Water environment monitoring method and device based on monitoring ship, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of environmental ecology, and particularly relates to a water environment monitoring method and device based on a monitoring ship, electronic equipment and a storage medium. A to-be-detected sample in a target water area is obtained; obtaining the color of the to-be-detected sample after reaction based on the to-be-detected sample and the detection reagent, and obtaining a spectrograph based on the color of the to-be-detected sample after reaction and the camera; determining the target water area water quality based on the spectrophotograph and the calculation unit. The water quality can be detected in real time by detecting the concentration level of nitrate in a sample, so that the pollution risk in the process of unloading the sample ashore and transporting the sample to a laboratory during water quality detection is reduced, and the detection efficiency and accuracy are improved. The computing unit on the monitoring ship can predict and detect algal blooms, detect and draw a garbage map in real time, and perform active management and intervention, so that the water quality is improved, the ecological system protection is enhanced, and the aquatic organisms are protected.

Description

Technical Field

[0001] The present invention belongs to the field of environmental ecology technology, and in particular relates to a water environment monitoring method, device, electronic equipment and storage medium based on a monitoring ship. Background Art

[0002] In recent decades, freshwater lakes such as Lake Erie in the United States and Lake Taihu in China have experienced “dead zones” caused by excess nitrates. Nitrates promote the growth of algae, during which the algae can rapidly multiply and form dense populations. As the algae eventually die and decompose, bacteria and other microorganisms break down the organic matter, consuming dissolved oxygen in the process. Increased microbial activity and increased algal biomass deplete oxygen in the water, which can have adverse effects on marine or river life, including fish kills.

[0003] The current testing method for water environments is mainly to transport water samples back to the laboratory for testing, but the aquatic environment is a constantly changing environment, and a lot of time is spent in the sample transportation process, which leads to inaccurate results. In addition, this method cannot obtain long-term real-time data, which means that it is impossible to accurately predict the possibility of algae blooms.

[0004] Therefore, how to monitor the water environment quickly and accurately has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a water environment monitoring method, device, electronic equipment and storage medium based on a monitoring ship, which can monitor the water quality in the water environment quickly and accurately.

[0006] In the first aspect, an embodiment of the present invention provides a water environment monitoring method based on a monitoring ship, wherein the monitoring ship includes: a computing unit, a lifting platform, a rotator and a camera, and the method includes: obtaining a sample to be detected in a target water area; obtaining the color of the sample to be detected after reaction based on the sample to be detected and a detection reagent, wherein the detection reagent changes color according to the nitrate content in the sample to be detected; obtaining a spectral photograph based on the color of the sample to be detected after reaction and the camera; and determining the water quality in the target water area based on the spectral photograph and the computing unit.

[0007] In a possible implementation, the monitoring ship also includes a positioning system, and after determining the water quality in the target waters based on the spectral photograph and the computing unit, the system includes: determining sampling locations of multiple samples to be detected in the target waters based on the positioning system; and determining the water quality distribution of the target waters based on the sampling locations of the multiple samples to be detected, the spectral photograph and the computing unit.

[0008] In one possible implementation, the rotator is used to obtain multiple groups of samples to be detected from different sampling locations in the target area, and place the multiple groups of samples to be detected into different chambers in the rotator respectively, and obtaining the colors of the samples to be detected after the reaction based on the samples to be detected and the detection reagents includes: obtaining the colors of the multiple groups of samples to be detected after the reaction based on the multiple groups of samples to be detected and the detection reagents in the different chambers.

[0009] In a possible implementation, the camera is installed on the lifting platform, and the camera captures image information of different sampling locations in the target water area through the lifting platform, and the image information includes fish image information and garbage image information.

[0010] In a possible implementation, the method further includes: acquiring underwater image information in the target water area based on the camera; determining underwater garbage based on the underwater image information and an underwater garbage recognition model, wherein the underwater garbage recognition model is obtained by training a convolutional neural network.

[0011] In a possible implementation, after determining the underwater garbage based on the underwater image information and the underwater garbage identification model, it also includes: determining the location of the underwater garbage based on the positioning system; and determining a garbage distribution heat map of the target water area based on the location of the underwater garbage and the target water area.

[0012] In a possible implementation, the monitoring ship further includes an unmanned driving system, and the unmanned driving system is combined with the camera to autonomously plan the driving path of the monitoring ship.

[0013] In a second aspect, an embodiment of the present invention provides a water environment monitoring device based on a monitoring ship, the device comprising: an acquisition module for acquiring a sample to be detected in a target water area; the acquisition module is also used to acquire the color of the sample to be detected after reaction based on the sample to be detected and a detection reagent, the detection reagent changes color according to the nitrate content in the sample to be detected; a processing module is used to obtain a spectral photograph based on the color of the sample to be detected after reaction and the camera; a determination module is used to determine the water quality of the target water area based on the spectral photograph and the calculation unit.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0016] In a fifth aspect, an embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the method described in the first aspect or any one of the implementations thereof.

[0017] The present invention provides a water environment monitoring method based on a monitoring ship, wherein the monitoring ship includes: a computing unit, a lifting platform, a rotator and a camera, and the method includes: obtaining a sample to be detected; obtaining the color of the sample to be detected after the reaction based on the sample to be detected and a detection reagent, and the detection reagent changes color according to the nitrate content in the sample to be detected; obtaining a spectral photo based on the color of the sample to be detected after the reaction and the camera; and determining the water quality of the sample to be detected based on the spectral photo and the computing unit. In the prior art, water environment monitoring is usually to transport water samples back to the laboratory for testing. In this case, during the transportation process, the sample is also a constantly changing environment. Long-term transportation will lead to inaccurate test results, so it is impossible to accurately predict whether algae will reproduce in large numbers. The present application improves the accuracy of water environment detection by analyzing the water environment in real time and quickly and accurately monitoring the water environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic flow chart of a water environment monitoring method based on a monitoring vessel provided in one embodiment of the present invention;

[0020] Figure 2 A monitoring ship provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of a spectrometer provided in accordance with an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of a first-order target detector provided by the present invention;

[0023] Figure 5 A loss graph of the feature extraction model provided by the present invention for the fish tail feature;

[0024] Figure 6 This is a recognition result diagram of the underwater garbage recognition model provided by the present invention;

[0025] Figure 7 A structural block diagram of a water environment monitoring device based on a monitoring ship provided by an embodiment of the present invention;

[0026] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0028] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0029] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0030] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0031] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0032] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0033] One common problem facing reservoirs is water quality monitoring. As the source of drinking water for local residents, water quality safety is of vital importance to management agencies. Current methods for monitoring algae and nitrate growth rely mainly on laboratory testing of water samples. These methods have two main drawbacks: low efficiency and data delay.

[0034] Additionally, large blooms of algae in reservoirs used to store drinking water can have a detrimental effect on residents in the area. While there are ways to kill these algae in reservoirs, such as chemicals like algaecides, the use of these chemicals can hinder the water purification process, leading to increased costs in producing drinking water. And chemical treatments can have unintended consequences for the aquatic environment. For example, algaecides can harm not only the target algae, but also other non-target organisms such as fish, invertebrates, and beneficial algae. Similarly, the use of nitrate reducers can also alter the natural nitrogen cycle and disrupt the balance of the ecosystem.

[0035] Frequent and repeated use of chemical treatments can lead to resistance in target organisms. For example, over time, algae can evolve and become resistant to certain algaecides. This requires the use of stronger or more effective chemicals, which results in increased chemical use.

[0036] Moreover, garbage in the reservoir is also the main reason for the massive reproduction of algae. During the water quality testing process, if the garbage cannot be identified and detected, simply testing the reservoir water quality will not achieve the purpose of improving the water quality.

[0037] In order to solve the above problems, the present application provides a water environment monitoring method based on a monitoring vessel, which can monitor the main harmful factors of underwater ecosystems, including water quality, aquatic organisms and water environment monitoring. During use, the monitoring vessel can monitor and predict the reproduction of algae in the water body. Through the Lambert-Beer law and deep learning technology, water samples are analyzed to determine the nitrate content, so that the monitoring vessel can provide valuable insights and predictions on the possibility and potential locations of algal blooms, assisting in active management and mitigation efforts. Finally, by applying a regression model and combining weather data with nitrate levels, the ship can predict and output a heat map of the estimated location of algal blooms.

[0038] Based on the monitoring, the monitoring ship can also identify garbage and species in the water environment through cameras. By integrating data analysis and machine learning technology, the computing unit can generate real-time maps or reports showing the location and density of detected garbage, the area where invasive species are located and the estimated number. This information can then be shared with relevant authorities and environmental organizations, allowing them to prioritize and efficiently allocate resources for garbage cleanup and alien invasive species prevention and control.

[0039] In summary, water environment monitoring vessels address deteriorating water quality in lakes and reservoirs by monitoring and mitigating harmful factors such as nitrate levels, invasive species, and trash. By leveraging advanced technologies such as deep learning and data analytics, onboard computers can predict and detect algal blooms, identify invasive species, and detect and map trash in real time. This enables proactive management and intervention to improve water quality, enhance ecosystem protection, and protect aquatic life.

[0040] Figure 1 A flow chart of a water environment monitoring method based on a monitoring ship is provided in accordance with an embodiment of the present invention. The method comprises:

[0041] S110, obtaining samples to be tested in the target water area.

[0042] For example, Figure 2 A monitoring ship is provided in one embodiment of the present invention, and the monitoring ship comprises: a computing unit, a lifting platform, a rotator and a camera. In the middle of the rotator is a mechanical arm, which can obtain samples from the water below the target water area.

[0043] S120, obtaining the color of the sample after reaction based on the sample and the detection reagent, wherein the detection reagent changes color according to the content of nitrate in the sample.

[0044] Exemplarily, the monitoring vessel detects nitrate concentration using the Lambert-Beer law, under which the absorbed light is equal to the nitrate concentration in the sample to be detected. The nitrate content in the sample to be detected is determined by the change in color of the sample to be detected.

[0045] In one possible implementation, the rotator is used to obtain multiple groups of samples to be tested from different sampling locations in the target area, and place the multiple groups of samples to be tested into different chambers in the rotator respectively, and obtain the colors of the samples to be tested after the reaction based on the samples to be tested and the detection reagents, including: obtaining the colors of the detection samples after the reaction based on the multiple groups of samples to be tested and the detection reagents in different chambers.

[0046] Exemplarily, the robotic arm acquires multiple groups of samples to be tested from different sampling locations in the target area, and distributes the multiple groups of samples to be tested into different chambers in the rotator. The monitoring ship can analyze samples from multiple locations / areas at the same time. During the sample acquisition process, the sampling locations of the test samples are recorded by the positioning system.

[0047] S130, obtaining a spectral photo based on the color of the sample to be detected after the reaction and the camera.

[0048] Exemplarily, the monitoring vessel further comprises a spectrometer, and the spectrometer is manufactured according to the Lambert-Beer law. Figure 3 Schematic diagram of a spectrometer provided in one embodiment of the present invention. First, the sample to be tested absorbs the test reagent, and then the color of the sample to be tested changes according to the nitrate content in the sample to be tested. Then, the light in the spectrometer turns on ( Figure 3 The light source shown in the figure) shines in through the small hole in the sample holder ( Figure 3 Focusing element shown). Depending on the amount of nitrate in the sample water, some of the light will be absorbed by the sample. The light will then pass through another slit ( Figure 3 entrance slit shown), into a mirror ( Figure 3 The optical signal is reflected by a small hole on the spectrometer, and the CCD (Charge-coupled Device) array converts the optical signal into an electrical signal, and then forms a spectral image based on the electrical signal. The camera captures the spectral image to obtain a spectral photo.

[0049] Before the spectrometer analysis, the spectrometer needs to be calibrated to avoid errors during the analysis. Three predetermined spectral samples are used for calibration. After that, the concentration of nitrate is stably calculated using the Lambert-Beer law.

[0050] Furthermore, by distributing multiple groups of samples to be tested into different chambers in the rotator, and then combining the spectrometer and spectral photographs, the monitoring ship can analyze samples from multiple locations at the same time.

[0051] Furthermore, the rotator may be polygonal or circular in shape.

[0052] S140, determining the water quality in the target water area based on the spectral photograph and the computing unit.

[0053] Exemplarily, based on the spectral photograph, the photograph is sent to a computing unit in the monitoring ship, and the computing unit determines the concentration of nitrate in the sample to be tested based on the python code, thereby determining the water quality of the sample to be tested.

[0054] Furthermore, through long-term data collection, long short-term memory neural networks are used to predict the long-term health of water bodies and determine when algae will reproduce in large numbers.

[0055] In a possible implementation, the monitoring ship also includes a positioning system. After determining the water quality in the target waters based on the spectral photograph and the computing unit, it includes: determining the sampling locations of multiple samples to be tested in the target waters based on the positioning system; determining the water quality distribution of the target waters based on the sampling locations of the multiple samples to be tested, the spectral photograph and the computing unit.

[0056] For example, based on the sampling locations of the samples to be tested in the area to be tested determined by the positioning system, and then based on the water quality corresponding to the samples to be tested, the water quality distribution of each area in the reservoir or other water environment can be determined, and the water quality distribution heat map can be determined based on the water quality distribution.

[0057] In a possible implementation, a camera is installed on a lifting platform, and the camera captures image information of different sampling locations in the target waters through the lifting platform, where the image information includes fish image information and garbage image information.

[0058] In one possible implementation, a camera is installed on a lifting platform. The camera captures image information above and below the water through the lifting platform, and identifies underwater fish species through the image information, thereby determining whether the identified fish species is an invasive species.

[0059] Exemplarily, the method for identifying species by image information is:

[0060] Obtain image information of the species to be detected.

[0061] Exemplarily, underwater fish are photographed by a camera to obtain image information of the fish.

[0062] Based on the image information of the species to be detected and the feature extraction model, the features of the species to be detected are determined. The feature extraction model includes a part feature extraction model and a length feature extraction model. The features of the species to be detected include part features and length features of the species to be detected.

[0063] Exemplarily, the fish features in the fish image information are obtained based on the image information of the fish and a part feature extraction model and a length feature extraction model in a computing unit on a monitoring vessel.

[0064] In a possible implementation, before determining the characteristics of the species to be detected based on the image information of the species to be detected and the feature extraction model, it also includes: obtaining the image information of the invasive species; determining a part feature extraction model and a length feature extraction model based on the image information of the invasive species and a convolutional neural network model, the part feature extraction model is used to extract part features, the part features include fish scale features and fish tail features, and the length feature extraction model is used to extract the head position and the tail position.

[0065] For example, before extracting features from fish image information, the part feature extraction model and the length feature extraction model must first be trained to ensure accurate extraction of features from the image information of the fish to be detected.

[0066] Specifically, by inputting the image information of common invasive species in the region, and then training the part feature extraction model and the length feature extraction model, the fish scale features and fish tail features, the head position and the tail position in the invasive species image information are extracted. The invasive species image information can be obtained through online pictures.

[0067] In one possible implementation, a part feature extraction model and a length feature extraction model are determined based on invasive species image information and a convolutional neural network model, including: determining a training data set, a verification data set, and a test data set based on the invasive species image information and a preset ratio; determining a trained convolutional neural network model based on the training data set and the convolutional neural network model; determining a loss value of the trained convolutional neural network model based on the trained convolutional neural network model and the verification data set; and determining a part feature extraction model and a length feature extraction model based on the loss value, the trained convolutional neural network model, and the test data set.

[0068] Exemplarily, image information of invasive species in the region is obtained through network images, and then the image information of invasive species is divided into a training data set, a verification data set, and a test data set.

[0069] Specifically, if 300 images of different types of fish and invasive species of different resolutions are collected, the invasive species image information is divided into three groups, with the training data set accounting for 70%, the verification data set accounting for 20%, and the test data set accounting for 10%.

[0070] As an example, the YOLOv5 architecture can be used to extract the fish scale features, fish tail features, head position, and tail position of the training data set in the invasive species image information. The YOLOv5 architecture is a first-order target detector. Figure 4The schematic diagram of the first-order target detector provided by the present invention, the first-order target detector consists of three parts: the first-order target detector consists of three parts: the backbone network (Backbone), the network layer (Neck) and the detection head (Head), which is used for prediction. The model backbone network is mainly used to obtain important features from a given image. In YOLOv5, the CSPDarknet-53 pre-trained network is used as the backbone network to extract information features (visual features in the image related to the identification of objects) from the input image. In addition, PANet is used as the network layer to obtain the feature pyramid, and three 1×1 convolutional layers are used as the detection head to predict the bounding box position, score and object category, that is, Figure 4 The YOLOv5 architecture uses a deep neural network to divide the image into a grid and predict bounding boxes and class probabilities simultaneously. YOLOv5 is able to capture fine-grained details and small objects. Its efficient architecture enables it to very accurately detect the various features of fish, such as scale features and tail features, head position, and tail position, even in complex underwater environments or when there are multiple fish species.

[0071] As an example, the part feature extraction model and the length feature extraction model are determined based on the invasive species image information and the convolutional neural network model. The invasive species image information used is 300 invasive species image information of different types of fish and different resolutions collected from the same group.

[0072] Furthermore, the convolutional neural network model trained with the training data set is trained with the validation data set to obtain its loss index and error index, i.e., the loss value. Figure 5 The feature extraction model provided by the present invention is a loss diagram for the fish tail feature, the horizontal axis represents the number of trainings, and the vertical axis represents the loss index or error index. It can be seen that as the number of trainings increases, the loss index or error index gradually decreases. When the loss value reaches the preset threshold, it can be determined that the training is completed, and then the convolutional neural network model after training and the test data set are tested to determine whether the convolutional neural network model after training accurately identifies the fish scale features and fish tail features, head position and tail position in the test data set. If they are all accurately identified, the training is completed, and the part feature extraction model and the length feature extraction model are obtained.

[0073] In a possible implementation, after determining the part feature extraction model and the length feature extraction model based on the invasive species image information and the convolutional neural network model, it also includes: determining the length feature of the invasive species based on the head position and the tail position and a preset formula.

[0074] Exemplarily, a length feature extraction model is obtained through a convolutional neural network model, the head position and tail position in the image information of the invasive species are determined, and the length feature of the invasive species is determined according to the head position and tail position and a preset formula. In order to calculate the length feature of the fish, the provided environment should be equipped with an RGB-D camera, which can not only output the received RGB image, but also output the depth information corresponding to each pixel in the image.

[0075] The specific formula is:

[0076]

[0077] z=d (3)

[0078] Where w' and h' are the width and height of the image, d represents the vertical distance from the camera to the object, α and β represent the vertical and horizontal viewing angles of the camera, and x' and y' represent the x and y coordinates of the object in the circular image received by the camera. The z-axis value is obtained by calculating the depth using the depth camera information d.

[0079] Combining formulas (1)-(3), the three-dimensional vector length calculation formula (4) is used to calculate the length of the fish, where l represents the length characteristic of the fish.

[0080]

[0081] The species type of the species to be detected is determined based on the part characteristics, length characteristics and a preset database of the species to be detected. The species types include invasive species and non-invasive species. The preset database includes the part characteristics, length characteristics and names of invasive species.

[0082] In one possible implementation, the species type is determined based on the part characteristics, length characteristics and preset database of the species to be detected, including: determining the species similarity through cosine similarity based on the part characteristics of the species to be detected and the preset database; determining the species type based on the species similarity, the length characteristics of the species to be detected and the preset database.

[0083] Exemplarily, the part feature extraction model and the length feature extraction model trained by the neural network are used to extract the part features of the species to be detected, that is, to extract the fish scale features and fish tail features, the head position and the tail position of the species to be detected.

[0084] Furthermore, after the part features of the species to be detected are extracted, the similarity of the images is found through the cosine similarity method. Cosine similarity is an indicator used to measure the similarity between two vectors. In image processing, it can be used to compare feature vectors extracted from images, such as the part features of fish.

[0085] Specifically, the species type is determined based on the cosine similarity of the scale features of the species to be detected and the scale features of invasive species in a preset database, based on the cosine similarity of the tail features of the species to be detected and the tail features of invasive species in a preset database, and the length features of the species to be detected and the preset database.

[0086] Exemplarily, the cosine similarity between two vectors is calculated based on the cosine value of the angle between them. Its range is -1 to 1, where 1 means that the vectors are exactly the same, 0 means they are not similar, and -1 means that the vectors are completely dissimilar.

[0087] Furthermore, the cosine similarity result is combined with the length characteristics of the species to be detected and the length characteristics of the invasive species to determine whether the species to be detected is an invasive species.

[0088] Specifically, the monitoring ship can combine the fish's characteristics and length characteristics to determine the type of fish, and then compare it with a pre-compiled list of invasive species for each continent or region, that is, a preset database, to see if it belongs to the species on the list. Then it will output whether the detected fish is an invasive species.

[0089] In a possible implementation, if the species to be detected is an invasive species, the location information of the species to be detected is obtained; based on the location information of the species to be detected, the occurrence frequency of the species to be detected is obtained; based on the occurrence frequency of the species to be detected, a frequency heat map of the species to be detected is determined.

[0090] Exemplarily, when the camera captures the image information of the species to be detected, the shooting location and the path of the monitoring ship will be recorded. After detecting the invasive species, the monitoring will mark the location where the invasive species was found. Over time, the monitoring ship will save the average frequency of invasive species appearing in the same location of the water body in its local database. Using the logistic regression method, the number of invasive species can be estimated. By forwarding the name and number of the invasive species, and then determining the frequency heat map of the species to be detected based on the frequency of occurrence of the species to be detected. The computing unit of the monitoring ship can be connected to a web-based artificial intelligence generator to obtain recommendations on how to remove these species.

[0091] In addition, the average frequency of invasive species occurrence can be used to draw species abundance heat maps, showing the density of fish populations in different areas of closed water bodies (lakes, reservoirs, etc.). This can provide a user-friendly interface for staff when dealing with these invasive species.

[0092] In a possible implementation, underwater image information in a target water area is acquired based on a camera; underwater garbage is determined based on the underwater image information and an underwater garbage recognition model, where the underwater garbage recognition model is obtained through training of a convolutional neural network.

[0093] In a possible implementation, the method of using a convolutional neural network to train an underwater garbage recognition model is similar to the method of using a convolutional neural network to train a part feature extraction model, which will not be repeated here. Figure 6 This is a diagram of the recognition result of the underwater garbage recognition model provided by the present invention. It can be seen that the underwater garbage recognition model can accurately recognize garbage in the water based on the image information.

[0094] In a possible implementation, after determining the underwater garbage based on the underwater image information and the underwater garbage recognition model, it also includes: determining the location of the underwater garbage based on the positioning system; and determining a garbage distribution heat map of the target water area based on the location of the underwater garbage and the target water area.

[0095] Specifically, according to the positioning system, after identifying the garbage, the monitoring ship will record its current location and the amount of garbage detected. Then, this information will be sent to the local database, which will use this information to output a real-time heat map.

[0096] In a possible implementation, the monitoring ship further includes an unmanned driving system, and the unmanned driving system is combined with the camera to autonomously plan the driving path of the monitoring ship.

[0097] Specifically, the monitoring vessel is equipped with a navigation and control system that can autonomously plan and navigate a preset route to a designated location while avoiding obstacles and adapting to changing environmental conditions. Autonomous planning enables the monitoring vessel to navigate to the predetermined location efficiently and safely. Once the monitoring vessel reaches the designated waypoint, it notifies the monitoring system of its arrival. In this way, the system can initiate various operations.

[0098] Furthermore, the monitoring vessel can be equipped with two modes:

[0099] 1. Regular patrol mode: It can monitor at specific points regularly to ensure that water quality remains stable. It provides a systematic way to actively monitor and maintain water quality, and timely intervention measures can be taken to protect a stable and healthy aquatic environment.

[0100] 2. Event-triggered monitoring: It can collect data in a targeted manner when a specific ecological event occurs, and has entered a more severe situation that requires all-weather real-time monitoring. High-density, long-term sampling and analysis are carried out at specific locations to find out the root cause.

[0101] After the samples are collected and analyzed, real-time data transmission is also provided. The monitoring vessel can transmit the data back to the monitoring system in real time, so that analysis and decisions can be made quickly based on the collected data.

[0102] Monitoring vessels can provide opportunities and buy time for early prevention by analyzing previously collected data sets and their relationship to overall environmental events. This approach involves collecting and analyzing large amounts of data from different sources, including environmental sensors and historical records.

[0103] Furthermore, advanced analytical techniques such as machine learning and data mining are used to identify patterns, correlations, and anomalies. By mining these insights, we can better understand the relationship between different environmental factors and events.

[0104] The technical solution provided by the present invention includes: obtaining a sample to be detected; obtaining the color of the sample to be detected after the reaction based on the sample to be detected and the detection reagent; obtaining a spectral photo based on the color of the sample to be detected after the reaction and the camera; and determining the water quality of the sample to be detected based on the spectral photo and the calculation unit. Information about certain water environments can be collected and analyzed relatively quickly on the monitoring ship. The nitrate concentration level can be calculated on the monitoring ship without collecting samples on the shore, thereby reducing the risk of pollution during unloading samples on the shore and transporting samples to the laboratory. In addition to water quality monitoring, the monitoring ship can also detect non-local species, thereby reducing the risk of competition between local species and local species, and can take less harmful actions against these species to limit collateral damage to local species, thereby protecting local aquatic biodiversity. Coupled with the ability to map water bodies and detect garbage in the water, it provides an extremely powerful tool for protecting aquatic biodiversity and reducing marine waste. Compared with the prior art, water samples are transported back to the laboratory for testing to obtain water quality; invasive species are identified manually. The method can analyze the water environment in real time, monitor the water environment quickly and accurately, and improve the accuracy of water environment detection.

[0105] Figure 7 This is a structural block diagram of a water environment monitoring device based on a monitoring ship provided by an embodiment of the present invention. For the sake of convenience, only the parts related to the embodiment of the present invention are shown. Figure 7 , the water environment monitoring device 700 based on the monitoring ship includes an acquisition module 701, a processing module 702, and a determination module 703;

[0106] In one implementation, the apparatus 700 may be used to implement the above Figure 1 For example, the acquisition module 701 is used to implement S110 and S120, the processing module 702 is used to implement S130, and the determination module 703 is used to implement S140.

[0107] The technical solution provided by the present invention includes: obtaining a sample to be detected; obtaining the color of the sample to be detected after the reaction based on the sample to be detected and the detection reagent; obtaining a spectral photo based on the color of the sample to be detected after the reaction and the camera; and determining the water quality of the sample to be detected based on the spectral photo and the calculation unit. Information about certain water environments can be collected and analyzed relatively quickly on the monitoring ship. The nitrate concentration level can be calculated on the monitoring ship without collecting samples on the shore, thereby reducing the risk of pollution during unloading samples on the shore and transporting samples to the laboratory. In addition to water quality monitoring, the monitoring ship can also detect non-local species, thereby reducing the risk of competition between local species and local species, and can take less harmful actions against these species to limit collateral damage to local species, thereby protecting local aquatic biodiversity. Coupled with the ability to map water bodies and detect garbage in the water, it provides an extremely powerful tool for protecting aquatic biodiversity and reducing marine waste. Compared with the prior art, water samples are transported back to the laboratory for testing to obtain water quality; invasive species are identified manually. The method can analyze the water environment in real time, monitor the water environment quickly and accurately, and improve the accuracy of water environment detection.

[0108] Figure 8 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 8 As shown, the electronic device 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the figure) a processor, a memory 81, and a computer program 82 stored in the memory 81 and executable on the at least one processor 80, and when the processor 80 executes the computer program 82, the steps in any of the above method embodiments are implemented.

[0109] The electronic device 8 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will appreciate that Figure 8 It is only an example of the electronic device 8 and does not constitute a limitation on the electronic device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0110] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0111] In some embodiments, the memory 81 may be an internal storage unit of the electronic device 8, such as a hard disk or memory of the electronic device 8. In other embodiments, the memory 81 may also be an external storage device of the electronic device 8, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 8. Further, the memory 81 may also include both an internal storage unit of the electronic device 8 and an external storage device. The memory 81 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 81 may also be used to temporarily store data that has been output or is to be output.

[0112] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0113] An embodiment of the present invention also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0114] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0115] An embodiment of the present invention provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0117] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A water environment monitoring method based on a monitoring ship, characterized in that: The monitoring ship comprises: a computing unit, a lifting platform, a rotator and a camera, and the method comprises: Obtain samples to be tested in target waters; Obtaining the color of the sample after the reaction based on the sample and the detection reagent, wherein the detection reagent changes color according to the content of nitrate in the sample; Obtaining a spectral photograph based on the color of the sample to be detected after the reaction and the camera; The water quality in the target water area is determined based on the spectral photograph and the calculation unit.

2. The method according to claim 1, characterized in that: The monitoring ship also includes a positioning system, which, after determining the water quality in the target waters based on the spectral photograph and the computing unit, includes: Determine the sampling locations of multiple samples to be detected in the target water area based on the positioning system; The water quality distribution of the target water area is determined based on the sampling locations, spectral photos and the calculation unit of the multiple samples to be detected.

3. The method according to claim 1, characterized in that: The rotator is used to obtain multiple groups of samples to be detected from different sampling locations in the target area, and respectively place the multiple groups of samples to be detected into different chambers in the rotator, and the color of the samples to be detected after the reaction based on the samples to be detected and the detection reagents is obtained, including: The colors of the multiple groups of samples to be detected after reaction are obtained based on the multiple groups of samples to be detected and the detection reagents in the different chambers.

4. The method according to claim 2, characterized in that: The camera is installed on the lifting platform, and the camera captures image information of different sampling locations in the target water area through the lifting platform, wherein the image information includes fish image information and garbage image information.

5. The method according to claim 4, characterized in that The method further comprises: Acquiring underwater image information in the target water area based on the camera; Underwater garbage is determined based on the underwater image information and an underwater garbage recognition model, wherein the underwater garbage recognition model is obtained by training a convolutional neural network.

6. The method according to claim 5, characterized in that After determining the underwater garbage based on the underwater image information and the underwater garbage recognition model, the method further includes: Determine the location of the underwater garbage based on the positioning system; A garbage distribution heat map of the target water area is determined based on the location of the underwater garbage and the target water area.

7. The method according to claim 1, characterized in that The monitoring ship also includes an unmanned driving system, which is combined with the camera to autonomously plan the driving path of the monitoring ship.

8. A water environment monitoring device based on a monitoring ship, characterized in that: The device comprises: An acquisition module is used to acquire samples to be tested in the target waters; The acquisition module is further used to acquire the color of the sample to be detected after the reaction based on the sample to be detected and the detection reagent, and the detection reagent changes color according to the content of nitrate in the sample to be detected; A processing module, used for obtaining a spectral photograph based on the color of the sample to be detected after reaction and the camera; A determination module is used to determine the water quality of the target water area based on the spectral photograph and the calculation unit.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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