CN115497342A - Intelligent bridge ship collision prevention early warning method and system based on multi-source data fusion - Google Patents
Intelligent bridge ship collision prevention early warning method and system based on multi-source data fusion Download PDFInfo
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Abstract
The invention belongs to the field of bridge collision avoidance, relates to a data processing technology, and is used for solving the problem that the existing intelligent bridge collision prevention early warning method cannot be combined with the traffic state of a bridge to feed back the severity of an accident, in particular to a bridge collision prevention intelligent early warning method and a system based on multi-source data fusion, wherein the intelligent early warning method comprises an intelligent early warning platform, and the intelligent early warning platform is in communication connection with a ship monitoring module, an early warning analysis module, a bridge analysis module and a storage module; the ship monitoring module is used for carrying out navigation monitoring analysis on a ship: setting a monitoring area for the bridge, marking a ship in the monitoring area as a monitoring object, and obtaining a risk coefficient WX; comparing the danger coefficient WX of the monitored object with a danger threshold WXmax and marking the monitored object as a safe object or a dangerous object according to the comparison result; the invention can monitor and analyze the navigation of the ship and comprehensively analyze the real-time running data of the ship and the bridge opening parameters of the bridge.
Description
Technical Field
The invention belongs to the field of bridge collision prevention, relates to a data processing technology, and particularly relates to a bridge ship collision prevention intelligent early warning method and system based on multi-source data fusion.
Background
In recent years, with the continuous development of the maritime traffic industry and the rising of inland river transportation requirements, the heat of collision avoidance and collision avoidance problems of inland ship piers is gradually increased, the collision risk of the inland ship piers is also gradually increased, and great losses such as bridge damage and collapse, channel obstruction, threat to lives and properties of people, environmental pollution and the like are caused.
The existing bridge ship collision prevention intelligent early warning method can only judge the collision risk of the bridge through numerical analysis of navigation data of ships and carry out early warning reminding on the ships when the collision risk occurs; however, the early warning reminding is performed in an information transmission mode, and the transmission and receiving processes of information are both abnormal, so that ship drivers cannot receive early warning signals, and finally ships bump the bridge; meanwhile, the severity of the accident cannot be fed back by combining the traffic state of the bridge when the collision risk exists.
In view of the above technical problems, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide a bridge ship collision prevention intelligent early warning method and system based on multi-source data fusion, which are used for solving the problem that the existing bridge ship collision prevention intelligent early warning method cannot be combined with the traffic state of a bridge to feed back the severity of an accident;
the technical problems to be solved by the invention are as follows: how to provide an intelligent early warning method and system for preventing ship collision of a bridge, which can feed back the severity of an accident by combining the traffic state of the bridge.
The purpose of the invention can be realized by the following technical scheme:
a bridge ship collision prevention intelligent early warning system based on multi-source data fusion comprises an intelligent early warning platform, wherein the intelligent early warning platform is in communication connection with a ship monitoring module, an early warning analysis module, a bridge analysis module and a storage module;
the ship monitoring module is used for carrying out navigation monitoring analysis on a ship: setting a monitoring area for the bridge, marking a ship in the monitoring area as a monitoring object, and obtaining a risk coefficient WX; acquiring a danger threshold WXmax through a storage module, comparing the danger coefficient WX of a monitored object with the danger threshold WXmax, marking the monitored object as a safe object or a dangerous object according to a comparison result, carrying out navigation monitoring analysis on the ship, carrying out comprehensive analysis by combining real-time data of ship operation and bridge opening parameters of a bridge, further feeding back the danger of the ship passing the bridge through the danger coefficient, and carrying out early warning in time when collision danger exists, so that the actual collision rate of the ship is reduced, the navigation safety of the ship is improved, and meanwhile, the bridge is protected;
the early warning analysis module is used for early warning dangerous objects, monitoring the transmission and receiving processes of dangerous signals, and taking corresponding measures in time when the transmission and receiving processes of the dangerous signals are abnormal, so that the damage caused by the collision which cannot be prevented and controlled is reduced to the minimum;
the bridge analysis module is used for detecting and analyzing the traffic state of the bridge after receiving the bridge early warning signal: acquiring maneuvering data JD, electric data DD and walking data BX of the passing bridge, and acquiring an early warning coefficient YJ of the bridge through a formula YJ = gamma 1 × JD + gamma 2 × DD + gamma 3 × BX, wherein gamma 1, gamma 2 and gamma 3 are proportionality coefficients, and gamma 1 is more than gamma 2 and more than gamma 3 is more than 1; the method comprises the steps of obtaining early warning thresholds YJmin and YJmax through a storage module, wherein YJmin is the minimum early warning threshold, YJmax is the maximum early warning threshold, comparing an early warning coefficient YJ of a bridge with the early warning thresholds YJmin and YJmax, marking the early warning level of the bridge as a first level, a second level or a third level according to a comparison result, detecting and analyzing the traffic state of the bridge, analyzing current traffic vehicles and personnel data on the bridge, judging the danger degree caused when the bridge is impacted, setting different early warning ranges according to different early warning levels, reminding the vehicles and the personnel not on the bridge to change paths, reducing the accident influence range and reducing the traffic jam degree.
As a preferred embodiment of the present invention, the process of acquiring the risk coefficient WX of the monitoring target includes: the method comprises the following steps of obtaining speed data SD, ship height data CG and quantity data SL of a monitoring object, wherein the speed data SD of the monitoring object is a real-time navigation speed value of the monitoring object, and the obtaining process of the ship height data CG of the monitoring object comprises the following steps: acquiring a distance value between the top of a monitored object and the water surface through an infrared thermal camera and marking the distance value as a ship distance value CJ, marking a maximum distance value between a bridge opening and the water surface as a hole distance value DJ, and marking a width value of the surface with the maximum distance between the bridge opening and the water surface as a hole width value DK; obtaining ship height data CG by a formula CG = alpha 1 (DJ-CJ) + alpha 2 DK, wherein alpha 1 and alpha 2 are both proportionality coefficients, and alpha 1 is larger than alpha 2 and larger than 1; the number data SL of the monitoring objects is the number value of the monitoring objects in the monitoring area; the risk coefficient WX of the monitored subject is obtained by the formula WX = (β 1 × sd + β 2 × sl)/(β 3 × cg), where β 1, β 2, and β 3 are all proportionality coefficients, and β 1 > β 2 > β 3 > 1.
As a preferred embodiment of the present invention, the specific process of comparing the risk coefficient WX with the risk threshold WXmax includes:
if the risk coefficient WX is smaller than the risk threshold WXmax, judging that the navigation state of the monitored object meets the requirement, and marking the corresponding monitored object as a safe object;
if the risk coefficient WX is larger than or equal to the risk threshold WXmax, judging that the navigation state of the monitored object does not meet the requirement, and marking the corresponding monitored object as a dangerous object; and sending the dangerous object to an intelligent early warning platform, and sending the dangerous object to an early warning analysis module after the intelligent early warning platform receives the dangerous object.
As a preferred embodiment of the present invention, the early warning analysis module sends a danger signal to a mobile phone terminal of a dangerous object manager through the intelligent early warning platform, marks a time when the early warning analysis module sends the danger signal as an early warning start time, obtains a danger coefficient of the dangerous object in real time within M1 minutes after the early warning start time, and determines whether an average value of the danger coefficients of the dangerous object within M1 minutes is less than a danger threshold: if so, marking the dangerous object as a safe object; if not, the early warning analysis module sends a bridge early warning signal to the intelligent early warning platform, and the intelligent early warning platform sends the bridge early warning signal to the bridge analysis module after receiving the bridge early warning signal.
In a preferred embodiment of the present invention, the maneuvering data JD for bridge passage is the total number of currently passing motorized vehicles on the bridge, the electric data DD for bridge passage is the total number of currently passing motorized vehicles on the bridge, and the acquiring process of the maneuvering data JD and the electric data DD includes: the method comprises the steps that license plates of motor vehicles and electric vehicles are scanned at two ends of a bridge, the license plates scanned at the two ends of the bridge are matched, license plates which cannot be matched are marked as passing license plates, the total number of the motor vehicles corresponding to the passing license plates is marked as motor data JD, and the total number of the electric vehicles corresponding to the passing license plates is marked as DD; and the walking data BX of the bridge passage is the total number of the current bridge passage pedestrians.
As a preferred embodiment of the present invention, the specific process of comparing the early warning coefficient YJ with the early warning threshold values YJmin and YJmax includes:
if YJ is less than or equal to YJmin, marking the early warning level of the bridge as a third level;
if YJmin is less than YJ and less than YJmax, marking the early warning level of the bridge as a second level;
and if YJ is larger than or equal to YJmax, marking the early warning level of the bridge as a level.
A bridge ship collision prevention intelligent early warning method based on multi-source data fusion comprises the following steps:
the method comprises the following steps: monitoring and analyzing the navigation of the ship: setting a monitoring area for a bridge, marking a ship in the monitoring area as a monitoring object, acquiring speed data SD, ship height data CG and quantity data SL of the monitoring object, carrying out numerical calculation to obtain a danger coefficient of the monitoring object, and marking the monitoring object as a dangerous object or a safe object according to the numerical value of the danger coefficient;
step two: carrying out early warning on dangerous objects: marking the time of the danger signal sent by the early warning analysis module as early warning starting time, acquiring the danger coefficient of the dangerous object in real time within M1 minutes after the early warning starting time, and feeding back the navigation state of the dangerous object according to the value of the danger coefficient of the dangerous object within M1 minutes;
step three: detecting and analyzing the traffic state of the bridge: acquiring maneuvering data JD, electric data DD and walking data BX for bridge passage, performing numerical calculation to obtain an early warning coefficient YJ of the bridge, and marking the early warning level of the bridge as a first level, a second level or a third level according to the numerical value of the early warning coefficient YJ; and allocating early warning distances for the bridge according to the early warning grades of the bridge.
The invention has the following beneficial effects:
1. the ship monitoring module can be used for monitoring and analyzing the navigation of the ship, comprehensively analyzing the real-time data of the running of the ship and the bridge opening parameters of the bridge, feeding back the danger of the ship passing through the bridge through the danger coefficient, and timely early warning when collision danger exists, so that the actual collision rate of the ship is reduced, the navigation safety of the ship is improved, and the bridge is protected;
2. the early warning analysis module can monitor the transmission and receiving process of the dangerous signals, and timely take corresponding measures when the transmission and receiving process of the dangerous signals is abnormal, so that the damage caused by the collision which cannot be prevented and controlled is reduced to the minimum;
3. the traffic state of the bridge is detected and analyzed through the bridge analysis module, current traffic vehicle and personnel data on the bridge are analyzed, and the danger degree caused when the bridge is impacted is judged, so that different early warning ranges are set according to different early warning levels, vehicles and personnel who are not on the bridge are reminded of changing paths, and the traffic jam degree is reduced while the accident influence range is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in figure 1, the intelligent early warning system for preventing the ship collision of the bridge based on multi-source data fusion comprises an intelligent early warning platform, wherein the intelligent early warning platform is in communication connection with a ship monitoring module, an early warning analysis module, a bridge analysis module and a storage module.
The ship monitoring module is used for monitoring and analyzing the navigation of the ship: setting a monitoring area for a bridge, marking a ship in the monitoring area as a monitoring object, and acquiring speed data SD, ship height data CG and quantity data SL of the monitoring object, wherein the speed data SD of the monitoring object is a real-time navigation speed value of the monitoring object, and the acquiring process of the ship height data CG of the monitoring object comprises the following steps: acquiring a distance value between the top of a monitored object and the water surface through an infrared thermal camera and marking the distance value as a ship distance value CJ, marking a maximum distance value between a bridge opening and the water surface as a hole distance value DJ, and marking a width value of a surface with the maximum distance between the bridge opening and the water surface as a hole width value DK; obtaining ship height data CG through a formula CG = alpha 1 (DJ-CJ) + alpha 2 DK, wherein alpha 1 and alpha 2 are proportionality coefficients, and alpha 1 is larger than alpha 2 and larger than 1; the number data SL of the monitoring objects is the number value of the monitoring objects in the monitoring area; obtaining a risk coefficient WX of the monitored object through a formula WX = (beta 1 × SD + beta 2 × SL)/(beta 3 × CG), wherein the risk coefficient is a numerical value reflecting the impact risk of the ship, and the larger the numerical value of the risk coefficient is, the larger the impact risk of the corresponding ship is; wherein beta 1, beta 2 and beta 3 are all proportionality coefficients, and beta 1 is more than beta 2 and more than beta 3 is more than 1; acquiring a danger threshold WXmax through a storage module, and comparing the danger coefficient WX of the monitored object with the danger threshold WXmax: if the risk coefficient WX is smaller than the risk threshold WXmax, judging that the navigation state of the monitored object meets the requirement, and marking the corresponding monitored object as a safe object; if the risk coefficient WX is larger than or equal to the risk threshold WXmax, judging that the navigation state of the monitored object does not meet the requirement, and marking the corresponding monitored object as a dangerous object; the intelligent early warning platform receives the dangerous objects and sends the dangerous objects to the early warning analysis module; carry out navigation monitoring analysis to boats and ships, combine the real-time data of boats and ships operation and the bridge opening parameter of bridge to carry out the integrated analysis, and then feed back the danger of boats and ships through the bridge through danger coefficient, in time carry out the early warning when there is the striking danger to reduce the actual striking rate of boats and ships, provide the protection to the bridge when improving boats and ships navigation safety.
The early warning analysis module is used for carrying out early warning on the dangerous object after receiving the dangerous object: the early warning analysis module sends a danger signal to a mobile phone terminal of a dangerous object manager through the intelligent early warning platform, the time mark of the early warning analysis module for sending the danger signal is early warning starting time, the danger coefficient of the dangerous object is obtained in real time within M1 minutes after the early warning starting time, and whether the average value of the danger coefficient of the dangerous object within M1 minutes is smaller than a danger threshold value is judged: if so, marking the dangerous object as a safe object; if not, the early warning analysis module sends a bridge early warning signal to the intelligent early warning platform, and the intelligent early warning platform sends the bridge early warning signal to the bridge analysis module after receiving the bridge early warning signal; the dangerous signal transmission and receiving process is monitored, corresponding measures are taken in time when the dangerous signal transmission and receiving process is abnormal, and damage caused by collision which cannot be prevented and controlled is reduced to the minimum.
The bridge analysis module is used for detecting and analyzing the traffic state of the bridge after receiving the bridge early warning signal: acquiring mobile data JD, electric data DD and walking data BX of bridge passage, wherein the mobile data JD of bridge passage is the total number of current passing motor vehicles of the bridge, the electric data DD of bridge passage is the total number of current electric vehicles of the bridge, and the acquisition process of the mobile data JD and the electric data DD comprises the following steps: the method comprises the steps that license plates of motor vehicles and electric vehicles are scanned at two ends of a bridge, the license plates scanned at the two ends of the bridge are matched, license plates which cannot be matched are marked as passing license plates, the total number of the motor vehicles corresponding to the passing license plates is marked as motor data JD, and the total number of the electric vehicles corresponding to the passing license plates is marked as DD; the bridge traffic walking data BX is the total number of current bridge traffic personnel, an early warning coefficient YJ of the bridge is obtained through a formula YJ = gamma 1 × JD + gamma 2 × DD + gamma 3 × BX, the early warning coefficient is a danger degree which reflects the bridge collision, the larger the numerical value of the early warning coefficient is, the larger the danger degree which is caused by the bridge collision is, wherein gamma 1, gamma 2 and gamma 3 are proportionality coefficients, and gamma 1 is more than gamma 2 and more than gamma 3 and more than 1; acquiring early warning thresholds YJmin and YJmax through a storage module, wherein YJmin is a minimum early warning threshold, YJmax is a maximum early warning threshold, and comparing an early warning coefficient YJ of the bridge with the early warning thresholds YJmin and YJmax: if YJ is less than or equal to YJmin, marking the early warning level of the bridge as a third level; if YJmin is less than YJ and less than YJmax, marking the early warning level of the bridge as a second level; if YJ is larger than or equal to YJmax, marking the early warning level of the bridge as a level; allocating early warning distances to the bridge according to the early warning level of the bridge, wherein the larger the early warning distance with the early warning level of one level is, the smallest early warning distance with the early warning level of three levels is; the traffic state of the bridge is detected and analyzed, the current traffic vehicle and personnel data on the bridge are analyzed, and the danger degree caused by collision of the bridge is judged, so that different early warning ranges are set according to different early warning levels, vehicles and personnel who are not on the bridge are reminded to change the path, and the traffic jam degree is reduced while the accident influence range is reduced.
Example two
As shown in FIG. 2, the intelligent bridge ship collision prevention early warning method based on multi-source data fusion comprises the following steps:
the method comprises the following steps: monitoring and analyzing the navigation of the ship: setting a monitoring area for a bridge, marking a ship in the monitoring area as a monitoring object, acquiring speed data SD, ship height data CG and quantity data SL of the monitoring object, carrying out numerical calculation to obtain a danger coefficient of the monitoring object, marking the monitoring object as a dangerous object or a safe object according to the numerical value of the danger coefficient, carrying out navigation monitoring analysis on the ship, carrying out comprehensive analysis by combining real-time data of ship operation and bridge opening parameters of the bridge, further carrying out feedback on the danger of the ship passing through the bridge through the danger coefficient, and carrying out early warning in time when the collision danger exists, so that the actual collision rate of the ship is reduced, the navigation safety of the ship is improved, and meanwhile, the bridge is protected;
step two: carrying out early warning on dangerous objects: marking the time of the dangerous signal sent by the early warning analysis module as early warning starting time, acquiring the danger coefficient of the dangerous object in real time within M1 minutes after the early warning starting time, feeding back the navigation state of the dangerous object according to the value of the danger coefficient of the dangerous object within M1 minutes, monitoring the transmission and receiving process of the dangerous signal, and taking corresponding measures in time when the transmission and receiving process of the dangerous signal is abnormal so as to minimize the damage caused by the collision which cannot be prevented and controlled;
step three: detecting and analyzing the traffic state of the bridge: acquiring maneuvering data JD, electric data DD and walking data BX of bridge traffic, performing numerical calculation to obtain an early warning coefficient YJ of the bridge, and marking the early warning level of the bridge as a first level, a second level or a third level according to the numerical value of the early warning coefficient YJ; the early warning method comprises the steps of distributing early warning distances for the bridge according to the early warning levels of the bridge, detecting and analyzing the traffic state of the bridge, analyzing current traffic vehicles and personnel data on the bridge, judging the danger degree caused when the bridge is impacted, setting different early warning ranges according to different early warning levels, reminding the vehicles and personnel not on the bridge to change the paths, and reducing the traffic jam degree while reducing the accident influence range.
A bridge ship collision prevention intelligent early warning method and system based on multi-source data fusion are disclosed, wherein during working, navigation monitoring analysis is carried out on ships: setting a monitoring area for a bridge, marking a ship in the monitoring area as a monitoring object, acquiring speed data SD, ship height data CG and quantity data SL of the monitoring object, carrying out numerical calculation to obtain a danger coefficient of the monitoring object, marking the monitoring object as a dangerous object or a safe object according to the numerical value of the danger coefficient, carrying out navigation monitoring analysis on the ship, carrying out comprehensive analysis by combining real-time data of ship operation and bridge opening parameters of the bridge, further carrying out feedback on the danger of the ship passing through the bridge through the danger coefficient, and carrying out early warning in time when the collision danger exists, so that the actual collision rate of the ship is reduced, the navigation safety of the ship is improved, and meanwhile, the bridge is protected; carrying out early warning on dangerous objects: marking the time of the early warning analysis module for sending out the danger signal as early warning starting time, and acquiring the danger coefficient of the dangerous object in real time within M1 minutes after the early warning starting time, wherein M1 is a numerical constant, and the specific numerical value of M1 is set by a manager; the navigation state of the dangerous object is fed back through the value of the danger coefficient of the dangerous object within M1 minutes, the transmission and receiving processes of the dangerous signal are monitored, corresponding measures are taken in time when the transmission and receiving processes of the dangerous signal are abnormal, and then damage caused by collision which cannot be prevented and controlled is reduced to the minimum; detecting and analyzing the traffic state of the bridge: acquiring maneuvering data JD, electric data DD and walking data BX for bridge passage, performing numerical calculation to obtain an early warning coefficient YJ of the bridge, and marking the early warning level of the bridge as a first level, a second level or a third level according to the numerical value of the early warning coefficient YJ; the early warning method comprises the steps of distributing early warning distances for the bridge according to the early warning levels of the bridge, detecting and analyzing the traffic state of the bridge, analyzing current traffic vehicles and personnel data on the bridge, judging the danger degree caused when the bridge is impacted, setting different early warning ranges according to different early warning levels, reminding the vehicles and personnel not on the bridge to change the paths, and reducing the traffic jam degree while reducing the accident influence range.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula WX = (β 1 × sd + β 2 × sl)/(β 3 × cg); collecting multiple groups of sample data by technicians in the field and setting corresponding danger coefficients for each group of sample data; substituting the set risk coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of beta 1, beta 2 and beta 3 which are 5.68, 3.92 and 2.16 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding danger coefficient preliminarily set by a person skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected, for example, the risk factor is proportional to the value of the speed data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. A bridge ship collision prevention intelligent early warning system based on multi-source data fusion comprises an intelligent early warning platform and is characterized in that the intelligent early warning platform is in communication connection with a ship monitoring module, an early warning analysis module, a bridge analysis module and a storage module;
the ship monitoring module is used for monitoring and analyzing the navigation of a ship: setting a monitoring area for the bridge, marking a ship in the monitoring area as a monitoring object, and obtaining a risk coefficient WX; acquiring a danger threshold WXmax through a storage module, comparing the danger coefficient WX of the monitored object with the danger threshold WXmax, and marking the monitored object as a safe object or a dangerous object according to a comparison result;
the early warning analysis module is used for early warning dangerous objects;
the bridge analysis module is used for detecting and analyzing the traffic state of the bridge after receiving the bridge early warning signal: acquiring maneuvering data JD, electric data DD and walking data BX of the passing bridge, and acquiring an early warning coefficient YJ of the bridge through a formula YJ = gamma 1 × JD + gamma 2 × DD + gamma 3 × BX, wherein gamma 1, gamma 2 and gamma 3 are proportionality coefficients, and gamma 1 is more than gamma 2 and more than gamma 3 is more than 1; and acquiring early warning thresholds YJmin and YJmax through a storage module, wherein YJmin is the minimum early warning threshold, YJmax is the maximum early warning threshold, comparing the early warning coefficient YJ of the bridge with the early warning thresholds YJmin and YJmax, and marking the early warning level of the bridge as a first level, a second level or a third level according to the comparison result.
2. The intelligent bridge ship collision prevention early warning system based on multi-source data fusion of claim 1, wherein the process of acquiring the risk coefficient WX of the monitored object comprises: the method comprises the following steps of obtaining speed data SD, ship height data CG and quantity data SL of a monitoring object, wherein the speed data SD of the monitoring object is a real-time navigation speed value of the monitoring object, and the obtaining process of the ship height data CG of the monitoring object comprises the following steps: acquiring a distance value between the top of a monitored object and the water surface through an infrared thermal camera and marking the distance value as a ship distance value CJ, marking a maximum distance value between a bridge opening and the water surface as a hole distance value DJ, and marking a width value of the surface with the maximum distance between the bridge opening and the water surface as a hole width value DK; obtaining ship height data CG by a formula CG = alpha 1 (DJ-CJ) + alpha 2 DK, wherein alpha 1 and alpha 2 are both proportionality coefficients, and alpha 1 is larger than alpha 2 and larger than 1; the number data SL of the monitoring objects is the number value of the monitoring objects in the monitoring area; the risk coefficient WX of the monitored object is obtained by the formula WX = (β 1 × sd + β 2 × sl)/(β 3 × cg), where β 1, β 2, and β 3 are all proportionality coefficients, and β 1 > β 2 > β 3 > 1.
3. The intelligent early warning system for preventing ship collision of bridge based on multi-source data fusion of claim 2, wherein the specific process of comparing the risk coefficient WX with the risk threshold WXmax comprises:
if the risk coefficient WX is smaller than the risk threshold WXmax, judging that the navigation state of the monitored object meets the requirement, and marking the corresponding monitored object as a safe object;
if the risk coefficient WX is larger than or equal to the risk threshold WXmax, judging that the navigation state of the monitored object does not meet the requirement, and marking the corresponding monitored object as a dangerous object; and sending the dangerous object to an intelligent early warning platform, and sending the dangerous object to an early warning analysis module by the intelligent early warning platform after receiving the dangerous object.
4. The intelligent bridge ship collision prevention early warning system based on multi-source data fusion of claim 3, wherein the early warning analysis module sends a danger signal to a mobile phone terminal of a dangerous object manager through the intelligent early warning platform, the time when the early warning analysis module sends the danger signal is marked as early warning start time, the danger coefficient of the dangerous object is obtained in real time within M1 minutes after the early warning start time, and whether the average value of the danger coefficient of the dangerous object within M1 minutes is smaller than a danger threshold value is judged: if so, marking the dangerous object as a safe object; if not, the early warning analysis module sends a bridge early warning signal to the intelligent early warning platform, and the intelligent early warning platform sends the bridge early warning signal to the bridge analysis module after receiving the bridge early warning signal.
5. The intelligent bridge ship collision prevention early warning system based on multi-source data fusion of claim 4, wherein maneuvering data JD of bridge passage is the total number of current passing motor vehicles of the bridge, electric data DD of bridge passage is the total number of current electric vehicles of the bridge, and the acquiring process of the maneuvering data JD and the electric data DD comprises: the method comprises the steps that license plates of motor vehicles and electric vehicles are scanned at two ends of a bridge, the license plates scanned at the two ends of the bridge are matched, license plates which cannot be matched are marked as passing license plates, the total number of the motor vehicles corresponding to the passing license plates is marked as motor data JD, and the total number of the electric vehicles corresponding to the passing license plates is marked as DD; and the walking data BX of the bridge traffic is the total number of the current traffic pedestrians of the bridge.
6. The intelligent bridge ship collision prevention early warning system based on multi-source data fusion of claim 5, wherein the specific process of comparing the early warning coefficient YJ with the early warning threshold values YJmin and YJmax comprises:
if YJ is less than or equal to YJmin, marking the early warning level of the bridge as a third level;
if YJmin is less than YJ and less than YJmax, marking the early warning level of the bridge as a second level;
and if the YJ is more than or equal to the YJmax, marking the early warning level of the bridge as a level.
7. The working method of the intelligent bridge anti-ship-collision early warning system based on multi-source data fusion according to any one of claims 1 to 6, characterized by comprising the following steps:
the method comprises the following steps: monitoring and analyzing the navigation of the ship: setting a monitoring area for the bridge, marking a ship in the monitoring area as a monitoring object, acquiring speed data SD, ship height data CG and quantity data SL of the monitoring object, carrying out numerical calculation to obtain a danger coefficient of the monitoring object, and marking the monitoring object as a dangerous object or a safe object according to the numerical value of the danger coefficient;
step two: carrying out early warning on dangerous objects: marking the time of the danger signal sent by the early warning analysis module as early warning starting time, acquiring the danger coefficient of the dangerous object in real time within M1 minutes after the early warning starting time, and feeding back the navigation state of the dangerous object according to the value of the danger coefficient of the dangerous object within M1 minutes;
step three: detecting and analyzing the traffic state of the bridge: acquiring maneuvering data JD, electric data DD and walking data BX of bridge traffic, performing numerical calculation to obtain an early warning coefficient YJ of the bridge, and marking the early warning level of the bridge as a first level, a second level or a third level according to the numerical value of the early warning coefficient YJ; and allocating early warning distances for the bridge according to the early warning grades of the bridge.
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