CN116343453B - Ship positioning information monitoring and early warning method based on big data and related equipment - Google Patents

Ship positioning information monitoring and early warning method based on big data and related equipment Download PDF

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CN116343453B
CN116343453B CN202310601156.4A CN202310601156A CN116343453B CN 116343453 B CN116343453 B CN 116343453B CN 202310601156 A CN202310601156 A CN 202310601156A CN 116343453 B CN116343453 B CN 116343453B
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ship
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electronic fence
parameter model
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CN116343453A (en
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严镔
邵长高
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Guangzhou Marine Geological Survey Sanya Institute Of South China Sea Geology
Guangzhou Marine Geological Survey
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Abstract

The invention discloses a ship positioning information monitoring and early warning method and related equipment based on big data, which relate to the technical field of ship automatic identification systems, and the implementation method comprises the following steps: acquiring AIS data of a ship; drawing a spatial position point of the ship according to the AIS data; establishing a ship parameter model according to the space position points of the ship; and monitoring the behavior of the ship according to the ship parameter model and carrying out early warning. According to the method, through accumulation of historical data, data mining and space analysis are utilized, a ship positioning information abnormality warning model is established according to characteristics of ship positioning track point characteristics, ship use, ship size and the like, and abnormality of ship behaviors and areas where the ship behaviors and the abnormal ship positioning information are located are timely identified by the model and early warning is sent out.

Description

Ship positioning information monitoring and early warning method based on big data and related equipment
Technical Field
The invention relates to the technical field of automatic ship identification systems, in particular to a ship positioning information monitoring and early warning method based on big data and related equipment.
Background
The automatic ship identification system (AIS system) utilizes a positioning device to transmit relevant information (such as GPS, beidou and the like) such as positioning in real time, and obtains positioning data (dynamic data: position, time, speed and the like) through a shore-based receiving station, a ship-borne receiving station and satellites. The positioning information is necessary information for ensuring normal navigation and safety of the ship. However, the information sent by the signal transmitting system of the marine vessel such as AIS can be tampered and deleted, which results in imperfect or unrealistic positioning information, for example: the cargo ships are marked as fishing ships or are marked as other types, particularly the large ships are marked as small ships, so that great challenges are brought to ship navigation safety, sea area safety and supervision, and no good method is available at present how to obtain real ship information.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a ship positioning information monitoring and early warning method and related equipment based on big data.
In order to achieve the above purpose, the present invention provides the following technical solutions:
in a first aspect, the present invention provides a method for monitoring and early warning of abnormality of ship positioning information based on big data, comprising:
acquiring AIS data of a ship;
drawing a spatial position point of the ship according to the AIS data;
establishing a ship parameter model according to the space position points of the ship;
and monitoring the behavior of the ship according to the ship parameter model and carrying out early warning.
In a second aspect, the present invention provides a ship positioning information monitoring and abnormality early warning system based on big data, comprising:
a data acquisition unit for acquiring AIS data of the ship;
a processing unit for performing the steps of:
drawing a spatial position point of the ship according to the AIS data;
establishing a ship parameter model according to the space position points of the ship;
and the early warning unit is used for monitoring the behavior of the ship according to the ship parameter model and carrying out early warning.
An electronic device comprising a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to realize the ship positioning information monitoring and abnormality early warning method based on big data as described above.
A computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set loaded and executed by a processor to implement the big data based vessel positioning information monitoring and anomaly early warning method as described above.
Compared with the prior art, the invention has the beneficial effects that: the invention changes the thought of analyzing the abnormal ship early warning behaviors by means of a single source model in the past, establishes a perfect ship abnormal behavior early warning method by utilizing big data and data mining analysis and rapidly and timely discovers the abnormality of ship positioning information. Compared with the traditional mode, the method has the advantages that the suspicious behavior of the ship is found more accurately and reliably, and particularly, the identification probability of camouflage ships which look like normal sailing is higher.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for monitoring and early warning of abnormality of ship positioning information based on big data in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Examples:
it should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
According to the embodiment of the invention, through accumulation of historical data, data mining and space analysis are utilized, a ship positioning information abnormality warning model is established according to characteristics of ship positioning track points, ship use, size and the like, abnormality of a ship behavior and a region where the ship behavior is located is timely identified by the model, early warning is sent, and relevant departments and personnel take measures to deal with the abnormality.
Referring to fig. 1, a method for monitoring and pre-warning abnormality of ship positioning information based on big data specifically includes the following steps:
step 1: and acquiring AIS data of the ship.
In this step, the spatial location data includes static data and dynamic data. The ship-borne positioning transmitting equipment stores, acquires and marks the static and dynamic information of the ship foundation, transmits related information through the satellite and the shore-based receiver, is used for other ships and supervision departments to acquire real-time information of the ship, and determines the position and the running state of the ship so as to ensure the smoothness and the safety of a route and ensure the legal compliance of marine or river transportation. Therefore, the normal opening and operation of the shipborne equipment and the capability of acquiring relevant data in real time are important supervision means and basis for the ship. A general positioning system is called an automatic ship identification system (AIS system), and refers to a novel navigation aid system applied to marine safety and communication between a ship and a shore, and between the ship and the ship. The relevant positioning information can be obtained through GPS, beidou and the like. The ship navigation track of a certain ship can be known through collection, analysis and arrangement of AIS data, the behavior characteristics, particularly the space traveling rule, of the ship can be obtained, and then a ship motion space model is built.
Step 2: and drawing the spatial position point of the ship according to the AIS data.
In this step, the implementation may map the spatial location points of the ship according to the following steps:
extracting all a certain amount of historical data of a certain ship according to the positioning data, and marking the positioning transmitting position points of the ship on a chart according to the time sequence; separating a berthing port point from a departure track in the coordinate points according to the positions; and classifying the navigation position points according to the purposes and the sizes to draw track points or routes. And (3) establishing different ship motion space tracks for ships (fishing boats, pleasure boats, cargo ships, passenger ships, scientific investigation ships and the like) in the fixed working area.
Step 3: and building a ship parameter model according to the space position points of the ship.
In this step, the implementation may respectively build a ship parameter model:
ship static data analysis subclass-ship use type/ship size
The static data is basic data of ships, the application and the size of the basic data determine the moving range and the space moving characteristics of the ships, and the main data parameters of the static data of the ships are the basic static data of the types (such as fishing boats, cargo ships, passenger ships, pleasure boats, scientific research ships and the like) and the sizes (small, medium and large, and are specifically set based on the parameters). Wherein,,indicating the type of ship>Representing the size of the vessel.
Ship dynamic data subclass-position/speed
Position and speed are important features of the ship's activities and are also important bases for monitoring its activities. The location is the distance from the nearest land. Wherein the method comprises the steps ofIndicating the position of the ship>Representing the speed of the vessel.
Ship time subclass
The time subclass of the ship is different from the time of the dynamic data subclass of the ship, and the scale is enlarged to the day, month, year and the like to find different rules. Dividing the time according to the timeThe definition is given by =season, month, day or night, etc. The formula is->Wherein->Indicating daytime and nighttime straddling +.>Representing cross month->Representing a cross quarter.
By usingRepresenting the time period departure time point, +.>Representing time period harborTime points. According to->The seasons, months, days or night of the time period are intercepted to define the sailing characteristics. If the navigation time is long, the attribute is defined by taking 12 hours in daytime or 12 hours in night as a period and taking more time.
Electronic fence subclass
Drawing a ship motion space regionAnd drawing track points of a certain time period on a chart, and building different types of ship electronic fence models according to different purposes. />Is the center point of the electronic fence, is->For track point set in electronic fence, < +.>Is a set of track points outside the electronic fence. />The value is +.>And->Is a ratio of (2). I.e. < ->。
Ship space environment association subclass
The related things are spatially related according to the type of the ship, such as destination, route, fishing ground, mining area, etc. Related information and surrounding important object information are acquired from static data to build a management model, wherein,for destination information->For associating context information->Is the closest distance of the vessel from the associated environment.
Step 4: and monitoring the behavior of the ship according to the ship parameter model and carrying out early warning.
In the step, a ship early warning neural network model algorithm is established according to the following formula:. Wherein: />Is the weight.
The ship behavior monitoring system is used for monitoring the behavior of the ship according to the ship parameter model and carrying out neural network analysis and early warning. Supplementary data was collected, 80% of the data was used for modeling, 20% for validation, and model parameters were modified to continuously improve accuracy.
Based on the same inventive concept, the embodiment of the invention also provides an offshore measurement ship monitoring and early warning system, which comprises: the system comprises a data acquisition unit, a processing unit and an early warning unit, wherein the data acquisition unit is used for acquiring AIS data of a ship; the processing unit is used for executing the following steps: drawing a spatial position point of the ship according to the AIS data; establishing a ship parameter model according to the space position points of the ship; and the early warning unit is used for monitoring the behavior of the ship according to the ship parameter model and carrying out neural network analysis and early warning.
Because the system is a system corresponding to the abnormal early warning method based on the ship positioning information monitoring of the big data, and the principle of solving the problem of the system is similar to that of the method, the implementation of the system can refer to the implementation process of the method embodiment, and the repetition is omitted.
Based on the same inventive concept, the embodiment of the invention also provides an electronic device, which comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor so as to realize the ship positioning information monitoring and abnormality early warning method based on big data.
It is understood that the Memory may include random access Memory (Random Access Memory, RAM) or Read-Only Memory (RAM). Optionally, the memory includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory may be used to store instructions, programs, code sets, or instruction sets. The memory may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the various method embodiments described above, and the like; the storage data area may store data created according to the use of the server, etc.
The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various portions of the overall server, perform various functions of the server, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in memory, and invoking data stored in memory. Alternatively, the processor may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU) and a modem etc. Wherein, the CPU mainly processes an operating system, application programs and the like; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor and may be implemented by a single chip.
Because the electronic device is the electronic device corresponding to the abnormal early warning method for monitoring the ship positioning information based on the big data, and the principle of solving the problem of the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the method embodiment, and the repetition is omitted.
Based on the same inventive concept, the embodiment of the invention further provides a computer readable storage medium, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the storage medium, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by a processor to realize the ship positioning information monitoring and abnormality early warning method based on big data.
Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the above embodiments may be implemented by a program that instructs associated hardware, the program may be stored in a computer readable storage medium including Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disk Memory, magnetic disk Memory, tape Memory, or any other medium that can be used for carrying or storing data that is readable by a computer.
Because the storage medium is the storage medium corresponding to the method for monitoring the ship positioning information based on the big data and pre-warning the abnormality, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the method embodiment, and the repetition is omitted.
In some possible implementations, the aspects of the method of the embodiments of the present invention may also be implemented in the form of a program product comprising program code for causing a computer device to carry out the steps of the big data based ship positioning information monitoring and anomaly early warning method according to the various exemplary embodiments of the present application as described in the present specification when the program product is run on the computer device. Wherein executable computer program code or "code" for performing the various embodiments may be written in a high-level programming language such as C, C ++, c#, smalltalk, java, javaScript, visual Basic, structured query language (e.g., act-SQL), perl, or in a variety of other programming languages.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the essence of the present invention are intended to be included within the scope of the present invention.

Claims (4)

1. The ship positioning information monitoring and abnormality early warning method based on big data is characterized by comprising the following steps of:
acquiring AIS data of a ship;
drawing a spatial position point of the ship according to the AIS data;
establishing a ship parameter model according to the space position points of the ship;
monitoring the behavior of the ship and performing early warning according to the ship parameter model, wherein,
the ship parameter model comprises ship time subclasses which are established according to the time period starting time point and the time period departure time point and are established according to time=season, month, day or night; since the normal AIS uses UTC time, the conversion formula is +.>Wherein->Indicating daytime and nighttime straddling +.>Representing cross month->Represents crossing quarters, with +.>Representing the time period departure time point, +.>Representing a time slot departure time point;
the ship parametric model comprises a ship static data analysis subclass, which is built according to a ship type and a ship size, wherein,indicating the type of ship>Representing the size of the ship;
the vessel parameter model comprises vessel dynamic data subclasses, which are established according to the position and the speed of the vessel, wherein,indicating the position of the ship>Representing the speed of the ship;
the ship parameter model comprises an electronic fence sub-class which is established according to an electronic fence center point, an electronic fence inner track point set and an electronic fence outer track point set,is the center point of the electronic fence, is->For track point set in electronic fence, < +.>For the set of track points outside the electronic fence, < +.>The value is +.>And->Ratio of (2), i.e.)>；
The ship parameter model comprises ship space environment association subclasses, and the ship space environment association subclasses are used for associating environment information according to destination informationAnd the closest distance of the vessel from the associated environment, wherein,in order to provide the destination information,for associating context information->The closest distance of the ship from the associated environment;
early warning is carried out according to the following method:
building a ship early warning neural network model algorithm according to the following formula:
in the method, in the process of the invention,as the weight, when the abnormal value appears, an early warning is sent out.
2. The big data based ship positioning information monitoring and abnormality pre-warning method according to claim 1, characterized in that the spatial position points of the ship are plotted according to the following steps: extracting historical data of a certain ship from the AIS data; marking the positioning transmitting position points of the ship on a chart according to a time sequence, wherein the berthing port points and the departure position point tracks in the coordinate points need to be distinguished; and classifying and drawing the space position points according to the track of the navigation position points and the application and the size of the ship.
3. The utility model provides a ship positioning information monitoring and unusual early warning system based on big data which characterized in that includes:
a data acquisition unit for acquiring AIS data of the ship;
a processing unit for performing the steps of:
drawing a spatial position point of the ship according to the AIS data;
establishing a ship parameter model according to the space position points of the ship;
an early warning unit for monitoring the behavior of the ship according to the ship parameter model and performing neural network analysis and early warning, wherein,
the ship parameter model comprises ship time subclasses which are established according to the time period starting time point and the time period departure time point and are established according to time=season, month, day or night; since the normal AIS uses UTC time, the conversion formula is +.>Wherein->Indicating daytime and nighttime straddling +.>Representing cross month->Represents crossing quarters, with +.>Representing the time period departure time point, +.>Representing a time slot departure time point;
the ship parametric model comprises a ship static data analysis subclass, which is built according to a ship type and a ship size, wherein,indicating the type of ship>Representing the size of the ship;
the vessel parameter model comprises vessel dynamic data subclasses, which are established according to the position and the speed of the vessel, wherein,indicating the position of the ship>Representing the speed of the ship;
the ship parameter model comprises an electronic fence sub-class which is established according to an electronic fence center point, an electronic fence inner track point set and an electronic fence outer track point set,is the center point of the electronic fence, is->For track point set in electronic fence, < +.>For the set of track points outside the electronic fence, < +.>The value is +.>And->Ratio of (2), i.e.)>；
The ship parameter model comprises a ship space environment gatewayA alliance class, the marine space environment association subclass being established according to destination information, association environment information, and a closest distance of a marine from the association environment, wherein,in order to provide the destination information,for associating context information->The closest distance of the ship from the associated environment;
building a ship early warning neural network model algorithm according to the following formula:
in the method, in the process of the invention,as the weight, when the abnormal value appears, an early warning is sent out.
4. An electronic device, characterized in that the electronic device comprises a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor, so as to implement the big data based ship positioning information monitoring and abnormality early warning method according to any one of claims 1 to 2.
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Publication number Priority date Publication date Assignee Title
WO2004053814A1 (en) * 2002-12-10 2004-06-24 Defence Science & Technology Agency Adaptive collision avoidance advisory system
CN108831194A (en) * 2016-05-12 2018-11-16 福建北斗星河通信有限公司 The highly-safe ship collision-proof method and system based on internet AIS
CN114462519A (en) * 2022-01-25 2022-05-10 中国船舶重工集团公司第七二四研究所 Ship abnormal behavior identification method based on sailing track characteristics
CN114550501A (en) * 2022-04-20 2022-05-27 迪泰（浙江）通信技术有限公司 AIS-based ship danger early warning system and method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004053814A1 (en) * 2002-12-10 2004-06-24 Defence Science & Technology Agency Adaptive collision avoidance advisory system
CN108831194A (en) * 2016-05-12 2018-11-16 福建北斗星河通信有限公司 The highly-safe ship collision-proof method and system based on internet AIS
CN114462519A (en) * 2022-01-25 2022-05-10 中国船舶重工集团公司第七二四研究所 Ship abnormal behavior identification method based on sailing track characteristics
CN114550501A (en) * 2022-04-20 2022-05-27 迪泰（浙江）通信技术有限公司 AIS-based ship danger early warning system and method

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