Unmanned ship operation method cooperating with fish and bird recognition and water ecology co-treatment

Abstract

The invention relates to the technical field of intelligent water area ecological management, and discloses an unmanned ship operation method cooperating with fish and bird identification and water ecology co-management, and the method comprises the following steps: S1, starting an unmanned ship, and completing the operation preparation; s2, acquiring real-time environment data including fish activity data in a water area, bird activity data and water body environment parameters; s3, analyzing the fish activity data and the bird activity data, and extracting population distribution, dynamic behaviors and activity rules of fishes and birds; s4, analyzing the environmental parameters of the water body; s5, analyzing a result based on the distribution characteristics of the fishes and the birds and the water quality; s6, executing an ecological restoration task according to the planned path; and S7, uploading the data after the task is completed to a cloud platform. According to the invention, the optimal operation path is generated through the path planning and scheduling module in combination with the real-time water quality data and the ecological demand, and the technical effect of efficiently scheduling the operation of the unmanned ship in the complex water area environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent water ecological governance, and particularly to an operation method of an unmanned ship for collaborative fish and bird recognition and water ecological co-governance. Background Art

[0002] With the development of unmanned technology, unmanned ships have gradually become important tools for water area governance. They show certain advantages in water quality monitoring, pollution treatment, and ecological restoration. However, existing technologies still face many limitations in practical applications, and these deficiencies lead to the operation efficiency of unmanned ships and the ecological governance effect being difficult to meet the governance requirements of complex water areas.

[0003] Existing unmanned ship technologies usually rely on a single type of data source in ecological monitoring. For example, the water quality status is judged by dissolved oxygen or turbidity data. However, the ecosystem is complex and multi-dimensional, and a single data source cannot comprehensively reflect the overall ecological health of the water body.

[0004] Secondly, the path planning technology of existing unmanned ships often assumes a static environment and uses a preset route to complete operations. However, the water area environment is dynamically changing. Pollutant sources may spread, and the water flow direction may change, while the path of the unmanned ship lacks the ability to flexibly adjust in these changes. For example, when the unmanned ship operates along a fixed path, it may not be able to cover a suddenly polluted area in time, and this lag significantly reduces the effectiveness of the restoration task. The lack of dynamic adaptability in the planning technology makes it difficult for existing unmanned ships to cope with complex water area environments.

[0005] Moreover, in terms of the emergency response ability to emergencies, the performance of existing unmanned ship systems is also not ideal. For example, when the unmanned ship encounters equipment failures or the rapid spread of polluted areas during operation, most systems rely on manual instructions to adjust operation tasks. This passive response method not only increases the delay time but also may lead to the missed opportunity for governance. Systems lacking remote monitoring and self-scheduling capabilities are even more difficult to independently complete task adjustments in emergencies, further reducing the safety and efficiency of governance. Summary of the Invention

[0006] In order to make up for the above deficiencies, the present invention provides an operation method of an unmanned ship for collaborative fish and bird recognition and water ecological co-governance, aiming to improve the problems of existing technologies in ecological information integration, dynamic adaptability of path planning, multi-functional cooperation of repair equipment, intelligent data analysis, and emergency response.

[0007] In a first aspect, the present invention provides the following technical solution, an operation method of an unmanned ship for collaborative fish and bird recognition and water ecological co-governance, including the following steps: S1. Start the unmanned ship and complete the operation preparation; S2. Obtain real-time environmental data, including fish activity data, bird activity data, and water environment parameters in the water area; S3. Analyze the fish activity data and bird activity data to extract the population distribution, dynamic behavior, and activity patterns of fish and birds; S4. Analyze the water environment parameters to determine the dynamic changes in dissolved oxygen level, pH value, water temperature, and turbidity, and evaluate the water quality restoration requirements; S5. Generate the priority of the ecological restoration task based on the distribution characteristics of fish and birds and the water quality analysis results, and plan the optimal operation path of the unmanned boat; S6. Execute the ecological restoration task according to the planned path, including adjusting the dissolved oxygen level in the water, putting in probiotics, cleaning up the garbage in the water area, and controlling harmful algae; S7. Upload the data after the task is completed to the cloud platform, conduct an evaluation of the ecological governance effect, and generate optimization suggestions.

[0008] Preferably, in S3, the fish activity data is analyzed by a target detection algorithm to extract the species distribution and dynamic behavior characteristics of fish, and the bird activity data is combined with an image classification algorithm and a time series model to analyze its activity patterns and habitat distribution characteristics.

[0009] Preferably, the analysis of the water environment parameters in S4 includes: a. Deduce the dissolved oxygen level based on the temperature and turbidity data, and evaluate the change trend in combination with historical data; b. Determine the restoration requirements through the water body pH value and hydrogen ion concentration; c. Judge whether ecological restoration is required for the water body based on the comprehensive analysis results of dissolved oxygen and pH value.

[0010] Preferably, in S5, the path planning of the unmanned boat is based on a path optimization algorithm, and in combination with the distance to the target point and the cost of obstacle distribution, the route is dynamically adjusted to adapt to the complex water area environment.

[0011] Preferably, the ecological restoration tasks in S6 include the following operations: a. Adjust the dissolved oxygen level in the water body, increase the dissolved oxygen through an aeration device, and adjust the operation rate according to the actual dissolved oxygen level and demand difference; b. Put in probiotics, restore the ecological balance of the water body by dynamically adjusting the input amount, and determine the input amount according to the degree of dissolved oxygen deficiency and the ecological restoration requirements; c. Start the garbage cleaning device to remove the floating objects on the water surface; d. Enable the algae control device to inhibit the excessive growth of harmful algae.

[0012] Preferably, the data uploaded to the cloud platform in S7 includes the population distribution and dynamic monitoring data of fish and birds, water quality change data, and the implementation status of restoration tasks. The cloud platform analyzes the above data, generates optimization suggestions for water area governance, and predicts potential ecological risks.

[0013] Preferably, when the unmanned boat detects an emergency, including a sharp deterioration of water pollution or equipment abnormality, it enters the emergency operation mode, preferentially executes the dissolved oxygen regulation or probiotic delivery task, and returns to the base for equipment maintenance if necessary.

[0014] In a second aspect, the present invention provides the following technical solution. An unmanned boat system for collaborative fish and bird recognition and water ecological co-governance includes: A fish recognition module for analyzing the species distribution and dynamic behavior characteristics of fish in the water area; A bird recognition module for analyzing the activity patterns and habitat distribution characteristics of birds around the water area; A water quality monitoring module for collecting environmental parameters such as dissolved oxygen, pH value, water temperature, and turbidity of the water body; An ecological restoration module including an aeration device, a probiotic delivery device, a garbage cleaning device, and an algae treatment device for dynamically adjusting the water area restoration operation; A path planning and scheduling module for generating an optimal operation path and adjusting the route in real time to adapt to environmental changes; A cloud platform for receiving and analyzing data, generating optimization suggestions for water area governance, and providing a remote control function.

[0015] In a third aspect, the present invention provides the following technical solution. A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned operation method of the unmanned boat for collaborative fish and bird recognition and water ecological co-governance.

[0016] In a fourth aspect, the present invention provides the following technical solution. A readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned operation method of the unmanned boat for collaborative fish and bird recognition and water ecological co-governance.

[0017] The present invention has the following beneficial effects: 1. Through the path planning and scheduling module, the present invention dynamically generates an optimal operation path by combining real-time water quality data and ecological requirements, effectively improving the operation efficiency of the unmanned boat in a complex water area environment. Compared with the traditional technology that relies on static path planning, the present invention can dynamically adjust the route according to real-time environments such as pollution diffusion and water flow changes, overcomes the problem of rigid path planning, and achieves the technical effects of efficient scheduling and accurate coverage of the target area.

[0018] 2. This invention uses multi-source data integration technology to comprehensively collect and analyze the activity characteristics of fish and birds, providing comprehensive and accurate dynamic information for ecological management. By combining the target detection algorithm with the image classification model, it can not only identify the population distribution and dynamic behavior of fish in real time, but also extract the activity patterns and habitat characteristics of birds. Compared with the limitations of traditional single biological population monitoring, it significantly improves the scientificity and pertinence of ecological restoration tasks.

[0019] 3. The present invention uses multifunctional collaborative equipment, including an aeration device, a probiotic delivery device, a garbage cleaning device, and an algae treatment device, to jointly perform ecological restoration tasks, significantly improving the overall efficiency of water area management. Compared with the limitations of the decentralized operation of single-function equipment in traditional technologies, the present invention achieves multi-dimensional pollution control, and can simultaneously carry out operations in multiple aspects such as dissolved oxygen regulation, harmful algae suppression, and surface garbage cleaning, and quickly restore the ecological balance of the water area.

[0020] 4. Based on the big data analysis function of the cloud platform, the present invention comprehensively processes the fish and bird activity data, water quality parameters and the execution of restoration tasks to generate scientific ecological governance optimization suggestions. Compared with the isolation of data and lack of comprehensive analysis in traditional systems, the present invention significantly improves the intelligence level of the system, solves the problems of feedback lag and lack of scientific basis for governance plans, and provides more accurate and sustainable support for ecological governance.

[0021] 5. The present invention adopts real-time environmental perception and dynamic response mechanism, which significantly improves the emergency response capability of unmanned boats in emergencies. When it detects that water pollution is deteriorating sharply or equipment is abnormal, the unmanned boat can give priority to key repair tasks (such as dissolved oxygen regulation or probiotic delivery), and choose to return for maintenance or continue operations according to actual conditions, avoiding the delay caused by relying on manual instructions in traditional technologies, and improving the safety and effectiveness of governance.

[0022] 6. The present invention comprehensively grasps the dynamic changes of water quality by real-time monitoring of key parameters such as dissolved oxygen, pH, turbidity and water temperature, and predicts future trends based on historical data, providing a scientific basis for formulating ecological restoration strategies. Compared with the traditional method of evaluating water quality by relying on a single parameter, the multi-dimensional analysis method of the present invention significantly improves the comprehensiveness and accuracy of water quality assessment.

[0023] 7. The multifunctional equipment design of the present invention realizes flexible allocation and efficient coordination of repair tasks, and can adjust equipment operating parameters according to real-time needs. The aeration device adjusts the operating rate according to the change of dissolved oxygen level, and the probiotic delivery device dynamically optimizes the delivery amount, thereby avoiding resource waste, significantly improving the repair efficiency and environmental protection, and showing higher energy-saving effect and treatment quality compared with traditional equipment.

[0024] 8. The present invention dynamically plans the route through a path optimization algorithm combined with real-time environmental data, overcoming the deficiency of static path planning in the prior art. By comprehensively considering the distance to the target point and the cost of obstacle distribution, the unmanned ship can flexibly avoid obstacles in complex waters and efficiently complete the coverage of the task area, greatly improving the adaptability and governance efficiency in complex water environments.

[0025] 9. The present invention utilizes a cloud platform to integrate multi-dimensional data, providing the ability to predict ecological risks in real time. Through the analysis and fusion of multi-source data such as fish, birds, and water quality, the platform can identify the diffusion trend of potential pollution sources in advance and generate targeted governance suggestions. Compared with traditional manual analysis, the present invention significantly shortens the response time and effectively improves the scientific nature of the governance plan.

[0026] 10. The modular design and highly scalable architecture of the present invention enable it to flexibly adapt to the governance requirements of different waters. Users can add or replace functional modules according to the characteristics of the waters, and at the same time, realize the collaborative operation of multiple unmanned ships through cloud remote control. Compared with traditional technologies, the present invention provides a more flexible, efficient, and easy-to-maintain solution, especially suitable for ecological governance tasks in large-scale waters. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of the operation method of the unmanned ship for collaborative fish and bird recognition and water ecological co-governance proposed by the present invention; Figure 2 is a system architecture diagram of an unmanned ship system for collaborative fish and bird recognition and water ecological co-governance proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Embodiment 1 Referring to Figure 1 , in the first embodiment of the present invention, the present invention provides an operation method of an unmanned ship for collaborative fish and bird recognition and water ecological co-governance, including the following steps: S1. Start the unmanned ship and complete the operation preparation; Specifically, in this embodiment, after the unmanned ship system is started, the hardware module is first self-checked. Specifically, the fish recognition module, the bird recognition module, the water quality monitoring module, the ecological restoration module, and the path planning module will conduct status detection one by one to ensure that the sensors, actuators, and communication devices are all in working condition. For example, in the fish recognition module, after startup, the image acquisition ability of the underwater camera will be checked, and the clarity and focusing ability of the camera will be calibrated through test frames to ensure the recognition accuracy of underwater fish.

[0030] In the water quality monitoring module, generally, function detection will be preferentially performed on devices such as dissolved oxygen sensors, pH sensors, thermometers, and turbidity meters. Taking the dissolved oxygen sensor as an example, after it is started, it will automatically collect reference data and calculate the reference dissolved oxygen value through the built-in calibration program . Specifically, this value can be expressed as:

[0031] Where: is the reference dissolved oxygen value; is the reference temperature; is the ambient temperature; is the theoretical saturated dissolved oxygen value; is the temperature correction coefficient.

[0032] This calibration process ensures the consistency of the sensor under different water temperature and turbidity conditions. As an option, after the pH sensor is started, it can automatically generate a correction factor by detecting the hydrogen ion concentration of the standard solution for precision adjustment of subsequent data collection.

[0033] In some embodiments, to improve the safety during the initialization phase, the system can also perform pressure and energy consumption detection on the actuators of the ecological restoration module. For example, after the aeration device is started, it will run at the lowest power for a period of time and record the power consumption and the output of the air pump to confirm whether the initial state of the device is normal. In addition, the probiotic delivery device can test the delivery path and strength by delivering simulated particles to ensure the accurate execution of subsequent tasks.

[0034] The initialization of the communication module plays an important role in the present invention, especially in the cloud control and data transmission functions. During startup, the unmanned ship will establish a preliminary connection with the cloud platform through wireless communication devices, verify the network stability, and synchronize the system clock. Specifically, the communication test includes the detection of data upload rate and command response latency. As a possible implementation, the communication module can simulate the transmission of a set of fish recognition data and record its transmission time. When the delay value exceeds the set threshold, the system will automatically adjust the communication protocol parameters.

[0035] In some embodiments, to further optimize the subsequent path planning effect, after the path planning module is started, it can perform simulation tests in combination with preset environmental data. Specifically, the path planning module can load the historical obstacle distribution data and use a path optimization algorithm to calculate a virtual flight path to verify the real-time planning ability of the system. The calculation formula for the path weight is as follows:

[0036] Where: is the path weight; is the distance between nodes; is the obstacle cost; is the adjustment coefficient.

[0037] This process not only verifies the correctness of the algorithm but also provides technical support for navigation in subsequent complex environments. In a possible implementation, the initialization process of this step can also include the positioning and attitude adjustment of the unmanned ship body. For example, the current position of the unmanned ship can be obtained through a GPS positioning device, and its initial attitude can be adjusted using a gyroscope. The results of the positioning and attitude adjustment will be uploaded to the central control system for initializing the operation state.

[0038] S2. Obtain real-time environmental data, including fish activity data, bird activity data, and water environment parameters in the water area; Specifically, in this embodiment, first start the underwater image acquisition device of the fish recognition module to obtain a continuous video stream of the target area through an underwater camera. Generally, this device will assist in locating the fish school position through sonar data and adjust the exposure parameters of the camera according to the ambient light intensity. For example, when the underwater light is weak, the camera will automatically switch to the low-light enhancement mode to ensure the image quality. As an option, the underwater camera can be combined with a filtering algorithm to perform preliminary processing on the image to reduce the interference caused by suspended particles in the water. The core of the filtering algorithm is to smooth the pixel values through a mean filter, and its mathematical expression is:

[0039] Where: represents the filtered pixel value; is the original pixel value; is the size of the filtering window; The filtered image will be used as the input for the deep learning model to extract the species, quantity, and behavioral characteristics of the fish.

[0040] The acquisition devices of the bird recognition module are started simultaneously. Generally, for bird monitoring, a water surface camera is used for wide-area shooting, covering the airspace and shore areas around the unmanned boat. Specifically, the camera will perform multi-angle rotational shooting on the specified area according to the scanning period set in the system, so as to ensure the integrity of data acquisition. In some embodiments, the bird image data can optimize the storage efficiency through real-time chunk processing technology, such as classifying and storing the images by time period or habitat area.

[0041] The water quality monitoring module also operates at this stage. As a possible implementation, the water quality monitoring module collects dissolved oxygen, acidity, water temperature, and turbidity parameters through multiple sensors in parallel.

[0042] In a possible implementation, the water quality monitoring module will also automatically generate an estimated value of water transparency based on the real-time data of dissolved oxygen and turbidity, and compare it with historical data to identify potential ecological problems.

[0043] In some embodiments, the data acquisition device can be linked with the path planning module. Specifically, the path planning module will dynamically adjust the acquisition route of the unmanned boat according to the real-time collected water quality data. For example, when an abnormal dissolved oxygen area is detected, the unmanned boat will give priority to going to this area to collect more sample data. This process can provide more targeted support for subsequent ecological restoration.

[0044] As an extension, the communication module undertakes the task of real-time data transmission during the data acquisition stage. For example, at fixed time intervals, the unmanned boat will upload the collected environmental data to the cloud platform through a wireless network. These data are not only used for real-time monitoring but can also be further processed by the analysis algorithms of the cloud platform to assist the unmanned boat in optimizing the acquisition strategy.

[0045] S3. Analyze the fish activity data and bird activity data to extract the population distribution, dynamic behavior, and activity patterns of fish and birds; the fish activity data is analyzed through a target detection algorithm to extract the species distribution and dynamic behavior characteristics of fish, and the bird activity data is combined with an image classification algorithm and a time series model to analyze its activity patterns and habitat distribution characteristics.

[0046] Specifically, in this embodiment, the analysis of fish activity data is based on a target detection algorithm. By using a deep learning model (such as YOLO), each frame of the underwater video is processed frame by frame to extract the species, quantity, and dynamic behavior characteristics of fish. Specifically, the model will generate multiple bounding boxes for each frame of the image, and the classification probability and confidence of the bounding boxes are used to determine the distribution of the fish population. The confidence calculation formula for the bounding box is:

[0047] Where: Total confidence of the bounding box; Probability of the category to which the target belongs; Intersection over Union (IoU) between the predicted bounding box and the ground truth box.

[0048] As an option, the system can also combine time series analysis methods to track the dynamic changes of fish schools. For example, the Kalman harmonic algorithm is used to predict the movement trajectories of fish, thereby reducing the interference of video noise on behavior analysis. The state update formula of the Kalman filter is:

[0049]

[0050] Where: System state vector; State transition matrix, Control input matrix; Control input; Process noise; Measurement vector; Measurement matrix; Measurement noise.

[0051] In the analysis of bird activity data, the system adopts a combination of image classification algorithms and time series models. Generally, an image classification model (such as ResNet) will first extract features from bird images, and the extracted features are used to determine the species distribution and habitat locations of birds. Specifically, this process includes convolutional feature extraction and fully connected layer classification, and the classification result is output as a probability distribution.

[0052] In a possible implementation, this step also includes the credibility assessment of the recognition results of fish and birds. The system can calculate the confidence interval of the recognition results based on historical data, so as to screen out the analysis data with higher reliability. For example, when the recognition frequency of a certain type of fish school is lower than the threshold, the system will automatically mark this data as a low-confidence result to avoid interference with subsequent tasks.

[0053] As an extended application, the analysis results of this step can also be uploaded to the cloud platform for cross-verification with the data of other unmanned boats. For example, when multiple unmanned boats are operating in adjacent areas, their fish and bird activity data can be fused and analyzed through the cloud to generate a more detailed ecological characteristic map.

[0054] S4. Analyze the water environment parameters to determine the dynamic changes of dissolved oxygen level, acidity and alkalinity, water temperature and turbidity, and evaluate the water quality restoration needs; The analysis of water environment parameters includes: a. Deduce the dissolved oxygen level based on temperature and turbidity data, and evaluate the change trend in combination with historical data; b. Determine the restoration needs based on the water body's acidity and alkalinity and hydrogen ion concentration; c. Judge whether ecological restoration of the water body is required by comprehensively analyzing the results of dissolved oxygen and acidity and alkalinity.

[0055] Specifically, in this embodiment, the analysis of dissolved oxygen is based on the original data collected by the sensor. The output value of the sensor will undergo multiple corrections to eliminate the interference of environmental temperature and water body turbidity. Specifically, the correction formula is as follows:

[0056] Where: is the corrected dissolved oxygen value; is the initial reading of the sensor; and are the reference temperature and the actual ambient temperature respectively; is the water body turbidity value; and are the environmental parameter correction coefficients.

[0057] In some embodiments, in order to further improve the accuracy of the data, the system calculates the dynamic trend of dissolved oxygen through the time-weighted average method. This method can reduce the impact of instantaneous fluctuations on the overall judgment.

[0058] Specifically, when the pH value of the water body is lower than a certain threshold, the system will preferentially mark this area as a potential restoration target. Combining the dynamic changes in the dissolved oxygen level, the system can more accurately identify problem areas.

[0059] In some embodiments, the system performs correlation analysis on multiple water quality parameters. For example, combining the data of dissolved oxygen and turbidity to judge whether there is an abnormal pollution source. When the comprehensive influence factor exceeds the set range, the system will issue a warning signal to indicate potential pollution risks.

[0060] As an extension, the analysis results of this step can be directly integrated with the activity data of fish and birds. Through the spatial overlay analysis model, the system can evaluate the sensitivity of different populations to water quality changes. For example, when the activity frequency of fish schools significantly decreases in areas with low dissolved oxygen, the system will mark this area as a priority restoration area.

[0061] In another possible implementation, the analysis results will also be uploaded to the cloud platform for comparison with historical data. The cloud platform predicts the future change trend of water quality through trend analysis algorithms and provides further operation suggestions to the unmanned ship system.

[0062] S5. Based on the distribution characteristics of fish and birds and the results of water quality analysis, generate the priority of ecological restoration tasks and plan the optimal operation path of the unmanned ship; the path planning of the unmanned ship is based on the path optimization algorithm, combines the distance to the target point and the cost of obstacle distribution, and dynamically adjusts the route to adapt to the complex water environment.

[0063] Specifically, after analyzing the water environment parameters, the system needs to generate the priority of ecological restoration tasks according to the activity data of fish and birds and the results of water quality analysis. Generally, the restoration requirements in different regions are different and need to be dynamically adjusted in combination with population distribution, environmental conditions and ecological risks. At the same time, to ensure that the unmanned ship can complete the tasks efficiently, the path planning module will design the optimal operation route according to the location of the restoration target area and the distribution of water obstacles.

[0064] In this embodiment, the generation of the priority relies on a set of weighted indicators for comprehensive evaluation. Specifically, the system quantifies the fish distribution density, the frequency of bird habitat activities and the degree of water quality abnormality into scores respectively, and calculates the restoration priority score through the following formula:

[0065] Where: is the restoration priority score of the area; is the fish distribution density factor; is the bird activity frequency factor; is the environmental abnormality factor; , , are the corresponding weight coefficients, and the weight values are dynamically adjusted by empirical data.

[0066] As an option, the generation of the priority of the restoration task can also be dynamically adjusted in combination with historical data. For example, when a certain area is marked as high priority for several consecutive times, the system will reduce the weight of this area to balance the restoration needs of other areas.

[0067] Specifically, the path planning module generates the optimal operation route of the unmanned ship through the path optimization algorithm. Generally, the selection of the optimal path comprehensively considers the distance between target areas, the operation priority and the distribution of obstacles. The calculation formula of the path weight is:

[0068] Where: is the path weight from node to node ; is the distance between nodes; is the obstacle cost; is node The corresponding repair priority score; and are adjustment coefficients that respectively control the influence of obstacle cost and priority on the path weight.

[0069] In some embodiments, the path planning module also collects the navigation data of the unmanned ship in real time, such as the sailing speed and steering angle. Combining the water area terrain and obstacle information obtained by the sensor, the path planning module can dynamically adjust the route to avoid potential collision risks. For example, when it is detected that the distance to an obstacle is less than the warning threshold, the system will trigger an obstacle avoidance mechanism and recalculate the sub-optimal path.

[0070] In a possible implementation, the path planning module can decompose the path of the unmanned ship into multiple sub-task points, and each point corresponds to an operation area. The system will sort the task points according to the priority order and generate an operation plan including time and path information.

[0071] This plan includes the following: The expected arrival time of each target area; The execution order of the operation tasks; The total length and estimated time consumption of each section of the path.

[0072] As an extension, the operation route generated by the path planning module will also be uploaded to the cloud platform and compared with the historical path data. The cloud platform evaluates the rationality of the route through big data analysis and provides optimization suggestions when necessary. For example, when multiple unmanned ships are operating simultaneously, the cloud platform can perform global scheduling on their respective paths to avoid duplicate operations or path conflicts.

[0073] In some embodiments, this step can also achieve higher efficiency through the linkage of the path planning and ecological restoration module. For example, when the system detects that the dissolved oxygen level in a certain area drops sharply, the path planning module will give priority to setting this area as an emergency restoration target and adjust the priorities of other areas in real time.

[0074] S6. Execute ecological restoration tasks according to the planned path, including adjusting the dissolved oxygen level of the water body, putting in probiotics, cleaning up water area garbage, and controlling harmful algae; the ecological restoration tasks include the following operations: a. Adjust the dissolved oxygen level of the water body, increase the dissolved oxygen through the aeration device, and the operation rate is adjusted according to the actual dissolved oxygen level and demand difference; b. Put in probiotics, restore the ecological balance of the water body by dynamically adjusting the dosage, and the dosage is determined according to the degree of dissolved oxygen deficiency and ecological restoration needs; c. Start the garbage cleaning device to remove floating objects on the water surface; d. Enable the algae control device to inhibit the overgrowth of harmful algae.

[0075] Specifically, in this embodiment, the aeration device is first started to adjust the dissolved oxygen level in the water body. Specifically, the aeration rate is dynamically adjusted according to the actual dissolved oxygen level and ecological requirements, and its calculation formula is:

[0076] Where: is the aeration rate; is the target dissolved oxygen level; is the currently measured dissolved oxygen value; is the aeration efficiency coefficient.

[0077] As a possible implementation, the system will detect the water temperature and flow rate before starting the aeration device to determine the optimal aeration mode. In some embodiments, to improve the aeration effect, the system will adjust the diameter and distribution range of the bubbles so that oxygen can be more evenly diffused to different depths of the water body.

[0078] Releasing probiotics is another key task in this step. Generally, the role of probiotics is to decompose organic pollutants, inhibit the growth of harmful algae, and promote the restoration of the water body ecological balance. Specifically, the calculation of the release amount will comprehensively consider the current pollution load and restoration target of the water body. Its calculation formula is:

[0079] Where: is the probiotic release amount; is the release efficiency coefficient; is a control parameter used to adjust the steepness of the release curve. As an option, the system can combine the probiotic release with the water flow direction to increase the coverage range of the probiotics.

[0080] In some embodiments, to avoid ecological imbalance caused by over-release, the system will regularly monitor the activity of the probiotics after release and adjust the release strategy according to the monitoring results.

[0081] Garbage cleaning is one of the important links in this step, mainly for the removal of floating objects on the water surface. The garbage collection device will automatically adjust the operating speed and cleaning range according to the garbage density in the target area. Specifically, the efficiency of garbage cleaning depends on the capture radius and storage capacity of the device. As a possible implementation, the system will real-time monitor the cleaning progress through a visual sensor and automatically trigger the return mode to empty the garbage when it detects that the storage capacity of the device is close to the upper limit.

[0082] The operation of the algae treatment device mainly targets the growth problems of harmful algae, especially the overgrowth of cyanobacteria. Generally, the system will activate the ultraviolet algae disinfection equipment to inhibit the growth and spread of algae. Specifically, the intensity and irradiation time of ultraviolet light will be dynamically adjusted according to the algae density. For example, when the algae density in the target area is significantly higher than the historical average, the system will increase the irradiation intensity and extend the operation time of the equipment.

[0083] In a possible implementation, the algae treatment device can also adjust the angle of the algae disinfection equipment in combination with the water flow direction to expand the treatment scope. In some embodiments, to further improve the efficiency of algae treatment, the system will simultaneously release specific algae inhibitors to form a synergistic effect with the ultraviolet equipment.

[0084] As an extension, the execution results of this step will be uploaded to the cloud platform in real time, including data such as the aeration rate, the dosage of probiotics, the progress of garbage cleaning, and the algae treatment effect. The cloud platform will provide a basis for optimizing subsequent tasks through comprehensive analysis of these data. For example, when the restoration effect in a certain area is better than expected, the system will lower the priority of this area to concentrate resources on dealing with other high-risk areas.

[0085] S7. Upload the data after the task is completed to the cloud platform, evaluate the ecological treatment effect, and generate optimization suggestions. The data uploaded to the cloud platform includes the population distribution and dynamic monitoring data of fish and birds, water quality change data, and the execution situation of the restoration task. The cloud platform analyzes the above data, generates optimization suggestions for water area treatment, and predicts potential ecological risks.

[0086] Specifically, in this embodiment, the communication module is first activated, and the system transmits the collected data to the cloud platform in real time through the wireless network. Generally, the uploaded data includes the population distribution of fish and birds, the dynamic changes of water environment parameters, and the execution situation of the restoration task. As an extended application, the optimization suggestions generated by the cloud platform can be directly sent to the unmanned boat system to adjust future operation parameters. For example, when the restoration effect in a certain area is significant, the system will reduce the restoration frequency of this area and allocate resources to other high-priority areas.

[0087] In some embodiments, this step can also achieve data sharing for multi-unmanned boat collaborative operations. Specifically, the data uploaded by each unmanned boat will be integrated and processed by the cloud platform to generate a global water area ecological status map. This method can avoid duplicate operation tasks and optimize the overall resource allocation.

[0088] In a possible implementation, the system will also evaluate the credibility of the uploaded data. For example, for fish recognition data with low confidence, the cloud platform will mark it as a status to be verified and require the unmanned boat to re-collect data in this area during future tasks.

[0089] When the unmanned ship detects an emergency, including a sharp deterioration of water pollution or equipment anomalies, it enters the emergency operation mode, gives priority to performing the dissolved oxygen regulation or probiotic delivery tasks, and returns to the base for equipment maintenance if necessary.

[0090] Specifically, when the unmanned ship detects an emergency during operation (such as a sharp deterioration of water pollution or equipment anomalies), the system will automatically enter the emergency operation mode. Generally, the core goal of the emergency operation mode is to quickly respond to abnormal situations, prioritize ensuring water ecological safety and equipment operation stability, and minimize potential losses at the same time. The emergency operation mode not only covers real-time ecological restoration tasks but also includes equipment self-check, communication optimization, and guarantee operations for returning to the base.

[0091] In this embodiment, the system monitors the water quality and equipment status in real time through a variety of sensors. Specifically: The detection of water anomalies depends on the changing trends of parameters such as dissolved oxygen, pH value, and turbidity. When it is detected that the dissolved oxygen level drops below the critical value or the water body pH value deviates sharply from the neutral range, the system will automatically trigger the emergency response mechanism.

[0092] Equipment anomalies are monitored through internal sensors, including operating power, temperature, and workload. When the operating parameter of a certain piece of equipment exceeds the safe range, for example, the power of the aeration device suddenly increases or the delivery volume of the probiotic delivery device decreases abnormally, the system will mark it as a faulty state.

[0093] After entering the emergency operation mode, the system will give priority to performing the dissolved oxygen regulation and probiotic delivery tasks to quickly relieve the water ecological pressure. The system will continuously and dynamically monitor the water quality changes during the execution of emergency tasks. If the repair effect is significant (for example, the dissolved oxygen concentration has returned to the target value range), the system will gradually reduce the operating power of the repair equipment to save energy consumption and reduce the secondary impact on the ecological environment.

[0094] As a possible implementation, the system will use lidar or sonar technology to draw a three-dimensional pollution distribution map of the target area to more accurately deliver repair resources.

[0095] In some special cases, such as when it is detected that the pollution source spreads too fast or the area of the polluted area continues to expand, the system will also notify other unmanned ships to cooperate. For example, coordinate neighboring unmanned ships through the cloud platform to jointly perform the dissolved oxygen regulation or probiotic delivery tasks to achieve rapid response and efficient repair.

[0096] Data on all emergencies, including the triggering time, abnormal parameters, and the execution effects of emergency tasks, are uploaded to the cloud platform in real time. The cloud platform evaluates the severity of events through big data analysis and generates optimized response strategies for possible similar events in the future.

[0097] In some embodiments, the cloud platform also predicts the trend of ecological risks in the current operation area and sends the prediction results to the unmanned ship system to guide it to adjust the execution order of subsequent tasks.

[0098] Embodiment 2: Refer to Figure 2 , in the second embodiment of the present invention, the present invention provides an unmanned ship system for collaborative fish and bird recognition and water ecological co-governance, including: A fish recognition module for analyzing the species distribution and dynamic behavior characteristics of fish in the water area; A bird recognition module for analyzing the activity patterns and habitat distribution characteristics of birds around the water area; A water quality monitoring module for collecting environmental parameters such as dissolved oxygen, pH, water temperature, and turbidity of the water body; An ecological restoration module, including an aeration device, a probiotic dosing device, a garbage cleaning device, and an algae treatment device, for dynamically adjusting the water area restoration operation; A path planning and scheduling module for generating the optimal operation path and adjusting the route in real time to adapt to environmental changes; A cloud platform for receiving and analyzing data, generating optimization suggestions for water area governance, and providing remote control functions.

[0099] Specifically, the fish recognition module uses an underwater camera and a target detection algorithm to obtain the species, quantity, and dynamic behavior characteristics of fish in the water area. The recognition data is transmitted to the cloud platform and the path planning and scheduling module in real time, serving as an important basis for generating task priorities. For example, when the fish population distribution density in a certain water area is high, the path planning module will give priority to including this area in the restoration plan. The fish data will also be integrated with the data of the water quality monitoring module to analyze the sensitivity of the fish population to the dissolved oxygen level in the water body, further optimizing the execution order of the restoration tasks.

[0100] The bird recognition module uses a surface camera and an image classification algorithm to monitor the activity patterns and habitat distribution characteristics of birds around the water area. The bird data is transmitted to the cloud platform through wireless communication and jointly analyzed with the fish recognition data to comprehensively understand the overall situation of the water area ecosystem. The bird recognition results are also fed back to the ecological restoration module. When the area with frequent bird activities is detected as a high-risk pollution area, the restoration module will give priority to treating this area. In addition, the bird data can be used to guide the path planning module to optimize the operation route to avoid bird habitats and reduce the interference of the unmanned ship operation on the ecological environment.

[0101] The water quality monitoring module collects the environmental parameters of the water body through a dissolved oxygen sensor, a pH sensor, a thermometer, and a turbidity meter. These data are directly transmitted to the cloud platform for real-time assessment of the ecological health status of the water body. At the same time, the monitoring data is transmitted to the ecological restoration module to dynamically adjust the operating parameters of the restoration equipment. For example, when the dissolved oxygen concentration is lower than the set threshold, the ecological restoration module will start the aeration device and increase the operating intensity. The path planning module also receives the water quality monitoring data to identify the pollution areas that need to be treated first, thereby optimizing the operation route of the unmanned boat.

[0102] The ecological restoration module includes an aeration device, a probiotic dosing device, a garbage cleaning device, and an algae treatment device, and realizes the ecological restoration of the target area by dynamically adjusting the operation of each device. The operation of the restoration module is controlled by the path planning and scheduling module and executed according to the task plan generated based on the fish and bird recognition results and the water quality monitoring data. For example, when the dissolved oxygen concentration in the target area decreases abnormally, the aeration device will be started first; when the garbage density on the water surface increases, the garbage cleaning device will increase the operation intensity. The task data after the restoration is completed is transmitted to the cloud platform for analyzing the restoration effect and optimizing the future operation strategy.

[0103] The path planning and scheduling module generates the optimal operation route of the unmanned boat by comprehensively analyzing the fish and bird recognition data, the water quality monitoring data, and the priority of the ecological restoration tasks. The planning result is transmitted to the ecological restoration module to guide the restoration equipment to treat each target area in a predetermined order. At the same time, the path planning module receives the real-time data from the water quality monitoring module and the fish and bird recognition module, and dynamically adjusts the route to cope with the changes in the water area environment. For example, when a new pollution source or obstacle is detected, the path planning module will recalculate the route to ensure the safe and efficient operation of the unmanned boat.

[0104] The cloud platform is the core data processing center of the entire system, receives the real-time data from the fish and bird recognition module, the water quality monitoring module, and the ecological restoration module, and generates optimization suggestions for water area treatment through big data analysis. The cloud platform will also provide a more refined path optimization plan based on the operation path data uploaded by the path planning module. For example, when multiple unmanned boats cooperate in operation, the cloud platform can adjust their routes and task sequences by analyzing their respective operation scopes and task progress to avoid operation duplication or resource waste. The cloud platform also has a remote control function, and managers can monitor the operation status of the unmanned boat through the platform and issue adjustment instructions according to actual needs.

[0105] Embodiment III The third embodiment of the present invention, based on the same inventive concept, provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the unmanned ship operation method for collaborative fish and bird recognition and water ecological co-governance in the above embodiment.

[0106] Embodiment 4 The fourth embodiment of the present invention, based on the same inventive concept, provides a computer device. The computer device includes: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to perform the unmanned ship operation method for collaborative fish and bird recognition and water ecological co-governance in the above embodiment.

[0107] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiment, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An unmanned boat operation method for coordinating fish and bird identification and water ecological management, characterized in that: The following steps are involved: S1. Start the unmanned ship and complete the operation preparation; S2. Obtain real-time environmental data, including fish activity data, bird activity data and water environment parameters in the water area; S3. Analyze the fish activity data and bird activity data to extract the population distribution, dynamic behavior and activity patterns of fish and birds; S4. Analyze water environmental parameters to determine the dynamics of dissolved oxygen levels, pH, water temperature and turbidity, and assess water quality restoration needs; S5. Based on the distribution characteristics of fish and birds and the results of water quality analysis, generate ecological restoration task priorities and plan the optimal operation path of the unmanned boat; S6. Carry out ecological restoration tasks according to the planned path, including regulating the dissolved oxygen level in the water, adding probiotics, cleaning up water garbage and controlling harmful algae; S7. Upload the data after the task is completed to the cloud platform to evaluate the ecological governance effect and generate optimization suggestions.

2. The unmanned boat operation method for coordinated fish-bird identification and water ecological management according to claim 1 is characterized in that: The fish activity data in S3 is analyzed by target detection algorithm to extract the species distribution and dynamic behavior characteristics of fish. The bird activity data is combined with image classification algorithm and time series model to analyze its activity patterns and habitat distribution characteristics.

3. The unmanned boat operation method for coordinated fish-bird identification and water ecological management according to claim 1 is characterized in that: The analysis of water environment parameters in S4 includes: a. Estimate dissolved oxygen levels based on temperature and turbidity data, and evaluate trends based on historical data; b. Determine the repair needs based on the pH and hydrogen ion concentration of the water; c. Comprehensively analyze the dissolved oxygen and pH results to determine whether the water body needs ecological restoration.

4. The unmanned boat operation method for coordinated fish-bird identification and water ecological management according to claim 1 is characterized in that: The path planning of the unmanned ship in S5 is based on a path optimization algorithm, which combines the distance to the target point and the obstacle distribution cost to dynamically adjust the route to adapt to the complex water environment.

5. The unmanned boat operation method for coordinated fish-bird identification and water ecological management according to claim 1 is characterized in that: The ecological restoration tasks in S6 include the following operations: a. Regulate the dissolved oxygen level in the water body, increase the dissolved oxygen through the aeration device, and adjust the operating rate according to the actual dissolved oxygen level and demand difference; b. Release probiotics to restore the ecological balance of the water body by dynamically adjusting the release amount. The release amount is determined based on the degree of dissolved oxygen deficiency and the needs of ecological restoration; c. Start the garbage cleaning device to remove floating objects on the water surface; d. Activate algae control devices to inhibit excessive growth of harmful algae.

6. The unmanned boat operation method for coordinated fish-bird identification and water ecological management according to claim 1 is characterized in that: The data uploaded to the cloud platform in S7 include fish and bird population distribution and dynamic monitoring data, water quality change data and restoration task execution status. The cloud platform analyzes the above data, generates water area management optimization suggestions, and predicts potential ecological risks.

7. The unmanned boat operation method for coordinated fish-bird identification and water ecological management according to claim 1 is characterized in that: When the unmanned boat detects an emergency, including a sharp deterioration in water pollution or equipment abnormality, it enters emergency operation mode, giving priority to performing dissolved oxygen regulation or probiotic delivery tasks, and returns for equipment maintenance when necessary.

8. An unmanned boat system for coordinated fish and bird identification and water ecological management, characterized in that: The unmanned boat operation method for coordinated fish-bird identification and water ecological co-governance as described in any one of claims 1 to 7 comprises: Fish identification module, used to analyze the species distribution and dynamic behavior characteristics of fish in waters; Bird identification module, used to analyze the activity patterns and habitat distribution characteristics of birds around water areas; Water quality monitoring module, used to collect environmental parameters such as dissolved oxygen, pH, water temperature and turbidity of water bodies; Ecological restoration module, including aeration device, probiotic delivery device, garbage cleaning device and algae treatment device, used to dynamically adjust water restoration operations; Path planning and scheduling module, used to generate the optimal operation path and adjust the route in real time to adapt to environmental changes; The cloud platform is used to receive and analyze data, generate optimization suggestions for water area management, and provide remote control functions.

9. A computer 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 unmanned boat operation method for coordinated fish and bird identification and water ecological management as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the unmanned boat operation method for coordinated fish-bird identification and water ecological management as described in any one of claims 1 to 7.

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