An optical cable monitoring and analysis method and system based on large language models

Abstract

The present application relates to an optical cable monitoring and analysis method and system based on a large language model, which includes: when receiving a retrieval instruction sent by a user terminal, obtaining the retrieval information in the retrieval text box and inputting it into a preset retrieval analysis model; when the retrieval analysis model receives the retrieval information, performing semantic recognition processing and label filling processing on the retrieval information to obtain a retrieved matching text; the retrieval analysis model indexes a matching service node from a preset service program based on the retrieved matching text, and filters out the monitoring data associated with the retrieved matching text; based on the retrieved matching text, determining whether the filtered monitoring data needs to be analyzed; when analysis needs to be performed, obtaining an associated analysis framework based on the retrieved matching text, sending the monitoring data to the analysis framework for analysis and calculation processing, outputting an analysis report and sending it to the user terminal. The present application has the effect of facilitating the targeted retrieval and analysis of optical cable monitoring data by the user terminal.

Description

Technical Field

[0001] This application relates to the technical field of optical cable detection, and in particular to an optical cable monitoring and analysis method and system based on a large language model. Background Art

[0002] Currently, the purpose of monitoring the data of optical cables is to analyze and predict the operating status of optical cables, so as to timely discover potential safety hazards and fault locations in the optical cable lines, and improve the reliability and maintenance efficiency of the optical cable network.

[0003] Generally, an optical time domain reflectometer is used in combination with environmental data to obtain monitoring data, and a large model is used to analyze the monitoring data to feedback the fault type and fault location to the user side. However, the data for training the current large model is relatively single, generally only including monitoring data and geographical location information. Therefore, when the user side needs to specifically retrieve and analyze the historical monitoring data of optical cables, multiple data screening steps such as data type, time, location, and fault type need to be performed, which is cumbersome to operate and results in a poor user experience, thus needing improvement. Summary of the Invention

[0004] In order to facilitate the targeted retrieval and analysis of optical cable monitoring data by the user side and improve the user experience, this application provides an optical cable monitoring and analysis method and system based on a large language model.

[0005] The first above-mentioned invention object of this application is achieved through the following technical solutions:

[0006] An optical cable monitoring and analysis method and system based on a large language model, including the steps of:

[0007] When receiving a monitoring data retrieval request sent by the user side, send a retrieval text box to the user side;

[0008] When receiving a retrieval instruction sent by the user side, obtain the retrieval information in the retrieval text box and input it into a preset retrieval analysis model, where the retrieval information includes text information;

[0009] When the retrieval analysis model receives the retrieval information, perform semantic recognition processing and label filling processing on the retrieval information to obtain a retrieval matching text;

[0010] The retrieval analysis model indexes the matching service nodes from a preset service program based on the retrieval matching text, and filters out the monitoring data associated with the retrieval matching text;

[0011] Based on the retrieval matching text, determine whether the filtered monitoring data needs to perform analysis processing;

[0012] When analysis processing needs to be performed, an associated analysis framework is obtained based on the retrieved matching text, and the monitoring data is sent to the analysis framework for analysis and calculation processing, and an analysis report is output and sent to the client.

[0013] By adopting the above technical solution, when the client needs to retrieve historical or current optical cable monitoring data, a monitoring data retrieval request is sent to obtain a retrieval text box for inputting the text content of the monitoring data to be retrieved and sending it to the retrieval analysis model. The retrieval analysis model integrates text semantic recognition of the retrieved information, matching of identification information of the retrieved information, and index matching with the business SOP. Through semantic analysis of the retrieved information, the text expressions of the name, location, time, etc. of the monitoring data that the user wants to retrieve are obtained. After identification filling processing, the identification filling of information such as the name of the monitoring data, the optical cable location number, and the time is realized. The identification filling includes, for example, the filling of path information and screening filling, so that the retrieved matching text can index to the matching business node in the preset business process, and the monitoring data required by the client is screened out according to the retrieved matching text. Further, it is determined whether there is an analysis requirement for the monitoring data in the retrieved matching text by the client. If so, the analysis framework is automatically matched for analysis and calculation. Finally, the monitoring data is sent to the client; through the large language model that integrates multi-modal data, the identification and conversion of the monitoring data and the analysis and matching of the user's retrieval intention for the monitoring data are realized. For the client, the operation is simple and the user experience is better.

[0014] Optionally, when the retrieval analysis model receives the retrieved information, it performs semantic recognition processing and identification filling processing on the retrieved information to obtain a retrieved matching text, including:

[0015] The retrieval analysis model outputs the semantic text corresponding to the retrieved information based on semantic recognition processing, and the semantic recognition includes context awareness and semantic reasoning;

[0016] Based on context awareness, preset fields are extracted from the semantic text. The preset fields include the time information, optical cable location number information, and data name information of the monitoring data to be retrieved, and the time information and optical cable location number information in the same preset field are the same;

[0017] Perform identification filling processing on the preset fields. The identification filling processing includes retrieving the identification information matching the preset fields from the preset database and filling it into the preset fields of the semantic text to obtain a retrieved matching text. The identification information includes path information.

[0018] By adopting the above technical solution, through context awareness and semantic reasoning, the retrieval analysis model outputs semantic text for the received retrieval information in a unified text format and text expression logic, accurately obtains the content to be obtained by the user side, extracts preset fields from the semantic text, and the preset fields represent the information of the monitoring data to be retrieved by the user side, including information such as data name, optical cable location number, data collection time, etc., and the time information and location information in the same preset field are the same, which is convenient for subsequent retrieval and matching of text in the business node index in the preset business process. For example, through context awareness, a preset field in the semantic text is obtained as the monitoring data D of the optical cable with A time, B location, and number C. Then, the identification information is filled in for the preset field, and the identification information includes the combination of A time, B location, optical cable number C, and the path information of the monitoring data D; to obtain the retrieval matching text for matching business nodes.

[0019] Optionally, based on the retrieval matching text, the retrieval analysis model indexes the matching business node from the preset business process and filters out the monitoring data associated with the retrieval matching text, including:

[0020] The retrieval analysis model identifies the identification information of different preset fields in the retrieval matching text; and indexes each preset field one by one;

[0021] Based on the time information and optical cable location number information in the preset field, the business node is indexed in the business process;

[0022] Based on the data name information in the preset field, the monitoring data associated with the retrieval matching text is filtered out from all the monitoring data of the business node.

[0023] By adopting the above technical solution, the retrieval analysis model first identifies all the preset fields in the retrieval matching text, separates and indexes the monitoring data with different time information and location information, improves the indexing efficiency and accuracy, indexes the business node of the target optical cable based on the time and optical cable location number, and at this time, all the monitoring data of the business node is indexed, and then according to the data name information, the monitoring data to be retrieved by the user side is filtered out.

[0024] Optionally, the retrieval analysis model outputs the semantic text corresponding to the retrieval information based on semantic recognition processing, and the semantic recognition includes context awareness and semantic reasoning; including:

[0025] When the retrieval analysis model receives the retrieval information, based on the pre-trained context awareness relationship and semantic reasoning decision, it identifies the text features of the current retrieval information and determines the text format information of the current retrieval information;

[0026] Obtain the text conversion template that matches the currently determined text format information;

[0027] Based on the text conversion template, convert the retrieved information into semantic text.

[0028] By adopting the above technical solution, since the retrieved information is text, the user side may express the monitored data to be retrieved in different text formats. Therefore, by constructing a knowledge graph in advance and feeding a large amount of corpus into the retrieval analysis model, training the decision logic of context-aware relationships and semantic reasoning through a large amount of retrieved information, obtaining a large number of custom text format information, further identifying the text format information of the current retrieved information through the recognition of text features, and converting the retrieved information into semantic text in a unified text format through the pre-stored text conversion template for each text format information. For example: when the user side inputs to retrieve all the monitored data with the optical cable number C at position B at time A, but excludes the D data in all the monitored data, and it is known that the monitored data of the optical cable number C includes DEFG, it can be uniformly converted through the text conversion template into: retrieve the EFG data of the optical cable number C at position B at time A. The unified semantic text facilitates the efficiency of subsequent indexing.

[0029] Optionally, identifying the text features of the current retrieved information and determining the text format information of the current retrieved information includes: the text features include the semantic types represented by different fields in the retrieved information, and the semantic structure features formed after sorting different semantic types in the retrieved information.

[0030] By adopting the above technical solution, by analyzing the text of the retrieved information, obtaining the semantic structure features composed of various semantic types in the text, where the semantic types include semantics such as retrieval, acquisition, spot check, screening, etc. that tend to be execution means, and also include the name nouns representing certain information and various general pronouns and conjunctions, so as to realize the determination of the text format information of the retrieved information.

[0031] Optionally, based on the retrieved matching text, determine whether the screened monitored data needs to be analyzed and processed; including:

[0032] Traverse the retrieved matching text, identify and judge based on the context whether the retrieved matching text contains an analysis requirement field, and the analysis field contains specific analysis methods;

[0033] If it contains an analysis requirement field, it is determined that the current retrieved matching text needs to be analyzed and processed;

[0034] If it does not contain an analysis requirement field, it is determined that the current retrieved matching text does not need to be analyzed and processed, and then the screened monitored data is sent to the user side.

[0035] By adopting the above technical solution, by identifying and retrieving fields in the matching text, including the fields representing the execution of analysis and the name of the analysis algorithm, it is determined whether the current monitoring data needs to be analyzed. The identification method is more flexible. If the analysis requirement fields are not identified, the retrieved matching text is directly sent to the user terminal.

[0036] Optionally, when analysis processing needs to be executed, based on the retrieved matching text, an associated analysis framework is obtained, and the monitoring data is sent to the analysis framework for analysis and calculation processing, and an analysis report is output and sent to the user terminal, including:

[0037] Based on the requirement analysis fields, an associated analysis framework is obtained from the preset algorithm database. The analysis framework includes analysis algorithms and report templates;

[0038] The monitoring data is calculated by the associated analysis algorithm, and the calculation results are filled into the corresponding positions of the report template to generate an analysis report and send it to the user terminal.

[0039] By adopting the above technical solution, based on the requirement analysis fields, an associated analysis algorithm is obtained and the monitoring data is substituted into the analysis algorithm for automatic calculation. Then, based on the calculation result mapping relationship of the analysis framework, the monitoring data and the calculation results are filled into the corresponding positions in the report template and sent to the user terminal, which is convenient for the user terminal to browse the data.

[0040] The second invention object of the present application is achieved by the following technical solutions:

[0041] An optical cable monitoring and analysis system based on a large language model, including:

[0042] A retrieval request module, which is used to send a retrieval text box to the user terminal when receiving a monitoring data retrieval request sent by the user terminal;

[0043] An information acquisition module, which is used to acquire the retrieval information in the retrieval text box and input it into a preset retrieval analysis model when receiving a retrieval instruction sent by the user terminal. The retrieval information includes text information;

[0044] A semantic recognition module, which is used to perform semantic recognition processing and label filling processing on the retrieval information when the retrieval analysis model receives the retrieval information to obtain a retrieved matching text;

[0045] A matching module, which is used to index a matching service node from a preset service program based on the retrieved matching text and screen out the monitoring data associated with the retrieved matching text;

[0046] An analysis and judgment module, which is used to judge whether the screened monitoring data needs to execute analysis processing based on the retrieved matching text;

[0047] An analysis sending module, when analysis processing needs to be performed, obtains an associated analysis framework based on retrieved matching texts, sends monitoring data to the analysis framework for analysis and calculation processing, outputs an analysis report and sends it to the user terminal.

[0048] The above object three of the present application is achieved by the following technical solutions:

[0049] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned optical cable monitoring analysis method based on a large language model are implemented.

[0050] The above object four of the present application is achieved by the following technical solutions:

[0051] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned optical cable monitoring analysis method based on a large language model are implemented.

[0052] In summary, the present application includes at least one of the following beneficial technical effects:

[0053] 1. After the identification filling process, the information such as the name of the monitoring data, the optical cable position number, and the time is filled. The identification filling is, for example, the filling of path information and screening filling, so that the retrieved matching text can index to the matching service node in the preset service program, and the monitoring data required by the user terminal is screened out according to the retrieved matching text. Further, it is judged whether there is an analysis requirement for the monitoring data at the user terminal in the retrieved matching text. If so, the analysis framework is automatically matched for analysis and calculation, and finally the monitoring data is sent to the user terminal; through the large language model that fuses multi-modal data, the identification and conversion of the monitoring data and the analysis and matching of the user's willingness to retrieve the monitoring data are realized. For the user terminal, the operation is simple and the user experience is better;

[0054] 2. The retrieval analysis model first identifies all preset fields in the retrieved matching text, separates the monitoring data with different time information and position information for indexing, improves the indexing efficiency and accuracy, indexes to the service node of the target optical cable based on time and optical cable position number. At this time, all the monitoring data of the service node is indexed, and then the monitoring data desired by the user terminal is screened out according to the data name information;

[0055] 3. Convert the retrieved information into a semantic text in a unified text format. For example, when the user inputs a request to retrieve all monitoring data of optical cable No. C at location B at time A, but excludes data D from all the monitoring data, and the monitoring data of optical cable No. C is known to include DEFG, it can be uniformly converted through a text conversion template to: Retrieve the EFG data of optical cable No. C at location B at time A. The unified semantic text facilitates the efficiency of subsequent indexing;

[0056] 4. By analyzing the text of the retrieved information, obtain the semantic structure features composed of various semantic types in the text. Among them, semantic types include semantics such as retrieval, acquisition, spot check, and screening that tend to be execution means, as well as noun names representing certain information and various general pronouns and conjunctions, so as to realize the determination of the text format information of the retrieved information. Brief Description of the Drawings

[0057] Figure 1 is a flowchart of an implementation of an optical cable monitoring and analysis method based on a large language model according to an embodiment of the present application;

[0058] Figure 2 is a flowchart of an implementation of step S30 in an optical cable monitoring and analysis method based on a large language model according to an embodiment of the present application;

[0059] Figure 3 is a flowchart of an implementation of step S31 in an optical cable monitoring and analysis method based on a large language model according to an embodiment of the present application;

[0060] Figure 4 is a flowchart of an implementation of step S40 in an optical cable monitoring and analysis method based on a large language model according to an embodiment of the present application;

[0061] Figure 5 is a schematic diagram of a computer device according to the present application. Detailed Description of the Embodiment

[0062] The following will Figures 1-5 further elaborate on the present application in detail.

[0063] In an embodiment, as Figure 1 shown, the present application discloses an optical cable monitoring and analysis method based on a large language model, which specifically includes the following steps:

[0064] S10: When receiving a monitoring data retrieval request sent by the user terminal, send a retrieval text box to the user terminal;

[0065] In this embodiment, the client refers to the PC or mobile terminal software used by cable data retrieval personnel. The monitoring data of the optical cable includes, but is not limited to, cable deformation data, cable temperature data, temperature data, vibration and intrusion signal data, bit error rate, optical signal-to-noise ratio, and environmental data at the cable burial location.

[0066] The client issues a data retrieval request to obtain a retrieval text box for inputting text at the client. Optionally, it also includes a picture input text box or a voice input box.

[0067] Specifically, the cable data retrieval personnel issue a retrieval request instruction for cable monitoring data through the client to obtain a retrieval text box at the client for inputting the text of the monitoring data to be retrieved.

[0068] S20: When receiving the retrieval instruction sent by the client, obtain the retrieval information in the retrieval text box and input it into a preset retrieval analysis model. The retrieval information includes text information.

[0069] In this embodiment, the retrieval information refers to the text of the monitoring data to be retrieved, and the retrieval instruction refers to the instruction sent by the client to trigger the sending of the retrieval information to the retrieval analysis model.

[0070] S30: When the retrieval analysis model receives the retrieval information, perform semantic recognition processing and label filling processing on the retrieval information to obtain a retrieval matching text.

[0071] In this embodiment, the retrieval analysis model is a large language model trained using neural networks, integrating functions such as text semantic recognition, text format conversion, text filling, and matching of text with business nodes of a preset business program. The semantic recognition processing includes format recognition of the text of the retrieval information and conversion of text format and sentence pattern, aiming to unify the text structure of the retrieval information for subsequent indexing of business nodes in the text of the retrieval information. The label filling processing is to fill the path index information of the monitoring data storage location in the text of the retrieval information.

[0072] Specifically, when the retrieval analysis model receives the text of the retrieval information, perform semantic recognition processing on the text to convert the text format and sentence pattern of the retrieval information, and fill the path index information of the monitoring data at the position expressing the location of the monitoring data to be retrieved in the text of the retrieval information. The retrieval matching text is the text that has completed text format conversion and filled the index path information of the monitoring data.

[0073] S40: Based on the retrieval matching text, the retrieval analysis model indexes the matching business nodes from the preset business program and filters out the monitoring data associated with the retrieval matching text.

[0074] In this embodiment, the preset business program is an SOP process for customizing the storage paths of different monitoring data according to business requirements, which is equivalent to a storage path tree diagram. Through the preset business program, the storage locations of different monitoring data can be indexed, and each storage location is a business node.

[0075] Specifically, based on the semantics in the retrieved matching text and the filled path index information, the retrieval analysis model indexes from the preset business GOP process to the business node where the monitoring data to be retrieved in the retrieved matching text exists, and further filters out the associated monitoring data based on the content of the retrieved matching text.

[0076] S50: Based on the retrieved matching text, determine whether the filtered monitoring data needs to be analyzed and processed;

[0077] In this embodiment, the analysis and processing include, but are not limited to, trend analysis of index data, predictive analysis of data trends, distribution analysis of abnormal index data, and emergency plan analysis of abnormal index data.

[0078] Specifically, character semantic recognition is performed based on the content of the retrieved matching text to determine whether the currently filtered monitoring data needs to be analyzed and processed.

[0079] S60: When analysis and processing are required, obtain the associated analysis framework based on the retrieved matching text, send the monitoring data to the analysis framework for analysis and calculation processing, and output the analysis report and send it to the user terminal.

[0080] In this embodiment, the analysis framework includes calculation logic steps arranged in a preset order and corresponding analysis report templates. Each logic step is set with an algorithm formula, and each logic step is used to identify and obtain the corresponding monitoring data and fill it into the calculation position corresponding to the algorithm formula. After the calculation is completed, the generated data is mapped to the report template correspondingly.

[0081] Specifically, when text representing that the user terminal needs to perform monitoring data analysis is recognized from the retrieved matching text, the corresponding analysis framework is associated based on the recognized text, and the monitoring data is sent to the calculation position of the algorithm formula of the corresponding logic step in the analysis framework. After gradually calculating according to the arrangement of the algorithm formula, the generated calculation result is input into the corresponding analysis report template to generate an analysis report and send it to the user terminal.

[0082] In one embodiment, referring to Figure 2 , step S30 includes the steps:

[0083] S31: The retrieval analysis model outputs the semantic text corresponding to the retrieval information based on semantic recognition processing, and the semantic recognition includes context awareness and semantic reasoning;

[0084] S32: Based on context awareness, extract preset fields from the semantic text. The preset fields include the time information, cable location number information, and data name information of the monitoring data to be retrieved, and the time information and cable location number information in the same preset field are the same;

[0085] S33: Perform identification filling processing on the preset fields. The identification filling processing includes retrieving the identification information matching the preset fields from the preset database and filling it into the preset fields of the semantic text to obtain the retrieved matching text. The identification information includes path information.

[0086] In this embodiment, semantic reasoning aims to extract implicit logical relationships, intentions, and knowledge from the text and obtain new conclusions through reasoning. Its essence is to establish the connection between language symbols and real-world knowledge, mainly relying on knowledge representation, logical reasoning, and distributed semantics. Context awareness refers to the system dynamically adjusting understanding and response according to the current conversation, environment, or user historical behavior. The key lies in capturing and utilizing multi-dimensional context information, including local context, global context, and dynamic context, and implementing it through attention mechanisms, memory networks, and state tracking.

[0087] The preset fields are distinguished by the cable number position. For example, for the cable with location number B at position A, the monitoring data of the same preset field are all the cables with location number B at position A, and the number can be single or multiple.

[0088] The path information is the path index information, which is used to index to the service node where the monitoring data is stored in the service SOP. The preset fields in the semantic text are ABCDE, and their corresponding path information is 1 / 2 / 3. After the identification filling processing, the ABCDE text in the retrieved matching text will be displayed as ABCDE(1 / 2 / 3).

[0089] In one embodiment, referring to Figure 3 , step S31 includes the steps:

[0090] S311: When the retrieval analysis model receives the retrieval information, based on the pre-trained context awareness relationship and semantic reasoning decision, identify the text features of the current retrieval information and determine the text format information of the current retrieval information;

[0091] S312: Obtain the text conversion template that matches the currently determined text format information;

[0092] S313: Based on the text conversion template, convert the retrieval information into semantic text.

[0093] In this embodiment, the text features include the semantic types represented by different fields in the retrieval information, and the semantic structure features formed after sorting different semantic types in the retrieval information;

[0094] Specifically, the conversion of the text format combines context awareness and semantic reasoning decisions to achieve the disassembly of the text format at the semantic level and the conversion of the text sorting. For example, when the retrieved information is to retrieve the monitoring data of all cable types at location A, after semantic recognition and database comparison in the retrieved analysis, it is analyzed that there are three cable types, B, C, and D, at location A. Then the semantic text is converted into retrieving the monitoring data of cable type B at location A, the monitoring data of cable type C at location A, and the monitoring data of three cable types, namely cable type D at location A, that is, the disassembly of 3 preset fields is formed, and the text format is all in the format of location - type - monitoring data, achieving the disassembly of the preset fields.

[0095] And during the conversion process, it is necessary to combine the semantics of the identified screening words, including but not limited to screening words such as obtaining, screening out, and excluding.

[0096] In one embodiment, referring to Figure 4 , step S40 includes the steps:

[0097] S41: The retrieved analysis model identifies and retrieves the identification information of different preset fields in the retrieved matching text; and indexes each preset field one by one;

[0098] S42: Based on the time information and cable location number information in the preset field, index to the service node in the service program;

[0099] S43: Based on the data name information in the preset field, screen out the monitoring data associated with the retrieved matching text from all the monitoring data of the service node.

[0100] In this embodiment, all the monitoring data corresponding to the cable location number is stored in each service node. Specifically, the monitoring data that needs to be retrieved further needs to screen and identify all or part or single monitoring data to be obtained through the data name information in the preset field.

[0101] In one embodiment, step S50 includes the steps:

[0102] S51: Traverse the retrieved matching text, identify and determine whether the retrieved matching text contains an analysis requirement field based on the context. The analysis field includes specific analysis methods;

[0103] S52: If it contains an analysis requirement field, it is determined that the current retrieved matching text needs to perform analysis processing;

[0104] S53: If it does not contain an analysis requirement field, it is determined that the current retrieved matching text does not need to perform analysis processing, and the screened monitoring data is sent to the user terminal.

[0105] In this embodiment, the analysis requirement field not only includes the analysis method, but also includes the context part of the analysis method, such as the word "not" in the case of not analyzing. By prestoring known analysis methods in the database, the analysis requirement field is analyzed through semantic recognition and matched with the known analysis methods in the database to determine whether the client needs to perform analysis processing.

[0106] In one embodiment, step S60 includes the steps of:

[0107] S61: Based on the requirement analysis field, obtain an associated analysis framework from a preset algorithm database, where the analysis framework includes an analysis algorithm and a report template;

[0108] S62: Perform operations on the monitoring data through the associated analysis algorithm, fill the calculation results into the corresponding positions of the report template, generate an analysis report, and send it to the client.

[0109] In this embodiment, the analysis algorithm is the algorithm formula in each logical step.

[0110] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0111] In one embodiment, a fiber optic cable monitoring and analysis system based on a large language model is provided. The fiber optic cable monitoring and analysis system based on a large language model corresponds one-to-one with a fiber optic cable monitoring and analysis method based on a large language model in the above embodiment. A fiber optic cable monitoring and analysis system based on a large language model includes:

[0112] A retrieval request module, configured to send a retrieval text box to the client when receiving a monitoring data retrieval request sent by the client;

[0113] An information acquisition module, configured to acquire the retrieval information in the retrieval text box and input it into a preset retrieval analysis model when receiving a retrieval instruction sent by the client, where the retrieval information includes text information;

[0114] A semantic recognition module, configured to perform semantic recognition processing and label filling processing on the retrieval information when the retrieval analysis model receives the retrieval information, to obtain a retrieval matching text;

[0115] A matching module, configured to index a matching service node from a preset service program based on the retrieval matching text, and filter out the monitoring data associated with the retrieval matching text;

[0116] An analysis and judgment module, configured to judge whether the filtered monitoring data needs to perform analysis processing based on the retrieval matching text;

[0117] An analysis sending module, which is configured to, when analysis processing needs to be performed, obtain an associated analysis framework based on retrieved matching text, send monitoring data to the analysis framework for analysis and calculation processing, and output an analysis report and send it to the client.

[0118] Optionally, the semantic recognition module includes:

[0119] A semantic text sub-module, which is configured to output a semantic text corresponding to the retrieved information based on semantic recognition processing by the retrieved analysis model, and the semantic recognition includes context awareness and semantic reasoning;

[0120] A preset field sub-module, which is configured to extract preset fields from the semantic text based on context awareness. The preset fields include time information, optical cable location number information, and data name information of the monitoring data to be retrieved, and the time information and optical cable location number information in the same preset field are the same;

[0121] A filling sub-module, which is configured to perform identification filling processing on the preset fields. The identification filling processing includes retrieving identification information matching the preset fields from a preset database and filling it into the preset fields of the semantic text to obtain retrieved matching text, and the identification information includes path information.

[0122] Optionally, the matching module includes:

[0123] An index recognition sub-module, which is configured to retrieve the analysis model to recognize the identification information of different preset fields in the retrieved matching text; and perform individual indexing on the preset fields;

[0124] An index sub-module, which is configured to index to a service node in a service program based on the time information and optical cable location number information in the preset fields;

[0125] An index screening sub-module, which is configured to screen out monitoring data associated with the retrieved matching text from all the monitoring data of the service node based on the data name information in the preset fields.

[0126] Optionally, the semantic text sub-module includes:

[0127] A text feature recognition unit, which is configured to, when the retrieved analysis model receives the retrieved information, recognize the text features of the current retrieved information based on pre-trained context awareness relationships and semantic reasoning decisions, and determine the text format information of the current retrieved information; the text features include semantic types represented by different fields in the retrieved information, and the semantic structure features formed after sorting different semantic types in the retrieved information;

[0128] A text format recognition unit, which is configured to obtain a text conversion template matching the determined text format information;

[0129] The text conversion unit converts the retrieved information into semantic text based on a text conversion template.

[0130] For the specific limitations of an optical cable monitoring and analysis system based on a large language model, reference can be made to the limitations of an optical cable monitoring and analysis method based on a large language model in the above text, which will not be elaborated here.

[0131] Optionally, the analysis and judgment module includes:

[0132] The analysis and recognition sub-module is used to traverse and retrieve matching texts, identify and judge whether the retrieved matching texts contain analysis requirement fields based on the context, and the analysis fields include specific analysis methods;

[0133] The first analysis execution module is used to determine that the currently retrieved matching text needs to perform analysis processing if it contains analysis requirement fields;

[0134] The second analysis execution module is used to determine that the currently retrieved matching text does not need to perform analysis processing if it does not contain analysis requirement fields, and then send the filtered monitoring data to the user side.

[0135] Optionally, the analysis and sending module includes:

[0136] The framework acquisition sub-module is used to acquire associated analysis frameworks from a preset algorithm database based on the requirement analysis fields. The analysis frameworks include analysis algorithms and report templates;

[0137] The calculation sub-module is used to perform operations on the monitoring data through the associated analysis algorithms, fill the calculation results into the corresponding positions of the report template, generate an analysis report and send it to the user side.

[0138] Each module in the above optical cable monitoring and analysis system based on a large language model can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0139] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 5As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements a method for monitoring and analyzing optical cables based on a large language model.

[0140] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements a method for monitoring and analyzing optical cables based on a large language model.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements a method for monitoring and analyzing optical cables based on a large language model.

[0142] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0144] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring and analyzing optical cables based on a large language model, characterized in that: When a monitoring data retrieval request sent by the user terminal is received, a retrieval text box is sent to the user terminal; When a retrieval instruction sent by the user terminal is received, the retrieval information in the retrieval text box is obtained and input into a preset retrieval analysis model, and the retrieval information includes text information; When the retrieval analysis model receives the retrieval information, it performs semantic recognition processing and label filling processing on the retrieval information to obtain a retrieval matching text; Based on the retrieval matching text, the retrieval analysis model indexes the matching service node from the preset service program and filters out the monitoring data associated with the retrieval matching text; Based on the retrieval matching text, it is judged whether the filtered monitoring data needs to be analyzed and processed; When analysis and processing are required, an associated analysis framework is obtained based on the retrieval matching text, and the monitoring data is sent to the analysis framework for analysis and calculation processing, and an analysis report is output and sent to the user terminal; When the retrieval analysis model receives the retrieval information, it performs semantic recognition processing and label filling processing on the retrieval information to obtain a retrieval matching text, including: The retrieval analysis model outputs a semantic text corresponding to the retrieval information based on semantic recognition processing, and the semantic recognition includes context awareness and semantic reasoning; Based on context awareness, preset fields are extracted from the semantic text. The preset fields include time information, optical cable position number information, and data name information of the monitoring data to be retrieved, and the time information and optical cable position number information in the same preset field are the same; Perform label filling processing on the preset fields. The label filling processing includes retrieving label information matching the preset fields from a preset database and filling it into the preset fields of the semantic text to obtain a retrieval matching text. The label information includes path information; the path information is path index information, which is used to index the service node where the monitoring data is stored in the service SOP; The retrieval analysis model outputs a semantic text corresponding to the retrieval information based on semantic recognition processing, and the semantic recognition includes context awareness and semantic reasoning, including: When the retrieval analysis model receives the retrieval information, it identifies the text features of the current retrieval information and determines the text format information of the current retrieval information based on the pre-trained context awareness relationship and semantic reasoning decision; Obtain a text conversion template that matches the currently determined text format information; Based on the text conversion template, convert the retrieval information into a semantic text; the conversion of the text format combines context awareness and semantic reasoning decision to achieve the decomposition of the text format at the semantic level and the conversion of the text sorting; The identification of the text features of the current retrieval information and the determination of the text format information of the current retrieval information include: the text features include the semantic types represented by different fields in the retrieval information and the semantic structure features formed after the sorting of different semantic types in the retrieval information.

2. The optical cable monitoring and analysis method based on a large language model according to claim 1, wherein, Based on the retrieval matching text, the retrieval analysis model indexes the matching service node from the preset service program and filters out the monitoring data associated with the retrieval matching text, including: Retrieve the analysis model to identify the identification information of different preset fields in the retrieved matching text; and index each preset field one by one; Based on the time information and optical cable location number information in the preset field, index to the service node in the service program; Based on the data name information in the preset field, filter out the monitoring data associated with the retrieved matching text from all the monitoring data of the service node.

3. The optical cable monitoring and analysis method based on a large language model according to claim 1, wherein, The judgment on whether the filtered monitoring data needs to be analyzed and processed based on the retrieved matching text includes: Traverse the retrieved matching text, identify and judge whether the retrieved matching text contains an analysis requirement field based on the context, and the analysis field includes specific analysis methods; If it contains an analysis requirement field, it is determined that the current retrieved matching text needs to be analyzed and processed; If it does not contain an analysis requirement field, it is determined that the current retrieved matching text does not need to be analyzed and processed, and the filtered monitoring data is sent to the user terminal.

4. A method for monitoring and analyzing optical cables based on a large language model according to claim 1, characterized in that When analysis and processing are required, the associated analysis framework is obtained based on the retrieved matching text, and the monitoring data is sent to the analysis framework for analysis and calculation processing, and the analysis report is output and sent to the user terminal, including: Based on the requirement analysis field, obtain the associated analysis framework from the preset algorithm database, and the analysis framework includes analysis algorithms and report templates; Perform operations on the monitoring data through the associated analysis algorithm, fill the calculation results into the corresponding positions of the report template, generate an analysis report and send it to the user terminal.

5. An optical cable monitoring and analysis system based on a large language model, characterized in that: A retrieval request module, configured to send a retrieval text box to the user terminal when receiving a monitoring data retrieval request sent by the user terminal; An information acquisition module, configured to obtain the retrieval information in the retrieval text box and input it into a preset retrieval analysis model when receiving a retrieval instruction sent by the user terminal, and the retrieval information includes text information; A semantic recognition module, configured to perform semantic recognition processing and identification filling processing on the retrieval information when the retrieval analysis model receives the retrieval information, and obtain a retrieved matching text; A matching module, configured to index to a matching service node from a preset service program based on the retrieved matching text, and filter out the monitoring data associated with the retrieved matching text; An analysis judgment module, configured to judge whether the filtered monitoring data needs to be analyzed and processed based on the retrieved matching text; An analysis sending module, configured to obtain the associated analysis framework based on the retrieved matching text when analysis and processing are required, send the monitoring data to the analysis framework for analysis and calculation processing, output an analysis report and send it to the user terminal; The semantic recognition module includes: A semantic text sub-module, configured to output the semantic text corresponding to the retrieval information based on semantic recognition processing by the retrieval analysis model, and the semantic recognition includes context awareness and semantic reasoning; A preset field sub-module, configured to extract preset fields from the semantic text based on context awareness, and the preset fields include the time information, optical cable location number information, and data name information of the monitoring data to be retrieved, and the time information and optical cable location number information in the same preset field are the same; A filling sub-module for performing identification filling processing on a preset field. The identification filling processing includes retrieving identification information matching the preset field from a preset database and filling it into the preset field of the semantic text to obtain a retrieved matching text. The identification information includes path information and is used to index the business node where the monitoring data is stored in the business SOP. The semantic text sub-module includes: A text feature recognition unit for, when the analysis model receives the retrieved information, identifying the text features of the current retrieved information and determining the text format information of the current retrieved information based on the pre-trained context-aware relationship and semantic reasoning decision. The text features include the semantic types represented by different fields in the retrieved information and the semantic structure features formed after sorting different semantic types in the retrieved information. A text format recognition unit for obtaining a text conversion template that matches the currently determined text format information. A text conversion unit for converting the retrieved information into a semantic text based on the text conversion template. The conversion of the text format combines context awareness and semantic reasoning decision to achieve the disassembly of the text format at the semantic level and the conversion of the text sorting.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the fiber optic cable monitoring and analysis method based on the large language model according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the fiber optic cable monitoring and analysis method based on the large language model according to any one of claims 1 to 4.

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