Internet of things card traffic speed limiting method and apparatus based on radius message, device, and medium

Abstract

Embodiments of the present application disclose an Internet of Things card traffic speed limiting method and apparatus based on a RADIUS message, a device, and a medium. The method belongs to the technical field of the Internet of Things, and comprises: receiving, in real time, monitoring data reported by each base station, determining hotspot base stations on the basis of the monitoring data and a preset identification threshold, and forming a hotspot base station list on the basis of the hotspot base stations; collecting, in real time, a RADIUS message of an Internet of Things card, carrying out statistical computation on the RADIUS message of the Internet of Things card in a preset time period by means of a multi-window algorithm, and determining, on the basis of a traffic statistics result, whether the Internet of Things card is a large traffic card; if the Internet of Things card is a large traffic card, determining, on the basis of the RADIUS message and the hotspot base station list, whether the Internet of Things card is in a hotspot area; and if the Internet of Things card is in a hotspot area, subscribing the Internet of Things card to a corresponding speed limiting package to control traffic of the Internet of Things card. According to the embodiments of the present application, an intelligent and flexible Internet of Things card speed limiting service can be provided.

Description

Method, device, equipment and medium for limiting traffic speed of IoT card based on Radius message Technical Field

[0001] The present application relates to the field of Internet of Things technology, and in particular to a method, device, equipment and medium for limiting the traffic speed of an Internet of Things card based on Radius messages. Background Art

[0002] In today's highly interconnected era, the rational allocation and management of network resources has become particularly critical. Among them, speed limit strategies, as an effective means, are widely used in hotspots to ensure high-quality network operation and meet the needs of different user groups.

[0003] However, traditional speed limiting methods have shown limitations when dealing with increasingly complex and changing network environments and demands. This is especially true in emerging scenarios such as IoT 4G high-volume SIM cards. In hotspots like shopping malls and stadiums, the high-volume demands of IoT 4G high-volume SIM cards can place significant pressure on network bandwidth resources, significantly reducing the data usage experience of regular 4G users with their personal SIM cards. Traditional speed limiting strategies often employ fixed rules, making them inflexible and unable to adapt to the changing characteristics of different hotspots. They also fail to effectively differentiate between personal SIM cards and IoT SIM cards, hindering the ability to provide flexible speed limiting services for different users. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, device, and medium for limiting the traffic speed of an Internet of Things card based on Radius messages, aiming to provide an intelligent and flexible Internet of Things card speed limiting service to improve the user experience.

[0005] In a first aspect, an embodiment of the present application provides a method for limiting the traffic speed of an IoT card based on a Radius message, which includes:

[0006] Receive monitoring data reported by each base station in real time, determine hotspot base stations based on the monitoring data and a preset identification threshold, and form a hotspot base station list based on the hotspot base stations;

[0007] Collect the Radius messages of the IoT card in real time, and perform statistical calculations on the Radius messages of the IoT card within a preset time period using a multi-window algorithm to obtain traffic statistics results;

[0008] Determine whether the IoT card corresponding to the Radius message is a high-traffic card according to the traffic statistics result;

[0009] If the IoT card is the high-traffic card, determining whether the IoT card is in a hotspot area according to the Radius message and the hotspot base station list;

[0010] If the Internet of Things card is in the hotspot area, a corresponding speed limit package is subscribed to the Internet of Things card to control the traffic of the Internet of Things card.

[0011] In a second aspect, an embodiment of the present application further provides an IoT card traffic rate limiting device based on Radius messages, which includes:

[0012] a receiving and determining unit, configured to receive monitoring data reported by each base station in real time, determine a hotspot base station based on the monitoring data and a preset identification threshold, and form a hotspot base station list based on the hotspot base stations;

[0013] The collection and calculation unit is used to collect the Radius messages of the Internet of Things card in real time, and perform statistical calculations on the Radius messages of the Internet of Things card within a preset time period through a multiple window algorithm to obtain traffic statistics results;

[0014] A first judgment unit, configured to judge whether the Internet of Things card corresponding to the Radius message is a high-traffic card according to the traffic statistics result;

[0015] a second judgment unit, configured to, if the IoT card is the high-traffic card, determine whether the IoT card is in a hotspot area according to the Radius message and the hotspot base station list;

[0016] A subscription unit is used to subscribe a corresponding speed limit package to the Internet of Things card to control the traffic of the Internet of Things card if the Internet of Things card is in the hotspot area.

[0017] In a third aspect, an embodiment of the present application further provides a computer device, wherein the computer device is equipped with a connection management platform, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0018] 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.

[0019] The embodiment of the present application provides a method, device, equipment and medium for limiting the flow rate of an Internet of Things card based on Radius messages. The method includes: receiving monitoring data reported by each base station in real time, determining the hotspot base station according to the monitoring data and a preset identification threshold, and forming a hotspot base station list according to the hotspot base station; collecting the Radius messages of the Internet of Things card in real time, and performing statistical calculations on the Radius messages of the Internet of Things card within a preset time period through a multiple window algorithm to obtain flow statistics results; judging whether the Internet of Things card corresponding to the Radius message is a high-flow card according to the flow statistics results; if the Internet of Things card is the high-flow card, judging whether the Internet of Things card is in a hotspot area according to the Radius message and the hotspot base station list; if the Internet of Things card is in the hotspot area, subscribing to a corresponding speed limit package for the Internet of Things card to control the flow of the Internet of Things card. The technical solution of the embodiment of the present application collects various indicator data of the base station to analyze the hotspot base station, and collects the Radius message of the Internet of Things traffic card in real time to identify the high-traffic Internet of Things card. If the Internet of Things card is identified as a high-traffic card and is in a hotspot area at the same time, a speed limit package is customized for the Internet of Things card to achieve speed limit, so as to provide a more flexible and intelligent speed limit mechanism for the Internet of Things traffic card, reduce the impact of the Internet of Things high-traffic card on the perception and experience of the users of the human network card in the hotspot area, and improve the user experience of the human network card. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] FIG1 is a flow chart of a method for limiting the flow rate of an Internet of Things card based on Radius messages provided in an embodiment of the present application;

[0022] FIG2 is a schematic diagram of a sub-flow of a method for limiting the flow rate of an Internet of Things card based on Radius messages provided in an embodiment of the present application;

[0023] FIG3 is a schematic diagram of a sub-flow of a method for limiting the flow rate of an Internet of Things card based on Radius messages provided in an embodiment of the present application;

[0024] FIG4 is a schematic diagram of a processing flow of a multi-window algorithm provided in an embodiment of the present application;

[0025] FIG5 is a schematic diagram of a sub-flow of a method for limiting the flow rate of an Internet of Things card based on a Radius message provided in an embodiment of the present application;

[0026] FIG6 is a schematic block diagram of a device for limiting Internet of Things card traffic based on Radius messages provided in an embodiment of the present application;

[0027] FIG7 is a schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] The Radius message-based method for limiting IoT card traffic speed, according to an embodiment of the present application, can be applied to an IoT card connection management platform. This connection management platform can communicate with IoT cards and base stations, receiving data from these cards and base stations to manage IoT card traffic speed limits based on these data. Refer to Figure 1, which is a schematic flow chart of a Radius message-based method for limiting IoT card traffic speed, according to an embodiment of the present application. As shown in Figure 1, the method includes the following steps, S100-S140.

[0034] S100: Receive monitoring data reported by each base station in real time, determine hotspot base stations according to the monitoring data and a preset identification threshold, and form a hotspot base station list based on the hotspot base stations.

[0035] In an embodiment of the present application, each base station counts the number of users connected to itself (i.e., the number of IoT cards) in real time, regularly monitors its own data, such as data traffic load, bandwidth utilization, resource occupancy and other information, and reports these data to the connection management platform. The connection management platform receives the various monitoring data reported by each base station in real time, and processes the various monitoring data to identify hotspot base stations. Among them, each monitoring data may include information such as the number of connected users, data traffic load, bandwidth utilization and resource occupancy. Each monitoring data is correspondingly provided with its own threshold for identifying hotspot base stations, that is, the preset identification threshold includes: a threshold for the number of connected users, a data traffic load threshold, a bandwidth utilization threshold and a resource utilization threshold. Based on the various monitoring data and the thresholds corresponding to the various monitoring data, it can be determined whether the base station that reports the monitoring data to the connection management platform is a hotspot base station, and then a hotspot base station list can be formed based on the information of the determined hotspot base station.

[0036] In some embodiments, such as this embodiment, as shown in FIG. 2 , step S100 may include steps S101 - S105 .

[0037] S101, recording the received monitoring data in a timestamp format to form a historical data set;

[0038] S102. For each base station, calculate, based on the historical data set, the average number of connected users, the average data traffic load, the average bandwidth utilization, and the average resource occupancy of the base station within the preset time period;

[0039] S103: Compare the average number of connected users, the average data traffic load, the average bandwidth utilization, and the average resource occupancy with corresponding preset thresholds;

[0040] S104: When the average number of connected users, the average data traffic load, the average bandwidth utilization, and the average resource occupancy rate all exceed their corresponding preset thresholds, marking the base station as a hotspot base station;

[0041] S105: Obtain information of all the hotspot base stations to form the hotspot base station list.

[0042] In the embodiment of the present application, the connection management platform will store the monitoring data reported by each base station, wherein these monitoring data will be recorded and stored in the form of timestamps to form a historical data set. Then, statistics and analysis are performed on the historical data set of each base station to calculate the average number of connected users, average data traffic load, average bandwidth utilization, and average resource occupancy of each base station in a preset time period. The calculation method can be calculated by arithmetic average or weighted average. For example, in this embodiment, let the number of connected users of base station i in the preset time period t be U i,t , the data traffic load is D i,t , the bandwidth utilization is B i,t , the resource occupancy rate is R i,t Assuming that the data of each base station is recorded according to the timestamp k, the historical data set, the average number of connected users, the average data traffic load, the average bandwidth utilization and the average resource occupancy can be expressed as follows: Historical data set = {(U i,k ,D i,k ,B i,k ,R i,k )|i=1,2,...,N;k=1,2,...,K};

[0043] Wherein, N represents the total number of base stations, and K represents the total number of timestamps.

[0044] Based on historical data analysis, the connection management platform can set a series of thresholds for identifying hotspot base stations. These thresholds can be set based on different time periods, geographic locations, base station types, and other factors to meet the needs of different scenarios. The calculated average indicators of each monitoring data are then compared with the set thresholds. When the average number of connected users, average data traffic load, average bandwidth utilization, and average resource utilization of a base station exceed their corresponding thresholds, the connection management platform identifies the base station as a hotspot. Finally, the information of the base stations identified as hotspots is obtained and compiled into a hotspot base station list.

[0045] S110. Collect Radius messages of the Internet of Things card in real time, and perform statistical calculations on the Radius messages of the Internet of Things card within a preset time period using a multiple window algorithm to obtain traffic statistics results.

[0046] In this embodiment of the present application, the data acquisition module of the connection management platform is responsible for collecting RADIUS messages from IoT cards. Specifically, the upstream and downstream traffic of RADIUS messages from IoT cards can be obtained through the Acct-Input-Octets and Acct-Output-Octets protocol fields. A multi-window algorithm is then used to calculate the traffic usage of a single IoT card in real time within a preset time period to generate traffic statistics. It should be noted that in this embodiment, RADIUS (Remote Authentication Dial-In User Service) messages are a protocol used for network access control and authentication. They are used to track user network resource usage. This includes recording user login and logout times, data transmission volume, and other information for billing and monitoring purposes. The multi-window algorithm is an improved method based on the sliding window algorithm, used for more granular data flow analysis and statistics. By using multiple windows of varying sizes, it enables data analysis at different time scales. This multi-window algorithm can more flexibly capture changing patterns in data flows and is suitable for application scenarios that require simultaneous consideration of multiple time scales.

[0047] In some embodiments, such as this embodiment, as shown in FIG3 , step S110 may include steps S111 - S115 .

[0048] S111, initializing multiple windows, setting sizes and sliding steps of the multiple windows, wherein each window corresponds to a different time scale;

[0049] S112: Continuously collect the Radius messages within the preset time period, and place the collected Radius messages into each window in chronological order;

[0050] S113. For each window, calculate the traffic data of the Radius message in the window to obtain an aggregation index for each time scale corresponding to the window;

[0051] S114: Sliding the window according to the sliding step size, removing the oldest data in the window and adding the latest data outside the window by sliding, and returning to the step of calculating the traffic data of the Radius message in the window to obtain the aggregation index of each time scale corresponding to the window;

[0052] S115: Utilize the aggregated indicators of all the windows as the traffic statistics results.

[0053] In an embodiment of the present application, please refer to Figure 4, which is a schematic diagram of the multiple sliding window processing flow. Step 1 Initialization: Initialize multiple windows. In Figure 4, window 1 (W1) and window 2 (W2), base stations A, B, C, and D are taken as examples. The size and sliding step of window W1 and window W2 can be set according to actual needs, and each window corresponds to a different time scale. Step 2 Data collection: Assuming that the preset time period is 20 minutes, the Radius messages within the 20-minute time period are continuously collected and placed in window W1 and window W2. Step 3 Data calculation: Statistical calculation is performed on the traffic data of the Radius messages in window W1 and window W2 to obtain the aggregation index of each time scale corresponding to each window. Among them, the preset 20-minute time period can be divided into four time points: T0, T1, T2, T3, and T4. The time scales of different time intervals can be formed between the four time points. Step 4 Window sliding: Slide according to the sliding step set for each window. By sliding, the oldest data in the window is removed and the latest data outside the window is added. The time scale corresponding to the sliding window will change. As shown in Figure 4, the IoT card reports a usage of 300M (Access Point A) at 09:55 (T0), 300M (Access Point A) at 10:00 (T1), 50M (Access Point B) at 10:05 (T2), 100M (Access Point C) at 10:10 (T3), and 300M (Access Point D) at 10:15 (T4). Therefore, for window W1: the aggregated metric (i.e., traffic data) counted within the T0-T1 timescale is 600M; the aggregated metric counted within the T0-T2 timescale is 650M; the aggregated metric counted within the T1-T3 timescale is 450M; and the aggregated metric counted within the T2-T4 timescale is 450M. For window W2: the aggregation index counted within the time scale of T0 to T1 is 600M; the aggregation index counted within the time scale of T0 to T2 is 650M; the aggregation index counted within the time scale of T0 to T3 is 750M; the aggregation index counted within the time scale of T0 to T4 is 1050M. Understandably, when the preset time period is greater than 20 minutes, steps 3 and 4 are repeated, and data calculation and window sliding are continuously performed to complete the continuous statistics of the traffic data of the Radius message within a period of time. Finally, the aggregation index of all windows (i.e., window W1 and window W2) is used as the traffic statistics result.

[0054] S120. Determine, based on the traffic statistics result, whether the IoT card corresponding to the Radius message is a high-traffic card.

[0055] In an embodiment of the present application, judging whether an Internet of Things card is a high-traffic card based on traffic statistics results requires: comparing the obtained aggregation index of each time scale with the high-traffic threshold preset for each window, and when the aggregation index reaches the high-traffic threshold, marking the Internet of Things card corresponding to the Radius message as a high-traffic card. For example, assuming that the identification rule for a high-traffic card (i.e., the preset high-traffic threshold) is: window W1: the traffic usage reaches 500M within 10 minutes; window W2: the traffic usage reaches 1G within 20 minutes. According to the traffic data counted by the above-mentioned window W1 and window W2, if there is only window W1 (traffic usage reaches 500M within 10 minutes), the Internet of Things card is only identified as a high-traffic card at T0~T1 and T0~T2. If there is only window W2 (traffic usage reaches 1G within 20 minutes), the Internet of Things card is only identified as a high-traffic card at T0~T4. When multiple sliding windows are used (i.e., window W1 and window W2 are effective at the same time), the IoT card will be identified as a high-traffic card at T0~T1, T0~T2, and T0~T4. Therefore, the success rate of identifying high-traffic cards is increased by adopting a multiple-window algorithm. Moreover, the multiple-window algorithm can adapt to different time scale requirements and can analyze minute-level and hour-level data at the same time. By analyzing data at different time scales, we can have a more comprehensive understanding of the changing trends of data flows, thereby making decisions and management more accurate. It should be noted that when the multiple-window algorithm is actually implemented, multiple windows and high-traffic thresholds can be configured, which can be set according to actual needs. This application does not make specific limitations here.

[0056] S130: If the Internet of Things card is the high-traffic card, determine whether the Internet of Things card is in a hotspot area according to the Radius message and the hotspot base station list.

[0057] In an embodiment of the present application, if the Internet of Things card is identified as a high-traffic card, it is further determined whether the Internet of Things card is in a hotspot area based on the Radius message and the hotspot base station list. Specifically, the access area information of the Internet of Things card is obtained through the preset field in the Radius message; the access area information is matched with the hotspot base station list to obtain a matching result, and whether the Internet of Things card is in a hotspot area is determined based on the matching result. Among them, the preset field is 3GPP-User-Location-Info in the Radius message, that is, the access area information of the Internet of Things card can be obtained through 3GPP-User-Location-Info, and then the access area information is matched with the previous hotspot base station list to determine whether the Internet of Things card is within the hotspot area.

[0058] S140: If the Internet of Things card is in the hotspot area, subscribe to a corresponding speed limit package for the Internet of Things card to control the traffic of the Internet of Things card.

[0059] In an embodiment of the present application, if it is determined that the IoT card is located in a hotspot area and has been identified as a high-traffic card, the connection management platform will subscribe to a corresponding speed-limited package for the IoT card, and based on the speed-limited package, the speed-limited policy corresponding to the speed-limited package is signed between the IoT card and the hotspot base station through the PCRF to control the traffic of the IoT card, thereby ensuring that the user's traffic is reasonably controlled. Among them, PCRF (Policy and Charging Rules Function) is an important functional element in the communication network, commonly used in the mobile network field. It is defined in the 3GPP (Third Generation Partnership Project) mobile communication standard and is used to implement policy control and charging control.

[0060] Furthermore, in some embodiments, such as this embodiment, as shown in FIG5 , steps S150 to S170 are further included after step S140 .

[0061] S150. Acquire the location information of the Internet of Things card through the Radius message collected in real time;

[0062] S160: If the location information is updated, return to the step of performing statistical calculations on the Radius messages of the IoT card within a preset time period using a multi-window algorithm to obtain traffic statistics results;

[0063] S170: If the IoT card is not in the hotspot area and / or the IoT card is not the high-traffic card, cancel the speed limit package for the IoT card and release the subscribed speed limit policy through the PCRF.

[0064] In an embodiment of the present application, the location information of the IoT card is obtained through real-time Radius messages collected, and the location information is used to determine whether the location of the IoT card has been updated. If the location information of the IoT card has been updated, the traffic data usage of the IoT card within a preset time period will be recalculated using a multi-window algorithm, and the user's new access area information will be analyzed. In other words, the step of performing statistical calculations on the Radius messages of the IoT card within the preset time period using the multi-window algorithm to obtain traffic statistics is returned to determine whether the IoT card is in a hotspot area and / or is a high-traffic card at its new location. If the IoT card is not in a hotspot area and / or is not a high-traffic card, it means that the IoT card is no longer in the hotspot base station area and / or the IoT card's traffic usage at the new location has not reached the high-traffic card threshold. The connection management platform will cancel the customized speed limit package and release the subscribed speed limit policy through the PCRF, allowing the IoT card to restore normal Internet access speed. It should be noted that in this embodiment, the location information of the IoT card can be set as a location area range. When the IoT card leaves the current location area range, it is determined that a location update has occurred.

[0065] The present invention's Radius message-based IoT card traffic speed limiting method combines a multi-window algorithm with Radius messages, enabling real-time data analysis without waiting for all data to arrive. Furthermore, Radius messages can be used to analyze user traffic usage and base station access information. This combination is ideal for real-time monitoring and speed limiting of high-traffic cards at hotspot base stations. Furthermore, by combining the IoT card management capabilities of the connection management platform with the policy control capabilities of the PCRF, Radius messages can be used to detect in real time whether an IoT card has left a hotspot base station and / or is no longer a high-traffic card. If the conditions for hotspot base stations and high-traffic cards are not met, speed limit control is promptly lifted, optimizing the IoT card's speed limit service. Compared to traditional speed limiting methods that use fixed rules, the present invention's speed limit control and lifting are more timely, providing a more flexible and intelligent IoT card speed limiting mechanism. This reduces the impact of high-traffic IoT cards on the user experience and user experience of IoT card users in hotspot areas, thereby optimizing mobile network performance and user experience.

[0066] FIG6 is a schematic block diagram of a RADIUS message-based Internet of Things (IoT) card traffic rate limiting device 200 provided in an embodiment of the present application. As shown in FIG6 , corresponding to the RADIUS message-based IoT card traffic rate limiting method applied to the IoT platform, the RADIUS message-based IoT card traffic rate limiting device 200 includes a unit for executing the RADIUS message-based IoT card traffic rate limiting method. Specifically, referring to FIG6 , the RADIUS message-based IoT card traffic rate limiting device 200 includes a receiving and determining unit 201, a collection and calculation unit 202, a first judgment unit 203, a second judgment unit 204, and an ordering unit 205.

[0067] Among them, the receiving and determining unit 201 is used to receive the monitoring data reported by each base station in real time, determine the hotspot base station according to the monitoring data and the preset identification threshold, and form a hotspot base station list according to the hotspot base station; the collecting and calculating unit 202 is used to collect the Radius message of the Internet of Things card in real time, and perform statistical calculations on the Radius message of the Internet of Things card within a preset time period through a multiple window algorithm to obtain traffic statistics results; the first judgment unit 203 is used to judge whether the Internet of Things card corresponding to the Radius message is a high-traffic card according to the traffic statistics results; the second judgment unit 204 is used to judge whether the Internet of Things card is in a hotspot area according to the Radius message and the hotspot base station list if the Internet of Things card is the high-traffic card; the ordering unit 205 is used to subscribe to the corresponding speed limit package for the Internet of Things card to control the traffic of the Internet of Things card if the Internet of Things card is in the hotspot area.

[0068] In some embodiments, such as this embodiment, the acquisition and calculation unit 202 includes an initialization subunit, an acquisition subunit, a first calculation subunit, a sliding subunit, and an acting subunit.

[0069] Among them, the initialization subunit is used to initialize multiple windows, set the size and sliding step of multiple windows, wherein each window corresponds to a different time scale; the collection subunit is used to continuously collect the Radius messages within the preset time period, and put the collected Radius messages into each window in chronological order; the first calculation subunit is used to calculate the traffic data of the Radius messages in the window for each window, and obtain the aggregation index of each time scale corresponding to the window; the sliding subunit is used to slide the window according to the sliding step, remove the oldest data in the window and add the latest data outside the window by sliding, and return to execute the step of calculating the traffic data of the Radius messages in the window to obtain the aggregation index of each time scale corresponding to the window; the acting subunit is used to use the aggregation index of all the windows as the traffic statistics result.

[0070] In some embodiments, such as this embodiment, the first determination unit 203 includes a first comparison subunit and a marking subunit.

[0071] Among them, the comparison subunit is used to compare the aggregation index with the high-traffic threshold preset for each window; the marking subunit is used to mark the Internet of Things card corresponding to the Radius message as the high-traffic card when the aggregation index reaches the high-traffic threshold.

[0072] In some embodiments, such as this embodiment, the receiving determination unit 201 includes a recording subunit, a second calculating subunit, a second comparing subunit, an identifying subunit, and a forming subunit.

[0073] Among them, the recording subunit is used to record the received monitoring data in the form of a timestamp to form a historical data set; the second calculation subunit is used to calculate the average number of connected users, average data traffic load, average bandwidth utilization and average resource occupancy of each base station within the preset time period according to the historical data set; the second comparison subunit is used to compare the average number of connected users, the average data traffic load, the average bandwidth utilization and the average resource occupancy with the corresponding preset thresholds; the identification subunit is used to identify the base station as a hotspot base station when the average number of connected users, the average data traffic load, the average bandwidth utilization and the average resource occupancy all exceed the corresponding preset thresholds; the formation subunit is used to obtain information of all the hotspot base stations to form the hotspot base station list.

[0074] In some embodiments, such as this embodiment, the second determination unit 204 includes an acquisition subunit and a matching subunit.

[0075] Among them, the acquisition subunit is used to obtain the access area information of the Internet of Things card through the preset field in the Radius message; the matching subunit is used to match the access area information with the hotspot base station list to obtain a matching result, and judge whether the Internet of Things card is in the hotspot area according to the matching result.

[0076] In some embodiments, such as this embodiment, the ordering unit 205 includes an ordering subunit and a contract signing subunit.

[0077] Among them, the subscription subunit is used to subscribe to a corresponding speed limit package for the Internet of Things card if the Internet of Things card is in a hotspot area; the signing subunit is used to sign a speed limit policy corresponding to the speed limit package between the Internet of Things card and the hotspot base station through PCRF based on the speed limit package to control the traffic of the Internet of Things card.

[0078] In some embodiments, such as the present embodiment, the Internet of Things card traffic rate limiting device 200 based on Radius messages further includes an acquisition unit, a return execution unit, and a release unit.

[0079] Among them, the acquisition unit is used to obtain the location information of the Internet of Things card through the Radius message collected in real time; the return execution unit is used to return to execute the step of performing statistical calculations on the Radius messages of the Internet of Things card within a preset time period through a multiple window algorithm to obtain traffic statistics results if the location information is updated; the release unit is used to cancel the speed limit package for the Internet of Things card if the Internet of Things card is not in the hotspot area and / or the Internet of Things card is not the high-traffic card, and release the signed speed limit policy through the PCRF.

[0080] It should be noted that technical personnel in the relevant field can clearly understand that the specific implementation process of the above-mentioned Internet of Things card traffic speed limiting device 200 based on Radius messages and each unit can refer to the corresponding description in the aforementioned method embodiment. For the convenience and conciseness of the description, it will not be repeated here.

[0081] The above-mentioned Internet of Things card traffic speed limiting device based on Radius message can be implemented in the form of a computer program, which can be run on the computer device shown in Figure 7.

[0082] 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 a connection management platform.

[0083] 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 .

[0084] 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 a method for limiting the flow rate of an Internet of Things card based on a Radius message.

[0085] The processor 302 is used to provide computing and control capabilities to support the operation of the entire computer device 300.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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. A method for limiting the flow rate of an Internet of Things card based on Radius messages, characterized in that: include: Receive monitoring data reported by each base station in real time, determine hotspot base stations according to the monitoring data and a preset identification threshold, and form a hotspot base station list according to the hotspot base stations; Collect the Radius messages of the IoT card in real time, and perform statistical calculations on the Radius messages of the IoT card within a preset time period through a multi-window algorithm to obtain traffic statistics results; Determine whether the IoT card corresponding to the Radius message is a high-traffic card according to the traffic statistics result; If the IoT card is the high-traffic card, judging whether the IoT card is in a hotspot area according to the Radius message and the hotspot base station list; If the IoT card is in the hotspot area, a corresponding speed limit package is subscribed to the IoT card to control the traffic of the IoT card.

2. According to the method for limiting the flow rate of Internet of Things cards based on Radius messages according to claim 1, it is characterized in that: The method of performing statistical calculation on the Radius messages of the IoT card within a preset time period by using a multiple window algorithm to obtain traffic statistics results includes: Initializing multiple windows, and setting the sizes and sliding steps of the multiple windows, wherein each window corresponds to a different time scale; Continuously collecting the Radius messages within the preset time period, and placing the collected Radius messages into each of the windows in chronological order; For each of the windows, the flow data of the Radius message in the window is calculated to obtain an aggregation index of each of the time scales corresponding to the window; Sliding the window according to the sliding step, removing the oldest data in the window and adding the latest data outside the window by sliding, and returning to execute the step of calculating the traffic data of the Radius message in the window to obtain the aggregation index of each time scale corresponding to the window; The aggregated index of all the windows is used as the traffic statistics result.

3. The method for limiting the flow rate of an Internet of Things card based on a Radius message according to claim 2 is characterized in that: The determining, according to the traffic statistics result, whether the Internet of Things card corresponding to the Radius message is a high-traffic card includes: Comparing the aggregation index with a preset high-flow threshold for each of the windows; When the aggregation index reaches the high-traffic threshold, the Internet of Things card corresponding to the Radius message is marked as the high-traffic card.

4. According to the method for limiting the flow rate of Internet of Things cards based on Radius messages according to claim 1, it is characterized in that: The determining of hotspot base stations according to the monitoring data and a preset identification threshold, and forming a hotspot base station list according to the hotspot base stations, includes: Recording the received monitoring data in a timestamp format to form a historical data set; For each of the base stations, calculating the average number of connected users, average data traffic load, average bandwidth utilization, and average resource occupancy of the base station within the preset time period according to the historical data set; Compare the average number of connected users, the average data traffic load, the average bandwidth utilization, and the average resource occupancy with corresponding preset thresholds; When the average number of connected users, the average data traffic load, the average bandwidth utilization rate, and the average resource occupancy rate all exceed the corresponding preset thresholds, marking the base station as a hotspot base station; The information of all the hotspot base stations is obtained to form the hotspot base station list.

5. According to the method for limiting the flow rate of Internet of Things cards based on Radius messages according to claim 1, it is characterized in that: The determining whether the Internet of Things card is in a hotspot area according to the Radius message and the hotspot base station list includes: Obtaining access area information of the Internet of Things card through a preset field in the Radius message; The access area information is matched with the hotspot base station list to obtain a matching result, and whether the Internet of Things card is in a hotspot area is determined according to the matching result.

6. The method for limiting the flow rate of an Internet of Things card based on a Radius message according to claim 1 is characterized in that: If the Internet of Things card is in the hotspot area, subscribing a corresponding speed limit package to the Internet of Things card to control the traffic of the Internet of Things card includes: If the IoT card is in a hotspot area, a corresponding speed-limited package is ordered for the IoT card; Based on the speed limit package, a speed limit policy corresponding to the speed limit package is signed between the Internet of Things card and the hotspot base station through PCRF to control the traffic of the Internet of Things card.

7. The method for limiting the flow rate of an Internet of Things card based on a Radius message according to claim 6 is characterized in that: The method further comprises: Acquire the location information of the IoT card through the Radius message collected in real time; If the location information is updated, return to the step of performing statistical calculations on the Radius messages of the IoT card within a preset time period using a multiple window algorithm to obtain traffic statistics results; If the Internet of Things card is not in the hot spot area and / or the Internet of Things card is not the high-traffic card, the speed limit package is canceled for the Internet of Things card, and the contracted speed limit policy is released through the PCRF.

8. A device for limiting the flow rate of an Internet of Things card based on Radius messages, characterized in that: include: A receiving and determining unit, the receiving and determining unit is used to receive monitoring data reported by each base station in real time, determine a hotspot base station according to the monitoring data and a preset identification threshold, and form a hotspot base station list according to the hotspot base stations; A collection and calculation unit, wherein the collection and statistics unit is used to collect the Radius messages of the Internet of Things card in real time, and perform statistical calculations on the Radius messages of the Internet of Things card within a preset time period through a multi-window algorithm to obtain traffic statistics results; A first judgment unit, the first judgment unit is used to judge whether the Internet of Things card corresponding to the Radius message is a high-traffic card according to the traffic statistics result; A second judgment unit, wherein if the Internet of Things card is the high-traffic card, the second judgment unit is used to judge whether the Internet of Things card is in a hotspot area according to the Radius message and the hotspot base station list; A subscription unit, wherein the subscription unit is used to subscribe a corresponding speed limit package to the Internet of Things card to control the traffic of the Internet of Things card if the Internet of Things card is in the hot spot area.

9. A computer device, characterized in that: The computer device is equipped with a connection management platform, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the Internet of Things card traffic speed limiting method based on Radius messages as described in any one of claims 1-7 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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