Intelligent network switching management method based on optimal signal selection of super SIM multi-network

Abstract

The present invention discloses an intelligent network switching management method based on optimal signal selection of super SIM multi-network, which relates to the field of intelligent network switching management. The main scheme is: by analyzing and calculating the operating status data, the network delay score, download rate score and network stability score in the current network environment are obtained, and a comprehensive analysis is performed to obtain the current network status score; by evaluating the current network status score, it is judged whether to execute the network switching instruction; and by analyzing and calculating the coverage data, network status data and switching cost data, the coverage matching index, network status index and switching cost-effectiveness ratio of different operators are respectively obtained; the switching priority index of different operators is obtained through comprehensive analysis, and the switching priority is determined, so as to solve the problem that the switching decision is out of touch with the demand because the switching is triggered only based on signal strength or a single rate threshold, and the core indicators affecting the user experience such as network delay and stability are not integrated.

Description

Technical Field

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

[0002] With the rapid development of 5G, the Internet of Things (IoT), and edge computing, users' demands for mobile network quality have evolved from simple "connectivity" to a comprehensive experience requiring "high stability, low latency, and high throughput." The maturity of Super SIM card technology has made multi-network (multi-carrier network access) possible with a single card, providing the hardware foundation for mobile devices to dynamically select the optimal network. In this context, intelligent network switching management methods hold broad application prospects.

[0003] For example, in scenarios such as mobile office, HD video live streaming, cloud gaming, and telemedicine, users need to switch to the optimal network in real time to ensure business continuity. By dynamically aggregating network resources from multiple operators, it is possible to overcome the coverage or congestion limitations of a single network, improve user experience, and combine network quality with pricing strategies to provide users with the most cost-effective access solution.

[0004] Although current mobile terminals support access to multiple operator networks, traditional switching mechanisms rely on manual selection or fixed rules (such as signal strength thresholds), which present the following problems:

[0005] Existing methods trigger switching based solely on signal strength or a single rate threshold, without integrating core indicators that affect user experience, such as network latency and stability. This results in a disconnect between switching decisions and business needs.

[0006] Traditional solutions use preset priorities or fixed cost models without dynamically evaluating coverage matching and switching cost-effectiveness. Summary of the Invention

[0007] (1) Technical problems solved

[0008] In response to the shortcomings of the existing technology, the present invention provides an intelligent network switching management method based on the optimal signal selection of super SIM multi-network, which at least solves the problem that the existing technology only triggers switching based on signal strength or a single rate threshold, fails to integrate core indicators affecting user experience such as network delay and stability, resulting in a disconnect between switching decisions and demand.

[0009] (2) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for intelligent network switching management based on optimal signal selection of multiple networks of a super SIM, comprising:

[0011] Step 1: Obtain the operating status data of the current network operating environment, and analyze and calculate the network latency score, download speed score, and network stability score under the current network environment;

[0012] Step 2: Calculate the current network status score by comprehensively analyzing the network latency score, download speed score, and network stability score. Further evaluate the current network status score to determine whether to execute the network disconnection command.

[0013] Step 3: When it is determined that the network switching instruction is to be executed; obtain coverage data, network status data and switching cost data of the operator to be connected;

[0014] Step 4: Analyze and calculate the coverage data to obtain the coverage matching index of different operators; analyze and calculate the network status data to obtain the network status index of different operators; and analyze the switching cost data to obtain the switching cost-benefit ratio;

[0015] Step 5: By comprehensively analyzing the coverage matching index, network status index, and switching cost-benefit ratio of different operators, the switching priority index of different operators is obtained and the switching priority is determined.

[0016] In the preferred embodiment of the above-mentioned method for intelligent network switching management based on optimal signal selection for Super SIM multi-network, the operating status data includes round-trip delay values, packet loss rates, and bandwidth utilization rates at multiple time points within a detection time period; the corresponding average round-trip delay values, average packet loss rates, and average bandwidth utilization rates are calculated based on the round-trip delay values, packet loss rates, and bandwidth utilization rates at multiple time points; the delay jitter value is obtained by calculating the standard deviation of the differences between the round-trip delay values ​​of adjacent data packets, and these parameters are input into a network delay score calculation model to obtain a network delay score (SCO) for the detection time period;

[0017] The operating 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 using the time value of the time period and the number of disconnections. The average reconnection time is obtained by summing the reconnection time after each disconnection and calculating the average. The network stability score (SCE) is calculated by inputting the disconnection frequency and average reconnection time into the network stability scoring model.

[0018] The operating status data also includes the download rate and buffering time of audio and video at different time points within the time period. The download rate and buffering time at different time points are summed and averaged to obtain the average download rate and average buffering time. The download-buffer score (SCQ) is calculated by inputting the average download rate and average buffering time into the download-buffer evaluation model.

[0019] In the preferred embodiment of the above-mentioned intelligent network switching management method for optimal signal selection based on super SIM multi-network, the current network status score is calculated by inputting the network delay score, download rate score, and network stability score into the network status score calculation model. The calculation formula is as follows:

[0020] ;

[0021] Among them, ZH represents the current network status score; , where e represents the base of natural logarithm, k SCO Represents the Sigmoid steepness coefficient, SCO thr Indicates the threshold value of network delay score; , among which, SCE max Indicates the theoretical maximum value of SCE; , where γ represents the stability index.

[0022] In the preferred embodiment of the above-mentioned intelligent network switching management method based on the optimal signal selection of 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:

[0023] Set a network status score threshold and compare the current network status score with the network status score threshold. When the current network status score is less than or equal to the network status score threshold, execute the network disconnection instruction.

[0024] In the preferred embodiment of the above-mentioned method for intelligent network switching management based on optimal signal selection for Super SIM multi-network, the coverage data includes the user's life trajectory obtained by the trajectory module, and the life trajectory is used to predict multiple predicted areas where the user will be located per unit time in the future, as well as the probability of appearing in each predicted area. The heat map of the operator to be connected is obtained by connecting to the API of different operators, and the median signal strength of the signal area corresponding to the predicted area in the historical heat map is obtained, and the predicted distance value between the predicted area and the base stations of different operators is calculated.

[0025] The network status data is the historical network status data obtained by different operators in different forecast areas, including network delay, channel fluctuation index, and signal uniformity index of different operators in different forecast areas;

[0026] Switching cost data includes the network rate unit price and current network rate unit price of different operators obtained through the official website rate disclosure page or operator app, as well as the historical switching success rate of different operators obtained through historical logs.

[0027] In the preferred embodiment of the intelligent network switching management method for optimal signal selection based on Super SIM multi-network, the coverage matching index is calculated by inputting the median signal strength and predicted distance values ​​corresponding to the heat maps of different operators into the coverage matching calculation model. The formula is as follows:

[0028] ;

[0029] Among them, FGD q represents the coverage index of the qth operator; YC q，i represents the distance between the base station of the qth operator and the i-th prediction area; RL q，i represents the median signal strength of the ith prediction area in the heat map of the qth operator; GL i represents the probability of the terminal appearing 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.

[0030] In the preferred embodiment of the above-mentioned intelligent network switching management method for optimal signal selection based on super SIM multi-network, the network status index of different operators in the prediction area is calculated based on the network delay, channel fluctuation index and signal uniformity index of different operators in different prediction areas. The formula is as follows:

[0031] ;

[0032] Among them, DWI q 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 evenness 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 evenness index.

[0033] In the preferred embodiment of the intelligent network switching management method for optimal signal selection based on Super SIM multi-network, a comprehensive analysis of network tariff unit prices of different operators, current network tariff unit prices, and historical switching success rates of different operators is performed to obtain a switching cost-benefit ratio based on the following formula:

[0034] ;

[0035] Among them, NCE q represents the switching cost-benefit ratio of the qth operator; Cta q represents the network tariff unit price of the qth operator; Ccu represents the current network tariff unit price; δ represents the second penalty factor; RSq Indicates the historical handover success rate of the qth operator.

[0036] In the preferred embodiment of the intelligent network switching management method for optimal signal selection based on Super SIM multi-network, a switching priority index is obtained by comprehensively analyzing the coverage matching index, network status index, and switching cost-benefit ratio of different operators. The formula is as follows:

[0037] ;

[0038] Among them, YXJ q represents the switching priority index of the qth operator; β1 represents the weight coefficient of the coverage matching index; β2 represents the weight coefficient of the network status index; β3 represents the weight coefficient of the switching cost-effectiveness ratio.

[0039] In the preferred solution of the above-mentioned intelligent network switching management method based on the optimal signal selection of super SIM multi-network, the method for determining the switching priority is:

[0040] Traverse the switching priority indexes of different operators, use the numerical value of the switching priority index as the switching priority, and switch the network.

[0041] (3) Beneficial effects

[0042] The present invention provides an eSIM automated network switching strategy management system and method based on multi-source data fusion, which has the following beneficial effects:

[0043] (1) By obtaining the operating status data of the current network operating environment and analyzing and calculating the network delay score, download rate score and network stability score under the current network environment, comprehensive and key basic data is provided 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 is possible to 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 the freezes and disconnections caused by network problems, and improving user satisfaction with mobile communication services.

[0044] (2) A comprehensive analysis is conducted on the scores of the three important indicators of network delay, download speed and network stability to obtain the current network status score, and based on this, it is determined whether to execute the network switching instruction. A clear trigger mechanism is set for the entire intelligent network switching management process, which can respond to changes in the current network status in a timely and proactive manner, avoiding the passive use of the network when the network quality deteriorates; a comprehensive analysis and decision-making is conducted based on multiple factors such as the coverage, network status and switching cost of the operator to be accessed, avoiding blind network switching, allowing 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 the waste of network resources, and also alleviating network congestion problems.

[0045] (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 connected. These three aspects of data are crucial for the subsequent selection of the optimal target network, ensuring that factors such as coverage, actual network performance and the cost of the switching process can be comprehensively considered when switching networks; determining the switching priority based on the switching cost-effectiveness ratio can effectively reduce the number of unnecessary network switches, reduce the terminal power consumption, system resources and signaling overhead during each switching process, thereby reducing the overall network switching cost, extending the battery life of terminal equipment, and improving the operating efficiency and stability of the system, which has important economic value for both operators and users.

[0046] (4) The coverage data, network status data, and switching cost data are analyzed and calculated separately to obtain the coverage matching index, network status indicator set, and switching cost-benefit ratio of different operators. The complex network-related information is quantified into specific indices and indicators, which facilitates the subsequent comprehensive and accurate comparison and evaluation of different operator networks and provides a strong basis for determining switching priorities. This solution can intelligently select the optimal network for switching under different network environments and conditions, based on the characteristics of different operator networks and user needs, making user devices more adaptable in the network and able to always maintain a good network connection status in a complex multi-network environment, thereby enhancing the reliability and availability of network services.

[0047] (5) Comprehensively analyze the coverage matching index, network status indicator set and switching cost-benefit ratio of different operators, obtain the switching priority index of different operators and determine the switching priority, realize intelligent decision-making based on multiple key factors in a multi-network environment, and be able to select the optimal network that best meets user needs and current actual conditions. It reflects the progress of communication technology in intelligence and automation, and helps to promote the development of the communication industry in a more intelligent and efficient direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a schematic diagram of the steps of the intelligent network switching management method based on the optimal signal selection of super SIM multi-network according to the present invention. DETAILED DESCRIPTION

[0049] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] Example 1

[0051] See also Figure 1 The present invention provides a super SIM multi-network optimal signal selection intelligent network switching management method, including:

[0052] Step 1: Obtain the operating status data of the current network operating environment, and analyze and calculate the network delay score, download speed score, and network stability score under the current network environment by analyzing the operating status data.

[0053] Step 101: The operating status data is the operating status data of the environment during the detection time period. The operating status data includes round-trip delay values, packet loss rates, and bandwidth utilization rates at multiple time points during the time period. The corresponding average round-trip delay values, average packet loss rates, and average bandwidth utilization rates are calculated using the round-trip delay values, packet loss rates, and bandwidth utilization rates at multiple time points. The delay jitter value is obtained by calculating the standard deviation of the difference between the round-trip delay values ​​of adjacent data packets. These parameters are then input into a network delay score calculation model to obtain a network delay score for the detection time period. The calculation formula is as follows:

[0054] ;

[0055] Among them, SCO represents the network delay score, RTT avg Indicates the average value of network delay; RTT JZ It represents the benchmark value of round-trip delay, which is used to convert the average round-trip delay into a relative proportional value. It can be adjusted according to demand and can be set according to international standards. For example, when RTT≤150ms, it is set to 100ms. avg If the value is 50ms, the calculated value is 100 / 50=2, which means that the round-trip delay is better than the benchmark. If the RTT avg If the value is 200ms, the calculated value is 100 / 200=0.5, which means the delay is poor; JIT represents the delay jitter value of the round-trip delay; JIT JZIndicates the benchmark value of delay jitter, which can be set according to international standards, such as JIT≤30ms; RAT avg represents the average value of broadband utilization; 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 bandwidth utilization, and ω1+ω2+ω3=1, and the possible values ​​are ω1=0.4, ω2=0.3, and ω3=0.3; CF represents the first penalty factor.

[0056] The calculation formula of CF is:

[0057] ;

[0058] Among them, RLT avg Indicates the average value of packet loss rate; RLT JZ Indicates the baseline value of packet loss rate, such as 20%; and when RLT JZ When the value is greater than 20%, the first penalty factor CF directly takes the maximum value of 1.

[0059] This formula is a network latency score calculation model. Its core is to comprehensively consider multiple key network parameters to quantitatively assess network latency. Specifically, the ratio of average round-trip latency to the round-trip latency baseline, the ratio of average latency jitter to the latency jitter baseline, and average bandwidth utilization are multiplied by their corresponding weight coefficients, added together, and finally multiplied to obtain the network latency score. The network congestion factor is used to further modify the score to make it more consistent with actual network conditions. The weight coefficients of each parameter can be adjusted based on actual application scenarios and needs to reflect the importance of different network parameters to network latency.

[0060] Traditional network latency assessments often rely solely on a single metric, round-trip delay (RTT), which fails to fully reflect actual network performance. For example, evaluating latency solely based on RTT may overlook network transmission instability caused by delay jitter or the indirect impact of bandwidth utilization on latency. However, multiple factors influencing latency exist in network environments, such as round-trip delay, delay jitter, and bandwidth utilization, which interact and impact network performance. This formula provides a method for comprehensively quantifying these diverse factors, incorporating them into a unified calculation model and deriving a comprehensive network latency score through a weighted summation. This solves the problem of integrating multiple complex network factors into a single, easily understandable and comparable metric, facilitating unified evaluation and comparison of network latency performance across different network environments or over time. Furthermore, the sensitivity and importance of network parameters vary across different network environments and application scenarios. By adjusting the weighting coefficients in the formula, the impact of certain network parameters can be flexibly emphasized or de-emphasized based on specific application scenarios and requirements, making the network latency score more aligned with actual business needs and network environment characteristics, thus achieving universality and flexibility in evaluating network latency across diverse scenarios. The network latency score calculated based on this formula accurately reflects the actual network latency, providing a strong basis for network performance evaluation, troubleshooting, and optimization. Translating network latency from a qualitative description to a quantitative assessment makes performance comparisons between different networks simple and intuitive. By comparing network latency scores across different network environments, network operators and users can clearly understand the strengths and weaknesses of each network, providing a sound basis for decision-making when selecting the appropriate network service provider or switching networks.

[0061] It should be noted that before calculating the network delay score and the first penalty factor, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0062] Step 102: The operating 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 using the time value of the time period and the number of disconnections. The average reconnection time is obtained by summing and averaging the reconnection time after each disconnection. The network stability score is calculated by inputting the disconnection frequency and average reconnection time into the network stability scoring model using the following formula:

[0063] ;

[0064] Among them, 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, which can be ω4=0.5 and ω5=0.5.

[0065] It should be noted that before calculating the network stability score, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0066] This formula is a network stability score calculation model designed to quantitatively assess network stability. Specifically, the network stability score is calculated by multiplying two key factors, disconnection frequency and average reconnection time, by their corresponding weight coefficients, and then adding the reciprocal of the sum to 1 (to avoid a denominator of 0). This score can be adjusted based on actual conditions to reflect the importance of different factors to network stability.

[0067] Traditional network stability assessments often rely solely on the number of disconnections or reconnection time, which fails to fully reflect the actual network situation. This formula combines disconnection frequency and average reconnection time, comprehensively considering the frequency of network outages and the time required to restore connections. This solves the challenge of comprehensively and accurately assessing network stability and avoids the one-sidedness that can arise from single-metric assessments.

[0068] In a multi-network environment, it's necessary to quantitatively compare the stability of different operators or network areas. This formula provides a method for converting network stability into a specific score. The calculated SCE value allows for intuitive comparison of the stability of different networks, providing strong data support for network selection and optimization. This formula transforms network stability from a qualitative description into a quantitative assessment, making the stability performance comparison between different networks simple and intuitive. By comparing SCE values ​​in different network environments, network operators and users can clearly understand the strengths and weaknesses of each network, providing a scientific basis for decision-making when selecting the appropriate network service provider or switching networks.

[0069] Step 103: The running status data also includes the download rate and the buffering duration of the audio and video at different time points during the time period. The download rate and buffering duration at different time points are summed and averaged to obtain the average download rate and average buffering duration. The average download rate and average buffering duration are input into the download-buffering evaluation model to calculate the download-buffering score based on the following formula:

[0070] ;

[0071] Among them, SCQ represents the download-buffer score, D avg Indicates the average download rate; D max Indicates the theoretical maximum download speed of the current network standard; B avg Indicates the average buffering time, such as the waiting time from request to start of playback; B minIt represents the minimum tolerable buffering time threshold. For example, if it is set to 0.5 seconds, a value lower than this is considered an instantaneous response. ω6 represents the weight coefficient of the average download rate. ω7 represents the weight coefficient of the average buffering time, and ω6+ω7=1. The possible values ​​are ω6=0.4 and ω7=0.6.

[0072] It should be noted that before calculating the download-buffer score, it is necessary to normalize the corresponding parameters to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0073] This formula is a Download-Buffering Score calculation model that comprehensively assesses a network's download and buffering capabilities. The ratio of the average download rate to the theoretical maximum download speed for the current network standard 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 indicates whether the buffering duration meets basic requirements. The Download-Buffering Score is calculated by multiplying these two ratios by their corresponding weighting coefficients. The weighting coefficients can be adjusted based on different application scenarios and requirements to balance the impact of download rate and buffering duration on the score.

[0074] Traditional methods often evaluate download speed or buffering time separately, which fails to fully reflect the overall performance of a network in real-world applications. This formula combines the two, creating a comprehensive scoring model that addresses the issue of how to simultaneously consider both download and buffering performance. This avoids the one-sidedness often associated with single-metric evaluations and provides a more comprehensive perspective for network performance evaluation.

[0075] In a multi-network environment, it's necessary to quantify and compare the download-buffering performance of different networks. This formula provides a method that integrates download rate and buffering duration into a single score, making performance comparisons between different networks more intuitive and convenient. This allows users and network operators to quickly understand the strengths and weaknesses of each network, providing strong support for network selection and switching. Quantifying network download-buffering performance into a specific score makes performance comparisons between different networks simple and intuitive. By comparing the SCQ values ​​of different networks, users can clearly understand the strengths and weaknesses of each network, allowing them to choose the network that best suits their needs and enjoy a better network experience. Network operators can also optimize their networks based on SCQ scores to enhance their competitiveness. In the intelligent network switching management method for optimal signal selection based on Super SIM multi-networks, accurate download-buffering scores are crucial 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 and other network status factors, such as network latency and stability scores. This helps achieve efficient utilization of network resources and smooth switching of user devices between different networks, improving the operating efficiency and intelligence level of the entire network system, while reducing the risk of degraded user experience and data transmission interruption caused by improper network switching.

[0076] Step 2: By comprehensively analyzing the network delay score, download speed score, and network stability score, the current network status score is obtained; and the current network status score is further evaluated to determine whether to execute the network disconnection instruction.

[0077] Step 201: Calculate the current network status score by inputting the network delay score, download rate score, and network stability score into the network status score calculation model. The calculation formula is as follows:

[0078] ;

[0079] Among them, ZH represents the current network status score; , where e represents the base of natural logarithm, k SCO Represents the Sigmoid steepness coefficient. When strict delay control is required, a larger k can be selected SCO , so that the score drops sharply after exceeding the standard, allowing a certain delay fluctuation, and choosing a smaller k SCO , can take values ​​of [0.01, 1], SCO thr Indicates the standard value of network delay score; , among which, SCE max Indicates the theoretical maximum value of SCE; , where γ represents the stability index, which is used to control the nonlinear amplification effect of the network stability score and determine the contribution intensity of the network stability score to the current network status score. It can be set in [0.1, 3]. When γ is greater than 1, the contribution of the network stability score to the current network status score is amplified. When γ is less than 1, the contribution of the network stability score to the current network status score is suppressed. It can be adjusted according to actual needs.

[0080] It should be noted that before calculating the current network status score, it is necessary to normalize the corresponding parameters to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0081] This formula is a comprehensive network status score calculation model designed to comprehensively assess the overall performance of the current network. Specifically, the network latency score, network stability score, and download-buffer score are processed by specific functions, multiplied together, and the cube root is taken to obtain the current network status score. 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) uses the minimum and power functions to process the download-buffer score. The parameters of these functions can be adjusted according to actual needs to achieve flexible control and highlight different network indicators.

[0082] Traditional network evaluation methods often focus on single metrics, such as latency, stability, and download speed, but these methods fail to fully reflect the overall network performance. This formula addresses the issue of how to conduct a comprehensive, multi-dimensional network evaluation by integrating three key network metrics (latency, stability, and download-buffering) into a single, comprehensive scoring model. This approach avoids the bias inherent in single-metric evaluations and provides a more comprehensive and accurate perspective for network performance assessment.

[0083] In a multi-network environment, it's necessary to quantitatively compare the overall performance of different networks. This formula provides a way to consolidate multiple network metrics into a single score, making performance comparisons between different networks more intuitive and convenient. This allows users and network operators to quickly understand the strengths and weaknesses of each network, providing strong support for network selection and switching.

[0084] The formula quantifies the multi-dimensional performance of the network into specific scores, making performance comparisons 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, thereby selecting the network access that best suits their needs and obtaining 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 real-time ZH scores and other network status factors, such as coverage. This helps to achieve efficient utilization of network resources and smooth switching of user devices between different networks, improving the operating efficiency and intelligence level of the entire network system, while reducing the risk of user experience degradation and data transmission interruption caused by improper network switching, ensuring that users can always access the optimal network and enjoy high-quality network services.

[0085] Step 202: Set a network status score threshold, and compare the current network status score with the network status score threshold. When the current network status score is less than or equal to the network status score threshold, execute the network switching instruction.

[0086] It should be noted that the network status score threshold can be calculated based on indicators of historical data. For example, network operation data at time points when users use the network normally and without adverse effects are selected, and the operation data at all time points are calculated to obtain the corresponding normal network status score. The lowest value or average value of the normal network status score is selected as the network status score threshold.

[0087] Step 3: When it is determined that the network switching instruction is to be executed; obtain coverage data, network status data and switching cost data of the operator to be connected.

[0088] Step 301: The coverage data includes the user's life trajectory obtained through the trajectory module, and the life trajectory is used to predict multiple predicted areas where the user will be located per unit time in the future and the probability of appearing in each predicted area; the heat map of the operator to be connected is obtained by connecting to the API of different operators, and the median signal strength of the signal area corresponding to the historical heat map and the predicted area is obtained, and the predicted distance value between the predicted area and the base stations of different operators is calculated.

[0089] 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 by the signal-to-noise ratio of multiple prediction areas, based on the following formula:

[0090] ;

[0091] Among them, CQV qrepresents the channel fluctuation index of the qth operator; SINR q,i represents the signal-to-noise ratio of the qth operator in the i-th prediction area; SINR q，avg Represents the average signal-to-noise ratio of all prediction areas of the qth operator; SINR q，QOS represents the minimum SINR of the qth operator; L q，t If there are multiple base stations, the load rate of the base station of the qth operator is calculated.

[0092] It should be noted that before calculating the channel fluctuation index, it is necessary to normalize the corresponding parameters and pre-process them to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0093] This formula is a channel fluctuation index calculation model used to quantitatively assess channel fluctuations across different operators and forecast areas. Specifically, the channel fluctuation index is calculated by calculating the standard deviation of the difference between the signal-to-noise ratio (SNR) and the average SNR across multiple forecast areas relative to the lowest SNR, and then multiplying it by the base station load factor exponential term. This model comprehensively considers SNR fluctuations and base station load, reflecting the stability and reliability of channel quality.

[0094] Traditional methods have difficulty in fully quantifying channel fluctuations, especially when involving multiple forecast areas and different operators. This formula solves the problem of how to quantitatively evaluate channel fluctuations by calculating the standard deviation of the signal-to-noise ratio fluctuations in multiple forecast areas and combining it with the base station load rate, providing a new dimension for network performance evaluation. Channel fluctuations are not only affected by the signal-to-noise ratio, but are also closely related to the base station load. This formula combines the signal-to-noise ratio fluctuations and the base station load rate to solve the problem of how to comprehensively evaluate channel quality and load impact, and provides a more comprehensive channel fluctuation assessment method. In a complex network environment with multiple operators and multiple forecast areas, it is necessary to compare and evaluate the channel fluctuations of different operators. This formula is applicable to different operators and forecast areas, solving the problem of how to uniformly evaluate channel fluctuations in a multi-operator, multi-region environment, and providing strong support for network selection and optimization.

[0095] The formula quantifies channel fluctuations into a specific index, making channel quality comparisons across different operators and forecasted regions more intuitive. In intelligent network switching management based on Super SIM multi-networks, 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 informed decisions based on real-time CQV and other network status data. This ensures efficient utilization of network resources and smooth user device handovers, enhancing network intelligence and reducing handover failure rates and user complaints.

[0096] The signal uniformity index is calculated from the signal strength values ​​of multiple prediction areas according to the following formula:

[0097] ;

[0098] Among them, DQS q Indicates the average signal strength value of the qth operator; RSRP q,i Indicates the signal strength value of the qth operator in the i-th prediction area; RSRP q,avg Represents the average signal strength of the qth operator in the i-th prediction area; RSRP q，QOS Indicates the lowest RSRP of the qth operator.

[0099] It should be noted that before calculating the signal strength value, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0100] Traditional methods struggle to comprehensively quantify signal strength uniformity across multiple forecast areas, especially in complex and changing multi-operator network environments. This formula, through mean calculation and normalization, addresses the issue of quantifying signal strength uniformity, providing a scientific basis for network coverage quality assessment. In complex network environments with multiple operators and forecast areas, it's necessary to compare and evaluate signal strength uniformity across different operators. This formula, applicable to different operators and forecast areas, addresses the challenge of uniformly evaluating signal strength uniformity across multiple operators and regions, providing strong support for network selection and optimization.

[0101] The formula quantifies signal strength uniformity into a specific index, making signal strength comparisons between different operators and forecast areas more intuitive. Network operators can optimize network configuration based on the DOS index and improve their competitiveness. Users can also use this index to select networks with more stable signals for a better communication experience. In intelligent network switching management based on Super SIM multi-network, accurate signal strength uniformity values ​​are 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 DOS index and other network status data, achieving efficient utilization of network resources and smooth handover of user devices, improving network intelligence, and reducing handover failure rates and user complaint rates.

[0102] Step 303: The switching cost data is obtained by obtaining the network tariff unit price and current network tariff unit price of different operators through the official website tariff disclosure page or the operator APP; and obtaining the historical switching success rate of different operators through historical logs.

[0103] Step 4: By analyzing and calculating the coverage data, the coverage matching index of different operators is obtained; by analyzing and calculating the network status data, the network status index of different operators is obtained; by analyzing the switching cost data, the switching cost-benefit ratio is obtained.

[0104] Step 401: Calculate the coverage matching index by inputting the median signal strength and predicted distance values ​​corresponding to the heat maps of different operators into the coverage matching calculation model. The formula is as follows:

[0105] ;

[0106] Among them, FGD q represents the coverage index of the qth operator; YC q，i represents the distance between the base station of the qth operator and the i-th prediction area; RL q，i represents the median signal strength of the ith prediction area in the heat map of the qth operator; GL i It represents the probability of the terminal appearing in the i-th prediction area, which can be calculated by dividing the number of appearances in each area by the total number of actions; q represents the serial number of different operators and can be a positive integer; i represents the serial number of the prediction area and can be a positive integer; n represents the total number of prediction areas.

[0107] It should be noted that before calculating the coverage matching index, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0108] The formula used to calculate the coverage match index (CMI) quantifies how well different operators' network coverage matches the predicted areas. Specifically, the median signal strength for each predicted area is combined with a function of base station distance, multiplied by the probability of that area appearing, and summed across all predicted areas to arrive at the CMI. The various parameters in the formula work together to comprehensively reflect the impact of signal strength, base station distance, and area probability on network coverage quality.

[0109] The formula quantifies the degree of network coverage matching into a specific index, making network coverage comparisons between different operators and forecast areas more intuitive. Network operators can use the FGD index to optimize network configurations and enhance their competitiveness. Users can also use this index to select operators with better network coverage and enjoy a better communication experience. For example, when choosing a mobile network operator, a user might compare the FGD indices of different operators in their frequently visited areas and choose the operator with the higher FGD index, resulting in a more stable network connection and a better communication experience.

[0110] 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 the real-time FGD index and other network status data. This ensures efficient utilization of network resources and smooth handover of user devices, improves network intelligence, and reduces handover failure rates and user complaints. In an area with multiple operators, user devices calculate the FGD index of different operators in real time and select the operator with the highest FGD index for connection. When users move to another area, the system automatically switches to the operator with better network coverage based on the new FGD index, ensuring that users always enjoy high-quality network service.

[0111] Step 402: Calculate the network status index of different operators in the prediction area based on the network delay, channel fluctuation index and signal uniformity index of different operators in the prediction area, according to the following formula:

[0112] ;

[0113] Among them, DWI q 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 evenness index of the qth operator; α1 represents the weight coefficient of network delay, which can be 0.3; α2 represents the weight coefficient of channel fluctuation index, which can be 0.4; α3 represents the weight coefficient of signal evenness index, which can be 0.3; and α1+α2+α3=1. The specific value can be adjusted according to the actual situation.

[0114] It should be noted that before calculating the network status index, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0115] The formula used to calculate the Network Status Index (NSI) comprehensively evaluates the network performance of different operators in the forecasted area. The Network Status Index is calculated by multiplying network latency, channel fluctuation index, and signal uniformity index by their respective weighting coefficients, then summing and normalizing the results. This index comprehensively reflects network latency, stability, and signal uniformity, providing a comprehensive quantitative indicator for network performance evaluation.

[0116] Traditional network evaluation methods often focus on single indicators such as latency, stability, and signal uniformity, but they are unable to fully reflect the overall performance of the network. The formula integrates three key network indicators into a comprehensive evaluation model, achieving a comprehensive quantitative assessment of network performance and avoiding the one-sidedness caused by single-indicator evaluation.

[0117] The DWI index accurately reflects the comprehensive performance of a 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 a network into a specific index, making performance comparisons between different networks simple and intuitive. By comparing the DWI values ​​of different networks, users can clearly understand the strengths and weaknesses of each network, thereby selecting the network access that best suits their needs and achieving a better network experience.

[0118] In intelligent network switching management based on Super SIM multi-network, an accurate network status index is crucial 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 DWI scores and other network status factors. This ensures efficient utilization of network resources and smooth handover of user devices between different networks, improving the operational efficiency and intelligence of the entire network system while reducing the risk of user experience degradation and data transmission interruption caused by improper network switching.

[0119] Step 403: By comprehensively analyzing the network tariff unit prices of different operators, the current network tariff unit prices, and the historical handover success rates of different operators, a handover cost-benefit ratio is obtained, based on the following formula:

[0120] ;

[0121] Among them, NCE q represents the switching cost-benefit ratio of the qth operator; Cta q represents the network tariff unit price of the qth operator; Ccu represents the current network tariff unit price; δ represents the second penalty factor, which is in the range of [1,3] and can adjust the impact of the handover failure rate on the overall cost. The larger the value, the greater the impact on the cost; RS q Indicates the historical handover success rate of the qth operator.

[0122] It should be noted that before calculating the switching cost-effectiveness ratio, the corresponding parameters need to be normalized and preprocessed to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0123] In a multi-operator network environment, users must consider not only network costs but also the success rate of switching to another operator's network when selecting a network. However, there is a lack of a comprehensive method to evaluate these two factors. Quantifying the cost-effectiveness of network switching to determine whether it is worthwhile remains a pressing issue. This formula combines network price differences with the success rate of switching, addressing the issue of comprehensively evaluating network costs and switching reliability. This provides users with a comprehensive quantitative metric to help them make more informed decisions when selecting a network. Furthermore, by calculating the switching cost-benefit ratio, it translates cost savings and switching risks into concrete numerical values, enabling a quantitative assessment of switching costs and benefits, enabling users and network management systems to intuitively assess the economic viability of switching.

[0124] This formula comprehensively considers both rates and handover success rates, helping users balance affordability and reliability when switching networks. Users can use the NCE index to select the operator that strikes the best balance between rates and handover success rates, avoiding the temptation to simply pursue lower rates while ignoring handover risks, or forego potential cost savings due to an over-focus on handover success rates. This quantitative assessment makes network handover decisions more scientific and accurate. The network management system can recommend the optimal network handover plan based on the real-time NCE index, improving network resource utilization and enhancing the user experience. The NCE index provides users with clear handover cost-effectiveness information, helping them select the most cost-effective network and reduce network costs. Furthermore, by considering handover success rates, it reduces the risks and additional costs associated with handover failures, improving user experience and satisfaction.

[0125] Step 5: By comprehensively analyzing the coverage matching index, network status index, and switching cost-benefit ratio of different operators, the switching priority index of different operators is obtained and the switching priority is determined.

[0126] Step 501: A handover priority index is obtained by comprehensively analyzing the coverage matching index, network status index, and handover cost-benefit ratio of different operators. The formula is as follows:

[0127] ;

[0128] Among them, YXJ q represents the switching priority index of the qth operator; β1 represents the weight coefficient of the coverage matching index; β2 represents the weight coefficient of the network status index; β3 represents the weight coefficient of the switching cost-effectiveness ratio; and β1+β2+β3=1. The specific values ​​can be adjusted according to actual conditions, and can be β1=0.3, β2=3, and β3=0.4 respectively.

[0129] It should be noted that before calculating the handover priority index, it is necessary to perform normalization preprocessing on the corresponding parameters to eliminate the dimensions of different parameters to facilitate subsequent formula calculations.

[0130] The formula used to calculate the handover priority index comprehensively evaluates the network coverage, performance, and handover cost-effectiveness of different operators. The handover priority index is calculated by multiplying the coverage matching index, network status index, and handover cost-effectiveness ratio by their respective weighting coefficients and then summing them. This index comprehensively reflects the network's coverage quality, performance, and economic efficiency, providing a comprehensive quantitative indicator for network handover decisions.

[0131] In a multi-operator network environment, users need to comprehensively consider network coverage, performance, and handover cost-effectiveness when selecting a network, but there has been a lack of a method to comprehensively evaluate these factors. This formula addresses this issue by combining the coverage match index, network status index, and handover cost-effectiveness ratio to provide users with a comprehensive quantitative metric, helping them make more informed decisions when selecting a network. By calculating the handover priority index, network coverage, performance, and affordability are converted into specific numerical values, enabling a quantitative assessment of handover priority, allowing users and network management systems to intuitively determine the optimal operator network to switch to.

[0132] The YXJ Index accurately reflects the priority of switching to different carrier networks, providing reliable data support for switching decisions by users and network management systems. This index allows for a quick assessment of whether switching to a particular carrier network will maximize overall benefits, avoiding the imbalance caused by a single factor dominating switching decisions. This quantitative evaluation based on this formula makes network switching decisions more scientific and accurate. Based on the real-time YXJ Index, the network management system can recommend the optimal network switching solution for users, improving network resource utilization and enhancing the user experience. The YXJ Index provides users with clear switching priority information, helping them select the most cost-effective network and reducing network usage costs.

[0133] Step 502: traverse the handover priority indexes of different operators, use the numerical value of the handover priority index as the handover priority, and switch the network.

[0134] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other 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 skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0136] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An intelligent network switching management method based on optimal signal selection of multiple super SIM networks, characterized in that: include: Step 1: Obtain the operating status data of the current network operating environment, and analyze and calculate the network delay score, download-buffer score, and network stability score of the current network environment by analyzing the operating status data; Specifically, the operating status data includes round-trip delay values, packet loss rates, and bandwidth utilization at multiple time points within a detection period. The corresponding average round-trip delay values, average packet loss rates, and average bandwidth utilization are calculated using the round-trip delay values, packet loss rates, and bandwidth utilization at multiple time points. The delay jitter value is calculated by calculating the standard deviation of the differences between the round-trip delay values ​​of adjacent data packets. These parameters are then input into a network delay score calculation model to obtain the network delay score (SCO) for the detection period. The operating 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 using the time value of the time period and the number of disconnections. The average reconnection time is obtained by summing the reconnection time after each disconnection and calculating the average. The network stability score (SCE) is calculated by inputting the disconnection frequency and average reconnection time into the network stability scoring model. The operational status data also includes the download rate and buffering duration of audio and video at different time points within the time period. The download rate and buffering duration at different time points are summed and averaged to obtain the average download rate and average buffering duration. The average download rate and average buffering duration are input into the download-buffering evaluation model to calculate the download-buffering score (SCQ). Step 2: Calculate the current network status score by comprehensively analyzing the network latency score, download speed score, and network stability score. Further evaluate the current network status score to determine whether to execute the network disconnection command. Step 3: When it is determined that the network switching instruction is to be executed; obtain coverage data, network status data and switching cost data of the operator to be connected; Step 4: Analyze and calculate the coverage data to obtain the coverage matching index of different operators; analyze and calculate the network status data to obtain the network status index of different operators; analyze the switching cost data to obtain the switching cost-benefit ratio; the coverage data includes the user's life trajectory obtained by the trajectory module, and the life trajectory is used to predict the multiple predicted areas where the user will be located per unit time in the future and the probability of appearing in each predicted area; obtain the heat map of the operator to be connected by connecting to the API of different operators, obtain the median signal strength of the signal area corresponding to the historical heat map and the predicted area, and calculate the predicted distance value between the predicted area and the base stations of different operators; Step 5: By comprehensively analyzing the coverage matching index, network status index, and switching cost-benefit ratio of different operators, the switching priority index of different operators is obtained and the switching priority is determined.

2. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 1, characterized in that: The current network status score is calculated by inputting the network delay score, download-buffer 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 natural logarithm, k SCO Represents the Sigmoid steepness coefficient, SCO thr Indicates the threshold value of network delay score; = , among which, SCE max Indicates the theoretical maximum value of SCE; , where γ represents the stability index.

3. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 2, characterized in that: The method for evaluating the current network status score and determining whether to execute the network disconnection instruction is as follows: Set a network status score threshold and compare the current network status score with the network status score threshold. When the current network status score is less than or equal to the network status score threshold, execute the network disconnection instruction.

4. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 1, characterized in that: The network status data is the historical network status data obtained by different operators in different forecast areas, including network delay, channel fluctuation index, and signal uniformity index of different operators in different forecast areas; Switching cost data includes the network rate unit price and current network rate unit price of different operators obtained through the official website rate disclosure page or operator app, as well as the historical switching success rate of different operators obtained through historical logs.

5. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 4 is characterized in that: The coverage match index is calculated by inputting the median signal strength and predicted distance values ​​corresponding to the heat maps of different operators into the coverage match calculation model. The formula is as follows: ； Among them, FGD q represents the coverage index of the qth operator; YC q，i represents the distance between the base station of the qth operator and the i-th prediction area; RL q，i represents the median signal strength of the ith prediction area in the heat map of the qth operator; GL i represents the probability of the terminal appearing 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.

6. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 5, characterized in that: The network status index of different operators in the prediction area is calculated based on the network delay, channel fluctuation index, and signal uniformity index of different operators in different prediction areas. The formula is as follows: ； Among them, DWI q SY represents the network status index of the qth operator; 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 evenness 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 evenness index.

7. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 6, characterized in that: The switching cost-benefit ratio is derived by comprehensively analyzing the network tariff unit prices of different operators, the current network tariff unit prices, and the historical switching success rates of different operators. The formula is as follows: ； Among them, NCE q represents the switching cost-benefit ratio of the qth operator; Cta q represents the network tariff unit price of the qth operator; Ccu represents the current network tariff unit price; δ represents the second penalty factor; RS q Indicates the historical handover success rate of the qth operator.

8. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 7, characterized in that: The handover priority index is derived by comprehensively analyzing the coverage matching index, network status index, and handover cost-benefit ratio of different operators. The formula is as follows: ； Among them, YXJ q represents the switching priority index of the qth operator; β1 represents the weight coefficient of the coverage matching index; β2 represents the weight coefficient of the network status index; β3 represents the weight coefficient of the switching cost-effectiveness ratio.

9. The method for intelligent network switching management based on optimal signal selection of super SIM multi-network according to claim 8, characterized in that: The method for determining switching priority is: Traverse the switching priority indexes of different operators, use the numerical value of the switching priority index as the switching priority, and switch the network.

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