Noise monitoring method and apparatus, computer device, and storage medium

Abstract

The present application relates to a noise monitoring method and apparatus, a computer device, a computer-readable storage medium, and a computer program product. The method comprises: during operation of a device under monitoring, acquiring environmental sound data and process quantity data, wherein the process quantity data comprises operation state data of the device and / or environmental state data of an area where the device is located; inputting the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is obtained by performing training on the basis of an environmental sound data set and a process quantity data set; and outputting the noise monitoring result. By adopting the method, the accuracy of noise monitoring can be improved.

Description

Noise monitoring method, device, computer equipment and storage medium Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a noise monitoring method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] When industrial units experience equipment failures during operation, noise monitoring can effectively detect these failures. Currently, noise monitoring research on units is based on specific monitoring targets, such as Karman vortex noise, blade vortex noise, draft tube noise, and cavitation noise.

[0003] However, monitoring these various noise types relies on high-precision acoustic sensors and high-precision, high-sampling-rate acquisition cards, and only monitors specific noise frequencies. Because the noise generated by various components of the unit varies in spectrum and intensity, noise signals can be distorted by the environment and interference at every stage of generation, conversion, and propagation. Sometimes, the distortion is so severe that it obscures the original signal.

[0004] Therefore, relevant technologies are unable to accurately collect and analyze noise, resulting in low accuracy in noise identification and monitoring. Summary of the Invention

[0005] Based on this, it is necessary to provide a noise monitoring method, device, computer equipment, computer-readable storage medium and computer program product that can improve the noise monitoring accuracy in response to the above technical problems.

[0006] In a first aspect, the present application provides a noise monitoring method, comprising:

[0007] During the operation of the device to be monitored, collecting environmental sound data and process quantity data, wherein the process quantity data includes operating status data of the device to be monitored and / or environmental status data in the area where the device to be monitored is located;

[0008] Inputting the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set;

[0009] The noise monitoring result is output.

[0010] In one embodiment, the step of inputting the ambient sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result further includes:

[0011] When the noise monitoring result indicates the presence of noise, determining the noise type according to the noise monitoring result and the process quantity data;

[0012] A noise source is determined according to the noise type.

[0013] In one embodiment, when the noise monitoring result indicates the presence of noise, determining the noise type according to the noise monitoring result and the process quantity data includes:

[0014] When the noise monitoring result indicates the presence of noise, processing and analyzing the ambient sound data to obtain a noise feature, wherein the noise feature includes at least one of a sound pressure feature, a level feature, and a spectrum feature;

[0015] The noise type is determined according to the noise characteristics and the process quantity data.

[0016] In one embodiment, the equipment to be monitored is a pumped storage unit, and during the operation of the equipment to be monitored, collecting environmental sound data and process quantity data includes:

[0017] During the operation of the pumped storage unit equipment, collecting environmental sound data at multiple measurement points in real time;

[0018] The operating status data and environmental status data at multiple measurement points are collected in real time, wherein the operating status data includes at least one of the rotational speed, guide vane opening, and power, and the environmental status data includes at least one of the wind speed, flow rate, temperature, and humidity.

[0019] In one embodiment, the pumped storage unit includes a plurality of functional modules and a plurality of functional parts, the plurality of measurement points include measurement points arranged on the functional module serving as a power source and measurement points arranged at the functional part serving as a vibration source of environmental factors; the real-time collection of operating status data and environmental status data at the plurality of measurement points includes:

[0020] In the case where the measuring point is arranged on the functional device, collecting the operating status data of the functional device where the measuring point is located in real time;

[0021] In the case where the measuring point is arranged at the functional part, the environmental status data in the area where the measuring point is located is collected in real time.

[0022] In one embodiment, the functional module includes at least one of a water inlet ball valve, a generator, a turbine, an auxiliary system, and a main transformer; and the functional part includes at least one of a generator wind tunnel, a waterwheel chamber, and a tailwater pipe.

[0023] In a second aspect, the present application further provides a noise monitoring device, comprising:

[0024] A data acquisition module is used to collect environmental sound data and process quantity data during the operation of the device to be monitored, wherein the process quantity data includes the operating status data of the device to be monitored and / or the environmental status data of the area where the device to be monitored is located;

[0025] a data processing module, configured to input the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set;

[0026] The result output module is used to output the noise monitoring result.

[0027] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] During the operation of the device to be monitored, collecting environmental sound data and process quantity data, wherein the process quantity data includes operating status data of the device to be monitored and / or environmental status data in the area where the device to be monitored is located;

[0029] Inputting the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set;

[0030] The noise monitoring result is output.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0032] During the operation of the device to be monitored, collecting environmental sound data and process quantity data, wherein the process quantity data includes operating status data of the device to be monitored and / or environmental status data in the area where the device to be monitored is located;

[0033] Inputting the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set;

[0034] The noise monitoring result is output.

[0035] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0036] During the operation of the device to be monitored, collecting environmental sound data and process quantity data, wherein the process quantity data includes operating status data of the device to be monitored and / or environmental status data in the area where the device to be monitored is located;

[0037] Inputting the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set;

[0038] The noise monitoring result is output.

[0039] The noise monitoring method, apparatus, computer device, storage medium, and computer program product described above collect environmental sound data and process quantity data during the operation of the monitored equipment to obtain operational status data of the monitored equipment and environmental status data of the area in which the monitored equipment is located. The environmental sound data and process quantity data are then input into a noise monitoring model to obtain and output noise monitoring results. Because the noise monitoring model is trained based on the environmental sound data set and process quantity data set, and through machine learning techniques, the noise monitoring model learns the characteristics of the operational status data and environmental status data under abnormal conditions of the equipment during training. The data input into the noise monitoring model characterizes the operational status of the operating equipment itself and the state of the environment in which the equipment is operating. Therefore, the noise monitoring model can determine whether the ambient sound data in the current situation contains noise data, thereby obtaining noise monitoring results. Because this method fully considers process quantity data such as operational status data and environmental status data, noise can be captured and monitored in multiple aspects, thereby improving noise monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] FIG1 is a diagram showing an application environment of a noise monitoring method according to an embodiment;

[0042] FIG2 is a schematic flow chart of a noise monitoring method according to an embodiment;

[0043] FIG3 is a flow chart of step S202 in a noise monitoring method according to an embodiment;

[0044] FIG4 is a block diagram of a noise monitoring device according to an embodiment;

[0045] FIG5 is a diagram showing the internal structure of a computer device according to one embodiment;

[0046] FIG6 is a diagram showing the internal structure of a computer device in another embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] The method provided in the embodiment of the present application can be applied in the application environment shown in Figure 1. Among them, the terminal 102 can be applied in the application environment shown in Figure 1. Among them, the terminal 102 communicates with the server 104 through the network, and the terminal 102 can be used to collect environmental sound data and process quantity data, and send the collected data to the server 104 and store it in the data storage system. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The data storage system can be used to store environmental sound data and process quantity data, and can also store environmental sound data sets and process quantity data sets for training noise monitoring models. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0049] In an exemplary embodiment, as shown in FIG2 , a noise monitoring method is provided, which can be applied in the application environment shown in FIG1 . The method is described by taking the system consisting of the terminal 102 and the server 104 in FIG1 as an example, and includes the following steps S202 to S206 . Among them:

[0050] Step S202: During the operation of the equipment to be monitored, environmental sound data and process quantity data are collected.

[0051] The process quantity data may include operating status data of the equipment to be monitored, and may also include environmental status data in the area where the equipment to be monitored is located.

[0052] For example, operating status data can be used to indicate whether the monitored device is currently operating, record the specific values ​​of the monitored device's operating parameters, or represent the interrelationships between the operating statuses of different modules within the monitored device. For example, operating status data can include device speed, guide vane opening, actuator on / off status, and real-time engine power.

[0053] Exemplarily, the environmental status data can be used to characterize the status of various natural environmental factors within a certain distance range around the monitored device when it is running. For example, the environmental status data can be the temperature, humidity, fluid flow rate, etc. of the surrounding area to be monitored.

[0054] For example, during the operation of the equipment to be monitored, the terminal 102 can collect environmental sound data and process quantity data through sensors, and then send the environmental sound data and process quantity data to the server 104 through the network. After the server 104 receives the data transmitted by the terminal 102, it can store the data in the data storage system.

[0055] Step S204: input the environmental sound data and process quantity data into the noise monitoring model to obtain the noise monitoring results.

[0056] Among them, the noise monitoring model is trained based on the environmental sound dataset and process quantity dataset.

[0057] For example, server 104 can pre-parse historical data sets to generate an ambient sound dataset and a process quantity dataset, and use these datasets as training sets to train the noise monitoring model. The ambient sound dataset and the process quantity dataset correspond to each other based on time periods, and both datasets include failure conditions of the equipment to be monitored within the corresponding time periods. Therefore, the noise monitoring model trained using machine learning techniques can identify situations in which ambient sound data contains noise data requiring fault warnings, thereby deriving noise monitoring results.

[0058] Step S206: output the noise monitoring result.

[0059] The noise monitoring result includes early warning result information, wherein the early warning result information is used to indicate whether the corresponding environmental sound data includes noise data indicating a fault early warning.

[0060] Exemplarily, the noise monitoring result may further include noise analysis information, wherein the noise analysis information is used to characterize possible sources of noise, noise intensity data, noise warning level and other information.

[0061] For example, the server 104 may directly send the noise monitoring result to the terminal 102 and display the noise monitoring result; and may also send an early warning signal corresponding to the noise early warning level through the terminal 102 according to the noise monitoring result.

[0062] In the above-mentioned noise monitoring method, during the operation of the equipment to be monitored, environmental sound data and process quantity data are collected to obtain the operating status data of the equipment to be monitored and the environmental status data of the area where the equipment to be monitored is located. The environmental sound data and process quantity data are then input into a noise monitoring model to obtain and output noise monitoring results. Because the noise monitoring model is trained based on the environmental sound data set and the process quantity data set, the noise monitoring model grasps the characteristics of the operating status data and environmental status data of the equipment under abnormal conditions during the training process through machine learning technology. The data input into the noise monitoring model represents the operating status of the operating equipment itself and the state of the environment in which the operating equipment is located. Therefore, the noise monitoring model can determine whether the environmental sound data in the current situation contains noise data, thereby obtaining noise monitoring results. Because this method fully considers process quantity data such as operating status data and environmental status data, noise can be captured and monitored in multiple aspects, thereby improving the accuracy of noise monitoring.

[0063] In an exemplary embodiment, step S204 further includes: when the noise monitoring result indicates the presence of noise, determining the noise type according to the noise monitoring result and process quantity data; and determining the noise source according to the noise type.

[0064] Exemplarily, the server 104 can also determine the noise type, such as water flow noise, based on the noise monitoring results and process quantity data, and thereby judge the equipment location where the noise may occur through the noise spectrum, and add the noise characteristics and source data to the noise monitoring results and send them to the terminal 102 for display by the terminal 102.

[0065] Exemplarily, when the noise monitoring results indicate the presence of noise, the step of determining the noise type based on the noise monitoring results and process quantity data includes: when the noise monitoring results indicate the presence of noise, processing and analyzing the ambient sound data to obtain noise characteristics, wherein the noise characteristics include at least one of sound pressure characteristics, level characteristics, and spectrum characteristics; and determining the noise type based on the noise characteristics and process quantity data.

[0066] For example, users can view real-time or historical data on noise sound pressure amplitude, level, and spectrum through terminal 102, and can also replay noise audio through terminal 102. Furthermore, terminal 102 can also visualize noise monitoring results to obtain graphical and charted analysis data, allowing users to more intuitively locate the most likely noise source.

[0067] In an exemplary embodiment, as shown in FIG3 , the device to be monitored may be a pumped storage unit, and step S202 includes steps S302 to S304 .

[0068] Step S302 : During the operation of the pumped storage unit, environmental sound data at multiple measurement points are collected in real time.

[0069] For example, the terminal 102 for collecting environmental sound data can be a microphone sensor. During the operation of the pumped storage unit, a microphone sensor is deployed at each measurement point. The microphone sensor collects environmental sound data and converts the sound of the monitored equipment and the surrounding environmental noise into electrical signals. The collected signals are sent to the acoustic collection unit, which then sends them to the server 104 through a switch. The server 104 can include a cloud server.

[0070] Furthermore, the performance parameters of the above-mentioned microphone sensor can be: microphone nominal diameter: 1 / 4; frequency response range: 20 to 10000Hz (±2dB); sensitivity: 45mV / Pa; since the speed of the hydropower unit is relatively low, the noise frequency below 200Hz is relatively important, and the lowest frequency of the microphone that can be selected can reach 5Hz.

[0071] Furthermore, the acoustic acquisition unit utilizes industrial-grade computer technology and modular hardware, consisting of a microphone module and a real-time DSP module for rapid data acquisition. It utilizes standard industrial interfaces, is compact, and can be fixed to the outside of a concrete pit using expansion screws.

[0072] Furthermore, the above-mentioned switch is used for unified transmission and communication of data at each measurement point. To ensure data security, it needs to have a fully functional intelligent firewall and multiple data ports (at least 6).

[0073] For example, the transmission cable between the microphone and the data acquisition unit must be no longer than 15 meters. Therefore, each data acquisition unit can be installed close to the microphone, allowing system operation to be checked without requiring the unit to be backed up. For example, the data acquisition unit at the generator wind tunnel measurement point is mounted on the wall outside the wind tunnel, while the data acquisition unit at the ball valve measurement point is mounted approximately 1.5 meters below the ball valve. The data acquisition units are connected to the switch via a network cable, which must be no longer than 100 meters.

[0074] Step S304: collecting the operating status data and environmental status data at multiple measurement points in real time.

[0075] The operating status data may include at least one of the rotational speed, guide vane opening, and power, and the environmental status data may include at least one of the wind speed, flow rate, temperature, and humidity.

[0076] For example, the terminal 102 for collecting operating status data and environmental status data can obtain the operating status data of the corresponding equipment from the monitoring system through hard wiring, and then collect the corresponding environmental status data through sensors installed at multiple measurement points. Next, the terminal 102 sends the collected process quantity data to the server 104, which can store the two types of process quantity data accordingly based on the collection time period.

[0077] In an exemplary embodiment, the pumped storage unit equipment includes multiple functional modules and multiple functional parts, and the multiple measurement points include measurement points arranged on the functional module serving as a power source and measurement points arranged at the functional part serving as a vibration source of environmental factors. Correspondingly, step S304 includes: when the measurement point is arranged on the functional device, real-time collection of operating status data of the functional device where the measurement point is located; when the measurement point is arranged at the functional part, real-time collection of environmental status data in the area where the measurement point is located.

[0078] Exemplarily, the above-mentioned functional modules may include at least one of a water inlet ball valve, a generator, a turbine, an auxiliary system, and a main transformer; the above-mentioned functional parts may include at least one of a generator wind tunnel, a waterwheel chamber, and a tailwater pipe. Among them, the pumped storage unit has more operating condition conversions and frequent starts and stops. Its operating conditions are more complicated than those of conventional hydropower units, and the types and quantities of unit equipment are more than those of conventional hydropower units. All kinds of mechanical and electrical equipment will generate vibration and noise during operation. Therefore, it is necessary to predict whether there is a fault by capturing small changes in equipment noise. The sound emitted by energy storage unit equipment is usually the result of mechanical vibration caused by periodic and non-periodic events. These sounds may be caused by many different phenomena, and the sources are mainly from three aspects:

[0079] (1) Mechanical factors: due to shaft misalignment, bearing anisotropy, bearing oil film instability, mechanical or electromagnetic imbalance and friction.

[0080] (2) Hydraulic factors: These are caused by the force generated by the fluid itself, cracks and defects in flow-passing components such as the runner and guide vanes, cavitation, and instability of the water flow in the tailwater pipe or flow channel.

[0081] (3) Electrical factors: magnetic imbalance or uneven generator gap.

[0082] Therefore, the aforementioned measurement points can be placed: 1. On functional modules that serve as power sources, such as the water inlet ball valve, generator, turbine, auxiliary system, and main transformer. This allows for the collection and analysis of the sound generated by the equipment during operation, capturing subtle changes. 2. On functional locations that serve as sources of environmental vibration, such as the generator wind tunnel, waterwheel chamber, and tailwater pipe. This allows for the analysis of changes in the unit's operating status through changes in the sound at the vibration source.

[0083] In another exemplary embodiment, during the operation of the pumped storage unit, a microphone sensor is deployed at each measuring point. The microphone sensor collects environmental sound data and converts the sound of the monitored equipment and the surrounding environmental noise into electrical signals. The collected signals are sent to the acoustic collection unit, which then sends them to the cloud server 104 via a switch. The terminal 102 for collecting operating status data and environmental status data obtains the operating status data (including speed, guide vane opening, and power) of the corresponding equipment from the monitoring system through hard wiring, and then collects the corresponding environmental status data (including wind speed, flow rate, temperature, and humidity) through sensors set at multiple measuring points. Next, the terminal 102 sends the collected process quantity data to the server 104 accordingly, and the server 104 stores the two types of process quantity data accordingly according to the collection time period.

[0084] Among them, the pumped storage unit equipment includes multiple functional modules (including water inlet ball valves, generators, turbines, auxiliary systems, and main transformers) and multiple functional parts (including generator wind tunnels, waterwheel chambers, and tailwater pipes). The multiple measurement points include measurement points arranged on functional modules serving as power sources and measurement points arranged at functional parts serving as vibration sources of environmental factors. When the measurement points are arranged on functional devices, the operating status data of the functional devices where the measurement points are located are collected in real time; when the measurement points are arranged at functional parts, the environmental status data in the area where the measurement points are located are collected in real time.

[0085] Next, server 104 inputs the ambient sound data and process data into the noise monitoring model to generate noise monitoring results. Finally, server 104 determines the noise type, such as water flow noise, based on the noise monitoring results and process data, to determine the likely location of the equipment. The noise characteristics and source data are then added to the noise monitoring results and sent to terminal 102 for display. Terminal 102 allows users to view real-time or historical data on noise pressure amplitude, level, and frequency spectrum, and playback noise audio through terminal 102. Furthermore, terminal 102 visualizes the noise monitoring results, generating graphical and charted analytical data, allowing for more intuitive identification of the most likely noise sources.

[0086] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0087] Based on the same inventive concept, embodiments of the present application also provide a noise monitoring device for implementing the aforementioned noise monitoring method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more noise monitoring device embodiments provided below can be found in the above-described limitations of the noise monitoring method and will not be further elaborated here.

[0088] In an exemplary embodiment, as shown in FIG4 , a noise monitoring device is provided, including: a data acquisition module 402 , a data processing module 404 , and a result output module 406 , wherein:

[0089] The data acquisition module 402 is used to collect environmental sound data and process quantity data during the operation of the monitored equipment, wherein the process quantity data includes the operating status data of the monitored equipment and / or the environmental status data of the area where the monitored equipment is located;

[0090] The data processing module 404 is used to input the environmental sound data and the process quantity data into the noise monitoring model to obtain noise monitoring results, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set;

[0091] The result output module 406 is used to output the noise monitoring result.

[0092] In one embodiment, the apparatus further comprises:

[0093] A type determination module is used to determine the noise type based on the noise monitoring results and process quantity data when the noise monitoring results indicate the presence of noise;

[0094] The source analysis module is used to determine the noise source according to the noise type.

[0095] In one embodiment, the type determination module includes:

[0096] a feature analysis unit, configured to process and analyze the ambient sound data to obtain noise features when the noise monitoring result indicates the presence of noise, wherein the noise features include at least one of a sound pressure feature, a level feature, and a spectrum feature;

[0097] The type determination unit is used to determine the noise type according to the noise characteristics and process quantity data.

[0098] In one embodiment, the device to be monitored is a pumped storage unit, and the data acquisition module 402 includes:

[0099] The first data acquisition submodule is used to collect environmental sound data at multiple measurement points in real time during the operation of the pumped storage unit equipment;

[0100] The second data acquisition submodule is used to collect operating status data and environmental status data at multiple measurement points in real time, wherein the operating status data includes at least one of the rotational speed, guide vane opening, and power, and the environmental status data includes at least one of the wind speed, flow rate, temperature, and humidity.

[0101] In one embodiment, the pumped storage unit includes a plurality of functional modules and a plurality of functional parts, the plurality of measurement points include measurement points arranged on the functional module serving as a power source and measurement points arranged at the functional part serving as a vibration source of environmental factors; the second data acquisition submodule includes:

[0102] The first collection unit is used to collect the operating status data of the functional device where the measuring point is located in real time when the measuring point is arranged on the functional device;

[0103] The second collection unit is used to collect environmental status data in the area where the measurement point is located in real time when the measurement point is arranged at a functional location.

[0104] In one embodiment, the functional module includes at least one of a water inlet ball valve, a generator, a turbine, an auxiliary system, and a main transformer; and the functional part includes at least one of a generator wind tunnel, a waterwheel chamber, and a tailwater pipe.

[0105] Each module in the noise monitoring device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0106] In an exemplary embodiment, a computer device is provided, which may be a server. Its internal structure diagram may be as shown in FIG5 . The computer device includes a processor, memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is configured to store ambient sound data and process quantity data, and may also store data such as ambient sound datasets and process quantity datasets used to train noise monitoring models. The I / O interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals via a network connection. When executed by the processor, the computer program implements a noise monitoring method.

[0107] In an exemplary embodiment, a computer device is provided, which may be a terminal. Its internal structure diagram may be as shown in FIG6 . The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and an external device. The communication interface of the computer device is configured to communicate with an external terminal via wired or wireless communication, where the wireless communication may be achieved via Wi-Fi, a mobile cellular network, NFC (near field communication), or other technologies. When executed by the processor, the computer program implements a noise monitoring method. The display unit of the computer device is configured to produce a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0108] Those skilled in the art will understand that the structure shown in FIG6 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0109] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: during the operation of the equipment to be monitored, collecting environmental sound data and process quantity data, wherein the process quantity data includes operating status data of the equipment to be monitored and / or environmental status data in the area where the equipment to be monitored is located; inputting the environmental sound data and process quantity data into a noise monitoring model to obtain noise monitoring results, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set; and outputting the noise monitoring results.

[0110] In one embodiment, when the processor executes the computer program, the following steps are further implemented: when the noise monitoring result indicates the presence of noise, determining the noise type according to the noise monitoring result and process quantity data; and determining the noise source according to the noise type.

[0111] In one embodiment, when the processor executes the computer program, the following steps are also implemented: when the noise monitoring results indicate the presence of noise, the ambient sound data is processed and analyzed to obtain noise characteristics, wherein the noise characteristics include at least one of sound pressure characteristics, level characteristics, and spectrum characteristics; and the noise type is determined based on the noise characteristics and process quantity data.

[0112] In one embodiment, when the processor executes the computer program, it also implements the following steps: during the operation of the pumped storage unit equipment, real-time collection of environmental sound data at multiple measurement points; real-time collection of operating status data and environmental status data at multiple measurement points, wherein the operating status data includes at least one of the rotational speed, guide vane opening, and power, and the environmental status data includes at least one of the wind speed, flow rate, temperature, and humidity.

[0113] In one embodiment, when the processor executes the computer program, it also implements the following steps: when the measuring point is arranged on a functional device, the operating status data of the functional device where the measuring point is located is collected in real time; when the measuring point is arranged at a functional part, the environmental status data in the area where the measuring point is located is collected in real time.

[0114] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: during the operation of the equipment to be monitored, environmental sound data and process quantity data are collected, wherein the process quantity data includes operating status data of the equipment to be monitored and / or environmental status data in the area where the equipment to be monitored is located; the environmental sound data and process quantity data are input into a noise monitoring model to obtain noise monitoring results, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set; and the noise monitoring results are output.

[0115] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: when the noise monitoring result indicates the presence of noise, determining the noise type according to the noise monitoring result and process quantity data; and determining the noise source according to the noise type.

[0116] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the noise monitoring results indicate the presence of noise, the ambient sound data is processed and analyzed to obtain noise characteristics, wherein the noise characteristics include at least one of sound pressure characteristics, level characteristics, and spectrum characteristics; and the noise type is determined based on the noise characteristics and process quantity data.

[0117] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: during the operation of the pumped storage unit equipment, environmental sound data at multiple measurement points are collected in real time; operating status data and environmental status data at multiple measurement points are collected in real time, wherein the operating status data includes at least one of the rotational speed, guide vane opening, and power, and the environmental status data includes at least one of the wind speed, flow rate, temperature, and humidity.

[0118] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the measuring point is arranged on a functional device, the operating status data of the functional device where the measuring point is located is collected in real time; when the measuring point is arranged at a functional part, the environmental status data in the area where the measuring point is located is collected in real time.

[0119] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: collecting environmental sound data and process quantity data during the operation of a device to be monitored, wherein the process quantity data includes operating status data of the device to be monitored and / or environmental status data in an area where the device to be monitored is located; inputting the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained based on the environmental sound data set and the process quantity data set; and outputting the noise monitoring result.

[0120] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: when the noise monitoring result indicates the presence of noise, determining the noise type according to the noise monitoring result and process quantity data; and determining the noise source according to the noise type.

[0121] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the noise monitoring results indicate the presence of noise, the ambient sound data is processed and analyzed to obtain noise characteristics, wherein the noise characteristics include at least one of sound pressure characteristics, level characteristics, and spectrum characteristics; and the noise type is determined based on the noise characteristics and process quantity data.

[0122] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: during the operation of the pumped storage unit equipment, environmental sound data at multiple measurement points are collected in real time; operating status data and environmental status data at multiple measurement points are collected in real time, wherein the operating status data includes at least one of the rotational speed, guide vane opening, and power, and the environmental status data includes at least one of the wind speed, flow rate, temperature, and humidity.

[0123] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the measuring point is arranged on a functional device, the operating status data of the functional device where the measuring point is located is collected in real time; when the measuring point is arranged at a functional part, the environmental status data in the area where the measuring point is located is collected in real time.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0125] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0126] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A noise monitoring method, characterized in that, The method includes: During the operation of the device to be monitored, environmental sound data and process quantity data are collected, where the process quantity data includes the operation status data of the device to be monitored and / or the environmental status data in the area where the device to be monitored is located; The environmental sound data and the process quantity data are input into a noise monitoring model to obtain a noise monitoring result, where the noise monitoring model is trained according to an environmental sound data set and a process quantity data set; The noise monitoring result is output.

2. The method according to claim 1, characterized in that, After the environmental sound data and the process quantity data are input into the noise monitoring model to obtain a noise monitoring result, it further includes: When the noise monitoring result indicates the existence of noise, the noise type is determined according to the noise monitoring result and the process quantity data; The noise source is determined according to the noise type.

3. The method according to claim 2, wherein The step of determining the noise type according to the noise monitoring result and the process quantity data when the noise monitoring result indicates the existence of noise includes: When the noise monitoring result indicates the existence of noise, the environmental sound data is processed and analyzed to obtain noise characteristics, where the noise characteristics include at least one of sound pressure characteristics, level characteristics, and spectrum characteristics; The noise type is determined according to the noise characteristics and the process quantity data.

4. The method according to claim 1, characterized in that The device to be monitored is a pumped-storage unit monitoring device. During the operation of the device to be monitored, collecting environmental sound data and process quantity data includes: During the operation of the pumped-storage unit device, environmental sound data at multiple measurement points is collected in real time; The operation status data and environmental status data at multiple measurement points are collected in real time, where the operation status data includes at least one of rotational speed, guide vane opening, and power, and the environmental status data includes at least one of wind speed, flow velocity, temperature, and humidity.

5. The method according to claim 4, wherein The pumped-storage unit device includes multiple functional modules and multiple functional parts. The multiple measurement points include measurement points arranged on the functional module serving as the power source and measurement points arranged at the functional part serving as the environmental factor vibration source; The step of collecting the operation status data and environmental status data at multiple measurement points in real time includes: When the measurement point is arranged on the functional device, the operation status data of the functional device where the measurement point is located is collected in real time; When the measurement point is arranged at the functional part, the environmental status data in the area where the measurement point is located is collected in real time.

6. The method according to claim 5, characterized in that, The functional module includes at least one of an inlet ball valve, a generator, a water turbine, an auxiliary system, and a main transformer; the functional part includes at least one of a generator air duct, a waterwheel chamber, and a draft tube.

7. A noise monitoring device, characterized in that, The device includes: A data acquisition module for collecting environmental sound data and process quantity data during the operation of the device to be monitored, where the process quantity data includes the operation status data of the device to be monitored and / or the environmental status data in the area where the device to be monitored is located; A data processing module, configured to input the environmental sound data and the process quantity data into a noise monitoring model to obtain a noise monitoring result, wherein the noise monitoring model is trained according to an environmental sound data set and a process quantity data set; A result output module, configured to output the noise monitoring result.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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