Large benthonic animal ecological acoustic monitoring method and device for mangrove forest area

Abstract

The invention relates to an ecological acoustic monitoring method and device for large benthonic animals in a mangrove forest area, and belongs to the technical field of ecological monitoring. Acoustic sensors are arranged on the bottom surface and the bottom inside of sediment in the mangrove forest area, acoustic signals of the large benthonic animals in the mangrove forest area are collected in real time, and in combination with sedimentary environment parameters of the monitoring area, the large benthonic animals in the mangrove forest area are monitored. And identifying and analyzing functional groups, relative abundance, activity rules and the like of the large benthonic animals by utilizing an acoustic signal processing algorithm according to the parameters such as moisture content, porosity, granularity, temperature, salinity and the like. The method can realize long-term, continuous and non-invasive monitoring of the large benthonic animals in the mangrove forest region, has the characteristics of high precision, high efficiency and low interference, and is suitable for health assessment and biodiversity protection of a mangrove forest ecosystem.

Description

Technical Field

[0001] The invention relates to the technical field of ecological monitoring, and in particular to a method and a device for acoustic monitoring of large benthic animals in mangrove areas. Background Art

[0002] Mangroves are important ecosystems in tropical and subtropical coastal areas, with extremely high biodiversity and ecological service functions. Large benthic animals (such as crabs, shellfish, polychaetes, etc.) in mangrove areas are important components of the ecosystem, and they play a key role in material circulation, energy flow and habitat construction. However, due to the complexity and dynamics of the mangrove environment, traditional large benthic animal monitoring methods (such as sampling methods, trap methods, etc.) have the following limitations, such as 1) strong invasiveness: traditional methods usually require direct interference with the habitat of large benthic animals, which may cause changes in animal behavior or habitat destruction, affecting the accuracy of monitoring results; 2) low efficiency: mangrove areas have complex terrain and frequent tidal changes, and traditional methods are time-consuming and labor-intensive, making it difficult to achieve large-scale, long-term continuous monitoring; 3) data limitations: traditional methods can usually only obtain data within a specific time and space range, and it is difficult to fully reflect the ecological characteristics and dynamic changes of benthic animals. In recent years, acoustic monitoring technology has been widely used in aquatic ecosystem research. Acoustic monitoring has the advantages of non-invasiveness, continuity and high temporal and spatial resolution, which can effectively make up for the shortcomings of traditional methods. However, existing acoustic monitoring technologies are mainly targeted at fish and marine mammals, and there is still a lack of research on acoustic monitoring of large benthic animals in mangrove areas. The acoustic environment in mangrove areas is complex, and the acoustic signals of large benthic animals are weak and easily disturbed by environmental noise. Therefore, it is necessary to develop acoustic monitoring methods and devices specifically suitable for mangrove areas.

[0003] The purpose of the present invention is to provide a method and device for ecological acoustic monitoring of large benthic animals in mangrove areas. By optimizing the layout of acoustic sensors, developing efficient acoustic signal processing algorithms and integrating environmental parameter monitoring, long-term, continuous and non-invasive monitoring of large benthic animals in mangrove areas can be achieved, providing scientific basis and technical support for health assessment of mangrove ecosystems and biodiversity protection. Summary of the invention

[0004] The present invention overcomes the shortcomings of the prior art and provides a method and a device for acoustic monitoring of macrobenthic animals in mangrove areas.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The present invention provides an ecological acoustic monitoring method for macrobenthic animals in mangrove areas, comprising the following steps:

[0007] Acquiring acoustic propagation characteristic data under various environmental characteristics through big data, and constructing an acoustic propagation characteristic data prediction model according to the acoustic propagation characteristic data under various environmental characteristics;

[0008] Predicting acoustic characteristic data in a mangrove area based on the acoustic propagation characteristic data prediction model, and arranging acoustic equipment in the mangrove area according to the acoustic propagation characteristic data in the mangrove area;

[0009] Acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of functional groups of large benthic animals is counted;

[0010] The relative abundance of each functional group of the macrobenthic animals is analyzed to obtain analysis results, and the diversity of the macrobenthic animal soundscape is evaluated based on the analysis results.

[0011] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, an acoustic propagation characteristic data prediction model is constructed based on the acoustic propagation characteristic data under the above-mentioned environmental characteristics, specifically:

[0012] Constructing an acoustic propagation feature data prediction model based on a deep neural network, introducing a graph neural network, inputting the acoustic propagation feature data under the environmental features into the graph neural network, and using the acoustic propagation feature data features as the first node of the graph neural network;

[0013] Using the acoustic propagation feature data as the second node of the graph neural network, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship so that the first node points to the second node, and constructing a topological structure graph;

[0014] Obtaining an adjacency matrix based on the topological structure graph, inputting the adjacency matrix into the acoustic propagation feature data prediction model for training, and determining whether the acoustic propagation feature data prediction model during the training process meets a training end condition;

[0015] When the acoustic propagation feature data prediction model in the training process meets the training end condition, the model parameters of the acoustic propagation feature data prediction model are saved, and the acoustic propagation feature data prediction model is output.

[0016] Furthermore, in the method for monitoring the ecological acoustics of macrobenthic animals in mangrove areas, predicting the acoustic propagation characteristic data in the mangrove area based on the acoustic propagation characteristic data prediction model specifically includes:

[0017] Acquiring environmental characteristics and real-time acoustic characteristic data of the current mangrove area, and inputting the environmental characteristics and real-time acoustic characteristic data of the current mangrove area into the acoustic propagation characteristic data prediction model for prediction;

[0018] Acoustic propagation characteristic data in the mangrove area are obtained through prediction, and the acoustic propagation characteristic data in the mangrove area are output.

[0019] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, the acoustic equipment in the mangrove area is arranged according to the acoustic propagation characteristic data in the mangrove area, specifically:

[0020] Obtaining the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data, and obtaining the acoustic device type corresponding to the sensing range area being greater than the preset sensing range area;

[0021] The acoustic device type corresponding to the sensing range area being larger than the preset sensing range area is used as the acoustic device type for collecting data, and the sensing range area of ​​the acoustic device type for collecting data is obtained according to the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data and the acoustic propagation characteristic data in the mangrove area;

[0022] Initialize the layout position and layout quantity of the acoustic device type for collecting data, and calculate the estimated sensing range area based on the sensing range area of ​​the acoustic device type for collecting data, the layout position and layout quantity of the acoustic device type for collecting data;

[0023] When the estimated sensing range area is not larger than the preset range information, the arrangement position and the arrangement quantity of the acoustic device type for collecting data are adjusted until it is larger than the preset range information.

[0024] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and the functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of the functional groups of large benthic animals is counted, specifically including:

[0025] Acoustic characteristic data in the mangrove area are obtained through acoustic equipment, and macrobenthic animal functional groups related to each acoustic characteristic data are obtained through big data, and a macrobenthic animal knowledge graph is constructed according to the macrobenthic animal functional groups related to each acoustic characteristic data;

[0026] The acoustic feature data in the mangrove area is input into the large benthic animal knowledge graph for identification, the large benthic animal type feature data in the mangrove area is obtained, and the large benthic animal functional group feature data in the mangrove area is counted to obtain the relative abundance of the large benthic animal functional groups.

[0027] Furthermore, in the method for ecological acoustic monitoring of macrobenthic animals in mangrove areas, the relative abundance of each functional group of the macrobenthic animals is analyzed, the analysis results are obtained, and the diversity of macrobenthic animal soundscapes is evaluated based on the analysis results, specifically including:

[0028] Setting a relative abundance characteristic threshold of a macrobenthic animal functional group, and determining whether the relative abundance of each macrobenthic animal functional group is greater than the relative abundance characteristic threshold of the macrobenthic animal functional group;

[0029] When the relative abundance of each functional group of the macrobenthic animals is greater than the relative abundance characteristic threshold of the functional group of the macrobenthic animals, an analysis result is generated, and the relative abundance of each functional group of the macrobenthic animals is output.

[0030] A second aspect of the present invention provides an ecological acoustic monitoring device for large benthic animals in mangrove areas, comprising a memory and a processor, wherein the memory comprises a program for an ecological acoustic monitoring method for large benthic animals in mangrove areas, and when the program for an ecological acoustic monitoring method for large benthic animals in mangrove areas is executed by the processor, any step of the ecological acoustic monitoring method for large benthic animals in mangrove areas is implemented.

[0031] A third aspect of the present invention provides a computer-readable storage medium, comprising a program for an ecological acoustic monitoring method for large benthic animals in mangrove areas. When the program for an ecological acoustic monitoring method for large benthic animals in mangrove areas is executed by a processor, any step of the ecological acoustic monitoring method for large benthic animals in mangrove areas is implemented.

[0032] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0033] The present invention obtains acoustic propagation characteristic data under various environmental characteristics through big data, and constructs an acoustic propagation characteristic data prediction model according to the acoustic propagation characteristic data under the various environmental characteristics, and then predicts the acoustic characteristic data in a mangrove area based on the acoustic propagation characteristic data prediction model, and arranges acoustic equipment in the mangrove area according to the acoustic propagation characteristic data in the mangrove area, so as to obtain the acoustic characteristic data in the mangrove area through the acoustic equipment, and monitors and identifies the functional groups of large benthic animals in the mangrove area according to the acoustic characteristic data in the mangrove area, and counts the relative abundance of the functional groups of large benthic animals, and finally analyzes the relative abundance of each functional group of the large benthic animals, obtains the analysis results, and evaluates the soundscape diversity of large benthic animals based on the analysis results. The purpose of the present invention is to provide a method and device for ecological acoustic monitoring of large benthic animals in mangrove areas. By optimizing the layout of acoustic sensors, developing efficient acoustic signal processing algorithms and integrating environmental parameter monitoring, long-term, continuous and non-invasive monitoring of large benthic animals in mangrove areas can be achieved, providing scientific basis and technical support for health assessment of mangrove ecosystems and biodiversity protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0035] Figure 1 The overall flow chart of the method for eco-acoustic monitoring of macrobenthic animals in mangrove areas is shown;

[0036] Figure 2 A partial method flow chart showing the method for eco-acoustic monitoring of macrobenthic animals in mangrove areas;

[0037] Figure 3 The device block diagram of the device for acoustic monitoring of macrobenthic animals in mangrove areas is shown;

[0038] Figure 4 A schematic diagram of the structure of the acoustic monitoring equipment is shown. DETAILED DESCRIPTION

[0039] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0041] like Figure 1 As shown, the present invention provides an ecological acoustic monitoring method for macrobenthic animals in mangrove areas, comprising the following steps:

[0042] S102: Acquiring acoustic propagation characteristic data under various environmental characteristics through big data, and constructing an acoustic propagation characteristic data prediction model according to the acoustic propagation characteristic data under various environmental characteristics;

[0043] S104: predicting acoustic characteristic data in the mangrove area based on the acoustic propagation characteristic data prediction model, and arranging acoustic equipment in the mangrove area according to the acoustic propagation characteristic data in the mangrove area;

[0044] S106: Acquiring acoustic characteristic data in the mangrove area through acoustic equipment, and monitoring and identifying the functional groups of macrobenthic animals in the mangrove area according to the acoustic characteristic data in the mangrove area, and counting the relative abundance of the functional groups of macrobenthic animals;

[0045] S108: Analyze the relative abundance of each functional group of the macrobenthic animals, obtain analysis results, and evaluate the diversity of macrobenthic animal soundscapes based on the analysis results.

[0046] It should be noted that the purpose of the present invention is to provide a method and device for acoustic monitoring of large benthic animals in mangrove areas. By optimizing the layout of acoustic sensors, developing efficient acoustic signal processing algorithms and integrating environmental parameter monitoring, long-term, continuous and non-invasive monitoring of large benthic animals in mangrove areas can be achieved, providing scientific basis and technical support for health assessment of mangrove ecosystems and biodiversity conservation.

[0047] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, an acoustic propagation characteristic data prediction model is constructed based on the acoustic propagation characteristic data under the above-mentioned environmental characteristics, specifically:

[0048] Constructing an acoustic propagation feature data prediction model based on a deep neural network, introducing a graph neural network, inputting the acoustic propagation feature data under the environmental features into the graph neural network, and using the acoustic propagation feature data as the first node of the graph neural network;

[0049] Using the acoustic propagation feature data as the second node of the graph neural network, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship so that the first node points to the second node, and constructing a topological structure graph;

[0050] Obtaining an adjacency matrix based on the topological structure graph, inputting the adjacency matrix into the acoustic propagation feature data prediction model for training, and determining whether the acoustic propagation feature data prediction model during the training process meets a training end condition;

[0051] When the acoustic propagation feature data prediction model in the training process meets the training end condition, the model parameters of the acoustic propagation feature data prediction model are saved, and the acoustic propagation feature data prediction model is output.

[0052] It should be noted that the acoustic propagation characteristic data includes data such as the amount of sound propagation loss per unit time, the speed of sound propagation per unit time, and the quality of sound propagation. Since different environments (such as temperature environment, humidity environment, soil environment, and seabed sediment environment) have different acoustic propagation characteristics, this method can construct an acoustic propagation characteristic data prediction model to predict the acoustic propagation characteristic data.

[0053] Furthermore, in the method for monitoring the ecological acoustics of macrobenthic animals in mangrove areas, predicting the acoustic propagation characteristic data in the mangrove area based on the acoustic propagation characteristic data prediction model specifically includes:

[0054] Acquiring environmental characteristics and real-time acoustic characteristic data of the current mangrove area, and inputting the environmental characteristics and real-time acoustic characteristic data of the current mangrove area into the acoustic propagation characteristic data prediction model for prediction;

[0055] Acoustic propagation characteristic data in the mangrove area are obtained through prediction, and the acoustic propagation characteristic data in the mangrove area are output.

[0056] like Figure 2 As shown, further, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, the acoustic equipment in the mangrove area is arranged according to the acoustic propagation characteristic data in the mangrove area, specifically:

[0057] S202: Obtaining the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data, and obtaining the acoustic device type corresponding to the sensing range area being larger than the preset sensing range area;

[0058] S204: taking the acoustic device type corresponding to the sensing range area larger than the preset sensing range area as the acoustic device type for collecting data, and acquiring the sensing range area of ​​the acoustic device type for collecting data according to the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data and the acoustic propagation characteristic data in the mangrove area;

[0059] S206: Initializing the layout position and layout quantity of the acoustic device type for collecting data, and calculating an estimated sensing range area based on the sensing range area of ​​the acoustic device type for collecting data, the layout position and layout quantity of the acoustic device type for collecting data;

[0060] S208: When the estimated sensing range area is not larger than the preset range information, adjusting the layout position and the layout quantity of the acoustic device type for collecting data until it is larger than the preset range information.

[0061] It should be noted that, since different types of acoustic equipment have different sound propagation capabilities, if the appropriate acoustic equipment type is not set, it will lead to the inability to collect acoustic data and the collected data will be abnormal, which is not conducive to the analysis and noise processing of acoustic data, thereby causing the collected data to be abnormal. The sensing range area of ​​the acoustic equipment type for collecting data is estimated based on the sensing range area of ​​each acoustic equipment type under different acoustic propagation characteristic data and the acoustic propagation characteristic data in the mangrove area. Therefore, this method can optimize the layout of the acoustic equipment, so that the data collected by the acoustic equipment is better, and the accuracy and rationality of acoustic data acquisition can be further improved, thereby improving the monitoring accuracy of large benthic animals.

[0062] The acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and the functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of the functional groups of large benthic animals is counted, specifically:

[0063] Acoustic characteristic data in the mangrove area are obtained through acoustic equipment, and macrobenthic animal functional groups related to each acoustic characteristic data are obtained through big data, and a macrobenthic animal knowledge graph is constructed according to the macrobenthic animal functional groups related to each acoustic characteristic data;

[0064] The acoustic feature data in the mangrove area is input into the large benthic animal knowledge graph for identification, the large benthic animal type feature data in the mangrove area is obtained, and the large benthic animal functional group feature data in the mangrove area is counted to obtain the relative abundance of the large benthic animal functional groups.

[0065] It should be noted that, after obtaining the acoustic characteristic data in the mangrove area, the method may also include noise processing of acoustic data, signal amplification of acoustic data (sound wave type, sound wave frequency, etc.), noise reduction processing of acoustic signals using digital signal processing technology, retaining the characteristic frequency of biological sound, etc., which are not limited to the content of this embodiment. The acoustic characteristic data includes data such as biological sound, geophysical sound, and artificial sound.

[0066] Furthermore, in the method for ecological acoustic monitoring of macrobenthic animals in mangrove areas, the relative abundance of each functional group of the macrobenthic animals is analyzed, the analysis results are obtained, and the diversity of macrobenthic animal soundscapes is evaluated based on the analysis results, specifically including:

[0067] Setting a relative abundance characteristic threshold of a macrobenthic animal functional group, and determining whether the relative abundance of each macrobenthic animal functional group is greater than the relative abundance characteristic threshold of the macrobenthic animal functional group;

[0068] When the relative abundance of each functional group of the macrobenthic animals is greater than the relative abundance characteristic threshold of the functional group of the macrobenthic animals, an analysis result is generated, and the relative abundance of each functional group of the macrobenthic animals is output.

[0069] It should be noted that this method can better monitor large benthic animals in mangrove areas and achieve unmanned monitoring.

[0070] Figure 4 A schematic diagram of the structure of the acoustic monitoring equipment is shown.

[0071] In addition, the method further comprises:

[0072] Acquire water environment characteristic data of each area of ​​the current mangrove forest, initialize the sound wave working parameters of the acoustic device according to the water environment characteristic data of each area of ​​the current mangrove forest, and acquire environmental noise data under the sound wave working parameters of the acoustic device;

[0073] Introducing a particle swarm algorithm, setting the number of iterations based on the particle swarm algorithm, and determining whether the ambient noise data under the sound wave working parameters of the acoustic device is greater than a preset noise data threshold;

[0074] When the environmental noise data under the sound wave working parameters of the acoustic device is greater than a preset noise data threshold, iterating based on the number of iterations, and re-adjusting the sound wave working parameters of the acoustic device until the environmental noise data under the sound wave working parameters of the acoustic device is no greater than the preset noise data threshold;

[0075] When the environmental noise data under the sound wave working parameters of the acoustic device is not greater than the preset noise data threshold, the sound wave working parameters of the acoustic device are output, and control is performed according to the sound wave working parameters of the acoustic device.

[0076] It should be noted that by introducing an adaptive algorithm, the acoustic wave parameters are automatically adjusted according to the environmental noise or water conditions (for example, the output intensity is dynamically adjusted using a power amplifier), thereby improving the monitoring accuracy.

[0077] In addition, the method further comprises:

[0078] Through continuous iterations, the environmental noise data of the current acoustic equipment under the maximum sound wave working parameters is obtained;

[0079] Determine whether the ambient noise data of the current acoustic device under the maximum sound wave working parameter is greater than the preset noise data threshold;

[0080] When the environmental noise data of the current acoustic device under the maximum sound wave working parameter is greater than the preset noise data threshold, the current acoustic device is controlled to stop working and an early warning prompt is issued;

[0081] When the environmental noise data of the current acoustic device under the maximum sound wave working parameter is not greater than the preset noise data threshold, the current acoustic device is controlled to continue working.

[0082] It should be noted that when the environmental noise data of the current acoustic device under the maximum sound wave working parameters is greater than the preset noise data threshold, it means that no matter how it is adjusted, it cannot meet the requirements. At this time, controlling the current acoustic device to stop working can save energy consumption, reduce monitoring costs, and improve the rationality of monitoring.

[0083] like Figure 3 As shown, the second aspect of the present invention provides a large benthic animal ecological acoustic monitoring device 4 for mangrove areas, including a memory 41 and a processor 42. The memory 41 includes a large benthic animal ecological acoustic monitoring method 42 program for mangrove areas. When the large benthic animal ecological acoustic monitoring method program for mangrove areas is executed by the processor 42, the following steps are implemented:

[0084] Acquiring acoustic propagation characteristic data under various environmental characteristics through big data, and constructing an acoustic propagation characteristic data prediction model according to the acoustic propagation characteristic data under various environmental characteristics;

[0085] Predicting acoustic characteristic data in a mangrove area based on the acoustic propagation characteristic data prediction model, and arranging acoustic equipment in the mangrove area according to the acoustic propagation characteristic data in the mangrove area;

[0086] Acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of functional groups of large benthic animals is counted;

[0087] The relative abundance of each functional group of the macrobenthic animals is analyzed to obtain analysis results, and the diversity of the macrobenthic animal soundscape is evaluated based on the analysis results.

[0088] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, an acoustic propagation characteristic data prediction model is constructed based on the acoustic propagation characteristic data under the above-mentioned environmental characteristics, specifically:

[0089] Constructing an acoustic propagation feature data prediction model based on a deep neural network, introducing a graph neural network, inputting the acoustic propagation feature data under the environmental features into the graph neural network, and using the acoustic propagation feature data features as the first node of the graph neural network;

[0090] Using the acoustic propagation feature data as the second node of the graph neural network, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship so that the first node points to the second node, and constructing a topological structure graph;

[0091] Obtaining an adjacency matrix based on the topological structure graph, inputting the adjacency matrix into the acoustic propagation feature data prediction model for training, and determining whether the acoustic propagation feature data prediction model during the training process meets a training end condition;

[0092] When the acoustic propagation feature data prediction model in the training process meets the training end condition, the model parameters of the acoustic propagation feature data prediction model are saved, and the acoustic propagation feature data prediction model is output.

[0093] Furthermore, in the method for monitoring the ecological acoustics of macrobenthic animals in mangrove areas, predicting the acoustic propagation characteristic data in the mangrove area based on the acoustic propagation characteristic data prediction model specifically includes:

[0094] Acquiring environmental characteristics and real-time acoustic characteristic data of the current mangrove area, and inputting the environmental characteristics and real-time acoustic characteristic data of the current mangrove area into the acoustic propagation characteristic data prediction model for prediction;

[0095] Acoustic propagation characteristic data in the mangrove area are obtained through prediction, and the acoustic propagation characteristic data in the mangrove area are output.

[0096] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, the acoustic equipment in the mangrove area is arranged according to the acoustic propagation characteristic data in the mangrove area, specifically:

[0097] Obtaining the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data, and obtaining the acoustic device type corresponding to the sensing range area being greater than the preset sensing range area;

[0098] The acoustic device type corresponding to the sensing range area being larger than the preset sensing range area is used as the acoustic device type for collecting data, and the sensing range area of ​​the acoustic device type for collecting data is obtained according to the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data and the acoustic propagation characteristic data in the mangrove area;

[0099] Initialize the layout position and layout quantity of the acoustic device type for collecting data, and calculate the estimated sensing range area based on the sensing range area of ​​the acoustic device type for collecting data, the layout position and layout quantity of the acoustic device type for collecting data;

[0100] When the estimated sensing range area is not larger than the preset range information, the arrangement position and the arrangement quantity of the acoustic device type for collecting data are adjusted until it is larger than the preset range information.

[0101] Furthermore, in the method for ecological acoustic monitoring of large benthic animals in mangrove areas, acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and the functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of the functional groups of large benthic animals is counted, specifically including:

[0102] Acoustic characteristic data in the mangrove area are obtained through acoustic equipment, and macrobenthic animal functional groups related to each acoustic characteristic data are obtained through big data, and a macrobenthic animal knowledge graph is constructed according to the macrobenthic animal functional groups related to each acoustic characteristic data;

[0103] The acoustic feature data in the mangrove area is input into the large benthic animal knowledge graph for identification, the large benthic animal type feature data in the mangrove area is obtained, and the large benthic animal functional group feature data in the mangrove area is counted to obtain the relative abundance of the large benthic animal functional groups.

[0104] Furthermore, in the method for ecological acoustic monitoring of macrobenthic animals in mangrove areas, the relative abundance of each functional group of the macrobenthic animals is analyzed, the analysis results are obtained, and the diversity of macrobenthic animal soundscapes is evaluated based on the analysis results, specifically including:

[0105] Setting a relative abundance characteristic threshold of a macrobenthic animal functional group, and determining whether the relative abundance of each macrobenthic animal functional group is greater than the relative abundance characteristic threshold of the macrobenthic animal functional group;

[0106] When the relative abundance of each functional group of the macrobenthic animals is greater than the relative abundance characteristic threshold of the functional group of the macrobenthic animals, an analysis result is generated, and the relative abundance of each functional group of the macrobenthic animals is output.

[0107] A second aspect of the present invention provides an ecological acoustic monitoring device for large benthic animals in mangrove areas, comprising a memory and a processor, wherein the memory comprises a program for an ecological acoustic monitoring method for large benthic animals in mangrove areas, and when the program for an ecological acoustic monitoring method for large benthic animals in mangrove areas is executed by the processor, any step of the ecological acoustic monitoring method for large benthic animals in mangrove areas is implemented.

[0108] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the 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 can be integrated into another device, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0109] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0110] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0111] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0112] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0113] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for ecological acoustic monitoring of macrobenthic animals in mangrove areas, characterized in that: The following steps are involved: Acquiring acoustic propagation characteristic data under various environmental characteristics through big data, and constructing an acoustic propagation characteristic data prediction model according to the acoustic propagation characteristic data under various environmental characteristics; Predicting acoustic characteristic data in a mangrove area based on the acoustic propagation characteristic data prediction model, and arranging acoustic equipment in the mangrove area according to the acoustic propagation characteristic data in the mangrove area; Acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of functional groups of large benthic animals is counted; The relative abundance of each functional group of the macrobenthic animals is analyzed to obtain analysis results, and the diversity of the macrobenthic animal soundscape is evaluated based on the analysis results.

2. The method for ecological acoustic monitoring of macrobenthic animals in mangrove areas according to claim 1, characterized in that: The acoustic propagation characteristic data prediction model is constructed according to the acoustic propagation characteristic data under the various environmental characteristics, specifically: Constructing an acoustic propagation feature data prediction model based on a deep neural network, introducing a graph neural network, inputting the acoustic propagation feature data under each environmental feature into the graph neural network, and using the environmental feature as the first node of the graph neural network; Using the acoustic propagation feature data as the second node of the graph neural network, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship so that the first node points to the second node, and constructing a topological structure graph; Obtaining an adjacency matrix based on the topological structure graph, inputting the adjacency matrix into the acoustic propagation feature data prediction model for training, and determining whether the acoustic propagation feature data prediction model during the training process meets a training end condition; When the acoustic propagation feature data prediction model in the training process meets the training end condition, the model parameters of the acoustic propagation feature data prediction model are saved, and the acoustic propagation feature data prediction model is output.

3. The method for ecological acoustic monitoring of macrobenthic animals in mangrove areas according to claim 1, characterized in that: Predicting the acoustic propagation characteristic data in the mangrove area based on the acoustic propagation characteristic data prediction model specifically includes: Acquiring environmental characteristics and real-time acoustic characteristic data of the current mangrove area, and inputting the environmental characteristics and real-time acoustic characteristic data of the current mangrove area into the acoustic propagation characteristic data prediction model for prediction; Acoustic propagation characteristic data in the mangrove area are obtained through prediction, and the acoustic propagation characteristic data in the mangrove area are output.

4. The method for ecological acoustic monitoring of macrobenthic animals in mangrove areas according to claim 1, characterized in that: The acoustic equipment in the mangrove area is arranged according to the acoustic propagation characteristic data in the mangrove area, specifically: Obtaining the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data, and obtaining the acoustic device type corresponding to the sensing range area being greater than the preset sensing range area; The acoustic device type corresponding to the sensing range area being larger than the preset sensing range area is used as the acoustic device type for collecting data, and the sensing range area of ​​the acoustic device type for collecting data is obtained according to the sensing range area of ​​each acoustic device type under different acoustic propagation characteristic data and the acoustic propagation characteristic data in the mangrove area; Initialize the layout position and layout quantity of the acoustic device type for collecting data, and calculate the estimated sensing range area based on the sensing range area of ​​the acoustic device type for collecting data, the layout position and layout quantity of the acoustic device type for collecting data; When the estimated sensing range area is not larger than the preset range information, the arrangement position and the arrangement quantity of the acoustic device type for collecting data are adjusted until it is larger than the preset range information.

5. The method for ecological acoustic monitoring of macrobenthic animals in mangrove areas according to claim 1, characterized in that: Acoustic characteristic data in the mangrove area are obtained by acoustic equipment, and the functional groups of large benthic animals in the mangrove area are monitored and identified based on the acoustic characteristic data in the mangrove area, and the relative abundance of the functional groups of large benthic animals is counted, including: Acoustic characteristic data in the mangrove area are obtained through acoustic equipment, and macrobenthic animal functional groups related to each acoustic characteristic data are obtained through big data, and a macrobenthic animal knowledge graph is constructed according to the macrobenthic animal functional groups related to each acoustic characteristic data; The acoustic feature data in the mangrove area is input into the large benthic animal knowledge graph for identification, the large benthic animal type feature data in the mangrove area is obtained, and the large benthic animal functional group feature data in the mangrove area is counted to obtain the relative abundance of the large benthic animal functional groups.

6. The method for ecological acoustic monitoring of macrobenthic animals in mangrove areas according to claim 1, characterized in that: Analyze the relative abundance of each functional group of the macrobenthic animals, obtain analysis results, and evaluate the diversity of macrobenthic animal soundscapes based on the analysis results, specifically including: Setting a relative abundance characteristic threshold of a macrobenthic animal functional group, and determining whether the relative abundance of each macrobenthic animal functional group is greater than the relative abundance characteristic threshold of the macrobenthic animal functional group; When the relative abundance of each functional group of the macrobenthic animals is greater than the relative abundance characteristic threshold of the functional group of the macrobenthic animals, an analysis result is generated, and the relative abundance of each functional group of the macrobenthic animals is output.

7. An acoustic monitoring device for macrobenthic animals in mangrove areas, characterized in that: It comprises a memory and a processor, wherein the memory comprises a program of an ecological acoustic monitoring method for large benthic animals in mangrove areas, and when the program of an ecological acoustic monitoring method for large benthic animals in mangrove areas is executed by the processor, the steps of the ecological acoustic monitoring method for large benthic animals in mangrove areas as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that: It comprises a method program for ecological acoustic monitoring of large benthic animals in mangrove areas. When the method program for ecological acoustic monitoring of large benthic animals in mangrove areas is executed by a processor, the steps of the method for ecological acoustic monitoring of large benthic animals in mangrove areas as described in any one of claims 1 to 6 are implemented.

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