A real-time research and judgment method and system based on a heat-loadable standard specification package

Abstract

This invention relates to the field of computer technology, specifically to a real-time analysis method based on a hot-loadable standard specification package. The method includes the following steps: A layered architecture is used to design the real-time analysis module, breaking down its functions into independent modules for data preprocessing, feature extraction, and pattern matching. A standardized interface protocol is defined to unify input and output data formats. A version control mechanism manages template changes. The modular analysis framework is embedded into a stream processing task. Parallel computation is achieved using window sharding and a state backend. Task parallelism is dynamically adjusted to adapt to hot loading. Analysis latency is extracted. An anomaly detection algorithm identifies performance degradation. When a rollback mechanism is triggered, fault logs are automatically recorded, resulting in a real-time analysis module with self-healing capabilities. This invention solves the problems of traditional real-time analysis, such as difficult module coupling and updates, inflexible event rule management, high data processing latency and poor parallelism, and a lack of effective monitoring and self-healing capabilities.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, specifically relating to a real-time analysis method and system based on hot-loadable standard specification packages. Background Technology

[0002] In today's rapidly developing digital age, real-time analysis technology plays a crucial role in numerous fields, such as financial risk control, cybersecurity monitoring, and intelligent traffic management. It enables timely analysis and processing of massive amounts of data, providing key information for decision-making. However, traditional real-time analysis methods have several limitations. In terms of system architecture, most adopt an integrated design with tightly coupled functional modules, making system maintenance and upgrades extremely difficult. If a function malfunctions, a complete system shutdown is often required for repair, severely impacting business continuity. Regarding module updates, there is a lack of effective hot-loading mechanisms. When optimizing a module or fixing vulnerabilities, the entire system must be stopped and the updated module redeployed, consuming significant time and manpower, and causing data processing delays, reducing system real-time performance. In event rule management, hard-coding is typically used; rule changes require code modification and recompilation, resulting in poor flexibility and difficulty in quickly adapting to dynamic changes in business needs. Furthermore, effective means of synchronizing and versioning rule bases are lacking, easily leading to rule inconsistencies. At the data processing level, traditional methods struggle to achieve efficient parallel computing. When dealing with large-scale data, processing speed is slow, failing to meet the requirements of low-latency analysis. At the same time, the system lacks comprehensive monitoring and self-healing capabilities, and cannot detect problems such as performance degradation in a timely manner and take automatic measures. Once a fault occurs, manual intervention is often required for troubleshooting and repair, which affects the stability and reliability of the system.

[0003] Existing technologies suffer from high module coupling, requiring downtime for updates which can lead to business interruptions; inflexible event rule management and difficulty in synchronization; poor data processing parallelism and high latency; and a lack of effective monitoring and self-healing capabilities, making it difficult to meet the dynamic and efficient requirements for real-time analysis. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a real-time analysis method and system based on a hot-loadable standard specification package. This method solves the problems of traditional real-time analysis, such as difficult module coupling and updates, inflexible event rule management, high data processing latency and poor parallelism, and a lack of effective monitoring and self-healing capabilities. To achieve the above objectives, this invention adopts the following technical solution:

[0005] The real-time analysis method based on a hot-loadable standard specification package includes the following steps: A layered architecture is used to design the real-time analysis module, breaking down the functionality into independent modules for data preprocessing, feature extraction, and pattern matching. A standardized interface protocol is defined to unify the input and output data formats, resulting in a modular analysis framework. The module's runtime environment is isolated by a loader, and a file module monitoring mechanism is used to detect module updates in real time. When a new version is loaded, the old module is automatically unloaded and intermediate states are migrated, resulting in a module management mechanism that supports hot loading. Event rules are defined as serializable template objects, and template changes are managed through a version control mechanism. An incremental update protocol is used to synchronize the template library, and the compatibility between the template and the data stream is verified before loading, resulting in a dynamically extensible event template library. A real-time data pipeline is built using Apache Flink, embedding the modular analysis framework into the stream processing task. Parallel computation is achieved using window sharding and a state backend, and the task parallelism is dynamically adjusted to adapt to hot loading, resulting in a low-latency analysis pipeline. Analysis latency and accuracy metrics are extracted to construct a monitoring panel. An anomaly detection algorithm identifies performance degradation, and when a rollback mechanism is triggered, fault logs are automatically recorded, resulting in a real-time analysis module with self-healing capabilities.

[0006] Furthermore, the real-time analysis module, designed with a layered architecture, decomposes its functions into independent modules for data preprocessing, feature extraction, and pattern matching. By defining a standardized interface protocol to unify the input and output data formats, a modular analysis framework is obtained. This includes the following steps: The real-time analysis module is designed with a layered architecture, divided from bottom to top into a data access layer, a core processing layer, and a result output layer. The core processing layer is further subdivided into a data preprocessing module, a feature extraction module, and a pattern matching module. The data preprocessing module is responsible for cleaning and standardizing the raw data. The feature extraction module extracts indicators based on time-series analysis algorithms. The pattern matching module identifies event patterns using dynamic programming algorithms. By defining a standardized interface protocol, the data preprocessing module, feature extraction module, and pattern matching module are unified. The input is a structured data stream, and the output is a feature vector and matching results, ensuring decoupling between modules. This integration of independent modules and the standardized protocol yields a modular analysis framework with high scalability and low coupling.

[0007] Furthermore, the module management mechanism that isolates the module runtime environment through a loader, uses a file module monitoring mechanism to detect module updates in real time, and automatically unloads the old module and migrates the intermediate state when loading a new version, thus achieving hot loading, includes the following steps: The loader isolates the module runtime environment, avoiding class conflicts between different version modules through the class loader's namespace isolation mechanism; the file monitoring service monitors file changes in the module directory in real time, extracting file modification time and checksum to determine update events; when loading a new version module, the currently processing intermediate state is serialized to shared memory, the old module class loader is unloaded to release resources, the intermediate state is deserialized to the new module instance, and this is encapsulated into a standardized hot loading process, thus achieving hot loading-supported module management mechanism.

[0008] Furthermore, the process of defining event rules as serializable template objects, managing template changes through a version control mechanism, synchronizing the template library using an incremental update protocol, and verifying the compatibility of templates with the data stream before loading to obtain a dynamically scalable event template library includes the following steps: encapsulating event rules into serializable template objects using a structured data format; assigning a unique version identifier to each template through an integrated version control tool, recording change history, and supporting rollback operations; using an incremental update protocol based on difference files to transmit the changed data in the template modification event template object, reducing synchronization overhead; extracting feature fields and performing type and dimension verification with real-time data stream metadata, integrating the process into an automated management pipeline, and obtaining an event template library that supports dynamic expansion and secure updates.

[0009] Furthermore, the process of building a real-time data pipeline using Apache Flink, embedding a modular judgment framework into stream processing tasks, utilizing window sharding and a state backend to achieve parallel computation, and dynamically adjusting task parallelism to adapt to hot reloading, resulting in a low-latency judgment pipeline, includes the following steps: Building a real-time data pipeline using Apache Flink, accessing multiple data streams by defining data source operators, encapsulating the modular judgment framework as custom processing operators and embedding it into stream processing tasks; using a rolling window mechanism to shard the data stream, combining it with a RocksDB state backend to persist intermediate computation results, enabling independent processing of multiple parallel subtasks; using Flink's dynamic scaling mechanism to monitor module hot reloading events in real time and adjust task parallelism to ensure resource allocation matches the load, integrating it into an end-to-end processing link, resulting in a real-time judgment pipeline that supports dynamic expansion and has an end-to-end latency of less than 100 milliseconds.

[0010] Furthermore, the step of extracting and assessing latency and accuracy indicators to construct a monitoring panel, identifying performance degradation through anomaly detection algorithms, and automatically recording fault logs when a rollback mechanism is triggered, thereby obtaining a real-time assessment module with self-healing capabilities, includes the following steps: extracting indicators for the assessment task using data tracking technology, collecting latency and result accuracy data in real time and storing them persistently; visualizing the data on the monitoring panel by statistically analyzing the mean and volatility of indicators through a sliding window, establishing a performance baseline model using an exponential smoothing algorithm, and identifying abnormal degradation by comparing the differences between real-time indicators and the baseline; automatically triggering the module rollback mechanism and generating a unique fault identifier when a performance anomaly is detected, recording the anomaly context and rollback operation in a structured log, and encapsulating the monitoring, detection, and self-healing processes into a closed-loop management component to obtain a real-time assessment module with automatic fault recovery capabilities.

[0011] The second aspect of this invention provides a real-time analysis system based on a hot-loadable standard specification package. This system includes the following modules: an analysis framework module, used to design a real-time analysis module using a layered architecture, decomposing functions into independent modules for data preprocessing, feature extraction, and pattern matching, and unifying input and output data formats through a standardized interface protocol to obtain a modular analysis framework; a dynamic hot-loading module, used to isolate the module's runtime environment through a loader, utilize a file module monitoring mechanism to detect module updates in real time, automatically unload old modules and migrate intermediate states when loading new versions, resulting in a module management mechanism that supports hot loading; a dynamic update module, used to define event rules as serializable template objects, manage template changes through a version control mechanism, synchronize the template library using an incremental update protocol, and verify the compatibility of templates and data streams before loading, resulting in a dynamically extensible event template library; and an integration processing module, used to integrate with Apache... Flink constructs a real-time data pipeline, embedding a modular judgment framework into stream processing tasks. It utilizes window sharding and a state backend to achieve parallel computation and dynamically adjusts task parallelism to adapt to hot loading, resulting in a low-latency judgment pipeline. The monitoring system module is used to extract judgment latency and accuracy indicators to build a monitoring panel. It identifies performance degradation through anomaly detection algorithms and automatically records fault logs when a rollback mechanism is triggered, thus obtaining a real-time judgment module with self-healing capabilities.

[0012] A third aspect of the present invention provides a real-time analysis device based on a hot-loadable standard specification package, the real-time analysis device based on a hot-loadable standard specification package includes a memory and at least one processor, the memory storing instructions; the at least one processor calls the instructions in the memory to cause the real-time analysis device based on a hot-loadable standard specification package to perform the various steps of the real-time analysis method based on a hot-loadable standard specification package as described in any of the preceding claims.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions, characterized in that, when executed by a processor, the instructions implement the steps of the real-time evaluation method based on a hot-loadable standard specification package as described in any one of the preceding claims.

[0014] In the technical solution provided by this invention, a layered architecture is adopted for the real-time judgment module. The function is divided into independent modules for data preprocessing, feature extraction, and pattern matching. By defining a standardized interface protocol to unify the input and output data format, a modular judgment framework is obtained. The module running environment is isolated by a loader, and a file module monitoring mechanism is used to detect module updates in real time. When loading a new version, the old module is automatically unloaded and the intermediate state is migrated, resulting in a module management mechanism that supports hot loading. Event rules are defined as serializable template objects, and template changes are managed through a version control mechanism. An incremental update protocol is used to synchronize the template library, and the compatibility between the template and the data stream is verified before loading, resulting in a dynamically scalable event template library. A real-time data pipeline is built using Apache Flink, and the modular judgment framework is embedded into the stream processing task. Parallel computing is achieved using window sharding and a state backend. The parallelism of the task is dynamically adjusted to adapt to hot loading, resulting in a low-latency judgment pipeline. Judgment latency and accuracy indicators are extracted to build a monitoring panel. An anomaly detection algorithm is used to identify performance degradation. When a rollback mechanism is triggered, fault logs are automatically recorded, resulting in a real-time judgment module with self-healing capabilities. This invention solves the problems of difficult module coupling and updating, inflexible event rule management, high data processing latency and poor parallelism, as well as lack of effective monitoring and self-healing capabilities in traditional real-time analysis. It realizes hot loading of modules, dynamic expansion of rules, low-latency analysis and system self-healing. Attached Figure Description

[0015] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0016] Figure 1 This is a schematic diagram of the first embodiment of a real-time evaluation method based on a hot-loadable standard specification package according to the present invention.

[0017] Figure 2 This is a schematic diagram of a second embodiment of a real-time evaluation method based on a hot-loadable standard specification package according to the present invention.

[0018] Figure 3 This is a schematic diagram of a third embodiment of a real-time evaluation method based on a hot-loadable standard specification package in this invention.

[0019] Figure 4This is a schematic diagram of the fourth embodiment of a real-time evaluation method based on a hot-loadable standard specification package in this invention.

[0020] Figure 5 This is a schematic diagram of the fifth embodiment of a real-time evaluation method based on a hot-loadable standard specification package in this invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] A real-time evaluation method based on hot-loadable standard specification packages, such as Figure 1 As shown, the process includes the following steps: A layered architecture is used to design the real-time analysis module, breaking down its functions into independent modules for data preprocessing, feature extraction, and pattern matching. A standardized interface protocol is defined to unify input and output data formats, resulting in a modular analysis framework. The module's runtime environment is isolated using a loader, and a file module monitoring mechanism is used to detect module updates in real time. When a new version is loaded, the old module is automatically unloaded and intermediate states are migrated, resulting in a module management mechanism that supports hot loading. Event rules are defined as serializable template objects, and template changes are managed through a version control mechanism. An incremental update protocol is used to synchronize the template library, and the compatibility between the template and the data stream is verified before loading, resulting in a dynamically scalable event template library. A real-time data pipeline is built using Apache Flink, embedding the modular analysis framework into stream processing tasks. Parallel computation is achieved using window sharding and a state backend, and the task parallelism is dynamically adjusted to adapt to hot loading, resulting in a low-latency analysis pipeline. Analysis latency and accuracy metrics are extracted to build a monitoring panel. An anomaly detection algorithm identifies performance degradation, and when a rollback mechanism is triggered, fault logs are automatically recorded, resulting in a real-time analysis module with self-healing capabilities.

[0024] like Figure 2As shown, in this embodiment, a layered architecture is adopted for the real-time judgment module, which is divided from bottom to top into a data access layer, a core processing layer, and a result output layer. The core processing layer is further subdivided into a data preprocessing module, a feature extraction module, and a pattern matching module. The data preprocessing module is responsible for cleaning and standardizing the raw data, the feature extraction module extracts indicators based on time series analysis algorithms, and the pattern matching module identifies event patterns through dynamic programming algorithms. By defining a standardized interface protocol, the data preprocessing module, feature extraction module, and pattern matching module are unified. The input is a structured data stream, and the output is a feature vector and the matching result, ensuring decoupling between modules. The independent modules are integrated with the standardized protocol to obtain a modular judgment framework with high scalability and low coupling characteristics.

[0025] A layered architecture is adopted, and the core processing layer functions are refined, making the responsibilities of each module clear and facilitating independent development and maintenance. The data preprocessing module effectively cleans and standardizes raw data, providing a high-quality foundation for subsequent processing; the feature extraction module accurately extracts indicators based on time-series analysis algorithms, enhancing data usability; and the pattern matching module efficiently identifies event patterns using dynamic programming algorithms. Standardized interface protocols are defined to decouple modules, greatly improving the system's scalability, allowing for flexible addition, removal, or replacement of modules, while reducing coupling and minimizing mutual interference between modules.

[0026] like Figure 3 As shown, in this embodiment, the loader isolates the module's runtime environment and avoids class conflicts between different version modules through the class loader's namespace isolation mechanism; it monitors file changes in the module directory in real time through a file monitoring service, extracts file modification time and checksum to determine update events; when loading a new version module, it serializes the intermediate state currently being processed to shared memory, unloads the old module class loader to release resources, deserializes the intermediate state to the new module instance, and encapsulates it into a standardized hot loading process, thus obtaining a module management mechanism that supports hot loading.

[0027] The class loader utilizes a namespace isolation mechanism to effectively avoid class conflicts between different version modules, ensuring system stability. The file monitoring service monitors changes to module directory files in real time, accurately extracting file modification times and checksums to determine update events, ensuring timely detection of module updates. When loading a new version module, a standardized hot-reload process is encapsulated by serializing the intermediate state to shared memory, unloading the old module to release resources, and then deserializing it to the new module instance. This achieves seamless module updates, greatly improving system maintainability and availability, and reducing business interruptions caused by module updates.

[0028] like Figure 4As shown, in this embodiment, the event rules are encapsulated into serializable template objects using a structured data format. A unique version identifier is assigned to each template through an integrated version control tool, the change history is recorded, and rollback operations are supported. An incremental update protocol based on difference files is used to transmit the data that has changed in the template modification event template object, reducing synchronization overhead. Feature fields are extracted and real-time data stream metadata is used for type and dimension verification. The process is integrated into an automated management pipeline to obtain an event template library that supports dynamic expansion and secure updates.

[0029] Event rules are encapsulated as serializable template objects and formatted in a structured data format for easy storage and transmission. An integrated version control tool assigns unique version identifiers, clearly recording change history and supporting rollback, ensuring the traceability and stability of rule management. An incremental update protocol based on difference files transmits only changed data, significantly reducing synchronization overhead and improving update efficiency. Type and dimension validation is performed on feature fields and real-time data stream metadata, and integrated into an automated management pipeline to ensure rule and data compatibility.

[0030] like Figure 5 As shown, in this embodiment, Apache Flink is used to build a real-time data pipeline. By defining data source operators to access multi-source data streams, the modular judgment framework is encapsulated as a custom processing operator and embedded into the stream processing task. The data stream is sharded using a rolling window mechanism, and intermediate calculation results are persisted in the RocksDB state backend to achieve independent processing of multiple parallel subtasks. Through Flink's dynamic scaling mechanism, module hot loading events are monitored in real time and the parallelism of tasks is adjusted to ensure that resource allocation matches the load. This is integrated into an end-to-end processing link, resulting in a real-time judgment pipeline that supports dynamic expansion and has an end-to-end latency of less than 100 milliseconds.

[0031] By leveraging Apache Flink to build a real-time data pipeline, it can efficiently access multi-source data streams and embed a modular analysis framework to ensure the continuity of the data processing flow. The rolling window mechanism, in conjunction with the RocksDB state backend, enables data sharding and intermediate result persistence, allowing multiple parallel subtasks to run independently, improving processing efficiency. Flink's dynamic scaling mechanism can respond to module hot-reload events in real time, dynamically adjust task parallelism, and rationally allocate resources to achieve system load balancing. Integrated into an end-to-end processing pipeline, it creates a real-time analysis pipeline that supports dynamic expansion and has end-to-end latency of less than 100 milliseconds, meeting the needs of business scenarios with high real-time requirements.

[0032] Specifically, Apache Flink is an open-source distributed stream processing framework designed for low-latency, high-throughput stateful computation. It supports both unbounded and bounded data stream processing, i.e., real-time stream processing and batch processing, providing a unified programming interface. Flink possesses a sophisticated state management mechanism that ensures state consistency during fault recovery and supports event-driven processing to accurately handle out-of-order and delayed data. Its flexible architecture allows deployment on resource managers such as YARN and Kubernetes, or it can run independently.

[0033] In this embodiment, data tracking technology is used to extract indicators for the assessment task, and latency and result accuracy data are collected in real time and stored persistently. The mean and volatility of the indicators are statistically analyzed using a sliding window, and the data is visualized on the monitoring panel. An exponential smoothing algorithm is used to establish a performance baseline model, and abnormal declines are identified by comparing the differences between real-time indicators and the baseline. When a performance anomaly is detected, the module rollback mechanism is automatically triggered and a unique fault identifier is generated. The anomaly context and rollback operation are recorded in a structured log. The monitoring, detection, and self-healing processes are encapsulated into a closed-loop management component, resulting in a real-time assessment module with automatic fault recovery capabilities.

[0034] Data tracking technology is used to extract and analyze task metrics, and latency and accuracy data are collected and persistently stored in real time, providing a reliable basis for monitoring. Sliding window statistics and visualization make performance data readily apparent, and the performance baseline model established by the exponential smoothing algorithm can accurately identify abnormal degradation. When performance anomalies are detected, a module rollback mechanism is automatically triggered, generating a unique fault identifier and recording the anomaly context and rollback operation in a structured log. The monitoring, detection, and self-healing processes are encapsulated into a closed-loop management component, effectively improving the system's stability and reliability.

[0035] This invention also provides a real-time analysis system based on a hot-loadable standard specification package, comprising the following modules: an analysis framework module, used to design a real-time analysis module using a layered architecture, decomposing functions into independent modules for data preprocessing, feature extraction, and pattern matching, and unifying input and output data formats through a standardized interface protocol to obtain a modular analysis framework; a dynamic hot-loading module, used to isolate the module's runtime environment through a loader, use a file module monitoring mechanism to detect module updates in real time, automatically unload old modules and migrate intermediate states when loading new versions, resulting in a module management mechanism that supports hot loading; a dynamic update module, used to define event rules as serializable template objects, manage template changes through a version control mechanism, synchronize the template library using an incremental update protocol, and verify the compatibility of templates and data streams before loading, resulting in a dynamically extensible event template library; and an integration processing module, used to integrate with Apache... Flink constructs a real-time data pipeline, embedding a modular judgment framework into stream processing tasks. It utilizes window sharding and a state backend to achieve parallel computation and dynamically adjusts task parallelism to adapt to hot loading, resulting in a low-latency judgment pipeline. The monitoring system module is used to extract judgment latency and accuracy indicators to build a monitoring panel. It identifies performance degradation through anomaly detection algorithms and automatically records fault logs when a rollback mechanism is triggered, thus obtaining a real-time judgment module with self-healing capabilities.

[0036] This invention also provides a real-time analysis device based on a hot-loadable standard specification package. This device may further include one or more power supplies, one or more wired or wireless network interfaces, one or more input / output interfaces, and / or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that the structure of the real-time analysis device based on the hot-loadable standard specification package does not constitute a limitation on the computer device provided by this invention, and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0037] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the various steps of the real-time evaluation method based on the hot-loadable standard specification package provided in the above embodiments.

[0038] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time evaluation method based on a hot-loadable standard specification package, characterized in that, The real-time analysis method based on a hot-loadable standard specification package includes the following steps: A layered architecture is adopted for the real-time analysis module, divided from bottom to top into a data access layer, a core processing layer, and a result output layer. The core processing layer is further subdivided into a data preprocessing module, a feature extraction module, and a pattern matching module. A standardized interface protocol is defined to unify the input and output data formats, resulting in a modular analysis framework. Specifically, the data preprocessing module is responsible for cleaning and standardizing the raw data; the feature extraction module extracts indicators based on time-series analysis algorithms; and the pattern matching module identifies event patterns using dynamic programming algorithms. The module's runtime environment is isolated by a loader, and a file module monitoring mechanism is used to detect module updates in real time. When a new version is loaded, the old module is automatically unloaded and intermediate states are migrated, resulting in a module management mechanism that supports hot loading. Event rules are defined as serializable template objects. Template changes are managed through a version control mechanism, and an incremental update protocol is used to synchronize the template library. Before loading, the compatibility between the template and the data stream is verified, resulting in a dynamically extensible event template library. The analysis is conducted using Apache... Flink constructs a real-time data pipeline, encapsulating a modular judgment framework as custom processing operators embedded in stream processing tasks. It utilizes a rolling window mechanism to shard the data stream and combines this with RocksDB state backend persistence of intermediate computation results to enable independent processing of multiple parallel subtasks. Through Flink's dynamic scaling mechanism, it monitors module hot-loading events in real-time and adjusts task parallelism to ensure resource allocation matches the load, integrating it into an end-to-end processing chain. This results in a real-time judgment pipeline that supports dynamic expansion and has end-to-end latency of less than 100 milliseconds. Judgment latency and accuracy metrics are extracted to build a monitoring panel. Anomaly detection algorithms identify performance degradation, and when a rollback mechanism is triggered, fault logs are automatically recorded, resulting in a real-time judgment module with self-healing capabilities.

2. The real-time analysis method based on a hot-loadable standard specification package according to claim 1, characterized in that, The above-described standard interface protocol unifies the data preprocessing module, feature extraction module, and pattern matching module. The input is a structured data stream, and the output is a feature vector and matching result. This ensures decoupling between modules and integrates independent modules with the standard protocol to obtain a modular judgment framework with high scalability and low coupling.

3. The real-time analysis method based on a hot-loadable standard specification package according to claim 1, characterized in that, The module management mechanism that isolates the module runtime environment through a loader, uses a file module monitoring mechanism to detect module updates in real time, and automatically unloads the old module and migrates the intermediate state when loading a new version, thus achieving hot reloading, includes the following steps: The loader isolates the module runtime environment, avoiding class conflicts between different versions of modules through the namespace isolation mechanism of the class loader; The file monitoring service monitors file changes in the module directory in real time, extracting file modification time and checksum to determine update events; When loading a new version of a module, the intermediate state currently being processed is serialized to shared memory, the old module class loader is unloaded to release resources, the intermediate state is deserialized to the new module instance, and this process is encapsulated into a standardized hot reloading process, resulting in a module management mechanism that supports hot reloading.

4. The real-time analysis method based on a hot-loadable standard specification package according to claim 1, characterized in that, The process of defining event rules as serializable template objects, managing template changes through a version control mechanism, synchronizing the template library using an incremental update protocol, verifying the compatibility between the template and the data stream before loading, and obtaining a dynamically scalable event template library includes the following steps: encapsulating event rules into serializable template objects using a structured data format, assigning a unique version identifier to each template through an integrated version control tool, recording change history, and supporting rollback operations; An incremental update protocol based on difference files is adopted to transmit the changed data in the template object of the template modification event, reducing synchronization overhead; Extracting feature fields and performing type and dimension validation with real-time data stream metadata, the process is integrated into an automated management pipeline, resulting in an event template library that supports dynamic expansion and secure updates.

5. The real-time analysis method based on a hot-loadable standard specification package according to claim 1, characterized in that, The process of building a real-time data pipeline using Apache Flink includes the following steps: A real-time data pipeline is built using Apache Flink, multi-source data streams are accessed by defining data source operators, and the modular analysis framework is encapsulated as a custom processing operator embedded into the stream processing task.

6. The real-time analysis method based on a hot-loadable standard specification package according to claim 1, characterized in that, The process of extracting and evaluating latency and accuracy indicators to construct a monitoring panel, identifying performance degradation through anomaly detection algorithms, automatically recording fault logs when a rollback mechanism is triggered, and obtaining a real-time evaluation module with self-healing capabilities includes the following steps: Extracting evaluation task indicators using data tracking technology, collecting and persistently storing latency and result accuracy data in real time; Visualizing the data on the monitoring panel by statistically analyzing the mean and volatility of indicators using a sliding window, establishing a performance baseline model using an exponential smoothing algorithm, and identifying abnormal degradation by comparing real-time indicators with the baseline. When a performance anomaly is detected, the module rollback mechanism is automatically triggered and a unique fault identifier is generated. The anomaly context and rollback operation are recorded in a structured log. The monitoring, detection and self-healing processes are encapsulated into a closed-loop management component, resulting in a real-time analysis module with automatic fault recovery capabilities.

7. A real-time analysis system based on a hot-loadable standard specification package, characterized in that, The real-time analysis system based on a hot-loadable standard specification package includes the following modules: An analysis framework module, designed with a layered architecture, divided from bottom to top into a data access layer, a core processing layer, and a result output layer. The core processing layer is further subdivided into a data preprocessing module, a feature extraction module, and a pattern matching module. A standardized interface protocol is defined to unify the input and output data formats, resulting in a modular analysis framework. The data preprocessing module cleans and standardizes the raw data; the feature extraction module extracts indicators based on time-series analysis algorithms; and the pattern matching module identifies event patterns using dynamic programming algorithms. A dynamic hot-loading module isolates the module's runtime environment through a loader, uses a file module monitoring mechanism to detect module updates in real time, and automatically unloads old modules and migrates intermediate states when loading new versions, resulting in a module management mechanism that supports hot loading. A dynamic update module defines event rules as serializable template objects, manages template changes through a version control mechanism, synchronizes the template library using an incremental update protocol, and verifies the compatibility of templates and data streams before loading, resulting in a dynamically scalable event template library. An integration processing module is used to integrate the system through Apache... Flink constructs a real-time data pipeline, encapsulating a modular judgment framework into custom processing operators embedded in stream processing tasks. It utilizes a rolling window mechanism to shard the data stream and combines this with RocksDB state backend persistence of intermediate computation results to enable independent processing of multiple parallel subtasks. Through Flink's dynamic scaling mechanism, it monitors module hot-loading events in real-time and adjusts task parallelism to ensure resource allocation matches the load, integrating them into an end-to-end processing chain. This results in a real-time judgment pipeline that supports dynamic expansion and has end-to-end latency of less than 100 milliseconds. A monitoring module is used to extract judgment latency and accuracy metrics to build a monitoring panel. Anomaly detection algorithms identify performance degradation, and when a rollback mechanism is triggered, fault logs are automatically recorded, providing a self-healing real-time judgment module.

8. A real-time analysis device based on a heat-loadable standard specification package, characterized in that, The real-time analysis device based on the hot-loadable standard specification package includes a memory and at least one processor. The memory stores instructions, and the at least one processor calls the instructions in the memory to cause the real-time analysis device based on the hot-loadable standard specification package to perform each step of the real-time analysis method based on the hot-loadable standard specification package as described in any one of claims 1-6.

9. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement each step of the real-time evaluation method based on a hot-loadable standard specification package as described in any one of claims 1-6.

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