CN118233392A

CN118233392A - Current limiting method and device, electronic equipment and storage medium

Info

Publication number: CN118233392A
Application number: CN202410402612.7A
Authority: CN
Inventors: 谢方敏; 周峰; 郭陟; 宋承录; 王灿; 洪文波
Original assignee: Guangzhou Fangzhou Information Technology Co ltd
Current assignee: Guangzhou Fangzhou Information Technology Co ltd
Priority date: 2024-04-03
Filing date: 2024-04-03
Publication date: 2024-06-21

Abstract

The application discloses a current limiting method, a current limiting device, electronic equipment and a storage medium. The method specifically comprises the following steps: acquiring real-time request quantity and response time of a target user in the mobile medical system; determining the flow fluctuation condition according to the request quantity and the response time; determining a current limiting strategy of a target user according to the flow fluctuation condition and a pre-trained user behavior model; and carrying out current limiting operation according to the current limiting strategy. In the technical scheme of the embodiment of the application, the flow fluctuation condition is determined by utilizing the real-time request quantity and response time of the target user, and then the flow fluctuation condition is identified by a pre-trained user behavior model, so as to analyze whether the abnormal condition occurs or not to limit the flow. The method has the advantages that the user behavior model can be fully utilized to analyze the occurrence condition of the current request quantity, the pointed and flexible current limiting is carried out, and the stability and the safety of the mobile medical system are improved.

Description

Current limiting method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a current limiting method, a device, an electronic apparatus, and a storage medium.

Background

With the development of the age and the progress of society, the internet technology has been extremely widely used. More and more industries and users are improving productivity levels in various fields with telecommunication technology and internet technology. The medical treatment is an indispensible field with the life density of people, and the medical treatment industry can fully play a role through a remote communication technology and an internet technology, thereby serving the masses.

Currently, the concept of ambulatory medical has begun to be generalized and generalized through the combination of telecommunications, the internet, and the medical industry. Medical and service information is provided by means of mobile communication technology, such as PDA (Personal DIGITAL ASSISTANT, palm computer), mobile phone, satellite communication, etc. As for the field of mobile internet, medical services are provided to users through medical application programs on mobile terminals. Although these modes have begun to be popularized, certain potential safety hazards exist, and sudden huge flow or malicious attack is encountered to easily cause system paralysis, so that the safety and stability of the mobile medical system are poor.

Disclosure of Invention

The application provides a current limiting method, a current limiting device, electronic equipment and a storage medium, which are used for improving the safety and stability of an ambulatory medical system.

According to an aspect of the present application, there is provided a current limiting method, comprising:

acquiring real-time request quantity and response time of a target user in the mobile medical system;

determining the flow fluctuation condition according to the request quantity and the response time;

Determining a current limiting strategy of a target user according to the flow fluctuation condition and a pre-trained user behavior model;

and carrying out current limiting operation according to the current limiting strategy.

According to another aspect of the present application, there is provided a current limiting device comprising:

the data acquisition module is used for acquiring real-time request quantity and response time of a target user in the mobile medical system;

the fluctuation determining module is used for determining the flow fluctuation condition according to the request quantity and the response time;

The strategy determining module is used for determining the current limiting strategy of the target user according to the flow fluctuation condition and a pre-trained user behavior model;

and the user current limiting module is used for carrying out current limiting operation according to the current limiting strategy.

According to another aspect of the present application, there is provided an electronic apparatus including:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the current limiting method according to any one of the embodiments of the present application.

According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a current limiting method according to any one of the embodiments of the present application.

In the technical scheme of the embodiment of the application, the flow fluctuation condition is determined by utilizing the real-time request quantity and response time of the target user, and then the flow fluctuation condition is identified by a pre-trained user behavior model, so as to analyze whether the abnormal condition occurs or not to limit the flow. The method has the advantages that the user behavior model can be fully utilized to analyze the occurrence condition of the current request quantity, the pointed and flexible current limiting is carried out, and the stability and the safety of the mobile medical system are improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method of limiting current according to a first embodiment of the present application;

FIG. 2 is a diagram of a user behavior model determination process according to a second embodiment of the present application;

Fig. 3 is a schematic structural view of a current limiting device according to a third embodiment of the present application;

Fig. 4 is a schematic structural diagram of an electronic device implementing a current limiting method according to an embodiment of the present application.

Detailed Description

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a current limiting method provided in an embodiment of the present application, where the current limiting method is applicable to a mobile medical system and a mobile medical application for current limiting, current diversion, and malicious attack prevention of a user, and the method may be performed by a current limiting device, where the current limiting device may be implemented in a form of hardware and/or software, and the current limiting device may be configured in an electronic device. As shown in fig. 1, the method includes:

s110, acquiring real-time request quantity and response time of a target user in the mobile medical system.

S120, determining the flow fluctuation condition according to the request quantity and the response time.

S130, determining a current limiting strategy of the target user according to the flow fluctuation condition and a pre-trained user behavior model.

S140, performing current limiting operation according to the current limiting strategy.

For S110, the mobile medical system may be a service system provided by a server corresponding to the mobile medical APP (Application). It will be appreciated that the user uses the medical APP installed on the mobile terminal to use the relevant medical services (e.g., appointment registration, online consultation, etc.), and the corresponding server provides those online medical services. The target user may be any user using medical services through the ambulatory medical APP. The request amount may be a data amount of request data received by the server for the target user in the case where the target user uses the medical service. The sources of the requested data may include, but are not limited to, case review applications, appointment registration applications, online consultation applications, and the like. The response time may be the duration of time that these requests occur. It will be appreciated that the target user may be a single user or a plurality of users who are simultaneously using medical services. By acquiring the request amount and response time generated by the user using the medical service in real time, the request amount generated in a short time can be monitored to help further determine whether huge flow which is difficult to bear by the medical system server or whether malicious attack is encountered or not later.

For S120, the flow fluctuation condition may be a condition whether the request amount and the response time are different from those of the flow in the normal state. The flow fluctuation condition can be further determined by the request amount and the response time. For example, a large number of users use medical services in the same time period, and a large amount of requests generated by the target users to the server due to medical service operation are transmitted to the server in a shorter response time, so that the requests and the response time are not concentrated compared with the requests and the response time in a normal state, and the requests and the response time can be determined as flow fluctuation. For example, the request amount and the response time of all the target users may be divided to obtain the request amount in the unit time, for example, the corresponding request amount in the unit time under the normal state may be set, and if the request amount in the unit time monitored in real time exceeds the request amount in the unit time under the normal state, the flow fluctuation may be considered to occur. Of course, an exceeding threshold may be set for a portion of the request amount per unit time exceeding the request amount per unit time in the normal state, and different levels may be set for the flow fluctuation condition according to the number of the exceeding request amount per unit time, so as to facilitate the subsequent targeted processing.

For S130, the user behavior model may be a model for performing user behavior analysis according to the request amount and response time of the target user. The model may be trained in advance, and the machine learning model is trained by a large amount of training data and test data, and the machine learning model may be selected by a relevant technician according to actual situations, for example, a convolutional neural network model may be adopted, which is not limited by the embodiment of the present application. The sources of training data and test data may be information of the corresponding hardware and software of the user request which has already occurred, and may include, for example, historical period user request data, mobile device data, medical data, and the like. The throttling policy may be a way to limit the traffic of the target user. It will be appreciated that the large volume of medical system traffic that occurs suddenly over a period of time puts considerable stress on the server. The cause of these sudden large flows can be identified and analyzed by means of a user behavior model. Because the user behavior model is trained according to behaviors of each target user in the past for a long time, which belong to normal demands of the users can be analyzed according to habits of the target users, and which belong to abnormal flows (such as malicious attacks and the like). And then based on the reason for identifying the abnormal flow of a certain target user, limiting the flow of the target user with the abnormal flow, limiting the flow using authority of the user, rejecting the request of the user, reducing the flow bandwidth of the user, and the like, the specific flow limiting strategy can be set by related technicians according to actual conditions, and the embodiment of the application is not limited herein.

For S140, a corresponding current limiting operation may be performed according to the current limiting policy determined in the previous step.

In an alternative embodiment, the determining the current limiting policy of the target user according to the flow fluctuation condition and the pre-trained user behavior model may include: and determining a current limiting strategy according to whether the flow fluctuation condition accords with the user behavior model.

It can be appreciated that, since the user behavior model is trained by the user request data, the mobile device data and the medical data related to the user behavior, the user behavior model can identify the user behavior and record the past historical behavior of the user. If the current flow fluctuation condition and the historical behavior recorded in the user behavior model have similar data, the historical data and the request data of the current user can be compared. If the similar proportion exceeds the preset threshold value or is smaller than the preset threshold value within a certain preset degree, the use habit of the target user is not changed greatly, so that the request quantity appearing in the corresponding response time can be considered to be normal, the current flow fluctuation condition can be determined to accord with the trained user behavior model, and the current flow fluctuation condition does not need to execute a current limiting strategy at the moment, namely the target user does not need to be limited.

In an alternative embodiment, the determining the current limiting policy according to whether the flow fluctuation situation accords with the user behavior model may include: and if the flow fluctuation condition does not accord with the user behavior model, carrying out request limiting operation of the target user.

Continuing the previous example, if the similar proportion is far below a preset threshold (including that the request amount of the target user in the response time is too small, the server of the mobile medical system is not affected, and only the situation that the request amount is too large is analyzed here), the request amount of the target user in the response time is greatly different from the historical data, too many requests which do not appear or a large amount of data which do not accord with the habit of the user are generated, namely, the flow fluctuation situation does not accord with a pre-trained user behavior model, and a current limiting strategy is implemented on the target user.

In the embodiment, the current request quantity and response time detected by the target user in real time are compared with the habit behaviors of the user in the user behavior model, and the multi-dimensional user behavior model can analyze whether the current request quantity and response time are reasonable or not and whether the current request quantity and response time are in accordance with the use habits of the target user or not according to the fact that the user behavior model absorbs a large amount of data in different dimensions during training, so that the mobile medical system is helped to find abnormal conditions in time, and the current limiting efficiency and accuracy are improved.

In an alternative embodiment, the request limiting operation may include a request number limit for a preset time period and a request flow limit for a preset time period.

The request times limit in the preset time period can be a limit for successfully generating request flow for the request operation of the target user in the preset time period, namely, the request application times of the target user to the server through the mobile medical APP of the mobile terminal in the preset time period is limited. Similarly, the request flow rate limitation in the preset duration may be to limit the flow rate of the request of the target user in the preset time period, for example, the request flow rate of the user may be limited by limiting the bandwidth or directly limiting the uplink and downlink speeds of the flow rate, so as to generally limit the maximum request amount generated by the user in a short time. The real-time mode provides two current limiting strategies, and is beneficial to limiting the current of a target user with abnormal conditions by a server of the mobile medical system.

In an alternative embodiment, after the preset time period of the current limiting is finished, the method may include: the request limiting operation is adjusted according to the amount of change in the request amount and the amount of change in the response time.

It can be understood that, since the request amount and the response time are obtained in real time, correspondingly, the change amount of the request amount and the change amount of the response time of the target user after the preset time of the current limit is finished, that is, the change amount of the request amount in the unit time of the target user after the preset time of the current limit is finished can be understood as the change amount of the request frequency of the target user. It will be appreciated that after the preset time period of the current limitation is finished, if the change of the request frequency is still increased and is increased to exceed the preset threshold value, the request limitation operation may be adjusted, for example, a time period longer than the last time of current limitation is set, and the current limitation is performed on the target user. Otherwise, if the request frequency change becomes small, and the request frequency change is so small that the server is not affected in processing, the current limiting strategy of the target user can be changed, and the current limiting of the target user is stopped. According to the embodiment, the current limiting strategy is changed according to the change of the request frequency of the user through a flexible dynamic current limiting mode, so that the processing performance of the server is exerted, and the mobile medical system can serve all users better.

In another alternative embodiment, after performing the request limiting operation, the method may further include: and adjusting the processing resource allocation of the mobile medical system according to the total request amount acquired after the request limiting operation adjustment.

And after limiting, adjusting the resource allocation of the server of the mobile medical system to process all the request quantities according to the total request quantity of all the target users. For example, according to the data sources of different request amounts in the total request amount, different numbers of threads are allocated to process the request, and the like. The dynamic flow limiting strategy can flexibly configure system resources according to real-time requirements. During peak traffic, resources may be added to cope with demand; when the flow is low, the resources can be reduced, the resource waste is avoided, and the cost is saved.

Example two

Fig. 2 is a diagram of a process for determining a user behavior model according to a second embodiment of the present application, where the process for constructing and training the user behavior model is refined based on the foregoing embodiments. As shown in fig. 2, the specific steps are as follows:

S210, acquiring historical behavior data, mobile device data and medical data of a user using an application program in the mobile medical system.

S220, training the user behavior model according to the historical behavior data, the mobile device data and the medical data.

Wherein, the historical behavior data can be request data of a user in the past period of time, and can include, but is not limited to, operation and request data of the user in the mobile medical APP, such as case checking, appointment registration, online consultation and the like; the mobile device data may be data of a mobile terminal used by a user, such as a device model number, an operating system, a network state, a network address, etc.; the medical data may be data generated by an ambulatory medical system, such as case information, medical reports, diagnostic results, and the like. A large amount of the above-described historical data may be used as a training data set and a test data set to train the machine learning model. Since these data can reflect the user's behavior and habits over the history period for the ambulatory medical APP. The trained user behavior model can output a judging result whether to accord with the habit behavior of the user or not according to real-time data of the target user as input, thereby helping the mobile medical system to identify behaviors such as abnormal conditions, malicious attacks and the like.

In the embodiment of the application, the model is trained through the request data, the equipment data and the medical data in the user history data, so that the user behavior model has multi-dimensional recognition capability, thereby helping the mobile medical system to recognize the behaviors of the user in all aspects, being beneficial to improving the recognition accuracy and the current limiting flexibility of the mobile medical system, and being further beneficial to the stable operation of the mobile medical system.

Example III

Fig. 3 is a schematic structural diagram of a current limiting device according to a third embodiment of the present application. As shown in fig. 3, the current limiting device 300 includes:

a data acquisition module 310, configured to acquire a real-time request amount and response time of a target user in the ambulatory medical system;

a fluctuation determining module 320, configured to determine a flow fluctuation condition according to the request amount and the response time;

A policy determining module 330, configured to determine a current limiting policy of the target user according to the flow fluctuation situation and a pre-trained user behavior model;

the user current limiting module 340 is configured to perform a current limiting operation according to a current limiting policy.

In an alternative embodiment, the policy determination module 330 may include:

And the flow limiting strategy determining unit is used for determining the flow limiting strategy according to whether the flow fluctuation situation accords with the user behavior model.

In an alternative embodiment, the current limit policy determining unit may be specifically configured to:

And if the flow fluctuation condition does not accord with the user behavior model, carrying out request limiting operation of the target user.

In an alternative embodiment, the request limit operation includes a request number limit for a preset duration and a request flow limit for the preset duration.

In an alternative embodiment, the current limiting device 300 may further include:

And the limiting operation adjusting module is used for adjusting the request limiting operation according to the change amount of the request amount and the change amount of the response time.

and the resource allocation adjustment module is used for adjusting the processing resource allocation of the mobile medical system according to the total request amount acquired after the request limiting operation adjustment.

The current limiting device provided by the embodiment of the application can execute the current limiting method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of executing each current limiting method.

Example IV

Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the current limiting method.

In some embodiments, the current limiting method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the current limiting method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the current limiting method in any other suitable way (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present application are achieved, and the present application is not limited herein.

The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims

1. A method of limiting current, comprising:

Determining flow fluctuation conditions according to the request quantity and the response time;

determining a current limiting strategy of the target user according to the flow fluctuation condition and a pre-trained user behavior model;

2. The method of claim 1, wherein said determining a current limiting policy for said target user based on said flow fluctuation conditions and a pre-trained user behavior model comprises:

and determining the current limiting strategy according to whether the flow fluctuation situation accords with the user behavior model.

3. The method of claim 2, wherein said determining said flow restriction strategy based on whether said flow fluctuation conditions are in accordance with said user behavior model comprises:

4. A method according to claim 3, wherein the request limiting operation comprises a request number limit for a preset duration and a request flow limit for a preset duration.

5. A method according to claim 3, wherein after the end of the preset time period of the restriction, the method comprises:

And adjusting the request limiting operation according to the change amount of the request amount and the change amount of the response time.

6. The method according to claim 1, wherein the method further comprises:

And adjusting the processing resource allocation of the mobile medical system according to the total request amount acquired after the request limiting operation adjustment.

7. The method according to any of claims 1-6, wherein the user behavior model is determined by:

acquiring historical behavior data, mobile equipment data and medical data of a user using an application program in the mobile medical system;

and training the user behavior model according to the historical behavior data, the mobile equipment data and the medical data.

8. A current limiting device, comprising:

9. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the current limiting method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to implement the current limiting method of any one of claims 1-7 when executed.