Environmental noise monitoring method and device based on sound level meter

Abstract

The present invention discloses a method and device for monitoring environmental noise based on a sound level meter, comprising: a cloud server receives sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored, and inputs all sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model; the cloud server analyzes the environmental noise information in the target scene according to the output result as the environmental noise monitoring result of the target scene. It can be seen that the implementation of the present invention can realize the automatic and intelligent monitoring of environmental noise, which is conducive to improving the monitoring efficiency and monitoring efficiency of environmental noise. In addition, it can also improve the convenience of environmental noise monitoring and the applicability of environmental noise monitoring methods.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental noise monitoring, and in particular to an environmental noise monitoring method and device based on a mobile monitoring station. Background Art

[0002] Environmental noise refers to the sound generated in industrial production, construction, transportation and social life that interferes with the surrounding living environment. In order to reduce the interference of environmental noise on the surrounding living environment, it is necessary to monitor the environmental noise so that corresponding measures can be taken in time after the environmental noise is monitored to exceed the normal sound range.

[0003] At present, the monitoring of environmental noise mainly relies on on-site monitoring by monitoring personnel. Practice has found that the current environmental noise monitoring method requires relevant monitoring personnel to be familiar with the environmental noise monitoring process and the operating procedures of relevant information collection equipment, and requires relevant monitoring personnel to be on-site, which is not conducive to improving the monitoring efficiency and reliability of environmental noise. Summary of the invention

[0004] The present invention provides an environmental noise monitoring method and device based on a sound level meter, which can realize automatic and intelligent monitoring of environmental noise, and is conducive to improving the monitoring efficiency and monitoring efficiency of environmental noise.

[0005] The first aspect of the present invention discloses an environmental noise monitoring method based on a sound level meter, the method comprising:

[0006] The cloud server receives sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored;

[0007] The cloud server inputs all the sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model;

[0008] The cloud server analyzes the environmental noise information of the target scene according to the output result as the environmental noise monitoring result of the target scene.

[0009] As an optional implementation, in the first aspect of the present invention, the cloud server inputs all the sound monitoring data into a pre-generated noise anomaly recognition model, and before obtaining the output result of the noise anomaly recognition model, the method further includes:

[0010] The cloud server determines identification information uniquely corresponding to each of the sound level meters that reports the sound monitoring data;

[0011] The cloud server obtains the current collection state information corresponding to each sound level meter according to the identification information uniquely corresponding to each sound level meter, the current collection state information corresponding to each sound level meter includes at least one of the collection position, collection direction, collection angle, activated sub-collector and collection-related device of each sound level meter in the target scene, and the collection-related device of the sound level meter includes a smart device whose distance value from the sound level meter in the target scene is less than or equal to a preset distance threshold and whose influence on the sound level meter performing the sound monitoring data collection operation is greater than or equal to a preset influence threshold;

[0012] The cloud server inputs all the sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model, including:

[0013] The cloud server inputs all the sound monitoring data and the current acquisition status information corresponding to the corresponding sound level meter into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model.

[0014] As an optional implementation, in the first aspect of the present invention, the output result includes a sub-noise identification result of each sound level meter at a corresponding acquisition position in the target scene;

[0015] And, the cloud server analyzes the environmental noise information of the target scene according to the output result as the environmental noise monitoring result of the target scene, including:

[0016] The cloud server calculates the environmental noise monitoring result of the target scene according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

[0017] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0018] The sound monitoring data includes sound monitoring data in seconds, sound monitoring data in minutes, sound monitoring data in hours or sound monitoring data in days;

[0019] Among them, the sound monitoring data in seconds is the sound monitoring data monitored by the corresponding sound level meter within a time period of 1 second and is stored in a created second data array, the sound monitoring data in minutes is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 seconds and is stored in a created minute data array, the sound monitoring data in hours is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 minutes and is stored in a created hour data array, and the sound monitoring data in days is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 24 hours and is stored in a created day data array.

[0020] As an optional implementation, in the first aspect of the present invention, the cloud server inputs all the sound monitoring data into a pre-generated noise anomaly recognition model, and before obtaining the output result of the noise anomaly recognition model, the method further includes:

[0021] The cloud server determines whether all the sound monitoring data belong to the same collection level. When the judgment result is yes, it triggers the execution of the operation of inputting all the sound monitoring data into a pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0022] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0023] When it is determined that all the sound monitoring data do not belong to the same collection level, the cloud server determines the target collection level with the highest occurrence frequency from all the collection levels corresponding to all the sound monitoring data;

[0024] The cloud server determines, according to the target collection level, the sound monitoring data to be replaced from all the sound monitoring data, wherein the collection level corresponding to the sound monitoring data to be replaced is not the target collection level;

[0025] The cloud server determines a target sound level meter that reports the sound monitoring data to be replaced, determines a target array corresponding to the target collection level from all arrays used to store the sound monitoring data collected by the target sound level meter, and extracts the target sound monitoring data collected by the target sound level meter with a collection level of the target collection level from the target array;

[0026] The cloud server replaces the sound monitoring data to be replaced with the target sound monitoring data to update all the sound monitoring data, and triggers the operation of inputting all the sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model.

[0027] As an optional implementation, in the first aspect of the present invention, before the cloud server calculates the environmental noise monitoring result of the target scene according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene, the method further includes:

[0028] The cloud server determines the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene;

[0029] The cloud server determines the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene, including:

[0030] The cloud server determines the first noise impact factors corresponding to all the sound level meters according to the collection levels corresponding to all the sound monitoring data;

[0031] The cloud server determines a second noise impact factor corresponding to each of the sound level meters according to current collection state information corresponding to each of the sound level meters that reports the sound monitoring data;

[0032] The cloud server calculates the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene according to the first noise impact factors corresponding to all the sound level meters and the second noise impact factors corresponding to all the sound level meters.

[0033] The second aspect of the present invention discloses an environmental noise monitoring method and device based on a sound level meter, the device is applied to a cloud server, the cloud server is applied to an environmental noise monitoring mobile station, and the device includes:

[0034] A receiving module, used to receive sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored;

[0035] A noise anomaly recognition module, used for inputting all the sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model;

[0036] The noise analysis module is used to analyze the environmental noise information in the target scene according to the output result as the environmental noise monitoring result of the target scene.

[0037] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0038] A determination module, used for determining the unique identification information of each sound level meter that reports the sound monitoring data before the noise anomaly identification module inputs all the sound monitoring data into a pre-generated noise anomaly identification model to obtain the output result of the noise anomaly identification model; and obtaining the current collection state information corresponding to each sound level meter according to the unique identification information corresponding to each sound level meter, the current collection state information corresponding to each sound level meter including at least one of the collection position, collection direction, collection angle, activated sub-collector and collection-related device of each sound level meter in the target scene, the collection-related device of the sound level meter including a smart device whose distance value from the sound level meter in the target scene is less than or equal to a preset distance threshold and whose influence on the sound level meter performing the sound monitoring data collection operation is greater than or equal to a preset influence threshold;

[0039] The noise anomaly recognition module inputs all the sound monitoring data into a pre-generated noise anomaly recognition model, and a specific method of obtaining the output result of the noise anomaly recognition model includes:

[0040] All the sound monitoring data and the current acquisition status information corresponding to the corresponding sound level meter are input into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model.

[0041] As an optional implementation, in the second aspect of the present invention, the output result includes a sub-noise identification result of each sound level meter at a corresponding acquisition position in the target scene;

[0042] Furthermore, the noise analysis module analyzes the environmental noise information of the target scene according to the output result, and the specific manner of using the environmental noise monitoring result of the target scene as the output result includes:

[0043] The environmental noise monitoring result of the target scene is calculated according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

[0044] As an optional implementation, in the second aspect of the present invention, the sound monitoring data includes sound monitoring data in seconds, sound monitoring data in minutes, sound monitoring data in hours, or sound monitoring data in days;

[0045] Among them, the sound monitoring data in seconds is the sound monitoring data monitored by the corresponding sound level meter within a time period of 1 second and is stored in a created second data array, the sound monitoring data in minutes is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 seconds and is stored in a created minute data array, the sound monitoring data in hours is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 minutes and is stored in a created hour data array, and the sound monitoring data in days is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 24 hours and is stored in a created day data array.

[0046] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0047] A judgment module is used to judge whether all the sound monitoring data belong to the same collection level before the noise anomaly recognition module inputs all the sound monitoring data into a pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model; when the judgment result is yes, the noise anomaly recognition module is triggered to execute the operation of inputting all the sound monitoring data into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0048] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0049] The updating module is used for, when the judgment module determines that all the sound monitoring data do not belong to the same collection level, determining the target collection level with the highest frequency from all the collection levels corresponding to all the sound monitoring data; determining the sound monitoring data to be replaced from all the sound monitoring data according to the target collection level, the collection level corresponding to the sound monitoring data to be replaced is not the target collection level; determining the target sound level meter for reporting the sound monitoring data to be replaced, and determining the target array corresponding to the target collection level from all arrays for storing the sound monitoring data collected by the target sound level meter, and extracting the target sound monitoring data collected by the target sound level meter with the collection level of the target collection level from the target array; replacing the sound monitoring data to be replaced with the target sound monitoring data to update all the sound monitoring data, and triggering the noise anomaly recognition module to perform the operation of inputting all the sound monitoring data into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0050] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0051] An impact factor determination module, used to determine the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene;

[0052] The specific manner in which the impact factor determination module determines the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene includes:

[0053] Determine the first noise impact factor corresponding to all the sound level meters according to the collection levels corresponding to all the sound monitoring data;

[0054] Determining a second noise impact factor corresponding to each of the sound level meters that reports the sound monitoring data according to current collection state information corresponding to each of the sound level meters;

[0055] According to the first noise impact factors respectively corresponding to all the sound level meters and the second noise impact factors respectively corresponding to all the sound level meters, the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene is calculated.

[0056] The third aspect of the present invention discloses another method and device for monitoring environmental noise based on a sound level meter, the device is applied to a cloud server, the cloud server is applied to an environmental noise monitoring mobile station, and the device includes:

[0057] A memory storing executable program code;

[0058] a processor coupled to the memory;

[0059] The processor calls the executable program code stored in the memory to execute part or all of the steps in the environmental noise monitoring method based on the sound level meter disclosed in the first aspect of the present invention.

[0060] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the environmental noise monitoring based on the sound level meter disclosed in the first aspect of the present invention.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The cloud server receives the sound monitoring data collected by at least one sound level meter set in the target scene of the environmental noise to be monitored, and inputs all the sound monitoring data into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model; the cloud server analyzes the environmental noise information in the target scene according to the output result as the environmental noise monitoring result of the target scene. It can be seen that the implementation of the present invention can realize the automatic and intelligent monitoring of environmental noise, which is conducive to improving the monitoring efficiency and monitoring efficiency of environmental noise. In addition, it can also improve the convenience of environmental noise monitoring and the applicability of environmental noise monitoring methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0064] Figure 1 It is a flow chart of an environmental noise monitoring method based on a sound level meter disclosed in an embodiment of the present invention;

[0065] Figure 2 It is a flow chart of another environmental noise monitoring method based on a sound level meter disclosed in an embodiment of the present invention;

[0066] Figure 3 is a structural schematic diagram of an environmental noise monitoring device based on a sound level meter disclosed in an embodiment of the present invention;

[0067] Figure 4 is a structural schematic diagram of another environmental noise monitoring device based on a sound level meter disclosed in an embodiment of the present invention;

[0068] Figure 5 It is a structural schematic diagram of another environmental noise monitoring device based on a sound level meter disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0070] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product, port or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products, ports or ends.

[0071] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0072] The present invention discloses a method and device for monitoring environmental noise based on a sound level meter, which can realize automatic and intelligent monitoring of environmental noise, and is conducive to improving the monitoring efficiency and monitoring efficiency of environmental noise. The following are detailed descriptions respectively.

[0073] Embodiment 1

[0074] See also Figure 1 , Figure 1 : is a flow chart of an environmental noise monitoring method based on a sound level meter disclosed in an embodiment of the present invention. Figure 1 The described method is applied in a cloud server, and the cloud server can be further applied in an environmental noise monitoring mobile station, which is not limited in the embodiment of the present invention. Figure 1 As shown, the method may include the following operations:

[0075] 101. A cloud server receives sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored.

[0076] In the embodiment of the present invention, a sound level meter is provided at a pre-determined collection position in the target scene, and the collection position may be manually determined or adaptively determined based on the scene parameters of the target scene. Optionally, the scene parameters of the target scene may include at least one of the scene range of the target scene, the area division result of the target scene, and the functions corresponding to different areas in the target scene, etc., which are not limited in the embodiment of the present invention.

[0077] The sound level meter can collect sound monitoring data and report the sound monitoring data through the following steps:

[0078] Step 1: Connect the sound level meter via a USB cable to monitor the working status of the sound level meter and obtain the sound monitoring data of the sound level meter on the environmental noise in real time.

[0079] Step 2: Connect to the cloud server through the 5G module, encode the acquired sound monitoring data according to the 212 protocol, and upload the sound monitoring data.

[0080] More specifically, the above step 1 may include:

[0081] 1) After the main control is turned on, power on the sound level meter;

[0082] 2) After 15 seconds, when the sound level meter is successfully powered on and initialized, the main control sends the #4,4; command to download the sound level meter configuration file;

[0083] 3) Modify the downloaded sound level meter configuration file according to the custom parameters saved by the main control, and then use the command #9,2; + to upload the configuration file to the sound level meter memory;

[0084] 4) Send command #1, S1; make the sound level meter start monitoring;

[0085] 5) Send #2,a,1 once every 500 milliseconds; obtain the sound monitoring data of the sound level meter with a period of 1 second and store it in the second data array;

[0086] 6) Whenever the number of seconds changes from 59 seconds to 0 seconds, the sound monitoring data of the previous 60 seconds stored in the second data array is calculated, and the average value is calculated as the minute-level sound monitoring data, which is stored in the minute data array;

[0087] 7) When the number of hours increases by 1, the average value of the minute data array of the previous 60 minutes saved in the previous minute data array is calculated as the hour data and stored in the hour data array;

[0088] 8) When the number of hours is 24, the average value of the hourly data array of the previous 24 hours saved in the hourly data array is calculated as the day data and stored in the hourly data array;

[0089] 9) When the acquisition of monitoring data fails for 10 consecutive times, restart the sound level meter and try to obtain the monitoring data again. If it fails, report the abnormality of the sound level meter to the server.

[0090] And, the above step 2 may include:

[0091] 1) After the main control is turned on, power on the 5G module;

[0092] 2) After the 5G module is automatically initialized successfully in 15 seconds, send the module AT command to configure the 5G module parameters so that the 5G module can automatically connect to the network;

[0093] 3) Wait for the 5G module to connect to the Internet successfully, or call the system synchronization network time program ntp to obtain the network time;

[0094] 4) Open the socket and connect to the cloud server;

[0095] 5) When the detection data array of the sound level meter has new minute data, hour data or day data, package the data according to the 212 protocol and send the data through the socket's send function.

[0096] 6) Use the recv function to obtain the confirmation value replied by the server to confirm that the uploaded data is received successfully by the server, otherwise repeat step 5).

[0097] 102. The cloud server inputs all sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model.

[0098] Optionally, before the cloud server inputs all sound monitoring data into a pre-generated noise anomaly recognition model and obtains an output result of the noise anomaly recognition model, the method may further include:

[0099] The cloud server removes abnormal data from all sound monitoring data according to a pre-set abnormal data removal model to update the sound monitoring data. This helps to improve the accuracy of the sound monitoring data and reduce the computational complexity of the noise anomaly recognition model, thereby improving the efficiency of obtaining the output results.

[0100] 103. The cloud server analyzes the environmental noise information of the target scene according to the output result as the environmental noise monitoring result of the target scene.

[0101] Among them, after obtaining the environmental noise monitoring results of the target scene, the environmental noise monitoring results of the target scene can be sent to the corresponding display terminal for display, and / or, if it is judged that the target scene currently has noise interference according to the environmental noise monitoring results of the target scene, a noise abnormality prompt is output.

[0102] Optionally, the cloud server can also remotely control the working parameters of the sound level meter, which is conducive to achieving the control efficiency and control flexibility of the upgrader. The specific control process can be as follows:

[0103] 1) After opening the socket, use the recv function in real time to listen to the data sent by the cloud server. If the data is parsed to be the parameter setting data, the parameter modification is executed.

[0104] 2) After the parameters are modified successfully, disconnect the power of the sound level meter through the IO port, and then power it on to restart the sound level meter.

[0105] It can be seen that the implementation of the method described in the embodiment of the present invention can realize the automatic and intelligent monitoring of environmental noise, which is conducive to improving the monitoring efficiency and monitoring efficiency of environmental noise.

[0106] In an optional embodiment, before executing step 102, the method may further include the following operations:

[0107] The cloud server determines the unique identification information corresponding to each sound level meter that reports the sound monitoring data;

[0108] The cloud server obtains the current collection status information corresponding to each sound level meter according to the unique identification information corresponding to each sound level meter.

[0109] In this optional embodiment, the cloud server inputs all sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model, which may include:

[0110] The cloud server inputs all sound monitoring data and the current acquisition status information corresponding to the corresponding sound level meter into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0111] In this optional embodiment, further optionally, the current collection status information corresponding to each sound level meter includes at least one of the collection position, collection direction, collection angle, activated sub-collector and collection-associated devices of each sound level meter in the target scene, and the collection-associated device of the sound level meter includes a smart device whose distance value from the sound level meter in the target scene is less than or equal to a preset distance threshold and whose influence on the sound level meter's execution of sound monitoring data collection operations is greater than or equal to a preset influence threshold.

[0112] It can be seen that this optional embodiment can also input the current collection status information corresponding to the sound level meter together with all sound monitoring data into the noise anomaly recognition model, which is conducive to improving the accuracy of the output results, and further conducive to improving the accuracy of the environmental noise monitoring results.

[0113] In another optional embodiment, the above output result may include the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene. The cloud server analyzes the environmental noise information in the target scene according to the output result, and the environmental noise monitoring result of the target scene may include:

[0114] The cloud server calculates the environmental noise monitoring result of the target scene according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

[0115] It can be seen that this optional embodiment can also determine the environmental noise monitoring results of the target scene based on the sub-noise identification results of each sound level meter that reports sound monitoring data in the target scene at the corresponding collection position in the target scene and further combine them with the corresponding noise influencing factors, which is beneficial to improving the accuracy of the environmental noise monitoring results of the target scene.

[0116] In another optional embodiment, before the cloud server calculates the environmental noise monitoring result of the target scene according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene, the method further includes:

[0117] The cloud server determines the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

[0118] Optionally, the cloud server determines the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene, including:

[0119] The cloud server determines the first noise impact factor corresponding to all sound level meters according to the collection level corresponding to all sound monitoring data;

[0120] The cloud server determines the second noise impact factor corresponding to each sound level meter according to the current collection state information corresponding to each sound level meter that reports the sound monitoring data;

[0121] The cloud server calculates the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene according to the first noise impact factors corresponding to all the sound level meters and the second noise impact factors corresponding to all the sound level meters.

[0122] It can be seen that this optional embodiment can also comprehensively calculate the noise impact factor of each sound level meter at the corresponding collection position in the target scene based on the current collection status information corresponding to the sound level meter and the collection level corresponding to the sound monitoring data, and provide an intelligent calculation method for the noise impact factor of each sound level meter at the corresponding collection position in the target scene, which is beneficial to improve the accuracy of the noise impact factor of each sound level meter at the corresponding collection position in the target scene, and further helps to improve the accuracy of the environmental noise monitoring results of the target scene determined based on the noise impact factor of each sound level meter at the corresponding collection position in the target scene.

[0123] Embodiment 2

[0124] See also Figure 2 , Figure 2 is a flow chart of another environmental noise monitoring method based on a sound level meter disclosed in an embodiment of the present invention. Figure 2The described method is applied in a cloud server, and the cloud server can be further applied in an environmental noise monitoring mobile station, which is not limited in the embodiment of the present invention. Figure 2 As shown, the method may include the following operations:

[0125] 201. A cloud server receives sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored.

[0126] In the embodiment of the present invention, optionally, the sound monitoring data includes sound monitoring data in seconds, sound monitoring data in minutes, sound monitoring data in hours, or sound monitoring data in days. The sound monitoring data in seconds is the sound monitoring data monitored by the corresponding sound level meter within a time period of 1 second and is stored in a created second data array, the sound monitoring data in minutes is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 seconds and is stored in a created minute data array, the sound monitoring data in hours is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 minutes and is stored in a created hour data array, and the sound monitoring data in days is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 24 hours and is stored in a created day data array.

[0127] 202. The cloud server determines whether all the sound monitoring data belong to the same collection level. When the judgment result of step 202 is yes, step 207 is executed; when the judgment result of step 202 is no, steps 203 to 206 are triggered.

[0128] 203. The cloud server determines the target collection level with the highest occurrence frequency from all collection levels corresponding to all sound monitoring data.

[0129] 204. The cloud server determines the sound monitoring data to be replaced from all the sound monitoring data according to the target collection level.

[0130] Among them, the collection level corresponding to the sound monitoring data to be replaced is not the target collection level.

[0131] 205. The cloud server determines a target sound level meter that reports the sound monitoring data to be replaced, determines a target array corresponding to a target collection level from all arrays used to store the sound monitoring data collected by the target sound level meter, and extracts the target sound monitoring data collected by the target sound level meter with a collection level of the target collection level from the target array.

[0132] 206. The cloud server replaces the sound monitoring data to be replaced with the target sound monitoring data to update all the sound monitoring data.

[0133] 207. The cloud server inputs all sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model.

[0134] 208. The cloud server analyzes the environmental noise information of the target scene according to the output result as the environmental noise monitoring result of the target scene.

[0135] It can be seen that the implementation of the method described in the embodiment of the present invention can realize the automatic and intelligent monitoring of environmental noise, which is conducive to improving the monitoring efficiency and monitoring efficiency of environmental noise. In addition, before analyzing and processing the sound monitoring data reported by multiple sound level meters received, it is first determined whether the sound monitoring data belongs to the same collection level (such as collection by seconds, collection by minutes, collection by hours or collection by days), and matching control operations are performed according to the corresponding judgment results, which is conducive to improving the consistency of the collection level of the sound monitoring data input to the noise anomaly recognition model, reducing the consistency processing process of the noise anomaly recognition model on the sound monitoring data, and is conducive to improving the data processing efficiency of the noise anomaly recognition model, and then It is conducive to improving the efficiency of sound anomaly monitoring.

[0136] Embodiment 3

[0137] See also Figure 3 , Figure 3 : is a schematic diagram of the structure of an environmental noise monitoring device based on a sound level meter disclosed in an embodiment of the present invention. Figure 3 The described device is applied in a cloud server, and the cloud server can be further applied in an environmental noise monitoring mobile station, which is not limited in the embodiment of the present invention. Figure 3 As shown, the device may include:

[0138] The receiving module 301 is used to receive sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored;

[0139] The noise anomaly recognition module 302 is used to input all sound monitoring data into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model;

[0140] The noise analysis module 303 is used to analyze the environmental noise information in the target scene according to the output result as the environmental noise monitoring result of the target scene.

[0141] It can be seen that the implementation Figure 3 The described device can realize automatic and intelligent monitoring of environmental noise, which is beneficial to improving the monitoring efficiency and monitoring efficiency of environmental noise.

[0142] In an optional embodiment, if Figure 4 As shown, the device may also include:

[0143] The determination module 304 is used to determine the unique identification information of each sound level meter that reports the sound monitoring data before the noise anomaly identification module 302 inputs all the sound monitoring data into the pre-generated noise anomaly identification model to obtain the output result of the noise anomaly identification model; and obtain the current collection state information corresponding to each sound level meter according to the unique identification information corresponding to each sound level meter, the current collection state information corresponding to each sound level meter includes at least one of the collection position, collection direction, collection angle, activated sub-collector and collection-related equipment of each sound level meter in the target scene, and the collection-related equipment of the sound level meter includes a smart device whose distance value to the sound level meter in the target scene is less than or equal to a preset distance threshold and whose influence on the sound level meter performing the sound monitoring data collection operation is greater than or equal to a preset influence threshold;

[0144] The noise anomaly recognition module 302 inputs all sound monitoring data into a pre-generated noise anomaly recognition model, and the specific method of obtaining the output result of the noise anomaly recognition model includes:

[0145] All sound monitoring data and the current acquisition status information corresponding to the corresponding sound level meter are input into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0146] It can be seen that this optional embodiment can also input the current collection status information corresponding to the sound level meter together with all sound monitoring data into the noise anomaly recognition model, which is conducive to improving the accuracy of the output results, and further conducive to improving the accuracy of the environmental noise monitoring results.

[0147] In another optional embodiment, the output result includes the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene. The noise analysis module 303 analyzes the environmental noise information in the target scene according to the output result, and the specific method of the environmental noise monitoring result of the target scene may include:

[0148] The environmental noise monitoring result of the target scene is calculated according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

[0149] It can be seen that this optional embodiment can also determine the environmental noise monitoring results of the target scene based on the sub-noise identification results of each sound level meter that reports sound monitoring data in the target scene at the corresponding collection position in the target scene and further combine them with the corresponding noise influencing factors, which is beneficial to improving the accuracy of the environmental noise monitoring results of the target scene.

[0150] In yet another optional embodiment, the sound monitoring data includes sound monitoring data in seconds, sound monitoring data in minutes, sound monitoring data in hours, or sound monitoring data in days.

[0151] Among them, the sound monitoring data in seconds is the sound monitoring data monitored by the corresponding sound level meter within a time period of 1 second and is stored in the created second data array, the sound monitoring data in minutes is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 seconds and is stored in the created minute data array, the sound monitoring data in hours is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 minutes and is stored in the created hour data array, and the sound monitoring data in days is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 24 hours and is stored in the created day data array.

[0152] It can be seen that this optional embodiment can also enable the sound level meter to flexibly collect and store sound monitoring data, which is conducive to improving the efficiency of obtaining sound monitoring data.

[0153] In yet another optional embodiment, Figure 4 As shown, the device may also include:

[0154] The judgment module 305 is used to judge whether all the sound monitoring data belong to the same collection level before the noise anomaly recognition module 303 inputs all the sound monitoring data into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model. When the judgment result is yes, the noise anomaly recognition module 302 is triggered to execute the above-mentioned operation of inputting all the sound monitoring data into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0155] It can be seen that this optional embodiment can also determine whether the sound monitoring data belongs to the same collection level (such as collection by seconds, minutes, hours or days) before analyzing and processing the sound monitoring data reported by multiple sound level meters, and perform matching control operations according to the corresponding judgment results, which is beneficial to improving the consistency of the collection level of the sound monitoring data input to the noise anomaly recognition model, reducing the consistency processing process of the noise anomaly recognition model on the sound monitoring data, and is beneficial to improving the data processing efficiency of the noise anomaly recognition model, and further beneficial to improving the sound anomaly monitoring efficiency.

[0156] In this optional embodiment, further optional, such as Figure 4 As shown, the device may also include:

[0157] The updating module 306 is used to determine the target collection level with the highest frequency from all the collection levels corresponding to all the sound monitoring data when the judging module 305 judges that all the sound monitoring data do not belong to the same collection level; determine the sound monitoring data to be replaced from all the sound monitoring data according to the target collection level, and the collection level corresponding to the sound monitoring data to be replaced is not the target collection level; determine the target sound level meter for reporting the sound monitoring data to be replaced, and determine the target array corresponding to the target collection level from all arrays used to store the sound monitoring data collected by the target sound level meter, and extract the target sound monitoring data collected by the target sound level meter with the collection level of the target collection level from the target array; replace the sound monitoring data to be replaced with the target sound monitoring data to update all the sound monitoring data, and trigger the noise anomaly recognition module 302 to perform the above-mentioned operation of inputting all the sound monitoring data into the pre-generated noise anomaly recognition model to obtain the output result of the noise anomaly recognition model.

[0158] It can be seen that this optional embodiment can also intelligently realize the update of sound monitoring data that does not meet the collection level requirements, which is beneficial to improving the consistency of the collection level of the sound monitoring data input into the noise anomaly recognition model, reducing the consistency processing process of the noise anomaly recognition model on the sound monitoring data, and is beneficial to improving the data processing efficiency of the noise anomaly recognition model, and further helping to improve the sound anomaly monitoring efficiency.

[0159] In yet another optional embodiment, Figure 4 As shown, the device may also include:

[0160] An impact factor determination module 307 is used to determine the noise impact factor corresponding to the corresponding acquisition position of each sound level meter in the target scene;

[0161] The specific manner in which the impact factor determination module 307 determines the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene includes:

[0162] Determine the first noise impact factor corresponding to all sound level meters according to the collection level corresponding to all sound monitoring data;

[0163] Determine a second noise impact factor corresponding to each sound level meter according to current collection state information corresponding to each sound level meter that reports sound monitoring data;

[0164] The noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene is calculated according to the first noise impact factors corresponding to all the sound level meters and the second noise impact factors corresponding to all the sound level meters.

[0165] It can be seen that this optional embodiment can also comprehensively calculate the noise impact factor of each sound level meter at the corresponding collection position in the target scene based on the current collection status information corresponding to the sound level meter and the collection level corresponding to the sound monitoring data, and provide an intelligent calculation method for the noise impact factor of each sound level meter at the corresponding collection position in the target scene, which is beneficial to improve the accuracy of the noise impact factor of each sound level meter at the corresponding collection position in the target scene, and further helps to improve the accuracy of the environmental noise monitoring results of the target scene determined based on the noise impact factor of each sound level meter at the corresponding collection position in the target scene.

[0166] Embodiment 5

[0167] See also Figure 5 , Figure 5 is a schematic diagram of the structure of another environmental noise monitoring device based on a sound level meter disclosed in an embodiment of the present invention. Figure 5 The described device is applied in a cloud server, and the cloud server can be further applied in an environmental noise monitoring mobile station, which is not limited in the embodiment of the present invention. Figure 5 As shown, the device may include:

[0168] A memory 401 storing executable program codes;

[0169] a processor 402 coupled to the memory 401;

[0170] The processor 402 calls the executable program code stored in the memory 401 to execute part or all of the steps in the environmental noise monitoring method based on the sound level meter disclosed in the first or second embodiment of the present invention.

[0171] Embodiment 5

[0172] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all steps of the environmental noise monitoring method based on the sound level meter disclosed in Embodiment 1 or Embodiment 2 of the present invention.

[0173] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0174] Through the specific description of the above embodiments, 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 such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0175] Finally, it should be noted that the environmental noise monitoring method and device based on a sound level meter disclosed in the embodiment of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. 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 various embodiments of the present invention.

Claims

1. A method for monitoring environmental noise based on a sound level meter, characterized in that: The method comprises: The cloud server receives sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored; The cloud server determines identification information uniquely corresponding to each of the sound level meters that reports the sound monitoring data; The cloud server obtains the current collection state information corresponding to each sound level meter according to the unique identification information corresponding to each sound level meter, the current collection state information corresponding to each sound level meter includes the collection position, collection direction, collection angle, activated sub-collector and collection-related equipment of each sound level meter in the target scene, and the collection-related equipment of the sound level meter includes a smart device whose distance value from the sound level meter in the target scene is less than or equal to a preset distance threshold and whose influence on the sound level meter performing the sound monitoring data collection operation is greater than or equal to a preset influence threshold; The cloud server determines whether all the sound monitoring data belong to the same collection level; When it is determined that all the sound monitoring data belong to the same collection level, the cloud server inputs all the sound monitoring data and the current collection state information corresponding to the corresponding sound level meter into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model; the output result includes a sub-noise recognition result of each sound level meter at a corresponding collection position in the target scene; When it is determined that all the sound monitoring data do not belong to the same collection level, the cloud server determines the target collection level with the highest occurrence frequency from all the collection levels corresponding to all the sound monitoring data; The cloud server determines, according to the target collection level, the sound monitoring data to be replaced from all the sound monitoring data, wherein the collection level corresponding to the sound monitoring data to be replaced is not the target collection level; The cloud server determines a target sound level meter that reports the sound monitoring data to be replaced, determines a target array corresponding to the target collection level from all arrays used to store the sound monitoring data collected by the target sound level meter, and extracts the target sound monitoring data collected by the target sound level meter with a collection level of the target collection level from the target array; The cloud server replaces the sound monitoring data to be replaced with the target sound monitoring data to update all the sound monitoring data, and inputs all the sound monitoring data and the current acquisition state information corresponding to the corresponding sound level meter into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model; the output result includes a sub-noise recognition result of each sound level meter at a corresponding acquisition position in the target scene; The cloud server determines the first noise impact factors corresponding to all the sound level meters according to the collection levels corresponding to all the sound monitoring data; The cloud server determines a second noise impact factor corresponding to each of the sound level meters according to current collection state information corresponding to each of the sound level meters that reports the sound monitoring data; The cloud server calculates the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene according to the first noise impact factors corresponding to all the sound level meters and the second noise impact factors corresponding to all the sound level meters; The cloud server calculates the environmental noise monitoring result of the target scene according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

2. The environmental noise monitoring method based on a sound level meter according to claim 1, characterized in that: The sound monitoring data includes sound monitoring data in seconds, sound monitoring data in minutes, sound monitoring data in hours or sound monitoring data in days; Among them, the sound monitoring data in seconds is the sound monitoring data monitored by the corresponding sound level meter within a time period of 1 second and is stored in a created second data array, the sound monitoring data in minutes is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 seconds and is stored in a created minute data array, the sound monitoring data in hours is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 60 minutes and is stored in a created hour data array, and the sound monitoring data in days is obtained by averaging the sound monitoring data collected by the corresponding sound level meter within a time period of 24 hours and is stored in a created day data array.

3. An environmental noise monitoring device based on a sound level meter, characterized in that: The device is applied in a cloud server, the cloud server is applied in an environmental noise monitoring mobile station and the cloud server is used to execute the environmental noise monitoring method based on a sound level meter according to claim 1 or 2, and the device includes: A receiving module, used to receive sound monitoring data collected by at least one sound level meter set in a target scene of environmental noise to be monitored; A determination module, used to determine the unique identification information corresponding to each of the sound level meters that reports the sound monitoring data; and, according to the unique identification information corresponding to each of the sound level meters, obtain the current collection state information corresponding to each of the sound level meters, wherein the current collection state information corresponding to each of the sound level meters includes the collection position, collection direction, collection angle, activated sub-collector and collection-related equipment of each of the sound level meters in the target scene, and the collection-related equipment of the sound level meter includes a smart device whose distance value from the sound level meter in the target scene is less than or equal to a preset distance threshold and whose influence on the sound level meter performing the sound monitoring data collection operation is greater than or equal to a preset influence threshold; A noise anomaly recognition module, used for inputting all the sound monitoring data and the current collection state information corresponding to the corresponding sound level meter into a pre-generated noise anomaly recognition model to obtain an output result of the noise anomaly recognition model; the output result includes a sub-noise recognition result of each sound level meter at a corresponding collection position in the target scene; An impact factor determination module is used to determine a first noise impact factor corresponding to all the sound level meters according to the collection level corresponding to all the sound monitoring data; determine a second noise impact factor corresponding to each sound level meter according to the current collection state information corresponding to each sound level meter that reports the sound monitoring data; and calculate the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene according to the first noise impact factors respectively corresponding to all the sound level meters and the second noise impact factors respectively corresponding to all the sound level meters; The noise analysis module is used to calculate the environmental noise monitoring result of the target scene according to the sub-noise identification result of each sound level meter at the corresponding collection position in the target scene and the noise impact factor corresponding to the corresponding collection position of each sound level meter in the target scene.

4. An environmental noise monitoring device based on a sound level meter, characterized in that: The device is applied in a cloud server, and the cloud server is applied in an environmental noise monitoring mobile station. The device includes: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the environmental noise monitoring method based on the sound level meter as claimed in claim 1 or 2.

5. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the environmental noise monitoring method based on the sound level meter as claimed in claim 1 or 2.

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