A multi-beam noise detection method and apparatus

Abstract

This invention provides a multi-beam noise detection method and apparatus. The method includes: determining the noise detection area of ​​the object under test and establishing a multi-beam array detection point; acquiring noise array signals based on the multi-beam array detection points and performing beamforming to form noise acoustic signal data of the detection area; performing a first noise identification analysis on the object under test based on the noise acoustic signal data of the detection area to form first noise identification information, and determining whether there is feasibility for performing a second noise identification analysis; performing a second noise identification analysis on the object under test based on the feasibility result and the noise acoustic signal data of the detection area to form second noise identification information; and issuing a noise detection warning based on the second noise identification information and the first noise identification information. The multi-beam noise detection method provided by this invention can effectively combine beamforming methods for noise detection processing, determining more accurate noise data information.

Description

Technical Field

[0001] This application relates to the field of noise detection technology, and more specifically, to a multi-beam noise detection method and apparatus. Background Technology

[0002] From a physics perspective, noise is any irregular signal; from a physiological point of view, it refers to any sound that interferes with people's rest, study, and work, or interferes with the sounds they want to hear. While noise is mostly unwanted in daily life, it plays a crucial role in production and daily life. In production and daily life, especially in product manufacturing and inspection, engineering projects still in use, and equipment defect detection, internal defects are often difficult or impossible to inspect directly, thus requiring indirect methods. The definite shape, materials, and structure of manufactured products, projects, and equipment determine the definite sound wave signals they exhibit during operation or sound wave transmission. When defects occur, they cause changes in the sound wave signals; that is, noise appears in the sound wave signal measured under relatively normal, defect-free conditions.

[0003] Currently, mature technologies for noise measurement of defects have been developed, enabling indirect defect confirmation through sound wave measurement. To improve detection accuracy, multi-beamforming can further determine the presence of noise. Beamforming effectively processes and analyzes the acquired sound waves to avoid the influence of environmental factors on the detection results. However, current noise detection focuses primarily on beamforming processing, specifically the appropriate sound signal processing methods for filtering and noise reduction to obtain more accurate sound data and achieve accurate noise assessment. Even after beamforming, signal data deviations still exist, particularly in gain focus direction and sound power. Given that beamforming data cannot always accurately identify noise, effectively combining beamforming with diverse noise detection processing methods may become a new research direction in noise detection.

[0004] Therefore, how to effectively combine beamforming methods for noise detection processing in order to accurately determine noise data information is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, it is necessary to provide a multi-beam noise detection method and apparatus to solve the technical problem that existing technologies cannot accurately determine noise data information.

[0006] To address the aforementioned technical problems, this invention provides a multi-beam noise detection method, comprising:

[0007] Determine a noise detection area for the object under test that is no less than the number of first detection positions, and establish multi-beam array detection points based on the noise detection area;

[0008] Based on the multi-beam array detection points, noise array signals are acquired and beamforming is performed to form noise acoustic signal data of the detection area.

[0009] Based on the noise acoustic signal data of the detection area, a first noise identification analysis is performed on the object under test to form first noise identification information, and it is determined whether there is a feasibility to perform a second noise identification analysis.

[0010] If it is feasible to perform a second noise identification analysis, the second noise identification analysis is performed on the object under test by combining the noise sound wave signal data of the detection area to form second noise identification information.

[0011] Noise detection and early warning are performed based on the second noise identification information and the first noise identification information.

[0012] In some possible implementations, determining a noise detection region of not less than a first detection position for the object under test, and establishing multi-beam array detection points based on the noise detection region, includes:

[0013] Establish a coordinate system for the detection area of ​​the object to be tested;

[0014] A first noise detection plane is determined in the coordinate system of the detection area, and multiple planar noise detection areas are established on the first noise detection plane in a number not less than the number of first planar noise detection positions;

[0015] Determine a plurality of spatial noise detection regions in the coordinate system of the detection area, the number of which is not less than the number of the first spatial noise detection positions, and the plurality of spatial noise detection regions are not located on the plane in which the plurality of planar noise detection regions are located;

[0016] A planar array of noise sensors is arranged within the plurality of planar noise detection areas;

[0017] A spatial array of noise sensors is arranged within the plurality of spatial noise detection areas;

[0018] Wherein, the first number of detection positions is the sum of the first number of planar noise detection positions and the first number of spatial noise detection positions, the multiple noise detection areas include multiple planar noise detection areas and multiple spatial noise detection areas, and the multi-beam array detection points include a planar array of noise sensors and a spatial array of noise sensors.

[0019] In some possible implementations, on the first noise detection plane, the effective interval angle between the plurality of planar noise detection areas and the origin of the detection area coordinate system is not less than 90 degrees, and the positions of the plurality of noise detection areas do not form a straight line.

[0020] In some possible implementations, the step of performing a first noise identification analysis on the target object based on the noise acoustic signal data of the detection area to form first noise identification information, and determining whether there is feasibility for performing a second noise identification analysis, includes:

[0021] Based on the noise sound wave detection data of the detection area, obtain the noise sound wave signal power W determined for each noise detection area;

[0022] The power of the noise acoustic signal is analyzed to generate noise power analysis data.

[0023] Acquire initial power analysis data, and perform a first noise identification analysis on the initial power analysis data and the noise power analysis data to form the first noise identification information;

[0024] Obtain the noise confirmation result from the first noise identity confirmation information, and determine whether there is a feasibility for conducting a second noise identity confirmation analysis based on the noise confirmation result.

[0025] In some possible implementations, the step of performing power analysis on the noise acoustic signal power to generate noise power analysis data includes:

[0026] The noise acoustic signal power W is arranged in order of magnitude to form an ordered noise acoustic signal power set Q1 = {W1, W2, ..., Wn}, where n is the sequence number of the noise detection area after sorting the noise acoustic signal power according to its magnitude, and n is a non-zero natural number.

[0027] Adjacent deviation processing is performed on the power data in the ordered noise acoustic wave signal power set to form an ordered noise acoustic wave signal power deviation set Q2={W2-W1,W3-W2,…,Wn-Wn-1};

[0028] Obtain the variance of the ordered noise acoustic wave signal power deviation set to form the noise acoustic wave signal power variance C.

[0029] In some possible implementations, the step of acquiring initial power analysis data and performing a first noise identification analysis on the initial power analysis data and the noise power analysis data to form the first noise identification information includes:

[0030] Acquire the initial power analysis data of the object under test, and perform non-noise power range analysis to determine the variance range of the non-noise acoustic signal power of the object under test, Cinitial = [Csmall, Clarge], where Csmall is the minimum value of the range and Clarge is the maximum value of the range.

[0031] The power variance range C of the non-noise sound wave signal is initially compared with the power variance C of the noise sound wave signal to confirm the existence of noise and form the first noise identification information.

[0032] In some possible implementations, if the feasibility of performing a second noise identification analysis exists, the second noise identification analysis is performed on the target object in conjunction with the noise acoustic signal data of the detection area to form second noise identification information, including:

[0033] Based on the noise acoustic signal data of the detection area, the gain focusing direction determined for each noise detection area is obtained, and a position function Sn(x, y, z) of the gain focusing direction is established in the coordinate system of the detection area, where x, y, and z represent position parameters on different coordinate axes of the coordinate system of the detection area, respectively.

[0034] Based on the position function Sn(x, y, z) determined for each noise detection region, focus confirmation is performed to form focus confirmation result data;

[0035] Based on the focus confirmation result data, the location of the noise source is analyzed to form the second noise identification information.

[0036] In some possible implementations, the focus confirmation based on the position function Sn(x, y, z) determined for each noise detection region, forming focus confirmation result data, includes:

[0037] By establishing equations for each pair of position functions, the focal position coordinates between any two position functions can be determined.

[0038] In some possible implementations, the step of analyzing the location of the noise source based on the focus confirmation result data to form the second noise identification information includes:

[0039] When the number of focal position coordinates is the same as the number of position functions, the focal position coordinates are determined as the noise source location;

[0040] When the number of focal position coordinates is less than the number of position functions, a noise source spatial position region is defined, and the noise source spatial position region is determined as the spatial region where the noise source is located. The noise source spatial position region is an effective spherical space. The effective center position coordinate point of the spherical space is formed with reference to the focal position coordinates determined by more than two of the position functions. The diameter of the spherical space is the maximum length from the effective center position coordinate point to any of the focal position coordinates.

[0041] On the other hand, the present invention also provides a multi-beam noise detection device, comprising:

[0042] A multi-beam array establishment unit is used to determine a noise detection area of ​​no less than a first detection position for the object under test, and to establish multi-beam array detection points based on the noise detection area;

[0043] The beamforming unit is used to acquire noise array signals based on the multi-beam array detection points and perform beamforming to form noise acoustic signal data in the detection area.

[0044] The first identity verification unit is used to perform a first noise identity verification analysis on the object under test based on the noise acoustic signal data of the detection area, form first noise identity verification information, and determine whether there is a feasibility to perform a second noise identity verification analysis.

[0045] The second identity verification unit is used to perform second noise identity verification analysis on the object under test in combination with the noise sound wave signal data of the detection area if there is a feasibility for second noise identity verification analysis, thereby forming second noise identity verification information.

[0046] The noise warning unit is used to perform noise detection and warning based on the second noise identification information and the first noise identification information.

[0047] The beneficial effects of the above embodiments are as follows: The multi-beam noise detection method provided by this invention establishes an effective noise detection system using multi-beam detection techniques. Compared to multi-beam detection at a single location, this results in a more comprehensive and accurate noise detection method. Furthermore, by combining the noise acoustic signal data after beamforming with noise identification based on this scheme's noise detection system, it is possible to more accurately determine whether the detected object is generating noise, thereby facilitating effective detection and processing of the detected object. The combination of beamforming techniques with multi-location detection and comprehensive analysis further optimizes the noise detection process, obtaining more accurate noise detection data and results. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating an embodiment of a multibeam noise detection method provided by the present invention;

[0050] Figure 2 This is a schematic diagram of an embodiment of a multibeam noise detection device provided by the present invention;

[0051] Figure 3 A schematic diagram of the structure of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0053] It should be understood that the illustrative drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0054] Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] The specific embodiments are described in detail below. It should be noted that the order of the following descriptions of the embodiments is not intended to limit the preferred order of the embodiments.

[0057] This invention provides a multi-beam noise detection method and apparatus.

[0058] like Figure 1 As shown, Figure 1 This is a flowchart illustrating an embodiment of a multi-beam noise detection method provided by the present invention. The multi-beam noise detection method includes:

[0059] S101. Determine a noise detection area for the object to be tested that is not less than the number of the first detection positions, and establish multi-beam array detection points based on the noise detection area;

[0060] S102. Obtain the noise array signal based on the multi-beam array detection points, and perform beamforming to form noise acoustic signal data of the detection area.

[0061] S103. Based on the noise acoustic signal data of the detection area, perform a first noise identity verification analysis on the object to be tested to form first noise identity verification information, and determine whether there is feasibility for performing a second noise identity verification analysis.

[0062] S104. If it is feasible to perform a second noise identification analysis, combine the noise sound wave signal data of the detection area to perform a second noise identification analysis on the object under test and form second noise identification information.

[0063] S105. Noise detection and early warning are performed based on the second noise identification information and the first noise identification information.

[0064] Compared with existing technologies, the multi-beam noise detection method provided by this invention establishes an effective noise detection system using multi-beam detection techniques, resulting in a more comprehensive and accurate noise detection approach. Simultaneously, by combining the noise acoustic signal data after beamforming with noise identification based on this scheme's noise detection system, it is possible to more accurately determine whether the detected object is generating noise, thereby facilitating effective detection and processing of the detected object. The beamforming technique combined with multi-position detection and comprehensive analysis further optimizes the noise detection process, obtaining more accurate noise detection data and results.

[0065] Optionally, in some embodiments of the present invention, step S101, which involves determining a noise detection area of ​​not less than a first detection position for the object under test, and establishing multi-beam array detection points based on the noise detection area, includes:

[0066] Establish a coordinate system for the detection area of ​​the object to be tested;

[0067] A first noise detection plane is determined in the coordinate system of the detection area, and multiple planar noise detection areas are established on the first noise detection plane in a number not less than the number of first planar noise detection positions;

[0068] Determine a plurality of spatial noise detection regions in the coordinate system of the detection area, the number of which is not less than the number of the first spatial noise detection positions, and the plurality of spatial noise detection regions are not located on the plane in which the plurality of planar noise detection regions are located;

[0069] A planar array of noise sensors is arranged within the plurality of planar noise detection areas;

[0070] A spatial array of noise sensors is arranged within the plurality of spatial noise detection areas;

[0071] Wherein, the first number of detection positions is the sum of the first number of planar noise detection positions and the first number of spatial noise detection positions, the multiple noise detection areas include multiple planar noise detection areas and multiple spatial noise detection areas, and the multi-beam array detection points include a planar array of noise sensors and a spatial array of noise sensors.

[0072] In a specific embodiment of the present invention, on the first noise detection plane, the effective interval angle between the multiple planar noise detection areas and the origin of the detection area coordinate system is not less than 90 degrees, and the positions of the multiple noise detection areas do not form a straight line.

[0073] It should be noted that the effective interval angle mentioned here is determined with reference to the positioning point defined in each planar noise detection area. The positioning point can be a reference point on the gain focusing direction determined under the initial noise-free detection conditions, or a relative reference point set based on spatial location. Based on the characteristics of sound waves, a detection position of at least 90 degrees can maximize the non-repeatability of data collected from each planar noise detection area. Under the constraint of at least 90 degrees, a maximum of four positions can be established for planar noise detection areas. Of course, this can be set according to the actual detection situation.

[0074] It is understandable that the acoustic signals collected from different locations of the inspection areas will differ for the test object. Compared to collecting acoustic signals from a single location and then confirming noise based on the collected data, the smaller amount of data obtained may lead to deviations or even distortions in the analysis results due to environmental or even human factors. Therefore, this solution establishes multiple noise detection areas to collect noise from different directions, which can minimize the analytical bias caused by the small amount of data. Simultaneously, to ensure that the data collected from different noise detection areas are not repetitive, this solution establishes a coordinate system with the test object as a reference to set the positions of the noise detection areas. There are various ways to set these positions; this solution mainly limits two aspects: the positional relationship between different noise detection areas on the same plane, and the spatial position of the noise detection areas around the detection plane. This ensures that when a noise source is present, all noise detection areas detect relative to the noise source, avoiding the acquisition of repetitive detection data, greatly increasing the amount of detection data, providing a sufficient data foundation for subsequent analysis, and further reducing the occurrence of biases. Of course, the noise detection area can be set according to the actual object to be tested and the environmental conditions. As long as the amount of data collected does not contain too much repetitive data, it is acceptable.

[0075] Optionally, in some embodiments of the present invention, in step S102, noise array signals are acquired based on the multi-beam array detection points, and beamforming is performed to form noise acoustic signal data of the detection area.

[0076] It should be noted that the processing method can be set as needed during beamforming to improve the processing effects of filtering, noise reduction, amplification, etc.

[0077] Optionally, in some embodiments of the present invention, step S103, based on the noise acoustic signal data of the detection area, performs a first noise identification analysis on the object to be tested to form first noise identification information, and determines whether there is feasibility for performing a second noise identification analysis, including:

[0078] Based on the noise sound wave detection data of the detection area, obtain the noise sound wave signal power W determined for each noise detection area;

[0079] The noise acoustic wave power signal is subjected to power analysis to generate noise power analysis data.

[0080] Acquire initial power analysis data, and perform a first noise identification analysis on the initial power analysis data and the noise power analysis data to form the first noise identification information;

[0081] Obtain the noise confirmation result from the first noise identity confirmation information, and determine whether there is a feasibility for conducting a second noise identity confirmation analysis based on the noise confirmation result.

[0082] Understandably, the purpose of noise detection is to determine the presence or absence of noise and to confirm the location of the noise source from the collected sound wave data. Therefore, further data processing steps are established around these two detection objectives to more accurately determine the noise situation. For these two objectives, the presence or absence of noise is primarily confirmed by analyzing the power information of the sound wave signal, while the location of the noise source is determined using directional data from multiple noise detection areas. Of course, confirming the noise source after the noise presence or absence analysis avoids ineffective noise source location analysis and optimizes the noise detection process and efficiency. Specifically, the confirmation of the presence or absence of noise is achieved by comparing it with data detected under the initial normal state.

[0083] The power analysis of the noise acoustic signal power to form noise power analysis data includes:

[0084] The noise acoustic signal power W is arranged in order of magnitude to form an ordered noise acoustic signal power set Q1 = {W1, W2, ..., Wn}, where n is the sequence number of the noise detection area after sorting the noise acoustic signal power according to its magnitude, and n is a non-zero natural number.

[0085] Adjacent deviation processing is performed on the power data in the ordered noise acoustic wave signal power set to form an ordered noise acoustic wave signal power deviation set Q2={W2-W1,W3-W2,…,Wn-Wn-1};

[0086] Obtain the variance of the ordered noise acoustic wave signal power deviation set to form the noise acoustic wave signal power variance C.

[0087] It is understandable that the power of different noises varies. Detection at a single location, and comparing the results with those under initial normal conditions, lacks comparability and is significantly affected by the limited scope of the detection location and the singularity of the data, leading to reduced accuracy. While the sound wave data collected at different locations may differ for the same noise, the relative magnitude of the data remains stable. Analyzing the presence or absence of noise based on this relative magnitude can significantly improve the accuracy of the analysis. In this scheme, this relative magnitude is established by the deviation between the power of the noise sound wave signals acquired from different noise detection areas. Specifically, this scheme uses variance as a reference to represent the deviation, meaning that the relative dispersion of the power data acquired from different detection locations for the same noise remains stable.

[0088] Additionally, initial power analysis data is acquired, and the initial power analysis data is combined with the noise power analysis data to perform a first noise identification analysis, forming the first noise identification information, including:

[0089] Acquire the initial power analysis data of the object under test, and perform non-noise power range analysis to determine the variance range of the non-noise acoustic signal power of the object under test, Cinitial = [Csmall, Clarge], where Csmall is the minimum value of the range and Clarge is the maximum value of the range.

[0090] The power variance range C of the non-noise sound wave signal is initially compared with the power variance C of the noise sound wave signal to confirm the existence of noise and form the first noise identification information.

[0091] It should be noted that, under normal circumstances, the variance data obtained from the initial detection can determine a range of variance values ​​for noise-free conditions. By comparing the variance of the noise sound wave signal power with this range, the presence or absence of noise can be determined.

[0092] Optionally, in some embodiments of the present invention, step S104, which involves performing a second noise identification analysis on the test object based on the feasibility of the feasibility result and in conjunction with the noise acoustic signal data of the detection area to form second noise identification information, includes:

[0093] The gain focusing direction for each noise detection area is obtained based on the noise acoustic signal data of the detection area, and a position function Sn(x, y, z) for the gain focusing direction is established in the coordinate system of the detection area, where x, y, and z represent position parameters on different coordinate axes of the coordinate system of the detection area.

[0094] Based on the position function Sn(x, y, z) determined for each noise detection region, focus confirmation is performed to form focus confirmation result data;

[0095] Based on the focus confirmation result data, the location of the noise source is analyzed to form the second noise identification information.

[0096] It should be noted that the directional information contained in the beamforming acoustic signal data mainly refers to the direction of gain focusing. Reversing the gain focusing and establishing it in the detection area coordinate system using a position function allows for a more intuitive and clear determination of the possible location lines of noise sources within each noise detection area.

[0097] The step of performing focus confirmation based on the position function Sn(x, y, z) determined for each noise detection region to form focus confirmation result data includes:

[0098] By establishing equations for each pair of position functions, the focal position coordinates between any two position functions can be determined.

[0099] It is understandable that since different noise detection areas do not overlap in terms of detection position, that is, the collected sound wave signal data is not repeatable, when the position function has a focal point, it can be determined that there is a noise source near the focal point, which further improves the accuracy of noise source identification.

[0100] Additionally, the step of analyzing the location of the noise source based on the focus confirmation result data to form the second noise identification information includes:

[0101] When the number of focal position coordinates is the same as the number of position functions, the focal position coordinates are determined as the noise source location;

[0102] When the number of focal position coordinates is less than the number of position functions, a noise source spatial position region is defined, and the noise source spatial position region is determined as the spatial region where the noise source is located. The noise source spatial position region is an effective spherical space. The effective center position coordinate point of the spherical space is formed with reference to the focal position coordinates determined by more than two of the position functions. The diameter of the spherical space is the maximum length from the effective center position coordinate point to any of the focal position coordinates.

[0103] It's important to note that noise sources are not necessarily singular; they are usually multiple. Therefore, the direction of gain focusing is based on the fitted direction formed by the processing methods used in beamforming, and the actual locations of the noise sources are distributed near the fitted inverse direction. It can be determined that if the position function of the gain focusing direction only has pairwise intersection points, it indicates that the different noise sources are relatively independent, and each focal point can be identified as the location of a noise source. However, if multiple points of position function intersection exist, it can be determined that the noise sources are interconnected, and the focal point is not necessarily the exact location of the noise source; it can only lock the location of the noise source within a certain range, but this still provides a basis for identifying the noise source.

[0104] Optionally, in some embodiments of the present invention, in step S105, noise detection and early warning are performed based on the second noise identification information and the first noise identification information.

[0105] Noise detection and early warning mainly involves identifying noise levels and providing data on noise power and source to inform subsequent noise management.

[0106] This invention utilizes multi-beam detection to establish an effective noise detection system, providing a more comprehensive and accurate noise detection method compared to single-location multi-beam detection. Furthermore, by combining beamforming noise acoustic signal data with noise identification based on this scheme, the system can more accurately determine whether the detected object is generating noise, thus facilitating effective detection and processing. The combination of beamforming and multi-location detection with comprehensive analysis further optimizes the noise detection process, yielding more accurate noise detection data and results.

[0107] To better implement the multi-beam noise detection method in the embodiments of the present invention, based on the multi-beam noise detection method, the embodiments of the present invention also provide a multi-beam noise detection device, such as... Figure 2 As shown, the multibeam noise detection device 200 includes:

[0108] The multi-beam array establishment unit 201 is used to determine a noise detection area of ​​no less than a first detection position for the object under test, and to establish multi-beam array detection points based on the noise detection area.

[0109] The beamforming unit 202 is used to acquire noise array signals based on the multi-beam array detection points and perform beamforming to form noise acoustic signal data of the detection area.

[0110] The first identity verification unit 203 is used to perform a first noise identity verification analysis on the object to be tested based on the noise acoustic signal data of the detection area, form first noise identity verification information, and determine whether there is a feasibility to perform a second noise identity verification analysis.

[0111] The second identity verification unit 204 is used to perform second noise identity verification analysis on the object under test in combination with the noise sound wave signal data of the detection area if there is a feasibility for second noise identity verification analysis, thereby forming second noise identity verification information.

[0112] The noise warning unit 205 is used to perform noise detection and warning based on the second noise identification information and the first noise identification information.

[0113] The multi-beam noise detection device 200 provided in the above embodiments can realize the technical solutions described in the above multi-beam noise detection method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above multi-beam noise detection method embodiments, and will not be repeated here.

[0114] like Figure 3 As shown, the present invention also provides an electronic device 300. The electronic device 300 includes a processor 301, a memory 302, and a display 303. Figure 3 Only some components of the electronic device 300 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0115] In some embodiments, processor 301 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 302 or process data, such as the multi-beam noise detection program in this invention.

[0116] In some embodiments, processor 301 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 301 may be local or remote. In some embodiments, processor 301 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0117] In some embodiments, memory 302 may be an internal storage unit of electronic device 300, such as a hard disk or memory of electronic device 300. In other embodiments, memory 302 may also be an external storage device of electronic device 300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 300.

[0118] Furthermore, the memory 302 may include both internal storage units of the electronic device 300 and external storage devices. The memory 302 is used to store application software and various types of data installed on the electronic device 300.

[0119] In some embodiments, display 303 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 303 is used to display information from electronic device 300 and to display a visual user interface. Components 301-303 of electronic device 300 communicate with each other via a system bus.

[0120] In one embodiment, when the processor 301 executes the multibeam noise detection program in the memory 302, the following steps can be implemented:

[0121] Determine a noise detection area for the object under test that is no less than the number of first detection positions, and establish multi-beam array detection points based on the noise detection area;

[0122] Based on the multi-beam array detection points, noise array signals are acquired and beamforming is performed to form noise acoustic signal data of the detection area.

[0123] Based on the noise acoustic signal data of the detection area, a first noise identification analysis is performed on the object under test to form first noise identification information, and it is determined whether there is a feasibility to perform a second noise identification analysis.

[0124] If it is feasible to perform a second noise identification analysis, the second noise identification analysis is performed on the object under test by combining the noise sound wave signal data of the detection area to form second noise identification information.

[0125] Noise detection and early warning are performed based on the second noise identification information and the first noise identification information.

[0126] It should be understood that when the processor 301 executes the multi-beam noise detection program in the memory 302, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0127] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 300 mentioned. Electronic device 300 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 300 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0128] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the multi-beam noise detection methods provided in the above-described method embodiments.

[0129] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0130] The multi-beam noise detection method and apparatus provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-beam noise detection method, characterized in that, include: Determine a noise detection area for the object under test that is no less than the number of first detection positions, and establish multi-beam array detection points based on the noise detection area; Based on the multi-beam array detection points, noise array signals are acquired and beamforming is performed to form noise acoustic signal data of the detection area. Based on the noise acoustic signal data of the detection area, a first noise identity verification analysis is performed on the object under test to form first noise identity verification information, and it is determined whether there is feasibility to perform a second noise identity verification analysis. If it is feasible to perform a second noise identification analysis, the second noise identification analysis is performed on the object under test by combining the noise sound wave signal data of the detection area to form second noise identification information. Noise detection and early warning are performed based on the second noise identification information and the first noise identification information.

2. The multi-beam noise detection method according to claim 1, characterized in that, The step of determining a noise detection area of ​​at least one number of first detection positions for the object under test, and establishing multi-beam array detection points based on the noise detection area, includes: Establish a coordinate system for the detection area of ​​the object to be tested; A first noise detection plane is determined in the coordinate system of the detection area, and multiple planar noise detection areas are established on the first noise detection plane in a number not less than the number of first planar noise detection positions; Determine multiple spatial noise detection regions in the coordinate system of the detection area, the number of which is not less than the number of the first spatial noise detection positions, and the multiple spatial noise detection regions are not located on the plane where the multiple planar noise detection regions are located; A planar array of noise sensors is arranged within the plurality of planar noise detection areas; A spatial array of noise sensors is arranged within the plurality of spatial noise detection areas; Wherein, the first number of detection positions is the sum of the first number of planar noise detection positions and the first number of spatial noise detection positions, the multiple noise detection areas include multiple planar noise detection areas and multiple spatial noise detection areas, and the multi-beam array detection points include a planar array of noise sensors and a spatial array of noise sensors.

3. The multi-beam noise detection method according to claim 2, characterized in that, On the first noise detection plane, the effective interval angle between the plurality of planar noise detection areas and the origin of the detection area coordinate system is not less than 90 degrees, and the positions of the plurality of noise detection areas do not form a straight line.

4. The multi-beam noise detection method according to claim 1, characterized in that, The step of performing a first noise identification analysis on the target object based on the noise acoustic signal data of the detection area to form first noise identification information, and determining whether there is feasibility for performing a second noise identification analysis, includes: Based on the noise sound wave detection data of the detection area, obtain the noise sound wave signal power W determined for each noise detection area; The power of the noise acoustic signal is analyzed to generate noise power analysis data. Acquire initial power analysis data, and perform a first noise identification analysis on the initial power analysis data and the noise power analysis data to form the first noise identification information; Obtain the noise confirmation result from the first noise identity confirmation information, and determine whether there is a feasibility for conducting a second noise identity confirmation analysis based on the noise confirmation result.

5. The multi-beam noise detection method according to claim 4, characterized in that, The step of performing power analysis on the noise acoustic signal power to generate noise power analysis data includes: The noise acoustic signal power W is arranged in order of magnitude to form an ordered noise acoustic signal power set Q1 = {W1, W2, ..., W...} n }, where n is the sequence number of the noise detection area after sorting according to the magnitude of the noise sound wave signal power, and n is a non-zero natural number; The power data in the ordered noise acoustic signal power set are subjected to adjacent deviation processing to form an ordered noise acoustic signal power deviation set Q2 = {W2-W1, W3-W2, ..., W...} n -W n-1 }; Obtain the variance of the ordered noise acoustic wave signal power deviation set to form the noise acoustic wave signal power variance C.

6. The multi-beam noise detection method according to claim 5, characterized in that, The step of acquiring initial power analysis data and performing a first noise identification analysis on the initial power analysis data and the noise power analysis data to form the first noise identification information includes: The initial power analysis data of the object under test is obtained, and a non-noise power range analysis is performed to determine the variance range C of the non-noise acoustic signal power of the object under test. 初始 =[C 小 C 大 ], C 小 C is the minimum value of the range. 大 It is the maximum value within the range; The power variance range C of the non-noise acoustic wave signal 初始 The noise is compared with the power variance C of the noise sound wave signal to confirm the existence of the noise and form the first noise identification information.

7. The multi-beam noise detection method according to claim 4, characterized in that, If the feasibility of performing a second noise identification analysis exists, the noise acoustic signal data of the detection area is combined to perform a second noise identification analysis on the object under test, forming second noise identification information, including: Based on the noise acoustic signal data of the detection area, the gain focusing direction determined for each noise detection area is obtained, and a position function S of the gain focusing direction is established in the coordinate system of the detection area. n (x, y, z), where x, y, and z represent the position parameters on different coordinate axes of the detection area coordinate system, respectively; Based on the position function S determined for each of the noise detection regions n (x, y, z) are used to confirm the focus and generate the focus confirmation result data; Based on the focus confirmation result data, the location of the noise source is analyzed to form the second noise identification information.

8. The multi-beam noise detection method according to claim 7, characterized in that, The position function S determined based on each of the noise detection regions n (x, y, z) is used for focus confirmation, generating focus confirmation result data, including: By establishing equations for each pair of position functions, the focal position coordinates between any two position functions can be determined.

9. The multi-beam noise detection method according to claim 8, characterized in that, The step of analyzing the location of the noise source based on the focus confirmation result data to form the second noise identification information includes: When the number of focal position coordinates is the same as the number of position functions, the focal position coordinates are determined as the noise source location; When the number of focal position coordinates is less than the number of position functions, a noise source spatial position region is defined, and the noise source spatial position region is determined as the spatial region where the noise source is located. The noise source spatial position region is an effective spherical space. The effective center position coordinate point of the spherical space is formed with reference to the focal position coordinates determined by more than two of the position functions. The diameter of the spherical space is the maximum length from the effective center position coordinate point to any of the focal position coordinates.

10. A multi-beam noise detection device, characterized in that, include: A multi-beam array establishment unit is used to determine a noise detection area of ​​no less than a first detection position for the object under test, and to establish multi-beam array detection points based on the noise detection area; The beamforming unit is used to acquire noise array signals based on the multi-beam array detection points and perform beamforming to form noise acoustic signal data in the detection area. The first identity verification unit is used to perform a first noise identity verification analysis on the object under test based on the noise acoustic signal data of the detection area, form first noise identity verification information, and determine whether there is a feasibility to perform a second noise identity verification analysis. The second identity verification unit is used to perform second noise identity verification analysis on the object under test in combination with the noise sound wave signal data of the detection area if there is a feasibility for second noise identity verification analysis, thereby forming second noise identity verification information. The noise warning unit is used to perform noise detection and warning based on the second noise identification information and the first noise identification information.

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