Noise abnormity monitoring and early warning method and system and readable storage medium

Abstract

The noise abnormity monitoring and early warning method comprises the following steps: S1, acquiring voiceprint monitoring data of fixed sound level detection devices distributed in a monitoring area in real time; s2, monitoring suspected noise abnormal data; s3, arranging a plurality of temporary sound level detection devices in communication connection with a background server in a preset range of the target fixed sound level detection device, and obtaining re-check voiceprint monitoring data newly detected by the temporary sound level detection devices and the first target fixed sound level detection device; and S4, according to the re-check voiceprint monitoring data, when noise abnormity occurs again, giving out noise abnormity early warning. According to the fixed sound level detection device based on fewer sound level detection devices, when the coarse mesh screening is abnormal, a plurality of temporary sound level detection devices are arranged in the preset range of the target fixed sound level detection device to perform noise abnormity recheck, so that the probability of misjudgment is reduced, and the accuracy of noise source positioning is improved.

Description

Technical Field

[0001] The present invention relates to the field of noise monitoring technology, and in particular to a noise anomaly monitoring and early warning method, system and readable storage medium. Background Art

[0002] Urban noise pollution such as late-night square dancing and bar disturbances can seriously affect the normal lives of residents, especially posing a major problem for local governments and residents. However, the management department lacks effective management methods for noise pollution complaints and it is difficult to collect effective noise pollution data. After receiving complaints, the management department usually handles the incident by visiting the door to dissuade the noise, but cannot confirm the on-site situation when the noise occurs, and the dissuasion effect is poor, which brings great inconvenience to management.

[0003] Noise monitoring instruments are used to monitor noise in living areas. They are widely used in noise monitoring of traffic arteries, noise detection at industrial enterprise boundaries, noise detection at construction site boundaries, environmental noise detection in urban areas, and noise detection, monitoring and evaluation of social living environments.

[0004] In the prior art, document CN107734044A proposes a noise remote 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 noise detection information through the communication operation base station; the data server, C / S client, and content server interact with data through the Internet. It detects the noise environment in the community through the noise detection devices installed in the community and the factory, and provides people with a healthy living environment. The data calibration module calibrates the sampled data, analyzes and processes it through the data analysis module, and stores the records of data processing to improve the accuracy of data monitoring. The stored data is transmitted through the wireless signal transmission module and the Internet, which facilitates data transmission to the data server, and is distributed to the C / S client and content server through the data server, so that users can consult it conveniently. However, although the above method realizes real-time monitoring and early warning of noise in people's living environment, the above noise monitoring equipment is fixedly arranged in living areas such as communities, and a large number of noise monitoring devices need to be arranged, which greatly increases the investment cost of urban noise monitoring.

[0005] In view of the above problems, it is necessary to propose a flexible and changeable noise anomaly monitoring and early warning method to reduce the cost of noise monitoring, improve the efficiency of noise monitoring, and minimize the impact of noise on people's lives. Summary of the invention

[0006] The main purpose of the present invention is to provide a noise anomaly monitoring and early warning method, system and readable storage medium to solve the above technical problems.

[0007] To achieve the above object, the present invention provides a noise anomaly monitoring and early warning method, comprising the steps of:

[0008] S1, real-time acquisition of voiceprint monitoring data from fixed sound level detection devices distributed in the monitoring area and connected to the backend server;

[0009] S2, monitoring the situation in which the fixed sound level detection device at the same time period and the same position detects abnormal noise within a continuous preset number of detection cycles, and recording it as suspected abnormal noise data; wherein the suspected abnormal noise data includes the target abnormal time period in which the suspected abnormal noise data is detected, the information of the first target fixed sound level detection device that monitors the suspected abnormal noise data, and the noise voiceprint data;

[0010] S3, deploying a plurality of temporary sound level detection devices in communication with the backend server within a preset range of the target fixed sound level detection device, and obtaining the latest verified voiceprint monitoring data detected by the temporary sound level detection device and the first target fixed sound level detection device;

[0011] S4, judging whether the noise abnormality occurs again within the target abnormal time period based on the reviewed voiceprint monitoring data; and when the noise abnormality occurs again, locating the position of the noise source based on the reviewed voiceprint monitoring data, and issuing a noise abnormality warning.

[0012] Preferably, the monitoring in step S2 includes:

[0013] If, during a preset number of consecutive detection cycles, a fixed sound level detection device at the same time period and the same location detects that the noise is greater than a preset threshold, and the number of occurrences is greater than a preset number, it is determined that a noise abnormality has been detected.

[0014] Preferably, the number is a discontinuous number.

[0015] Preferably, the step S4 further includes: analyzing the type of the noise according to the verified voiceprint monitoring data; the type of noise includes music noise, motor vehicle noise, construction noise and human voice noise.

[0016] Preferably, in step S4, when the noise abnormality occurs continuously within the target abnormal time period, it is determined that the noise abnormality occurs again.

[0017] Preferably, when there are multiple first target fixed sound level detection devices that monitor suspected noise anomaly data, step S22 is also included, judging whether the target abnormal time period in the multiple suspected noise anomaly data is continuous and whether the information of the multiple first target fixed sound level detection devices is continuous, judging whether there is a timeline relationship between the multiple first target fixed sound level detection devices and the target abnormal time period and whether the noise source is a mobile noise source.

[0018] Preferably, the method further includes step S23, determining whether the noise source is a fixed noise source according to whether the target abnormal time periods in the plurality of suspected noise abnormality data are the same and whether the plurality of the first target fixed sound level detection devices have a radial shape.

[0019] Preferably, the step S4 further includes:

[0020] S5, initiating a manual review request to remind the detection personnel to perform noise monitoring and evidence collection near the location of the noise source during the target abnormal time period;

[0021] S6, issuing a noise abnormality warning or canceling the noise abnormality warning according to the detection result of the detection personnel in step S5.

[0022] The present invention also provides a noise anomaly monitoring and early warning system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the noise anomaly monitoring and early warning method as described in any one of the above items when executing the computer program.

[0023] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the noise abnormality monitoring and early warning method as described in any one of the above items are implemented.

[0024] The noise anomaly monitoring and early warning method of the present invention comprises the steps of: S1, acquiring in real time the voiceprint monitoring data of the fixed sound level detection devices which are distributedly arranged in the monitoring area and are connected to the background server in communication; S2, monitoring the situation in which the fixed sound level detection devices in the same time period and the same position detect the noise anomaly within a preset number of consecutive detection cycles, and recording it as suspected noise anomaly data; wherein the suspected noise anomaly data comprises the target abnormal time period in which the suspected noise anomaly data is detected, the information of the first target fixed sound level detection device which monitors the suspected noise anomaly data, and the noise voiceprint data; S3, arranging a plurality of temporary sound level detection devices which are connected to the background server in communication within the preset range of the target fixed sound level detection device, acquiring the latest verified voiceprint monitoring data detected by the temporary sound level detection device and the first target fixed sound level detection device; S4, judging whether the noise anomaly occurs again within the target abnormal time period according to the verified voiceprint monitoring data; and when the noise anomaly occurs again, locating the position of the noise source according to the verified voiceprint monitoring data, and issuing a noise anomaly early warning. By monitoring the situation that the fixed sound level detection device at the same time period and the same position detects abnormal noise within a preset number of consecutive detection cycles, and recording it as suspected abnormal noise data; deploying multiple temporary sound level detection devices in communication with the background server within the preset range of the target fixed sound level detection device, obtaining the latest detected verification voiceprint monitoring data of the temporary sound level detection device and the first target fixed sound level detection device; judging according to the verification voiceprint monitoring data, when the abnormal noise occurs again, issuing a noise abnormality warning; thereby realizing that the fixed sound level detection device based on fewer sound level detection devices, when an abnormality occurs in the coarse network screening, deploying multiple temporary sound level detection devices within the preset range of the target fixed sound level detection device, and conducting a noise abnormality verification according to the latest detected verification voiceprint monitoring data of the temporary sound level detection device and the first target fixed sound level detection device, can not only save a certain number of fixed sound level detection devices, but also can conduct noise detection verification through the coarse positioning and precise temporary deployment of sound level detection devices, thereby reducing the probability of misjudgment and increasing the accuracy of noise source positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are part of the present invention and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the drawings described below are only some embodiments. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 The figure is a flow chart of a noise anomaly monitoring and early warning method in one embodiment of the present invention.

[0027] Figure 2 A schematic diagram of the hardware structure for running a noise anomaly monitoring and early warning method in one embodiment of the present invention.

[0028] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0029] The following is a clear and complete description of the technical problems solved by the embodiments of the present invention, the technical solutions adopted, and the technical effects achieved in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other equivalent or obviously modified embodiments obtained by ordinary technicians in this field without creative work fall within the scope of protection of the present invention. The embodiments of the present invention can be concretized in a variety of different ways as defined and covered in the claims.

[0030] It should be noted that in the following description, many specific details are given for ease of understanding, but it is obvious that the present invention can be implemented without these specific details.

[0031] It should be noted that, in the absence of clear limitation or conflict, the various embodiments of the present invention and the technical features therein can be combined with each other to form a technical solution.

[0032] Please refer to Figure 1 The present invention proposes a noise abnormality monitoring and early warning method, comprising the steps of:

[0033] S1, real-time acquisition of voiceprint monitoring data from fixed sound level detection devices distributed in the monitoring area and connected to the backend server;

[0034] S2, monitoring the situation in which the fixed sound level detection device at the same time period and the same position detects abnormal noise within a preset number of consecutive detection cycles, and recording it as suspected abnormal noise data; wherein the suspected abnormal noise data includes the target abnormal time period in which the suspected abnormal noise data is detected, the information of the first target fixed sound level detection device that monitors the suspected abnormal noise data, and the noise voiceprint data;

[0035] S3, deploying a plurality of temporary sound level detection devices in communication with the backend server within a preset range of the target fixed sound level detection device, and obtaining the latest verified voiceprint monitoring data detected by the temporary sound level detection device and the first target fixed sound level detection device;

[0036] S4, judging whether the noise abnormality occurs again within the target abnormal time period based on the reviewed voiceprint monitoring data; and when the noise abnormality occurs again, locating the position of the noise source based on the reviewed voiceprint monitoring data, and issuing a noise abnormality warning.

[0037] The noise anomaly monitoring and early warning method of the present invention comprises the steps of: S1, acquiring in real time the voiceprint monitoring data of the fixed sound level detection devices which are distributedly arranged in the monitoring area and are connected to the background server in communication; S2, monitoring the situation in which the fixed sound level detection devices in the same time period and the same position detect the noise anomaly within a preset number of consecutive detection cycles, and recording it as suspected noise anomaly data; wherein the suspected noise anomaly data comprises the target abnormal time period in which the suspected noise anomaly data is detected, the information of the first target fixed sound level detection device which monitors the suspected noise anomaly data, and the noise voiceprint data; S3, arranging a plurality of temporary sound level detection devices which are connected to the background server in communication within the preset range of the target fixed sound level detection device, acquiring the latest verified voiceprint monitoring data detected by the temporary sound level detection device and the first target fixed sound level detection device; S4, judging whether the noise anomaly occurs again within the target abnormal time period according to the verified voiceprint monitoring data; and when the noise anomaly occurs again, locating the position of the noise source according to the verified voiceprint monitoring data, and issuing a noise anomaly early warning. By monitoring the situation that the fixed sound level detection device at the same time period and the same position detects abnormal noise within a preset number of consecutive detection cycles, and recording it as suspected abnormal noise data; deploying multiple temporary sound level detection devices in communication with the background server within the preset range of the target fixed sound level detection device, obtaining the latest detected verification voiceprint monitoring data of the temporary sound level detection device and the first target fixed sound level detection device; judging according to the verification voiceprint monitoring data, when the abnormal noise occurs again, issuing a noise abnormality warning; thereby realizing that the fixed sound level detection device based on fewer sound level detection devices, when an abnormality occurs in the coarse network screening, deploying multiple temporary sound level detection devices within the preset range of the target fixed sound level detection device, and conducting a noise abnormality verification according to the latest detected verification voiceprint monitoring data of the temporary sound level detection device and the first target fixed sound level detection device, can not only save a certain number of fixed sound level detection devices, but also can conduct noise detection verification through the coarse positioning and precise temporary deployment of sound level detection devices, thereby reducing the probability of misjudgment and increasing the accuracy of noise source positioning.

[0038] Preferably, the monitoring in step S2 includes:

[0039] If, during a preset number of consecutive detection cycles, a fixed sound level detection device at the same time period and the same location detects that the noise is greater than a preset threshold, and the number of occurrences is greater than a preset number, it is determined that a noise abnormality has been detected.

[0040] Preferably, the number is a discontinuous number.

[0041] In this embodiment, in addition to occasional events, noise pollution caused by square dancing, racing, night construction, etc. is usually periodic and regular. If the fixed sound level detection device at the same time period and the same location detects that the noise is greater than the preset threshold and the number of occurrences is greater than the preset number, it can represent that the probability of continuous noise pollution is high.

[0042] Among them, the preset number of times can be set to 2 times or 3 times.

[0043] The preset threshold of noise can be set to have a certain degree of noise pollution, but not be particularly serious; it affects residents' rest to a certain extent; at this time, it can be monitored through continuous monitoring.

[0044] If the noise is detected to be in a very large noise decibel range, manual detection or manual on-site inspection can be directly initiated. Preferably, in one embodiment, this embodiment also includes the step of: according to the acquired voiceprint monitoring data of the fixed sound level detection device, when the noise is greater than a preset safety threshold, issuing a noise abnormality warning and / or issuing a manual detection request, so as to arrange on-site personnel to conduct noise detection confirmation.

[0045] Preferably, in step S4, when the noise abnormality occurs continuously within the target abnormal time period, it is determined that the noise abnormality occurs again.

[0046] Preferably, the step S4 further includes: analyzing the type of the noise according to the verified voiceprint monitoring data; the type of noise includes music noise, motor vehicle noise, construction noise and human voice noise.

[0047] Generally speaking, current voiceprint analysis technology can already distinguish the type of noise, and subsequent analysis operations can be performed based on the type of noise. The details are as follows:

[0048] Preferably, when there are multiple first target fixed sound level detection devices that monitor suspected noise anomaly data, step S22 is also included, judging whether the target abnormal time period in the multiple suspected noise anomaly data is continuous and whether the information of the multiple first target fixed sound level detection devices is continuous, judging whether there is a timeline relationship between the multiple first target fixed sound level detection devices and the target abnormal time period and whether the noise source is a mobile noise source.

[0049] Preferably, the method further includes step S23, determining whether the noise source is a fixed noise source according to whether the target abnormal time periods in the plurality of suspected noise abnormality data are the same and whether the plurality of the first target fixed sound level detection devices have a radial shape.

[0050] When the types of noise are music noise, construction noise and human voice noise, they are usually fixed noise sources.

[0051] When the type of noise is motor vehicle noise, it is typically a mobile noise source.

[0052] Preferably, the step S4 further includes:

[0053] S5, initiating a manual review request to remind the detection personnel to perform noise monitoring and evidence collection near the location of the noise source during the target abnormal time period;

[0054] S6, issuing a noise abnormality warning or canceling the noise abnormality warning according to the detection result of the detection personnel in step S5.

[0055] The present invention also provides a noise anomaly monitoring and early warning system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the noise anomaly monitoring and early warning method as described in any one of the above items when executing the computer program.

[0056] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the noise abnormality monitoring and early warning method as described in any one of the above items are implemented.

[0057] Figure 2 FIG. 1 is a schematic diagram of the hardware structure of a method for monitoring and warning of abnormal noise according to an embodiment of the present invention. Figure 2 As shown, the 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 executing a noise abnormality monitoring and early warning method. 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 the modules / units in the above-mentioned device embodiments are implemented.

[0058] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the computer 6.

[0059] The computer 6 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 2 It is only an example of computer 6 and does not constitute a limitation of computer 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer 6 may also include input and output devices, network access devices, buses, etc.

[0060] The processor 60 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0061] The memory 61 may be an internal storage unit of the computer 6, such as a 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, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer 6. Further, the memory 61 may include both an internal storage unit of the computer 6 and an external storage device. 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 is to be output.

[0062] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, 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. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0063] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0065] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0066] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0068] 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 this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained 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, computer-readable media do not include electric carrier signals and telecommunication signals.

[0069] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A noise abnormality monitoring and early warning method, characterized in that: Includes steps: S1, real-time acquisition of voiceprint monitoring data from fixed sound level detection devices distributed in the monitoring area and connected to the backend server; S2, monitoring the situation in which the fixed sound level detection device at the same time period and the same position detects abnormal noise within a preset number of consecutive detection cycles, and recording it as suspected abnormal noise data; wherein the suspected abnormal noise data includes the target abnormal time period in which the suspected abnormal noise data is detected, the information of the first target fixed sound level detection device that monitors the suspected abnormal noise data, and the noise voiceprint data; S3, deploying a plurality of temporary sound level detection devices in communication with the backend server within a preset range of the target fixed sound level detection device, and obtaining the latest verified voiceprint monitoring data detected by the temporary sound level detection device and the first target fixed sound level detection device; S4, judging whether the noise abnormality occurs again within the target abnormal time period based on the reviewed voiceprint monitoring data; and when the noise abnormality occurs again, locating the position of the noise source based on the reviewed voiceprint monitoring data, and issuing a noise abnormality warning.

2. The noise abnormality monitoring and early warning method according to claim 1 is characterized in that: The monitoring in step S2 includes the following steps: within a preset number of consecutive detection cycles, the fixed sound level detection device at the same time period and at the same location detects abnormal noise. If, during a preset number of consecutive detection cycles, a fixed sound level detection device at the same time period and the same location detects that the noise is greater than a preset threshold, and the number of occurrences is greater than a preset number, it is determined that a noise abnormality has been detected.

3. The noise abnormality monitoring and early warning method according to claim 2 is characterized in that: The number of times is discontinuous.

4. The noise abnormality monitoring and early warning method according to claim 1 is characterized in that: The step S4 further includes: analyzing the type of the noise according to the verified voiceprint monitoring data; the type of noise includes music noise, motor vehicle noise, construction noise and human voice noise.

5. The noise abnormality monitoring and early warning method according to claim 1 is characterized in that: In the step S4, when the noise abnormality occurs continuously within the target abnormality time period, it is determined that the noise abnormality occurs again.

6. The noise abnormality monitoring and early warning method according to claim 4 is characterized in that: When there are multiple first target fixed sound level detection devices that monitor suspected noise anomaly data, and / or when the type of the noise is motor vehicle noise, the method further includes step S22, judging whether the target abnormal time period in the multiple suspected noise anomaly data is continuous and whether the information of the multiple first target fixed sound level detection devices is continuous, judging whether there is a timeline relationship between the multiple first target fixed sound level detection devices and the target abnormal time period and whether the noise source is a mobile noise source.

7. The noise abnormality monitoring and early warning method according to claim 4 is characterized in that: The method further includes step S23, determining whether the noise source is a fixed noise source according to whether the target abnormal time periods in the plurality of suspected noise abnormality data are the same and whether the plurality of the first target fixed sound level detection devices have a radial shape.

8. The noise abnormality monitoring and early warning method according to claim 1 is characterized in that: After step S4, the following steps are also included: S5, initiating a manual review request to remind the detection personnel to perform noise monitoring and evidence collection near the location of the noise source during the target abnormal time period; S6, issuing a noise abnormality warning or canceling the noise abnormality warning according to the detection result of the detection personnel in step S5.

9. A noise anomaly monitoring and early warning system, 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 noise abnormality monitoring and early warning method according to any one of claims 1 to 8 are implemented.

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 noise abnormality monitoring and early warning method according to any one of claims 1 to 8 are implemented.

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