An environmental noise on-line monitoring system and method with high data acquisition rate

Abstract

The application discloses an environmental noise online monitoring system and method with high data acquisition rate, and belongs to the technical field of noise online monitoring in sound function areas.The system monitors noise values in the environment around the sound function area in real time, and performs real-time sampling on the data of the noise sensor.After the data is collected, data storage is performed, real-time noise data is stored in an SD card storage unit, and the data is used for data supplement.The real-time noise data is subjected to threshold value judgment, when the threshold value exceeds a recording standard threshold value, a recording unit starts real-time recording, recording file data is stored in the SD card storage unit, meanwhile, the over-standard noise recording is transmitted to a cloud server grid data collection platform, and the cloud server grid data collection platform is used for noise sound print recognition.Ridiculous transmission technology is adopted to transmit the recording data, so that the reliability and integrity of the noise data and the recording data are ensured.Real-time data or historical data is transmitted to the cloud server grid data collection platform through the Internet of Things technology, and is displayed on the cloud server grid data collection platform.

Description

Technical Field

[0001] This invention relates to an online environmental noise monitoring system and method with high data acquisition rate, belonging to the field of online noise monitoring technology for acoustic functional zones. Background Technology

[0002] A survey of similar devices across numerous markets revealed that all devices use a 1-second sampling interval, resulting in a massive 86,400 data points per day. Furthermore, the use of IoT wireless transmission means that occasional disconnections due to weak signals or base station malfunctions inevitably lead to data loss. Additionally, the large data transfer volume of noise recording files presents a significant challenge for real-time noise monitoring data transmission. Current products cannot achieve 100% data acquisition rates due to weak signals or base station malfunctions. Consequently, subsequent noise event analysis frequently encounters situations where missing noise data prevents on-site reconstruction and hinders the analysis of noise exceeding standards events.

[0003] Meanwhile, for noise recording files, since the file size is often tens of KB or several MB, in order to ensure the real-time transmission of noise data, the common practice of existing online noise monitoring systems is to export the recording files after the event and then review and analyze them. This results in the inability to remotely locate noise exceeding the standard event in the first instance, which is not conducive to the real-time analysis of noise events. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides an online environmental noise monitoring system and method with high data acquisition rate.

[0005] An online environmental noise monitoring system with high data acquisition rate includes a data acquisition unit, a noise sensor, a noise recording unit, a noise storage unit, a data transmission unit, and a cloud server, and features a 100% data acquisition rate.

[0006] Data acquisition unit: The system monitors the noise level in the environment surrounding the acoustic functional area in real time and samples the data from the noise sensor in real time.

[0007] Noise storage unit: After the data is collected, it stores the data and stores the real-time noise data in the SD card storage unit for data supplementation.

[0008] Noise recording unit: The noise recording unit performs threshold judgment on real-time noise data. When the threshold exceeds the recording standard threshold, the recording unit will start real-time recording and store the recording file data in the SD card storage unit. At the same time, the noise recording exceeding the standard will be transmitted to the cloud server grid data aggregation platform for use in noise voiceprint recognition.

[0009] Data transmission unit: Redundant transmission technology is used to transmit recording data to ensure the reliability and integrity of noise data and recording data.

[0010] Cloud server: Real-time or historical data is transmitted to a gridded data aggregation platform on a cloud server for display via Internet of Things (IoT) technology.

[0011] A method for online environmental noise monitoring with high data acquisition rate includes the following steps: data acquisition step, data storage step, data transmission (redundancy) step, and data supplementation step.

[0012] The data acquisition process involves collecting data through a noise acquisition unit.

[0013] Data storage steps include intelligent recording startup, compressed transmission, and real-time recording of noise data.

[0014] The data transmission (redundancy) step adopts a dual-carrier mechanism with dual-link DTU mode, transforming link quality into a visual operation mode. When the signal of one link is weak at a certain moment, the other link is automatically switched over.

[0015] The data completion process employs local completion, platform completion, and intelligent completion.

[0016] This invention solves the technical challenge of maintaining a 100% data acquisition rate in online noise monitoring systems under conditions of simultaneous massive transmission of noise data and audio recordings. By employing intelligent data supplementation, redundant communication links, and intelligent storage technology for massive amounts of data, this invention achieves a complete 100% data acquisition rate, providing a strong guarantee for analyzing changes in ambient noise. This invention realizes a high-data-acquisition-rate online noise monitoring system, enabling real-time, second-by-second transmission of noise data and real-time audio recordings of noise events, thus overcoming the technical challenge of achieving a 100% data acquisition rate. Attached Figure Description

[0017] When considered in conjunction with the accompanying drawings, the invention will be more fully and better understood, and its many accompanying advantages will become readily apparent, by referring to the following detailed description. However, the accompanying drawings, which are provided to further illustrate the invention and form part of this invention, are used to explain the invention and do not constitute an undue limitation thereof, as shown in the figures:

[0018] Figure 1 This is a schematic diagram of the structure of the present invention.

[0019] Figure 2 This is a schematic diagram of the intelligent storage of massive amounts of noise data according to the present invention.

[0020] Figure 3 This is a schematic diagram of the intelligent storage structure of the recording unit of the present invention.

[0021] Figure 4 This is a schematic diagram of the overall frame structure of the noise unit of the present invention.

[0022] Figure 5 This is a schematic diagram of the data communication redundancy link mechanism scheme of the present invention.

[0023] Figure 6 This is a schematic diagram of the intelligent data supplementation mechanism of the present invention.

[0024] Figure 7 This is a schematic diagram of the intelligent storage mechanism for massive amounts of noise and recording data of the present invention.

[0025] Figure 8 This is a flowchart illustrating the data acquisition logic of the present invention.

[0026] Figure 9 This is a data storage flowchart of the present invention.

[0027] Figure 10 This is a flowchart of the database storage process of the present invention.

[0028] Figure 11 This is a flowchart of the data query process of the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] Obviously, many modifications and variations made by those skilled in the art based on the spirit of this invention fall within the scope of protection of this invention.

[0031] Those skilled in the art will understand that, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art.

[0032] To facilitate understanding of the embodiments, further explanations and descriptions will be provided below, and the various embodiments do not constitute a limitation on the embodiments.

[0033] Example 1: As Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 As shown, an online environmental noise monitoring system and method with high data acquisition rate is presented.

[0034] like Figure 1As shown, an online environmental noise monitoring system with a high data acquisition rate is available. It can record the decibel values ​​and sounds in the surrounding environment in real time, and can independently perform cumulative statistics on sound values ​​and record excessive noise. The data is aggregated on a cloud server to visualize the characteristics of environmental noise.

[0035] An online environmental noise monitoring system with high data acquisition rate includes a data acquisition unit, a noise sensor, a noise recording unit, a noise storage unit, a data transmission unit, and a cloud server.

[0036] Data acquisition unit: The system monitors the noise level in the environment surrounding the acoustic functional area in real time and samples the data from the noise sensor in real time at a sampling interval of 1 second.

[0037] Noise storage unit: After the data is collected, it stores the data and stores a large amount of real-time noise data in the SD card storage unit.

[0038] Noise recording unit: such as Figure 3 As shown, a threshold judgment is performed on the real-time noise data. When the threshold exceeds the recording standard threshold, the recording unit will start real-time recording and store the recording file data in the SD card storage unit.

[0039] Data transmission unit: Redundant transmission technology is used to transmit recording data to ensure the reliability and integrity of noise data and recording data.

[0040] Cloud server: By using IoT technology to transmit real-time or historical data to the cloud server grid data aggregation platform for display, it can achieve a noise data collection rate of up to 100% for online monitoring of noise in acoustic functional areas, and complete the visualization of the acoustic environment quality of acoustic functional areas.

[0041] Data storage is mainly divided into real-time noise data and audio file data. Both noise data and audio file data are stored in a separate SD card storage unit. The data storage serves as a backup to complete the data recovery function. When data is missing, the missing data can be retrieved and uploaded automatically.

[0042] The storage of audio file data provides real-time backup of the recording data. In the event of a platform crash, the recording files can be recovered through automatic retrieval and uploading. The uploading of noise data and audio file data employs redundant dual-link DTU mode, ensuring that the uploading of audio file data and noise data does not interfere with each other. The storage diagram of noise data stored in the audio file is shown below. Figure 2 As shown.

[0043] like Figure 4As shown, a high-data-acquisition-rate online environmental noise monitoring method first determines whether there is any audio data to be uploaded after the noise acquisition unit has collected the data. If there is, real-time recording is processed first, followed by noise data storage. Noise data and audio data adopt a dual-link DTU mode, and both types of data can be uploaded to the cloud platform simultaneously, meaning they work concurrently without interference. The DTU also has a built-in redundant transmission mechanism and a dual-carrier mechanism. During operation, the current link quality factor q is judged. When the link quality factor q < 5, the DTU can automatically switch to the network with better current status for data communication, effectively avoiding the risk of data loss due to network issues. Even if data loss is caused by current network problems or other issues, the data can be recovered through the data recovery function. After a data transmission is completed, the cloud platform determines whether the data recovery function needs to be activated. If so, the time period of missing data needs to be found. After finding the time period, this time period is sent to the noise storage unit or the audio storage unit, and then the data for that time period is uploaded. Once the required data recovery event is completed, the next round of data processing and judgment begins.

[0044] A method for online environmental noise monitoring with high data acquisition rate includes the following steps: data acquisition, data storage, data transmission (redundancy), and data supplementation.

[0045] Data acquisition steps: After the noise acquisition unit completes data acquisition, it first determines whether there is any audio data uploaded. If so, the data acquisition unit notifies the sub-board that the audio data needs to be processed. After notifying the sub-board that the audio data needs to be processed, the data acquisition unit continues to acquire real-time noise values, effectively avoiding the loss of noise data. Then, the sub-board stores the real-time audio data in an SD card. After the recording ends, it reads the recording from the SD card to the cloud server. Due to the existence of the dual-center network, the real-time acquisition of noise values ​​will not be interrupted when the audio data is uploaded again. The logic flowchart is as follows: Figure 8 As shown.

[0046] Data storage steps: Intelligent recording starts, compressed transmission, and noise data is recorded in real time.

[0047] The recording storage unit and noise storage unit are dual-channel storage units, ensuring data integrity even when both are uploaded to the cloud platform simultaneously. In this solution, the recording module uses a high-capacity SD card for data storage, while noise data is stored in real-time using an SD card. When simultaneously uploading recording and real-time noise data, a dual-DTU intelligent real-time upload mode is employed, eliminating the need for mode switching and providing a robust hardware foundation for high data acquisition rates. Specifically, when the noise acquisition unit and recording acquisition unit acquire data simultaneously, or either one acquires data, the system acquires the current time in real-time, then retrieves device information, and finally stores the acquired real-time data at the end of the storage information. Once all data has been acquired, the storage of one noise or recording data point is complete. Tag-based storage lays the foundation for rapid device upload and retrieval, as well as data completion functionality. The flowchart is shown below. Figure 9 As shown.

[0048] Data transmission (redundancy) steps: Dual-link DTU mode with dual-carrier mechanism; if one link has a weak signal, it will switch to another link to ensure the links are online in real time. The process is as follows: Figure 5 As shown, firstly, the DTU's link unit will determine the signal link quality q for each path. The link switching logic judgment formula is as follows:

[0049] q = w1*P + w2*t

[0050] q: Link quality factor;

[0051] w1: Signal strength weighting factor;

[0052] P: Wireless signal strength;

[0053] w2: Link online time weighting factor;

[0054] t: Link offline time;

[0055] This formula takes into account DTU signal strength and total link offline time to calculate the link quality factor. When the primary link quality factor q < 5, the backup link is automatically activated. When the primary link quality recovers, it automatically switches back to the primary link. The signal link quality is assessed every 20 seconds, and this assessment interval can be configured. This formula transforms link quality into a visual operation mode. When one link signal is weak at a certain moment, the other link is automatically switched over, ensuring high acquisition rate transmission through hardware mechanisms.

[0056] Data completion steps: such as Figure 6As shown, local data completion, platform data completion, and scheduled querying of missing data are all implemented using intelligent data completion (completion mechanism: opportunistic). First, the cloud platform iterates through the original data time frame to check if there are any time gaps exceeding five sampling periods within the data sampling period. If so, these time gaps are identified and recorded. The time gaps are then selected and sent to the data acquisition unit along with a data completion marker. Upon receiving the data, the data acquisition unit activates the platform data completion function. At this point, the cloud platform sends the device number requiring data completion and the missing time period to the data acquisition unit via instructions. The data acquisition unit enables the background data correction function, starts the storage unit, opens the file stream of the storage unit, iterates through the missing data for the specified time period, and then uploads the data within that time period sequentially and inserts it into the database. During data upload, it first checks if the device is currently transmitting real-time data. If so, the data correction enable flag is set, and the correction function is disabled while waiting for the real-time data upload to complete. After the real-time data upload is complete, the correction flag is deactivated, and the correction function is re-enabled. The data requiring correction is then uploaded again, using a "squeezing in" approach, meaning the device's correction function is enabled only when real-time data acquisition is not affected. After data upload, the device checks if the correction was successful. If the device is still online 10 seconds after correction, the data correction for that time period was successful, and the next timed operation is initiated to prepare for subsequent data correction. Otherwise, the device sends a command to the data acquisition unit indicating that the correction failed, saves the flag, and adds the correction operation for that time period back to the next timed operation.

[0057] The storage structure employs associative storage, linking real-time data with statistical data. When real-time data is uploaded, statistical data from the most recent period is also uploaded to the cloud platform for display. The storage process involves first obtaining the starting address of the storage unit, calculating the total size of the storage unit, and then obtaining the intermediate address. Real-time data is stored starting from the starting address, while statistical data is stored starting from the intermediate address. A statistical time threshold is then set; once this threshold is exceeded, the statistical data for that period is calculated and saved, and the starting address of this statistical data is appended to the real-time data. This provides a solid foundation for high-speed data retrieval. The database storage flowchart is shown below. Figure 10 As shown.

[0058] like Figure 11As shown, to save time as much as possible during data querying, a binary search data structure is used for linked list lookup. First, after confirming the time period for supplementation, the program performs time indexing. It first divides the real-time data storage block of the storage unit into two parts to index the time period to be queried. After confirming the time period, it performs binary search again within that time period until the index value range is less than 100. Then, it starts traversing and searching precisely. If the data at that address is empty, it starts indexing again from half of the storage block using binary search until the difference between the indexed time value and the binary search value is less than the quantization value of 100. At this point, the binary search algorithm ends, traversing and locating the index value, and then starting to upload the data. This process continues in this manner.

[0059] Example 2: As Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 As shown, an online environmental noise monitoring system and method with high data acquisition rate is presented.

[0060] Terminology Explanation:

[0061] Data acquisition rate: The percentage of actual noise automatic monitoring raw data collected during the monitoring period compared to the theoretically required number of noise automatic monitoring raw data collected, due to instrument software and hardware malfunctions.

[0062] Environmental noise refers to the sounds that disturb the surrounding living environment generated during industrial production, construction, transportation, and social life.

[0063] Online monitoring: Real-time monitoring of noise level changes in the environment, and can independently perform statistical functions on noise data.

[0064] This invention aims to develop an online environmental noise monitoring system with a data acquisition rate of 100%, thereby solving the problem that current online environmental noise monitoring systems cannot achieve a 100% data acquisition rate.

[0065] The noise monitoring system can measure the decibel value of the surrounding environment in real time and can independently perform cumulative statistical functions of sound values, thus enabling visualization of environmental noise levels.

[0066] This invention realizes an online environmental noise monitoring system with a 100% data acquisition rate, achieving seamless transmission of real-time noise data and real-time noise recording data, and providing an advanced technical approach for the integrity of online noise monitoring data and noise event analysis.

[0067] Based on the above demonstration and practice, after a long period of testing, the data acquisition rate of the equipment was 100%, which was the same as the expected result and met the testing requirements.

[0068] 1. Data Acquisition: Description of noise data storage and fast pagination retrieval; Recording: High-capacity SD card or other storage media, compressed storage, and fast transmission.

[0069] 2. Data completion: local completion, platform completion, intelligent completion, and scheduled query for missing data issues.

[0070] 3. For data transmission: dual-link data transmission mechanism and intelligent storage mechanism for noise and recording data.

[0071] This invention achieves 100% data acquisition rate through data collection and storage, data transmission, and data supplementation, providing a strong guarantee for analyzing changes in ambient noise.

[0072] Other solutions cannot guarantee a 100% daily acquisition rate of raw equipment data.

[0073] As described above, the embodiments of the present invention have been explained in detail. However, many modifications are possible as long as they do not substantially depart from the inventive point and effects of the present invention, which will be obvious to those skilled in the art. Therefore, all such modifications are also included within the protection scope of the present invention.

Claims

1. A high data acquisition rate online environmental noise monitoring system, characterized in that... It includes a data acquisition unit, a noise sensor, a noise recording unit, a noise storage unit, a data transmission unit, and a cloud server. Data storage is divided into real-time noise data and audio file data. It adopts massive data intelligent storage technology to store noise data and audio file data in different SD card storage units. Through intelligent data supplementation, redundant communication links, and massive data intelligent storage technology, it achieves 100% data acquisition rate and completes the visualization of the acoustic environment quality of the acoustic functional area. Data acquisition unit: The system monitors the noise level in the environment surrounding the acoustic functional area in real time and samples the data from the noise sensor in real time; Noise storage unit: After the data is collected, it stores the data and stores the real-time noise data in the SD card storage unit for data supplementation. The data supplementation process employs local supplementation, platform supplementation, and intelligent supplementation. When uploading data, it first determines whether the device is currently transmitting real-time data. If real-time data is being uploaded, the supplementation data is enabled and the supplementation function is turned off. After the real-time data upload is complete, the supplementation setting is canceled and the supplementation function is re-enabled. The data that needs to be supplemented is then uploaded again. This adopts a "squeezing in" approach, that is, the device's supplementation function is enabled only when real-time data acquisition is not affected. Noise Recording Unit: This unit performs threshold judgment on real-time noise data. When the noise data exceeds the recording standard threshold, the noise recording unit will start real-time recording and store the recorded data in the SD card storage unit. At the same time, the recorded data of the noise exceeding the standard will be transferred to the cloud server grid data aggregation platform for use in noise voiceprint recognition. The noise recording unit and the noise storage unit are two storage units, so even if both are uploaded to the cloud server grid data aggregation platform at the same time, the cloud platform data will not be lost. Data transmission unit: Redundant transmission technology is used to transmit noise data and recording file data to ensure the reliability and integrity of noise data and recording file data; The uploading of noise data and audio file data is implemented using a dual-link DTU mode. When uploading audio file data and real-time noise data simultaneously, a dual-DTU intelligent real-time upload mode is adopted to ensure that the uploading of audio file data and noise data does not interfere with each other and that the communication links serve as backups for each other. If the signal of a certain link is weak, it will switch to another link to ensure that the links are online in real time. First, the link unit of the DTU will judge the signal link quality q of each channel. The link switching logic judgment formula is as follows: q = w1*P + w2*t q: Link quality factor; w1: Signal strength weighting factor; P: Wireless signal strength; w2: Link offline time weighting factor; t: Link offline time; This formula takes into account DTU signal strength and link offline time to calculate the link quality factor. When the primary link quality factor q < 5, the backup link is automatically activated. When the primary link quality recovers, it automatically switches back to the primary link. The signal link quality is assessed every 20 seconds, and this assessment interval can be configured. Cloud server: Real-time or historical data is transmitted to a gridded data aggregation platform on a cloud server for display via Internet of Things (IoT) technology.

2. The high data acquisition rate online environmental noise monitoring system according to claim 1, characterized in that... The noise data storage serves as a backup for completing the data supplementation function. When data is missing, the missing data can be automatically retrieved and uploaded.

3. The high data acquisition rate online environmental noise monitoring system according to claim 1, characterized in that... The storage of audio recording data includes real-time backup. If the cloud server's grid-based data aggregation platform crashes, the audio recording data can be recovered through automatic searching and uploading. Among them, the audio file data is uploaded in real time, and the cloud server grid data aggregation platform can analyze and locate noise exceeding the standard, and guide the handling of emergency events caused by noise exceeding the standard.

4. The high data acquisition rate online environmental noise monitoring system according to claim 1, characterized in that... The data transmission link adopts a redundant link approach. When the main communication link fails or the signal is weak, it automatically switches to the backup communication link, and the switching time is no more than 3 seconds. The IoT cards of the backup communication link and the main communication link use different communication operators to avoid link failure due to the failure of the same operator's communication network.

5. The high data acquisition rate online environmental noise monitoring system according to claim 1, characterized in that... Massive amounts of noise data and large-capacity audio files are completely transmitted to the data aggregation cloud server, achieving a data acquisition rate of up to 100%.

6. A method for online monitoring of environmental noise with high data acquisition rate, characterized in that... The high data acquisition rate online environmental noise monitoring system as described in any one of claims 1 to 5 includes the following steps: a data acquisition step, a data storage step, a data redundancy transmission step, and a data supplementation step. The data acquisition step involves acquiring data through a data acquisition unit. Data storage steps include intelligent recording startup, compressed transmission, and real-time recording of noise data. The data redundancy transmission steps adopt a dual-carrier mechanism with dual-link DTU mode. When uploading audio file data and real-time noise data at the same time, the dual-DTU intelligent real-time upload mode is adopted to transform the link quality into a visual operation mode. When one link signal is weak at a certain moment, the other link is automatically switched. The data completion process employs local completion, platform completion, and intelligent completion.

7. The method for online environmental noise monitoring with high data acquisition rate according to claim 6, characterized in that... The data acquisition process includes the following steps: After the data acquisition unit completes data acquisition, it first determines whether there is any audio file data uploaded. If there is audio file data uploaded, the data acquisition unit will notify the noise recording unit to process the audio file data. After notifying the noise recording unit to process the audio file data, the data acquisition unit will continue to acquire real-time noise data. Then, the noise recording unit will store the real-time audio file data in the SD card. After the audio file data is finished, the audio file data will be read from the SD card to the cloud server gridded data aggregation platform.

8. The method for online environmental noise monitoring with high data acquisition rate according to claim 6, characterized in that... The data storage process includes the following steps: Recording file data is stored on a high-capacity SD card, while noise data is stored in real-time on an SD card. When simultaneously uploading recording file data and real-time noise data, a dual-DTU intelligent real-time upload mode is used. The storage principle is that when the data acquisition unit and the noise recording unit acquire data simultaneously, or either one acquires data, the time is obtained in real-time, and then the device information is retrieved. The acquired real-time data is saved at the end of the stored information. Once all data has been acquired, the storage of one piece of noise data or recording file data is complete. Tagging-based storage lays the foundation for the device's rapid upload and retrieval, as well as data supplementation functions.

9. The method for online environmental noise monitoring with high data acquisition rate according to claim 6, characterized in that... The data completion process includes the following steps: Local and platform-based data completion are used to periodically query missing data. Intelligent data completion first involves the cloud server's gridded data aggregation platform traversing the original data time period to check if there are any time periods with gaps exceeding five sampling periods within the data's sampling cycle. If so, these time periods are identified as needing completion and recorded. The selected time periods, along with a completion marker, are then sent to the data acquisition unit. Upon receiving the data, the data acquisition unit activates the platform's data completion function. At this point, the cloud server's gridded data aggregation platform sends the device number requiring completion and the missing time period as instructions. The data acquisition unit then activates its local data completion function, starts the noise storage unit, opens the file in the noise storage unit, iterates through the missing data for that time period, uploads the data within that time period sequentially, and inserts it into the database. After uploading the data, the device needs to determine whether the data supplementation was successful. If the device is still online 10 seconds after the supplementation, it proves that the data supplementation for this time period was successful. Then, the next timer is started to prepare for subsequent data supplementation. Otherwise, the device sends a command to the data acquisition unit that the supplementation failed, saves the flag, and adds the supplementation operation for this time period to the next timer after completion.

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