CN113642811A - Dynamic hydrogen energy freight route planning method and device and computer equipment - Google Patents
Dynamic hydrogen energy freight route planning method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a dynamic hydrogen energy freight route planning method, a dynamic hydrogen energy freight route planning device, computer equipment and a storage medium, which realize the optimization of resource allocation and improve the efficiency and safety of hydrogen energy transportation. According to the method, candidate hydrogenation stations and candidate freight vehicles corresponding to the candidate hydrogenation stations are firstly determined according to freight requirements, then the special transportation requirements of hydrogen energy, the transportation efficiency and the safety are considered, static path planning is carried out through a graph model algorithm to obtain an initial hydrogen energy freight path, then the path is dynamically adjusted in real time through a path dynamic model based on road conditions and real-time change information of the vehicles during hydrogen energy transportation, reasonable hydrogenation stations and transportation vehicles are selected for hydrogen energy transportation, optimized resource allocation is achieved, meanwhile, the transportation path is dynamically adjusted by considering the special transportation requirements, the transportation efficiency and the safety of the hydrogen energy, optimization of the resource allocation is achieved, and the efficiency and the safety of the hydrogen energy transportation are improved.
Description
Technical Field
The application relates to the technical field of new energy, in particular to a dynamic hydrogen energy freight route planning method, a dynamic hydrogen energy freight route planning device, computer equipment and a storage medium.
Background
China is facing a difficult task of energy revolution and industrial structure adjustment, and the development of the hydrogen energy industry is an important component of the energy safety strategy of China and an important way for optimizing an energy consumption structure and realizing interconnection and intercommunication of a power grid and an air grid. The development of the hydrogen energy industry can also effectively drive the development of the manufacturing industry of high-end equipment such as new materials, new energy automobiles, hydrogen storage and transportation and the like, and has important significance for accelerating the adjustment of industrial structures and realizing high-quality development in China. The new energy automobile and the big data are fused together, and are the model of industrialization and informatization deep fusion, and the intelligent new energy automobile based on the big data is the strategic key direction of transformation and upgrading of the automobile industry in China.
However, researches show that due to specific transportation requirements of hydrogen energy transportation, the problems of unreasonable resource scheduling and low transportation efficiency exist during hydrogen energy transportation.
Disclosure of Invention
In view of the above, there is a need to provide a method, an apparatus, a computer device and a storage medium for planning a transportation route of hydrogen energy safely, efficiently and reasonably.
A dynamic hydrogen energy freight route planning method, the method comprising:
according to the demand information of hydrogen energy freight, the position and the residual fuel reserve of a hydrogenation station and the state of a hydrogen energy freight vehicle, selecting a candidate hydrogenation station and a candidate freight vehicle corresponding to each solitary hooke candidate hydrogenation station from the hydrogenation station and the hydrogen energy freight vehicle;
taking each candidate hydrogenation site as a freight starting point, taking a freight target point as a terminal point, taking a service site of a hydrogen energy freight vehicle between each starting point and the corresponding terminal point as an intermediate site, and constructing a freight digraph corresponding to each candidate hydrogenation site;
inputting the freight directed graphs and the vehicle information of the corresponding candidate vehicles into a preset graph model, and outputting target candidate paths and corresponding target candidate vehicles; the weight parameters of the graph model comprise path safety, transportation efficiency and vehicle evaluation coefficients; the hydrogen transportation requirement is used as an influence factor of path safety;
monitoring the running state of the target candidate vehicle and acquiring transportation change information in real time; the transportation change information comprises the real-time position and vehicle condition change information of the freight vehicle, the state change information of the intermediate station, the road condition change information and the state change information of the driver;
and inputting the transportation change information into a dynamic path planning model, and informing the target candidate vehicle to adjust a running path when the path output by the dynamic path planning model is different from the target candidate path.
In one embodiment, before the executing step inputs each of the shipping directed graphs and the vehicle information of the corresponding candidate vehicle into a preset graph model, and outputs the target candidate route and the corresponding target candidate vehicle, the method further includes:
constructing an original graph model;
acquiring a plurality of pieces of historical hydrogen energy freight data; each historical hydrogen energy freight data comprises a freight vehicle and a corresponding freight path;
carrying out freight evaluation marking on each piece of historical hydrogen energy freight data to construct a training sample set;
inputting the constructed training sample set into the original graph model, and performing model training by using path safety, transportation efficiency and vehicle evaluation coefficients as model parameters to obtain the graph model.
In one embodiment, before the step of inputting the transportation change information into the dynamic path planning model, the method further comprises:
constructing an original path dynamic planning model;
acquiring traffic information with time stamps from station to station of freight vehicles;
acquiring road section passing time of freight vehicles in a plurality of time periods between stations according to the traffic information with the time stamps;
and inputting the road section passing time of the freight vehicles in a plurality of time periods among the stations, the energy consumption of the corresponding freight vehicles, the state information of the corresponding intermediate stations and the corresponding driver state information into the original path dynamic planning model, and training the original path dynamic planning model by taking the special geographic information in the road section as an influence parameter to obtain the path dynamic planning model.
In one embodiment, the monitoring the driving state of the target candidate vehicle and acquiring the transportation change information in real time includes:
acquiring current Beidou positioning information, GPS positioning information and vehicle-mounted environment perception information of the target candidate vehicle;
the Beidou positioning information and GPS positioning information of the current time are used as the input of a Kalman filtering algorithm, the vehicle-mounted environment perception information is used as the constraint of the Kalman filtering algorithm, and the real-time position of the target candidate vehicle is output;
and acquiring the running state and transportation change information of the target candidate vehicle according to the change of the real-time position of the vehicle.
In one embodiment, the monitoring the driving state of the target candidate vehicle and acquiring the transportation change information in real time includes:
obtaining real-time vehicle condition information and vehicle body behaviors;
acquiring human eye fatigue recognition characteristic parameters and expression characteristic parameters according to the real-time face image of the driver;
and inputting the human eye fatigue recognition characteristic parameters, the expression characteristic parameters, the corresponding vehicle condition information, the vehicle behavior information and the running track deviation information into a driving behavior evaluation model to obtain the state information of the driver.
In one embodiment, the body behavior comprises: holding posture, vehicle speed, acceleration trend and steering wheel rotation amplitude.
In one embodiment, the method further comprises:
acquiring a target candidate path of the target candidate vehicle and a position of a target candidate vehicle driving route;
acquiring a real-time running path of the target candidate vehicle according to the position of the target candidate vehicle;
and calculating the running track deviation information according to the real-time running path and the target candidate path.
A dynamic hydrogen energy freight routing apparatus, the apparatus comprising:
the information acquisition module is used for selecting a candidate hydrogenation station and candidate freight vehicles corresponding to the candidate hydrogenation stations of the Soxhlet from the hydrogenation station and the hydrogen energy freight vehicles according to the demand information of hydrogen energy freight, the position and the residual fuel reserve of the hydrogenation station and the state of the hydrogen energy freight vehicles;
the directed graph building module is used for taking each candidate hydrogenation site as a freight starting point, taking a freight target point as an end point, taking a service site of the hydrogen energy freight vehicle between each starting point and the corresponding end point as an intermediate site, and building a freight directed graph corresponding to each candidate hydrogenation site;
the model processing module is used for inputting each freight digraph and the vehicle information of the corresponding candidate vehicle into a preset map model and outputting a target candidate path and the corresponding target candidate vehicle; the weight parameters of the graph model comprise path safety, transportation efficiency and vehicle evaluation coefficients; the hydrogen transportation requirement is used as an influence factor of path safety;
the monitoring module is used for monitoring the running state of the target candidate vehicle and acquiring transportation change information in real time; the transportation change information comprises the real-time position and vehicle condition change information of the freight vehicle, the state change information of the intermediate station, the road condition change information and the state change information of the driver;
and the dynamic adjustment module is used for inputting the transportation change information into a dynamic path planning model and informing the target candidate vehicle of adjusting a running path when the path output by the dynamic path planning model is different from the target candidate path.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when executing the computer program.
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 above-mentioned method.
The dynamic hydrogen energy freight route planning method, the device, the computer equipment and the storage medium realize the optimization of resource allocation and improve the efficiency and the safety of hydrogen energy transportation. According to the method, candidate hydrogenation stations and candidate freight vehicles corresponding to the candidate hydrogenation stations are firstly determined according to freight requirements, then the special transportation requirements of hydrogen energy, the transportation efficiency and the safety are considered, static path planning is carried out through a graph model algorithm to obtain an initial hydrogen energy freight path, then the path is dynamically adjusted in real time through a path dynamic model based on road conditions and real-time change information of the vehicles during hydrogen energy transportation, reasonable hydrogenation stations and transportation vehicles are selected for hydrogen energy transportation, optimized resource allocation is achieved, meanwhile, the transportation path is dynamically adjusted by considering the special transportation requirements, the transportation efficiency and the safety of the hydrogen energy, optimization of the resource allocation is achieved, and the efficiency and the safety of the hydrogen energy transportation are improved.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of a dynamic hydrogen energy shipment route planning method;
FIG. 2 is a schematic flow chart diagram illustrating a dynamic hydrogen energy freight route planning method according to an embodiment;
FIG. 3 is a flowchart illustrating step S104 according to an embodiment;
FIG. 4 is a flowchart illustrating step S104 in another embodiment;
FIG. 5 is a block diagram of a dynamic hydrogen energy freight route planning apparatus according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The dynamic hydrogen energy freight route planning method can be applied to the application environment shown in fig. 1. Wherein the server 104 communicates with the terminal 102 and the service site 106. Service site 106 may be, among other things, a hydrogen station, or other type of vehicle service site. The terminal 102 is a smart terminal for communication between the freight vehicle and the server, and may be in the form of, but not limited to, various vehicle-mounted terminals, personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a dynamic hydrogen energy freight route planning method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S110: according to the demand information of hydrogen energy freight, the position and the residual fuel storage of the hydrogenation station and the state of the hydrogen energy freight vehicle, selecting a candidate hydrogenation station and a candidate freight vehicle corresponding to each candidate hydrogenation station from the hydrogenation station and the hydrogen energy freight vehicle.
The demand information of hydrogen energy freight includes the demand of hydrogen energy source, the demand side, the demand time and the transportation requirement. After obtaining the demand information of the hydrogen energy freight, the server 104 obtains a hydrogen adding station meeting the demand and an idle freight vehicle meeting the transportation demand, and takes the hydrogen adding station meeting the demand and the idle freight vehicle meeting the transportation demand as a candidate hydrogen adding station and a candidate freight vehicle. The remaining fuel inventory may be considered first, followed by location, in determining whether the hydroprocessing site meets demand. Further, evaluation information of historical transportation of each hydrogenation site may be considered. The historical evaluation information can be evaluated from the aspects of distribution frequency of hydrogenation sites, diversity of selectable intermediate sites, positions, scales, equipment specification states and the like. Wherein the intermediate site is a service site of the freight vehicle. The candidate vehicle corresponding to the candidate station is a freight vehicle which executes the hydrogen energy task of transporting the corresponding candidate station and can meet the transportation requirement. The source of the information for the intermediate site may be the service site 106 communicating with the server 104. It is also possible to obtain by means of V2X such as vehicle-to-vehicle communication (V2V), vehicle-to-road communication (V2 r), and the like.
Step S120: and taking each candidate hydrogenation site as a freight starting point, taking a freight target point as an end point, taking a service site of a freight vehicle between each starting point and the corresponding end point as an intermediate site, and constructing a freight digraph corresponding to each candidate hydrogenation site.
After determining the candidate hydrogenation site, the server 104 obtains an intermediate site based on the candidate hydrogenation site and the destination, where the intermediate site is a service site where the corresponding candidate vehicle can stop. It should be clear that dockable is not to be equated with an inevitable dock, and that the driver of the freight vehicle during transport can autonomously choose whether to enter a docking station or at which docking station to dock, depending on the driving situation. After the starting point, the end point and the intermediate point are determined, the construction of the freight directed graph can be carried out. The shipping directed graph corresponds to the alternative transport paths for the vehicle.
Step S130: inputting the freight directed graphs and the vehicle information of the corresponding candidate vehicles into a preset graph model, and outputting target candidate paths and corresponding target candidate vehicles; the weight parameters of the graph model comprise path safety, transportation efficiency and vehicle evaluation coefficients; the hydrogen transport requirement is taken as an influencing factor for the path safety.
The vehicle information comprises the real-time position of the vehicle, the vehicle speed, the driver state and the state information of the hydrogen energy tank. The server 104 takes the shipping directed graph and the shipping directed graph as input of the graph model, and obtains the target candidate route and the corresponding target candidate vehicle.
Before the step is executed, a graph model is usually constructed and trained, and the specific process may be as follows:
a. constructing an original graph model; b. acquiring a plurality of pieces of historical hydrogen energy freight data; each historical hydrogen energy freight data comprises a freight vehicle and a corresponding freight path; c. carrying out freight evaluation marking on each piece of historical hydrogen energy freight data to construct a training sample set; and d, inputting the constructed training sample set into the original graph model, and performing model training by using path safety, transportation efficiency and vehicle evaluation coefficients as model parameters to obtain the graph model.
Step S140: and monitoring the running state of the target candidate vehicle, and acquiring transportation change information in real time. The transportation change information comprises real-time position and vehicle condition change information of the freight vehicle, state change information of the intermediate station, road condition change information and driver state change information. The vehicle condition change information may include a change in speed of the vehicle, temperature of vehicle tires, degree of wear, state of the hydrogen tank (seal, air pressure, etc.). The state change information of the intermediate station may include: vehicle density within a site, at an exit, at an entrance, personnel configuration changes, energy storage changes, and the like. The road condition change information may include traffic efficiency of vehicles, road surface temperature, road surface flatness, and the like. The driver state change information may include a continuous driving time period of the driver, a driving behavior evaluation result, and the like.
The server 104 monitors the running state of the target candidate vehicle and the transportation change information in real time. Alternatively, the server may derive the vehicle real-time position information and the change in the vehicle condition based on the acquired running state of the vehicle. The running state of the co-worker vehicle can also be used as reference information of the state change of the driver.
Step S150: and inputting the transportation change information and the current state of the vehicle into a dynamic path planning model, and informing the target candidate vehicle of adjusting a running path when the path output by the dynamic path planning model is different from the target candidate path.
The server 104 inputs the transportation change information and the current state of the vehicle into a path dynamic planning model to obtain the current planned path of the vehicle. And when the difference between the current planned path and the target candidate path is larger than a set threshold value, judging that the output current planned path is different from the target candidate path. At this time, the target candidate vehicle needs to be notified to adjust the travel route. Alternatively, the adjustment of the travel path may include an adjustment of the shipment volume and a path adjustment based on the adjustment of the shipment volume. For example, when the vehicle travels to the a station, the dynamic path planning model considers that the vehicle travels on the X road (transportation passing road) with the current loading capacity of the vehicle, which may cause transportation hazards due to the temperature of the vehicle tires and the ground, and at this time, the transportation capacity needs to be adjusted at the a station. But the target transportation amount is needed to be reached during transportation, at the moment, the dynamic planning model can acquire the current residual fuel reserves of each hydrogen filling station, and the path adjustment is carried out on the basis of the station A where the vehicle is currently located, the destination to be reached and the hydrogen energy to be supplemented so as to complete the hydrogen energy transportation task. The server may notify the target candidate vehicle of adjusting the travel path by issuing a message to the terminal 102 on the target candidate vehicle.
Optionally, before the step of inputting the transportation change information into a path dynamic planning model, the method further comprises: a. constructing an original path dynamic planning model; b. acquiring traffic information with time stamps from station to station of freight vehicles; c. acquiring road section passing time of freight vehicles in a plurality of time periods between stations according to the traffic information with the time stamps; d. and inputting the road section passing time of the freight vehicles in a plurality of time periods among the stations, the energy consumption of the corresponding freight vehicles, the state information of the corresponding intermediate stations and the corresponding driver state information into the original path dynamic planning model, and training the original path dynamic planning model by taking the special geographic information in the road section as an influence parameter to obtain the path dynamic planning model.
According to the dynamic hydrogen energy freight route planning method, firstly, candidate hydrogenation stations and candidate freight vehicles corresponding to the candidate hydrogenation stations are determined according to freight requirements, then special transportation requirements of hydrogen energy, transportation efficiency and safety are considered, static path planning is carried out through a graph model algorithm to obtain an initial hydrogen energy freight route, then, real-time dynamic adjustment of the route is carried out through a route dynamic model based on road conditions and real-time change information of the vehicles during hydrogen energy transportation, the purpose that reasonable hydrogenation stations and transportation vehicles are selected for hydrogen energy transportation is achieved, optimized resource allocation is achieved, meanwhile, dynamic adjustment is carried out on the transportation route according to special transportation requirements of the hydrogen energy, the transportation efficiency and the safety, optimization of the resource allocation is achieved, and the efficiency and the safety of the hydrogen energy transportation are improved.
In one embodiment, as shown in fig. 3, step S140 includes:
step S141: and acquiring the current Beidou positioning information, the GPS positioning information and the vehicle-mounted environment perception information of the target candidate vehicle. The vehicle-mounted environment perception information may include an image obtained by an image sensor of a vehicle or a point cloud obtained by a radar sensor.
Step S142: and outputting the real-time position of the target candidate vehicle by taking the Beidou positioning information and the GPS positioning information at the current time as the input of a Kalman filtering algorithm and taking the vehicle-mounted environment perception information as the constraint of the Kalman filtering algorithm.
Step S143: and acquiring the running state and transportation change information of the target candidate vehicle according to the change of the real-time position of the vehicle.
The method of this embodiment processes the positioning information obtained by the multiple position information acquisition sources through the kalman filter algorithm, and the obtained position is more accurate, and further, since the position information is the basis for calculating the multiple state change information, when the position information is used to perform the corresponding state change calculation, the obtained result is more accurate, for example: when the position information is used for the calculation of the change of the driving state of the vehicle and the calculation of the change of the transportation information, the obtained result is necessarily more accurate.
In one embodiment, as shown in fig. 4, step S140 further includes:
step S144: and acquiring real-time vehicle condition information and vehicle behavior information. Wherein the vehicle body behavior information includes: holding posture, vehicle speed, acceleration trend and steering wheel rotation amplitude.
Step S145: and acquiring human eye fatigue recognition characteristic parameters and expression characteristic parameters according to the real-time face image of the driver. Optionally, the real-time face image of the driver is input into a feature extraction network to obtain human eye fatigue recognition feature parameters and expression feature parameters.
Step S146: and inputting the human eye fatigue recognition characteristic parameters, the expression characteristic parameters, the corresponding vehicle condition information, the vehicle behavior information and the running track deviation information into a driving behavior evaluation model to obtain the state information of the driver.
Alternatively, the acquisition of the travel track deviation information may be obtained by: acquiring a target candidate path of the target candidate vehicle and a position of a target candidate vehicle driving route; acquiring a real-time running path of the target candidate vehicle according to the position of the target candidate vehicle; and calculating the running track deviation information according to the real-time running path and the target candidate path.
According to the embodiment, the driving behavior evaluation is comprehensively carried out by acquiring the vehicle body data, the face data of the driver and the driving track information, and when the evaluation result obtained by the method is used for the transportation evaluation of the hydrogen energy vehicle, the evaluation result can be objectively and reliably presented while whether the transportation route is in compliance or not is evaluated.
It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 5, there is provided a dynamic hydrogen energy freight route planning apparatus, including:
an information obtaining module 510, configured to select, according to the demand information of hydrogen energy freight, the location and the remaining fuel storage of the hydrogen energy freight transportation station, and the state of the hydrogen energy freight vehicle, a candidate hydrogen transportation station and a candidate freight vehicle corresponding to each solitary beard candidate hydrogen transportation station from the hydrogen energy freight transportation station and the hydrogen energy freight vehicle;
the directed graph construction module 520 is configured to construct a directed graph of freight transportation corresponding to each candidate hydrogenation station, using each candidate hydrogenation station as a freight transportation starting point, using a freight transportation target point as an end point, and using a service station of a hydrogen energy freight vehicle between each starting point and a corresponding end point as an intermediate station;
the model processing module 530 is used for inputting each freight directed graph and the vehicle information of the corresponding candidate vehicle into a preset graph model and outputting a target candidate path and the corresponding target candidate vehicle; the weight parameters of the graph model comprise path safety, transportation efficiency and vehicle evaluation coefficients; the hydrogen transportation requirement is used as an influence factor of path safety;
the monitoring module 540 is used for monitoring the running state of the target candidate vehicle and acquiring transportation change information in real time; the transportation change information comprises the real-time position and vehicle condition change information of the freight vehicle, the state change information of the intermediate station, the road condition change information and the state change information of the driver;
and a dynamic adjustment module 550, configured to input the transportation change information into a dynamic path planning model, and notify the target candidate vehicle to adjust a driving path when a path output by the dynamic path planning model is different from the target candidate path.
In one optional embodiment, the model processing module 530 is further configured to construct an original graph model;
acquiring a plurality of pieces of historical hydrogen energy freight data; each historical hydrogen energy freight data comprises a freight vehicle and a corresponding freight path; carrying out freight evaluation marking on each piece of historical hydrogen energy freight data to construct a training sample set; inputting the constructed training sample set into the original graph model, and performing model training by using path safety, transportation efficiency and vehicle evaluation coefficients as model parameters to obtain the graph model.
In an alternative embodiment, the dynamic adjustment module 550 is further configured to construct an original path dynamic planning model; acquiring traffic information with time stamps from station to station of freight vehicles; acquiring road section passing time of freight vehicles in a plurality of time periods between stations according to the traffic information with the time stamps; and inputting the road section passing time of the freight vehicles in a plurality of time periods among the stations, the energy consumption of the corresponding freight vehicles, the state information of the corresponding intermediate stations and the corresponding driver state information into the original path dynamic planning model, and training the original path dynamic planning model by taking the special geographic information in the road section as an influence parameter to obtain the path dynamic planning model.
In one optional embodiment, the monitoring module 540 is configured to obtain current beidou positioning information, GPS positioning information, and vehicle-mounted environment perception information of the target candidate vehicle; the Beidou positioning information and GPS positioning information of the current time are used as the input of a Kalman filtering algorithm, the vehicle-mounted environment perception information is used as the constraint of the Kalman filtering algorithm, and the real-time position of the target candidate vehicle is output; and acquiring the running state and transportation change information of the target candidate vehicle according to the change of the real-time position of the vehicle.
In one optional embodiment, the monitoring module 540 is configured to obtain real-time vehicle condition information and vehicle behavior; acquiring human eye fatigue recognition characteristic parameters and expression characteristic parameters according to the real-time face image of the driver; and inputting the human eye fatigue recognition characteristic parameters, the expression characteristic parameters, the corresponding vehicle condition information, the vehicle behavior information and the running track deviation information into a driving behavior evaluation model to obtain the state information of the driver. Optionally the body behavior comprises: holding posture, vehicle speed, acceleration trend and steering wheel rotation amplitude.
In one optional embodiment, the monitoring module 540 is configured to obtain a target candidate path of the target candidate vehicle and a position where the target candidate vehicle travels; acquiring a real-time running path of the target candidate vehicle according to the position of the target candidate vehicle; and calculating the running track deviation information according to the real-time running path and the target candidate path.
For specific limitations of the dynamic hydrogen energy freight route planning device, reference may be made to the above limitations of the dynamic hydrogen energy freight route planning method, and details are not described herein again. All or part of each module in the dynamic hydrogen energy freight route planning device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing freight vehicle related data, hydrogenerator related data and intermediate station and road related data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a dynamic hydrogen energy freight route planning method.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the dynamic hydrogen energy freight route planning method in the above embodiments when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the dynamic hydrogen energy freight route planning method in the above-described embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A dynamic hydrogen energy freight route planning method is characterized by comprising the following steps:
according to the demand information of hydrogen energy freight, the position and the residual fuel storage of a hydrogenation station and the state of a hydrogen energy freight vehicle, selecting a candidate hydrogenation station and a candidate freight vehicle corresponding to each candidate hydrogenation station from the hydrogenation station and the hydrogen energy freight vehicle;
taking each candidate hydrogenation site as a freight starting point, taking a freight target point as a destination, taking a service site of a freight vehicle between each starting point and the corresponding destination as an intermediate site, and constructing a freight digraph corresponding to each candidate hydrogenation site;
inputting the freight directed graphs and the vehicle information of the corresponding candidate vehicles into a preset graph model, and outputting target candidate paths and corresponding target candidate vehicles; the weight parameters of the graph model comprise path safety, transportation efficiency and vehicle evaluation coefficients; the hydrogen transportation requirement is used as an influence factor of path safety;
monitoring the running state of the target candidate vehicle and acquiring transportation change information in real time; the transportation change information comprises the real-time position and vehicle condition change information of the freight vehicle, the state change information of the intermediate station, the road condition change information and the state change information of the driver;
and inputting the transportation change information and the current state of the vehicle into a dynamic path planning model according to the transportation change information, and informing the target candidate vehicle of adjusting a running path when the path output by the dynamic path planning model is different from the target candidate path.
2. The method of claim 1, wherein before the performing step inputs each of the shipping directed graphs and vehicle information of the corresponding candidate vehicle into a preset graph model, and outputs a target candidate route and the corresponding target candidate vehicle, the method further comprises:
constructing an original graph model;
acquiring a plurality of pieces of historical hydrogen energy freight data; each historical hydrogen energy freight data comprises a freight vehicle and a corresponding freight path;
carrying out freight evaluation marking on each piece of historical hydrogen energy freight data to construct a training sample set;
inputting the constructed training sample set into the original graph model, and performing model training by using path safety, transportation efficiency and vehicle evaluation coefficients as model parameters to obtain the graph model.
3. The method of claim 1, wherein prior to the performing step inputting the transportation change information into a path dynamic planning model, the method further comprises:
constructing an original path dynamic planning model;
acquiring traffic information with time stamps from station to station of freight vehicles;
acquiring road section passing time of freight vehicles in a plurality of time periods between stations according to the traffic information with the time stamps;
and inputting the road section passing time of the freight vehicles in a plurality of time periods among the stations, the energy consumption of the corresponding freight vehicles, the state information of the corresponding intermediate stations and the corresponding driver state information into the original path dynamic planning model, and training the original path dynamic planning model by taking the special geographic information in the road section as an influence parameter to obtain the path dynamic planning model.
4. The method of claim 1, wherein the monitoring of the driving status of the target candidate vehicle and the obtaining of transportation change information in real time comprises:
acquiring current Beidou positioning information, GPS positioning information and vehicle-mounted environment perception information of the target candidate vehicle;
the Beidou positioning information and GPS positioning information of the current time are used as the input of a Kalman filtering algorithm, the vehicle-mounted environment perception information is used as the constraint of the Kalman filtering algorithm, and the real-time position of the target candidate vehicle is output;
and acquiring the running state and transportation change information of the target candidate vehicle according to the change of the real-time position of the vehicle.
5. The method of claim 4, wherein the monitoring of the driving status of the target candidate vehicle and the obtaining of transportation change information in real time comprises:
obtaining real-time vehicle condition information and vehicle body behaviors;
acquiring human eye fatigue recognition characteristic parameters and expression characteristic parameters according to the real-time face image of the driver;
and inputting the human eye fatigue recognition characteristic parameters, the expression characteristic parameters, the corresponding vehicle condition information, the vehicle behavior information and the running track deviation information into a driving behavior evaluation model to obtain the state information of the driver.
6. The method of claim 5, wherein the body behavior comprises: holding posture, vehicle speed, acceleration trend and steering wheel rotation amplitude.
7. The method of claim 5, further comprising:
acquiring a target candidate path of the target candidate vehicle and a position of a target candidate vehicle driving route;
acquiring a real-time running path of the target candidate vehicle according to the position of the target candidate vehicle;
and calculating the running track deviation information according to the real-time running path and the target candidate path.
8. A dynamic hydrogen energy freight route planning apparatus, characterized in that the apparatus comprises:
the information acquisition module is used for selecting a candidate hydrogenation station and candidate freight vehicles corresponding to the candidate hydrogenation stations of the Soxhlet from the hydrogenation station and the hydrogen energy freight vehicles according to the demand information of hydrogen energy freight, the position and the residual fuel reserve of the hydrogenation station and the state of the hydrogen energy freight vehicles;
the directed graph building module is used for taking each candidate hydrogenation site as a freight starting point, taking a freight target point as an end point, taking a service site of the hydrogen energy freight vehicle between each starting point and the corresponding end point as an intermediate site, and building a freight directed graph corresponding to each candidate hydrogenation site;
the model processing module is used for inputting each freight digraph and the vehicle information of the corresponding candidate vehicle into a preset map model and outputting a target candidate path and the corresponding target candidate vehicle; the weight parameters of the graph model comprise path safety, transportation efficiency and vehicle evaluation coefficients; the hydrogen transportation requirement is used as an influence factor of path safety;
the monitoring module is used for monitoring the running state of the target candidate vehicle and acquiring transportation change information in real time; the transportation change information comprises the real-time position and vehicle condition change information of the freight vehicle, the state change information of the intermediate station, the road condition change information and the state change information of the driver;
and the dynamic adjustment module is used for inputting the transportation change information into a dynamic path planning model and informing the target candidate vehicle of adjusting a running path when the path output by the dynamic path planning model is different from the target candidate path.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. 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 of any one of claims 1 to 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114519551A (en) * | 2022-04-20 | 2022-05-20 | 小柿子(北京)汽车供应链管理有限公司 | Scheduling method and device for hydrogen energy freight vehicle and storage medium |
| CN114881574A (en) * | 2022-05-27 | 2022-08-09 | 小柿子(北京)汽车供应链管理有限公司 | Order receiving method and device for cargo transportation and computer equipment |
| CN115019496A (en) * | 2022-04-21 | 2022-09-06 | 内蒙古中科装备有限公司 | Mobile hydrogenation vehicle scheduling method and device |
| CN117436777A (en) * | 2023-12-20 | 2024-01-23 | 江苏运满满同城信息科技有限公司 | Multi-type intermodal method and system for intelligent logistics freight transportation |
| CN117472006A (en) * | 2023-11-09 | 2024-01-30 | 宁波舟山港集团有限公司 | Hydrogen energy system monitoring and safety control method |
| CN117970822A (en) * | 2024-04-02 | 2024-05-03 | 陕西黑石绿能能源科技有限公司 | Automatic unmanned hydrogenation system and hydrogenation method thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020138197A1 (en) * | 2001-03-26 | 2002-09-26 | Schramke Richard W. | Hazardous materials information management system |
| CN101943902A (en) * | 2010-08-18 | 2011-01-12 | 北京物资学院 | Safety monitoring system and method for dangerous goods logistics |
| CN111415048A (en) * | 2020-04-10 | 2020-07-14 | 大连海事大学 | Vehicle path planning method based on reinforcement learning |
| CN112434849A (en) * | 2020-11-19 | 2021-03-02 | 上海交通大学 | Dangerous goods transportation path dynamic planning method based on improved multi-objective algorithm |
| CN113361997A (en) * | 2021-06-04 | 2021-09-07 | 南京大学 | Dangerous waste transportation path real-time planning method based on risk minimization |
-
2021
- 2021-10-12 CN CN202111189318.5A patent/CN113642811B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020138197A1 (en) * | 2001-03-26 | 2002-09-26 | Schramke Richard W. | Hazardous materials information management system |
| CN101943902A (en) * | 2010-08-18 | 2011-01-12 | 北京物资学院 | Safety monitoring system and method for dangerous goods logistics |
| CN111415048A (en) * | 2020-04-10 | 2020-07-14 | 大连海事大学 | Vehicle path planning method based on reinforcement learning |
| CN112434849A (en) * | 2020-11-19 | 2021-03-02 | 上海交通大学 | Dangerous goods transportation path dynamic planning method based on improved multi-objective algorithm |
| CN113361997A (en) * | 2021-06-04 | 2021-09-07 | 南京大学 | Dangerous waste transportation path real-time planning method based on risk minimization |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114519551A (en) * | 2022-04-20 | 2022-05-20 | 小柿子(北京)汽车供应链管理有限公司 | Scheduling method and device for hydrogen energy freight vehicle and storage medium |
| CN115019496A (en) * | 2022-04-21 | 2022-09-06 | 内蒙古中科装备有限公司 | Mobile hydrogenation vehicle scheduling method and device |
| CN115019496B (en) * | 2022-04-21 | 2023-08-25 | 内蒙古中科装备有限公司 | Mobile hydrogenation vehicle scheduling method and device |
| CN114881574A (en) * | 2022-05-27 | 2022-08-09 | 小柿子(北京)汽车供应链管理有限公司 | Order receiving method and device for cargo transportation and computer equipment |
| CN117472006A (en) * | 2023-11-09 | 2024-01-30 | 宁波舟山港集团有限公司 | Hydrogen energy system monitoring and safety control method |
| CN117436777A (en) * | 2023-12-20 | 2024-01-23 | 江苏运满满同城信息科技有限公司 | Multi-type intermodal method and system for intelligent logistics freight transportation |
| CN117436777B (en) * | 2023-12-20 | 2024-03-08 | 江苏运满满同城信息科技有限公司 | Multi-type intermodal method and system for intelligent logistics freight transportation |
| CN117970822A (en) * | 2024-04-02 | 2024-05-03 | 陕西黑石绿能能源科技有限公司 | Automatic unmanned hydrogenation system and hydrogenation method thereof |
| CN117970822B (en) * | 2024-04-02 | 2024-06-25 | 陕西黑石绿能能源科技有限公司 | Automatic unmanned hydrogenation system and hydrogenation method thereof |
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