CN114932929B - Train control method, device, equipment, storage medium and program product - Google Patents
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Abstract
The application discloses a control method, a device, equipment, a storage medium and a program product of a train. The method comprises the following steps: acquiring N scene types, wherein N is a positive integer; extracting a knowledge graph sub-graph from the knowledge graph based on the N scene types, wherein the knowledge graph sub-graph comprises the N scene types and N treatment flows which are in one-to-one correspondence with the N scene types; determining a control flow of the train according to the N treatment flows; and controlling the train based on the control flow. According to the embodiment of the application, the autonomous control of the train can be realized, so that the problem of low efficiency of the existing train control mode is solved.
Description
Technical Field
The application belongs to the technical field of artificial intelligence, and particularly relates to a control method, a device, equipment, a storage medium and a program product of a train.
Background
With the continuous improvement of the running speed and the automation degree of urban rail transit trains, the application range of the full-automatic running (Fully Automatic Operation, FAO) system is continuously expanded, and the availability of the FAO system, particularly the disposal efficiency and the safety under the fault and emergency conditions, are particularly important in the running process. Currently, in the operation process of the FAO, if a fault condition occurs, the general treatment process is 'field perception-information pushing-center confirmation-manual decision-field execution'.
For example, when a fire occurs in a vehicle, a general disposal procedure is that a dispatcher in a control center takes up a monitor screen of a specified vehicle after receiving a vehicle fire alarm, confirms the fire situation, schedules a vehicle crew, and a crew at a front station for evacuation of the personnel, emergency disposal of the fire, and the like. In the whole process, staff needs to collect information for confirming all aspects so as to finally make decisions. And under the condition that a plurality of abnormal scenes are compounded, more time is needed for manually collecting information and making a decision again, so that the treatment efficiency of abnormal treatment is seriously affected.
Disclosure of Invention
The embodiment of the application provides a control method, a device, equipment, a storage medium and a program product of a train, which can solve the problem of low efficiency of the existing train control mode.
In a first aspect, an embodiment of the present application provides a method for controlling a train, where the method includes:
acquiring N scene types according to the current running state of the train, wherein N is a positive integer;
Extracting a knowledge graph sub-graph from the knowledge graph based on the N scene types, wherein the knowledge graph sub-graph comprises the N scene types and N treatment flows corresponding to the N scene types one by one;
Determining a control flow of the train according to the N treatment flows;
and controlling the train based on the control flow.
In some embodiments, the determining the control flow of the train according to the N treatment flows includes:
When N is 1, the process flow is determined as the control flow of the train.
In some embodiments, the determining the control flow of the train according to the N treatment flows includes:
Determining the execution sequence of the N treatment flows according to the priorities of the N scene types under the condition that the N is a positive integer greater than 1;
And determining the N treatment processes arranged according to the execution sequence as the control process of the train.
In some embodiments, before the determining the control flow of the train according to the N treatment flows, the method further comprises:
Obtaining the loss degree corresponding to each scene type;
And setting the priority of each scene type according to each loss degree.
In some embodiments, the determining the control flow of the train according to the N treatment flows includes:
When the N treatment processes include only treatment steps, determining a control process of the train according to a sequential relationship between the treatment steps and the treatment steps;
detecting whether the current running state of the train meets the precondition or not in the case that the N treatment processes comprise treatment steps and the precondition;
And determining the control flow of the train according to the first precondition, the treatment step and the sequence relation between the first precondition and the treatment step which are met by the current running state.
In an embodiment, each of the treatment procedures comprises at least one treatment step; before the acquiring the N scene types, the method further includes:
constructing a mode layer of a knowledge graph, wherein the mode layer comprises entity nodes and relations between the entity nodes;
receiving input information, wherein the input information comprises running states of the train in a warehouse and during positive line running, treatment principles corresponding to the running states and occurrence relations between the running states and the treatment principles;
And filling a mode layer of the knowledge graph according to the input information to generate the knowledge graph.
In an embodiment, filling the pattern layer of the knowledge graph according to the input information to generate the knowledge graph includes:
Detecting the running state, the treatment principle and the occurrence relation in the input information;
Mapping the running state to the entity node of the mode layer to generate P scene types;
Mapping the treatment principle to the entity node of the mode layer, and generating P treatment flows corresponding to the P scene types one by one, and treatment steps and preconditions included in the P treatment flows;
Mapping the occurrence relation to the relation among the entity nodes in the mode layer, generating a first relation among the P scene types, the P treatment processes, the treatment steps and the preconditions, and generating the knowledge graph.
In an embodiment, after the determining the control flow of the train according to the N treatment flows, the method further includes:
And displaying the control flow through a visual interface.
In a second aspect, an embodiment of the present application provides a control device for a train, including:
The acquisition module is used for acquiring N scene types according to the current running state of the train, wherein N is a positive integer;
The extraction module is used for extracting a knowledge graph subgraph from the knowledge graph based on the N scene types, wherein the knowledge graph subgraph comprises the N scene types and N treatment flows which are in one-to-one correspondence with the N scene types;
the determining module is used for determining the control flow of the train according to the N treatment flows;
and the control module is used for controlling the train based on the control flow.
In a third aspect, an embodiment of the present application provides a control device for a train, including: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the control method of the train as described above.
In a fourth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method of controlling a train as above.
In a fifth aspect, embodiments of the present application provide a computer program product comprising computer program instructions which, when executed by a processor, implement a method of controlling a train as above.
In the method, N scene types are obtained, wherein N is a positive integer, a knowledge graph sub-graph is extracted from a knowledge graph based on the N scene types, and the knowledge graph sub-graph comprises the N scene types and N disposal flows corresponding to the N scene types one by one; determining a control flow of the train according to the N treatment flows; and controlling the train based on the control flow. In this way, compared with the prior art, when an abnormal scene occurs, the method and the device have the advantages that the autonomous decision of the train can be performed based on the knowledge graph by manually making the decision according to the acquired information, so that the control efficiency of the train is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a flow chart of a method for controlling a train according to an embodiment of the present application;
Fig. 2 is a schematic view of a control device of a train according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for controlling a train according to yet another embodiment of the present application;
fig. 4 is a flow chart of a control method of a train according to still another embodiment of the present application;
FIG. 5 is a flow chart of a method for controlling a train according to yet another embodiment of the present application;
fig. 6 is a schematic view of a control device of a train according to still another embodiment of the present application;
fig. 7 is a schematic view of a control device of a train according to an embodiment of the present application;
Fig. 8 is a schematic view of a control device of a train according to an embodiment of the present application;
fig. 9 is a schematic flow chart of a control device of a train according to another embodiment of the present application;
fig. 10 is a schematic view of a control device of a train according to still another embodiment of the present application;
fig. 11 is a schematic structural view of a control device for a train according to still another embodiment of the present application;
fig. 12 is a schematic structural view of a control device for a train according to still another embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
With the continuous improvement of the running speed and the automation degree of urban rail transit trains, the application range of the full-automatic running (Fully Automatic Operation, FAO) system is continuously expanded, and the availability of the FAO system, particularly the disposal efficiency and the safety under the fault and emergency conditions, are particularly important in the running process. Currently, in the operation process of the FAO, if a fault condition occurs, the general treatment process is 'field perception-information pushing-center confirmation-manual decision-field execution'.
For example, when a fire occurs in a vehicle, a general disposal procedure is that a dispatcher in a control center takes up a monitor screen of a specified vehicle after receiving a vehicle fire alarm, confirms the fire situation, schedules a vehicle crew, and a crew at a front station for evacuation of the personnel, emergency disposal of the fire, and the like. In the whole process, staff needs to collect information for confirming all aspects so as to finally make decisions. And when a plurality of abnormal scenes are compounded, more time is needed for manually collecting information and making a decision, so that the treatment efficiency of abnormal treatment is seriously influenced, even the influence range of faults is enlarged under certain conditions, and the problems of late operation and the like are directly caused.
Therefore, in order to solve the technical problems in the prior art, the application provides a train autonomous decision and control method for coping with composite multi-scene based on a knowledge graph, which comprises the following steps: firstly, combing the possibly-occurring compound multi-scene situations when the urban rail transit FAO system runs, and then establishing a disposal principle and a criterion relation when the compound multi-scene situations occur; further, a knowledge graph associated with the composite multi-scene is constructed according to the composite multi-scene and the criterion relation thereof, after the knowledge graph construction is completed, the decision can be automatically made on the basis of the knowledge graph under the condition that the train faces various composite multi-scenes, and the train is controlled on the basis of the decision, so that the train is not required to be controlled manually, and the control efficiency of the train is improved.
Specifically, in order to solve the problems in the prior art, the embodiment of the application provides a control method, a device, equipment, a storage medium and a program product of a train. The following first describes a control method of a train provided by an embodiment of the present application.
Fig. 1 shows a flow chart of a control method of a train according to an embodiment of the present application. The method comprises the following steps:
S110, acquiring N scene types according to the current running state of the train, wherein N is a positive integer.
In this embodiment, the N scene types are scene types of an operation scene that is currently running and determined by the train based on the collected data, where the scene types may include a normal scene and an abnormal scene; normal scenarios mean that the train is running normally, while abnormal scenarios mean that a fault event is currently occurring, requiring corresponding analysis and handling. Wherein the normal scenario may include early power up, wake up, leave, enter a positive line service, etc., while the abnormal scenario may include a vehicle fire, a station fire, obstacle derailment detection, a rain and snow mode, etc.
It should be noted that N is a positive integer, that is, the current scene type may be not only a single abnormal scene, but also a composite scene composed of a plurality of abnormal scenes, or a composite scene of at least one normal scene and at least one abnormal scene.
S120, extracting a knowledge graph sub-graph from the knowledge graph based on the N scene types, wherein the knowledge graph sub-graph comprises the N scene types and N treatment flows corresponding to the N scene types one by one.
In this embodiment, the control of the train may be performed using a knowledge graph. The knowledge graph can be obtained in advance, and the obtaining mode can be the construction obtaining or the receiving obtaining. The entities in the knowledge graph include all scene types that may be encountered by the train operation, and the treatment flows, treatment steps, preconditions, and treatment evaluations that correspond to those scene types. The relationship in the knowledge graph is the relationship between the entities.
After the current N scene types are obtained, extracting entities and relations related to the N scene types from the knowledge graph, namely extracting N scene types, N treatment processes corresponding to the N scene types one by one, all pre-conditions and treatment steps included in the N treatment processes, treatment evaluations corresponding to the N treatment processes, and relations among the entities, and jointly forming a knowledge graph sub-graph;
In an embodiment, fig. 2 is a knowledge graph sub-graph, where the scene types in the knowledge graph sub-graph are 2, and are respectively a rain and snow scene and a station fire scene, so that the knowledge graph sub-graph includes a rain and snow scene and a station fire scene, two disposal processes corresponding to the two scenes one by one, and preconditions and disposal steps associated with the disposal processes.
S130, determining the control flow of the train according to the N treatment flows.
In this embodiment, since the current N scene types each correspond to one treatment process, the train may sort the N treatment processes according to a predetermined treatment rule, so as to obtain a control process of the train.
And S140, controlling the train based on the control flow.
In this embodiment, the train may autonomously control the train according to the determined control flow. In this way, compared with the situation that in the prior art, when an abnormal scene occurs, the decision is needed to be made manually according to the acquired information, the method and the device can make the autonomous decision of the train based on the knowledge graph, so that the control efficiency of the train is improved.
As an alternative embodiment, referring to fig. 3, in order to make a decision of train control in a single scenario, S130 may include:
s210, determining the treatment flow as the control flow of the train when the N is 1.
In this embodiment, when N is1, the current scene includes only a single scene type, and then the treatment flow corresponding to the scene type may be determined as the control flow of the train, and the train may be controlled according to the control flow.
As an alternative embodiment, referring to fig. 4, in order to make a decision of train control in a composite scenario, S130 may further include:
s310, determining the execution sequence of the N treatment flows according to the priorities of the N scene types under the condition that the N is a positive integer greater than 1;
S320, determining the N treatment processes arranged according to the execution sequence as the control process of the train.
In this embodiment, in the case where N is a positive integer greater than 1, it is necessary to determine the execution order of N treatment flows corresponding to the N scene types according to the priorities of the N scene types, and then determine the N treatment flows arranged in the execution order as the control flow of the train.
In general, the priority of an abnormal scene is higher than that of a normal scene. For example, N is 2, and the current 2 scene types are a skip scene and a rain and snow mode scene. The jump stop scene is of a scene type of a normal mode, and the rain and snow mode scene is of a scene type of an abnormal mode. The control flow of the train is to execute the disposal flow corresponding to the rain and snow mode scene first, and then execute the disposal flow corresponding to the jump stop scene.
Specifically, in the process of entering a station, the train receives instructions of a rain and snow mode scene and a jump stop scene sent by a dispatching center. At this time, since the priority of the rain and snow mode scene is higher than the priority of the skip stop scene, the train preferentially responds to the instruction of the rain and snow mode scene, executes the treatment flow corresponding to the rain and snow mode scene, and executes the treatment flow corresponding to the skip stop scene after the treatment flow corresponding to the rain and snow mode scene is executed. Namely, the train enters a rain and snow mode after stopping according to preset service braking, and the maximum traction and the maximum braking output are limited. Particularly, if the off-station parking condition is met, the train is preferentially parked outside the station until the rain and snow mode is converted, and then the treatment flow corresponding to the jump parking scene, namely the jump parking command, is executed.
In addition, if all the 2 scenes compounded currently are abnormal scenes, the priority relation of the 2 abnormal scenes needs to be compared, so that the control flow of the train is determined.
For example, when the train re-section runs and simultaneously receives the command of the rain and snow mode scene and the station fire scene sent by the dispatching center, if the priority of the rain and snow mode scene is higher than that of the station fire scene, the processing flow corresponding to the rain and snow mode scene is executed first, and then the processing flow of the station fire scene is executed. The train is stopped according to the preset service brake preferentially to enter a rain and snow mode, the maximum traction and the maximum brake output are limited, and the execution of the treatment flow of the rain and snow mode scene is finished.
Then, a treatment process of the fire scene is performed, namely: if the front outbound annunciator is opened to meet the skip stop condition and the station fire emergency command is not canceled, the skip stop is directly implemented after the mobile authorization extends. Otherwise, the ATO controls the train to stop at an off-station proper position or to execute according to the operation instruction of the driving dispatcher (for example, to return to the last station). Meanwhile, the train autonomously transmits a broadcast to notify and pacify passengers.
As an alternative embodiment, referring to fig. 5, in order to determine the priority relationship of each scene type, before S130, the method may further include:
s410, obtaining the loss degree corresponding to each scene type;
S420, setting the priority of each scene type according to each loss degree.
In this embodiment, the degree of loss corresponding to the results of different scene types is also different. In order to establish the criterion relation of the multi-scene treatment, the loss degree corresponding to each scene type needs to be determined, and the scene type with the larger loss degree is set to be higher in priority.
In one embodiment, the loss level determination principle is firstly based on the safety of passengers, line equipment and trains, and then is determined according to line requirements and related emergency strategies. For example, normally the loss of normal scene type is zero or the degree of loss is small, so the priority of normal scene type is low, and the degree of loss of abnormal scene type is large, so the priority of abnormal scene type is high.
As an alternative embodiment, S130 includes:
s510, when the N treatment processes only comprise treatment steps, determining a control process of the train according to a sequence relation between the treatment steps and the treatment steps;
S520, detecting whether the current running state of the train meets the precondition or not under the condition that the N treatment processes comprise treatment steps and the precondition;
S530, determining the control flow of the train according to the first precondition, the treatment step and the sequence relation between the first precondition and the treatment step which are met by the current running state.
As an optional embodiment, before S110, the method may further include:
s610, constructing a mode layer of the knowledge graph, wherein the mode layer comprises entity nodes and relations between the entity nodes;
S620, receiving input information, wherein the input information comprises running states of the train in a warehouse and during positive line running, treatment principles corresponding to the running states and occurrence relations between the running states and the treatment principles;
S630, filling a mode layer of the knowledge graph according to the input information to generate the knowledge graph.
In this embodiment, in the process of constructing the knowledge graph for assisting the autonomous decision of the train, a bottom-up construction mode, a top-down construction mode, or a mixed construction mode may be adopted.
In one embodiment, as shown in fig. 6, a top-down manner is adopted to construct a knowledge graph, a pattern layer of the knowledge graph is firstly constructed, the pattern layer is used for defining rules of an organization form of knowledge data, and then information extraction is completed to the knowledge graph construction based on input data. I.e. the top-most concept starts to define, progressively refines down, and adds the extracted entity to the corresponding predefined concept. Specifically, in the construction process of the mode layer, firstly constructing a composite multi-scene concept system, then constructing a composite multi-scene body, and finally constructing a composite multi-scene attribute; in the construction process of the data layer, data acquisition is firstly carried out, then entities and relations (attributes) are determined, and finally, instances are added based on the entities and the relations.
As an alternative embodiment, S630 includes:
s710, detecting the running state, the treatment principle and the occurrence relation in the input information;
s720, mapping the running state to the entity node of the mode layer to generate P scene types;
s730, mapping the treatment principle to the entity node of the mode layer, and generating P treatment flows corresponding to the P scene types one by one, and treatment steps and preconditions included in the P treatment flows;
And S740, mapping the occurrence relation to the relation among the entity nodes in the mode layer, generating the first relation among the P scene types, the P treatment processes, the treatment steps and the preconditions, and generating the knowledge graph.
Specifically, firstly, a pattern layer of the knowledge graph is constructed, and according to requirements of the pattern layer in the embodiment, as shown in fig. 7, the nodes in the embodiment include two types of entity types and attribute types. Entity types may include scenes, treatment flows, preconditions, treatment steps, treatment evaluations, and the like; the attribute types may include scene descriptions, scene categories, flow descriptions, etc.; the black directed edges with arrows are the relationship types between nodes and may include following relationships, membership, sequential relationships, and the like.
The concurrency in the knowledge graph means that in a certain period, two scenes may occur simultaneously, and two treatment steps of the treatment flow may be performed simultaneously; the following relationship means that when a certain scene occurs, its corresponding treatment flow is activated immediately; the membership means that the preconditions and the treatment steps corresponding to the same scene are all affiliated to the treatment flow corresponding to the scene; the order relation means an execution order between the treatment steps included in the same treatment flow; mutually exclusive relationships mean that the treatment steps of different treatment flows may be mutually exclusive.
After the pattern layer of the knowledge graph is constructed, a data layer of the knowledge graph needs to be further constructed, namely, the data layer is filled according to the manually carded input information. In an embodiment, the input information may be a plurality of scene types possibly compounded when the FAO system operates by combing the urban rail transit FAO system, and the scene types are not only the basis of each equipment and each post function setting in the fully automatic operation system and the logical tie of linkage between the systems, but also the concept and the requirement of operation can be reflected, and the scene and the operation rule are taken as main lines.
As shown in fig. 8, in an embodiment, the FAO is fully automatically operated, and there may be a composite scene divided into 41, including 18 normal scene types and 23 abnormal scene types, covering all states of train operation in the warehouse and on the positive line. The main operation scenes are as follows: early power-up, wake-up, warehouse-out, entering positive line service, operation of a rolling train, automatic shunting, car washing, obstacle detection, fire disaster, rain and snow mode, shield door fault and the like.
And, these scene types mentioned above, each scene type includes a set of corresponding treatment flows, which are used to ensure the normal operation of the urban rail system. And a treatment flow may include at least one treatment step, preconditions, and treatment evaluations.
In the present embodiment, the input information includes the above-described scene types, the treatment flows, treatment steps, preconditions, and treatment evaluations corresponding to the scene types, and the first relationship therebetween. The scene type, the treatment flow, the treatment step, the preconditions and the treatment evaluation can be filled into the nodes of the corresponding knowledge graph according to the input information, and the relations between the nodes can be further filled according to the input information.
The filling process is firstly aimed at the entity in the input information, then the input information is subjected to entity identification under the guidance of the pattern layer knowledge graph to obtain the corresponding entity, and the corresponding entity is linked to the corresponding concept node, so that the related abstract concept is instantiated. After the entity extraction is completed, under the guidance of a pattern layer knowledge graph, the input information is subjected to relation and attribute detection and mapped into a predefined relation type, so that a first relation between nodes is obtained.
In addition, after the relation and nodes of the knowledge graph are filled, the knowledge graph can be further subjected to manual examination, so that the quality of the knowledge graph is improved, and the high efficiency of decision making is improved.
As an alternative embodiment, referring to fig. 9, in order to facilitate the staff to make an auxiliary decision on the control flow of the train, after S130, the method may further include:
and S810, displaying the control flow through a visual interface.
In this embodiment, the control flow of the train obtained based on the knowledge graph is often expressed by a machine language, possibly in the form of a formatted character string, and the like, and is only used for controlling the machine to execute, but is unfavorable for the staff of the train to examine. Therefore, in order to facilitate the staff to inspect and evaluate the control flow of the train in real time and to evaluate or assist in decision making of the control flow, the control flow needs to be converted into a visual text or picture, audio/video and other forms.
Further, the display of the control flow can be performed on a visual interface before the control flow is executed, so that a worker can conveniently adjust the control flow in real time, and the train can be assisted in making decisions; the control flow can be displayed on a visual interface after being executed, so that a worker can conveniently adjust the knowledge graph.
In an embodiment, as shown in fig. 10, the combed knowledge is processed and collected to complete the construction of the knowledge graph, after the knowledge graph is constructed, the current scene can be input into an execution module which performs bidirectional interaction with the knowledge graph, the execution module can perform process searching by means of the knowledge graph, that is, the knowledge stored in the knowledge graph is utilized to search and extract the related process of the composite multi-scene, a knowledge graph sub-graph is obtained, the search result is processed, that is, the control flow of the train is obtained, and the display and visualization of the result can be performed on the control flow of the train autonomous decision.
Based on the control method of the train provided by the embodiment, correspondingly, the application further provides a specific implementation mode of the control device of the train. Please refer to the following examples.
Referring first to fig. 11, a control device 700 for a train according to an embodiment of the present application includes the following modules:
The acquiring module 701 is configured to acquire N scene types according to a current running state of a train, where N is a positive integer;
An extracting module 702, configured to extract a knowledge graph sub-graph from a knowledge graph based on the N scene types, where the knowledge graph sub-graph includes the N scene types and N treatment flows corresponding to the N scene types one-to-one;
a determining module 703, configured to determine a control flow of the train according to the N treatment flows;
And the control module 704 is used for controlling the train based on the control flow.
The device can extract a knowledge graph subgraph from the knowledge graph based on N scene types by acquiring N scene types, wherein N is a positive integer, and the knowledge graph subgraph comprises N scene types and N treatment flows which are in one-to-one correspondence with the N scene types; determining a control flow of the train according to the N treatment flows; and controlling the train based on the control flow. In this way, compared with the prior art that when an abnormal scene occurs, the decision is needed to be made manually according to the acquired information, the method and the device can make the autonomous decision of the train based on the knowledge graph, so that the control efficiency of the train is improved.
As an implementation manner of the present application, in order to make a decision of train control in a single scenario, the determining module 703 may further include:
And the first determining unit is used for determining the treatment flow as the control flow of the train when the N is 1.
As an implementation manner of the present application, in order to make a decision of train control in a composite scenario, the determining module 703 may further include:
a sequence determining unit, configured to determine, when the N is a positive integer greater than 1, an execution sequence of the N treatment flows according to priorities of the N scene types;
and the second determining unit is used for determining the N treatment processes arranged according to the execution sequence as the control process of the train.
As an implementation manner of the present application, in order to construct a knowledge graph for assisting autonomous decision of a train, the acquiring module 701 may further include:
The node determining unit is used for determining P scene types according to the input information, P treatment processes corresponding to the P scene types one by one, and processing steps included in the P treatment processes, wherein the P scene types comprise the N scene types, and P is an integer greater than or equal to N;
A relationship determining unit configured to generate a first relationship among the P scene types, the P treatment flows, and processing steps included in the P treatment flows according to the input information;
and the construction unit is used for constructing the knowledge graph based on the P scene types, the P treatment flows, the processing steps included in the P treatment flows and the first relation.
As an implementation manner of the present application, in order to determine the priority relationship of each scene type, the determining module 703 may further include:
the acquisition unit is used for acquiring the loss degree corresponding to each scene type;
and a setting unit configured to set a priority of each scene type according to each loss degree.
As an implementation manner of the present application, in order to facilitate the staff to make an auxiliary decision on the control flow of the train, the determining module 703 may further include:
and the display unit is used for displaying the control flow through a visual interface.
The control device for a train provided by the embodiment of the present invention can implement each step in the method embodiments of fig. 1 to 6, and in order to avoid repetition, a description is omitted here.
Fig. 12 shows a schematic hardware structure of a control device of a train according to an embodiment of the present application.
The control equipment at the train may include a processor 1001 and a memory 1002 storing computer program instructions.
In particular, the processor 1001 may include a Central Processing Unit (CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 1002 may include mass storage for data or instructions. By way of example, and not limitation, memory 1002 may include a hard disk drive (HARD DISK DRIVE, HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) drive, or a combination of two or more of the foregoing. The memory 1002 may include removable or non-removable (or fixed) media, where appropriate. Memory 1002 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 1002 is a non-volatile solid state memory.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 1001 reads and executes the computer program instructions stored in the memory 1002 to implement the control method of any of the trains of the above embodiments.
In one example, the control devices of the train may also include a communication interface 1003 and a bus 1010. As shown in fig. 10, the processor 1001, the memory 1002, and the communication interface 1003 are connected to each other by a bus 1010, and perform communication with each other.
The communication interface 1003 is mainly used for implementing communication among the modules, devices, units and/or apparatuses in the embodiment of the application.
Bus 1010 includes hardware, software, or both, coupling components of the control devices of the train to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 1010 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The control device of the train may be based on the above-described embodiments, thereby implementing the control method and apparatus of the train described in connection with fig. 1 to 9.
In addition, in combination with the control method of the train in the above embodiment, the embodiment of the application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions when executed by the processor implement any one of the train control methods in the above embodiments, and achieve the same technical effects, and in order to avoid repetition, a detailed description is omitted here. The computer readable storage medium may include a non-transitory computer readable storage medium, such as Read-Only Memory (ROM), random access Memory (Random Access Memory RAM), magnetic disk or optical disk, and the like, which are not limited herein.
In addition, the embodiment of the application also provides a computer program product, which comprises computer program instructions, wherein the computer program instructions can realize the steps and corresponding contents of the embodiment of the method when being executed by a processor.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. The method processes of the present application are not limited to the specific steps described and shown, but various changes, modifications and additions, or the order between steps may be made by those skilled in the art after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. The present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (9)
1. A method of controlling a train, the method comprising:
constructing a mode layer of a knowledge graph, wherein the mode layer comprises entity nodes and relations between the entity nodes;
receiving input information, wherein the input information comprises running states of the train in a warehouse and during positive line running, treatment principles corresponding to the running states and occurrence relations between the running states and the treatment principles;
filling a mode layer of the knowledge graph according to the input information to generate the knowledge graph;
acquiring N scene types according to the current running state of the train, wherein N is a positive integer;
Extracting a knowledge graph sub-graph from the knowledge graph based on the N scene types, wherein the knowledge graph sub-graph comprises the N scene types and N treatment flows which are in one-to-one correspondence with the N scene types, and each treatment flow comprises a treatment step;
Determining a control flow of the train according to the N treatment flows;
controlling the train based on the control flow;
Wherein the determining the control flow of the train according to the N treatment flows includes:
When the N treatment processes include only treatment steps, determining a control process of the train according to a sequential relationship between the treatment steps and the treatment steps;
detecting whether the current running state of the train meets the precondition or not in the case that the N treatment processes comprise treatment steps and the precondition;
determining a control flow of the train according to a first precondition, the treatment step and a sequence relation between the first precondition and the treatment step which are met by the current running state;
The step of filling the mode layer of the knowledge graph according to the input information to generate the knowledge graph comprises the following steps:
Detecting the running state, the treatment principle and the occurrence relation in the input information;
Mapping the running state to the entity node of the mode layer to generate P scene types;
Mapping the treatment principle to the entity node of the mode layer, and generating P treatment flows corresponding to the P scene types one by one, and treatment steps and preconditions included in the P treatment flows;
Mapping the occurrence relation to the relation among the entity nodes in the pattern layer, generating a first relation among the P scene types, the P treatment processes, the treatment steps and the preconditions, and generating the knowledge graph.
2. The method of controlling a train according to claim 1, wherein the determining the control flow of the train according to the N treatment flows includes:
When N is 1, the process flow is determined as the control flow of the train.
3. The method of controlling a train according to claim 1, wherein the determining the control flow of the train according to the N treatment flows includes:
Determining the execution sequence of the N treatment flows according to the priorities of the N scene types under the condition that the N is a positive integer greater than 1;
And determining the N treatment processes arranged according to the execution sequence as the control process of the train.
4. A control method of a train according to claim 3, wherein before the determining the control flow of the train according to the N treatment flows, the method further comprises:
Obtaining the loss degree corresponding to each scene type;
And setting the priority of each scene type according to each loss degree.
5. The method for controlling a train according to claim 1, wherein after the control flow of the train is determined according to the N treatment flows, the method further comprises:
And displaying the control flow through a visual interface.
6. A control device for a train, the device comprising:
the building module is used for building a mode layer of the knowledge graph, wherein the mode layer comprises entity nodes and relations between the entity nodes;
The receiving module is used for receiving input information, wherein the input information comprises running states of the train in a warehouse and during positive line running, treatment principles for coping with the running states and occurrence relations between the running states and the treatment principles;
the generation module is used for filling a mode layer of the knowledge graph according to the input information to generate the knowledge graph;
the generation module is specifically configured to detect the running state, the treatment principle and the occurrence relationship in the input information;
Mapping the running state to the entity node of the mode layer to generate P scene types;
Mapping the treatment principle to the entity node of the mode layer, and generating P treatment flows corresponding to the P scene types one by one, and treatment steps and preconditions included in the P treatment flows;
Mapping the occurrence relation to the relation among the entity nodes in the pattern layer, generating a first relation among the P scene types, the P treatment processes, the treatment steps and the preconditions, and generating the knowledge graph;
the acquisition module is used for acquiring N scene types according to the current running state of the train, wherein N is a positive integer;
The extraction module is used for extracting a knowledge graph subgraph from the knowledge graph based on the N scene types, wherein the knowledge graph subgraph comprises the N scene types and N treatment flows which are in one-to-one correspondence with the N scene types, and each treatment flow comprises a treatment step;
the determining module is used for determining the control flow of the train according to the N treatment flows;
the determining module is specifically configured to: when the N treatment processes include only treatment steps, determining a control process of the train according to a sequential relationship between the treatment steps and the treatment steps;
detecting whether the current running state of the train meets the precondition or not in the case that the N treatment processes comprise treatment steps and the precondition;
determining a control flow of the train according to a first precondition, the treatment step and a sequence relation between the first precondition and the treatment step which are met by the current running state;
and the control module is used for controlling the train based on the control flow.
7. A control device of a train, characterized in that the control device of the train comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the control method of a train as claimed in any one of claims 1-5.
8. A computer storage medium, characterized in that it has stored thereon computer program instructions which, when executed by a processor, implement the method of controlling a train according to any of claims 1-5.
9. A computer program product, characterized in that it comprises computer program instructions which, when executed by a processor, implement the method of controlling a train according to any of claims 1-5.
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