarkably improved, meanwhile, the dependence on professional experience is reduced, quick generation and dynamic adjustment of the silencing scheme are realized, and the method is suitable for popularization and application. The operation economy and environmental friendliness of the power plant are improved; through cooperative work of the modules, the adaptability and expandability of the system are enhanced, and the system is suitable for various fan types and complex working conditions; in addition, the system can reduce manual intervention, reduce design cost and improve reliability and durability of a silencing scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer systems, in particular to a noise reduction scheme generation system for power plant aeration fans. BACKGROUND

[0002] Power plant aeration fans generate high-intensity noise during operation, which not only affects the working environment, but also may adversely affect equipment life and personnel health. In the prior art, noise reduction schemes usually rely on manual experience or simple simulation software, lacking systematic generation methods, resulting in low design efficiency, poor adaptability, and difficulty in optimizing different fan types and working conditions. In addition, traditional methods often ignore multi-factor coupling effects such as airflow characteristics, structural vibration, and acoustic propagation paths, making the noise reduction effect unstable and the cost high. SUMMARY

[0003] Therefore, the present application provides a noise reduction scheme generation system for power plant aeration fans to solve the problem of unstable noise reduction effect and high cost in the prior art.

[0004] To achieve the above purpose, the present application provides the following technical scheme:

[0005] A noise reduction scheme generation system for power plant aeration fans, comprising an input module, a processing module, an output module and a control module:

[0006] The input module is configured to receive operating parameters, environmental parameters and acoustic demand parameters of the power plant aeration fan, wherein the operating parameters include the type, speed, power and airflow of the fan, the environmental parameters include the installation location, surrounding noise level and temperature conditions, and the acoustic demand parameters include the target noise reduction amount and frequency range. The input module is also configured to verify the validity of the parameters and provide real-time feedback through a user interface;

[0007] The processing module receives data output by the input module and is configured to perform noise reduction scheme generation operations based on the received parameters. The processing module includes a data preprocessing unit, an acoustic analysis unit and a scheme optimization unit. The data preprocessing unit is responsible for normalizing the input parameters and identifying noise sources. The acoustic analysis unit applies an acoustic simulation model to calculate noise propagation characteristics and resonance frequencies. The scheme optimization unit generates multiple candidate noise reduction schemes using a genetic algorithm or a multi-objective optimization algorithm, and scores and ranks the schemes;

[0008] The output module receives data output by the processing module and is configured to display the generated noise reduction scheme in a visual form. The visual form display includes a scheme structure diagram, a material list and a performance prediction report, and also supports exporting the scheme to a standard document format;

[0009] The control module can control the input module, the processing module and the output module respectively, coordinate the data flow and operation sequence among the modules, monitor the system state and provide error handling function;

[0010] Each module communicates through a bus or a network interface to realize data sharing and real-time interaction.

[0011] Preferably, the input module further comprises a parameter database storing historical fan data, standard acoustic parameters and typical environmental configurations, and the input module can automatically retrieve relevant data from the parameter database to assist parameter input and verification; the parameter database also supports user-defined addition and update of data, and ensures information security through data encryption.

[0012] Preferably, the acoustic analysis unit in the processing module further comprises a finite element analysis subunit and a boundary element analysis subunit, the finite element analysis subunit is used to simulate the vibration of the fan structure and the sound field distribution, and the boundary element analysis subunit is used to calculate the propagation and reflection of noise in open space; the acoustic analysis unit is also configured to combine a machine learning model, optimize the acoustic simulation accuracy according to historical data, and adjust the model parameters in real time to cope with dynamic working condition changes.

[0013] Preferably, the output module further comprises a scheme comparison unit for parallel display and comparative analysis of multiple candidate noise elimination schemes, and provides a comprehensive evaluation report based on cost, effect and implementation difficulty; the output module is also integrated with a virtual reality interface, allowing users to preview the actual effect of the noise elimination scheme through three-dimensional visualization and interactively modify it.

[0014] Preferably, the scheme optimization unit in the processing module further comprises a sensitivity analysis subunit for evaluating the influence of parameter changes in the noise elimination scheme on the overall effect, and performing uncertainty analysis through Monte Carlo simulation; the scheme optimization unit is also configured to be connected with an external sensor network to obtain fan operation data in real time for dynamic adjustment of the scheme.

[0015] Preferably, the control module further comprises a log recording unit and a performance monitoring unit, the log recording unit is used to record system operation history, parameter changes and error events, the performance monitoring unit tracks the running state and resource usage of each module in real time, and prompts potential faults through an early warning mechanism; the control module also supports remote access function, allowing authorized users to configure and maintain the system through the Internet.

[0016] Preferably, the scheme optimization unit in the processing module is further integrated with a cost evaluation subunit for calculating the material cost, installation cost and maintenance cost of each candidate silencing scheme, and optimizing and screening in combination with the power plant budget constraint; the cost evaluation subunit can also dynamically update the cost parameters according to market data, and provide an economic analysis report.

[0017] Preferably, the scheme comparison unit works in cooperation with the cost evaluation subunit of the processing module to generate a comprehensive comparison report integrating the technical performance index and the economic index, and recommend the optimal silencing scheme through a weighted scoring system; the output module is further configured to generate an implementation guide including installation instructions and operation and maintenance suggestions.

[0018] The present application has the following advantages: when the present application is implemented, it can automatically integrate the fan parameters, environmental data and acoustic model, quickly generate customized silencing schemes, the system uses intelligent algorithms to optimize the design process, significantly improves the accuracy and generation efficiency of the schemes, reduces the dependence on professional experience, realizes the rapid generation and dynamic adjustment of the silencing schemes, and improves the operation economy and environmental friendliness of the power plant; through the cooperative work of the modules, the adaptability and scalability of the system are enhanced, and the system is suitable for various fan types and complex working conditions; in addition, the system can reduce manual intervention, reduce design cost, and improve the reliability and durability of the silencing schemes. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more intuitively illustrate the prior art and the present application, exemplary drawings are given below. It should be understood that the specific shapes, structures shown in the drawings should not be regarded as limiting conditions in the implementation of the present application; for example, based on the technical concepts and exemplary drawings disclosed in the present application, those skilled in the art can easily make routine adjustments or further optimizations to the increase / decrease / assignment of certain units (components), specific shapes, positional relationships, connection methods, size ratio relationships, etc.

[0020] Figure 1 A module diagram of a silencing scheme generation system for power plant aeration fans is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0021] The following embodiments of the present application are illustrated by way of specific examples, and other advantages and effects of the present application will be readily appreciated by those skilled in the art upon reading the foregoing description. It is obvious that the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. It should be understood that the embodiments are only used to further explain the present application, and cannot be understood as limiting the scope of protection of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] Please refer to Figure 1 A noise reduction scheme generation system for power plant aeration fans includes an input module, a processing module, an output module, and a control module:

[0023] The input module is configured to receive operating parameters, environmental parameters, and acoustic demand parameters of the power plant aeration fan, wherein the operating parameters include the type, speed, power, and airflow of the fan, the environmental parameters include the installation location, ambient noise level, and temperature conditions, and the acoustic demand parameters include the target noise reduction amount and frequency range. The input module is also configured to verify the validity of the parameters and provide real-time feedback through a user interface.

[0024] The processing module receives data output by the input module and is configured to perform noise reduction scheme generation operations based on the received parameters. The processing module includes a data preprocessing unit, an acoustic analysis unit, and a scheme optimization unit. The data preprocessing unit is responsible for normalizing the input parameters and identifying noise sources. The acoustic analysis unit applies an acoustic simulation model to calculate noise propagation characteristics and resonance frequencies. The scheme optimization unit generates multiple candidate noise reduction schemes using a genetic algorithm or a multi-objective optimization algorithm, and scores and ranks the schemes.

[0025] The output module receives data output by the processing module and is configured to display the generated noise reduction schemes in a visual form, including a scheme structure diagram, a material list, and a performance prediction report. The output module also supports exporting the schemes to standard document formats.

[0026] The control module is capable of controlling the input module, the processing module, and the output module respectively, and is configured to coordinate the data flow and operation sequence between the modules. The control module is also configured to monitor system status and provide error handling functions to ensure stable operation of the system.

[0027] Each module communicates through a bus or a network interface to achieve data sharing and real-time interaction.

[0028] In the implementation of the present scheme, first, the input module collects fan operating parameters, environmental parameters and acoustic demand parameters. This data collection step ensures that the subsequent analysis can accurately match the characteristics of the specific fan and the on-site environment, thereby ensuring that the generated noise reduction scheme has a high degree of pertinence. Subsequently, the processing module performs multi-level analysis on the collected data: the data preprocessing unit normalizes the parameters to eliminate dimensional differences and improve analysis accuracy; the acoustic analysis unit uses an acoustic simulation model to calculate noise propagation characteristics, accurately identifying the main noise sources and resonance points; the scheme optimization unit uses intelligent algorithms to generate multiple candidate schemes and score them, which can quickly filter out technically feasible and superior schemes, greatly improving design efficiency. The output module displays the optimized scheme in a visual form and provides detailed implementation documents, allowing power plant personnel to intuitively understand the scheme content and quickly organize implementation. The control module coordinates the workflow of each module to ensure stable system operation. The entire workflow realizes automated processing from data input to scheme output, not only significantly shortening the time required for traditional manual design, but also avoiding design deviations caused by insufficient human experience through scientific analysis, ultimately generating an optimized scheme that balances technical effectiveness and economic efficiency.

[0029] The input module further comprises a parameter database storing historical fan data, standard acoustic parameters and typical environmental configurations. The input module can automatically retrieve relevant data from the parameter database to assist parameter input and verification; the parameter database also supports user-defined addition and update of data, and ensures information security through data encryption; wherein the integration of the parameter database and the input module enables the system to quickly adapt to different power plant scenarios, improving the accuracy and efficiency of parameter input.

[0030] The parameter database stores a wealth of historical fan data, standard acoustic parameters and typical environmental configurations. When the user inputs basic parameters, the system can automatically retrieve relevant reference data from the database to assist in completing parameter supplementation and verification. For example, when the user inputs the power parameter of a certain type of fan, the system can automatically match the typical noise spectrum characteristics of that type of fan to provide reference values for the user. This design not only reduces the workload of manual input, but more importantly, improves the accuracy of parameter input through data comparison. The database supports users to add new data records according to actual operating experience, and uses encryption technology to protect data security. In practical applications, this function enables the system to accumulate more and more actual operating data, forming a virtuous cycle and continuously optimizing the adaptability and accuracy of the system. Especially when dealing with special working conditions or new types of fans, the reference value of the parameter database is more prominent, which can effectively reduce the risk of scheme deviation caused by missing or incorrect parameters.

[0031] The acoustic analysis unit in the processing module further includes a finite element analysis subunit and a boundary element analysis subunit, the finite element analysis subunit is used for simulating vibration of the fan structure and distribution of an acoustic field, and the boundary element analysis subunit is used for calculating propagation and reflection of noise in an open space; the acoustic analysis unit is further configured to combine a machine learning model, optimize acoustic simulation precision according to historical data, and be capable of adjusting model parameters in real time to cope with dynamic working condition changes; wherein the acoustic analysis unit ensures effectiveness and reliability of the noise elimination scheme in a complex environment through multi-physical field coupling analysis.

[0032] The finite element analysis subunit is specially responsible for simulating vibration characteristics and near-field acoustic field distribution of the fan structure, and is capable of accurately predicting vibration noise of components such as a fan casing and a blade; the boundary element analysis subunit focuses on calculating propagation law of noise in an open space, and considers reflection, diffraction and attenuation effects of sound waves in a complex environment. This analysis mode of division of labor and cooperation can more comprehensively cover the whole process of noise generation and propagation. In actual application, when the system analyzes noise problems of an aerator fan in a power plant, the finite element analysis can accurately identify vibration noise sources at key positions such as an inlet and an outlet of the fan and a casing, and the boundary element analysis can predict propagation paths and distribution characteristics of noise in a power plant workshop. In addition, through learning of historical data by the machine learning model, the system can continuously optimize parameter settings of the simulation model, so that the analysis result is closer to measured data, and this feature is particularly important when facing complex and variable field working conditions.

[0033] The output module further includes a scheme comparison unit, which is used for parallel display and comparative analysis of multiple candidate noise elimination schemes, and provides a comprehensive evaluation report based on cost, effect and implementation difficulty; the output module is further integrated with a virtual reality interface, allowing users to preview actual effects of the noise elimination scheme through three-dimensional visualization and interactively modify; wherein the introduction of the scheme comparison unit and the virtual reality interface enhances user experience and scientificity of scheme decision-making, facilitating power plant operation and maintenance personnel to quickly select an optimal scheme.

[0034] The scheme comparison unit can parallelly display multiple candidate noise elimination schemes generated by the processing module, compare and analyze from multiple dimensions such as noise reduction effect, implementation cost and engineering difficulty, and generate a detailed comparison report. For example, the system can simultaneously provide multiple schemes based on different technical routes such as silencers, soundproof covers and sound absorbers, and clearly list advantages and limitations of each scheme. The virtual reality interface further enhances the immersion of scheme display, and users can intuitively view installation positions, spatial layouts of the noise elimination device in the fan system, and even visual effects in a simulated running state through the VR device. This visual display not only helps non-professionals to understand scheme content, but also can timely find possible space conflicts or installation difficulty problems in the design stage, effectively reducing risks and change costs in the subsequent engineering implementation stage.

[0035] The scheme optimization unit in the processing module also includes a sensitivity analysis subunit for evaluating the influence of changes in each parameter in the noise reduction scheme on the overall effect and conducting uncertainty analysis through Monte Carlo simulation; the scheme optimization unit is also configured to be connected with an external sensor network to obtain real-time fan operation data to dynamically adjust the scheme and ensure the adaptability of the noise reduction scheme in long-term operation; wherein the sensitivity analysis subunit and the real-time data integration function enable the system to cope with power plant operating condition fluctuations and improve the robustness and sustainability of the scheme.

[0036] The sensitivity analysis subunit uses advanced algorithms such as Monte Carlo simulation to evaluate the degree of influence of changes in each design parameter (such as muffler length, sound-absorbing material thickness, etc.) in the noise reduction scheme on the final noise reduction effect. This function can identify the core parameters that are most critical to the scheme's effectiveness, providing a clear direction for scheme optimization. At the same time, the system's connection capability with the external sensor network enables it to obtain real-time operation state data of the fan, and when the fan operating conditions change, the system can timely adjust the relevant parameters of the noise reduction scheme. For example, in actual operation of the power plant, the load of the aeration fan may change with the power generation demand, and the system can dynamically optimize the configuration of the noise reduction scheme by monitoring the fan speed, flow rate, and other parameters in real time, ensuring ideal noise reduction effect under different operating conditions. This adaptive capability greatly extends the effective life of the noise reduction scheme.

[0037] The control module also includes a log recording unit and a performance monitoring unit, the log recording unit is used to record system operation history, parameter changes and error events, the performance monitoring unit tracks the running state and resource usage of each module in real time, and prompts potential faults through the early warning mechanism; the control module also supports remote access function, allowing authorized users to configure and maintain the system through the Internet; wherein the setting of the log recording unit and the performance monitoring unit guarantees the maintainability and security of the system, which is suitable for the high reliability requirement environment of the power plant.

[0038] The log recording unit records in detail the key operations, parameter modifications and system state changes in each scheme generation process, forming a complete operation history archive. When problems occur, operation and maintenance personnel can quickly locate the root cause of the problem by querying the log. The performance monitoring unit tracks the running load, response time and other key indicators of each module in real time, and sends an early warning when an abnormal situation is found. For example, when the calculation time of the processing module is abnormally prolonged, the system will prompt possible hardware or software problems. The addition of remote access function further improves the maintainability of the system, and technical support personnel can diagnose and maintain the system through network connection without going to the scene. These functions are particularly important in environments with high reliability requirements such as power plants, and can maximize the stable operation of the system and reduce project delays caused by system failures.

[0039] The scheme optimization unit in the processing module is also integrated with a cost evaluation subunit for calculating the material cost, installation cost and maintenance cost of each candidate noise elimination scheme, and optimizing and screening in combination with the budget constraints of the power plant; the cost evaluation subunit can also dynamically update the cost parameters according to market data and provide an economic analysis report; wherein the application of the cost evaluation subunit enables the system to consider not only technical effects but also economic feasibility when generating noise elimination schemes, helping the power plant to maximize cost-effectiveness.

[0040] The cost evaluation subunit can calculate in detail the material cost, installation cost and long-term maintenance cost of each noise elimination scheme, and screen the schemes based on the budget constraints of the power plant. For example, when generating noise elimination schemes, the system will consider factors such as price differences of different brands of sound-absorbing materials, labor costs of installation and construction, and maintenance costs during the expected service life. The cost evaluation subunit also has a market data updating function, which can adjust the cost calculation model in a timely manner according to fluctuations in raw material prices. This function has significant value in practical application, helping the power plant to control cost expenditure while meeting noise reduction requirements. Especially in the case of limited budget, the system can recommend the most cost-effective scheme, avoiding resource waste caused by overdesign and truly realizing the unity of technical feasibility and economic rationality.

[0041] The scheme comparison unit of the output module works in coordination with the cost evaluation subunit of the processing module to generate a comprehensive comparison report that integrates technical performance indicators and economic indicators, and recommends the optimal noise elimination scheme through a weighted scoring system; the output module is also configured to generate an implementation guide including step-by-step installation instructions and operation and maintenance suggestions to ensure smooth implementation of the scheme; wherein this coordinated working mechanism improves the practicality and integrity of the system output, enabling power plant personnel to make decisions based on multi-dimensional information, while reducing risks and uncertainties in the implementation process of the scheme.

[0042] Through the collaborative work of the scheme comparison unit and the cost evaluation subunit, multi-dimensional comprehensive evaluation of the noise reduction scheme is realized. This collaborative mechanism can generate a comprehensive comparison report containing technical performance indicators and economic indicators, and recommend the optimal scheme to the user through a scientific weighted scoring system. For example, the system may evaluate multiple indicators such as noise reduction effect, service life, implementation period, total investment cost, operation and maintenance cost, etc. at the same time, and set different weight coefficients according to the specific needs of the power plant. The output module further generates detailed implementation guidelines, including specific installation steps, construction precautions and daily maintenance requirements. In a typical embodiment, when the system generates a noise reduction scheme for the aeration fan of a power plant, not only will it provide detailed design drawings and material lists for the optimal scheme, but also complete construction guidance documents and maintenance plans to ensure that the scheme can be successfully implemented.

[0043] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A noise reduction scheme generation system for aeration blowers in power plants, characterized in that, It includes an input module, a processing module, an output module, and a control module: The input module is used to receive operating parameters, environmental parameters, and acoustic requirement parameters of the power plant aeration blower. The operating parameters include the type, speed, power, and airflow of the blower. The environmental parameters include the installation location, ambient noise level, and temperature conditions. The acoustic requirement parameters include the target noise reduction amount and frequency range. The input module is also configured to verify the validity of the parameters and provide real-time feedback through the user interface. The processing module receives data output from the input module and performs noise reduction scheme generation based on the received parameters. The processing module includes a data preprocessing unit, an acoustic analysis unit, and a scheme optimization unit. The data preprocessing unit is responsible for normalizing the input parameters and identifying noise sources. The acoustic analysis unit uses an acoustic simulation model to calculate noise propagation characteristics and resonant frequencies. The scheme optimization unit uses a genetic algorithm or a multi-objective optimization algorithm to generate multiple candidate noise reduction schemes and scores and ranks the schemes. The output module receives data output by the processing module and uses it to display the generated noise reduction scheme in a visual form. The visual form includes a scheme structure diagram, a bill of materials, and a performance prediction report. It also supports exporting the scheme to a standard document format. The control module can control the input module, processing module and output module respectively, and is used to coordinate the data flow and operation sequence between the modules. The control module is also configured to monitor the system status and provide error handling functions. The modules communicate with each other through a bus or network interface to achieve data sharing and real-time interaction.

2. The noise reduction scheme generation system for power plant aeration blowers according to claim 1, characterized in that, The input module also includes a parameter database, which stores historical wind turbine data, standard acoustic parameters, and typical environmental configurations. The input module can automatically retrieve relevant data from the parameter database to assist in parameter input and verification. The parameter database also supports user-defined addition and update of data, and ensures information security through data encryption.

3. The noise reduction scheme generation system for power plant aeration blowers according to claim 2, characterized in that, The acoustic analysis unit in the processing module also includes a finite element analysis sub-unit and a boundary element analysis sub-unit. The finite element analysis sub-unit is used to simulate the vibration and sound field distribution of the wind turbine structure, and the boundary element analysis sub-unit is used to calculate the propagation and reflection of noise in open space. The acoustic analysis unit is also configured to combine a machine learning model to optimize the acoustic simulation accuracy based on historical data, and to adjust the model parameters in real time to cope with dynamic operating condition changes.

4. The noise reduction scheme generation system for power plant aeration blowers according to claim 3, characterized in that, The output module also includes a scheme comparison unit, which is used to display and compare multiple candidate noise reduction schemes in parallel and provide a comprehensive evaluation report based on cost, effectiveness and implementation difficulty. The output module also integrates a virtual reality interface, allowing users to preview the actual effect of the noise reduction scheme through three-dimensional visualization and make interactive modifications.

5. The noise reduction scheme generation system for power plant aeration blowers according to claim 4, characterized in that, The scheme optimization unit in the processing module also includes a sensitivity analysis subunit, which is used to evaluate the impact of changes in various parameters in the noise reduction scheme on the overall effect and to perform uncertainty analysis through Monte Carlo simulation. The scheme optimization unit is also configured to connect to an external sensor network to acquire wind turbine operating data in real time to dynamically adjust the scheme.

6. The noise reduction scheme generation system for power plant aeration blowers according to claim 5, characterized in that, The control module also includes a log recording unit and a performance monitoring unit. The log recording unit is used to record the system operation history, parameter changes and error events. The performance monitoring unit tracks the operating status and resource usage of each module in real time and alerts potential faults through an early warning mechanism. The control module also supports remote access, allowing authorized users to configure and maintain the system via the Internet.

7. A noise reduction scheme generation system for power plant aeration blowers according to claim 5, characterized in that, The scheme optimization unit in the processing module also integrates a cost evaluation subunit. The cost evaluation subunit is used to calculate the material cost, installation cost, and maintenance cost of each candidate noise reduction scheme, and to optimize and screen the scheme in combination with the power plant's budget constraints. The cost evaluation subunit can also dynamically update cost parameters based on market data and provide an economic analysis report.

8. A noise reduction scheme generation system for power plant aeration blowers according to claim 7, characterized in that, The scheme comparison unit and the cost evaluation subunit of the processing module work together to generate a comprehensive comparison report. The comprehensive comparison report integrates technical performance indicators and economic indicators, and recommends the optimal noise reduction scheme through a weighted scoring system. The output module is also configured to generate an implementation guide, including step installation instructions and operation and maintenance suggestions.

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