ESIM automatic network switching strategy management system and method based on multi-source data fusion

Abstract

The invention discloses an eSIM automatic network switching strategy management system and method based on multi-source data fusion, and relates to the field of network switching management, and the main scheme is that the method comprises the following steps: analyzing network state data, calculating a basic performance index of a network state, and judging whether a network to be accessed meets a basic performance evaluation standard or not; when the network to be accessed meets the basic performance evaluation standard, obtaining network security data, analyzing the network security data, and judging whether the network security index meets the network security standard or not; when the network security indexes all meet the network security standard, executing a network switching instruction; the problems that in the prior art, only network performance indexes are emphasized, network security is ignored, equipment is switched to a network which is good in seeking performance but has potential safety hazards, such as a forged network which is maliciously built by an attacker or a network with vulnerabilities, so that risks of data leakage, network attack and the like are increased, and the network security is influenced are solved. And serious privacy and security problems are brought to the user.

Description

Technical Field

[0001] The present invention relates to the technical field of network switching management, and particularly to an eSIM automatic network switching policy management system and method based on multi-source data fusion. Background Art

[0002] With the booming development of the Internet of Things, as one of the key enabling technologies, eSIM technology is widely used in many fields such as smartphones, smart watches, smart cars, industrial Internet of Things devices, etc. According to market research institutions' predictions, the global eSIM market size is expected to continue to break through, and its application prospects in the field of network connection are extremely broad.

[0003] In the consumer electronics field, consumers' demand for seamless network connection experience is increasing day by day. They expect devices to automatically switch to the best network according to the network conditions to ensure smooth Internet access, calls, video playback, etc. For example, when users move in different operator network coverage areas, the automatic network switching function of eSIM can enable the device to automatically switch to a network with stronger signal and faster speed, improving the user experience. In the industrial Internet of Things, a large number of devices require stable and reliable network connections to transmit data and receive instructions. The automatic network switching technology can ensure that these devices can switch to a better-quality network in a timely manner when facing a complex and changeable network environment, guarantee the continuity and efficiency of production, and reduce the production stoppage risk and economic losses caused by network failures.

[0004] Currently, most eSIM network automatic network switching solutions only focus on network performance indicators such as signal strength and network speed for network switching judgment, but lack consideration for network security. This may lead to the device switching to a network that seemingly has good performance but has security risks, such as a forged network maliciously set up by an attacker or a network with vulnerabilities, thus increasing risks such as data leakage and network attacks, and bringing serious privacy and security problems to users. Summary of the Invention

[0005] (1) Technical Problems to be Solved Aiming at the deficiencies of the prior art, the present invention provides an eSIM automatic network switching policy management system and method based on multi-source data fusion, which at least solves the problem in the prior art that only focuses on network performance indicators while ignoring network security, thus increasing risks such as data leakage and network attacks, and bringing serious security hazards to users.

[0006] (2) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: The eSIM automatic network switching policy management method based on multi-source data fusion includes: Step 1: Obtain real-time network status data, and calculate the basic performance indicators of the network status by analyzing the network status data; Step 2: Evaluate the basic performance indicators to determine whether the network to be accessed meets the basic performance evaluation criteria; Step 3: When the network to be accessed meets the basic performance evaluation criteria, obtain network security data, analyze the network security data, and calculate network security indicators; Step 4: Determine whether the network security indicators meet the network security standards; when all network security indicators meet the network security standards, execute the network switching instruction; Step 5: When the network to be accessed meets the basic performance evaluation criteria but does not meet the network security standards, if an emergency security warning instruction is received, the exemption instruction can be temporarily activated, ignoring the evaluation results of the network security assessment, execute the instruction to switch the network, and after the emergency security warning instruction is eliminated, then refer to the network assessment strategy in Steps 1 to 4 to determine whether to switch to other networks.

[0007] In the preferred solution of the above eSIM automatic network switching strategy management method based on multi-source data fusion, the basic performance indicators include network quality evaluation index, roaming cost index, and reference signal theoretical received power.

[0008] In the preferred solution of the above eSIM automatic network switching strategy management method based on multi-source data fusion, the method for obtaining the network quality evaluation index is as follows: Obtain the measurement configuration through the measurement control message in the system information block SIB broadcast by the base station, and then measure the reference signal theoretical received power; after receiving the signal from the base station, calculate the signal-to-noise ratio according to the received useful signal power and the interference and noise floor power; in the wireless network, use the round-trip time of the RRC signaling or the round-trip time based on the TCP transmission control protocol connection as the round-trip delay; Perform normalization preprocessing on the reference signal theoretical received power, signal-to-noise ratio, and round-trip delay to obtain the received signal score, signal-to-noise ratio score, and round-trip delay score respectively, and perform comprehensive analysis through the network quality evaluation value index calculation model to obtain the network quality evaluation index.

[0009] In the preferred solution of the above eSIM automatic network switching strategy management method based on multi-source data fusion, the method for obtaining the roaming cost index is as follows: Perform API docking with the billing system of the network operator to obtain the real-time tariff value of the user in different network environments in real time; query the tax database of the target country or region to obtain the applicable tax rate value of the communication service; obtain the service contract signed between the enterprise and the operator from the enterprise's contract management system, and extract the benchmark tariff value from the service contract; perform normalization preprocessing on the real-time tariff value, applicable tax rate value, and benchmark tariff value, and perform comprehensive analysis through the roaming cost calculation model to obtain the roaming cost index.

[0010] In the preferred solution of the above eSIM automated network switching strategy management method based on multi-source data fusion, the method for obtaining the received power of the reference signal is as follows: Obtain the real-time rainfall within the area through the meteorological API, and obtain the altitude difference between the terminal and the base station; obtain the material attenuation coefficient of the obstacles or obstructions within the area; normalize the real-time rainfall, altitude difference, and material attenuation coefficient, and analyze and calculate to obtain the environmental compensation factor. The formula is as follows: ; Where: HBC represents the environmental compensation factor; JY represents the real-time rainfall within the area; HB represents the altitude difference between the area and the base station; JZ j represents the material attenuation coefficient of the j-th obstacle within the area; kr is the rain attenuation coefficient, representing the dynamic influence weight of rainfall on signal attenuation; kt is the terrain coefficient, representing the non-linear compensation factor of terrain undulation on signal attenuation; β j represents the proportion weight of different building materials in the environment. The calculation formula is: β j = the material attenuation coefficient of the j-th obstacle / the sum of the material attenuation coefficients of all obstacles; j represents the serial number of the obstacle, and the value is a positive integer; Calculate the received power of the reference signal according to the original signal strength and the environmental compensation factor. Specifically: the received power of the reference signal = the original signal strength - the environmental compensation factor.

[0011] In the preferred solution of the above eSIM automated network switching strategy management method based on multi-source data fusion, comprehensively evaluate the network quality evaluation index, roaming cost index, and received power of the reference signal to determine whether it meets the basic performance evaluation criteria. The specific method is as follows: Set the network quality index threshold, roaming cost index threshold, and received power threshold; compare the network quality evaluation index, roaming cost index, and environmental compensation factor with the network quality index threshold, roaming cost index threshold, and environmental state threshold respectively. When the network quality evaluation index is greater than or equal to the network quality index threshold, it is determined that this index is qualified; when the roaming cost index ≤ the roaming cost index threshold, it is determined that this index is qualified; when the received power of the reference signal ≥ the received power threshold, it is determined that this index is qualified; When all three indicators are qualified, it is determined that the network to be accessed meets the basic performance evaluation criteria.

[0012] In the preferred solution of the above eSIM automated network switching strategy management method based on multi-source data fusion, the network security data includes the signal spatio-temporal conflict index, Doppler frequency shift deviation, and behavior pattern entropy.

[0013] In the preferred solution of the above eSIM automated network switching strategy management method based on multi-source data fusion, the method for obtaining the signal spatio-temporal conflict index is as follows: Obtain the GPS positioning coordinates of the terminal through the GNSS module built into the terminal, and query the geographical location coordinates registered by the base station through the core network MME database; obtain the moving speed of the terminal through the GNSS Doppler frequency shift or inertial navigation system, and obtain the base station location update time interval through the base station broadcast system information block SIB; obtain the base station authentication level value of the base station identity by the operator's core network, perform normalization preprocessing on the GPS positioning coordinates of the terminal, the geographical location coordinates registered by the base station, the moving speed of the terminal, the base station location update time interval, and the base station authentication level value, and calculate the signal spatio-temporal conflict index. The formula is as follows: ; where STC represents the signal spatio-temporal conflict index; L GPS represents the GPS positioning coordinates of the terminal; L CEL represents the geographical location coordinates registered by the base station; represents the base station location update time interval; v represents the moving speed of the terminal; ZHD represents the base station authentication level value; Use the baseband processor to measure the carrier frequency offset value actually measured by the terminal in real time through the phase-locked loop PLL; obtain the carrier center frequency from the carrier frequency parameters broadcast by the base station, and calculate the Doppler frequency shift value based on the carrier center frequency. The formula is: , in the formula, f ca represents the Doppler frequency shift value, f0 represents the carrier center frequency, v represents the moving speed of the terminal, θ represents the included angle between the base station movement direction and the base station, and c represents the speed of light; use the built-in magnetometer and accelerometer of the terminal to obtain the included angle area between the movement direction and the base station connection line, and calculate the cosine value of the included angle as the movement direction weight; perform normalization preprocessing on the carrier frequency offset value, the carrier center frequency, the Doppler frequency shift value, and the movement direction weight, and input the processed parameters into the Doppler frequency shift deviation calculation model to calculate the Doppler frequency shift deviation; Statistically calculate the proportion of each signaling type within a unit period to obtain the occurrence probability of the kth type of signaling within the time window; dynamically adjust the federated learning weight coefficient according to the number of terminals by the cloud; call the federated learning server API, input the local entropy value and time series features, and obtain the abnormal probability as the federated learning abnormal score. The calculation method of the local entropy value is: ; Perform normalization preprocessing on the occurrence probability of the kth type of signaling within the time window, the federated learning weight coefficient, and the abnormal probability, and calculate the behavior pattern entropy according to the behavior pattern entropy calculation model.

[0014] In the preferred solution of the above eSIM automatic network switching strategy management method based on multi-source data fusion, the method for determining whether the network security index meets the network security standard is as follows: Set the signal spatio-temporal conflict threshold, Doppler frequency shift deviation threshold, and behavior pattern entropy threshold respectively; compare the signal spatio-temporal conflict index, Doppler frequency shift deviation, and behavior pattern entropy with the corresponding thresholds respectively; when the signal spatio-temporal conflict index < the signal spatio-temporal conflict threshold, this index meets the security standard; when the Doppler frequency shift deviation < the Doppler frequency shift deviation threshold, this index meets the security standard; when the behavior pattern entropy < the behavior pattern entropy threshold, this index meets the security standard.

[0015] (III) Beneficial effects The present invention provides an eSIM automatic network switching strategy management system and method based on multi-source data fusion, having the following beneficial effects: (1) Step 1 and Step 2 together constitute a comprehensive evaluation technical point for the network basic performance. By obtaining real-time network status data and calculating the basic performance indicators, and then determining whether the network to be accessed meets the basic performance evaluation standard, this process can accurately screen out the networks with qualified performance. Compared with the existing methods that only judge based on a single or a few network parameters, this solution comprehensively combines various real-time network status data to determine the basic performance, greatly enhancing the comprehensiveness and accuracy of the network performance evaluation, effectively avoiding the misjudgment of the poor performance of the accessed network due to only referring to partial data, thus laying a solid foundation for subsequent more in-depth network selection and switching decisions, and ensuring that the user equipment can preferentially consider the network environment with truly reliable performance for access attempts.

[0016] (2) Step 3 and Step 4 focus on the evaluation of the network security level. On the premise that the network to be accessed meets the basic performance evaluation standard, further obtain network security data and calculate network security indicators to determine whether they meet the network security standard. This technical point realizes a comprehensive and in-depth consideration of the network. Existing technologies often have a gap between performance and security evaluations, or do not pay enough attention to security factors. However, this solution organically combines the two, ensuring that even if the network performance is excellent, if there are security risks, it will not be switched rashly. Only when the network security indicators also meet the standards will the network switching instruction be executed, which effectively improves the overall quality and reliability of the device accessing the network, greatly reducing the probability of security risks such as the user equipment being attacked by the network and data leakage, and ensuring the information security and system stability of the user during the network usage process.

[0017] (3) The emergency safety warning instruction and exemption instruction mechanism involved in Step Five is such that when an emergency safety warning instruction is received, it can temporarily activate the exemption instruction to ignore the network security assessment result and execute the instruction to switch networks. After the emergency safety warning instruction is eliminated, it then determines whether to switch to other networks according to the conventional network assessment strategy. This mechanism demonstrates extremely high flexibility and practicality in the face of sudden network security threats or special emergency service requirement scenarios.

[0018] (4) The entire solution orderly constructs a set of eSIM network switching strategy management processes based on multi-source data fusion from Step One to Step Five. Through the integrated use of multi-source data, it not only comprehensively considers the two key dimensions of network performance and security, but also introduces an emergency handling mechanism, enabling the network switching decision to no longer be a mechanical and single judgment, but rather to make a flexible and intelligent response according to the ever-changing real-time network conditions and special emergency requirements. Compared with the traditional and relatively rigid eSIM automated network switching solution that considers only single factors, this solution greatly optimizes the intelligence level of network switching, enabling the device to more accurately match the network most suitable for the current actual usage requirements. Whether it is for smooth and stable daily use or to ensure basic connection in emergency moments, it has corresponding usage strategies and is more practical. Brief Description of the Drawings

[0019] Figure 1 It is a schematic diagram of the steps of the eSIM automated network switching strategy management method based on multi-source data fusion of the present invention. Detailed Embodiments

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Embodiment 1

[0022] Please refer to Figure 1 , the present invention provides an eSIM automated network switching strategy management method based on multi-source data fusion, including: Step One: Obtain real-time network status data, and calculate the basic performance indicators of the network status by analyzing the network status data.

[0023] Step 101: Obtain the measurement configuration through the measurement control message in the system information block SIB broadcast by the base station, and then measure the theoretical received power of the reference signal; after receiving the signal from the base station, calculate the signal-to-noise ratio based on the received useful signal power and the interference and noise floor power; in a wireless network, the round-trip delay can be estimated by measuring the round-trip time of the RRC signaling or the round-trip time based on the TCP (Transmission Control Protocol) connection.

[0024] It should be noted that the specific method for obtaining the measurement configuration and then measuring the theoretical received power of the reference signal is as follows: From the information in the measurement configuration, receive the reference signal at the specified SSB time-frequency position, and then calculate the theoretical received power RSR of the reference signal by performing energy integration on the CRS / SSB resource units. The formula is as follows: ; where r s represents the received amplitude of the s-th reference signal, s represents the serial number of the reference signal, taking positive integer values, and S represents the number of reference signals.

[0025] This formula calculates a comprehensive theoretical received power of the reference signal by performing energy integration on the received amplitudes of multiple reference signals. In the formula, r s represents the received amplitude of the s-th reference signal. The RSR value is obtained by adding the squared amplitudes of all reference signals, taking the average, and then performing logarithmic conversion; it provides a more accurate received signal strength index for network quality assessment, enabling a more reliable judgment of the network's received quality in subsequent network handover decisions, avoiding problems such as frequent handovers or handovers to networks with poor signal quality due to inaccurate signal strength assessment, and avoiding the problem that evaluating the network's received quality based on only the strength of a single reference signal or partial signal characteristics cannot accurately reflect the overall received power. By integrating multiple reference signals and using the method of energy integration, the error caused by signal fluctuations and interference is effectively reduced, and the accuracy and stability of the received power assessment are improved.

[0026] It should be noted that the formula for calculating the signal-to-noise ratio SIN based on the received useful signal power and the interference and noise floor power is as follows: ; where P singnal represents the useful signal power; P noise represents the noise floor power; P interference represents the co-channel / adjacent-channel interference power.

[0027] The formula calculates the signal-to-noise ratio based on the ratio of the received useful signal power to the sum of the interference and background noise power, providing an index that accurately reflects the signal quality and offering a more reliable basis for network handover decisions. When switching networks, it is possible to select the network based on the signal-to-noise ratio, thereby enhancing the stability and reliability of communication and reducing communication quality issues such as the bit error rate. This avoids the problem that when only relying on signal strength to evaluate network quality, it cannot effectively reflect the quality of the signal, because even if the signal strength is high, if the interference and background noise power are also high, the actual communication quality may still be poor. This solution comprehensively considers the useful signal, interference, and background noise power, comprehensively evaluates the signal quality, and solves the problem of one-sided evaluation of signal quality by traditional methods.

[0028] Step 102: Perform normalization preprocessing on the reference signal theoretical received power, signal-to-noise ratio, and round-trip delay to obtain the received signal score, signal-to-noise ratio score, and round-trip delay score respectively, and perform comprehensive analysis through the network quality evaluation value index calculation model to obtain the network quality evaluation index. The formula is as follows: ; Among them, NQI represents the network quality evaluation index; RSR represents the received signal score; SIN represents the signal-to-noise ratio score; RTT represents the round-trip delay score; α1 represents the weight coefficient of the received signal score; α2 represents the signal-to-noise ratio score; α3 represents the weight coefficient of the round-trip delay score, which can be adjusted according to user needs, and α1 + α2 + α3 = 1. Here, the values can be: α1 = 0.3, α2 = 0.3, α3 = 0.4.

[0029] This formula calculates the network quality evaluation index after normalizing and preprocessing the three key network indicators of the reference signal theoretical received power, signal-to-noise ratio, and round-trip delay; it can perform a more comprehensive and accurate quantitative evaluation of network quality and provide a comprehensive quantitative basis for network handover decisions. During the eSIM automatic network switching process, based on the evaluation of this index, the optimal network can be selected more scientifically and reasonably, thereby improving the success rate of network switching and communication quality and enhancing the user experience.

[0030] Step 103: Through API docking with the billing system of the network operator, obtain the real-time tariff value of the user in different network environments in real time; obtain the applicable tax rate value of the communication service by querying the tax database of the target country or region; obtain the service contract signed between the enterprise and the operator through the enterprise's contract management system, and extract the benchmark tariff value from the service contract.

[0031] Step 104: Perform normalization preprocessing on the real-time tariff value, applicable tax rate value, and benchmark tariff value, and perform comprehensive analysis through the roaming cost calculation model to obtain the roaming cost index. The calculation formula is as follows: ; Among them, RCL represents the roaming cost index; SZ represents the real-time tariff value; SL represents the applicable tax rate; QJ represents the benchmark tariff value.

[0032] By performing API docking with the billing system of the network operator, it is possible to obtain the tariff situation of users in different network environments in real time. At the same time, the applicable tax rate is obtained by combining with the tax database of the target country or region, and the benchmark tariff is extracted from the service contract signed between the enterprise and the operator. These tariff-related data are preprocessed by normalization for subsequent comprehensive analysis; it provides a reliable data basis for the accurate calculation of subsequent roaming costs, enabling the roaming cost index to more truly reflect the actual roaming costs of users, which is beneficial to fully considering cost factors when performing eSIM network switching, selecting a more cost-effective network, and reducing the communication costs of users; traditional methods often have difficulty in obtaining tariff information in different network environments in a timely and accurate manner, and the evaluation of tariffs may be relatively lagged or inaccurate, unable to fully consider the impact of factors such as tax rates and service contracts on actual tariffs. This solution solves the problem of untimely and incomplete acquisition of tariff information through API docking and multi-source data fusion, and improves the accuracy of tariff evaluation.

[0033] Step 105: Obtain the real-time rainfall within the regional scope through the meteorological API, and obtain the altitude difference between the terminal and the base station; obtain the material attenuation coefficient of obstacles or obstructions within the region.

[0034] It should be noted that the material attenuation coefficients of different materials are obtained based on known public data such as the city-level BIM database to obtain building information within the region, and different material attenuation coefficients correspond to different building categories; for example, the value of a reinforced concrete wall can be 0.8 - 1.2, and the value of a double-layer insulating glass wall can be 0.3 - 0.5, etc.

[0035] Step 106: Normalize the real-time rainfall, altitude difference, and material attenuation coefficient, and analyze and calculate to obtain the environmental compensation factor; the calculation formula is as follows: ; Among them: HBC represents the environmental compensation factor; JY represents the real-time rainfall within the region; HB represents the altitude difference between the region and the base station; JZ j represents the material attenuation coefficient of the j-th obstruction within the region; kr is the rain attenuation coefficient, representing the dynamic influence weight of rainfall on signal attenuation; kt is the terrain coefficient, representing the non-linear compensation factor of terrain undulation on signal attenuation; β j represents the proportion weight of different building materials in the environment, and the calculation formula is: β j= Material attenuation coefficient of the j-th obstacle / Sum of material attenuation coefficients of all obstacles; j represents the serial number of the obstacle, and the value is a positive integer.

[0036] During rainfall, raindrops will absorb and scatter electromagnetic wave signals. When an electromagnetic wave signal propagates in the atmosphere, water molecules in the raindrops will absorb the signal energy, resulting in a decrease in signal strength. Generally speaking, the greater the rainfall, the more raindrops and water content per unit volume, and the more severe the signal attenuation. Generally, the greater the rainfall, the more raindrops and water content per unit volume, and the more severe the signal attenuation. A large altitude difference usually leads to a longer signal propagation path and may increase the number of times the signal passes through different atmospheric layers, intensifying the influence of atmospheric refraction and scattering on the signal, thereby increasing the degree of signal attenuation. Different building materials have different signal attenuation capabilities. The thickness, density of the obstacle, and electromagnetic characteristics of the material, etc., will all affect the magnitude of the material attenuation coefficient. Materials that are thicker and denser usually have a larger attenuation coefficient and cause more severe signal attenuation. By calculating the environmental compensation factor and applying it to the calculation of the theoretical received power of the reference signal, the actual signal strength received by the terminal can be predicted more accurately. During the communication network planning stage, considering the environmental compensation factor can more accurately evaluate the signal coverage in different areas.

[0037] Normalize the real-time rainfall, altitude difference, and material attenuation coefficient to eliminate the influence of different dimensions and data ranges. By calculating the environmental compensation factor, the impact of environmental factors on the received power of the signal can be quantified, providing an accurate compensation basis for the subsequent calculation of the theoretical received power of the reference signal, thereby more precisely predicting the actual signal reception situation and enhancing the scientificity of network handover decisions.

[0038] It should be noted that the rain attenuation coefficient can be dynamically predicted by the LSTM model and can take values from 0.5 to 1.5; the calculation formula based on the LSTM model is: ; Among them, is the measured received power of the reference signal of the i-th historical sample; Predicted received power of the reference signal of the i-th historical sample; R represents the actual rainfall of the i historical samples; and the constraint is kr ∈ [0.5, 1.5], i represents the serial number of the historical sample, and the value is a positive integer.

[0039] Dynamically predict the rain attenuation coefficient using an LSTM model, train the model with historical sample data (including the measured received power of the reference signal, the predicted received power, and the actual rainfall), determine the optimal rain attenuation coefficient value, improve the accuracy of signal attenuation assessment in a rainfall environment, enable the environmental compensation factor to more realistically reflect the impact of rainfall on the signal, and further improve the accuracy of calculating the theoretical received power of the reference signal. The impact of rainfall on signal attenuation is dynamically changing, and traditional fixed rain attenuation coefficients cannot accurately reflect this change. This solution uses the dynamic prediction ability of the LSTM model to adjust the rain attenuation coefficient in real time, solving the problem of inaccurate signal attenuation assessment caused by a fixed rain attenuation coefficient.

[0040] It should be noted that before formula calculation, all parameters need to be preprocessed by normalization. The calculation formula is as follows: ; where, Ptx represents the transmit power of the base station; PL0 represents the loss of the reference path; γ represents the path loss exponent, which can be set according to industry standards. For example, in urban areas, the value is 3.7, and in suburban areas, the value is 3.0, etc.; d represents the actual physical distance between the receiving end and the transmitting end; d0 represents the reference distance between the receiving end and the transmitting end, providing a standardized path loss calculation benchmark, usually taking the typical distance of free space propagation. G ant represents the directional gain of the antenna, with the unit of dBi, obtained from the performance parameter description of the antenna.

[0041] It should be noted that the terrain coefficient is obtained through the regional optimization method under the federated learning framework. The method is as follows: Calculate the gradient of the terrain coefficient through the terminal device. The kt value needs to be preset according to the actual situation. For example, the initial setting for plain terrain is 0.8; for hilly terrain, it is 1.2; for mountainous terrain, it is 1.5, etc. The formula is as follows: ; Among them, represents the gradient of the mountain coefficient, ‖(RSRP 实测 -RSRP 预测 )‖ 2 represents the square error operation of RSRP 实测 and RSRP 预测 ; ∂ is the partial integration operation.

[0042] Calculate the terrain coefficient based on the gradient of the terrain coefficient. The formula is as follows:

[0043] Among them, kt oldrepresents the terrain coefficient before update; σ represents the learning rate, with a value range of [0.001, 0.1], which can be selected according to historical experience. For example, in urban / plain areas: σ = 0.02, in mountain / hill areas: σ = 0.02, and so on.

[0044] The terrain coefficient is obtained through the regional optimization method under the federated learning framework, enabling the terrain coefficient to better reflect the impact of the actual terrain on the signal, improving the accuracy of calculating the environmental compensation factor, and further enhancing the prediction accuracy of the theoretical received power of the reference signal, making the network switching decision more accurate and effective under different terrain conditions. Since the attenuation effects of different terrains on the signal vary greatly, traditional fixed terrain coefficients cannot adapt to various complex terrain environments. This solution dynamically optimizes the terrain coefficient through the gradient descent method, solving the problem of inaccurate signal attenuation assessment caused by fixed terrain coefficients.

[0045] Step 107: Calculate the theoretical received power of the reference signal based on the environmental compensation factor, specifically: Theoretical received power of the reference signal = Original signal strength - Environmental compensation factor.

[0046] Calculating the theoretical received power of the reference signal based on the calculated environmental compensation factor can more accurately predict the actual theoretical received power of the reference signal, providing a more realistic and reliable signal strength evaluation index for the eSIM automated network switching strategy. During the network switching process, more reasonable network selection decisions can be made based on more accurate signal strength information, improving the success rate of network switching and communication quality, and enhancing the user experience. When calculating the theoretical received power of the reference signal using traditional methods, the influence of environmental factors is often not fully considered, resulting in a deviation between the calculated result and the actual received power. This solution solves the problem of inaccurate calculation of the theoretical received power of the reference signal under the influence of environmental factors by introducing the environmental compensation factor to correct the original signal strength.

[0047] Step two: Evaluate the basic performance indicators to determine whether the network to be accessed meets the basic performance evaluation criteria.

[0048] Conduct a comprehensive evaluation of the network quality evaluation index, roaming cost index, and theoretical received power of the reference signal to determine whether they meet the basic performance evaluation criteria. The specific method is as follows: Set the network quality index threshold, roaming cost index threshold, and received power threshold; compare the network quality evaluation index, roaming cost index, and environmental compensation factor with the network quality index threshold, roaming cost index threshold, and environmental state threshold respectively. When the network quality evaluation index is greater than or equal to the network quality index threshold, it is determined that this index is qualified; when the roaming cost index ≤ roaming cost index threshold, it is determined that this index is qualified; when the theoretical received power of the reference signal ≥ received power threshold, it is determined that this index is qualified.

[0049] When all three indicators are qualified, it is determined that the network to be connected meets the basic performance evaluation criteria.

[0050] It should be noted that the threshold of the network quality index refers to the relevant standards and specifications of the communication industry for network quality, or collects the quality evaluation index data of the network in different scenarios and time periods in the past, analyzes its distribution and the feedback of users on network quality, and determines a threshold that can ensure the basic network usage experience. For example, count the network quality evaluation index in the past month and take its average value or a certain percentile value (such as the 90th percentile) as the threshold. The threshold of the roaming cost index can be determined by analyzing the expected revenue and cost expenditure under different roaming costs according to the enterprise's cost control objectives and users' willingness to pay. The receiving power threshold can be set according to the requirements of the signal receiving power in the adopted communication standards and protocols. Communication protocols usually stipulate the minimum received signal power in different scenarios to ensure the reliability and compatibility of communication.

[0051] By comprehensively evaluating the network quality evaluation index, the roaming cost index, and the theoretical received power of the reference signal, it is judged whether it meets the basic performance evaluation criteria. The specific method is to set the corresponding thresholds and compare each indicator with the thresholds. When all three indicators are qualified, it is determined that the network to be connected meets the basic performance evaluation criteria; it can more accurately evaluate the overall performance of the network, provide a comprehensive and reliable evaluation result for network handover decisions, make the network handover decisions more scientific and reasonable, and improve the user experience and network reliability; traditional network evaluation methods often only focus on a single indicator and cannot comprehensively consider the impact of multiple factors on network performance. This solution solves the problem of insufficient comprehensiveness in network performance evaluation in the prior art by constructing a comprehensive evaluation system and improves the accuracy and scientificity of network performance evaluation.

[0052] Step 3: When the network to be connected meets the basic performance evaluation criteria, obtain network security data and analyze the network security data to calculate network security indicators.

[0053] Step 301: Obtain the GPS positioning coordinates of the terminal through the GNSS module built in the terminal, and query the geographical location coordinates registered by the base station through the core network MME database; obtain the moving speed of the terminal through the GNSS Doppler frequency shift or the inertial navigation system, and obtain the base station location update time interval through the base station broadcast system information block SIB; obtain the base station authentication level of the operator's core network for the base station identity, where 0 indicates unauthenticated and 1 indicates fully authenticated.

[0054] Step 302; Perform normalization preprocessing on the GPS positioning coordinates of the terminal, the geographical location coordinates registered by the base station, the moving speed of the terminal, the base station location update time interval, and the base station authentication level value; and calculate the signal spatio-temporal conflict index based on the preprocessed data. The formula is as follows: ; where STC represents the signal spatio-temporal conflict index; L GPS represents the GPS positioning coordinates of the terminal; L CEL represents the geographical location coordinates registered by the base station; represents the base station location update time interval; v represents the moving speed of the terminal; ZHD represents the base station authentication level value, which takes values of 0 or 1.

[0055] The formula comprehensively evaluates the signal spatio-temporal conflict situation by calculating the square of the Euclidean distance between the GPS positioning coordinates of the terminal and the geographical location coordinates registered by the base station, dividing it by the product of the moving speed of the terminal and the base station location update time interval, and finally multiplying it by the base station authentication level value. It can more accurately evaluate the spatio-temporal conflict risk during signal transmission, providing a quantitative basis for the calculation of network security indicators. During network handover, the security of the target network can be more accurately judged based on this index, so as to preferentially select the network with low spatio-temporal conflict risk, improve the overall security of the network, and reduce the possibility of security threats such as malicious attacks or data tampering on user equipment; traditional network security assessment methods are difficult to comprehensively consider the impact of multi-dimensional factors such as terminal location, base station location update, and authentication on network security, which easily leads to inaccurate and untimely assessment of the network security situation and cannot effectively identify potential security risks. This solution solves the problem of incomplete and inaccurate network security assessment in the existing technology by integrating these multi-source data and improves the ability to identify network security threats.

[0056] Step 303: Use the baseband processor to measure the carrier frequency offset value actually measured by the terminal in real time through the phase-locked loop PLL; obtain the carrier center frequency from the carrier frequency parameters broadcast by the base station, and calculate the Doppler frequency shift value based on the carrier center frequency; use the built-in magnetometer and accelerometer of the terminal to obtain the included angle area between the moving direction and the connection line of the base station, and calculate the cosine value of the included angle as the moving direction weight.

[0057] Step 304: Perform normalization preprocessing on the carrier frequency offset value, carrier center frequency, Doppler frequency shift value, and moving direction weight, and input the processed parameters into the Doppler frequency shift deviation calculation model to calculate the Doppler frequency shift deviation. The formula on which the Doppler frequency shift deviation calculation model is based is as follows: ; where DSD represents the Doppler frequency shift deviation, f obrepresents the carrier frequency offset value; f ca represents the Doppler frequency shift value; f0 represents the carrier center frequency, and YD represents the motion direction weight; Among them, the formula for calculating the carrier frequency offset value based on the carrier center frequency is: , where θ in the formula represents the angle between the base station motion direction and the base station, and c represents the speed of light.

[0058] By calculating the difference between the carrier frequency offset value actually measured by the terminal and the Doppler frequency shift value calculated based on the carrier center frequency, then dividing by the carrier center frequency, and multiplying the result by the motion direction weight, the Doppler frequency shift deviation is comprehensively evaluated; it can calculate the Doppler frequency shift deviation more accurately, providing a more accurate quantitative basis for the calculation of network security indicators. When the network switches, the signal stability of the target network can be judged more accurately based on this deviation, so as to preferentially select the network with a small Doppler frequency shift deviation, improve the overall stability of the network, reduce problems such as signal demodulation errors and communication quality degradation caused by a large Doppler frequency shift deviation, and enhance the user experience; traditional methods often ignore the influence of the terminal motion direction on the frequency shift when evaluating the Doppler frequency shift, resulting in inaccurate evaluation results. At the same time, in a complex environment, the relative motion speed between the terminal and the base station changes rapidly, and traditional fixed-parameter calculations are difficult to adapt. This solution solves the problems of inaccurate evaluation of Doppler frequency shift deviation and insufficient consideration of the influence of the motion direction in the prior art by integrating multi-source data such as carrier frequency offset value, Doppler frequency shift value, and motion direction weight, improving the accuracy and reliability of the evaluation of Doppler frequency shift deviation.

[0059] Step 305: Count the proportion of each signaling type within the last 1 hour to obtain the occurrence probability of the kth type of signaling within the time window; dynamically adjust the federated learning weight coefficient according to the number of terminals through the cloud; call the federated learning server API, input the local entropy value and time series features, and obtain the abnormal probability, which takes values in [0, 1], as the federated learning abnormal score; the calculation method of the local entropy value is: .

[0060] Traditional methods are difficult to accurately identify abnormal behaviors in the network in real time. Especially in a complex network environment, the concealment of abnormal behaviors is relatively high, and traditional methods are prone to false negatives or false positives. This solution uses federated learning technology to solve the problem of inaccurate identification of abnormal behaviors in the prior art by integrating local entropy values and time series features, improving the accuracy and timeliness of abnormal behavior identification.

[0061] Step 306: Normalize the occurrence probability, federated learning weight coefficient, and anomaly probability of the k-th type of signaling within the time window, and then calculate the behavior pattern entropy through the behavior pattern entropy calculation model. The formula is as follows: ; where BPE represents the behavior pattern entropy; p k represents the occurrence probability of the k-th type of signaling within the time window; k represents the serial number of the signaling type, taking positive integer values; ρ represents the federated learning weight coefficient, and the calculation formula is: ρ = 0.5 + 0.1 * log(N 终端 ), in the formula, N 终端 represents the number of terminals; YC represents the federated learning anomaly score, taking values in [0, 1].

[0062] In different network environments, the number of terminals varies greatly. The traditional fixed weight coefficient cannot adapt to this change, resulting in a decline in the adaptability and accuracy of network security assessment results. By dynamically adjusting the weight coefficient, the problem of fixed weight coefficient in the prior art is solved, and the flexibility and adaptability of network security assessment are improved. It enables network security assessment to better reflect the impact of the terminal scale in the actual network environment, enhancing the reliability and accuracy of the assessment results. In a network environment with a large number of terminals, potential security threats can be more effectively identified, ensuring the security and stability of the network.

[0063] Traditional network security assessment methods mainly focus on the physical layer parameters of network signals, while ignoring the impact of network behavior patterns on network security. This solution solves the problem of insufficient consideration of network behavior patterns in the prior art by integrating multi-source data such as signaling occurrence probability, federated learning weight coefficient, and anomaly probability, making network security assessment more comprehensive and accurate. It can more precisely assess the network security situation, providing a comprehensive quantitative basis for the calculation of network security indicators. When the network switches, the security of the target network can be more accurately judged according to the behavior pattern entropy, so as to preferentially select the network with a low behavior pattern entropy, improving the overall security of the network and reducing the possibility of user equipment being attacked by malicious attacks or interfered by abnormal behaviors.

[0064] Step Four: Determine whether the network security indicators meet the network security standards; when all network security indicators meet the network security standards, execute the network switching instruction.

[0065] Step 401: Set the signal spatio-temporal conflict threshold, Doppler frequency shift deviation threshold, and behavior pattern entropy threshold respectively; compare the signal spatio-temporal conflict index, Doppler frequency shift deviation, and behavior pattern entropy with their corresponding thresholds respectively; when the signal spatio-temporal conflict index < the signal spatio-temporal conflict threshold, this index meets the safety standard; when the Doppler frequency shift deviation < the Doppler frequency shift deviation threshold, this index meets the safety standard; when the behavior pattern entropy < the behavior pattern entropy threshold, this index meets the safety standard.

[0066] It should be noted that the signal spatio-temporal conflict threshold can be determined by collecting the signal spatio-temporal conflict index data of the network in different past scenarios, analyzing its distribution and change trend, and determining a threshold that can ensure network security. For example, count the signal spatio-temporal conflict index in different regions and different time periods in the past month, and take its average value or a certain percentile (such as the 95th percentile) as the threshold; or by studying the signal spatio-temporal conflict index when network security incidents occurred in the past, find the index range related to security incidents, and set the threshold slightly lower than the lower limit of this range to trigger the security assessment mechanism in a timely manner when similar situations occur. The Doppler frequency shift deviation threshold, in the actual network environment, through field tests, record the network quality assessment index under different Doppler frequency shift deviations, count the Doppler frequency shift deviations for which the network quality assessment index meets the standard, and take its average value or a certain percentile (such as the 95th percentile) as the threshold. The behavior pattern entropy threshold can be set at the critical point between the entropy values of normal behavior and abnormal behavior by collecting and analyzing known abnormal behavior samples and calculating their behavior pattern entropy as the threshold.

[0067] Step 402: When all three indicators are qualified, determine that the network to be accessed meets the security assessment standard and execute the network switching instruction.

[0068] When all three network security indicators are qualified, quickly determine that the network to be accessed meets the security assessment standard and immediately execute the network switching instruction to ensure that the user device can be connected to a safe and reliable network in a timely manner; the user device can be connected to a safer and better-quality network faster, reducing problems such as a decline in communication quality or data loss that may be caused by network switching delay, and improving the user's satisfaction and communication experience.

[0069] Step Five: When the network to be accessed meets the basic performance assessment standard but does not meet the network security standard, if an emergency security warning instruction is received, it can temporarily activate an exemption instruction, ignore the evaluation result of the network security assessment, execute the instruction to switch the network, and after the emergency security warning instruction is eliminated, then refer to the network evaluation strategy in Steps One to Four to determine whether to switch to other networks.

[0070] It should be noted that the emergency safety warning instruction can be to respond to the emergency instructions issued by the official, such as the red safety warning (war, natural disasters, etc.). It is necessary to verify the digital signature certificate PKI and the physical key HSM at the same time. After judging that the instruction is true, the exemption instruction is triggered.

[0071] When the network to be accessed meets the basic performance evaluation criteria but does not meet the network security standards, if an emergency safety warning instruction is received, the exemption instruction can be temporarily started, ignoring the evaluation results of the network security evaluation, and executing the instruction to switch the network. After the emergency safety warning instruction is eliminated, refer to the network evaluation strategy in Steps 1 to 4 to determine whether to switch to other networks.

[0072] In some special emergency situations (such as war, natural disasters, etc.), the network security standards may conflict with the timeliness and continuity of communication. The traditional network switching strategy will reject the switch when the network security evaluation fails. However, in emergency situations, this approach may lead to communication interruption, affecting the transmission of critical information and emergency rescue work. By introducing an emergency exemption mechanism, this solution solves the problem of inability to flexibly switch networks in the prior art under emergency situations on the premise of ensuring the authenticity of the instruction, improving the emergency response ability and flexibility of the system; it can quickly establish a communication connection in emergency situations, ensure the continuity of critical services and the timely transmission of information, reduce the switching delay caused by network security evaluation, and reduce the risk of communication interruption, providing strong support for emergency rescue, command and dispatch, etc. At the same time, the security of the system is guaranteed through the instruction verification mechanism, preventing malicious attacks from using the exemption mechanism for destruction.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0074] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. An eSIM automatic network switching strategy management method based on multi-source data fusion, characterized in that Including: Step 1: Obtain real-time network status data, and calculate the basic performance indicators of the network status by analyzing the network status data; Step 2: Evaluate the basic performance indicators to determine whether the network to be accessed meets the basic performance evaluation criteria; Step 3: When the network to be accessed meets the basic performance evaluation criteria, obtain network security data, analyze the network security data, and calculate network security indicators; Step 4: Judge whether the network security indicators meet the network security standards; when all the network security indicators meet the network security standards, execute the network switching instruction; Step 5: When the network to be accessed meets the basic performance evaluation criteria but does not meet the network security standards, if an emergency security warning instruction is received, the exemption instruction can be temporarily activated, ignoring the evaluation result of the network security evaluation, execute the instruction to switch the network, and after the emergency security warning instruction is eliminated, then refer to the network evaluation strategy in Steps 1 to 4 to judge whether to switch to other networks.

2. The eSIM automated network switching policy management method based on multi-source data fusion according to claim 1, wherein The basic performance indicators include the network quality evaluation index, the roaming cost index, and the reference signal theoretical received power.

3. The method for managing eSIM automatic network switching strategy based on multi-source data fusion according to claim 2, wherein The method for obtaining the network quality evaluation index is: Obtain the measurement configuration through the measurement control message in the system information block SIB broadcast by the base station, and then measure the reference signal theoretical received power; after receiving the signal from the base station, calculate the signal-to-noise ratio according to the received useful signal power and the interference and noise floor power; in the wireless network, measure the round-trip time of the RRC signaling or the round-trip time based on the TCP (Transmission Control Protocol) connection as the round-trip delay; Perform normalization preprocessing on the reference signal theoretical received power, the signal-to-noise ratio, and the round-trip delay to obtain the received signal score, the signal-to-noise ratio score, and the round-trip delay score respectively, and perform comprehensive analysis through the network quality evaluation value index calculation model to obtain the network quality evaluation index.

4. The eSIM automatic network switching strategy management method based on multi-source data fusion according to claim 2, wherein The method for obtaining the roaming cost index is: Perform API docking with the billing system of the network operator to obtain the real-time tariff value of the user in different network environments in real time; query the tax database of the target country or region to obtain the applicable tax rate value of the communication service; obtain the service contract signed between the enterprise and the operator from the enterprise's contract management system, and extract the benchmark tariff value from the service contract; Perform normalization preprocessing on the real-time tariff value, the applicable tax rate value, and the benchmark tariff value, and perform comprehensive analysis through the roaming cost calculation model to obtain the roaming cost index.

5. The eSIM automated network switching policy management method based on multi-source data fusion according to claim 4, characterized in that The method for obtaining the reference signal theoretical received power is: Obtain the real-time rainfall within the regional range through the meteorological API, and obtain the altitude difference between the terminal and the base station; obtain the material attenuation coefficient of the obstacles or obstructions within the region; perform normalization processing on the real-time rainfall, the altitude difference, and the material attenuation coefficient, and analyze and calculate to obtain the environmental compensation factor. The formula is as follows: ； Among them: HBC represents the environmental compensation factor; JY represents the real-time rainfall in the area; HB represents the altitude difference between the area and the base station; JZ j represents the material attenuation coefficient of the j-th obstacle in the area; kr is the rain attenuation coefficient, representing the dynamic influence weight of rainfall on signal attenuation; kt is the terrain coefficient, representing the non-linear compensation factor of terrain undulation on signal attenuation; β j represents the proportion weight of different building materials in the environment, and the calculation formula is: β j = material attenuation coefficient of the j-th obstacle / sum of material attenuation coefficients of all obstacles; j represents the serial number of the obstacle, and the value is a positive integer; Calculate the reference signal theoretical received power according to the original signal strength and the environmental compensation factor. Specifically: Reference signal theoretical received power = Original signal strength - Environmental compensation factor.

6. The eSIM automated network switching policy management method based on multi-source data fusion according to claim 5, characterized in that Perform comprehensive evaluation on the network quality evaluation index, the roaming cost index, and the reference signal theoretical received power to judge whether they meet the basic performance evaluation criteria. The specific method is as follows: Set the network quality index threshold, roaming cost index threshold, and received power threshold; compare the network quality evaluation index, roaming cost index, and environmental compensation factor with the network quality index threshold, roaming cost index threshold, and environmental status threshold respectively. When the network quality evaluation index is greater than or equal to the network quality index threshold, it is determined that this index is qualified; when the roaming cost index ≤ the roaming cost index threshold, it is determined that this index is qualified; when the reference signal theoretical received power ≥ the received power threshold, it is determined that this index is qualified; When all three indicators are qualified, it is determined that the network to be accessed meets the basic performance evaluation criteria.

7. The eSIM automated network switching strategy management method based on multi-source data fusion according to claim 6, wherein, Network security data includes signal spatio-temporal conflict index, Doppler frequency shift deviation, and behavior pattern entropy.

8. The method for managing eSIM automatic network switching policies based on multi-source data fusion according to claim 7, wherein The method for obtaining the signal spatio-temporal conflict index is as follows: Obtain the GPS positioning coordinates of the terminal through the built-in GNSS module of the terminal, and query the geographical location coordinates registered by the base station through the core network MME database; obtain the moving speed of the terminal through GNSS Doppler frequency shift or inertial navigation system, and obtain the base station location update time interval through the base station broadcast system information block SIB; Obtain the base station authentication level value of the base station identity by the operator's core network, perform normalization preprocessing on the GPS positioning coordinates of the terminal, the geographical location coordinates registered by the base station, the moving speed of the terminal, the base station location update time interval, and the base station authentication level value, and calculate the signal spatio-temporal conflict index. The formula is as follows: ； Among them, STC represents the signal spatio-temporal conflict index; L GPS represents the GPS positioning coordinates of the terminal; L CEL represents the geographical location coordinates registered by the base station; represents the base station location update time interval; v represents the moving speed of the terminal; ZHD represents the base station authentication level value; Use the baseband processor to measure the carrier frequency offset value actually measured by the terminal in real time through the phase-locked loop PLL; obtain the carrier center frequency from the carrier frequency parameters broadcast by the base station, and calculate the Doppler frequency shift value based on the carrier center frequency. The formula is: , where f in the formula ca represents the Doppler frequency shift value, f0 represents the carrier center frequency, v represents the moving speed of the terminal, θ represents the angle between the base station movement direction and the base station, and c represents the speed of light; the cosine value of the angle between the movement direction and the base station connection line is obtained by using the built-in magnetometer and accelerometer of the terminal, and the cosine value of the angle is calculated as the movement direction weight; the carrier frequency offset value, the carrier center frequency, the Doppler frequency shift value, and the movement direction weight are normalized and preprocessed, and the processed parameters are input into the Doppler frequency shift deviation calculation model to calculate the Doppler frequency shift deviation; Statistically calculate the proportion of each signaling type within a unit period to obtain the occurrence probability of the k-th type of signaling within the time window; dynamically adjust the federated learning weight coefficient according to the number of terminals by the cloud; call the federated learning server API, input the local entropy value and time series features, and obtain the abnormal probability as the federated learning abnormal score. The calculation method of the local entropy value is: ; p k represents the occurrence probability of the k-th type of signaling within the time window Perform normalization preprocessing on the occurrence probability of the k-th type of signaling within the time window, the federated learning weight coefficient, and the abnormal probability, and calculate the behavior pattern entropy according to the behavior pattern entropy calculation model.

9. The eSIM automated network switching policy management method based on multi-source data fusion according to claim 8, characterized in that, The method for determining whether the network security indicators meet the network security standards is as follows: Set the signal spatio-temporal conflict threshold, Doppler frequency shift deviation threshold, and behavior pattern entropy threshold respectively; compare the signal spatio-temporal conflict index, Doppler frequency shift deviation, and behavior pattern entropy with the corresponding thresholds respectively; when the signal spatio-temporal conflict index < the signal spatio-temporal conflict threshold, this index meets the security standards; when the Doppler frequency shift deviation < the Doppler frequency shift deviation threshold, this index meets the security standards; when the behavior pattern entropy threshold < the behavior pattern entropy threshold, this index meets the security standards.

10. An eSIM automatic network switching strategy management system based on multi-source data fusion, characterized in that An eSIM automated network switching strategy management method based on multi-source data fusion for implementing any one of the above claims 1-9.

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