CN116597693A - Inland navigation monitoring system and method - Google Patents
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- G—PHYSICS
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- G08G3/00—Traffic control systems for marine craft
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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, marine radar, shipborne 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 accidentsWhen 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 satisfiedAnd 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, p a Representing the probability of collision accident of the target ship;
b=18% represents the weight of the target vessel's probability of occurrence of a self-sinking accident, p b Representing the probability of the self-sinking accident of the target ship;
c=13% represents the weight of the probability of stranding accident of the target ship, p c Representing the probability of a stranded accident of the target vessel;
d=12% represents the weight of the probability of touch accident of the target ship, p d The probability of touch accidents of the target ship is represented;
e=10% represents the weight of the target vessel for other accident probability, p e Indicating the probability of other incidents of the target vessel.
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 accidentsWhen 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 satisfiedWhen 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, p a Representing the probability of collision accident of the target ship;
b=18% represents the weight of the target vessel's probability of occurrence of a self-sinking accident, p b Representing the probability of the self-sinking accident of the target ship;
c=13% represents the weight of the probability of stranding accident of the target ship, p c Representing the probability of a stranded accident of the target vessel;
d=12% represents the weight of the probability of touch accident of the target ship, p d The probability of touch accidents of the target ship is represented;
e=10% represents the weight of the target vessel for other accident probability, p e Indicating the probability of other incidents of the target vessel.
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 (10)
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; 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.
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 navigation monitoring system of claim 1, wherein the data processing module obtains a predicted result of the future navigation 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.
4. A inland shipping monitoring system of claim 3, wherein the accident types are classified as collision, autosinking, stranding, touch or others.
5. The inland navigation monitoring system of claim 4, wherein 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.
6. The inland navigation monitoring system of claim 5, wherein the outcome 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.
7. The inland navigation monitoring system of claim 6, wherein the pre-alarm module determines whether the target vessel needs to be pre-warned or warned according to the obtained prediction result, specifically:
when the probability of occurrence of any type of accident is more than 75%, or the weighted total probability of accidentsWhen 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 satisfiedAnd when the number of the ship is more than 90%, judging that the target ship needs to alarm.
8. The inland shipping monitoring system of claim 7, wherein 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, p a Representing the probability of collision accident of the target ship;
b=18% represents the weight of the target vessel's probability of occurrence of a self-sinking accident, p b Representing the probability of the self-sinking accident of the target ship;
c=13% represents the weight of the probability of stranding accident of the target ship, p c Representing the probability of a stranded accident of the target vessel;
d=12% represents the weight of the probability of touch accident of the target ship, p d The probability of touch accidents of the target ship is represented;
e=10% represents the weight of the target vessel for other accident probability, p e Indicating the probability of other incidents of the target vessel.
9. The inland shipping monitoring system of claim 1, wherein the wireless ad hoc communication network is a MANET network.
10. A method of inland navigation monitoring, characterized by applying a inland navigation monitoring system according to any one of claims 1-9, 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, the early warning information or the alarming information is transmitted to other ships and/or shore bases through the wireless self-organizing communication network.
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