Upload control method and device for refueling information of hydrogen refueling station and electronic equipment

Abstract

The embodiment of the invention discloses an uploading control method and device for refueling information of a hydrogen refueling station and electronic equipment. The method comprises the following steps: determining a network recovery probability within a preset duration; determining an information reporting mode based on the network recovery probability within a preset time length, the information reporting mode comprises a first mode for centralized reporting after being delayed to a preset time length, a second mode for batch reporting by an exponential back-off algorithm for dynamically adjusting an initial reporting interval based on a network recovery probability, and a third mode for continuous reporting based on a reporting interval corresponding to the network recovery probability. And reporting the filling information locally stored in the hydrogen refueling station based on the information reporting mode. According to the embodiment of the invention, after the hydrogen filling machine fails to upload to the server through networking, the filling information which is temporarily and locally stored is complementarily transmitted, the local reporting mode can be dynamically switched according to the predicted network recovery probability in a period of time in the future, and the filling information is timely reported under the condition of maximizing the network and system resource utilization rate.

Description

Technical Field

[0001] The embodiments of this specification belong to the field of local storage information upload control, and particularly relate to a method, device, and electronic device for uploading and controlling refueling information in a hydrogen refueling station. Background Art

[0002] A hydrogen refueling station is a site that provides hydrogen to fuel cell vehicles. During the process of a hydrogen refueling vehicle entering the hydrogen refueling station for hydrogen refueling interaction, refueling information containing interaction data will be generated. To avoid the refueling information occupying the limited local memory for a long time, this refueling information will be uploaded to the cloud for storage and management after being generated. When the hydrogen refueling station has a network disconnection due to network problems, it generally continuously attempts to report the refueling information at fixed time intervals until the reporting is successful. Such a method will continuously generate reporting requests to occupy system resources, and the reporting efficiency is relatively low. Summary of the Invention

[0003] The embodiments of the present disclosure provide a method, device, and electronic device for uploading and controlling refueling information in a hydrogen refueling station, aiming to solve one or more of the above problems and other potential problems.

[0004] According to the first aspect of the present disclosure, a method for uploading and controlling refueling information in a hydrogen refueling station is provided. The method includes determining the network recovery probability within a preset duration based on the network interruption time and signal strength at the current moment. The method further includes determining an information reporting mode based on the network recovery probability within the preset duration. The information reporting mode includes a first mode of delaying and centrally reporting after a preset duration, a second mode of reporting in batches based on the exponential backoff algorithm that dynamically adjusts the initial reporting interval according to the network recovery probability, and a third mode of continuously reporting based on the reporting interval corresponding to the network recovery probability. In addition, the method further includes reporting the refueling information stored locally in the hydrogen refueling station based on the information reporting mode.

[0005] According to the second aspect of the present disclosure, a device for uploading and controlling refueling information in a hydrogen refueling station is provided. The device includes a recovery probability determination module configured to determine the network recovery probability within a preset duration based on the network interruption time and signal strength at the current moment. The device further includes a reporting mode determination module configured to determine an information reporting mode based on the network recovery probability within the preset duration. The information reporting mode includes a first mode of delaying and centrally reporting after a preset duration, a second mode of reporting in batches based on the exponential backoff algorithm that dynamically adjusts the initial reporting interval according to the network recovery probability, and a third mode of continuously reporting based on the reporting interval corresponding to the network recovery probability. In addition, the device further includes a refueling information reporting module configured to report the refueling information stored locally in the hydrogen refueling station based on the information reporting mode.

[0006] According to a third aspect of the present disclosure, there is provided an electronic device, including one or more processors, and a memory associated with the one or more processors, where the memory is used to store program instructions, and when the program instructions are read and executed by the one or more processors, the method provided according to the first solution is executed.

[0007] According to a fourth aspect of the present disclosure, there is provided a computer program product, including a computer program, where when the computer program is executed by a processor, the method provided according to the first aspect is implemented.

[0008] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more obvious. In the drawings, the same or similar reference numerals denote the same or similar elements, where: Figure 1 A schematic diagram showing an example environment in which multiple embodiments of the present disclosure can be implemented; Figure 2 A flowchart showing a method for controlling the upload of refueling information of a hydrogen refueling station according to some embodiments of the present disclosure; Figure 3 A flowchart showing a processing process of each information reporting mode according to some embodiments of the present disclosure; Figure 4 A flowchart showing a process of batch division of refueling information according to some embodiments of the present disclosure; Figure 5 A flowchart showing a training process of a prediction model according to some embodiments of the present disclosure; Figure 6 A schematic diagram showing the structure of a device for controlling the upload of refueling information of a hydrogen refueling station according to some embodiments of the present disclosure; Figure 7 A schematic block diagram showing an electronic device according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the embodiments of the present specification will be clearly and completely described below in combination with the corresponding drawings of the embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0011] As used in this specification, the claims and the above drawings, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting".

[0012] As mentioned above, after a hydrogen vehicle is refueled at a hydrogen refueling station, the hydrogen refueling station will generate refueling information according to this refueling process. The refueling information may include the date of hydrogen filling, the license plate number of the hydrogen vehicle, the registration number of the gas cylinder for use, the mass of hydrogen consumed during filling, the transaction amount, the identity information of the inspector, the identity information of the filler, the invoice QR code, and the invoice document. The refueling information needs to be uploaded to the cloud server in a timely manner for storage to record and manage each filling situation and avoid the loss of some refueling information locally at the hydrogen refueling station due to force majeure. Moreover, the local memory of the hydrogen refueling station is generally limited. After the refueling information is uploaded to the cloud server in a timely manner, the local system can operate effectively and stably. In actual situations, the network of the hydrogen refueling station may fluctuate, resulting in occasional network disconnections, making the refueling information unable to be uploaded immediately and then stored locally at the hydrogen refueling station for a long time. Currently, the general solution to the above situation is to continuously attempt to report the refueling information at a certain waiting interval after the network is disconnected until the refueling information is successfully reported due to network recovery. This method will frequently occupy the system resources of the hydrogen refueling station to continuously attempt to report data, resulting in a relatively heavy network load, and may cause a secondary network paralysis due to intensive retries, and even affect the stable operation of the local system of the hydrogen refueling station.

[0013] In view of this, the embodiments of the present disclosure propose an upload control scheme for refueling information at a hydrogen refueling station. In the embodiments of the present disclosure, the network recovery probability within a certain period of time is estimated based on the network parameters (such as network interruption time, signal strength, etc.) at the moment when the network is interrupted, and different information reporting modes are selected to execute the reporting strategy for this interruption according to the different magnitudes of the estimated network recovery probability, so as to report the refueling information stored locally according to the corresponding reporting strategy.

[0014] Through the above method, the present application can estimate the network recovery probability within a future period of time about to occur based on the network interruption time and signal strength at the moment when the network is interrupted, and select an information reporting mode that can maximize the resource utilization rate to report the refueling information stored locally. In this way, the information reporting mode can be determined more accurately based on the nearby network interruption situation, and under the selected information reporting mode, the refueling information can be reported as early as possible, more efficiently and accurately, so as to reduce the number of attempts of ineffective reporting, and thus reduce the system load caused by reporting.

[0015] Figure 1 FIG. shows a schematic diagram of an example environment 100 in which multiple embodiments of the present disclosure can be implemented. As Figure 1 shown, the environment 100 may include a terminal 110, a hydrogen refueling dispenser 120, and a cloud 130. The terminal 110 may be any device having computing power or processing power. For example, the terminal 110 may include, but is not limited to, a mobile phone, a tablet computer, a desktop computer, a server, etc. After receiving the refueling instruction from the terminal 110, the hydrogen refueling dispenser 120 will perform a refueling operation on the currently connected hydrogen vehicle according to the refueling instruction, and generate corresponding refueling information 115 during the refueling operation and send it to the terminal 110 for local storage. When the terminal 110 locally stores the refueling information 115, the terminal 110 will upload the refueling information 115 to the cloud 130, and delete the locally stored refueling information 115 after the upload to the cloud is successful to save local resources. If the terminal 110 detects a network interruption with the cloud 130, resulting in the failure of uploading the refueling information 115, the terminal 110 will estimate the network recovery probability 113 based on the network interruption time 111 and the signal strength 112. Then, the terminal 110 will select the information reporting mode 114 according to the network recovery probability 113, and report the refueling information 115 according to the selected information reporting mode 114, so as to attempt to report the refueling information 115 to the cloud 130.

[0016] Figure 2 FIG. shows a schematic flow chart of a method 200 for controlling the upload of refueling information at a hydrogen refueling station according to some embodiments of the present disclosure. The method 200 may be executed by the terminal 110, for example. As Figure 2As shown, in box 202, method 200 can determine the network recovery probability within a preset time based on the network interruption time and signal strength at the current moment. In this embodiment, when a network interruption problem occurs, the terminal can first obtain the network interruption time and the signal strength of the network signal at the current moment, and estimate the network recovery probability within a preset time (for example, 10 minutes) according to the network interruption time and signal strength. Among them, the moment when the filling information fails to be reported, or the moment when the network interruption is detected, can be used as the network interruption time, and the strength data of the network signal collected at this time is used as the signal strength. As an example, a prediction model can be trained in advance based on historical data when the historical network is interrupted, and the prediction model can be a long short-term memory network (Long Short-Term Memory, LSTM) model. The obtained network interruption time and signal strength are input into the prediction model, and the network recovery probability output by the prediction model can be obtained. As another example, a mapping relationship between a network interruption time, signal strength and network recovery probability can be pre-set based on historical data and manual experience, and the corresponding network recovery probability can be obtained by querying the mapping relationship. In order to avoid the huge amount of data, which makes the construction of the prediction model or mapping relationship too difficult or complicated, the network interruption time can be accurate to only minutes, and the signal strength can be divided into intensity levels according to the interval. For example, the length of an interval is 5dBm, and the signal strength in the same interval is regarded as the intensity level corresponding to the interval, and the preset signal strength corresponding to the intensity level is used as the actual signal strength. For example, assuming that the signal strength at this time is 84dBm, which belongs to the interval of 81dBm to 85dBm, and the interval uses the middle value of 83dBm as the preset signal strength, then when actually training, the signal strength at this time will be considered to be 83dBm. In other embodiments, if the amount of data used for training is not large, or in order to further improve the accuracy of the model, the network interruption event can be accurate to seconds, for example, and the signal strength is not divided by interval but directly uses the original value.

[0017] At block 204, method 200 may determine an information reporting mode based on the network recovery probability within a preset duration. The information reporting modes include a first mode of deferring to report centrally after the preset duration, a second mode of reporting in batches using an exponential backoff algorithm that dynamically adjusts the initial reporting interval based on the network recovery probability, and a third mode of continuously reporting based on the reporting interval corresponding to the network recovery probability. In this embodiment, three probability intervals may be pre-divided, and an information reporting mode may be set for each probability interval, and the corresponding mode may be selected as the information reporting mode for this time according to the probability interval corresponding to the network recovery probability. The relationship between different probability intervals and different modes may be set according to actual needs. As an example, if the network recovery probability is high, it can be considered that at the corresponding moment after the preset duration, the network has a high probability of being restored. In this case, the first mode may be set to attempt to report the data uniformly at the moment when the network is likely to have been restored after the preset duration, so as to reduce the unnecessary reporting attempt times within the preset duration and save system resources. If the network recovery probability is low, it can be considered that after the preset duration, it cannot be guaranteed that the network will definitely be restored. This means that waiting until after the preset duration to report uniformly is not very meaningful, and instead, it will cause more unuploadable annotation information to accumulate during this period and occupy system memory. At the same time, due to limited system resources and the continuous generation of annotation information, reporting cannot be completely abandoned because of poor network, otherwise, if the accumulated annotation information exceeds the local storage capacity, some annotation information will be lost. Therefore, at this time, the third mode may be set to determine the reporting interval according to the network recovery probability (for example, the lower the network recovery probability, the longer the reporting interval, so as to reduce the resource occupation frequency when the recovery probability is extremely low), and continuously attempt to report the annotation information according to this reporting interval, thereby, at the cost of still occupying a part of the system resources, attempt to report multiple times to consume as much redundant accumulated annotation information as possible and avoid the continuous accumulation of annotation information and long-term resource occupation. Among them, if the network recovery probability is too low (for example, less than 10%), then no mode may be selected for reporting, and a system alarm may be directly triggered to remind the staff to manually repair the network. If the network recovery probability is medium, the second mode may be set to report the accumulated annotation information in batches through the exponential backoff algorithm, and the specific value of the initial reporting interval used in the exponential backoff algorithm may be dynamically adjusted according to the actual network recovery probability to balance the success rate and resource consumption. Among them, when the second mode is selected, the annotation information may be batch-divided in ways such as according to the time period of data storage, the order of data storage, and a fixed number of data as a batch. In addition, if the number of successfully uploaded annotation information is less than the preset number during two consecutive rounds of reporting attempts, a system alarm will also be triggered.

[0018] At block 206, method 200 may report the refueling information locally stored at the hydrogen refueling station based on the information reporting mode. In this embodiment, after determining the information reporting mode, the locally stored refueling information will be reported in this information reporting mode within a preset duration until the refueling information is successfully reported, and then the corresponding refueling information will be deleted locally. If the refueling information is still not successfully reported after the preset duration, a new round of reporting process will be carried out. At this time, the moment after the preset duration can be used as the new network interruption time, and the network recovery probability will be re-estimated in combination with the signal strength at this moment, and a corresponding mode will be selected according to the re-obtained network recovery probability for the next round of reporting.

[0019] In this way, according to the determined magnitude of the network recovery probability, the selected information reporting model can be switched to adjust the reporting strategy of the refueling information, maximize the utilization rate of network and system resources, and minimize the number of attempts of ineffective reporting, so that the refueling information can be reported as early as possible, efficiently and accurately.

[0020] Figure 3 The flowchart of the processing process 300 of each information reporting mode of some embodiments of the present disclosure is shown. In process 300, according to the network interruption time 311 and signal strength 312 at the current moment, the network recovery probability 320 within the preset duration can be determined, and the probability range 330 corresponding to this network recovery probability 320 can be determined. If the probability range 330 is a preset high probability range (for example, more than 80%), the first mode 340 will be selected as the information reporting mode for this time. In block 350, it will wait for the preset duration, and it is considered that the network has probably recovered at the moment after the preset duration, and all the locally stored refueling information will be reported together. If the probability range 330 is a preset low probability range (for example, between 10% - 30%), the third mode 342 will be selected as the information reporting mode for this time. In block 370, the refueling information will be reported one by one continuously according to the reporting interval corresponding to the network recovery probability (for example, 30 seconds), that is, after the previous refueling information is successfully reported, the next refueling information will be reported.

[0021] If the probability range 330 is a preset medium probability range (for example, between 30% and 80%), then the second mode 341 is selected as the information reporting mode for this time. In the second mode 341, first, according to the probability difference 360 between the obtained network recovery probability and the preset probability (for example, a reference value arbitrarily selected within the medium probability range, such as 50%), different probability differences 360 can be pre-correspondingly set with different initial reporting intervals, and the probability difference 360 can be negative. The larger the probability difference 360, the greater the probability that the network will recover after the preset duration. Then, there is no need to continuously retry too frequently within the preset duration. Instead, you can wait for a longer time and try again when the network is more likely to recover. Therefore, the initial reporting interval in the exponential backoff algorithm is larger. At the same time, the backoff factor 362 in the exponential backoff algorithm can also be set according to the signal strength 312. Since the network recovery probability is only an estimated value, in this embodiment, it can be considered that under the same network recovery probability, if the signal strength 312 is greater, the network signal is considered to be more stable and more likely to recover faster within the preset duration. To minimize the system load as much as possible, the backoff factor can be set smaller at this time to shorten the interval for the next retry after a reporting failure, so as to report the redundant refueling information as soon as possible. After the refueling information is batch-divided, the refueling information of each batch will be reported in order of batch priority. In box 363, the current reported batch will be determined, and the refueling information will be reported according to the current reporting interval 361. The current reporting interval 361 can be the initial reporting interval in the initial state. After a report is made, box 380 will determine whether the current report is successful. If the report fails, the current reporting interval 361 will be multiplied by the backoff factor 362 to obtain a new current reporting interval 361, and the next report will be made with the new current reporting interval 361. Among them, each time the current reporting interval 361 is multiplied when the report fails, the backoff factor 362 can be re-determined according to the signal strength re-obtained at the current moment during the multiplication, so as to achieve dynamic adjustment of the backoff factor 362, and thus achieve dynamic adjustment of the current reporting interval 361. In box 390, if the report is successful, the current reporting interval 361 will be restored to the initial state, and the refueling information of the next batch will be reported. In addition, if the cumulative reporting interval is greater than the preset duration, the reporting process will also be stopped, the network recovery probability 320 will be re-determined according to the network interruption time 311 and the signal strength 312, and a new information reporting mode will be re-selected for the next round of reporting.

[0022] Figure 4The flowchart of the batch division process 400 of the filling information showing some embodiments of the present disclosure is illustrated. In process 400, the filling information 410 can be classified to obtain the core data 420 and the non-core data 430. Then, according to the current network bandwidth, it is determined whether the current network bandwidth is small (i.e., whether it is less than the preset bandwidth). If the bandwidth is small, in order to avoid the situation that even if the network recovers, the information will cause the system to freeze and crash due to exceeding the bandwidth, resulting in the failure of information reporting. At this time, the data will be compressed. The core data 420 is more important and will be compressed losslessly, while the non-core data 430 will be compressed lossily. If the bandwidth is sufficient, the core data 420 will not be compressed, and only the non-core data 430 will be compressed losslessly. When dividing the filling information into batches, in the order of batch priority from high to low, the uncompressed core data 421 can be divided into the first batch 440, the compressed core data 422 can be divided into the second batch 441, the uncompressed non-core data 432 can be divided into the third batch 442, and the compressed non-core data 431 can be divided into the fourth batch 443.

[0023] Figure 5A flowchart of a training process 500 of a prediction model according to some embodiments of the present disclosure is shown. In process 500, each piece of historical interruption data 510 includes a historical network interruption time 511, a historical recovery duration 512, and a historical signal strength 513. By comparing the historical recovery duration 512 with a preset duration 514, it can be determined whether the network is restored within the preset duration for the historical interruption data 510, and then a label is set for each piece of historical interruption data 510 according to the comparison result. Then, all pieces of historical interruption data 510 with the same historical network interruption time 511 and historical signal strength 513 are taken as a set, and each set is processed separately. For any set, the proportion of the pieces of historical interruption data 510 with the determined label indicating network recovery in all the pieces of historical interruption data 510 in the set is used to determine the historical network recovery probability 520-2 of the historical interruption data 510 under the combination 520-1 of the historical network interruption time 511 and the historical signal strength 513. After determining the historical network recovery probabilities 520-2 corresponding to all combinations 520-1, a training set 520 can be constructed, and the prediction model 530 is trained using the training set 520. During the training process, the generator 531 of the prediction model 530 can generate a predicted network recovery probability 532 based on the combination 520-1. A loss judgment is made on the predicted network recovery probability 532 using the generator loss 533. The loss judgment can be made using a contrast loss function. The generator loss 533 can be a larger value, and then the generator loss 533 can be backpropagated to the generator 531 to guide the optimization of the parameters of the generator 531. Such a training process can be iteratively executed until the generator 531 can generate a more accurate predicted network recovery probability 532. After the training process ends, the prediction model 530 can output a network recovery probability 540 within the preset duration.

[0024] As an example, assume that there are a total of 500 pieces of historical interruption data for training. All historical interruption data with the same historical network interruption time and historical signal strength are grouped together. For example, there are 10 groups, and each group contains multiple pieces of historical interruption data. For any one group, assume that the preset duration is 10 minutes. A certain group contains 50 pieces of historical interruption data, and among the historical interruption data corresponding to the group, the number of historical interruption data with a historical recovery duration less than 10 minutes is 30 (that is, the number of tags representing network recovery within the preset duration is 30). Calculate the proportion of historical interruption data with tags representing network recovery within the preset duration in all historical interruption data in this group as 30 / 50. Then the historical network recovery probability corresponding to this group is 60%. The trained prediction model is not used to predict a specific recovery time (the predicted recovery time is set according to the recovery time in the ideal state and is often different from the actual recovery time, making it difficult to be used as a judgment benchmark), but is used to predict the network recovery probability within the preset duration, so as to select different information reporting modes according to different probabilities. In this way, it is not necessary to pay attention to the specific moment when the network recovers, and only need to adjust the reporting strategy according to different information reporting models to report the refueling information as early as possible in a way that minimizes system resource occupancy.

[0025] Figure 6 FIG. shows a schematic structural diagram of a refueling information upload control device 600 according to some embodiments of the present disclosure. Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply, and the relevant parts can refer to the partial description of the method embodiments. As Figure 6 shown, the device 600 includes a recovery probability determination module 601, configured to determine the network recovery probability within a preset duration based on the network interruption time and signal strength at the current moment. The device 600 further includes a reporting mode determination module 602, configured to determine the information reporting mode based on the network recovery probability within the preset duration. The information reporting mode includes a first mode of postponing and centrally reporting after the preset duration has passed, a second mode of reporting in batches using an exponential backoff algorithm that dynamically adjusts the initial reporting interval based on the network recovery probability, and a third mode of continuously reporting based on the reporting interval corresponding to the network recovery probability. In addition, the device 600 further includes a refueling information reporting module 603, configured to report the refueling information stored locally in the hydrogen refueling station based on the information reporting mode.

[0026] The reporting mode determination module 602 includes a first determination unit configured to determine the information reporting mode as the first mode in response to the probability range corresponding to the network recovery probability within a preset duration being a preset high probability range. The reporting mode determination module 602 further includes a second determination unit configured to determine the information reporting mode as the second mode in response to the probability range corresponding to the network recovery probability within a preset duration being a preset medium probability range. The reporting mode determination module 602 further includes a third determination unit configured to determine the information reporting mode as the third mode in response to the probability range corresponding to the network recovery probability within a preset duration being a preset low probability range.

[0027] The fueling information reporting module 603 includes a fourth determination unit configured to determine an initial reporting interval based on the probability difference between the network recovery probability and a preset probability within a preset duration in response to the second mode, and the probability difference is positively correlated with the initial reporting interval. The fueling information reporting module 603 further includes a fifth determination unit configured to determine a backoff factor based on the signal strength, and the signal strength is negatively correlated with the backoff factor. The fueling information reporting module 603 further includes a batch reporting unit configured to perform batch division on each fueling information and then report the fueling information in batches based on the exponential backoff algorithm.

[0028] The device 600 further includes a batch division module configured to classify the fueling information based on the data priority, and the classified data categories include core data and non-core data. The device 600 further includes a first compression module configured to perform lossless compression on the core data and lossy compression on the non-core data in response to the current network bandwidth being less than a preset bandwidth. The device 600 further includes a second compression module configured to perform lossless compression on the non-core data in response to the current network bandwidth being not less than a preset bandwidth. The batch reporting unit includes a batch division element configured to divide each fueling information into a first batch corresponding to uncompressed core data, a second batch corresponding to compressed core data, a third batch corresponding to uncompressed non-core data, and a fourth batch corresponding to compressed non-core data in descending order of batch priority.

[0029] The batch reporting unit further includes a first response element configured to report the fueling information of the batch based on the current reporting interval in response to the currently reported batch until the batch reporting is successful or the cumulative reporting interval is greater than a preset duration, and the current reporting interval is the initial reporting interval in the initial state. The batch reporting unit further includes a second response element configured to determine a backoff factor based on the signal strength at the current moment in response to the batch reporting failure, and use the product of the backoff factor and the current reporting interval as the new current reporting interval. The batch reporting unit further includes a third response element configured to report the next batch in response to the batch reporting being successful.

[0030] In device 600, the network recovery probability within a preset duration is obtained based on a prediction model. Device 600 further includes a training set construction module configured to determine a training set from historical interruption data. The training set includes combinations composed of historical network interruption times and historical signal strengths, as well as the corresponding historical network recovery probabilities for the combinations. Device 600 further includes a training module configured to train the prediction model based on the combinations and the historical network recovery probabilities.

[0031] The training set construction module includes a label setting unit configured to set labels for the historical interruption data based on the comparison result between the recovery duration and the preset duration. Each piece of historical interruption data includes a historical network interruption time, a historical recovery duration, and a historical signal strength. The labels are used to represent whether the historical interruption data has recovered the network within the preset duration. The training set construction module includes a proportion calculation unit configured to determine the historical network recovery probability corresponding to the combination of the historical network interruption time and the historical signal strength based on the proportion of the historical interruption data labeled as having recovered the network within the preset duration among the pieces of historical interruption data with the same historical network interruption time and historical signal strength. The training set construction module includes a training set construction unit configured to construct a training set based on the combinations and the historical network recovery probabilities.

[0032] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a Digital Versatile Disc (DVD)), or a semiconductor medium (such as a Solid State Disk (SSD)), etc.

[0033] Figure 7FIG. 0 shows a block diagram of an electronic device 700 that can implement multiple embodiments of the present disclosure. As Figure 7 shown, the electronic device 700 includes a processor 710, a disk drive 720, an input / output interface 730, a network interface 740, and a memory 750. The above-mentioned processor 710, disk drive 720, input / output interface 730, network interface 740, and memory 750 can be communicatively connected through a communication bus 760.

[0034] Among them, the processor 710 can be implemented in the form of a general-purpose CPU, a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the present application.

[0035] The memory 750 can be implemented in the form of a ROM (Read Only Memory), a RAM (Read Access Memory), a static memory, a dynamic storage device, etc. The memory 750 can store an operating system 751 for controlling the operation of the electronic device 700, and a basic input / output system (BIOS) 752 for controlling the low-level operations of the electronic device 700. In addition, a web browser 753, a data storage management system 754, etc. can also be stored. In short, when implementing the technical solutions provided by the present application through software or firmware, the relevant program codes are stored in the memory 750 and are called and executed by the processor 710.

[0036] The input / output interface 730 is used to connect to an input / output module to implement information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, a warning light, etc.

[0037] The network interface 740 is used to connect to a communication module (not shown in the figure) to implement communication and interaction between the device and other devices. Among them, the communication module can implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as a mobile network, WIFI, Bluetooth, etc.).

[0038] The bus 760 includes a path for transmitting information between various components of the device (such as the processor 710, the disk drive 720, the input / output interface 730, the network interface 740, and the memory 750).

[0039] It should be noted that although the above device only shows the processor 710, disk drive 720, input / output interface 730, network interface 740, and memory 750, bus 760, etc., in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may only include the components necessary to implement the method of the present application, and does not necessarily include all the components shown in the figure.

[0040] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, so that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0041] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In addition, although the operations are depicted in a particular order, this should be understood to require that the operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although a number of specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation can also be implemented separately or in any suitable sub-combination in multiple implementations.

[0042] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms for implementing the claims.

Claims

1. A method for uploading and controlling refueling information of a hydrogen refueling station, characterized in that, The method includes: Determining a network recovery probability within a preset duration based on the network interruption time and signal strength at the current moment; Determining an information reporting mode based on the network recovery probability within the preset duration, where the information reporting mode includes a first mode of delaying and centrally reporting after the preset duration, a second mode of batch reporting using an exponential backoff algorithm that dynamically adjusts the initial reporting interval based on the network recovery probability, and a third mode of continuously reporting based on the reporting interval corresponding to the network recovery probability; and Reporting the refueling information locally stored in the hydrogen refueling station based on the information reporting mode.

2. The method according to claim 1, wherein The determining the information reporting mode based on the network recovery probability within the preset duration includes: Responding to the probability range corresponding to the network recovery probability within the preset duration being a preset high probability range, and determining the information reporting mode as the first mode; Responding to the probability range corresponding to the network recovery probability within the preset duration being a preset medium probability range, and determining the information reporting mode as the second mode; and Responding to the probability range corresponding to the network recovery probability within the preset duration being a preset low probability range, and determining the information reporting mode as the third mode.

3. The method according to claim 1 or 2, characterized in that The reporting the refueling information locally stored in the hydrogen refueling station based on the information reporting mode includes: Responding to the second mode, determining an initial reporting interval based on the probability difference between the network recovery probability within the preset duration and a preset probability, where the probability difference is positively correlated with the initial reporting interval; Determining a backoff factor based on the signal strength, where the signal strength is negatively correlated with the backoff factor; and After batch-dividing each of the refueling information, reporting the refueling information in batches based on the exponential backoff algorithm.

4. The method according to claim 3, wherein The method further includes: Classifying the refueling information based on data priority, and the classified data categories include core data and non-core data; Responding to the current network bandwidth being less than a preset bandwidth, performing lossless compression on the core data and lossy compression on the non-core data; and Responding to the current network bandwidth being not less than the preset bandwidth, performing lossless compression on the non-core data; The batch-dividing each of the refueling information includes: Dividing each of the refueling information into a first batch corresponding to the uncompressed core data, a second batch corresponding to the compressed core data, a third batch corresponding to the uncompressed non-core data, and a fourth batch corresponding to the compressed non-core data in the order of decreasing batch priority.

5. The method according to claim 3, wherein The reporting the refueling information in batches based on the exponential backoff algorithm includes: Responding to the currently reported batch, reporting the refueling information of the batch based on the current reporting interval until the batch reporting is successful or the cumulative reporting interval is greater than the preset duration, and the current reporting interval is the initial reporting interval in the initial state; Responding to the batch reporting failure, determining the backoff factor based on the signal strength at the current moment, and using the product of the backoff factor and the current reporting interval as the new current reporting interval; and Responding to the batch reporting being successful, reporting the next batch.

6. The method according to claim 1, wherein The network recovery probability within the preset duration is obtained based on a prediction model; The method further includes: Determining a training set from historical interruption data, where the training set includes combinations composed of historical network interruption times and historical signal strengths, and the corresponding historical network recovery probabilities; and Training the prediction model based on the combinations and the historical network recovery probabilities.

7. The method according to claim 6, wherein The determining of the training set from historical interruption data includes: Setting labels for the historical interruption data based on the comparison result between the recovery duration and the preset duration. Each piece of the historical interruption data includes a historical network interruption time, a historical recovery duration, and a historical signal strength, and the label is used to characterize whether the historical interruption data recovers the network within the preset duration; Among the pieces of historical interruption data with the same historical network interruption time and historical signal strength, determining the historical network recovery probability corresponding to the combination of the historical network interruption time and the historical signal strength based on the proportion of the historical interruption data characterized as recovering the network within the preset duration by the label; and Constructing a training set based on the combination and the historical network recovery probability.

8. An upload control device for refueling information of a hydrogen refueling station, characterized in that, The device includes: A recovery probability determination module configured to determine the network recovery probability within the preset duration based on the network interruption time and signal strength at the current moment; A reporting mode determination module configured to determine an information reporting mode based on the network recovery probability within the preset duration. The information reporting mode includes a first mode of deferring to report centrally after the preset duration has elapsed, a second mode of reporting in batches using an exponential backoff algorithm that dynamically adjusts the initial reporting interval based on the network recovery probability, and a third mode of continuously reporting based on the reporting interval corresponding to the network recovery probability; and A refueling information reporting module configured to report the refueling information stored locally at the hydrogen refueling station based on the information reporting mode.

9. An electronic device, including: One or more processors, and A memory associated with the one or more processors, where the memory is used to store program instructions, and when the program instructions are read and executed by the one or more processors, the steps of the method according to any one of claims 1-7 are executed.

10. A computer program product, including a computer program, where when the computer program is executed by a processor, the method according to any one of claims 1-7 is implemented.

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