Flow sharing method and system of artificial intelligence Internet of Things card

Abstract

The invention relates to the field of Internet of Things cards, in particular to an artificial intelligence Internet of Things card traffic sharing method and system, and the method comprises the steps: receiving feature parameters, collected by Internet of Things cards, at the current moment, and calculating traffic consumption parameters of each Internet of Things card at the current moment according to the feature parameters at the current moment; predicting the traffic consumption parameter of each Internet of Things card at the next moment according to the traffic consumption parameters of all the Internet of Things cards at the current moment and the traffic consumption parameters of all the Internet of Things cards at other predetermined moments; according to the total traffic and the residual traffic in the shared traffic pool, the total duration and the residual duration of the charging period, the traffic consumption parameter of each Internet of Things card at the next moment and the traffic consumption parameters of other moments, performing traffic distribution for each Internet of Things card; and limiting the flow consumption of each Internet of Things card at the next moment according to the flow distributed for each Internet of Things card. The total flow of the shared flow pool can be prevented from being exhausted in advance, and network congestion is avoided.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things cards, and in particular to a traffic sharing method and system for an artificial intelligence Internet of Things card. Background Art

[0002] With the popularization and development of IoT applications, more and more devices need to be connected to the Internet, such as equipment in the fields of smart agriculture and smart medical care. In order for these devices to be connected to the network smoothly, a large number of IoT cards are needed, and different devices require different traffic in a computing cycle. Therefore, in order to reasonably utilize the traffic of all / part of the IoT cards belonging to the same user, all / part of the IoT cards belonging to the same user will currently form a shared traffic pool on the operator side. All IoT cards in the shared traffic pool can share the total traffic in the shared traffic pool, as long as the total traffic of these IoT cards does not exceed the size of the shared traffic pool, thereby realizing traffic sharing of IoT cards.

[0003] However, if all / part of the IoT cards in the shared traffic pool have used a lot of traffic at the beginning or in the middle of the billing cycle, or all / part of the IoT cards in the shared traffic pool have abnormal traffic, the remaining traffic in the shared traffic pool will be small, which is insufficient to maintain the traffic usage of these IoT cards for the remainder of the billing cycle, resulting in unnecessary fees or service interruptions due to excessive traffic, and even monitoring abnormalities in the user's IoT device.

[0004] In addition, when all / part of the IoT cards belonging to the same user share traffic at the same time, it may cause network congestion, affecting the connection stability and data transmission speed of each IoT card. For example: in a smart factory, a large number of IoT devices work at the same time. If there is a problem with the traffic sharing of their IoT cards, it may cause the production process to be interrupted, thereby affecting the normal operation of the smart factory and causing serious consequences for the smart factory.

[0005] Therefore, how to avoid exhausting the total traffic of the shared traffic pool in advance and avoid network congestion is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0006] The present application provides a traffic sharing method and system for an artificial intelligence Internet of Things card, so as to share the traffic of multiple Internet of Things cards belonging to the same user, and adjust the available traffic of each Internet of Things card in real time to avoid exhausting the total traffic of the shared traffic pool in advance and avoid network congestion.

[0007] In order to solve the above technical problems, this application provides the following technical solutions:

[0008] A traffic sharing method for an artificial intelligence Internet of Things card is applied to a data analysis platform, comprising the following steps: step S110, receiving characteristic parameters at the current moment collected by all Internet of Things cards belonging to the same shared traffic pool within a billing cycle, and calculating the traffic consumption parameters of each Internet of Things card at the current moment according to the characteristic parameters at the current moment; step S120, predicting the traffic consumption parameters of each Internet of Things card at the next moment according to the traffic consumption parameters of all Internet of Things cards at the current moment and the traffic consumption parameters of all Internet of Things cards at other predetermined moments within the billing cycle; step S130, allocating traffic to each Internet of Things card according to the total traffic in the shared traffic pool, the remaining traffic in the shared traffic pool, the total duration of the billing cycle, the remaining duration of the billing cycle, the predicted traffic consumption parameters of each Internet of Things card at the next moment, and the traffic consumption parameters at other moments; step S140, limiting the traffic consumption of each Internet of Things card at the next moment according to the traffic allocated to each Internet of Things card.

[0009] In the traffic sharing method of the artificial intelligence Internet of Things card as described above, preferably, the characteristic parameters include: data transmission frequency, data transmission volume, network protocol, network status and abnormal conditions of the device where the Internet of Things card is located.

[0010] In the traffic sharing method of the artificial intelligence Internet of Things card as described above, preferably, the data transmission frequency includes: the data collection frequency of the device where the Internet of Things card is located and the interaction frequency between the device where the Internet of Things card is located and the data analysis platform; the data transmission volume includes: the size of the transmitted data and the compression of the data; the network protocol includes: the protocol type and the amount of protocol header information; the network status includes: the network signal strength and the network delay; the abnormal conditions of the device where the Internet of Things card is located include: equipment failure and equipment network attack.

[0011] The traffic sharing method of the artificial intelligence Internet of Things card as described above, wherein preferably, at the operator end, the following steps are completed: step S210, creating a unique virtual card number for the user; step S220, establishing a mapping relationship between multiple Internet of Things cards under the user and the virtual card number; step S230, creating a shared traffic pool for the virtual number, and allowing the Internet of Things cards that have a mapping relationship with the virtual card number to share the total traffic in the shared traffic pool.

[0012] In the traffic sharing method of the artificial intelligence Internet of Things card as described above, it is preferred to determine whether the traffic allocated to the Internet of Things card at the next moment reaches the traffic warning threshold of the Internet of Things card; if the traffic allocated to the Internet of Things card at the next moment is greater than the traffic warning threshold of the Internet of Things card, the traffic consumption of the Internet of Things card at the next moment is limited according to the traffic warning threshold of the Internet of Things card; if the traffic allocated to the Internet of Things card at the next moment is not greater than the traffic warning threshold of the Internet of Things card, the traffic consumption of the Internet of Things card at the next moment is limited according to the traffic allocated to the Internet of Things card at the next moment.

[0013] A traffic sharing system for an artificial intelligence Internet of Things card comprises: a data analysis platform, a device and an Internet of Things card installed on the device; wherein the data analysis platform comprises: a data receiving module, a parameter calculation module, a parameter prediction module, a traffic allocation module and a traffic limiting module; within a billing cycle, the data receiving module receives characteristic parameters collected by all Internet of Things cards belonging to the same shared traffic pool at the current moment, and the parameter calculation module calculates the traffic consumption parameters of each Internet of Things card at the current moment according to the characteristic parameters at the current moment; the parameter prediction module predicts the traffic consumption parameters of each Internet of Things card at the next moment according to the traffic consumption parameters of all Internet of Things cards at the current moment and the traffic consumption parameters of all Internet of Things cards at other predetermined moments within the billing cycle; the traffic allocation module allocates traffic to each Internet of Things card according to the total traffic in the shared traffic pool, the remaining traffic in the shared traffic pool, the total duration of the billing cycle, the remaining duration of the billing cycle, the predicted traffic consumption parameters of each Internet of Things card at the next moment and the traffic consumption parameters at other moments; the traffic limiting module limits the traffic consumption of each Internet of Things card at the next moment according to the traffic allocated to each Internet of Things card.

[0014] In the traffic sharing system of the artificial intelligence Internet of Things card as described above, preferably, the characteristic parameters include: data transmission frequency, data transmission volume, network protocol, network status and abnormal conditions of the device where the Internet of Things card is located.

[0015] In the traffic sharing system of the artificial intelligence Internet of Things card as described above, preferably, the data transmission frequency includes: the data collection frequency of the device where the Internet of Things card is located and the interaction frequency between the device where the Internet of Things card is located and the data analysis platform; the data transmission volume includes: the size of the transmitted data and the compression of the data; the network protocol includes: the protocol type and the amount of protocol header information; the network status includes: the network signal strength and the network delay; the abnormal conditions of the device where the Internet of Things card is located include: equipment failure and equipment network attack.

[0016] The traffic sharing system of the artificial intelligence Internet of Things card as described above, wherein, preferably, also includes: an operator side, the operator side includes: a card number creation module, a relationship establishment module and a traffic pool creation module; the card number creation module creates a unique virtual card number for the user; the relationship establishment module establishes a mapping relationship between multiple Internet of Things cards under the user and the virtual card number; the traffic pool creation module creates a shared traffic pool for the virtual number, and enables the Internet of Things cards that have a mapping relationship with the virtual card number to share the total traffic in the shared traffic pool.

[0017] In the traffic sharing system of the artificial intelligence Internet of Things card as described above, preferably, the data analysis platform determines whether the traffic allocated to the Internet of Things card at the next moment reaches the traffic warning threshold of the Internet of Things card; if the traffic allocated to the Internet of Things card at the next moment is greater than the traffic warning threshold of the Internet of Things card, the traffic limiting module limits the traffic consumption of the Internet of Things card at the next moment according to the traffic warning threshold of the Internet of Things card; if the traffic allocated to the Internet of Things card at the next moment is not greater than the traffic warning threshold of the Internet of Things card, the traffic limiting module limits the traffic consumption of the Internet of Things card at the next moment according to the traffic allocated to the Internet of Things card.

[0018] Compared with the above background technology, the present application can share traffic for multiple IoT cards belonging to the same user, and adjust the available traffic of each IoT card in real time to avoid exhausting the total traffic of the shared traffic pool in advance, thereby avoiding unnecessary fees or service interruptions due to excess traffic, and further avoiding monitoring anomalies in user IoT devices, and can also avoid network congestion, thereby avoiding affecting the connection stability and data transmission speed of each IoT card. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 It is a flow chart of the traffic sharing method of the artificial intelligence Internet of Things card provided in the embodiment of the present application;

[0021] Figure 2 It is a schematic diagram of the traffic sharing system of the artificial intelligence Internet of Things card provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. In addition, spatial relationship terms such as "upper", "lower", "front", "back", "left" and "right" are used to facilitate the description to explain the positional relationship between two components. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0023] Embodiment 1

[0024] like Figure 1 As shown, the present application provides a traffic sharing method of an artificial intelligence Internet of Things card, which is applied to a data analysis platform and includes the following steps:

[0025] Step S110: within a billing cycle, receiving characteristic parameters at the current moment collected by all IoT cards belonging to the same shared traffic pool, and calculating the traffic consumption parameters of each IoT card at the current moment according to the characteristic parameters at the current moment;

[0026] During a billing cycle, all / part of the IoT cards belonging to the same user collect their current characteristic parameters (for example: data transmission frequency (data collection frequency of the device where the IoT card is located, interaction frequency between the device where the IoT card is located and the data analysis platform), data transmission volume (transmitted data size, data compression), network protocol (protocol type, amount of protocol header information), network status (network signal strength, network delay), abnormal conditions of the device where the IoT card is located (equipment failure, device network attack), location information of the device where the IoT card is located, etc.). These characteristic parameters are used to calculate the traffic consumption parameters of the corresponding IoT card at the current moment.

[0027] After the IoT card collects its characteristic parameters at the current moment, it uploads the characteristic parameters at the current moment to the data analysis platform, and the data analysis platform calculates the traffic consumption parameters of all IoT cards belonging to the same shared traffic pool at the current moment. The specific data analysis platform calculates the traffic consumption parameters of each IoT card at the current moment based on the characteristic parameters of each IoT card at the current moment, and analyzes the traffic consumption of these IoT cards on the data analysis platform.

[0028] Specifically, according to the following formula:

[0029]

[0030] Calculate the traffic consumption parameters of the nth IoT card at the current time t in, is the jth characteristic parameter among the i-th characteristic parameters collected by the n-th IoT card at the current time t; α ij is the weight of the jth feature parameter in the i-th feature parameter; β i is the total weight of the i-th type feature parameter; is the jth characteristic parameter of the fourth category of characteristic parameters collected by the nth IoT card at the current time t; α 4j is the weight of the jth feature parameter in the fourth category of feature parameters; β4 is the total weight of the fourth category of feature parameters; is the first adjustment value, which is a constant of 0.578; is the second adjustment value, which is a constant of 1.249; is the abnormal situation of the device where the nth IoT card is located at the current time t. The abnormal situation of the device where the IoT card is located includes: device failure and device network attack. If the device fails, then If the device network is attacked, This is only possible if the device is not faulty and the device network is not attacked.

[0031] In the event of a device failure or an attack on the device network, the abnormal condition of the device where the nth IoT card is located at the current time t is reflected =0, so the data analysis platform calculates the traffic consumption parameter of the nth IoT card at the current time t It is also equal to 0. When this happens, the data analysis platform sends an alarm message to the user.

[0032] When calculating the traffic consumption parameters, the data analysis platform does not need to use the location information of the device where the IoT card is located. When it is equal to 0, the data analysis platform will call the collected location information of the device where the corresponding IoT card is located, and send an alarm message indicating the location information to the user to remind the user to inspect and repair the corresponding IoT card and the device where the IoT card is located based on the location information.

[0033] When i=1 and j=1,2, is the data collection frequency of the device where the nth IoT card is located at the current time t, is the interaction frequency between the device where the nth IoT card is located and the data analysis platform at the current time t, α 11 is the weight of data collection frequency, α 12 is the weight of the interaction frequency, β1 is the total weight of the data transmission frequency; when i = 2 and j = 1, 2, is the data size transmitted by the nth IoT card at the current time t, is the data compression status of the nth IoT card at the current time t, α 21 is the weight of the transmitted data size, α 22 is the weight of the data compression, β2 is the total weight of the data transmission volume; when i = 3 and j = 1, 2, is the protocol type of the nth IoT card at the current time t, is the amount of protocol header information of the nth IoT card at the current time t, α 31 is the weight of the protocol type, α 32 is the weight of the protocol header information, β3 is the total weight of the network protocol; when i = 4 and j = 1, 2, is the network signal strength corresponding to the nth IoT card at the current time t, is the network delay corresponding to the nth IoT card at the current time t, α 41 is the weight of network signal strength, α 42 is the weight of network delay, and β4 is the total weight of network status.

[0034] Before this, in order to form these IoT cards into a shared traffic pool, the following steps need to be completed on the operator side:

[0035] Step S210: Create a unique virtual card number for the user;

[0036] Step S220: Establish a mapping relationship between multiple IoT cards of the user and the virtual card number;

[0037] Step S230: create a shared traffic pool for the virtual number, and enable the IoT cards that have a mapping relationship with the virtual card number to share the total traffic in the shared traffic pool;

[0038] A shared traffic pool is created on the operator side for the user's unique virtual card number. The shared traffic pool has a certain amount of total traffic within a billing cycle, and all IoT cards that are mapped to the virtual card number share the total traffic in the shared traffic pool created for the virtual card number, thereby achieving the sharing of the total traffic of the virtual card number among the user's various IoT cards.

[0039] After the construction of the shared traffic pool is completed on the operator side, the traffic sharing method is executed on the data analysis platform, that is, steps S110 to S140 are executed.

[0040] Step S120, predicting the traffic consumption parameters of each Internet of Things card at the next moment based on the traffic consumption parameters of all Internet of Things cards at the current moment and the traffic consumption parameters of all Internet of Things cards at other predetermined moments in the billing cycle;

[0041] After calculating the traffic consumption parameters of each IoT card at the current moment t, the data analysis platform can call the traffic consumption parameters of all IoT cards belonging to the same shared traffic pool at other scheduled moments during the billing period stored in the memory. The traffic consumption parameters at other scheduled moments are calculated according to step S110 at their corresponding scheduled moments and stored in the memory, thereby using the traffic consumption parameters of all IoT cards in the same shared traffic pool at the current moment and the traffic consumption parameters of all IoT cards belonging to the same shared traffic pool at other scheduled moments during the billing period to predict the traffic consumption parameters of each IoT card belonging to the shared traffic pool at the next moment.

[0042] Specifically, according to the following formula:

[0043]

[0044] Predict the traffic consumption parameters of the nth IoT card at the next moment t+1 Among them, a is the weight corresponding to the traffic consumption parameter at the current time t; is the traffic consumption parameter of the nth IoT card at the previous moment t-1; b is the weight corresponding to the traffic consumption parameter at the previous moment t-1; is the traffic consumption parameter of the nth IoT card at time tm; c is the weight corresponding to the traffic consumption parameter at time tm; m is between 2 and M, where M is a predetermined value, a positive integer greater than 2, and can be determined according to actual needs; τ is the adjustment coefficient, which is 1.362.

[0045] Step S130, allocating traffic to each IoT card according to the total traffic in the shared traffic pool, the remaining traffic in the shared traffic pool, the total duration of the billing cycle, the remaining duration of the billing cycle, the predicted traffic consumption parameters of each IoT card at the next moment, and the traffic consumption parameters at other moments;

[0046] At the beginning of the billing cycle, the data analysis platform already knows the total traffic L in the shared traffic pool. z And the total duration of the billing cycle H z , and the total flow L in the shared flow pool z And the total duration of the billing cycle H z In addition, the memory also stores the predicted traffic consumption parameters of each IoT card at the next moment, and the calculated traffic consumption parameters at other moments before are stored in the memory. In addition, after the billing cycle starts, the data analysis platform also monitors the remaining traffic L of the shared traffic pool in real time. s and the remaining duration of the billing cycle H s .

[0047] When allocating traffic to each IoT card, the data analysis platform calls the total traffic L in the shared traffic pool stored in the memory. z , the total duration of the billing cycle H z , the traffic consumption parameters of each IoT card at the next moment obtained by prediction and the traffic consumption parameters of other moments obtained by calculation, as well as the remaining traffic L of the shared traffic pool monitored in real time s and the remaining duration of the billing cycle H s , calculate the traffic allocated to each IoT card at the next moment.

[0048] Specifically, according to the following formula:

[0049]

[0050] Calculate the traffic allocated to the nth IoT card at the next time t+1 Where N is the number of IoT cards belonging to the same shared traffic pool; D is the weight of the traffic consumption parameter at the current time t on the traffic allocation; is the traffic consumption parameter of the nth IoT card at time tk; K is the number at other previous times, which can be reserved as needed; E is the influence weight of the traffic consumption parameters at other previous times on traffic allocation; δ is the total influence weight of the traffic consumption parameters on traffic allocation; γ is the influence weight of the proportion of the remaining duration of the billing cycle on traffic allocation; θ is the influence weight of the proportion of the remaining traffic in the shared traffic pool on traffic allocation.

[0051] Step S140: Based on the traffic allocated to each IoT card, limit the traffic consumption of each IoT card at the next moment.

[0052] After calculating the traffic allocated to each IoT card at the next moment, the data analysis platform determines whether the traffic allocated to the IoT card at the next moment reaches the traffic warning threshold of the IoT card. If the traffic allocated to the IoT card at the next moment is greater than the traffic warning threshold of the IoT card, the traffic consumption of the IoT card at the next moment is limited according to the traffic warning threshold of the IoT card. If the traffic allocated to the IoT card at the next moment is not greater than the traffic warning threshold of the IoT card, the traffic consumption of the IoT card at the next moment is limited according to the traffic allocated to the IoT card at the next moment.

[0053] For example: if it is determined that the traffic allocated to the IoT card at the next moment is greater than the traffic warning threshold preset for the IoT card, the data analysis platform promptly sends instructions to control the data transmission frequency of the IoT card (the data collection frequency of the device where the IoT card is located, the interaction frequency between the device where the IoT card is located and the data analysis platform) to decrease, or control the data transmission volume of the IoT card (the size of the transmitted data, the compression of the data) to decrease, or control the IoT card to stop working, or remind the IoT card in a preset manner, for example, automatically sending text messages, emails, etc. to promptly notify the target IoT card of the renewal purchase operation.

[0054] Embodiment 2

[0055] like Figure 2 As shown, the present application provides a traffic sharing system for an artificial intelligence Internet of Things card, including: a data analysis platform 210, a device 220 and an Internet of Things card 230 installed on the device 220; wherein, the data analysis platform 210 includes: a data receiving module 211, a parameter calculation module 212, a parameter prediction module 213, a traffic allocation module 214 and a traffic limitation module 215.

[0056] In a billing cycle, the data receiving module 211 receives the characteristic parameters of the current moment collected by all IoT cards 230 belonging to the same shared traffic pool, and the parameter calculation module 212 calculates the traffic consumption parameters of each IoT card 230 at the current moment based on the characteristic parameters at the current moment.

[0057] During a billing cycle, all / part of the Internet of Things cards 230 belonging to the same user collect their current characteristic parameters (for example: data transmission frequency (data collection frequency of the device 220 where the Internet of Things card 230 is located, the interaction frequency between the device 220 where the Internet of Things card 230 is located and the data analysis platform 210), data transmission volume (transmitted data size, data compression), network protocol (protocol type, amount of protocol header information), network status (network signal strength, network delay), abnormal conditions of the device 220 where the Internet of Things card 230 is located (equipment failure, equipment network attack), etc.), and these characteristic parameters are used to calculate the traffic consumption parameters of the corresponding Internet of Things card 230 at the current moment.

[0058] After the Internet of Things card 230 has collected its characteristic parameters at the current moment, the characteristic parameters at the current moment are uploaded to the data receiving module 211 of the data analysis platform 210, and the parameter calculation module 212 of the data analysis platform 210 calculates the traffic consumption parameters of all Internet of Things cards 230 belonging to the same shared traffic pool at the current moment. Specifically, the parameter calculation module 212 of the data analysis platform 210 calculates the traffic consumption parameters of each Internet of Things card 230 at the current moment based on the characteristic parameters of each Internet of Things card 230 at the current moment, so as to analyze the traffic consumption of these Internet of Things cards 230 on the data analysis platform 210.

[0059] Specifically, the parameter calculation module 212 is based on the following formula:

[0060]

[0061] Calculate the traffic consumption parameters of the nth IoT card at the current time t in, is the jth characteristic parameter among the i-th characteristic parameters collected by the n-th IoT card at the current time t; α ij is the weight of the jth feature parameter in the i-th feature parameter; β i is the total weight of the i-th type feature parameter; is the jth characteristic parameter of the fourth category of characteristic parameters collected by the nth IoT card at the current time t; α 4j is the weight of the jth feature parameter in the fourth category of feature parameters; β4 is the total weight of the fourth category of feature parameters; is the first adjustment value, which is a constant of 0.578; is the second adjustment value, which is a constant of 1.249; is the abnormal situation of the device where the nth IoT card is located at the current time t. The abnormal situation of the device where the IoT card is located includes: device failure and device network attack. If the device fails, then If the device network is attacked, This is only possible if the device is not faulty and the device network is not attacked.

[0062] In the event of a device failure or an attack on the device network, the abnormal condition of the device where the nth IoT card is located at the current time t is reflected =0, so the data analysis platform calculates the traffic consumption parameter of the nth IoT card at the current time t It is also equal to 0. When this happens, the data analysis platform sends an alarm message to the user.

[0063] When calculating the traffic consumption parameters, the data analysis platform does not need to use the location information of the device where the IoT card is located. When it is equal to 0, the data analysis platform will call the collected location information of the device where the corresponding IoT card is located, and send an alarm message indicating the location information to the user to remind the user to inspect and repair the corresponding IoT card and the device where the IoT card is located based on the location information.

[0064] When i=1 and j=1,2, is the data collection frequency of the device where the nth IoT card is located at the current time t, is the interaction frequency between the device where the nth IoT card is located and the data analysis platform at the current time t, α 11 is the weight of data collection frequency, α 12 is the weight of the interaction frequency, β1 is the total weight of the data transmission frequency; when i = 2 and j = 1, 2, is the data size transmitted by the nth IoT card at the current time t, is the data compression status of the nth IoT card at the current time t, α 21 is the weight of the transmitted data size, α 22 is the weight of the data compression, β2 is the total weight of the data transmission volume; when i = 3 and j = 1, 2, is the protocol type of the nth IoT card at the current time t, is the amount of protocol header information of the nth IoT card at the current time t, α 31 is the weight of the protocol type, α 32 is the weight of the protocol header information, β3 is the total weight of the network protocol; when i = 4 and j = 1, 2, is the network signal strength corresponding to the nth IoT card at the current time t, is the network delay corresponding to the nth IoT card at the current time t, α 41 is the weight of network signal strength, α 42 is the weight of network delay, and β4 is the total weight of network status.

[0065] Prior to this, in order to form these IoT cards 230 into a shared traffic pool, the following steps need to be completed on the operator side 300 , and the operator side 300 includes: a card number creation module 310 , a relationship establishment module 320 and a traffic pool creation module 330 .

[0066] The card number creation module 310 creates a unique virtual card number for the user; the relationship establishment module 320 establishes a mapping relationship between multiple IoT cards under the user and the virtual card number; the traffic pool creation module 330 creates a shared traffic pool for the virtual number, and enables the IoT cards that have a mapping relationship with the virtual card number to share the total traffic in the shared traffic pool.

[0067] A shared traffic pool is created on the operator side 300 for the user's unique virtual card number. The shared traffic pool has a certain amount of total traffic within a billing cycle, and all IoT cards that are mapped to the virtual card number share the total traffic in the shared traffic pool created for the virtual card number, thereby achieving sharing of the total traffic of the virtual card number among the user's various IoT cards.

[0068] After the operator side 300 completes the construction of the shared traffic pool, the traffic sharing method is executed on the data analysis platform 210.

[0069] The parameter prediction module 213 predicts the traffic consumption parameters of each IoT card 230 at the next moment based on the traffic consumption parameters of all IoT cards 230 at the current moment and the traffic consumption parameters of all IoT cards 230 at other predetermined moments in the billing cycle.

[0070] After calculating the traffic consumption parameters of each Internet of Things card 230 at the current moment t, the data analysis platform 210 can call the traffic consumption parameters of all Internet of Things cards 230 belonging to the same shared traffic pool at other scheduled moments during the billing period stored in the memory. The traffic consumption parameters at other scheduled moments are calculated according to the above method at their corresponding scheduled moments and stored in the memory, thereby using the traffic consumption parameters of all Internet of Things cards 230 in the same shared traffic pool at the current moment and the traffic consumption parameters of all Internet of Things cards 230 belonging to the same shared traffic pool at other scheduled moments during the billing period to predict the traffic consumption parameters of each Internet of Things card 230 belonging to the shared traffic pool at the next moment.

[0071] Specifically, the parameter prediction module 213 is based on the following formula:

[0072]

[0073] Predict the traffic consumption parameters of the nth IoT card at the next moment t+1 Among them, a is the weight corresponding to the traffic consumption parameter at the current time t; is the traffic consumption parameter of the nth IoT card at the previous moment t-1; b is the weight corresponding to the traffic consumption parameter at the previous moment t-1; is the traffic consumption parameter of the nth IoT card at time tm; c is the weight corresponding to the traffic consumption parameter at time tm; m is between 2 and M, where M is a predetermined value, a positive integer greater than 2, and can be determined according to actual needs; τ is the adjustment coefficient, which is 1.362.

[0074] The traffic allocation module 214 allocates traffic to each Internet of Things card 230 based on the total traffic in the shared traffic pool, the remaining traffic in the shared traffic pool, the total duration of the billing cycle, the remaining duration of the billing cycle, the predicted traffic consumption parameters of each Internet of Things card 230 at the next moment, and the traffic consumption parameters at other moments.

[0075] At the beginning of the billing cycle, the data analysis platform 210 already knows the total traffic L in the shared traffic pool. z And the total duration of the billing cycle H z , and the total flow L in the shared flow pool z And the total duration of the billing cycle H z In addition, the memory also stores the predicted traffic consumption parameters of each IoT card 230 at the next moment, and the calculated traffic consumption parameters at other moments before are stored in the memory. In addition, after the billing cycle starts, the data analysis platform 210 also monitors the remaining traffic L of the shared traffic pool in real time. s and the remaining duration of the billing cycle H s .

[0076] When allocating traffic to each IoT card 230, the data analysis platform 210 calls the total traffic L in the shared traffic pool stored in the memory. z , the total duration of the billing cycle H z , the traffic consumption parameters of each IoT card 230 at the next moment obtained by prediction and the traffic consumption parameters of other moments obtained by calculation, and the remaining traffic L of the shared traffic pool monitored in real time s and the remaining duration of the billing cycle H s , calculate and obtain the traffic allocated to each IoT card 230 at the next moment.

[0077] Specifically, the flow distribution module 214 is based on the following formula:

[0078]

[0079] Calculate the traffic allocated to the nth IoT card at the next time t+1 Where N is the number of IoT cards belonging to the same shared traffic pool; D is the weight of the traffic consumption parameter at the current time t on the traffic allocation; is the traffic consumption parameter of the nth IoT card at time tk; K is the number at other previous times, which can be reserved as needed; E is the influence weight of the traffic consumption parameters at other previous times on traffic allocation; δ is the total influence weight of the traffic consumption parameters on traffic allocation; γ is the influence weight of the proportion of the remaining duration of the billing cycle on traffic allocation; θ is the influence weight of the proportion of the remaining traffic in the shared traffic pool on traffic allocation.

[0080] The traffic limiting module 215 limits the traffic consumption of each Internet of Things card at the next moment according to the traffic allocated to each Internet of Things card.

[0081] After calculating the flow rate at the next moment allocated to each Internet of Things card 230, the data analysis platform 210 determines whether the flow rate at the next moment allocated to the Internet of Things card 230 reaches the flow warning threshold of the Internet of Things card 230. If the flow rate at the next moment allocated to the Internet of Things card 230 is greater than the flow warning threshold of the Internet of Things card 230, the flow limitation module 215 limits the flow consumption of the Internet of Things card 230 at the next moment according to the flow warning threshold of the Internet of Things card 230. If the flow rate at the next moment allocated to the Internet of Things card 230 is not greater than the flow warning threshold of the Internet of Things card 230, the flow limitation module 215 limits the flow consumption of the Internet of Things card 230 at the next moment according to the flow rate at the next moment allocated to the Internet of Things card 230.

[0082] For example: if it is determined that the traffic flow at the next moment allocated to the IoT card 230 is greater than the traffic warning threshold preset for the IoT card 230, the data analysis platform 210 promptly sends instructions to control the data transmission frequency of the IoT card 230 (the data collection frequency of the device 220 where the IoT card 230 is located, the interaction frequency between the device 220 where the IoT card 230 is located and the data analysis platform 210) to be reduced, or the data transmission volume of the IoT card 230 (the size of the transmitted data, the compression of the data) is controlled to be reduced, or the IoT card 230 is controlled to stop working, or the IoT card 230 is reminded in a preset manner, for example, automatically sending text messages, emails, etc. to promptly notify the target IoT card 230 of the renewal purchase operation.

[0083] Since the present application can share traffic among multiple IoT cards belonging to the same user and adjust the available traffic of each IoT card in real time, it can avoid exhausting the total traffic of the shared traffic pool in advance, thereby avoiding unnecessary fees or service interruptions due to excess traffic, and further avoiding monitoring anomalies in user IoT devices. It can also avoid network congestion, thereby avoiding affecting the connection stability and data transmission speed of each IoT card.

[0084] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0085] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A traffic sharing method for an artificial intelligence Internet of Things card, characterized in that: Applied to the data analysis platform, including the following steps: Step S110: within a billing cycle, receiving characteristic parameters at the current moment collected by all IoT cards belonging to the same shared traffic pool, and calculating the traffic consumption parameters of each IoT card at the current moment according to the characteristic parameters at the current moment; Step S120, predicting the traffic consumption parameters of each Internet of Things card at the next moment based on the traffic consumption parameters of all Internet of Things cards at the current moment and the traffic consumption parameters of all Internet of Things cards at other predetermined moments in the billing cycle; Step S130, allocating traffic to each IoT card according to the total traffic in the shared traffic pool, the remaining traffic in the shared traffic pool, the total duration of the billing cycle, the remaining duration of the billing cycle, the predicted traffic consumption parameters of each IoT card at the next moment, and the traffic consumption parameters at other moments; Step S140: Based on the traffic allocated to each IoT card, limit the traffic consumption of each IoT card at the next moment.

2. The traffic sharing method of the artificial intelligence Internet of Things card according to claim 1 is characterized in that: Characteristic parameters include: data transmission frequency, data transmission volume, network protocol, network status and abnormal conditions of the device where the IoT card is located.

3. The traffic sharing method of the artificial intelligence Internet of Things card according to claim 2 is characterized in that: The data transmission frequency includes: the data collection frequency of the device where the IoT card is located and the interaction frequency between the device where the IoT card is located and the data analysis platform; Data transmission volume includes: the size of the data transmitted and the compression of the data; Network protocols include: protocol type and protocol header information volume; Network conditions include: network signal strength and network delay; Abnormal conditions of the device where the IoT card is located include: device failure and device network attack.

4. The traffic sharing method of the artificial intelligence Internet of Things card according to any one of claims 1 to 3 is characterized in that: On the operator side, complete the following steps: Step S210: Create a unique virtual card number for the user; Step S220: Establish a mapping relationship between multiple IoT cards of the user and the virtual card number; Step S230: create a shared traffic pool for the virtual number, and enable the Internet of Things cards that have a mapping relationship with the virtual card number to share the total traffic in the shared traffic pool.

5. The traffic sharing method of the artificial intelligence Internet of Things card according to any one of claims 1 to 3 is characterized in that: Determine whether the traffic allocated to the IoT card at the next moment reaches the traffic warning threshold of the IoT card; If the traffic allocated to the IoT card at the next moment is greater than the traffic warning threshold of the IoT card, the traffic consumption of the IoT card at the next moment is limited according to the traffic warning threshold of the IoT card; If the traffic allocated to the IoT card at the next moment is not greater than the traffic warning threshold of the IoT card, the traffic consumption of the IoT card at the next moment is limited according to the traffic allocated to the IoT card at the next moment.

6. A traffic sharing system for an artificial intelligence Internet of Things card, characterized in that: include: A data analysis platform, a device, and an IoT card installed on the device; wherein the data analysis platform includes: a data receiving module, a parameter calculation module, a parameter prediction module, a flow distribution module, and a flow restriction module; In a billing cycle, the data receiving module receives the characteristic parameters of the current moment collected by all IoT cards belonging to the same shared traffic pool, and the parameter calculation module calculates the traffic consumption parameters of each IoT card at the current moment based on the characteristic parameters at the current moment; The parameter prediction module predicts the traffic consumption parameters of each IoT card at the next moment based on the traffic consumption parameters of all IoT cards at the current moment and the traffic consumption parameters of all IoT cards at other scheduled moments in the billing cycle; The traffic allocation module allocates traffic to each IoT card based on the total traffic in the shared traffic pool, the remaining traffic in the shared traffic pool, the total duration of the billing cycle, the remaining duration of the billing cycle, the predicted traffic consumption parameters of each IoT card at the next moment, and the traffic consumption parameters at other moments; The traffic limiting module limits the traffic consumption of each IoT card at the next moment according to the traffic allocated to each IoT card.

7. The traffic sharing system of the artificial intelligence Internet of Things card according to claim 6 is characterized in that: Characteristic parameters include: data transmission frequency, data transmission volume, network protocol, network status and abnormal conditions of the device where the IoT card is located.

8. The traffic sharing system of the artificial intelligence Internet of Things card according to claim 7 is characterized in that: The data transmission frequency includes: the data collection frequency of the device where the IoT card is located and the interaction frequency between the device where the IoT card is located and the data analysis platform; Data transmission volume includes: the size of the data transmitted and the compression of the data; Network protocols include: protocol type and protocol header information volume; Network conditions include: network signal strength and network delay; Abnormal conditions of the device where the IoT card is located include: device failure and device network attack.

9. The traffic sharing system of the artificial intelligence Internet of Things card according to any one of claims 6 to 8, characterized in that: Also includes: The operator side includes: card number creation module, relationship establishment module and traffic pool creation module; The card number creation module creates a unique virtual card number for the user; the relationship establishment module establishes a mapping relationship between multiple IoT cards under the user and the virtual card number; the traffic pool creation module creates a shared traffic pool for the virtual number, and enables the IoT cards that have a mapping relationship with the virtual card number to share the total traffic in the shared traffic pool.

10. The traffic sharing system of the artificial intelligence Internet of Things card according to any one of claims 6 to 8, characterized in that: The data analysis platform determines whether the traffic allocated to the IoT card at the next moment reaches the traffic warning threshold of the IoT card; If the flow rate allocated to the IoT card at the next moment is greater than the flow rate warning threshold of the IoT card, the flow limiting module limits the flow consumption of the IoT card at the next moment according to the flow rate warning threshold of the IoT card; If the traffic allocated to the IoT card at the next moment is not greater than the traffic warning threshold of the IoT card, the traffic limiting module limits the traffic consumption of the IoT card at the next moment according to the traffic allocated to the IoT card at the next moment.

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