A method and device and equipment for monitoring disaster-causing organisms under water near a coastal nuclear power plant by combining light and sound

Abstract

The application discloses a kind of photo-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring method.Method includes: obtaining the image data and sonar data of different kinds of underwater disaster-causing organisms;Image data is counted corresponding to underwater disaster-causing organisms, and the dominant species classification matrix of underwater disaster-causing organisms is output;Based on sonar data and the dominant species classification matrix of underwater disaster-causing organisms, the quantity statistics corresponding to underwater disaster-causing organisms are carried out.The application uses optical imaging image recognition monitoring method, without salvage identification, can obtain the disaster-causing organism species existing in target sea area, timeliness is high and low to artificial dependence.By the method of optical recognition, the species information of the target is obtained, which provides a reliable basis for acoustic biological quantity calculation.The photo-acoustic combined cooperative monitoring method combines the advantages of optical means that can accurately identify species and acoustic means that can monitor biomass in a large area, and has low dependence on manpower, high automation level, effectively improves the intelligent level of monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater monitoring, in particular to a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring method, device and equipment. BACKGROUND

[0002] In recent years, marine organism outbreaks have caused the cooling water intake of coastal nuclear power plants to be blocked, which in turn has caused nuclear power unit shutdown events to occur from time to time. The cooling water supply for the circulating cooling water system and other important equipment of the coastal nuclear power plant depends on the cooling water intake of the nuclear power plant. According to statistics from the World Association of Nuclear Power Operators (WANO), from 2004 to 2015, a total of 104 cooling water intake blockage events occurred, of which marine organism-caused blockage accounted for more than 58%. Therefore, the safety protection of the cooling water intake is of the utmost importance for the stable operation of the coastal nuclear power plant, and the target sea area needs to be monitored to estimate the biomass, thereby providing technical support for the safe operation of the nuclear power plant.

[0003] Optical imaging is a commonly used biological monitoring method. The main process of conventional underwater camera monitoring is as follows: cameras are arranged according to the water depth, power supply and data transmission for the cameras are provided through wiring, and long-term monitoring of the returned data is manually performed. When disaster-causing organisms appear, the species of the organisms and the number of the organisms need to be manually identified. The disadvantage is that long-term manual monitoring is required, the optical imaging range is small due to the influence of the marine environment, and it is difficult to estimate the biomass on a large scale.

[0004] Acoustic detection can obtain target information over a long distance and a large range, and is an important means for underwater medium and long distance detection and large-scale investigation of underwater organisms. The main working process of the existing sonar detection system is as follows: the existing disaster-causing organisms in the sea area are investigated and biological samples are collected, the acoustic target intensity of different samples is measured in the laboratory, then the sonar is used to collect the echo intensity in the target sea area within a certain time, the species of the disaster-causing organisms in the actual sea area is determined according to the collection situation, the target intensity is determined according to the species, and finally the biomass is estimated according to the echo intensity. The disadvantage is that it is time-consuming and labor-intensive to collect the main biological species in the current sea area. SUMMARY

[0005] Therefore, the present application aims to provide a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring method, device and equipment, which can combine the advantages of acoustic and optical technologies, estimate the biomass in a large range without the need for collection, reduce the dependence on manual work, and improve the intelligent level of monitoring.

[0006] According to one aspect of the present application, there is provided a photo-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring method, comprising: acquiring image data and sonar data of different types of underwater disaster-causing organisms; performing statistics on the image data corresponding to the underwater disaster-causing organisms, and outputting a dominant species classification matrix of the underwater disaster-causing organisms; and performing quantity statistics on the underwater disaster-causing organisms based on the sonar data and the dominant species classification matrix of the underwater disaster-causing organisms.

[0007] According to another aspect of the present application, there is provided a photo-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring device, comprising: an acquisition module, an image statistics module, and a photo-acoustic analysis module; the acquisition module is configured to acquire image data and sonar data of different types of underwater disaster-causing organisms; the image statistics module is configured to perform statistics on the image data corresponding to the underwater disaster-causing organisms, and output a dominant species classification matrix of the underwater disaster-causing organisms; and the photo-acoustic analysis module is configured to perform quantity statistics on the underwater disaster-causing organisms based on the sonar data and the dominant species classification matrix of the underwater disaster-causing organisms.

[0008] According to still another aspect of the present application, there is provided a photo-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring equipment, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the photo-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring method according to any one of the above.

[0009] According to still another aspect of the present application, there is provided a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the photo-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring method according to any one of the above.

[0010] It can be found that, according to the above scheme, the present application adopts an optical imaging image recognition monitoring method, which can acquire the types of disaster-causing organisms existing in a target sea area without fishing and identification, has high timeliness and low dependence on manpower. The type information of the target acquired by the optical recognition method provides a reliable basis for acoustic biological quantity calculation. The photo-acoustic combined cooperative monitoring method combines the advantages of optical means capable of accurately identifying types and acoustic means capable of monitoring biological quantity in a large area, has low dependence on manpower, high automation level, and effectively improves the intelligent level of monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0012] Figure 1 is a flowchart of an embodiment of the present application of a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring method;

[0013] Figure 2 is a structural diagram of a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring device of an embodiment of the present application;

[0014] Figure 3 is a principle diagram of a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring device of an embodiment of the present application;

[0015] Figure 4 is a schematic diagram of underwater bottom-sitting platform probe installation of an embodiment of the present application of a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring device. DETAILED DESCRIPTION

[0016] The present application will be further described in detail below in combination with the drawings and embodiments. It is particularly pointed out that the following embodiments are only used to illustrate the present application, but do not limit the scope of the present application. Similarly, the following embodiments are only some embodiments of the present application, but not all embodiments, and all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] The present application provides a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring method, which can combine the advantages of both sound and light technologies, complete the biomass estimation in a large range without fishing, reduce the dependence on artificial labor, and improve the intelligent level of monitoring.

[0018] Please refer to Figure 1 , Figure 1 is a flowchart of an embodiment of the present application of a light-sound combined coastal nuclear power underwater disaster-causing organism monitoring method. It should be noted that the method of the present application is not limited to the flow order shown in Figure 1 . As shown in Figure 1 , the method comprises the following steps:

[0019] S101: Obtain image data and sonar data of different types of underwater disaster-causing organisms.

[0020] In the embodiment, the different kinds of underwater disaster-causing organisms are collected in parallel by the image and the sonar.

[0021] In the embodiment, the parallel collection means that the sonar probe is installed at the right side of the center of the bottom standing platform, the optical lens is installed at the left side of the center of the bottom standing platform, the two probes are installed in parallel, and the same sea area is detected without interference between them.

[0022] S102: Statistics of the underwater disaster-causing organisms corresponding to the image data are performed, and a dominant species classification matrix of the underwater disaster-causing organisms is output.

[0023] In the embodiment, the number of the underwater disaster-causing organisms is determined based on an image algorithm, the category n of the underwater disaster-causing organisms is determined according to the image algorithm, and a 1xn column matrix N is output based on the algorithm; and when the underwater disaster-causing organisms in the ith category account for 60% or more of the total number, the dominant species classification matrix N corresponding to the underwater disaster-causing organisms in the ith category is output, and the above steps are repeated until the dominant species classification matrix of the underwater disaster-causing organisms corresponding to all the image data is obtained.

[0024] Specifically,

[0025] The number of different organisms is determined by classifying the organisms in the image through an intelligent algorithm. In the embodiment, the intelligent algorithm can be a machine learning algorithm, a deep learning algorithm, etc., and the embodiment is not limited. The category of all target organisms in the sea area is n, and a classification matrix N of size 1xn is output by the algorithm. If organism 1 accounts for more than 60% of the total number, it can be considered that organism 1 is the current dominant species, and the algorithm outputs the dominant species classification matrix N=[1, 0, 0, …, 0]; if organism 2 accounts for more than 60% of the total number, it can be considered that organism 2 is the current dominant species, and the algorithm outputs the dominant species classification matrix N=[1, 0, 0, …, 0]; and the subsequent is similar. It should be understood that if there is no biological outbreak in normal times, it is normal. If there is a biological outbreak, it is certain that a certain organism is dominant, and such dominant organisms exist in large quantities in the sea area.

[0026] S103: Statistics of the underwater disaster-causing organisms corresponding to the underwater disaster-causing organisms are performed based on the sonar data and the dominant species classification matrix of the underwater disaster-causing organisms.

[0027] In the embodiment, based on the sonar data, a single individual target intensity matrix T=[TS 生物1 , TS 生物2 , TS 生物3 , …, TS 生物nand the single individual target strength matrix of the corresponding species of the underwater disaster-causing organisms in the sonar scanning surface is calculated according to the dominant species classification matrix of the underwater disaster-causing organisms and the single individual target strength matrix of all the underwater disaster-causing organisms in the sonar scanning surface obtained according to the sonar data, and the single individual average strength TS of the dominant species of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning surface is calculated, and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning surface is calculated through the single individual average strength TS of the dominant species of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning surface bs , and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning surface is calculated through the scattering cross section of the pulse echo obtained according to the sonar data and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning surface bs The number of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning surface is calculated.

[0028] Specifically,

[0029] First, the single individual average target strength matrix T of the corresponding species of all the target organisms in the sea area is T = [TS 生物1 , TS 生物2 , TS 生物3 , …, TS 生物n ], and the single individual average strength TS of the dominant disaster-causing organisms in the current sea area is calculated according to the dominant species classification matrix and the single individual average target strength matrix, and the calculation formula is as follows:

[0030] TS = T x NT (I)

[0031] Secondly, the corresponding single individual average scattering cross section σ of the disaster-causing organisms is obtained through formula (2). bs

[0032]

[0033] The working mode of the sonar is that the sonar transmits sound waves to the target sea area, and the underwater sound waves will produce echoes after encountering organisms during propagation. At the same time, the sonar controls the reception of the reflected sound waves with information, converts the sound signals into electrical signals, and then returns to the industrial computer to obtain the scattering cross section S V of the pulse echo.

[0034] Finally, the scattering cross section S V of the pulse echo is directly obtained through the transducer, and the biomass quantity n of the entire target area at present is obtained by dividing the single individual average scattering cross section σ bs of the disaster-causing organisms obtained by the above method, and the calculation formula is as follows (3).

[0035] ​

[0036] It can be found that, in the embodiment, the monitoring method of the application adopts optical imaging image recognition, and the species of the disaster-causing organisms existing in the target sea area can be obtained without fishing identification, which is high in timeliness and low in dependence on manpower. The species information of the target obtained by the optical recognition method provides a reliable basis for acoustic biomass calculation. The light-acoustic combined cooperative monitoring method combines the advantages of optical method in accurately identifying species and acoustic method in large-area monitoring of biomass, is low in dependence on manpower, high in automation level, and effectively improves the intelligent level of monitoring.

[0037] The application further provides a light-acoustic combined coastal nuclear power underwater disaster-causing organism monitoring device.

[0038] The acquisition module, the image statistical module and the sound-light analysis module are included.

[0039] The acquisition module is used to acquire image data and sonar data of different species of underwater disaster-causing organisms.

[0040] The image statistical module is used to statistically analyze the image data corresponding to the underwater disaster-causing organisms and output a dominant species classification matrix of the underwater disaster-causing organisms.

[0041] The sound-light analysis module is used to statistically analyze the number of the underwater disaster-causing organisms based on the sonar data and the dominant species classification matrix of the underwater disaster-causing organisms.

[0042] Optionally, the acquisition module can be specifically used for:

[0043] Parallelly collecting the images and the sonar data of the different species of underwater disaster-causing organisms.

[0044] Optionally, the image statistical module can be specifically used for:

[0045] Determining the number of the underwater disaster-causing organisms based on an image algorithm, determining the category n of the underwater disaster-causing organisms based on the image algorithm, outputting a 1×n column matrix N based on the algorithm, and when the underwater disaster-causing organisms of the ith category account for 60% or more of the total number, outputting a dominant species classification matrix N=[1, 0, 0, …, 0] corresponding to the underwater disaster-causing organisms of the ith category, and repeating the above steps until the dominant species classification matrix of the underwater disaster-causing organisms corresponding to all the image data is obtained.

[0046] Optionally, the sound-light analysis module can be specifically used for:

[0047] Based on the sonar data, a single individual target intensity matrix T=[TS生物1 , TS 生物2 , TS 生物3 , …, TS 生物n ] and the scattering cross section of the pulse echo, and the single individual target strength matrix of the corresponding species of underwater disaster-causing organisms in the sonar scanning plane is calculated according to the dominant species classification matrix of the underwater disaster-causing organisms and the single individual target strength matrix of the corresponding species of underwater disaster-causing organisms of all underwater disaster-causing organisms in the sonar scanning plane obtained from the sonar data, and the single individual average strength TS of the dominant species of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning plane is calculated by the single individual average strength TS of the dominant species of the underwater disaster-causing organisms in the sonar scanning plane corresponding to the sonar data, and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms in the sonar scanning plane corresponding to the sonar data is calculated by the scattering cross section of the pulse echo obtained from the sonar data and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms in the sonar scanning plane corresponding to the sonar data bs , and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms in the sonar scanning plane corresponding to the sonar data is calculated by the scattering cross section of the pulse echo obtained from the sonar data and the single individual average scattering cross section σ of the dominant species of the underwater disaster-causing organisms in the sonar scanning plane corresponding to the sonar data bs , and the number of the underwater disaster-causing organisms corresponding to the sonar data in the sonar scanning plane is calculated.

[0048] The various unit modules of the light-sound combined coastal nuclear power underwater disaster-causing organism monitoring device can respectively perform the corresponding steps in the above method embodiments, so the unit modules will not be described in detail here, please refer to the above description of the corresponding steps.

[0049] In this embodiment, please refer to Figures 2-4 for the actual case description of the device. Figure 2 is a structural schematic diagram of the light-sound combined coastal nuclear power underwater disaster-causing organism monitoring device; Figure 3 is a principle schematic diagram of the light-sound combined coastal nuclear power underwater disaster-causing organism monitoring device; Figure 4 is a schematic diagram of the installation of the underwater bottom-sitting platform probe.

[0050] In the figure, 1 is a shore-based work cabinet, 2 is an industrial computer, 3 is an underwater bottom-sitting platform, 4 is an optical camera, and 5 is a sonar. Among them, the optical camera, the sonar, and the underwater bottom-sitting platform are acquisition modules; the industrial computer and the shore-based work cabinet are image statistical modules and sound-light analysis modules.

[0051] The shore-based work cabinet is composed of stainless steel material, and the overall size is 80 centimeters long, 58 centimeters wide, and 120 centimeters high. The work cabinet can be moved or fixed to the ground through bolts, which can effectively reduce the impact of wind, rain, and humidity on the system and provide stable power supply for the system. The shore-based work cabinet contains a power supply module that can provide 220V mains power for the system, contains an industrial computer, and contains a sonar control host.

[0052] The optical camera is a three-in-one optical camera composed of an optical lens, an electric brush, a light supplement lamp and an outer frame. The imaging resolution of the optical lens is 1920*1080, and the imaging bit depth is 24 bits. The electric brush is arranged in front of the camera to clean the lens attachments. The light supplement lamp provides 1000 lumen light supplement. In addition, the electric brush is fixed above the lens and can swing 70 degrees left and right under the drive of the motor. The light supplement lamp is located on the side of the optical lens, and the light supplement direction is parallel to the focusing direction of the lens. Its function is to collect underwater optical signals. When the target organism appears in the lens range, the camera can collect the target image and return the data to the industrial computer for further processing.

[0053] The sonar is a transceiver combined sonar, which can transmit and receive sound signals. The appearance of the sonar is cylindrical, the probe diameter is 26 cm, the probe height is 22 cm, and the probe weight is 17 kg. The working frequency of the sonar is 38 kHz, the beam width is 10°*10°, the sound source level is 220.4 dB, and the pulse width is 0.5 ms. When the sonar is used as a transmitting end, it can transmit sound waves to the target sea area. When the sonar is used as a receiving end, it can receive the echo signals reflected by the organisms that may exist in the target sea area, and save the signals for further processing by the industrial computer.

[0054] The industrial computer includes an optical image processing program and an acoustic signal processing software. The industrial computer controls the whole acoustic-optical monitoring system to realize intelligent monitoring of the disaster-causing organisms. The optical image processing program realizes the functions of electric brush control (working time, working frequency), light control (working time, light intensity), camera control (whether to work), intelligent identification of targets (identifying targets, saving target species, displaying targets, obtaining the average single target intensity of the species), and monitoring data transmission, saving, etc. The optical software system is compiled by Python language and can be developed again on the basis of existing functions. The acoustic signal processing software realizes the control of the sonar (signal transmission, signal reception, working time, etc.), displays the received acoustic signals, saves the data, and calculates the biomass according to the feedback results of the image processing program.

[0055] The underwater bottom platform is made of corrosion-resistant special steel and has some additional weights to ensure that the bottom platform can be stably fixed on the seabed and does not shift or shake when facing tidal and water flow impact, thereby reducing the influence on the optical camera and the sonar and ensuring the quality of the collected data.

[0056] The sonar probe is installed at the right side of the center of the bottom platform, and the optical lens is installed at the left side of the center of the bottom platform. The two probes are installed in parallel and detect the same sea area without interference between them.

[0057] Firstly, according to the above-mentioned Figure 2 , Figure 4The connection system hardware part is connected according to the description in the foregoing. After the industrial computer 2 is started, the control program is started, the camera 4 and the sonar 5 are placed in the shallow water on the shore, the camera 4 and the sonar 5 are respectively controlled using the control program, and the functions of the camera 4 and the sonar 5, such as on-off, working mode setting, working time setting, data transmission and saving, are tested. The tested camera 4 and the sonar 5 are installed on the underwater bottom-sitting platform 3.

[0058] According to the description in the foregoing, the underwater bottom-sitting platform is deployed into the target sea area, the sonar can measure a large range of biological signals in the target sea area and obtain biomass information, and the camera can measure the signals of specific organisms in the target sea area and obtain biological species information. Through the combination of optical observation and acoustic observation, qualitative and quantitative information of organisms in the target sea area can be obtained. Figure 3 The working mode of the camera is that the control program controls the camera 4 to passively receive optical signals and form images, the imaging data are returned to the industrial computer 2 through a signal line, the data are saved locally, the target species in the image are automatically identified by the control program, and the single individual target intensity of the species is obtained. The specific calculation method is as follows.

[0059] First, the number of different organisms is determined by classifying the organisms in the image through an intelligent algorithm. In the embodiment, the intelligent algorithm can be a machine learning algorithm, a deep learning algorithm, etc., and the embodiment is not limited thereto. The species of all target organisms in the sea area is n, and a classification matrix N of size 1 x n is output by the algorithm. If organism 1 accounts for more than 60% of the total number, it can be considered that organism 1 is the dominant species at present, and the dominant species classification matrix N output by the algorithm is [1, 0, 0, …, 0]. If organism 2 accounts for more than 60% of the total number, it can be considered that organism 2 is the dominant species at present, and the dominant species classification matrix N output by the algorithm is [1, 0, 0, …, 0]. The subsequent is similar.

[0060] In addition, there is a single individual average target intensity matrix T of the species corresponding to all target organisms in the sea area, which is [TS 生物1 , TS 生物2 , TS 生物3 , …, TS 生物n ], so the single individual average intensity TS of the dominant disaster-causing organism in the current sea area can be calculated according to the dominant species classification matrix and the single individual average target intensity matrix, and the calculation formula is as follows.

[0061] TS = T x NT (I)

[0062] Secondly, the corresponding single individual average scattering cross section σ bs of the disaster-causing organism can be obtained by formula (2).

[0063]

[0064] ​

[0065] The sonar operates as follows: The sonar emits sound waves towards the target sea area. When these underwater sound waves encounter marine life during propagation, they generate echoes. Simultaneously, the sonar receives the reflected sound waves carrying information, converts the acoustic signals into electrical signals, and transmits them back to an industrial computer to obtain the scattering cross section S of the pulse echo. V .

[0066] Finally, the scattering cross section S of the pulse echo is obtained directly through the transducer. V Then divide by the average scattering cross section σ of a single individual of the causative organism obtained by the aforementioned method. bs The current biomass quantity n of the entire target area can be obtained by formula (3).

[0067]

[0068] The present invention also provides a photo-acoustic combined underwater disaster-causing biological monitoring device for coastal nuclear power plants, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described photo-acoustic combined underwater disaster-causing biological monitoring method for coastal nuclear power plants.

[0069] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0070] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0071] The present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described method embodiments.

[0072] It can be found that the above scheme, the monitoring method of the present application adopts optical imaging image recognition, without fishing identification, the target sea area can obtain the disaster-causing biological species, the timeliness is high and the artificial dependence is low. The species information of the target obtained by the optical recognition method provides a reliable basis for the calculation of the acoustic biomass. The light-acoustic combined cooperative monitoring method combines the advantages of optical method that can accurately identify the species and acoustic method that can monitor the biomass in a large area, and has low dependence on artificial, high automation level, and effectively improves the intelligent level of monitoring.

[0073] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiment is merely an example, and there can be other division manners. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0074] The unit described as a separate component can or can not be physically separate, and the component shown as a unit can or can not be a physical unit, that is, it can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment scheme.

[0075] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware, or in the form of a software functional unit.

[0076] If the integrated unit is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0077] The above only describes some embodiments of the present application, and does not limit the protection scope of the present application. Any equivalent device or equivalent process transformation, or direct or indirect application in other related technical fields based on the content of the present application specification and drawings are also included in the patent protection scope of the present application.

Claims

1. A light-sound combined method for underwater monitoring of hazardous organisms in coastal nuclear power plants, characterized in that, include: Acquire image and sonar data of different types of underwater disaster-causing organisms; The image data is statistically analyzed to identify the underwater disaster-causing organisms, and a dominant species classification matrix of the underwater disaster-causing organisms is output. Based on sonar data and the dominant species classification matrix of the underwater disaster-causing organisms, the quantity statistics of the corresponding underwater disaster-causing organisms were performed. The step of statistically analyzing the image data corresponding to the underwater disaster-causing organisms and outputting a dominant species classification matrix of the underwater disaster-causing organisms includes: The algorithm determines the number of underwater disaster-causing organisms based on image analysis, and determines the category n of the underwater disaster-causing organisms based on image analysis, and outputs a 1×n column matrix N based on the algorithm; and when the underwater disaster-causing organisms in the i-th category account for 60% or more of the total, the algorithm outputs a classification matrix N=[1, 0, 0, ..., 0] of the dominant species of the underwater disaster-causing organisms in the i-th category, and repeats the above steps until the classification matrix of the dominant species of the underwater disaster-causing organisms corresponding to all the image data is obtained; The method of statistically analyzing the number of underwater disaster-causing organisms based on sonar data and the dominant species classification matrix of the underwater disaster-causing organisms includes: Based on sonar data, obtain the individual target intensity matrix T=[TS] for each species of underwater pest within the sonar scanning area. 生物1 TS 生物2 TS 生物3 , ..., TS 生物n The text describes the calculation of the average intensity TS of a single individual of the dominant species of the underwater disaster-causing organisms within the sonar scanning plane, based on the dominant species classification matrix of the underwater disaster-causing organisms and the sonar data. It also mentions calculating the average scattering cross section of a single individual of the dominant species of the underwater disaster-causing organisms within the sonar scanning plane using the average intensity TS of the single individual of the dominant species of the underwater disaster-causing organisms within the sonar scanning plane. The scattering cross section of the pulse echo obtained through sonar data and the average scattering cross section of a single individual of the dominant species of the underwater hazard-causing organism corresponding to the sonar data within the sonar scanning plane. Calculate the number of underwater disaster-causing organisms corresponding to the sonar data within the sonar scanning area.

2. The optical-acoustic combined underwater disaster-causing biological monitoring method for coastal nuclear power plants as described in claim 1, characterized in that, The acquisition of image data and sonar data of different types of underwater disaster-causing organisms includes: Images and sonar were collected in parallel for the different species of underwater disaster-causing organisms.

3. A light-sound combined underwater biological monitoring device for coastal nuclear power plants, characterized in that, include: Acquisition module, image statistics module, and acoustic-optical analysis module; The acquisition module is used to acquire image data and sonar data of different types of underwater disaster-causing organisms; The image statistics module is used to perform statistics on the image data corresponding to the underwater disaster-causing organisms, and output the dominant species classification matrix of the underwater disaster-causing organisms; The acoustic-optical analysis module is used to perform quantity statistics of the corresponding underwater disaster-causing organisms based on sonar data and the dominant species classification matrix of the underwater disaster-causing organisms. The image statistics module is specifically used for: The number of underwater disaster-causing organisms is determined based on the image algorithm, and the category n of the underwater disaster-causing organisms is determined based on the image algorithm, and a 1×n column matrix N is output based on the algorithm. When the underwater disaster-causing organisms in category i account for 60% or more of the total, output the classification matrix N=[1, 0, 0, ..., 0] of the dominant species of the underwater disaster-causing organisms in category i, and repeat the above steps until the classification matrix of the dominant species of the underwater disaster-causing organisms corresponding to all the image data is obtained; The acousto-optic analysis module is specifically used for: Based on sonar data, obtain the individual target intensity matrix T=[TS] for each species of underwater pest within the sonar scanning area. 生物1 TS 生物2 TS 生物3 , ..., TS 生物n The text describes the calculation of the average intensity TS of a single individual of the dominant species of the underwater disaster-causing organisms within the sonar scanning plane, based on the dominant species classification matrix of the underwater disaster-causing organisms and the sonar data. It also mentions calculating the average scattering cross section of a single individual of the dominant species of the underwater disaster-causing organisms within the sonar scanning plane using the average intensity TS of the single individual of the dominant species of the underwater disaster-causing organisms within the sonar scanning plane. The scattering cross section of the pulse echo obtained through sonar data and the average scattering cross section of a single individual of the dominant species of the underwater hazard-causing organism corresponding to the sonar data within the sonar scanning plane. Calculate the number of underwater disaster-causing organisms corresponding to the sonar data within the sonar scanning area.

4. The optical-acoustic combined underwater disaster-causing biological monitoring device for coastal nuclear power plants as described in claim 3, characterized in that, The acquisition module is specifically used for: Images and sonar were collected in parallel for the different species of underwater disaster-causing organisms.

5. A light-sound combined underwater biological monitoring device for coastal nuclear power plants, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the photo-acoustic combined underwater disaster-causing biological monitoring method for coastal nuclear power plants as described in any one of claims 1 to 2.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the optical-acoustic combined underwater disaster-causing biological monitoring method for coastal nuclear power plants as described in any one of claims 1 to 2.

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