Marine organism invasion real-time monitoring and automatic response method
By deploying intelligent sensor nodes and edge computing technology in marine environments, combining deep learning models for real-time monitoring and automatic response, the real-time and stability problems of traditional monitoring methods are solved, and efficient and reliable monitoring and response of marine biological invasions are achieved.
Patent Information
- Application Number
- CN202411802312.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-09
- Publication Date
- 2025-03-25
AI Technical Summary
Traditional marine biological invasion monitoring methods have problems such as high cost, long cycles, and difficulty in real-time monitoring, and existing solutions based on remote sensing technology and automatic identification systems are unstable in complex and changeable marine environments.
Intelligent sensor nodes and edge computing technology are used to monitor marine biological invasion in real time, and potential invasive organisms and their invasion degree are judged through deep learning models, and preset response programs are retrieved based on the invasion degree, and automatic response measures are initiated, such as physical isolation or chemical expulsion.
Real-time monitoring and timely response to marine biological invasion events are achieved, and response strategies are dynamically optimized, which improves the effectiveness and reliability of responding to invasive events and reduces the impact of invasive species on the monitoring area.
Smart Images
Figure CN119694046A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of intelligent monitoring, and in particular to a method for real-time monitoring and automatic response to marine biological invasion. Background Art
[0002] With the acceleration of globalization and the increase of human activities, the problem of marine biological invasion is becoming increasingly serious, causing great damage to local ecosystems. Traditional monitoring methods mainly rely on manual sampling and laboratory analysis, which have disadvantages such as high cost, long cycle, and difficulty in achieving real-time monitoring. In recent years, although some solutions based on remote sensing technology and automatic identification systems have been proposed, they usually can only provide limited data support and are unstable in the complex and changing marine environment.
[0003] From the above, we can see that how to minimize the impact of invasive species on the region remains to be resolved. Summary of the invention
[0004] In order to minimize the impact of invasive species on a region, the present application provides a method for real-time monitoring and automatic response to marine biological invasion.
[0005] In the first aspect, the present application provides a method for real-time monitoring and automatic response of marine biological invasion, which adopts the following technical solution:
[0006] A method for real-time monitoring and automatic response of marine biological invasion, comprising:
[0007] Determine a corresponding monitoring area, deploy multiple smart sensor nodes in the monitoring area, obtain corresponding preliminary monitoring data based on the smart sensor nodes, process and filter the preliminary monitoring data based on edge computing technology, and obtain corresponding target monitoring data, wherein the preliminary monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images, and videos;
[0008] Input the target monitoring data into a pre-trained deep learning model to obtain corresponding monitoring results, determine whether there are potential invasive organisms based on the monitoring results, and when a suspected invasion event is detected, determine the corresponding invasion degree;
[0009] Retrieve a preset relationship mapping table between the intrusion degree and the response procedure, determine the corresponding response procedure based on the intrusion degree and the relationship mapping table, after starting the response procedure, record the corresponding response process and obtain the response result in the monitoring area, and determine whether to optimize the relationship mapping table based on the response result.
[0010] By adopting the above technical solution, multiple intelligent sensor nodes are deployed in the monitoring area, and edge computing technology is used to process and filter the collected data to generate target monitoring data. These data are input into the pre-trained deep learning model to determine whether there are potential invasive organisms and the degree of invasion. The system calls the preset response program according to the degree of invasion, initiates the corresponding automatic response measures, such as physical isolation, chemical repellent, etc., and records the response process and results. By continuously monitoring and evaluating the response effect, the system can dynamically optimize the response strategy to ensure timely and effective response to invasion events, thereby minimizing the impact of invasive species on the monitoring area.
[0011] Optionally, in the process of determining whether to optimize the relationship mapping table based on the response result, the method further includes:
[0012] After the response procedure is initiated, the smart sensor nodes are used to continuously obtain the subsequent monitoring data after the response in the monitoring area, where the subsequent monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images, videos, and the actual effect of the response measures;
[0013] Comparing the subsequent monitoring data after the response with the preliminary monitoring data before the response, generating a corresponding evaluation report based on the comparison result, and judging whether the current response procedure has achieved the expected effect based on the evaluation report;
[0014] If the expectation is not met, the response procedure in the relationship mapping table is modified according to the evaluation report, wherein the modification operation includes modifying the type, intensity, and execution order of the response measures.
[0015] By adopting the above technical solution, in the process of judging whether to optimize the relationship mapping table based on the response results, the actual effect of the current response measures can be judged by continuously acquiring and comparing the monitoring data before and after the response, generating an evaluation report. If the expected effect is not achieved, the response procedure is dynamically adjusted according to the evaluation report, including modifying the type, intensity and execution order of the response measures, so as to ensure that the system can continuously optimize the response strategy and improve the effectiveness and reliability of responding to intrusion events.
[0016] Optionally, the method further comprises:
[0017] Obtain the corresponding initial node location information of each intelligent sensor node and retrieve the geographical feature data of the monitoring area;
[0018] Determining whether the position of the smart sensor node needs to be adjusted based on the initial node position information and the geographic feature data;
[0019] If necessary, the target intelligent sensor node that needs to adjust its position is determined, and the optimal moving path of the target intelligent sensor node is calculated, and the target intelligent sensor node adjusts its position along the optimal moving path through a built-in motion device.
[0020] By adopting the above technical solution, by obtaining the initial location information of the intelligent sensor nodes and the geographical feature data of the monitoring area, the system can determine and automatically adjust the location of the sensor nodes to ensure the best monitoring coverage. This process optimizes the layout of the sensor network, improves the accuracy and efficiency of monitoring, and ensures that the system can respond to marine biological invasion events more effectively.
[0021] Optionally, the method further comprises:
[0022] When a suspected intrusion event is detected, the corresponding timestamp is obtained and determined as the preliminary intrusion time;
[0023] Retrieve retrospective monitoring data for a period of time prior to the initial intrusion time;
[0024] Based on the retrospective monitoring data, the timestamp of the first appearance of the invasive organism is determined as the precise invasion time; the retrospective monitoring data corresponding to the precise invasion time is compared with the preliminary monitoring data corresponding to the preliminary invasion time, and whether there is an invasion event is determined based on the comparison result.
[0025] By adopting the above technical solution, the system can determine the precise time when the invading organisms first appeared by obtaining the initial invasion time of the suspected invasion event and looking back at the monitoring data in the previous period. The monitoring data of the precise invasion time and the initial invasion time are compared to ensure the accurate judgment of the invasion event and improve the detection accuracy and response speed of the system.
[0026] Optionally, the method further comprises:
[0027] When an intrusion event is detected, the response procedure is divided into multiple response measures, and the first phase response measures are initiated, the first phase monitoring data of the intrusion event is continuously obtained, and the effect of the first phase response measures is evaluated based on the first phase monitoring data;
[0028] If the first-stage response measures fail to effectively control the intrusion incident, the second-stage response measures are automatically initiated, the second-stage monitoring data of the intrusion incident is continuously obtained, and the effectiveness of the second-stage response measures is evaluated based on the second-stage monitoring data;
[0029] If the second-stage response measures still fail to effectively control the intrusion incident, the third-stage response measures are automatically initiated, the third-stage monitoring data of the intrusion incident is continuously obtained, and the effectiveness of the third-stage response measures is evaluated based on the third-stage monitoring data;
[0030] If the third-stage response measures can effectively control the intrusion incident, then the next-stage response measures will be stopped, otherwise the next-stage response measures will be started.
[0031] By adopting the above technical solution, the system can dynamically adjust the response strategy according to the development of the intrusion incident by dividing the response procedure into multiple stages and gradually launching the response measures at different stages. The effect of the response measures at each stage is evaluated through the continuously acquired monitoring data to ensure timely and effective control of the intrusion incident and improve the response efficiency and coping ability of the system.
[0032] Optionally, the method further comprises:
[0033] When an intrusion event is detected, the distribution range and diffusion speed of the intrusion event are obtained, and the start time of the response program is dynamically adjusted according to the distribution range and diffusion speed of the intrusion event;
[0034] Among them, include:
[0035] If the intrusion incident is urgent and spreads quickly, initiate the response procedure in advance;
[0036] If the intrusion incident is mild and spreads slowly, the response procedure will be initiated at the default time;
[0037] After the response measures are initiated, the response effect corresponding to the intrusion event is obtained, and the duration of the response program is adjusted based on the response effect.
[0038] By adopting the above technical solution, the system can make timely and effective responses according to the urgency and spread speed of the intrusion event by dynamically adjusting the start time and duration of the response program. Urgent and fast-spreading intrusion events will start the response program in advance, while mild and slow-spreading intrusion events will start at the default time. After the response measures are started, the system dynamically adjusts the duration according to the actual response effect to ensure that the intrusion event is effectively controlled and improve the response efficiency and flexibility of the system.
[0039] In the second aspect, the present application provides a real-time monitoring and automatic response device for marine biological invasion, which adopts the following technical solution:
[0040] A real-time monitoring and automatic response system for marine biological invasion, comprising:
[0041] The target monitoring data acquisition module determines the corresponding monitoring area, deploys multiple intelligent sensor nodes in the monitoring area, obtains the corresponding preliminary monitoring data based on the intelligent sensor nodes, and processes and filters the preliminary monitoring data based on edge computing technology to obtain the corresponding target monitoring data, wherein the preliminary monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images and videos;
[0042] An invasion degree determination module, which inputs the target monitoring data into a pre-trained deep learning model, obtains corresponding monitoring results, determines whether there are potential invasive organisms based on the monitoring results, and determines the corresponding invasion degree when a suspected invasion event is detected;
[0043] A response result acquisition module retrieves a preset relationship mapping table between the intrusion degree and the response program, determines a corresponding response program based on the intrusion degree and the relationship mapping table, records the corresponding response process after starting the response program and uses it to obtain a response result in the monitoring area, and determines whether to optimize the relationship mapping table based on the response result;
[0044] A memory for storing a program of a method for real-time monitoring and automatic response to marine biological invasion;
[0045] The program in the processor and the memory can be loaded and executed by the processor to realize a real-time monitoring and automatic response method for marine biological invasion.
[0046] In a third aspect, the present application provides a computer storage medium capable of storing a corresponding program, which has the effect of minimizing the impact of invasive species on a region, and adopts the following technical solution:
[0047] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the above-mentioned real-time monitoring and automatic response method for marine biological invasion.
[0048] In a fourth aspect, the present application provides a circulating water pump, which adopts the following technical solution:
[0049] An intelligent terminal comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute the above-mentioned method for real-time monitoring and automatic response to marine biological invasion.
[0050] In summary, the present application includes at least one of the following beneficial technical effects:
[0051] 1. Through intelligent sensor nodes and edge computing technology, the monitoring data is efficiently processed and screened, and real-time monitoring of marine biological invasion events is achieved to ensure early detection of potential invasive organisms; according to the degree of invasion, the preset response program is called up, the corresponding automatic response measures are initiated, and the response process and results are recorded. By continuously monitoring and evaluating the response effect, the response strategy is dynamically optimized to improve the effectiveness and reliability of responding to invasion events.
[0052] 2. By obtaining the initial location information of the intelligent sensor nodes and the geographic feature data of the monitoring area, the position of the sensor nodes is automatically adjusted to ensure the best monitoring coverage, optimize the layout of the sensor network, and improve the accuracy and efficiency of monitoring; by backtracking the monitoring data, the precise time when the invasive organisms first appeared is determined to ensure accurate judgment of the invasion event and improve the detection accuracy and response speed of the system.
[0053] 3. Divide the response procedure into multiple stages, gradually initiate response measures at different stages, dynamically adjust the response strategy according to the development of the intrusion incident, ensure timely and effective control of the intrusion incident, and improve the response efficiency and coping capabilities of the system; dynamically adjust the start time and duration of the response procedure according to the urgency and spread speed of the intrusion incident to ensure that the system can respond to different types of intrusion incidents in a timely and effective manner, and improve the flexibility and efficiency of the response. BRIEF DESCRIPTION OF THE DRAWINGS
[0054] Figure 1 The present invention is a flow chart of a method for real-time monitoring and automatic response to marine biological invasion according to an exemplary embodiment.
[0055] Figure 2 It is a structural block diagram of a method and device for real-time monitoring and automatic response to marine biological invasion according to an exemplary embodiment. DETAILED DESCRIPTION
[0056] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.
[0057] A method, system, storage medium and terminal for real-time monitoring and automatic response to marine biological invasions. Through intelligent sensor nodes and edge computing technology, it efficiently processes and filters monitoring data, realizes real-time monitoring of marine biological invasion events, and ensures early detection of potential invasive organisms; according to the degree of invasion, it calls the preset response program, starts the corresponding automatic response measures, and records the response process and results. By continuously monitoring and evaluating the response effect, the response strategy is dynamically optimized to improve the effectiveness and reliability of responding to invasion events.
[0058] Reference Figure 1 The present application embodiment discloses a method for real-time monitoring and automatic response of marine biological invasion, comprising:
[0059] Step S100, determine the corresponding monitoring area, deploy multiple smart sensor nodes in the monitoring area, obtain corresponding preliminary monitoring data based on the smart sensor nodes, process and screen the preliminary monitoring data based on edge computing technology, and obtain corresponding target monitoring data.
[0060] Among them, select the sea areas or specific waters that need to be monitored. These areas are usually ecologically sensitive areas, economically important areas, or areas where biological invasions have occurred in history; deploy multiple smart sensor nodes evenly or according to geographical features in the selected monitoring area. These smart sensor nodes can collect a variety of environmental parameters, such as water temperature, salinity, pH value, dissolved oxygen, images and videos, etc. Each sensor node has the ability to self-organize the network and can automatically adjust its position without human intervention to ensure the best monitoring coverage.
[0061] Then, the intelligent sensor nodes collect environmental parameters in real time, including water temperature, salinity, pH value, dissolved oxygen, images and videos, etc., and pre-process and screen the preliminary monitoring data on the sensor nodes or nearby edge computing devices to remove invalid or redundant data and extract key feature information. This step reduces the amount of data transmission, reduces the burden on the cloud server, and can improve data processing efficiency.
[0062] Finally, the data after processing and screening are determined as target monitoring data. These data are more refined and useful and can be directly used for subsequent analysis and judgment.
[0063] Step S110, input the target monitoring data into a pre-trained deep learning model to obtain the corresponding monitoring results, and determine whether there are potential invasive organisms based on the monitoring results. When a suspected invasion event is detected, the corresponding invasion degree is determined.
[0064] Among them, the target monitoring data is input into a pre-trained deep learning model. The deep learning model has been trained with a large amount of historical data and can recognize and classify different biological characteristics. At this time, the deep learning model outputs the monitoring results. The system determines whether there are potential invasive organisms based on the monitoring results. If a suspected invasion incident is detected, the system further analyzes the number, type and distribution range of the invasive organisms to determine the severity of the invasion incident. It should be pointed out here that the degree of invasion can be divided into multiple levels, such as mild, moderate, severe, etc.
[0065] Step S120, retrieve the preset relationship mapping table between the intrusion degree and the response procedure, determine the corresponding response procedure based on the intrusion degree and the relationship mapping table, after starting the response procedure, record the corresponding response process and obtain the response result in the monitoring area, and determine whether to optimize the relationship mapping table based on the response result.
[0066] Among them, the system calls up the pre-set relationship mapping table between the invasion degree and the response procedure. The relationship mapping table defines the response measures corresponding to different invasion degrees. According to the invasion degree and the relationship mapping table, the system determines and starts the corresponding response procedure, such as physical isolation, chemical expulsion, biological control, etc.
[0067] After the response program is started, the system records all operations and data in the response process, including the type of response measures, execution time, execution effect, etc., and then continuously obtains post-response monitoring data through intelligent sensor nodes to evaluate the actual effects of the response measures; based on the response results, the system generates an evaluation report to determine whether the current response program achieves the expected effect; if the expected effect is not achieved, the response program in the relationship mapping table is modified according to the evaluation report, including modifying the type, intensity and execution order of the response measures.
[0068] By ensuring comprehensive coverage of the monitoring area and efficient data processing, the accuracy and real-time nature of data collection are improved, and then the deep learning model is used to accurately determine potential invasive organisms and their invasion degree, ensuring early detection and accurate judgment of invasion events; corresponding response measures are initiated according to the invasion degree, and the response effects are recorded and evaluated, and the response strategy is dynamically optimized to ensure timely and effective response to invasion events. Through these steps and measures, this method can effectively monitor and respond to marine biological invasion events, minimize the impact of invasive species on the monitoring area, and maintain the health and stability of the marine ecosystem.
[0069] In the process of determining whether to optimize the relationship mapping table based on the response result, the method further includes:
[0070] Step S1201, after starting the response program, continuously acquiring subsequent monitoring data after the response in the monitoring area through the intelligent sensor node.
[0071] Among them, according to the degree of invasion and the relationship mapping table, the system initiates the corresponding response program, such as physical isolation, chemical repellent, biological control, etc.; and after the response program is started, the intelligent sensor node continues to collect environmental parameters in the monitoring area, including water temperature, salinity, pH value, dissolved oxygen, images and videos, etc. At the same time, it evaluates the actual effect of the response measures and records the implementation and results of the response measures.
[0072] By continuously collecting follow-up monitoring data, we can ensure that the system can monitor the effectiveness of response measures in real time and promptly detect and record any changes.
[0073] Step S1202, compare the subsequent monitoring data after the response with the preliminary monitoring data before the response, generate a corresponding evaluation report based on the comparison result, and judge whether the current response procedure achieves the expected effect based on the evaluation report.
[0074] Among them, before the response program is initiated, the system has collected preliminary monitoring data for a period of time. After the response program is initiated, the system continues to collect subsequent monitoring data of the same type, compares the subsequent monitoring data after the response with the preliminary monitoring data before the response, and analyzes changes in environmental parameters and biological behaviors.
[0075] Then, based on the results of the data comparison, a detailed evaluation report is generated, and the evaluation report includes the implementation of response measures, changes in environmental parameters, changes in biological behavior, etc. It should be pointed out here that the evaluation report should clearly indicate the effectiveness of the response measures, including situations where invasive organisms were successfully controlled and situations where they were not effectively controlled.
[0076] At the same time, according to the content of the assessment report, it is judged whether the current response procedures have achieved the expected results. The expected results usually include the reduction of the number of invasive organisms, the recovery of environmental parameters, etc., and clear assessment criteria need to be set, such as the percentage of reduction in the number of invasive organisms, the recovery of environmental parameters to normal range, etc.
[0077] Step S1203: If the expectation is not met, modify the response program in the relationship mapping table according to the evaluation report.
[0078] Among them, if the evaluation report shows that the current response procedure does not achieve the expected effect, the response procedure in the relationship mapping table is modified according to the suggestions in the evaluation report. Specific modification content: The modification operation includes modifying the type, intensity, execution order, etc. of the response measures. For example, if the physical isolation measures are not effective, chemical repellent measures can be added; if the intensity of the response measures is not enough, the drug concentration can be increased; if the order of the response measures is unreasonable, the execution order can be adjusted.
[0079] Based on the above steps S1201 to S1203, the actual effects of the response measures are evaluated through data comparison to determine whether the response measures are effective. A detailed evaluation report is generated to provide a scientific basis for decision makers to help them understand the actual effects of the response measures. At the same time, by dynamically adjusting the response procedures, it is ensured that the system can continuously optimize the response strategy and improve the effectiveness and reliability of responding to intrusion incidents.
[0080] Correspondingly, after determining it as a suspected intrusion event, further confirmation is required, so the method also includes:
[0081] Step S111: when a suspected intrusion event is detected, a corresponding timestamp is obtained and determined as a preliminary intrusion time.
[0082] The system uses a deep learning model to analyze the target monitoring data to determine whether there are potential invasive organisms. When a suspected invasion event is detected, the system records the timestamp of the event and determines it as the initial invasion time. This can provide a time benchmark for subsequent retrospective analysis to ensure that the suspected invasion event can be accurately traced back to the time point.
[0083] Step S112, retrieve the backtracking monitoring data within a period of time before the initial intrusion time.
[0084] Among them, according to the initial invasion time, a period of time is traced back, such as 1 hour or 24 hours, and the specific time can be adjusted according to the actual situation; and the monitoring data during this period is retrieved from the smart sensor node, including water temperature, salinity, pH value, dissolved oxygen, images and videos, etc.; by tracing back the data, it is ensured that the environmental conditions before the suspected invasion incident are covered, and early signs of invasive organisms can be detected.
[0085] Step S113, determining the timestamp of the first appearance of the invading organisms based on the retrospective monitoring data as the precise invasion time.
[0086] Among them, the retrieved retrospective monitoring data is analyzed in detail, especially the image and video data, to find the first signs of the invasive organisms, find the time when the invasive organisms first appear, and record the timestamp of that time point to determine it as the precise invasion time, so as to more accurately assess the occurrence and development of the invasion event.
[0087] Step S114, comparing the retrospective monitoring data corresponding to the precise intrusion time with the preliminary monitoring data corresponding to the preliminary intrusion time, and determining whether an intrusion event exists based on the comparison result.
[0088] Among them, preliminary monitoring data for a period of time are collected before and after the initial invasion time point, and retrospective monitoring data for the same time period are collected before and after the precise invasion time point; then the preliminary monitoring data corresponding to the initial invasion time are compared with the retrospective monitoring data corresponding to the precise invasion time to analyze the changes in environmental parameters and biological behavior.
[0089] Then, based on the results of the data comparison, a detailed comparison report is generated. The report should include the comparison results of the initial invasion time and the precise invasion time, as well as the changes in environmental parameters and biological behaviors. At the same time, based on the comparison report, it is determined whether there is a real invasion event. If the initial invasion time and the precise invasion time are consistent and there are significant changes in environmental parameters and biological behaviors, it can be determined that an invasion event has occurred.
[0090] Based on the above steps S111 to S114, by determining the precise time when the invasive organisms first appear, it is helpful to more accurately assess the occurrence and development of invasion events and ensure early intervention; then, through data comparison, the accuracy of the initial invasion time is verified to improve the detection accuracy of the system; at the same time, based on the comparison report, the accurate judgment of the invasion event is ensured, false alarms and missed alarms are reduced, and a basis for scientific decision-making is provided. The above steps and measures ensure that the system can accurately judge and respond to marine biological invasion events and minimize the impact of invasive species on the monitored area.
[0091] After determining that it is an intrusion event, the method further includes:
[0092] Step S115, when an intrusion event is detected, the response procedure is divided into multiple response measures, and the first phase response measures are started, the first phase monitoring data of the intrusion event is continuously obtained, and the effect of the first phase response measures is evaluated based on the first phase monitoring data.
[0093] Among them, the system confirms the existence of an invasion incident and determines the extent of the invasion through deep learning models and data analysis, and divides the response procedure into multiple stages. Each stage corresponds to different response measures. For example, the first stage can be physical isolation, the second stage can be chemical expulsion, and the third stage can be biological control, etc.
[0094] Then, according to the degree of intrusion and the relationship mapping table, the first phase of response measures, such as physical isolation, is initiated. After the first phase of response measures is initiated, the intelligent sensor nodes continue to collect environmental parameters in the monitoring area, including water temperature, salinity, pH value, dissolved oxygen, images and videos, etc., as well as evaluate the actual effect of the response measures.
[0095] Step S116: If the first-stage response measures fail to effectively control the intrusion event, the second-stage response measures are automatically initiated, the second-stage monitoring data of the intrusion event is continuously obtained, and the effectiveness of the second-stage response measures is evaluated based on the second-stage monitoring data.
[0096] Among them, based on the first-stage monitoring data, the effectiveness of the first-stage response measures is evaluated. If the number of invasive organisms has not decreased significantly, or the environmental parameters have not returned to the normal range, it is judged that the first-stage response measures have failed to effectively control the invasion incident. Then, based on the evaluation results, the second-stage response measures are automatically started, such as chemical repellent. After the second-stage response measures are started, the intelligent sensor nodes continue to collect environmental parameters in the monitoring area to evaluate the actual effect of the response measures.
[0097] Step S117, if the second-stage response measures still fail to effectively control the intrusion event, the third-stage response measures are automatically started, the third-stage monitoring data of the intrusion event is continuously obtained, and the effectiveness of the third-stage response measures is evaluated based on the third-stage monitoring data.
[0098] Among them, based on the second-stage monitoring data, the effectiveness of the second-stage response measures is evaluated. If the number of invasive organisms still does not decrease significantly, or the environmental parameters do not return to the normal range, it is judged that the second-stage response measures have failed to effectively control the invasion event, and based on the evaluation results, the third-stage response measures, such as biological control, are automatically started. After the third-stage response measures are started, the intelligent sensor nodes continue to collect environmental parameters in the monitoring area to evaluate the actual effect of the response measures. If the second-stage response measures fail to effectively control the invasion event, the system automatically starts the third-stage response measures to ensure further adjustment of the response strategy.
[0099] Step S118: If the third stage response measures can effectively control the intrusion event, then the next stage response measures are stopped from being initiated; otherwise, the next stage response measures are initiated.
[0100] Among them, based on the third-stage monitoring data, the effectiveness of the third-stage response measures is evaluated. If the number of invasive organisms is significantly reduced or the environmental parameters return to the normal range, it is judged that the third-stage response measures have effectively controlled the invasion event. If the third-stage response measures effectively control the invasion event, the system stops starting the next-stage response measures; if the third-stage response measures still fail to effectively control the invasion event, the system will start the next-stage response measures according to the evaluation results, such as higher-level biological control or other emergency measures.
[0101] Based on the above steps S115 to S118, by dividing the response procedure into multiple stages, gradually taking different response measures, and continuously acquiring monitoring data at each stage, it is ensured that the system can monitor the effectiveness of the response measures in real time and promptly discover and record any changes. If the response measures at a certain stage fail to effectively control the intrusion event, the system automatically starts the response measures at the next stage to ensure timely adjustment of the response strategy, and by evaluating the effectiveness of the response measures at each stage, ensure that the intrusion event is effectively controlled and reduce unnecessary waste of resources.
[0102] At the same time, when an intrusion event is detected, the corresponding diffusion speed factor needs to be considered, so the method also includes:
[0103] Step S1141, obtaining the distribution range and diffusion speed of the intrusion event, and dynamically adjusting the start time of the response program according to the distribution range and diffusion speed of the intrusion event;
[0104] Among them, the system confirms the existence of invasion events and determines the extent of invasion through deep learning models and data analysis; it can also determine the distribution range of invasive organisms through data collected by smart sensor nodes, including their specific location and scope of influence in the monitoring area, and calculate the spread rate of invasive organisms by analyzing changes in the number and distribution range of invasive organisms.
[0105] If the invasion event is urgent and spreads quickly, the system will start the response program in advance. For example, if the spread rate of the invading organism exceeds a certain threshold, the system will immediately start the response measures. If the invasion event is mild and spreads slowly, the system will start the response program at the default time. For example, if the spread rate of the invading organism is lower than a certain threshold, the system will start the response measures according to the preset schedule.
[0106] Step S1142, after the response measures are initiated, the response effect corresponding to the intrusion event is obtained, and the duration of the response program is adjusted based on the response effect.
[0107] Among them, after the response measures are initiated, the intelligent sensor nodes continue to collect environmental parameters in the monitoring area, including water temperature, salinity, pH value, dissolved oxygen, images and videos, and evaluate the actual effects of the response measures; they can also evaluate the actual effects of the response measures based on the collected monitoring data, including changes in the number of invasive organisms, recovery of environmental parameters, etc.
[0108] In addition, based on the evaluation results, it is determined whether the current response measures have achieved the expected effect. If the number of invasive organisms has decreased significantly or the environmental parameters have returned to the normal range, the response measures are considered effective. If the response measures are effective, the system can shorten the duration of the response measures and reduce resource consumption. If the response measures fail to effectively control the invasion event, the system will extend the duration of the response measures until the expected effect is achieved.
[0109] Through the above steps, for urgent and fast-spreading invasion incidents, the response procedure is initiated in advance to ensure timely control of the spread of invasive organisms and reduce the impact on the monitored area; for mild and slow-spreading invasion incidents, the response procedure is initiated at the default time to avoid unnecessary waste of resources and ensure the rational use of resources.
[0110] By continuously obtaining response results, the system can monitor the effectiveness of response measures in real time and promptly detect and record any changes. By evaluating the response effect, it can be determined whether the response measures are effective, providing a basis for subsequent adjustments. The duration of response measures can be dynamically adjusted according to the response effect to ensure that the system can flexibly respond to different situations and improve its ability to respond to intrusion incidents.
[0111] For multiple smart sensor nodes in the monitoring area, regular adjustments are required, so the method also includes:
[0112] Step S131, obtaining the corresponding initial node position information of each intelligent sensor node, and retrieving the geographical feature data of the monitoring area.
[0113] Among them, each intelligent sensor node will record its initial location information, including longitude and latitude, altitude, etc. when deployed, and retrieve the geographic feature data of the monitoring area from the geographic information system (GI step S) or other data sources, including terrain, water flow, vegetation distribution, important ecological areas, etc.
[0114] Step S132: determining whether the position of the smart sensor node needs to be adjusted based on the initial node position information and the geographic feature data.
[0115] Among them, the initial location information of the intelligent sensor nodes is integrated with the geographic feature data of the monitoring area to form a comprehensive geographic information map. Then, based on the comprehensive geographic information map, the current layout of the sensor nodes is analyzed to see whether it is reasonable. The factors to be considered here include coverage, monitoring blind spots, monitoring needs of important ecological areas, etc. If it is found that the monitoring density in some areas is insufficient or there are monitoring blind spots, or that some important ecological areas need to strengthen monitoring, the system will determine that the position of the intelligent sensor nodes needs to be adjusted.
[0116] Step S133: if necessary, determine the target smart sensor node that needs to adjust its position, calculate the optimal moving path of the target smart sensor node, and adjust the position of the target smart sensor node along the optimal moving path through the built-in motion device.
[0117] Among them, according to the analysis results, the smart sensor nodes that need to be adjusted are determined. These smart sensor nodes may be in areas with insufficient monitoring density or important ecological areas that need to strengthen monitoring. Farmers use path planning algorithms and combine geographic feature data to calculate the best movement path for the target smart sensor nodes. Factors to be considered include the shortest distance, avoiding obstacles, and minimum energy consumption.
[0118] In addition, after receiving the movement command, the target intelligent sensor node starts the built-in motion devices such as motors, wheels, propellers, etc., and then the intelligent sensor node autonomously navigates and moves to the new position according to the calculated optimal movement path. After arriving at the new position, the intelligent sensor node re-records its position information and sends the new position information back to the central control system.
[0119] Through the above process, the intelligent sensor nodes can autonomously adjust their positions to ensure full coverage of the monitoring area, improve the accuracy and efficiency of monitoring, and calculate the optimal moving path to ensure that the intelligent sensor nodes can move to new locations efficiently and safely, reducing energy consumption and moving time.
[0120] Based on the same inventive concept, the present application embodiment discloses a real-time monitoring and automatic response system for marine biological invasion, referring to Figure 2 , the system includes but is not limited to:
[0121] The target monitoring data acquisition module 200 determines the corresponding monitoring area, deploys multiple smart sensor nodes in the monitoring area, obtains the corresponding preliminary monitoring data based on the smart sensor nodes, and processes and filters the preliminary monitoring data based on edge computing technology to obtain the corresponding target monitoring data, wherein the preliminary monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images and videos;
[0122] The invasion degree determination module 210 inputs the target monitoring data into a pre-trained deep learning model, obtains the corresponding monitoring results, determines whether there are potential invasive organisms based on the monitoring results, and determines the corresponding invasion degree when a suspected invasion event is detected;
[0123] The response result acquisition module 220 calls up a preset relationship mapping table between the intrusion degree and the response procedure, determines the corresponding response procedure based on the intrusion degree and the relationship mapping table, and after starting the response procedure, records the corresponding response process and uses it to obtain the response result in the monitoring area, and determines whether to optimize the relationship mapping table based on the response result.
[0124] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute a road stain cleaning method.
[0125] The various processes and treatments described above, such as a method for real-time monitoring and automatic response to marine biological invasion, can be executed by a CPU. For example, in some embodiments, a method for real-time monitoring and automatic response to marine biological invasion can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a memory. In some embodiments, part or all of the computer program can be loaded and / or installed on a computing device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, a method for real-time monitoring and automatic response to marine biological invasion or multiple actions described above can be executed.
[0126] The present disclosure relates to methods, computing devices, computer-readable storage media, and / or computer program products. The computer program product may include computer-readable program instructions for executing various aspects of the present disclosure.
[0127] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.
[0128] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge computing devices. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.
[0129] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.
[0130] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of a method for real-time monitoring and automatic response to marine biological invasion or multiple actions.
[0131] Computer-readable program instructions can also be loaded onto a computer, other programmable data processing device, or other device, so that a series of operating steps are executed on the computer, other programmable data processing device, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing device, or other device implement a real-time monitoring and automatic response method for marine biological invasion or multiple actions.
[0132] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.
[0133] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute a method for real-time monitoring and automatic response to marine biological invasion.
Claims
1. A method for real-time monitoring and automatic response to marine biological invasion, characterized in that: include: Determine a corresponding monitoring area, deploy multiple smart sensor nodes in the monitoring area, obtain corresponding preliminary monitoring data based on the smart sensor nodes, process and filter the preliminary monitoring data based on edge computing technology, and obtain corresponding target monitoring data, wherein the preliminary monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images, and videos; Input the target monitoring data into a pre-trained deep learning model to obtain corresponding monitoring results, determine whether there are potential invasive organisms based on the monitoring results, and when a suspected invasion event is detected, determine the corresponding invasion degree; Retrieve a preset relationship mapping table between the intrusion degree and the response procedure, determine the corresponding response procedure based on the intrusion degree and the relationship mapping table, after starting the response procedure, record the corresponding response process and obtain the response result in the monitoring area, and determine whether to optimize the relationship mapping table based on the response result.
2. The method for real-time monitoring and automatic response to marine biological invasion according to claim 1, characterized in that: In the process of determining whether to optimize the relationship mapping table based on the response result, the method further includes: After the response procedure is initiated, the smart sensor nodes are used to continuously obtain the subsequent monitoring data after the response in the monitoring area, where the subsequent monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images, videos, and the actual effect of the evaluation response measures; Comparing the subsequent monitoring data after the response with the preliminary monitoring data before the response, generating a corresponding evaluation report based on the comparison result, and judging whether the current response procedure has achieved the expected effect based on the evaluation report; If the expectation is not met, the response procedure in the relationship mapping table is modified according to the evaluation report, wherein the modification operation includes modifying the type, intensity, and execution order of the response measures.
3. The method for real-time monitoring and automatic response to marine biological invasion according to claim 1, characterized in that: The method also includes: Obtain the corresponding initial node location information of each intelligent sensor node and retrieve the geographical feature data of the monitoring area; Determining whether the position of the smart sensor node needs to be adjusted based on the initial node position information and the geographic feature data; If necessary, the target intelligent sensor node that needs to adjust its position is determined, and the optimal moving path of the target intelligent sensor node is calculated, and the target intelligent sensor node adjusts its position along the optimal moving path through a built-in motion device.
4. The method for real-time monitoring and automatic response to marine biological invasion according to claim 2, characterized in that: The method also includes: When a suspected intrusion event is detected, the corresponding timestamp is obtained and determined as the preliminary intrusion time; Retrieve retrospective monitoring data for a period of time prior to the initial intrusion time; The timestamp of the first appearance of the invasive organisms is determined based on the retrospective monitoring data as the precise invasion time; The retrospective monitoring data corresponding to the precise intrusion time is compared with the preliminary monitoring data corresponding to the preliminary intrusion time, and whether there is an intrusion event is determined based on the comparison result.
5. The method for real-time monitoring and automatic response to marine biological invasion according to claim 4, characterized in that: The method also includes: When an intrusion event is detected, the response procedure is divided into multiple response measures, and the first phase response measures are initiated, the first phase monitoring data of the intrusion event is continuously obtained, and the effect of the first phase response measures is evaluated based on the first phase monitoring data; If the first-stage response measures fail to effectively control the intrusion incident, the second-stage response measures are automatically initiated, the second-stage monitoring data of the intrusion incident is continuously obtained, and the effectiveness of the second-stage response measures is evaluated based on the second-stage monitoring data; If the second-stage response measures still fail to effectively control the intrusion incident, the third-stage response measures are automatically initiated, the third-stage monitoring data of the intrusion incident is continuously obtained, and the effectiveness of the third-stage response measures is evaluated based on the third-stage monitoring data; If the third-stage response measures can effectively control the intrusion incident, then the next-stage response measures will be stopped, otherwise the next-stage response measures will be started.
6. The method for real-time monitoring and automatic response to marine biological invasion according to claim 4, characterized in that: The method also includes: When an intrusion event is detected, the distribution range and diffusion speed of the intrusion event are obtained, and the start time of the response program is dynamically adjusted according to the distribution range and diffusion speed of the intrusion event; These include: if the intrusion incident is urgent and spreads quickly, start the response procedure in advance; If the intrusion incident is mild and spreads slowly, the response procedure will be initiated at the default time; After the response measures are initiated, the response effect corresponding to the intrusion event is obtained, and the duration of the response program is adjusted based on the response effect.
7. A real-time monitoring and automatic response system for marine biological invasion, characterized in that: include: The target monitoring data acquisition module determines the corresponding monitoring area, deploys multiple intelligent sensor nodes in the monitoring area, obtains the corresponding preliminary monitoring data based on the intelligent sensor nodes, and processes and filters the preliminary monitoring data based on edge computing technology to obtain the corresponding target monitoring data, wherein the preliminary monitoring data includes water temperature, salinity, pH value, dissolved oxygen, images and videos; An invasion degree determination module, which inputs the target monitoring data into a pre-trained deep learning model, obtains corresponding monitoring results, determines whether there are potential invasive organisms based on the monitoring results, and determines the corresponding invasion degree when a suspected invasion event is detected; A response result acquisition module retrieves a preset relationship mapping table between the intrusion degree and the response program, determines a corresponding response program based on the intrusion degree and the relationship mapping table, records the corresponding response process after starting the response program and uses it to obtain the response result in the monitoring area, and determines whether to optimize the relationship mapping table based on the response result; A memory for storing a program of a method for real-time monitoring and automatic response to marine biological invasion according to any one of claims 1 to 6; The program in the processor and the memory can be loaded and executed by the processor to implement a real-time monitoring and automatic response method for marine biological invasion as described in any one of claims 1 to 6.
8. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute a method for real-time monitoring and automatic response to marine biological invasion as claimed in any one of claims 1 to 6.
9. An intelligent terminal, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes a method for real-time monitoring and automatic response to marine biological invasion as described in any one of claims 1 to 6.