Multi-network converged internet of things card cost query method, system, device and storage medium

Abstract

The embodiment of the application provides a multi-network converged Internet of Things card fee query method, system, device and storage medium, and belongs to the computer technical field. The method obtains a first fee query request of a multi-network converged Internet of Things card, converts the first fee query request into a second fee query request of a target network operator through an API pipeline model, then accesses the fee query service system of the corresponding network operator according to the second fee query request of each target network operator, and obtains the fee query result of the multi-network converged Internet of Things card. The application uses the API pipeline model to uniformly interface externally, converts the first fee query request into the second fee query request conforming to the interface of the fee query service system of the network operator after receiving the first fee query request of the user, realizes efficient query of the multi-network converged Internet of Things card fee, and improves the billing convenience of the multi-network converged Internet of Things card.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computers, and in particular to a multi-network converged Internet of Things card fee query method, system, device and storage medium. BACKGROUND

[0002] The Internet of Things technology is rapidly developing and becoming a dynamic and ever-changing industry. Although the Internet of Things user scale shows a rapid growth trend, the signal strength of Internet of Things cards of major operators still differs significantly in different regions. In addition, the Internet of Things card of a single operator may cause network jitter and failure in some scenarios, thereby affecting the stability of data transmission and the availability of services, which is unacceptable in industries with strict requirements for network service SLA, such as unmanned retail, mobile media and Internet of Vehicles. To solve this problem, multi-network converged Internet of Things cards have emerged, which can combine the network advantages of multiple operators and automatically switch network services to ensure network reliability and stability.

[0003] Currently, research on multi-network converged Internet of Things cards mainly focuses on network connection and switching of multi-network convergence. For the billing mechanism of multi-network converged Internet of Things cards, different operators have their own API query interfaces, billing systems and traffic restrictions, which make it difficult to query the fees of multi-network converged Internet of Things cards. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a multi-network converged Internet of Things card fee query method, system, device and storage medium, which aims to efficiently query the fees of multi-network converged Internet of Things cards and improve the billing convenience of multi-network converged Internet of Things cards.

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

[0006] Obtaining a first fee query request of a multi-network converged Internet of Things card;

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

[0008] 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 converged Internet of Things card.

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

[0010] The first fee query request is parsed to obtain a plurality of 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 strategy 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 password information of each query channel, and the 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] The query content is allocated a query channel according to the multi-query channel configuration of the API interface strategy to obtain account password information of the allocated query channel;

[0014] The account 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] Obtaining a request traffic value change rate, a request response time change rate, a system load change rate, and an overall Internet of Things card activity;

[0017] Using a weighted algorithm, a scaling factor is determined according to the request traffic value change rate, the request response time change rate, the system load change rate, and the overall Internet of Things card activity;

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

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

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

[0021]

[0022] Wherein, F represents the request traffic value change rate, F t represents the request traffic value at time t, R represents the request response time change rate, R t represents the request response time at time t, denotes the system load change rate, L t denotes the system load at time t, A total denotes the overall Internet of Things card activity, and α, β, γ, and δ are weight parameters.

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

[0024] The first fee query request is parsed to obtain a request label;

[0025] The request priority is obtained by querying a priority mapping table according to the request label;

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

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

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

[0029] The query frequency is determined according to the scaling factor and the target Internet of Things card activity;

[0030] The first fee query request of the multi-network converged Internet of Things card is generated according to the query frequency.

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

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

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

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

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

[0036] To achieve the above object, another aspect of the embodiment of the present application provides a storage medium, which is a computer readable storage medium, for computer readable storage, and stores one or more programs, which 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 provided by the present application obtain a first fee query request of a multi-network converged Internet of Things card, convert the first fee query request into a second fee query request of a target network operator through an API pipeline model, then access the fee query service system of the corresponding network operator according to the second fee query request of each target network operator, and 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, converts the first fee query request into a second fee query request conforming to the interface of the fee query service system of the network operator after receiving the first fee query request of the user, and realizes efficient query of the fee of the multi-network converged Internet of Things card, thereby improving the billing convenience of the multi-network converged Internet of Things card. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flowchart of the multi-network converged Internet of Things card fee query method provided by the embodiment of the present application;

[0039] Figure 2 is Figure 1 is a flowchart of step S102 in

[0040] Figure 3 is Figure 2 is a flowchart of step S202 in

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

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

[0043] Figure 6 is Figure 1 is a flowchart of step S101 in

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

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

[0046] Figure 9is a whole conception diagram of a multi-network converged Internet of Things card fee query method provided by an embodiment of the present application.

[0047] Figure 10 is a whole flow chart of the multi-network converged Internet of Things card fee query method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in combination with the 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 flow chart, in some cases, the steps shown or described can be executed in a different order from the module division in the system or the order in the flow chart. The terms "first", "second", etc. in the specification and claims and the above drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

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

[0051] First, the several terms involved in the present application are analyzed:

[0052] API (Application Programming Interface, Application Programming Interface): It is a pre-defined function or interface, and the purpose is to provide the ability for developers to access based on software or hardware, commonly used for function development and inter-system call.

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

[0054] SLA (Service Level Agreement, Service Level Agreement): It is a kind of agreement recognized by both parties between service providers and users or between service providers in order to guarantee the performance and reliability of services under certain cost (usually this cost is the main factor driving the quality of service provided).

[0055] TPS (Transaction Per Second) is a key indicator for measuring system performance, which represents the number of transactions that the system can handle per second. TPS bearing value refers to the maximum TPS value that the system can achieve under certain conditions, which is of great significance for evaluating the processing capacity of the system, optimizing system performance, and formulating performance testing schemes.

[0056] The embodiment of the present application provides a multi-network converged Internet of Things card fee query method, system, device and storage medium, aiming at efficiently querying the multi-network converged Internet of Things card fee and improving the billing convenience 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 by the embodiment of the present application are specifically described through the following embodiment. First, the multi-network converged Internet of Things card fee query method in the embodiment of the present application is described.

[0058] The multi-network converged Internet of Things card fee query method provided by the embodiment of the present application relates to the technical field of computers. The multi-network converged Internet of Things card fee query method provided by the embodiment of the present application can be applied to a terminal, can be applied to a server end, and can also be software running in the terminal or the server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server end can be configured as an independent physical server, can be configured as a server cluster or a distributed system composed of multiple physical servers, can also be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and big data and artificial intelligence platform; and the software can be an application that implements the multi-network converged Internet of Things card fee query method, 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 devices or portable devices, tablet devices, multi-processor 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 a distributed computing environment, in which tasks are performed by remote processing devices connected by 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 is an optional flowchart of the multi-network converged Internet of Things card fee query method provided by the embodiment of the present application, Figure 1 The method in the embodiment of the present application can include, but is not limited to, steps S101 to S103.

[0061] In step S101, a first fee query request of a multi-network converged Internet of Things card is acquired.

[0062] In step S102, the first fee query request is converted into a second fee query request of a target network operator through an API pipeline model.

[0063] In step S103, a corresponding network operator's fee query service system is accessed according to the second fee query request of each target network operator, and a fee query result of the multi-network converged Internet of Things card is obtained.

[0064] The steps S101 to S103 shown in the embodiment of the present application, by acquiring the first fee query request of the multi-network converged Internet of Things card, converting the first fee query request into the second fee query request of the target network operator through the API pipeline model, and then accessing the corresponding network operator's fee query service system according to the second fee query request of each target network operator, the fee query result of the multi-network converged Internet of Things card is obtained. The embodiment of the present application uses the API pipeline model to unify the external interface. After receiving the first fee query request of the user, the first fee query request is converted into the second fee query request conforming to the interface of the network operator's fee query service system, which realizes efficient query of the multi-network converged Internet of Things card fee and improves the convenience of multi-network converged Internet of Things card billing.

[0065] In step S101 of some embodiments, the multi-network converged Internet of Things card refers to an Internet of Things card converged with multiple network operators, which can switch between multiple operator networks. The triple-network converged Internet of Things card is one of the multi-network converged Internet of Things cards, which refers to an Internet of Things card supporting mobile, telecommunications, and Unicom three major operator networks. This card integrates multiple sizes of SIM card functions, 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 the traffic usage, voice usage, and fees generated in the operator, etc.

[0066] In step S102 of some embodiments, the API pipeline model uniformly encapsulates API interfaces of different operators and provides a standardized interface for upper system calling, and extracts key and common information and other query contents through an efficient API standardized encapsulation layer. Exemplarily, a terminal where a multi-network converged IoT card is located initiates a first fee query request to a platform implementing the method of the embodiment, the first fee query request can include an IoT card identification number and query content (for example, query traffic or query balance, etc.), the platform receives the first fee query request, queries each target network operator converged by the IoT card according to the IoT card identification number in the request, and then queries an API interface format of the target operator, so as to convert 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 second fee query request of each target network operator is used to access a fee query service system of the corresponding network operator, the fee query service system of each network operator returns network resource consumption of the IoT card on the corresponding network operator, the platform integrates return data of each target network operator to obtain a 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] Please refer to Figure 2 In some embodiments, the API pipeline model includes API interface strategies of different network operators, and step S102 can include but is not limited to steps S201 to S202:

[0069] Step S201, parsing the first fee query request to obtain a plurality of target network operators carrying a multi-network converged IoT card and query content;

[0070] Step S202, 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.

[0071] In the embodiment, the terminal where the multi-network converged IoT card is located initiates a first fee query request to the platform implementing the method of the embodiment, and the first fee query request can include a plurality of target network operators and query content. The platform receives the first fee query request, parses the request according to the API interface format of the API pipeline model facing the user end to extract the target network operators and the query content in the request.

[0072] In the embodiment, the API pipeline model includes API interface strategies of different network operators, the API interface strategies include API interface formats adopted by the network operators, and the API pipeline model can query the API interface format corresponding to 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 network operator according to the API interface format.

[0073] Referring to 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 password information of each query channel, and step S202 can include but is not limited to steps S301 to S302:

[0074] In step S301, the query content is distributed to a query channel according to the multi-query channel configuration of the API interface strategy, and account password information of the distributed query channel is obtained.

[0075] In step S302, the account password information and the query content are encapsulated according to the API interface format of the API interface strategy, and a second fee query request of the target network operator is obtained.

[0076] In the embodiment, the API interface strategy also includes a multi-query channel configuration, and the multi-query channel configuration includes account password information of each query channel. An API load strategy of multiple channels is constructed inside the API pipeline model for API channels of each network operator, and the API load of each channel is configured with related account password and TPS bearing value in advance, so that in the case of subsequent high load, the pressure of high concurrency request can be effectively shared, and key requests can be processed preferentially, so that the efficiency of API calling is maximized. In particular, under extreme load conditions, stable services can still be provided. The multi-channel API load and standardized encapsulation construct the API pipeline model, which can be expanded and scaled according to the multi-factor analysis result in the subsequent scheme to cope with the API calling demand in peak period.

[0077] In the embodiment, the query content can be randomly distributed to a query channel according to the multi-query channel configuration of the API interface strategy corresponding to the target network operator, so as to determine the account password information of the distributed query channel, and then the account password information and the query content are encapsulated according to the API interface format of the API interface strategy, and a second fee query request of the target network operator is obtained. The platform can access the API channel corresponding to the fee query service system of the target network operator through the account password information in the second fee query request, so as to obtain the query result returned by the API channel.

[0078] Referring to Figure 4In some embodiments, the multi-network converged Internet of Things card cost query method of the embodiments of the present application can further include, but is not limited to, steps S401 to S404:

[0079] Step S401, obtaining a request traffic value change rate, a request response time change rate, a system load change rate, and an overall Internet of Things card activity level;

[0080] Step S402, using a weighting algorithm to determine a scale-in / out factor according to the request traffic value change rate, the request response time change rate, the system load change rate, and the overall Internet of Things card activity level;

[0081] Step S403, when the scale-in / out factor is greater than a first expected threshold, expanding resources of a pipeline model of an API pipeline model;

[0082] Step S404, when the scale-in / out factor is less than a second expected threshold, reducing resources of the pipeline model of the API pipeline model.

[0083] In the embodiment, the request traffic change value represents a change in API traffic value received by the platform in a unit time, and the request traffic 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 a change in response time of the platform processing API requests in a unit time, and the response time R t can reflect the current load status of the system. The system load change rate represents a change in usage of resources such as CPU, memory, and network of the system platform in a unit time, and the system load is represented by L t . The overall Internet of Things card activity level A total is calculated based on the weighted average of all single card activity levels, and the calculation formula is:

[0084]

[0085] Where N is the total number of Internet of Things cards, W i is the weight of the i th card, which can be usually set to 1, and is reserved for adjustment according to certain priorities, A i is the single card activity level of the i th card, and the calculation formula of the single card activity level is as follows:

[0086]

[0087] Where U t is the current time period package usage, and U t-1 is the last time period package usage.

[0088] The scale-in / out factor is calculated by the following formula:

[0089]

[0090] wherein, represents the request traffic value change rate, F t represents the request traffic value at time t, represents the request response time change rate, R t represents the request response time at time t, represents the system load change rate, L t represents the system load at time t, A total represents the overall Internet of Things card activity, and α, β, γ, and δ are weight parameters. The scaling factor takes into account the dynamic change of TPS, API response time, system load, and Internet of Things card activity, wherein the weight parameters of each factor can be optimized according to system requirements; By taking the logarithm of the change rate of TPS, the fluctuation is smoothed, and extreme values are prevented from affecting decision-making; is the response time change rate, reflecting the response speed of the system under the current load; is the change rate of the system load, indicating the change in the use of system resources.

[0091] When S t exceeds a first expected threshold T expand , the API pipeline model resources are automatically expanded, for example, the upper limit of the traffic value F t is increased, that is, the TPS bearing value of the query channel in the multi-query channel configuration in each API interface strategy is increased, to cope with high load demand.

[0092] When S t is lower than a second expected threshold T shrink , the API pipeline model resources are automatically reduced, for example, the upper limit of the traffic value F t is reduced, that is, the TPS bearing value of the query channel in the multi-query channel configuration in each API interface strategy is reduced, to save resources.

[0093] Through the above API pipeline scaling algorithm based on multi-factor analysis, by comprehensively considering factors such as API response time, system load, and Internet of Things card activity, the scaling factor is calculated to give the characteristic index of dynamic scaling, which is convenient for the use of multi-network fusion Internet of Things card query packages and related billing operations.

[0094] Please refer to Figure 5 In some embodiments, the multi-query channel configuration further includes priority configuration of each query channel, and the multi-network fusion Internet of Things card fee query method of the embodiments of the application can further include but is not limited to steps S501 to S503:

[0095] Step S501, the first fee query request is parsed to obtain a request label;

[0096] Step S502, the request label is queried according to the priority mapping table to obtain a request priority;

[0097] Step S503, the query content of the first fee query request is distributed to the query channel of the corresponding priority according to the request priority, wherein the network resource proportion of the query channel with higher priority is higher.

[0098] In the embodiment, the first fee query request can be parsed to obtain the Internet of Things card identifier and the query content, etc. According to the parsed content, the corresponding request label can be mapped, for example, the request label of the user group type can be determined according to the Internet of Things card identifier, such as high consumption group, ordinary consumption group or low consumption group; or the request label of the query service type can be determined according to the query content, such as query traffic, query broadband or query fee, etc. The request priority is obtained by querying the priority mapping table according to the request label. The priority mapping table records the priority order corresponding to various request labels, for example, query traffic is greater than query broadband.

[0099] In the embodiment, after determining the priority of the first fee query request, when distributing the query content of the first fee query request to the query channel, the query content of the first fee query request can be distributed to the corresponding priority channel according to the request priority, so that the first fee query request can be preferentially allocated resources to ensure its fast response. In the embodiment, the network resource proportion of the query channel with higher priority is higher, for example, the transmission bandwidth, CPU computing resource occupied by the query channel with higher priority is more, that is, its TPS bearing value is higher. The embodiment dynamically allocates the first fee query request to the channel of different priority through the real-time calculation of the scale-out factor. For high priority service request, the system preferentially allocates resources to ensure its fast response. The embodiment can also use concurrent control and flow limiting strategy to further optimize the utilization efficiency of resources, prevent the system from overloading under high load, and ensure the stability and performance of the overall system.

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

[0101] Step S601, the query frequency is determined according to the scale-out factor and the target Internet of Things card activity;

[0102] Step S602, the first fee query request of the multi-network fusion Internet of Things card is generated according to the query frequency.

[0103] In the 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 first fee query request, so as to query the resource consumption generated by the Internet of Things card based on the first fee query request to the fee query service system, and monitor the fee situation of the Internet of Things card.

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

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

[0106] First, the first fee query request of the multi-network converged Internet of Things card is triggered by a timing task or a user, and then the network resource usage of the Internet of Things card is queried through the API channel of the corresponding network operator in the API pipeline model to the fee query system of the operator, and the single card activity level and the overall Internet of Things card activity level are calculated. In the API pipeline model, the system load characteristic factor, the response time characteristic factor, the request traffic TPS characteristic factor, and the overall Internet of Things card activity level characteristic factor are read. Then, the scaling factor is calculated based on the read characteristic factors, and the scaling factor is compared with the expected threshold value. If the scaling factor is greater than the first expected threshold value, it indicates that the current processing amount is large, and the API pipeline model can be expanded by increasing the API channel to improve the processing efficiency of the request. If the scaling factor is less than the second expected threshold value, it indicates that the current processing amount is small, and the API pipeline model can be scaled down by reducing the API channel to save network resources. If no scaling operation is performed, the Internet of Things card query frequency can be dynamically adjusted according to the Internet of Things card activity level and the scaling factor.

[0107] According to some embodiments of the present application, the embodiments are directed to the connection management and daily traffic and billing operation of the multi-network converged Internet of Things card, which faces the problems of differentiated operator billing system, non-uniform traffic model, open API query limitation, and large number of Internet of Things cards. By integrating the standardized packaging interface and dynamic adaptation mechanism of the API of multiple operators, an API pipeline model is constructed to facilitate efficient querying of resource consumption generated by the multi-network converged Internet of Things card in each operator. The embodiments monitor the API response and pipeline load in real time, introduce a scaling factor, and combine the 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 the API pipeline resources, improve the stability under high load conditions, and improve the resource utilization rate. According to the scaling factor and the dynamic adjustment of the query frequency of the Internet of Things card activity, the accuracy of the query and the timeliness of the billing are improved. The API requests are intelligently distributed to different priority channels, and the system performance is optimized through concurrent control and flow limiting strategies. In this way, flexible management of the multi-network converged Internet of Things card traffic billing is achieved, the efficient, stable, accurate, and convenient billing operation management is maximized, and the overall efficiency and user experience of the Internet of Things card in traffic operation are further improved.

[0108] Referring to Figure 7 The embodiments of the present application also provide a multi-network converged Internet of Things card fee query system, comprising:

[0109] A first module is configured to obtain a first fee query request of a multi-network converged Internet of Things card.

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

[0111] A third module is configured 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 converged Internet of Things card.

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

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

[0114] Please refer to Figure 8 , Figure 8 The hardware structure of the electronic device of another embodiment is illustrated, and the electronic device comprises:

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

[0116] The memory 802 can be implemented in the form of a ROM (ReadOnly Memory), a static storage device, a dynamic storage device or a RAM (Random Access Memory). The memory 802 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 802 and are called and executed by the processor 801 to realize the multi-network converged Internet of Things card fee query method of the embodiments of the present application.

[0117] The input / output interface 803 is used to realize information input and output.

[0118] The communication interface 804 is used to realize the communication interaction between the device and other devices, and can realize communication through a wired manner (for example, a USB, a network cable and the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth and the like).

[0119] The bus 805 is used to transmit information between various components (for example, the processor 801, the memory 802, the input / output interface 803 and the communication interface 804) of the device.

[0120] The processor 801, the memory 802, the input / output interface 803 and the communication interface 804 realize the communication connection between each other in the device through the bus 805.

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

[0122] The memory, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through 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 a combination thereof.

[0123] The embodiments described in the embodiments of the present application are used 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 by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

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

[0125] The system embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the modules can be selected to realize the purposes of the embodiments of the present application.

[0126] Those skilled in the art can understand that all or some steps in the above-mentioned method, the functions of the modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0127] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, based on stated preference or the like, and that "default" or other orderings are thus permissible. Further, the terms "comprise", "comprising", "include", "including", and the like, are specifically intended to be open-ended. That is, references to individual steps and the like do not suhstantially exclude the presence of two or more of a recited step or its integral sub-steps or additional steps whether or not readily ascertainable from the description or the like. Further, the words "a" or "an", as used herein in the disclosure and elsewhere, are used indiscriminately and are to be interpreted in the same way, i.e. as meaning "one or more".

[0128] It should be understood that, in this application, "at least one" means one or more, "multiple" means two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that there are only A, only B, and A and B at the same time. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or the like means any combination of these items, including any combination of single or multiple 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, and c can be single or multiple.

[0129] In several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described system embodiments are only illustrative, for example, the division of the above-mentioned units is only a logical functional division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between systems or units, which can be electrical, mechanical or other forms.

[0130] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0131] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0132] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0133] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and the scope of the rights of the embodiments of the present application is not limited thereto. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A multi-network converged Internet of Things card fee inquiry method, characterized in that: The following steps are involved: Obtain the request traffic value change rate, request response time change rate, system load change rate, and overall IoT card activity; Using a weighted algorithm, determine the expansion and contraction factor according to the request traffic value change rate, the request response time change rate, the system load change rate, and the overall IoT card activity; Performing a scaling operation according to the scaling factor, wherein the scaling operation is: when the scaling factor is greater than a first expected threshold, expanding the load value of the corresponding query channel in the API pipeline model; when the scaling factor is less than a second expected threshold, reducing the load value of the corresponding query channel in the API pipeline model; In the absence of a scaling operation, adjusting the query frequency according to the scaling factor and the activity of the target Internet of Things card, generating a first fee query request for the multi-network converged Internet of Things card according to the query frequency, and determining a plurality of target network operators that carry the multi-network converged Internet of Things card according to the Internet of Things card identification number in the first fee query request; The target IoT card activity is calculated using the following formula: ； in, The usage of the package in the current time period. The usage of the package in the previous time period; Converting the first fee query request into a second fee query request for the target network operator through an API pipeline model; the API pipeline model includes API interface strategies for different network operators, the API interface strategies including an API interface format and a multi-query channel configuration, the multi-query channel configuration being used to represent multiple query channels for the corresponding network operator and a bearer value for each query channel; According to the second fee inquiry request of each target network operator, the fee inquiry service system of the corresponding network operator is accessed to obtain the fee inquiry result of the multi-network integration Internet of Things card.

2. The multi-network converged Internet of Things card fee inquiry method according to claim 1, 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 inquiry method according to claim 2, characterized in that: The API interface policy includes an API interface format and a multi-query channel configuration, wherein the multi-query channel configuration includes account and password information for each query channel. Encapsulating 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 includes the following steps: 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; The account 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 from the target network operator.

4. The multi-network converged Internet of Things card fee inquiry method according to claim 3 is characterized in that: The expansion factor is calculated by the following formula: ； in, Indicates the change rate of the request traffic value. Indicates the request traffic value at time t, Indicates the change rate of request response time, Indicates the request response time at time t, Indicates the system load change rate, represents the system load at time t, Indicates the overall IoT card activity, , are all weight parameters.

5. The multi-network converged Internet of Things card fee inquiry method according to claim 3 is characterized in that: 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: Parsing the first fee query request to obtain a request tag; Querying a 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.

6. A multi-network integrated Internet of Things card fee inquiry system, characterized in that: include: A first module is configured to, when no scaling operation is performed, adjust a query frequency based on a scaling factor and an activity level of a target Internet of Things card, generate a first fee query request for the multi-network converged Internet of Things card according to the query frequency, and determine a plurality of target network operators that carry the multi-network converged Internet of Things card based on an Internet of Things card identification number in the first fee query request; The target IoT card activity is calculated using the following formula: ； in, The usage of the package in the current time period. The usage of the package in the previous time period; a second module configured to convert the first fee query request into a second fee query request for a target network operator through an API pipeline model; the API pipeline model including API interface policies for different network operators, the API interface policies including an API interface format and a multi-query channel configuration, the multi-query channel configuration being configured to represent multiple query channels for the corresponding network operator and a bearer value for each query channel; 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 converged Internet of Things card; The multi-network convergence Internet of Things card fee query method further includes the following steps: Obtain the request traffic value change rate, request response time change rate, system load change rate, and overall IoT card activity; Using a weighted algorithm, determine the expansion and contraction factor according to the request traffic value change rate, the request response time change rate, the system load change rate, and the overall IoT card activity; Scaling operations are performed according to the scaling factor, where the scaling operations are: when the scaling factor is greater than a first expected threshold, the load value of the corresponding query channel in the API pipeline model is expanded; when the scaling factor is less than a second expected threshold, the load value of the corresponding query channel in the API pipeline model is reduced.

7. 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 implementing 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 5 are implemented.

8. A storage medium, which is a computer-readable storage medium and is 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 5.

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