Power plant noise simulation prediction analysis method

Abstract

The invention provides a power plant noise simulation prediction analysis method, and belongs to the technical field of analysis prediction, and the method comprises the steps: obtaining the noise data, environment data and operation data of all equipment of a power plant, and obtaining the equipment distribution and spatial layout of the power plant; analyzing the noise data to determine a sound source feature matrix, and constructing a noise propagation model based on the sound source feature matrix, the equipment distribution of the power plant and the spatial layout; analyzing the operation data to determine a main feature vector related to a noise level, and constructing a noise prediction model based on the main feature vector, the environment data and a noise propagation model; and performing simulation based on the noise prediction model to determine a real-time noise prediction result, and making a noise control strategy based on the real-time noise prediction result. Potential noise can be identified, noise prediction and control precision can be improved, equipment operation can be dynamically adjusted, sound insulation measures can be deployed, noise pollution can be effectively reduced, accurate noise prediction and control in a complex environment of the power plant can be realized, and the environmental protection and safety level of the power plant can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of analysis and prediction, and in particular to a method for simulating and predicting analysis of power plant noise. Background Art

[0002] Existing power plant noise methods mostly focus on the simulation and prediction of a single sound source or a simplified model, usually ignoring the complex impact of environmental conditions and equipment operating status on noise levels, resulting in low prediction accuracy. At the same time, some technologies lack in-depth analysis of the correlation between noise and equipment operating characteristics, making it difficult to optimize noise control strategies in a targeted manner. In addition, noise prediction models cannot adapt to the dynamic changes in the operating status of power plant equipment. There are limitations in noise propagation simulation and accurate prediction, which makes it difficult to meet the needs of noise control.

[0003] Therefore, the present invention provides a method for simulating, predicting and analyzing power plant noise. Summary of the invention

[0004] The present invention provides a method for simulating, predicting and analyzing noise in a power plant. The method constructs a noise propagation model by analyzing the noise data, equipment distribution and spatial layout of the power plant. The method constructs a noise prediction model by analyzing the operation data and environmental data in combination with the noise propagation model. The method performs simulation according to the noise prediction model to determine the real-time noise prediction result and formulates a noise control strategy. The method can identify potential noise, improve the accuracy of noise prediction and control, dynamically adjust equipment operation and deploy sound insulation measures, effectively reduce noise pollution, realize accurate prediction and control of noise in a complex environment of the power plant, and improve the environmental protection and safety level of the power plant.

[0005] The present invention provides a power plant noise simulation prediction and analysis method, comprising:

[0006] 101: Obtain the noise data, environmental data and operation data of all equipment in the power plant, and obtain the equipment distribution and spatial layout of the power plant;

[0007] 102: Analyze noise data to determine the sound source characteristic matrix, and build a noise propagation model based on the sound source characteristic matrix, equipment distribution and spatial layout of the power plant;

[0008] 103: Analyze the operation data to determine the main eigenvectors related to the noise level, and build a noise prediction model based on the main eigenvectors, environmental data and noise propagation model;

[0009] 104: Perform simulation based on the noise prediction model to determine the real-time noise prediction result, and formulate a noise control strategy based on the real-time noise prediction result.

[0010] According to a power plant noise simulation prediction and analysis method provided by the present invention, noise data and environmental data of all equipment in the power plant are obtained, including:

[0011] Acquire sub-noise data and corresponding sub-environment data of each device in the power plant within a specified time period based on the first sensor group;

[0012] Preprocessing all sub-noise data and all corresponding sub-environmental data, determining the noise data of the power plant based on all preprocessed sub-noise data, and determining the environmental data of the power plant based on all preprocessed sub-environmental data;

[0013] Sub-operation data related to noise of each device within a specified time period is acquired based on the second sensor group, and operation data is determined based on the pre-processed sub-operation data of all devices.

[0014] According to a power plant noise simulation prediction and analysis method provided by the present invention, noise data is analyzed to determine a sound source characteristic matrix, and a noise propagation model is constructed based on the sound source characteristic matrix, the equipment distribution and spatial layout of the power plant, including:

[0015] Performing spectrum analysis on the preprocessed sub-noise data of each device in the noise data to determine multiple sub-frequency domain features of each device, and at the same time, performing time domain feature extraction on the preprocessed sub-noise data of each device in the noise data to determine multiple sub-time domain features of each device;

[0016] Determine a sound source feature vector of each device based on all sub-frequency domain features and all sub-time domain features of each device;

[0017] Determine the sound source characteristic matrix of the power plant based on the sound source characteristic vectors of all equipment;

[0018] Determine the sub-noise propagation path and sub-noise propagation attenuation of each device based on the equipment distribution and spatial layout of the power plant;

[0019] A noise propagation model is constructed based on the sub-noise propagation paths of all devices, the adjusted sub-noise propagation attenuation of all devices, and the sound source characteristic matrix.

[0020] According to a power plant noise simulation prediction and analysis method provided by the present invention, operating data is analyzed to determine the main characteristic vectors related to the noise level, including:

[0021] Marking each piece of operation data in the sub-operation data of each device with a state and a noise level, classifying the sub-operation data of each device with a state based on the state mark, and determining state operation data, wherein the state operation data includes a plurality of sub-state operation data;

[0022] Perform feature extraction on the sub-state operation data of each state of each device to determine the operation feature vector of the sub-state operation data of each state of each device;

[0023] Determine a fitted noise level of the sub-state operation data of each state of each device based on all noise level markers in the sub-state operation data of each state of each device;

[0024] Based on the running feature vectors of all sub-state running data of all devices in all states, calculate the correlation value of each feature with the noise level;

[0025] Determining a correlation feature threshold based on the number of features in the running feature vector and the density of all correlation values;

[0026] Sort the correlation values ​​of all features in the running feature vector with the noise level from large to small, and select the first correlation feature threshold feature values ​​after sorting as the main features related to the noise level;

[0027] The main feature vectors are determined based on all the selected main features.

[0028] According to a power plant noise simulation prediction and analysis method provided by the present invention, based on the operation feature vectors of all sub-state operation data of all equipment in all states, the correlation value between each feature and the noise level is calculated, including:

[0029]

[0030]

[0031] in, represents the first sub-correlation value of the p-th eigenvalue in all sub-state operation data of the ith device and the fitting noise level of all sub-state operation data of the ith device, N1 represents the number of sub-state operation data, represents the pth eigenvalue in the operation feature vector of the jth sub-state operation data of the ith device, represents the pth average eigenvalue of the operation feature vector of the ith device in all sub-state operation data, Y ij represents the fitted noise level of the j-th sub-state operation data of the ith device, represents the average noise level of all sub-state operation data of the i-th device, Represents the standard deviation of the pth eigenvalue in the operating eigenvector of all sub-state operating data of all devices, σY ij represents the standard deviation of the fitted noise level of all sub-state operation data for all devices, represents the second sub-correlation value of the p-th feature value of the i-th device and the k-th device in the j-th sub-state operation data, N2 represents the number of devices, represents the pth average eigenvalue in the operating eigenvector of the operating data of all devices in the jth sub-state, represents the pth eigenvalue in the operation feature vector of the jth sub-state operation data of the kth device, represents the second correlation value of the ith device and all other devices except the ith device, R p Represents the correlation value of the pth eigenvalue in the running eigenvector and the noise level.

[0032] According to a power plant noise simulation prediction and analysis method provided by the present invention, a noise prediction model is constructed based on main feature vectors, environmental data and noise propagation model, including:

[0033] Perform feature extraction on all preprocessed sub-environment data in the environment data, and determine the sub-environment vector of each preprocessed sub-environment data;

[0034] Performing cluster analysis on the sub-environment vectors of all sub-environment data, determining each cluster in the cluster analysis result as a sub-category, and determining the environment category based on all sub-categories;

[0035] Determine a subcategory vector for each subcategory based on the sub-environment vectors of all sub-environment data in the cluster corresponding to each subcategory;

[0036] Determine, based on the preprocessed sub-environmental data corresponding to the preprocessed sub-operational data in the input operation data, a subcategory corresponding to the preprocessed sub-environmental data;

[0037] adjusting the sub-noise propagation attenuation of each device based on a sub-category vector of a sub-category corresponding to the sub-environment data;

[0038] Optimize the noise propagation model based on the adjusted sub-noise propagation attenuation;

[0039] The main feature vectors and the optimized noise propagation model are input into the noise prediction model, and the noise prediction model is trained based on the operating data and noise data.

[0040] According to a power plant noise simulation prediction and analysis method provided by the present invention, a real-time noise prediction result is determined by simulation based on a noise prediction model, and a noise control strategy is formulated based on the real-time noise prediction result, including:

[0041] Real-time collection of real-time environmental data, real-time operation data and real-time noise data of the power plant;

[0042] Input real-time environmental data and real-time operation data into the noise prediction model to simulate the actual operation status of the power plant;

[0043] A real-time noise prediction result is determined based on the output result of the noise prediction model.

[0044] According to a power plant noise simulation prediction and analysis method provided by the present invention, a real-time noise prediction result is determined by simulation based on a noise prediction model, and a noise control strategy is formulated based on the real-time noise prediction result, and the method also includes:

[0045] Analyze the real-time noise prediction results of the noise prediction model and the consistency of the real-time noise data collected in real time, and evaluate and optimize the noise prediction model;

[0046] Formulate noise control strategies based on the optimized noise prediction model.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] By analyzing the noise data, equipment distribution and spatial layout of the power plant, a noise propagation model is constructed. The noise prediction model is constructed by analyzing the operation data and environmental data in combination with the noise propagation model. Simulations are performed based on the noise prediction model to determine the real-time noise prediction results and formulate noise control strategies. This can identify potential noise, improve the accuracy of noise prediction and control, dynamically adjust equipment operation and deploy sound insulation measures, effectively reduce noise pollution, achieve accurate prediction and control of noise in the complex environment of the power plant, and improve the environmental protection and safety level of the power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a flow chart of a method for simulating, predicting and analyzing power plant noise provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Embodiment 1:

[0053] The embodiment of the present invention provides a method for simulating and predicting power plant noise. Figure 1 As shown, including:

[0054] 101: Obtain the noise data, environmental data and operation data of all equipment in the power plant, and obtain the equipment distribution and spatial layout of the power plant;

[0055] 102: Analyze noise data to determine the sound source characteristic matrix, and build a noise propagation model based on the sound source characteristic matrix, equipment distribution and spatial layout of the power plant;

[0056] 103: Analyze the operation data to determine the main eigenvectors related to the noise level, and build a noise prediction model based on the main eigenvectors, environmental data and noise propagation model;

[0057] 104: Perform simulation based on the noise prediction model to determine the real-time noise prediction result, and formulate a noise control strategy based on the real-time noise prediction result.

[0058] In this embodiment, the equipment distribution and spatial layout clearly define the location of each equipment in the power plant and the overall spatial layout of the power plant, so as to simulate the noise propagation path and impact range.

[0059] In this embodiment, the noise propagation model is based on the equipment location and spatial layout in the power plant, combined with environmental conditions, to simulate the noise propagation from the sound source to the outside. This model is used to predict the propagation effect of noise at different locations, taking into account factors such as spatial attenuation.

[0060] In this embodiment, the noise data includes sub-noise data of all equipment in the power plant; the environmental data includes environmental data that affects noise propagation, such as temperature, humidity, wind speed, etc.; the operating data includes operating data of all equipment in the power plant, such as operating status, power output, speed, etc., and this information is closely related to the noise level.

[0061] In this embodiment, a noise prediction model is used to perform real-time simulation prediction to obtain the noise level of the power plant under the current operating state and environmental conditions.

[0062] The beneficial effects of the above technical solution are as follows: by analyzing the noise data, equipment distribution and spatial layout of the power plant to build a noise propagation model, analyzing the operation data and environmental data in combination with the noise propagation model to build a noise prediction model, and simulating according to the noise prediction model to determine the real-time noise prediction results, and formulating noise control strategies, potential noise can be identified, the accuracy of noise prediction and control can be improved, equipment operation can be dynamically adjusted and sound insulation measures can be deployed, noise pollution can be effectively reduced, and accurate noise prediction and control in the complex environment of the power plant can be achieved, thereby improving the environmental protection and safety level of the power plant.

[0063] Embodiment 2:

[0064] The embodiment of the present invention provides a power plant noise simulation prediction and analysis method, which obtains noise data and environmental data of all equipment in the power plant, including:

[0065] Acquire sub-noise data and corresponding sub-environment data of each device in the power plant within a specified time period based on the first sensor group;

[0066] Preprocessing all sub-noise data and all corresponding sub-environmental data, determining the noise data of the power plant based on all preprocessed sub-noise data, and determining the environmental data of the power plant based on all preprocessed sub-environmental data;

[0067] Sub-operation data related to noise of each device within a specified time period is acquired based on the second sensor group, and operation data is determined based on the pre-processed sub-operation data of all devices.

[0068] In this embodiment, the first sensor group refers to a sensor set for collecting device noise and environmental information, including noise sensors and environmental sensors.

[0069] In this embodiment, the sub-noise data represents the noise data generated by each device within a specified time period.

[0070] In this embodiment, the sub-environmental data represents the environmental data of each device within a specified time period, such as temperature, humidity, and other environmental factors that affect noise propagation.

[0071] In this embodiment, preprocessing refers to performing operations such as cleaning, smoothing, and denoising on the data to improve data quality and ensure the accuracy of subsequent analysis.

[0072] In this embodiment, the noise data represents the overall noise information of the power plant obtained after preprocessing and integrating all sub-noise data, and represents the overall noise level in the power plant.

[0073] In this embodiment, the environmental data represents the overall environmental information of the power plant obtained after preprocessing and integrating all sub-environmental data, and is used to describe the environmental conditions inside the power plant.

[0074] In this embodiment, the second sensor group refers to a sensor group used to collect the operating status of the device, such as a speed sensor, etc., which is closely related to the generation of noise.

[0075] In this embodiment, the sub-operation data represents the operation status data of each device within a specified time period, including the load, speed, temperature, etc. of the device, which data affect the noise generation of the device.

[0076] In this embodiment, the operation data represents the overall operation status of the power plant obtained after preprocessing and integrating all sub-operation data, which is used for noise prediction and control analysis.

[0077] The beneficial effects of the above technical solution are: obtaining the noise data and environmental data of all equipment in the power plant can achieve detailed monitoring and response to the noise source of each equipment, providing a more efficient data basis for noise management.

[0078] Embodiment 3:

[0079] The embodiment of the present invention provides a method for simulating, predicting and analyzing noise in a power plant, analyzing noise data to determine a sound source characteristic matrix, and constructing a noise propagation model based on the sound source characteristic matrix, the equipment distribution and spatial layout of the power plant, including:

[0080] Performing spectrum analysis on the preprocessed sub-noise data of each device in the noise data to determine multiple sub-frequency domain features of each device, and at the same time, performing time domain feature extraction on the preprocessed sub-noise data of each device in the noise data to determine multiple sub-time domain features of each device;

[0081] Determine a sound source feature vector of each device based on all sub-frequency domain features and all sub-time domain features of each device;

[0082] Determine the sound source characteristic matrix of the power plant based on the sound source characteristic vectors of all equipment;

[0083] Determine the sub-noise propagation path and sub-noise propagation attenuation of each device based on the equipment distribution and spatial layout of the power plant;

[0084] A noise propagation model is constructed based on the sub-noise propagation paths of all devices, the adjusted sub-noise propagation attenuation of all devices, and the sound source characteristic matrix.

[0085] In this embodiment, the frequency domain features represent the noise signal features obtained by analysis in the frequency domain, including main frequency components, frequency peaks, frequency band energy, etc., and describe the frequency distribution of the noise signal.

[0086] In this embodiment, the time domain feature refers to the noise signal feature obtained by analyzing in the time domain, such as mean, variance, peak value, etc., which describes the change characteristics of the signal over time.

[0087] In this embodiment, the sound source feature vector represents a vector formed by integrating frequency domain features and time domain features, and is used to describe the sound source characteristics of each device.

[0088] In this embodiment, the sound source characteristic matrix represents a matrix containing characteristic vectors of sound sources of all equipment, and is a characteristic data set of the noise sources of the entire power plant.

[0089] In this embodiment, the sub-noise propagation path represents the propagation route of the noise of each device in the power plant space, which is affected by the location of the device and the layout of the power plant.

[0090] In this embodiment, the sub-noise propagation attenuation indicates the amount of energy loss caused by distance and blocking factors during the propagation of sound waves, and is generally related to the propagation distance, medium and obstacles.

[0091] In this embodiment, the noise propagation model refers to a model constructed based on a sound source characteristic matrix, a propagation path, and attenuation characteristics, and is used to simulate the propagation mode and attenuation of noise in a power plant.

[0092] The beneficial effects of the above technical solution are as follows: constructing a noise propagation model based on the sound source characteristic matrix, the equipment distribution of the power plant and the spatial layout can enhance the understanding and prediction accuracy of noise propagation, provide a more scientific basis for noise control in the power plant, and effectively improve the noise control effect.

[0093] Embodiment 4:

[0094] The embodiment of the present invention provides a power plant noise simulation prediction and analysis method, which analyzes operation data to determine the main feature vectors related to the noise level, including:

[0095] Marking each piece of operation data in the sub-operation data of each device with a state and a noise level, classifying the sub-operation data of each device with a state based on the state mark, and determining state operation data, wherein the state operation data includes a plurality of sub-state operation data;

[0096] Perform feature extraction on the sub-state operation data of each state of each device to determine the operation feature vector of the sub-state operation data of each state of each device;

[0097] Determine a fitted noise level of the sub-state operation data of each state of each device based on all noise level markers in the sub-state operation data of each state of each device;

[0098] Based on the running feature vectors of all sub-state running data of all devices in all states, calculate the correlation value of each feature with the noise level;

[0099] Determining a correlation feature threshold based on the number of features in the running feature vector and the density of all correlation values;

[0100] Sort the correlation values ​​of all features in the running feature vector with the noise level from large to small, and select the first correlation feature threshold feature values ​​after sorting as the main features related to the noise level;

[0101] The main feature vectors are determined based on all the selected main features.

[0102] In this embodiment, the status mark represents a device status label added to each piece of operating data, such as "idle", "operating", "fault", etc., which describes the current operating status of the device.

[0103] In this embodiment, the noise level mark indicates recording the noise level corresponding to each piece of operation data, which is used to analyze the change of noise.

[0104] In this embodiment, the state classification means classifying the sub-operation data of the device according to the state mark to form an operation data set in each state.

[0105] In this embodiment, the state operation data represents a set of overall operation data of a device in all states, including a plurality of sub-state operation data.

[0106] In this embodiment, the sub-state operation data represents the operation data of a certain device in a certain state, which facilitates the extraction of state features.

[0107] In this embodiment, the operation feature vector represents a feature set extracted from sub-state operation data of a certain device in a certain state, and each state of each device corresponds to an operation feature vector.

[0108] In this embodiment, the fitting noise level represents a representative noise level value determined according to all noise level marks of the corresponding device in the corresponding state, and is used to describe the noise situation of the device in the state.

[0109] In this embodiment, the correlation feature threshold represents a value set according to the number and density of correlation values, and is used to filter the features most correlated with the noise level.

[0110] In this embodiment, the main features refer to several features that are most closely related to the noise level among all the features, and are used for subsequent analysis.

[0111] In this embodiment, the main feature vector refers to a vector composed of all main features, and represents a feature set that has a key impact on the noise level.

[0112] The beneficial effects of the above technical solution are as follows: by analyzing the operating data to determine the main characteristic vectors related to the noise level, the key operating factors affecting the noise can be identified, providing a scientific basis for accurate noise reduction and optimization of equipment operating status.

[0113] Embodiment 5:

[0114] The embodiment of the present invention provides a power plant noise simulation prediction and analysis method, which calculates the correlation value between each feature and the noise level based on the operation feature vector of all sub-state operation data of all devices in all states, including:

[0115]

[0116] in, represents the first sub-correlation value of the p-th eigenvalue in all sub-state operation data of the ith device and the fitting noise level of all sub-state operation data of the ith device, N1 represents the number of sub-state operation data, represents the pth eigenvalue in the operation feature vector of the jth sub-state operation data of the ith device, represents the pth average eigenvalue of the operation feature vector of the ith device in all sub-state operation data, Y ij represents the fitted noise level of the j-th sub-state operation data of the ith device, represents the average noise level of all sub-state operation data of the i-th device, Represents the standard deviation of the pth eigenvalue in the operating eigenvector of all sub-state operating data of all devices, σY ij represents the standard deviation of the fitted noise level of all sub-state operation data for all devices, represents the second sub-correlation value of the p-th feature value of the i-th device and the k-th device in the j-th sub-state operation data, N2 represents the number of devices, represents the pth average eigenvalue in the operating eigenvector of the operating data of all devices in the jth sub-state, represents the pth eigenvalue in the operation feature vector of the jth sub-state operation data of the kth device, represents the second correlation value of the ith device and all other devices except the ith device, R p Represents the correlation value of the pth eigenvalue in the running eigenvector and the noise level.

[0117] In this embodiment, the correlation value represents the correlation between the corresponding feature in the running feature vector and all noise levels of all devices.

[0118] In this embodiment, Represents the correlation value of the p-th eigenvalue and the noise level in all sub-state operation data of the ith device and all other devices except the ith device.

[0119] In this embodiment, Represents the comprehensive second correlation value among all devices.

[0120] The beneficial effect of the above technical solution is as follows: according to the operating feature vectors of all sub-state operating data in all states of all devices, the correlation value of each feature and the noise level is calculated, which can improve the data basis for screening the main features and determining the main feature vectors.

[0121] Embodiment 6:

[0122] The embodiment of the present invention provides a power plant noise simulation prediction and analysis method, which constructs a noise prediction model based on main feature vectors, environmental data and a noise propagation model, including:

[0123] Perform feature extraction on all preprocessed sub-environment data in the environment data, and determine the sub-environment vector of each preprocessed sub-environment data;

[0124] Performing cluster analysis on the sub-environment vectors of all sub-environment data, determining each cluster in the cluster analysis result as a sub-category, and determining the environment category based on all sub-categories;

[0125] Determine a subcategory vector for each subcategory based on the sub-environment vectors of all sub-environment data in the cluster corresponding to each subcategory;

[0126] Determine, based on the preprocessed sub-environmental data corresponding to the preprocessed sub-operational data in the input operation data, a subcategory corresponding to the preprocessed sub-environmental data;

[0127] adjusting the sub-noise propagation attenuation of each device based on a sub-category vector of a sub-category corresponding to the sub-environment data;

[0128] Optimize the noise propagation model based on the adjusted sub-noise propagation attenuation;

[0129] The main feature vectors and the optimized noise propagation model are input into the noise prediction model, and the noise prediction model is trained based on the operating data and noise data.

[0130] In this embodiment, the sub-environment vector represents a feature vector extracted from the corresponding sub-environment data, describing the multi-dimensional environment characteristics of the sub-environment data.

[0131] In this embodiment, a cluster represents a group in a cluster analysis result, and each cluster represents a subcategory, reflecting a data set with similar environmental characteristics.

[0132] In this embodiment, the subcategory represents a subcategory with similar environmental characteristics obtained by cluster analysis, and includes a data set of specific environmental characteristics.

[0133] In this embodiment, the environment category is a comprehensive classification of multiple subcategories, which is used to represent the overall characteristics of the environment.

[0134] In this embodiment, the subcategory vector represents the vector of the feature center of each subcategory, indicating the typical environmental characteristics of the category.

[0135] In this embodiment, the noise propagation model is a mathematical model that simulates the propagation of noise in different environments and spaces, and takes into account the sound source, path and attenuation of the noise.

[0136] In this embodiment, the noise prediction model is a model trained according to operation data, environmental data and an optimized noise propagation model, and is used to predict the noise level of the equipment.

[0137] The beneficial effects of the above technical solution are as follows: constructing a noise prediction model based on the main eigenvectors, environmental data and noise propagation model can more accurately reflect the impact of environmental changes on noise, and provide reliable support for noise prediction and control in complex environments.

[0138] Embodiment 7:

[0139] The embodiment of the present invention provides a power plant noise simulation prediction and analysis method, which performs simulation based on a noise prediction model to determine a real-time noise prediction result, and formulates a noise control strategy based on the real-time noise prediction result, including:

[0140] Real-time collection of real-time environmental data, real-time operation data and real-time noise data of the power plant;

[0141] Input real-time environmental data and real-time operation data into the noise prediction model to simulate the actual operation status of the power plant;

[0142] A real-time noise prediction result is determined based on the output result of the noise prediction model.

[0143] In this embodiment, the real-time environmental data includes current environmental parameters, such as temperature, humidity, air pressure, wind speed, etc. These factors will affect the propagation characteristics of noise.

[0144] In this embodiment, the real-time operation data includes the operation parameters of each device in the power plant, such as operation status, power, speed, load, etc., reflecting the working condition of the equipment.

[0145] In this embodiment, the real-time noise data includes actual measured values ​​of the current noise level, which are usually provided by a noise sensor and used for subsequent noise prediction result comparison and model optimization.

[0146] In this embodiment, the collected real-time environmental data and real-time operation data are used as inputs to a noise prediction model. The noise prediction model simulates the noise level of the equipment in the power plant under current environmental conditions based on the trained equipment noise characteristics and environmental noise propagation laws.

[0147] In this embodiment, the real-time noise prediction result is a noise level prediction value output by the noise prediction model under current conditions, indicating the predicted noise situation of the device under the real-time environment and operating state.

[0148] The beneficial effects of the above technical solution are as follows: by simulating and determining the real-time noise prediction results according to the noise prediction model, and formulating a noise control strategy according to the real-time noise prediction results, the noise propagation model can be optimized and the accuracy of the noise prediction model can be improved.

[0149] Embodiment 8:

[0150] The embodiment of the present invention provides a power plant noise simulation prediction and analysis method, which performs simulation based on a noise prediction model to determine a real-time noise prediction result, and formulates a noise control strategy based on the real-time noise prediction result, and also includes:

[0151] Analyze the real-time noise prediction results of the noise prediction model and the consistency of the real-time noise data collected in real time, and evaluate and optimize the noise prediction model;

[0152] Formulate noise control strategies based on the optimized noise prediction model.

[0153] In this embodiment, analyzing the consistency of the real-time noise prediction results of the noise prediction model and the real-time noise data collected in real time includes checking the deviation between the noise prediction results and the actual noise data, analyzing the degree of consistency between the prediction results and the actual noise data under different environments and operating conditions, and finding out the causes of the prediction errors, such as the impact of environmental conditions, equipment parameters, etc. on noise prediction.

[0154] After determining the prediction error of the model under certain conditions through consistency analysis, the noise prediction model is evaluated and optimized. The optimization process includes parameter adjustment: adjusting the parameters in the model according to the characteristics of the noise data and the actual situation to improve the adaptability of the model to different environments and operating conditions; data update: adding new real-time noise data and environmental data for training to enhance the generalization ability of the model and adapt it to new operating scenarios; model structure optimization: according to the results of error analysis, optimizing the model structure (such as adding new input features or improving the model algorithm) to improve prediction accuracy.

[0155] Based on the optimized noise prediction model, a noise control strategy is formulated. By utilizing the model's predictive capabilities, power plants can take preventive measures for noise situations that may exceed the standard or pose risks, such as equipment operation adjustments: reducing the operating power of certain equipment or changing its working mode in high noise risk situations to reduce noise generation; noise shielding: taking measures such as sound insulation barriers and sound-absorbing materials to reduce noise propagation during noise peak periods or in high-risk areas; dynamic control: dynamically adjusting equipment operating parameters or using noise control devices based on real-time predicted noise levels to keep noise within a safe range, etc.

[0156] The beneficial effects of the above technical solution are as follows: by analyzing the noise data, equipment distribution and spatial layout of the power plant to build a noise propagation model, analyzing the operation data and environmental data in combination with the noise propagation model to build a noise prediction model, and simulating according to the noise prediction model to determine the real-time noise prediction results, and formulating noise control strategies, potential noise can be identified, the accuracy of noise prediction and control can be improved, equipment operation can be dynamically adjusted and sound insulation measures can be deployed, noise pollution can be effectively reduced, and accurate noise prediction and control in the complex environment of the power plant can be achieved, thereby improving the environmental protection and safety level of the power plant.

[0157] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0158] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating and predicting power plant noise, characterized in that: include: 101: Obtain the noise data, environmental data and operation data of all equipment in the power plant, and obtain the equipment distribution and spatial layout of the power plant; 102: Analyze noise data to determine the sound source characteristic matrix, and build a noise propagation model based on the sound source characteristic matrix, equipment distribution and spatial layout of the power plant; 103: Analyze the operation data to determine the main eigenvectors related to the noise level, and build a noise prediction model based on the main eigenvectors, environmental data and noise propagation model; 104: Perform simulation based on the noise prediction model to determine the real-time noise prediction result, and formulate a noise control strategy based on the real-time noise prediction result.

2. A power plant noise simulation prediction and analysis method according to claim 1, characterized in that: Obtain noise data and environmental data for all equipment in the power plant, including: Acquire sub-noise data and corresponding sub-environment data of each device in the power plant within a specified time period based on the first sensor group; Preprocessing all sub-noise data and all corresponding sub-environmental data, determining the noise data of the power plant based on all preprocessed sub-noise data, and determining the environmental data of the power plant based on all preprocessed sub-environmental data; Sub-operation data related to noise of each device within a specified time period is acquired based on the second sensor group, and operation data is determined based on the pre-processed sub-operation data of all devices.

3. A power plant noise simulation prediction and analysis method according to claim 2, characterized in that: Analyze noise data to determine the sound source characteristic matrix, and build a noise propagation model based on the sound source characteristic matrix, equipment distribution and spatial layout of the power plant, including: Performing spectrum analysis on the preprocessed sub-noise data of each device in the noise data to determine multiple sub-frequency domain features of each device, and at the same time, performing time domain feature extraction on the preprocessed sub-noise data of each device in the noise data to determine multiple sub-time domain features of each device; Determine a sound source feature vector of each device based on all sub-frequency domain features and all sub-time domain features of each device; Determine the sound source characteristic matrix of the power plant based on the sound source characteristic vectors of all equipment; Determine the sub-noise propagation path and sub-noise propagation attenuation of each device based on the equipment distribution and spatial layout of the power plant; A noise propagation model is constructed based on the sub-noise propagation paths of all devices, the adjusted sub-noise propagation attenuation of all devices, and the sound source characteristic matrix.

4. A power plant noise simulation prediction and analysis method according to claim 2, characterized in that: The operational data was analyzed to determine the main eigenvectors associated with the noise level, including: Marking each piece of operation data in the sub-operation data of each device with a state and a noise level, classifying the sub-operation data of each device with a state based on the state mark, and determining state operation data, wherein the state operation data includes a plurality of sub-state operation data; Perform feature extraction on the sub-state operation data of each state of each device to determine the operation feature vector of the sub-state operation data of each state of each device; Determine a fitted noise level of the sub-state operation data of each state of each device based on all noise level markers in the sub-state operation data of each state of each device; Based on the running feature vectors of all sub-state running data of all devices in all states, calculate the correlation value of each feature with the noise level; Determining a correlation feature threshold based on the number of features in the running feature vector and the density of all correlation values; Sort the correlation values ​​of all features in the running feature vector with the noise level from large to small, and select the first correlation feature threshold feature values ​​after sorting as the main features related to the noise level; The main feature vectors are determined based on all the selected main features.

5. A power plant noise simulation prediction and analysis method according to claim 4, characterized in that: Based on the running feature vectors of all sub-state running data in all states of all devices, the correlation value between each feature and the noise level is calculated, including: in, represents the first sub-correlation value of the p-th eigenvalue in all sub-state operation data of the ith device and the fitting noise level of all sub-state operation data of the ith device, N1 represents the number of sub-state operation data, represents the pth eigenvalue in the operation feature vector of the jth sub-state operation data of the ith device, represents the pth average eigenvalue of the operation feature vector of the ith device in all sub-state operation data, Y ij represents the fitted noise level of the j-th sub-state operation data of the ith device, represents the average noise level of all sub-state operation data of the i-th device, Represents the standard deviation of the pth eigenvalue in the operating eigenvector of all sub-state operating data of all devices, σY ij represents the standard deviation of the fitted noise level of all sub-state operation data for all devices, represents the second sub-correlation value of the p-th feature value of the i-th device and the k-th device in the j-th sub-state operation data, N2 represents the number of devices, represents the pth average eigenvalue in the operating eigenvector of the operating data of all devices in the jth sub-state, represents the pth eigenvalue in the operation feature vector of the jth sub-state operation data of the kth device, represents the second correlation value of the ith device and all other devices except the ith device, R p Represents the correlation value of the pth eigenvalue in the running eigenvector and the noise level.

6. A power plant noise simulation prediction and analysis method according to claim 2, characterized in that: A noise prediction model is constructed based on the main feature vectors, environmental data and noise propagation model, including: Perform feature extraction on all preprocessed sub-environment data in the environment data, and determine the sub-environment vector of each preprocessed sub-environment data; Performing cluster analysis on the sub-environment vectors of all sub-environment data, determining each cluster in the cluster analysis result as a sub-category, and determining the environment category based on all sub-categories; Determine a subcategory vector for each subcategory based on the sub-environment vectors of all sub-environment data in the cluster corresponding to each subcategory; Determine, based on the preprocessed sub-environmental data corresponding to the preprocessed sub-operational data in the input operation data, a subcategory corresponding to the preprocessed sub-environmental data; adjusting the sub-noise propagation attenuation of each device based on a sub-category vector of a sub-category corresponding to the sub-environment data; Optimize the noise propagation model based on the adjusted sub-noise propagation attenuation; The main eigenvectors and the optimized noise propagation model are input into the noise prediction model, and the noise prediction model is trained based on the operating data and noise data.

7. A power plant noise simulation prediction and analysis method according to claim 1, characterized in that: Based on the noise prediction model, simulation is performed to determine the real-time noise prediction results, and noise control strategies are formulated based on the real-time noise prediction results, including: Real-time collection of real-time environmental data, real-time operation data and real-time noise data of the power plant; Input real-time environmental data and real-time operation data into the noise prediction model to simulate the actual operation status of the power plant; A real-time noise prediction result is determined based on the output result of the noise prediction model.

8. A power plant noise simulation prediction and analysis method according to claim 7, characterized in that: Based on the noise prediction model, the real-time noise prediction results are simulated and determined, and the noise control strategy is formulated based on the real-time noise prediction results, which also includes: Analyze the real-time noise prediction results of the noise prediction model and the consistency of the real-time noise data collected in real time, and evaluate and optimize the noise prediction model; Formulate noise control strategies based on the optimized noise prediction model.

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