Method, device, equipment, storage medium and computer program product for automatically diagnosing session initiation protocol signaling system problems

Abstract

This application relates to the field of communication technology, and in particular to an automatic diagnosis method, apparatus, device, storage medium, and computer program product for problems in a session initiation protocol signaling system. The method acquires session initiation protocol signaling data from multiple signaling entities and constructs a signaling dataset based on this data; extracts structured fields related to extended fields in the signaling dataset to generate a structured signaling representation; performs semantic mapping on the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with a unified semantic representation; acquires receiver capability information, determines a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapts the normalized signaling according to the signaling transmission strategy to obtain the target signaling; performs anomaly identification based on the target signaling to obtain anomaly identification results, and performs root cause analysis on the signaling interaction relationships based on the anomaly identification results to generate diagnostic results.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, device, storage medium, and computer program product for automatic diagnosis of problems in a session initiation protocol signaling system. Background Technology

[0002] Session initiation protocols, as key protocols for session establishment and control in real-time communication systems, are widely deployed among various signaling entities such as terminals, servers, and gateways. Due to the collaboration between multiple vendors, versions, and forms of devices in real-world network environments, signaling messages typically carry vendor-defined extended fields in addition to basic fields, leading to differences in signaling expression between different signaling entities. Cross-device signaling sharing and adaptation often rely on static configuration and rule maintenance by operations and maintenance personnel based on experience, such as using point-to-point mapping rules or manually adjusting field mapping relationships. When devices are added, upgraded, or the capabilities of peer devices change, the existing static configuration methods need to be readjusted, and sharing failures are easily caused by rule omissions or ambiguities, resulting in poor stability of signaling interaction in cross-vendor environments. Therefore, improving the stability of signaling interaction in cross-vendor environments has become an urgent technical problem to be solved. Summary of the Invention

[0003] The main objective of this application is to provide an automatic diagnosis method, apparatus, device, storage medium, and computer program product for Session Initiation Protocol (SIP) signaling system problems, aiming to solve the technical problem of how to improve the stability of signaling interaction in cross-vendor environments.

[0004] To achieve the above objectives, this application provides an automatic diagnostic method for problems in a session initiation protocol signaling system, the method comprising the following steps: Acquire session initiation protocol signaling data from multiple signaling entities, and construct a signaling dataset based on the session initiation protocol signaling data; Extract the structured fields related to the extended fields in the signaling dataset to generate a structured signaling representation; Based on a preset semantic association library, semantic mapping is performed on the extended fields in the structured signaling representation to obtain normalized signaling with unified semantic representation; Obtain receiving side capability information, determine signaling transmission strategy based on the receiving side capability information and the normalized signaling, and perform adaptation processing on the normalized signaling according to the signaling transmission strategy to obtain target signaling; Anomaly identification is performed based on the target signaling to obtain anomaly identification results. Root cause analysis is then performed on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results.

[0005] In one embodiment, the step of extracting structured fields related to extended fields in the signaling dataset and generating a structured signaling representation includes: Based on a preset signaling parsing model, the signaling messages in the signaling dataset are processed by sequence parsing, and field identification information and field content information used to represent the extended field are output, as well as session association field information associated with the extended field are output. Based on the field identifier information, the field content information, and the session association field information, the extended fields are organized into a field-level structured structure to obtain a set of structured fields; The structured field set is combined and encoded with the basic fields of the signaling message, and the association between the structured field set and the corresponding signaling message is established to obtain a structured signaling representation.

[0006] In one embodiment, the step of semantically mapping the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with a unified semantic representation includes: Based on the structured signaling representation, a set of extended fields to be mapped is determined, and the field identifier information, field value information, and basic signaling field information associated with each extended field are extracted. Based on the field identification information, semantic tag information and / or semantic mapping rules corresponding to the extended field are retrieved from the preset semantic association library, and the target unified semantic identifier is determined based on the semantic mapping rules; Based on the target unified semantic identifier, the extended fields are semantically labeled and / or semantically replaced, and the semantically labeled and / or semantically replaced extended fields are combined with the basic signaling field information to obtain normalized signaling with unified semantic representation.

[0007] In one embodiment, the steps of obtaining receiver capability information, determining a signaling delivery strategy based on the receiver capability information and the normalized signaling, and adapting the normalized signaling according to the signaling delivery strategy to obtain the target signaling include: Obtain the capability information corresponding to the receiving side, and determine the receiving side's support for unified semantic representation and the carrying of necessary fields based on the capability information to obtain the receiving side capability information; Based on the receiving side capability information and the normalized signaling, a signaling transmission strategy corresponding to the receiving side is determined by a preset strategy. The signaling transmission strategy is used to indicate the transmission method and adaptation method of the normalized signaling. The normalized signaling is adapted according to the signaling transmission strategy to obtain the target signaling. When the receiving side supports unified semantic representation, the normalized signaling is directly transmitted. When the receiving side does not support unified semantic representation, the normalized signaling is converted into a receiving side compatible format through a preset format conversion model. When the receiving side lacks necessary fields, the necessary fields are completed based on the preset semantic association library.

[0008] In one embodiment, the step of obtaining an anomaly identification result based on the target signaling, and performing root cause analysis on the signaling interaction relationship based on the anomaly identification result to generate a diagnostic result includes: Based on the target signaling, a signaling flow feature is constructed to characterize the signaling timing correlation, and the signaling flow feature is input into a preset anomaly detection model to obtain the corresponding anomaly identification result; Based on the anomaly identification results, signaling entities and interaction records related to the anomaly are determined from the target signaling, and a signaling interaction relationship representation is constructed to characterize the association between the signaling entities and the interaction records. The signaling interaction relationship is input into a preset root cause analysis model to obtain root cause analysis results, and a diagnostic result is generated based on the root cause analysis results.

[0009] In one embodiment, after the steps of obtaining an anomaly identification result based on the target signaling and performing root cause analysis on the signaling interaction relationship based on the anomaly identification result to generate a diagnostic result, the method further includes: Based on the diagnostic results, a processing strategy is determined, and the target signaling is configured and adjusted based on the processing strategy. The diagnostic results and the execution results of the processing strategy are written into a preset feedback dataset; Based on the pre-defined feedback dataset after it has been written, the model parameters related to the anomaly identification and the root cause analysis are updated.

[0010] Furthermore, to achieve the above objectives, this application also proposes an automatic diagnostic device for Session Initiation Protocol (SIP) signaling system problems, the automatic diagnostic device for Session Initiation Protocol (SIP) signaling system problems comprising: The data acquisition module is used to acquire session initiation protocol signaling data from multiple signaling entities and construct a signaling dataset based on the session initiation protocol signaling data; The structuring module is used to extract structured fields related to the extended fields in the signaling dataset and generate a structured signaling representation; The semantic mapping module is used to perform semantic mapping on the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with a unified semantic representation; The signaling adaptation module is used to obtain receiver capability information, determine a signaling transmission strategy based on the receiver capability information and the normalized signaling, and perform adaptation processing on the normalized signaling according to the signaling transmission strategy to obtain the target signaling. The diagnostic results module is used to identify anomalies based on the target signaling to obtain anomaly identification results, and to perform root cause analysis on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results.

[0011] Furthermore, to achieve the above objectives, this application also proposes an automatic diagnostic device for Session Initiation Protocol (SIP) signaling system problems. The device includes: a memory, a processor, and an automatic diagnostic program for Session Initiation Protocol (SIP) signaling system problems stored in the memory and executable on the processor. The automatic diagnostic program for Session Initiation Protocol (SIP) signaling system problems is configured to implement the steps of the automatic diagnostic method for Session Initiation Protocol (SIP) signaling system problems as described above.

[0012] In addition, to achieve the above objectives, this application also proposes a storage medium storing an automatic diagnostic program for Session Initiation Protocol (SIP) signaling system problems. When the SIP signaling system problem automatic diagnostic program is executed by a processor, it implements the steps of the automatic diagnostic method for SIP signaling system problems as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the automatic diagnosis method for session initiation protocol signaling system problems as described above.

[0014] This application acquires session initiation protocol signaling data from multiple signaling entities and constructs a signaling dataset based on this data. It extracts structured fields related to extended fields in the signaling dataset to generate a structured signaling representation. Based on a pre-defined semantic association library, it performs semantic mapping on the extended fields in the structured signaling representation to obtain normalized signaling with a unified semantic representation. It acquires receiver capability information, determines a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapts the normalized signaling according to the signaling transmission strategy to obtain the target signaling. Based on the target signaling, it performs anomaly identification to obtain anomaly identification results, and based on the anomaly identification results, it performs root cause analysis on the signaling interaction relationships to generate diagnostic results. This application constructs a signaling dataset by acquiring session initiation protocol signaling data from multiple signaling entities, providing a unified data input for cross-entity interaction. It extracts structured content related to extended fields from the signaling dataset and forms a structured signaling representation, ensuring consistent and processable expression for the extended fields. Then, based on a pre-defined semantic association library, it performs semantic mapping on the extended fields to generate normalized signaling with a unified semantic representation, thus mitigating semantic inconsistencies caused by differences in extended field expressions from different vendors. Furthermore, it introduces receiver capability information and combines it with the normalized signaling to determine the signaling transmission strategy. The normalized signaling is then adapted according to the strategy to obtain target signaling that matches the receiver's capabilities, thereby reducing interaction failures caused by incompatibility. Finally, it identifies anomalies in the target signaling and performs root cause analysis based on signaling interaction relationships to generate diagnostic results, enabling anomalies to be identified and attributed to promote problem convergence, thereby enhancing the stability of cross-vendor signaling interaction. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the first embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application; Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application; Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application; Figure 4 This is a schematic diagram of the module structure of the automatic diagnostic device for session initiation protocol signaling system problems in an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the automatic diagnosis method for session initiation protocol signaling system problems in the embodiments of this application.

[0016] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0018] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0019] It should be noted that the Session Initiation Protocol (SIP), as a key protocol for session establishment and control in real-time communication systems, is widely deployed among various signaling entities such as terminals, servers, and gateways. Due to the collaboration between devices from multiple vendors, versions, and forms in real-world network environments, signaling messages typically carry vendor-defined extended fields in addition to basic fields, leading to differences in signaling expression between different signaling entities. Cross-device signaling sharing and adaptation often rely on static configuration and rule maintenance by operations and maintenance personnel based on experience, such as using point-to-point mapping rules or manually adjusting field mapping relationships. When devices are added, upgraded, or the capabilities of peer devices change, the existing static configuration methods need to be readjusted, and sharing failures are easily caused by rule omissions or ambiguities, resulting in poor stability of signaling interaction in cross-vendor environments. Therefore, improving the stability of signaling interaction in cross-vendor environments has become an urgent technical problem to be solved.

[0020] The main solution of this application is as follows: First, acquire session initiation protocol signaling data from multiple signaling entities and construct a signaling dataset based on this data. Second, extract structured fields related to extended fields in the signaling dataset to generate a structured signaling representation. Third, perform semantic mapping on the extended fields in the structured signaling representation based on a pre-defined semantic association library to obtain normalized signaling with a unified semantic representation. Fourth, acquire receiver capability information, determine a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapt the normalized signaling according to the signaling transmission strategy to obtain the target signaling. Fifth, perform anomaly identification based on the target signaling to obtain anomaly identification results, and perform root cause analysis on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results.

[0021] This application constructs a signaling dataset by acquiring session initiation protocol signaling data from multiple signaling entities, providing a unified data input for cross-entity interaction. It extracts structured content related to extended fields from the signaling dataset and forms a structured signaling representation, ensuring consistent and processable expression for the extended fields. Then, based on a pre-defined semantic association library, it performs semantic mapping on the extended fields to generate normalized signaling with a unified semantic representation, thus mitigating semantic inconsistencies caused by differences in extended field expressions from different vendors. Furthermore, it introduces receiver capability information and combines it with the normalized signaling to determine the signaling transmission strategy. The normalized signaling is then adapted according to the strategy to obtain target signaling that matches the receiver's capabilities, thereby reducing interaction failures caused by incompatibility. Finally, it identifies anomalies in the target signaling and performs root cause analysis based on signaling interaction relationships to generate diagnostic results, enabling anomalies to be identified and attributed to promote problem convergence, thereby enhancing the stability of cross-vendor signaling interaction.

[0022] It should be noted that the executing entity of the method in this embodiment can be a computing service device with data processing, network communication, and program execution functions, or it can be the aforementioned automatic diagnostic device for session initiation protocol signaling system problems with the same or similar functions. This embodiment and the following embodiments will be described using an automatic diagnostic device for session initiation protocol signaling system problems as an example.

[0023] Based on this, a first embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application is proposed. Please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application.

[0024] In this embodiment, the method includes the following steps: S1: Obtain session initiation protocol signaling data from multiple signaling entities, and construct a signaling dataset based on the session initiation protocol signaling data; It should be noted that the Session Initiation Protocol (SIP) is a signaling protocol used for establishing, modifying, and releasing communication sessions. A signaling entity is a network-side or device-side functional entity that participates in SIP signaling interactions. Session Initiation Protocol signaling data is SIP signaling-related data generated or forwarded by the signaling entity. Extension fields are extension headers / information defined by the vendor or system and appended to the signaling message, in addition to the standard SIP fields. A signaling dataset is a collection of data formed by aggregating and grouping the collected Session Initiation Protocol signaling data according to a preset organization method.

[0025] Specifically, in the operating environment of the Session Initiation Protocol (SIP) signaling system, distributed acquisition agents are deployed on the signaling device side or network nodes to collect SIP signaling generated or forwarded by multiple signaling entities in real time. The collected SIP signaling data at least covers standard signaling messages, vendor extended fields, and associated fields used to characterize the interaction context, and retains data source information corresponding to different signaling entities to support subsequent organization and processing based on session association.

[0026] Furthermore, the acquisition side can adopt a passive listening method of port mirroring, actively obtain device logs through interface calls, or be compatible with SIP transmission of different bearer forms through protocol adaptation. After the acquisition is completed, the session initial protocol signaling data from each signaling entity is aggregated and collected to form a signaling dataset. The signaling dataset is used to carry the unified input of signaling messages, extended fields and related fields required for subsequent steps.

[0027] By acquiring session initial protocol signaling data from multiple signaling entities and constructing a signaling dataset, signaling messages, extended fields, and session association fields can be uniformly aggregated and organized within the same dataset. This provides a consistent data input foundation for subsequent structured processing around extended fields and cross-entity session association analysis. Simultaneously, by combining the deployment of distributed acquisition agents on the device side or network nodes and compatibility with different acquisition / bearing methods, multi-source signaling data can be continuously and stably acquired and incorporated into the signaling dataset, thereby reducing the uncertainty of subsequent processing caused by data loss or fragmented sources.

[0028] S2: Extract the structured fields related to the extended fields in the signaling dataset and generate a structured signaling representation; S3: Based on the preset semantic association library, perform semantic mapping on the extended fields in the structured signaling representation to obtain normalized signaling with unified semantic representation; It should be noted that structured fields are key field information extracted from unstructured signaling and can be organized and processed according to field dimensions. Structured signaling representation is the signaling representation form formed after the key signaling fields are organized in a structured manner. The pre-defined semantic association library is a pre-set knowledge base used to provide the basis for semantic mapping of extended fields. Semantic mapping refers to the process of mapping extended fields from different vendors to a unified semantic space under the constraints of the semantic association library. Normalized signaling with unified semantic representation is the signaling representation expressed in a unified semantic space after the extended fields have undergone semantic mapping.

[0029] Specifically, the signaling messages in the signaling dataset are used as input to a pre-trained language model to parse the signaling text. During the parsing process, the model extracts key fields from the signaling and identifies the corresponding semantic label information for the extended fields. It also obtains key fields such as session-related identifier fields and media-related fields. Subsequently, the extraction results are organized according to the field dimension to form a structured signaling representation that characterizes the signaling messages and their extended fields.

[0030] Furthermore, after obtaining the structured signaling representation, a preset semantic association library is invoked to perform semantic mapping processing on the extended fields therein. Specifically, based on the semantic association information about protocol standards, vendor extension specifications, and historical cases in the semantic association library, the extended fields of different vendors are aligned under a unified semantic space and mapped to the corresponding unified semantic identifiers. Then, the extended fields in the structured signaling representation are semantically normalized using the unified semantic identifiers to obtain normalized signaling with a unified semantic representation.

[0031] First, key fields related to extended fields in the signaling dataset are extracted in a structured manner and a structured signaling representation is formed, transforming the extended fields from unstructured text into a field-based representation that can be processed consistently. Then, based on a pre-defined semantic association library, extended fields from different vendors are mapped to a unified semantic space and normalized signaling is generated. This eliminates the differences in extended field expression caused by vendor differences, thus providing a consistent semantic input basis for subsequent transmission strategy determination, adaptation processing, anomaly identification, and root cause analysis based on normalized signaling.

[0032] S4: Obtain receiving side capability information, determine signaling transmission strategy based on the receiving side capability information and the normalized signaling, and perform adaptation processing on the normalized signaling according to the signaling transmission strategy to obtain target signaling; S5: Based on the target signaling, perform anomaly identification to obtain anomaly identification results, and based on the anomaly identification results, perform root cause analysis on the signaling interaction relationship to generate diagnostic results.

[0033] It should be noted that receiver capability information is used to characterize the receiver's support for signaling expression formats and field carrying requirements. The signaling delivery strategy is generated based on the receiver capability information. Adaptation processing refers to the processing performed on normalized signaling according to the signaling delivery strategy. Target signaling is the signaling generated after the normalized signaling has undergone adaptation processing and is delivered to the receiver. Anomaly identification is the process of analyzing the signaling flow to identify anomalies. Signaling interaction relationships are information used to characterize the interactive relationships between signaling entities. Root cause analysis is the process of tracing and locating the root cause of anomalies based on signaling interaction relationships. Diagnostic results are the result information output by root cause analysis used to characterize the cause of the anomaly.

[0034] Specifically, the receiving side capability information is obtained, and based on this information, the receiving side's support for unified semantic representation, support for the original vendor format, and whether any necessary fields are missing are determined. Based on this, the receiving side capability information is matched with the normalized signaling to determine the signaling delivery strategy corresponding to the receiving side. Subsequently, the normalized signaling is adapted according to the signaling delivery strategy: if the receiving side supports normalized semantics, the normalized signaling is directly delivered; if the receiving side only supports the original vendor format, the normalized signaling undergoes format conversion to obtain a signaling format compatible with the receiving side; if the receiving side lacks necessary fields, the necessary fields are completed based on semantic association information, thereby obtaining the target signaling.

[0035] Furthermore, after obtaining the target signaling, a signaling flow to be analyzed is formed based on the target signaling and input into the anomaly detection model to complete anomaly identification and output the anomaly identification result; subsequently, based on the anomaly identification result, the signaling entities and their interaction records related to the anomaly are determined, and the signaling entities and signaling interactions are modeled into a signaling interaction relationship structure; then, the signaling interaction relationship structure is input into the root cause localization model for root cause analysis to trace the root cause of the anomaly and generate diagnostic results.

[0036] By introducing receiver capability information and determining the signaling transmission strategy accordingly, normalized signaling can be adapted to different paths, such as direct transmission, format conversion, or field completion, based on receiver support before transmission, thereby reducing signaling mismatch caused by differences in receiver capabilities. After the target signaling is formed, anomalies are identified, and signaling interaction relationships are constructed based on the anomaly identification results to conduct root cause analysis, enabling anomalies to transition from "being identified" to "being located," thus providing a diagnostic basis for subsequent anomaly convergence and stable operation in cross-vendor signaling interaction processes.

[0037] This embodiment acquires session initiation protocol signaling data from multiple signaling entities and constructs a signaling dataset based on this data. It extracts structured fields related to extended fields in the signaling dataset to generate a structured signaling representation. Based on a preset semantic association library, it performs semantic mapping on the extended fields in the structured signaling representation to obtain normalized signaling with a unified semantic representation. It acquires receiver capability information, determines a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapts the normalized signaling according to the signaling transmission strategy to obtain the target signaling. Based on the target signaling, it performs anomaly identification to obtain anomaly identification results, and performs root cause analysis on the signaling interaction relationships based on the anomaly identification results to generate diagnostic results. This embodiment constructs a signaling dataset by acquiring session initiation protocol signaling data from multiple signaling entities, providing a unified data input for cross-entity interaction. It extracts structured content related to extended fields from the signaling dataset to form a structured signaling representation, ensuring consistent and processable expression for the extended fields. Then, based on a pre-defined semantic association library, it performs semantic mapping on the extended fields to generate normalized signaling with a unified semantic representation, mitigating semantic inconsistencies caused by differences in extended field expressions from different vendors. Furthermore, it introduces receiver capability information and combines it with the normalized signaling to determine a signaling transmission strategy. The normalized signaling is then adapted according to the strategy to obtain target signaling that matches the receiver's capabilities, thereby reducing interaction failures caused by incompatibility. Finally, it identifies anomalies in the target signaling and performs root cause analysis based on signaling interaction relationships to generate diagnostic results, enabling anomalies to be identified and attributed to promote problem convergence, thus enhancing the stability of cross-vendor signaling interaction.

[0038] Based on the first embodiment described above, a second embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application is proposed. Please refer to... Figure 2 , Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application.

[0039] like Figure 2 As shown, in this embodiment, step S2 includes: S21: Based on the preset signaling parsing model, perform sequence parsing processing on the signaling messages in the signaling dataset, output field identification information and field content information used to characterize the extended field, and output session association field information associated with the extended field; S22: Based on the field identification information, the field content information, and the session association field information, the extended fields are organized into a field-level structured structure to obtain a set of structured fields; S23: Combine and encode the structured field set with the basic fields of the signaling message, and establish the association between the structured field set and the corresponding signaling message to obtain a structured signaling representation.

[0040] It should be noted that the preset signaling parsing model is a pre-defined model used to parse signaling messages and output key information. Sequence parsing processing is a method of parsing signaling messages as a sequentially arranged sequence of text / fields. Field identification information is a type of information used to characterize "what kind of field / which type of field" an extended field is. Field content information is a type of information used to characterize "what content" an extended field carries. Field-level structuring is based on the field identification information and field content information output by parsing. The structured field set is the set of fields obtained from field-level structuring. Basic fields are the basic class of field information in the signaling message other than extended fields. Combination encoding is the process of combining the structured field set and basic fields into a unified representation according to a preset encoding method. Association relationship is information used to characterize the correspondence between the structured field set and its source signaling message.

[0041] Specifically, based on a preset signaling parsing model, the signaling messages in the signaling dataset are processed by sequence parsing. Each signaling message is input into the signaling parsing model as a sequence to be parsed. During the parsing process, the signaling parsing model extracts key information from the signaling messages and outputs at least field identifier information and field content information to represent extended fields. At the same time, it outputs session association field information associated with the extended fields so that the extended fields can establish a correspondence with the corresponding session context.

[0042] Furthermore, after obtaining field identification information, field content information, and session-related field information, the extended fields are organized and structured at the field level based on the above information. The expressions of the same extended field in different signaling messages are organized and merged at the field granularity to obtain a structured field set. Further, the structured field set is combined and encoded with the basic fields of the signaling message, and the association between the structured field set and the corresponding signaling message is established to obtain a structured signaling representation to support subsequent unified processing around the extended fields.

[0043] By using a pre-defined signaling parsing model, the signaling message is sequence-parsed and the field identifier and content information of the extended fields are output. At the same time, the session association field information associated with the extended fields is also output, transforming the extended fields from "unstructured signaling fragments" into "locatable, organizeable, and referable" field-based information. On this basis, the extended fields are organized into a structured field set at the field level, and further combined and encoded with the basic fields and established with the original signaling message to obtain a structured signaling representation. This enables subsequent processing to perform consistent operations on the extended fields on a unified structured representation, reducing the uncertainty introduced by the scattered and difficult-to-align extended field expressions.

[0044] Based on the first embodiment described above, in this embodiment, step S3 includes: S31: Based on the structured signaling representation, determine the set of extended fields to be mapped, and extract the field identifier information, field value information and basic signaling field information associated with each extended field; S32: Based on the field identification information, retrieve the semantic tag information and / or semantic mapping rules corresponding to the extended field from the preset semantic association library, and determine the target unified semantic identifier based on the semantic mapping rules; S33: Based on the target unified semantic identifier, perform semantic annotation and / or semantic replacement on the extended field, and combine the semantically annotated and / or semantically replaced extended field with the basic signaling field information to obtain normalized signaling with unified semantic representation.

[0045] It should be noted that the set of extended fields to be mapped is a set of extended fields that need to undergo semantic mapping processing, obtained based on the structured signaling representation. Field value information refers to the parameter values / content information carried by the extended field. Basic signaling field information refers to the basic class signaling field information associated with the extended field. Semantic tag information refers to the tag information used to characterize the "semantic category / meaning" of the extended field. Semantic mapping rules are the rule information in the semantic association library used to map extended fields (and their values) to a unified semantic space. The target unified semantic identifier is an identifier determined according to the semantic mapping rules, used to represent the meaning of the extended field in the unified semantic space. Semantic annotation is the process of attaching the target unified semantic identifier to the extended field, enabling the extended field to carry unified semantic information. Semantic replacement is the process of replacing the original vendor expression (e.g., extended header name / category expression) of the extended field with the target unified semantic identifier.

[0046] Specifically, based on the structured signaling representation, the extended fields are traversed and filtered to determine the set of extended fields to be mapped. For each extended field to be mapped, its field identifier information, field value information, and associated basic signaling field information are extracted, so that each extended field has input elements that can be used for retrieval and judgment in subsequent mapping, while retaining its association with the basic signaling fields as the basis for subsequent combination to generate normalized signaling.

[0047] Furthermore, for each extended field to be mapped, the corresponding semantic tag information and / or semantic mapping rules are retrieved from the preset semantic association library based on the field identification information, and the target unified semantic identifier is determined according to the semantic mapping rules; then, the extended fields are semantically labeled and / or semantically replaced based on the target unified semantic identifier, and the extended fields after semantic labeling and / or semantic replacement are combined with the aforementioned basic signaling field information to form normalized signaling expressed in a unified semantic space.

[0048] By determining the set of extended fields to be mapped from the structured signaling representation and extracting field identifier information, field value information, and associated basic signaling field information, the extended fields have an input basis that can be retrieved and matched by the semantic association library. Then, based on the semantic label information and / or semantic mapping rules output by the semantic association library, the target unified semantic identifier is determined, and semantic annotation and / or semantic replacement are performed on the extended fields to align the expressions of extended fields from different vendors to a unified semantic space. Finally, the mapped extended fields are combined with the basic signaling field information to generate normalized signaling, thereby providing consistent semantic input for subsequent determination and adaptation of signaling delivery strategies based on the unified semantic representation.

[0049] This embodiment acquires session initiation protocol signaling data from multiple signaling entities and constructs a signaling dataset based on this data. It extracts structured fields related to extended fields in the signaling dataset to generate a structured signaling representation. Based on a preset semantic association library, it performs semantic mapping on the extended fields in the structured signaling representation to obtain normalized signaling with a unified semantic representation. It acquires receiver capability information, determines a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapts the normalized signaling according to the signaling transmission strategy to obtain the target signaling. Based on the target signaling, it performs anomaly identification to obtain anomaly identification results, and performs root cause analysis on the signaling interaction relationships based on the anomaly identification results to generate diagnostic results. This embodiment constructs a signaling dataset by acquiring session initiation protocol signaling data from multiple signaling entities, providing a unified data input for cross-entity interaction. It extracts structured content related to extended fields from the signaling dataset to form a structured signaling representation, ensuring consistent and processable expression for the extended fields. Then, based on a pre-defined semantic association library, it performs semantic mapping on the extended fields to generate normalized signaling with a unified semantic representation, mitigating semantic inconsistencies caused by differences in extended field expressions from different vendors. Furthermore, it introduces receiver capability information and combines it with the normalized signaling to determine a signaling transmission strategy. The normalized signaling is then adapted according to the strategy to obtain target signaling that matches the receiver's capabilities, thereby reducing interaction failures caused by incompatibility. Finally, it identifies anomalies in the target signaling and performs root cause analysis based on signaling interaction relationships to generate diagnostic results, enabling anomalies to be identified and attributed to promote problem convergence, thus enhancing the stability of cross-vendor signaling interaction.

[0050] Based on the second embodiment described above, a third embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application is proposed. Please refer to... Figure 3 , Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the automatic diagnosis method for session initiation protocol signaling system problems in this application.

[0051] In this embodiment, step S4 includes: S41: Obtain the capability information corresponding to the receiving side, and determine the receiving side's support for unified semantic representation and the carrying of necessary fields based on the capability information to obtain the receiving side capability information; S42: Based on the receiving side capability information and the normalized signaling, a signaling transmission strategy corresponding to the receiving side is determined by a preset strategy. The signaling transmission strategy is used to indicate the transmission method and adaptation method of the normalized signaling. S43: Adapt the normalized signaling according to the signaling transmission strategy to obtain the target signaling. When the receiving side supports unified semantic representation, the normalized signaling is directly transmitted. When the receiving side does not support unified semantic representation, the normalized signaling is converted into a receiving side compatible format through a preset format conversion model. When the receiving side lacks necessary fields, the necessary fields are completed based on the preset semantic association library.

[0052] It should be noted that capability information is a source of information used to characterize the receiving side's signaling processing capabilities. Required fields are a set of fields that the receiving side is required to possess during signaling processing or session interaction to ensure that signaling can be processed correctly. The preset policy determination model is a model used to dynamically generate sharing / transmission policies based on differences in receiving side capabilities. The preset format conversion model is a pre-defined model used to convert normalized signaling into a format that the receiving side can process in scenarios where "the receiving side only supports the original vendor's format". Receiver-compatible formats are signaling format forms that the receiving side can parse and process.

[0053] Specifically, the capability information corresponding to the receiving side is obtained, and the support status of the receiving side for normalized semantics and the carrying status of necessary fields are determined based on the capability information, thereby obtaining the receiving side capability information; after obtaining the receiving side capability information, the receiving side capability information and the normalized signaling are used as inputs for strategy generation, and a signaling transmission strategy corresponding to the receiving side is generated by the model through a preset strategy. The signaling transmission strategy is used to indicate the transmission method and adaptation method adopted when the normalized signaling is transmitted to the receiving side.

[0054] Furthermore, the normalized signaling is adapted according to the signaling transmission strategy to obtain the target signaling; wherein, when the receiving side supports normalized semantics, the normalized signaling is directly transmitted according to the signaling transmission strategy; when the receiving side only supports the original vendor format, the normalized signaling is converted according to the signaling transmission strategy by calling a preset format conversion model to obtain signaling in a receiving side compatible format; when the receiving side lacks necessary fields, the necessary fields are completed according to the signaling transmission strategy based on a preset semantic association library so that the adapted signaling meets the field carrying conditions of the receiving side, thereby obtaining the target signaling.

[0055] First, based on capability information, the receiving side's support status for normalized semantics and the carrying status of necessary fields are determined. Then, a preset strategy is used to determine the signaling transmission strategy for the receiving side generated by the model. This allows the transmission and adaptation methods of normalized signaling to be dynamically determined according to the differences in the receiving side's capabilities. Subsequently, according to the strategy, adaptation processing is carried out in paths such as "direct transmission", "format conversion to receiving side compatible format" and "complete necessary fields based on semantic association library" to obtain the target signaling. This ensures that the target signaling output by the sending side matches the semantic support capabilities and field carrying conditions of the receiving side, reducing interaction instability caused by inconsistent capabilities or missing fields.

[0056] Based on the second embodiment described above, in this embodiment, step S5 includes: S51: Construct signaling flow features based on the target signaling to characterize the signaling timing correlation, and input the signaling flow features into a preset anomaly detection model to obtain the corresponding anomaly identification result; S52: Based on the anomaly identification result, determine the signaling entities and interaction records related to the anomaly from the target signaling, and construct a signaling interaction relationship representation to characterize the association between the signaling entities and the interaction records; S53: Input the signaling interaction relationship representation into the preset root cause analysis model to obtain the root cause analysis result, and generate a diagnostic result based on the root cause analysis result.

[0057] It should be noted that signaling timing correlation is temporal information used to characterize the dependencies and order of signaling messages during session progression. Signaling flow features are a set of features extracted from target signaling organized according to session dimension and time sequence. The preset anomaly detection model is a pre-defined model used to identify anomalies in the normalized / target signaling flow. The signaling interaction relationship representation is a structured representation used to characterize the relationship between signaling entities and signaling interaction records. The preset root cause analysis model is a model used for root cause localization / tracing based on the signaling interaction relationship representation. The root cause analysis result is the output of the root cause analysis model, used to characterize the anomaly cause chain or key causal entities / interactions.

[0058] Specifically, based on the target signaling, the target signaling is merged according to the session association relationship, and the merged target signaling is sorted according to the time order to form a signaling stream for representing the temporal association of signaling. On this basis, signaling stream features for representing field local information and temporal dependency information are extracted from the signaling stream, and the signaling stream features are input into a preset anomaly detection model to output the anomaly identification result corresponding to the target signaling.

[0059] Furthermore, after obtaining the anomaly identification result, the signaling entity related to the anomaly and the interaction record associated with the signaling entity are determined from the target signaling, and a signaling interaction relationship representation is constructed based on the signaling entity and the interaction record; subsequently, the signaling interaction relationship representation is input into a preset root cause analysis model to obtain the root cause analysis result, and a diagnostic result is generated based on the root cause analysis result.

[0060] By constructing signaling flow features representing the temporal correlation of signaling based on target signaling and inputting them into an anomaly detection model, anomaly identification results are obtained. This establishes anomaly identification on the basis of joint representation of signaling field information and temporal dependency information. Furthermore, based on the anomaly identification results, signaling entities and interaction records related to the anomaly are identified from the target signaling, and a signaling interaction relationship representation is constructed. This interaction relationship representation is then input into a root cause analysis model to obtain root cause analysis results and generate diagnostic results. This transforms the anomaly from being "identified" into "traceable causal information," thereby providing a structured diagnostic basis for subsequent anomaly localization and handling.

[0061] In this embodiment, after step S5, the following steps are also included: S5a: Determine a processing strategy based on the diagnostic results, and adjust the configuration of the target signaling based on the processing strategy; S5b: Write the diagnostic results and the execution results of the processing strategy into a preset feedback dataset; S5c: Based on the preset feedback dataset after writing, update the model parameters related to the anomaly identification and the root cause analysis.

[0062] It should be noted that the treatment strategy is a solution determined based on the diagnostic results. Configuration adjustments are operations that modify relevant configuration parameters according to the treatment strategy. Execution results are the information obtained after the treatment strategy is executed. The feedback dataset is a collection of data used to record the diagnostic results and the effectiveness of the treatment strategy execution.

[0063] Specifically, based on the diagnostic results, the cause of the anomaly, the involved signaling entities, and the anomaly-related signaling content are used as inputs for strategy generation. A preset strategy generation model outputs a processing strategy that matches the diagnostic results. Subsequently, the target signaling is configured and adjusted based on the processing strategy. Specifically, for signaling format incompatibility issues, unsupported extended content in the target signaling is deleted and / or converted according to the processing strategy, and the signaling is retransmitted. For configuration issues, the relevant device interfaces are called to modify the configuration items according to the processing strategy so that the configuration status related to the processing of the target signaling meets the interaction requirements.

[0064] Furthermore, after the processing strategy is executed, the diagnostic results and the execution results of the processing strategy are written into a preset feedback dataset. The execution results are used at least to characterize the success / failure status of the processing strategy. Based on the feedback dataset after writing, the model parameters related to the anomaly identification and the root cause analysis are updated. The updates include: adding new vendor extended field samples to the training data used for signaling structured processing, incorporating unidentified anomaly patterns into the sample / feature set of the anomaly detection model, and using failed repair cases to adjust the training elements of the strategy generation model, thereby completing the online learning and updating of the relevant models.

[0065] By determining the handling strategy based on the diagnostic results and making corresponding configuration adjustments to the target signaling, the anomaly handling is further reduced from "cause localization" to the operational stage of "strategy execution". At the same time, the diagnostic results and the execution results of the handling strategy are written into the feedback dataset, and the parameters of the anomaly identification and root cause analysis related models are updated online accordingly. This allows the models to be continuously corrected and improved using newly added extended field samples, uncovered anomaly patterns, and failure repair cases. This improves the consistency of anomaly identification and attribution in subsequent similar scenarios and enhances the adaptability of strategy output and system configuration status to anomaly scenarios.

[0066] This embodiment acquires session initiation protocol signaling data from multiple signaling entities and constructs a signaling dataset based on this data. It extracts structured fields related to extended fields in the signaling dataset to generate a structured signaling representation. Based on a preset semantic association library, it performs semantic mapping on the extended fields in the structured signaling representation to obtain normalized signaling with a unified semantic representation. It acquires receiver capability information, determines a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapts the normalized signaling according to the signaling transmission strategy to obtain the target signaling. Based on the target signaling, it performs anomaly identification to obtain anomaly identification results, and performs root cause analysis on the signaling interaction relationships based on the anomaly identification results to generate diagnostic results. This embodiment constructs a signaling dataset by acquiring session initiation protocol signaling data from multiple signaling entities, providing a unified data input for cross-entity interaction. It extracts structured content related to extended fields from the signaling dataset to form a structured signaling representation, ensuring consistent and processable expression for the extended fields. Then, based on a pre-defined semantic association library, it performs semantic mapping on the extended fields to generate normalized signaling with a unified semantic representation, mitigating semantic inconsistencies caused by differences in extended field expressions from different vendors. Furthermore, it introduces receiver capability information and combines it with the normalized signaling to determine a signaling transmission strategy. The normalized signaling is then adapted according to the strategy to obtain target signaling that matches the receiver's capabilities, thereby reducing interaction failures caused by incompatibility. Finally, it identifies anomalies in the target signaling and performs root cause analysis based on signaling interaction relationships to generate diagnostic results, enabling anomalies to be identified and attributed to promote problem convergence, thus enhancing the stability of cross-vendor signaling interaction.

[0067] This application also provides an automatic diagnostic device for problems in the Session Initiation Protocol signaling system. Please refer to... Figure 4 , Figure 4 This is a schematic diagram of the module structure of the automatic diagnosis device for session initiation protocol signaling system problems according to an embodiment of this application. The automatic diagnosis device for session initiation protocol signaling system problems includes: The data acquisition module 401 is used to acquire session initiation protocol signaling data from multiple signaling entities and construct a signaling dataset based on the session initiation protocol signaling data; The structuring module 402 is used to extract structured fields related to the extended fields in the signaling dataset and generate a structured signaling representation; The semantic mapping module 403 is used to perform semantic mapping on the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with unified semantic representation; The signaling adaptation module 404 is used to obtain receiving side capability information, determine a signaling transmission strategy based on the receiving side capability information and the normalized signaling, and perform adaptation processing on the normalized signaling according to the signaling transmission strategy to obtain the target signaling. The diagnostic result module 405 is used to perform anomaly identification based on the target signaling to obtain anomaly identification results, and to perform root cause analysis on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results.

[0068] The automatic diagnostic device for session initiation protocol signaling system problems provided in this application adopts the automatic diagnostic method for session initiation protocol signaling system problems in the above embodiments, and can solve the technical problem of how to improve the stability of signaling interaction in a cross-vendor environment. Compared with the prior art, the beneficial effects of the automatic diagnostic device for session initiation protocol signaling system problems provided in this application are the same as the beneficial effects of the automatic diagnostic method for session initiation protocol signaling system problems provided in the above embodiments, and other technical features in the automatic diagnostic device for session initiation protocol signaling system problems are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0069] This application provides an automatic diagnostic device for Session Initiation Protocol (SIP) signaling system problems. The automatic diagnostic device for Session Initiation Protocol (SIP) signaling system problems includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the automatic diagnostic method for Session Initiation Protocol (SIP) signaling system problems in the above embodiments.

[0070] The following is for reference. Figure 5 , Figure 5 This is a schematic diagram of the hardware operating environment involved in the automatic diagnosis method for session initiation protocol signaling system problems in the embodiments of this application. It shows a schematic diagram of the structure of the device suitable for implementing the automatic diagnosis device for session initiation protocol signaling system problems in the embodiments of this application. Figure 5 The illustrated automatic diagnostic device for session initiation protocol signaling system problems is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0071] like Figure 5As shown, the automatic diagnostic device for session initiation protocol signaling system problems may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the automatic diagnostic device for session initiation protocol signaling system problems. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the Session Initiation Protocol Signaling System Problem Automatic Diagnosis Device to exchange data wirelessly or via wired communication with other devices. Although the diagram shows a Session Initiation Protocol Signaling System Problem Automatic Diagnosis Device with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0072] In particular, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. When the computer program is executed by the processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0073] The automatic diagnostic device for session initiation protocol signaling system problems provided in this application, employing the automatic diagnostic method for session initiation protocol signaling system problems in the above embodiments, can solve the technical problem of how to improve the stability of signaling interaction in cross-vendor environments. Compared with the prior art, the beneficial effects of the automatic diagnostic device for session initiation protocol signaling system problems provided in this application are the same as the beneficial effects of the automatic diagnostic method for session initiation protocol signaling system problems provided in the above embodiments, and other technical features in this automatic diagnostic device for session initiation protocol signaling system problems are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0074] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0075] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0076] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the automatic diagnosis method for session initiation protocol signaling system problems in the above embodiments.

[0077] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the Automatic Diagnosis Device for Session Initiation Protocol (SIP) Signaling System Problems, the Automatic Diagnosis Device for Session Initiation Protocol (SIP) Signaling System Problems causes the device to: acquire SIP signaling data from multiple signaling entities and construct a signaling dataset based on the SIP signaling data; extract structured fields related to extended fields in the signaling dataset and generate a structured signaling representation; perform semantic mapping on the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with a unified semantic representation; acquire receiver capability information, determine a signaling transmission strategy based on the receiver capability information and the normalized signaling, and adapt the normalized signaling according to the signaling transmission strategy to obtain the target signaling; perform anomaly identification based on the target signaling to obtain anomaly identification results, and perform root cause analysis on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results. Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0079] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0080] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described automatic diagnosis method for session initiation protocol signaling system problems, thereby solving the technical problem of how to improve the stability of signaling interaction in a cross-vendor environment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the automatic diagnosis method for session initiation protocol signaling system problems provided in the above embodiments, and will not be repeated here.

[0081] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the automatic diagnosis method for session initiation protocol signaling system problems as described above.

[0082] The computer program product provided in this application can solve the technical problem of how to improve the stability of signaling interaction in a cross-vendor environment. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the automatic diagnosis method for session initiation protocol signaling system problems provided in the above embodiments, and will not be repeated here.

[0083] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. An automatic diagnostic method for problems in a session initiation protocol signaling system, characterized in that, The method includes: Acquire session initiation protocol signaling data from multiple signaling entities, and construct a signaling dataset based on the session initiation protocol signaling data; Extract the structured fields related to the extended fields in the signaling dataset to generate a structured signaling representation; Based on a preset semantic association library, semantic mapping is performed on the extended fields in the structured signaling representation to obtain normalized signaling with unified semantic representation; Obtain receiving side capability information, determine signaling transmission strategy based on the receiving side capability information and the normalized signaling, and perform adaptation processing on the normalized signaling according to the signaling transmission strategy to obtain target signaling; Anomaly identification is performed based on the target signaling to obtain anomaly identification results. Root cause analysis is then performed on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results.

2. The method as described in claim 1, characterized in that, The step of extracting structured fields related to the extended fields in the signaling dataset and generating a structured signaling representation includes: Based on a preset signaling parsing model, the signaling messages in the signaling dataset are processed by sequence parsing, and field identification information and field content information used to represent the extended field are output, as well as session association field information associated with the extended field are output. Based on the field identifier information, the field content information, and the session association field information, the extended fields are organized into a field-level structured structure to obtain a set of structured fields; The structured field set is combined and encoded with the basic fields of the signaling message, and the association between the structured field set and the corresponding signaling message is established to obtain a structured signaling representation.

3. The method as described in claim 1, characterized in that, The step of semantically mapping the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with a unified semantic representation includes: Based on the structured signaling representation, a set of extended fields to be mapped is determined, and the field identifier information, field value information, and basic signaling field information associated with each extended field are extracted. Based on the field identification information, semantic tag information and / or semantic mapping rules corresponding to the extended field are retrieved from the preset semantic association library, and the target unified semantic identifier is determined based on the semantic mapping rules; Based on the target unified semantic identifier, the extended fields are semantically labeled and / or semantically replaced, and the semantically labeled and / or semantically replaced extended fields are combined with the basic signaling field information to obtain normalized signaling with unified semantic representation.

4. The method as described in claim 1, characterized in that, The steps of obtaining receiver capability information, determining a signaling delivery strategy based on the receiver capability information and the normalized signaling, and adapting the normalized signaling according to the signaling delivery strategy to obtain the target signaling include: Obtain the capability information corresponding to the receiving side, and determine the receiving side's support for unified semantic representation and the carrying of necessary fields based on the capability information to obtain the receiving side capability information; Based on the receiving side capability information and the normalized signaling, a signaling transmission strategy corresponding to the receiving side is determined by a preset strategy. The signaling transmission strategy is used to indicate the transmission method and adaptation method of the normalized signaling. The normalized signaling is adapted according to the signaling transmission strategy to obtain the target signaling. When the receiving side supports unified semantic representation, the normalized signaling is directly transmitted. When the receiving side does not support unified semantic representation, the normalized signaling is converted into a receiving side compatible format through a preset format conversion model. When the receiving side lacks necessary fields, the necessary fields are completed based on the preset semantic association library.

5. The method as described in claim 1, characterized in that, The steps of obtaining anomaly identification results based on the target signaling, and performing root cause analysis on the signaling interaction relationships based on the anomaly identification results to generate diagnostic results include: Based on the target signaling, a signaling flow feature is constructed to characterize the signaling timing correlation, and the signaling flow feature is input into a preset anomaly detection model to obtain the corresponding anomaly identification result; Based on the anomaly identification results, signaling entities and interaction records related to the anomaly are determined from the target signaling, and a signaling interaction relationship representation is constructed to characterize the association between the signaling entities and the interaction records. The signaling interaction relationship is input into a preset root cause analysis model to obtain root cause analysis results, and a diagnostic result is generated based on the root cause analysis results.

6. The method as described in claim 1, characterized in that, After the steps of obtaining an anomaly identification result based on the target signaling, and performing root cause analysis on the signaling interaction relationship based on the anomaly identification result to generate a diagnostic result, the method further includes: Based on the diagnostic results, a processing strategy is determined, and the target signaling is configured and adjusted based on the processing strategy. The diagnostic results and the execution results of the processing strategy are written into a preset feedback dataset; Based on the pre-defined feedback dataset after it has been written, the model parameters related to the anomaly identification and the root cause analysis are updated.

7. An automatic diagnostic device for problems in a session initiation protocol signaling system, characterized in that, The device includes: The data acquisition module is used to acquire session initiation protocol signaling data from multiple signaling entities and construct a signaling dataset based on the session initiation protocol signaling data; The structuring module is used to extract structured fields related to the extended fields in the signaling dataset and generate a structured signaling representation; The semantic mapping module is used to perform semantic mapping on the extended fields in the structured signaling representation based on a preset semantic association library to obtain normalized signaling with a unified semantic representation; The signaling adaptation module is used to obtain receiver capability information, determine a signaling transmission strategy based on the receiver capability information and the normalized signaling, and perform adaptation processing on the normalized signaling according to the signaling transmission strategy to obtain the target signaling. The diagnostic results module is used to identify anomalies based on the target signaling to obtain anomaly identification results, and to perform root cause analysis on the signaling interaction relationship based on the anomaly identification results to generate diagnostic results.

8. A computer device, characterized in that, The device includes: a memory, a processor, and an automatic diagnostic program for session initiation protocol signaling system problems stored in the memory and executable on the processor, the automatic diagnostic program for session initiation protocol signaling system problems being configured to implement the steps of the automatic diagnostic method for session initiation protocol signaling system problems as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores an automatic diagnostic program for Session Initiation Protocol (SIP) signaling system problems. When the SIP signaling system problem automatic diagnostic program is executed by the processor, it implements the steps of the automatic diagnostic method for SIP signaling system problems as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the automatic diagnosis method for session initiation protocol signaling system problems as described in any one of claims 1 to 6.

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