High-reliability programmable intelligent message processing method and device

Abstract

The invention discloses a high-reliability programmable intelligent message processing method and device, and the method specifically comprises the steps: inputting a message generated in a task processing process into a message processor, the message comprising a message header, a message body and message content; the message processor carries out aes decryption on message content in the message to obtain decrypted message content, the decrypted message content is sent to chat gpt, the chat gpt returns text information, the text information is an interest tag token with a standard data format, the interest tag token is input into a user personalized cache group, and the user personalized cache group stores the text information. The user personalized cache group performs user matching according to an interest tag token in the text information, outputs a user matching message, and inputs the user matching message into a queue object corresponding to a user for waiting execution processing; and the message processor enters spring ioc according to the name of the message header to search for a processor implementation class self-defined by a developer, and executes a corresponding hand method.

Description

Technical Field

[0001] The present application relates to the fields of information technology and communication technology, and mainly to a high-reliability programmable intelligent message processing method and device. Background Art

[0002] With the rapid development of the Internet and the diversification of user needs, network data traffic has shown explosive growth, posing huge challenges to network transmission and service quality. Traditional caching technology mainly focuses on improving data access speed and reducing broadband consumption, but it often seems powerless when dealing with complex and diverse user needs. In addition, at present, the existing message processing system has certain limitations in high concurrency and high availability. Traditional systems often rely on single-point architecture, which is prone to system failures. In addition, when processing different types of information, the existing system lacks flexible configuration and customization functions and cannot meet diverse needs. Therefore, it is particularly important to develop a device with high availability and customized message processing functions.

[0003] Existing cache technologies and data forwarding technologies mainly focus on improving data access speed and reducing bandwidth consumption. However, they have the following major problems when dealing with complex and diverse user needs:

[0004] 1. Lack of personalization: Traditional caching technology lacks consideration for user personalized needs and cannot dynamically adjust caching strategies based on users’ specific needs, resulting in poor user experience.

[0005] 2. Inefficiency: Each user will store cache, and each user will process message information according to cache information. This design is to enable each user to control the cache processing behavior and intercept operations on the cache in an orderly manner. Improve cache efficiency.

[0006] 3. Insufficient expansion capabilities: The user's ability to process cached messages is limited, and the ability cannot be horizontally expanded. Enhance the user's ability to process messages based on custom cache configurations. Summary of the invention

[0007] In view of the problems existing in the current prior art, the present application proposes a highly reliable programmable intelligent message processing method and device.

[0008] According to one aspect of the present invention, a high-reliability programmable intelligent message processing method is proposed, comprising:

[0009] Inputting the message generated during the task processing into the message processor, the message including a message header, a message body and a message content;

[0010] The message processor performs AES decryption on the message content in the message to obtain the decrypted message content, and sends the decrypted message content to Chat GPT, which returns text information, which is an interest tag token with a standard data format, and inputs the interest tag token into a user personalized cache group, which performs user matching according to the interest tag token in the text information, outputs a user matching message, and inputs the user matching message into a queue object corresponding to the user to wait for execution processing;

[0011] The message processor enters the spring ioc according to the name of the message header to find the developer's customized processor implementation class and executes the corresponding handle method.

[0012] Furthermore, the message includes a message header, a message body and message content, wherein the message header begins with a user interest identification processor name, the message body is the main body of the message, and the message content is set to be encrypted.

[0013] The message header starts with the name of the user interest identification processor, and the message body is the main body. The message content can be set to be encrypted. The message header uses spring ioc for automatic matching and dependency injection of objects, which simplifies the management of the message manager, so that the corresponding message processor can be found in the message manager for message processing, simplifies object management and improves the scalability and maintainability of the system. The message processing process decrypts the message body and persists the message body.

[0014] Furthermore, the generation process of the message processor includes: relying on an access device, the device provides a template interface, the developer implements the handle method of the template interface through custom programming according to the template interface, implements the processing logic of the directional type message, and generates a message processor.

[0015] In the present invention, the use of the template interface allows developers to easily add, modify or replace message processing logic without modifying the core framework code. This means that the system can quickly make adjustments based on changes in demand without worrying about affecting the functions of other parts. Among them, the template interface provides basic structures and functions, allowing developers to reuse these common code blocks and reduce duplication of work. For example, common error handling, logging, input validation and other functions can be implemented in a unified template to avoid repeatedly writing the same code in each processor; through the template interface, message processors can be designed as independent modules, which communicate through interfaces, reducing the coupling between modules. This design is conducive to system maintenance and upgrading, because changes in one module will not easily affect other modules.

[0016] Furthermore, the user personalized cache group specifically includes: relying on the access device, configuring the corresponding user behavior data source, generating user behavior data, using the AI ​​big model to perform data training and analysis on the user behavior data to generate big model data, chat gpt performs text answers on the big model data to generate interest tag tokens, generating user group information based on the interest tag tokens, and organizing the user group information into a user personalized cache group.

[0017] The user personalized cache group uses the user's unique identifier as the primary key to store the user's corresponding interest tag token and the corresponding message processing queue object.

[0018] The user personalized cache group stores a large number of interest tag tokens. These interest tag tokens come from chatgpt's text answers and user interest classification of user behavior data sources. When the interest tag token is passed in during the message processing process, the user personalized cache group can quickly match the corresponding user to achieve precise delivery.

[0019] The large AI model used for data training and analysis can be an interactive chat GPT or a BERT model for natural language processing, etc.

[0020] Furthermore, the step of inputting the message generated during the task processing into the message processor specifically includes: first starting a message timer, wherein the message timer includes a scheduled execution task of the message processor and an inactive user clearing task.

[0021] The specific execution steps of the scheduled execution task include: the scheduled execution task first enters the user counter to obtain a license, and the user counter maintains the current executable meter number of each user; when the current user's license number is greater than 0, it means that there is a license. After obtaining the license, the corresponding license number is reduced by 1, and the task execution thread task in the message queue is pulled at the same time; when the current user's license number is equal to 0, it means that the current queue thread task execution number is full, and it is necessary to enter the queue to wait for the license number to be released. The license will be released after each thread task ends.

[0022] Furthermore, the specific execution steps of the inactive user clearing task include: the inactive user clearing task monitors the user data in the user personalized cache group, and when the user matching message enters the corresponding queue, the inactive user clearing task maintains the latest message timestamp of the user.

[0023] The maintaining of the latest message timestamp of the user specifically includes: performing calculation with the current time, and if the timestamp exceeds a configured threshold, clearing the cache information of the user from the user personalized cache group.

[0024] In the present invention, the message timer realizes the regular collection of messages within a certain period of time, and then performs batch processing, wherein the processing order is controlled by the number of licenses. This method can reduce the number of I / O operations of the system, improve processing efficiency, and reduce resource consumption.

[0025] The number of licenses can also be customized to dynamically adjust resource allocation based on system load, such as executing time-consuming tasks during off-peak hours to make full use of resources.

[0026] The inactive user cleanup task regularly cleans up expired or no longer needed messages to free up storage space and ensure the health and performance of the queue.

[0027] According to a second aspect of the present invention, a computer-readable storage medium is provided, on which one or more computer programs are stored. When the one or more computer programs are executed by a computer processor, the above method is implemented.

[0028] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0029] 1. Improve processing efficiency: By using technologies such as personalized cache groups, message managers, and timers, the present invention can improve the processing efficiency of the system in user behavior data analysis. The message manager can dynamically set message tags, and the cache group can regularly clean up and process messages to ensure the rational use of system resources.

[0030] 2. Accurate matching and processing: The system can match according to the preset message cache tags and automatically push messages to the target users to ensure the accuracy of information transmission. At the same time, the use of spring ioc can realize automatic matching and dependency injection of objects, reduce management complexity, and improve the flexibility and scalability of the system.

[0031] 3. Resource optimization and conservation: By using the hash type of reids for cache storage, resource usage is reduced. The use of message managers and cache groups makes the system more efficient in processing user behavior data, reducing resource waste and redundancy.

[0032] 4. Flexibility and scalability: The modular design in the technical solution of the present invention, such as the use of spring ioc and semaphore technologies, makes the system have good flexibility and scalability. The user behavior data analysis and processing process can be adjusted and expanded according to actual needs to adapt to different application scenarios.

[0033] 5. Reliability and stability: The system ensures the reliability and stability of message transmission and processing by using technologies such as blocking queue and semaphore, avoiding problems such as system overload and resource competition. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present invention. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0035] Figure 1 A schematic diagram of a high-reliability programmable intelligent message processing method according to an embodiment of the present invention is shown;

[0036] Figure 2 It shows a schematic diagram of a specific process flow of timing execution of tasks according to a specific embodiment of the present invention;

[0037] Figure 3 A schematic diagram of the horizontal expansion process of message processing according to a specific embodiment of the present invention is shown. DETAILED DESCRIPTION

[0038] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0039] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0040] Figure 1 FIG. 4 shows a flow chart of a high-reliability programmable intelligent message processing method according to an embodiment of the present invention. Figure 1 As shown:

[0041] Inputting the message generated during the task processing into the message processor, the message including a message header, a message body and a message content;

[0042] The message includes a message header, a message body and message content, wherein the message header begins with a user interest identification processor name, the message body is the main body of the message, and the message content is set to be encrypted.

[0043] The message header starts with the name of the user interest identification processor, and the message body is the main body. The message content can be set to be encrypted. The message header uses spring ioc for automatic matching and dependency injection of objects, which simplifies the management of the message manager, so that the corresponding message processor can be found in the message manager for message processing, simplifies object management and improves the scalability and maintainability of the system. The message processing process decrypts the message body and persists the message body.

[0044] The generation process of the message processor includes: relying on an access device, the device provides a template interface, the developer implements the handle method of the template interface through custom programming according to the template interface, implements the processing logic of the directional type message, and generates a message processor.

[0045] The message processor performs AES decryption on the message content in the message to obtain the decrypted message content, and sends the decrypted message content to Chat GPT, which returns text information, which is an interest tag token with a standard data format, and inputs the interest tag token into a user personalized cache group, which performs user matching according to the interest tag token in the text information, outputs a user matching message, and inputs the user matching message into a queue object corresponding to the user to wait for execution processing;

[0046] In this embodiment, the message processor is identified by the unique name of the message header name, for example, userHandler message processor, and enters spring ioc according to the name to find the developer's customized processor implementation class, executes the corresponding handle method, and intelligently and automatically enters the corresponding message processor to process the message, decrypts the content of the message body with AES, and then sends the message to gpt to inquire which interest identifiers the message matches, requiring gpt to return data in the format of {"mark":["color:yellow","color:red"]}, and pushes user message pairing according to the returned text information.

[0047] The user personalized cache group specifically includes: relying on the access device, configuring the corresponding user behavior data source, generating user behavior data, using the AI ​​big model to perform data training and analysis on the user behavior data to generate big model data, chat gpt performs text answers on the big model data to generate interest tag tokens, generating user group information based on the interest tag tokens, and organizing the user group information into a user personalized cache group.

[0048] The user personalized cache group uses the user's unique identifier as the primary key to store the user's corresponding interest tag token and the corresponding message processing queue object.

[0049] The user behavior data includes but is not limited to user browsing data and filled-in information data.

[0050] In this embodiment, the message is pushed into the user cache group, and the corresponding user is matched according to the mark identifier. The yellow.* in the cache group represents multiple groups of words after matching yellow, and # represents matching a word. The user is matched according to the message body returned by gpt. The message is pushed into the queue object corresponding to the user to wait for execution.

[0051] In this embodiment, the design and implementation of the personalized cache group uses the hash type of redis to cache and store user behavior data, ensuring fast access and efficient management of data.

[0052] The step of inputting the message generated during the task processing into the message processor specifically includes: first starting a message timer, wherein the message timer includes a scheduled execution task and an inactive user clearing task of the message processor.

[0053] The scheduled execution task, such as Figure 2 As shown, the specific execution steps include: the scheduled execution task first enters the user counter to obtain a license, and the user counter maintains the current executable metering number of each user; when the current user's license number is greater than 0, it means that there is a license. After obtaining the license, the corresponding license number is reduced by 1, and the task execution thread task in the message queue is pulled at the same time; the current user's license number is equal to 0, indicating that the current queue thread task execution number is full, and it is necessary to enter the queue to wait for the license number to be released. Each thread task releases the license after the task is completed.

[0054] Scheduled tasks are executed at the configured frequency for queue task execution. Each message task in a queue corresponding to each user corresponds to a thread. The number of threads executed by each user at the same time is subject to the device configuration and is not allowed to exceed the configuration threshold.

[0055] When the scheduled task is executed to pull the cached message, the user's corresponding count will be obtained from the user count manager to determine whether there is a license. If the license is exceeded, the message processing will be intercepted and the user's current count manager will be waited for to release the license. The number of licenses for each user at the same time can be configured. The count here uses semaphore to prevent system overload. It will maintain the number of licenses for each user.

[0056] The specific execution steps of the inactive user clearing task include: the inactive user clearing task monitors the user data in the user personalized cache group, and when the user matching message enters the corresponding queue, the inactive user clearing task maintains the latest message timestamp of the user.

[0057] The maintaining of the latest message timestamp of the user specifically includes: performing calculation with the current time, and if the timestamp exceeds a configured threshold, clearing the cache information of the user from the user personalized cache group.

[0058] When a message enters the message processor, it will match the interest identifier and record the user message time that matches the interest identifier. When the message enters the message manager, the timer will be started. The timer will execute the task at a certain frequency, obtain the current system time and the message time corresponding to the user for calculation. If the time exceeds the set inactivity threshold, the user information will be cleared to avoid wasting system memory.

[0059] Further, such as Figure 3 As described above, the message processor, message counter, message timer, and user personalized cache group are used as a broker, and the broker is horizontally expanded to achieve high-availability and high-efficiency task message execution. By combining blockingqueue and semaphore technology, the reliability and stability of the message transmission process are ensured to avoid system overload and resource competition. The message manager can be horizontally expanded to expand the processing capacity of user behavior cache data. Accurate user portraits and personalized message push are achieved.

[0060] In summary, based on the method of the present invention, a customizable and programmable high-availability message processing device is realized through artificial intelligence service empowerment, a data source is configured to execute data training, and user interest tag information is stored, so as to realize a message device that can intelligently push message service information, and realize a highly available and efficient message device. Users only need to pay attention to the processing logic of the message, and do not need to consider the message processing efficiency and the message push mechanism too much to realize intelligent message full-process push. It is suitable for message reception, parsing, processing, storage and sending under a distributed architecture, and is particularly suitable for application scenarios that require high concurrency, high availability and flexible custom configuration.

[0061] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0062] The modules involved in the embodiments of the present application may be implemented by software or by hardware.

[0063] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

Claims

1. A highly reliable programmable intelligent message processing method, characterized in that: The following steps are involved: Inputting the message generated during the task processing into the message processor, the message including a message header, a message body and a message content; The message processor performs AES decryption on the message content in the message to obtain the decrypted message content, and sends the decrypted message content to Chat GPT, which returns text information, which is an interest tag token with a standard data format, and inputs the interest tag token into a user personalized cache group, which performs user matching according to the interest tag token in the text information, outputs a user matching message, and inputs the user matching message into a queue object corresponding to the user to wait for execution processing; The message processor enters springioc according to the name of the message header to find the developer's customized processor implementation class and executes the corresponding handle method.

2. The message processing method according to claim 1, characterized in that: The message includes a message header, a message body and message content, wherein the message header begins with a user interest identification processor name, the message body is the main body of the message, and the message content is set to be encrypted.

3. The message processing method according to claim 1, characterized in that: The generation process of the message processor includes: relying on an access device, the device provides a template interface, the developer implements the handle method of the template interface through custom programming according to the template interface, implements the processing logic of the directional type message, and generates a message processor.

4. The message processing method according to claim 1, characterized in that: The user personalized cache group specifically includes: relying on the access device, configuring the corresponding user behavior data source, generating user behavior data, using the AI ​​big model to perform data training and analysis on the user behavior data to generate big model data, chat gpt performs text answers on the big model data to generate interest tag tokens, generating user group information based on the interest tag tokens, and organizing the user group information into a user personalized cache group.

5. The message processing method according to claim 4, characterized in that: The user personalized cache group uses the user's unique identifier as the primary key to store the user's corresponding interest tag token and the corresponding message processing queue object.

6. The message processing method according to claim 1, characterized in that: The step of inputting the message generated during the task processing into the message processor specifically includes: first starting a message timer, wherein the message timer includes a scheduled execution task and an inactive user clearing task of the message processor.

7. The message processing method according to claim 6, characterized in that: The specific execution steps of the scheduled execution task include: the scheduled execution task first enters the user counter to obtain a license, and the user counter maintains the current executable metering number of each user; when the current user's license number is greater than 0, it means that a license exists. After obtaining the license, the corresponding license number is reduced by 1, and the task execution thread task in the message queue is pulled at the same time; the current user's license number is equal to 0, which means that the current queue thread task execution number is full, and it is necessary to enter the queue to wait for the license number to be released.

8. The message processing method according to claim 7, characterized in that: The thread task releases the license after the task is completed.

9. The message processing method according to claim 6, characterized in that: The specific execution steps of the inactive user clearing task include: the inactive user clearing task monitors the user data in the user personalized cache group, and when the user matching message enters the corresponding queue, the inactive user clearing task maintains the latest message timestamp of the user.

10. The message processing method according to claim 9, characterized in that: The maintaining of the latest message timestamp of the user specifically includes: performing calculation with the current time, and if the timestamp exceeds a configured threshold, clearing the cache information of the user from the user personalized cache group.

11. A computer program product, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

12. A computing system, characterized in that: The method comprises a processor and a memory, wherein the processor is configured to execute the method according to any one of claims 1 to 10.

Images