Self-adaptive short message sending method and system

Abstract

The invention relates to the technical field of communication, in particular to a self-adaptive short message sending method and system.The method comprises the steps that abnormal information of a target system is collected, then the abnormal information is analyzed, an analysis result of standardized packaging is obtained, then a corresponding notification object is matched with the analysis result based on a preset multi-dimensional matching mechanism, and finally the notification object is sent to the target system. And sending an analysis result to a notification object in a short message form. Compared with the prior art, the method provided by the invention solves the problems of low information response speed, high short message forwarding error rate and poor adaptive capacity in the prior art.

Description

Technical Field

[0001] This invention relates to the field of communication technology. More specifically, this invention relates to an adaptive SMS sending method and system. Background Technology

[0002] In today's rapidly developing digital business environment, various software systems, network applications, and hardware-based business systems are widely used across various fields. These systems are becoming increasingly large-scale and complex, often requiring the collaborative operation of multiple modules, various technology stacks, and a large number of hardware devices. During operation, these systems inevitably encounter various anomalies or failures, such as data errors, reception timeouts, or reception failures.

[0003] In traditional technologies, once such anomalies or malfunctions occur, it is generally necessary to manually send the corresponding information to the appropriate notification recipients for handling. This approach is slow, has a high error rate, and lacks the ability to adaptively select notification recipients, which often exacerbates the time spent on handling anomalies or malfunctions and increases system operating costs. Summary of the Invention

[0004] To address the aforementioned technical problems of slow response speed, high error rate, and poor adaptability, this invention discloses an adaptive SMS sending method and system.

[0005] In a first aspect, the present invention discloses an adaptive SMS sending method, comprising: Collect abnormal information from the target system; The abnormal information is parsed to obtain standardized and encapsulated parsing results; Based on a preset multidimensional matching mechanism, the parsing results are matched with the corresponding notification objects. The parsing results will be sent to the notification recipient via SMS.

[0006] Beneficial effects: After collecting abnormal information from the target system, the method of the present invention parses the abnormal information to obtain standardized and encapsulated parsing results, so as to avoid incompatibility problems caused by inconsistent parsing text formats. Then, a multi-dimensional matching mechanism is used to match the parsing results with corresponding notification objects, and the parsing results are sent to the corresponding notification objects in the form of SMS, thereby achieving the effect of automated, adaptive and fast SMS push.

[0007] Preferably, the abnormal information is parsed to obtain a standardized and encapsulated parsing result, including: Receive exception information using a message queue or API interface, and generate a unique ID for each exception message to obtain the information to be processed. The information to be processed is preprocessed and converted into data to be parsed in a uniform format; Extract key features from the data to be analyzed, and perform statistical and correlation analysis on these key features to identify key influencing factors; Key influencing factors are placed into a pre-defined rule base for classification to obtain anomaly classification results; Key influencing factors are placed into a pre-defined assessment model for classification to obtain anomaly levels; The anomaly classification results, anomaly levels, and anomaly information are standardized and encapsulated to obtain the parsing results.

[0008] Preferably, the multidimensional matching mechanism includes a matching mechanism based on multidimensional rules, a matching mechanism based on weighted scoring, a matching mechanism based on anomaly level, and / or a matching mechanism based on duty list.

[0009] Preferably, before matching the parsing result with the corresponding notification object, the method of the present invention further includes: Link the recipient's role information, job responsibilities, skill level, and work priority level; Based on the scope of responsibilities and skill level, define the anomaly classification results corresponding to the role information.

[0010] Preferably, if the multidimensional matching mechanism adopts a weighted scoring-based matching mechanism, the corresponding notification object is matched to the parsing result, specifically as follows: The role information, scope of function, skill level, and workload of the notification recipients are put into a preset weighted scoring model to obtain a comprehensive score for all notification recipients. The notification with the highest overall score will be used as the matching result.

[0011] Preferably, if the abnormal information is received at night or on holidays, the multi-dimensional matching mechanism adopts a matching mechanism based on the duty list.

[0012] Preferably, the parsing results are sent to the notification recipients via SMS, including: Based on the anomaly classification results, set the corresponding information sending template for the parsing results; Write the parsing results into the message sending template to obtain the SMS message to be sent; For SMS messages of the same anomaly level to be sent, a merged sending strategy is used to distribute them to the corresponding notification recipients; For multiple unsent SMS messages with identical content, a progressive strategy is used to send them to the corresponding notification recipients.

[0013] Preferably, the anomaly levels include a first anomaly level, a second anomaly level, a third anomaly level, and a fourth anomaly level; the notification recipients for the first anomaly level are limited to the system / module engineering end; the notification recipients for the second anomaly level include the system / module engineering end and the technical team management end; the notification recipients for the third anomaly level include the system / module engineering end, the technical team management end, and the technical department management end; and the notification recipients for the fourth anomaly level include the system / module engineering end, the technical team management end, the technical department management end, and the company management end.

[0014] Preferably, the anomaly information includes at least the anomaly time, anomaly location, anomaly description, server indicator information, and / or context information.

[0015] Secondly, the present invention also discloses an adaptive SMS sending system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the adaptive SMS sending method described in the first aspect is implemented.

[0016] The beneficial effects of this invention are as follows: (1) Compared with the prior art, the method of the present invention solves the problems of slow information response speed, high error rate of SMS forwarding and poor adaptability in the prior art.

[0017] (2) Compared with the prior art, the multi-dimensional matching mechanism of the method of the present invention includes a matching mechanism based on multi-dimensional rules, a matching mechanism based on weight scoring, a matching mechanism based on anomaly level and / or a matching mechanism based on duty list. These four matching mechanisms can be adaptively switched according to the specific application environment, thereby further improving the adaptability of the method of the present invention.

[0018] (3) Compared with the prior art, the method of the present invention generates a corresponding SMS template according to the actual situation when generating SMS, and switches the SMS sending strategy according to the actual scenario to achieve a more efficient and targeted SMS push function. Attached Figure Description

[0019] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a flowchart of the adaptive SMS sending method in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the adaptive SMS sending system in Embodiment 2 of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses an adaptive SMS sending method and system to solve the technical problems of slow response speed, high error rate and poor adaptability in the prior art.

[0022] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] Example 1 like Figure 1 As shown, this embodiment discloses an adaptive SMS sending method, including: S10: Collect abnormal information from the target system.

[0024] In this embodiment, the target system can be any system used for server operation management, hardware device monitoring, or network communication security management. The determination of whether abnormal information exists can be achieved using a predefined rule engine. Generally, a corresponding threshold or matching value can be defined for the parameters to be monitored within the monitoring information. When the value exceeds the threshold or a mismatch occurs, it is determined to be abnormal information. Additionally, when a NullPointerException, ArrayIndexOutOfBoundsException, or SQLSyntaxErrorException occurs, the above-mentioned collection actions will be automatically triggered based on the script. Abnormal information includes the abnormal trigger type, abnormal time, abnormal location, abnormal description, server indicator information, and / or context information.

[0025] Furthermore, a monitoring module is used to monitor the system's operational status in real time, achieving the effect of collecting the aforementioned abnormal information in real time. Specifically, for network communication security management, the abnormal location includes the network connection establishment point, data transmission / reception point, and protocol parsing point. This location information can be server IP, port number, application name, module name, or line number, etc. For locating faulty devices, the abnormal location can be the coordinates obtained using GPS positioning. The abnormal description mainly includes error code, error message, abnormal stack information, and communication connection timeout. Server metrics information includes quantifiable information such as CPU utilization, memory usage, disk space, and network traffic. The aforementioned contextual information mainly includes user operations, request parameters, and environment variables.

[0026] Through the above step S10, the method of this embodiment uses the monitoring module to collect multi-dimensional abnormal information of the target system and automatically sends this information to the parsing module to improve the efficiency of abnormal information transmission.

[0027] S20: Parse the exception information to obtain a standardized encapsulated parsing result.

[0028] Step S20 above includes: S21: Receive exception information using a message queue or API interface, generate a unique ID for each exception message, and obtain the information to be processed.

[0029] Specifically, the parsing module receives abnormal information sent by the monitoring module through a message queue or API interface, generates a unique ID for each abnormal message, and finally obtains the information to be processed with a unique ID, so as to facilitate subsequent information tracing and information recording.

[0030] S22: Preprocess the information to be processed and convert it into data to be parsed in a uniform format.

[0031] In this embodiment, the preprocessing process mainly includes the removal of duplicate and invalid information. This preprocessing improves the quality of the data to be parsed and reduces data redundancy. After preprocessing, the information is uniformly converted into JSON format for parsing.

[0032] It should be explained that JSON is a lightweight data exchange format that is independent of programming languages ​​and platforms. This format supports cross-platform compatibility, which is beneficial for integration with other systems. Step S22 above helps to improve the adaptability of the method in this embodiment.

[0033] S23: Extract the key features of the data to be analyzed, and perform statistical and correlation analysis on the key features to identify the key influencing factors.

[0034] It should be noted that the key features mentioned above include, but are not limited to, anomaly trigger type, network latency, packet loss rate, and concurrent connection count. The statistical analysis primarily focuses on the distribution of these key features and the frequency of anomalies. For correlation analysis, the `corr` function in pandas can be used to calculate the correlation coefficients between these key features to understand their interrelationships.

[0035] For example, if statistical analysis reveals a high average network latency with significant fluctuations, it indicates that network latency is a key factor affecting server performance and causing anomalies. Similarly, if the evaluation logic for other key influencing factors is not significantly different from the above description, it will not be elaborated upon here.

[0036] For example, if the correlation coefficient between network latency and concurrent connections calculated from the feature correlation matrix is ​​higher than a predetermined correlation threshold, it indicates that network latency may increase as the number of concurrent connections increases. When either network latency or concurrent connections is identified as a key influencing factor, another related data point will also be identified as a key influencing factor.

[0037] S24: Place key influencing factors into a preset rule base for classification to obtain anomaly classification results.

[0038] For example, the rule base table in step S24 above can be:

[0039] It should be explained that in traditional technologies, the anomaly classification result is simply the anomaly trigger type mentioned in step S10 above. This approach is too general and not conducive to the accurate anomaly location and elimination for the notified object. Therefore, in this embodiment, step S24 mainly involves a detailed classification process for anomaly trigger types. Among them, code logic errors mainly refer to situations where the definition of related code does not conform to the specifications. As for anomalies such as unstable network nodes, improper concurrency handling, and abnormal database interaction, these are mainly related to the hardware undertaking the communication task.

[0040] S25: Place key influencing factors into a pre-set evaluation model to classify them into levels, and obtain the abnormality level.

[0041] In this embodiment, the evaluation model can employ either a traditional rule-based evaluation model or an emerging artificial intelligence model. Anomaly levels include a first anomaly level, a second anomaly level, a third anomaly level, and a fourth anomaly level. The first anomaly level represents a minor anomaly, which can be handled by the corresponding engineer; therefore, notifications for the first anomaly level are limited to the system / module engineering team. The second anomaly level represents a general anomaly, requiring special attention from the relevant technical team and its leader / supervisor; therefore, notifications for the second anomaly level include the system / module engineering team and the technical team management team. The third anomaly level represents a serious anomaly, requiring special attention from the technical department and its head; therefore, notifications for the third anomaly level include the system / module engineering team, the technical team management team, and the technical department management team. The fourth anomaly level represents a major anomaly that may jeopardize the normal operation of the company's system and requires special attention from company management; therefore, notifications for the fourth anomaly level include the system / module engineering team, the technical team management team, the technical department management team, and the company management team.

[0042] By setting up a progressively escalating notification mechanism through step S25 above, it is possible to ensure that anomalies of the corresponding level can be promptly monitored and handled.

[0043] S26: Standardize and encapsulate the anomaly classification results, anomaly levels, and anomaly information to obtain the parsing results.

[0044] It should be noted that the standardized encapsulation mentioned above will also include information such as the cause of the exception, the scope of its impact, and handling suggestions. This information can be obtained by matching with a knowledge base or by analyzing with a large language model.

[0045] Through steps S21-S26 above, the method of the present invention achieves the above-mentioned multi-dimensional fusion and standardized encapsulation to realize information compatibility under different application systems, thereby further improving the adaptability of the method in this embodiment.

[0046] Preferably, after step S26 above, the method of this embodiment further includes: S200: Collect feedback information corresponding to the analysis results.

[0047] S201: Update the rule base based on the feedback information.

[0048] It should be explained that the above steps S200-S201 collect feedback information on the parsing results, such as the processing effect or accuracy evaluation, and then input the feedback information into the artificial intelligence model to update the rule base, thereby improving the accuracy and efficiency of parsing.

[0049] S30: Based on a preset multi-dimensional matching mechanism, match the parsing results with the corresponding notification objects.

[0050] Prior to step S30 above, the method in this embodiment further includes: S301: Associate the role information, job scope, skill level, and work priority level of the notification recipient; S302: Define the anomaly classification results corresponding to role information based on the scope of functions and skill level.

[0051] By going through the above steps S301-S302, a multi-dimensional rule matching library is defined, which is beneficial for achieving faster information matching when calling the matching mechanism in the future.

[0052] In this embodiment, the multidimensional matching mechanism includes a matching mechanism based on multidimensional rules, a matching mechanism based on weighted scoring, a matching mechanism based on anomaly level, and / or a matching mechanism based on duty list.

[0053] The choice between using a multi-dimensional rule-based matching mechanism and a weighted scoring matching mechanism can be configured by the management system.

[0054] If the above-described multi-dimensional rule-based matching mechanism is executed, step S30 is completed using the predefined matching library in S301-S302.

[0055] If the above weighted scoring matching mechanism is executed, then step S30 above includes: S31: Input the role information, functional scope, skill level and workload of the notification recipients into the preset weighted scoring model to obtain the comprehensive score of all notification recipients.

[0056] In this embodiment, the weighted scoring model adopts a multiple linear regression model. By defining corresponding weight coefficients for role information, functional scope, skill level and workload, and performing weighted summation during calculation, the corresponding comprehensive score is obtained.

[0057] S32: Use the notification with the highest overall score as the matching result.

[0058] Through the above steps S31-S32, the method of this embodiment can quickly and conveniently obtain relatively accurate matching results.

[0059] It should be further noted that if the abnormal information is received at night or on holidays, both the multi-dimensional rule-based matching mechanism and the weighted scoring matching mechanism mentioned above are turned off, and a pre-set script is used to call the matching mechanism based on the duty list. Specifically, for special time periods such as night, weekends, or holidays, the method of this invention will automatically retrieve the duty personnel list and match the notification recipients in order according to the preset emergency contact methods.

[0060] Furthermore, the above-mentioned matching mechanism based on anomaly level can be used in parallel with any of the three mechanisms mentioned above. The determination of the specific notification targets has been clearly discussed in step S25 and will not be repeated here.

[0061] S40: Send the parsing results to the notification recipient via SMS.

[0062] Specifically, step S40 is executed by the encapsulated SMS sending module, as follows: S41: Based on the anomaly classification results, set the corresponding information sending template for the parsing results.

[0063] S42: Write the parsing result into the message sending template to obtain the SMS to be sent.

[0064] It should be explained that in step S42 above, the content of the SMS message to be sent can also be personalized according to the position, skills and responsibilities of the recipient. For example, for technical personnel, more detailed technical information and log snippets can be provided; for managers, the scope of impact, business losses and handling suggestions can be highlighted.

[0065] S43: For SMS messages to be sent at the same anomaly level, a merged sending strategy is used to distribute them to the corresponding notification recipients.

[0066] It should be explained that merging multiple SMS messages of the same anomaly level into a single sending request reduces the number of interactions with the SMS gateway, thereby reducing network overhead and sending latency, and significantly improving the notification distribution capability per unit time.

[0067] S44: For multiple SMS messages with identical content to be sent, a progressive strategy is used to send them to the corresponding notification recipients.

[0068] It should be explained that the progressive strategy only sends new notifications when the content changes, thereby avoiding redundant transmission and improving information transmission efficiency.

[0069] Through steps S10-S40, by collecting and parsing target system anomaly information in real time and generating standardized encapsulated parsing results, the accuracy and consistency of information processing are ensured, reducing parsing errors caused by inconsistent formats. Matching based on a preset multi-dimensional matching mechanism can accurately and quickly match the corresponding notification object, avoiding delays and omissions caused by manual configuration. Furthermore, this method adapts to different anomaly types and notification requirements, dynamically adjusting the sending strategy, such as merging or progressive sending, which improves response speed and reduces the load and error risk on the SMS gateway, ensuring the timeliness and effectiveness of anomaly notifications.

[0070] Following step S40 above, the method of this embodiment further includes: S50: Monitors the sending status of SMS messages in real time and determines whether to execute the corresponding automatic retry policy based on the sending status.

[0071] For example, the content of the above-mentioned sending status includes: Condition 1: Whether the API call was successful; Condition 2: Whether the SMS receipt status has been sent; Condition 3: Whether the terminal delivery status has been delivered.

[0072] If any of the above three conditions is "No", then an automatic retry will be executed. The retry strategy includes: (1) Index retreat and retry, for example, retrying after 1 minute, 3 minutes or 5 minutes; (2) Channel switching retry, that is, after one channel fails, try to send through other channels; (3) Content optimization retry, that is, adjusting the content that may be blocked and then retrying.

[0073] Through the above step S50, this embodiment can adaptively switch the corresponding retry strategy according to the actual situation of the SMS, so as to ensure that the corresponding notification object can receive the SMS in a timely manner, thereby further improving the adaptive capability of the method of this embodiment.

[0074] It should be noted that in the method of this embodiment, sensitive information (such as user data, system configuration, etc.) in the SMS content will be encrypted or de-identified to comply with the requirements of relevant regulations.

[0075] Preferably, if the above three conditions still cannot be met after the retry strategy, other notification methods (email or telephone) will be used. Simultaneously, the SMS sending status will be recorded in the log for later querying and analysis.

[0076] Example 2 like Figure 2 As shown, this embodiment discloses an adaptive SMS sending system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the adaptive SMS sending method described in the first aspect.

[0077] The system in this embodiment also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art, and therefore will not be described in detail here.

[0078] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0079] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0080] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. An adaptive SMS sending method, characterized in that, include: Collect abnormal information from the target system; The abnormal information is parsed to obtain a standardized and encapsulated parsing result; Based on a preset multi-dimensional matching mechanism, the parsing result is matched with the corresponding notification object; The parsing results are sent to the notification recipient via SMS.

2. The adaptive SMS sending method according to claim 1, characterized in that, The abnormal information is parsed to obtain a standardized, encapsulated parsing result, including: The exception information is received using a message queue or API interface, and a unique ID is generated for each exception information to obtain the information to be processed. The information to be processed is preprocessed and converted into data to be parsed in a uniform format; Key features of the data to be analyzed are extracted, and statistical and correlation analyses are performed on these key features to identify key influencing factors. The key influencing factors are placed into a preset rule base for classification to obtain anomaly classification results; The key influencing factors are placed into a preset evaluation model for classification to obtain anomaly levels; The anomaly classification result, the anomaly level, and the anomaly information are standardized and encapsulated to obtain the parsing result.

3. The adaptive SMS sending method according to claim 2, characterized in that, The multidimensional matching mechanism includes a matching mechanism based on multidimensional rules, a matching mechanism based on weighted scoring, a matching mechanism based on the anomaly level, and / or a matching mechanism based on the duty roster.

4. The adaptive SMS sending method according to claim 3, characterized in that, Before matching the parsed result with the corresponding notification object, the method further includes: The notification recipient's role information, functional scope, skill level, and work priority level are associated. Based on the defined scope of functions and skill level, define the anomaly classification results corresponding to the role information.

5. The adaptive SMS sending method according to claim 4, characterized in that, If the multidimensional matching mechanism adopts a weighted scoring-based matching mechanism, the corresponding notification object is matched to the parsing result, specifically as follows: The role information, functional scope, skill level, and workload of the notification recipients are put into a preset weighted scoring model to obtain a comprehensive score for all notification recipients. The notification with the highest overall score will be used as the matching result.

6. The adaptive SMS sending method according to claim 3, characterized in that, If the abnormal information is received at night or on a holiday, the multi-dimensional matching mechanism adopts a matching mechanism based on the duty list.

7. The adaptive SMS sending method according to claim 2, characterized in that, Sending the parsing results to the notification recipient via SMS includes: Based on the anomaly classification results, set a corresponding information sending template for the parsing results; The parsing result is written into the information sending template to obtain the SMS message to be sent; For SMS messages of the same anomaly level to be sent, a merged sending strategy is used to distribute them to the corresponding notification recipients; For multiple unsent SMS messages with identical content, a progressive strategy is used to send them to the corresponding notification recipients.

8. The adaptive SMS sending method according to claim 1, characterized in that, The anomaly levels include a first anomaly level, a second anomaly level, a third anomaly level, and a fourth anomaly level; the notification recipients for the first anomaly level are limited to the system / module engineering end; the notification recipients for the second anomaly level include the system / module engineering end, the technical team management end, and the technical department management end; the notification recipients for the fourth anomaly level include the system / module engineering end, the technical team management end, the technical department management end, and the company management end.

9. The adaptive SMS sending method according to claim 1, characterized in that, The abnormal information includes at least the abnormal time, abnormal location, abnormal description, server indicator information, and / or context information.

10. An adaptive SMS sending system, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the adaptive SMS sending method according to any one of claims 1-9.

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