Cloud mobile phone service expiration multistage early warning method and system and medium

Abstract

The invention discloses a cloud mobile phone service expiration multi-level early warning method and system and a medium. The method comprises the steps that expiration cloud mobile phone instances in a cloud mobile phone instance database are periodically retrieved; obtaining service expiration time associated with each expired cloud mobile phone instance; calculating the service remaining time of each expired cloud mobile phone instance according to the associated service expiration time; matching an early warning threshold according to the service remaining time, and triggering corresponding graded early warning according to a matching result of each expired cloud mobile phone instance; and sending the triggered graded early warning information to the corresponding expired cloud mobile phone instance. Therefore, the attention requirements of different users are met through a grading early warning mechanism, the user experience can be improved, and the user charge rate of a cloud service provider can be increased.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud service management, and particularly relates to a cloud mobile service expiration multi-level warning method and system and a medium. BACKGROUND

[0002] With the development of cloud computing technology, cloud mobile as a kind of virtual mobile device service based on cloud computing is widely used in office, entertainment, testing and other scenarios. When using cloud mobile service, users need to pay fees according to the agreed period to maintain service effectiveness. If the service expires without timely renewal, the cloud mobile instance will be disabled, the temporary data storage will be invalid, and even important data will be lost.

[0003] At the same time, there is a lack of hierarchical warning mechanism, which cannot dynamically adjust the warning intensity according to the remaining validity period, for example, only sending a notification one day before expiration, which cannot meet the needs of different users for reminder time. Therefore, this not only reduces the user experience, but also leads to a decrease in the user renewal rate of cloud service providers, resulting in idle service resources and increased operating costs. SUMMARY

[0004] The present application provides a cloud mobile service expiration multi-level warning method, system and medium, which can adapt to different user attention needs through a hierarchical warning mechanism and improve user experience and increase the user renewal rate of cloud service providers. In a first aspect, a cloud mobile service expiration multi-level warning method is provided, comprising: Periodically retrieving expired cloud mobile instances in a cloud mobile instance database; Obtaining the service expiration time associated with each of the expired cloud mobile instances; Calculating the service remaining time of each expired cloud mobile instance according to the associated service expiration time; Matching the warning threshold according to the service remaining time, and triggering the corresponding hierarchical warning according to the matching result of each expired cloud mobile instance; Sending the triggered hierarchical warning information to the corresponding expired cloud mobile instance.

[0005] In some embodiments, the service remaining time of each expired cloud mobile instance is calculated according to the associated service expiration time, comprising: Subtracting the current service time from the service expiration time associated with each expired cloud mobile instance to obtain the corresponding service remaining time.

[0006] In some embodiments, the warning threshold is matched according to the service remaining time, and the corresponding hierarchical warning is triggered according to the matching result of each expired cloud mobile instance, comprising: When it is detected that the service remaining time is less than or equal to the first early warning threshold and greater than the second early warning threshold, and the state flag in the user instance data associated with the expired cloud mobile phone instance is not sent, a first-level early warning is triggered; When it is detected that the service remaining time is less than or equal to the second early warning threshold and greater than the third early warning threshold, and the state flag in the user instance data associated with the expired cloud mobile phone instance is not sent, a second-level early warning is triggered; When it is detected that the service remaining time is less than or equal to the third early warning threshold, and the state flag in the user instance data associated with the expired cloud mobile phone instance is not sent, a third-level early warning is triggered.

[0007] In some embodiments, the sending of the triggered hierarchical early warning information to the corresponding expired cloud mobile phone instance comprises: connecting to an operator SMS gateway, and sending the triggered hierarchical early warning information to the corresponding expired cloud mobile phone instance through an SMS; sending the hierarchical early warning information in JSON format to a third-party port associated with the corresponding expired cloud mobile phone instance through an API interface.

[0008] In some embodiments, after the sending of the triggered hierarchical early warning information to the corresponding expired cloud mobile phone instance, the method further comprises: obtaining user behavior characteristic data associated with a cloud mobile phone instance database; constructing a user multi-dimensional portrait according to the user behavior characteristic data using an analytic hierarchy process; classifying the user multi-dimensional portrait based on a trained XGBoost model; correspondingly adjusting a search cycle and hierarchical early warning according to the classification result of the user.

[0009] In some embodiments, the constructing of the user multi-dimensional portrait according to the user behavior characteristic data using the analytic hierarchy process comprises: obtaining behavior dimension quantitative indicators, preference dimension quantitative indicators, and risk dimension quantitative indicators according to the user behavior characteristic data; determining dimension weights based on the analytic hierarchy process; generating a user multi-dimensional portrait based on the dimension weights and the quantitative indicators of the corresponding dimensions.

[0010] In some embodiments, the training process of the trained XGBoost model comprises: collecting user behavior characteristic training samples, and performing sample division on the user behavior characteristic training samples to obtain a training set and a validation set; The training set is divided into training folds and validation folds, the hyperparameters of the XGBoost model are initialized, the hyperparameters are dynamically optimized in the k-fold cross-validation process on the training folds through the Bayesian optimization algorithm, and early stopping is triggered based on the fact that the evaluation index based on the validation folds does not improve within a preset number of rounds during the optimization process to determine the optimal hyperparameters. The XGBoost model with optimized parameters is iteratively trained using the training set, and the loss of the validation set is used as the convergence criterion until the validation set loss converges, and the trained XGBoost model is obtained.

[0011] In some embodiments, the adjustment of the retrieval period and the hierarchical early warning according to the classification result of the user includes: If the classification result is fast response type, the retrieval period of the user corresponding to the fast response type is extended, and only the second early warning and the third early warning in the hierarchical early warning are triggered; If the classification result is delayed response type, the sending number of the first early warning in the hierarchical early warning is increased; If the classification result is forgetful type, the retrieval period of the user corresponding to the fast response type is shortened, and the sending number of all early warnings in the hierarchical early warning is increased.

[0012] In a second aspect, a cloud phone service expiration multi-level early warning system is provided, which includes: A periodical retrieval module for periodically retrieving expired cloud phone instances in a cloud phone instance database; An expiration time acquisition module in communication connection with the periodical retrieval module, configured to acquire a service expiration time associated with each of the expired cloud phone instances; A remaining time calculation module in communication connection with the expiration time acquisition module, configured to calculate a service remaining time of each of the expired cloud phone instances according to the associated service expiration time; An early warning module in communication connection with the remaining time calculation module, configured to match an early warning threshold according to the service remaining time, and trigger a corresponding hierarchical early warning according to the matching result of each of the expired cloud phone instances; and An information sending module in communication connection with the early warning module, configured to send the triggered hierarchical early warning information to the corresponding expired cloud phone instance.

[0013] In a third aspect, the embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the cloud phone service expiration multi-level early warning method as described above.

[0014] Compared with the prior art, the advantages of the present application are as follows: by periodically retrieving the expired cloud mobile phone instance in the cloud mobile phone instance database; obtaining the service expiration time associated with each of the expired cloud mobile phone instances; calculating the service remaining time of each expired cloud mobile phone instance according to the associated service expiration time; matching the warning threshold according to the service remaining time, and triggering the corresponding hierarchical warning according to the matching result of each expired cloud mobile phone instance; and sending the triggered hierarchical warning information to the corresponding expired cloud mobile phone instance. Therefore, the hierarchical warning mechanism is used to adapt to different user attention needs, and the user experience can be improved and the user renewal of the cloud service provider can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a flowchart of a cloud mobile phone service expiration multi-level warning method of the present application; Figure 2 is a structural diagram of a cloud mobile phone service expiration multi-level warning system of the present application. DETAILED DESCRIPTION

[0016] Reference will now be made in detail to the present application, examples of which are illustrated in the accompanying drawings. While the present application will be described in conjunction with the specific embodiments, it will be understood that the present application is not limited to the embodiments described. To the contrary, the present application is intended to cover alternatives, modifications and equivalents, which are included within the spirit and scope of the present application as defined by the appended claims. It should be noted that the method steps described herein can all be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0017] In order for those skilled in the art to better understand the present application, the present application will be further described in detail below in conjunction with the drawings and specific embodiments.

[0018] Note: The examples to be introduced next are only one specific example, and are not as a limitation on the embodiments of the present application must be as follows: specific steps, values, conditions, data, order, etc. Those skilled in the art can use the concept of the present application to construct more embodiments not mentioned in the present specification by reading the present specification.

[0019] Please refer to Figure 1 , the flowchart of a cloud mobile phone service expiration multi-level warning method provided by the embodiments of the present application, the method of the present application comprises: Step S100, periodically retrieve the expired cloud mobile phone instance in the cloud mobile phone instance database; the running period can be preset to 30 minutes, and the cloud mobile phone instances with "remaining valid period <= 48 hours" are filtered from the cloud mobile phone instance database.

[0020] Step S200, obtaining the service expiration time associated with each of the expired cloud mobile phone instances; Step S300, according to the service expiration time associated with each expiration cloud phone instance, the service remaining time of each expiration cloud phone instance is calculated, including: The service expiration time associated with each expiration cloud phone instance is subtracted from the current service time to obtain the corresponding service remaining time.

[0021] Step S400, according to the service remaining time matching the warning threshold, and according to the matching result of each expiration cloud phone instance, the corresponding hierarchical warning is triggered, including: When it is detected that the service remaining time is less than or equal to the first warning threshold and greater than the second warning threshold, and the state flag in the user instance data associated with the expiration cloud phone instance is not sent, a first-level warning is triggered; When it is detected that the service remaining time is less than or equal to the second warning threshold and greater than the third warning threshold, and the state flag in the user instance data associated with the expiration cloud phone instance is not sent, a second-level warning is triggered; When it is detected that the service remaining time is less than or equal to the third warning threshold, and the state flag in the user instance data associated with the expiration cloud phone instance is not sent, a third-level warning is triggered.

[0022] Specifically, in the embodiment of the present application, the preset three-level warning thresholds (48 hours, 24 hours, 15 minutes) and the "notification sending state flag" are judged as follows: When the remaining time is ≤48 hours and >24 hours, and the state flag is "not sent", a first-level warning (2 days in advance) is triggered; When the remaining time is ≤24 hours and >15 minutes, and the state flag is "not sent", a second-level warning (1 day in advance countdown reminder) is triggered; When the remaining time is within 15 minutes of the expiration time, and the state flag is "not sent", a third-level warning (expiration instant reminder) is triggered; If a notification of a certain level has been sent (the state flag is "has been sent"), the level is skipped; if the previous sending fails (the state flag is "sending failure"), the notification of the level is retriggered.

[0023] Step S500, the triggered hierarchical warning information is sent to the corresponding expiration cloud phone instance, including: The triggered hierarchical warning information is sent to the corresponding expiration cloud phone instance through the short message by connecting the operator short message gateway; The JSON format hierarchical warning information is sent to the third party port associated with the corresponding expiration cloud phone instance through the API interface.

[0024] Specifically, in this embodiment of the invention, SMS notification: By connecting to the operator's SMS gateway, an SMS message containing "cloud phone instance ID, remaining time, and renewal link" is sent to the user's mobile phone number, such as "[XX Cloud Service] Your cloud phone instance (ID: 12345) will expire in 48 hours. Please click https: / / xxx to renew to avoid data loss." API Notification: Push structured alert data in JSON format to the user's associated third-party systems (such as enterprise OA, personal cloud service clients) through the preset API interface. The data includes instance information, expiration timestamp, and renewal interface address. The third-party system can customize the reminder display format.

[0025] The S500, after sending the triggered tiered early warning information to the corresponding expired cloud phone instance, includes: S510 retrieves user behavior characteristic data associated with the cloud phone instance database. The user behavior characteristic data includes: the response time of the last n renewals, the number of historical service interruptions, the recovery time after the interruption, the frequency of cloud phone usage, the preferred renewal channel, the best response time, and the number of historical overdue payments.

[0026] S520, using the analytic hierarchy process (AHP) to construct a multi-dimensional user profile based on the user behavior feature data, including: Based on the user behavior feature data, quantitative indicators for behavior dimension, quantitative indicators for preference dimension, and quantitative indicators for risk dimension are obtained. Behavioral dimension quantitative indicators: Response speed metric: mean response time × 0.6 + variance of response time × 0.4 (the smaller the value, the more stable and faster the response). Performance stability index: 1 - (number of interruptions / total number of service cycles), that is, the higher the value, the more stable the performance.

[0027] Quantitative indicators for preference dimensions: Channel Dependence: The weighted sum of open rates and usage frequencies for each channel, such as SMS channel score = SMS open rate × the proportion of subscribers choosing that channel for renewal; Time sensitivity: The degree of overlap between the optimal response period and the historical warning sending time. The higher the value, the higher the acceptance of reminders for a specific time period.

[0028] Quantitative indicators for risk dimensions: Overdue Risk Index: Number of overdue payments × 0.5 + Percentage of low balance status × 0.3 + Default rate of related services × 0.2. The higher the value, the greater the risk of forgetting to renew. Recovery Willingness Index: 1 / Average recovery time after interruption. The higher the value, the stronger the willingness to actively restore services after an interruption.

[0029] Meanwhile, the weights of each dimension were determined using the Analytic Hierarchy Process (AHP), resulting in a weight of 40% for the behavioral dimension, 30% for the preference dimension, and 30% for the risk dimension.

[0030] Based on the weights of each dimension and the corresponding quantitative indicators, a multi-dimensional user profile is generated.

[0031] Therefore, the quantitative indicators for each dimension are: B, P, and R, which represent the scores (0–1) for the behavior, preference, and risk dimensions, respectively.

[0032] The possible comprehensive score S = 0.4×B + 0.3×P + 0.3×R, therefore the score S is the user's multi-dimensional profile.

[0033] S530, classify the multi-dimensional user profile based on the trained XGBoost model.

[0034] The training process of the completed XGBoost model includes: Collect user behavior feature training samples, and divide the user behavior feature training samples to obtain a training set and a validation set; The training set is divided into training folds and validation folds. The hyperparameters of the XGBoost model are initialized. The hyperparameters are dynamically optimized during the k-fold cross-validation process on the training fold using a Bayesian optimization algorithm. During the optimization process, if the evaluation index of the validation fold does not improve within a preset number of rounds, an early stop is triggered to determine the optimal hyperparameters. The XGBoost model includes two types of hyperparameters: structural parameters, which control the overall model architecture, and learning parameters, which control the growth of individual decision trees and the ensemble performance. Both types of parameters need to be initialized before training; the initial values ​​are usually based on experience or default settings and are subsequently adjusted through cross-validation.

[0035] Cross-validation, in order to evaluate the generalization ability of different parameter combinations, requires dividing the training data into k folds, using k-1 folds for training and 1 folds for validation in turn, and finally taking the average performance as the evaluation metric for the parameter combination.

[0036] In k-fold cross-validation, the Bayesian optimization algorithm dynamically adjusts hyperparameters through a closed loop of "surrogate model fitting multi-fold evaluation data - acquisition function guiding hyperparameter selection - iterative update," ultimately finding the hyperparameter combination with optimal generalization ability on the training set. This process leverages both the stability of k-fold cross-validation and the efficient search capability of Bayesian optimization.

[0037] Meanwhile, the early stopping mechanism is to prevent model overfitting. This is because the model performs well on the training set but degrades on the validation set. By monitoring the loss on the validation set, training is dynamically terminated to avoid unnecessary consumption of computational resources.

[0038] The XGBoost model with optimized parameters is iteratively trained using the training set, and the loss on the validation set is used as the convergence criterion until the validation set loss converges to obtain the trained XGBoost model.

[0039] It should be noted that XGBoost (eXtreme Gradient Boosting) is a high-efficiency machine learning framework based on Gradient Boosting Decision Tree (GBDT). It has made significant optimizations to the traditional GBDT, significantly improving the training speed, performance, and flexibility of the model, and is widely used in tasks such as classification, regression, and ranking.

[0040] S540 adjusts the retrieval cycle and tiered alerts based on the user's classification results, including: If the classification result is a fast response type, the search period for the user corresponding to the fast response type will be extended, and only the second-level and third-level warnings in the graded warning system will be triggered. If the classification result is delayed response type, then increase the number of times the first-level warning is sent in the hierarchical warning system; If the classification result is forgetful, then the retrieval cycle for the user corresponding to the fast response type will be shortened, and the number of times all warnings in the graded warning system will be increased.

[0041] In summary, this invention aims to solve the following problems existing in current cloud phone service expiration notification systems: (1) The timeliness of notifications is insufficient. Single or delayed reminders may cause users to miss the renewal, resulting in service interruption and data loss; (2) There is no graded early warning mechanism, and the reminder strategy cannot be dynamically adjusted according to the remaining validity period to adapt to the user's attention needs at different stages; (3) Lack of intelligent adaptation capability; the uniform reminder parameters cannot meet the behavioral habits of different users, resulting in low reminder efficiency; (4) There is no notification status record, which may lead to duplicate or missed sending.

[0042] See also Figure 2 As shown, this embodiment of the invention also provides a multi-level early warning system for cloud phone service expiration, including: The periodic retrieval module is used to periodically retrieve expired cloud phone instances from the cloud phone instance database. The expiration time acquisition module is communicatively connected to the periodic retrieval module and is used to acquire the service expiration time associated with each of the expiration cloud phone instances. The remaining time calculation module is communicatively connected to the expiration time acquisition module and is used to calculate the remaining service time of each expired cloud phone instance based on the associated service expiration time. The early warning module, communicatively connected to the remaining time calculation module, is used to match an early warning threshold based on the remaining service time, and trigger corresponding tiered early warnings based on the matching result of each expiring cloud phone instance; and, The information sending module is communicatively connected to the early warning module and is used to send the triggered graded early warning information to the corresponding expired cloud phone instance.

[0043] In summary, the present invention has the following specific beneficial effects: (1) Reduce the risk of service interruption: Through tiered early warning measures, ensure that users receive reminders at different stages and avoid service interruption caused by a single delayed notification.

[0044] (2) Improve user renewal rate: Personalized early warning strategies are adapted to the behavioral habits of different users, such as increasing reminder channels and frequency for "forgetful" users.

[0045] (3) Optimize resource efficiency: Avoid duplicate notifications by using "notification sending status flag", reduce invalid calls to SMS gateway and API interface, and reduce communication resource consumption.

[0046] (4) Strong compatibility: This invention can directly connect to the database and interface of the existing cloud mobile phone service management system without the need for large-scale modification of the original system. It has low deployment cost and wide adaptability.

[0047] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0048] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements all or part of the method steps of the above method.

[0049] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0050] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor. When the processor executes the computer program, it implements all or part of the method steps described above.

[0051] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0052] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A cloud phone service expiration multi-level warning method, characterized in that, The method comprises the following steps: Periodically retrieving expired cloud mobile phone instance in cloud mobile phone instance database; Obtaining service expiration time associated with each of the expired cloud mobile phone instance; Calculating service remaining time of each of the expired cloud mobile phone instance according to the associated service expiration time; Matching the service remaining time with the warning threshold, and triggering corresponding hierarchical warning according to the matching result of each of the expired cloud mobile phone instance; Sending the triggered hierarchical warning information to the corresponding expired cloud mobile phone instance.

2. The cloud phone service expiration multi-level alerting method of claim 1, wherein, The step of calculating the service remaining time of each of the expired cloud mobile phone instance according to the associated service expiration time comprises the following steps: Subtracting the current service time from the service expiration time associated with each of the expired cloud mobile phone instance to obtain the corresponding service remaining time.

3. The cloud phone service expiration multi-stage alerting method of claim 1, wherein, The step of matching the service remaining time with the warning threshold, and triggering corresponding hierarchical warning according to the matching result of each of the expired cloud mobile phone instance comprises the following steps: When detecting that the service remaining time is less than or equal to the first warning threshold and greater than the second warning threshold, and the state flag in the user instance data associated with the expired cloud mobile phone instance is not sent, triggering a first-level warning; When detecting that the service remaining time is less than or equal to the second warning threshold and greater than the third warning threshold, and the state flag in the user instance data associated with the expired cloud mobile phone instance is not sent, triggering a second-level warning; When detecting that the service remaining time is less than or equal to the third warning threshold, and the state flag in the user instance data associated with the expired cloud mobile phone instance is not sent, triggering a third-level warning.

4. The cloud phone service expiration multi-stage alerting method of claim 1, wherein, The step of sending the triggered hierarchical warning information to the corresponding expired cloud mobile phone instance comprises the following steps: Connecting to the operator SMS gateway, and sending the triggered hierarchical warning information to the corresponding expired cloud mobile phone instance through SMS; Sending the JSON format hierarchical warning information to the third party port associated with the corresponding expired cloud mobile phone instance through the API interface.

5. The cloud phone service expiration multi-stage alerting method of claim 1, wherein, After the step of sending the triggered hierarchical warning information to the corresponding expired cloud mobile phone instance, the method further comprises the following steps: Obtaining user behavior characteristic data associated with the cloud mobile phone instance database; Using the analytic hierarchy process to construct a user multi-dimensional portrait according to the user behavior characteristic data; Classifying the user multi-dimensional portrait based on the trained XGBoost model; Adjusting the retrieval period and the hierarchical warning according to the classification result of the user.

6. The cloud phone service expiration multi-stage alerting method of claim 5, wherein, The step of using the analytic hierarchy process to construct a user multi-dimensional portrait according to the user behavior characteristic data comprises the following steps: Obtaining behavior dimension quantitative indicators, preference dimension quantitative indicators and risk dimension quantitative indicators according to the user behavior characteristic data; Determining the weight of each dimension based on the analytic hierarchy process; Generating a user multi-dimensional portrait based on the weight of each dimension and the quantitative indicators of the corresponding dimension.

7. The cloud phone service expiration multi-stage alerting method of claim 5, wherein, The training process of the trained XGBoost model comprises the following steps: Collecting user behavior characteristic training samples, and dividing the user behavior characteristic training samples into a training set and a validation set; The training set is divided into training folds and validation folds, the hyperparameters of the XGBoost model are initialized, the hyperparameters are dynamically optimized in the k-fold cross-validation process on the training folds through the Bayesian optimization algorithm, and early stopping is triggered based on the evaluation index of the validation folds not improving within a preset number of rounds during the optimization process to determine the optimal hyperparameters; The XGBoost model with optimized parameters is iteratively trained using the training set, and the loss of the validation set is used as the convergence criterion until the validation set loss converges, and the trained XGBoost model is obtained.

8. The cloud phone service expiration multi-stage alerting method of claim 5, wherein, The retrieval period and hierarchical early warning are adjusted according to the classification results of the user, including: If the classification result is fast response type, the retrieval period of the user corresponding to the fast response type is extended, and only the second and third level early warnings in the hierarchical early warning are triggered; If the classification result is a delayed response type, the number of sending of the first level early warning in the hierarchical early warning is increased; If the classification result is a forgetful type, the retrieval period of the user corresponding to the fast response type is shortened, and the number of sending of all early warnings in the hierarchical early warning is increased.

9. A cloud phone service expiration multi-level warning system, characterized in that, It includes: The periodic retrieval module periodically retrieves the expired cloud phone instance in the cloud phone instance database; The expiration time acquisition module is in communication connection with the periodic retrieval module, and is used to acquire the service expiration time associated with each of the expired cloud phone instances; The remaining time calculation module is in communication connection with the expiration time acquisition module, and is used to calculate the service remaining time of each expired cloud phone instance according to the associated service expiration time; The early warning module is in communication connection with the remaining time calculation module, and is used to match the early warning threshold according to the service remaining time, and trigger the corresponding hierarchical early warning according to the matching result of each expired cloud phone instance; And, The information sending module is in communication connection with the early warning module, and is used to send the triggered hierarchical early warning information to the corresponding expired cloud phone instance.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the cloud phone service expiration multi-level early warning method according to any one of claims 1-8.

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