Gridding noise monitoring method and system based on distributed sound level detection device

Abstract

The invention relates to the technical field of urban noise monitoring, in particular to a gridding noise monitoring method and system based on a distributed sound level detection device. According to the gridding noise monitoring method based on the distributed sound level detection devices, a gridding noise monitoring system of the distributed sound level detection devices is constructed by taking the minimum estimation precision error rate P of a monitoring network for monitoring surrounding noise and the minimum number of the sound level detection devices as targets; and then the multi-objective optimization function can be conveniently solved based on a genetic algorithm, and an optimal solution with the minimum estimation precision error rate P and the minimum number of sound level detection devices can be obtained through iterative calculation of limited times.

Description

Technical Field

[0001] The present invention relates to the technical field of urban noise monitoring, and particularly to a grid noise monitoring method and system based on a distributed sound level detection device. Background Art

[0002] Real-time monitoring of living area noise is carried out through a noise monitor, which is widely used in traffic artery noise monitoring, industrial enterprise boundary noise detection, construction site boundary noise detection, urban area environmental noise detection, social living environment noise detection, monitoring and assessment.

[0003] In the prior art, Document CN107734044A proposes a remote noise monitoring system for public places, including a noise detection device and a data server. The noise detection device sends information to a communication operation base station through a wireless signal transmission module; the data server obtains the noise detection information through the communication operation base station; the data server, the C / S client, and the content server perform data interaction through the Internet. It detects the noise environment in the community through the noise detection devices installed in the community and provides a healthy living environment for people. The data calibration module calibrates the sampled data, and the data analysis module analyzes and processes it. The records of the processed data are stored to improve the accuracy of data monitoring. The stored data is transmitted through the wireless signal transmission module and the Internet, which is convenient for the data to be transmitted to the data server and distributed to the C / S client and the content server by the data server for users to consult.

[0004] Although the existing automatic monitoring systems can achieve continuous monitoring for 24 hours, the technical means in aspects such as noise source identification, spatial positioning, and pollution tracing are still relatively lacking. At the same time, due to the lack of a scientific layout model of sound level detectors and precise positioning means, it is impossible to comprehensively detect the sources of noise pollution and accurately judge the sources and degrees of noise pollution, which seriously affects the pertinence and effectiveness of noise pollution prevention and control.

[0005] Most of the common environmental noise monitoring methods rely on the comprehensive layout of acoustic sensors, but this seriously increases the cost of noise monitoring. At the same time, with the acceleration of the urbanization process, environmental noise sources are becoming increasingly complex and diverse, posing higher requirements for the comprehensiveness and accuracy of noise monitoring. Summary of the Invention

[0006] The main purpose of the present invention is to provide a grid noise monitoring method and system based on a distributed sound level detection device to solve the above technical problems.

[0007] To achieve the above object, a grid noise monitoring method based on a distributed sound level detection device provided by the present invention includes the steps:

[0008] S1. Construct a minimized grid model M based on the known noise data within the target monitoring area: The minimized grid model M divides the target monitoring area into N identical square grids, and the noise monitoring data Q of the minimized grid model is Q = {(x 1 , y 1 , z 1 ), (x 2 , y 2 , z 2 ),... (x i , y i , z i ),... (x N , y N , z N ), where x i represents the abscissa of grid i, y i represents the ordinate of grid i, z i represents the noise monitoring value of grid i, and i ∈ [1, N];

[0009] S2. Construct a grid-based noise monitoring model M': Randomly select a data subset Q' from the noise monitoring data Q of the minimized grid model. Q' contains J grids, and the noise monitoring value z' i of grid i not included in the J grids is estimated by spatial interpolation to form the grid-based noise monitoring model M'. The noise monitoring data record of the grid-based noise monitoring model M' is

[0010]

[0011]

[0012] where ω j is the weighted average coefficient of the noise monitoring data of grid i in the J grids;

[0013] S3. Construct the objective optimization function S of the grid-based noise monitoring model M':

[0014] where P represents the estimation accuracy error rate of the grid-based noise monitoring model M';

[0015] S4. Solve the objective optimization function S to obtain the optimal grid-based noise monitoring model M' with the minimum estimation accuracy error rate P and the minimum number of grids;

[0016] S5. Set sound level detection devices in the grids corresponding to the optimal grid-based noise monitoring model M' in the target monitoring area to construct a grid-based noise monitoring based on distributed sound level detection devices.

[0017] Preferably, the noise monitoring data z' of grid i not included in the J grids i is calculated using the natural neighbor interpolation method.

[0018] Preferably, the formula for ω j is represents the area of grid i in the Voronoi cell space O i , represents the area of the space O i occupied by the natural neighbor grid j before introducing grid i in the original Voronoi cell space O j ;

[0019] d(k, i) represents the Euclidean distance between grid k and grid i, and d(k, h) represents the Euclidean distance between grid k and grid h.

[0020] Preferably, in step S4, a genetic algorithm is used to solve, including the steps of:

[0021] S41, define the population size X, the maximum memory bank capacity X cache , the maximum number of iterations S, the minimum number of monitoring points l, and the maximum acceptable number of monitoring points L;

[0022] S42, randomly generate an initial population, where the initial population includes x randomly generated grid-based noise monitoring models M', x ≤ X + X cache , l ≤ |J| ≤ L;

[0023] S43, evaluate the estimation accuracy error rate P of each grid-based noise monitoring model M' in the population;

[0024] S44, according to the estimation accuracy error rate P of each grid-based noise monitoring model M' and the preset concentration weighting value, calculate the reproduction rate R of each grid-based noise monitoring model M', and extract Y preset data from the initial population as the parental population in ascending order of the reproduction rate;

[0025] S45, generate a new grid-based noise monitoring model M' as the offspring through the population update strategy, then use the parental population and the offspring population as the new population, and return to step S43 for repeated iteration;

[0026] S46, after reaching the maximum number of iterations S or reaching the preset convergence condition, output the grid-based noise monitoring model M' with the minimum estimation accuracy error rate P in the population, which is the optimal solution.

[0027] Preferably, in S42, the initial population is randomly generated by using two dimensions based on the uniform distribution of the positions of the detection points in the noise monitoring data Q and the uniform distribution of the noise detection values, and randomly extracting in the noise monitoring data Q through the KS algorithm.

[0028] Preferably, the population update strategy includes performing crossover and mutation on the parental population to generate a new grid-based noise monitoring model M' and randomly generating a grid-based noise monitoring model M' as the offspring.

[0029] Preferably, the known noise data is obtained through automatic and / or manual monitoring by setting fixed sound level detection devices and / or mobile sound level detection devices.

[0030] Preferably, in the N grids of the minimized grid model M, noise monitoring is performed for a preset time period for each grid, and the average noise data of each grid is used as the noise detection value.

[0031] The present invention also provides a grid-based noise monitoring system based on a distributed sound level detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the grid-based noise monitoring method based on a distributed sound level detection device described in any one of the above are implemented.

[0032] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the grid-based noise monitoring method based on a distributed sound level detection device described in any one of the above are implemented.

[0033] In the present invention, the layout design problem of the sound level detection device in the grid-based noise monitoring method based on a distributed sound level detection device is transformed into an optimization problem of finding the best detection positions and the best combination of the least number of sound level detection devices. With the goal of minimizing the estimation accuracy error rate P of the monitoring network for surrounding noise monitoring and minimizing the number of sound level detection devices, a grid-based noise monitoring system of a distributed sound level detection device is constructed. Then, based on the genetic algorithm, it is convenient to solve the multi-objective optimization function, and through a finite number of iterative calculations, the optimal solution with the minimum estimation accuracy error rate P and the least number of sound level detection devices can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings, as a part of the present invention, are used to provide a further understanding of the present invention. The schematic embodiments and descriptions of the present invention are used to explain the present invention, but do not constitute an improper limitation to the present invention. Obviously, the drawings in the following description are only some embodiments, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0035] Figure 1 This is a schematic flowchart of a grid-based noise monitoring method based on a distributed sound level detection device in an embodiment of the present invention.

[0036] Figure 2 This is a schematic diagram of the hardware structure for running the grid-based noise monitoring method based on a distributed sound level detection device in an embodiment of the present invention.

[0037] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0038] The following clearly and completely describes the technical problems to be solved, the technical solutions adopted, and the technical effects achieved by the embodiments of the present invention in combination with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of them. Based on the embodiments in the present application, all other equivalent or significantly variant embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. The embodiments of the present invention can be embodied in various different ways as defined and covered in the claims.

[0039] It should be noted that, for the convenience of understanding, many specific details are given in the following description. However, it is obvious that the implementation of the present invention can be without these specific details.

[0040] It should be noted that, without clear limitation or conflict, the various embodiments and their technical features in the present invention can be combined with each other to form technical solutions.

[0041] Please refer to Figure 1 , the present invention proposes a grid-based noise monitoring method based on a distributed sound level detection device, including the steps of:

[0042] S1. Construct a minimized grid model M through the known noise data in the target monitoring area: The minimized grid model M divides the target monitoring area into N identical square grids, and the noise monitoring data Q of the minimized grid model = {(x 1 , y 1 , z 1 ), (x 2 , y 2 , z 2 ),... (x i , y i , z i ),... (x N , y N , z N ), x iDenote the abscissa of grid i as y i Denote the ordinate of grid i as z i Denote the noise monitoring value of grid i, where i ∈ [1, N];

[0043] S2. Construct a gridded noise monitoring model M': Randomly select a data subset Q' from the noise monitoring data Q of the minimized grid model. Q' contains J grids, and the noise monitoring value z' of grid i not included in the J grids i is estimated through spatial interpolation to form the gridded noise monitoring model M'. The noise monitoring data of the gridded noise monitoring model M' is recorded as

[0044]

[0045]

[0046] where ω j is the weighted average coefficient of the noise monitoring data of grid i in the J grids;

[0047] S3. Construct the objective optimization function S of the gridded noise monitoring model M':

[0048] where P represents the estimation accuracy error rate of the gridded noise monitoring model M';

[0049] S4. Solve the objective optimization function S to obtain the optimal gridded noise monitoring model M' with the minimum estimation accuracy error rate P and the minimum number of grids;

[0050] S5. Set sound level detection devices in the grids corresponding to the optimal gridded noise monitoring model M' in the target monitoring area to construct a gridded noise monitoring based on distributed sound level detection devices.

[0051] Preferably, the noise monitoring data z' of grid i not included in the J grids i is calculated using the natural neighbor interpolation method.

[0052] Preferably, the calculation formula of ω j is represents the area of grid i in the Voronoi cell space O i and represents the space O i occupies the area of the original Voronoi cell space O of the natural neighbor grid j before introducing grid i j ;

[0053] d(k,i) represents the Euclidean distance between grid k and grid i, and d(k,h) represents the Euclidean distance between grid k and grid h.

[0054] The calculation formula for the Euclidean distance between grid a and grid b is

[0055] Preferably, in step S4, a genetic algorithm is used for solving, including the steps of:

[0056] S41, defining the population size X, the maximum memory bank capacity X cache , the maximum number of iterations S, the minimum number of monitoring points l, and the maximum acceptable number of monitoring points L;

[0057] S42, randomly generating an initial population, where the initial population includes x randomly generated grid-based noise monitoring models M', x ≤ X + X cache , l ≤ |J| ≤ L;

[0058] S43, evaluating the estimation accuracy error rate P of each grid-based noise monitoring model M' in the population;

[0059] S44, calculating the reproduction rate R of each grid-based noise monitoring model M' according to the estimation accuracy error rate P of each grid-based noise monitoring model M' and a preset concentration weighting value, and extracting Y preset data from the initial population as the parent population in ascending order of the reproduction rate;

[0060] S45, generating a new grid-based noise monitoring model M' as the offspring through a population update strategy, then taking the parent population and the offspring population as the new population, and returning to step S43 for repeated iteration;

[0061] S46, after reaching the maximum number of iterations S or reaching a preset convergence condition, outputting the grid-based noise monitoring model M' with the smallest estimation accuracy error rate P in the population as the optimal solution.

[0062] Preferably, the method of randomly generating the initial population in S42 is to randomly extract from the noise monitoring data Q through the KS algorithm based on two dimensions of the uniform distribution of the positions of the detection points and the uniform distribution of the noise detection values in the noise monitoring data Q.

[0063] The KS algorithm selects data with large differences (the farthest Euclidean distance) into the sample set, effectively ensuring that representative data enters the sample set, and making the data distribution in the sample set as uniform as possible to the greatest extent. The principle of the KS algorithm is to maximize the minimum Euclidean distance between the selected sample points and the remaining sample points. Preferably, the population update strategy includes performing crossover and mutation on the parent population to generate a new grid-based noise monitoring model M' and randomly generating a grid-based noise monitoring model M' as the offspring.

[0064] Preferably, the known noise data is obtained through automatic and / or manual monitoring by setting fixed sound level detection devices and / or mobile sound level detection devices.

[0065] Preferably, among the N grids of the minimized grid model M, noise monitoring is performed for each grid within a preset time period, and the average noise data of each grid is used as the noise detection value.

[0066] In the present invention, the layout design problem of the sound level detection device in the grid-based noise monitoring method based on the distributed sound level detection device is transformed into an optimization problem of finding the best detection positions and the best combination of the fewest sound level detection devices. With the goal of minimizing the estimation accuracy error rate P of the surrounding noise monitoring by the monitoring network and minimizing the number of sound level detection devices, a grid-based noise monitoring system for the distributed sound level detection device is constructed. Then, based on the genetic algorithm, it is convenient to solve the multi-objective optimization function. Through a finite number of iterative calculations, the optimal solution with the minimum estimation accuracy error rate P and the fewest number of sound level detection devices can be obtained.

[0067] Figure 2 It is a schematic diagram of the hardware structure for running the grid-based noise monitoring method based on the distributed sound level detection device provided by an embodiment of the present invention. As Figure 2 shown, this embodiment / computer 6 includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as a program for running the industrial equipment cooperative control method based on TSN. When the processor 60 executes the computer program 62, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 60 executes the computer program 62, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0068] Exemplarily, the computer program 62 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 61 and executed by the processor 60 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the computer 6.

[0069] The computer 6 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer 6 device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand, Figure 2This is only an example of the computer 6 and does not constitute a limitation on the computer 6. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, the computer 6 may also include input / output devices, network access devices, buses, etc.

[0070] The so-called processor 60 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0071] The memory 61 may be an internal storage unit of the computer 6, such as the hard disk or memory of the computer 6. The memory 61 may also be an external storage device of the computer 6, such as a plug-in hard disk equipped on the computer 6, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 61 may also include both the internal storage unit and the external storage device of the computer 6. The memory 61 is used to store the computer program 62 and other programs and data required by the computer 6. The memory 61 may also be used to temporarily store data that has been output or will be output.

[0072] The present invention also provides a grid noise monitoring system based on a distributed sound level detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the grid noise monitoring method based on the distributed sound level detection device described in any one of the above are implemented.

[0073] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the grid noise monitoring method based on the distributed sound level detection device described in any one of the above are implemented.

[0074] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0075] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0077] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0080] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0081] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A grid noise monitoring method based on a distributed sound level detection device, characterized in that: Includes steps: S1, construct a minimized grid model M based on the known noise data in the target monitoring area: the minimized grid model M divides the target monitoring area into N identical square grids, and the noise monitoring data Q of the minimized grid model is Q={(x1, y1, z1), (x2, y2, z2), ... (x i ,y i ,z i ),...(x N ,y N ,z N )},x i Represents the horizontal coordinate of grid i, y i represents the ordinate of grid i, z i represents the noise monitoring value of grid i, i∈[1,N]; S2, constructing a gridded noise monitoring model M': randomly selecting a data subset Q' from the noise monitoring data Q of the minimized grid model, Q' containing J grids, and the noise monitoring value z' of the grid i not contained in the J grids i The gridded noise monitoring model M' is estimated by spatial interpolation. The noise monitoring data of the gridded noise monitoring model M' is recorded as follows: Among them, ω j is the weighted average coefficient of noise monitoring data of grid i among J grids; S3, construct the objective optimization function S of the gridded noise monitoring model M': Wherein, P represents the estimation accuracy error rate of the gridded noise monitoring model M'; S4, solving the target optimization function S to obtain the optimal gridded noise monitoring model M' with the minimum estimation accuracy error rate P and the least number of grids; S5, setting a sound level detection device in the grid corresponding to the optimal grid noise monitoring model M' in the target monitoring area, and constructing a grid noise monitoring based on a distributed sound level detection device.

2. The grid noise monitoring method based on the distributed sound level detection device according to claim 1 is characterized in that: The noise monitoring data z' of the grid i not included in the J grids i The natural neighbor interpolation method is used for calculation.

3. The grid noise monitoring method based on the distributed sound level detection device according to claim 2 is characterized in that: The ω j The calculation formula is Represents the grid i in the Voronoi cell space O i The area of Representation space O i Occupies the original space O of the Voronoi cell of the natural neighboring grid j before the introduction of grid i j area; d(k,i) represents the Euclidean distance between grid k and grid i, and d(k,h) represents the Euclidean distance between grid k and grid h.

4. The grid noise monitoring method based on the distributed sound level detection device according to claim 1 is characterized in that: In step S4, a genetic algorithm is used to solve the problem, including the following steps: S41, define population size X, maximum memory capacity X cache , maximum number of iterations S, minimum number of monitoring points l, maximum acceptable number of monitoring points L; S42, randomly generate an initial population, the initial population includes x randomly generated gridded noise monitoring models M', x≤X+X cache , l≤|J|≤L; S43, evaluating the estimation accuracy error rate P of each gridded noise monitoring model M' of the population; S44, calculating the reproduction rate R of each gridded noise monitoring model M' according to the estimation accuracy error rate P and the preset concentration weighted value of each gridded noise monitoring model M', and extracting preset Y data from the initial population as the parent population according to the reproduction rate from small to large; S45, generating a new gridded noise monitoring model M' as a child through a population update strategy, then taking the parent population and the child population as a new population, and returning to step S43 for repeated iterations; S46, after reaching the maximum number of iterations S or reaching the preset convergence condition, the gridded noise monitoring model M' with the smallest estimation accuracy error rate P in the population is output, which is the optimal solution.

5. The grid noise monitoring method based on the distributed sound level detection device according to claim 4 is characterized in that: The method of randomly generating the initial population in S42 adopts two dimensions based on the uniform distribution of the positions of the detection points in the noise monitoring data Q and the uniform distribution of the noise detection values, and randomly extracts from the noise monitoring data Q through the KS algorithm.

6. The grid noise monitoring method based on the distributed sound level detection device according to claim 4 is characterized in that: The population update strategy includes performing crossover and mutation on the parent population to generate a new gridded noise monitoring model M' and a randomly generated gridded noise monitoring model M' as a child.

7. The grid noise monitoring method based on the distributed sound level detection device according to claim 1 is characterized in that: The known noise data is obtained by setting a fixed sound level detection device and / or a mobile sound level detection device through automatic and / or manual monitoring.

8. The grid noise monitoring method based on the distributed sound level detection device according to claim 7 is characterized in that: In the N grids of the minimized grid model M, each grid performs noise monitoring within a preset time period, and obtains average noise data of each grid as the noise detection value.

9. A gridded noise monitoring system based on a distributed sound level detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the gridded noise monitoring method based on a distributed sound level detection device are implemented as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the gridded noise monitoring method based on a distributed sound level detection device according to any one of claims 1 to 8 are implemented.

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