Network switching method and device for embedded subscriber identity module card, equipment and medium
By acquiring the current location and historical traffic data of the embedded user identification card, candidate operators are identified and scores are calculated, enabling automated switching of operator networks during device cross-regional movement. This solves the problems of signal quality and excessive costs, and provides more accurate estimated cost calculations.
Patent Information
- Application Number
- CN202511558852.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-29
- Publication Date
- 2026-01-23
AI Technical Summary
During the movement of equipment across regions, the dynamically changing network environment leads to problems such as drastic fluctuations in signal quality and excessive costs. Existing technologies are insufficient to achieve automated switching of operator networks, taking into account both signal quality and cost.
By obtaining the current location of the embedded user identification card, candidate operators are identified and network signal strength is acquired. Traffic demand is predicted based on historical traffic usage data, and estimated costs are calculated in conjunction with billing rules. The optimal operator is selected for network switching using the scoring value.
It enables automated switching of operator networks, taking into account both network signal strength and estimated costs to avoid excessive costs or weak signals, and makes cost estimates more accurate.
Smart Images
Figure CN121397673A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of network switching technology, and in particular to a network switching method, apparatus, device and medium for an embedded user identification card. Background Technology
[0002] With the explosive growth of global IoT devices, embedded SIM (eSIM) technology, with its remote configuration capabilities and multi-carrier switching features, has become a core solution for cross-border device connectivity. However, during the movement of devices across regions (such as cross-border logistics vehicles and wearable devices), the dynamically changing network environment presents significant challenges. For example, geographical differences lead to drastic fluctuations in signal quality, and fragmented pricing strategies among different regional operators can easily result in excessive costs. Currently, there is an urgent need for an automated method for switching carriers that comprehensively considers both signal quality and data costs. Summary of the Invention
[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a network switching method, apparatus, device, and medium for an embedded subscriber identification card, which can automatically switch operator networks and comprehensively consider network signal strength and estimated costs to avoid excessive costs or weak network signal strength.
[0004] A network handover method for an embedded user identification card according to a first aspect embodiment of this application includes: Get the current location of the embedded user identification card; Based on the current location, determine each candidate operator in the current location of the embedded identification card, and obtain the network signal strength of each candidate operator; Based on the historical traffic usage data of the embedded user identification card, traffic demand is predicted to obtain the predicted traffic demand for each candidate operator; wherein, the predicted traffic demand includes targeted traffic demand and non-targeted traffic demand. Based on the targeted traffic demand and the non-targeted traffic demand, and the corresponding billing rules of the candidate operators, calculate the estimated cost of each candidate operator. Based on the network signal strength and the estimated cost, a score value is determined for each candidate operator, and the candidate operator with the highest score value is selected as the target operator. If the current operator is detected to be different from the target operator, the network of the embedded user identification card is switched to the network of the target operator.
[0005] The network switching method for an embedded subscriber identity card according to embodiments of this application has at least the following advantages: The method first obtains the current location of the embedded subscriber identity card, determines each candidate operator based on the current location, and determines the network signal strength of each candidate operator. Then, it predicts traffic demand based on historical traffic usage data to obtain the predicted traffic demand for each candidate operator. Based on directional and non-directional traffic demand, and the corresponding billing rules of the candidate operators, it calculates the estimated cost for each candidate operator. Based on the network signal strength and the estimated cost, it determines the score of each candidate operator, selects the candidate operator with the highest score as the target operator, and then switches the network of the embedded subscriber identity card to the network of the target operator. In the switching method of this application, when predicting traffic demand, prediction is performed separately for each candidate operator. Since some operators have directional traffic while others do not, the predicted traffic demand of this application corresponds one-to-one with the candidate operators. Then, based on the predicted traffic demand, the estimated cost for each candidate operator is calculated separately, resulting in a more accurate estimated cost. Thus, the network switching method of the embedded user identification card in this application can automatically switch operator networks, and comprehensively consider network signal strength and estimated cost to avoid excessive cost or weak network signal strength, and the calculated estimated cost is more accurate.
[0006] According to some embodiments of the first aspect of this application, the candidate operators include operators without purchased packages; The estimated cost from the operator for the unpurchased package is calculated through the following steps: Obtain the billing rules of the operator of the unpurchased package; The estimated cost is calculated based on the billing rules of the operator who has not purchased a package and the non-targeted data traffic demand.
[0007] According to some embodiments of the first aspect of this application, the candidate operator includes operators with purchased packages; The estimated cost from the operator of the purchased package is calculated through the following steps: Obtain the remaining targeted and untargeted data traffic from the purchased data plan operator; wherein, the remaining targeted data traffic includes the remaining targeted data traffic for at least one software; the remaining targeted data traffic corresponds one-to-one with the targeted data traffic demand; Based on the remaining targeted traffic and corresponding targeted traffic demand of the same software, as well as the billing rules of the purchased package operator, the targeted cost for a single software is calculated. Based on the individual software targeted fees, the targeted fees of the purchased package operator are obtained; The non-targeted remaining traffic, the non-targeted traffic demand, and the billing rules of the purchased package operator are used to calculate the non-targeted fee. Based on the targeted fees and the non-targeted fees, the estimated fees of the operators whose packages have been purchased are obtained.
[0008] According to some embodiments of the first aspect of this application, determining the score value for each candidate operator based on the network signal strength and the estimated cost includes: Obtain a first weight for the network signal strength and a second weight for the estimated cost; The score is calculated based on the network signal strength, the first weight, the estimated cost, and the second weight.
[0009] According to some embodiments of the first aspect of this application, the first weight for obtaining the network signal strength and the second weight for the estimated cost include: Obtain the device type of the carrier device of the embedded user identification card; Based on the device type, the first weight and the second weight corresponding to the device type are obtained from a preset weight mapping table.
[0010] According to some embodiments of the first aspect of this application, the step of predicting traffic demand based on historical traffic usage data of the embedded subscriber identification card to obtain the predicted traffic demand for each of the candidate operators includes: The historical traffic usage data of the carrier device of the embedded user identification card for each software to be predicted is obtained within a historical time period; wherein, the historical traffic usage data includes the consumption sequence of each time period, the total traffic consumption value, the traffic consumption value per unit time, and the distance coefficient; the distance coefficient represents the degree of difference between the historical location and the current location within the historical time period; The consumption sequence of each time period, the total flow consumption value, the flow consumption value per unit time, and the distance coefficient are input into a pre-trained flow prediction model to obtain the target predicted flow. When the traffic used by the software to be predicted is directed traffic, the target predicted traffic is taken as the single software directed predicted traffic of the software to be predicted; when the traffic used by the software to be predicted is non-directed traffic, the target predicted traffic is taken as the single software non-directed predicted traffic. Based on the directional predicted traffic of each of the individual software and the non-directional predicted traffic of each of the individual software, the predicted traffic demand of each of the candidate operators is obtained.
[0011] According to some embodiments of the first aspect of this application, the distance coefficient is obtained through the following steps: Calculate the location distance between the historical location and the current location; The reciprocal of the location distance is used as the distance coefficient.
[0012] A second aspect of this application provides a network switching device for an embedded user identification card, comprising: The first acquisition module is used to acquire the current location of the embedded user identification card; The second acquisition module is used to determine each candidate operator in the current location of the embedded identification card based on the current location, and to acquire the network signal strength of each candidate operator. The prediction module is used to predict traffic demand based on the historical traffic usage data of the embedded user identification card, and obtain the predicted traffic demand for each candidate operator; wherein, the predicted traffic demand includes targeted traffic demand and non-targeted traffic demand. The billing module is used to calculate the estimated cost of each candidate operator based on the targeted traffic demand and the non-targeted traffic demand, as well as the corresponding billing rules of the candidate operators. The scoring module is used to determine the score value of each candidate operator based on the network signal strength and the estimated cost, and select the candidate operator with the highest score value as the target operator. The switching module is used to switch the network of the embedded user identification card to the network of the target operator when it is detected that the current operator is different from the target operator.
[0013] A third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the network switching method for an embedded user identification card as described in any one of the first aspects of the embodiment.
[0014] A fourth aspect of this application provides a computer-readable storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the network switching method for an embedded user identification card as described in any one of the first aspects of the embodiment.
[0015] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0016] The present application will be further described below with reference to the accompanying drawings and embodiments, wherein: Figure 1 This is a flowchart illustrating the steps of the network switching method for an embedded user identification card according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the calculation steps for the estimated cost of an operator without a purchased package in an embodiment of this application. Figure 3 This is a schematic diagram illustrating the calculation steps for the estimated cost of a purchased package from an operator, as described in an embodiment of this application. Figure 4 This is a schematic diagram illustrating the process of obtaining the first weight and the second weight in an embodiment of this application; Figure 5 This is a schematic diagram illustrating the steps for obtaining predicted traffic demand in an embodiment of this application; Figure 6 This is a schematic diagram of the network switching device for an embedded user identification card according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation
[0017] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.
[0018] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.
[0019] In the description of this application, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.
[0020] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.
[0021] In the description of this application, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0022] The first aspect of this application provides a network switching method for an embedded user identification card. This network switching method for an embedded user identification card can be applied to a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet computer, laptop computer, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the network switching method for the embedded user identification card, etc., but is not limited to the above forms.
[0023] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0024] Reference Figure 1 , Figure 1 This is a flowchart illustrating the steps of a network switching method for an embedded user identification card according to an embodiment of this application. The network switching method for an embedded user identification card according to an embodiment of this application includes, but is not limited to, steps S110 to S160.
[0025] Step S110: Obtain the current location of the embedded user identification card; In some embodiments, the device on which the embedded user identification card is installed is called a carrier device. The carrier device for the embedded user identification card can be a mobile phone, desktop computer, laptop computer, tablet computer, smart car system, smartwatch, medical device, factory processing control equipment, smart grid equipment, security equipment, logistics tracking equipment, etc. The carrier device is typically equipped with a positioning device, such as a GPS device, which allows the current location of the embedded user identification card to be obtained.
[0026] Step S120: Based on the current location, determine each candidate operator in the current location of the embedded identification card, and obtain the network signal strength of each candidate operator; It is worth noting that after determining the current location, operators that provide network services at the current location can be selected as candidate operators, and the network signal strength of each candidate operator at the current location can be obtained. The network signal strength is used to characterize the network quality of the candidate operators.
[0027] Step S130: Based on the historical traffic usage data of the embedded user identification card, traffic demand is predicted to obtain the predicted traffic demand for each candidate operator; wherein, the predicted traffic demand includes targeted traffic demand and non-targeted traffic demand. It is worth noting that candidate operators are divided into operators with and without service contracts. Operators with service contracts refer to mobile network operators with whom the device has pre-registered an account, activated services, and paid the corresponding fees, thus establishing a formal service contract. Operators without service contracts refer to other mobile network operators within the signal coverage area of the device's current location, with whom the device has not yet established a service contract. In some embodiments, such as when the device is roaming, the embedded subscriber identity card (SIM card) can still directly use the operator's network traffic even if it has not established a service contract with an operator.
[0028] It should be noted that some operators with pre-purchased plans offer dedicated traffic, while others do not. Dedicated traffic refers to traffic specifically allocated to a particular software application on a carrier device. For example, if operator A with a pre-purchased plan offers dedicated traffic for software B, then when the carrier device's network is connected to operator A's network and software B is running on the carrier device, all traffic consumed by software B is dedicated traffic. Therefore, the predicted traffic demand differs for each operator. This application forecasts traffic demand separately for each candidate operator, and the forecasted traffic demand corresponds one-to-one with the candidate operator. This results in more accurate subsequent cost estimates.
[0029] Step S140: Calculate the estimated cost for each candidate operator based on the targeted traffic demand and the non-targeted traffic demand, as well as the billing rules of the corresponding candidate operators. Step S150: Determine the score of each candidate operator based on network signal strength and estimated cost, and select the candidate operator with the highest score as the target operator. Step S160: If the current operator is detected to be different from the target operator, the network of the embedded user identification card is switched to the network of the target operator.
[0030] It should be noted that if the current operator is the same as the target operator, there is no need to switch networks.
[0031] It is worth noting that the network switching method for the embedded subscriber identification card in this application embodiment, through steps S110 to S160, first obtains the current location of the embedded subscriber identification card, determines each candidate operator based on the current location, and determines the network signal strength of each candidate operator. Then, it predicts traffic demand based on historical traffic usage data to obtain the predicted traffic demand for each candidate operator. Based on the directional and non-directional traffic demand and the corresponding billing rules of the candidate operators, it calculates the estimated cost of each candidate operator. Based on the network signal strength and the estimated cost, it determines the score value of each candidate operator, selects the candidate operator with the highest score value as the target operator, and then switches the network of the embedded subscriber identification card to the network of the target operator when it is detected that the current operator and the target operator are different. In the switching method of this application, when predicting traffic demand, prediction is performed for each candidate operator separately. Since some operators have directional traffic while others do not, the predicted traffic demand of this application corresponds one-to-one with the candidate operators. Then, the estimated cost of each candidate operator is calculated based on the predicted traffic demand, resulting in a more accurate estimated cost. Thus, the network switching method of the embedded user identification card in this application can automatically switch operator networks, and comprehensively consider network signal strength and estimated cost to avoid excessive cost or weak network signal strength, and the calculated estimated cost is more accurate.
[0032] In some embodiments, refer to Figure 2 , Figure 2 This is a schematic diagram illustrating the calculation steps for the estimated cost of an operator who has not purchased a package according to an embodiment of this application. Figure 2 The illustrated process includes, but is not limited to, steps S210 to S220.
[0033] Step S210: Obtain the billing rules of the operator for the unpurchased package; Step S220: Calculate the estimated cost based on the billing rules of the operator for the unpurchased package and the non-targeted data traffic demand.
[0034] It is worth noting that since the predicted traffic demand for operators without purchased packages only includes non-targeted traffic demand, in step 130, the predicted traffic demand equals the non-targeted traffic demand. Therefore, the estimated cost is calculated directly based on the billing rules of the operator without purchased packages and the non-targeted traffic demand. For example, the non-targeted traffic demand is S, and the unit of S is M; while the billing rule of the operator without purchased packages is: the price per M of traffic is Y yuan, then the estimated cost is S*Y.
[0035] In some embodiments, refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the calculation steps for the estimated cost of a purchased package from an operator, as described in an embodiment of this application. Figure 3 The illustrated process includes, but is not limited to, steps S310 to S350.
[0036] Step S310: Obtain the remaining targeted and non-targeted traffic from the purchased data plan operator; wherein, the remaining targeted traffic includes the remaining targeted traffic of at least one software; the remaining targeted traffic corresponds one-to-one with the targeted traffic demand; For example, the contract between the embedded user identification card and the operator of the purchased package may include multiple types of targeted traffic, such as multiple software programs, each software program corresponding to a specific type of targeted traffic, or multiple software programs corresponding to a specific type of targeted traffic. Therefore, in step S130, the predicted traffic demand includes multiple targeted traffic demands, and the remaining targeted traffic corresponds one-to-one with the targeted traffic demand.
[0037] Step S320: Calculate the single software targeted cost based on the remaining targeted traffic of the same software, the corresponding targeted traffic demand, and the billing rules of the purchased package operator. For example, the remaining targeted traffic for software C is C1, and the targeted traffic demand for software C is C2. The billing rule is: the price for each MB of traffic exceeding the package limit is C3. When C2 is greater than C1, the single targeted traffic cost for the targeted traffic demand C2 is (C2-C1)*A3. It should be noted that when C2 is less than or equal to C1, the single targeted traffic cost is set to 0. The units for C1 and C2 are both MB.
[0038] It is worth noting that the cost of a single software-specific service corresponds one-to-one with the required traffic volume.
[0039] Step S330: Based on the targeted fees for each individual software, obtain the targeted fees for the purchased package operators; It is worth noting that the targeted fees for each individual software are directly summed up, and the sum is used as the targeted fee for the purchased package operator.
[0040] Step S340: Calculate the non-targeted fee based on the remaining non-targeted traffic, non-targeted traffic demand, and the billing rules of the operator of the purchased package. For example, the remaining non-targeted data allowance of a purchased data plan operator is D1, and the non-targeted data allowance requirement is D2; the billing rule is D3 yuan per megabyte of non-targeted data allowance exceeding the limit. When D2 is greater than D1, the non-targeted data allowance fee for the purchased data plan operator is (D2-D1)*D3. It should be noted that when D2 is less than or equal to D1, the non-targeted data allowance is set to 0. The unit for both D1 and D2 is megabytes (MB).
[0041] Step S350: Based on the targeted and non-targeted fees, obtain the estimated fees from the operators of the purchased packages.
[0042] It is worth noting that through steps S310 to S350, the estimated cost of the purchased package can be calculated based on the billing rules of the purchased package operator. Furthermore, the calculation process involves calculating both targeted and non-targeted costs separately, resulting in a more accurate estimated cost.
[0043] In some embodiments, step S150 includes steps S151 and S152.
[0044] Step S151: Obtain the first weight of network signal strength and the second weight of estimated cost; Step S152: Calculate the score based on network signal strength, first weight, estimated cost, and second weight.
[0045] For example, the formula for calculating the score is as follows: F = F1 * W1 - F2 * W2; Where F1 is the network signal strength, W1 is the first weight, F2 is the estimated cost, and W2 is the second weight.
[0046] The scoring formula can be used to calculate the score of each candidate operator, and the candidate operator with the highest score can be selected as the target operator.
[0047] In some embodiments, refer to Figure 4 , Figure 4 This is a schematic diagram illustrating the process of obtaining the first weight and the second weight in an embodiment of this application. Figure 4 The illustrated process includes, but is not limited to, steps S410 and S420.
[0048] Step S410: Obtain the device type of the carrier device for the embedded user identification card; Step S420: Based on the device type, obtain the first weight and second weight corresponding to the device type from the preset weight mapping table.
[0049] It is worth noting that a weight mapping table is pre-set, which records the specific values of the first and second weights for each device type.
[0050] Table 1
[0051] It is worth noting that, referring to Table 1, equipment types are categorized into general equipment, medical equipment, and logistics equipment. Generally, general equipment refers to smart devices such as mobile phones and laptops used by ordinary users. Medical equipment refers to equipment used for medical services. Logistics equipment refers to equipment used for logistics tracking. Typically, J1 is less than K1; J2 is greater than K2, and J3 is greater than K3. Those skilled in the art can set the values of J1, J2, J3, K1, K2, and K3 according to actual circumstances. Because different equipment types have different network requirements, different weights are pre-set to adapt the network switching process to different devices.
[0052] In some embodiments, refer to Figure 5 , Figure 5 This is a schematic diagram illustrating the steps for obtaining predicted traffic demand in an embodiment of this application. Figure 5 The illustrated steps include, but are not limited to, steps S510 to S540.
[0053] Step S510: Obtain historical traffic usage data of each software to be predicted in the carrier device of the embedded user identification card within a historical time period; wherein, the historical traffic usage data includes the consumption sequence of each time period, the total traffic consumption value, the traffic consumption value per unit time, and the distance coefficient; the distance coefficient represents the degree of difference between the historical location and the current location within the historical time period; It's important to note that total traffic consumption refers to the total traffic consumed by the embedded SIM card within a historical time period on a candidate operator's network. Traffic consumption per unit time equals total traffic consumption divided by the historical time period. The historical time period is divided into multiple time segments, and the traffic usage in each segment is recorded and sorted chronologically to obtain the consumption sequence for each time segment. For example, if the historical time period is the past 24 hours, it is divided into 24 time segments, each lasting one hour, and the traffic usage in each segment is recorded and sorted chronologically to obtain the time segment consumption sequence. This time segment consumption sequence uses a total of 24 elements.
[0054] It should be noted that the embodiments of this application do not impose specific limitations on the historical time period, and those skilled in the art can set it according to the actual situation.
[0055] Step S520: Input the consumption sequence of each time period, the total flow consumption value, the flow consumption value per unit time, and the distance coefficient into the pre-trained flow prediction model to obtain the target predicted flow. It is worth noting that the traffic prediction model can employ a Long Short-Term Memory (LSTM) model. LSTM is a variant of recurrent neural networks designed specifically for processing time-series data. Its core lies in overcoming the shortcomings of traditional RNN models in capturing long-term dependencies through ingenious "memory cells" and gating mechanisms.
[0056] It is worth noting that historical traffic usage data is a typical time series (especially the consumption sequence of each time period). Through its unique "gating mechanism" (forget gate, input gate, output gate), the LSTM network can selectively remember and forget information. It can learn the dependence of traffic usage from historical traffic data, thereby making more forward-looking and accurate predictions of future traffic demand.
[0057] It is worth noting that existing technologies typically make coarse predictions based on the average traffic usage of a user group, failing to fully consider the specific software usage habits of individual users. This application's embodiments, by acquiring historical traffic usage data of the software, refine the prediction granularity from the "user group" level to the "individual user - individual software" level. This allows the prediction results to truly reflect the user's actual usage needs for the software, thereby greatly improving the accuracy and personalization of the prediction results. Furthermore, by introducing a distance coefficient in the prediction process, traffic usage is correlated with location, enabling the traffic prediction model to dynamically perceive and adapt to the impact of location changes. This maintains reliable prediction accuracy even in user mobility scenarios, effectively overcoming the interference of location changes on traffic prediction and improving the model's robustness and practicality.
[0058] Furthermore, this application does not simply use total traffic volume, but comprehensively employs four interrelated yet distinct features: traffic consumption sequences over different time periods, total traffic consumption, traffic consumption per unit time, and distance coefficient. The total traffic consumption reflects the user's macro-level traffic usage, the traffic consumption per unit time reflects the intensity and habits of user traffic usage, the distance coefficient reflects the environmental context, and the traffic consumption sequences over different time periods reflect the relationship between traffic usage and duration. This multi-dimensional feature combination provides the prediction model with richer and deeper information input, enabling the model to more comprehensively learn user behavior patterns, uncover the intrinsic correlation between traffic consumption and time and space, and thus make more scientific and reasonable predictions, avoiding prediction biases caused by single features. It should be noted that this application does not elaborate on the training process of the traffic prediction model. Those skilled in the art can train the traffic prediction model based on historical traffic usage data from two different historical time periods. For example, inputting the historical traffic usage data of software A for the historical period of March 24th into the traffic prediction model yields the target predicted traffic for software A. Then, the total traffic used by software A on March 25th is used as the true label. Based on the true label, the target predicted traffic, and the loss function, a loss value is calculated, and the target predicted traffic is updated based on the loss value. After the number of updates reaches a preset threshold, the trained target predicted traffic is obtained.
[0059] Step S530: When the traffic used by the software to be predicted is directional traffic, the target prediction traffic is used as the single software directional prediction traffic of the software to be predicted; when the traffic used by the software to be predicted is non-directional traffic, the target prediction traffic is used as the single software non-directional prediction traffic. Step S540: Based on the directional predicted traffic and the non-directional predicted traffic of each individual software, the predicted traffic demand of each candidate operator is obtained.
[0060] It is worth noting that, in this embodiment, the target predicted traffic for each software is first calculated through steps S510 to S520. Then, in step S530, if the traffic used by the software to be predicted is directional traffic, the target predicted traffic is used as the single software directional predicted traffic for the software to be predicted; if the traffic used by the software to be predicted is non-directional traffic, the target predicted traffic is used as the single software non-directional predicted traffic. In step S540, for each operator without a subscription plan, the sum of all single software directional predicted traffic and single software non-directional predicted traffic is directly calculated as the non-directional traffic requirement of the operator without a subscription plan, while the directional traffic requirement of the operator without a subscription plan is 0.
[0061] For each purchased service provider, one provider is selected as the target provider. Software with targeted traffic defined in the contract between the embedded user identification card and the target provider is identified as associated software. The single-software targeted traffic forecast for each associated software is the target provider's targeted traffic demand. If there are multiple associated software providers, there will be multiple targeted traffic demands, with each demand corresponding to a specific associated software. Then, the sum of all single-software non-targeted predicted traffic and the single-software targeted predicted traffic for all other software is calculated, and this sum is taken as the target provider's targeted traffic demand. This process is repeated to calculate the target provider's predicted traffic demand for each purchased service provider. The target provider is then changed to iterate through each purchased service provider to calculate the predicted traffic demand for each of these providers.
[0062] It is worth noting that, through steps S510 to S540, the present application embodiment, in the process of predicting traffic demand, accurately predicts the traffic of a single software with ...
[0063] In some embodiments, the distance coefficient is obtained through the following steps: Calculate the distance between the historical location and the current location; Use the reciprocal of the location distance as the distance coefficient.
[0064] A second aspect of this application provides a network switching device for an embedded user identification card. (See also...) Figure 6 , Figure 6 This is a schematic diagram of the network switching device for an embedded user identification card according to an embodiment of this application. The network switching device for the embedded user identification card includes: The first acquisition module 610 is used to acquire the current position of the embedded user identification card; The second acquisition module 620 is used to determine the candidate operators of the current location of the embedded identification card based on the current location, and to acquire the network signal strength of each candidate operator. The prediction module 630 is used to predict traffic demand based on historical traffic usage data of the embedded user identification card, and obtain the predicted traffic demand for each candidate operator; wherein, the predicted traffic demand includes targeted traffic demand and non-targeted traffic demand. The billing module 640 is used to calculate the estimated cost of each candidate operator based on the targeted traffic demand and the non-targeted traffic demand, as well as the billing rules of the corresponding candidate operators. The scoring module 650 is used to determine the score of each candidate operator based on network signal strength and estimated cost, and select the candidate operator with the highest score as the target operator. The switching module 660 is used to switch the network of the embedded subscriber identity card to the network of the target operator when the current operator is detected to be different from the target operator.
[0065] The network switching apparatus for an embedded subscriber identity card according to a second aspect of this application is used to execute the network switching method for an embedded subscriber identity card according to a first aspect of this application. When executing the method, the current location of the embedded subscriber identity card is first obtained. Based on the current location, each candidate operator is determined, and the network signal strength of each candidate operator is determined. Then, traffic demand is predicted based on historical traffic usage data to obtain the predicted traffic demand for each candidate operator. Based on directional and non-directional traffic demand, and the corresponding billing rules of the candidate operators, the estimated cost of each candidate operator is calculated. Based on the network signal strength and the estimated cost, a score value for each candidate operator is determined, and the candidate operator with the highest score value is selected as the target operator. Then, if the current operator and the target operator are detected to be different, the network of the embedded subscriber identity card is switched to the network of the target operator. In the switching method of this application, when predicting traffic demand, prediction is performed separately for each candidate operator. Since some operators have directional traffic while others do not, the predicted traffic demand of this application corresponds one-to-one with the candidate operators. Then, the estimated cost of each candidate operator is calculated based on the predicted traffic demand, resulting in a more accurate estimated cost. Thus, the network switching method of the embedded user identification card in this application can automatically switch operator networks, and comprehensively consider network signal strength and estimated cost to avoid excessive cost or weak network signal strength, and the calculated estimated cost is more accurate.
[0066] It should be noted that the specific implementation of the network switching device for the embedded user identification card is basically the same as the specific embodiment of the network switching method for the embedded user identification card described above, and will not be repeated here. Subject to meeting the requirements of the embodiments of this application, the network switching device for the embedded user identification card may also be equipped with other functional units to implement the network switching method for the embedded user identification card in the above embodiments.
[0067] A third aspect of this application provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the network switching method for an embedded user identification card according to any one of the first aspects of the embodiment. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
[0068] Reference Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device according to one embodiment. The electronic device includes: The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701 to execute the network switching method of the embedded user identification card in the embodiments of this application. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.
[0069] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the network switching method for an embedded user identification card according to any one of the first aspects of this application.
[0070] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0071] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0072] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0073] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.
[0074] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0075] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0076] It should be understood that in this application, "at least one (item)" means one or more, and "more than one" means two or more. "And / or" is used to describe the mapping relationship between the mapped objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following mapped objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0077] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.
[0078] The units described above 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 units can be selected to achieve the purpose of this embodiment according to actual needs.
[0079] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0081] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. A network handover method for an embedded user identification card, characterized in that, include: Get the current location of the embedded user identification card; Based on the current location, determine each candidate operator in the current location of the embedded identification card, and obtain the network signal strength of each candidate operator; Based on the historical traffic usage data of the embedded user identification card, traffic demand is predicted to obtain the predicted traffic demand for each candidate operator; wherein, the predicted traffic demand includes targeted traffic demand and non-targeted traffic demand. Based on the targeted traffic demand and the non-targeted traffic demand, and the corresponding billing rules of the candidate operators, calculate the estimated cost of each candidate operator. Based on the network signal strength and the estimated cost, a score value is determined for each candidate operator, and the candidate operator with the highest score value is selected as the target operator. If the current operator is detected to be different from the target operator, the network of the embedded user identification card is switched to the network of the target operator.
2. The network switching method for an embedded user identification card according to claim 1, characterized in that, The candidate operators include those who have not purchased service packages; The estimated cost from the operator for the unpurchased package is calculated through the following steps: Obtain the billing rules of the operator of the unpurchased package; The estimated cost is calculated based on the billing rules of the operator who has not purchased a package and the non-targeted data traffic demand.
3. The network switching method for an embedded user identification card according to claim 1, characterized in that, The candidate operators include those with purchased packages; The estimated cost from the operator of the purchased package is calculated through the following steps: Obtain the remaining targeted and untargeted data traffic from the purchased data plan operator; wherein, the remaining targeted data traffic includes the remaining targeted data traffic for at least one software; the remaining targeted data traffic corresponds one-to-one with the targeted data traffic demand; Based on the remaining targeted traffic and corresponding targeted traffic demand of the same software, as well as the billing rules of the purchased package operator, the targeted cost for a single software is calculated. Based on the individual software targeted fees, the targeted fees of the purchased package operator are obtained; The non-targeted remaining traffic, the non-targeted traffic demand, and the billing rules of the purchased package operator are used to calculate the non-targeted fee. Based on the targeted fees and the non-targeted fees, the estimated fees of the operators whose packages have been purchased are obtained.
4. The network switching method for an embedded user identification card according to claim 1, characterized in that, The process of determining the score for each candidate operator based on the network signal strength and the estimated cost includes: Obtain a first weight for the network signal strength and a second weight for the estimated cost; The score is calculated based on the network signal strength, the first weight, the estimated cost, and the second weight.
5. The network switching method for an embedded user identification card according to claim 4, characterized in that, The first weight for obtaining the network signal strength and the second weight for the estimated cost include: Obtain the device type of the carrier device of the embedded user identification card; Based on the device type, the first weight and the second weight corresponding to the device type are obtained from a preset weight mapping table.
6. The network switching method for an embedded user identification card according to claim 1, characterized in that, The step of predicting traffic demand based on historical traffic usage data from the embedded user identification card to obtain the predicted traffic demand for each candidate operator includes: The historical traffic usage data of the carrier device of the embedded user identification card for each software to be predicted is obtained within a historical time period; wherein, the historical traffic usage data includes the consumption sequence of each time period, the total traffic consumption value, the traffic consumption value per unit time, and the distance coefficient; the distance coefficient represents the degree of difference between the historical location and the current location within the historical time period; The consumption sequence of each time period, the total flow consumption value, the flow consumption value per unit time, and the distance coefficient are input into a pre-trained flow prediction model to obtain the target predicted flow. When the traffic used by the software to be predicted is directed traffic, the target predicted traffic is taken as the single software directed predicted traffic of the software to be predicted; when the traffic used by the software to be predicted is non-directed traffic, the target predicted traffic is taken as the single software non-directed predicted traffic. Based on the directional predicted traffic of each of the individual software and the non-directional predicted traffic of each of the individual software, the predicted traffic demand of each of the candidate operators is obtained.
7. The network switching method for an embedded user identification card according to claim 6, characterized in that, The distance coefficient is obtained through the following steps: Calculate the location distance between the historical location and the current location; The reciprocal of the location distance is used as the distance coefficient.
8. A network switching device for an embedded user identification card, characterized in that, include: The first acquisition module is used to acquire the current location of the embedded user identification card; The second acquisition module is used to determine each candidate operator in the current location of the embedded identification card based on the current location, and to acquire the network signal strength of each candidate operator. The prediction module is used to predict traffic demand based on the historical traffic usage data of the embedded user identification card, and obtain the predicted traffic demand for each candidate operator; wherein, the predicted traffic demand includes targeted traffic demand and non-targeted traffic demand. The billing module is used to calculate the estimated cost of each candidate operator based on the targeted traffic demand and the non-targeted traffic demand, as well as the corresponding billing rules of the candidate operators. The scoring module is used to determine the score value of each candidate operator based on the network signal strength and the estimated cost, and select the candidate operator with the highest score value as the target operator. The switching module is used to switch the network of the embedded user identification card to the network of the target operator when it is detected that the current operator is different from the target operator.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the network switching method for the embedded user identification card according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the network switching method for the embedded user identification card as described in any one of claims 1 to 7.