阅读说明：本技术 一种基于大数据的加氢站工作计划智能提醒方法及系统 (Intelligent reminding method and system for hydrogen station working plan based on big data ) 是由 刘可飞 于 2021-07-09 设计创作，主要内容包括：本发明提供了一种基于大数据的加氢站工作计划智能提醒方法及系统。其中的方法包括：S1,响应于加氢请求,确定目标加氢站；S2,调取与该目标加氢站相关的大数据,对该目标加氢站的工作计划进行预测；S3,将所述工作计划输出至终端。本发明的技术方案能够基于与加氢站相关的大数据来预测出加氢站的工作计划,进而给氢能源车的加氢操作提供指导,降低加氢成本及提高加氢效率。(The invention provides a hydrogen station work plan intelligent reminding method and system based on big data. The method comprises the following steps: s1, responding to the hydrogenation request, and determining a target hydrogenation station; s2, retrieving big data related to the target hydrogen station, and predicting the work plan of the target hydrogen station; and S3, outputting the work plan to a terminal. According to the technical scheme, the working plan of the hydrogen station can be predicted based on the big data related to the hydrogen station, so that guidance is provided for the hydrogenation operation of the hydrogen energy vehicle, the hydrogenation cost is reduced, and the hydrogenation efficiency is improved.)

1. A hydrogen station work plan intelligent reminding method based on big data is characterized in that: the method comprises the following steps:

s1, responding to the hydrogenation request, and determining a target hydrogenation station;

s2, retrieving big data related to the target hydrogen station, and predicting the work plan of the target hydrogen station;

and S3, outputting the work plan to a terminal.

2. The method of claim 1, wherein: the hydrogenation request is triggered by the hydrogen energy vehicle autonomously based on the residual hydrogen storage condition or manually by a passenger of the hydrogen energy vehicle.

3. The method of claim 1, wherein: the big data comprises big data of the hydrogen energy vehicle, big data of a distribution station and real-time big data of traffic;

in step S2, the retrieving big data related to the target hydrogen station and predicting the work plan of the target hydrogen station includes:

s20, calculating a first time sequence of hydrogenation demand in a period after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic;

s21, calculating a second time sequence of the delivery vehicle reaching the target hydrogen adding station based on the delivery station big data and the traffic real-time big data;

and S22, calculating a predicted work plan of the target hydrogen adding station based on the first time series and the second time series of the hydrogen adding demand.

4. The method of claim 3, wherein: the big data of the hydrogen energy vehicle comprises historical track data and real-time track data;

in step S20, the calculating a first time series of hydrogenation demand in a period of time after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic includes:

s201, classifying roads in a first range into high-level roads and low-level roads;

s202, determining all exits of the high-grade road in the first range, and determining a third time sequence of the number of hydrogen energy vehicles driven out of the exits based on historical track data;

s203, determining a fourth time sequence of the number of hydrogen energy source vehicles of the low-grade road in the first range based on the real-time track data;

s204, determining a first time series of hydrogenation demands based on the third time series and the fourth time series.

5. The method of claim 4, wherein: the historical track data also comprises driving plan data;

the step S202 further includes:

and judging whether the route from the hydrogen energy vehicle driven out of the corresponding outlet to the target hydrogenation station after hydrogenation needs to be folded back or not based on the road network structure, and if so, correcting the third time sequence by adopting a fifth weight.

6. The method according to any one of claims 3-5, wherein: in step S21, the calculating a second time sequence of the delivery vehicle arriving at the target hydrogen refueling station based on the delivery station big data and the traffic real-time big data includes:

s210, calculating a sixth time sequence of the preparation duration of the delivery vehicle based on the big data of the delivery station;

s211, predicting a seventh time sequence consumed by delivery based on the traffic real-time big data;

s212, determining the second time series based on the sixth time series and the seventh time series.

7. The method of claim 6, wherein: the work plan comprises a hydrogen supply stopping time period and a coincidence condition between an estimated time period when the hydrogen energy vehicle arrives at the hydrogen station and the hydrogen supply stopping time period.

8. A hydrogen station work plan intelligent reminding system based on big data comprises a determining module, a predicting module and a communication module; wherein the content of the first and second substances,

the determining module is used for responding to a hydrogenation request and determining a target hydrogenation station;

the prediction module is used for calling big data related to the target hydrogen station and predicting the working plan of the target hydrogen station;

the communication module is used for receiving the hydrogenation request and outputting the work plan to a terminal.

9. An electronic device, the device comprising: a memory storing executable program code; a processor coupled with the memory; the method is characterized in that: the processor calls the executable program code stored in the memory to implement the method of any one of claims 1-7.

10. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of claims 1-7.

Technical Field

The invention relates to the technical field of hydrogen energy, in particular to a hydrogen station work plan intelligent reminding method and system based on big data, electronic equipment and a computer storage medium.

Background

Carbon peaking, carbon neutralization have become a national task that china and countries around the world must face. The method is an optimal choice in two-carbon battle. According to the prediction of relevant organizations, 8% of the energy structures in China depend on completely clean hydrogen energy under the condition of 2060 year carbon neutralization. The green hydrogen energy end product is water, and its popularity is a good prospect but a turning round of the process, which requires the break-through of various industry chains.

Among them, the hydrogen refueling station is a crucial ring of the hydrogen energy industry. The reality of the hydrogen station is important for the driver of a hydrogen powered vehicle. For example, if a hydrogen energy vehicle arrives at a hydrogen refueling station, the hydrogen refueling station is in a non-operating state or a congested state, which obviously results in waste of the distance of the hydrogen energy vehicle and energy, and further increases the operation cost thereof, which is particularly sensitive to operating the hydrogen energy vehicle. However, if the driver can know the operation state of the hydrogen refueling station in time, it is possible to determine whether to add hydrogen to the hydrogen refueling station or to continue traveling to another hydrogen refueling station based on the operation state.

The inventor searches the existing relevant documents that the working state of the hydrogen station is reported externally, and meanwhile, by analogy with the knowledge of the close neighborhood, the reminding of the working state of the station is realized by the working personnel of the station based on the real-time condition of the station by adopting gate hang tags and website publishing.

Disclosure of Invention

In order to solve the technical problems in the background art, the invention provides a big data-based intelligent reminding method, a big data-based intelligent reminding system, an electronic device and a computer storage medium for a hydrogen refueling station working plan, wherein the working plan of the hydrogen refueling station is predicted and provided for a driver of a hydrogen energy vehicle to assist the driver to decide whether to make a decision on hydrogenation or not, so that the hydrogenation efficiency and the economy of the hydrogen energy vehicle are improved.

The invention provides a hydrogen station work plan intelligent reminding method based on big data, which comprises the following steps:

s1, responding to the hydrogenation request, and determining a target hydrogenation station;

s2, retrieving big data related to the target hydrogen station, and predicting the work plan of the target hydrogen station;

and S3, outputting the work plan to a terminal.

Preferably, the hydrogenation request is triggered autonomously by the hydrogen-powered vehicle based on the hydrogen storage remaining condition, or manually by an occupant of the hydrogen-powered vehicle.

Preferably, the big data comprises big data of the hydrogen energy vehicle, big data of a distribution station and real-time big data of traffic;

in step S2, the retrieving big data related to the target hydrogen station and predicting the work plan of the target hydrogen station includes:

s20, calculating a first time sequence of hydrogenation demand in a period after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic;

s21, calculating a second time sequence of the delivery vehicle reaching the target hydrogen adding station based on the delivery station big data and the traffic real-time big data;

and S22, calculating a predicted work plan of the target hydrogen adding station based on the first time series and the second time series of the hydrogen adding demand.

Preferably, the big data of the hydrogen energy vehicle comprises historical track data and real-time track data;

in step S20, the calculating a first time series of hydrogenation demand in a period of time after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic includes:

s201, classifying roads in a first range into high-level roads and low-level roads;

s202, determining all exits of the high-grade road in the first range, and determining a third time sequence of the number of hydrogen energy vehicles driven out of the exits based on historical track data;

s203, determining a fourth time sequence of the number of hydrogen energy source vehicles of the low-grade road in the first range based on the real-time track data;

s204, determining a first time series of hydrogenation demands based on the third time series and the fourth time series.

Preferably, the historical trajectory data further includes driving plan data;

the step S202 further includes:

and judging whether the route from the hydrogen energy vehicle driven out of the corresponding outlet to the target hydrogenation station after hydrogenation needs to be folded back or not based on the road network structure, and if so, correcting the third time sequence by adopting a fifth weight.

Preferably, in step S21, the calculating a second time sequence of the delivery vehicle arriving at the target hydrogen refueling station based on the delivery station big data and the traffic real-time big data includes:

s210, calculating a sixth time sequence of the preparation duration of the delivery vehicle based on the big data of the delivery station;

s211, predicting a seventh time sequence consumed by delivery based on the traffic real-time big data;

s212, determining the second time series based on the sixth time series and the seventh time series.

Preferably, the work plan comprises a hydrogen supply stopping time period, and the estimated time period when the hydrogen energy vehicle arrives at the hydrogen station coincides with the hydrogen supply stopping time period.

The invention provides a hydrogen station work plan intelligent reminding system based on big data, which comprises a determining module, a predicting module and a communication module, wherein the determining module is used for determining the work plan of a hydrogen station; wherein the content of the first and second substances,

the determining module is used for responding to a hydrogenation request and determining a target hydrogenation station;

the prediction module is used for calling big data related to the target hydrogen station and predicting the working plan of the target hydrogen station;

the communication module is used for receiving the hydrogenation request and outputting the work plan to a terminal.

Preferably, the hydrogenation request is triggered autonomously by the hydrogen-powered vehicle based on the hydrogen storage remaining condition, or manually by an occupant of the hydrogen-powered vehicle.

Preferably, the big data comprises big data of the hydrogen energy vehicle, big data of a distribution station and real-time big data of traffic;

the calling of big data related to the target hydrogen station and prediction of the work plan of the target hydrogen station comprise:

calculating a first time sequence of hydrogenation demand in a time period after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic;

calculating to obtain a second time sequence of the delivery vehicle reaching the target hydrogen adding station based on the delivery station big data and the traffic real-time big data;

and calculating a predicted working plan of the target hydrogenation station based on the first time series and the second time series of the hydrogenation demand.

Preferably, the big data of the hydrogen energy vehicle comprises historical track data and real-time track data;

calculating a first time series of hydrogenation demands in a period of time after the hydrogenation request based on the hydrogen energy vehicle big data and the traffic real-time big data, wherein the first time series comprises:

classifying roads in a first range into high-level roads and low-level roads;

determining all exits of the high-grade road in the first range, and determining a third time series of the number of hydrogen energy vehicles driven out of the exits based on historical track data;

determining a fourth time series of the number of hydrogen energy source vehicles of the low-grade road in the first range based on the real-time trajectory data;

determining a first time series of hydrogenation demand based on the third time series and the fourth time series.

Preferably, the historical trajectory data further includes driving plan data;

the prediction module is further configured to: and judging whether the route from the hydrogen energy vehicle driven out of the corresponding outlet to the target hydrogenation station after hydrogenation needs to be folded back or not based on the road network structure, and if so, correcting the third time sequence by adopting a fifth weight.

Preferably, the calculating, by the prediction module, a second time sequence of the delivery vehicle reaching the target hydrogen refueling station based on the delivery station big data and the traffic real-time big data includes:

calculating a sixth time sequence of the preparation duration of the distribution vehicle based on the big data of the distribution station;

predicting a seventh time sequence of delivery time consumption based on the traffic real-time big data;

determining the second time series based on the sixth time series and the seventh time series.

Preferably, the work plan comprises a hydrogen supply stopping time period, and the estimated time period when the hydrogen energy vehicle arrives at the hydrogen station coincides with the hydrogen supply stopping time period.

A third aspect of the present invention provides an electronic device, which is applied to the foregoing operation platform: the apparatus comprises: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory to implement the method of any of the preceding claims.

A fourth aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any preceding claim.

The invention has the beneficial effects that:

the intelligent reminding scheme of the hydrogen station working plan based on the big data can accurately predict the working plan of the target hydrogen station based on the relevant big data of the target hydrogen station, and further can guide a driver of a hydrogen energy vehicle to select a proper hydrogen station more quickly and accurately, so that the transportation efficiency and the economical efficiency of the hydrogen energy vehicle are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

FIG. 1 is a schematic flow chart diagram of a method for intelligently reminding a workstation working plan based on big data according to an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of a big data-based intelligent reminding system for a workstation working plan, according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. indicate an orientation or a positional relationship based on that shown in the drawings or that the product of the present invention is used as it is, this is only for convenience of description and simplification of the description, and it does not indicate or imply that the device or the element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention.

Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.

It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.

Example one

Referring to fig. 1, fig. 1 is a schematic flow chart of a big data-based intelligent reminding method for a workstation working plan according to an embodiment of the present invention. As shown in fig. 1, in an embodiment of the present invention, a method for intelligently reminding a workstation working plan based on big data includes:

s1, responding to the hydrogenation request, and determining a target hydrogenation station;

s2, retrieving big data related to the target hydrogen station, and predicting the work plan of the target hydrogen station;

and S3, outputting the work plan to a terminal.

In the embodiment of the invention, when the hydrogen energy source vehicle needs to be hydrogenated, the server can quickly determine the target hydrogenation station based on the request signal of the hydrogen energy source vehicle, then call all data (including historical data and real-time data) related to the target hydrogenation station to predict the work plan of the target hydrogenation station, and feed the prediction result back to the hydrogen energy source vehicle sending the hydrogenation request to guide the hydrogenation operation of the target hydrogenation station. Thus, the driver of the hydrogen energy source vehicle can determine whether the hydrogen station is reasonable based on the predicted operation plan of the hydrogen station, for example, whether the hydrogen energy source vehicle is in a non-operation state when arriving at the hydrogen station, whether the hydrogen energy source vehicle waits for a long time, and the like.

The method of the present invention is applied to a server, which includes, but is not limited to, a computer, a network host, a database server, a storage server, and a Cloud server formed by an application server or a plurality of servers, wherein the Cloud server is formed by a large number of computers or network servers based on Cloud Computing (Cloud Computing).

In addition, the terminal may be a vehicle-mounted terminal of the hydrogen energy vehicle, and of course, the terminal may also be various mobile terminals, such as a smart phone, a tablet computer (including but not limited to an IOS, an Android, a Windows, a BlackBerry OS, and other operating systems), a PDA (Personal Digital Assistant), and the like, and these mobile terminals only need to be bound with the hydrogen energy vehicle in advance to realize the same function as the vehicle-mounted terminal.

Preferably, the hydrogenation request is triggered autonomously by the hydrogen-powered vehicle based on the hydrogen storage remaining condition, or manually by an occupant of the hydrogen-powered vehicle.

In the embodiment of the present invention, when the hydrogen energy vehicle determines that the hydrogen storage remaining value is insufficient, the hydrogen addition request may be automatically sent to the server through the vehicle-mounted network, or may be manually triggered by the occupants of the hydrogen energy vehicle (including the driver and other occupants) through the terminal devices (vehicle-mounted terminals, or the aforementioned various mobile terminals) of the hydrogen energy vehicle. The hydrogen storage residual condition can be a hydrogen storage residual value, or a matching relation between the hydrogen storage residual value and the residual mileage in the driving plan, namely whether the hydrogen storage residual value can support the hydrogen energy source vehicle to complete the residual driving plan or not.

Preferably, the big data comprises big data of the hydrogen energy vehicle, big data of a distribution station and real-time big data of traffic;

in step S2, the retrieving big data related to the target hydrogen station and predicting the work plan of the target hydrogen station includes:

s20, calculating a first time sequence of hydrogenation demand in a period after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic;

s21, calculating a second time sequence of the delivery vehicle reaching the target hydrogen adding station based on the delivery station big data and the traffic real-time big data;

and S22, calculating a predicted work plan of the target hydrogen adding station based on the first time series and the second time series of the hydrogen adding demand.

In the embodiment of the invention, hydrogen distribution to the hydrogen refueling station is mainly based on two aspects, namely: 1) sales of hydrogen; 2) and (4) distribution condition of a hydrogenation station. The invention determines the working plan of the target hydrogenation station based on the influence factors of the two aspects, namely the time period for the hydrogenation station to suspend providing hydrogenation service for the outside due to hydrogen supplement.

Firstly, a hydrogenation demand time sequence can be determined based on the big data of the hydrogen energy vehicle and the real-time big data of traffic. Specifically, when the hydrogen storage residual quantity of the hydrogen adding station is less than a certain value (for example, when 20% of hydrogen is left), the hydrogen adding station calls the distribution station to dispatch hydrogen adding, and the hydrogen storage residual quantity is directly and negatively related to the sales condition, and the sales condition has periodicity and regularity. According to the invention, the hydrogenation demand time sequence is obtained by predicting the regular data of hydrogenation carried out by the hydrogen energy source vehicle to the target hydrogenation station through the step S20, and moreover, the hydrogenation demand time sequence obtained by prediction is more accurate by considering the hydrogenation demand of the hydrogen energy source vehicle and the real-time traffic condition.

Secondly, when the delivery vehicle reaches the target hydrogen filling station can be determined based on the delivery station big data and the traffic real-time big data. Specifically, after the delivery station receives the scheduling request of the hydrogen filling station, the time required for the delivery station to deliver the prepared hydrogen gas to the target hydrogen filling station mainly comprises an in-station preparation time length and a delivery time length, and the time when the delivery vehicle reaches the target hydrogen filling station can be predicted based on the big data of the two aspects.

Finally, when the hydrogen selling sequence (corresponding to the hydrogenation demand time sequence) of the target hydrogenation station and the moment when the delivery vehicle arrives at the target hydrogenation station are determined, the time period that the target hydrogenation station suspends the external hydrogenation service due to hydrogen supplement can be predicted. For example, based on the hydrogen demand time sequence and the hydrogen storage data, it may be determined that the time when the hydrogen storage remaining value decreases to the scheduling trigger value (for example, 20%) is 13:20, at this time, the hydrogen station may send a scheduling request to the distribution station in an automatic or manual manner, the distribution station may schedule a corresponding distribution vehicle after receiving the request, and load a corresponding amount of compressed hydrogen to the target hydrogen station, and finally arrive at 13:50, so that the time period 13:50-14:10 may be determined as a time period for suspending the external provision of the hydrogen service (assuming that the hydrogen replenishment operation time period of the distribution vehicle is 20 minutes, and of course, the hydrogen replenishment operation time period may also be determined according to the actual hydrogen replenishment amount).

Preferably, the big data of the hydrogen energy vehicle comprises historical track data and real-time track data;

in step S20, the calculating a first time series of hydrogenation demand in a period of time after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic includes:

s201, classifying roads in a first range into high-level roads and low-level roads;

s202, determining all exits of the high-grade road in the first range, and determining a third time sequence of the number of hydrogen energy vehicles driven out of the exits based on historical track data;

s203, determining a fourth time sequence of the number of hydrogen energy source vehicles of the low-grade road in the first range based on the real-time track data;

s204, determining a first time series of hydrogenation demands based on the third time series and the fourth time series.

In the embodiment of the invention, the hydrogenation demand is directly and positively correlated with the number of hydrogen energy source vehicles in a certain range around the hydrogenation station, that is, the more hydrogen energy source vehicles are operated on the surrounding roads, the higher the sales data of the hydrogenation station is, and correspondingly, the first time sequence of the hydrogenation demand is changed along with the positive change. Meanwhile, the possibility of hydrogenation by the hydrogenation station for vehicles running on different road grades is different, for example, vehicles on a closed high-grade road such as an expressway generally do not depart from the expressway for hydrogenation (unless no hydrogenation station is arranged in the closed road), while vehicles on a low-grade road do not have the situation at all, and hydrogen supplement by the hydrogenation station is basically based on objective requirements.

Aiming at the vehicle characteristics of the roads with different grades, the scheme of the invention analyzes and calculates the first time sequence based on the grades of the roads. And counting data of vehicles which exit from the high-grade road, wherein the vehicles can be regarded as equivalent to vehicles on the low-grade road, determining a fifth time series of the number of the integrated hydrogen energy vehicles on the low-grade road in the first range based on the third time series and the fourth time series, and determining the first time series through a deep learning model. The deep learning model is trained by utilizing the number of the hydrogen energy vehicles and the historical data of the hydrogenation demand, and the trained deep learning model can learn to obtain the corresponding relation between the number of the hydrogen energy vehicles and the hydrogenation demand. The fifth time series is then input into the deep learning model, which can output a first time series of predicted hydrogenation demands. The deep learning model can be constructed based on CNN (including but not limited to Lenet5, GoogleNet, ResNet, DenseNet, VGGNet, etc.), LSTM (including but not limited to Coupled LSTM, Peephole LSTM, GRU, etc.), BP neural network (including but not limited to traditional BP neural network, BP neural network based on wavelet noise filtering improvement, BP neural network based on Kalman filtering improvement, BP neural network based on wolf colony algorithm improvement, etc.), etc. since the deep learning technology is mature, the deep learning model is not described in detail herein, and a person skilled in the art can freely select a proper deep learning model according to actual conditions.

Further, since a hydrogen-powered vehicle traveling on a high-grade road generally travels a long distance and generally does not depart from a closed high-grade road due to hydrogenation, the possibility that hydrogen is required for the hydrogen-powered vehicle traveling on the high-grade road is actually higher than for the hydrogen-powered vehicle traveling on a low-grade road. For this practical situation, the present invention limits step S204 as follows:

s2041, detecting whether a hydrogenation station exists in a second range of upstream and downstream of the high-grade road, if so, correcting the third time sequence by a first weight, otherwise, correcting the third time sequence by a second weight, wherein the first weight is smaller than the second weight;

s2042, correspondingly fusing the corrected third time sequence and the fourth time sequence to obtain a fifth time sequence, and inputting the fifth time sequence into a deep learning model to obtain a first time sequence.

If the hydrogen stations exist upstream and downstream, the possibility that the outgoing hydrogen energy vehicles are used for hydrogenation at the target hydrogen station is not high, and the number of the hydrogen energy vehicles in the third time sequence is reduced by using a relatively smaller first weight; on the contrary, it indicates that the hydrogen energy vehicles are likely to have to drive away from the high-grade road for hydrogenation because the number of the hydrogenation stations in the high-grade road is not enough, and at this time, the second weight which is relatively larger is used for increasing the number of the hydrogen energy vehicles in the third time sequence.

In addition, as for the first weight, the following may be more specifically made: if a hydroprocessing station is present upstream of the outlet, the first weight is made a third weight, and if a hydroprocessing station is present downstream of the outlet, the first weight is made a fourth weight, wherein the third weight is less than the fourth weight. When a hydrogenation station exists at the downstream and the hydrogen energy vehicle runs off a high-grade road, the fact that the residual travel of the hydrogen energy vehicle is not enough to reach the hydrogenation station is indicated, the possibility of hydrogenation is higher, a larger fourth weight is adopted, and a smaller third weight is adopted.

The specific values of the first weight, the second weight, the third weight and the fourth weight can be freely determined and adjusted by those skilled in the art based on the above relative magnitude relationship, which is not limited by the invention.

Preferably, the historical trajectory data further includes driving plan data;

the step S202 further includes:

and judging whether the route from the hydrogen energy vehicle driven out of the corresponding outlet to the target hydrogenation station after hydrogenation needs to be folded back or not based on the road network structure, and if so, correcting the third time sequence by adopting a fifth weight.

In the embodiment of the invention, whether the route of the hydrogen energy vehicle running out from each outlet to the target hydrogenation station needs to be folded can be judged based on the actual road structure in the first range, and the folding of the route means excessive consumption of energy, time and distance, which can negatively influence the choice of the driver. In order to solve the problem, the invention judges the turn-back situation based on the road network structure, and if the road network structure where a certain exit is located is judged to cause the hydrogen energy vehicles to be hydrogenated by the target hydrogenation station and then to turn back to the route for running, the fifth weight is used for reducing the number of the hydrogen energy vehicles at the corresponding intersection, so that the correction of the third time sequence is realized.

Preferably, in step S21, the calculating a second time sequence of the delivery vehicle arriving at the target hydrogen refueling station based on the delivery station big data and the traffic real-time big data includes:

s210, calculating a sixth time sequence of the preparation duration of the delivery vehicle based on the big data of the delivery station;

s211, predicting a seventh time sequence consumed by delivery based on the traffic real-time big data;

s212, determining the second time series based on the sixth time series and the seventh time series.

In the embodiment of the invention, the time required by the delivery station after receiving the hydrogen delivery request of the hydrogen filling station until the delivery vehicle reaches the target hydrogen filling station mainly comprises two parts, namely the in-station preparation time (including the delivery vehicle in-place time, the hydrogen filling time and the like) and the on-road time consumption. The preparation time in the station can be calculated based on the big data of the distribution station, and the time spent on the road can be calculated based on the traffic real-time big data.

Preferably, in step S210, the calculating a sixth time series of the preparation time of the delivery vehicle based on the big data of the delivery station includes:

and acquiring historical distribution preparation time length data of the distribution station, and carrying out cluster analysis on the historical distribution preparation time length to obtain a sixth time sequence.

In the embodiment of the invention, the preparation time length data of the distribution station can be obtained in advance, then clustering and segmentation of the data can be realized through clustering analysis, and further, a sixth time sequence of the preparation time length in one day of corresponding date attributes (working day/non-working day, whether to save or leave a holiday, and the like) is obtained, namely, how long the preparation time length is usually required in corresponding time periods of different dates is described. When the cluster analysis is performed, the density of the fourth time series can be determined based on the traffic condition, for example, 05:00-7:00 is an early shift period, the period belongs to a distribution peak period, hands of a distribution station are abundant, and the fluctuation of the preparation time is not large, so that the period can be divided into 2 sub-periods; for 9:00-11:00, as hydrogen supplement is carried out in the early shift, the demand of each hydrogen adding station is unstable, the distribution task is greatly fluctuated, and the distribution station is unlikely to fill a plurality of vehicles with hydrogen in advance for standby in the time period, so that the time period is divided into 4 sub-time periods to realize accurate analysis and predicted use of the prepared time length data of each sub-time period.

Preferably, in step S211, the predicting a seventh time sequence of delivery time consumption based on the real-time traffic big data includes:

s2110, predicting distribution time consumed by a distribution vehicle to reach each hydrogen filling station based on the traffic real-time big data, and further obtaining a station arrival time sequence;

s2111, correcting the station arrival time sequence based on a distribution plan to obtain the seventh time sequence.

In the embodiment of the invention, the real-time traffic condition is a main factor influencing the time consumption on the distribution road, so the distribution time consumption of the distribution vehicle reaching each hydrogenation station can be calculated based on traffic big data, and the time of reaching each hydrogenation station is correspondingly determined.

Meanwhile, the less the goods are transported, the more the driver tends to drive aggressively, and the delivery plan usually includes delivery tasks for a plurality of hydrogen refueling stations, so that the delivery speed of the driver is actually significantly increased as the delivery plan progresses. For example, when the delivery schedule of the delivery vehicle is: if the target hydrogen station is D, the load of the distribution vehicle after the distribution vehicle is pulled out from the hydrogen station C is only about 200Kg, and the driver is more inclined to drive aggressively, wherein the reaching time sequence indicates that the ratio 9:20 reaches D, but the ratio 9:10 actually reaches because the driver drives aggressively. In view of the situation, the present invention can determine the degree of aggressive driving of the driver in different delivery road sections (i.e., road sections between adjacent delivery stations in the delivery plan) based on the order of the target hydrogen refueling station in the delivery plan and the delivery shares corresponding to each delivery order, and accordingly correct the station arrival time sequence to obtain a more accurate seventh time sequence. In addition, the individual driving attribute of the driver may be further considered, the driving-oriented degree may be corrected based on the individual driving attribute, and the station arrival time series may be corrected using the corrected driving-oriented degree. The relationship between the aggressive driving degree and the delivered cargo share (weight and volume) can be calculated by real data statistical analysis and a data fitting algorithm, and the relationship is not repeated in the invention because the relationship belongs to a conventional mathematical analysis method.

Preferably, in step S2, the retrieving big data related to the target hydrogen station and predicting the work plan of the target hydrogen station further includes a prediction updating mechanism:

and determining real-time traffic conditions based on the traffic real-time big data, and determining the update frequency of the work plan based on the traffic conditions, wherein the update frequency is higher when the traffic conditions are worse.

In the embodiment of the invention, because the working plan is predicted and is not completely accurate, and meanwhile, the real-time traffic condition directly influences the time when each hydrogen energy vehicle reaches the target hydrogen station, further influences the time sequence of the required hydrogenation amount, the time when the delivery vehicle reaches the target hydrogen station and finally influences the working plan of the target hydrogen station, the prediction result of the working plan needs to be updated for many times, and the most accurate prediction data can be provided before the driver of the hydrogen energy vehicle makes a final decision. In addition, the updating frequency is determined based on the traffic condition, and the updating frequency is higher when the traffic condition is worse, so that the accuracy of the prediction result under the severe traffic condition is improved.

Preferably, the work plan comprises a hydrogen supply stopping time period, and the estimated time period when the hydrogen energy vehicle arrives at the hydrogen station coincides with the hydrogen supply stopping time period.

In the embodiment of the invention, after the work plan of the hydrogen station is predicted, the hydrogen supply stopping time period, namely the hydrogen replenishing operation time period of the delivery vehicle at the hydrogen station can be known, and the information can effectively guide the hydrogen operation of the driver of the hydrogen energy vehicle. In addition, the superposition condition of the estimated time period when the hydrogen energy vehicle reaches the hydrogen station and the hydrogen supply stopping time period can be sent to the vehicle-mounted terminal of the hydrogen energy vehicle, so that a driver can more clearly know how long the waiting time is for hydrogen supply when the vehicle goes to the hydrogen station, and then whether the hydrogen station is suitable or not is determined. During specific implementation, the information can be visually displayed on a display screen of the hydrogen energy vehicle-mounted terminal, and the information can be provided for a driver in a sound-light broadcasting mode according to setting selection.

Example two

Referring to fig. 2, fig. 2 is a schematic flow chart of a big data-based intelligent reminding system for a workstation working plan according to an embodiment of the present invention. As shown in fig. 2, the intelligent reminding system (1) for the hydrogen refueling station working plan based on big data according to the embodiment of the present invention includes a determining module (101), a predicting module (102), and a communication module (103); wherein the content of the first and second substances,

the determining module (101) is used for responding to a hydrogenation request and determining a target hydrogenation station;

the prediction module (102) is used for calling big data related to the target hydrogen station and predicting the work plan of the target hydrogen station;

the communication module (103) is used for receiving the hydrogenation request and outputting the work plan to a terminal.

For specific functions of the intelligent reminding system for the hydrogen refueling station working plan based on the big data in this embodiment, reference is made to the first embodiment, and since the system in this embodiment adopts all technical solutions of the first embodiment, at least all beneficial effects brought by the technical solutions of the first embodiment are achieved, and details are not repeated here.

Preferably, the hydrogenation request is triggered autonomously by the hydrogen-powered vehicle based on the hydrogen storage remaining condition, or manually by an occupant of the hydrogen-powered vehicle.

Preferably, the big data comprises big data of the hydrogen energy vehicle, big data of a distribution station and real-time big data of traffic;

the prediction module (102) calls big data related to the target hydrogen station to predict the work plan of the target hydrogen station, and comprises the following steps:

calculating a first time sequence of hydrogenation demand in a time period after the hydrogenation request based on the big data of the hydrogen energy vehicle and the real-time big data of traffic;

calculating to obtain a second time sequence of the delivery vehicle reaching the target hydrogen adding station based on the delivery station big data and the traffic real-time big data;

and calculating a predicted working plan of the target hydrogenation station based on the first time series and the second time series of the hydrogenation demand.

Preferably, the big data of the hydrogen energy vehicle comprises historical track data and real-time track data;

the prediction module (102) calculates a first time series of hydrogenation demand in a period of time after the hydrogenation request based on the hydrogen energy vehicle big data and traffic real-time big data, including:

classifying roads in a first range into high-level roads and low-level roads;

determining all exits of the high-grade road in the first range, and determining a third time series of the number of hydrogen energy vehicles driven out of the exits based on historical track data;

determining a fourth time series of the number of hydrogen energy source vehicles of the low-grade road in the first range based on the real-time trajectory data;

determining a first time series of hydrogenation demand based on the third time series and the fourth time series.

Preferably, the prediction module (102) determines a first time series of hydrogenation demands based on the third time series and the fourth time series, further comprising:

detecting whether a hydrogenation station exists in a second range of upstream and downstream of the high-grade road, if so, correcting the third time sequence by a first weight, otherwise, correcting the third time sequence by a second weight, wherein the first weight is smaller than the second weight;

correspondingly fusing the corrected third time sequence and the fourth time sequence to obtain a fifth time sequence, and inputting the fifth time sequence into a deep learning model to obtain a first time sequence.

Preferably, the historical trajectory data further includes driving plan data;

the prediction module (102) is further configured to: and judging whether the route from the hydrogen energy vehicle driven out of the corresponding outlet to the target hydrogenation station after hydrogenation needs to be folded back or not based on the road network structure, and if so, correcting the third time sequence by adopting a fifth weight.

Preferably, the calculating and obtaining a second time sequence of the delivery vehicle arriving at the target hydrogen adding station by the prediction module (102) based on the big data of the delivery station and the real-time big data of the traffic comprises:

calculating a sixth time sequence of the preparation duration of the distribution vehicle based on the big data of the distribution station;

predicting a seventh time sequence of delivery time consumption based on the traffic real-time big data;

determining the second time series based on the sixth time series and the seventh time series.

Preferably, the prediction module (102) calculates a sixth time series of preparation periods of the delivery vehicles based on the big data of the delivery stations, and the sixth time series comprises:

and acquiring historical distribution preparation time length data of the distribution station, and carrying out cluster analysis on the historical distribution preparation time length to obtain a sixth time sequence.

Preferably, the prediction module (102) predicts a seventh time sequence of delivery time consumption based on the traffic real-time big data, and comprises:

predicting distribution time consumed by a distribution vehicle to reach each hydrogenation station based on the traffic real-time big data, and further obtaining a station arrival time sequence;

and correcting the station arrival time sequence based on a delivery plan to obtain the seventh time sequence.

Preferably, the prediction module (102) retrieves big data related to the target hydrogen station, predicts the work plan of the target hydrogen station, and further comprises a prediction update mechanism:

and determining real-time traffic conditions based on the traffic real-time big data, and determining the update frequency of the work plan based on the traffic conditions, wherein the update frequency is higher when the traffic conditions are worse.

Preferably, the work plan comprises a hydrogen supply stopping time period, and the estimated time period when the hydrogen energy vehicle arrives at the hydrogen station coincides with the hydrogen supply stopping time period.

EXAMPLE III

Referring to fig. 3, fig. 3 is an electronic device disclosed in the embodiment of the present invention, which is applied to the operation platform described in the foregoing embodiment: the apparatus comprises:

a memory storing executable program code;

a processor coupled with the memory;

the processor calls the executable program code stored in the memory to implement the method according to embodiment one.

Example four

The embodiment of the invention also discloses a computer storage medium which is applied to the operation platform in the embodiment; the storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to the first embodiment.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, an optical disk) as described above, and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.