CN113592692A - Method, device, medium and equipment for identifying scene hazards of rail transit scene - Google Patents

Method, device, medium and equipment for identifying scene hazards of rail transit scene Download PDF

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CN113592692A
CN113592692A CN202010366078.0A CN202010366078A CN113592692A CN 113592692 A CN113592692 A CN 113592692A CN 202010366078 A CN202010366078 A CN 202010366078A CN 113592692 A CN113592692 A CN 113592692A
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梁鸿煜
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BYD Co Ltd
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Abstract

The disclosure relates to a method, a device, a medium and equipment for identifying scene hazards in a rail transit scene, wherein the method comprises the following steps: obtaining hazard factors of a scene to be identified, wherein the hazard factors at least comprise train factors, place factors and personnel factors; determining a target state corresponding to the hazard factors; and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the target state corresponding to the damage factors. Therefore, the hazard factors of the rail transit scene and the target states corresponding to the hazard factors can be used for identifying the scene hazard corresponding to the rail transit scene, so that the scene hazard identification result of the rail transit scene can be more comprehensive and clear, so that safety personnel can more accurately control the scene hazard in the rail transit scene, and the possibility of safety accidents in the rail transit scene is reduced.

Description

Method, device, medium and equipment for identifying scene hazards of rail transit scene
Technical Field
The present disclosure relates to the field of train safety, and in particular, to a method, an apparatus, a medium, and a device for identifying a scene hazard in a rail transit scene.
Background
With the rapid development of rail transit, an independent safety management method for rail transit is still deficient. In order to ensure the safety of rail transit, the method is crucial to the safety management of rail transit, and the safety management usually adopts an up-down working mode, and at the top of the safety management activity, the most important point is that the method can identify the scene hazard in the rail transit scene first, so that the risk can be controlled according to the identified scene hazard.
In the prior art, the standards commonly used for identifying the scene hazards include three general international standards and group standards, but in practical applications, for example, in the process of identifying the scene hazards in the rail transit scene, the general standards cannot be well combined with the practical applications, so that the scene hazard identification result of the rail transit scene is not comprehensive and clear.
Disclosure of Invention
The invention aims to provide a method, a device, a medium and equipment for identifying scene hazards of a rail transit scene, which can identify the scene hazards corresponding to the rail transit scene through the hazard factors aiming at the rail transit scene and the target state corresponding to the hazard factors, so that the scene hazard identification result of the rail transit scene can be more comprehensive and clear, so that safety personnel can more accurately control the scene hazards in the rail transit scene, and the possibility of safety accidents in the rail transit scene is reduced.
In order to achieve the above object, the present disclosure provides a method for identifying a scene hazard of a rail transit scene, the method including:
obtaining hazard factors of a scene to be identified, wherein the hazard factors at least comprise train factors, place factors and personnel factors;
determining a target state corresponding to the hazard factors;
and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the target state corresponding to the damage factors.
Optionally, the target states include a primary target state and a secondary target state, and a one-to-many or one-to-one correspondence relationship is between the primary target state and the secondary target state;
the identifying the scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor comprises:
and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the primary target state and the secondary target state corresponding to the damage factors.
Optionally, the identifying the scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor includes:
and searching and identifying scene hazards corresponding to the scene to be identified in a preset scene hazard database according to the hazard factors of the scene to be identified and the target state corresponding to the hazard factors.
Optionally, before the obtaining of the hazard factors of the scene to be identified, the method further includes:
and constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors.
Optionally, the method further comprises:
acquiring scene attributes of the scene to be identified, wherein the scene data at least comprises one of a driving mode, an operation level and a line characteristic;
the searching and identifying the scene hazard corresponding to the scene to be identified in a preset scene hazard database according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor comprises:
and searching and identifying the scene hazard corresponding to the scene to be identified in the preset scene hazard database according to the hazard factors of the scene to be identified, the target state corresponding to the hazard factors and the scene attribute.
Optionally, the constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors includes:
and constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified, the state corresponding to the hazard factors and all the scene attributes of the scene to be identified.
Optionally, the constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors includes:
acquiring all the hazard factors in the scene to be identified;
acquiring all factor values corresponding to the hazard factors;
successively selecting one factor value from each hazard factor as a target factor value, and arranging and combining all the target factor values;
and combining each permutation combination with the state corresponding to the hazard factors to construct the preset scene hazard database.
The present disclosure also provides a scene hazard recognition apparatus for a rail transit scene, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a recognition module, wherein the first acquisition module is used for acquiring hazard factors of a scene to be recognized, and the hazard factors at least comprise train factors, place factors and personnel factors;
the determining module is used for determining a target state corresponding to the hazard factors;
and the identification module is used for identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the target state corresponding to the damage factors.
The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, realizes the steps of the above-mentioned method for identifying a hazard in a rail traffic scene.
The present disclosure also provides an electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the above method for identifying a hazard in a rail traffic scene.
Through the technical scheme, the scene damage corresponding to the rail transit scene can be identified through the damage factors aiming at the rail transit scene and the target state corresponding to the damage factors, so that the scene damage identification result of the rail transit scene can be more comprehensive and clear, so that safety personnel can more accurately control the scene damage in the rail transit scene, and the possibility of safety accidents in the rail transit scene is reduced.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario, according to yet another exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario, according to yet another exemplary embodiment of the present disclosure.
Fig. 4 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario, according to yet another exemplary embodiment of the present disclosure.
Fig. 5 is a flowchart illustrating a method for building a preset scene hazard database in a method for identifying a scene hazard of a rail transit scene according to yet another exemplary embodiment of the present disclosure.
Fig. 6 is a block diagram illustrating a configuration of a scene hazard recognition apparatus for a rail transit scene according to an exemplary embodiment of the present disclosure.
Fig. 7 is a block diagram illustrating a configuration of a scene hazard recognition apparatus for a rail transit scene according to yet another exemplary embodiment of the present disclosure.
Fig. 8 is a block diagram illustrating a configuration of a scene hazard recognition apparatus for a rail transit scene according to yet another exemplary embodiment of the present disclosure.
FIG. 9 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 10 is a block diagram illustrating an electronic device in accordance with yet another example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the method includes steps 101 to 103.
In step 101, hazard factors of a scene to be identified are obtained, wherein the hazard factors at least include train factors, location factors and personnel factors.
Each hazard factor may include a plurality of factor values, for example, a train factor may include four factor values of train movement, train standstill, train inside, and no train, a location factor may include four factor values of a center (e.g., a main/standby control center), a field section (including a parking lot or a vehicle section), a section, and a station (including a test lane), and a personnel factor may include three factor values of a passenger, an employee, and a public (including an emergency rescue group). The factor value of each hazard factor is determined according to the hazard factors which can appear in the rail transit scene.
In addition, the hazard factors may include other hazard factors not exemplified in the present disclosure, in addition to the train factor, the location factor, and the personnel factor.
When the hazard factors in the scene to be identified are obtained, each obtained hazard factor may include only one factor value, or may include multiple factor values. For example, when the scene to be identified is a scene in which a train is in operation, the factor values of the train factor may include train movement and the inside of the train among the hazard factors of the scene to be identified, and since the train may pass through various places in operation, the place factor of the hazard factor of the scene to be identified may include four factor values, such as a center, an interval, a field and a station. Similarly, for the scene to be identified in which the train is in operation, the personnel factors of the hazard factors may also include the values of three factors, namely, passengers, staff and the public, because in the scene to be identified, three kinds of personnel are likely to be present.
In step 102, a target state corresponding to the hazard factor is determined.
After the hazard factors in the scene to be identified are acquired, a target state corresponding to the hazard factors can be determined. The target state may be determined by the factor value of the hazard factor, or may also be determined in real time by determining the monitoring condition of the scene to be identified. The target state may be a state that may exist in the scene to be identified, such as a collision between a train and a train, a collision between a train and an obstacle, or a fire and an explosion, an environment that does not meet the safety environment requirement of the scene to be identified, and the like.
For example, in the case that the target state is determined according to the factor value of the hazard factor, in the case that the scene to be identified is a scene in which a train is in operation, the factor value of the train factor is train motion and inside of the train, and in the case that the train factor is train motion, the state that may occur in the scene to be identified may be that the train collides with the train, the train collides with an obstacle, the train collides with a person, the train is derailed, a fire and an explosion occur, the environment does not meet the safety environment requirement of the scene to be identified, a situation that emergency and rescue are required occurs, or other unexpected situations, for example, when blockade is not performed, staff performs maintenance in a rail area, a turnout suddenly rotates, and the like; in the case where the train factor is inside a train, a state that may occur in the scene to be recognized is, for example, occurrence of a hazard inside the train.
Because the hazard factors in the scene to be identified include not only train factors but also location factors and personnel factors, when determining the target state corresponding to the hazard factors, the state that may occur in the scene to be identified is determined according to the factor values of the train factors, and the states that may occur in the scene to be identified are also determined according to the factor values of the location factors and the factor values of the personnel factors. And finally, only determining the states respectively corresponding to the train factor, the location factor and the personnel factor as the target states. For example, the state of the train colliding with the train may correspond to the train movement in the train element, the section, the yard, or the station in the site element, or the passenger, the staff, or the public in the staff element, and the train colliding with the train may be determined as the target state corresponding to the hazard factor.
The correspondence between the states and the factor values of the different hazard factors may be preset, for example, may be a preset correspondence table shown in table 1.
TABLE 1
Figure BDA0002476575950000071
The "environment" state may represent a state in which the environment of the scene to be identified does not meet the requirement of the security environment of the scene to be identified, the environment of the scene to be identified may include, for example, a natural environment and a social environment, the natural environment may include, for example, a temperature environment, a humidity environment, an electromagnetic environment, and the like, and the social environment may include, for example, an artificial malicious destruction situation, a terrorist attack, insufficient bearing of a basic setting, and the like. The "emergency and rescue" state can represent a state in which emergency and rescue are needed in the scene to be identified, and the emergency and rescue can include rescue, evacuation and the like. This "outside station hazard" can characterize, for example, when the train is stationary or not, some hazards inherent in the station, such as: when taking in and falling off operation is carried out, the platform door and the vehicle door are not correspondingly opened, and the platform door is in a fault state, the gap between the vehicle door and the platform door is too large, so that personnel fall from the platform into a rail and other vehicle stations in a special state. The 'harm in the train' can represent the harm caused by the self fault of the train, such as opening of a train door in the running process, putting of the fault train into operation, creeping running of the train and the like.
The state may also include other states, and the state shown in table 1 is merely one example according to the present disclosure. According to the preset correspondence table of this example, the target state corresponding to the hazard factor may be determined in the case where the hazard factor of the scene to be recognized is determined.
In step 103, identifying a scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and a target state corresponding to the hazard factor.
The method for determining the scene hazard corresponding to the scene to be recognized may be to automatically generate the scene hazard directly through a preset generation model by using the hazard factor determined in step 101 and the target state corresponding to the hazard factor determined in step 102.
For example, when the scene to be identified is a scene in which a train is in operation, the factor value of the train factor may include train movement, the factor value of the location factor may include a section, the factor value of the personnel factor may include passengers, and the target state corresponding to the hazard factor in the scene to be identified may include a collision between the train and the train. In this case, the following scenario hazard may be generated according to the hazard factor and the target state corresponding to the hazard factor directly through the preset generation model: in the process of train movement, the train collides with the train in the interval, and passengers are injured.
Specifically, an example of automatically generating a scene hazard through the preset generation model according to a location factor and a personnel factor corresponding to the target state and the train factor when the target state is that the train collides with the train and the factor value of the train factor is that the train moves in the scene where the scene to be identified is that the train is in operation is given in table 2.
TABLE 2
Figure BDA0002476575950000081
Figure BDA0002476575950000091
As shown in table 2, the preset generating model can automatically generate a speech segment for representing the scene hazard corresponding to the scene to be recognized according to the target state and the hazard factor, so as to recognize the scene hazard corresponding to the scene to be recognized.
In the above scenario that the to-be-identified scene is a scene in which the train is in operation, the target state determined according to the hazard factor may also have other states, the hazard factor, for example, the factor value included in the train factor may also include other factor values, and table 2 is only an example of generating, by the preset generation model, a language segment representing the hazard of the scene according to the hazard factor and the target loading.
In addition, identifying the scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor can also be realized by presetting a scene hazard database, and the detailed description will be specifically described later.
By the method provided by the technical scheme, the hazard factors of the rail transit scene and the target states corresponding to the hazard factors can be identified, so that the scene hazard identification result of the rail transit scene can be more comprehensive and clear, safety personnel can more accurately control the scene hazard of the rail transit scene, and the possibility of safety accidents in the rail transit scene is reduced.
In one possible implementation, the target states include a primary target state and a secondary target state, and there is a one-to-many or one-to-one correspondence between the primary target state and the secondary target state; step 103 shown in fig. 1 may also be: and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the primary target state and the secondary target state corresponding to the damage factors.
Taking the target state as the train-to-train collision as an example, the primary target state included in the target state may be the train-to-train collision, and the secondary target state corresponding to the primary target state may be, for example, the train-to-tail collision, the train-to-back collision, the train-to-side collision, the train-to-head collision, the train-to-train collision, and the like.
The primary target state and the secondary target state included in the target state are both preset, and the corresponding relationship between the primary target state and the secondary target state is also preset. In the case where the target state corresponding to the hazard factor in the scene to be recognized is determined by the above method, the target state, the secondary target state, and the correspondence relationship therebetween included therein can be determined.
And when the target state includes the primary target state and the secondary target state, and the scene hazard is automatically generated according to the preset generation model, the scene hazard needs to be generated together according to the primary target state and the secondary target state.
Table 3 shows an example of identifying scene hazards through the preset generation model when the first-level target state included in the target state is a train-to-train collision, and the second-level target state corresponding to the first-level target state is a train-to-train rear-end collision, a train backward collision, a train side collision, and a train disassembly collision under the same condition as table 2.
TABLE 3
Figure BDA0002476575950000111
Through the technical scheme, the scene hazards which are possibly corresponding to the scene to be identified and represented by the target state can be divided into fine-grained hazards through setting the first-level target state and the second-level target state in the target state, so that when the scene hazards corresponding to the scene to be identified are identified in the scene to be identified, the scene hazards can be identified more specifically, and then security personnel can more accurately control the scene hazards in the rail traffic scene, and the possibility of safety accidents in the rail traffic scene is further reduced.
Fig. 2 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario, according to yet another exemplary embodiment of the present disclosure. As shown in fig. 2, the method includes steps 101 and 102, and further includes step 201.
In step 201, a scene hazard corresponding to the scene to be identified is searched and identified in a preset scene hazard database according to the hazard factor of the scene to be identified and a target state corresponding to the hazard factor.
The preset scene hazard database may be a database that is generated in advance after all states and hazard factors are listed in advance and includes all scene hazards, including all known scene hazards, and a correspondence between a factor value of each scene hazard and each hazard factor and the state. Under the condition that scene damage needs to be identified on the scene to be identified, the scene damage corresponding to the damage factor and the target state can be found in the preset scene damage database only by acquiring the damage factor of the scene to be identified and determining the target state corresponding to the damage factor.
Through the technical scheme, the process of identifying the scene hazards of the rail transit scene can be further simplified, the more comprehensive scene hazards can be identified, and the speed of identifying the scene hazards is increased.
Fig. 3 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario, according to yet another exemplary embodiment of the present disclosure. As shown in fig. 3, the method includes step 101, step 102, step 201, and further includes step 301.
In step 301, the preset scene hazard database corresponding to the scene to be identified is constructed based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors.
Since the target states and hazard factors applicable to different scenes to be recognized may be different, when a scene hazard corresponding to the scene to be recognized is recognized according to the preset scene hazard database, all the hazard factors that may be included and possible states corresponding to the hazard factors may be determined in advance according to the different scenes to be recognized, so as to construct the preset scene hazard database for the scene to be recognized.
Fig. 4 is a flowchart illustrating a method for identifying a hazard scenario for a rail transit scenario, according to yet another exemplary embodiment of the present disclosure. As shown in fig. 4, the method includes step 101, step 102, and step 301, and further includes step 402 and step 403.
In step 402, scene attributes of the scene to be identified are obtained, and the scene data at least includes one of a driving mode, an operation level, and a route characteristic.
The driving mode may be, for example, a fully automatic unmanned mode (FAM) or an automatic Train driving mode (AM), the Operation level may be, for example, a Driverless Train Operation (DTO) or an Unattended Operation (UTO), and the line characteristic may be, for example, an above-ground line or an underground line. In addition, the scene attribute may further include, for example, a product system of a train included in the scene, such as a subway or a magnetic levitation. The scene attributes can be determined accordingly when the scene to be recognized is determined.
In step 403, according to the hazard factors of the scene to be identified, the target state corresponding to the hazard factors, and the scene attributes, the scene hazard corresponding to the scene to be identified is searched and identified in the preset scene hazard database.
In the above example where the scene to be identified is a scene in which a train is in operation, the scene hazard identified according to the hazard factor obtained in the scene to be identified and the target state corresponding to the hazard factor may be more than the scene hazard that actually occurs, for example, as shown in table 1, a state where the train collides with an obstacle is determined as a target state, where the state may include a state where the train collides with the obstacle as a primary target state, and the primary target state may correspond to but not only correspond to two secondary target states: the train collides with the flood gate; the train collides with the civil air defense door. The two secondary target states only exist in a scene that the train runs in an underground line, and if the train in the scene to be identified runs in the above-ground line or the product system of the train in the scene to be identified is, for example, a suspension type, the scene damage that the train collides with a flood gate and the people's defense gate does not generally occur in the scene to be identified.
Therefore, before the scene hazard is identified, the scene attribute of the scene to be identified is acquired, and the scene hazard is identified according to the scene attribute when the scene hazard is identified, so that the identification accuracy of the scene hazard can be further improved, safety personnel can more accurately control the scene hazard in the rail transit scene, and the possibility of safety accidents in the rail transit scene is further reduced.
In one possible embodiment, as shown in fig. 4, the method may further include step 401. In step 401, based on all the hazard factors in the scene to be identified, the state corresponding to the hazard factors, and all the scene attributes of the scene to be identified, the preset scene hazard database corresponding to the scene to be identified is constructed. That is, before the preset scene hazard database according to the scene attribute, the preset scene hazard database is constructed based on all the scene attributes in the scene to be identified. Therefore, the efficiency of recognizing the damage of the scene can be further improved.
Fig. 5 is a flowchart illustrating a method for building a preset scene hazard database in a method for identifying a scene hazard of a rail transit scene according to yet another exemplary embodiment of the present disclosure. As shown in fig. 5, the method includes steps 501 to 504.
In step 501, all the hazard factors in the scene to be identified are acquired.
For example, all hazard factors in the scene to be identified may be as shown in table 1, including a train factor, a location factor, and a personnel factor.
In step 502, all factor values corresponding to each of the hazard factors are obtained.
All the factor values corresponding to each hazard factor may also be as shown in table 1, all the factor values corresponding to the train may include four factor values of train movement, train standstill, train inside, and train absence, all the factor values corresponding to the location factor may include four factor values of center, field, section, and station, for example, and all the factor values corresponding to the personnel factor may include three factor values of passenger, employee, and public, for example.
In step 503, one factor value is selected from each of the hazard factors as a target factor value, and all the target factor values are arranged and combined.
That is, the train movement may be selected as the target factor value among the train factors, the section may be selected as the target factor value among the location factors, and the passenger may be selected as the target factor value among the personnel factors, thus forming a combination, and thus, a plurality of permutation combinations may be obtained each time one target factor is selected among one hazard factor, and each permutation combination includes one factor value among each hazard factor.
In step 504, each permutation and combination is combined with the state corresponding to the hazard factor to construct the preset scene hazard database.
Finally, the permutation and combination determined in step 503 are respectively combined with the states that may appear in the scene to be identified, so as to obtain the final preset scene hazard database.
In a possible implementation manner, in order to make the preset scene hazard database more reasonable and reduce the occupation of the preset scene hazard database on the storage space, security personnel may delete the unreasonable combination according to the actual situation when constructing the preset scene hazard database, and may delete the unreasonable scene hazard corresponding to the unreasonable combination.
Fig. 6 is a block diagram illustrating a configuration of a scene hazard recognition apparatus 100 for a rail transit scene according to an exemplary embodiment of the present disclosure. As shown in fig. 6, the apparatus 100 includes: the system comprises a first acquisition module 10, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring hazard factors of a scene to be identified, and the hazard factors at least comprise train factors, place factors and personnel factors; a determining module 20, configured to determine a target state corresponding to the hazard factor; and the identifying module 30 is configured to identify a scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and a target state corresponding to the hazard factor.
Through the device that above-mentioned technical scheme provided, can come to discern the scene harm that the track traffic scene corresponds through the harm factor to this track traffic scene and the target state that corresponds with the harm factor to make the scene harm identification result to this track traffic scene can be more comprehensive, clear, thereby make the security personnel can control the scene harm in this track traffic scene more accurately, reduce the possibility that the incident takes place in this track traffic scene.
In one possible implementation, the target states include a primary target state and a secondary target state, and there is a one-to-many or one-to-one correspondence between the primary target state and the secondary target state; the identification module 30 is further configured to: and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the primary target state and the secondary target state corresponding to the damage factors.
In a possible embodiment, the identification module 30 is further configured to: and searching and identifying scene hazards corresponding to the scene to be identified in a preset scene hazard database according to the hazard factors of the scene to be identified and the target state corresponding to the hazard factors.
Fig. 7 is a block diagram illustrating a configuration of a scene hazard recognition apparatus 100 for a rail transit scene according to yet another exemplary embodiment of the present disclosure. As shown in fig. 7, the apparatus 100 further includes: the building module 40 is configured to build the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors before the hazard factors of the scene to be identified are obtained.
In a possible implementation manner, as shown in fig. 7, the apparatus 100 further includes a second obtaining module 50, configured to obtain a scene attribute of the scene to be identified, where the scene data includes at least one of a driving mode, a running level, and a route characteristic; the identification module 30 is further configured to: and searching and identifying the scene hazard corresponding to the scene to be identified in the preset scene hazard database according to the hazard factors of the scene to be identified, the target state corresponding to the hazard factors and the scene attribute.
In a possible embodiment, the building module 40 is further configured to: and constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified, the state corresponding to the hazard factors and all the scene attributes of the scene to be identified.
Fig. 8 is a block diagram illustrating a configuration of a scene hazard recognition apparatus 100 for a rail transit scene according to yet another exemplary embodiment of the present disclosure. As shown in fig. 8, the building block 40 includes: a first obtaining submodule 401, configured to obtain all the hazard factors in the scene to be identified; a second obtaining submodule 402, configured to obtain all factor values corresponding to each hazard factor; a first processing submodule 403, configured to successively select one factor value from each of the hazard factors as a target factor value, and perform permutation and combination on all the target factor values; and a second processing sub-module 404, configured to combine each permutation and combination with the state corresponding to the hazard factor to construct the preset scene hazard database.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 9 is a block diagram illustrating an electronic device 900 in accordance with an example embodiment. As shown in fig. 9, the electronic device 900 may include: a processor 901 and a memory 902. The electronic device 900 may also include one or more of a multimedia component 903, an input/output (I/O) interface 904, and a communications component 905.
The processor 901 is configured to control the overall operation of the electronic device 900, so as to complete all or part of the steps in the method for identifying a hazard in a rail transit scene. The memory 902 is used to store various types of data to support operation of the electronic device 900, such as instructions for any application or method operating on the electronic device 900 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 902 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia component 903 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 902 or transmitted through the communication component 905. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 904 provides an interface between the processor 901 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 905 is used for wired or wireless communication between the electronic device 900 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, which is not limited herein. The corresponding communication component 905 may thus include: Wi-Fi module, Bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic Device 900 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing the scene hazard recognition method for the rail traffic scene.
In another exemplary embodiment, a computer readable storage medium is also provided, which comprises program instructions, which when executed by a processor, implement the steps of the above-described method for identifying a hazard scenario for a rail traffic scenario. For example, the computer readable storage medium may be the memory 902 described above comprising program instructions executable by the processor 901 of the electronic device 900 to perform the method for identifying a hazard scenario for a rail traffic scenario described above.
Fig. 10 is a block diagram illustrating an electronic device 1000 in accordance with an example embodiment. For example, the electronic device 1000 may be provided as a server. Referring to fig. 10, the electronic device 1000 includes a processor 1022, which may be one or more in number, and a memory 1032 for storing computer programs executable by the processor 1022. The computer programs stored in memory 1032 may include one or more modules that each correspond to a set of instructions. Further, the processor 1022 may be configured to execute the computer program to perform the scene hazard identification method of the rail transit scene described above.
Additionally, the electronic device 1000 may also include a power component 1026 and a communication component 1050, the power component 1026 may be configured to perform power management for the electronic device 1000, and the communication component 1050 may be configured to enable communication for the electronic device 1000, e.g., wired or wireless communication. In addition, the electronic device 1000 may also include input/output (I/O) interfaces 1058. The electronic device 1000 may operate based on an operating system stored in the memory 1032, such as Windows ServerTM，Mac OS XTM，UnixTM，LinuxTMAnd so on.
In another exemplary embodiment, a computer readable storage medium is also provided, which comprises program instructions, which when executed by a processor, implement the steps of the above-described method for identifying a hazard scenario for a rail traffic scenario. For example, the computer-readable storage medium may be the memory 1032 comprising program instructions executable by the processor 1022 of the electronic device 1000 to perform the method for identifying a hazard scenario for a rail traffic scenario described above.
In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable device, the computer program having code portions for performing the above-mentioned method for identifying a hazard scenario for a rail traffic scenario when being executed by the programmable device.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.

Claims (10)

1. A method for identifying scene hazards of a rail transit scene is characterized by comprising the following steps:
obtaining hazard factors of a scene to be identified, wherein the hazard factors at least comprise train factors, place factors and personnel factors;
determining a target state corresponding to the hazard factors;
and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the target state corresponding to the damage factors.
2. The method of claim 1, wherein the target states comprise primary target states and secondary target states, and the primary target states and the secondary target states have one-to-many or one-to-one correspondence;
the identifying the scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor comprises:
and identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the primary target state and the secondary target state corresponding to the damage factors.
3. The method according to claim 1 or 2, wherein the identifying the scene hazard corresponding to the scene to be identified according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor comprises:
and searching and identifying scene hazards corresponding to the scene to be identified in a preset scene hazard database according to the hazard factors of the scene to be identified and the target state corresponding to the hazard factors.
4. The method of claim 3, wherein prior to said obtaining hazard factors for a scene to be identified, the method further comprises:
and constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors.
5. The method of claim 4, further comprising:
acquiring scene attributes of the scene to be identified, wherein the scene data at least comprises one of a driving mode, an operation level and a line characteristic;
the searching and identifying the scene hazard corresponding to the scene to be identified in a preset scene hazard database according to the hazard factor of the scene to be identified and the target state corresponding to the hazard factor comprises:
and searching and identifying the scene hazard corresponding to the scene to be identified in the preset scene hazard database according to the hazard factors of the scene to be identified, the target state corresponding to the hazard factors and the scene attribute.
6. The method of claim 5, wherein the constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors comprises:
and constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified, the state corresponding to the hazard factors and all the scene attributes of the scene to be identified.
7. The method of claim 3, wherein the constructing the preset scene hazard database corresponding to the scene to be identified based on all the hazard factors in the scene to be identified and the states corresponding to the hazard factors comprises:
acquiring all the hazard factors in the scene to be identified;
acquiring all factor values corresponding to the hazard factors;
successively selecting one factor value from each hazard factor as a target factor value, and arranging and combining all the target factor values;
and combining each permutation combination with the state corresponding to the hazard factors to construct the preset scene hazard database.
8. A scene hazard identification apparatus for a rail transit scene, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a recognition module, wherein the first acquisition module is used for acquiring hazard factors of a scene to be recognized, and the hazard factors at least comprise train factors, place factors and personnel factors;
the determining module is used for determining a target state corresponding to the hazard factors;
and the identification module is used for identifying scene damage corresponding to the scene to be identified according to the damage factors of the scene to be identified and the target state corresponding to the damage factors.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 7.
CN202010366078.0A 2020-04-30 2020-04-30 Method, device, medium and equipment for identifying scene hazards of rail transit scene Pending CN113592692A (en)

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