Multi-network integration Internet of Things card fee query method, system and device, and storage medium

Abstract

The embodiment of the invention provides a multi-network integration Internet of Things card fee query method, system and device and a storage medium, and belongs to the technical field of computers. According to the method, a first cost query request of a multi-network integration Internet of Things card is obtained, the first cost query request is converted into a second cost query request of a target network operator through an API pipeline model, and then a cost query service system of a corresponding network operator is accessed according to the second cost query request of each target network operator. And obtaining a cost query result of the multi-network integration Internet of Things card. According to the method, the external interface is unified by using the API pipeline model, and after the first cost query request of the user is received, the first cost query request is converted into the second cost query request conforming to the cost query service system interface of the network operator, so that efficient query of the multi-network integration Internet of Things card cost is realized, and the user experience is improved. And the charging convenience of the multi-network integration Internet of Things card is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, system, device and storage medium for querying fees for a multi-network converged Internet of Things card. Background Art

[0002] IoT technology is developing rapidly and has become a dynamic and ever-changing industry. Although the scale of IoT users is showing a rapid growth trend, the signal strength of IoT cards from major operators in different regions still varies significantly. In addition, IoT cards from a single operator may cause network jitter and failure in certain scenarios, thereby affecting the stability of data transmission and the availability of services. This is unacceptable in industries with strict requirements on network service SLA, such as unmanned retail, mobile media, and Internet of Vehicles. To address this problem, multi-network converged IoT cards came into being. They can combine the network advantages of multiple operators and automatically switch network services to ensure network reliability and stability.

[0003] At present, the research on multi-network convergence IoT cards mainly focuses on the network connection and switching of multi-network convergence. As for the billing mechanism of multi-network convergence IoT cards, the API query interface, billing system and traffic restrictions of different operators make it difficult to query the fees of multi-network convergence IoT cards. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method, system, device and storage medium for querying the fees of a multi-network converged Internet of Things card, aiming to efficiently query the fees of a multi-network converged Internet of Things card and improve the convenience of billing of a multi-network converged Internet of Things card.

[0005] To achieve the above purpose, one aspect of an embodiment of the present application proposes a multi-network converged Internet of Things card fee query method, comprising the following steps:

[0006] Obtain the first fee query request of the multi-network converged IoT card;

[0007] Converting the first fee query request into a second fee query request of the target network operator through an API pipeline model;

[0008] According to the second fee query request of each target network operator, the fee query service system of the corresponding network operator is accessed to obtain the fee query result of the multi-network integration Internet of Things card.

[0009] In some embodiments, the API pipeline model includes API interface strategies of different network operators, and converting the first fee query request into a second fee query request of the target network operator through the API pipeline model includes the following steps:

[0010] Parsing the first fee query request to obtain multiple target network operators carrying the multi-network converged Internet of Things card and query content;

[0011] The query content is encapsulated according to the API interface policy of the target network operator to obtain a second fee query request of the target network operator.

[0012] In some embodiments, the API interface strategy includes an API interface format and a multi-query channel configuration, the multi-query channel configuration includes account and password information for each query channel, and encapsulating the query content according to the API interface strategy of the target network operator to obtain a second fee query request of the target network operator includes the following steps:

[0013] Allocate a query channel for the query content according to the multi-query channel configuration of the API interface strategy, and obtain the account and password information of the allocated query channel;

[0014] The account number and password information and the query content are encapsulated according to the API interface format of the API interface strategy to obtain a second fee query request of the target network operator.

[0015] In some embodiments, the multi-network converged Internet of Things card fee query method further includes the following steps:

[0016] Obtain the change rate of request traffic value, request response time, system load and overall IoT card activity;

[0017] A weighted algorithm is used to determine the expansion and contraction factor according to the change rate of the request flow value, the change rate of the request response time, the change rate of the system load and the overall activity of the Internet of Things card;

[0018] When the expansion factor is greater than a first expected threshold, resources are expanded on the pipeline model of the API pipeline model;

[0019] When the scaling factor is less than a second expected threshold, resources of the pipeline model of the API pipeline model are reduced.

[0020] In some embodiments, the expansion factor is calculated by the following formula:

[0021]

[0022] in, Indicates the change rate of the request flow value, F t represents the request flow value at time t, Indicates the request response time change rate, R t represents the request response time at time t, Indicates the system load change rate, L t represents the system load at time t, A total Represents the overall IoT card activity, α, β, γ, and δ are all weight parameters.

[0023] In some embodiments, the multi-query channel configuration further includes a priority configuration for each query channel, and the multi-network converged Internet of Things card fee query method further includes the following steps:

[0024] Parsing the first fee query request to obtain a request tag;

[0025] Query the priority mapping table according to the request tag to obtain the request priority;

[0026] Allocating the query content of the first fee query request to a query channel of a corresponding priority according to the request priority;

[0027] Among them, the higher the priority of the query channel, the higher the proportion of network resources.

[0028] In some embodiments, obtaining the first fee query request of the multi-network converged Internet of Things card includes the following steps:

[0029] Determine the query frequency according to the expansion and contraction factor and the activity of the target IoT card;

[0030] Generate a first fee query request for the multi-network convergence Internet of Things card according to the query frequency.

[0031] To achieve the above purpose, another aspect of the embodiment of the present application proposes a multi-network converged Internet of Things card fee query system, including:

[0032] The first module is used to obtain a first fee query request for a multi-network converged Internet of Things card;

[0033] A second module is used to convert the first fee query request into a second fee query request of the target network operator through an API pipeline model;

[0034] The third module is used to access the fee query service system of the corresponding network operator according to the second fee query request of each target network operator to obtain the fee query result of the multi-network integration Internet of Things card.

[0035] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program implements the method described in the above embodiment when executed by the processor.

[0036] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in the above embodiment.

[0037] The multi-network converged Internet of Things card fee query method, system, device and storage medium proposed in the present application obtain the first fee query request of the multi-network converged Internet of Things card, convert the first fee query request into the second fee query request of the target network operator through the API pipeline model, and then access the fee query service system of the corresponding network operator according to the second fee query request of each target network operator to obtain the fee query result of the multi-network converged Internet of Things card. The present application uses the API pipeline model to unify the external interface, and after receiving the first fee query request of the user, converts the first fee query request into the second fee query request that conforms to the fee query service system interface of the network operator, so as to realize efficient query of the fees of the multi-network converged Internet of Things card and improve the convenience of billing of the multi-network converged Internet of Things card. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a multi-network converged Internet of Things card fee query method provided in an embodiment of the present application;

[0039] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0040] Figure 3 yes Figure 2 Flow chart of step S202 in FIG.

[0041] Figure 4 It is a flow chart of a multi-network converged Internet of Things card fee query method provided by another embodiment of the present application;

[0042] Figure 5 It is a flow chart of a multi-network converged Internet of Things card fee query method provided by another embodiment of the present application;

[0043] Figure 6 yes Figure 1 Flow chart of step S101 in FIG.

[0044] Figure 7 It is a schematic diagram of a multi-network converged Internet of Things card fee query system provided in an embodiment of the present application;

[0045] Figure 8 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application;

[0046] Fig. 9It is a schematic diagram of the overall concept of the multi-network converged Internet of Things card fee query method provided in the embodiment of the present application;

[0047] Fig.10 It is an overall flow chart of the multi-network integrated Internet of Things card fee query method provided in the embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] It should be noted that, although the functional modules are divided in the system and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0051] First, some nouns involved in this application are analyzed:

[0052] API (Application Programming Interface): is a predefined function or interface that aims to provide developers with access capabilities based on software or hardware. It is often used for function development and inter-system calls.

[0053] CMP (Connectivity Management Platform): is an application that manages connected devices or connection information. It is a unified management platform that supports functions such as data reporting, issuing commands, monitoring and analysis.

[0054] SLA (Service Level Agreement) is a mutually agreed agreement between a service provider and a user or between service providers to ensure the performance and reliability of the service at a certain cost (usually this cost is the main factor driving the quality of service).

[0055] TPS (Transaction Per Second) is a key indicator for measuring system performance. It represents the number of transactions that the system can process per second. The TPS carrying value refers to the maximum TPS value that the system can achieve under certain conditions. This value is of great significance for evaluating the system's processing capacity, optimizing system performance, and formulating performance testing plans.

[0056] The embodiments of the present application provide a method, system, device and storage medium for querying the fees of a multi-network converged Internet of Things card, aiming to efficiently query the fees of a multi-network converged Internet of Things card and improve the convenience of billing of the multi-network converged Internet of Things card.

[0057] The multi-network converged Internet of Things card fee query method, system, device and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the multi-network converged Internet of Things card fee query method in the embodiments of the present application is described.

[0058] The multi-network convergence Internet of Things card fee query method provided in the embodiment of the present application relates to the field of computer technology. The multi-network convergence Internet of Things card fee query method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the multi-network convergence Internet of Things card fee query method, etc., but is not limited to the above forms.

[0059] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0060] Figure 1 This is an optional flow chart of the multi-network converged Internet of Things card fee query method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S103.

[0061] Step S101, obtaining a first fee query request of a multi-network converged Internet of Things card;

[0062] Step S102, converting the first fee query request into a second fee query request of the target network operator through the API pipeline model;

[0063] Step S103, accessing the fee query service system of the corresponding network operator according to the second fee query request of each target network operator to obtain the fee query result of the multi-network integration Internet of Things card.

[0064] In the steps S101 to S103 shown in the embodiment of the present application, by obtaining the first fee query request of the multi-network converged Internet of Things card, the first fee query request is converted into the second fee query request of the target network operator through the API pipeline model, and then the fee query service system of the corresponding network operator is accessed according to the second fee query request of each target network operator to obtain the fee query result of the multi-network converged Internet of Things card. The embodiment of the present application uses the API pipeline model to unify the external interface, and after receiving the first fee query request of the user, the first fee query request is converted into the second fee query request that conforms to the fee query service system interface of the network operator, so as to realize efficient query of the fees of the multi-network converged Internet of Things card and improve the convenience of billing of the multi-network converged Internet of Things card.

[0065] In step S101 of some embodiments, the multi-network converged Internet of Things card refers to an Internet of Things card that integrates multiple network operators and can switch between multiple operator networks. The three-network-in-one Internet of Things card is one of the multi-network converged Internet of Things cards, which refers to an Internet of Things card that supports the three major operator networks of China Mobile, China Telecom, and China Unicom. This card integrates the functions of SIM cards of various sizes, such as standard size, micro SIM card, and Nano SIM card, providing greater flexibility and compatibility for various devices. The first fee query request of the multi-network converged Internet of Things card refers to an instruction for querying the resources used by the Internet of Things card in each network operator. For example, the first fee query request can query traffic usage, voice usage, and fees incurred by the operator.

[0066] In step S102 of some embodiments, the API pipeline model uniformly encapsulates the API interfaces of different operators and provides a standardized interface for the upper-layer system to call, and extracts query content such as key and common information through an efficient API standardized encapsulation layer. Exemplarily, the terminal where the multi-network converged Internet of Things card is located initiates a first fee query request to the platform that implements the method of this embodiment. The first fee query request may include the Internet of Things card identification number and the query content (for example, querying traffic or querying balance, etc.). After receiving the first fee query request, the platform queries the target network operators integrated by the Internet of Things card according to the Internet of Things card identification number in the request, and then queries the API interface format of the target operator, thereby converting the first fee query request into a second fee query request of the target operator.

[0067] In step S103 of some embodiments, after obtaining the second fee query request of each target network operator, the fee query service system of the corresponding network operator is accessed according to the second fee query request of each target network operator, and the fee query service system of each network operator returns the network resource consumption generated by the IoT card on the corresponding network operator. The platform integrates the data returned by each target network operator to obtain the fee query result of the multi-network converged IoT card, and returns the fee query result to the terminal where the multi-network converged IoT card is located through a unified API interface.

[0068] See also Figure 2 In some embodiments, the API pipeline model includes API interface strategies of different network operators, and step S102 may include but is not limited to steps S201 to S202:

[0069] Step S201, parsing the first fee query request to obtain multiple target network operators and query contents that carry the multi-network converged Internet of Things card;

[0070] Step S202: encapsulate the query content according to the API interface policy of the target network operator to obtain a second fee query request of the target network operator.

[0071] In this embodiment, the terminal where the multi-network convergence Internet of Things card is located initiates a first fee query request to the platform implementing the method of this embodiment, and the first fee query request may include multiple target network operators and query contents. After receiving the first fee query request, the platform parses the request according to the API interface format facing the user end of the API pipeline model to extract the target network operator and query content in the request.

[0072] In this embodiment, the API pipeline model includes API interface strategies of different network operators, and the API interface strategies include the API interface formats adopted by the network operators. The API pipeline model can query the corresponding API interface format of the target network operator indicated by the first fee query request, and then convert the first fee query request into a second fee query request of the target operator according to the API interface format.

[0073] See also Figure 3 In some embodiments, the API interface strategy includes an API interface format and a multi-query channel configuration, the multi-query channel configuration includes account and password information for each query channel, and step S202 may include but is not limited to steps S301 to S302:

[0074] Step S301, assigning a query channel to the query content according to the multi-query channel configuration of the API interface strategy, and obtaining the account and password information of the assigned query channel;

[0075] Step S302: encapsulate the account password information and the query content according to the API interface format of the API interface strategy to obtain a second fee query request of the target network operator.

[0076] In this embodiment, the API interface strategy also has multiple query channel configurations, and the multiple query channel configurations include the account and password information of each query channel. A multi-channel API load strategy is constructed for the API channels of each network operator within the API pipeline model. The API load of each channel is configured with relevant account passwords and TPS carrying values ​​in advance, so that in the case of subsequent high loads, the pressure of high concurrent requests can be effectively shared, and key requests can be processed with priority, so that the efficiency of API calls can be maximized, especially under extreme load conditions, stable services can still be provided. Multi-channel API loads and standardized encapsulation construct an API pipeline model, which can be expanded or reduced according to the results of multi-factor analysis in subsequent solutions to cope with API call demands during peak hours.

[0077] In this embodiment, a query channel can be randomly assigned to the query content according to the multi-query channel configuration of the corresponding API interface policy of the target network operator, thereby determining the account password information of the assigned query channel, and then encapsulating the account password information and the query content according to the API interface format of the API interface policy to obtain the second fee query request of the target network operator. The platform can access the corresponding API channel of the fee query service system of the target network operator through the account password information in the second fee query request, thereby obtaining the query result returned by the API channel.

[0078] See also Figure 4In some embodiments, the multi-network converged Internet of Things card fee query method of the embodiment of the present application may also include but is not limited to steps S401 to S404:

[0079] Step S401, obtaining the request flow value change rate, request response time change rate, system load change rate and overall IoT card activity;

[0080] Step S402, using a weighted algorithm to determine the expansion and contraction factor according to the change rate of the request flow value, the change rate of the request response time, the change rate of the system load and the overall activity of the IoT card;

[0081] Step S403, when the scaling factor is greater than the first expected threshold, resources of the pipeline model of the API pipeline model are expanded;

[0082] Step S404: When the scaling factor is less than the second expected threshold, resources of the pipeline model of the API pipeline model are reduced.

[0083] In this embodiment, the request flow change value represents the change in the API flow value received by the platform within a unit time. The request flow value F t The current number of transactions per second can be used to represent the current traffic load of the system. The response time change rate represents the change in the response time of the platform processing API requests within a unit time. The response time R t It can reflect the current load status of the system. The system load change rate indicates the change in the usage of the system platform's CPU, memory, network and other resources within a unit time. The system load is expressed in L t Indicates. Overall IoT card activity A total It is calculated based on the weighted average of all single card activity. The calculation formula is:

[0084]

[0085] Where N is the total number of IoT cards, W i is the weight of the i-th card, which can usually be set to 1 and reserved for adjustment based on certain priorities. i is the single card activity of the i-th card. The single card activity calculation formula is as follows:

[0086]

[0087] Among them, U t is the package usage in the current time period, U t-1 It is the package usage in the previous time period.

[0088] The expansion factor is calculated by the following formula:

[0089]

[0090] in, Indicates the change rate of the request flow value, F t represents the request flow value at time t, Indicates the request response time change rate, R t represents the request response time at time t, Indicates the system load change rate, L t represents the system load at time t, A total Represents the overall IoT card activity, α, β, γ, δ are all weight parameters. The scaling factor takes into account the dynamic changes of TPS, API response time, system load and IoT card activity. The weight parameters of each factor can be adjusted according to system requirements; By taking the logarithm of the rate of change of TPS, fluctuations can be smoothed to prevent extreme values ​​from affecting decision-making; is the response time change rate, which reflects the response speed of the system under the current load; It is the rate of change of system load, indicating the change of system resource usage.

[0091] When S t Exceeding the preset first expected threshold T expand Automatically expand API pipeline model resources when, for example, increase the flow value F t The upper limit is to increase the TPS carrying value of the query channel in the multi-query channel configuration in each API interface strategy to cope with high load requirements.

[0092] When S t Below the second desired threshold T shrink Automatically reduce API pipeline model resources when, for example, reduce the flow value F t The upper limit is to reduce the TPS carrying value of the query channel in the multi-query channel configuration in each API interface strategy to save resources.

[0093] Through the above-mentioned API pipeline scaling algorithm based on multi-factor analysis, by comprehensively considering factors such as API response time, system load, IoT card activity, etc., the scaling factor is calculated to provide characteristic indicators of dynamic scaling, which is convenient for multi-network integrated IoT cards to query package usage and perform related billing operations.

[0094] See also Figure 5 In some embodiments, the multi-query channel configuration further includes a priority configuration for each query channel. The multi-network converged IoT card fee query method of the embodiment of the present application may also include but is not limited to steps S501 to S503:

[0095] Step S501, parsing the first fee query request to obtain a request tag;

[0096] Step S502, querying the priority mapping table according to the request tag to obtain the request priority;

[0097] Step S503: Allocate the query content of the first fee query request to a query channel of a corresponding priority according to the request priority, wherein a query channel with a higher priority has a higher proportion of network resources.

[0098] In this embodiment, the first fee query request is parsed to obtain the IoT card identifier and query content, etc., and the parsed content can be mapped to the corresponding request tag. For example, the request tag of the user group type can be determined according to the IoT card identifier, such as a high-consumption group, a normal-consumption group, or a low-consumption group; or the request tag of the query service type can be determined according to the query content, such as querying traffic, querying broadband, or querying phone charges, etc. The request priority is obtained by querying the priority mapping table according to the request tag. The priority mapping table records the priority order corresponding to various request tags, for example, querying traffic is greater than querying broadband.

[0099] In this embodiment, after determining the corresponding priority of the first fee query request, when allocating query channels to its query content, the query content of the first fee query request can be allocated to the corresponding priority channel according to the request priority, so that the first fee query request can be allocated resources preferentially to ensure its rapid response. In this embodiment, the higher the priority of the query channel, the higher the proportion of network resources. For example, the higher the priority of the query channel, the more transmission bandwidth and CPU computing resources it occupies, that is, its TPS carrying value is higher. This embodiment dynamically allocates the first fee query request to channels of different priorities through real-time calculation of the expansion and contraction factors. For high-priority business requests, the system preferentially allocates resources to ensure its rapid response. This embodiment can also use concurrency control and current limiting strategies to further optimize resource utilization efficiency, prevent the system from overloading under high load, and ensure the stability and performance of the overall system.

[0100] See also Figure 6 In some embodiments, step S101 includes but is not limited to steps S601 to S602:

[0101] Step S601, determining the query frequency according to the expansion and contraction factor and the activity of the target IoT card;

[0102] Step S602, generating a first fee query request for the multi-network converged Internet of Things card according to the query frequency.

[0103] In this embodiment, there are two schemes for obtaining the first fee query request of the Internet of Things card on the platform. One is that the terminal where the Internet of Things card is located initiates the first fee query request to the platform, and the platform receives the first fee query request; the other is that the platform simulates the terminal to actively trigger the generation of the first fee query request, so as to query the fee query service system for the resource consumption generated by the Internet of Things card based on the first fee query request, so as to monitor the fee situation of the Internet of Things card.

[0104] In the process of the platform actively triggering the generation of the first fee query request, a timed query task can be set to generate the first fee query request. Furthermore, the frequency of the generation of the first fee query request of the Internet of Things card by the timed query task, that is, the fee query frequency, can be dynamically adjusted according to the real-time changes in the scaling factor and the activity of a single Internet of Things card. For example, the fee query frequency is proportional to the scaling factor and the activity of a single Internet of Things card. Specifically, when the scaling factor and the activity of the Internet of Things card are high, the platform increases the fee query frequency to improve the timeliness of the fee query. When the scaling factor is low, the platform appropriately reduces the query frequency to reduce resource consumption. At the same time, it can trigger appropriate billing operations according to different business needs to achieve the goal of flexible billing. In this embodiment, in addition to setting a timed query task to trigger the generation of the first fee query request, a trigger mode of customer foreground clicks or active queries can be added during the timed query task to compensate for the situation where the Internet of Things card consumption is not updated in time due to the low frequency of the timed query.

[0105] According to some embodiments of this application, please refer to Fig. 9 and Fig.10 The specific process of the multi-network converged Internet of Things card fee query method of the embodiment of the present application is as follows:

[0106] First, the first fee query request of the multi-network converged IoT card is triggered by a scheduled task or a user, and then the network resource usage of the IoT card is queried from the operator's fee query system through the API channel of the corresponding network operator in the API pipeline model. At the same time, the platform calculates the single card activity and the overall IoT card activity. In the API pipeline model, the system load characteristic factor, response time characteristic factor, request flow TPS characteristic factor and overall IoT card activity characteristic factor are read. Then, the expansion and contraction factor is calculated based on the read characteristic factors, and the expansion and contraction factor is compared with the expected threshold. If the expansion and contraction factor is greater than the first expected threshold, it indicates that the current processing volume is large, and the capacity can be expanded in the API pipeline model by adding API channels to improve the processing efficiency of the request; if the expansion and contraction factor is less than the second expected threshold, it means that the current processing volume is small, and the capacity can be reduced in the API pipeline model by reducing API channels to save network resources. If the expansion and contraction operation is not performed, the query frequency of the IoT card can be dynamically adjusted according to the IoT card activity and the expansion and contraction factor.

[0107] According to some embodiments of the present application, this embodiment faces the problems of differentiated operator billing systems, inconsistent traffic models, open API query restrictions, and a large number of Internet of Things cards in terms of connection management and daily traffic and billing operations of multi-network converged Internet of Things cards. By integrating the standardized encapsulation interfaces and dynamic adaptation mechanisms of multiple operators' APIs, an API pipeline model is constructed to facilitate the query of multi-network converged Internet of Things cards to efficiently query resource consumption generated by each operator. This embodiment monitors the API response and pipeline load in real time, introduces expansion and contraction factors, and combines comprehensive analysis of real-time API transaction processing speed, API response time, system load, and Internet of Things card activity to dynamically adjust and optimize API pipeline resources, improve stability under high load conditions, and improve resource utilization. Dynamically adjust the query frequency according to the expansion and contraction factors and the activity of the Internet of Things card to improve the accuracy of the query and the timeliness of the billing. Intelligently allocate API requests to different priority channels, and optimize system performance through concurrency control and current limiting strategies. In this way, flexible management of traffic billing of multi-network converged Internet of Things cards is achieved, and efficient, stable, accurate, and convenient billing operation management is met to the maximum extent, further improving the overall efficiency and user experience of the Internet of Things card in traffic operation.

[0108] See also Figure 7 The embodiment of the present application also provides a multi-network converged Internet of Things card fee query system, including:

[0109] The first module is used to obtain a first fee query request for a multi-network converged Internet of Things card;

[0110] A second module is used to convert the first fee query request into a second fee query request of the target network operator through the API pipeline model;

[0111] The third module is used to access the fee inquiry service system of the corresponding network operator according to the second fee inquiry request of each target network operator to obtain the fee inquiry result of the multi-network integration Internet of Things card.

[0112] It can be understood that the contents of the above-mentioned multi-network converged Internet of Things card fee query method embodiment are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above-mentioned multi-network converged Internet of Things card fee query method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned multi-network converged Internet of Things card fee query method embodiment.

[0113] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned multi-network converged Internet of Things card fee query method. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0114] See also Figure 8 , Figure 8 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0115] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0116] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the multi-network convergence Internet of Things card fee query method of the embodiment of this application;

[0117] Input / output interface 803, used to implement information input and output;

[0118] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0119] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0120] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0121] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned multi-network converged Internet of Things card fee query method.

[0122] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0124] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0125] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0126] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0127] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0128] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0129] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0130] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0133] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A multi-network converged Internet of Things card fee query method, characterized in that: The following steps are involved: Obtain the first fee query request of the multi-network converged IoT card; Converting the first fee query request into a second fee query request of the target network operator through an API pipeline model; According to the second fee query request of each target network operator, the fee query service system of the corresponding network operator is accessed to obtain the fee query result of the multi-network integration Internet of Things card.

2. The multi-network converged Internet of Things card fee query method according to claim 1 is characterized in that: The API pipeline model includes API interface strategies of different network operators, and converting the first fee query request into a second fee query request of the target network operator through the API pipeline model includes the following steps: Parsing the first fee query request to obtain multiple target network operators carrying the multi-network converged Internet of Things card and query content; The query content is encapsulated according to the API interface policy of the target network operator to obtain a second fee query request of the target network operator.

3. The multi-network converged Internet of Things card fee query method according to claim 2 is characterized in that: The API interface strategy includes an API interface format and a multi-query channel configuration, wherein the multi-query channel configuration includes account and password information of each query channel, and encapsulating the query content according to the API interface strategy of the target network operator to obtain a second fee query request of the target network operator includes the following steps: Allocate a query channel for the query content according to the multi-query channel configuration of the API interface strategy, and obtain the account and password information of the allocated query channel; The account number and password information and the query content are encapsulated according to the API interface format of the API interface strategy to obtain a second fee query request of the target network operator.

4. The multi-network converged Internet of Things card fee query method according to claim 3 is characterized in that: The multi-network integration Internet of Things card fee query method also includes the following steps: Obtain the change rate of request traffic value, request response time, system load and overall IoT card activity; A weighted algorithm is used to determine the expansion and contraction factor according to the change rate of the request flow value, the change rate of the request response time, the change rate of the system load and the overall activity of the Internet of Things card; When the scaling factor is greater than a first expected threshold, resources are expanded on the pipeline model of the API pipeline model; When the scaling factor is less than a second expected threshold, resources of the pipeline model of the API pipeline model are reduced.

5. The multi-network converged Internet of Things card fee query method according to claim 4 is characterized in that: The expansion factor is calculated by the following formula: in, Indicates the change rate of the request flow value, F t represents the request flow value at time t, Indicates the request response time change rate, R t represents the request response time at time t, Indicates the system load change rate, L t represents the system load at time t, A total Represents the overall IoT card activity, α, β, γ, and δ are all weight parameters.

6. The multi-network converged Internet of Things card fee query method according to claim 4 is characterized in that: The multi-query channel configuration also includes a priority configuration for each query channel, and the multi-network converged Internet of Things card fee query method also includes the following steps: Parsing the first fee query request to obtain a request tag; Query the priority mapping table according to the request tag to obtain the request priority; Allocating the query content of the first fee query request to a query channel of a corresponding priority according to the request priority; Among them, the higher the priority of the query channel, the higher the proportion of network resources.

7. The multi-network converged Internet of Things card fee query method according to claim 4 is characterized in that: The method of obtaining the first fee query request of the multi-network converged Internet of Things card comprises the following steps: Determine the query frequency according to the expansion and contraction factor and the activity of the target IoT card; Generate a first fee query request for the multi-network convergence Internet of Things card according to the query frequency.

8. A multi-network integrated Internet of Things card fee inquiry system, characterized in that: include: The first module is used to obtain a first fee query request for a multi-network converged Internet of Things card; A second module is used to convert the first fee query request into a second fee query request of the target network operator through an API pipeline model; The third module is used to access the fee query service system of the corresponding network operator according to the second fee query request of each target network operator to obtain the fee query result of the multi-network integration Internet of Things card.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of claims 1 to 7.

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