A method, device, medium, and product for laser radar signal peak detection of aerosol clusters

Abstract

This application discloses a method, device, medium, and product for detecting lidar signal peaks in aerosol clusters, relating to the field of data processing. The method includes processing the lidar signal of an aerosol cluster using an outlier detection algorithm to obtain the signal peak closest to the lidar signal zero point; extracting the peak positions corresponding to the rising and falling edges of the signal peak; correcting the signal peak using the two peak positions to obtain the detection result; segmenting the lidar signal of the aerosol cluster based on the peak position corresponding to the falling edge; and iterating multiple times on a segment of the signal farthest from the lidar signal zero point until a set condition is met to obtain the lidar signal peak detection result for the aerosol cluster. This application can improve the accuracy of signal peak detection, greatly preserve the detailed information of the aerosol signal peak, and extract the complete shape of the aerosol signal peak.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, device, medium, and product for detecting lidar signal peaks in aerosol clusters. Background Technology

[0002] Aerosols are an important component of the atmosphere. Some aerosol clusters, such as soot and polycyclic aromatic hydrocarbons (PAHs) produced by biomass combustion and fossil fuel combustion, pose significant risks to human health and daily life. The longer they survive, the greater their harm.

[0003] LiDAR is a solution for efficient and real-time detection of aerosols. In practical applications such as aerosol cluster detection and pollutant monitoring, it is often necessary to detect the echo signal peaks of lidar to facilitate subsequent data analysis and processing.

[0004] The results of signal peak detection have a significant impact on subsequent data analysis. Noise exists in the echo signals of lidar; directly processing them using outlier detection or peak detection algorithms can lead to false detections. Furthermore, low-amplitude peaks and overlapping peaks in the signal are easily hidden during smoothing, and the presence of noise prevents complete detection of the signal peak shape. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, medium, and product for detecting lidar signal peaks of aerosol clusters, which can improve the accuracy of signal peak detection, greatly protect the detailed information of aerosol signal peaks, and extract the complete shape of aerosol signal peaks.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a method for detecting lidar signal peaks in aerosol clusters, including:

[0008] An outlier detection algorithm is used to process the lidar signal of aerosol clusters to obtain the signal peak closest to the zero point of the lidar signal;

[0009] Extract the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak;

[0010] The signal peak is corrected by using the peak position corresponding to the rising edge and the peak position corresponding to the falling edge to obtain the corrected signal peak, and the corrected signal peak is used as the detection result of the signal peak.

[0011] Based on the peak position corresponding to the falling edge, the lidar signal of the aerosol cloud is divided into the first segment and the second segment.

[0012] The second segment of signal is used as the lidar signal of the new aerosol cluster, and the process of processing the lidar signal of the aerosol cluster using the outlier detection algorithm is returned to obtain the signal peak closest to the zero point of the lidar signal. This process continues until the set conditions are met, and then the lidar signal peak detection result of the aerosol cluster is generated based on the detection results of all obtained signal peaks.

[0013] Optionally, the setting condition is that the largest signal peak value in the signal peak detection results is less than the intensity threshold.

[0014] Optionally, the setting condition is that the distance between the lidar signal of the processed aerosol cloud and the lidar signal zero point reaches a distance threshold.

[0015] Optionally, the outlier detection algorithm is the median absolute deviation method, the Quartiles non-normal outlier method, the Tukey's Fences method, the mean absolute deviation method, the Grubbs normal outlier method, and / or the GESD outlier method.

[0016] Optionally, extracting the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak specifically includes:

[0017] The peak position corresponding to the rising edge and the peak position corresponding to the falling edge of the signal peak are extracted by changing the sign of the derivative.

[0018] Optionally, extracting the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak specifically includes:

[0019] The peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak are extracted based on the changing trend of the slope of the signal peak.

[0020] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described lidar signal peak detection method for aerosol clusters.

[0021] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the lidar signal peak detection method for aerosol clusters provided above.

[0022] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for detecting lidar signal peaks for aerosol clusters.

[0023] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0024] This application provides a method, device, medium, and product for detecting lidar signal peaks in aerosol clusters. Based on the characteristic that aerosol cluster signals are generally significantly stronger than normal atmospheric echo signals, the lidar signal of aerosol clusters is progressively segmented and detected through multiple iterations. An outlier detection algorithm is used to detect the signal peaks one by one, thereby reducing the impact of noise on the signal peak detection effect, improving the accuracy of signal peak detection, and greatly preserving the detailed information of the aerosol signal peaks, thus revealing the complete shape of the aerosol signal peaks. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is an application environment diagram of a lidar signal peak detection method for aerosol clusters according to an embodiment of this application;

[0027] Figure 2 This is a schematic flowchart illustrating a lidar signal peak detection method for aerosol clusters, provided in an embodiment of this application.

[0028] Figure 3 A schematic diagram of the lidar signal and signal peak stripping results of aerosol clusters provided in an embodiment of this application;

[0029] Figure 4 for Figure 3 A magnified view of the key location 1 in the middle;

[0030] Figure 5 for Figure 3 Enlarged view of two key locations in the middle;

[0031] Figure 6 for Figure 3 Enlarged views of three key locations in the middle;

[0032] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] The lidar signal peak detection method for aerosol clusters provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the lidar signal of the aerosol cluster to server 104. After receiving the lidar signal, server 104 processes the aerosol cluster lidar signal using an outlier detection algorithm to obtain the signal peak closest to the lidar signal zero point. The peak position corresponding to the rising edge and the peak position corresponding to the falling edge of the signal peak are extracted. The signal peak is corrected using the peak position corresponding to the rising and falling edges to obtain a corrected signal peak, which is used as the detection result of the signal peak. Based on the peak position corresponding to the falling edge, the lidar signal of the aerosol cluster is divided into a first signal segment and a second signal segment. The second signal segment is used as the new lidar signal for the aerosol cluster, and the above steps are repeated until the set conditions are met. Based on the detection results of all obtained signal peaks, a lidar signal peak detection result for the aerosol cluster is generated. The server 104 can feed back the obtained lidar signal peak detection result to the terminal 102. Furthermore, in some embodiments, the lidar signal peak detection method for aerosol clusters can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform signal peak detection on the lidar signal of the aerosol cluster, or the server 104 can obtain the lidar signal of the aerosol cluster from the data storage system and perform signal peak detection on the lidar signal of the aerosol cluster.

[0036] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0037] In one exemplary embodiment, such as Figure 2 As shown, a method for detecting lidar signal peaks in aerosol clusters is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 200 to 204. Wherein:

[0038] Step 200: The outlier detection algorithm is used to process the lidar signal of the aerosol cloud to obtain the signal peak closest to the zero point of the lidar signal.

[0039] Step 201: Extract the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak.

[0040] Step 202: Correct the signal peak using the peak position corresponding to the rising edge and the peak position corresponding to the falling edge to obtain the corrected signal peak, and use the corrected signal peak as the detection result of the signal peak.

[0041] Step 203: Using the peak position corresponding to the falling edge as a reference, divide the lidar signal of the aerosol cloud into a first signal segment and a second signal segment. The second signal segment is the lidar signal segment farther from the lidar signal zero point.

[0042] Step 204: Use the second signal segment as the lidar signal of the new aerosol cluster, and return to step 200 until the set conditions are met. Then, generate the lidar signal peak detection result of the aerosol cluster based on the detection results of all obtained signal peaks.

[0043] Laser energy attenuates during transmission, especially after passing through dense aerosols, where the attenuation is significant. This leads to a greater impact of noise on echo signals at greater distances. Compared to traditional algorithms that simply use fixed thresholds or estimate the signal-to-noise ratio for signal peak detection, this application, based on the characteristic that aerosol cluster signals are generally significantly stronger than normal atmospheric echo signals, implements steps 200 to 204 to progressively segment and detect the lidar signal of the aerosol cluster. An outlier detection algorithm is used to detect each signal peak individually, reducing the impact of noise on signal peak detection, improving the accuracy of signal peak detection, and greatly preserving the detailed information of the aerosol signal peaks, thus revealing the complete shape of the aerosol signal peaks.

[0044] In another exemplary embodiment of this application, the condition is set as follows: the largest signal peak value in the signal peak detection results is less than the intensity threshold, or the distance between the processed aerosol cluster's lidar signal and the lidar signal zero point reaches the distance threshold. Then, step 204 can be changed to using the second signal segment as the new aerosol cluster's lidar signal and returning to steps 200-203 until the largest signal peak value in the signal peak detection results is less than the intensity threshold, or the distance between the processed aerosol cluster's lidar signal and the lidar signal zero point reaches the distance threshold. Based on the detection results of all obtained signal peaks, a lidar signal peak detection result for the aerosol cluster is generated.

[0045] In this embodiment, the intensity threshold, which is one of the stopping conditions, can be determined based on the user's specific needs or the noise level of the signal. The distance threshold can be determined based on the user's specific needs, or it can be left unset. If left unset, the distance threshold is the original length of the lidar signal of the aerosol cloud.

[0046] In another exemplary embodiment of this application, the outlier detection algorithm may be the median absolute deviation method, the Quartiles non-normal outlier method, the Tukey's Fences method, the mean absolute deviation method, the Grubbs normal outlier method, or the GESD outlier method.

[0047] In another exemplary embodiment of this application, outlier detection algorithms alone are often insufficient to accurately locate peaks. To prevent distortion of peak information, precise peak positions need to be determined. Therefore, in this embodiment, the peak positions corresponding to the rising and falling edges of the signal peak can be extracted by changing the sign of the derivative. Alternatively, the peak positions corresponding to the rising and falling edges of the signal peak can be extracted based on the trend of the signal peak slope. However, this approach is not limited to these two methods; any method capable of accurately extracting peak positions is acceptable.

[0048] In another exemplary embodiment of this application, a signal peak detection is demonstrated using the lidar signal of an aerosol cloud obtained using a horizontally probing lidar. In this embodiment, the lidar signal of the aerosol cloud is processed using the Quartiles nonnormal outlier method, and steps 201 to 204 described above are performed. The final signal peak stripping result is as follows: Figure 3 As shown. Based on Figures 4-6 It can be seen that the lidar signal peak detection method provided in this application can detect the signal peak very completely, ensuring that the details of the signal peak are not distorted. It has technical advantages such as scientific design, good effect, simple and reliable operation.

[0049] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores lidar signal peak detection data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a lidar signal peak detection method for aerosol clusters.

[0050] Those skilled in the art will understand that Figure 7 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0051] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0052] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0053] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0054] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0055] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0056] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of laser radar signal peak detection for aerosol clusters, characterized in that, The method for detecting lidar signal peaks for aerosol clusters includes: An outlier detection algorithm is used to process the lidar signal of aerosol clusters to obtain the signal peak closest to the zero point of the lidar signal; Extract the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak; The signal peak is corrected by using the peak position corresponding to the rising edge and the peak position corresponding to the falling edge to obtain the corrected signal peak, and the corrected signal peak is used as the detection result of the signal peak. Based on the peak position corresponding to the falling edge, the lidar signal of the aerosol cloud is divided into the first segment and the second segment. The second segment of signal is used as the lidar signal of the new aerosol cluster, and the process of processing the lidar signal of the aerosol cluster using the outlier detection algorithm is returned to obtain the signal peak closest to the zero point of the lidar signal. This process continues until the set conditions are met, and then the lidar signal peak detection result of the aerosol cluster is generated based on the detection results of all obtained signal peaks.

2. The method for laser radar signal peak detection for aerosol clusters according to claim 1, characterized in that, The set condition is that the largest signal peak value in the detection results of the signal peak is less than the intensity threshold.

3. The method for laser radar signal peak detection for aerosol clusters according to claim 1, characterized in that, The set condition is that the distance between the lidar signal of the processed aerosol cluster and the lidar signal zero point reaches a distance threshold.

4. The method for laser radar signal peak detection for aerosol clusters according to claim 1, characterized in that, The outlier detection algorithm is the median absolute deviation method, the Quartiles non-normal outlier method, the Tukey's Fences method, the mean absolute deviation method, the Grubbs normal outlier method, and / or the GESD outlier method.

5. The method for laser radar signal peak detection for aerosol clusters of claim 1, wherein, Extracting the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak, specifically including: The peak position corresponding to the rising edge and the peak position corresponding to the falling edge of the signal peak are extracted by changing the sign of the derivative.

6. The method for detecting lidar signal peaks for aerosol clusters according to claim 1, characterized in that, Extracting the peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak, specifically including: The peak position corresponding to the rising edge of the signal peak and the peak position corresponding to the falling edge of the signal peak are extracted based on the changing trend of the slope of the signal peak.

7. A computer device, comprising: The memory and processor contain a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the lidar signal peak detection method for aerosol clusters as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the lidar signal peak detection method for aerosol clusters as described in any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the lidar signal peak detection method for aerosol clusters as described in any one of claims 1-6.

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