CN115310673A - Navigation risk identification, prevention and control method, system, device and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a storage medium for identifying, preventing and controlling navigation risks. The method comprises the steps of acquiring basic event data; constructing a navigation risk fault tree model; then, analyzing the basic event data through a fault tree model to obtain key risk factors; constructing an event tree model according to the key risk factors and the traffic accident report; evaluating the basic event data through an event tree model to obtain a key accident consequence; constructing a Bow-Tie model according to the key risk factors and the key accident consequences; and then, generating a prevention and control strategy through a Bow-Tie model. According to the method, the fault tree model, the event tree model and the Bow-Tie model are combined by acquiring basic event data, a specific ship navigation risk prevention and control strategy can be generated for a user to refer to, navigation safety under a hybrid navigation scene is effectively improved, and the purpose of risk prevention and control is further achieved. The method can be widely applied to the technical field of accident risk prediction 9.
Description
Technical Field
The invention relates to the technical field of accident risk prediction, in particular to a navigation risk identification, prevention and control method, a navigation risk identification, prevention and control system, a navigation risk identification, prevention and control device and a storage medium.
Background
At present, the technology for dealing with the cooperative failure accident of the traditional/intelligent ships in the inland waterway still remains blank. In actual navigation, when personnel on the ship face various severe complex conditions, the importance of current mastered information cannot be accurately grasped, most of the information is judged by means of navigation experience, the intelligent ship has high experience requirements on workers, the risk of interaction failure with the intelligent ship is easy to occur, unexpected consequences are caused, and the navigation safety of the ship cannot be effectively guaranteed.
Disclosure of Invention
The present invention aims to solve at least to some extent one of the technical problems existing in the prior art.
Therefore, an object of the embodiments of the present invention is to provide a method, a system, a device and a storage medium for identifying and controlling a navigation risk.
The technical scheme adopted by the embodiment of the invention comprises the following steps:
on one hand, the embodiment of the invention provides a navigation risk identification and prevention and control method, which comprises the following steps:
acquiring basic event data according to a traffic accident report, wherein the basic events comprise conventional events corresponding to conventional ships and unconventional events corresponding to intelligent ships;
constructing a fault tree model of a mixed navigation scene of a plurality of ships under the inland navigation condition, wherein the fault tree model comprises a top event, a middle event and a bottom event;
analyzing the basic event data through the fault tree model to obtain key risk factors;
constructing an event tree model according to the key risk factors and the traffic accident report;
evaluating the basic event data through the event tree model to obtain a key accident consequence;
constructing a Bow-Tie model according to the key risk factors and the key accident consequences;
and generating a prevention and control strategy corresponding to the top event through the Bow-Tie model. Further, the fault tree model is constructed by the following steps:
constructing a mixed navigation scene of various ships under the inland navigation condition;
decomposing the interactive cooperation process among various ships in the scene into a cooperative perception stage, a cooperative cognition stage, a cooperative decision stage and a cooperative control stage;
constructing a cooperative framework according to the cooperative sensing stage, the cooperative cognition stage, the cooperative decision stage and the cooperative control stage;
setting a top event according to the collaboration frame;
analyzing according to the top incident and the traffic accident report to obtain a middle incident and a bottom incident corresponding to the top incident;
and constructing the fault tree model according to the top event, the middle event and the bottom event.
Further, the step of analyzing according to the top event and the traffic accident report specifically includes:
extracting accident data in the traffic accident report, and taking the accident type appearing in the accident data as an intermediate event;
classifying the intermediate event, wherein the classification comprises personnel risk, ship risk, environmental risk and technical risk.
Further, the step of performing qualitative and quantitative analysis on the basic event data through the fault tree model to obtain a key risk factor specifically includes:
acquiring the occurrence probability of the basic event;
according to the occurrence probability of the basic event, obtaining the probability importance and the key importance corresponding to the basic event through the fault tree model, wherein the key importance is used for representing the influence weight of the bottom event on the top event;
and sorting according to the key importance, and obtaining key risk factors according to a sorting result.
Further, the obtaining the basic event occurrence probability includes:
acquiring historical accident data;
extracting bottom event data in the historical accident data;
and calculating the occurrence probability of the basic event according to the bottom event data.
Further, the method further comprises the steps of:
and displaying the prevention and control strategy generated by the Bow-Tie model through a display module.
Further, the prevention and control strategy includes preventive measures and preventive measures.
On the other hand, an embodiment of the present invention provides a system for identifying, preventing and controlling a navigation risk, including:
the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring basic event data according to a traffic accident report, and the basic events comprise conventional events corresponding to conventional ships and unconventional events corresponding to intelligent ships;
the second module is used for constructing a fault tree model of a mixed navigation scene of various ships under the inland navigation condition, wherein the fault tree model comprises a top event, a middle event and a bottom event;
the third module is used for carrying out qualitative and quantitative analysis on the basic event data through the fault tree model to obtain key risk factors;
a fourth module, configured to construct an event tree model according to the key risk factors and the traffic accident report;
a fifth module, configured to evaluate the basic event data through the event tree model to obtain a key accident consequence;
a sixth module, configured to construct a Bow-Tie model according to the key risk factor and the key accident consequence;
and the seventh module is used for generating a prevention and control strategy corresponding to the top event through the Bow-Tie model.
On the other hand, an embodiment of the present invention provides an identification, prevention and control device for a navigation risk, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one program causes the at least one processor to implement the method for identifying and controlling a voyage risk.
In another aspect, an embodiment of the present invention provides a storage medium, in which processor-executable instructions are stored, and when the processor-executable instructions are executed by a processor, the method for identifying and controlling a navigation risk is implemented.
The invention discloses a navigation risk identification, prevention and control method, which has the following beneficial effects:
the embodiment acquires basic event data according to a traffic accident report, wherein the basic event comprises a conventional event corresponding to a conventional ship and an unconventional event corresponding to a smart ship; constructing a fault tree model of a mixed navigation scene of a plurality of ships under the inland navigation condition, wherein the fault tree model comprises a top event, a middle event and a bottom event; analyzing the basic event data through the fault tree model to obtain key risk factors; constructing an event tree model according to the key risk factors and the traffic accident report; evaluating the basic event data through the event tree model to obtain a key accident consequence; constructing a Bow-Tie model according to the key risk factors and the key accident consequence; and generating a prevention and control strategy corresponding to the top event through the Bow-Tie model. According to the method, the fault tree model, the event tree model and the Bow-Tie model are combined by acquiring basic event data, a specific ship navigation risk prevention and control strategy can be generated for a user to refer to, navigation safety under a hybrid navigation scene is effectively improved, and the purpose of risk prevention and control is further achieved.
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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 following description is made on the drawings of the embodiments of the present invention or the related technical solutions in the prior art, it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic view of an implementation environment of a method for identifying and controlling a navigation risk provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for identifying, preventing and controlling a navigation risk according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a navigation risk identification and prevention system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a navigation risk identification and prevention and control device according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a navigation risk identification and prevention and control device according to an embodiment of the present invention
Detailed Description
Reference will now be made in detail to the present embodiments of the present invention, preferred embodiments of which are illustrated in the accompanying drawings, wherein the drawings are provided for the purpose of visually supplementing the description in the specification and so forth, and which are not intended to limit the scope of the invention.
In the description of the embodiments of the present invention, several means are one or more, a plurality means two or more, more than, less than, more than, etc. are understood as excluding the essential numbers, more than, less than, inner, etc. are understood as including the essential numbers, "at least one" means one or more, "at least one item below" and the like, and any combination of these items, including any combination of a single item or a plurality of items, is meant. If the description of "first", "second", etc. is used for the purpose of distinguishing technical features, it is not intended to indicate or imply relative importance or to implicitly indicate the number of indicated technical features or to implicitly indicate the precedence of the indicated technical features.
It should be noted that terms such as setting, installing, connecting and the like in the embodiments of the present invention should be understood in a broad sense, and a person skilled in the art may reasonably determine specific meanings of the terms in the embodiments of the present invention by combining specific contents of the technical solutions. For example, the term "coupled" may be mechanical, electrical, or may be in communication with each other; may be directly connected or indirectly connected through an intermediate.
In the description of embodiments of the present disclosure, reference to the description of the terms "one embodiment/implementation," "another embodiment/implementation," or "certain embodiments/implementations," "in the above embodiments/implementations," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least two embodiments or implementations of the present disclosure. In the present disclosure, a schematic representation of the above terms does not necessarily refer to the same exemplary embodiment or implementation. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or implementations.
It should be noted that the technical features related to the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
From the safety situation of waterway traffic, the national policy and the development route of intelligent ships, the remote driving of ships is the key and the essential trend of the future waterway traffic field development. The appearance of the remote ship breaks through the mixed navigation pattern of the conventional ship and the conventional ship in the inland river, and a mixed navigation scene of the conventional ship and the remote ship is gradually formed. According to the navigation experimental conditions of intelligent ships developed by various countries, the hybrid navigation scene sprouts in an inland river scene, and then is gradually expanded to coastal and ocean scenes, and the current academic research on risk identification, prevention and control of the inland river hybrid navigation scene is blank. At present, the technology for dealing with the self-sinking accident of the traditional/intelligent ship cooperative failure accident of the future inland waterway is not enough and is still blank. In actual navigation, when personnel on the ship face various severe complex conditions, the importance of current mastered information cannot be accurately grasped, most of the information is judged by means of navigation experience, and the occurrence of the intelligent ship has high requirements on the extremely high experience of the personnel, so that risks of interaction failure of danger and the intelligent ship easily occur, unexpected consequences are caused, and the navigation safety of the ship cannot be effectively guaranteed.
Therefore, the application provides a navigation risk identification, prevention and control method, which comprises the steps of obtaining basic event data; constructing a navigation risk fault tree model comprising a top event, a middle event and a bottom event; then, carrying out qualitative and quantitative analysis on the basic event data through the fault tree model to obtain key risk factors; constructing an event tree model according to the key risk factors and the traffic accident report; then, evaluating the basic event data through the event tree model to obtain a key accident consequence; constructing a Bow-Tie model according to the key risk factors and the key accident consequence; and then, generating a prevention and control strategy corresponding to the top event through the Bow-Tie model. According to the method, the fault tree model, the event tree model and the Bow-Tie model are combined by acquiring basic event data, a specific ship navigation risk prevention and control strategy can be generated for a user to refer to, navigation safety under a hybrid navigation scene is effectively improved, and the purpose of risk prevention and control is further achieved.
Fig. 1 is a schematic diagram of an implementation environment of a training method for an intelligent interaction model according to an embodiment of the present application. Referring to fig. 1, the software and hardware main body of the implementation environment mainly includes an operation terminal 101 and a server 102, and the operation terminal 101 is connected to the server 102 in a communication manner. The training method of the intelligent interaction model may be separately configured to be executed by the operation terminal 101, may also be separately configured to be executed by the server 102, or may be executed based on the interaction between the operation terminal 101 and the server 102, which may be appropriately selected according to the actual application situation, and this embodiment is not particularly limited thereto. In addition, the operation terminal 101 and the server 102 may be nodes in a block chain, which is not limited in this embodiment.
Specifically, the operation terminal 101 in the present application may include, but is not limited to, any one or more of a smart watch, a smart phone, a computer, a Personal Digital Assistant (PDA), an intelligent voice interaction device, an intelligent household appliance, or a vehicle-mounted terminal. The server 102 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform. The operation terminal 101 and the server 102 may establish a communication connection through a wireless Network or a wired Network, which uses standard communication technologies and/or protocols, and the Network may be set as the internet, or may be any other Network, such as, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wired, or wireless Network, a private Network, or any combination of virtual private networks.
Fig. 2 is a flowchart of a method for identifying and controlling a navigation risk according to an embodiment of the present application, where an execution subject of the method may be at least one of an operation terminal or a server, and fig. 2 illustrates an example of an implementation in which the method for identifying and controlling a navigation risk is configured at an operation terminal. Referring to fig. 2, the method for identifying and controlling navigation risk includes, but is not limited to, steps 110 to 170.
Step 110: and acquiring basic event data according to the traffic accident report, wherein the basic events comprise a conventional event corresponding to a conventional ship and an unconventional event corresponding to a smart ship.
In this step, the regular events refer to events that can be extracted from historical accident data, for example, intervention timing errors, host failures, and heavy rains. And the non-conventional events refer to some events which can occur after the introduction of a remote ship, such as low intelligent monitoring capacity of a channel, failure of a decision algorithm and failure of information transmission. In this embodiment, the obtaining channel of the basic event data is not limited, for example, in some embodiments, the basic event data may be obtained by downloading from an associated resource server, or may be obtained from other electronic devices and computer systems through a data transmission interface or a remote communication transmission.
Step 120: and constructing a fault tree model of a mixed navigation scene of various ships under the inland navigation condition, wherein the fault tree model comprises a top event, a middle event and a bottom event.
It should be noted that, in the embodiments described herein, specific technical details are specifically described by taking the top event as an example of the risk of the hybrid sailing of the future conventional/remote ship.
Step 130: and carrying out qualitative and quantitative analysis on the basic event data through the fault tree model to obtain key risk factors.
Step 140: and constructing an event tree model according to the key risk factors and the traffic accident report.
Step 150: and evaluating the basic event data through the event tree model to obtain the key accident consequence.
Step 160: and constructing a Bow-Tie model according to the key risk factors and the key accident consequences.
Step 170: and generating a prevention and control strategy corresponding to the top event through the Bow-Tie model.
The embodiment obtains the basic event data; constructing a navigation risk fault tree model comprising a top event, a middle event and a bottom event; then, carrying out qualitative and quantitative analysis on the basic event data through the fault tree model to obtain key risk factors; constructing an event tree model according to the key risk factors and the traffic accident report; then, evaluating the basic event data through the event tree model to obtain a key accident consequence; constructing a Bow-Tie model according to the key risk factors and the key accident consequence; and then, generating a prevention and control strategy corresponding to the top event through the Bow-Tie model. According to the method, the fault tree model, the event tree model and the Bow-Tie model are combined by acquiring basic event data, a specific ship navigation risk prevention and control strategy can be generated for a user to refer to, navigation safety under a hybrid navigation scene is effectively improved, and the purpose of risk prevention and control is further achieved.
Referring to fig. 5, further as an alternative embodiment, the fault tree model is constructed by the following steps:
constructing a mixed navigation scene of various ships under the inland navigation condition;
decomposing the interactive collaborative process among multiple ships in the scene into a collaborative perception stage, a collaborative cognition stage, a collaborative decision stage and a collaborative control stage;
constructing a cooperative framework according to the cooperative sensing stage, the cooperative cognition stage, the cooperative decision stage and the cooperative control stage;
setting a top event according to the collaboration frame;
analyzing according to the top event and the traffic accident report to obtain a middle event and a bottom event corresponding to the top event;
and constructing the fault tree model according to the top event, the middle event and the bottom event.
As a further optional implementation, the step of analyzing according to the top event and the traffic accident report specifically includes:
extracting accident data in the traffic accident report, and taking the accident type appearing in the accident data as an intermediate event;
classifying the intermediate event, wherein the classification comprises personnel risk, ship risk, environmental risk and technical risk.
Specifically, the top incident is analyzed in combination with a traffic accident report and expert consulting opinions, and an intermediate incident is made clear. Different inland navigation scenarios, such as bridge regions, curves, dams, and narrow waterways, introduce different intermediate events. The intermediate events mainly take personnel risks, ship risks, environmental risks and technical risks into consideration. The results of the analysis of the intermediate event and the basic event in the present embodiment are shown in the following table.
TABLE 1 intermediate event and basic event table
As a further optional implementation manner, the step of performing qualitative and quantitative analysis on the basic event data through the fault tree model to obtain a key risk factor specifically includes:
acquiring the occurrence probability of the basic event;
according to the occurrence probability of the basic event, obtaining the probability importance and the key importance corresponding to the basic event through the fault tree model, wherein the key importance is used for representing the influence weight of the bottom event on the top event;
and sorting according to the key importance, and obtaining key risk factors according to a sorting result.
Specifically, referring to table 2, for the regular event probability among the basic events, the accident data collected by the maritime regulatory authority may be approximated as the probability of occurrence of the regular event with the frequency of occurrence thereof. For convenience of calculation, the present embodiment reserves the event occurrence probability as a five-bit decimal, and the basic event probability calculation formula is as follows:
the conventional event occurrence probability is shown in the following table:
the probability of unconventional events in the basic events can be obtained by training a targeted machine learning model. After the training data set is obtained, the training data set can be input into the initialized irregular event probability calculation model for training. Specifically, after the data in the training data set is input into the initialized irregular event probability calculation model, the recognition result output by the model, that is, the irregular event probability calculation result, can be obtained, and the accuracy of calculation of the calculation model can be evaluated according to the irregular event probability calculation result, so that the parameters of the model are updated. For the unconventional event probability calculation model, the accuracy of the model calculation result may be measured by a Loss Function (Loss Function), where the Loss Function is defined on a single training data and is used to measure the prediction error of a piece of training data, and specifically, the Loss value of the piece of training data is determined by the label of the single training data and the prediction result of the model on the piece of training data. In actual training, a training data set has many training data, so a Cost Function (Cost Function) is generally adopted to measure the overall error of the training data set, and the Cost Function is defined on the whole training data set and is used for calculating the average value of prediction errors of all the training data, so that the prediction effect of the model can be measured better. For a general machine learning model, based on the cost function, and a regularization term for measuring the complexity of the model, the regularization term can be used as a training objective function, and based on the objective function, the loss value of the whole training data set can be obtained. There are many kinds of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc. all can be used as the loss function of the machine learning model, and are not described one by one here. In the embodiment of the application, a loss function can be selected from the loss functions to determine the loss value of the training. And updating the parameters of the model by adopting a back propagation algorithm based on the trained loss value, and iterating for several rounds to obtain the trained unconventional event probability calculation model. Specifically, the number of iteration rounds may be preset, or training may be considered to be completed when the test set meets the accuracy requirement.
Referring to table 3, for the determination of the key importance of the fault tree, parsing is mainly performed from the aspects of the key logic, structural hierarchy, and the like of the fault tree model, for example, solving the minimal cut set. The minimum cut set was obtained by the ascending method, and the results are shown in table 3.
TABLE 3 minimum cut set of cooperative decision failure model
Solving the key importance in the fault tree model can reveal the magnitude of the influence of various factors on the decision failure. The calculation formula is as follows:
wherein I φ (i) Representing the structural importance of the ith basic event; n represents the number of basic events; phi (I) i X) denotes that the ith primitive event is 1, Σ Φ (1) i X) represents a base event x i Number of state combinations that occur simultaneously with the top event. Phi (0) i X) indicates that the ith primitive event is 0, Σ φ (0) i X) represents a base event x i Number of state combinations that do not occur and the top event occurs. The basic event structure importance result of the cooperative decision failure risk fault tree model is calculated according to a formula as follows:
I(X 37 )＝I(X 36 )＝I(X 35 )＝I(X 34 )＝I(X 31 )＝I(X 30 )＝I(X 29 )＝I(X 26 )＝I(X 25 )＝I(X 24 )＝I(X 23 )＝I(X 22 )＝I(X 21 )＝I(X 20 )＝I(X 19 )＝I(X 18 )＝I(X 17 )＝I(X 16 )＝I(X 15 )＝I(X 14 )＝I(X 13 )＝I(X 12 )＝I(X 11 )＝I(X 10 )＝I(X 9 )＝I(X 8 )＝I(X 7 )＝I(X 6 )＝I(X 5 )＝I(X 4 )＝I(X 3 )＝I(X 2 )＝I(X 1 )>I(X 33 )＝I(X 32 )＝I(X 28 )＝I(X 27 )
the results of the probability importance and key importance calculations are shown in the following table:
TABLE 4 calculation of probability and key importance
Further as an optional implementation manner, the obtaining the occurrence probability of the basic event includes:
acquiring historical accident data;
extracting bottom event data in the historical accident data;
and calculating the occurrence probability of the basic event according to the bottom event data.
Specifically, referring to table 5, the regular event probability may be calculated by acquiring historical accident data at the marine regulatory body, then extracting bottom event data from the historical accident data, and using it as a regular event. And combining the conventional event probability and the unconventional event probability to obtain the occurrence probability of the basic event. The basic event probabilities are shown in the following table:
TABLE 5 elementary event probabilities
As a further optional embodiment, the method further comprises the steps of:
and displaying the prevention and control strategy generated by the Bow-Tie model through a display module.
Specifically, the display module includes, but is not limited to, a smart watch, a smart phone, a tablet computer, a Personal Digital Assistant (PDA), an intelligent voice interaction device, a notebook computer, a desktop computer, an intelligent household appliance, or a vehicle-mounted terminal, and the display module is wirelessly connected to the communication module of the Bow-Tie model, so that a real-time state feedback function can be realized.
Further as an alternative embodiment, the prevention strategy includes both preventive and preventative measures.
Specifically, referring to table 6, the bow-Tie model provides a cooperative decision-making of prevention and control measures for failure risk in both prevention and prevention aspects, and the prevention and control measures cover several aspects of civil air defense, physical air defense and technical air defense, and aim to reduce the occurrence probability of risks and improve the safety of the conventional/remote-driving ship hybrid navigation. The preventive and deterrent measures for cooperative decision-making failure events are shown in the following table:
TABLE 6 cooperative decision prevention and prevention of failure events
Referring to fig. 3, an identification, prevention and control system for a navigation risk provided in an embodiment of the present invention includes:
a first module 301, configured to obtain basic event data, where the basic event includes a regular event and an irregular event;
a second module 302, configured to construct a fault tree model of the navigation risk, where the fault tree model includes a top event, a middle event, and a bottom event;
a third module 303, configured to perform qualitative and quantitative analysis on the basic event data through the fault tree model to obtain a key risk factor;
a fourth module 304, configured to construct an event tree model according to the key risk factors and the traffic accident report;
a fifth module 305, configured to evaluate the basic event data through the event tree model to obtain a critical accident consequence;
a sixth module 306, configured to construct a Bow-Tie model according to the key risk factor and the key accident consequence;
a seventh module 307, configured to generate, through the Bow-Tie model, a prevention and control policy corresponding to the top event.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
Referring to fig. 4, an embodiment of the present invention provides an apparatus for identifying, preventing and controlling a navigation risk, including:
at least one processor 401;
at least one memory 402 for storing at least one program;
the at least one program, when executed by the at least one processor 401, causes the at least one processor 401 to implement the method for identifying and controlling navigation risk illustrated in fig. 2.
The contents in the method embodiments are all applicable to the device embodiments, the functions specifically implemented by the device embodiments are the same as those in the method embodiments, and the beneficial effects achieved by the device embodiments are also the same as those achieved by the method embodiments.
An embodiment of the present invention further provides a storage medium, in which processor-executable instructions are stored, and when the processor-executable instructions are executed by a processor, the storage medium is used for implementing the method for identifying and controlling navigation risks shown in fig. 2.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A navigation risk identification, prevention and control method is characterized by comprising the following steps:
acquiring basic event data according to a traffic accident report, wherein the basic events comprise conventional events corresponding to conventional ships and unconventional events corresponding to intelligent ships;
constructing a fault tree model of a mixed navigation scene of various ships under the inland navigation condition, wherein the fault tree model comprises a top event, a middle event and a bottom event;
analyzing the basic event data through the fault tree model to obtain key risk factors;
constructing an event tree model according to the key risk factors and the traffic accident report;
evaluating the basic event data through the event tree model to obtain a key accident consequence;
constructing a Bow-Tie model according to the key risk factors and the key accident consequence;
and generating a prevention and control strategy corresponding to the top event through the Bow-Tie model.
2. The method for identifying, preventing and controlling sailing risks according to claim 1, characterized in that the fault tree model is constructed by the following steps:
constructing a mixed navigation scene of various ships under the inland navigation condition;
decomposing the interactive collaborative process among multiple ships in the scene into a collaborative perception stage, a collaborative cognition stage, a collaborative decision stage and a collaborative control stage;
constructing a cooperative framework according to the cooperative sensing stage, the cooperative cognition stage, the cooperative decision stage and the cooperative control stage;
setting a top event according to the collaborative frame;
analyzing according to the top incident and the traffic accident report to obtain a middle incident and a bottom incident corresponding to the top incident;
and constructing the fault tree model according to the top event, the middle event and the bottom event.
3. The method for identifying and controlling sailing risks according to claim 2, characterized in that the step of analyzing the top incident and the traffic accident report includes:
extracting accident data in the traffic accident report, and taking the accident type appearing in the accident data as an intermediate event; classifying the intermediate event, wherein the classification comprises personnel risk, ship risk, environmental risk and technical risk.
4. The method for identifying, preventing and controlling navigation risks according to claim 1, wherein the step of performing qualitative and quantitative analysis on the basic event data through the fault tree model to obtain key risk factors specifically comprises:
acquiring the occurrence probability of the basic event;
according to the occurrence probability of the basic event, obtaining the probability importance and the key importance corresponding to the basic event through the fault tree model, wherein the key importance is used for representing the influence weight of the bottom event on the top event;
and sequencing according to the key importance, and obtaining key risk factors according to a sequencing result.
5. The method for identifying and controlling sailing risks according to claim 4, wherein the obtaining of the probability of occurrence of the basic event comprises:
acquiring historical accident data;
extracting bottom event data in the historical accident data;
and calculating the occurrence probability of the basic event according to the bottom event data.
6. Method for identification and control of voyage risks according to claim 1, characterized in that it comprises the following further steps:
and displaying the prevention and control strategy generated by the Bow-Tie model through a display module.
7. Method for identification and control of voyage risks according to any one of claims 1-6, characterized in that the control strategy comprises preventive and preventive measures.
8. An identification and prevention and control system for navigation risks, comprising:
the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring basic event data according to a traffic accident report, and the basic events comprise conventional events corresponding to conventional ships and unconventional events corresponding to intelligent ships;
the second module is used for constructing a fault tree model of a mixed navigation scene of various ships under the inland navigation condition, wherein the fault tree model comprises a top event, a middle event and a bottom event;
the third module is used for carrying out qualitative and quantitative analysis on the basic event data through the fault tree model to obtain key risk factors;
the fourth module is used for constructing an event tree model according to the key risk factors and the traffic accident report;
a fifth module, configured to evaluate the basic event data through the event tree model to obtain a key accident consequence;
a sixth module, configured to construct a Bow-Tie model according to the key risk factor and the key accident consequence;
and the seventh module is used for generating a prevention and control strategy corresponding to the top event through the Bow-Tie model.
9. An identification and prevention and control device for navigation risks, which is characterized by comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a method for identifying and controlling a voyage risk according to any one of claims 1-7.
10. A computer readable storage medium having stored therein processor executable instructions for implementing a method for identifying and controlling a voyage risk according to any one of claims 1-7 when executed by a processor.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116010886A (en) * | 2022-12-22 | 2023-04-25 | 航安云创科技(北京)有限公司 | Security monitoring method, device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116010886A (en) * | 2022-12-22 | 2023-04-25 | 航安云创科技(北京)有限公司 | Security monitoring method, device, electronic equipment and storage medium |
| CN116010886B (en) * | 2022-12-22 | 2023-09-12 | 航安云创科技(北京)有限公司 | Security monitoring method, device, electronic equipment and storage medium |
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