Regional boundary use limiting method and device of Internet of Things card

Abstract

The invention provides a method and a device for limiting the use of an internet of things card at a region boundary, and the method comprises the steps: under the condition that the internet of things card is connected to an adjacent unauthorized region base station at an authorized region boundary, calculating the use limit of the internet of things card on the basis of a base station abnormal call proportion, a historical abnormal risk coefficient, a geographic position risk coefficient and a corresponding weight coefficient; calculating a base station overall risk coefficient of the uplink base station of the Internet of Things card; calculating an Internet of Things card risk coefficient based on the Internet of Things card risk behavior coefficient, the abnormal use risk coefficient, the abnormal movement risk coefficient, the potential business risk coefficient and the corresponding weight coefficient; calculating a total risk coefficient based on the base station overall risk coefficient and the Internet of Things card risk coefficient; and based on the total risk coefficient, determining whether the current Internet access behavior of the Internet of Things card is legal so as to determine whether to limit the network access of the Internet of Things card. The Internet of Things card of the area boundary is limited to be used in the risk range, the problem that the area boundary is connected to the adjacent area and cannot be used is solved, and the user experience is improved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more particularly to a method and apparatus for restricting the use of IoT cards at regional boundaries. Background Technology

[0002] An IoT SIM card is a card-type device based on Internet of Things (IoT) technology. It is typically used to connect various smart devices, such as smart meters, smart water meters, smart homes, smart transportation, and industrial automation control devices, to enable data transmission and device control.

[0003] With the diversification of IoT application scenarios and the continuous development of business scale, cases of IoT cards being used for illegal activities occur frequently. Restricting the use of IoT cards in certain high-risk or remote areas can effectively reduce these illegal activities and maintain social stability. Proactively restricting the areas where IoT cards are used can mitigate business risks and ensure the normal operation of customer businesses.

[0004] Currently, IoT operators support provincial-level usage area restrictions. However, when using IoT SIM cards at the border of certain regions, there is a problem where the card is actually located in a permitted area but registered in an unauthorized area, rendering the SIM card unusable. This does not meet the requirements of actual business scenarios and affects customer business operations. Summary of the Invention

[0005] This invention provides a method and apparatus for restricting the use of IoT cards at regional boundaries, in order to solve the defect in the prior art where the actual location of the IoT card at the regional boundary is legal, but it cannot be used because it is connected to an adjacent unauthorized area base station. This invention enables the IoT card to access the network within the risk range when it is connected to an adjacent unauthorized area base station at the boundary of an authorized area.

[0006] This invention provides a method for restricting the use of IoT cards at regional boundaries, comprising: When an IoT SIM card connects to an adjacent unauthorized base station at the boundary of an authorized area, the overall base station risk coefficient of the uplink base station of the IoT SIM card is calculated based on the proportion of abnormal calls to the base station, the historical abnormal risk coefficient, the geographical location risk coefficient, and the corresponding weight coefficient. The IoT card risk coefficient is calculated based on the IoT card risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, potential business risk coefficient, and corresponding weight coefficients. Calculate the total risk coefficient based on the overall risk coefficient of the base station and the risk coefficient of the IoT card; Based on the total risk coefficient, it is determined whether the IoT card's current internet access behavior is legal, in order to determine whether to restrict the IoT card's network access.

[0007] According to the method for restricting the use of IoT cards at regional boundaries provided by the present invention, the proportion of abnormal calls at the base station is calculated in the following way: Based on the abnormal call determination criteria, it is determined whether multiple calls are abnormal calls; the abnormal call determination criteria include at least one of the following: call duration determination criteria, call frequency determination criteria, caller ID credibility determination criteria, and user abnormal call behavior determination criteria. The proportion of abnormal calls to a base station is calculated based on the number of abnormal calls and the total number of calls.

[0008] According to the method for restricting the use of IoT cards at regional boundaries provided by the present invention, the historical anomaly risk coefficient is calculated in the following manner: Determine the number of abnormal behaviors within the coverage area of ​​the uplink base station of the IoT card within a historical time frame; Based on the number of abnormal behaviors and the preset number threshold, the historical abnormal risk coefficient is calculated.

[0009] According to a method for restricting the use of IoT cards at regional boundaries provided by the present invention, the geographical location risk coefficient is determined in the following manner: Obtain at least one set of risk regions; wherein, each set of risk regions corresponds to a different risk coefficient; If the region to which the uplink base station of the IoT card belongs is a risk region in any set of risk regions, then the risk coefficient corresponding to the risk set of any risk region is determined as the geographical location risk coefficient.

[0010] According to a method for restricting the use of IoT cards at regional boundaries provided by the present invention, the abnormal use risk coefficient is determined in the following manner: The standard deviation of daily usage is calculated based on the average daily usage of IoT cards in the first preset period and the daily usage of each day in the first preset period. Based on the average daily usage and the standard deviation of daily usage, the scope of abnormal usage risk assessment is determined; Determine whether the daily usage of the IoT card on the access day is within the range of abnormal usage risk assessment to determine the abnormal usage risk coefficient.

[0011] According to the method for restricting the use of IoT cards at regional boundaries provided by the present invention, the abnormal movement risk coefficient is calculated in the following manner: The standard deviation of displacement distance is calculated based on the average displacement distance of the IoT card in the second preset period and the daily displacement distance in the second preset period. Based on the average displacement distance and the standard deviation of the displacement distance, the range of abnormal movement risk is determined; To determine whether the distance the IoT card travels on the access day is within the range of abnormal movement risk assessment, an abnormal movement risk coefficient is determined.

[0012] According to the method for restricting the use of IoT cards at regional boundaries provided by the present invention, the potential business risk coefficient is calculated in the following manner: The potential business risk coefficient is calculated based on the number of risky IoT card transactions and the total number of IoT card transactions.

[0013] According to the method for restricting the use of IoT cards at regional boundaries provided by the present invention, before calculating the overall base station risk coefficient of the uplink base station of the IoT card based on the proportion of abnormal calls at the base station, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weight coefficients, the method further includes: If the area to which the uplink base station of the IoT card belongs is not an available area of ​​the IoT card, a group of base stations near the uplink base station is selected based on the latitude and longitude of the uplink base station. Calculate the distance between the base stations in the base station group that belong to the available area and the uplink base station of the IoT card, and determine the candidate base station that is closest to the uplink base station of the IoT card; Based on the latitude and longitude of the candidate base stations, determine the target base station belonging to the region that is closest to the candidate base station; If the uplink base station of the IoT card and the target base station are the same base station, then it is determined that the IoT card is connected to the base station of the adjacent unauthorized area at the boundary of the authorized area.

[0014] The present invention also provides a regional boundary usage restriction device for an Internet of Things (IoT) card, comprising: The base station overall risk coefficient calculation unit is used to calculate the base station overall risk coefficient of the uplink base station of the IoT card when the IoT card connects to the adjacent unauthorized area base station at the boundary of the authorized area. This is based on the proportion of abnormal calls at the base station, the historical abnormal risk coefficient, the geographical location risk coefficient, and the corresponding weight coefficient. The IoT card risk coefficient calculation unit is used to calculate the IoT card risk coefficient based on the IoT card risk behavior coefficient, abnormal use risk coefficient, abnormal movement risk coefficient, potential business risk coefficient and corresponding weight coefficient. The total risk coefficient calculation unit is used to calculate the total risk coefficient based on the overall risk coefficient of the base station and the risk coefficient of the IoT card; The communication function control unit is used to determine whether the Internet of Things (IoT) card's current Internet access behavior is legal based on the total risk coefficient, so as to determine whether to restrict the IoT card's network access.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the area boundary usage restriction method of any of the above-described IoT cards.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the area boundary usage restriction method of any of the above-described IoT cards.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the area boundary usage restriction method of any of the above-described IoT cards.

[0018] The IoT card area boundary usage restriction method and apparatus provided by this invention determines whether the IoT card’s current internet access behavior at the area boundary is legal by calculating the total risk coefficient, and thus determines whether to restrict the IoT card’s network access, rather than simply determining the legality of the IoT card’s internet access behavior based on base station affiliation. This limits the use of IoT cards at the area boundary to within the risk range, solves the problem that IoT cards cannot be used when connecting to adjacent areas at the authorized area boundary, and improves the user experience. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the method for restricting the use of IoT cards at regional boundaries provided in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the process for determining whether an IoT card is connected to a base station in an adjacent area at the regional boundary, provided by an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of a non-regional boundary usage scenario provided by an embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of a regional boundary usage scenario provided in an embodiment of the present invention.

[0024] Figure 5 This is one of the structural schematic diagrams of the area boundary usage restriction device for IoT cards provided in the embodiments of the present invention.

[0025] Figure 6 This is the second structural schematic diagram of the IoT card area boundary usage restriction device provided in the embodiment of the present invention.

[0026] Figure 7 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] In the description of embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Those skilled in the art will understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0029] In the current regional restriction management method, the usage area of ​​an IoT SIM card is obtained through RADUIS (Remote Authentication Dial-In User Service) (4G) or CHF (Charging Function) (5G) call detail records on the network element side, or by comparing the base station ID (IdentityDocument) with the base station affiliation table. In actual operation, RADUIS / CHF / base stations all have clearly defined affiliation provinces, so the usage area is strongly correlated with the uplink network element. However, at the borders of different provinces, due to issues such as cellular coverage signal strength and terminal selection, there may be a problem where the IoT SIM card is actually in a legitimate location, but it cannot be used because it is connected to a base station in an adjacent area (an unauthorized area).

[0030] To address the aforementioned issues, the legality of an IoT SIM card's internet access behavior at the regional boundary is determined by calculating the total risk coefficient, thus deciding whether to restrict the IoT SIM card's network access. This approach goes beyond simply relying on base station affiliation to determine the legality of the IoT SIM card's internet access behavior. As a result, the use of IoT SIM cards at the regional boundary is limited to a risk range, resolving the issue of IoT SIM cards being unable to connect to adjacent areas from authorized regional boundaries and improving the user experience.

[0031] Figure 1 This is a flowchart illustrating the method for restricting the use of IoT cards at regional boundaries according to an embodiment of the present invention. (Refer to...) Figure 1 This invention provides a method for restricting the use of IoT cards at regional boundaries, the method specifically including the following steps: Step 101: When the IoT card connects to an adjacent unauthorized area base station at the boundary of the authorized area, calculate the overall base station risk coefficient of the uplink base station of the IoT card based on the base station abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient and corresponding weight coefficient.

[0032] It should be noted that the executing entity of the IoT card area boundary usage restriction method provided in this embodiment of the invention can be an electronic device, a component in the electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, a mobile electronic device can be a mobile phone, tablet computer, laptop computer, PDA, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., while a non-mobile electronic device can be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This embodiment of the invention does not specifically limit the specific implementation of these methods.

[0033] In this embodiment of the invention, the abnormal call ratio of a base station can refer to the proportion of abnormal calls within the coverage area of ​​an adjacent unauthorized area base station connected to an IoT card, the historical abnormal risk coefficient can refer to the risk coefficient obtained by risk assessment based on historical abnormal behavior within the coverage area of ​​the base station, and the geographical location risk coefficient can refer to the risk coefficient obtained by risk assessment based on the geographical location of the base station.

[0034] In this embodiment of the invention, the overall risk coefficient of the IoT SIM card's uplink base station can be obtained by weighting the proportion of abnormal calls, historical abnormal risk coefficient, and geographical location risk coefficient of the uplink base station (i.e., the adjacent unauthorized area base station connected to by the IoT SIM card at the area boundary). The formula for calculating the overall risk coefficient of the base station is as follows: R X =α·R1+β·R2+γ·R3(1); Where R1 is the proportion of abnormal calls to the base station, R2 is the historical abnormal risk coefficient, R3 is the geographical location risk coefficient, α is the weight coefficient corresponding to the proportion of abnormal calls to the base station, β is the weight coefficient corresponding to the historical abnormal risk coefficient, and γ is the weight coefficient corresponding to the geographical location risk coefficient.

[0035] In some embodiments, the weighting coefficients α, β, and γ can be set to 0.2, 0.4, and 0.4, respectively, based on historical business analysis. It should be noted that these weighting coefficients can be dynamically adjusted according to actual needs.

[0036] Step 102: Calculate the IoT card risk coefficient based on the IoT card risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, potential business risk coefficient, and corresponding weight coefficients.

[0037] In this embodiment of the invention, IoT card risk behavior can refer to the identification of prohibited risk behaviors in the use of the IoT card by a risk control model based on the IoT card.

[0038] In some embodiments, if there are prohibited risk behaviors in the use of the IoT card, the IoT card risk behavior coefficient can be 1; if there are no prohibited risk behaviors in the use of the IoT card, the IoT card risk behavior coefficient can be 0.

[0039] In this embodiment of the invention, the abnormal usage risk coefficient can refer to the risk coefficient obtained by risk assessment based on the data traffic used by the IoT card, the abnormal movement risk coefficient can refer to the risk coefficient obtained by risk assessment based on the displacement distance of the IoT card, and the potential business risk coefficient can refer to the risk coefficient obtained by risk assessment based on the business situation of the IoT card.

[0040] In this embodiment of the invention, the IoT card risk coefficient can be obtained by weighting the IoT card risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, and potential business risk coefficient. The formula for calculating the IoT card risk coefficient is as follows: R a =L·(δ·R4+ε·R5+η·R6)(2); Where L is the IoT card risk behavior coefficient, R4 is the abnormal use risk coefficient, R5 is the abnormal movement risk coefficient, R6 is the potential business risk coefficient, δ is the weight coefficient corresponding to the abnormal use risk coefficient, ε is the weight coefficient corresponding to the abnormal movement risk coefficient, and η is the weight coefficient corresponding to the potential business risk coefficient.

[0041] In some embodiments, the weighting coefficients δ, ε, and η can be set to 0.4, 0.4, and 0.2, respectively, based on historical business analysis. It should be noted that these weighting coefficients can be dynamically adjusted according to actual needs.

[0042] Step 103: Calculate the total risk coefficient based on the overall risk coefficient of the base station and the risk coefficient of the IoT card.

[0043] In this embodiment of the invention, the overall risk coefficient of the base station and the risk coefficient of the IoT card can be weighted and calculated to obtain the total risk coefficient. The formula for calculating the total risk coefficient is as follows: R=φ·R X +λ·R a (3); Among them, R X R represents the overall risk coefficient of the base station. a φ is the risk coefficient of the IoT card, φ is the weight coefficient corresponding to the overall risk coefficient of the base station, and λ is the weight coefficient corresponding to the risk coefficient of the IoT card.

[0044] In some embodiments, the weighting coefficients φ and λ can both be set to 1. It should be noted that the weighting coefficients can be dynamically adjusted according to actual needs.

[0045] Step 104: Based on the total risk coefficient, determine whether the IoT card's current internet access behavior is legal, so as to determine whether to restrict the IoT card's network access.

[0046] In this embodiment of the invention, after calculating the total risk coefficient, it can be determined whether the total risk coefficient exceeds the risk coefficient threshold.

[0047] In some embodiments, the risk coefficient threshold can be set to 1. If the total risk coefficient R < 1, it indicates that the IoT card is actually located at the boundary of the area but is connected to an unauthorized base station in an adjacent area, and the IoT card's current internet access behavior can be marked as legal; if the total risk coefficient R ≥ 1, the IoT card's current internet access behavior can be marked as illegal.

[0048] In some embodiments, taking the available area of ​​the IoT card as area A and normal business use as an example: When the IoT card's uplink base station belongs to area A, the current internet access behavior can be recorded as legal and network access can be allowed; when the IoT card's uplink base station belongs to area B, it is necessary to determine whether the base station risk and IoT card risk are within a controllable risk range (i.e., whether the total risk coefficient exceeds the risk coefficient threshold). If the determination is "yes", the current behavior is recorded as legal and network access is allowed; if the determination is "no", the current behavior is recorded as illegal and network access is denied.

[0049] In this embodiment of the invention, if it is determined that the current Internet access behavior of the IoT card is legal, network access can be restricted; if it is determined that the current Internet access behavior of the IoT card is illegal, network access can be allowed.

[0050] This invention, in the case of an IoT SIM card connecting to an adjacent unauthorized base station at the boundary of an authorized area, calculates the overall base station risk coefficient of the uplink base station based on the base station's abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weighting coefficients. It also calculates the IoT SIM card risk coefficient based on the IoT SIM card's risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, potential service risk coefficient, and corresponding weighting coefficients. Finally, it calculates the total risk coefficient based on the overall base station risk coefficient and the IoT SIM card risk coefficient. This achieves a quantitative assessment of IoT SIM card usage risk, enabling more accurate risk control. This invention determines the legality of an IoT SIM card's internet access behavior at the area boundary by calculating the total risk coefficient, rather than solely relying on base station affiliation. This limits the use of IoT SIM cards at the area boundary to a risky range, solving the problem of IoT SIM cards being unable to use when connecting to adjacent areas at the boundary of an authorized area, thus improving the user experience.

[0051] In an optional embodiment, the proportion of abnormal calls at the base station can be calculated through the following steps S11-S12: Step S11: Based on the abnormal call determination criteria, determine whether multiple calls are abnormal calls; the abnormal call determination criteria include at least one of the following: call duration determination criteria, call frequency determination criteria, caller ID credibility determination criteria, and user abnormal call behavior determination criteria.

[0052] In some embodiments, a call can be determined to be an abnormal call if any three of the following conditions are met: call duration determination condition, call frequency determination condition, caller ID credibility determination condition, and user abnormal call behavior determination condition.

[0053] In some embodiments, the value of the binary variable SCF can be used to characterize whether each phone call is an abnormal phone call. If any three conditions in the abnormal phone call determination criteria are met, the SCF value is 1, and the phone call is determined to be an abnormal phone call; if any three conditions in the abnormal phone call determination criteria are not met, the SCF value is 0, and the phone call is determined to be a normal phone call.

[0054] In this embodiment of the invention, the call duration determination condition may refer to a call duration that is not within a preset call duration range. In some embodiments, the preset call duration range may be set to a call duration of less than 10 seconds or more than 5 minutes. It should be noted that the preset call duration range can be set according to actual business needs, and this invention does not impose any limitations on it.

[0055] In this embodiment of the invention, the call frequency determination condition may refer to the number of calls made by the calling number within a preset time exceeding a preset number of calls. In some embodiments, the preset time may be set to 1 hour, and the preset number of calls may be set to 10. It should be noted that the values ​​of the preset time and the preset number of calls can be set according to actual business needs, and this invention does not impose any restrictions on them.

[0056] In this embodiment of the invention, the criteria for determining the credibility of the calling number may be that the calling number is similar to a known abnormal number or has been marked as an abnormal number.

[0057] In this embodiment of the invention, the condition for determining abnormal user call behavior can be that the average number of voice call minutes in the most recent preset time period exceeds a preset multiple of the average call minutes in the historical preset time period. In some embodiments, the most recent preset time period can be set to 7 days, the historical preset time period can be set to the previous 3 months, and the preset multiple can be set to 2 times. It should be noted that the values ​​of the most recent preset time period, the historical preset time period, and the preset multiple can be set according to actual business needs, and this invention does not impose any restrictions on them.

[0058] Step S12: Calculate the proportion of abnormal calls to the base station based on the number of abnormal calls and the total number of calls.

[0059] In this embodiment of the invention, the ratio of the number of abnormal calls to the total number of calls can be calculated to obtain the abnormal call ratio of the base station. The formula for calculating the abnormal call ratio of the base station can be as follows: (4); Where N is the number of phone calls; SCF is a binary variable, taking the value 1 or 0; SCF i Let SCF be the value of the i-th phone call, and R1 be the proportion of abnormal calls at the base station.

[0060] This invention, through its embodiment, determines whether multiple calls are abnormal based on abnormal call criteria, and calculates the proportion of abnormal calls at a base station based on the number of abnormal calls and the total number of calls. This can be further used for quantitative assessment of the overall risk of IoT card uplink base stations, which is beneficial for better control of IoT card usage risks.

[0061] In an optional embodiment, the historical anomaly risk coefficient can be calculated through the following steps S21-S22: Step S21: Determine the number of abnormal behaviors within the coverage area of ​​the uplink base station of the IoT card within a historical time range; Step S22: Calculate the historical anomaly risk coefficient based on the number of abnormal behaviors and the preset quantity threshold.

[0062] In some embodiments, the abnormal behavior can be classified according to the relationship between the number of abnormal behaviors and a preset threshold. Based on the number of abnormal behaviors reported or confirmed by users within the coverage area of ​​the IoT card uplink base station within a historical time range and the preset threshold, a historical abnormal risk coefficient is calculated. The historical time range can be set to 1 year as needed.

[0063] The formula for calculating the historical anomaly risk coefficient can be as follows: (5); Where Y1 is the number of abnormal behaviors, K1 is the preset threshold, and R2 is the historical abnormal risk coefficient.

[0064] This invention calculates a historical anomaly risk coefficient based on the number of abnormal behaviors within the coverage area of ​​an IoT card uplink base station and a preset threshold within a historical time frame. This coefficient can be further used to quantitatively assess the overall risk of the IoT card uplink base station, which is beneficial for better controlling the risks associated with using IoT cards.

[0065] In an optional embodiment, the geographical location risk factor can be determined by the following steps S31-S32: Step S31: Obtain at least one set of risk areas; wherein, each set of risk areas corresponds to a different risk coefficient; Step S32: If the region to which the uplink base station of the IoT card belongs is a risk region in any risk region set, then the risk coefficient corresponding to the risk region risk set is determined as the geographical location risk coefficient.

[0066] In some embodiments, taking two risk area sets—an abnormal behavior area set and a sensitive roaming area set—as examples, the geographical location risk coefficient can be determined as follows: (6); Where L represents the area to which the base station belongs, determined based on the uplink base station location of the IoT card; Area1 is the set of abnormal behavior areas; Area2 is the set of sensitive roaming areas; and K... a K represents the risk coefficient corresponding to Area1, the set of abnormal behavior regions. b Let Area2 be the set of sensitive roaming areas, and 1 ≥ K. b >K a >0, R3 is the geographical location risk coefficient.

[0067] This invention determines whether the region to which the uplink base station of the IoT card belongs is a risk region in any risk region set. If so, the risk coefficient corresponding to any risk region risk set is determined as the geographical location risk coefficient. This can be further used to quantitatively assess the overall base station risk of the IoT card uplink base station, which is beneficial for better controlling the risk of IoT card usage.

[0068] In an optional embodiment, the abnormal use risk factor can be determined by the following steps S41-S42: Step S41: Calculate the standard deviation of daily usage based on the average daily usage of the IoT card in the first preset period and the daily usage of each day in the first preset period. Step S42: Determine the range of abnormal usage risk assessment based on the average daily usage and the standard deviation of the daily usage; Step S43: Determine whether the daily usage of the IoT card on the access day is within the range of abnormal usage risk assessment, so as to determine the abnormal usage risk coefficient.

[0069] In some embodiments, the standard deviation of daily consumption can be calculated using the following formula: (7); Where T is the number of days in the first preset cycle, U T U represents the average daily usage of IoT cards over days T. t Let θ1 be the daily consumption on day t within day T, and let θ1 be the standard deviation of daily consumption.

[0070] In some embodiments, the risk factor for abnormal use can be determined in the following ways: (8); Among them, U X Let θ1 be the daily usage of IoT cards connected to base station X on the access day, and U be the standard deviation of daily usage. T R4 represents the average daily usage of IoT cards over T days, and R4 is the abnormal usage risk coefficient.

[0071] This invention utilizes the standard deviation of daily usage and the average daily usage to construct a range for judging abnormal usage risks. By determining which range of abnormal usage risks the daily usage of the IoT card falls within on the access day, the abnormal usage risk coefficient is determined. This can be further used for the quantitative assessment of the risk coefficient of the IoT card, which is beneficial for better control of the IoT card usage risks.

[0072] In an optional embodiment, the abnormal movement risk coefficient can be calculated through the following steps S51-S53: Step S51: Calculate the standard deviation of displacement distance based on the average displacement distance of the IoT card in the second preset period and the daily displacement distance in the second preset period. Step S52: Determine the range of abnormal movement risk based on the average displacement distance and the standard deviation of the displacement distance; Step S53: Determine whether the movement distance of the IoT card on the access day is within the range of abnormal movement risk assessment, so as to determine the abnormal movement risk coefficient.

[0073] In some embodiments, the standard deviation of displacement distance can be calculated using the following formula: (9); Where T is the number of days in the second preset cycle, and D T Let D be the average displacement distance of the IoT card within T days. t Let θt be the displacement distance on day t within T days, and θ2 be the standard deviation of the displacement distance.

[0074] In some embodiments, the abnormal movement risk factor can be determined in the following ways: (10); Among them, D X Let θ1 be the displacement distance of the IoT card connected to base station X on the access day, and D be the standard deviation of the displacement distance. T R5 represents the average displacement distance of the IoT card within T days, and R5 is the abnormal movement risk coefficient.

[0075] This invention utilizes the standard deviation of displacement distance and the average displacement distance to construct an abnormal movement risk judgment range. By determining which abnormal movement risk judgment range the displacement distance of the IoT card falls within on the access date, the abnormal movement risk coefficient is determined. This can be further used for the quantitative assessment of the risk coefficient of the IoT card, which is beneficial for better control of the risk of IoT card use.

[0076] In an optional embodiment, the potential business risk coefficient can be calculated as follows: based on the number of risky IoT card services and the total number of IoT card services, the potential business risk coefficient is calculated.

[0077] In some embodiments, risky services may include targeted voice calls, targeted high-volume data transfers, and unlinked SIM cards. The more risky services an IoT SIM card has, the higher the risk coefficient. The potential service risk coefficient can be calculated using the following formula: (11); Where Y represents the number of risky IoT card transactions, N represents the total number of IoT card transactions, and R6 represents the potential business risk coefficient.

[0078] The embodiments of the present invention calculate the potential business risk coefficient based on the number of risky services of IoT cards and the total number of IoT card services. This can be further used for the quantitative assessment of IoT card risk coefficients, which is beneficial for better control of IoT card usage risks.

[0079] Figure 2 This is a schematic diagram illustrating the process of determining whether an IoT card is connected to a base station in an adjacent area at the area boundary, as provided in an embodiment of the present invention. (Refer to...) Figure 2 In an optional embodiment, before calculating the overall base station risk coefficient of the uplink base station of the IoT card based on the base station abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weighting coefficient, the following steps may be included: Step 201: If the area to which the uplink base station of the IoT card belongs is not an available area of ​​the IoT card, filter out the base station group near the uplink base station based on the latitude and longitude of the uplink base station. Step 202: Calculate the distance between the base stations in the base station group that belong to the available area and the uplink base station of the IoT card, and determine the candidate base station that is closest to the uplink base station of the IoT card; Step 203: Based on the latitude and longitude of the candidate base stations, determine the target base station belonging to the region that is closest to the candidate base station; Step 204: If the uplink base station of the IoT card and the target base station are the same base station, then it is determined that the IoT card is connected to the base station of the adjacent unauthorized area at the boundary of the authorized area.

[0080] In this embodiment of the invention, before calculating the overall risk coefficient of the uplink base station of the IoT card and the risk coefficient of the IoT card, and thus calculating the total risk coefficient, it can be determined whether the IoT card is connected to an adjacent unauthorized area base station at the area boundary.

[0081] In some embodiments, the available area of ​​an IoT card can be obtained through manual settings or the initial call detail record (CDR) of the IoT card. The following example uses area A as the available area of ​​the IoT card and area B as the area to which the IoT card's uplink base station belongs.

[0082] First, it can be determined whether the area to which the uplink base station NodeB.X of the IoT SIM card network belongs is area A. If the area to which the uplink base station NodeB.X belongs is area A, then the IoT SIM card's current action is marked as legal; if the area to which the uplink base station NodeB.X belongs is area B, then a boundary roaming determination is performed to determine whether the uplink base station NodeB.X is the base station closest to area A among the base stations near area B.

[0083] 4G base station coverage distance is typically 1-10 kilometers, with one degree of latitude and longitude corresponding to an actual distance of approximately 100 kilometers. This embodiment of the invention can be based on the latitude and longitude of the uplink base station as NodeB.X (lat... x , lng x ), through (lat x ±0.5, lng x Within a latitude and longitude range of ±0.5, the NodeB clusters near the uplink base station [1, 2, 3, ...] are selected.

[0084] The Haversine formula can be used to calculate the distance d between the base station in area A and Node B.X in a base station cluster. x The candidate base station NodeB.Y closest to the uplink base station NodeB.X is obtained by sorting by distance. The distance calculation formula is as follows: (12); Based on the latitude and longitude information of the candidate base station NodeB.Y, the method described above, which sorts and filters the closest base stations by distance, can be used to obtain the target base station NodeB.Z in the B region that is closest to the candidate base station NodeB.Y, and it can be determined whether NodeB.X and NodeB.Z are the same base station.

[0085] Figure 3 This is a schematic diagram of a non-regional boundary usage scenario provided by an embodiment of the present invention. (Refer to...) Figure 3 If it is determined that NodeB.X and NodeB.Z are not the same base station, it indicates that it belongs to the boundary usage scenario. At this time, the IoT card (the IoT card is set in the terminal device) is connected to the base station of the authorized area of ​​the current actual location at the area boundary.

[0086] Figure 4 This is a schematic diagram illustrating a usage scenario of a region boundary provided in an embodiment of the present invention. (Refer to...) Figure 4 If NodeB.X and NodeB.Z are determined to be the same base station, it indicates that they belong to the boundary usage scenario. In this case, the actual location of the IoT card (which is set in the terminal device) belongs to the authorized area, but it is connected to the base station in the adjacent unauthorized area at the boundary of the area.

[0087] This invention proposes the concepts of base station risk factors and IoT SIM card risk factors. By dynamically calculating the risk values ​​of the base station and IoT SIM card based on their behavior, the total risk value is used to determine whether the IoT SIM card can be used. This can solve practical usage problems and further control risks. Furthermore, when the IoT SIM card is not used in a legal area, the nearest legal area base station is found based on the IoT SIM card's uplink base station, and the relationship and distance between the two base stations determine whether the IoT SIM card is used in a boundary area.

[0088] This invention uses the uplink base station information from the IoT terminal to determine whether the IoT card is being used at the regional boundary. It also defines the security risk level between the IoT card and the base station, limiting the use of the IoT card to a risk range. This solves the problem of the IoT card becoming unusable when connected to a base station in an adjacent area at the regional boundary, improving the user experience while taking into account the risks of using the IoT card.

[0089] The area boundary usage restriction device for IoT cards provided by the present invention is described below. The area boundary usage restriction device for IoT cards described below can be referred to in correspondence with the area boundary usage restriction method for IoT cards described above.

[0090] Figure 5 This is one of the structural schematic diagrams of the area boundary usage restriction device for an IoT card provided in an embodiment of the present invention. (Refer to...) Figure 5 This invention provides a regional boundary usage restriction device for IoT cards, which may specifically include the following units: The base station overall risk coefficient calculation unit 510 is used to calculate the base station overall risk coefficient of the uplink base station of the IoT card when the IoT card connects to the adjacent unauthorized area base station at the boundary of the authorized area. This is based on the base station abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient and corresponding weight coefficient. The IoT card risk coefficient calculation unit 520 is used to calculate the IoT card risk coefficient based on the IoT card risk behavior coefficient, abnormal use risk coefficient, abnormal movement risk coefficient, potential business risk coefficient and corresponding weight coefficient. The total risk coefficient calculation unit 530 is used to calculate the total risk coefficient based on the overall risk coefficient of the base station and the risk coefficient of the IoT card; The communication function control unit 540 is used to determine whether the Internet of Things card's current Internet access behavior is legal based on the total risk coefficient, so as to determine whether to restrict the Internet of Things card's network access.

[0091] Figure 6 This is the second structural schematic diagram of the area boundary usage restriction device for the Internet of Things card provided in this embodiment of the invention. (Refer to...) Figure 6 In one optional embodiment, the area boundary usage restriction device for the IoT card may include the following units: The base station information storage unit 610 is used to establish a base station information table and store base station information including cell ID, latitude and longitude, and province of origin. Risk level setting unit 620 is used to set the risk level of the provinces and regions where the IoT card can be used; The legal area setting unit 630 is used to set and record the legal provinces and regions where the IoT card can be used. The area determination unit 640 is used to determine whether the current area of ​​use is a legal area of ​​use based on the uplink information of the IoT card, thereby marking the Internet access behavior; Internet access behavior recording unit 650 is used to record the Internet access behavior of IoT cards and provide auxiliary information for area determination; The communication function control unit 660 is used to allow or deny Internet access behavior of the Internet card based on the determination result of the Internet card behavior.

[0092] This invention determines the legality of an IoT card's internet access behavior at the regional boundary by calculating the total risk coefficient, in order to determine whether to restrict the IoT card's network access, rather than simply judging the legality of the IoT card's internet access behavior based on the base station affiliation. This limits the use of IoT cards at the regional boundary to within the risk range, solves the problem of being unable to use the network when connected to adjacent areas, and improves the user experience.

[0093] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, communications interface 720, and memory 730 communicate with each other through the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a method for restricting the use of an IoT card at its regional boundaries. This method includes: when the IoT card connects to an adjacent unauthorized base station at the boundary of an authorized area, calculating the overall base station risk coefficient of the uplink base station of the IoT card based on the base station's abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weighting coefficients; calculating the IoT card risk coefficient based on the IoT card's risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, potential service risk coefficient, and corresponding weighting coefficients; calculating the total risk coefficient based on the overall base station risk coefficient and the IoT card risk coefficient; and determining whether the IoT card's current internet access behavior is legal based on the total risk coefficient, thereby determining whether to restrict the IoT card's network access.

[0094] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the IoT card area boundary usage restriction method provided by the above methods. The method includes: when the IoT card connects to an adjacent unauthorized area base station at the authorized area boundary, calculating the overall base station risk coefficient of the uplink base station of the IoT card based on the base station abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weight coefficient; calculating the IoT card risk coefficient based on the IoT card risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, potential business risk coefficient, and corresponding weight coefficient; calculating the total risk coefficient based on the base station overall risk coefficient and the IoT card risk coefficient; and determining whether the IoT card's current internet access behavior is legal based on the total risk coefficient, so as to determine whether to restrict the IoT card's network access.

[0096] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the method for restricting the regional boundary use of an IoT card provided by the above methods. The method includes: when the IoT card connects to an adjacent unauthorized area base station at the boundary of an authorized area, calculating the overall base station risk coefficient of the uplink base station of the IoT card based on the base station's abnormal call ratio, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weight coefficients; calculating the IoT card risk coefficient based on the IoT card's risk behavior coefficient, abnormal use risk coefficient, abnormal movement risk coefficient, potential service risk coefficient, and corresponding weight coefficients; calculating the total risk coefficient based on the base station's overall risk coefficient and the IoT card's risk coefficient; and determining whether the IoT card's current internet access behavior is legal based on the total risk coefficient, thereby determining whether to restrict the IoT card's network access.

[0097] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for restricting the use of IoT cards at regional boundaries, characterized in that, include: When an IoT SIM card connects to an adjacent unauthorized base station at the boundary of an authorized area, the overall base station risk coefficient of the uplink base station of the IoT SIM card is calculated based on the proportion of abnormal calls to the base station, the historical abnormal risk coefficient, the geographical location risk coefficient, and the corresponding weight coefficient. The IoT card risk coefficient is calculated based on the IoT card risk behavior coefficient, abnormal usage risk coefficient, abnormal movement risk coefficient, potential business risk coefficient, and corresponding weight coefficients. Calculate the total risk coefficient based on the overall risk coefficient of the base station and the risk coefficient of the IoT card; Based on the total risk coefficient, it is determined whether the IoT card's current internet access behavior is legal, in order to determine whether to restrict the IoT card's network access.

2. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, The proportion of abnormal calls at the base station was calculated in the following way: Based on the abnormal call determination criteria, it is determined whether multiple calls are abnormal calls; the abnormal call determination criteria include at least one of the following: call duration determination criteria, call frequency determination criteria, caller ID credibility determination criteria, and user abnormal call behavior determination criteria. The proportion of abnormal calls to a base station is calculated based on the number of abnormal calls and the total number of calls.

3. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, The historical anomaly risk coefficient is calculated in the following way: Determine the number of abnormal behaviors within the coverage area of ​​the uplink base station of the IoT card within a historical time frame; Based on the number of abnormal behaviors and the preset number threshold, the historical abnormal risk coefficient is calculated.

4. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, The geographical location risk factor is determined in the following way: Obtain at least one set of risk regions; wherein, each set of risk regions corresponds to a different risk coefficient; If the region to which the uplink base station of the IoT card belongs is a risk region in any set of risk regions, then the risk coefficient corresponding to the risk set of any risk region is determined as the geographical location risk coefficient.

5. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, The abnormal use risk factor is determined in the following way: The standard deviation of daily usage is calculated based on the average daily usage of IoT cards in the first preset period and the daily usage of each day in the first preset period. Based on the average daily usage and the standard deviation of daily usage, the scope of abnormal usage risk assessment is determined; Determine whether the daily usage of the IoT card on the access day is within the range of abnormal usage risk assessment to determine the abnormal usage risk coefficient.

6. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, The abnormal movement risk coefficient is calculated in the following way: The standard deviation of displacement distance is calculated based on the average displacement distance of the IoT card in the second preset period and the daily displacement distance in the second preset period. Based on the average displacement distance and the standard deviation of the displacement distance, the range of abnormal movement risk is determined; To determine whether the distance the IoT card travels on the access day is within the range of abnormal movement risk assessment, an abnormal movement risk coefficient is determined.

7. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, The potential business risk coefficient is calculated in the following way: The potential business risk coefficient is calculated based on the number of risky IoT card transactions and the total number of IoT card transactions.

8. The method for restricting the use of IoT cards at regional boundaries according to claim 1, characterized in that, Before calculating the overall base station risk coefficient of the uplink base station of the IoT card based on the proportion of abnormal calls at the base station, historical abnormal risk coefficient, geographical location risk coefficient, and corresponding weighting coefficients, the following steps are also included: If the area to which the uplink base station of the IoT card belongs is not an available area of ​​the IoT card, a group of base stations near the uplink base station is selected based on the latitude and longitude of the uplink base station. Calculate the distance between the base stations in the base station group that belong to the available area and the uplink base station of the IoT card, and determine the candidate base station that is closest to the uplink base station of the IoT card; Based on the latitude and longitude of the candidate base stations, determine the target base station belonging to the region that is closest to the candidate base station; If the uplink base station of the IoT card and the target base station are the same base station, then it is determined that the IoT card is connected to the base station of the adjacent unauthorized area at the boundary of the authorized area.

9. A regional boundary usage restriction device for an Internet of Things (IoT) card, characterized in that, include: The base station overall risk coefficient calculation unit is used to calculate the base station overall risk coefficient of the uplink base station of the IoT card when the IoT card connects to the adjacent unauthorized area base station at the boundary of the authorized area. This is based on the proportion of abnormal calls at the base station, the historical abnormal risk coefficient, the geographical location risk coefficient, and the corresponding weight coefficient. The IoT card risk coefficient calculation unit is used to calculate the IoT card risk coefficient based on the IoT card risk behavior coefficient, abnormal use risk coefficient, abnormal movement risk coefficient, potential business risk coefficient and corresponding weight coefficient. The total risk coefficient calculation unit is used to calculate the total risk coefficient based on the overall risk coefficient of the base station and the risk coefficient of the IoT card; The communication function control unit is used to determine whether the Internet of Things (IoT) card's current Internet access behavior is legal based on the total risk coefficient, so as to determine whether to restrict the IoT card's network access.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the method for restricting the use of the Internet of Things card at regional boundaries as described in any one of claims 1 to 8.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for restricting the use of IoT cards at regional boundaries as described in any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for restricting the use of IoT cards at regional boundaries as described in any one of claims 1 to 8.

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