False AIS ship trajectory detection method and system based on time slot conflict detection, and storage medium

Abstract

The invention provides a false AIS ship trajectory detection method and system based on time slot conflict detection and a storage medium. The method comprises the following steps: processing AIS data to be analyzed, filtering out AIS data related to a ship to be analyzed, and storing the AIS data in a database; arranging AIS data related to the to-be-analyzed ship according to the corresponding ship; calculating time slot switching times of the to-be-analyzed ship in the to-be-analyzed time period and specific time slots with time slot conflicts; a specific environment is simulated, and correction parameters are trained; and counting the time slot conflict condition of the AIS dynamic message actually broadcasted by the target ship, and analyzing the probability that the ship trajectory is a false trajectory. In order to solve the problem that a false broadcasting station cannot master other ship dynamic message information which can be received by a ship during actual navigation, that is, correct SOTDMA time slot access cannot be carried out, some false AIS ship trajectories which are disguised perfectly are effectively detected through a false AIS ship trajectory detection method based on time slot conflict detection, and the false AIS ship trajectory detection method based on time slot conflict detection is improved. And the purpose of accurately identifying the illegal ship is achieved.

Description

Technical Field

[0001] The present invention relates to the field of maritime communication technology, and in particular to a false AIS ship track detection method, system and storage medium based on time slot conflict detection. Background Art

[0002] Since the Automatic Identification System (AIS) became popular, seagoing ships must be equipped with AIS systems. Law enforcement agencies have deployed AIS base stations in coastal areas to monitor the track information of each ship in real time and detect ship violations. Therefore, some ships will turn off the broadcast of their own AIS dynamic information when performing illegal operations. At the same time, they will broadcast false AIS ship tracks on the shore to forge complete ship tracks to evade real-time monitoring by AIS base stations, such as Figure 3 As shown in the figure, the dotted circle represents the AIS data receiving range of each device; the illegal ship turns off the AIS dynamic message when arriving at point A, and the false broadcasting station starts broadcasting the false route. After completing the illegal operation, it starts the dynamic message broadcast when arriving at point B, and the false broadcasting station stops broadcasting the false route to create a complete route track to evade big data analysis inspection.

[0003] In response to the above situation, law enforcement agencies used signal strength detection, remote data reception detection, ship dynamic data rationality analysis (analysis of navigation speed, navigation direction, number of receiving ships, SOTDMA status and other parameters) and other means to detect false AIS ship tracks. Although these methods can detect some false AIS ship track broadcasting activities, some false broadcasting stations with relatively high technical levels can also forge more realistic AIS dynamic messages to broadcast false navigation tracks to evade detection by dynamically changing the transmission power and broadcasting by multiple false broadcasting stations on the spot. Summary of the invention

[0004] According to the technical problems raised above, a false AIS ship track detection method based on time slot conflict detection is provided. The present invention is based on AIS big data analysis. The false broadcast station cannot grasp the dynamic message information of other ships that can be received when the ship is actually sailing, that is, it cannot perform correct SOTDMA time slot access. The false AIS ship track detection method based on time slot conflict detection can effectively detect some false AIS ship tracks that are disguised as real, so as to achieve the purpose of accurately identifying illegal ships.

[0005] The technical means adopted by the present invention are as follows:

[0006] A false AIS ship track detection method based on time slot conflict detection, comprising:

[0007] Process the AIS data to be analyzed, filter out the AIS data related to the ship to be analyzed, and store it in the database;

[0008] According to the corresponding ships, sort out the AIS data related to the ships to be analyzed;

[0009] Calculate the number of time slot switching times of the ship to be analyzed during the time period to be analyzed and the specific time slots where time slot conflicts occur;

[0010] Simulate the specific environment and correct the parameters during training;

[0011] The time slot conflicts when the target ship actually broadcasts AIS dynamic messages are counted, and the probability that the ship's trajectory is a false trajectory is analyzed.

[0012] Further, the processing of the AIS data to be analyzed, filtering out the AIS data related to the ship to be analyzed, and storing the data in a database specifically includes:

[0013] Obtain AIS data within the period to be analyzed from the data center;

[0014] Determine the receiving range of the target ship according to the receiving ship number field in the SOTDMA status information in the AIS dynamic message;

[0015] Filter out the ship data within the receiving range of the target ship during the SOTDMA timeout period;

[0016] Sort out the time slots actually used for broadcasting by the ships to be analyzed.

[0017] Furthermore, the AIS data related to the ship to be analyzed is sorted according to the corresponding ship, specifically including:

[0018] The ship data within the receiving range of the target ship during the SOTDMA timeout period are sorted and selected according to the ship MMSI number.

[0019] Further, the calculation of the number of time slot switching of the ship to be analyzed within the time period to be analyzed and the specific time slot where the time slot conflict occurs specifically includes:

[0020] Based on the AIS data of the ship to be analyzed, the number of changes is counted, with the SOTDMA time slot timeout being reduced to 0 as a unit.

[0021] Furthermore, the statistical target ship actually broadcasts the AIS dynamic message and the time slot conflict occurs, and analyzes the probability that the ship track is a false track, specifically including:

[0022] Assume that the number of times the ship to be analyzed selects a time slot in the period to be analyzed is t, each SOTDMA timeout is recorded as 0, and the number of ships that have time slot conflicts is recorded as n;

[0023] Assume that the number of time slot conflicts between the ship and the ship to be analyzed is m, and the distance between the two ships each time a time slot conflict occurs is S1, S2, ···, S m , calculate the time slot conflict parameter P for each ship that has a time slot conflict with the ship to be analyzed, and the calculation formula is as follows:

[0024]

[0025] Among them, a and b are correction parameters, which are affected by base station layout, channel distribution and channel load. The channel state of the time period to be analyzed is simulated by channel load, base station layout and channel distribution, and false ships are simulated to train the correction parameters and obtain the values ​​of the correction parameters.

[0026] Based on the calculation formula of the time slot conflict parameter P, the conflict parameters of all relevant ships are calculated and recorded as P1, P2, ···, P k ;

[0027] Based on the conflict parameters of all relevant ships, the false ship trajectory probability Q of the target ship is calculated as follows:

[0028]

[0029] By calculating the false ship trajectory probability Q of the target ship, it is determined whether the ship's navigation trajectory is a false ship navigation trajectory.

[0030] The present invention also provides a false AIS ship track detection system based on time slot conflict detection, which is implemented based on the false AIS ship track detection method based on time slot conflict detection, comprising: a big data processing unit, a ship list processing unit, a ship data processing unit to be analyzed, an environment simulation unit and a conflict analysis unit, wherein:

[0031] The big data processing unit is used to process the AIS data to be analyzed, filter out the AIS data related to the ship to be analyzed, and store it in the database;

[0032] The ship list processing unit is used to sort out the AIS data related to the ship to be analyzed according to the corresponding ship;

[0033] The ship data processing unit to be analyzed is used to calculate the number of time slot switching times of the ship to be analyzed within the time period to be analyzed and the specific time slots where time slot conflicts occur;

[0034] The environment simulation unit is used to simulate a specific environment and train correction parameters;

[0035] The conflict analysis unit is used to count the time slot conflicts that occur when the target ship actually broadcasts the AIS dynamic message, and analyze the probability that the ship's trajectory is a false trajectory.

[0036] The present invention also provides a storage medium, which includes a stored program, wherein when the program is run, the false AIS ship track detection method based on time slot conflict detection is executed.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] The present invention provides a false AIS ship track detection method based on time slot conflict detection. Based on the SOTDMA time slot access algorithm adopted by AIS dynamic messages and the original data of AIS base stations that cannot be obtained by illegal ships, the behavior of false ship navigation tracks that transmit perfect data near the shore of illegal ships can be identified.

[0039] Based on the above reasons, the present invention can be widely promoted in fields such as marine communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 The figure is a flow chart of the method of the present invention.

[0042] Figure 2 A system framework diagram provided for an embodiment of the present invention.

[0043] Figure 3 An example of an environment provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme 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.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0046] like Figure 1 As shown, the present invention provides a false AIS ship track detection method based on time slot conflict detection, comprising:

[0047] S1. Process the AIS data to be analyzed, filter out the AIS data related to the ship to be analyzed, and store it in the database;

[0048] S2. Arrange the AIS data related to the ship to be analyzed according to the corresponding ship;

[0049] S3, calculating the number of time slot switching times of the ship to be analyzed within the time period to be analyzed and the specific time slots where time slot conflicts occur;

[0050] S4, simulate the specific environment and correct the parameters at the training place;

[0051] S5. Count the time slot conflicts that occur when the target ship actually broadcasts the AIS dynamic message, and analyze the probability that the ship's trajectory is a false trajectory.

[0052] In specific implementation, as a preferred embodiment of the present invention, step S1, processing the AIS data to be analyzed, filtering out the AIS data related to the ship to be analyzed, and storing the database, specifically includes:

[0053] S11, obtaining AIS data within the period to be analyzed from the data center;

[0054] S12, defining the receiving range of the target ship according to the receiving ship quantity field in the SOTDMA status information in the AIS dynamic message;

[0055] S13, filtering out the ship data within the receiving range of the target ship during the SOTDMA timeout period for use by the ship list processing unit;

[0056] S14. Sort out the time slots actually used for broadcasting by the ship to be analyzed, for use by the data processing unit of the ship to be analyzed.

[0057] In specific implementation, as a preferred embodiment of the present invention, step S2, sorting out AIS data related to the ship to be analyzed according to the corresponding ship, specifically includes:

[0058] The ship data within the receiving range of the target ship during the SOTDMA timeout period are sorted and selected according to the ship MMSI number.

[0059] In specific implementation, as a preferred embodiment of the present invention, step S3, calculating the number of time slot switching times of the ship to be analyzed in the time period to be analyzed and the specific time slot where the time slot conflict occurs, specifically includes:

[0060] Based on the AIS data of the ship to be analyzed, the number of changes is counted, with the SOTDMA time slot timeout being reduced to 0 as a unit.

[0061] In the specific implementation, as a preferred embodiment of the present invention, step S4 counts the time slot conflicts that occur when the target ship actually broadcasts the AIS dynamic message, and analyzes the probability that the ship's trajectory is a false trajectory, which specifically includes:

[0062] S41, assuming that the number of times the ship to be analyzed selects a time slot in the time period to be analyzed is t, each SOTDMA timeout is 0 and recorded as one, and the number of ships that have time slot conflicts is recorded as n;

[0063] S42, assuming that the number of time slot conflicts between the ship and the ship to be analyzed is m, and the distance between the two ships each time a time slot conflict occurs is S1, S2, . . . , S m , calculate the time slot conflict parameter P for each ship that has a time slot conflict with the ship to be analyzed, and the calculation formula is as follows:

[0064]

[0065] Among them, a and b are correction parameters, which are affected by base station layout, channel distribution and channel load. The channel state of the time period to be analyzed is simulated by channel load, base station layout and channel distribution, and fake ships are simulated to train the correction parameters (the training goal is that the Q value obtained by the fake ship and the real ship is significantly different, and the Q value of the fake ship is above 60%) to obtain the value of the correction parameter;

[0066] S43. Based on the calculation formula of the time slot conflict parameter P, the conflict parameters of all relevant ships are calculated, which are recorded as P1, P2, ···, P k ;

[0067] S44. Based on the conflict parameters of all relevant ships, the false ship trajectory probability Q of the target ship is calculated, and the calculation formula is as follows:

[0068]

[0069] By calculating the false ship trajectory probability Q of the target ship, it is determined whether the ship's navigation trajectory is a false ship navigation trajectory.

[0070] like Figure 2 As shown, corresponding to the false AIS ship track detection method based on time slot conflict detection in the present application, the present application also provides a false AIS ship track detection system based on time slot conflict detection, including: a big data processing unit, a ship list processing unit, a ship data processing unit to be analyzed, an environment simulation unit and a conflict analysis unit, wherein:

[0071] The big data processing unit is used to process the AIS data to be analyzed, filter out the AIS data related to the ship to be analyzed, and store it in the database;

[0072] The ship list processing unit is used to sort out the AIS data related to the ship to be analyzed according to the corresponding ship;

[0073] The ship data processing unit to be analyzed is used to calculate the number of time slot switching times of the ship to be analyzed within the time period to be analyzed and the specific time slots where time slot conflicts occur;

[0074] The environment simulation unit is used to simulate a specific environment and train correction parameters;

[0075] The conflict analysis unit is used to count the time slot conflicts that occur when the target ship actually broadcasts the AIS dynamic message, and analyze the probability that the ship's trajectory is a false trajectory. In this embodiment, the conflict analysis unit calculates the possibility that the navigation trajectory reported by the analyzed ship during the time period to be analyzed is a false trajectory for the user based on the results obtained by the above units.

[0076] As for the embodiments of the present invention, since they correspond to the embodiments above, the description is relatively simple. For the relevant similarities, please refer to the description of the above embodiments, which will not be described in detail here.

[0077] The embodiment of the present application further discloses a computer-readable storage medium, which stores a computer instruction set. When the computer instruction set is executed by a processor, the false AIS ship track detection method based on time slot conflict detection as provided in any of the above embodiments is implemented.

[0078] Example

[0079] like Figure 3As shown in the figure, the law enforcement department deploys 3 AIS base stations on the shore to cover the coastal area and receive AIS data. When the illegal ship enters position A, it deviates from the route and turns off the dynamic message broadcast. At the same time, the false broadcast station on the shore begins to broadcast false AIS dynamic information to simulate the navigation track of the illegal ship. When the ship completes the illegal operation and returns to position B, the false broadcast station is turned off again and the normal dynamic message broadcast of the ship is turned on. Now the law enforcement personnel need to analyze the navigation track of the illegal ship (assuming MMSI number 999999999), and the analysis period is from 0:00 to 12:00.

[0080] The big data processing unit obtains the AIS data between 0:00 and 12:00 from the data center and filters the AIS data in the following way:

[0081] First, parse all the dynamic data of ships with MMSI number 9999999999, and according to the number of received ships reported in the ship dynamic data, draw a circle with the latitude and longitude information reported in the dynamic data to cover the same number of ships, pass the AIS data of all covered ships to the ship list processing unit, and pass the ship data with MMSI number 9999999999 to the ship data processing unit to be analyzed.

[0082] The ship list processing unit sorts the relevant AIS data filtered by the big data processing unit according to the ship MMSI number for use in the conflict analysis part. Here it is assumed that there are 50 ships, each of which has 4 time slot conflicts with the ship to be analyzed. The distance between the two ships is 1 nautical mile, 3 nautical miles, 7 nautical miles, and 15 nautical miles each time a time slot conflict occurs.

[0083] The data processing unit of the ship to be analyzed organizes the AIS data of the MMSI number 9999999999, and takes the SOTDMA time slot timeout reduced to 0 as a unit, and counts the number of times, which is assumed to be 1000 times. Among these 1000 times, time slot conflicts occurred 200 times.

[0084] The environment simulation unit simulates a specific environment and trains the correction parameters. Under this condition, the correction parameters trained by the environment simulation are: a=0.05, b=0.05;

[0085] The conflict analysis unit counts the time slot conflicts that occur when the target ship actually broadcasts AIS dynamic messages, and analyzes the probability that the ship's trajectory is a false trajectory. The specific analysis process is as follows:

[0086] Assume that the number of times the ship to be analyzed selects a time slot in the period to be analyzed is t = 1000, each SOTDMA timeout is recorded as 0, and the number of ships that have time slot conflicts is recorded as n = 50;

[0087] Calculate the time slot conflict parameter P for each ship that has a time slot conflict with the ship to be analyzed:

[0088]

[0089] Based on the time slot conflict parameters P of all relevant ships, the false ship trajectory probability Q of the target ship is calculated:

[0090] Q = 18.75 × 50 ÷ 1000 = 94%

[0091] The result is far greater than 60%, based on which it can be judged that the ship's navigation track is a false navigation track, and law enforcement agencies can focus on this ship.

[0092] In summary, with the continuous improvement of scientific and technological means, the false AIS ship track detection methods at the traditional signal level can be hidden by scientific and technological means, and the detection of dynamic report content is gradually ineffective in the process of continuous upgrading of anti-reconnaissance means. In this case, the present invention uses the SOTDMA channel access mechanism of AIS dynamic report and the asymmetry of information data to detect false AIS ship navigation tracks, and the illegal ships cannot be hidden by technical means, so they can't hide.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units 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 units or modules, which can be electrical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A false AIS ship track detection method based on time slot conflict detection, characterized in that: include: Process the AIS data to be analyzed, filter out the AIS data related to the ship to be analyzed, and store it in the database; According to the corresponding ships, sort out the AIS data related to the ships to be analyzed; Calculate the number of time slot switching times of the ship to be analyzed during the period to be analyzed and the specific time slots where time slot conflicts occur; Simulate the specific environment and train the correction parameters; The time slot conflicts when the target ship actually broadcasts AIS dynamic messages are counted, and the probability that the ship's trajectory is a false trajectory is analyzed.

2. A false AIS ship track detection method based on time slot conflict detection according to claim 1, characterized in that: The processing of the AIS data to be analyzed, filtering out the AIS data related to the ship to be analyzed, and storing the data in a database specifically includes: Obtain AIS data within the period to be analyzed from the data center; Determine the receiving range of the target ship according to the receiving ship number field in the SOTDMA status information in the AIS dynamic message; Filter out the ship data within the receiving range of the target ship during the SOTDMA timeout period; Sort out the time slots actually used for broadcasting by the ships to be analyzed.

3. A false AIS ship track detection method based on time slot conflict detection according to claim 1, characterized in that: The AIS data related to the ship to be analyzed are sorted according to the corresponding ship, specifically including: The ship data within the receiving range of the target ship during the SOTDMA timeout period are sorted and selected according to the ship MMSI number.

4. A false AIS ship track detection method based on time slot conflict detection according to claim 1, characterized in that: The calculating of the number of time slot switching of the ship to be analyzed in the time period to be analyzed and the specific time slot where the time slot conflict occurs specifically includes: Based on the AIS data of the ship to be analyzed, the number of changes is counted, with the SOTDMA time slot timeout being reduced to 0 as a unit.

5. A false AIS ship track detection method based on time slot conflict detection according to claim 1, characterized in that: The statistical target ship actually broadcasts the AIS dynamic message and the time slot conflict occurs, and analyzes the probability that the ship track is a false track, specifically including: Assume that the number of times the ship to be analyzed selects a time slot in the period to be analyzed is t, each SOTDMA timeout is recorded as 0, and the number of ships that have time slot conflicts is recorded as n; Assume that the number of time slot conflicts between the ship and the ship to be analyzed is m, and the distance between the two ships each time a time slot conflict occurs is S1, S2, ···, S m , calculate the time slot conflict parameter P for each ship that has a time slot conflict with the ship to be analyzed, and the calculation formula is as follows: Among them, a and b are correction parameters, which are affected by base station layout, channel distribution and channel load. The channel state of the time period to be analyzed is simulated by channel load, base station layout and channel distribution, and false ships are simulated to train the correction parameters and obtain the values ​​of the correction parameters. Based on the calculation formula of the time slot conflict parameter P, the conflict parameters of all relevant ships are calculated and recorded as P1, P2, ···, P k ; Based on the conflict parameters of all relevant ships, the false ship trajectory probability Q of the target ship is calculated as follows: By calculating the false ship trajectory probability Q of the target ship, it is determined whether the ship's navigation trajectory is a false ship navigation trajectory.

6. A false AIS ship track detection system based on time slot conflict detection implemented by the false AIS ship track detection method based on time slot conflict detection according to any one of claims 1 to 5, characterized in that: include: Big data processing unit, ship list processing unit, ship data processing unit to be analyzed, environment simulation unit and conflict analysis unit, among which: The big data processing unit is used to process the AIS data to be analyzed, filter out the AIS data related to the ship to be analyzed, and store it in the database; The ship list processing unit is used to sort out the AIS data related to the ship to be analyzed according to the corresponding ship; The ship data processing unit to be analyzed is used to calculate the number of time slot switching times of the ship to be analyzed within the time period to be analyzed and the specific time slots where time slot conflicts occur; The environment simulation unit is used to simulate a specific environment and train correction parameters; The conflict analysis unit is used to count the time slot conflicts that occur when the target ship actually broadcasts the AIS dynamic message, and analyze the probability that the ship's trajectory is a false trajectory.

7. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the false AIS ship track detection method based on time slot conflict detection described in any one of claims 1 to 5 is executed.

Images