CN111583052A - Fishing boat trajectory tracking and fishing situation analysis system - Google Patents
Fishing boat trajectory tracking and fishing situation analysis system Download PDFInfo
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Abstract
The invention relates to the technical field of marine vessel tracking and artificial intelligence auxiliary devices, in particular to a fishing boat track tracking and fishing situation analysis system which can help a user to better plan the navigation track of a fishing boat and ensure that fishing activities are more efficient; the fishing boat track generation module and the fish condition acquisition module are connected with the ship and the fish condition comparison module in a data transmission mode.
Description
Technical Field
The invention relates to the technical field of marine vessel tracking and artificial intelligence auxiliary devices, in particular to a fishing vessel track tracking and fishing situation analysis system.
Background
As is well known, the production efficiency problem of the marine fishery industry is related to the vital interests of fishermen. The sea-going navigation direction of the fishing boat is usually judged according to the experience of fishermen, the distribution of fish schools is influenced by various factors such as seasons, marine environments, climate and the like, the fish school distribution position which cannot be accurately obtained is judged only through the experience, even the condition of wrong judgment is caused, and certain time and economic loss is caused. Therefore, the precise positioning of the moving range of the fish school has a crucial influence on the fishing activities of fishermen.
At present, the detection means for fish shoal distribution mainly comprises the following three methods. The first is direct detection of fish shoal, and by visual observation or using sensors such as aerial photography, video camera, laser radar, and aerial radar, sea surface hydrological information formed by various fish shoal activities can be obtained, such as: the image of the near-water surface fish school, the color of the fish school, the wave caused by the fish school, the distribution of the seabirds and the like, thereby obtaining the fish inhabitation habit information such as the spatial and temporal distribution characteristics of the fish school, the size of the population organism, the migration and the reproduction of the fish school and the like. The second is to monitor sea surface temperature information (SST), and comprehensively analyze the fishing ground by monitoring the information of characteristic temperature value, temperature frontal surface, surface water mass analysis, temperature field space position and the like of the sea. And the third is to monitor the color information of the ocean water, and to analyze the fishery and evaluate the fishery resources or the ocean biomass by observing the content and the change of the phytoplankton in the ocean water. However, the indexes required by the three methods are difficult to obtain, and the calculation is complex, so that the method is difficult to be applied to the analysis of the large-range fishing activities.
Disclosure of Invention
In order to solve the technical problems, the invention provides a fishing vessel trajectory tracking and fishing situation analysis system which can help a user to plan the navigation trajectory of a fishing vessel better and enables fishing activities to be more efficient.
The fishing boat track tracking and fishing situation analyzing system comprises a fishing boat track generating module, a fish situation obtaining module and a ship and fish school comparing module, wherein the fishing boat track generating module and the fish situation obtaining module are in data transmission connection with the ship and fish school comparing module.
The invention relates to a fishing boat track tracking and fishing situation analysis system.A fishing boat track positioning method is based on an Automatic Identification System (AIS).
The fishing vessel trajectory tracking and fishing situation analysis system of the invention is based on artificial intelligence.
According to the fishing boat trajectory tracking and fishing situation analysis system, ship information can be searched and obtained from related websites.
Compared with the prior art, the invention has the beneficial effects that: the track data of the current ship mainly comes from an automatic ship identification system (AIS), the AIS data relate to dynamic information such as the position (longitude and latitude), the navigational speed and the course of the ship, and static information such as the name, the type, the MMSI, the IMO, the call sign, the ship's book, the ship width and the draught of the ship, the original track data may have a condition of long recorded acquisition time interval, so that part of data is in a missing state, the missing value processing accuracy is not high due to the fact that the missing data amount is large in the track with the overlong interval time, the missing value processing is performed on the adjacent data with the time interval exceeding 24h temporarily, and the missing value processing of an interpolation method is performed on the adjacent data with the time interval difference exceeding 20 minutes. Then converting track coordinate data, orderly recombining discrete coordinate points, converting the discrete coordinate points into a ship navigation track line for geographical feature storage, creating conditions for geographical analysis application of the ship navigation track, then performing feature extraction, taking course change points capable of reflecting ship track changes and speed change points capable of reflecting ship motion state changes as track data feature points, setting a threshold value, setting a feature point extraction threshold value according to actual analysis requirements, storing original track points with course change rates exceeding the course threshold value and original track points with speed change rates exceeding the speed threshold value into a track feature database as track feature points, performing spatial storage by taking the ship track as an independent individual object, and dynamically displaying the ship track on a map bottom layer in a point-line connection mode according to the passage of time; firstly, enough fishing boat sailing tracks of fishing activities are collected, and the sailing time, position, type and other information of the boat are recorded and made into structured data. The well-made data set is then divided into two parts, 70% of which are training sets and 30% of which are testing sets. Inputting the training set into a neural network model for training, inputting the data of the test set into the model after the model training is finished, checking the accuracy of the model classification, by debugging each hyper-parameter of the neural network, the accuracy of model classification is improved as much as possible, collecting the sailing tracks of most ships in the researched range after the model classification accuracy reaches the standard, classifying the sailing tracks by using the trained model, if the ship tracks are judged to be fishing activities, the ship positions are lightened on the bottom layer of the map and are dynamically displayed according to time points, when all ships are completely displayed on the map, the more bright spots indicate that the fishing activities of the fishing ships in the area are more intensive, therefore, the fish shoal information can be judged to be the area where the fish shoals are gathered, and the fish information can be dynamically displayed on the bottom layer of the map according to the time lapse through the method; the ship track layer generated by the fishing boat track generation module and the fishing situation layer generated by the fishing situation acquisition module are superposed together, and dynamic demonstration is carried out according to the time lapse, so that whether the sailing track of the fishing boat moves in the distribution range of fish schools can be clearly observed, and the fishing boat fishing path can be better analyzed by a user.
Drawings
FIG. 1 is a sample fishing activity area;
FIG. 2 is a sample ship trajectory;
in the drawings, the reference numbers: 1. land; 2. an ocean; 3. fishing the activity area; 4. the trajectory of the ship.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Example (b): as shown in fig. 1 and 2
Step a, acquiring the sailing track information of the ship in the corresponding time range from the AIS data supply related website according to the ship call number and the sailing time provided by the user.
And b, carrying out missing value processing on the data. And (3) temporarily not processing adjacent data with time intervals exceeding 24h, and processing missing values of an interpolation method between the adjacent data with the time intervals differing by more than 20 minutes.
And c, carrying out feature extraction on the ship track.
And d, performing space speech storage on the ship track by methods of extracting geographic coordinates, performing coordinate projection conversion, creating and storing a space data point object, creating and storing a space data line object and the like.
And e, visually displaying the time node and the latitude and longitude coordinates provided by the ship navigation track information on the bottom layer of the map in a point form in the map, and connecting the points through lines.
And f, acquiring a large amount of ship information from related websites, recording the information such as the running track, running time and the like of the ship, marking the samples which carry out fishing activities as 1, marking the samples which do not carry out fishing activities as 0, and making the samples into structured data serving as a data set of the neural network.
And g-1, randomly extracting 70% of samples in the data set as a training set, and taking the rest samples as a testing set.
And g-2, inputting the training set into the neural network model for training, and inputting the test set into the trained model for testing the classification precision after the training is finished. And judging whether the precision meets the requirement.
And g-3, if the precision meets the requirement, carrying out the next step. And if the precision does not meet the requirement, returning to the previous step after debugging the hyper-parameters of the model.
And h-1, collecting ship tracks of the researched area for several voyage times as much as possible, making the ship tracks into structured data, inputting the structured data into a trained model, and judging fishing behaviors of the trained model.
And h-2, if the ship track is judged to be fishing activity, marking the ship track in a point mode on the bottom layer of the map, and dynamically displaying according to the time point. When all ships are completely displayed on the map, the more densely dotted area indicates that the fishing activities of the fishing boat in the area are more densely covered, and thus it can be judged as an area where fish schools are gathered.
Step i, superposing the ship track layer required by the user and the fishing activity layer, analyzing whether the ship track is matched with the intensive fishing activity area or not, and analyzing the reason.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (4)
1. The utility model provides a fishing boat orbit is tracked and fishing feelings analytic system, its characterized in that, includes fishing boat orbit generation module, fish feelings acquisition module and ship and shoal of fish contrast module, fishing boat orbit generation module and fish feelings acquisition module all with ship and shoal of fish contrast module data transmission connection.
2. The fishing vessel trajectory tracking and fishing situation analysis system of claim 1, wherein the fishing vessel trajectory location method is based on an Automatic Identification System (AIS).
3. The fishing vessel trajectory tracking and fishing situation analysis system of claim 2, wherein the fishing activity determination method is based on artificial intelligence.
4. The fishing vessel trajectory tracking and fishing situation analysis system of claim 3, wherein vessel information is available for retrieval from a related website.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010437887.6A CN111583052A (en) | 2020-05-21 | 2020-05-21 | Fishing boat trajectory tracking and fishing situation analysis system |
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| CN202010437887.6A CN111583052A (en) | 2020-05-21 | 2020-05-21 | Fishing boat trajectory tracking and fishing situation analysis system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112418521A (en) * | 2020-11-23 | 2021-02-26 | 青岛科技大学 | Short-term marine fish school and fish quantity prediction method |
| CN113341407A (en) * | 2021-06-02 | 2021-09-03 | 中国水产科学研究院南海水产研究所 | Fishing tracking system and method based on radar detection |
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| CN103714717A (en) * | 2012-10-09 | 2014-04-09 | 北京东方道迩信息技术股份有限公司 | Method for dynamically tracing ships and identifying behavior patterns of ships based SAR data |
| CN104899263A (en) * | 2015-05-22 | 2015-09-09 | 华中师范大学 | Ship trajectory mining, analysis and monitoring method based on specific region |
| CN109685086A (en) * | 2017-10-18 | 2019-04-26 | 中电科海洋信息技术研究院有限公司 | The recognition methods of marine ships job state, device, equipment and storage medium |
| CN109919113A (en) * | 2019-03-12 | 2019-06-21 | 北京天合睿创科技有限公司 | Ship monitoring method and system and harbour operation prediction technique and system |
| US20190263484A1 (en) * | 2018-02-27 | 2019-08-29 | Benjamin Michael Wilson | Remote controlled unmanned fishing boat |
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2020
- 2020-05-21 CN CN202010437887.6A patent/CN111583052A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714717A (en) * | 2012-10-09 | 2014-04-09 | 北京东方道迩信息技术股份有限公司 | Method for dynamically tracing ships and identifying behavior patterns of ships based SAR data |
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| US20190263484A1 (en) * | 2018-02-27 | 2019-08-29 | Benjamin Michael Wilson | Remote controlled unmanned fishing boat |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112418521A (en) * | 2020-11-23 | 2021-02-26 | 青岛科技大学 | Short-term marine fish school and fish quantity prediction method |
| CN112418521B (en) * | 2020-11-23 | 2023-02-24 | 青岛科技大学 | Short-term marine fish school and fish quantity prediction method |
| CN113341407A (en) * | 2021-06-02 | 2021-09-03 | 中国水产科学研究院南海水产研究所 | Fishing tracking system and method based on radar detection |
| CN113341407B (en) * | 2021-06-02 | 2024-02-06 | 中国水产科学研究院南海水产研究所 | Fishery fishing tracking system and method based on radar detection |
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