SIM card switching system and switching method

Abstract

The invention provides an SIM (Subscriber Identity Module) card switching system and an SIM card switching method. Belongs to the technical field of mobile communication. The method comprises the following steps: analyzing behavior information of a user based on a machine learning algorithm, and establishing a user behavior model based on an analysis result; collecting network quality data of the current position in real time through the Internet of Things communication module, evaluating the network quality, and generating a network quality report based on an evaluation result; based on the user behavior model, the network quality report and a preset switching strategy, intelligent switching decision making is carried out through an intelligent decision making engine, and the optimal SIM card is selected for communication. Through application of a machine learning algorithm and a deep learning algorithm, intelligent decision making can be performed according to a behavior model of a user and real-time network quality, it is ensured that an optimal scheme is selected when the SIM card is switched, and the reliability and efficiency of communication are improved.

Description

Technical Field

[0001] The present invention provides a SIM card switching system and a switching method, belonging to the technical field of mobile communications. Background Art

[0002] With the rapid development of mobile communication technology, more and more intelligent terminal devices support the multi-SIM card function to meet users' needs for different communication networks, tariff packages or international roaming. However, most of the existing multi-SIM card switching methods rely on manual selection by users, which is not only cumbersome to operate, but also may not be able to respond in a timely manner in the case of quickly switching network environments or emergency communication requirements, affecting the user experience. In addition, traditional methods lack intelligent evaluation and optimization of the states of different SIM cards (such as signal strength, tariff balance, international roaming status, etc.), resulting in unreasonable resource allocation and increasing the communication cost of users. Summary of the Invention

[0003] The present invention provides a SIM card switching system and a switching method to solve the problems mentioned in the above background art: A SIM card switching method proposed by the present invention, the method includes: S1. Analyze the user's behavior information based on a machine learning algorithm, and establish a user behavior model based on the analysis results; S2. Collect network quality data of the current location in real time through an Internet of Things communication module, evaluate the network quality, and generate a network quality report based on the evaluation results; S3. Based on the user behavior model, the network quality report, and a preset switching strategy, make an intelligent switching decision through an intelligent decision-making engine, and select the optimal SIM card for communication; S4. According to the decision result of the intelligent decision-making engine, perform the SIM card switching operation, and display the switching result and the current network status to the user through the user interface; S5. The system continuously monitors the changes in the user's behavior pattern and the network environment, and optimizes the switching strategy according to the monitoring results.

[0004] A SIM card switching system proposed by the present invention, the system includes: An information collection module: Analyze the user's behavior information based on a machine learning algorithm, and establish a user behavior model based on the analysis results; A quality evaluation module: Collect network quality data of the current location in real time through an Internet of Things communication module, evaluate the network quality, and generate a network quality report based on the evaluation results; A communication selection module: Based on the user behavior model, the network quality report, and a preset switching strategy, make an intelligent switching decision through an intelligent decision-making engine, and select the optimal SIM card for communication; Status display module: According to the decision result of the intelligent decision-making engine, perform the SIM card switching operation, and display the switching result and the current network status to the user through the user interface; Policy optimization module: The system continuously monitors the changes in the user behavior pattern and the network environment, and optimizes the switching policy according to the monitoring results.

[0005] Advantages of the present invention: Through the application of machine learning and deep learning algorithms, it is possible to make intelligent decisions based on the user's behavior model and real-time network quality, ensuring the selection of the optimal solution when switching SIM cards, improving the reliability and efficiency of communication; The collection and analysis of user behavior data enable the system to understand the user's communication habits and preferences, thereby providing more personalized network services and enhancing the user experience; The system can continuously monitor the changes in the user behavior pattern and the network environment, and continuously optimize the switching policy according to the user feedback to ensure that the policy always adapts to the user's needs and network conditions; Through the integration of multiple data sources, including user behavior data, network quality data, and user feedback data, comprehensive decision support is provided to make the switching decision more accurate and effective; By analyzing the user's communication needs, preferences, and the tariff structures of different SIM cards, it is possible to help the user select a more cost-effective communication plan at different times and scenarios, thereby achieving cost optimization; Through the continuous evaluation of network quality and the comparison of the service quality of different SIM cards, it is possible to effectively select the SIM card that provides better network performance, enhancing the stability and speed of communication; Throughout the process, encryption algorithms are used to protect the data to ensure that the user's privacy information is not leaked, enhancing the user's trust in the system; Based on the continuous training and update of deep learning algorithms, the system can adjust the switching policy according to the changes in user behavior and the dynamic network environment to ensure the long-term adaptability and forward-looking of the system. Description of the Drawings

[0006] Figure 1 It is the method step diagram of the present invention; Figure 2 It is the system module diagram of the present invention. Detailed Embodiment

[0007] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0008] An embodiment of the present invention, as Figure 1 shown, a SIM card switching method, the method includes: S1. Based on machine learning algorithms, analyze the user's behavior information, and establish a user behavior model based on the analysis results; S2. Collect network quality data at the current location in real time through the Internet of Things communication module, evaluate the network quality, and generate a network quality report based on the evaluation results; S3. Based on the user behavior model, network quality report, and preset switching strategy, perform intelligent switching decisions through the intelligent decision-making engine to select the optimal SIM card for communication; S4. According to the decision result of the intelligent decision-making engine, execute the SIM card switching operation, and display the switching result and the current network status to the user through the user interface; S5. The system continuously monitors changes in the user behavior pattern and network environment, and optimizes the switching strategy according to the monitoring results.

[0009] The working principle of the above technical solution is as follows: Deeply mine and analyze the user's behavior information using machine learning algorithms. The behavior information includes the user's communication habits (such as call duration, data usage, common contacts, etc.), preference settings (for example, whether the user tends to use a specific SIM card for data traffic or voice calls at a specific time or location), and historical SIM card switching records. Based on the analysis results, a user behavior model is established. The user behavior model reflects the user's preferences and habits for SIM card usage in different situations. Through the Internet of Things communication module, the system can collect network quality data at the user's current location in real time, including key indicators such as signal strength, network speed, and latency. Then, the system evaluates these data and generates a network quality report. The network quality report lists the quality status of each available network (corresponding to different SIM cards). At this step, the intelligent decision-making engine makes a comprehensive judgment based on the user behavior model, network quality report, and preset switching strategy. The switching strategy includes preferentially using the network with the highest signal strength, switching to a more economical SIM card when the data usage reaches a threshold, etc. The intelligent decision-making engine comprehensively considers these factors and selects the optimal SIM card for communication in the current situation. According to the decision result of the intelligent decision-making engine, the system automatically executes the SIM card switching operation. At the same time, through the user interface, the system displays the switching result and the current network status to the user, enabling the user to clearly understand which SIM card is currently being used and the network status. The system continuously monitors changes in the user behavior pattern and network environment. Over time, the user's communication habits may change, and the network environment may also be different. Therefore, the system needs to optimize the switching strategy according to these changes to ensure that the best communication experience can always be provided to the user. Among them, optimization includes adjusting the weight of the switching strategy, updating the user behavior model, etc.

[0010] The effect of the above technical solution is: through the in-depth analysis of user behavior information by machine learning algorithms, the system can establish an accurate user behavior model, and through the user behavior model, the personalized characteristics of the user's communication habits, preference settings, etc. are reflected, so that the system can intelligently switch according to the actual needs of the user, ensuring that the user can obtain the most suitable communication service in different situations (such as work, entertainment, travel, etc.), thereby improving the user's communication experience. The Internet of Things communication module can collect network quality data of the user's current location in real time, including key indicators such as signal strength, network speed, and delay. After evaluating these data, the system can generate a network quality report to provide an important reference for intelligent switching decisions. By selecting a SIM card with better network quality for communication, users can enjoy more stable and faster network services. The intelligent decision engine can comprehensively consider the user behavior model, network quality report, and preset switching strategy to make intelligent switching decisions, taking into account both the personalized needs of the user and the actual situation of the network quality, making the switching decision more scientific and reasonable. At the same time, the intelligent decision engine can also make adaptive adjustments according to changes in user behavior patterns and network environment, and continuously optimize the switching strategy. After the system performs the SIM card switching operation, it will display the switching result and current network status to the user through the user interface, which not only allows the user to clearly understand their communication status, but also increases the user's trust and satisfaction with the system. In addition, users can also adjust preferences and switching strategies through the user interface, further improving the user-friendliness of the system. Through intelligent switching decisions, the system can avoid unnecessary SIM card switching and waste of communication resources, reduce users' communication costs, and reduce equipment energy consumption and carbon emissions to achieve green communication. The system can continuously monitor changes in user behavior patterns and network environments, and optimize switching strategies based on monitoring results. This ability to continuously learn and optimize enables the system to continuously adapt to new communication environments and user needs, maintaining its competitiveness and advancement.

[0011] In one embodiment of the present invention, the S1 includes: S11, collecting multivariate data of the user through built-in sensors and applications of the mobile communication device, wherein the multivariate data includes communication habits, preference settings, and historical switching records, and preprocessing the collected multivariate data; S12. Based on the preprocessed data, a user behavior feature library is constructed by a machine learning algorithm, wherein the feature library includes user activity patterns, time period preferences, geographic location preferences, and communication type preferences; S13. Use deep learning algorithms to train user behavior characteristics, build a user behavior prediction model, and predict users' future communication needs and preferences through the behavior prediction model.

[0012] The working principle of the above technical solution is as follows: Mobile communication devices (such as smartphones, tablets, etc.) are built-in with a variety of sensors (such as acceleration sensors, position sensors, etc.) and application programs (such as call applications, data traffic monitoring applications, etc.). Through these sensors and application programs, diverse data of users can be collected in real time or regularly, including communication habits (such as call duration, text message sending frequency, data usage, etc.), preference settings (such as which SIM card the user sets to preferentially use for data traffic or voice calls), and historical switching records (such as the records of the user switching SIM cards at different locations or time periods). The raw data collected is preprocessed. Based on the preprocessed data, the characteristics of user behavior are extracted through machine learning algorithms. The said characteristics include user activity patterns (such as the communication frequency on weekdays is higher than that on weekends), time period preferences (such as the user tends to use data traffic at night), geographical location preferences (such as the user often uses a certain SIM card at home), and communication type preferences (such as the user prefers to use text messages rather than phone calls for communication). The feature library contains key information that can reflect user communication behaviors and preferences. Deep learning algorithms (such as neural networks, recurrent neural networks, etc.) are used to train the user behavior characteristics. Deep learning algorithms can automatically learn the complex patterns and relationships in the data, thereby constructing an accurate user behavior prediction model. Through the training process, deep learning algorithms can generate a user behavior prediction model. This model can predict the user's future communication needs and preferences based on the user's historical behaviors and the current environment (such as time, location, etc.). The prediction results are used to guide the intelligent switching decision of the SIM card to ensure that the user can obtain the most suitable communication service in different situations.

[0013] The effects of the above technical solutions are as follows: By means of the built-in sensors and applications in the mobile communication device, it is possible to comprehensively collect diverse data of users, including communication habits, preference settings, and historical switching records, etc. Preprocessing the collected diverse data can remove noise, duplicates, and missing values, improving the data quality. The preprocessed data is cleaner and tidier, which is beneficial to the subsequent processing and analysis by machine learning algorithms. Based on the preprocessed data, a user behavior feature library is constructed through machine learning algorithms, and personalized features of users can be extracted, including user activity patterns, time period preferences, geographical location preferences, and communication type preferences, etc., which can comprehensively reflect the communication behaviors and preferences of users. The user behavior feature library is the basis for constructing the subsequent user behavior prediction model. Through the feature library, the system can more accurately understand the communication needs and behavior patterns of users, so as to provide more personalized services for users. Using deep learning algorithms to train the user behavior features can construct an accurate user behavior prediction model. This model can predict the future communication needs and preferences of users based on the historical behaviors of users and the current environment (such as time, location, etc.). The user behavior prediction model can provide strong support for the intelligent switching decision of the SIM card. By predicting the communication needs and behavior patterns of users, the system can automatically select the optimal SIM card for communication, improving the communication efficiency and user experience. With the changes in user behaviors and the development of communication technologies, the user behavior prediction model needs to be continuously optimized and improved. By continuously collecting and analyzing user data, the system can automatically update the user behavior feature library and the prediction model to adapt to the new communication environment and user needs.

[0014] In one embodiment of the present invention, the S2 includes: S21. Through the Internet of Things communication module, real-time collect the network quality data of the current location, and the network quality data includes signal strength, network speed, latency, network type, network stability, and network coverage; S22. Integrate the collected multi-source network quality data and remove redundant information; S23. Based on the integrated data, through machine learning algorithms, construct a network quality evaluation model, quantitatively score the network quality, and generate a network quality report.

[0015] The working principle of the above technical solution is as follows: The Internet of Things communication module is a bridge connecting mobile communication devices and the external network environment. It can collect network quality data at the current location in real time, including key indicators such as signal strength, network speed, latency, network type (such as 4G, 5G, Wi-Fi, etc.), network stability, and network coverage. The Internet of Things communication module can continuously collect network quality data to ensure the timeliness and accuracy of the data. The collected network quality data may come from multiple different sources (such as different communication operators, different network devices, etc.). The system fuses these multi-source data to form a comprehensive network quality data set. During the data fusion process, some redundant information (such as duplicate data, useless data, etc.) may be generated. The system needs to remove this redundant information. Based on the fused data, the system constructs a network quality assessment model through machine learning algorithms. The model can automatically learn the features and rules in the data to quantitatively score the network quality. The network quality assessment model will score each network indicator and generate a comprehensive network quality report by integrating these scores. The report may include information such as the scores of each network indicator, the score of the overall network quality, and the ranking of the network quality.

[0016] The effects of the above technical solution are as follows: Through the Internet of Things communication module, the system can collect multiple network quality data at the current location in real time, including key indicators such as signal strength, network speed, and latency. The data collected in real time can reflect the real situation of the current network environment. Decisions made based on these data (such as SIM card switching, network optimization, etc.) will be more accurate and effective. Users can timely understand the advantages and disadvantages of the current network environment, so as to make reasonable communication choices and improve the communication experience and satisfaction of users. Fusing multi-source network quality data can form a unified and comprehensive data set, simplify the data processing process, and improve the efficiency and accuracy of data processing. Removing redundant information can reduce the repetition and uselessness of data, improve the quality and usability of data, and provide stronger support for subsequent network quality assessment. The network quality assessment model constructed through machine learning algorithms can quantitatively score the network quality. This makes the assessment of network quality more objective, accurate, and comparable. The network quality report generated based on the assessment model can detail the scores of each network indicator and the score of the overall network quality. Users and managers can comprehensively understand the situation of the current network environment and provide a basis for subsequent decisions. The network quality assessment model can provide strong support for the intelligent switching decision of SIM cards. When the network quality does not meet the user's needs, the system can automatically select a better network for communication to ensure the continuity and stability of communication. Through continuous network quality assessment, problems and bottlenecks in the network can be discovered, enabling operators and network managers to timely optimize and improve the network, and enhancing the overall network quality and service level.

[0017] One embodiment of the present invention, S3 includes: S31. Based on the user behavior prediction model and the network quality assessment report, predict the current and future communication requirements of the user through a deep learning algorithm; S32. Based on the user requirement prediction result, combine the user preference settings and the preset switching strategy to formulate an intelligent switching strategy; S33. Through the intelligent decision-making engine, comprehensively consider the user requirements, network quality, switching cost, and user feedback, and select the optimal SIM card for communication based on the comprehensive consideration result, and automatically execute the switching operation.

[0018] The working principle of the above technical solution is as follows: The user behavior prediction model is constructed based on the user's historical behavior data (such as communication habits, preference settings, etc.) and can predict the user's future communication requirements; the network quality assessment report reflects the network quality status of the current location in real time, including key indicators such as signal strength, network speed, and latency. Use a deep learning algorithm to predict the user's current and future communication requirements, including predicting the user's communication volume, communication time, communication type (such as voice, data, SMS, etc.), and possible communication locations, etc. The deep learning algorithm can automatically learn the features and rules in the data, so as to achieve accurate prediction of the user's communication requirements. According to the prediction result, analyze the user's current communication requirements and possible future changes in communication requirements. Consider the user's preference settings, such as preferring to use the network of a certain operator, the requirement for network speed, etc. Combine the preset switching strategy, and the switching strategy includes factors such as cost-benefit (such as choosing a network with lower tariffs), network coverage (such as choosing a network with better signal), service quality (such as choosing a network with lower latency), and security (such as choosing a network with stronger encryption). Comprehensively analyze the user requirement prediction result, user preference, and preset strategy to formulate an intelligent switching strategy that meets the user's requirements. The intelligent decision-making engine comprehensively considers multiple factors such as user requirements (such as communication volume, communication type, etc.), network quality (such as signal strength, network speed, etc.), switching cost (such as the cost of replacing the SIM card, time cost, etc.), and user feedback (such as the user's satisfaction with the network quality). Based on the comprehensive consideration result, the intelligent decision-making engine selects the current optimal SIM card for communication. Usually, among multiple possible SIM cards, it is selected according to the principle of the highest comprehensive score. Once the optimal SIM card is determined, the system will automatically execute the switching operation, switching the user's communication device to this SIM card, including steps such as updating the device's network settings and activating the new SIM card.

[0019] The effects of the above technical solution are as follows: By combining the user behavior prediction model and the network quality assessment report, it is possible to more comprehensively understand the user's communication habits and network environment, thereby improving the prediction accuracy of the user's current and future communication needs. Accurate prediction results help operators or network equipment providers plan the allocation of network resources and SIM cards in advance, avoiding resource waste and overcongestion. Users can enjoy network services that better meet their own needs, such as automatically switching to a better network when the network quality is poor, thereby enhancing the communication experience. Combining user preference settings and preset switching strategies can provide personalized network services for users. For example, for users who pay attention to cost-effectiveness, they can choose a network with lower tariffs; for users who pursue network coverage, they can choose a network with better signal. The intelligent switching strategy can automatically adjust the user's communication method according to the changes in the network environment, thereby enhancing the stability and reliability of the network. The preset switching strategy includes security factors to ensure that users will not disclose sensitive information or be attacked by the network when switching networks. The intelligent decision-making engine can comprehensively consider multiple factors, such as user needs, network quality, switching costs, and user feedback, etc., and thus quickly make the optimal decision. Automatically executing the switching operation can reduce manual intervention and the time cost during the switching process, improving the switching efficiency. Users can enjoy the optimal network service without manually switching the network, thereby enhancing user satisfaction and loyalty. By continuously collecting user feedback and network quality data, operators can continuously optimize network services and switching strategies, improving the overall network quality and service level.

[0020] In one embodiment of the present invention, the S32 includes: Analyze the user's current and future communication needs predicted by the deep learning algorithm, clarify the user's communication needs in different time periods and different scenarios, including data traffic, voice call duration, SMS requirements, etc., and at the same time, analyze the future change trend of the user's needs; According to the user demand prediction results, prioritize the user's communication needs in different time periods; Obtain the user's preference information for communication services through user research, in-app settings, or user behavior analysis, including but not limited to network speed, stability, cost, coverage, service quality (QoS), and assign weights to different preference settings according to the user preference information; Based on the tariff structures of different SIM cards, calculate the communication costs of users in different time periods and different scenarios, and consider the long-term cost-effectiveness, such as package discounts, traffic accumulation, etc., and use the network quality assessment report to evaluate the network coverage of different SIM cards in different regions, including signal strength, network speed, stability, etc.; Analyze the quality of service provided by different SIM cards, including latency, jitter, packet loss rate, etc., to ensure that the selected SIM card can meet the user's QoS requirements, and evaluate the security of different SIM cards, including data encryption, authentication, privacy protection, etc., to ensure the security of user communication; Combine the user demand prediction results, user preference settings, and preset switching strategies to design the rules of the intelligent switching strategy; the rules should be able to dynamically adjust the switching strategy according to the user's needs, network quality, cost - effectiveness, and security in different scenarios. Based on the design of the strategy rules, prioritize different switching options. The priority ranking should comprehensively consider factors such as user needs, network quality, cost - effectiveness, and security to ensure that the selected SIM card can maximize cost - effectiveness while meeting the user's needs.

[0021] The working principle of the above - mentioned technical solution is as follows: Analyze in detail the current and future communication needs of users predicted by the deep - learning algorithm to clarify the specific communication needs of users in different time periods (such as weekdays, weekends, holidays, etc.) and different scenarios (such as home, office, outdoors, etc.), including data traffic, voice call duration, SMS requirements, etc. Analyze the future change trends of user needs. For example, as the working day ends, users may shift from a high - data - demand state to a low - data - demand state. According to the user demand prediction results, prioritize the communication needs of users in different time periods. For example, during working hours, video conferencing and data transmission may have higher priorities; while during non - working hours, social media and entertainment applications may be dominant. Obtain the user's preference information for communication services through various methods such as user research, in - app settings, or user behavior analysis. This information includes, but is not limited to, network speed, stability, cost, coverage, quality of service (QoS), etc. Assign weights to different preference settings according to the user preference information. For example, for users who often travel, network coverage may have a higher weight; while for users who focus on cost, the cost factor may account for a larger proportion. Based on the tariff structure of different SIM cards, calculate the communication costs of users in different time periods and different scenarios; at the same time, consider the long - term cost - effectiveness, such as package discounts, traffic accumulation, etc. Use the network quality assessment report to evaluate the network coverage of different SIM cards in different regions, including signal strength, network speed, stability, etc. Analyze the quality of service provided by different SIM cards, including key indicators such as latency, jitter, packet loss rate, etc., to ensure that the selected SIM card can meet the user's QoS requirements. Evaluate the security of different SIM cards, including key aspects such as data encryption, authentication, privacy protection, etc., to ensure the security of user communication; for example, assume there are two SIM cards, A and B, with the following tariff structures: SIM card A: Monthly rent: 50 yuan; Call charge: $0.15 per minute; Data charge: The first 1GB is $10, and the excess is $5 per GB; Package discount: 50 minutes of call duration and 1GB of data are given as a monthly gift; SIM card B: Monthly rent: $30; Call charge: $0.2 per minute; Data charge: $3 per GB, no tiered pricing; Data accumulation: The data not used this month can be accumulated for use next month and is valid for one year; Scenario 1: Weekday commute: Time period: 7:00 - 9:00 in the morning, 17:00 - 19:00 in the evening; Call duration: A total of 30 minutes of calls during daily commuting; Data usage: 500MB of data is used during daily commuting; Cost of SIM card A: Call cost: 30 minutes × $0.15 per minute = $4.5 per day, approximately $135 per month; Data cost: 500MB per day, a total of 15GB per month (1GB + 14GB after considering the package discount), the cost is $10 (for the first GB) + 14GB × $5 per GB = $70 per month; Total cost: $50 (monthly rent) + $135 (call) + $70 (data) = $255 per month; Cost of SIM card B: Call cost: 30 minutes × $0.2 per minute = $6 per day, approximately $180 per month; Data cost: 15GB × $3 per GB = $45 per month; Total cost: $30 (monthly rent) + $180 (call) + $45 (data) = $255 per month; Scenario 2: Weekend leisure: Time period: All day on Saturday and Sunday; Call duration: A total of 60 minutes of calls on weekends; Data usage: A total of 2GB of data is used on weekends; Cost of SIM card A: Call cost: 60 minutes × $0.15 per minute = $9; Data cost: 2GB is outside the package, and the cost is 2GB × $5 per GB = $10; Additional cost: $9 (call) + $10 (data) = $19; Cost of SIM card B: Call cost: 60 minutes × $0.2 per minute = $12; Data cost: 2GB × $3 per GB = $6; Extra cost: $12 (call) + $6 (data) = $18; Long - term cost - benefit analysis: SIM card A: Although the monthly rent is relatively high, the included call duration and data in the package reduce the daily cost to a certain extent. However, for users with low data usage, its tiered pricing may not be cost - effective; SIM card B: The monthly rent is low, and data can be accumulated for use, which is more user - friendly for users with fluctuating data usage. In the long run, the data accumulation function can reduce the extra costs caused by excessive or insufficient data; Network quality assessment: Suppose there is a network quality assessment report that evaluates the network coverage of SIM card A and B in different regions (such as cities, suburbs, and rural areas): Urban area: SIM card A: Strong signal strength, fast network speed, and good stability; SIM card B: Relatively strong signal strength, slightly slower network speed than A, but also good stability; Suburban area: SIM card A: Medium signal strength, decreased network speed, but still within an acceptable range; SIM card B: Weak signal strength, significantly slower network speed than A, and occasional instability; Rural area: SIM card A: Wide signal coverage, but weak strength and slow network speed; SIM card B: Limited signal coverage, no signal in some areas, and extremely slow network speed; Comprehensive evaluation and selection: For the scenarios of weekday commuting and weekend leisure, the total costs of SIM card A and B are comparable. However, when considering the long - term cost - benefit, the data accumulation function of SIM card B may be more attractive; In terms of network quality, SIM card A performs better in urban and suburban areas, while SIM card B performs relatively poorly in rural areas; Therefore, the user's choice should be based on their usage scenarios, long-term communication needs, and the network coverage in their area. For example, for users who often move between cities and suburbs and want to enjoy the benefits of cumulative data, they can choose SIM card B; while for users who need to frequently use the network in rural areas, they may be more inclined to choose SIM card A with a wider signal coverage. Based on the user demand prediction results, user preference settings, and preset switching strategies, design the rules of the intelligent switching strategy. These rules should be able to dynamically adjust the switching strategy according to the user's needs, network quality, cost-effectiveness, and security in different scenarios. On the basis of the strategy rule design, prioritize different switching options. The priority ranking should comprehensively consider factors such as user needs, network quality, cost-effectiveness, and security to ensure that the selected SIM card can maximize cost-effectiveness while meeting the user's needs.

[0022] The effects of the above technical solution are as follows: By using deep learning algorithms to accurately predict the current and future communication needs of users, and combining the demand changes of users in different time periods and different scenarios, it is possible to provide users with communication services that better meet their actual needs; reduce resource waste, improve the utilization rate of communication resources, and at the same time enhance the user experience; according to the user demand prediction results, prioritize the communication needs of users in different time periods, and design the rules of the intelligent switching strategy, which can ensure that under the condition of limited resources, the high-priority needs of users are preferentially satisfied; improve the response speed and efficiency of communication services, and ensure that users can enjoy stable and efficient communication services at critical moments; obtain the preference information of users for communication services through user research, in-app settings or user behavior analysis, and assign weights to different preference settings according to this information, which can provide users with personalized communication services; enhance user satisfaction and loyalty, and improve the brand image and market competitiveness; based on the tariff structures of different SIM cards, calculate the communication costs of users in different time periods and different scenarios, and consider the long-term cost-benefit, such as package discounts, traffic accumulation, etc., which can save communication costs for users; reduce the communication costs of users, improve user satisfaction and loyalty, and at the same time promote the long-term development of operators; use the network quality assessment report to evaluate the network coverage of different SIM cards in different regions, and analyze the service quality provided by different SIM cards, which can ensure that the SIM card selected by users has good network quality and stable service; reduce communication failures and interruptions, improve the reliability and stability of communication services, and enhance the user experience; evaluate the security of different SIM cards, including data encryption, authentication, privacy protection, etc., which can ensure the security of user communication; protect the privacy and communication content of users from being leaked or misused, and enhance the user's trust and sense of security in communication services; combine the user demand prediction results, user preference settings and preset switching strategies, and design the rules of the intelligent switching strategy, which can comprehensively consider multiple factors such as user needs, network quality, cost-benefit and security, and maximize the overall benefits; improve the overall level and competitiveness of communication services, and bring greater value to users and operators.

[0023] In one embodiment of the present invention, step S4 includes: S41. Through the user interface, the switching result and the current network status are displayed in real time. The real-time display content includes the network type, speed, cost, and stability before and after switching; S42. Collect the feedback opinions of users on the switching result through various methods; the various methods include user surveys, satisfaction ratings, and feedback of opinions; S43. Based on the user feedback, use machine learning algorithms to iteratively optimize the user behavior prediction model and the network quality assessment model, and adjust the switching strategy.

[0024] The working principle of the above technical solution is as follows: Through the user interface, the switching results and specific information on the current network status are displayed in real time.

[0025] The display content includes: Network type: Displays the network types before and after switching, such as 4G, 5G, Wi-Fi, etc., to help users understand the current network environment.

[0026] Speed: Displays the download and upload speeds of the current network, allowing users to intuitively feel the speed of the network.

[0027] Cost: Estimates and displays the network costs before and after switching based on the user's package situation and current usage, to help users make more economical choices.

[0028] Stability: Evaluates and displays the stability of the current network, such as signal strength, packet loss rate, etc., to ensure that users can enjoy stable network services.

[0029] The system monitors the network status and user behavior, and updates the display information on the user interface in real time to ensure that users can always keep track of the network situation.

[0030] Feedback from users on the switching results is collected through multiple methods, and the multiple methods include: User surveys: Design questionnaires to collect users' satisfaction and opinions on the switching results through online or offline methods.

[0031] Satisfaction rating: Provide a simple rating system for users to rate the switching results, to quickly understand users' satisfaction.

[0032] Feedback: Set up feedback channels, such as customer service hotlines, online messages, etc., to allow users to feedback problems and suggestions at any time.

[0033] The system collects users' feedback through multiple methods, and sorts out and analyzes these opinions to provide a basis for subsequent optimization.

[0034] Iteratively optimize the user behavior prediction model and the network quality assessment model to improve the accuracy and reliability of the models; adjust the switching strategy according to user feedback and model optimization results to ensure that the switching process is smoother and more efficient. The system analyzes the user's historical behavior and current network status through machine learning algorithms, continuously optimizes the prediction model, and improves the accuracy of switching. At the same time, the system also adjusts the switching strategy in real time according to users' feedback to ensure that the switching process can meet users' needs and expectations. Through this continuous optimization method, the system can continuously improve the user's network experience and service quality.

[0035] The effects of the above technical solutions are as follows: Users can intuitively see the changes in network status before and after switching, including key information such as network type, speed, cost, and stability, enhancing users' sense of participation and trust in network management; The real-time display of information provides users with instant feedback on network status, helping users make more informed decisions, such as whether to switch networks and when to switch. Users' perception of network status is more accurate, reducing misunderstandings or dissatisfaction caused by information asymmetry. Users' satisfaction and trust in network services are improved, which helps to enhance user loyalty and brand image. By collecting user feedback in various ways, it can be ensured that the information collected is more comprehensive and objective, avoiding biases or omissions that may be brought about by a single method. User feedback is an important basis for service improvement. By continuously collecting and analyzing user feedback, problems existing in the service can be discovered and solved in a timely manner, promoting the continuous improvement of service quality. Service providers can understand users' needs and expectations more accurately, so as to provide services that better meet users' needs. The user feedback mechanism promotes communication and interaction between service providers and users, enhancing users' sense of participation and belonging. By iteratively optimizing the user behavior prediction model and network quality assessment model through machine learning algorithms, the accuracy of prediction can be improved, so as to more accurately meet users' needs. Adjusting the switching strategy based on user feedback can ensure that the strategy is always synchronized with users' needs, improving the flexibility and adaptability of the service. The switching strategy is more intelligent and personalized, and can better meet users' network needs and service expectations. By continuously optimizing the model and adjusting the strategy, service providers can continuously improve service quality and enhance market competitiveness.

[0036] In one embodiment of the present invention, the S43 includes: S431. Preprocess the feedback data and integrate data from different sources to form a complete user feedback data set; S432. Use natural language processing technology to perform sentiment analysis on user opinion feedback, identify positive or negative evaluations of different switching results and network status by users, and quantify the sentiment analysis results; S433. Evaluate the performance of the user behavior prediction model and network quality assessment model based on user feedback data; and analyze the model performance evaluation results to identify existing problems; S434. Combine the specific content of user feedback to conduct attribution analysis on the problems, and according to the problem identification results, re-examine and optimize the features of the user behavior prediction model and network quality assessment model; S435. Use the hyperparameter tuning technology of machine learning algorithms to adjust the parameters of the model; through parameter tuning, find the parameter combination with the best model performance; S436. Integrate or ensemble multiple machine learning models, and evaluate the effectiveness of the current switching strategy based on user feedback data and model optimization results; S437. Adjust and optimize the switching strategy according to the evaluation results of the strategy effectiveness; through a monitoring mechanism, conduct real-time monitoring and analysis of user feedback data, model performance, switching strategy effectiveness, etc.; S438. Incorporate user feedback data, model optimization results, and switching strategy adjustments into an iterative loop for continuous iteration and optimization.

[0037] The working principle of the above technical solution is as follows: First, the feedback data collected from multiple sources (such as user surveys, satisfaction ratings, opinion feedback, etc.) is preprocessed. Then, the data from these different sources is integrated to form a complete user feedback dataset. Natural language processing techniques are used to perform sentiment analysis on user opinion feedback to identify positive or negative evaluations of different handover results and network states. Through methods such as sentiment lexicons, machine learning algorithms, or deep learning models, the sentiment analysis results are quantified, such as giving positive, negative, or neutral sentiment tendency scores. Based on the user feedback data, the performance of the user behavior prediction model and the network quality assessment model is evaluated, including indicators such as accuracy, recall rate, and F1 score. By analyzing the model performance evaluation results, problems such as prediction bias, overfitting, and underfitting are identified to provide directions for subsequent optimization. Combining the specific content of the user feedback, attribution analysis of the problems is carried out to identify the key factors affecting the model performance. According to the problem identification results, the features of the user behavior prediction model and the network quality assessment model are reexamined and optimized, such as introducing new features, adjusting the weights of existing features, etc., to improve the prediction ability and robustness of the model. Using hyperparameter tuning techniques of machine learning algorithms, such as grid search, random search, or Bayesian optimization, etc., the parameters of the model are adjusted. Through parameter tuning, the parameter combination with the best model performance is found to improve the accuracy and generalization ability of the model. Multiple machine learning models are fused or integrated to improve the stability and accuracy of the prediction results through methods such as voting and weighted averaging. At the same time, based on the user feedback data and the model optimization results, the effectiveness of the current handover strategy is evaluated, and indicators such as the handover success rate and user satisfaction of the handover strategy in different scenarios are analyzed to identify the problems and deficiencies of the strategy. According to the strategy effectiveness evaluation results, the handover strategy is adjusted and optimized, including setting handover conditions, choosing handover times, prioritizing standby SIM cards, etc. By adjusting the strategy, the handover process is made more in line with the actual needs of users and the network state, and the efficiency and success rate of the handover are improved. The user feedback data, model optimization results, and handover strategy adjustment are incorporated into an iterative loop for continuous iteration and optimization. By continuously monitoring and analyzing user feedback data, model performance, and handover strategy effectiveness, problems are timely discovered and solved to ensure the stability and effectiveness of the model and the handover strategy. At the same time, according to the changes in new user needs and network states, the model and strategy are continuously updated and optimized to adapt to the changing market environment.

[0038] The effects of the above technical solutions are as follows: By performing data preprocessing and integration, the accuracy and consistency of the data are improved, providing a reliable basis for subsequent analysis and optimization. By integrating data from different sources, a complete user feedback dataset is formed, which helps to comprehensively understand user needs and reduces analysis errors caused by inconsistent or missing data. It provides comprehensive and accurate data support for subsequent steps. By quantifying the positive or negative evaluations of users on different switching results and network states, it helps to more intuitively understand user satisfaction. By providing a quantitative indicator of user sentiment, it provides a basis for subsequent optimization, improves the utilization rate of user feedback, and makes the optimization process more precise. It helps to timely discover and solve problems that users are not satisfied with, and improves user satisfaction. It identifies problems existing in the model, provides a direction for subsequent optimization, improves the accuracy and reliability of the model, and reduces decision-making errors caused by inaccurate models. By introducing new features or adjusting the weights of existing features, the prediction ability and robustness of the model are improved, the problem of model performance degradation caused by improper feature selection is solved, the prediction accuracy and generalization ability of the model are improved, and the model better meets the actual business needs. By parameter tuning, the parameter combination with the best model performance is found, the accuracy and efficiency of the model are improved, the model performance degradation caused by improper parameter settings is reduced, and the running speed and stability of the model are improved. By accurately evaluating the effects of switching strategies in different scenarios, the rationality and effectiveness of the switching strategies are improved, providing strong data support for subsequent optimization. According to the evaluation results, the switching strategies are adjusted and optimized, and the problem of decreased user satisfaction caused by unreasonable strategies is solved. It improves the adaptability and flexibility of the switching strategies; improves user satisfaction and loyalty. Incorporating user feedback data, model optimization results, and switching strategy adjustments into an iterative loop realizes continuous optimization. It improves the overall performance and stability of the system. Ensures that the model and switching strategies are always synchronized with user needs. Improves the competitiveness and market share of the system.

[0039] In one embodiment of the present invention, the S431 includes: Removing duplicate, irrelevant or incorrect feedback data through a hash algorithm and text similarity detection, and complementing missing key information through data interpolation; Unifying text encoding, correcting spelling mistakes, converting non-standard terms into standard vocabulary, and converting timestamps from different sources into a unified time zone format; defining clear classification labels for feedback content; Associate data from different channels through unique identifiers such as user ID and device ID to form a complete user profile. Combine user behavior data (such as APP usage frequency, browsing habits), network status data (such as signal strength, latency time) with feedback text to construct a multi-modal dataset; and align user feedback with network status and behavior data on the time axis.

[0040] The working principle of the above technical solution is as follows: Use the hash algorithm to calculate the unique identifier of the feedback data, and identify and remove duplicate data by comparing the hash values. At the same time, use text similarity detection algorithms (such as cosine similarity, Jaccard similarity, etc.) to identify and remove feedback data with highly similar content, as well as irrelevant or incorrect feedback data, such as advertising information, garbled characters, etc.; for missing key information (such as user ID, timestamp, specific feedback content, etc.), use data interpolation methods (such as linear interpolation, nearest neighbor interpolation, etc.) to complete; unify the text encoding of the feedback data into UTF-8 or other standard encodings to avoid garbled problems caused by inconsistent encoding; use spelling check algorithms (such as dictionary-based methods, machine learning-based methods, etc.) to correct spelling mistakes in the feedback data; convert non-standard terms (such as abbreviations, Internet terms, etc.) in the feedback data into standard vocabulary; convert timestamps from different sources into a unified time zone format to ensure the consistency of time data; define clear classification labels for the feedback content, such as network quality, handover success rate, user experience, etc., for subsequent data analysis and model training; through unique identifiers such as user ID and device ID, associate data from different channels to form a complete user profile, including combining user behavior data (such as APP usage frequency, browsing habits, etc.), network status data (such as signal strength, latency time, etc.) with feedback text; integrate the associated data into a multi-modal dataset, that is, a dataset containing various types of data such as text, numerical values, time series, etc. This multi-modal dataset helps to more comprehensively understand user needs and problems; align user feedback with network status and behavior data on the time axis to analyze the association between user feedback and network status and behavior data at different time points. This helps to identify the specific time and scenario when problems occur and provides strong data support for subsequent optimization.

[0041] The effects of the above technical solutions are as follows: Through the hash algorithm and text similarity detection, duplicate, irrelevant, or incorrect feedback data, such as advertising information and garbled codes, are effectively removed, significantly improving the quality and usability of the data. The data interpolation technology is used to complement missing key information, ensuring the integrity and coherence of the data and providing a more reliable basis for subsequent analysis. Unifying text encoding and correcting spelling mistakes eliminate data confusion problems caused by inconsistent encoding or spelling mistakes, improving the readability and accuracy of the data. Converting non-standard terms into standard vocabulary and converting timestamps from different sources into a unified time zone format ensure the consistency and comparability of the data. Defining clear classification tags for feedback content, such as network quality, handover success rate, user experience, etc., helps to quickly identify and analyze the key points and trends of user feedback. The introduction of classification tags also facilitates subsequent data mining and machine learning model training, improving the efficiency and accuracy of analysis. By using unique identifiers such as user ID and device ID, data from different channels are associated to form a complete user profile, which helps to comprehensively understand the behavior and needs of users. Combining user behavior data, network status data with feedback text constructs a multi-modal data set, providing rich data resources for in-depth analysis of the correlation between user feedback and network status and behavior data. Aligning user feedback with network status and behavior data on the timeline helps to identify the specific time and scenario when problems occur, providing strong data support for subsequent optimization and improvement. The improvement of data quality and standardization processing makes subsequent data analysis more efficient and accurate, reducing analysis errors and repetitive work caused by data problems. By deeply analyzing user feedback and behavior data, enterprises can more accurately identify user needs and pain points, thereby optimizing products and services and enhancing the user experience. The complete and accurate data set provides strong data support for the decision-making of enterprises, helping enterprises to make more scientific and reasonable decisions. Timeline-aligned data helps enterprises quickly identify product problems and improvement points, thus accelerating the product iteration and optimization process.

[0042] In one embodiment of the present invention, by using unique identifiers such as user ID and device ID, data from different channels are associated to form a complete user profile, and user behavior data (such as APP usage frequency, browsing habits), network status data (such as signal strength, latency) are combined with feedback text to construct a multi-modal data set; and aligning user feedback with network status and behavior data on the timeline includes: Generating a globally unique identifier for each piece of user feedback data, and using UUID or other associated fields to match and associate data from different channels to form a complete user feedback chain; During the association process, through the hash algorithm and text similarity detection, duplicate data are removed, and similar but not exactly the same data are merged. Combine the user's basic information with the feedback data to construct a preliminary user profile; associate the user's behavior data with the feedback data through a unique identifier to analyze the user's behavior patterns and preferences; Combine the user's network status data with the feedback data to analyze the impact of network status on user feedback; continuously update and improve the user profile based on the continuous accumulation of user feedback, behavior data, and network status data; Preprocess the feedback text; use word embedding technology to convert the text data into vector representations; Fuse the text vectors, user behavior data, and network status data in the feature space to construct a multimodal dataset; Align the user feedback, behavior data, and network status data on the time axis; Check whether there are missing values or outliers in the multimodal dataset; Use machine learning algorithms to evaluate the effectiveness of the multimodal dataset, use natural language processing techniques to perform sentiment analysis and emotion recognition on user feedback, and use machine learning algorithms to predict user behavior based on the multimodal dataset; Analyze the problems and bottlenecks in the network based on user feedback and network status data.

[0043] The working principle of the above technical solution is as follows: Generate a globally unique identifier (UUID) for each piece of user feedback data, which serves as the unique identity of the data throughout the processing flow; Use the UUID or other associated fields (such as user ID, device ID, etc.) to match and associate data from different channels (such as in-app feedback, social media, customer service hotline, etc.). In this way, regardless of which channel the user uses to provide feedback, the system can regard it as the feedback of the same user, forming a complete user feedback chain; During the association process, remove duplicate data through hash algorithms and text similarity detection. Hash algorithms can quickly determine whether two pieces of data are the same, while text similarity detection can identify similar but not exactly the same data and merge them to ensure the uniqueness and accuracy of the data; Clean the feedback text, including removing stop words, punctuation marks, special characters, etc., to improve the efficiency and accuracy of text processing; Combine the user's basic information (such as name, age, gender, region, etc.) with the feedback data to construct a preliminary user profile; Through the unique identifier, associate the user's behavioral data (such as clicks, views, purchases, shares, etc.) with the feedback data. By analyzing this data, the user's behavioral patterns and preferences can be understood, further enriching the user profile; Combine the user's network status data (such as network type, speed, latency, etc.) with the feedback data to analyze the impact of network status on user feedback. This data helps identify network problems and improve the user experience; Use word embedding techniques (such as Word2Vec, BERT, etc.) to convert text data into vector representations. These vectors can capture the semantic information in the text, facilitating subsequent text analysis and processing; Fuse the text vectors, user behavioral data, and network status data in the feature space to construct a multimodal dataset. The fusion methods can include concatenation, weighted summation, attention mechanisms, etc., and the specific choice depends on the requirements of the task and the characteristics of the data; Align the user feedback, behavioral data, and network status data on the time axis to ensure the timeliness and consistency of the data. This helps analyze the user's behavior and feedback changes at different time periods, as well as the immediate impact of network status on user feedback; Check whether there are missing values or outliers in the multimodal dataset, and verify the consistency and reliability of the data by comparing data from different sources (such as user feedback and behavioral data, network status data, etc.). Use machine learning algorithms to evaluate the effectiveness of the multimodal dataset to ensure that the dataset can accurately reflect the user's real behavior and feedback; Use natural language processing techniques to perform sentiment analysis and emotion recognition on user feedback to understand the user's emotional tendency and satisfaction. Based on the multimodal dataset, use machine learning algorithms to predict user behavior, such as predicting the user's purchase intention, usage frequency, etc. According to the user feedback and network status data, analyze the problems and bottlenecks in the network, and propose network optimization suggestions and improvement measures.This helps to improve network quality and user experience; by combining user portraits and multimodal datasets, personalized recommendations and services can be provided to users.

[0044] The effects of the above technical solutions are as follows: By integrating data from different channels (such as in-APP feedback, social media, customer service hotlines, etc.) and using unique identifiers (such as UUID, user ID, device ID) for association, a complete user feedback chain can be formed, thereby constructing a comprehensive and accurate user portrait; during the association process, duplicate data is removed through hash algorithms and text similarity detection, and similar but not identical data is merged to ensure the uniqueness and accuracy of the data, avoiding data redundancy and errors; by combining user behavior data (such as clicks, browsing, purchases, sharing, etc.), network status data (such as network type, speed, latency, etc.) with feedback text, a multimodal dataset is constructed. This dataset contains rich information dimensions and can more comprehensively reflect users' real behaviors and needs; through preprocessing operations such as cleaning, tokenization, and stop word removal on the feedback text, and using word embedding technology to convert text data into vector representations, the efficiency and accuracy of text processing are improved; this provides strong support for subsequent text analysis and processing; in the feature space, text vectors, user behavior data, and network status data are fused to construct a multimodal dataset. The fusion methods can include concatenation, weighted summation, attention mechanisms, etc., and the most suitable fusion method is selected according to specific task requirements; by aligning user feedback, behavior data, and network status data on the time axis, the timeliness and consistency of the data are ensured; this helps to analyze the changes in users' behaviors and feedback at different time periods, as well as the immediate impact of network status on user feedback; by checking for missing values or outliers in the multimodal dataset and comparing data from different sources to verify the consistency and reliability of the data, the quality of the dataset is ensured; using natural language processing technology to perform sentiment analysis and emotion recognition on user feedback can understand users' sentiment tendencies and satisfaction levels, and timely discover users' dissatisfaction and pain points; based on the multimodal dataset, machine learning algorithms are used to predict user behaviors, such as predicting users' purchase intentions, usage frequencies, etc. This helps enterprises to formulate more precise marketing strategies and personalized services; according to user feedback and network status data, analyze the problems and bottlenecks existing in the network, and propose network optimization suggestions and improvement measures. This helps to improve network quality and user experience; as user feedback, behavior data, and network status data continue to accumulate, the user portrait will be continuously updated and improved, including users' interests, needs, pain points, etc. This provides strong support for personalized recommendations and services; by combining user portraits and multimodal datasets, products and services that better meet users' needs can be provided to users, improving user satisfaction and loyalty.

[0045] In one embodiment of the present invention, the S436 includes: Based on the user feedback data and the model optimization results, select the machine learning models with excellent performance as candidates; Based on the model fusion technology, preliminarily fuse the candidate models; Design the strategy for model integration; Use the user feedback data and the model optimization results to train the integrated model, use the cross-validation method to verify the integrated model, and evaluate its generalization ability and prediction accuracy on new data; Based on the prediction results of the integrated model, evaluate the effectiveness of the current switching strategy, and further optimize the integrated model according to the evaluation results; Based on the continuous monitoring mechanism of the integrated model's performance, real-time track the performance of the model on new data, discover and solve problems in a timely manner; incorporate the user feedback data, model optimization results, and switching strategy adjustments into the iterative loop for continuous iteration and optimization.

[0046] The working principle of the above technical solution is as follows: based on user feedback data and model optimization results, select excellent machine learning models as candidates. These models may include user behavior prediction models, network quality assessment models, sentiment analysis models, etc., each of which performs well in different application scenarios and data sets; use model fusion techniques, such as weighted average, voting mechanism or stacking model, to initially fuse the candidate models. These technologies aim to reduce the bias and variance of a single model and improve the accuracy and stability of the overall prediction by combining the prediction results of multiple models; design model integration strategies, including the selection of integration methods (such as parallel integration, serial integration, etc.), the allocation of integration weights (based on factors such as model performance and user feedback), and the output method of the integrated model. These strategies aim to further leverage the advantages of each model and improve the performance and adaptability of the integrated model; use user feedback data and model optimization results to train the integrated model to ensure that the model can fully learn the complementarity between different data sources and models; use cross-validation to verify the integrated model and evaluate its generalization ability and prediction accuracy on new data. The cross-validation method evaluates the performance of the model more comprehensively by dividing the data set into multiple subsets, which are used as training sets and test sets in turn. Based on the prediction results of the integrated model, the current switching strategy is evaluated, including indicators such as switching success rate, user satisfaction, and network quality. These indicators can fully reflect the performance of the switching strategy and user experience. According to the evaluation results, the integrated model is further optimized, such as adjusting the integrated strategy, adding new candidate models, optimizing model parameters, etc., to improve the overall performance of the model. These optimization measures are aimed at continuously iterating and improving the model to better adapt to actual application scenarios and needs; establishing a continuous monitoring mechanism for the effects of the integrated model to track the performance of the model on new data in real time. This helps to promptly discover problems and deficiencies in the model so that timely adjustments and optimizations can be made; user feedback data, model optimization results, and switching strategy adjustments are included in the iterative cycle for continuous iteration and optimization. This cycle mechanism ensures that the technical solution can continue to learn and improve, thereby continuously improving user satisfaction and network quality.

[0047] The effects of the above technical solution are as follows: By integrating multiple excellent machine learning models, such as user behavior prediction models, network quality assessment models, sentiment analysis models, etc., the advantages of each model can be fully utilized, reducing the bias and uncertainty of a single model, thereby improving the accuracy and stability of overall prediction; Different models may be good at handling different types of data or problems. Through integration, a more comprehensive and in-depth understanding of the data can be achieved, thus improving the prediction effect; By integrating the prediction results of the models, the effect of the current handover strategy can be evaluated, which can more objectively reflect the performance of the strategy in actual applications, including indicators such as handover success rate, user satisfaction, and network quality; According to the evaluation results, the handover strategy can be optimized specifically, such as adjusting the handover threshold, optimizing the handover algorithm, etc., thereby enhancing the user experience and network performance; Establishing a continuous monitoring mechanism for the effect of the integrated model can track the performance of the model on new data in real time, discover and solve problems in a timely manner, and ensure that the model always maintains the best state; Incorporating user feedback data, model optimization results, and handover strategy adjustments into an iterative loop, through continuous learning and optimization, the technical solution can continuously adapt to and meet the changing needs of users, maintaining competitiveness; Through multi-model integration, even if a certain model has problems or its performance deteriorates, other models can still provide effective prediction results, thereby enhancing the robustness of the system; The technical solution can flexibly handle complex scenarios under different users and different network environments. By dynamically adjusting the handover strategy and model parameters, the system can always be maintained in the best state; Through model fusion and integration, the existing model resources can be fully utilized, avoiding duplicate development and resource waste; The integrated model is easier to maintain and update compared to a single model because it can quickly adapt to new application scenarios and data by adjusting the integration strategy or adding new candidate models.

[0048] In one embodiment of the present invention, the S5 includes: S51. Monitor the changes in user behavior patterns (such as new preferences, changes in communication habits) and network environment (such as network quality fluctuations, new base station construction, network policy adjustments) in real time; S52. Based on the real-time monitoring results, continuously train and update the user behavior prediction model and the network quality assessment model through deep learning algorithms; S53. Dynamically adjust the handover strategy according to the model update results and user feedback, and during the monitoring, learning, and optimization process, encrypt the data and protect the privacy through encryption algorithms.

[0049] The working principle of the above technical solution is as follows: By monitoring the user's behaviors within the application or on the website, such as clicks, browsing, purchases, etc., analyze the possible new preferences of the user; Monitor the user's communication frequency, duration, objects, etc., and analyze the changes in the user's communication habits; Use sensors and network monitoring tools to monitor parameters such as network bandwidth, latency, and jitter in real time, and analyze the fluctuations in network quality; By monitoring changes in network infrastructure, such as the construction and commissioning of new base stations, evaluate its impact on network quality; Pay attention to policy adjustments of network operators, such as traffic tariffs, network access strategies, etc., and analyze their possible impacts on user behaviors and network quality; Use deep learning algorithms, such as neural networks, recurrent neural networks, etc., to train the user behavior prediction model and the network quality evaluation model. Through real-time monitoring data, continuously update the parameters and weights of the model to improve the prediction accuracy and generalization ability of the model. Based on the real-time monitoring results, continuously collect new user behavior data and network environment data. Input the new data into the model for continuous training and updating to adapt to changes in user behaviors and network environments. According to the model update results and user feedback, analyze the effectiveness of the current handover strategy. Dynamically adjust the handover strategy, such as handover thresholds, handover algorithms, etc., to improve the accuracy and timeliness of handover. During the monitoring, learning, and optimization process, use encryption algorithms to encrypt the data. Strictly comply with privacy protection regulations and standards to ensure the security and privacy of user data.

[0050] The effects of the above technical solution are as follows: By monitoring the changes in the user behavior pattern and network environment in real time, it is ensured that the enterprise can quickly capture key information such as the new preferences of users, the changes in communication habits, and the fluctuations in network quality. This helps the enterprise to respond in a timely manner, adjust strategies, and meet the needs of users and cope with the changes in the network environment. Based on the real-time monitoring results, the user behavior prediction model and network quality evaluation model are continuously trained and updated through deep learning algorithms, enabling the models to continuously adapt to new data and changes, improving the accuracy of prediction and evaluation, and providing more reliable data support for the enterprise. According to the model update results and user feedback, the switching strategy is dynamically adjusted, which helps the enterprise to flexibly handle various situations, ensure the stability and reliability of the service, and at the same time improve user satisfaction. In the whole process of monitoring, learning, and optimization, the data is encrypted through encryption algorithms and the privacy is protected to ensure the security and privacy of user data and enhance the user's trust in the enterprise. By monitoring the user behavior pattern and network environment in real time, the enterprise can more accurately understand the needs and preferences of users, and thus provide more personalized services and products. At the same time, the dynamic adjustment of the switching strategy helps to ensure the stability and reliability of the service, reduce the inconvenience caused by network problems, and improve the user experience. Based on the real-time monitoring results and the prediction of the deep learning model, the enterprise can more reasonably allocate resources such as network bandwidth and server resources, which helps to reduce operating costs and improve resource utilization efficiency. By continuously optimizing the model and adjusting the strategy, the enterprise can more quickly adapt to the changes in the market and user needs, which helps the enterprise to maintain a leading position in the fierce market competition and enhance its competitiveness. By strengthening data encryption and privacy protection, the enterprise can comply with the requirements of relevant laws and regulations, protect the legitimate rights and interests of users, enhance the user's trust in the enterprise, and promote the compliance operation and sustainable development of the enterprise.

[0051] An embodiment of the present invention is as follows Figure 2 shown, a SIM card switching system, the system includes: Information collection module: Based on machine learning algorithms, analyze the behavior information of users, and establish a user behavior model based on the analysis results; Quality evaluation module: Real-time collect the network quality data of the current location through the Internet of Things communication module, evaluate the network quality, and generate a network quality report based on the evaluation results; Communication selection module: Based on the user behavior model, network quality report, and preset switching strategy, make an intelligent switching decision through an intelligent decision-making engine, and select the optimal SIM card for communication; Status display module: According to the decision result of the intelligent decision-making engine, perform the SIM card switching operation, and display the switching result and the current network status to the user through the user interface; Policy Optimization Module: The system continuously monitors the changes in the user's behavior patterns and network environment, and optimizes the switching policy based on the monitoring results.

[0052] The working principle of the above technical solution is as follows: Deeply mine and analyze the user's behavior information using machine learning algorithms. The behavior information includes the user's communication habits (such as call duration, data usage, common contacts, etc.), preference settings (for example, whether the user tends to use a specific SIM card for data traffic or voice calls at a specific time or location), and historical SIM card switching records. Based on the analysis results, a user behavior model is established. The user behavior model reflects the user's preferences and habits for SIM card usage in different situations. Through the Internet of Things communication module, the system can collect real-time network quality data of the user's current location, including key indicators such as signal strength, network speed, and latency. Then, the system will evaluate these data and generate a network quality report. The network quality report lists the quality status of each available network (corresponding to different SIM cards). At this step, the intelligent decision-making engine will make a comprehensive judgment based on the user behavior model, network quality report, and the preset switching policy. The switching policy includes preferentially using the network with the highest signal strength, switching to a more economical SIM card when the data usage reaches a threshold, etc. The intelligent decision-making engine will comprehensively consider these factors and select the optimal SIM card for communication in the current situation. According to the decision result of the intelligent decision-making engine, the system will automatically perform the SIM card switching operation. At the same time, through the user interface, the system will display the switching result and the current network status to the user, allowing the user to clearly understand which SIM card is currently being used and the network situation. The system will continuously monitor the changes in the user's behavior patterns and network environment. Over time, the user's communication habits may change, and the network environment may also be different. Therefore, the system needs to optimize the switching policy according to these changes to ensure that it can always provide the user with the best communication experience. Among them, the optimization includes adjusting the weight of the switching policy, updating the user behavior model, etc.

[0053] The effect of the above technical solution is: through the in-depth analysis of user behavior information by machine learning algorithms, the system can establish an accurate user behavior model, and through the user behavior model, the personalized characteristics of the user's communication habits, preference settings, etc. are reflected, so that the system can intelligently switch according to the actual needs of the user, ensuring that the user can obtain the most suitable communication service in different situations (such as work, entertainment, travel, etc.), thereby improving the user's communication experience. The Internet of Things communication module can collect network quality data of the user's current location in real time, including key indicators such as signal strength, network speed, and delay. After evaluating these data, the system can generate a network quality report to provide an important reference for intelligent switching decisions. By selecting a SIM card with better network quality for communication, users can enjoy more stable and faster network services. The intelligent decision engine can comprehensively consider the user behavior model, network quality report, and preset switching strategy to make intelligent switching decisions, taking into account both the personalized needs of the user and the actual situation of the network quality, making the switching decision more scientific and reasonable. At the same time, the intelligent decision engine can also make adaptive adjustments according to changes in user behavior patterns and network environment, and continuously optimize the switching strategy. After the system performs the SIM card switching operation, it will display the switching result and current network status to the user through the user interface, which not only allows the user to clearly understand their communication status, but also increases the user's trust and satisfaction with the system. In addition, users can also adjust preferences and switching strategies through the user interface, further improving the user-friendliness of the system. Through intelligent switching decisions, the system can avoid unnecessary SIM card switching and waste of communication resources, reduce users' communication costs, and reduce equipment energy consumption and carbon emissions to achieve green communication. The system can continuously monitor changes in user behavior patterns and network environments, and optimize switching strategies based on monitoring results. This ability to continuously learn and optimize enables the system to continuously adapt to new communication environments and user needs, maintaining its competitiveness and advancement.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A SIM card switching method, characterized in that: The method comprises: S1. Analyze user behavior information based on machine learning algorithms and establish a user behavior model based on the analysis results; S2. Collect network quality data of the current location in real time through the IoT communication module, evaluate the network quality, and generate a network quality report based on the evaluation results; S3, based on the user behavior model, network quality report and preset switching strategy, the intelligent decision engine makes intelligent switching decisions and selects the best SIM card for communication; S4. According to the decision result of the intelligent decision engine, the SIM card switching operation is executed, and the switching result and the current network status are displayed to the user through the user interface; S5. The system continuously monitors user behavior patterns and changes in the network environment, and optimizes the switching strategy based on the monitoring results.

2. A SIM card switching method according to claim 1, characterized in that: Said S1 comprises: S11, collecting multivariate data of the user through the built-in sensors and applications of the mobile communication device, and preprocessing the collected multivariate data; S12. Based on the preprocessed data, a user behavior feature library is constructed through machine learning algorithms; S13. Use deep learning algorithms to train user behavior characteristics, build a user behavior prediction model, and predict users' future communication needs and preferences through the behavior prediction model.

3. A SIM card switching method according to claim 1, characterized in that: The S2 comprises: S21. Collect network quality data of the current location in real time through the Internet of Things communication module; S22, integrating the collected multi-source network quality data and removing redundant information; S23. Based on the fused data, a network quality assessment model is constructed through machine learning algorithms to quantify the network quality and generate a network quality report.

4. A SIM card switching method according to claim 1, characterized in that: The S3 includes: S31. Based on the user behavior prediction model and network quality assessment report, the user's current and future communication needs are predicted through deep learning algorithms; S32, formulating an intelligent switching strategy based on the user demand prediction result and in combination with the user preference setting and the preset switching strategy; S33. Comprehensively consider user needs, network quality, switching costs and user feedback through an intelligent decision-making engine, select the best SIM card for communication based on the comprehensive consideration results, and automatically perform the switching operation.

5. A SIM card switching method according to claim 4, characterized in that: The S32 includes: Analyze the current and future communication needs of users predicted by deep learning algorithms, clarify the communication needs of users in different time periods and scenarios, and analyze the future changing trends of user needs; According to the user demand prediction results, prioritize the user's communication needs in different time periods; Obtain user preference information for communication services through user surveys, in-app settings or user behavior analysis, and assign weights to different preference settings based on user preference information; Based on the tariff structure of different SIM cards, calculate the communication costs of users in different time periods and scenarios, and use the network quality assessment report to evaluate the network coverage of different SIM cards in different areas; Analyze the quality of services provided by different SIM cards and evaluate the security of different SIM cards; Design the rules of intelligent switching strategy based on the prediction results of user demand, user preference settings and preset switching strategy; and prioritize different switching options based on the strategy rule design.

6. A SIM card switching method according to claim 1, characterized in that: The S4 comprises: S41, displaying the switching result and the current network status in real time through the user interface; S42, collecting user feedback on the switching result in various ways; S43. Based on user feedback, the user behavior prediction model and the network quality assessment model are iteratively optimized using machine learning algorithms, and the switching strategy is adjusted.

7. A SIM card switching method according to claim 6, characterized in that: The S43 includes: S431, pre-processing the feedback data, and integrating data from different sources to form a complete user feedback data set; S432, using natural language processing technology to perform sentiment analysis on user feedback, identifying users' positive or negative comments on different switching results and network states, and quantifying sentiment analysis results; S433. Based on user feedback data, evaluate the performance of the user behavior prediction model and the network quality assessment model; and analyze the model performance evaluation results to identify existing problems; S434. Based on the specific content of user feedback, perform attribution analysis on the problem, and review and optimize the features of the user behavior prediction model and the network quality assessment model based on the problem identification results; S435. Use the hyperparameter tuning technology of the machine learning algorithm to adjust the parameters of the model; through parameter tuning, find the parameter combination with the best model performance; S436, fusing or integrating multiple machine learning models, and evaluating the effectiveness of the current switching strategy based on user feedback data and model optimization results; S437. Adjust and optimize the switching strategy according to the strategy effect evaluation result; perform real-time monitoring and analysis through the monitoring mechanism; S438. Incorporate user feedback data, model optimization results, and switching strategy adjustments into an iterative loop for continuous iteration and optimization.

8. A SIM card switching method according to claim 7, characterized in that: The S431 includes: Through hashing algorithms and text similarity detection, duplicate, irrelevant or erroneous feedback data is removed, and missing key information is supplemented through data interpolation; Unify text encoding, correct spelling errors, convert non-standard terms to standard vocabulary, and convert timestamps from different sources to a unified time zone format; define classification labels for feedback content; Through unique identifiers, data from different channels are associated to form a complete user portrait, and user behavior data, network status data and feedback text are combined to construct a multimodal data set; and user feedback is aligned with network status and behavior data on the timeline.

9. A SIM card switching method according to claim 1, characterized in that: The S5 comprises: S51, real-time monitoring of changes in user behavior patterns and network environment; S52. Based on the real-time monitoring results, the user behavior prediction model and the network quality assessment model are continuously trained and updated through the deep learning algorithm; S53. Dynamically adjust the switching strategy based on the model update results and user feedback, and encrypt the data and protect the privacy through encryption algorithms during the monitoring, learning and optimization process.

10. A SIM card switching system, characterized in that: The system comprises: Information collection module: Analyze user behavior information based on machine learning algorithms and establish user behavior models based on the analysis results; Quality assessment module: collects network quality data of the current location in real time through the IoT communication module, assesses the network quality, and generates a network quality report based on the assessment results; Communication selection module: Based on the user behavior model, network quality report and preset switching strategy, the intelligent decision engine makes intelligent switching decisions and selects the best SIM card for communication; Status display module: executes SIM card switching operation according to the decision result of the intelligent decision engine, and displays the switching result and current network status to the user through the user interface; Policy optimization module: The system continuously monitors user behavior patterns and changes in the network environment, and optimizes the switching strategy based on the monitoring results.

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