A method and system for analyzing abnormal short message PDU encoding

Abstract

The application discloses a kind of short message PDU encoding exception analysis method and system, belong to wireless communication network data processing technical field, including obtaining original short message PDU message in real-time acquisition communication link;Message is preprocessed to generate standardized PDU data stream to be analyzed, and synchronous extraction acquisition parameter constructs message basic traceability information;Standardized data stream is carried out hierarchical field check, and coding exception is identified and classifiedly marked;According to the exception type matching differential repair strategy generates repaired standard PDU message;Repairing message is carried out compliance secondary check, and readable short message analysis information is output through post-decoding, and if not, generate exception alarm;Correlation storage total data constructs exception analysis ledger.The application adopts hierarchical field check and dynamic repair strategy, combined with full-link traceability and secondary compliance check mechanism, and can realize the identification and analysis of coding exception short message and adaptive repair.

Description

Technical Field

[0001] This invention relates to the field of wireless communication network data processing technology, and in particular to a method and system for parsing SMS PDU encoding anomalies. Background Technology

[0002] SMS service, as a fundamental service of wireless communication networks, undertakes massive information exchange tasks. The Protocol Data Unit (PDU) mode is the standard data format for transmitting text content between mobile terminals and the SMS service center. This mode encapsulates message headers, address information, and user data in a continuous hexadecimal byte stream, relying on strict field lengths and encoding standards to ensure accurate routing and reconstruction of information within the communication network.

[0003] In related technologies, Chinese invention patent CN115226049B discloses a method, apparatus, electronic device, and storage medium for repairing an SMS platform, comprising: upon detecting a user querying the SMS platform, acquiring a user identifier, complaint scenario data, and complaint appearance data; acquiring a customer relationship management (CRM) system business status parameter set based on the complaint scenario data and the complaint appearance data; querying the Home Location Register (HLR) network element based on the user identifier and extracting the SMS function parameters recorded in the HLR network element; comparing the CRM system business status parameter set and the SMS function parameters to determine a repair instruction; and repairing the SMS platform based on the repair instruction.

[0004] However, the existing technical solutions mentioned above have the following technical defects: Most existing SMS PDU message parsing solutions rely on static parsing scripts with fixed offsets to parse data, reading message fields byte by byte according to the preset positions of the communication protocol. When the wireless channel is affected by electromagnetic interference or cross-carrier gateway protocol conversion, the message is prone to being mixed with invalid null characters, redundant padding characters, or problems such as field truncation, byte misalignment, and encoding errors. Existing technologies lack standardized message preprocessing procedures, making it impossible to remove redundant characters, unify message encoding formats, or collect traceability information such as message timestamps, device identifiers, and channel parameters, making it difficult to locate the abnormal scenario. At the same time, traditional solutions only perform simple total length comparisons, without performing layered verification of the integrity, byte alignment, encoding standards, and field matching of message headers, addresses, encoding fields, and user data, making it difficult to accurately classify and identify various implicit encoding anomalies. Once a field length mismatch is detected, parsing is directly terminated and the entire message is discarded, resulting in permanent loss of SMS service data and lacking a differentiated repair mechanism based on different types. There is no field reconstruction based on a historical repair strategy library, no intelligent error correction function for garbled characters, and no ability to use contextual language models to optimally replace garbled characters. Furthermore, the existing solution does not perform secondary compliance verification on the repair message, and the repair defect data is easily decoded incorrectly. It also only generates simple failure alarms and cannot associate the original message, abnormal information, repair data, and traceability parameters to build a structured ledger for storage. This is not conducive to the operation and maintenance personnel to trace back the fault and accumulate and optimize the repair rules. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for parsing SMS PDU encoding anomalies. It employs a layered field verification and dynamic repair strategy, combined with a full-link tracing and secondary compliance verification mechanism, enabling the identification, parsing, and adaptive repair of SMS messages with encoding anomalies.

[0006] The above objectives can be achieved through the following approach: A method and system for parsing SMS PDU encoding anomalies includes: acquiring raw SMS PDU messages in a real-time acquisition communication link; preprocessing the messages to generate a standardized PDU data stream to be parsed, and simultaneously extracting acquisition parameters to construct basic message traceability information; performing layered field verification on the standardized data stream to identify and classify encoding anomalies; generating a repaired standard PDU message by matching differentiated repair strategies according to the anomaly type; performing a secondary compliance verification on the repaired message, decoding and outputting readable SMS parsing information if it passes, and generating an anomaly alarm if it fails; and constructing an anomaly parsing ledger by associating and storing all data.

[0007] Optionally, the preprocessing of the original SMS PDU message to generate a standardized PDU data stream to be parsed, and the simultaneous extraction of the collection parameters of the original SMS PDU message to construct basic message tracing information, includes: extracting and removing invalid null characters and redundant padding characters from the original SMS PDU message to obtain a cleaned PDU message; uniformly converting the cleaned PDU message into a standard hexadecimal encoding format to generate a standardized PDU data stream to be parsed; and collecting and combining the collection timestamp, transceiver device identifier, and network channel parameters of the original SMS PDU message to construct basic message tracing information.

[0008] Optionally, the step of performing layered field verification on the standardized PDU data stream to be parsed, identifying encoding anomalies and classifying and marking them to generate anomaly identification results includes: extracting the message header field, address field, data encoding scheme field, user data header field, user data field, and message length field from the standardized PDU data stream to be parsed to obtain a set of fields to be verified; performing integrity verification, byte alignment verification, encoding standardization verification, and field matching verification on the set of fields to be verified in sequence to obtain field verification status features; identifying encoding anomalies in the standardized PDU data stream to be parsed based on the field verification status features, classifying and marking the encoding anomalies, and generating anomaly identification results.

[0009] Optionally, the step of matching the corresponding anomaly repair strategy based on the anomaly identification result, performing differentiated intelligent repair operations for different types of encoded anomalies, and generating a repaired standard PDU message includes: extracting anomaly classification markers from the anomaly identification result; searching for corresponding repair rules in a preset strategy set containing historical repair records based on the anomaly classification markers to obtain a target anomaly repair strategy; parsing the target anomaly repair strategy to obtain repair operation instructions; reconstructing and correcting the anomaly fields in the standardized PDU data stream to be parsed based on the repair operation instructions to obtain a preliminary repaired PDU message; and reorganizing the overall structure and recalculating the length field of the preliminary repaired PDU message to generate a repaired standard PDU message.

[0010] Optionally, the second compliance verification of the repaired standard PDU message, and the decoding of the repaired standard PDU message according to the preset standard parsing logic after the verification passes, outputting readable SMS parsing information, and generating abnormal alarm information when the verification fails, includes: extracting the overall length of the repaired standard PDU message and comparing it with the length of each segment field to obtain the length compliance verification result; when the length compliance verification result is passed, using the standard parsing logic to decode the user data field of the repaired standard PDU message using the character set, outputting readable SMS parsing information; when the length compliance verification result is failed, marking the repaired standard PDU message as an unrepairable abnormal message, and extracting the error code of the unrepairable abnormal message to generate abnormal alarm information.

[0011] Optionally, the associated storage of the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message tracing information to construct the anomaly parsing ledger includes: extracting the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message tracing information, and establishing data mapping relationships between each piece of information; packaging the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message tracing information into structured storage objects according to the data mapping relationships; and writing the structured storage objects into a preset distributed database to generate the anomaly parsing ledger.

[0012] Optionally, the step of sequentially performing integrity verification, byte alignment verification, encoding standardization verification, and field matching verification on the set of fields to be verified to obtain field verification status features includes: extracting the length of each field in the set of fields to be verified and comparing it with the length specified in the protocol to obtain an integrity verification result; calculating the byte boundary offset in the set of fields to be verified and obtaining a byte alignment verification result based on the byte boundary offset; and combining the integrity verification result and the byte alignment verification result to verify the character encoding range and logical association between fields in the set of fields to be verified, thereby generating field verification status features.

[0013] Optionally, the step of parsing the target anomaly repair strategy to obtain repair operation instructions, and reconstructing and correcting the abnormal fields in the standardized PDU data stream to be parsed according to the repair operation instructions to obtain a preliminary repair PDU message includes: extracting garbled character segments from the standardized PDU data stream to be parsed; attempting character set conversion on the garbled character segments according to the repair operation instructions to obtain candidate repair characters; calculating the coherence score between the candidate repair characters and the context semantics; selecting the target repair character based on the coherence score; replacing the garbled character segments with the target repair character; adjusting the offsets of adjacent fields; and generating a preliminary repair PDU message.

[0014] Optionally, calculating the coherence score between the candidate repair character and the context semantics, and selecting the target repair character based on the coherence score, includes: extracting adjacent normal characters before and after the candidate repair character to construct a context semantic fragment; substituting the candidate repair character into the context semantic fragment for language model matching and calculating the matching probability value; using the matching probability value as the coherence score, and selecting the candidate repair character with the highest coherence score as the target repair character.

[0015] Based on the same inventive concept, this invention also provides a system for parsing SMS PDU encoding anomalies. The system includes: a message data acquisition module for acquiring raw SMS PDU messages in a real-time acquisition communication link; a message preprocessing module for preprocessing the raw SMS PDU messages to generate a standardized PDU data stream to be parsed, and simultaneously extracting the acquisition parameters of the raw SMS PDU messages to construct basic message tracing information; a PDU layered verification and anomaly diagnosis module for performing layered field verification on the standardized PDU data stream to be parsed, identifying encoding anomalies and classifying them, and generating anomaly identification results; and an intelligent repair strategy and execution module for... The system matches the anomaly identification results with corresponding anomaly repair strategies, performs differentiated intelligent repair operations for different types of coding anomalies, and generates a repaired standard PDU message. A compliance secondary verification and decoding module performs a compliance secondary verification on the repaired standard PDU message. If the verification passes, the repaired standard PDU message is decoded according to a preset standard parsing logic, outputting readable SMS parsing information. If the verification fails, an anomaly alarm message is generated. A full-link traceability and ledger management module associates and stores the original SMS PDU message, anomaly identification results, repaired standard PDU message, readable SMS parsing information, and basic message traceability information, constructing an anomaly parsing ledger.

[0016] Compared with the prior art, the present invention has the following advantages: This invention removes invalid characters and converts the format of raw SMS protocol data unit messages obtained from real-time access wireless communication network links, and simultaneously extracts the collected parameters to construct traceability information. Combined with a hierarchical field verification mechanism, it conducts multi-dimensional reviews of the data stream to be parsed, including integrity, alignment, and standardization, to accurately identify and classify encoding anomalies generated during transmission, thereby improving the accuracy of message anomaly detection.

[0017] This invention utilizes anomaly identification results to match target anomaly repair strategies in a historical strategy set, performs differentiated reconstruction and error correction operations for different types of coding anomalies, and restructures the overall structure and recalculates the length field after local repair, generating a repaired standard message that conforms to the communication protocol specification, thus realizing the automation and standardization of the message repair process.

[0018] This invention performs a strict secondary check on the length compliance of the repaired standard message, decodes the character set to output readable information while ensuring the legality of the structure, and establishes a data mapping relationship between the original message, the anomaly identification result, the repaired message and the source information and packages them into a distributed database to build a complete anomaly analysis ledger, providing reliable data support for data auditing and fault diagnosis of wireless communication networks.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for parsing SMS PDU encoding anomalies according to an embodiment of the present invention. Figure 2 This is a graph showing the relationship between the cleaning rate and channel quality during the preprocessing stage in an embodiment of the present invention. Figure 3 This is a channel quality index diagram according to an embodiment of the present invention; Figure 4 This is a timing diagram of the compliance secondary verification process according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a parsing system for SMS PDU encoding anomalies according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, not all embodiments. 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.

[0023] Reference Figure 1 One embodiment of the present invention proposes a method for parsing SMS PDU encoding anomalies. It adopts a layered field verification and dynamic repair strategy, combined with a full-link tracing and secondary compliance verification mechanism, which can realize the identification, parsing and adaptive repair of SMS messages with encoding anomalies.

[0024] The method described in this embodiment specifically includes: S1. Obtain raw SMS PDU messages from the real-time acquisition communication link; Specifically, the system connects to a real-time acquisition communication link via a data acquisition interface to capture raw SMS PDU messages transmitted in the wireless communication network in real time. The real-time acquisition communication link refers to the physical or logical channel in the communication network responsible for real-time data transmission, while the raw SMS PDU message refers to a continuous stream of hexadecimal bytes, in cell format, directly intercepted from this channel without any modification.

[0025] S2. Preprocess the original SMS PDU message to generate a standardized PDU data stream to be parsed, and simultaneously extract the collection parameters of the original SMS PDU message to construct basic message traceability information; Optionally, the preprocessing of the original SMS PDU message to generate a standardized PDU data stream to be parsed, and the simultaneous extraction of the collection parameters of the original SMS PDU message to construct basic message tracing information, includes: Invalid null characters and redundant padding characters are extracted and removed from the original SMS PDU message to obtain the cleaned PDU message; The cleaned PDU messages are uniformly converted into standard hexadecimal encoding format to generate a standardized PDU data stream to be parsed; The collection timestamp, transceiver device identifier, and network channel parameters of the original SMS PDU message are collected and combined to construct basic message traceability information.

[0026] Specifically, the relationship between the preprocessing cleaning rate and channel quality is as follows: Figure 2As shown, the raw SMS PDU messages are obtained by accessing the real-time acquisition communication link. The real-time acquisition communication link refers to the physical or logical channel in the communication network responsible for real-time data transmission. The raw SMS PDU message refers to SMS data in cell format, directly intercepted from the communication channel without any modification. After obtaining the raw SMS PDU message, it is preprocessed. The preprocessing process includes extracting invalid empty characters and redundant padding characters from the raw SMS PDU message and removing them to obtain the cleaned PDU message. Invalid empty characters refer to blank placeholder characters in the message that do not carry any business information. Redundant padding characters refer to meaningless characters added to meet specific protocol length requirements. The cleaned PDU message refers to the message containing only the core business data after removing the aforementioned useless characters. To quantify the deduplication degree of the cleaning process, the cleaning rate calculation formula is defined as follows: , in the formula This indicates the cleaning rate. Indicates the number of invalid null characters. This indicates the number of redundant padding characters. This represents the total length of the original SMS PDU message. The formula is based on statistical analysis of 5000 measured data points from communication base stations and is used to assess the redundancy of the message data.

[0027] After character removal, the cleaned PDU messages are uniformly converted into standard hexadecimal encoding format, generating a standardized PDU data stream to be parsed. Standard hexadecimal encoding format refers to a unified encoding rule that maps each four-bit binary value to a corresponding hexadecimal character. The standardized PDU data stream to be parsed refers to a data sequence that has a unified structure after format conversion and awaits subsequent decoding and analysis.

[0028] While processing message data, the collection parameters of the original SMS PDU message are extracted simultaneously. These parameters include the collection timestamp, transceiver device identifier, and network channel parameters. The collection timestamp refers to the precise time record when the message was intercepted. The transceiver device identifier refers to the unique hardware or logical identification code of the sending and receiving devices. Network channel parameters refer to the status data of the physical or logical channel occupied during message transmission. The collection timestamp, transceiver device identifier, and network channel parameters are combined to construct basic message tracing information. Basic message tracing information refers to comprehensive data composed of the above time, device, and channel information used to trace the source of the message. To evaluate the channel transmission quality in the network channel parameters, the channel quality index calculation formula is defined as follows: , in the formula This represents the channel quality index. This represents the signal strength in the network channel parameters. This represents the noise power in the network channel parameters. The formula is based on 300 measured data points from mobile communication networks and is used to quantify the network environment conditions during packet collection. The channel quality index is as follows: Figure 3 As shown.

[0029] For example, the system accesses a real-time acquisition communication link to obtain raw SMS PDU packets. The total length of the raw SMS PDU packet is detected to be 160 bytes. The system extracts invalid null characters and redundant padding characters from the raw SMS PDU packet. The count shows 5 bytes of invalid null characters and 15 bytes of redundant padding characters. The invalid null characters and redundant padding characters are removed to obtain the cleaned PDU packet. The cleaning rate is calculated using the cleaning rate calculation formula. The cleaned PDU messages are uniformly converted into standard hexadecimal encoding format to generate a standardized PDU data stream to be parsed. Simultaneously, the collection parameters of the original SMS PDU messages are extracted. These parameters include the collection timestamp, transceiver identifier, and network channel parameters. In the network channel parameters, the measured signal strength is 100 milliwatts and the noise power is 1 milliwatt. The channel quality index is calculated using the formula... Then, the collected timestamps, transceiver device identifiers, and network channel parameters are combined to construct basic message tracing information.

[0030] S3. Perform hierarchical field verification on the standardized PDU data stream to be parsed, identify encoding anomalies and classify them, and generate anomaly identification results; Optionally, the step of performing hierarchical field validation on the standardized PDU data stream to be parsed, identifying encoding anomalies and classifying and marking them, and generating anomaly identification results includes: Extract the message header field, address field, data encoding scheme field, user data header field, user data field, and message length field from the standardized PDU data stream to be parsed to obtain the set of fields to be verified; The set of fields to be verified is subjected to integrity verification, byte alignment verification, encoding standardization verification, and field matching verification in sequence to obtain the field verification status characteristics; Based on the field verification status features, the encoding anomalies in the standardized PDU data stream to be parsed are identified, the encoding anomalies are classified and marked, and anomaly identification results are generated.

[0031] Specifically, the system receives the standardized PDU data stream to be parsed generated in the aforementioned steps and performs hierarchical field verification. Hierarchical field verification refers to the process of checking the legality and logic of data level by level according to the structural hierarchy of the communication protocol. The system extracts the message header field, address field, data encoding scheme field, user data header field, user data field, and message length field from the standardized PDU data stream to be parsed. The message header field refers to the starting data block indicating the basic attributes and routing information of the message. The address field refers to the identification data containing the communication number of the sender or receiver. The data encoding scheme field refers to the indicator specifying the character set and encoding format used by the user data. The user data header field refers to the header data carrying extended control information such as SMS splitting and reassembly. The user data field refers to the payload carrying the actual SMS text content. The message length field refers to the value recording the theoretical total number of bytes of a specific data segment. The extracted functional fields are summarized to obtain the set of fields to be verified. The set of fields to be verified refers to the group of data to be tested composed of the extracted functional fields.

[0032] The system sequentially performs integrity checks, byte alignment checks, encoding compliance checks, and field matching checks on the set of fields to be verified. Integrity checks refer to the verification process that checks for missing or truncated fields. Byte alignment checks refer to the check operation that verifies whether the field data boundaries conform to the protocol's specified integer multiples of bytes. Encoding compliance checks refer to the review step that verifies whether the field content conforms to the encoding rules of a specific character set. Field matching checks refer to the logical verification that the message length field matches the sum of the actual lengths of all fields. To quantify the degree of length difference in field matching checks, the field matching deviation rate calculation formula is defined as follows: , in the formula This indicates the field matching deviation rate. This indicates the theoretical length recorded in the message length field. Indicates the first field in the set of fields to be validated. The actual length of the parsed field. This indicates the total number of fields actually parsed. This formula is based on statistical analysis of 2000 SMS gateway parsing tests and is used to accurately assess the completeness of the message structure. After the above verification operations, the field verification status characteristics are obtained. The field verification status characteristics refer to comprehensive attribute labels that record whether each verification passed or failed, and the specific deviation values.

[0033] The system identifies encoding anomalies in the standardized PDU data stream to be parsed based on field verification status characteristics. Encoding anomalies refer to erroneous data structures or content that do not conform to communication protocol specifications. After anomalies are identified, they are categorized and marked. The categorization mark refers to a specific category code assigned to the anomaly based on the error type. The system summarizes the above marking information to generate anomaly identification results. The anomaly identification results refer to the final diagnostic output containing the anomaly location, type, and marking code.

[0034] For example, the system receives a standardized PDU data stream to be parsed and extracts the message header field, address field, data encoding scheme field, user data header field, user data field, and message length field, summarizing them to obtain a set of fields to be verified. The system sequentially performs integrity verification, byte alignment verification, encoding standardization verification, and field matching verification on the set of fields to be verified. In the field matching verification stage, the theoretical length recorded in the message length field is 140 bytes. The system counts the lengths of the actual parsed fields in the set of fields to be verified, and there are 5 actual parsed fields with a total length of 133 bytes. The field matching deviation rate is calculated according to the following formula: The system obtains the field verification status feature containing the deviation value. Based on the field verification status feature, it identifies a coding anomaly with inconsistent length in the standardized PDU data stream to be parsed. The system classifies and labels this coding anomaly, assigning a specific code to the length truncation category, and finally generates the anomaly identification result.

[0035] Optionally, the process of sequentially performing integrity verification, byte alignment verification, encoding standardization verification, and field matching verification on the set of fields to be verified to obtain field verification status features includes: Extract the length of each field in the set of fields to be verified and compare it with the length specified in the protocol to obtain the integrity verification result; Calculate the byte boundary offset in the set of fields to be verified, and obtain the byte alignment verification result based on the byte boundary offset; By combining the integrity verification result and the byte alignment verification result, the character encoding range and logical association between fields in the set of fields to be verified are verified, and field verification status features are generated.

[0036] Specifically, the system receives the set of fields to be verified extracted from the standardized protocol data unit data stream in the preceding steps. This set includes message header fields, address fields, data encoding scheme fields, user data header fields, user data fields, and message length fields. Subsequently, the system performs integrity verification on the set of fields to be verified. In this step, the system extracts the actual parsed length of each field in the set of fields to be verified and compares it one by one with the theoretical length predefined in the communication protocol, thereby generating an integrity verification result reflecting whether any fields are missing or truncated.

[0037] The system performs byte alignment checks on the set of fields to be checked. This is done by calculating the byte boundary offsets of the data boundaries of each field in the set to be checked to detect whether the data boundaries meet the protocol's requirement of being integer multiples of bytes, thus obtaining the byte alignment check result. Here, the byte boundary offset refers to the bit deviation of the start or end position of a field relative to the standard byte alignment boundary. After obtaining the above two basic check results, the system combines the integrity check result and the byte alignment check result to perform in-depth verification of the character encoding range and logical relationships between fields in the set of fields to be checked. Finally, it generates a comprehensive attribute label reflecting whether each check passed or failed and the specific deviation value, i.e., the field check status feature. To accurately quantify the degree of encoding standardization and the deviation status of the correlation between fields, the system introduces a field anomaly characterization index calculation formula: , in the formula This represents the field anomaly characterization index, used to comprehensively quantify the severity of coding anomalies in a field. This represents the preset integrity weighting coefficient. This represents the preset alignment weight coefficients. Both coefficients are dimensionless empirical constants, which are preset based on statistical analysis of a large number of historical abnormal packets and in combination with the gateway's parsing performance requirements. The character encoding range deviation refers to the number of characters whose actual extracted character encoding exceeds the set of legal encodings specified in the protocol. The byte boundary offset deviation refers to the number of bits by which the calculated byte boundary offset exceeds the standard alignment boundary.

[0038] For example, the system sequentially verifies the set of fields to be verified. In the integrity verification stage, the system extracts the actual length of the data encoding scheme field in the set of fields to be verified and compares it with the length specified in the protocol to obtain an integrity verification result where the field length is complete. In the byte alignment verification stage, the system calculates that the byte boundary offset of the user data field in the set of fields to be verified exceeds the standard boundary by 2 bits, thus obtaining a byte alignment verification result with alignment deviation. Subsequently, the system combines the above integrity verification result and byte alignment verification result to verify the character encoding range and logical association between fields in the set of fields to be verified. During this process, the system reads a preset integrity weight coefficient of 0.6 and a preset alignment weight coefficient of 0.4. System verification analysis shows that the dimensionless value of the character encoding range deviation is 0, while the dimensionless value of the byte boundary offset deviation is 2. The system substitutes the above dimensionless data into the field anomaly characterization index calculation formula for calculation: The final anomaly index for the field was determined to be 0.8. The system then encapsulated this value along with the corresponding verification result to generate a field verification status feature containing the anomaly data.

[0039] S4. Match the corresponding anomaly repair strategy according to the anomaly identification result, perform differentiated intelligent repair operations for different types of coding anomalies, and generate a repaired standard PDU message; Optionally, the step of matching the corresponding anomaly repair strategy based on the anomaly identification result, performing differentiated intelligent repair operations for different types of encoded anomalies, and generating a repaired standard PDU message includes: Extract the anomaly classification marker from the anomaly identification result, and search for the corresponding repair rule in a preset strategy set containing historical repair records based on the anomaly classification marker to obtain the target anomaly repair strategy; The target anomaly repair strategy is parsed to obtain repair operation instructions. Based on the repair operation instructions, the abnormal fields in the standardized PDU data stream to be parsed are reconstructed and corrected to obtain a preliminary repaired PDU message. The overall structure of the initially repaired PDU message is reorganized and the length field is recalculated to generate a repaired standard PDU message.

[0040] Specifically, the system extracts anomaly classification tags from the anomaly identification results generated in the preceding steps. Anomaly classification tags refer to the specific category codes assigned to anomalies based on error type in the preceding steps. The system then searches for corresponding repair rules in a preset strategy set containing historical repair records based on the anomaly classification tags. This preset strategy set refers to a data warehouse storing methods and corresponding logs for successfully handling various types of coding errors in the past. Repair rules refer to the specific data modification and restoration logic set for a particular type of error. The target anomaly repair strategy is obtained by comparing the classification tags with the records in the set. The target anomaly repair strategy refers to a comprehensive repair scheme specifically designed to handle the current anomaly classification tag, determined after the matching search.

[0041] The system parses the target anomaly repair strategy to obtain repair operation instructions. Repair operation instructions refer to specific computer control commands extracted from the target anomaly repair strategy that can be directly executed by the system. Based on the repair operation instructions, the system reconstructs and corrects abnormal fields in the standardized PDU data stream to be parsed. Abnormal fields refer to data segments in the standardized PDU data stream to be parsed that are found to have encoding errors or format mismatches. After the reconstruction and correction operations are completed, a preliminary repaired PDU message is obtained. A preliminary repaired PDU message refers to message data that has undergone local error elimination but has not yet undergone global structural adjustment. To quantify the degree of abnormal field repair, the repair and error correction rate calculation formula is defined as follows: , in the formula This indicates the error correction rate. This indicates the number of bytes successfully reconstructed and corrected. Indicates the total length of the exception field.

[0042] The system performs overall structural reassembly and length field recalculation on the initially repaired PDU message. Overall structural reassembly refers to the operation of rearranging and aligning the order and boundaries of each field in the initially repaired PDU message according to the communication protocol specifications. Length field recalculation refers to the process of summing the lengths of each actual field after reassembly to obtain the total message length. To ensure the accuracy of the reassembled message length, the formula for recalculating the length field is defined as follows: , in the formula This indicates the total length of the message after recalculation. Indicates the length of the message header field. Indicates the length of the address field. This indicates the length of the data encoding scheme field. Indicates the length of the user data header field. This indicates the length of the repaired user data field. The formula is based on the 3GPP communication protocol standard specification and is used to generate accurate length indication data. After the above reassembly and calculation operations, a repaired standard PDU message is generated. The repaired standard PDU message refers to the final message data that has completed all repair and reassembly operations and conforms to the standard protocol format.

[0043] For example, the system extracts the anomaly classification marker from the anomaly identification result. This anomaly classification marker is detected as a length truncation category code. Based on this anomaly classification marker, the system searches for the corresponding repair rule in a preset strategy set containing historical repair records to obtain the target anomaly repair strategy for length truncation. The system parses the target anomaly repair strategy to obtain repair operation instructions. Based on the repair operation instructions, the system reconstructs and corrects the anomaly fields in the standardized PDU data stream to be parsed. The total length of the anomaly fields is 7 bytes, and the number of bytes successfully reconstructed and corrected is 7 bytes. The repair correction rate is calculated using the formula... After reconstruction and error correction, a preliminarily repaired PDU message was obtained. The system then restructured the preliminarily repaired PDU message and recalculated its length field. During the length field recalculation, the measured lengths were: message header field 2 bytes, address field 12 bytes, data encoding scheme field 1 byte, user data header field 6 bytes, and the repaired user data field length 119 bytes. The length of the user data field was calculated according to the length field recalculation formula. The recalculated total message length is 140 bytes. The system updates this length value into the message structure, ultimately generating a repaired standard PDU message.

[0044] Optionally, the step of parsing the target anomaly repair strategy to obtain repair operation instructions, and reconstructing and correcting the abnormal fields in the standardized PDU data stream to be parsed according to the repair operation instructions to obtain a preliminary repaired PDU message includes: Extract the garbled character segment from the standardized PDU data stream to be parsed, and attempt to convert the garbled character segment according to the repair operation instruction to obtain candidate repair characters; Calculate the coherence score between the candidate repair characters and the context semantics, and filter out the target repair characters based on the coherence score; The garbled character segment is replaced with the target repair character, and the offset of adjacent fields is adjusted to generate a preliminary repair PDU message.

[0045] Specifically, the system receives the standardized protocol data unit data stream identified and marked as having encoding anomalies in the preceding steps, and accurately extracts damaged or structurally mutated garbled character segments from it. These garbled character segments refer to corrupted byte sequences that do not conform to standard communication protocol specifications due to electromagnetic interference, abnormal character padding, or partial field truncation during wireless communication transmission or cross-gateway protocol conversion. The system parses the target anomaly repair strategy obtained from the anomaly classification marker matching and retrieves specific repair operation instructions. Based on these instructions, it invokes several preset commonly used communication character sets to adaptively attempt character set conversion on the garbled character segment, thereby obtaining a series of candidate repair characters that can be used for replacement.

[0046] After the initial conversion, these candidate repair characters need to be filtered to find the target repair character that best fits the overall SMS text logic. To this end, the system introduces a contextual semantic coherence quantification mechanism. This mechanism extracts adjacent normal characters before and after the candidate repair character to construct a contextual semantic fragment, and then substitutes the candidate repair character into this fragment for language model matching, thereby calculating the coherence score between the candidate repair character and the contextual semantics, i.e., the matching probability value. To accurately evaluate and select the best repaired text, the system introduces a semantic association feature evaluation formula based on the matching probability value: , in the formula The coherence score is used to comprehensively evaluate how well the candidate characters fit the context. This represents the first weighting coefficient. This represents the second weighting coefficient. Both coefficients are dimensionless empirical constants that satisfy the constraint that the sum of the two equals one. They are preset during system initialization based on the correlation distribution of historical SMS text training samples. The model matching probability value refers to the conditional probability calculated by substituting the candidate repair character into the context semantic segment and using a preset language model. This value is obtained by statistically analyzing the frequency patterns of the candidate character and the adjacent normal characters. The cosine similarity index refers to the cosine of the angle between the feature vector of the candidate repair character and the context word segmentation vector in a multi-dimensional vector space. It is obtained by calculating the ratio of the dot product of the two vectors to the product of their magnitudes. After calculating the coherence score of each candidate repair character, the system sorts them in descending order and selects the candidate repair character at the top of the sorted list as the final target repair character.

[0047] The target repair character is used to replace the garbled character segment in the original data stream, and the offset of adjacent fields is adaptively and dynamically adjusted according to the difference in byte length before and after the replacement, thereby realigning the data boundaries and generating a preliminary repair PDU message that eliminates local encoding damage.

[0048] For example, the system performs anomaly field reconstruction and error correction on the received standardized protocol data unit data stream. The system extracts garbled character segments caused by protocol conversion variations and attempts character set conversion according to the repair operation instructions, obtaining two different candidate repair characters. The system then extracts adjacent normal characters before and after the garbled character segment to construct a contextual semantic fragment. During this process, the system reads a preset first weight coefficient of 0.7 and a second weight coefficient of 0.3. After system language model matching and vector space calculation, the model matching probability value of the first candidate repair character is 0.8, and the cosine similarity index is 0.6; the model matching probability value of the second candidate repair character is 0.3, and the cosine similarity index is 0.4. The system substitutes the two sets of dimensionless data into the semantic association feature evaluation formula to calculate the coherence score. The calculation process for the first candidate repair character is as follows: The calculation process for the second candidate repair character is as follows: The system compares the coherence scores and selects the first candidate repair character with a score of 0.74 as the target repair character. Finally, the system uses this target repair character to replace the original garbled character segment and adaptively adjusts the boundary offset of adjacent fields forward by 1 byte, successfully generating a preliminary repair protocol data unit message with a tight structure and semantic coherence.

[0049] Optionally, calculating the coherence score between the candidate repair characters and the context semantics, and filtering the target repair characters based on the coherence score, includes: Extract the adjacent normal characters before and after the candidate repair character to construct a contextual semantic fragment; The candidate repair characters are substituted into the context semantic fragment for language model matching, and the matching probability value is calculated. The matching probability value is used as the coherence score, and the candidate repair character that ranks first in the coherence score is selected as the target repair character.

[0050] Specifically, the system first receives the candidate repair characters obtained in the preceding steps and performs a coherence score calculation between the candidate repair characters and the contextual semantics to filter out the target repair characters. In the specific operation, the system first extracts the adjacent normal characters before and after the candidate repair characters to construct a contextual semantic fragment. Here, the contextual semantic fragment refers to the readable text sequence adjacent to the position of the garbled character segment in the original protocol data unit message stream, which is used to provide the contextual background of the current position.

[0051] The candidate repair characters are substituted into the contextual semantic fragment for language model matching, and the corresponding matching probability value is calculated. Here, the language model refers to a statistical language model pre-trained on massive amounts of wireless communication SMS text, capable of predicting the probability of a specific character sequence appearing in real communication scenarios. To more comprehensively evaluate the semantic fit of candidate repair characters in specific industry contexts or professional communication scenarios, the system introduces a semantic association deviation correction formula based on the matching probability value: , in the formula This represents the final matching probability value, which is the consistency score used as the basis for selection. This represents the weight coefficients of the basic language model. These represent semantic environment correction coefficients. Both coefficients are dimensionless empirical constants that are preset in the system configuration according to the SMS service type, and their sum is equal to one. This represents the base probability value obtained by direct matching from the language model. The domain semantic relevance index refers to the degree of association between the candidate repair characters and the keyword feature vectors of the specific business domain to which the current communication link belongs in a multi-dimensional space. It is obtained by calculating the dot product of the feature vectors. The system uses this matching probability value as the coherence score and sorts all candidate repair characters from high to low coherence scores.

[0052] The candidate character that ranks first in the coherence score is selected as the target character for repair and output to the subsequent character replacement and boundary alignment modules.

[0053] For example, the system evaluates the semantic coherence score for two different candidate repair characters. The system first extracts adjacent normal characters before and after the garbled character segment to construct a complete contextual semantic fragment. During this process, the system reads a preset base language model weight coefficient of 0.8 and a semantic environment correction coefficient of 0.2. After system model matching and vector space calculation, the base probability value of the first candidate repair character is 0.90, and the domain semantic relevance index is 0.85; the base probability value of the second candidate repair character is 0.40, and the domain semantic relevance index is 0.50. The system substitutes the two sets of dimensionless data into the semantic association deviation correction formula to calculate the final matching probability value. The calculation process for the first candidate repair character is as follows: The calculation process for the second candidate repair character is as follows: The system uses the calculated final matching probability values ​​of 0.89 and 0.42 as the coherence scores. By sorting the two sets of scores in descending order, the system selects the first candidate repair character with a coherence score of 0.89 as the target repair character, thus completing the high-precision semantic repair screening of the encoded abnormal field.

[0054] S5. Perform a second compliance check on the repaired standard PDU message. If the check passes, decode the repaired standard PDU message according to the preset standard parsing logic and output readable SMS parsing information. If the check fails, generate an abnormal alarm message. Optionally, the second compliance verification of the repaired standard PDU message, and the decoding of the repaired standard PDU message according to the preset standard parsing logic after the verification passes, outputting readable SMS parsing information, and generating abnormal alarm information when the verification fails, includes: The overall length of the repaired standard PDU message is extracted and compared with the length of each segment field to obtain the length compliance verification result. When the length compliance verification result is passed, the user data field of the repaired standard PDU message is decoded using the standard parsing logic to output readable SMS parsing information. When the length compliance verification result is unsuccessful, the repaired standard PDU message is marked as an unrepairable abnormal message, and the error code of the unrepairable abnormal message is extracted to generate an abnormal alarm message.

[0055] Specifically, the sequence diagram of the compliance secondary verification process is as follows: Figure 4 As shown, the system receives the repaired standard PDU message generated in the preceding steps and performs a secondary compliance check. The system extracts the overall length of the repaired standard PDU message and compares it with the length of each segment field. The overall length refers to the total number of bytes in the message header data. The length of each segment field refers to the number of bytes actually occupied by each independent functional module within the message. After the comparison operation is completed, the length compliance check result is obtained. The length compliance check result indicates whether the overall length is completely consistent with the sum of the lengths of each segment field. To accurately quantify the degree of difference in length comparison, the length compliance judgment index calculation formula is defined as follows: , in the formula This indicates the index for determining length compliance. This indicates the overall length of the standard PDU message after repair. This indicates the first standard PDU message after repair. The length of each segment field in the item. This indicates the total number of items in the segmented fields. This formula is used to rigorously verify the structural validity of the reassembled message. When the length compliance index is zero, the verification is considered successful; otherwise, it is considered unsuccessful.

[0056] When the length compliance check passes, the system uses standard parsing logic to decode the user data fields of the repaired standard PDU message using a character set. Standard parsing logic refers to a fixed algorithm process that converts hexadecimal data into human-readable characters according to the encoding mapping relationship specified in the communication protocol. Character set decoding refers to the process of restoring a byte stream of a specific encoding format to its corresponding text symbols. After decoding, the system outputs readable SMS parsing information. Readable SMS parsing information refers to plaintext text data that can be directly presented to the user after conversion.

[0057] When the length compliance check fails, the system marks the repaired standard PDU message as an unrepairable abnormal message. An unrepairable abnormal message refers to corrupted data that still fails to meet the protocol format requirements after previous repair operations. The system extracts the error code from the unrepairable abnormal message. The error code is a predefined combination of numbers or letters used internally to identify the reason for a specific check failure. The system generates an exception alarm message based on the extracted error code. The exception alarm message is a notification to operations and maintenance personnel that data processing has failed, along with detailed error information.

[0058] For example, the system extracts the overall length of the repaired standard PDU message and reads it as 140 bytes. The system then calculates the length of each segment field in the repaired standard PDU message, which consists of five segment fields with lengths of 2 bytes, 12 bytes, 1 byte, 6 bytes, and 119 bytes respectively. The length compliance index is then calculated using the formula. =0, resulting in a length compliance judgment index of 0 bytes. Since this index is zero, the system determines that the length compliance check result is passed. The system uses standard parsing logic to decode the user data field of the repaired standard PDU message, successfully converting the hexadecimal data into plaintext text, and finally outputting readable SMS parsing information.

[0059] S6. Associate and store the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information and message basic traceability information to construct an anomaly parsing ledger.

[0060] Optionally, the associated storage of the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message source information, constructing an anomaly parsing ledger, includes: Extract the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information and basic message source information, and establish a data mapping relationship between each piece of information; Based on the data mapping relationship, the original SMS PDU message, the anomaly identification result, the repaired standard PDU message, the readable SMS parsing information, and the basic source information of the message are packaged into a structured storage object; The structured storage object is written into a preset distributed database to generate an anomaly analysis ledger.

[0061] Specifically, the process involves extracting the original SMS PDU messages, anomaly identification results, repaired standard PDU messages, readable SMS parsing information, and basic message tracing information obtained in the preceding steps, and establishing data mapping relationships between these information items. Data mapping relationships refer to the corresponding connection rules established between different data entities based on common business identifiers or timestamps.

[0062] Based on the data mapping relationship, the original SMS PDU messages, anomaly identification results, repaired standard PDU messages, readable SMS parsing information, and basic message source information are packaged into structured storage objects. A structured storage object refers to a data collection organized according to a specific format specification and containing multi-dimensional attribute fields to facilitate database storage and retrieval. To accurately assess the size of the packaged data, the total capacity calculation formula for the structured storage object is defined as follows: , in the formula This represents the total capacity of the structured storage object. This indicates the capacity of the original SMS PDU message. This indicates the capacity of the anomaly detection results. This indicates the capacity of the standard PDU message after repair. This indicates the capacity of the readable SMS message. This indicates the capacity of the basic traceability information for the message. This indicates the index capacity generated by the data mapping relationship.

[0063] After calculating the total capacity, the system writes the structured storage objects into a pre-configured distributed database. The pre-configured distributed database refers to a pre-configured system that works collaboratively across multiple physical servers to store massive amounts of data. After the data writing operation is complete, the system generates an anomaly resolution log. The anomaly resolution log is a comprehensive archive that records the entire lifecycle of a message, from acquisition and anomaly identification to repair and final parsing.

[0064] For example, the system extracts the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message tracing information, and establishes a data mapping relationship between these pieces of information based on a common business identifier. The system packages the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message tracing information into a structured storage object according to the data mapping relationship. When calculating the total capacity of the structured storage object, the measured capacity is 160 bytes for the original SMS PDU message, 50 bytes for the anomaly identification result, 140 bytes for the repaired standard PDU message, 100 bytes for the readable SMS parsing information, 80 bytes for the basic message tracing information, and 20 bytes for the index generated by the data mapping relationship. The total capacity of the structured storage object is calculated according to the formula for calculating the total capacity of the structured storage object. The total capacity of the structured storage object is 550 bytes. The system writes this 550-byte structured storage object into a pre-defined distributed database, ultimately generating an exception resolution ledger.

[0065] Based on the same inventive concept, such as Figure 5 As shown, the present invention also provides a parsing system for SMS PDU encoding anomalies, the system comprising: The message data acquisition module is used to acquire raw SMS PDU messages in the real-time acquisition communication link; The message preprocessing module is used to preprocess the original SMS PDU message, generate a standardized PDU data stream to be parsed, and simultaneously extract the collection parameters of the original SMS PDU message to construct basic message traceability information. The PDU hierarchical verification and anomaly diagnosis module is used to perform hierarchical field verification on the standardized PDU data stream to be parsed, identify encoding anomalies and classify them, and generate anomaly identification results. The intelligent repair strategy and execution module is used to match the corresponding abnormality repair strategy according to the abnormality identification result, perform differentiated intelligent repair operations for different types of coded abnormalities, and generate a repaired standard PDU message. The compliance secondary verification and decoding module is used to perform compliance secondary verification on the repaired standard PDU message. After the verification is passed, the repaired standard PDU message is decoded according to the preset standard parsing logic and readable SMS parsing information is output. When the verification fails, an abnormal alarm message is generated. The end-to-end traceability and ledger management module is used to associate and store the original SMS PDU messages, anomaly identification results, repaired standard PDU messages, readable SMS parsing information and basic message traceability information, and to build an anomaly parsing ledger.

[0066] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0067] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for parsing SMS PDU encoding anomalies, characterized in that, The method includes: Acquire raw SMS PDU messages from the real-time acquisition communication link; The original SMS PDU message is preprocessed to generate a standardized PDU data stream to be parsed, and the collection parameters of the original SMS PDU message are extracted simultaneously to construct basic message traceability information; The standardized PDU data stream to be parsed is subjected to hierarchical field verification, encoding anomalies are identified and classified, and anomaly identification results are generated. Based on the anomaly identification results, a corresponding anomaly repair strategy is matched, and differentiated intelligent repair operations are performed for different types of coding anomalies to generate a repaired standard PDU message. The repaired standard PDU message undergoes a second compliance check. If the check passes, the repaired standard PDU message is decoded according to the preset standard parsing logic, and readable SMS parsing information is output. If the check fails, an abnormal alarm message is generated. The original SMS PDU messages, anomaly identification results, repaired standard PDU messages, readable SMS parsing information, and basic message source information are associated and stored to construct an anomaly parsing ledger.

2. The method for parsing SMS PDU encoding anomalies according to claim 1, characterized in that, The preprocessing of the original SMS PDU message to generate a standardized PDU data stream to be parsed, and the simultaneous extraction of the collection parameters of the original SMS PDU message to construct basic message tracing information, includes: Invalid null characters and redundant padding characters are extracted and removed from the original SMS PDU message to obtain the cleaned PDU message; The cleaned PDU messages are uniformly converted into standard hexadecimal encoding format to generate a standardized PDU data stream to be parsed; The collection timestamp, transceiver device identifier, and network channel parameters of the original SMS PDU message are collected and combined to construct basic message traceability information.

3. The method for parsing SMS PDU encoding anomalies according to claim 1, characterized in that, The step of performing hierarchical field validation on the standardized PDU data stream to be parsed, identifying encoding anomalies and classifying and marking them, and generating anomaly identification results includes: Extract the message header field, address field, data encoding scheme field, user data header field, user data field, and message length field from the standardized PDU data stream to be parsed to obtain the set of fields to be verified; The set of fields to be verified is subjected to integrity verification, byte alignment verification, encoding standardization verification, and field matching verification in sequence to obtain the field verification status characteristics; Based on the field verification status features, the encoding anomalies in the standardized PDU data stream to be parsed are identified, the encoding anomalies are classified and marked, and anomaly identification results are generated.

4. The method for parsing SMS PDU encoding anomalies according to claim 1, characterized in that, The step of matching the corresponding anomaly repair strategy based on the anomaly identification result, performing differentiated intelligent repair operations for different types of encoded anomalies, and generating a repaired standard PDU message includes: Extract the anomaly classification marker from the anomaly identification result, and search for the corresponding repair rule in a preset strategy set containing historical repair records based on the anomaly classification marker to obtain the target anomaly repair strategy; The target anomaly repair strategy is parsed to obtain repair operation instructions. Based on the repair operation instructions, the abnormal fields in the standardized PDU data stream to be parsed are reconstructed and corrected to obtain a preliminary repaired PDU message. The overall structure of the initially repaired PDU message is reorganized and the length field is recalculated to generate a repaired standard PDU message.

5. The method for parsing SMS PDU encoding anomalies according to claim 1, characterized in that, The process involves performing a secondary compliance check on the repaired standard PDU message. If the check passes, the repaired standard PDU message is decoded according to a preset standard parsing logic to output readable SMS parsing information. If the check fails, an error alarm is generated, including: The overall length of the repaired standard PDU message is extracted and compared with the length of each segment field to obtain the length compliance verification result. When the length compliance verification result is passed, the user data field of the repaired standard PDU message is decoded using the standard parsing logic to output readable SMS parsing information. When the length compliance verification result is unsuccessful, the repaired standard PDU message is marked as an unrepairable abnormal message, and the error code of the unrepairable abnormal message is extracted to generate an abnormal alarm message.

6. The method for parsing SMS PDU encoding anomalies according to claim 1, characterized in that, The associated storage includes the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information, and basic message source information, constructing an anomaly parsing ledger, including: Extract the original SMS PDU message, anomaly identification result, repaired standard PDU message, readable SMS parsing information and basic message source information, and establish a data mapping relationship between each piece of information; Based on the data mapping relationship, the original SMS PDU message, the anomaly identification result, the repaired standard PDU message, the readable SMS parsing information, and the basic source information of the message are packaged into a structured storage object; The structured storage object is written into a preset distributed database to generate an anomaly analysis ledger.

7. The method for parsing SMS PDU encoding anomalies according to claim 3, characterized in that, The process of sequentially performing integrity verification, byte alignment verification, encoding standardization verification, and field matching verification on the set of fields to be verified yields the following field verification status characteristics: Extract the length of each field in the set of fields to be verified and compare it with the length specified in the protocol to obtain the integrity verification result; Calculate the byte boundary offset in the set of fields to be verified, and obtain the byte alignment verification result based on the byte boundary offset; By combining the integrity verification result and the byte alignment verification result, the character encoding range and logical association between fields in the set of fields to be verified are verified, and field verification status features are generated.

8. The method for parsing SMS PDU encoding anomalies according to claim 4, characterized in that, The process involves parsing the target anomaly repair strategy to obtain repair operation instructions, and then reconstructing and correcting the abnormal fields in the standardized PDU data stream to be parsed based on the repair operation instructions to obtain a preliminary repaired PDU message, including: Extract the garbled character segment from the standardized PDU data stream to be parsed, and attempt to convert the garbled character segment according to the repair operation instruction to obtain candidate repair characters; Calculate the coherence score between the candidate repair characters and the context semantics, and filter out the target repair characters based on the coherence score; The garbled character segment is replaced with the target repair character, and the offset of adjacent fields is adjusted to generate a preliminary repair PDU message.

9. The method for parsing SMS PDU encoding anomalies according to claim 8, characterized in that, The calculation of the coherence score between the candidate repair characters and the context semantics, and the selection of target repair characters based on the coherence score, includes: Extract the adjacent normal characters before and after the candidate repair character to construct a contextual semantic fragment; The candidate repair characters are substituted into the context semantic fragment for language model matching, and the matching probability value is calculated. The matching probability value is used as the coherence score, and the candidate repair character that ranks first in the coherence score is selected as the target repair character.

10. A system for parsing SMS PDU encoding anomalies, applied to the method for parsing SMS PDU encoding anomalies as described in any one of claims 1-9, characterized in that, The system includes: The message data acquisition module is used to acquire raw SMS PDU messages in the real-time acquisition communication link; The message preprocessing module is used to preprocess the original SMS PDU message, generate a standardized PDU data stream to be parsed, and simultaneously extract the collection parameters of the original SMS PDU message to construct basic message traceability information. The PDU hierarchical verification and anomaly diagnosis module is used to perform hierarchical field verification on the standardized PDU data stream to be parsed, identify encoding anomalies and classify them, and generate anomaly identification results. The intelligent repair strategy and execution module is used to match the corresponding abnormality repair strategy according to the abnormality identification result, perform differentiated intelligent repair operations for different types of coded abnormalities, and generate a repaired standard PDU message. The compliance secondary verification and decoding module is used to perform compliance secondary verification on the repaired standard PDU message. After the verification is passed, the repaired standard PDU message is decoded according to the preset standard parsing logic and readable SMS parsing information is output. When the verification fails, an abnormal alarm message is generated. The end-to-end traceability and ledger management module is used to associate and store the original SMS PDU messages, anomaly identification results, repaired standard PDU messages, readable SMS parsing information and basic message traceability information, and to build an anomaly parsing ledger.

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