Automated management method and apparatus for internet of things cards, computer device and storage medium

Abstract

Disclosed in embodiments of the present application are an automated management method and apparatus for Internet of Things cards, a computer device and a storage medium. The method relates to the technical field of Internet of Things, and comprises: inputting acquired source data into a data gain and offloading device for gain filtering processing to obtain preprocessed source data, and sending the preprocessed source data to a conditional inference engine; on the basis of the conditional inference engine and a pre-configured strategy condition, performing inference calculation on the preprocessed source data to obtain an inference calculation result; if the inference calculation result is that the preprocessed source data meets the strategy condition, storing into a database a trigger record corresponding to the preprocessed source data, and sending generated action trigger information to an action trigger engine; and on the basis of the action trigger engine and the action trigger information, managing an Internet of Things card corresponding to the preprocessed source data. The embodiments of the present application can achieve automated management of Internet of Things cards, thereby improving the operation efficiency and the management efficiency of the Internet of Things cards

Description

Internet of Things card automated management method, device, computer equipment and storage medium Technical Field

[0001] The present application relates to the field of Internet of Things technology, and in particular to an automated management method, device, computer equipment, and storage medium for Internet of Things cards. Background Art

[0002] The Internet of Things (IoT) is a rapidly evolving industry sector with promising development prospects. Unlike the Internet of Humans (IoH), the IoT exhibits significant differences in regulatory policies, user targets, operational models, and functional requirements. IoT access management, in particular, faces challenges such as intensive management, massive amounts of data, and diverse customer needs.

[0003] For operators, the Internet of Things (IoT) is a business-oriented service (i.e., a 2B business), distinct from the consumer-oriented Internet of People (2C business). Enterprise customers require robust, effective, unified management and convenient operations for IoT applications. Unlike Internet of People users, they prefer not to frequently navigate self-service portals for various operations. In the IoT sector, enterprise customers hope to reduce the number of operational personnel and improve operational and management efficiency. For large-scale IoT card management, they seek automated management through configuration-based methods, supporting notification methods such as SMS, email, and APIs for early warning and secondary development, thereby automating the entire lifecycle of IoT cards, from activation and testing, through use, shutdown, and removal. Operators also want to open up relevant capabilities to customers to enable highly customized service configurations, thereby reducing feature development and operational personnel. Effective monitoring of the entire service chain facilitates subsequent maintenance and problem identification and remediation. Existing physical network card management methods require a high level of manual effort from development and operations personnel, resulting in low operational and management efficiency. Faced with challenges such as intensive management, massive amounts of data, and diverse customer needs, automated management throughout the entire lifecycle is impossible. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, computer equipment, and storage medium for automated management of Internet of Things (IoT) cards, aiming to achieve automated management of physical network cards to improve operational efficiency and management efficiency.

[0005] In a first aspect, an embodiment of the present application provides an automated management method for an Internet of Things card, which includes:

[0006] Inputting the acquired source data into the data gain and splitter for gain filtering to obtain pre-processed source data, and sending the pre-processed source data to the conditional inference engine;

[0007] Based on the conditional inference engine, the pre-processed source data is subjected to inference calculation according to pre-configured policy conditions to obtain an inference calculation result;

[0008] If the inference calculation result is that the pre-processed source data meets the policy condition, the trigger record corresponding to the pre-processed source data is saved in the database, and the generated action trigger information is sent to the action trigger engine;

[0009] Based on the action trigger engine, the Internet of Things card corresponding to the pre-processed source data is managed according to the action trigger information.

[0010] In a second aspect, an embodiment of the present application further provides an IoT card automated management device, comprising:

[0011] A preprocessing unit, configured to input the acquired source data into a data gain and splitter for gain filtering to obtain preprocessed source data, and send the preprocessed source data to a conditional inference engine;

[0012] An inference calculation unit, configured to perform inference calculation on the preprocessed source data based on the conditional inference engine and according to preconfigured policy conditions to obtain an inference calculation result;

[0013] a judgment and sending unit, configured to save a trigger record corresponding to the preprocessed source data into a database and send generated action trigger information to an action trigger engine if the inference calculation result indicates that the preprocessed source data satisfies the policy condition;

[0014] A management unit is configured to manage the Internet of Things card corresponding to the preprocessed source data based on the action triggering engine and according to the action triggering information.

[0015] In a third aspect, an embodiment of the present application further provides a computer device, which is equipped with an Internet of Things card management platform. The computer device includes a memory and a processor, and a computer program is stored in the memory. The above method is implemented when the processor executes the computer program.

[0016] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program can implement the above method when executed by a processor.

[0017] The present invention provides an automated management method, apparatus, computer device, and storage medium for Internet of Things (IoT) cards. The method comprises: inputting acquired source data into a data gain and splitter for gain filtering to obtain preprocessed source data, and sending the preprocessed source data to a conditional inference engine; performing inference calculations on the preprocessed source data based on preconfigured policy conditions based on the conditional inference engine to obtain inference calculation results; if the inference calculation results indicate that the preprocessed source data meets the policy conditions, saving a trigger record corresponding to the preprocessed source data to a database, and sending the generated action trigger information to an action trigger engine; and managing the IoT cards corresponding to the preprocessed source data based on the action trigger information based on the action trigger information based on the action trigger information. The technical solution of the present invention is that after the source data is gain-filtered and distributed by the data gain and splitter, the distributed preprocessed data is inferred and calculated by the conditional inference engine based on the preconfigured policy conditions. If the preprocessed source data meets the policy conditions, the trigger record is saved and the action trigger information is sent to the action trigger engine. The action trigger engine manages the physical network card based on the action trigger information to execute the corresponding action. The IoT card data is processed efficiently and quickly through the three-layer processing model of "data gain and diverter, conditional inference engine, and action trigger engine", which improves operational efficiency and management efficiency. It can quickly process massive data while avoiding the tediousness and errors of manual operations; and in the process of processing data, the IoT card is automatically managed through pre-configured policy conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] FIG1 is an architecture diagram of an automated management method for Internet of Things cards provided in an embodiment of the present application;

[0021] FIG2 is a flow chart of an automated management method for IoT cards according to an embodiment of the present application;

[0022] FIG3 is a schematic diagram of a sub-process of an automated management method for Internet of Things cards provided in an embodiment of the present application;

[0023] FIG4 is a schematic diagram of a configuration flow of policy rules of an automated management method for IoT cards provided in an embodiment of the present application;

[0024] FIG5 is a schematic diagram of a sub-process of an automated management method for an Internet of Things card provided in an embodiment of the present application;

[0025] FIG6 is a schematic block diagram of an IoT card automation management device provided in an embodiment of the present application;

[0026] FIG7 is a schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0029] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0032] The method for automated management of Internet of Things cards according to the embodiment of the present application can be applied to an Internet of Things card management platform. The Internet of Things card management platform includes a three-layer processing model, namely: a data gain and splitter, a conditional inference engine, and an action trigger engine. The composition relationship and communication process are shown in Figure 1. Figure 1 is an architectural diagram of the method for automated management of Internet of Things cards provided by the embodiment of the present application. The Internet of Things card management platform can communicate with the Internet of Things card, receive data from the Internet of Things card, and send corresponding management instructions to the Internet of Things card to achieve management of the Internet of Things card. Please refer to Figure 2, which is a flow chart of an automated management method for Internet of Things cards provided by the embodiment of the present application. The method for automated management of Internet of Things cards is described in detail below. As shown in Figure 2, the method includes the following steps S100-S130.

[0033] S100: Input the acquired source data into a data gain and splitter for gain filtering to obtain pre-processed source data, and send the pre-processed source data to a conditional inference engine.

[0034] In an embodiment of the present application, source data can be input into the data gain and separator through various message components or APIs for gain filtering. The source data can be real-time massive source data such as RADIUS (Remote Authentication Dial In User Service) and CHF (Charging Function), or it can be detailed order data, data change data, other data sources, etc. After the source data is gain filtered, it will be distributed to the conditional inference engine of the next layer model for further reasoning and calculation processing. It should be noted that in this embodiment, the RADIUS: is an authentication, authorization and accounting protocol widely used in computer networks; the RADIUS protocol plays an important role in 4G wireless networks. In 4G LTE networks, the RADIUS protocol is used to authenticate and authorize users of wireless terminal devices (such as smartphones and tablets). The CHF: is responsible for generating billing data records, serving as a quota control node for online billing, and performing online billing rate processing for various services of users.

[0035] In some embodiments, such as this embodiment, as shown in FIG3 , step S100 may include steps S101 - S103 .

[0036] S101, inputting the acquired source data into the data enhancement module for information enhancement to obtain enhanced source data;

[0037] S102, inputting the enhanced source data into the data filtering module for filtering and screening to obtain the pre-processed source data;

[0038] S103: Distribute the pre-processed source data to the conditional inference engine through the data distribution module.

[0039] In an embodiment of the present application, the data gain and diverter includes a message enhancement module (MEM), a data filtering module (DFM), and a data distribution module (DDM). MEM can be used to enhance data information and supplement relevant system information to facilitate subsequent processing. DFM can be used to filter data based on specific conditions to reduce unnecessary data messages in massive data processing and reduce resource consumption. DDM can perform specific diversion and distribution based on data characteristics and type.

[0040] In this embodiment, MEM can enhance relevant information based on keywords such as the IoT card number, such as the card number's custId (customer ID), groupId, and prodInstId. For messages like RADIUS and CHF, MEM can also enhance regional location information based on the base station IP address.

[0041] After enhancing the data, an enhanced source data set is obtained, and then the enhanced source data is filtered through DFM to obtain pre-processed source data. Specifically, pre-configured policy rules are read from the database, and customer information ID and business plan ID are selected from the policy rules; the enhanced source data is filtered according to the customer information ID and the business plan ID to obtain the pre-processed source data. It should be noted that if the enterprise to which the IoT card belongs has not set any policy rules, the pre-processed source data can be discarded and no longer processed, thereby improving the resource utilization of the IoT card management platform.

[0042] The pre-processed source data obtained after screening and filtering is distributed by DDM to each business (Plan) module in the conditional reasoning engine of the next layer according to the matching customer information ID and business PlanId, so as to realize diversion and distribution processing through DDM.

[0043] It should be noted that, in this embodiment, the pre-configured policy rules in the database are shown in Figure 4, which is a flow chart of the policy rules configured by the customer in the embodiment of this application. First, the customer creates the policy rules, and then starts to configure: the first step is to configure the policy business type (Plan), for example, select the policy business type PlanId, and determine the processing module entry of the subsequent policy rules. The second step is to configure the policy trigger condition (Rule), for example, you can set the trigger condition of the policy rule, select the trigger function Functions and the trigger parameter Param, such as the trigger condition in the usage monitoring is set to: trigger when the cumulative traffic usage is >2G. The third step is to configure the policy filter (filter), the policy filter is the scope covered by the policy rule, for example, the specific ranges that can be selected are: 1. Customer level; 2. Card number group level; 3. Single card level, etc. The fourth step is to configure the policy action executor (Action). The policy action executor represents the action to be executed after a policy rule is matched and triggered. You need to select: 1. Action type ActionId; 2. Sending template Template; 3. Destination address DA, etc. The DA (Destination Address) is used to identify the execution destination address of each action when the action is triggered, such as a URL, email, or mobile phone number. Finally, after the policy rule configuration is complete, you can save the configured policy rule to the database.

[0044] S110 , based on the conditional inference engine, perform inference calculation on the pre-processed source data according to pre-configured policy conditions to obtain an inference calculation result.

[0045] In an embodiment of the present application, the inference calculation of various services (Plans) is supported in the conditional inference engine. Specifically, the conditional inference engine receives the pre-processed source data distributed by DDM, reads the customer information ID from the pre-processed source data, and loads the corresponding policy conditions from the policy rules according to the customer information ID, wherein the policy conditions include trigger functions; the pre-processed source data is inferred and calculated according to the trigger function to obtain the inference calculation results. For example, after a piece of pre-processed source data has been processed by the usage monitoring service, the usage monitoring policy condition set by the customer to whom the IoT card belongs is loaded: "ApnAg monthly traffic exceeds 10G policy". Among them, the Rule of this policy condition: PlanId is usage monitoring; ApnRg is the monthly traffic monitoring type; the trigger function Functions of this policy condition includes: Apn judgment function, Rg judgment function, and traffic size comparison function; the function expression finally composed of these three trigger functions is: Apn judgment function & Rg judgment function & traffic size comparison function. In this embodiment, the policy condition will be triggered only when the trigger parameters Params of the three trigger functions are all met, where the trigger parameters Params include: param1 containing customer settings in the Apn judgment function: Apn name; param2 containing customer settings in the Rg judgment function: Rg name; param3 containing customer settings in the traffic size comparison function: 10G.

[0046] S120. If the inference calculation result is that the pre-processed source data meets the policy condition, the trigger record corresponding to the pre-processed source data is saved in the database, and the generated action trigger information is sent to the action trigger engine.

[0047] In an embodiment of the present application, continuing to take the above-mentioned usage monitoring trigger condition as an example, an inference calculation is performed on a pre-processed source data of a service type of usage monitoring according to the above-mentioned trigger function. If the inference calculation result is that the pre-processed source data satisfies the trigger function, the trigger record is recorded in the database (for example, if the monthly traffic is 12G, the policy condition is triggered, and the monthly traffic of 12G is saved in the database as a trigger record), and an action trigger information is generated and sent to the next layer action trigger engine to execute the corresponding action. Among them, the action trigger information can be generated according to the policy conditions and the pre-configured policy rules in the database. For example, the generated action trigger information can be: sending API information to a certain URL, sending text messages to a certain mobile phone number, sending emails to a certain Email address, etc.

[0048] Furthermore, if the inference calculation result is that the preprocessed source data does not meet the policy conditions, the preprocessed source data will be discarded, and the steps of saving the trigger record corresponding to the preprocessed source data to the database and sending the generated action trigger information to the action trigger engine will be stopped, and no subsequent processing will be performed.

[0049] S130. Based on the action trigger engine, manage the Internet of Things card corresponding to the pre-processed source data according to the action trigger information.

[0050] In this embodiment of the present application, the action trigger engine decouples action triggering from other modules, assembling and automatically sending action messages. The action trigger engine includes an action factory module, a trigger, and an action execution module. These three modules work together to execute corresponding actions based on the action trigger information sent by the upper-level conditional inference engine, thereby achieving management of IoT cards.

[0051] In some embodiments, such as this embodiment, as shown in FIG5 , step S130 may include steps S131 - S134 .

[0052] S131, determining the corresponding action factory module according to the action trigger information;

[0053] S132. Based on the trigger, determine a corresponding message template, template parameters, and destination address according to the action factory module and the action trigger information;

[0054] S133. Based on the action execution module, concatenate the message template and the template parameters to obtain a target message;

[0055] S134: Send the target message to the destination address, and change the action execution state of the Internet of Things card corresponding to the pre-processed source data.

[0056] In an embodiment of the present application, an action trigger engine determines a corresponding action factory module (Actions) based on action trigger information. The trigger loads the corresponding message template, template parameters, and destination address based on the determined action factory module and action trigger information. The action execution module concatenates the message template and template parameters to form a target message, automatically sends the target message to the target address, and modifies the action execution status of the IoT card corresponding to the pre-processed source data, wherein the action execution status is stored in the trigger record. For example, continuing with the aforementioned policy condition "ApnAg monthly traffic exceeds 10G policy" as an example, the action trigger information generated based on this policy condition and pre-configured policy rules is "Send API information to a certain URL." This action trigger information is sent to the API module in the action factory module. The trigger automatically loads the corresponding message template and template parameters from the pre-configured policy rules based on the API module. The trigger then concatenates the message template and template parameters to form the content required for the target message. Finally, it calls the client URL via the http or https protocol to send the message, and modifies the action execution status in the trigger record.

[0057] Furthermore, the IoT card automated management method of the embodiment of the present application also includes: obtaining multiple call link nodes when executing the data gain and splitter, the conditional inference engine, and the action trigger engine; and saving the call chain formed by the multiple call link nodes into the trigger record. Specifically, to ensure high trigger reliability and subsequent location determination by operations and maintenance, the embodiment of the present application records call chains in both the data source and the three-layer processing model. According to the three-layer processing module, the resulting call chain mainly includes the following node information: source data ID - owned customer CustId - customer strategy StrategyId - business PlanId - data gain and splitter process Name - conditional inference engine process Name - trigger action type ActionId - action trigger engine process Name - action result Flag. The resulting call chain record is saved in the trigger record to provide a trace to facilitate operation and maintenance problem location and discovery. The call chain also serves as a chain of evidence for highly reliable triggering, achieving full lifecycle and link tracking of IoT cards.

[0058] The automated management method for IoT cards implemented in this application proposes a three-layer processing model consisting of "data gain and diverter, conditional inference engine, and action trigger engine" to quickly and effectively process the massive amount of real-time and non-real-time data in the IoT field. Based on the relevant policy rules set by the customer, the data gain and diverter performs layer-by-layer data enhancement, filtering, and diversion distribution. The conditional inference engine performs policy condition matching and inference calculations, and finally, the action trigger engine triggers the customer-configured actions to perform functions such as early warning prompts, API sending, and package renewal. The trigger records can be persisted for easy viewing by customers, thereby meeting the requirements of efficient, accurate, and highly customized automated management of IoT cards throughout their entire life cycle. At the same time, for triggered records, a field table can be used to record the triggering message source data ID, as well as the module IDs, business type, policy conditions, action factory, and other information. The call chain composed of these fields is persisted together with the trigger record, facilitating the subsequent backtracking of trigger records and problem location and maintenance.

[0059] Therefore, in summary, the method for automated management of IoT cards according to the embodiment of the present application has the following beneficial effects:

[0060] 1. Automated management is achieved through configuration-based means, which can be applied to all stages of the IoT card's life cycle. This enables automated rule processing throughout the IoT card's life cycle, quickly processing massive amounts of data while avoiding the tediousness and errors of manual operations.

[0061] 2. The call chain enables full-link tracking and tagging of IoT cards, providing a track to facilitate operation and maintenance and problem location and discovery. The call chain can also serve as a chain of evidence for highly reliable triggering.

[0062] 3. Based on the characteristics and needs of IoT cards, a more professional, efficient, real-time and accurate automated management solution is provided through the three-layer processing model of "data gain and splitter, conditional inference engine, and action trigger engine";

[0063] 4. Supports custom rule configuration and adjustment, with stronger adaptability and high customization to meet customers' various custom needs;

[0064] 5. It has a wide range of applications and strong portability, and can be used in other industries that require massive data processing and automated management.

[0065] Figure 6 is a schematic block diagram of an IoT card automated management device 200 provided in an embodiment of the present application. As shown in Figure 6 , corresponding to the aforementioned IoT card automated management method applied to an IoT card management platform, the IoT card automated management device 200 includes units for executing the aforementioned IoT card automated management method. Specifically, referring to Figure 6 , the IoT card automated management device 200 includes a preprocessing unit 201, an inference and calculation unit 202, a judgment and transmission unit, and a management unit 204.

[0066] Among them, the preprocessing unit 201 is used to input the acquired source data into the data gain and splitter for gain filtering processing to obtain preprocessed source data, and distribute the preprocessed source data to the conditional inference engine; the inference unit 202 is used to perform inference calculation on the preprocessed source data based on the conditional inference engine according to the preconfigured policy conditions to obtain the inference calculation result; the judgment and sending unit 203 is used to save the trigger record corresponding to the preprocessed source data into the database if the inference calculation result is that the preprocessed source data meets the policy condition, and send the generated action trigger information to the action trigger engine; the management unit 204 is used to manage the Internet of Things card corresponding to the preprocessed source data according to the action trigger information based on the action trigger engine.

[0067] In some embodiments, such as this embodiment, the pre-processing unit 201 includes an enhancement sub-unit, a filtering sub-unit, and a distribution sub-unit.

[0068] Among them, the enhancement subunit is used to input the acquired source data into the data enhancement module for information enhancement to obtain enhanced source data; the filtering and screening subunit is used to input the enhanced source data into the data filtering module for filtering and screening to obtain the preprocessed source data; the distribution subunit is used to distribute the preprocessed source data to the conditional inference engine through the data distribution module.

[0069] In some embodiments, such as this embodiment, the filtering and screening subunit includes a reading subunit and a filtering and screening subunit.

[0070] Among them, the reading subunit is used to read the pre-configured policy rules from the database, and select customer information Id and business PlanId from the policy rules; the filtering subunit is used to filter and filter the enhanced source data according to the customer information Id and the business PlanId to obtain the pre-processed source data.

[0071] In some embodiments, such as this embodiment, the inference unit 202 includes a loading subunit and an inference calculation subunit.

[0072] Among them, the loading sub-unit is used to read the customer information Id from the pre-processed source data, and load the corresponding policy conditions according to the customer information Id, wherein the policy conditions include a trigger function; the inference calculation sub-unit is used to perform inference calculation on the pre-processed source data according to the trigger function to obtain the inference calculation result.

[0073] In some embodiments, such as this embodiment, the Internet of Things card automatic management device 200 further includes a discarding unit.

[0074] Among them, the discarding unit is used to discard the preprocessed source data if the reasoning calculation result is that the preprocessed source data does not meet the policy conditions, and stop executing the steps of saving the trigger record corresponding to the preprocessed source data into the database and sending the generated action trigger information to the action trigger engine.

[0075] In some embodiments, such as this embodiment, the management unit 204 includes a first determination subunit, a second determination subunit, a splicing subunit, and a sending change subunit.

[0076] Among them, the first determination subunit is used to determine the corresponding action factory module according to the action trigger information; the second determination subunit is used to determine the corresponding message template, template parameters and destination address based on the trigger, the action factory module and the action trigger information; the splicing subunit is used to splice the message template and the template parameters based on the action execution module to obtain the target message; the sending change subunit is used to send the target message to the destination address and change the action execution status of the Internet of Things card corresponding to the pre-processed source data.

[0077] In some embodiments, such as the present embodiment, the IoT card automatic management device 200 further includes an acquisition unit and a storage unit.

[0078] Among them, the acquisition unit is used to obtain multiple call link nodes when executing the data gain and splitter, the conditional inference engine and the action trigger engine; the saving unit is used to save the call link formed by the multiple call link nodes to the trigger record.

[0079] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned Internet of Things card automatic management device 200 and each unit can refer to the corresponding description in the aforementioned method embodiment. For the convenience and brevity of the description, it will not be repeated here.

[0080] The above-mentioned Internet of Things card automatic management device can be implemented in the form of a computer program, which can be run on a computer device as shown in Figure 7.

[0081] Please refer to Figure 7, which is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 300 is a computer device equipped with an Internet of Things card management platform.

[0082] 7 , the computer device 300 includes a processor 302 , a memory, and a network interface 305 connected via a system bus 301 , wherein the memory may include a non-volatile storage medium 303 and an internal memory 304 .

[0083] The non-volatile storage medium 303 can store an operating system 3031 and a computer program 3032. When the computer program 3032 is executed, the processor 302 can execute an automatic management method for Internet of Things cards.

[0084] The processor 302 is used to provide computing and control capabilities to support the operation of the entire computer device 300.

[0085] The internal memory 304 provides an environment for the operation of the computer program 3032 in the non-volatile storage medium 303. When the computer program 3032 is executed by the processor 302, the processor 302 can execute a face detection method.

[0086] The network interface 305 is used to communicate with other devices over the network. Those skilled in the art will appreciate that the structure shown in FIG7 is merely a block diagram of a portion of the structure related to the present invention, and does not limit the computer device 300 to which the present invention is applied. The specific computer device 300 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0087] It should be understood that in the embodiment of the present application, the processor 302 may be a central processing unit (CPU), and the processor 302 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0088] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0089] Therefore, the present application also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform any embodiment of the above-mentioned face detection method.

[0090] The storage medium may be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0091] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and other division methods may be used in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not implemented.

[0093] The steps in the method of the embodiment of the present application can be adjusted in order, combined, and deleted according to actual needs. The units in the device of the embodiment of the present application can be combined, divided, and deleted according to actual needs. In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit.

[0094] 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 storage medium. Based on this understanding, the technical solution of the present application is essentially 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. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0095] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] Obviously, those skilled in the art may make various modifications and variations to this application without departing from the spirit and scope of this application. Thus, as long as these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0097] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An automated management method for Internet of Things cards, characterized in that: include: Input the acquired source data into the data gain and splitter for gain filtering to obtain pre-processed source data, and send the pre-processed source data to the conditional inference engine; Based on the conditional inference engine, the preprocessed source data is subjected to inference calculation according to preconfigured policy conditions to obtain an inference calculation result; If the inference calculation result is that the pre-processed source data satisfies the policy condition, the trigger record corresponding to the pre-processed source data is saved in the database, and the generated action trigger information is sent to the action trigger engine; Based on the action trigger engine, the Internet of Things card corresponding to the pre-processed source data is managed according to the action trigger information.

2. The method for automatic management of Internet of Things cards according to claim 1, characterized in that: The data gain and splitter includes a data enhancement module, a data filtering module and a data distribution module. The acquired source data is input into the data gain and splitter for gain filtering to obtain pre-processed source data, and the pre-processed source data is sent to the conditional inference engine, including: Inputting the acquired source data into the data enhancement module to perform information enhancement to obtain enhanced source data; Inputting the enhanced source data into the data filtering module for filtering and screening to obtain the pre-processed source data; The pre-processed source data is distributed to the conditional reasoning engine through the data distribution module.

3. The method for automatic management of Internet of Things cards according to claim 2, characterized in that: The inputting the enhanced source data into the data filtering module for filtering and screening to obtain the pre-processed source data comprises: Reading pre-configured policy rules from the database, and selecting customer information Id and business PlanId from the policy rules; The enhanced source data is filtered and screened according to the customer information Id and the business PlanId to obtain the pre-processed source data.

4. The method for automatic management of Internet of Things cards according to claim 3, characterized in that: The performing inference calculation on the preprocessed source data according to the preconfigured policy conditions to obtain the inference calculation result includes: Reading the customer information ID from the pre-processed source data, and loading the corresponding policy condition from the policy rule according to the customer information ID, wherein the policy condition includes a trigger function; The preprocessed source data is inferred and calculated according to the trigger function to obtain the inference and calculation result.

5. The method for automatic management of Internet of Things cards according to claim 1, characterized in that: The method further comprises: If the inference calculation result is that the preprocessed source data does not meet the policy condition, the preprocessed source data is discarded, and the steps of saving the trigger record corresponding to the preprocessed source data in the database and sending the generated action trigger information to the action trigger engine are stopped.

6. The method for automatic management of Internet of Things cards according to claim 1, characterized in that: The action trigger engine includes an action factory module, a trigger, and an action execution module. Based on the action trigger engine, the Internet of Things card corresponding to the pre-processed source data is managed according to the action trigger information, including: Determine the corresponding action factory module according to the action trigger information; Based on the trigger, determining a corresponding message template, template parameters and destination address according to the action factory module and the action trigger information; Based on the action execution module, the message template and the template parameters are concatenated to obtain a target message; The target message is sent to the destination address, and the action execution state of the Internet of Things card corresponding to the pre-processed source data is changed.

7. The method for automatic management of Internet of Things cards according to claim 1, characterized in that: The method further comprises: Acquire multiple call link nodes when executing the data gain and splitter, the conditional inference engine, and the action trigger engine; The call link formed by the plurality of call link nodes is saved in the trigger record.

8. An IoT card automation management device, characterized in that: include: A preprocessing unit, the preprocessing unit is used to input the acquired source data into the data gain and splitter for gain filtering processing to obtain preprocessed source data, and send the preprocessed source data to the conditional reasoning engine; An inference calculation unit, the inference calculation unit is used to perform inference calculation on the preprocessed source data based on the conditional inference engine and according to preconfigured policy conditions to obtain an inference calculation result; A judgment sending unit, wherein if the inference calculation result is that the pre-processed source data satisfies the policy condition, the judgment sending unit is used to save the trigger record corresponding to the pre-processed source data into a database, and send the generated action trigger information to the action trigger engine; A management unit, wherein the management unit is used to manage the Internet of Things card corresponding to the pre-processed source data based on the action trigger engine and according to the action trigger information.

9. A computer device, characterized in that: The computer device is equipped with an Internet of Things card management platform, and the computer device includes a memory and a processor. The memory stores a computer program, and the processor implements the Internet of Things card automatic management method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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