Dynamic capacity expansion and contraction method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a dynamic capacity expansion and contraction method and device, equipment and a storage medium. The method comprises the steps that time series data in a first time period are obtained, the time series data are used for representing the situation that the number of online vehicles changes along with time in the first time period, and the first time period is a time period before the current moment; the time sequence data are input into a prediction model, the number of online vehicles in a second time period is predicted, and the second time period is a time period after the current moment; according to the number of online vehicles in the second time period, determining a capacity expansion and contraction strategy of resources in the second time period; and adjusting the resource quantity provided for the online vehicle in the second time period according to the capacity expansion and contraction strategy. According to the method, resource allocation in the next time period is arranged in advance by predicting the number of online vehicles in the next time period, the hysteresis of capacity expansion operation can be avoided, and the stability of the system is better maintained.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of intelligent connected vehicle technology, and in particular to a dynamic expansion and contraction method, device, equipment and storage medium. Background Art

[0002] With the rapid development of intelligent connected vehicles, the demand for various hardware resources and network resources in the cloud is also increasing to ensure the safe and stable driving of connected vehicles.

[0003] Traditional resource allocation uses a fixed quota approach. However, in actual applications, there are peak and off-peak periods for vehicle use. During the daytime when vehicle use is at peak, the demand for resources is high, which can easily lead to insufficient demand for resources. On the contrary, during the night when vehicle use is at low trough, the demand for resources is low, which can easily lead to waste of resources. Moreover, resource demand will fluctuate during different periods of peak or off-peak periods. Therefore, in order to maximize resource utilization, capacity can be expanded based on the current resource utilization rate to better meet resource demand at different times.

[0004] However, there is a certain lag in expanding capacity based on the current resource utilization rate, which causes the system to be under great pressure during the expansion, affecting the stability of the system. Summary of the invention

[0005] In view of this, embodiments of the present application provide a method, apparatus, device and storage medium for dynamic expansion and contraction to at least solve or alleviate the above-mentioned problems.

[0006] According to one aspect of an embodiment of the present application, a dynamic expansion and contraction method is provided, the method comprising:

[0007] Acquire time series data within a first time period, where the time series data is used to indicate how the number of online vehicles changes over time within the first time period, where the first time period is a time period before a current moment;

[0008] Inputting the time series data into a prediction model to predict the number of online vehicles in a second time period, where the second time period is a time period after the current moment;

[0009] Determining a resource expansion and contraction strategy within the second time period according to the number of online vehicles within the second time period;

[0010] The amount of resources provided to the online vehicles during the second time period is adjusted according to the expansion and contraction strategy.

[0011] According to another aspect of an embodiment of the present application, a dynamic expansion and contraction device is provided, the device comprising:

[0012] An acquisition module, used for acquiring time series data in a first time period, wherein the time series data is used for indicating how the number of online vehicles changes over time in the first time period, wherein the first time period is a time period before a current moment;

[0013] A prediction module, used for inputting the time series data into a prediction model to predict the number of online vehicles in a second time period, where the second time period is a time period after the current moment;

[0014] A determination module, configured to determine a resource expansion and contraction strategy within the second time period according to the number of online vehicles within the second time period;

[0015] An adjustment module is used to adjust the amount of resources provided to the online vehicles during the second time period according to the expansion and contraction strategy.

[0016] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and when the executable instruction is executed by the processor, the dynamic expansion and contraction method provided in the above aspect is implemented.

[0017] According to another aspect of an embodiment of the present application, a computer storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the dynamic scaling method described in the above aspect is implemented.

[0018] According to another aspect of an embodiment of the present application, a computer program product is provided, wherein the computer program product includes computer instructions, and the computer instructions instruct a computing device to execute the dynamic scaling method described in one aspect above.

[0019] According to the dynamic expansion and contraction method provided in the embodiment of the present application, the time series data in the first time period before the current moment is obtained, and then the number of online vehicles in the second time period after the current moment is predicted by the change of the number of online vehicles over time during this time period, and then the expansion and contraction strategy is generated according to the number of online vehicles in the second time period, and the amount of resources provided to the online vehicles in the second time period is adjusted according to the expansion and contraction strategy, so as to realize the early layout of resource allocation in the next time period, and avoid the situation that insufficient resource allocation of online vehicles in the next time period leads to excessive system pressure, or excessive resource allocation of online vehicles leads to waste of system resources. And by predicting the number of online vehicles in the next time period, the resource allocation in the next time period is arranged, which can avoid the lag of expansion operation and better maintain the stability of the system compared to expanding according to the current resource utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0021] Figure 1 is a schematic diagram of an exemplary platform used in one embodiment of the present application;

[0022] Figure 2 is a flow chart of a dynamic expansion and contraction method according to an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of a dynamic expansion and contraction device according to an embodiment of the present application;

[0024] Figure 4 It is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] The present application is described below based on embodiments, but the present application is not limited to these embodiments. In the detailed description of the present application below, some specific details are described in detail. It is possible for a person skilled in the art to fully understand the present application without the description of these details. In order to avoid confusing the essence of the present application, known methods, processes, and flows are not described in detail. In addition, the drawings are not necessarily drawn to scale.

[0026] Exemplary Systems

[0027] Figure 1 An exemplary vehicle network platform is shown, which includes a monitoring system 110 and a scaling system 120 .

[0028] The monitoring system 110 is used to monitor and record the online operation of the intelligent networked vehicle. For example, it monitors and records the communication between the intelligent networked vehicle and the Internet of Vehicles platform; for example, it monitors and records the number of online vehicles, resource usage rate, and bandwidth flow of the intelligent networked vehicle at each time.

[0029] The scaling system 120 is used to adjust the allocation of resources according to the online operation of the intelligent networked vehicle. For example, the scaling method can be used to adjust the hardware and / or network resources provided to the intelligent networked vehicle in different time periods. Exemplarily, the scaling system includes a task scheduling module 122 and a task execution module 124. The scaling system 120 is provided with a prediction model, which can be called by the task scheduling module 122 to complete resource calculation; the task scheduling module 122 in the scaling system 120 obtains the time series data of the number of online vehicles, resource utilization rate, and bandwidth flow in the first time period, inputs the obtained time series data into the prediction model, and predicts the number of online vehicles in the second time period; performs resource calculation according to the predicted number of online vehicles in the second time period, and calculates the amount of hardware resources and / or network resources required in the second time period; then determines the scaling strategy of resources in the second time period according to the amount of hardware resources and / or network resources used in the current time period, combined with the predicted amount of hardware resources and / or network resources required in the second time period. Then, before entering the second time period, or at the same time as entering the second time period, the task execution module 124 generates a scaling task corresponding to the scaling strategy through the task template, and executes the scaling task, such as generating a container adapter, using the container adapter to manage the container, adjusting the allocation of hardware resources, and calling the operator interface to adjust the network resource bandwidth.

[0030] The scaling system 120 also includes a data training module 126, which is used to perform training tasks of the prediction model. For example, the data training module 126 reads data to obtain a series of historical time series data, which includes time series data of the number of online vehicles, resource utilization, and bandwidth flow in the historical time period, and then performs data cleaning, obtains the historical time series data used for training from the historical time series data as sample data, and standardizes the format of the sample data to the data input format supported by the model to be trained, and then uses the sample data to train the model to be trained, and the final model output is a prediction model that predicts the number of online vehicles through time series data.

[0031] Dynamic Scaling Method

[0032] Based on the above system, an embodiment of the present application provides a dynamic expansion and contraction method, which can be executed by an Internet of Vehicles platform. The method is described in detail through multiple embodiments below.

[0033] Figure 2 FIG. 1 is a flow chart of a method for dynamically scaling capacity according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0034] Step 210, obtaining time series data within a first time period, the time series data is used to represent the change in the number of online vehicles over time within the first time period, and the first time period is the time period before the current moment.

[0035] The Internet of Vehicles platform obtains time series data of the number of online vehicles in a first time period. The first time period may be a time period in minutes or hours; for example, the first time period is the time series data of the number of online vehicles within 30 minutes before the current moment; or, for another example, the first time period is the time series data of the number of online vehicles within 2 hours before the current moment.

[0036] Optionally, the time series data includes: the number of online vehicles at each moment in the first time period. The interval between each moment is in seconds or minutes. For example, the time series data includes: the number of online vehicles per second in the first time period; for another example, the time series data includes: the number of online vehicles per minute in the first time period.

[0037] Exemplary, combined Figure 1 The Internet of Vehicles platform shown in the figure obtains time series data within a first time period from the monitoring system by the expansion and contraction system.

[0038] Optionally, the time series data includes the number of online vehicles and at least one of the following: resource usage at each moment in the first time period; bandwidth flow at each moment in the first time period. The time series data of resource usage and / or bandwidth flow is used to indicate how resource usage changes over time in the first time period.

[0039] Step 220, input the time series data into the prediction model to predict the number of online vehicles in a second time period, where the second time period is a time period after the current moment.

[0040] The Internet of Vehicles platform inputs the time series data into the prediction model and uses the prediction model to predict the number of online vehicles in the second time period.

[0041] Optionally, the Internet of Vehicles platform inputs time series data into a prediction model to obtain a curve showing how the number of online vehicles changes over time in a second time period; based on the curve showing how the number of online vehicles changes over time in the second time period, the average number of online vehicles in the second time period is determined, and the average number of online vehicles is used as the number of online vehicles in the entire second time period.

[0042] Alternatively, the median or mode of the number of online vehicles in the second time period may be determined based on a curve showing the number of online vehicles changing over time in the second time period as the number of online vehicles in the entire second time period.

[0043] Step 230, determining a resource expansion and contraction strategy within the second time period according to the number of online vehicles within the second time period.

[0044] The Internet of Vehicles platform predicts the amount of resources required in the second time period based on the number of online vehicles in the second time period, and determines the expansion and contraction strategy based on the amount of resources required in the second time period. For example, the Internet of Vehicles platform plans the expansion and contraction strategy of resources in the second time period based on the predicted amount of resources required in the second time period and the amount of resources currently used by online vehicles, including strategies for expanding or contracting hardware resources and expanding or contracting network bandwidth.

[0045] Optionally, the amount of resources required for a single vehicle is pre-set on the Internet of Vehicles platform; the Internet of Vehicles platform determines the required amount of resources in the second time period based on the number of online vehicles in the second time period and the amount of resources required for a single vehicle; and determines the resource expansion and contraction strategy in the second time period based on the required amount of resources in the second time period.

[0046] Optionally, a linear relationship between the number of online vehicles and the amount of resources is set on the Internet of Vehicles platform; the Internet of Vehicles platform determines the amount of resources corresponding to the number of online vehicles in the second time period based on the linear relationship between the number of online vehicles and the amount of resources, and obtains the required amount of resources in the second time period; based on the required amount of resources in the second time period, determines the resource expansion and contraction strategy in the second time period.

[0047] The linear relationship between the number of online vehicles and the amount of resources is a linear relationship fitted based on the number of online vehicles and the amount of resources in the historical time period. The Internet of Vehicles platform also updates the linear relationship at preset intervals, collects historical time series data in the recent period, and fits the linear relationship so that the linear relationship can better express the linear relationship between the number of online vehicles and the amount of resources in the recent period.

[0048] The above resources include at least one of hardware resources and network resources.

[0049] Step 240: Adjust the amount of resources provided to the online vehicles in the second time period according to the expansion and contraction strategy.

[0050] The Internet of Vehicles platform can adjust the amount of resources provided to online vehicles in the second time period according to the expansion and contraction strategy before entering the second time period or at the initial moment of entering the second time period.

[0051] Optionally, the Internet of Vehicles platform generates and deploys a container adapter according to the expansion and contraction strategy, and adjusts the amount of hardware resources through the container adapter; and calls the operator interface to adjust the network resource bandwidth according to the expansion and contraction strategy.

[0052] In summary, the dynamic capacity expansion method provided in this embodiment obtains the time series data in the first time period before the current moment, and then predicts the number of online vehicles in the second time period after the current moment by the change of the number of online vehicles in this time period over time, and then generates a capacity expansion and contraction strategy based on the number of online vehicles in the second time period, and adjusts the amount of resources provided to online vehicles in the second time period according to the capacity expansion and contraction strategy, so as to realize the early layout of resource allocation in the next time period, and avoid the situation that insufficient resource allocation of online vehicles in the next time period leads to excessive system pressure, or excessive resource allocation of online vehicles leads to waste of system resources. And by predicting the number of online vehicles in the next time period, the resource allocation in the next time period is arranged, which can avoid the lag of capacity expansion operation and better maintain the stability of the system compared to capacity expansion based on the current resource utilization rate.

[0053] based on Figure 2 In the embodiment shown, the above prediction model is constructed based on an algorithm for time series prediction. The algorithms that can be used to construct the prediction model include but are not limited to the following:

[0054] Moving average model; Exponential Smoothing model; eXtreme Gradient Boosting (Xgboost) model; Autoregressive Integrated Moving Average (ARIMA) model; Random Forest model; Deep Learning model; Vector Autoregressive (VAR) model; Long Short-Term Memory (LSTM) model; Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model; Prophet model; Gated Recurrent Unit (GRU) model; Transformer model.

[0055] The IoV platform obtains the historical time series data obtained through monitoring, performs data training on the constructed model to be trained, and finally obtains a prediction model for predicting the number of online vehicles in a time period. The IoV platform also updates and trains the prediction model at preset intervals, such as retraining the prediction model with the historical time series data in the recent period, to ensure the accuracy of the prediction model's prediction of the number of online vehicles.

[0056] In some possible implementations, the Internet of Vehicles platform predicts the number of online vehicles according to a pre-set prediction cycle. For example, each cycle predicts the number of online vehicles in the next cycle, the next cycle is the second time period, and the first time period may include one or more cycles before the second time period, such as the first time period including the current cycle, and the first time period including the current cycle and the previous cycle.

[0057] Optionally, the prediction cycles corresponding to different time periods within a day are different. For example, the prediction cycle during the day is 1 hour, and the prediction cycle at night is 3 hours. Alternatively, each day is divided into a peak period of vehicle usage and a trough period of vehicle usage, and the prediction cycle can also be divided according to the peak period of vehicle usage and the trough period of vehicle usage. For example, the prediction cycle for the peak period of vehicle usage is 30 minutes, and the prediction cycle for the trough period of vehicle usage is 4 hours. The peak period of vehicle usage and the trough period of vehicle usage are also different on holidays and weekdays. The peak period of vehicle usage and the trough period of vehicle usage can be further determined based on holidays or weekdays, and the prediction cycle is determined based on the peak period of vehicle usage or the trough period of vehicle usage. A correspondence between time periods and prediction cycles is set on the Internet of Vehicles platform, and the prediction cycle can be determined based on the correspondence.

[0058] The Internet of Vehicles platform determines the required amount of resources in the second time period based on the predicted number of online vehicles in the second time period; before determining the resource expansion and contraction strategy in the second time period, it compares the amount of resources used by online vehicles in the current period with the amount of resources required in the second time period, and determines whether to adjust the resources based on the difference between the two. If the difference between the two is less than a preset threshold, no resource adjustment is required. Otherwise, the expansion and contraction strategy is determined based on the amount of resources used by online vehicles in the current period and the amount of resources required in the second time period, and further adjusts the resources according to the expansion and contraction strategy.

[0059] In some other possible implementations, the vehicle networking platform predicts the curve of the number of online vehicles changing over time in the second time period, and can also calculate the change rate curve of the number of online vehicles in the second time period based on the curve of the number of online vehicles changing over time in the second time period; divide the second time period into multiple (two or more) sub-time periods based on the change rate curve, and the difference in the change rate between each time in each sub-time period is less than the difference threshold; determine the number of online vehicles in each sub-time period based on the curve of the number of online vehicles changing over time in each sub-time period. Accordingly, the generated expansion and contraction strategy includes the expansion and contraction strategy of each sub-time period.

[0060] This method can make the number of online vehicles determined in each sub-period more accurate, thereby improving the accuracy of the expansion and contraction strategy, ensuring that the resources in each sub-period are neither excessive nor insufficient, and better maintaining system stability.

[0061] Dynamic expansion device

[0062] Corresponding to the above method embodiment, Figure 3 A schematic diagram of a dynamic expansion and contraction device is shown. Figure 3 As shown, the dynamic expansion and contraction device includes:

[0063] An acquisition module 310 is used to acquire time series data in a first time period, where the time series data is used to represent how the number of online vehicles changes over time in the first time period, where the first time period is a time period before the current moment;

[0064] A prediction module 320 is used to input the time series data into a prediction model to predict the number of online vehicles in a second time period, where the second time period is a time period after the current moment;

[0065] A determination module 330, configured to determine a resource expansion and contraction strategy within the second time period according to the number of online vehicles within the second time period;

[0066] The adjustment module 340 is used to adjust the amount of resources provided to the online vehicles in the second time period according to the expansion and contraction strategy.

[0067] In another possible implementation, the prediction module 320 inputs the time series data into the prediction model to predict the number of online vehicles in the second time period, including:

[0068] The time series data is input into the prediction model to predict the curve of the number of online vehicles changing with time in the second time period; based on the curve of the number of online vehicles changing with time in the second time period, the average number of online vehicles in the second time period is determined.

[0069] In another possible implementation, the determination module 330 determines the resource expansion and contraction strategy in the second time period according to the number of online vehicles in the second time period, including:

[0070] According to the number of online vehicles in the second time period, the required amount of resources in the second time period is predicted; according to the required amount of resources in the second time period, the expansion and contraction strategy is determined.

[0071] In another possible implementation, the determination module 330 predicts the required amount of resources in the second time period according to the number of online vehicles in the second time period, including:

[0072] According to the linear relationship between the number of online vehicles and the amount of resources, the amount of resources corresponding to the number of online vehicles in the second time period is determined to obtain the required amount of resources in the second time period.

[0073] In another possible implementation, the time series data includes: the number of online vehicles, resource usage rate, and bandwidth flow at each moment in the first time period.

[0074] In another possible implementation, the adjustment module 340 adjusts the amount of resources provided to the online vehicle in the second time period according to the expansion and contraction strategy, including:

[0075] Generate and deploy container adapters according to the capacity expansion and contraction strategy, and adjust the amount of hardware resources through the container adapter; and call the operator interface to adjust the network resource bandwidth according to the capacity expansion and contraction strategy.

[0076] It should be noted that the dynamic expansion and contraction device of this embodiment is used to implement the corresponding dynamic expansion and contraction method in the aforementioned method embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0077] Electronic devices

[0078] Figure 4 is a schematic block diagram of an electronic device provided in an embodiment of the present application. The specific embodiment of the present application does not limit the specific implementation of the electronic device. For example, the electronic device may be a server or a terminal. Figure 4 As shown, the electronic device may include: a processor (processor) 702, a communication interface (Communications Interface) 704, a memory (memory) 706, and a communication bus 708. Among them:

[0079] The processor 702 , the communication interface 704 , and the memory 706 communicate with each other via a communication bus 708 .

[0080] The communication interface 704 is used to communicate with other electronic devices or servers.

[0081] The processor 702 is used to execute the program 710, and specifically can execute the relevant steps in any of the aforementioned dynamic expansion and contraction method embodiments.

[0082] Specifically, the program 710 may include program codes, which include computer operation instructions.

[0083] The processor 702 may be a CPU, or an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0084] RISC-V is an open source instruction set architecture based on the Reduced Instruction Set (RISC) principle. It can be applied to various aspects such as single-chip microcomputers and FPGA chips. Specifically, it can be applied to the fields of IoT security, industrial control, mobile phones, personal computers, etc., and because it is designed with small, fast, and low-power realities in mind, it is particularly suitable for modern computing devices such as warehouse-scale cloud computers, high-end mobile phones, and tiny embedded systems. With the rise of artificial intelligence Internet of Things (AIoT), the RISC-V instruction set architecture has also received more and more attention and support, and is expected to become the next generation of widely used CPU architecture.

[0085] The computer operation instructions in the embodiment of the present application may be computer operation instructions based on the RISC-V instruction set architecture, and correspondingly, the processor 702 may be designed based on the RISC-V instruction set. Specifically, the chip of the processor in the electronic device provided in the embodiment of the present application may be a chip designed with the RISC-V instruction set, and the chip may execute executable code based on the configured instructions, thereby realizing the dynamic expansion and contraction method in the above embodiment.

[0086] The memory 706 is used to store the program 710. The memory 706 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0087] The program 710 may be specifically used to enable the processor 702 to execute the dynamic expansion and contraction method in any of the aforementioned embodiments.

[0088] The specific implementation of each step in program 710 can refer to the corresponding description of the corresponding steps and units in any of the above-mentioned dynamic expansion and contraction method embodiments, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiments, which will not be repeated here.

[0089] Computer storage media

[0090] The present application also provides a computer-readable storage medium storing instructions for causing a machine to execute the dynamic expansion and contraction method as described herein. Specifically, a system or device equipped with a storage medium may be provided, on which a software program code implementing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device is enabled to read and execute the program code stored in the storage medium.

[0091] In this case, the program code read from the storage medium itself can implement the function of any one of the above embodiments, so the program code and the storage medium storing the program code constitute part of the present application.

[0092] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0093] Computer program product

[0094] An embodiment of the present application also provides a computer program product, including computer instructions, which instruct a computing device to perform any corresponding operation in the above-mentioned multiple method embodiments.

[0095] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0096] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0097] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0098] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. A dynamic expansion and contraction method, characterized in that: The method comprises: Acquire time series data within a first time period, where the time series data is used to indicate how the number of online vehicles changes over time within the first time period, where the first time period is a time period before a current moment; Inputting the time series data into a prediction model to predict the number of online vehicles in a second time period, where the second time period is a time period after the current moment; Determining a resource expansion and contraction strategy within the second time period according to the number of online vehicles within the second time period; The amount of resources provided to the online vehicles during the second time period is adjusted according to the expansion and contraction strategy.

2. The method according to claim 1, characterized in that The step of inputting the time series data into a prediction model to predict the number of online vehicles in a second time period includes: Inputting the time series data into a prediction model to predict a curve showing the number of online vehicles changing with time in the second time period; The average number of online vehicles in the second time period is determined according to a curve showing the change of the number of online vehicles over time in the second time period.

3. The method according to claim 1, characterized in that The determining, according to the number of online vehicles in the second time period, a resource expansion and contraction strategy in the second time period includes: Predicting the required amount of resources in the second time period according to the number of online vehicles in the second time period; The scaling strategy is determined according to the required amount of resources in the second time period.

4. The method according to claim 3, characterized in that The step of predicting the required amount of resources in the second time period according to the number of online vehicles in the second time period includes: According to the linear relationship between the number of online vehicles and the amount of resources, the amount of resources corresponding to the number of online vehicles in the second time period is determined to obtain the required amount of resources in the second time period.

5. The method according to any one of claims 1 to 4, characterized in that: The time series data includes: the number of online vehicles, resource utilization rate and bandwidth flow at each moment in the first time period.

6. The method according to any one of claims 1 to 4, characterized in that: The adjusting the amount of resources provided to the online vehicles in the second time period according to the expansion and contraction strategy includes: Generate and deploy a container adapter according to the expansion and contraction strategy, and adjust the amount of hardware resources through the container adapter; and According to the expansion and contraction strategy, the operator interface is called to adjust the network resource bandwidth.

7. A dynamic expansion and contraction device, characterized in that: The device comprises: An acquisition module, used for acquiring time series data in a first time period, wherein the time series data is used for indicating how the number of online vehicles changes over time in the first time period, wherein the first time period is a time period before a current moment; A prediction module, used for inputting the time series data into a prediction model to predict the number of online vehicles in a second time period, where the second time period is a time period after the current moment; A determination module, configured to determine a resource expansion and contraction strategy within the second time period according to the number of online vehicles within the second time period; An adjustment module is used to adjust the amount of resources provided to the online vehicles during the second time period according to the expansion and contraction strategy.

8. An electronic device, characterized in that: The electronic device comprises: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and when the executable instruction is executed by the processor, the dynamic expansion and contraction method according to any one of claims 1 to 6 is implemented.

9. A computer storage medium, characterized in that The computer storage medium stores a computer program, and when the computer program is executed by the processor, the dynamic expansion and contraction method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes computer instructions, and the computer instructions instruct a computer device to execute the dynamic expansion and contraction method according to any one of claims 1 to 6.

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