Fishing port disaster early warning method and system, terminal and storage medium

Abstract

The invention relates to the technical field of data processing, and particularly provides a fishing port disaster early warning method and system, a terminal and a storage medium, and the method comprises the steps: carrying out the aerial photographing of a region video of a fishing port through an unmanned plane, and extracting the region asset information from the region video; typhoon information is obtained from the meteorological prediction platform, and actual meteorological data of the fishing port are collected by using a sensor; matching the actual meteorological data with the typhoon information to obtain typhoon transit prediction data; and extracting the highest wind power and the time for reaching the highest wind power from the prediction data, and generating a disaster coping scheme based on the disaster grade and the regional asset information. The emergency capacity and efficiency of the fishing port are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a fishing port disaster early warning method, system, terminal and storage medium. Background Art

[0002] As an important hub for marine economic activities, the fishing port's ability to respond to marine disasters and meteorological warnings is directly related to the safety and stability of coastal areas. However, fishing ports currently generally face the problem of insufficient accuracy and timeliness in this area. Specifically, the construction of marine disaster and meteorological warning systems is still imperfect, and there may be lags or errors in each link of data collection, analysis and information transmission, resulting in the inability to quickly and accurately convey warning information to fishermen and relevant management departments. This lag in warnings directly weakens the timeliness and effectiveness of preventive measures, making fishermen often passive in the face of extreme weather such as typhoons and tsunamis, and the scientificity and practicality of wind and disaster reduction strategies are greatly reduced.

[0003] Especially during extreme weather such as typhoons, the management of fishing boats has become a particularly thorny issue. Due to the uncertainty and rapid development of typhoon paths, fishermen and management departments need to make decisions in a very short period of time to arrange for fishing boats to take shelter and evacuate. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a fishing port disaster early warning method, system, terminal and storage medium to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a fishing port disaster early warning method, comprising: Using drones to take aerial photos of regional videos of fishing ports, and extracting regional asset information from the regional videos; Obtain typhoon information from the weather forecast platform and use sensors to collect actual weather data at the fishing port; Matching the actual meteorological data with the typhoon information to obtain typhoon transit forecast data; The maximum wind speed and the time when the maximum wind speed is reached are extracted from the forecast data, and a disaster response plan is generated based on the disaster level and regional asset information.

[0006] In an optional implementation, using a drone to take aerial photos of a fishing port area video, and extracting area asset information from the area video, includes: Use drones to take aerial photos of the entire fishing port and obtain regional videos; Utilize a pre-trained target detection model to identify a target asset in the regional video, and parse the asset information of the target asset based on the position of the identified target asset in the image frame and the coordinate system of the image frame, wherein the asset information includes the asset type and the location; Save the identified asset information to the regional asset list.

[0007] In an optional implementation, typhoon information is obtained from a weather forecast platform, and actual weather data of the fishing port is collected using sensors, including: Crawl typhoon information from the weather forecast platform, the typhoon information including the typhoon center location, impact range, wind force distribution, precipitation information, and movement trajectory; Through the sensors deployed in the fishing port area, actual meteorological data are continuously collected, and the actual meteorological data include wind force level, wave parameters, and precipitation.

[0008] In an optional implementation, matching the actual meteorological data with the typhoon information to obtain typhoon transit prediction data includes: Match the geographical location of the fishing port with the typhoon center location, movement trajectory and speed in the typhoon information to obtain the relative position of the fishing port and the typhoon; Based on the impact range data in the typhoon information, the relative position of the fishing port and the typhoon and the actual meteorological data, the prediction data of the typhoon passage is generated, and the prediction data includes the wind force level that changes with time.

[0009] In an optional embodiment, the maximum wind speed and the time when the maximum wind speed is reached are extracted from the forecast data, and a disaster response plan is generated based on the disaster level and regional asset information, including: Pre-set response plans for asset types under different disaster levels; According to the asset type and asset location in the regional asset information, count the number of asset types in each area of ​​the fishing port and the number of assets corresponding to each asset type; Based on the disaster level of the fishing port, the corresponding response plan is retrieved, and the corresponding response plan and asset location are organized into an overall response plan for the fishing port.

[0010] In a second aspect, the present invention provides a fishing port disaster warning system, comprising: An asset acquisition module is used to use a drone to take aerial photos of the fishing port area and extract regional asset information from the area video; The data acquisition module is used to obtain typhoon information from the weather forecast platform and collect actual weather data of the fishing port using sensors; A data checking module, used to match the actual meteorological data with the typhoon information to obtain the forecast data of the typhoon passing; A plan generation module is used to extract the maximum wind speed and the time when the maximum wind speed is reached from the forecast data, and generate a disaster response plan based on the disaster level and regional asset information.

[0011] In an optional implementation, the asset acquisition module includes: The video shooting unit is used to use a drone to take aerial photos of the entire fishing port to obtain regional videos; An asset identification unit, configured to identify a target asset in the regional video using a pre-trained target detection model, and parse the asset information of the target asset based on the position of the identified target asset in the image frame and the coordinate system of the image frame, wherein the asset information includes the asset type and the position; The asset saving unit is used to save the identified asset information to the regional asset list.

[0012] In an optional embodiment, the data acquisition module includes: A first acquisition unit is used to crawl typhoon information from a weather forecast platform, wherein the typhoon information includes the typhoon center location, impact range, wind force distribution, precipitation information, and movement trajectory; The second acquisition unit is used to continuously collect actual meteorological data through sensors deployed in the fishing port area, and the actual meteorological data includes wind force level, wave parameters, and precipitation.

[0013] In a third aspect, a terminal is provided, including: processor, memory, wherein: The memory is used to store computer programs. The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0014] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0015] The beneficial effect of the present invention is that the fishing port disaster warning method, system, terminal and storage medium provided by the present invention improve the accuracy of regional typhoon warning by combining meteorological forecast data with actual monitoring data, and dynamically monitor asset information to arrange disaster response plans for actual assets. The present invention improves the emergency response capability and efficiency of fishing ports.

[0016] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0019] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0020] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] The fishing port disaster warning method provided in the embodiment of the present invention is executed by a computer device, and accordingly, the fishing port disaster warning system runs in the computer device.

[0024] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a fishing port disaster warning system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0025] like Figure 1 As shown, the method includes: Step 110, using a drone to take aerial photos of the fishing port's regional video, and extracting regional asset information from the regional video; Step 120, obtaining typhoon information from a weather forecasting platform, and using sensors to collect actual weather data of the fishing port; Step 130, matching the actual meteorological data with the typhoon information to obtain typhoon transit prediction data; Step 140, extracting the maximum wind speed and the time when the maximum wind speed is reached from the forecast data, and generating a disaster response plan based on the disaster level and regional asset information.

[0026] To facilitate understanding of the present invention, the fishing port disaster warning method provided by the present invention is further described below based on the principle of the fishing port disaster warning method of the present invention and in combination with the process of early warning management of fishing port disasters in the embodiment.

[0027] Specifically, the fishing port disaster early warning methods include: S1. Use a drone to take aerial photos of the fishing port area, and extract regional asset information from the area video.

[0028] Use a drone to take aerial photos of the entire fishing port to obtain a regional video; use a pre-trained target detection model to identify asset information from the regional video, the asset information including asset type and location; save the identified asset information to the regional asset list.

[0029] Specifically, the process includes the following: Target setting: The aerial photography target is to fully cover the fishing port area, including key areas such as port facilities, fishing boat berthing areas, cargo yards, and storage facilities.

[0030] Route planning: Use GIS (Geographic Information System) or professional drone aerial photography software to plan the drone flight route based on the fishing port map to ensure full coverage and avoid no-fly zones and obstacles.

[0031] Time selection: Choose the best time to shoot, considering weather conditions (e.g. clear and windless), light quality (avoid overexposure or heavy shadows), and the operating hours of the fishing port.

[0032] Equipment preparation: Choose a drone equipped with a high-definition camera and stabilizer to ensure that the shooting picture is clear and stable. At the same time, check the battery power, memory card capacity, etc. to ensure that the shooting task is completed smoothly.

[0033] Drone takeoff: Remotely or autonomously control the drone to take off and start shooting according to the preset route and altitude.

[0034] Real-time monitoring: Through the real-time transmission system carried by the drone, the ground control station can view the shooting images in real time to ensure the shooting quality.

[0035] Data collection: The drone flies along a predetermined path, capturing comprehensive video footage of the fishing port area, paying special attention to close-up shots of assets for subsequent identification.

[0036] Video import: Import the video footage shot by the drone to a computer or cloud server.

[0037] Preprocessing: Perform preprocessing operations such as denoising and contrast enhancement on the video to improve image quality and facilitate subsequent analysis.

[0038] Target detection: Using pre-trained deep learning models (such as target detection models based on convolutional neural networks), each frame in the video is analyzed frame by frame to identify various types of assets in the fishing port, including asset types (such as fishing boats, warehouses, loading and unloading equipment, etc.). Further, by transforming the image coordinate system and the actual geodetic coordinate system, the location information of the target asset is obtained.

[0039] Information integration: Organize the identified asset information (including timestamp, coordinates, type, etc.) to form a structured asset information database.

[0040] Save to Regional Asset List: Save the identified and organized asset information to the fishing port’s regional asset list, which can be a spreadsheet, database or professional asset management software.

[0041] Dynamic update: Fishing ports are re-photographed regularly and asset information is updated using the same process to ensure the timeliness and accuracy of the data.

[0042] Analysis application: Based on the regional asset list, further asset management, optimization of resource allocation, and safety hazard investigation can be carried out to improve the overall operational efficiency and management level of the fishing port.

[0043] S2. Obtain typhoon information from the weather forecast platform and use sensors to collect actual weather data of the fishing port.

[0044] Typhoon information is crawled from the weather forecast platform, and the typhoon information includes the typhoon center location, impact range, wind distribution, precipitation information, and movement trajectory; actual meteorological data is continuously collected through sensors deployed in the fishing port area, and the actual meteorological data includes wind level, wave parameters, and precipitation.

[0045] Specifically, they include: (1) Crawl typhoon information from the weather forecast platform Data crawling and parsing: First, use automated scripts or API interfaces to regularly crawl the latest typhoon information from authoritative weather forecast platforms. These data are usually provided in JSON, XML or HTML format and need to be parsed to extract key fields. The crawled typhoon information includes but is not limited to: typhoon number, center location (latitude and longitude), impact range (areas expected to be affected), wind distribution map (coverage range of different levels of wind), precipitation information (expected precipitation and precipitation intensity), movement trajectory (movement path forecast in the next few hours to days).

[0046] Data processing and storage: The crawled typhoon information is cleaned and sorted to remove redundant or erroneous data. The processed data is stored in the database for subsequent query and analysis. At the same time, key information can also be displayed on the map in a visual form to intuitively reflect the real-time status and future trends of the typhoon.

[0047] (2) Collecting actual meteorological data through sensors deployed in the fishing port area Sensor deployment: Deploy a variety of sensors at key locations in the fishing port area, including but not limited to anemometers, wind vanes, wave meters, rain gauges, etc., to fully monitor actual weather conditions. Ensure that the sensor network covers the entire fishing port area, including key areas such as port entrances, berthing areas, and cargo yards, so as to accurately capture weather changes in each area.

[0048] Data collection and transmission: Sensors continuously monitor and collect actual meteorological data such as wind force level, wave parameters (such as wave height, period, direction), precipitation, etc. The collected data is transmitted to the data center or cloud server in real time via wireless methods (such as Wi-Fi, LoRa, NB-IoT, etc.) to ensure the timeliness and accuracy of the data.

[0049] Data processing and analysis: Preprocess the collected actual meteorological data, including data cleaning, denoising, interpolation, etc., to improve the reliability of the data. Compare and analyze the processed data with the typhoon information on the meteorological forecast platform to evaluate the actual impact of the typhoon on the fishing port, such as whether it exceeds the forecast range, whether there are new meteorological changes, etc.

[0050] S3. Match the actual meteorological data with the typhoon information to obtain typhoon transit prediction data.

[0051] The geographical location of the fishing port is matched with the typhoon center position, movement trajectory and speed in the typhoon information to obtain the relative position of the fishing port and the typhoon; based on the impact range data in the typhoon information, the relative position of the fishing port and the typhoon and the actual meteorological data, the prediction data of the typhoon passage is generated, and the prediction data includes the wind force level that changes over time.

[0052] Specifically, they include: (1) Data collection and preprocessing Fishing port geographic location data: collect the precise latitude and longitude coordinates of the fishing port, as well as possible environmental information such as the surrounding sea terrain and water depth.

[0053] Typhoon information: real-time acquisition of the latitude and longitude of the typhoon center, its trajectory (i.e. past path and future predicted path), moving speed, maximum wind speed, central air pressure, radius of the seven-level wind circle (or other impact range indicators), etc. These data are usually derived from meteorological satellites, radar observations, meteorological model predictions, etc.

[0054] Actual meteorological data: collect real-time meteorological parameters such as wind direction, wind speed, air pressure, humidity, etc. of the fishing port and surrounding sea areas, as well as historical meteorological data for comparative analysis.

[0055] (2) Calculation of the relative position between the fishing port and the typhoon Using GIS technology, the geographical location of the fishing port is spatially matched with the typhoon center location, and the straight-line distance and azimuth between the two are calculated.

[0056] Combined with the typhoon's trajectory and speed, the system dynamically simulates the typhoon's position changes relative to the fishing port in the future (such as 6 hours, 12 hours, 24 hours, etc.), including the estimated time, distance and direction of the closest point of approach (CPA).

[0057] (3) Assessment of typhoon impact area Based on the impact range data in the typhoon information (such as the radius of a level 7 wind circle) and the relative position of the fishing port and the typhoon center, the direct impact area that the typhoon may cause to the fishing port and the theoretical wind force level are evaluated.

[0058] Calculate the difference between the theoretical wind force level and the actual wind force level of the fishing port, and arrange the differences in order of generation time as an error data sequence. Import the error data sequence into the pre-trained LSTM model (Long Short-Term Memory Network Model) to predict the wind force error.

[0059] Based on the predicted typhoon track and moving speed, the relative position between the typhoon center and the fishing port is predicted, and based on the predicted relative position and the typhoon's impact range data, the theoretical wind force level is predicted. The sum of the predicted theoretical wind force level and the predicted wind force error is output as the prediction data.

[0060] S4. Extracting the maximum wind speed and the time when the maximum wind speed is reached from the forecast data, and generating a disaster response plan based on the disaster level and regional asset information.

[0061] Response plans under different disaster levels are determined based on pre-set asset types; according to the asset type and asset location in the regional asset information, as well as the disaster level of the fishing port, the corresponding response plans are retrieved, and the corresponding response plans and asset locations are organized into disaster response plans.

[0062] Specifically include: Classification of asset types: First, the assets within the fishing port area are classified in detail, including but not limited to fishing vessels, port facilities (such as docks, warehouses, breakwaters), residential buildings, commercial facilities, key infrastructure (such as communication towers, power stations), etc.

[0063] Definition of disaster level: Clarify the standards for different disaster levels, such as dividing disasters into minor, moderate, severe, and extremely severe levels based on wind force levels.

[0064] Response plan development: Develop detailed response plans in advance for each asset type and each disaster level. These plans should cover but not be limited to: Preventive measures: such as strengthening the structure, improving wind and water resistance, and evacuating people in advance.

[0065] Emergency response: emergency measures taken when a disaster occurs, such as shutting down non-essential facilities, activating emergency communication systems, organizing rescue teams, etc.

[0066] Recovery and reconstruction: Planning for repair, cleanup, and reconstruction after a disaster.

[0067] Response plan matching: According to the disaster level and asset type, the corresponding response plan is automatically retrieved from the pre-set response plan library. This process may involve complex matching logic to ensure that the selected plan meets the disaster level requirements and is targeted at specific asset types.

[0068] Spatial location association: Associate the selected response plan with the specific asset location, and use the spatial analysis function of GIS to intuitively display on the map which areas and assets need to take which response measures.

[0069] Disaster response plan compilation: The matched response plans and asset location information are compiled into a detailed disaster response plan report. The report should clearly list the response measures, responsible persons, execution time, required resources and other key information for each asset or asset area to facilitate rapid response and effective execution.

[0070] Dynamic adjustment and optimization: During the disaster response process, the response plan is dynamically adjusted according to the real-time monitoring data and changes in the actual situation. At the same time, lessons learned are summarized after the disaster, and the pre-set response plan is continuously optimized and improved.

[0071] Through the above steps, we can ensure that when natural disasters such as typhoons occur, we can quickly and accurately retrieve and implement targeted response plans to minimize the impact of disasters on assets in the fishing port area.

[0072] In some embodiments, the fishing port disaster warning system may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the fishing port disaster warning system may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) The function of fishing port disaster warning.

[0073] In this embodiment, the fishing port disaster warning system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of the system 200 may include: an asset acquisition module 210, a data acquisition module 220, a data proofreading module 230, and a solution generation module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0074] An asset acquisition module is used to use a drone to take aerial photos of the fishing port area and extract regional asset information from the area video; The data acquisition module is used to obtain typhoon information from the weather forecast platform and collect actual weather data of the fishing port using sensors; A data checking module, used to match the actual meteorological data with the typhoon information to obtain the forecast data of the typhoon passing; A plan generation module is used to extract the maximum wind speed and the time when the maximum wind speed is reached from the forecast data, and generate a disaster response plan based on the disaster level and regional asset information.

[0075] Optionally, as an embodiment of the present invention, the asset acquisition module includes: The video shooting unit is used to use a drone to take aerial photos of the entire fishing port to obtain regional videos; An asset identification unit, configured to identify a target asset in the regional video using a pre-trained target detection model, and parse the asset information of the target asset based on the position of the identified target asset in the image frame and the coordinate system of the image frame, wherein the asset information includes the asset type and the position; The asset saving unit is used to save the identified asset information to the regional asset list.

[0076] Optionally, as an embodiment of the present invention, the data acquisition module includes: A first acquisition unit is used to crawl typhoon information from a weather forecast platform, wherein the typhoon information includes the typhoon center location, impact range, wind force distribution, precipitation information, and movement trajectory; The second acquisition unit is used to continuously collect actual meteorological data through sensors deployed in the fishing port area, and the actual meteorological data includes wind force level, wave parameters, and precipitation.

[0077] Figure 3 The present invention is a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the fishing port disaster warning method provided in an embodiment of the present invention.

[0078] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0079] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0080] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0081] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0082] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0083] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0084] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0085] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0086] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0088] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A fishing port disaster early warning method, characterized in that: include: Using drones to take aerial photos of regional videos of fishing ports, and extracting regional asset information from the regional videos; Obtain typhoon information from the weather forecast platform and use sensors to collect actual weather data at the fishing port; Matching the actual meteorological data with the typhoon information to obtain typhoon transit forecast data; The maximum wind force and the time when the maximum wind force is reached are extracted from the forecast data, and a disaster response plan is generated based on the maximum wind force, the time when the maximum wind force is reached, and regional asset information.

2. The method according to claim 1, characterized in that: Use drones to take aerial photos of the fishing port area, and extract regional asset information from the area video, including: Use drones to take aerial photos of the entire fishing port and obtain regional videos; Utilize a pre-trained target detection model to identify a target asset in the regional video, and parse the asset information of the target asset based on the position of the identified target asset in the image frame and the coordinate system of the image frame, wherein the asset information includes the asset type and the location; Save the identified asset information to the regional asset list.

3. The method according to claim 1, characterized in that Obtain typhoon information from the weather forecast platform and use sensors to collect actual weather data at the fishing port, including: Crawl typhoon information from the weather forecast platform, the typhoon information including the typhoon center location, impact range, wind force distribution, precipitation information, and movement trajectory; Through the sensors deployed in the fishing port area, actual meteorological data are continuously collected, and the actual meteorological data include wind force level, wave parameters, and precipitation.

4. The method according to claim 3, characterized in that: Matching the actual meteorological data with the typhoon information to obtain typhoon transit forecast data includes: The geographical location of the fishing port is matched with the typhoon center location, movement trajectory and speed in the typhoon information to obtain the relative position of the fishing port and the typhoon; Based on the impact range data in the typhoon information, the relative position of the fishing port and the typhoon and the actual meteorological data, the prediction data of the typhoon passage is generated, and the prediction data includes the wind force level that changes with time.

5. The method according to claim 1, characterized in that Extracting the maximum wind speed and the time when the maximum wind speed is reached from the forecast data, and generating a disaster response plan based on the disaster level and regional asset information, including: Pre-set response plans for asset types under different disaster levels; According to the asset type and asset location in the regional asset information, count the number of asset types in each area of ​​the fishing port and the number of assets corresponding to each asset type; Based on the disaster level of the fishing port, the corresponding response plan is retrieved, and the corresponding response plan and asset location are organized into an overall response plan for the fishing port.

6. A fishing port disaster early warning system, characterized in that: include: An asset acquisition module, used to use a drone to take aerial photos of the fishing port area video and extract regional asset information from the area video; The data acquisition module is used to obtain typhoon information from the weather forecast platform and collect actual weather data of the fishing port using sensors; A data checking module, used to match the actual meteorological data with the typhoon information to obtain the forecast data of the typhoon passing; A plan generation module is used to extract the maximum wind speed and the time when the maximum wind speed is reached from the forecast data, and generate a disaster response plan based on the disaster level and regional asset information.

7. The system according to claim 6, characterized in that The asset acquisition module includes: The video shooting unit is used to use a drone to take aerial photos of the entire fishing port to obtain regional videos; An asset identification unit, configured to identify a target asset in the regional video using a pre-trained target detection model, and parse the asset information of the target asset based on the position of the identified target asset in the image frame and the coordinate system of the image frame, wherein the asset information includes the asset type and the position; The asset saving unit is used to save the identified asset information to the regional asset list.

8. The system according to claim 6, characterized in that The data acquisition module comprises: A first acquisition unit is used to crawl typhoon information from a weather forecast platform, wherein the typhoon information includes the typhoon center location, impact range, wind force distribution, precipitation information, and movement trajectory; The second acquisition unit is used to continuously collect actual meteorological data through sensors deployed in the fishing port area, and the actual meteorological data includes wind force level, wave parameters, and precipitation.

9. A terminal, characterized in that: include: A memory, used for storing a fishing port disaster warning program; A processor, used to implement the steps of the fishing port disaster warning method as described in any one of claims 1-5 when executing the fishing port disaster warning program.

10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a fishing port disaster warning program, which, when executed by a processor, implements the steps of the fishing port disaster warning method as described in any one of claims 1 to 5.

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