Optimal signal selection intelligent network switching management method based on super SIM (Subscriber Identity Module) multiple networks

Abstract

The invention discloses an optimal signal selection intelligent network switching management method based on a super SIM multi-network, and relates to the field of intelligent network switching management, and the main scheme is as follows: analyzing and calculating operation state data to obtain a network delay score, a downloading rate score and a network stability score in a current network environment, and carrying out comprehensive analysis to obtain an optimal signal selection score; obtaining a current network state score; judging whether to execute a network switching instruction or not by evaluating the current network state score; the coverage degree data, the network state data and the switching cost data are analyzed and calculated to respectively obtain coverage matching degree indexes, network state indexes and switching cost-benefit ratios of different operators; switching priority indexes of different operators are obtained through comprehensive analysis, the switching priority is determined, and the problem that switching decisions and requirements are disjointed due to the fact that switching is triggered only based on signal strength or a single rate threshold and core indexes such as network delay and stability which affect user experience are not fused is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of network management, and specifically to an intelligent network switching management method for optimal signal selection based on a super SIM multi-network. Background Art

[0002] With the rapid development of 5G, the Internet of Things (IoT), and edge computing, users' demands for mobile network quality have been upgraded from simple "connectivity" to comprehensive experience requirements of "high stability, low latency, and high throughput". The maturity of super SIM card technology makes it possible for a single card to access multiple networks (access to multiple operator networks), providing a hardware foundation for dynamically selecting the optimal network for mobile devices. In this context, the application prospect of intelligent network switching management methods is broad.

[0003] For example, in scenarios such as mobile office, high-definition video live broadcast, cloud gaming, and remote medical treatment, users need to switch to the best network in real time to ensure business continuity; by dynamically aggregating network resources of multiple operators, the limitations of single network coverage or congestion can be broken through, the user experience can be improved, and by combining network quality and tariff strategies, the most cost-effective access solution can be provided for users.

[0004] Although current mobile terminals support access to multiple operator networks, the traditional switching mechanism relies on manual selection or fixed rules (such as signal strength thresholds), and there are the following problems: Existing methods only trigger switching based on signal strength or a single rate threshold, without integrating core indicators such as network latency and stability that affect the user experience, resulting in the disconnection between switching decisions and business needs.

[0005] Traditional solutions adopt preset priorities or fixed cost models, without dynamically evaluating coverage matching and switching cost-effectiveness. Summary of the Invention

[0006] (I) Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides an intelligent network switching management method for optimal signal selection based on a super SIM multi-network, which at least solves the problem in the prior art that switching is only triggered based on signal strength or a single rate threshold, without integrating core indicators such as network latency and stability that affect the user experience, resulting in the disconnection between switching decisions and requirements.

[0007] (II) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent network switching management method for optimal signal selection based on a super SIM multi-network, including: Step 1: Obtain the operation status data of the current network operation environment, and calculate the network latency score, download rate score, and network stability score in the current network environment through analysis and calculation of the operation status data; Step 2: By comprehensively analyzing the network latency score, download rate score, and network stability score, obtain the current network status score; and further evaluate the current network status score to determine whether to execute the network switching instruction. Step 3: When it is determined to execute the network switching instruction; obtain the coverage data, network status data, and switching cost data of the operator to be accessed. Step 4: By analyzing and calculating the coverage data, obtain the coverage matching index of different operators; by analyzing and calculating the network status data, obtain the network status index of different operators; by analyzing the switching cost data, obtain the switching cost-benefit ratio. Step 5: By comprehensively analyzing the coverage matching index, network status index, and switching cost-benefit ratio of different operators, obtain the switching priority index of different operators and determine the switching priority.

[0008] In the preferred scheme of the above optimal signal selection intelligent network switching management method based on super SIM multi-network, the operating state data includes the round-trip delay values, packet loss rate, and broadband utilization rate at multiple time points within a detection time period; calculate the corresponding average round-trip delay value, average packet loss rate, and average bandwidth utilization rate through the round-trip delay values, packet loss rate, and broadband utilization rate at multiple time points; obtain the delay jitter value by calculating the standard deviation of the difference between the round-trip delay values of adjacent data packets, and input these parameters into the network latency scoring calculation model to obtain the network latency score SCO of the detection time period. The operating state data also includes the number of disconnections counted within the time period and the reconnection time after each disconnection. Calculate the disconnection frequency through the time value of the time period and the number of disconnections; calculate the average reconnection time by summing up the reconnection times after each disconnection and calculating the mean value; input the disconnection frequency and average reconnection time into the network stability scoring model to calculate the network stability score SCE. The operating state data also includes the download rates at different time points and the buffering duration of audio and video counted within the time period. Sum up and calculate the average values of the download rates and buffering durations at different time points respectively to obtain the average download rate and average buffering duration; input the average download rate and average buffering duration into the download-buffering evaluation model to calculate the download-buffering score SCQ.

[0009] In the preferred scheme of the above optimal signal selection intelligent network switching management method based on super SIM multi-network, the current network status score is calculated by inputting the network latency score, download rate score, and network stability score into the network status scoring calculation model. The calculation formula is as follows: ; where, ZH represents the current network status score; , where \(e\) represents the base of the natural logarithm, and \(k\) SCO represents the Sigmoid steepness coefficient, and SCO thr represents the threshold of the network delay score; , where SCE max represents the theoretical maximum value of SCE; , where \(\gamma\) represents the stability index.

[0010] In the above preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, the method for evaluating the current network status score and determining whether to execute the network switching instruction is as follows: Set the network status score threshold, and compare the current network status score with the network status score threshold. When the current network status score ≤ the network status score threshold, execute the network switching instruction.

[0011] In the above preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, the coverage data includes the user's life trajectory obtained through the trajectory module, and predicts multiple prediction areas where the user will be in the future unit time and the probability of appearing in each prediction area through the life trajectory; obtain the heat map of the operator to be accessed by docking the APIs of different operators, and obtain the median signal strength of the signal area corresponding to the historical heat map and the prediction area, and calculate the predicted distance value between the prediction area and the base stations of different operators; The network status data is the historical network status data of different operators in different prediction areas, including the network delay, channel fluctuation index, and signal uniformity index of different operators in different prediction areas; The switching cost data includes the network tariff unit prices of different operators and the current network tariff unit price obtained through the official website tariff publicity page or the operator APP, and the historical switching success rate of different operators obtained through the historical log.

[0012] In the above preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, by inputting the median signal strength corresponding to the heat maps of different operators and the predicted distance value into the coverage matching degree calculation model, calculate the coverage matching degree index, and the formula is as follows: ; where FGD q represents the coverage index of the \(q\)-th operator; YC q，i represents the distance value between the base station of the \(q\)-th operator and the \(i\)-th prediction area; RL q，i represents the median signal strength corresponding to the \(i\)-th prediction area in the heat map of the \(q\)-th operator; GL iIndicates the probability that the terminal appears in the i-th predicted area; q represents the serial number of different operators; i represents the serial number of the predicted area, and n represents the total number of predicted areas.

[0013] In the above-mentioned preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, the network state index of different operators in the predicted area is calculated through the network delay, channel fluctuation index, and signal uniformity index of different operators in different predicted areas. The formula is as follows: ; Among them, DWI q represents the network state index of the q-th operator, SY q represents the network delay of the q-th operator; CQV q represents the channel fluctuation index of the q-th operator; DOS q represents the signal uniformity index of the q-th operator; α1 represents the weight coefficient of the network delay; α2 represents the weight coefficient of the channel fluctuation index; α3 represents the weight coefficient of the signal uniformity index.

[0014] In the above-mentioned preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, by comprehensively analyzing the network tariff unit price, current network tariff unit price, and historical switching success rate of different operators, the switching cost-benefit ratio is obtained. The formula is as follows: ; Among them, NCE q represents the switching cost-benefit ratio of the q-th operator; Cta q represents the network tariff unit price of the q-th operator; Ccu represents the current network tariff unit price; δ represents the second penalty factor; RS q represents the historical switching success rate of the q-th operator.

[0015] In the above-mentioned preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, by comprehensively analyzing the coverage matching degree index, network state index, and switching cost-benefit ratio of different operators, the switching priority index is obtained. The formula is as follows: ; Among them, YXJ q represents the switching priority index of the q-th operator; β1 represents the weight coefficient of the coverage matching degree index; β2 represents the weight coefficient of the network state index; β3 represents the weight coefficient of the switching cost-benefit ratio.

[0016] In the above-mentioned preferred solution of the optimal signal selection intelligent network switching management method based on the super SIM multi-network, the method for determining the switching priority is: Traverse the handover priority indices of different operators, and take the numerical value of the handover priority index as the handover priority, and then switch the network.

[0017] (III) Beneficial effects The present invention provides an eSIM automatic network switching strategy management system and method based on multi-source data fusion, which has the following beneficial effects: (1) By obtaining the operation status data of the current network operation environment and analyzing and calculating the network delay score, download rate score, and network stability score in the current network environment, it provides comprehensive and key basic data for subsequent network status evaluation, which helps to accurately understand the performance of the current network; through the comprehensive evaluation of multi-dimensional indicators such as network delay, download rate, and network stability, it can switch to a better network in time when the current network status deteriorates, ensuring that users can enjoy a smoother, more stable, and faster network experience when using mobile data services, reducing situations such as card lags and disconnections caused by network problems, and improving user satisfaction with mobile communication services.

[0018] (2) By comprehensively analyzing the scores of the three important indicators of network delay, download rate, and network stability, the current network status score is obtained, and based on this, it is judged whether to execute the network switching instruction, which sets a clear triggering mechanism for the entire intelligent network switching management process, enabling it to respond to changes in the current network status in a timely and proactive manner, avoiding passively using the network when the network quality deteriorates; through comprehensive analysis and decision-making based on various factors such as the coverage, network status, and switching cost of the operator to be accessed, it avoids blindly switching networks, enabling the user equipment to switch between the most suitable networks, making full use of the network resources of each operator, improving the utilization efficiency of the entire network resources, avoiding waste of network resources, and at the same time alleviating network congestion problems.

[0019] (3) When it is necessary to execute the network switching instruction, further obtain the coverage data, network status data, and switching cost data of the operator to be accessed. These three aspects of data are crucial for subsequent selection of the optimal target network, ensuring that factors such as coverage range, actual network performance, and cost during the switching process can be comprehensively considered when switching networks; determining the handover priority based on the consideration of the switching cost-benefit ratio can effectively reduce unnecessary network switching times, reduce the terminal power consumption, system resources, and signaling overhead consumed during each switching process, thereby reducing the overall network switching cost, extending the battery life of the terminal device, and improving the operation efficiency and stability of the system, which has important economic value for both operators and users.

[0020] (4)Analyze and calculate the coverage data, network status data, and handover cost data respectively to obtain the coverage matching index, network status indicator set, and handover cost-benefit ratio for different operators, quantifying complex network-related information into specific indexes and indicators, which is convenient for subsequent comprehensive and accurate comparison and evaluation of different operators' networks, and provides a strong basis for determining the handover priority. This solution can intelligently select the optimal network for handover according to the characteristics of different operators' networks and user needs in different network environments and conditions, making the user equipment more adaptable in the network, able to always maintain a good network connection state in a complex multi-network environment, and enhancing the reliability and availability of network services.

[0021] (5)Comprehensively analyze the coverage matching index, network status indicator set, and handover cost-benefit ratio of different operators to obtain the handover priority index of different operators and determine the handover priority, realizing intelligent decision-making based on multiple key factors in a multi-network environment, being able to select the optimal network that best meets the user needs and the current actual situation, reflecting the progress of communication technology in terms of intelligence and automation, and contributing to promoting the development of the communication industry towards a more intelligent and efficient direction. Description of the Drawings

[0022] Figure 1 It is a schematic diagram of the steps of the intelligent network handover management method for optimal signal selection based on super SIM multi-network of the present invention. Detailed Embodiments

[0023] 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 making creative efforts shall fall within the protection scope of the present invention.

[0024] Embodiment 1

[0025] Please refer to Figure 1 , the present invention provides an intelligent network handover management method for optimal signal selection based on super SIM multi-network, including: Step 1: Obtain the operation status data of the current network operation environment, and analyze and calculate the operation status data to obtain the network delay score, download rate score, and network stability score in the current network environment.

[0026] Step 101: The operation status data is the operation status data in the environment during the detection time period. The operation status data includes round-trip delay values, packet loss rates, and broadband utilization rates at multiple time points within the time period. Calculate the corresponding average round-trip delay value, average packet loss rate, and average broadband utilization rate through the round-trip delay values, packet loss rates, and broadband utilization rates at multiple time points. Calculate the standard deviation of the differences between adjacent packet round-trip delay values to obtain the delay jitter value. And by inputting these parameters into the network delay score calculation model, obtain the network delay score for the detection time period. The calculation formula is as follows: ; where SCO represents the network delay score, and RTT avg represents the average value of the network delay; RTT JZ represents the reference value of the round-trip delay, which is used to convert the average round-trip delay into a relative proportion value; it can be adjusted according to requirements and can be set with reference to international standards. For example, when RTT ≤ 150ms and is taken as 100ms, if RTT avg takes a value of 50ms, the calculated value is 100 / 50 = 2, indicating that the round-trip delay is better than the reference. If, RTT avg takes a value of 200ms, the calculated value is 100 / 200 = 0.5, indicating a poor delay; JIT represents the delay jitter value of the round-trip delay; JIT JZ represents the reference value of the delay jitter value, which can be set with reference to international standards, such as JIT ≤ 30ms; RAT avg represents the average value of the broadband utilization rate; ω1 represents the weight coefficient of the round-trip delay term, ω2 represents the weight coefficient of the delay jitter term; ω3 represents the weight coefficient of the broadband utilization rate term, and ω1 + ω2 + ω3 = 1. It can be taken as ω1 = 0.4, ω2 = 0.3, ω3 = 0.3; CF represents the first penalty factor.

[0027] The calculation formula of CF is: ; where RLT avg represents the average value of the packet loss rate; RLT JZ represents the reference value of the packet loss rate, such as taking a value of 20%; and when RLT JZ > 20%, the first penalty factor CF directly takes the maximum value of 1.

[0028] This formula is a network delay scoring calculation model, the core of which is to comprehensively consider multiple key network parameters to quantitatively evaluate the network delay situation. Specifically, the ratio of the average round-trip delay to the round-trip delay reference value, the ratio of the average delay jitter to the delay jitter reference value, and the average bandwidth utilization rate, these three indicators, are respectively multiplied by the corresponding weight coefficients, then added together, and finally multiplied to obtain the network delay score. Among them, the network congestion factor is used to further correct the score to make it more in line with the actual network situation. The weight coefficients of each parameter can be adjusted according to the actual application scenario and requirements to reflect the importance of different network parameters to network delay.

[0029] Traditional network delay assessments often rely solely on a single round-trip delay indicator and cannot comprehensively reflect the actual performance of the network. For example, when evaluating delay only through RTT, it may ignore the unstable network transmission caused by delay jitter, or the indirect impact of bandwidth utilization on delay; however, there are various factors affecting delay in the network environment, such as round-trip delay, delay jitter, and bandwidth utilization, etc. These factors interact with each other and jointly affect network performance. This formula provides a method to comprehensively quantify these different factors, incorporate them into a unified calculation model, and obtain a comprehensive network delay score through weighted summation, solving the problem of how to integrate multiple complex network factors into an easy-to-understand and comparable indicator, facilitating the unified evaluation and comparison of network delay performance in different network environments or at different times; and different network environments and application scenarios have different sensitivities and importance to network parameters. By adjusting the weight coefficients in the formula, according to the specific application scenario and requirements, the influence of certain network parameters can be flexibly emphasized or weakened, making the network delay score more in line with the actual business needs and network environment characteristics, achieving the generality and flexibility of network delay assessment in different scenarios. The network delay score calculated based on this formula can accurately reflect the actual delay situation of the network, providing a strong basis for network performance evaluation, fault troubleshooting, and optimization. It transforms the qualitative description of network delay into a quantitative assessment, making the performance comparison between different networks simple and intuitive. By comparing the network delay scores in different network environments, network operators and users can clearly understand the advantages and disadvantages of each network, providing a scientific decision-making basis for choosing a suitable network service provider or making network switches.

[0030] It should be noted that before calculating the network delay score and the first penalty factor, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0031] Step 102: The running status data also includes the number of disconnections counted within a time period and the reconnection time after each disconnection. The disconnection frequency is calculated by the time value of the time period and the number of disconnections. The average reconnection time is obtained by summing up the reconnection times after each disconnection and calculating the mean value. By inputting the disconnection frequency and the average reconnection time into the network stability scoring model, the network stability score is calculated, and the formula is as follows: ; where SCE represents the network stability score; DX represents the disconnection frequency; CL represents the average reconnection time; ω4 represents the weight coefficient of the disconnection frequency; ω5 represents the weight coefficient of the average reconnection time, and ω4 + ω5 = 1. It can take values of ω4 = 0.5 and ω5 = 0.5.

[0032] It should be noted that before calculating the network stability score, several corresponding parameters need to be pre - processed by normalization to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0033] This formula is a network stability scoring calculation model, aiming to quantitatively evaluate the stability of the network. Specifically, the network stability score is calculated by taking the reciprocal of the sum of multiplying the two key factors of the disconnection frequency and the average reconnection time by their corresponding weight coefficients and then adding 1 (to avoid a denominator of 0), and it can be adjusted according to the actual situation to reflect the importance of different factors to network stability.

[0034] Traditional network stability evaluations often rely only on a single number of disconnections or reconnection time, making it difficult to comprehensively reflect the actual situation of the network. This formula combines the disconnection frequency and the average reconnection time, comprehensively considering the frequency of network interruptions and the time required to restore the connection, solving the problem of how to comprehensively and accurately evaluate network stability and avoiding the one - sidedness caused by single - indicator evaluations.

[0035] In a multi - network environment, it is necessary to quantitatively compare the stabilities of different operators or different network regions. This formula provides a method to convert network stability into a specific score. Through the calculated SCE value, the stabilities of different networks can be intuitively compared, providing strong data support for network selection and optimization. This formula transforms the qualitative description of network stability into a quantitative evaluation, making the comparison of stability performance between different networks simple and intuitive. By comparing the SCE values in different network environments, network operators and users can clearly understand the advantages and disadvantages of each network, providing a scientific decision - making basis for choosing a suitable network service provider or switching networks.

[0036] Step 103: The running state data also includes the download rate at different time points and the buffering duration of audio and video statistics within a time period. Sum and calculate the average value of the download rate and buffering duration at different time points respectively to obtain the average download rate and average buffering duration. By inputting the average download rate and average buffering duration into the download-buffering evaluation model, calculate the download-buffering score. The basis formula is as follows: ; where SCQ represents the download-buffering score, D avg represents the average value of the download rate; D max represents the theoretical maximum download speed of the current network mode; B avg represents the average value of the buffering duration, such as the waiting duration from the request to the start of playback; B min represents the minimum tolerable buffering time threshold, such as set to 0.5 seconds. Values below this are regarded as instantaneous response; ω6 represents the weight coefficient of the average download rate; ω7 represents the weight coefficient of the average buffering duration, and ω6 + ω7 = 1. It can take values of ω6 = 0.4 and ω7 = 0.6.

[0037] It should be noted that before calculating the download-buffering score, several corresponding parameters need to be preprocessed by normalization to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0038] This formula is a download-buffering score calculation model for comprehensively evaluating the network's capabilities in terms of download and buffering. Among them, the ratio of the average download rate to the theoretical maximum download speed of the current network mode reflects the gap between the actual download rate and the ideal state; the ratio of the average buffering duration to the minimum tolerable buffering time threshold reflects whether the buffering duration meets the basic requirements. By multiplying these two ratios by the corresponding weight coefficients respectively and then multiplying them, the download-buffering score is obtained. The weight coefficients can be adjusted according to different application scenarios and requirements to balance the influence of the download rate and buffering duration on the score.

[0039] Traditional methods often evaluate the download rate or buffering duration separately and are difficult to comprehensively reflect the network's overall performance in actual applications. This formula combines the two, constructs a comprehensive scoring model, solves the problem of how to consider both download and buffering performance simultaneously, avoids the one-sidedness caused by single-index evaluation, and provides a more comprehensive perspective for network performance evaluation.

[0040] In a multi-network environment, it is necessary to quantitatively compare the download-buffering performance of different networks. This formula provides a method to integrate the download rate and buffering duration into a single score, making the performance comparison between different networks more intuitive and convenient. It helps users and network operators quickly understand the advantages and disadvantages of each network, providing strong support for network selection and switching. Quantifying the network download-buffering performance into a specific score makes the performance comparison between different networks simple and intuitive. By comparing the SCQ values of different networks, users can clearly understand the advantages and disadvantages of each network, and thus select the network access that best suits their needs to obtain a better network experience. At the same time, network operators can also optimize and adjust the network according to the SCQ score to enhance network competitiveness. In the intelligent network switching management method for optimal signal selection based on Super SIM multi-networks, an accurate download-buffering score is of great significance for network switching decisions. This formula provides key data support for intelligent network switching management, enabling the system to make more intelligent and reasonable network switching decisions based on real-time SCQ scores, combined with other network status factors such as network latency scores and stability scores. This helps to achieve the efficient utilization of network resources and the smooth switching of user devices between different networks, improving the operating efficiency and intelligent level of the entire network system, while reducing the risks of user experience degradation and data transmission interruption caused by improper network switching.

[0041] Step 2: Through comprehensive analysis of the network latency score, download rate score, and network stability score, obtain the current network status score; and further evaluate the current network status score to determine whether to execute the network switching instruction.

[0042] Step 201: Calculate the current network status score by inputting the network latency score, download rate score, and network stability score into the network status score calculation model. The calculation formula is as follows: ; where ZH represents the current network status score; , where e represents the base of the natural logarithm, and k SCO represents the Sigmoid steepness coefficient. When strict latency control is required, a larger k can be selected SCO to cause the score to drop sharply after exceeding the standard, allowing a certain delay fluctuation, and selecting a smaller k SCO , which can take values in [0.01, 1], and SCO thr represents the standard value of the network latency score; , where SCE max represents the theoretical maximum value of SCE; , where γ represents the stability index, which is used to control the non-linear amplification effect of the network stability score, determine the contribution intensity of the network stability score to the current network state score, and can take values in the range of [0.1, 3]. When γ > 1, the contribution of the network stability score to the current network state score is amplified; when γ < 1, the contribution of the network stability score to the current network state score is suppressed. It can be adjusted according to actual needs.

[0043] It should be noted that before calculating the current network state score, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0044] This formula is a comprehensive network state score calculation model, aiming to comprehensively evaluate the overall performance of the current network. Specifically, after the network latency score, network stability score, and download-buffer score are respectively processed by specific functions, the product of these three processed values is taken and the cube root is calculated to obtain the current network state score. Among them, f(SCO) uses the Sigmoid function to process the network latency score, g(SCE) uses the hyperbolic tangent function to process the network stability score, and h(SCQ) processes the download-buffer score through the minimum function and power function. The parameters of these functions can be adjusted according to actual needs to achieve flexible control and highlighting of different network metrics.

[0045] Traditional network evaluation methods often focus on single metrics such as latency, stability, and download speed, etc., but it is difficult to comprehensively reflect the overall performance of the network. This formula integrates three key network metrics (latency, stability, and download-buffer) into a comprehensive scoring model, solves the problem of how to conduct multi-dimensional comprehensive evaluation of the network, avoids the one-sidedness caused by single-metric evaluation, and provides a more comprehensive and accurate perspective for network performance evaluation.

[0046] In a multi-network environment, it is necessary to quantitatively compare the overall performance of different networks. This formula provides a method to integrate multiple network metrics into a single score, making the performance comparison between different networks more intuitive and convenient, facilitating users and network operators to quickly understand the advantages and disadvantages of each network, and providing strong support for network selection and switching.

[0047] The formula quantifies the multi-dimensional performance of the network into a specific score, making the performance comparison between different networks simple and intuitive. By comparing the ZH values of different networks, users can clearly understand the advantages and disadvantages of each network, so as to select the network access that best suits their needs and obtain a better network experience. This formula provides key data support for intelligent network switching management, enabling the system to make more intelligent and reasonable network switching decisions based on the real-time ZH score, combined with other network status factors such as coverage. This helps to achieve the efficient utilization of network resources and the smooth switching of user devices between different networks, improving the operation efficiency and intelligence level of the entire network system. At the same time, it reduces the risk of deterioration of user experience and data transmission interruption caused by improper network switching, ensuring that users can always access the optimal network and enjoy high-quality network services.

[0048] Step 202: Set the network status score threshold, and compare the current network status score with the network status score threshold. When the current network status score ≤ the network status score threshold, execute the network switching instruction.

[0049] It should be noted that the network status score threshold can be calculated based on the indicators of historical data. For example, select the network operation data at the time point when the user is marked as using the network normally without adverse effects, calculate the operation data at all time points, obtain the corresponding network status score in the normal state, and select the lowest value or the average value of the network status score in the normal state as the network status score threshold.

[0050] Step 3: When it is determined to execute the network switching instruction; obtain the coverage data, network status data, and switching cost data of the operator to be accessed.

[0051] Step 301: The coverage data includes the life trajectory of the user obtained through the trajectory module, and predicts multiple prediction areas where the user will be in the future unit time and the probability of appearing in each prediction area through the life trajectory; obtain the heat map of the operator to be accessed by docking the APIs of different operators, and obtain the median signal strength of the signal area corresponding to the historical heat map and the prediction area, and calculate the predicted distance value between the prediction area and the base stations of different operators.

[0052] Step 302: The network status data is the historical network status data of different operators in different prediction areas, including the network delay, channel fluctuation index, and signal uniformity index of different operators in different prediction areas; the channel fluctuation index is calculated through the signal-to-noise ratio of multiple prediction areas, and the formula is as follows: ; Among them, CQV q represents the channel fluctuation index of the qth operator; SINR q,iDenote the signal-to-noise ratio of the $i$-th prediction area of the $q$-th operator; SINR q，avg Denote the average signal-to-noise ratio of all prediction areas of the $q$-th operator; SINR q，QOS Denote the lowest SINR of the $q$-th operator; L q，t Denote the base station load rate of the $q$-th operator. If there are multiple base stations, calculate the average load rate of multiple base stations.

[0053] It should be noted that before calculating the channel fluctuation index, several corresponding parameters need to be normalized preprocessing to eliminate the dimension of different parameters, which is convenient for subsequent formula calculation.

[0054] This formula is a channel fluctuation index calculation model, which is used to quantitatively evaluate the channel fluctuation of different operators in different prediction areas. Specifically, by calculating the standard deviation of the difference between the signal-to-noise ratio and the average signal-to-noise ratio of multiple prediction areas relative to the lowest signal-to-noise ratio, and then multiplying by the exponential term of the base station load rate, the channel fluctuation index is obtained. This model comprehensively considers the fluctuation degree of the signal-to-noise ratio and the base station load situation, and can reflect the stability and reliability of the channel quality.

[0055] Traditional methods are difficult to comprehensively quantify the channel fluctuation situation, especially when involving multiple prediction areas and different operators. This formula solves the problem of how to quantitatively evaluate the channel fluctuation by calculating the standard deviation of the signal-to-noise ratio fluctuation of multiple prediction areas and combining with the base station load rate, providing a new dimension for network performance evaluation. Channel fluctuation is not only affected by the signal-to-noise ratio, but also closely related to the base station load. This formula combines the signal-to-noise ratio fluctuation and the base station load rate, solves the problem of how to comprehensively evaluate the influence of channel quality and load, and provides a more comprehensive method for evaluating channel fluctuation. In a complex network environment with multiple operators and multiple prediction areas, it is necessary to compare and evaluate the channel fluctuations of different operators. This formula is applicable to different operators and prediction areas, solves the problem of how to uniformly evaluate the channel fluctuation in a multi-operator and multi-area environment, and provides strong support for network selection and optimization.

[0056] The formula quantifies the channel fluctuation into a specific index, making the comparison of channel quality between different operators and prediction areas more intuitive. In the intelligent network switching management based on Super SIM multi-network, an accurate channel fluctuation index is crucial for network selection and handover decisions. This formula provides data support for intelligent network management, enabling the system to make reasonable decisions based on real-time CQV index and other network status data, realizing efficient utilization of network resources and smooth handover of user equipment, improving the network intelligence level, and reducing the handover failure rate and user complaint rate.

[0057] The signal uniformity index is calculated through the signal strength values of multiple prediction areas, and the formula is as follows: ; Among them, DQS q represents the average value of the signal strength of the q-th operator; RSRP q,i represents the signal strength value of the i-th prediction area of the q-th operator; RSRP q,avg represents the average signal strength of the i-th prediction area of the q-th operator; RSRP q，QOS represents the lowest RSRP of the q-th operator.

[0058] It should be noted that before calculating the signal strength value, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0059] Traditional methods are difficult to comprehensively quantify the uniformity of signal strength in multiple prediction areas, especially in a complex and changing multi-operator network environment. This formula solves the problem of how to quantify the signal strength uniformity through mean calculation and standardization processing, providing a scientific basis for network coverage quality assessment. In a complex network environment with multiple operators and multiple prediction areas, it is necessary to compare and evaluate the signal strength uniformity of different operators. This formula is applicable to different operators and prediction areas, solving the problem of how to uniformly evaluate the signal strength uniformity in a multi-operator and multi-area environment, providing strong support for network selection and optimization.

[0060] The formula quantifies the signal strength uniformity into a specific index, making the comparison of signal strength between different operators and prediction areas more intuitive. Network operators can optimize network configuration according to the DOS index to enhance competitiveness. Users can also select a network with more stable signals based on this index to obtain a better communication experience. In the intelligent network switching management based on the super SIM multi-network, accurate signal strength average values are crucial for network selection and switching decisions. This formula provides data support for intelligent network management, enabling the system to make reasonable decisions based on real-time DOS indices and other network status data, realizing efficient utilization of network resources and smooth switching of user devices, improving the level of network intelligence, and reducing the switching failure rate and user complaint rate.

[0061] Step 303: The switching cost data is obtained by getting the network tariff unit prices of different operators and the current network tariff unit price from the official website tariff publicity page or the operator APP; the historical switching success rates of different operators are obtained through historical logs.

[0062] Step 4: By analyzing and calculating the coverage data, the coverage matching degree indices of different operators are obtained; by analyzing and calculating the network status data, the network status indices of different operators are obtained; by analyzing the switching cost data, the switching cost-benefit ratio is obtained.

[0063] Step 401: Calculate the coverage matching degree index by inputting the median signal strength corresponding to the heat maps of different operators and the predicted distance values into the coverage matching degree calculation model. The formula is as follows: ; where FGD q represents the coverage degree index of the q-th operator; YC q，i represents the distance value from the base station of the q-th operator to the i-th predicted area; RL q，i represents the median signal strength corresponding to the i-th predicted area in the heat map of the q-th operator; GL i represents the probability that the terminal appears in the i-th predicted area, which can be calculated by dividing the number of times it appears in each area by the total number of actions; q represents the serial number of different operators and can take positive integer values; i represents the serial number of the predicted area and takes positive integer values, and n represents the total number of predicted areas.

[0064] It should be noted that before calculating the coverage matching degree index, several corresponding parameters need to be preprocessed by normalization to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0065] The formula is used to calculate the coverage matching degree index, aiming to quantitatively evaluate the matching degree between the network coverage of different operators and the predicted areas. Specifically, by combining the median signal strength of each predicted area with the function of the base station distance and multiplying it by the probability of the area appearing, the sum of all predicted areas is obtained to get the coverage matching degree index. The various parameters in the formula work together to comprehensively reflect the influence of signal strength, base station distance, and area appearance probability on the network coverage quality.

[0066] The formula quantifies the network coverage matching degree into a specific index, making the comparison of network coverage between different operators and predicted areas more intuitive. Network operators can optimize network configurations according to the FGD index to enhance competitiveness. Users can also select operators with better network coverage based on this index to obtain a better communication experience; for example, when a user selects a mobile network operator, by comparing the FGD indices of different operators in the areas where the user often goes, the user selects the operator with a higher FGD index, thus obtaining a more stable network connection and a better communication experience.

[0067] In intelligent network switching management based on Super SIM multi-network, an accurate coverage matching index is crucial for network selection and handover decisions. This formula provides data support for intelligent network management, enabling the system to make reasonable decisions based on real-time FGD indices and other network status data, achieving efficient utilization of network resources and smooth handover of user devices, improving the level of network intelligence, and reducing handover failure rates and user complaint rates. In an area covered by multiple operators, user devices calculate the FGD indices of different operators in real time and select the operator with the highest FGD index for connection. When the user moves to another area, the system automatically switches to the operator with better network coverage according to the new FGD index, ensuring that users always enjoy high-quality network services.

[0068] Step 402: Calculate the network status indices of different operators in the prediction area based on the network latency, channel fluctuation index, and signal uniformity index of different operators in different prediction areas. The formula is as follows: ; where DWI q represents the network status index of the q-th operator, SY q represents the network latency of the q-th operator; CQV q represents the channel fluctuation index of the q-th operator; DOS q represents the signal uniformity index of the q-th operator; α1 represents the weight coefficient of network latency, which can take a value of 0.3; α2 represents the weight coefficient of the channel fluctuation index, which can take a value of 0.4; α3 represents the weight coefficient of the signal uniformity index, which can take a value of 0.3; and α1 + α2 + α3 = 1. The specific values can be adjusted according to the actual situation.

[0069] It should be noted that before calculating the network status index, several corresponding parameters need to be preprocessed by normalization to eliminate the dimensions of different parameters, facilitating subsequent formula calculations.

[0070] The formula is used to calculate the network status index, aiming to comprehensively evaluate the network performance of different operators in the prediction area. By multiplying the network latency, channel fluctuation index, and signal uniformity index by their respective weight coefficients, then summing and normalizing, the network status index is obtained. This index comprehensively reflects the network's latency, stability, and signal uniformity, providing a comprehensive quantitative indicator for network performance evaluation.

[0071] Traditional network evaluation methods often focus on single indicators such as latency, stability, and signal uniformity, but it is difficult to comprehensively reflect the overall performance of the network; the formula integrates three key network indicators into a comprehensive evaluation model, achieving a comprehensive quantitative evaluation of network performance and avoiding one-sidedness caused by single-indicator evaluation.

[0072] The DWI index can accurately reflect the comprehensive performance of the network in terms of latency, stability, and signal uniformity, providing reliable data support for network performance evaluation and optimization. The formula quantifies the multi-dimensional performance of the network into a specific index, making it simple and intuitive to compare the performance between different networks. By comparing the DWI values of different networks, users can clearly understand the advantages and disadvantages of each network, and thus select the network access that best suits their needs to obtain a better network experience.

[0073] In the intelligent network switching management based on the super SIM multi-network, an accurate network status index is of great significance for network switching decisions. This formula provides key data support for intelligent network switching management, enabling the system to make more intelligent and reasonable network switching decisions based on the real-time DWI score and combined with other network status factors, realizing the efficient utilization of network resources and the smooth switching of user devices between different networks, improving the operation efficiency and intelligent level of the entire network system, and at the same time reducing the risk of user experience degradation and data transmission interruption caused by improper network switching.

[0074] Step 403: By comprehensively analyzing the network tariff unit prices of different operators, the current network tariff unit price, and the historical switching success rates of different operators, the switching cost-benefit ratio is obtained, and the formula is as follows: ; where NCE q represents the switching cost-benefit ratio of the q-th operator; Cta q represents the network tariff unit price of the q-th operator; Ccu represents the current network tariff unit price; δ represents the second penalty factor, with a value range of [1, 3], which can adjust the impact degree of the switching failure rate on the overall cost. The larger the value, the greater the impact on the cost; RS q represents the historical switching success rate of the q-th operator.

[0075] It should be noted that before calculating the switching cost-benefit ratio, several corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters for subsequent formula calculations.

[0076] In a multi-operator network environment, when users select a network, they not only need to consider the tariff cost but also the success rate of switching to other operators' networks. However, there is a lack of a method to comprehensively evaluate these two factors. When performing network switching, how to quantify the cost-benefit of the switch to determine whether the switch is worthwhile is an urgent problem to be solved. The formula combines the network tariff difference with the switching success rate, solves the problem of how to comprehensively evaluate network tariffs and switching reliability, provides users with a comprehensive quantitative index, and helps users make more informed decisions when choosing a network. By calculating the switching cost-benefit ratio, the tariff savings and switching risks are converted into specific values, realizing the quantitative evaluation of the switching cost-benefit, enabling users and network management systems to intuitively judge the economy and feasibility of the switch.

[0077] By comprehensively considering tariffs and switching success rates, the formula helps users balance economy and reliability when switching networks. Users can select the operator that achieves the best balance between tariffs and switching success rates based on the NCE index, avoiding simply pursuing low tariffs and ignoring switching risks, or giving up potential tariff savings opportunities due to excessive focus on switching success rates. Through quantitative evaluation, network switching decisions are more scientific and accurate. The network management system can recommend the optimal network switching plan for users based on the real-time NCE index, improving the utilization efficiency of network resources and enhancing the user's network experience at the same time. The NCE index provides users with clear switching cost-benefit information, helps users choose the most cost-effective network, and reduces the network usage cost. At the same time, by considering the switching success rate, the risks and additional costs caused by switching failures are reduced, improving the user's network usage experience and satisfaction.

[0078] Step Five: By comprehensively analyzing the coverage matching degree index, network status index, and switching cost-benefit ratio of different operators, obtain the switching priority index of different operators and determine the switching priority.

[0079] Step 501: By comprehensively analyzing the coverage matching degree index, network status index, and switching cost-benefit ratio of different operators, obtain the switching priority index. The formula is as follows: ; Among them, YXJ q represents the switching priority index of the q-th operator; β1 represents the weight coefficient of the coverage matching degree index; β2 represents the weight coefficient of the network status index; β3 represents the weight coefficient of the switching cost-benefit ratio; and β1 + β2 + β3 = 1. The specific values can be adjusted according to the actual situation and can be taken as β1 = 0.3, β2 = 3, β3 = 0.4 respectively.

[0080] It should be noted that before calculating the handover priority index, several corresponding parameters need to be pre - processed by normalization to eliminate the dimensions of different parameters, which is convenient for subsequent formula calculations.

[0081] The formula is used to calculate the handover priority index, aiming to comprehensively evaluate the network coverage, performance, and handover cost - effectiveness of different operators. By multiplying the coverage matching degree index, network status index, and handover cost - effectiveness ratio by their respective weight coefficients and then summing them up, the handover priority index is obtained. This index comprehensively reflects the coverage quality, performance, and economy of the network, providing a comprehensive quantitative indicator for network handover decisions.

[0082] In a multi - operator network environment, when users choose a network, they need to comprehensively consider network coverage, performance, and handover cost - effectiveness, but there is a lack of a method to comprehensively evaluate these factors. The formula combines the coverage matching degree index, network status index, and handover cost - effectiveness ratio, solving the problem of how to comprehensively evaluate network handover factors, providing a comprehensive quantitative indicator for users, and helping users make more informed decisions when choosing a network. By calculating the handover priority index, the network coverage, performance, and economy are transformed into specific numerical values, realizing the quantitative evaluation of handover priority, enabling users and network management systems to intuitively judge which operator's network is the best to switch to.

[0083] The YXJ index can accurately reflect the priority of switching to different operators' networks, providing reliable data support for the handover decisions of users and network management systems. Through this index, it can be quickly judged whether switching to a certain operator's network can bring the maximum comprehensive benefit, avoiding the imbalance problem caused by a single factor dominating the handover decision. Based on the quantitative evaluation of this formula, network handover decisions are more scientific and accurate. The network management system can recommend the optimal network handover plan for users according to the real - time YXJ index, improving the utilization efficiency of network resources and at the same time enhancing the user's network experience. The YXJ index provides users with clear handover priority information, helping users choose the network with the most comprehensive benefits and reducing the network usage cost.

[0084] Step 502: Traverse the handover priority indexes of different operators, take the numerical value of the handover priority index as the handover priority, and switch the network.

[0085] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. 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 will realize that the units and algorithm steps of the examples described in connection 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.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., 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.

[0087] As described above, the above is only a 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. The intelligent network switching management method for optimal signal selection based on super SIM multi-network, characterized in that, Including: Step 1: Obtain the operation status data of the current network operating environment, and calculate the network latency score, download rate score, and network stability score in the current network environment through analysis and calculation of the operation status data; Step 2: Through comprehensive analysis of the network latency score, download rate score, and network stability score, obtain the current network status score; and further evaluate the current network status score to determine whether to execute the network switching instruction; Step 3: When it is determined to execute the network switching instruction; obtain the coverage data, network status data, and switching cost data of the operator to be accessed; Step 4: Through analysis and calculation of the coverage data, obtain the coverage matching index of different operators; through analysis and calculation of the network status data, obtain the network status index of different operators; through analysis of the switching cost data, obtain the switching cost-benefit ratio; Step 5: Through comprehensive analysis of the coverage matching index, network status index, and switching cost-benefit ratio of different operators, obtain the switching priority index of different operators and determine the switching priority.

2. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 1, characterized in that The operation status data includes round-trip delay values, packet loss rates, and broadband utilization rates at multiple time points within a detection time period; calculate the corresponding average round-trip delay value, average packet loss rate, and average bandwidth utilization rate through the round-trip delay values, packet loss rates, and broadband utilization rates at multiple time points; obtain the delay jitter value by calculating the standard deviation of the difference between the round-trip delay values of adjacent data packets, and input these parameters into the network latency score calculation model to obtain the network latency score SCO of the detection time period; The operation status data also includes the number of disconnection times and the reconnection time after each disconnection counted within the time period. Calculate the disconnection frequency through the time value of the time period and the number of disconnection times; obtain the average reconnection time by summing up the reconnection times after each disconnection and calculating the mean value; input the disconnection frequency and the average reconnection time into the network stability score model to calculate the network stability score SCE; The operation status data also includes the download rates at different time points and the buffering durations of audio and video counted within the time period. Sum up and calculate the average values of the download rates at different time points and the buffering durations respectively to obtain the average download rate and the average buffering duration; input the average download rate and the average buffering duration into the download-buffering evaluation model to calculate the download-buffering score SCQ.

3. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 2, wherein The current network status score is calculated by inputting the network latency score, download rate score, and network stability score into the network status score calculation model. The calculation formula is as follows: ； Among them, ZH represents the current network status score; , where e represents the base of the natural logarithm, and k SCO represents the Sigmoid steepness coefficient, and SCO thr represents the threshold of the network delay score; , where SCE max represents the theoretical maximum value of SCE; , where γ represents the stability index.

4. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 3, characterized in that, The method for evaluating the current network status score to determine whether to execute the network switching instruction is: Set a network status score threshold, compare the current network status score with the network status score threshold, and execute the network switching instruction when the current network status score ≤ the network status score threshold.

5. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 1, characterized in that The coverage data includes the user's life trajectory obtained through the trajectory module, and predicts multiple predicted areas where the user will be in the future unit time and the probability of appearing in each predicted area through the life trajectory; obtains the heat map of the operator to be connected by docking the APIs of different operators, and obtains the median signal strength of the signal area corresponding to the historical heat map and the predicted area, and calculates the predicted distance value between the predicted area and the base stations of different operators; The network status data is the historical network status data of different operators in different predicted areas, including the network delay, channel fluctuation index, and signal uniformity index of different operators in different predicted areas; The handover cost data includes the network tariff unit price of different operators and the current network tariff unit price obtained through the official website tariff publicity page or the operator APP, and the historical handover success rate of different operators obtained through the historical log.

6. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 5, characterized in that By inputting the median signal strength corresponding to the heat map of different operators and the predicted distance value into the coverage matching degree calculation model, the coverage matching degree index is calculated, and the formula is as follows: ； Among them, FGD q represents the coverage index of the q-th operator; YC q，i represents the distance value from the base station of the q-th operator to the i-th prediction area; RL q，i represents the median signal strength corresponding to the i-th prediction area in the heat map of the q-th operator; GL i represents the probability that the terminal appears in the i-th prediction area; q represents the serial number of different operators; i represents the serial number of the prediction area, and n represents the total number of prediction areas.

7. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 5, characterized in that By calculating the network delay, channel fluctuation index, and signal uniformity index of different operators in different predicted areas, the network status index of different operators in the predicted area is calculated, and the formula is as follows: ； Among them, DWI q represents the network status index of the qth operator; SY q represents the network delay of the qth operator; CQV q represents the channel fluctuation index of the qth operator; DOS q represents the signal uniformity index of the qth operator; α1 represents the weight coefficient of network delay; α2 represents the weight coefficient of channel fluctuation index; α3 represents the weight coefficient of signal uniformity index.

8. The optimal signal selection intelligent network switching management method based on a super SIM multi-network according to claim 5, characterized in that By comprehensively analyzing the network tariff unit price of different operators, the current network tariff unit price, and the historical handover success rate of different operators, the handover cost-benefit ratio is obtained, and the formula is as follows: ； Among them, NCE q represents the handover cost-benefit ratio of the q-th operator; Cta q represents the unit network tariff of the q-th operator; Ccu represents the current network tariff unit price; δ represents the second penalty factor; RS q represents the historical handover success rate of the q-th operator.

9. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 8, characterized in that, By comprehensively analyzing the coverage matching degree index, network status index, and handover cost-benefit ratio of different operators, the handover priority index is obtained, and the formula is as follows: ； Among them, YXJ q represents the handover priority index of the q-th operator; β1 represents the weight coefficient of the coverage matching degree index; β2 represents the weight coefficient of the network status index; β3 represents the weight coefficient of the handover cost-benefit ratio.

10. The intelligent network switching management method for optimal signal selection based on super SIM multi-network according to claim 9, characterized in that The method for determining the handover priority is: Traverse the handover priority indexes of different operators, and use the numerical size of the handover priority index as the handover priority and switch the network.

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