A High-Efficiency Decoding System and Method for RRC Messages Based on Dynamic Configuration

Abstract

The application belongs to the technical field of message decoding, and particularly relates to a high-efficiency decoding system and method for RRC messages based on dynamic configuration, which comprises a scene sensing module, a configuration generation module, a resource management module, a decoding execution module and an adaptive learning module. The scene sensing module collects the running state and communication environment information of a communication device in real time, processes the collected information, and abstracts the current communication scene as a set of feature vectors. The configuration generation module combines the feature vectors with a pre-stored RRC protocol rule library to generate a decoding configuration file for the current scene. The resource management module allocates and dynamically manages the computing resources and memory resources of the system according to the decoding configuration file. The decoding execution module decodes the RRC messages according to the decoding configuration file and the resources allocated by the resource management module. The adaptive learning module analyzes and learns new rules to realize decoding when the decoding operation cannot correctly decode, so as to improve the compatibility of the system. The application can improve the compatibility and decoding efficiency.

Description

Technical Field

[0001] This invention relates to the field of message decoding technology, and specifically to an efficient RRC message decoding system and method based on dynamic configuration. Background Technology

[0002] The rapid development of mobile wireless network communication has led to increasingly larger data payloads in wireless communication protocols, placing higher demands on the ability to parse protocol data. Most existing technologies employ full-data decoding, but traditional recursive descent decoding algorithms suffer from high time complexity and significant performance degradation when handling complex nested structures.

[0003] As wireless communication standards are continuously updated, the Radio Resource Control (RRC) protocol is also revised and expanded accordingly. Whenever the protocol changes, even with minor adjustments to the format, meaning, or value range of a single parameter, traditional decoding methods typically require large-scale code modifications and debugging of the entire decoding program, resulting in poor compatibility. This not only increases software development and maintenance costs and prolongs the development cycle but also easily introduces new errors during modifications, affecting system reliability and stability, and leading to low decoding efficiency. Summary of the Invention

[0004] In view of the technical deficiencies mentioned in the background art, the purpose of this invention is to provide a high-efficiency decoding system and method for RRC messages based on dynamic configuration, so as to improve decoding efficiency.

[0005] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a dynamically configured, high-efficiency RRC message decoding system, the system comprising:

[0006] The scene perception module is used to collect the operating status and communication environment information of communication devices in real time, process the collected information, and abstract the current communication scene into a set of feature vectors.

[0007] A configuration generation module is used to combine the feature vector with a pre-stored RRC protocol rule base to generate a decoding configuration file for the current scenario; wherein, the RRC protocol rule base includes detailed RRC protocol rules for multiple versions and multiple application scenarios;

[0008] The resource management module is used to allocate and dynamically manage the system's computing and memory resources based on the decoding configuration file generated by the configuration generation module.

[0009] The decoding execution module is used to perform decoding operations on RRC messages according to the decoding configuration file and the resources allocated by the resource management module;

[0010] The adaptive learning module is used to analyze and learn new rules to achieve decoding when the decoding operation fails to decode correctly, thereby improving the system's compatibility. At the same time, the new rules are fed back to the configuration generation module to achieve system self-optimization.

[0011] As one specific implementation of this application, the scene perception module specifically includes:

[0012] The information collection unit collects information related to communication equipment and the communication environment through various means, and interacts with the GPS module built into the communication equipment to obtain the device's geographical location, speed, and direction information to determine whether the communication equipment is in a high-speed movement scenario; it obtains the network parameters of the current connection through the network interface; at the same time, it interacts with the upper-layer application to understand the communication services currently in operation.

[0013] The information processing and feature extraction unit is used to normalize the collected information and convert it into a unified and usable data format. Then, using a preset feature extraction algorithm, it extracts multiple key features from the processed information and combines these key features into a feature vector to comprehensively describe the current communication scenario.

[0014] As one specific implementation of this application, the configuration generation module specifically includes:

[0015] The rule matching unit is used to perform precise matching in the RRC protocol rule base based on the output feature vector to find the RRC message type and its corresponding parameter set that are closely related to the current communication scenario;

[0016] The configuration file generation unit is used to determine the exact location, data type, and applicable decoding algorithm information of each parameter in the RRC message based on the selected parameter set, and to organize this information in a preset format to generate the decoding configuration file.

[0017] As one specific implementation of this application, the resource management module specifically includes:

[0018] The priority determination unit is used to determine the priority of each parameter decoding task according to the generated decoding configuration file; wherein, parameter decoding tasks with high real-time requirements and high impact on communication quality are given high priority, while some non-critical auxiliary parameter decoding tasks have low priority.

[0019] The computing resource allocation unit is used to monitor the available computing resources of the system in real time, and allocate CPU time slices and core resources to each decoding task according to the priority of the decoding task and a preset scheduling algorithm; for high-priority tasks, more CPU time slices and higher-performance cores are allocated to ensure that they can be executed quickly and with priority.

[0020] The memory resource allocation unit is used to allocate corresponding memory space for each decoding task based on the size of each parameter, the amount of intermediate data expected to be generated during the decoding process, and storage requirements.

[0021] At the same time, memory reuse and caching technologies are adopted to release memory in a timely manner for data that has been decoded and is no longer in use, and to cache and manage intermediate data that needs to be reused, so as to avoid memory waste and fragmentation.

[0022] As one specific implementation of this application, the decoding execution module specifically includes:

[0023] The message reading and parameter positioning unit is used to read RRC messages from the communication link and accurately locate the storage location of the corresponding parameter in the message according to the location information of each parameter in the decoding configuration file.

[0024] The parameter decoding unit is used to perform decoding operations on each located parameter according to the data type and decoding algorithm determined in the decoding configuration file.

[0025] The verification and error handling unit is used to perform real-time verification of the decoding result of each parameter during the decoding process.

[0026] As one specific implementation of this application, the adaptive learning module specifically includes:

[0027] An abnormal message capture unit is used to pass an RRC message to the adaptive learning module when the decoding execution module cannot correctly decode it according to the existing decoding configuration file.

[0028] The feature extraction and analysis unit is used to perform comprehensive feature extraction on RRC messages that cannot be correctly decoded; then, the extracted features are compared in detail with the stored RRC protocol rule base and other variant rules learned in the past to analyze the differences between the message and the existing rules.

[0029] The rule learning and updating unit is used to decode the message by adjusting existing decoding parameters or learning new decoding rules. This process includes multiple trial decodings and result verifications of the message. Once an effective decoding method is found, the newly learned rules or parameter adjustments are recorded and updated in the RRC protocol rule base. At the same time, the new rules are fed back to the configuration generation module so that this new message variant can be taken into account when generating decoding configuration files in the future, thereby improving the system's decoding capability for different RRC message variants.

[0030] Secondly, embodiments of the present invention also provide a method for efficient decoding of RRC messages based on dynamic configuration, applied to the efficient decoding system for RRC messages based on dynamic configuration described in the first aspect, the method comprising the following steps:

[0031] The system collects real-time information on the operating status of communication devices and the communication environment, processes the collected information, and abstracts the current communication scenario into a set of feature vectors.

[0032] The feature vector is combined with a pre-stored RRC protocol rule base to generate a decoding configuration file for the current scenario; wherein, the RRC protocol rule base includes detailed RRC protocol rules for multiple versions and multiple application scenarios;

[0033] The system's computing and memory resources are allocated and dynamically managed based on the decoding configuration file generated by the configuration generation module.

[0034] Based on the decoding configuration file and the resources allocated by the resource management module, the RRC message is decoded.

[0035] When the decoding operation fails to decode correctly, new rules are analyzed and learned to achieve decoding, thereby improving the system's compatibility. At the same time, the new rules are fed back to the configuration generation module to achieve system self-optimization.

[0036] The technical solution provided by this invention abstracts the current communication scenario into a set of feature vectors based on the real-time requirements of the communication scenario. These feature vectors are then combined with a pre-stored RRC protocol rule base to generate a decoding configuration file for the current scenario. This allows subsequent decoding to prioritize the decoding of key feature parts in the RRC message, avoiding the ineffective processing of redundant information and significantly improving decoding speed and efficiency. Simultaneously, when encountering undecoding messages, the system improves compatibility by analyzing and learning new rules, and feeds these new rules back to the configuration generation module, enabling system self-optimization. This modular and intelligent solution achieves efficient, low-cost, flexible, and compatible decoding of RRC messages. Attached Figure Description

[0037] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0038] Figure 1 This is a block diagram illustrating the principle of a dynamically configured, high-efficiency RRC message decoding system provided in this embodiment of the invention.

[0039] Figure 2 This is a schematic diagram of a high-efficiency RRC message decoding system based on dynamic configuration provided in an embodiment of the present invention;

[0040] Figure 3 This is a flowchart of an efficient RRC message decoding method based on dynamic configuration provided in an embodiment of the present invention. Detailed Implementation

[0041] 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.

[0042] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0043] Please refer to Figure 1 and Figure 2 This invention provides an efficient RRC message decoding system based on dynamic configuration, the system comprising:

[0044] The scene perception module is used to collect the operating status and communication environment information of communication devices in real time, process the collected information, and abstract the current communication scene into a set of feature vectors.

[0045] A configuration generation module is used to combine the feature vector with a pre-stored RRC protocol rule base to generate a decoding configuration file for the current scenario; wherein, the RRC protocol rule base includes detailed RRC protocol rules for multiple versions and multiple application scenarios;

[0046] The resource management module is used to allocate and dynamically manage the system's computing and memory resources based on the decoding configuration file generated by the configuration generation module.

[0047] The decoding execution module is used to perform decoding operations on RRC messages according to the decoding configuration file and the resources allocated by the resource management module;

[0048] The adaptive learning module is used to analyze and learn new rules to achieve decoding when the decoding operation fails to decode correctly, thereby improving the system's compatibility. At the same time, the new rules are fed back to the configuration generation module to achieve system self-optimization.

[0049] In this embodiment, the scene perception module specifically includes:

[0050] The information collection unit collects information related to communication equipment and the communication environment through various means, and interacts with the GPS module built into the communication equipment to obtain the device's geographical location, speed, and direction information to determine whether the communication equipment is in a high-speed movement scenario; it obtains the network parameters of the current connection through the network interface; at the same time, it interacts with the upper-layer application to understand the communication services currently in operation.

[0051] The network parameters include the current network standard (such as 4G, 5G), signal strength, bandwidth and other network-related parameters; communication services include, for example, real-time voice calls, high-definition video streaming, ordinary data downloads, or low-power data interaction between IoT devices.

[0052] The information processing and feature extraction unit normalizes the collected information, converting it into a unified and usable data format. Then, using a pre-defined feature extraction algorithm, it extracts multiple key features from the processed information and combines these features into a feature vector to comprehensively describe the current communication scenario. For example, in a scenario involving high-speed movement and video calls, the feature vector might include key features such as movement speed exceeding a certain threshold, the service type being a video call, and network signal strength falling within a certain range.

[0053] The configuration generation module specifically includes:

[0054] The rule matching unit is used to perform precise matching in the RRC protocol rule base based on the output feature vector to find the RRC message types and their corresponding parameter sets that are closely related to the current communication scenario. For example, if the scenario awareness module determines that the device is in a high-speed moving state and is making a video call, the configuration generation module will find the RRC messages related to mobility management and video transmission from the rule base, such as the parts of the RRC connection reconfiguration message related to handover parameters (such as handover threshold, neighbor cell list), video encoding parameters, and parameters related to video transmission quality assurance (such as encoding method, resource allocation parameters).

[0055] The configuration file generation unit is used to determine the exact location (in byte offset or bit) of each parameter in the RRC message, its data type (such as unsigned integer, enumeration type, string, etc.), and the applicable decoding algorithm (such as a specific binary decoding algorithm or lookup table decoding algorithm) based on the selected parameter set. This information is then organized according to a preset format to generate the decoding configuration file. The decoding configuration file uses an easy-to-read and parse format, such as XML or JSON, which contains specific configuration items for each parameter that needs to be decoded, such as "Parameter Name: Switch Threshold, Location: Bytes 10-13 of the message body, Data Type: Unsigned Integer, Decoding Algorithm: Bitwise parsing followed by numerical conversion".

[0056] Through in-depth analysis of the RRC protocol and real-time awareness of communication scenario requirements, dynamic adjustment of decoding configuration is achieved. Specifically, before receiving an RRC message, the system predicts key information that may need to be parsed based on the current status of the communication device (such as whether it is in a mobile state, the type of network currently connected, etc.), the type of communication service (such as voice call, video streaming, data download, etc.), and the pre-stored RRC protocol rule base, and generates a corresponding decoding configuration file. For example, when the device is in a high-speed mobile state and is conducting a video call, the system predicts that it may frequently receive RRC messages related to mobility management and video transmission, and thus focuses on configuring fast parsing rules for these related parameters in the decoding configuration file. In this way, during the actual decoding process, the system can selectively prioritize the decoding of key parts of the RRC message, skipping redundant information that is unlikely to be used in the current scenario, greatly improving decoding efficiency.

[0057] The resource management module specifically includes:

[0058] The priority determination unit is used to determine the priority of each parameter decoding task based on the generated decoding configuration file. Among them, parameter decoding tasks with high real-time requirements and significant impact on communication quality are given high priority, while some non-critical auxiliary parameter decoding tasks have low priority. For example, in real-time video calls, decoding tasks related to video encoding parameters have higher priority than non-critical parameter decoding tasks used for statistical purposes.

[0059] The computing resource allocation unit is used to monitor the current available computing resource information of the system in real time (such as the number of available CPU cores, computing frequency, etc.), and allocate CPU time slices and core resources to each decoding task according to the priority of the decoding task and a preset scheduling algorithm (such as a priority-based preemptive scheduling algorithm). For high-priority tasks, more CPU time slices and higher-performance cores are allocated to ensure that they can be executed quickly and with priority. For example, CPU time slices and core resources are allocated to each decoding task according to priority order. For the highest priority video encoding parameter decoding task, multiple high-performance CPU cores may be allocated and given a longer continuous time slice for computation.

[0060] The memory resource allocation unit is used to allocate corresponding memory space for each decoding task based on the size of each parameter, the amount of intermediate data expected to be generated during the decoding process, and storage requirements.

[0061] Meanwhile, memory reuse and caching techniques are employed to promptly release memory for decoded data that is no longer in use, and to cache intermediate data that needs to be reused, thus avoiding memory waste and fragmentation. For example, when decoding a large RRC message, temporary data structures used only in specific stages are immediately reclaimed from memory after use, making their space available for other tasks.

[0062] In application, the decoding execution module specifically includes:

[0063] The message reading and parameter positioning unit is used to read RRC messages from the communication link and accurately locate the storage location of the corresponding parameter in the message according to the position information of each parameter in the decoding configuration file. For example, for the switching threshold parameter specified in the configuration file as "bytes 10-13", the decoding execution module directly extracts 4 bytes of data starting from the 10th byte of the RRC message as the original data of the parameter.

[0064] The parameter decoding unit is used to perform decoding operations on each located parameter according to the data type and decoding algorithm determined in the decoding configuration file. For example, for an unsigned integer parameter encoded in two's complement form, the decoding execution module converts the binary data into a decimal value according to the corresponding decoding algorithm. When decoding complex data structures (such as nested structures and arrays), the decoding is performed step by step according to the defined structural hierarchy and element parsing rules.

[0065] The verification and error handling unit is used to perform real-time verification of the decoding results for each parameter during the decoding process. Verification includes checking whether the data format meets expectations (e.g., whether integers are within the specified range, whether the string length is correct), and the logical consistency with other related parameters (e.g., whether the logical relationship between the switching threshold and the signal strength of neighboring cells is reasonable). If an error is found during verification, it is handled according to a preset error handling mechanism. For minor errors, such as small deviations in data format that do not affect overall understanding, an attempt is made to correct the decoding using an error correction algorithm. For serious errors, such as key parameter values ​​exceeding reasonable ranges, a detailed error log is recorded (including the name, location, actual decoded value, expected value, etc.) and reported to the system. Simultaneously, the current decoding task is paused, awaiting further processing (e.g., re-acquiring messages, manual intervention, etc.).

[0066] In this embodiment, the adaptive learning module specifically includes:

[0067] An abnormal message capture unit is used to pass the message to the adaptive learning module when the decoding execution module cannot correctly decode an RRC message according to the existing decoding configuration file (equivalent to determining whether the decoding was successful or not). These abnormal messages may be caused by minor changes in the RRC protocol, new operator-customized formats, or device-specific encoding methods.

[0068] The feature extraction and analysis unit is used to perform comprehensive feature extraction on RRC messages that cannot be correctly decoded; then, the extracted features are compared in detail with the stored RRC protocol rule base and other variant rules learned in the past to analyze the differences between the message and the existing rules.

[0069] Specifically, a comprehensive feature extraction process is performed on abnormal messages, including the overall structure of the message (such as the order of fields and hierarchical relationships), the distribution patterns of parameters (such as the frequency of parameter occurrence and the correlation between adjacent parameters), and the data encoding method (such as whether a new encryption algorithm or special binary encoding rules are used). Then, the extracted features are compared in detail with the standard RRC protocol rules stored in the system and other variant rules learned in the past to analyze the differences between the message and the existing rules.

[0070] The rule learning and updating unit is used to decode the message by adjusting existing decoding parameters or learning new decoding rules. This process includes multiple trial decodings and result verifications of the message (e.g., attempting decoding by changing the decoding algorithm of a parameter, adjusting the parameter position, or redefining the data type). Once an effective decoding method is found, the newly learned rules or parameter adjustments are recorded and updated in the RRC protocol rule base. At the same time, the new rules are fed back to the configuration generation module so that this new message variant can be taken into account when generating decoding configuration files in the future, thereby improving the system's decoding capability for different RRC message variants.

[0071] The above solution abstracts the current communication scenario into a set of feature vectors based on the real-time requirements of the communication scenario. These feature vectors are then combined with a pre-stored RRC protocol rule base to generate a decoding configuration file specific to the current scenario. This allows subsequent decoding to prioritize the decoding of key feature parts in the RRC message, avoiding the ineffective processing of redundant information and significantly improving decoding speed and efficiency. Simultaneously, when encountering undecoding messages, the system improves compatibility by analyzing and learning new rules, and feeds these new rules back to the configuration generation module, enabling system self-optimization. This modular and intelligent solution achieves efficient, low-cost, flexible, and compatible decoding of RRC messages.

[0072] Based on the same inventive concept, embodiments of the present invention also provide a method for efficient decoding of RRC messages based on dynamic configuration, applied to the efficient decoding system for RRC messages based on dynamic configuration described in the first aspect, referring to... Figure 3 The method includes the following steps:

[0073] S101 collects real-time information on the operating status of communication devices and the communication environment, processes the collected information, and abstracts the current communication scenario into a set of feature vectors.

[0074] S102, combine the feature vector with the pre-stored RRC protocol rule base to generate a decoding configuration file for the current scenario; wherein, the RRC protocol rule base includes detailed RRC protocol rules for multiple versions and multiple application scenarios;

[0075] S103, Based on the decoding configuration file generated by the configuration generation module, the computing resources and memory resources of the system are allocated and dynamically managed;

[0076] S104, Decode the RRC message according to the decoding configuration file and the resources allocated by the resource management module;

[0077] S105, when the decoding operation fails to decode correctly, the system analyzes and learns new rules to achieve decoding, thereby improving the system's compatibility. At the same time, the new rules are fed back to the configuration generation module to achieve system self-optimization.

[0078] Furthermore, the current communication scenario is abstracted into a set of feature vectors, specifically including:

[0079] The system collects information about communication devices and the communication environment through various means, and interacts with the GPS module built into the communication device to obtain the device's geographical location, speed, and direction information, thereby determining whether the communication device is in a high-speed movement scenario; it obtains the network parameters of the current connection through the network interface; at the same time, it interacts with the upper-layer application to understand the communication services currently in operation.

[0080] The collected information is normalized and converted into a unified and usable data format. Then, a preset feature extraction algorithm is used to extract multiple key features from the processed information. These key features are then combined into a feature vector to comprehensively describe the current communication scenario.

[0081] In this embodiment, generating the decoding configuration file specifically includes:

[0082] Based on the output feature vector, a precise match is performed in the RRC protocol rule base to identify the RRC message type and its corresponding parameter set that are closely related to the current communication scenario;

[0083] The configuration file generation unit is used to determine the exact location, data type, and applicable decoding algorithm information of each parameter in the RRC message based on the selected parameter set, and to organize this information in a preset format to generate the decoding configuration file.

[0084] Decoding is achieved by analyzing and learning new rules to improve system compatibility. The process is carried out according to the following steps:

[0085] A comprehensive feature extraction is performed on RRC messages that cannot be correctly decoded; then, the extracted features are compared in detail with the stored RRC protocol rule base and other variant rules learned in the past to analyze the differences between the message and the existing rules.

[0086] The message is then decoded by adjusting existing decoding parameters or learning new decoding rules. This process includes multiple trial decodings and result verifications of the message. Once an effective decoding method is found, the newly learned rules or parameter adjustments are recorded and updated in the RRC protocol rule base.

[0087] At the same time, the new rules are fed back to the configuration generation module so that this new message variant can be taken into account when generating decoding configuration files in the future, thereby improving the system's ability to decode different RRC message variants.

[0088] It should be noted that for a more detailed description of the workflow of the method embodiments, please refer to the aforementioned system embodiments section, which will not be repeated here.

[0089] The entire solution abstracts the current communication scenario into a set of feature vectors based on the real-time requirements of the communication scenario. Then, it combines the feature vectors with a pre-stored RRC protocol rule base to generate a decoding configuration file for the current scenario. This enables subsequent decoding to prioritize the decoding of key feature parts in the RRC message, avoiding the ineffective processing of redundant information, thereby significantly improving decoding speed and efficiency. At the same time, when encountering messages that cannot be decoded, the system improves its compatibility by analyzing and learning new rules.

[0090] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-efficiency decoding system for RRC messages based on dynamic configuration, characterized in that, The system comprises: a scene awareness module for collecting the running state of the communication device and the communication environment information in real time, processing the collected information, and abstracting the current communication scene as a set of feature vectors; a configuration generation module for combining the feature vectors with a pre-stored RRC protocol rule library to generate a decoding configuration file for the current scene; wherein the RRC protocol rule library comprises RRC protocol detailed rules in multiple versions and multiple application scenarios; a resource management module for allocating and dynamically managing the computing resources and memory resources of the system according to the decoding configuration file generated by the configuration generation module; a decoding execution module for decoding the RRC message according to the decoding configuration file and the resources allocated by the resource management module; an adaptive learning module for realizing decoding by analyzing and learning new rules when the decoding operation cannot correctly decode, so as to improve the compatibility of the system, and feeding back the new rules to the configuration generation module to realize self-optimization of the system; the configuration generation module specifically comprises: a rule matching unit for accurately matching in the RRC protocol rule library according to the output feature vectors, and finding out the RRC message type closely related to the current communication scene and the corresponding parameter set thereof; a configuration file generation unit for determining the accurate position of each parameter in the RRC message, the data type and the applicable decoding algorithm information based on the filtered parameter set, and arranging and generating the decoding configuration file in a preset format.

2. The system of claim 1, wherein, The scene awareness module specifically comprises: an information collection unit for collecting communication device and communication environment related information through various channels, interacting with the GPS module built-in in the communication device to obtain the geographical position, moving speed and direction information of the device, so as to determine whether the communication device is in a high-speed moving scene; obtaining the network parameters of the current connection through the network interface; and interacting with the upper application program to understand the current running communication service; an information processing and feature extraction unit for normalizing the collected various types of information, converting them into a unified available data format; then, using a preset feature extraction algorithm to extract a plurality of key features from the processed information, and then grouping the plurality of key features into a feature vector for comprehensively describing the current communication scene.

3. The system of claim 1, wherein, The resource management module specifically comprises: a priority determination unit for determining the priority of each parameter decoding task according to the generated decoding configuration file; wherein the parameter decoding tasks with high real-time requirements and great influence on communication quality are given high priority, and some non-critical auxiliary parameter decoding tasks are given low priority; a computing resource allocation unit for monitoring the current available computing resource information of the system in real time, and allocating CPU time slices and core resources for each decoding task according to the priority of the decoding task and a preset scheduling algorithm; for high-priority tasks, more CPU time slices and cores with better performance are allocated to ensure that they can be executed preferentially and quickly; Memory resource allocation unit, for allocating corresponding memory space for each decoding task according to the size of each parameter, the amount of intermediate data expected to be generated in the decoding process and the storage requirement; At the same time, by using memory multiplexing and caching technology, the memory of the decoded data temporarily not used is released in time, and the intermediate data needing to be reused is managed by caching to avoid memory waste and fragmentation.

4. The system of claim 3, wherein, The decoding execution module specifically comprises: Message reading and parameter positioning unit, for reading RRC messages from the communication link, and accurately positioning the storage location of each parameter in the message according to the position information of each parameter in the decoding configuration file; Parameter decoding unit, for decoding each parameter positioned according to the data type and decoding algorithm determined in the decoding configuration file; Verification and error handling unit, for verifying the decoding result of each parameter in real time during the decoding process.

5. The system of any one of claims 1 to 4, wherein, The adaptive learning module specifically comprises: Abnormal message capturing unit, for passing the RRC message that the decoding execution module cannot decode correctly according to the existing decoding configuration file to the adaptive learning module; Feature extraction and analysis unit, for comprehensively extracting the features of the RRC message that cannot be decoded correctly; then, comparing the extracted features with the stored RRC protocol rule library and other learned variant rules in history in detail to analyze the differences between the message and the existing rules; Rule learning and updating unit, for realizing the decoding of the message by adjusting the existing decoding parameters or learning new decoding rules; wherein, the process includes multiple tentative decoding of the message and result verification; once an effective decoding method is found, the newly learned rule or parameter adjustment is recorded and updated to the RRC protocol rule library; at the same time, the new rule is fed back to the configuration generation module, so that the new message variant situation can be considered when generating the decoding configuration file in the future, thereby improving the decoding ability of the system to different RRC message variants. 6.A method for efficient decoding of a dynamic configuration based RRC message, the method comprising: The method is applied to the RRC message efficient decoding system based on dynamic configuration in claim 1, and the method comprises the following steps: Collecting the running state and communication environment information of the communication equipment in real time, and processing the collected information to abstract the current communication scene as a group of feature vectors; Combining the feature vectors with the pre-stored RRC protocol rule library to generate a decoding configuration file for the current scene; wherein, the RRC protocol rule library comprises detailed rules of RRC protocols under multiple versions and multiple application scenarios; Allocating and dynamically managing the computing resources and memory resources of the system according to the decoding configuration file generated by the configuration generation module; Decoding the RRC message according to the decoding configuration file and the resources allocated by the resource management module; When the decoding operation cannot correctly decode, new rules are analyzed and learned to realize decoding, so as to improve the compatibility of the system, and the new rules are fed back to the configuration generation module to realize self-optimization of the system; The decoding configuration file is generated, specifically comprising: According to the output feature vector, accurate matching is performed in the RRC protocol rule base to find out the RRC message type closely related to the current communication scenario and the corresponding parameter set; The configuration file generation unit is configured to determine the accurate position of each parameter in the RRC message, the data type and the applicable decoding algorithm information based on the screened parameter set, and to generate the decoding configuration file in the preset format.

7. The method of claim 6, wherein, The current communication scenario is abstracted as a set of feature vectors, specifically comprising: Through various ways to collect communication equipment and communication environment related information, and interact with the built-in GPS module of the communication equipment to obtain the geographical position, moving speed and direction information of the equipment, so as to judge whether the communication equipment is in a high-speed moving scenario; Through the network interface to obtain the network parameters of the current connection; At the same time, interact with the upper layer application program to understand the current running communication service; The collected various information is normalized and converted into a unified data format; Then, a preset feature extraction algorithm is used to extract a plurality of key features from the processed information, and then a plurality of key features are combined to form a feature vector for comprehensively describing the current communication scenario.

8. The method of claim 7, wherein, The decoding is realized by analyzing and learning new rules to improve the compatibility of the system, and the following steps are processed: The RRC message that cannot be correctly decoded is comprehensively characterized; Then, the extracted features are compared with the stored RRC protocol rule base and other variant rules learned from history to analyze the differences between the message and the existing rules; Then, the decoding of the message is realized by adjusting the existing decoding parameters or learning new decoding rules; The process includes multiple trial decodings and result verifications of the message; Once an effective decoding method is found, the newly learned rules or parameter adjustments are recorded and updated to the RRC protocol rule base; At the same time, the new rule is fed back to the configuration generation module, so that the new message variant situation can be considered when generating the decoding configuration file in the future, thereby improving the decoding ability of the system to different RRC message variants.

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