A multi-source heterogeneous data fusion optical cable resource digital modeling method and system

Abstract

This invention relates to a method and system for digital modeling of optical cable resources using multi-source heterogeneous data fusion, belonging to the field of knowledge graph technology. It includes the following steps: constructing an optical cable resource knowledge graph; collecting vibration signals of optical cable segments corresponding to the knowledge graph based on preset distributed acoustic sensors, and obtaining the coordinates of the vibration signal occurrence points using distributed fiber optic acoustic wave data analysis technology; performing spatial mapping processing on the knowledge graph based on the vibration signal occurrence point coordinates to determine the optical cable segment corresponding to the vibration signal occurrence point coordinates; and updating the event information corresponding to the vibration signal to the dynamic attributes of the corresponding optical cable segment in the knowledge graph.

Description

Technical Field

[0001] This invention relates to a digital modeling method and system for optical cable resources based on multi-source heterogeneous data fusion, belonging to the field of knowledge graph technology. Background Technology

[0002] In power communication systems, optical cables serve as the core transmission medium for critical services such as relay protection, dispatch automation, and the Internet of Things in distribution. The accuracy and real-time performance of their resource management directly impact the safe and stable operation of the power grid. Currently, power companies generally adopt a static ledger management model based on the power grid geographic information system (GIS). This model inputs basic information such as the routing, laying method, joint location, and equipment attributes of power communication optical cables into the GIS in the form of graphics and attribute data, forming a two-dimensional or three-dimensional digital ledger corresponding to the actual geographical location. This model relies on manual surveying, design drawings, and as-built documentation for one-time data entry. Once the data is established, it remains fixed for a long time, lacking the dynamic perception and automatic updating capability for changes in the actual state of optical cables caused by construction modifications, external damage, or environmental changes during operation. This easily leads to discrepancies between the map and reality, making it difficult to support accurate operation and maintenance and emergency response.

[0003] Traditional static ledger management has obvious drawbacks: in actual operation, optical cables may experience route deviations, joint replacements, or physical damage due to municipal construction, geological disasters, maintenance and renovation, but these changes are difficult to be fed back to the management system in a timely manner, resulting in "inconsistency between map and reality," which seriously affects the accuracy of fault location and the efficiency of emergency response; traditional maintenance relies on manual inspections or passive reporting after a fault occurs, which cannot provide real-time perception and risk warning of disturbances in the surrounding environment of the optical cable; although distributed optical fiber sensing can acquire vibration and acoustic signals along the line, Beidou and GPS can provide high-precision positioning, and the power grid work order system records work events, these data sources are heterogeneous, semantically fragmented, and have inconsistent spatiotemporal benchmarks, and an effective joint analysis mechanism has not yet been formed, making it difficult to support dynamic modeling and automatic updating of the optical cable status.

[0004] Therefore, there is an urgent need for a new optical cable resource management method that integrates IoT sensing, spatiotemporal information technology and artificial intelligence, which can organically integrate multi-source heterogeneous data, construct a dynamically evolving digital model of optical cables, and realize the automatic correction and updating of static ledgers, thereby improving the digital and intelligent management level of power communication infrastructure. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a digital modeling method and system for optical cable resources based on multi-source heterogeneous data fusion.

[0006] The technical solution of the present invention is as follows: On the one hand, this invention proposes a digital modeling method for optical cable resources based on multi-source heterogeneous data fusion, comprising the following steps: Construct a knowledge graph of optical cable resources; Based on a pre-set distributed acoustic sensor, vibration signals of the optical cable segments corresponding to the optical cable resource knowledge graph are collected, and the coordinates of the vibration signal occurrence point are obtained using distributed optical fiber acoustic wave data analysis technology. Based on the coordinates of the vibration signal occurrence point, spatial mapping processing is performed in the optical cable resource knowledge graph to determine the optical cable segment corresponding to the coordinates of the vibration signal occurrence point. The event information corresponding to the vibration signal is updated to the dynamic attributes of the corresponding optical cable segment in the optical cable resource knowledge graph.

[0007] Preferably, the optical cable resource knowledge graph is constructed according to the following steps: The physical entities in the optical fiber network to be modeled are used as nodes in the knowledge graph. Based on the physical connections and spatial inclusion relationships between the knowledge graph nodes, construct the edge relationships of the knowledge graph; Based on knowledge graph nodes, node attributes are set, and combined with the edge relationships of the knowledge graph, a knowledge graph of optical cable resources is obtained. The physical entities in the optical cable network to be modeled include optical cable segments, junction boxes, poles, substations, and terminal equipment.

[0008] Preferably, the node attributes include static attributes and dynamic attributes, wherein: The static attributes include the name of the physical entity device in the optical cable network to be modeled, the fiber segment affiliation, the fiber segment length range, multiple consecutive geographic coordinate points and their corresponding multiple cumulative fiber distances. The dynamic attributes include historical events, historical alarm records, the type of the last vibration event, the time and level of the last alarm, and the associated work order ID; The geographic coordinates are arranged in the order of the physical equipment's installation path to fully represent the actual connection path of the equipment and construct a mapping between the physical equipment and the real geographic location; the cumulative fiber optic distance is used to represent the length of each geographic coordinate relative to the starting end of the optical cable segment.

[0009] Preferably, the coordinates of the vibration signal generation point are calculated according to the following steps: Using pre-set distributed acoustic sensors, the raw vibration signals of the optical cable segment are collected and pre-processed. The preprocessed original vibration signal is input into the large language model to obtain the vibration event type; at the same time, the Rayleigh scattering signal analysis algorithm is used to obtain the distance along the optical fiber length from the starting end of the optical cable segment where the vibration event occurred, as well as the optical fiber identifier corresponding to the preprocessed vibration signal. Based on the fiber identifier, the corresponding fiber segment node is matched in the fiber cable resource knowledge graph, the static attributes of the fiber segment node are extracted, and multiple continuous geographic coordinate points and their corresponding multiple cumulative fiber distances are obtained. Based on multiple consecutive geographic coordinate points, the first and second target coordinate points that are sequentially adjacent are determined; based on the first and second target coordinate points, the geographic coordinates of the vibration signal generation point are calculated using linear interpolation. Among them, the cumulative fiber distance corresponding to the first target coordinate point is less than the distance along the fiber length, and the cumulative fiber distance corresponding to the second target coordinate point is greater than the distance along the fiber length.

[0010] Preferably, the optical cable segment corresponding to the coordinates of the vibration signal generation point is determined according to the following steps: Based on the fiber identifier and the distance along the fiber, the corresponding end-to-end optical cable is searched in the optical cable resource knowledge graph. Combined with the start and end fiber distance range in the static attributes of each optical cable segment node, the candidate optical cable segment to which the vibration event belongs is initially determined. Calculate the orthogonal projection distance from the geographical coordinates of the vibration signal occurrence point to the candidate optical cable segment, and determine whether the orthogonal projection distance is less than a preset threshold: if the orthogonal projection distance is less than the preset threshold, then the candidate optical cable segment is the optical cable segment corresponding to the coordinates of the vibration signal occurrence point.

[0011] Preferably, when the orthogonal projection distance is not less than a preset threshold, the optical cable segment corresponding to the vibration signal occurrence point coordinates is determined according to the following steps: Using the geographical coordinates of the vibration signal occurrence point as the center, search all optical cable segments within a preset radius in the optical cable resource knowledge graph to construct a new set of candidate optical cable segments; Each optical cable segment in the new candidate optical cable segment set is scored in multiple dimensions; The candidate optical cable segment with the highest multi-dimensional score was selected as the optical cable segment corresponding to the coordinates of the vibration signal occurrence point.

[0012] Preferably, the multi-dimensional scoring includes spatial matching degree, fiber optic distance reasonableness, and historical event support degree, wherein: Spatial matching degree: calculated based on the orthogonal projection distance from the geographical coordinates of the vibration signal occurrence point to the optical cable segment; the smaller the distance, the higher the score. Fiber optic distance rationality: whether the distance along the fiber optic length falls within the fiber optic segment length range in the static attributes of the current optical cable segment; full marks are awarded if it falls within the range, partial marks are awarded if it exceeds the range but is within the preset deviation range, and no marks are awarded if it exceeds both the range and the preset deviation range. Historical event support: Query the historical events in the dynamic attributes of candidate optical cable segments to determine whether the current optical cable segment has experienced a historical event of the same type as the current vibration event in the same area corresponding to the geographical coordinates of the vibration signal occurrence point. If so, add points according to the preset score; if not, do not add points.

[0013] Preferably, the updating of the dynamic attributes also includes updating historical alarm information, which is performed according to the following steps: The alarm-related information is extracted from the preset power grid work order system; the alarm-related information includes alarm type, alarm occurrence time, and alarm-associated physical entity device identifier; Based on the identified vibration event type, it is determined whether the preset alarm triggering conditions are met. If they are met, a new alarm message is generated, which includes the alarm type, alarm triggering time, and alarm level. The new alarm information is updated to the dynamic attributes of the corresponding optical cable segment node, and the historical alarm records, the time and level of the last alarm are updated synchronously, and the corresponding work order ID is associated.

[0014] On the other hand, the present invention also proposes a digital modeling system for optical cable resources that integrates multi-source heterogeneous data, comprising the following modules: Knowledge graph construction module: Constructs a knowledge graph of optical cable resources; Vibration identification and positioning module: Based on a preset distributed acoustic sensor, it collects vibration signals of the optical cable segment corresponding to the optical cable resource knowledge graph, and uses distributed optical fiber acoustic wave data analysis technology to obtain the coordinates of the vibration signal occurrence point; Vibration mapping module: Based on the coordinates of the vibration signal occurrence point, spatial mapping processing is performed in the optical cable resource knowledge graph to determine the optical cable segment corresponding to the coordinates of the vibration signal occurrence point; Knowledge graph update module: Updates the event information corresponding to the vibration signal to the dynamic attributes of the corresponding optical cable segment in the optical cable resource knowledge graph.

[0015] In another aspect, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0016] The present invention has the following beneficial effects: (1) This invention proposes a digital modeling method and system for optical cable resources that integrates multi-source heterogeneous data. By constructing a knowledge graph of optical cable resources that integrates static and dynamic data, it breaks through the limitations of traditional static ledgers. By modeling the physical entities of the optical cable network in the form of a graph, it records both the inherent static attributes of the equipment and sets dynamic attribute fields to achieve synchronous updates of real-time events and alarm information. At the same time, it embeds geographic coordinate sequences and corresponding cumulative fiber distances in nodes to establish a two-way mapping between spatial location and fiber distance, providing reliable data support for subsequent accurate positioning.

[0017] (2) This invention proposes a digital modeling method and system for optical cable resources based on multi-source heterogeneous data fusion. By constructing a two-level matching and multi-dimensional scoring event attribution determination mechanism, the matching accuracy and robustness in complex scenarios are improved. Based on the optical fiber identifier and event distance, the initial attribution of the optical cable segment is quickly located, and then the spatial rationality is verified by combining distance coordinate transformation with orthogonal projection distance. When the matching fails, the nearest search and multi-dimensional re-scoring are initiated, covering spatial matching degree, optical fiber distance rationality, and historical event support degree. This breaks through the limitations of the traditional single matching method and achieves highly reliable alignment between physical perception and digital model.

[0018] (3) This invention proposes a digital modeling method and system for optical cable resources by fusing multi-source heterogeneous data. By using vibration events to automatically trigger dynamic attribute updates of the spectrum nodes, synchronously recording conflict information in the matching process, and driving the ledger correction and model optimization in reverse, a complete dynamic update closed loop is constructed to improve the real-time performance and accuracy of optical cable resource management and get rid of the dilemma of traditional ledgers relying on manual updates and data lag. Attached Figure Description

[0019] Figure 1 This is a flowchart of the digital modeling method for optical cable resources proposed in Embodiment 1 of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0022] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0024] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0025] Example 1: See Figure 1 This embodiment proposes a digital modeling method for optical cable resources based on multi-source heterogeneous data fusion, including the following steps: S100. Identify key physical entities in the power communication optical cable network as graph nodes; Node types include optical cable segments, junction boxes, towers, substations, and terminal equipment (ODF, optical transceivers, etc.), with each entity having a unique identifier for accurate differentiation.

[0026] Edge relationships are defined based on the actual connections between entities. Edge relationships include physical connection relationships and spatial containment relationships, where: Physical connection relationships represent linear connection links between entities, such as the sequential connection between an optical cable segment and a junction box, and between a junction box and another optical cable segment. Spatial inclusion relationships reflect the geographical affiliation between entities, such as: an optical cable segment is laid between two towers, or a junction box is located inside a substation. A complete optical cable network topology is formed through the relationship between nodes and edges.

[0027] S101. Set static and dynamic attributes for each node in the knowledge graph, where: Static attributes are used to define the inherent parameters of physical devices, including device name, fiber segment ownership unit, fiber segment length, a sequence of consecutive geographic coordinate points arranged in the order of the laying path, and the cumulative fiber distance corresponding to each geographic coordinate point. It should be noted that the cumulative fiber distance is the actual length along the fiber from the starting end of the optical cable segment to the corresponding geographical coordinate point. At the same time, the fiber distance interval between the start and end of the optical cable segment is recorded, that is, the cumulative fiber distance range corresponding to the starting and ending ends of the optical cable segment.

[0028] Dynamic attributes are used to record real-time updated operating status information, including historical event records, historical alarm records, the type of the most recent vibration event, the time and level of the most recent alarm, and the associated work order ID. In the initial state, all dynamic attributes are set to blank or default values.

[0029] Based on the knowledge graph nodes with configured static and dynamic attributes, and combined with the edge relationships of the knowledge graph, a knowledge graph of optical cable resources is obtained.

[0030] S200: Collect vibration signals along the optical cable using distributed acoustic sensors deployed on the optical cable; It should be noted that the working principle of this sensor is as follows: continuous laser pulses are emitted into the optical fiber, and the backscattered light signal returned by Rayleigh scattering in the optical fiber is detected. When the optical fiber is subjected to external vibration disturbance, the refractive index or length of the local optical fiber changes slightly, which in turn causes a change in the phase or intensity of the returned light signal. The vibration signal is acquired by capturing this change.

[0031] Furthermore, the collected raw vibration signals are preprocessed. First, high-pass filtering is used to remove low-frequency noise interference, and then signal normalization is performed to adjust the amplitude to a uniform range. The preprocessed vibration signal is input into a large language model, and the vibration event type is identified through feature extraction and pattern matching, such as: excavator digging, car passing by, mechanical crushing, etc. Meanwhile, the Rayleigh scattering signal analysis algorithm is used to calculate core parameters from changes in optical signals, including: the distance along the fiber length from the location of the vibration event to the starting end of the optical cable segment and the unique identifier of the optical fiber that generated the vibration signal. The optical fiber identifier is used to associate the target optical cable node in the knowledge graph.

[0032] S201. Based on the optical fiber identifier, match the corresponding optical cable segment node in the optical cable resource knowledge graph, extract the static attributes of the optical cable segment node, and obtain multiple continuous geographical coordinate points and their corresponding multiple cumulative optical fiber distances. Based on multiple consecutive geographic coordinate points, determine the first and second target coordinate points that are sequentially adjacent; based on the first and second target coordinate points; Among them, the cumulative fiber distance corresponding to the first target coordinate point is less than the distance along the fiber length, and the cumulative fiber distance corresponding to the second target coordinate point is greater than the distance along the fiber length.

[0033] Furthermore, based on the geographic coordinates of the first and second target coordinate points and the cumulative fiber optic distance, the geographic coordinates of the vibration signal generation point are calculated using linear interpolation, expressed by the formula: ; ; In the formula, Indicates the longitude of the point where the vibration signal occurs. Indicates the latitude of the point where the vibration signal occurs. Indicates the longitude of the first target coordinate point. Indicates the latitude of the first target coordinate point. Indicates the longitude of the second target coordinate point. Indicates the latitude of the second target coordinate point. This indicates the distance along the fiber optic cable from the vibration event. This represents the cumulative fiber optic distance corresponding to the first target coordinate point. This represents the cumulative fiber optic distance corresponding to the second target coordinate point.

[0034] S300. Based on the fiber identification and the distance along the fiber length, find the corresponding end-to-end optical cable in the knowledge graph, and combine the start and end fiber distance ranges of each segment node under the optical cable to filter out the optical cable segments that contain the distance along the fiber length and determine them as candidate optical cable segments. The orthogonal projection distance from the vibration signal occurrence point to the candidate optical cable segment route, i.e., the shortest distance between them, is calculated to verify the spatial rationality. This is expressed by the formula: ; In the formula, This represents the orthogonal projection distance from the vibration signal origin to the candidate optical cable segment route; When the orthogonal projection distance is less than a preset threshold, which is set according to the optical cable laying scenario and accuracy requirements, it is used to determine whether the event is located within a reasonable range around the optical cable, and then the candidate optical cable segment is confirmed as the target optical cable segment.

[0035] When the orthogonal projection distance is not less than a preset threshold, the optical cable segment corresponding to the vibration signal occurrence point coordinates is determined according to the following steps: Centered on the vibration signal occurrence point, search the knowledge graph for all optical cable segments within a preset radius to form a new set of candidate optical cable segments; Each optical cable segment in the candidate set is scored from multiple dimensions. The scoring dimensions include spatial matching degree, fiber optic distance rationality, and historical event support degree, among which: Spatial matching score is calculated based on orthogonal projection distance; the smaller the distance, the higher the score. The rationality of the fiber optic distance is evaluated based on whether the distance along the fiber optic length falls within the range of the starting and ending fiber optic distances of the candidate optical cable segment and the degree of deviation. Full marks are awarded if the distance falls within the range, and higher marks are awarded for smaller deviations. Historical event support is determined by querying historical event records in the dynamic attributes of candidate optical cable segments. If a similar vibration event has occurred in the same area, a bonus is awarded; otherwise, a base score is awarded.

[0036] Furthermore, a weighted summation method is used to calculate the comprehensive score for each candidate optical cable segment, expressed by the formula: ; In the formula, This represents the overall score of the candidate optical cable segment. Indicates the spatial matching score. The score indicates the reasonableness of the fiber optic distance. This indicates the support score for historical events. The weights represent the spatial matching score. This indicates the weighting of the fiber optic distance rationality score. This indicates the weighting of the support score for historical events; The candidate optical cable segment with the highest comprehensive score is selected as the final target optical cable segment. Simultaneously, the differences between the initial and final matches are recorded, including the original candidate optical cable segment, the final matched optical cable segment, and the distance deviation value. S400: Update the identified vibration event type, occurrence time, and other information to the dynamic attributes of the target optical cable segment node, and synchronously update the "most recent vibration event type" field to ensure that the dynamic attributes can reflect the operating status of the optical cable in real time. Furthermore, alarm-related information is extracted from the preset power grid work order system; the alarm-related information includes alarm type, alarm occurrence time, and alarm-associated physical entity device identifier; Based on the identified vibration event type, it is determined whether the preset alarm triggering conditions are met. If they are met, a new alarm message is generated, which includes the alarm type, alarm triggering time, and alarm level. The new alarm information is updated to the dynamic attributes of the corresponding optical cable segment node, and the historical alarm records, the time and level of the last alarm are updated synchronously, and the corresponding work order ID is associated.

[0037] Example 2: This embodiment proposes a digital modeling system for optical cable resources based on multi-source heterogeneous data fusion, which includes the following modules: Knowledge graph construction module: Constructs a knowledge graph of optical cable resources; Vibration identification and positioning module: Based on a preset distributed acoustic sensor, it collects vibration signals of the optical cable segment corresponding to the optical cable resource knowledge graph, and uses distributed optical fiber acoustic wave data analysis technology to obtain the coordinates of the vibration signal occurrence point; Vibration mapping module: Based on the coordinates of the vibration signal occurrence point, spatial mapping processing is performed in the optical cable resource knowledge graph to determine the optical cable segment corresponding to the coordinates of the vibration signal occurrence point; Knowledge graph update module: Updates the event information corresponding to the vibration signal to the dynamic attributes of the corresponding optical cable segment in the optical cable resource knowledge graph.

[0038] Example 3: This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention.

[0039] Example 4: This embodiment proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0040] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0041] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0042] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0043] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A digital modeling method for optical cable resources based on multi-source heterogeneous data fusion, characterized in that, Includes the following steps: Construct a knowledge graph of optical cable resources; Based on a pre-set distributed acoustic sensor, vibration signals of the optical cable segments corresponding to the optical cable resource knowledge graph are collected, and the vibration signals are analyzed using distributed optical fiber acoustic data to obtain the coordinates of the vibration signal occurrence point. Based on the coordinates of the vibration signal occurrence point, spatial mapping processing is performed in the optical cable resource knowledge graph to determine the optical cable segment corresponding to the coordinates of the vibration signal occurrence point. The event information corresponding to the vibration signal is updated to the dynamic attributes of the corresponding optical cable segment in the optical cable resource knowledge graph.

2. The method for data-driven modeling of optical cable resources based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The optical cable resource knowledge graph is constructed according to the following steps: The physical entities in the optical fiber network to be modeled are used as nodes in the knowledge graph. Based on the physical connections and spatial inclusion relationships between the knowledge graph nodes, construct the edge relationships of the knowledge graph; Based on knowledge graph nodes, node attributes are set, and combined with the edge relationships of the knowledge graph, a knowledge graph of optical cable resources is obtained. The physical entities in the optical cable network to be modeled include one or more of the following: optical cable segments, junction boxes, poles, substations, and terminal equipment.

3. The method for data-driven modeling of optical cable resources based on multi-source heterogeneous data fusion according to claim 2, characterized in that, The node attributes include static attributes and dynamic attributes, wherein: The static attributes include the name of the physical entity device in the optical cable network to be modeled, the fiber segment affiliation, the fiber segment length range, multiple consecutive geographic coordinate points and their corresponding multiple cumulative fiber distances. The dynamic attributes include one or more of the following: historical events, historical alarm records, type of the last vibration event, time and level of the last alarm, and associated work order ID. The geographic coordinates are arranged in the order of the physical equipment's installation path to fully represent the actual connection path of the equipment and construct a mapping between the physical equipment and the real geographic location; the cumulative fiber optic distance is used to represent the length of each geographic coordinate relative to the starting end of the optical cable segment.

4. The method for data-driven modeling of optical cable resources based on multi-source heterogeneous data fusion according to claim 3, characterized in that, The coordinates of the vibration signal generation point are calculated according to the following steps: Using pre-set distributed acoustic sensors, the raw vibration signals of the optical cable segment are collected and pre-processed. The preprocessed original vibration signal is input into the large language model to obtain the vibration event type; at the same time, the Rayleigh scattering signal analysis algorithm is used to obtain the distance along the optical fiber length from the starting end of the optical cable segment where the vibration event occurred, as well as the optical fiber identifier corresponding to the preprocessed vibration signal. Based on the fiber identifier, the corresponding fiber segment node is matched in the fiber optic cable resource knowledge graph, and the static attributes of the fiber segment node are extracted to obtain multiple continuous geographic coordinate points and their corresponding multiple cumulative fiber distances. Based on multiple consecutive geographic coordinate points, the first and second target coordinate points that are sequentially adjacent are determined; based on the first and second target coordinate points, the geographic coordinates of the vibration signal generation point are calculated using linear interpolation. Among them, the cumulative fiber distance corresponding to the first target coordinate point is less than the distance along the fiber length, and the cumulative fiber distance corresponding to the second target coordinate point is greater than the distance along the fiber length.

5. The method for digitized modeling of optical cable resources by multi-source heterogeneous data fusion according to claim 4, characterized in that, The optical cable segment corresponding to the coordinates of the vibration signal generation point is determined according to the following steps: Based on the fiber identifier and the distance along the fiber length, the corresponding end-to-end optical cable is searched in the optical cable resource knowledge graph. Combined with the start and end fiber distance range in the static attributes of each optical cable segment node, the candidate optical cable segment to which the vibration event belongs is initially determined. Calculate the orthogonal projection distance from the geographical coordinates of the vibration signal occurrence point to the candidate optical cable segment, and determine whether the orthogonal projection distance is less than a preset threshold: if the orthogonal projection distance is less than the preset threshold, then the candidate optical cable segment is the optical cable segment corresponding to the coordinates of the vibration signal occurrence point.

6. The method for data-driven modeling of optical cable resources based on multi-source heterogeneous data fusion according to claim 5, characterized in that, When the orthogonal projection distance is not less than a preset threshold, the optical cable segment corresponding to the vibration signal occurrence point coordinates is determined according to the following steps: Using the geographical coordinates of the vibration signal occurrence point as the center, search all optical cable segments within a preset radius in the optical cable resource knowledge graph to construct a new set of candidate optical cable segments; Each optical cable segment in the new candidate optical cable segment set is scored in multiple dimensions; The candidate optical cable segment with the highest multi-dimensional score was selected as the optical cable segment corresponding to the coordinates of the vibration signal occurrence point.

7. The method for data-driven modeling of optical cable resources based on multi-source heterogeneous data fusion according to claim 6, characterized in that, The multi-dimensional scoring includes spatial matching degree, fiber optic distance reasonableness, and historical event support degree, among which: Spatial matching degree: calculated based on the orthogonal projection distance from the geographical coordinates of the vibration signal occurrence point to the optical cable segment; the smaller the distance, the higher the score. Fiber optic distance rationality: whether the distance along the fiber optic length falls within the fiber optic segment length range in the static attributes of the current optical cable segment; full marks are awarded if it falls within the range, partial marks are awarded if it exceeds the range but is within the preset deviation range, and no marks are awarded if it exceeds both the range and the preset deviation range. Historical event support: Query the historical events in the dynamic attributes of candidate optical cable segments to determine whether the current optical cable segment has experienced a historical event of the same type as the current vibration event in the same area corresponding to the geographical coordinates of the vibration signal occurrence point. If so, add points according to the preset score; if not, do not add points.

8. The method for data-driven modeling of optical cable resources based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The updating of the dynamic attributes also includes updating historical alarm information, which is performed according to the following steps: The alarm-related information is extracted from the preset power grid work order system; the alarm-related information includes alarm type, alarm occurrence time, and alarm-associated physical entity device identifier; Based on the identified vibration event type, it is determined whether the preset alarm triggering conditions are met. If they are met, a new alarm message is generated, which includes the alarm type, alarm triggering time, and alarm level. The new alarm information is updated to the dynamic attributes of the corresponding optical cable segment node, and the historical alarm records, the time and level of the last alarm are updated synchronously, and the corresponding work order ID is associated.

9. A digital modeling system for optical cable resources based on multi-source heterogeneous data fusion, characterized in that, Includes the following modules: Knowledge graph construction module: Constructs a knowledge graph of optical cable resources; Vibration identification and positioning module: Based on a preset distributed acoustic sensor, it collects vibration signals of the optical cable segment corresponding to the optical cable resource knowledge graph, and uses distributed optical fiber acoustic wave data analysis technology to obtain the coordinates of the vibration signal occurrence point; Vibration mapping module: Based on the coordinates of the vibration signal occurrence point, spatial mapping processing is performed in the optical cable resource knowledge graph to determine the optical cable segment corresponding to the coordinates of the vibration signal occurrence point; Knowledge graph update module: Updates the event information corresponding to the vibration signal to the dynamic attributes of the corresponding optical cable segment in the optical cable resource knowledge graph.

10. An electronic 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 program, it implements the method as described in any one of claims 1 to 8.

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