CN116597693B - Inland navigation monitoring system and method - Google Patents
Inland navigation monitoring system and method Download PDFInfo
- Publication number
- CN116597693B CN116597693B CN202310603239.7A CN202310603239A CN116597693B CN 116597693 B CN116597693 B CN 116597693B CN 202310603239 A CN202310603239 A CN 202310603239A CN 116597693 B CN116597693 B CN 116597693B
- Authority
- CN
- China
- Prior art keywords
- ship
- accident
- probability
- target ship
- navigation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 32
- 238000012545 processing Methods 0.000 claims abstract description 18
- 230000005540 biological transmission Effects 0.000 claims abstract description 13
- 230000003993 interaction Effects 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 12
- 238000013507 mapping Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000002349 favourable effect Effects 0.000 abstract description 2
- 238000010276 construction Methods 0.000 description 5
- 230000009429 distress Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000015654 memory Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G3/00—Traffic control systems for marine craft
- G08G3/02—Anti-collision systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G3/00—Traffic control systems for marine craft
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Mathematical Physics (AREA)
- Strategic Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Tourism & Hospitality (AREA)
- Computational Mathematics (AREA)
- Remote Sensing (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Radar, Positioning & Navigation (AREA)
- Pure & Applied Mathematics (AREA)
- Ocean & Marine Engineering (AREA)
- Marketing (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Game Theory and Decision Science (AREA)
- Probability & Statistics with Applications (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Algebra (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
Abstract
The present disclosure relates to a inland navigation monitoring system and method, the system comprising: the acquisition module is used for acquiring multisource information of navigation of the target ship; the data processing module is in communication connection with the acquisition module and is used for receiving the multi-source information and obtaining a prediction result of the future navigation condition of the target ship according to the multi-source information; the pre-warning module is in communication connection with the data processing module and is used for judging whether the target ship needs to perform pre-warning or warning according to the obtained prediction result; the data transmission module is respectively in communication connection with the data processing module and the pre-alarm module and is used for constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node. The method is applied to the system. The method has the advantages of high coverage and reliability, can accurately monitor, early warn and alarm the inland shipping ships, is favorable for reducing the accident occurrence probability of the inland shipping ships, reduces personnel and property loss, has low application cost, and is suitable for inland shipping scenes.
Description
Technical Field
The disclosure relates to the technical field of shipping monitoring, in particular to a inland shipping monitoring system and method.
Background
The inland shipping industry occupies an important position in the economic development of national economy of China, at present, inland shipping ships are basically provided with AIS (Automatic Identification System, automatic ship identification system), and are simultaneously assisted with ship positioning equipment, ship radar, ship-borne sensing instruments and the like, but at present, all systems or equipment are independently applied, are not well integrated in inland shipping guarantee systems, and the current shipping monitoring system is mainly applied to marine shipping ships and has the following defects:
firstly, the current shipping monitoring system adopts a traditional wireless network for communication, natural conditions and construction cost limits of a inland waterway are ignored, effective coverage is difficult to realize in remote inland waterways and most navigation areas lacking base station construction, and coverage and reliability of the shipping monitoring system are affected;
secondly, no reliable early warning and alarming system aiming at special navigation conditions of inland navigation and common accidents of ships is specially used in inland navigation scenes at present, so that early warning and alarming cannot be carried out on inland navigation ships in time, and personnel and property losses are caused.
In summary, the existing marine shipping monitoring system cannot be well applied to a inland shipping system, and has the defects of low coverage and reliability and incapability of effectively early warning and alarming.
Disclosure of Invention
In order to solve the problems of the prior art, the disclosure is directed to a inland navigation monitoring system and method. The method has the advantages of high coverage and reliability, can accurately monitor, early warn and alarm the inland shipping ships, is favorable for reducing the accident occurrence probability of the inland shipping ships, reduces personnel and property loss, has low application cost, and is suitable for inland shipping scenes.
The utility model provides a inland river shipping monitored control system, include:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring multi-source information of target ship navigation, and the multi-source information comprises environment information, ship positioning information and ship state information;
The data processing module is in communication connection with the acquisition module and is used for receiving the multi-source information and obtaining a prediction result of the future navigation condition of the target ship according to the multi-source information;
The pre-warning module is in communication connection with the data processing module and is used for judging whether the target ship needs to perform pre-warning or warning according to the obtained prediction result;
The data transmission module is respectively in communication connection with the data processing module and the pre-alarm module and is used for constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node; and when the pre-warning module judges that the target ship needs to perform pre-warning or warning, the pre-warning information or the warning information is transmitted to other ships and/or shore bases through the wireless self-organizing communication network.
Preferably, the acquisition module comprises:
the ship-borne sensor unit comprises at least one or more of a ship attitude sensor, a wind speed sensor, a ship draft sensor and a positioning module;
the AIS service unit is used for acquiring ship name, sign, ship position, ship speed and ship direction information of the target ship;
and the marine radar unit is used for assisting in acquiring positioning information of the target ship.
Preferably, the data processing module obtains the predicted result of the future sailing condition of the target ship according to the multi-source information specifically as follows:
Accident classification: acquiring multiple shipping accident report analysis with different accident reasons of a target ship navigation channel, and classifying the multiple shipping accident report analysis as a training set according to the accident types;
Model training: the obtained training set is used as input data of an ISM-BN model, and a prediction model for predicting the probability of various accidents of the ship is constructed;
and (3) predicting results: the method comprises the steps of acquiring multi-source information of a target ship in real time, inputting the multi-source information of the target ship into an obtained prediction model, and obtaining the probability of various types of accidents of the target ship, namely, the prediction result of the future sailing condition of the target ship.
Preferably, in the accident classification, accident types are classified as collision, autosinking, stranding, touching or others.
Preferably, the model training is specifically:
Setting ship navigation parameters according to accident reasons, wherein the ship navigation parameters comprise ship navigation conditions, ship age, ship size, ship type, cargo conditions, weather conditions, hydrologic conditions, channel conditions and water conditions;
Constructing an adjacent matrix for representing the mutual relation among ship navigation parameters according to the existence of mutual influence among the ship navigation parameters, calculating to obtain an reachable matrix according to the obtained adjacent matrix, and determining grading among different ship navigation parameters according to the obtained reachable matrix to obtain a navigation accident influence factor ISM about the channel ship;
Constructing a fuzzy logic relation about each sailing accident influence factor ISM;
And taking the obtained training set as input data, and constructing a topology network for representing the mapping relation between the accident type and each navigation accident influence factor ISM, wherein the topology network is the prediction model.
Preferably, the result prediction is specifically:
And inputting corresponding parameters in the multi-source information of the target ship into the prediction model to obtain the probability of various types of accidents of the target ship.
Preferably, the pre-warning module judges whether the target ship needs to perform pre-warning or warning according to the obtained prediction result specifically comprises:
when the probability of occurrence of any type of accident is more than 75%, or the weighted total probability of accidents When the number of the ship is more than 80%, judging that the target ship needs to be subjected to early warning;
when the probability of occurrence of any type of accident is more than 95%, or the total probability of accident weighting is satisfied And when the number of the ship is more than 90%, judging that the target ship needs to alarm.
Preferably, the accident weighted total probabilityThe method is calculated according to the following formula:
wherein a=47% represents the weight of the probability of collision accident of the target ship, and p a represents the probability of collision accident of the target ship;
b=18% represents the weight of the target vessel's probability of occurrence of an autodeposition accident, and p b represents the probability of occurrence of an autodeposition accident of the target vessel;
c=13% represents the weight of the probability of the stranded accident of the target ship, and p c represents the probability of the stranded accident of the target ship;
d=12% represents the weight of the probability of the touch accident of the target ship, and p d represents the probability of the touch accident of the target ship;
e=10% denotes the weight of the probability of other accidents of the target ship, and p e denotes the probability of other accidents of the target ship.
Preferably, the wireless ad hoc communication network is a MANET network.
The inland navigation monitoring method is characterized by comprising the following steps of:
constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node;
Collecting multisource information of target ship navigation, wherein the multisource information comprises environment information, ship positioning information and ship state information;
according to the multi-source information, a prediction result of the future navigation condition of the target ship is obtained;
Judging whether the target ship needs to be pre-warned or alarmed according to the obtained prediction result;
When the target ship is judged to need to perform early warning or alarming, the early warning information or the alarming information is transmitted to other ships and/or shore bases through the wireless self-organizing communication network.
The inland navigation monitoring system and method disclosed by the disclosure have the advantages that:
1. Aiming at the characteristics of small tonnage, low transportation speed, low operation cost and large quantity of inland shipping vessels, the wireless self-organizing communication network technology is utilized to establish a multi-node interconnection network for each shipping vessel as an interaction node, a large amount of information transmission services can be provided for the inland vessels, early warning information and alarm information of the vessels can be timely transmitted to nearby shipping vessels and shore bases, the early warning information and alarm information can be quickly and efficiently transmitted, a fixed backbone network can be quickly established and is not relied on, the wireless self-organizing network is particularly suitable for remote inland natural navigation channels and most navigation areas lack of base station construction, the coverage and reliability of the inland shipping system can be effectively improved, and the wireless self-organizing network has the advantages of small application difficulty, low application cost and wide applicability;
2. The prediction model for predicting the probability of various types of accidents of the ship is built, the multi-source information of the target ship is obtained in real time through the acquisition module, the prediction probability of various types of accidents of the target ship can be obtained based on the multi-source information, and early warning and alarming are timely and accurately carried out on the target ship based on the obtained prediction probability, so that the probability of the occurrence of accidents of the inland navigation ship is reduced, and personnel and property losses are reduced.
Drawings
FIG. 1 is a block diagram of a inland navigation monitoring system according to the present embodiment;
Fig. 2 is a hierarchical relationship diagram of inland vessel navigation accident influencing factors ISM according to the present embodiment;
Fig. 3 is a schematic diagram of a topology network for representing mapping relation between accident types and ISM of each navigation accident influence factor according to the present embodiment.
Detailed Description
As shown in fig. 1, a inland navigation monitoring system according to the present disclosure includes:
the acquisition module is used for acquiring multisource information of target ship navigation, the multisource information comprises environment information, ship positioning information and ship state information, and specifically the acquisition module comprises:
The ship-borne sensor unit comprises a ship attitude sensor, a wind speed sensor, a ship draft sensor and a positioning module, wherein the ship attitude sensor is used for sensing the ship attitude, the wind speed sensor is used for sensing the wind speed of a sailing environment, the ship draft sensor is used for sensing the ship draft, the positioning module is used for acquiring ship positioning information, and the ship-borne sensor unit is mainly used for acquiring ship sailing information and channel natural environment information which cannot be acquired by the AIS system, so that the prediction precision of a subsequent construction prediction model is improved.
The AIS service unit is particularly an existing AIS system, and mainly comprises a radar, a VHF communication machine and a GPS/Beidou positioning system, and is mainly used for acquiring ship name, symbol, ship position, ship speed and ship direction information of a target ship;
The inland ship radar unit is mainly applied to inland shipping ships provided with inland ship radars and mainly plays a role in assisting in positioning and acquiring accurate specific ship directions.
The data processing module is in communication connection with the acquisition module and is used for receiving the multi-source information acquired by the acquisition module, collecting the multi-source information, constructing a mathematical model of the ship navigation condition by using a related algorithm, judging the current navigation condition by using a ship distress experience coefficient and combining accident data of a target channel, and further predicting occurrence probability of various types of accidents to obtain a prediction result of the future navigation condition of the target ship.
The inland ship accident prediction model is mainly constructed by taking an ISM-BN mathematical model as a basis, and specifically comprises the following steps:
accident classification: collecting multiple shipping accident report analyses with different accident causes, such as Zhujiang channels, from a China maritime office network, classifying the multiple shipping accident report analyses according to accident types as a training set, wherein the exemplary classifications are as follows:
the types of incidents are classified as a-bump, b-auton, c-shelf, d-touch, or e-other, where other incidents include minor shipping incidents such as explosions, capsizes, etc.
Model training: the obtained training set is used as input data of an ISM-BN model, and a prediction model for predicting the probability of various accidents of the ship is constructed; the method comprises the following steps:
Setting ship navigation parameters according to accident reasons, wherein the ship navigation parameters comprise ship navigation conditions x1, ship conditions x2, ship age x3, ship size x4, ship type x5, cargo conditions x6, weather conditions x7, hydrological conditions x8, channel conditions x9 and water area conditions x10;
The different parameter acquisition modes are that the ship condition x2, the ship age x3, the ship size x4 and the ship type x5 are static data, and the static data are unchanged in the course of navigation and can be acquired by a shipping company or AIS;
The cargo condition x6 is a variable value, and the shipman sets input before transportation and can adjust along with the shipping process, and the weather condition x7, the hydrological condition x8, the channel condition x9 and the water area condition x10 are acquired and input through the shipborne sensor and the AIS system.
The ship navigation condition x1 is a plurality of dynamic data including ship attitude, draft, host rotation speed and the like during ship navigation, and can be obtained according to corresponding sensors or a read ship control system.
According to whether the ship navigation parameters have mutual influence or not, constructing an adjacent matrix for representing the mutual relation among the ship navigation parameters, setting 1 when the influence exists among different parameters, setting 0 when the influence does not exist, and then obtaining an reachable matrix according to the adjacent matrix through Boolean algebra rule operation, wherein the reachable matrix is shown in the following table 1:
TABLE 1 reachability matrix
Determining the hierarchical relationship of the navigation accident influencing factors ISM about the channel ship according to the hierarchy of the navigation parameters of different ships according to the accessibility matrix, wherein the hierarchical relationship is shown in figure 2;
based on the above-mentioned reachability matrix, a fuzzy logic relationship about each sailing accident influencing factor ISM is constructed as follows in table 2:
TABLE 2 fuzzy logic relationship of parameters
After the hierarchical relation of the navigation accident influence factors ISM of the channel ship is obtained, a topology network for representing the mapping relation between the accident type and each navigation accident influence factor ISM is constructed by inputting the obtained training set, wherein the topology network is shown in fig. 3 and comprises 11 nodes, and the topology network is the prediction model.
And (3) predicting results: the method comprises the steps of acquiring multi-source information of a target ship in real time, inputting the multi-source information of the target ship into an obtained prediction model, and obtaining the probability of various types of accidents of the target ship, namely, the prediction result of the future sailing condition of the target ship.
The result prediction is specifically as follows:
And inputting corresponding parameters in the multi-source information of the target ship into the prediction model to obtain the probability of various types of accidents of the target ship.
Specifically, corresponding parameters in the multi-source information are input into the obtained prediction model, and each node calculates posterior probability through a counting learning method.
The pre-warning module is in communication connection with the data processing module and is used for judging whether the target ship needs to perform pre-warning or warning according to the obtained prediction result; the method comprises the following steps:
when the probability of occurrence of any type of accident is more than 75%, or the weighted total probability of accidents When the number of the ship is more than 80%, judging that the target ship needs to be subjected to early warning;
when the probability of occurrence of any type of accident is more than 95%, or the total probability of accident weighting is satisfied When the number of the ship is more than 90%, judging that the target ship needs to alarm;
more specifically, in order to improve the prediction accuracy in the specific scene of the inland waterway, weighting calculation is performed on each accident type according to the common accident type of the inland waterway, which is specifically as follows:
wherein a=47% represents the weight of the probability of collision accident of the target ship, and p a represents the probability of collision accident of the target ship;
b=18% represents the weight of the target vessel's probability of occurrence of an autodeposition accident, and p b represents the probability of occurrence of an autodeposition accident of the target vessel;
c=13% represents the weight of the probability of the stranded accident of the target ship, and p c represents the probability of the stranded accident of the target ship;
d=12% represents the weight of the probability of the touch accident of the target ship, and p d represents the probability of the touch accident of the target ship;
e=10% denotes the weight of the probability of other accidents of the target ship, and p e denotes the probability of other accidents of the target ship.
The prediction model keeps the structure unchanged in the subsequent application process, and future sailing condition prediction is carried out on different target ships by modifying and inputting information of different ships.
The data transmission module is respectively in communication connection with the data processing module and the pre-alarm module and is used for constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node; specifically, the wireless self-organizing communication network is a MANET network, and each ship or shore in the shipping system is used as an interactive node to realize the transmission of ship early warning and alarm information.
The pre-warning module is in communication connection with the data processing module and is used for judging whether the target ship needs to perform pre-warning or warning according to the obtained prediction result;
the pre-alarm module is a whole-course generating system, generally makes a decision before the data transmission module and penetrates through all subsequent rescue links, but can skip all links if special conditions are met, directly plays a subjective role before the data transmission module, can be flexibly used, stores solidified international distress signal information and transmission system starting information in the data transmission module, and transmits worst distress data of a ship to other distress organizations such as ships or shore bases once the data transmission module is started in a non-program mode.
The working process of the inland navigation monitoring system in the embodiment is as follows:
the method comprises the steps that a collection module acquires ship navigation information in real time during normal shipping tasks of inland ships, the data processing module timely analyzes ship information parameters after the ship navigation information is transmitted to the data processing module at intervals, the analysis result is transmitted to a pre-alarm module to make judgment, if the result reaches the condition of sending pre-alarm information or alarm information, the data transmission module sends the pre-alarm information or the alarm information to a shore-based rescue system and ships loaded with the system nearby through a wireless self-organizing network, and a help seeking process is finished.
If the analysis result does not reach the condition of sending out the early warning information or the alarm information, the analysis result is stored in a ship server or other mobile memories, and the information is used for predicting the possibility of the change of the ship parameters in different time periods in the future.
In other preferred embodiments, the data processing module can also combine the collected multisource information to display the navigation condition of the offshore two-dimensional plane through integration to form a three-dimensional-like structure, so that the analysis model is more visual and accurate.
Aiming at the characteristics of small tonnage, low transportation speed, low operation cost and large quantity of inland shipping vessels, the wireless self-organizing communication network technology is utilized to establish a multi-node interconnection network for each shipping vessel as an interaction node, a large amount of information transmission services can be provided for the inland vessels, early warning information and alarm information of the vessels can be timely transmitted to nearby shipping vessels and shore bases, the early warning information and alarm information can be quickly and efficiently transmitted, a fixed backbone network can be quickly established and is not relied on, the wireless self-organizing network is particularly suitable for remote inland natural navigation channels and most navigation areas lack of base station construction, the coverage and reliability of the inland shipping system can be effectively improved, and the wireless self-organizing network has the advantages of small application difficulty, low application cost and wide applicability;
The prediction model for predicting the probability of various types of accidents of the ship is built, the multi-source information of the target ship is obtained in real time through the acquisition module, the prediction probability of various types of accidents of the target ship can be obtained based on the multi-source information, and early warning and alarming are timely and accurately carried out on the target ship based on the obtained prediction probability, so that the probability of the occurrence of accidents of the inland navigation ship is reduced, and personnel and property losses are reduced.
The embodiment also provides a inland navigation monitoring method, which is applied to the inland navigation monitoring system and comprises the following steps:
constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node;
Collecting multisource information of target ship navigation, wherein the multisource information comprises environment information, ship positioning information and ship state information;
according to the multi-source information, a prediction result of the future navigation condition of the target ship is obtained;
Judging whether the target ship needs to be pre-warned or alarmed according to the obtained prediction result;
When the target ship is judged to need to perform early warning or alarming, the early warning information or the alarming information is transmitted to other ships and/or shore bases through the wireless self-organizing communication network.
The method of this embodiment belongs to the same inventive concept as the monitoring system described above, and can be understood with reference to the above description, and will not be repeated here.
In the description of the present disclosure, it should be understood that the azimuth or positional relationships indicated by the azimuth terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal", and "top, bottom", etc., are generally based on the azimuth or positional relationships shown in the drawings, merely to facilitate description of the present disclosure and simplify the description, and without being otherwise described, these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be configured and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present disclosure.
It will be apparent to those skilled in the art from this disclosure that various other changes and modifications can be made which are within the scope of the invention as defined in the claims.
Claims (5)
1. A inland navigation monitoring system, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring multi-source information of target ship navigation, and the multi-source information comprises environment information, ship positioning information and ship state information;
The data processing module is in communication connection with the acquisition module and is used for receiving the multi-source information and obtaining a prediction result of the future navigation condition of the target ship according to the multi-source information;
The pre-warning module is in communication connection with the data processing module and is used for judging whether the target ship needs to perform pre-warning or warning according to the obtained prediction result;
the data transmission module is respectively in communication connection with the data processing module and the pre-alarm module and is used for constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node; when the pre-warning module judges that the target ship needs to perform pre-warning or warning, the pre-warning information or the warning information is transmitted to other ships and/or shore bases through the wireless self-organizing communication network;
The data processing module obtains the prediction result of the future navigation condition of the target ship according to the multi-source information, specifically:
Accident classification: acquiring multiple shipping accident report analysis with different accident reasons of a target ship navigation channel, and classifying the multiple shipping accident report analysis as a training set according to the accident types;
Model training: the obtained training set is used as input data of an ISM-BN model, and a prediction model for predicting the probability of various accidents of the ship is constructed;
And (3) predicting results: the method comprises the steps of acquiring multi-source information of a target ship in real time, inputting the multi-source information of the target ship into an obtained prediction model, and obtaining the probability of various types of accidents of the target ship, namely, a prediction result of future sailing conditions of the target ship;
In the accident classification, accident types are classified into collision, self-sinking, stranding, touch or others;
the model training is specifically as follows:
Setting ship navigation parameters according to accident reasons, wherein the ship navigation parameters comprise ship navigation conditions, ship age, ship size, ship type, cargo conditions, weather conditions, hydrologic conditions, channel conditions and water conditions;
Constructing an adjacent matrix for representing the mutual relation among ship navigation parameters according to the existence of mutual influence among the ship navigation parameters, calculating to obtain an reachable matrix according to the obtained adjacent matrix, and determining grading among different ship navigation parameters according to the obtained reachable matrix to obtain a navigation accident influence factor ISM about the channel ship;
Constructing a fuzzy logic relation about each sailing accident influence factor ISM;
taking the obtained training set as input data, constructing a topology network for representing the mapping relation between the accident type and each navigation accident influence factor ISM, wherein the topology network is the prediction model;
The result prediction is specifically as follows:
Inputting corresponding parameters in the multi-source information of the target ship into the prediction model to obtain the probability of various types of accidents of the target ship;
the pre-warning module judges whether the target ship needs to be pre-warned or warned according to the obtained prediction result specifically comprises the following steps:
when the probability of occurrence of any type of accident is more than 75%, or the weighted total probability of accidents When the number of the ship is more than 80%, judging that the target ship needs to be subjected to early warning;
when the probability of occurrence of any type of accident is more than 95%, or the total probability of accident weighting is satisfied When the number of the ship is more than 90%, judging that the target ship needs to alarm;
The weighted total probability of the accident The method is calculated according to the following formula:
wherein a represents the weight of the collision accident probability of the target ship, and p a represents the probability of the collision accident of the target ship;
b represents the weight of the self-sinking accident probability of the target ship, and p b represents the self-sinking accident probability of the target ship;
c represents the weight of the probability of the stranding accident of the target ship, and p c represents the probability of the stranding accident of the target ship;
d represents the weight of the probability of the touch accident of the target ship, and p d represents the probability of the touch accident of the target ship;
e denotes the weight of the probability of other accidents of the target ship, and p e denotes the probability of other accidents of the target ship.
2. The inland shipping monitoring system of claim 1, wherein the acquisition module comprises:
the ship-borne sensor unit comprises at least one or more of a ship attitude sensor, a wind speed sensor, a ship draft sensor and a positioning module;
the AIS service unit is used for acquiring ship name, sign, ship position, ship speed and ship direction information of the target ship;
and the marine radar unit is used for assisting in acquiring positioning information of the target ship.
3. The inland shipping monitoring system of claim 1, wherein the accident weighted total probabilityIn the calculation formula of (a):
a＝47％，b＝18％，c＝13％，d＝12％，e＝10％。
4. the inland shipping monitoring system of claim 1, wherein the wireless ad hoc communication network is a MANET network.
5. A method of inland navigation monitoring, characterized by applying a inland navigation monitoring system according to any of claims 1-4, comprising the steps of:
constructing a wireless self-organizing communication network by taking each ship and/or shore as an interaction node;
Collecting multisource information of target ship navigation, wherein the multisource information comprises environment information, ship positioning information and ship state information;
according to the multi-source information, a prediction result of the future navigation condition of the target ship is obtained;
Judging whether the target ship needs to be pre-warned or alarmed according to the obtained prediction result;
when the target ship is judged to need to perform early warning or alarming, transmitting early warning information or alarming information to other ships and/or shore bases through the wireless self-organizing communication network;
according to the multi-source information, the predicted result of the future navigation condition of the target ship is specifically obtained as follows:
Accident classification: acquiring multiple shipping accident report analysis with different accident reasons of a target ship navigation channel, and classifying the multiple shipping accident report analysis as a training set according to the accident types;
Model training: the obtained training set is used as input data of an ISM-BN model, and a prediction model for predicting the probability of various accidents of the ship is constructed;
And (3) predicting results: the method comprises the steps of acquiring multi-source information of a target ship in real time, inputting the multi-source information of the target ship into an obtained prediction model, and obtaining the probability of various types of accidents of the target ship, namely, a prediction result of future sailing conditions of the target ship;
In the accident classification, accident types are classified into collision, self-sinking, stranding, touch or others;
the model training is specifically as follows:
Setting ship navigation parameters according to accident reasons, wherein the ship navigation parameters comprise ship navigation conditions, ship age, ship size, ship type, cargo conditions, weather conditions, hydrologic conditions, channel conditions and water conditions;
Constructing an adjacent matrix for representing the mutual relation among ship navigation parameters according to the existence of mutual influence among the ship navigation parameters, calculating to obtain an reachable matrix according to the obtained adjacent matrix, and determining grading among different ship navigation parameters according to the obtained reachable matrix to obtain a navigation accident influence factor ISM about the channel ship;
Constructing a fuzzy logic relation about each sailing accident influence factor ISM;
taking the obtained training set as input data, constructing a topology network for representing the mapping relation between the accident type and each navigation accident influence factor ISM, wherein the topology network is the prediction model;
The result prediction is specifically as follows:
Inputting corresponding parameters in the multi-source information of the target ship into the prediction model to obtain the probability of various types of accidents of the target ship;
judging whether the target ship needs to be pre-warned or warned according to the obtained prediction result specifically comprises the following steps:
when the probability of occurrence of any type of accident is more than 75%, or the weighted total probability of accidents When the number of the ship is more than 80%, judging that the target ship needs to be subjected to early warning;
when the probability of occurrence of any type of accident is more than 95%, or the total probability of accident weighting is satisfied And when the number of the ship is more than 90%, judging that the target ship needs to alarm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310603239.7A CN116597693B (en) | 2023-05-25 | 2023-05-25 | Inland navigation monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310603239.7A CN116597693B (en) | 2023-05-25 | 2023-05-25 | Inland navigation monitoring system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116597693A CN116597693A (en) | 2023-08-15 |
| CN116597693B true CN116597693B (en) | 2024-08-02 |
Family
ID=87589611
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310603239.7A Active CN116597693B (en) | 2023-05-25 | 2023-05-25 | Inland navigation monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116597693B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117273197B (en) * | 2023-08-28 | 2024-05-28 | 长江水上交通监测与应急处置中心 | Ship operation state prediction method and system based on track and production information fusion |
| CN118278320B (en) * | 2024-05-24 | 2024-08-16 | 中交第四航务工程勘察设计院有限公司 | Inland waterway navigation early warning method and system based on hydrodynamic force simulation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739345A (en) * | 2020-07-21 | 2020-10-02 | 范文峰 | AIS-based intelligent water monitoring and management method and system |
| CN112966949A (en) * | 2021-03-15 | 2021-06-15 | 北京市市政工程研究院 | Tunnel construction risk assessment method and device and storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FI20095914A0 (en) * | 2009-09-04 | 2009-09-04 | Valtion Teknillinen | INTELLIGENT RISK INDICATION SYSTEM FOR WATER DRAINAGE AND RELATED METHOD |
| CN212208570U (en) * | 2020-05-15 | 2020-12-22 | 广州航保科技有限公司 | Ship yaw monitoring system |
| CN113012472B (en) * | 2021-03-17 | 2022-06-14 | 武汉理工大学 | Ship early warning method and equipment suitable for VTS system and storage medium |
| CN114662575B (en) * | 2022-03-09 | 2024-06-14 | 集美大学 | Wind power water area ship navigation risk prediction method, system and storage medium |
| CN114662979A (en) * | 2022-04-12 | 2022-06-24 | 西南交通大学 | BN-ISM model-based railway traffic accident early warning method and system |
-
2023
- 2023-05-25 CN CN202310603239.7A patent/CN116597693B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739345A (en) * | 2020-07-21 | 2020-10-02 | 范文峰 | AIS-based intelligent water monitoring and management method and system |
| CN112966949A (en) * | 2021-03-15 | 2021-06-15 | 北京市市政工程研究院 | Tunnel construction risk assessment method and device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116597693A (en) | 2023-08-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116597693B (en) | Inland navigation monitoring system and method | |
| Zhang et al. | A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditions | |
| CN103714718B (en) | A kind of inland river bridge area ship safe navigation precontrol system | |
| Fang et al. | Automatic identification system-based approach for assessing the near-miss collision risk dynamics of ships in ports | |
| CN113553682B (en) | Data-driven multi-level ship route network construction method | |
| CN114333424B (en) | Bridge prevents ship and hits monitoring early warning system | |
| CN108281043A (en) | A kind of ship collision Warning System and method for early warning | |
| CN114742128A (en) | Method, device, terminal and storage medium for identifying ship abnormal behavior | |
| Zhang et al. | Big data–based estimation for ship safety distance distribution in port waters | |
| CN105204092A (en) | Thunder and lightening prewarning system with honeycomb layout | |
| Wu et al. | A new multi-sensor fusion approach for integrated ship motion perception in inland waterways | |
| CN117647812B (en) | Navigation radar-based ship travel route detection and early warning method and system | |
| CN108061572A (en) | A kind of ocean nuclear power platform comprehensive situation display ＆ control system and method | |
| CN117037547B (en) | Congestion degree evaluation method, system and storage medium for channel key nodes | |
| CN118247072A (en) | Fishing condition prediction method based on fishery application digital twin system | |
| CN117688349A (en) | Maritime search and rescue method and system based on digital twinning | |
| CN113866849A (en) | Intelligent inland river shipping meteorological service guarantee system | |
| CN113642993A (en) | Position-based marine comprehensive service platform and rescue method platform thereof | |
| Newaliya et al. | A review of maritime spatio-temporal data analytics | |
| CN112446811A (en) | Fishery maritime search and rescue system and search and rescue method based on 5G communication technology | |
| CN116630907A (en) | Ship navigation digital twin method, system, terminal and storage medium based on remote sensing | |
| CN116312062A (en) | Ship navigation early warning method, device, equipment and storage medium | |
| Ming et al. | Intelligent marine area supervision based on AIS and radar fusion | |
| Berbić et al. | Overview of machine learning methods in maritime traffic monitoring based on Automatic Identification System | |
| KR102581419B1 (en) | System and method for constructing hierarchical database reflecting tracking characteristics of ships |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |