Power transmission line monitoring and early warning system and method based on distributed optical fiber sensing

Abstract

The invention discloses a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, building a distributed optical fiber sensing transmission channel and a segmented reference coordinate system; s2, performing temperature-strain coupling frequency shift feature extraction based on the reference coordinate mapping matrix; s3, time sequence disturbance intensity inversion is carried out according to the coupling frequency shift characteristics; s4, performing adjacent section disturbance coupling rate analysis based on the disturbance intensity evolution sequence; s5, performing disturbance source position inversion and classification identification according to the coupling disturbance structure; s6, performing dynamic threshold early warning by using a disturbance source distribution structure; by setting a disturbance evolution tensor model and a spatial response function, system modeling of disturbance propagation paths, energy evolution and nonlinear interaction characteristics in a power transmission line is realized, the limitation of traditional analysis based on a single-point threshold value is broken through, and the positioning precision and the response analysis capability in a complex disturbance scene are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line status monitoring, and in particular to a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing. Background Art

[0002] As power systems develop towards higher voltage, longer-distance operation, and greater intelligence, transmission lines in complex environments are susceptible to interference from external factors such as breeze vibration, ice shedding, mechanical disturbances, and lightning induction. These factors can lead to abnormal local structural vibration frequencies, phase shifts, or coupling interference. Distributed fiber optic sensing (DOFS) technology, as a monitoring method with continuous spatial perception capabilities, has been initially applied in transmission line scenarios.

[0003] 1. Existing disturbance identification methods are mostly based on single-point vibration characteristics or fixed threshold recognition models. These methods lack the ability to model the spatial evolution characteristics of disturbances along the line and their coupling path relationships. This makes it difficult to achieve structured identification and positioning in complex disturbance contexts. Misjudgments and missed detections are particularly prone to occur in scenarios involving disturbance resonance or multi-point nonlinear interactions.

[0004] 2. In existing technologies, the analysis of disturbance evolution trends often uses linear superposition or frequency-weighted models, which lack the ability to systematically reconstruct the evolutionary structure of disturbance clusters and cannot reveal the spatial aggregation and regional intensity evolution laws between disturbances, resulting in delayed early warning response mechanisms and distorted regional risk assessments. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a transmission line monitoring and early warning system and method based on distributed optical fiber sensing, comprising the following steps:

[0008] S1. Establish a distributed optical fiber sensing transmission channel and a segmented reference coordinate system;

[0009] S2, temperature-strain coupled frequency shift feature extraction based on reference coordinate mapping matrix;

[0010] S3. Inversion of the temporal disturbance intensity based on the coupled frequency shift characteristics;

[0011] S4. Analysis of the coupling rate of disturbances between adjacent segments based on the disturbance intensity evolution sequence;

[0012] S5. Invert and classify the disturbance source position according to the coupled disturbance structure;

[0013] S6. Use the disturbance source distribution structure for dynamic threshold warning.

[0014] Further optimizing the technical solution, in step S1, the optical fiber is arranged as a continuous sensing medium along the transmission line to form a one-dimensional sensing path ,in For the total length of the line, a segmented reference coordinate system is constructed synchronously to divide the line into monitoring segments of equal length. , each segment length is ,satisfy ;

[0015] The initial Brillouin frequency reference value matrix is ​​established in the step:

[0016] .

[0017] Further optimize this technical solution, in step S2, the Brillouin frequency at the current moment is Perform differential mapping to extract the comprehensive frequency shift characteristics of temperature and strain:

[0018] ;

[0019] According to temperature and strain The influence of Brillouin frequency shift is established by establishing a joint characteristic equation:

[0020] .

[0021] To further optimize this technical solution, in step S3, a disturbance response function mapping model is constructed to further identify and classify the disturbance wave structure characteristics. The disturbance response function mapping model is:

[0022] ;

[0023] in,

[0024] : first-order disturbance response term, corresponding to the traditional disturbance intensity;

[0025] : The second-order perturbation curvature term reflects the perturbation acceleration and sharp turning characteristics;

[0026] : Adjust the parameters to adjust the mapping proportion of different disturbance dimensions to the overall response;

[0027] : Local micro-magnitude structure factor, defined as:

[0028] ;

[0029] in, is the average frequency shift value of the local window;

[0030] is the disturbance point in the local micro interval.

[0031] To further optimize this technical solution, the disturbance response function mapping model in step S3 has the following usage method:

[0032] Extraction of local disturbance field evolution trend:

[0033] Auxiliary features for abnormal disturbance classification identification:

[0034] Construction of feature space for clustering of dynamic disturbance patterns.

[0035] To further optimize this technical solution, in step S4, the spatial propagation mode and structural coupling characteristics are expressed by perturbing the spatial evolution tensor function, and its formula model is as follows:

[0036] ;

[0037] in, , :respectively for position 、 The disturbance response function is obtained by step S3;

[0038] : Spatial coupling coefficient function, used to reflect the disturbance from Spread to The spatial path coupling strength of:

[0039] ;

[0040] in, : physical distance, in meters;

[0041] : The disturbance response phase difference is defined as:

[0042] ;

[0043] : Spatial propagation attenuation factor, set based on line material and wave speed;

[0044] : Phase response amplification factor, used to highlight the change in disturbance direction.

[0045] To further optimize this technical solution, the perturbation space evolution tensor function in step S4 has the following usage method:

[0046] Spatial propagation path identification;

[0047] disturbance aggregation and reflection feature recognition;

[0048] Classification of perturbation spatiotemporal patterns aided feature extraction.

[0049] Further optimize this technical solution, in step S5, based on the disturbance space evolution tensor field model, use the disturbance aggregation trend function to reconstruct the model The disturbance space is quantitatively inverted, and the reconstruction formula is:

[0050] ;

[0051] in, , reflecting the disturbance segment The average spatial coupling strength of

[0052] The disturbance source location result is determined by the following formula:

[0053] ;

[0054] That is, in the sliding window The line segment with the largest coupling disturbance trend , which is determined to be the disturbance source location.

[0055] To further optimize this technical solution, in step S5, the structural discrimination capability of disturbance recognition is improved by introducing a normalized disturbance function:

[0056] ;

[0057] And based on its first-order and second-order time derivatives, a set of disturbance morphology identification rules is constructed:

[0058] Sudden disturbance:

[0059] When the absolute value of the first-order derivative of the disturbance function is significantly greater than the criterion threshold , and its second-order derivative is positive, that is:

[0060] ,and ;

[0061] Slowly varying disturbances:

[0062] When the second-order derivative of the disturbance function is less than zero and is accompanied by obvious response lag characteristics, that is:

[0063] , and the response time is delayed significant;

[0064] Continuous disturbance:

[0065] When the integral value of the disturbance function within a certain time window is significantly greater than the criterion threshold ,Right now:

[0066] .

[0067] To further optimize this technical solution, the distributed fiber optic sensing-based transmission line monitoring and early warning system includes the following functional modules: fiber optic sensing data fusion and analysis module, spatial disturbance propagation modeling module, spatial coupling tensor modeling and response function construction module, coupled disturbance structure tensor field reconstruction module, disturbance inversion and type identification module, and dynamic threshold early warning module.

[0068] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing as described in the first aspect of the present invention are implemented.

[0069] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing as described in the first aspect of the present invention are implemented.

[0070] Compared with the existing technology, the present invention provides a transmission line monitoring and early warning system and method based on distributed optical fiber sensing, which has the following beneficial effects:

[0071] This distributed fiber-optic sensing-based transmission line monitoring and early warning system and method establishes a multi-point disturbance coupling identification mechanism by setting a disturbance evolution tensor model and a spatial response function. This system models the disturbance propagation path, energy evolution, and nonlinear interaction characteristics in the transmission line, breaking through the limitations of traditional single-point threshold analysis and effectively improving positioning accuracy and response analysis capabilities in complex disturbance scenarios. Furthermore, by constructing a disturbance level mapping function and a regional early warning response model, combining the disturbance trend quantity, spatial intensity tensor, and response function, it achieves nonlinear mapping and dynamic output of disturbance evolution information to regional early warning levels, enhancing the system's trend identification and early warning response capabilities in multi-source disturbance environments. This method has excellent spatial coupling modeling capabilities and adaptability to evolutionary trends, significantly improving the comprehensive performance of the transmission line monitoring system in terms of early warning sensitivity, response timeliness, and structural identification accuracy, effectively solving the key problem of insufficient reliability of disturbance identification and regional early warning in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0073] Figure 1 This is a schematic diagram of the structure of a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing proposed by the present invention;

[0074] Figure 2 A schematic diagram of a flow chart of a disturbance response function mapping model for a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing proposed by the present invention;

[0075] Figure 3 This is a schematic diagram of the flow of using the disturbance space evolution tensor function of the transmission line monitoring and early warning system and method based on distributed optical fiber sensing proposed by the present invention. DETAILED DESCRIPTION

[0076] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0077] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0078] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0079] Example 1:

[0080] Reference Figures 1 to 3 , which is the first embodiment of the present invention, provides a transmission line monitoring and early warning method based on distributed optical fiber sensing, comprising the following steps:

[0081] S1. Establish a distributed optical fiber sensing transmission channel and a segmented reference coordinate system;

[0082] In step S1, the optical fiber is arranged as a continuous sensing medium along the transmission line to form a one-dimensional sensing path. ,in For the total length of the line, a segmented reference coordinate system is constructed synchronously to divide the line into monitoring segments of equal length. , each segment length is ,satisfy ;

[0083] The initial Brillouin frequency reference value matrix is ​​established in the step:

[0084] .

[0085] S2, temperature-strain coupled frequency shift feature extraction based on reference coordinate mapping matrix;

[0086] In step S2, the Brillouin frequency at the current moment is Perform differential mapping to extract the comprehensive frequency shift characteristics of temperature and strain:

[0087] ;

[0088] According to temperature and strain The influence of Brillouin frequency shift is established by establishing a joint characteristic equation:

[0089] .

[0090] S3. Inversion of the temporal disturbance intensity based on the coupled frequency shift characteristics;

[0091] In step S3, a disturbance response function mapping model is constructed to further identify and classify the disturbance wave structure characteristics. The disturbance response function mapping model is:

[0092] ;

[0093] in,

[0094] : first-order disturbance response term, corresponding to the traditional disturbance intensity;

[0095] : The second-order perturbation curvature term reflects the perturbation acceleration and sharp turning characteristics;

[0096] : Adjust the parameters to adjust the mapping proportion of different disturbance dimensions to the overall response;

[0097] : Local micro-magnitude structure factor, defined as:

[0098] ;

[0099] in, is the average frequency shift value of the local window;

[0100] is the disturbance point in the local micro interval;

[0101] The disturbance response function mapping model in step S3 has the following usage method:

[0102] Extraction of local disturbance field evolution trend:

[0103] Can directly replace the original disturbance intensity model , used to draw the disturbance time series profile;

[0104] Higher-order response terms can significantly amplify the non-stationary section of the disturbance and improve the boundary recognition ability;

[0105] Auxiliary features for abnormal disturbance classification identification:

[0106] If a disturbance is mainly caused by Dominant, can be judged as high-frequency vibration type disturbance;

[0107] like If it is greater than the first-order term, it is a catastrophic disturbance;

[0108] Dynamic disturbance pattern clustering feature space construction:

[0109] For all calculate , and construct the disturbance response space matrix:

[0110] ,

[0111] It can be used for subsequent cluster analysis of disturbance source types;

[0112] A spatially coupled disturbance model is constructed to characterize the nonlinear interaction relationship of multi-point disturbances in the transmission line. Different from the existing mode based on single-point signal or fixed transmission path analysis, this method can identify the implicit coupling and nonlinear response evolution mechanism between disturbances, realize structured modeling of the disturbance propagation process, and improve the accuracy of abnormal source positioning and the expressive ability of the system model.

[0113] S4. Analysis of the coupling rate of disturbances between adjacent segments based on the disturbance intensity evolution sequence;

[0114] In step S4, the spatial propagation mode and structural coupling characteristics are expressed by perturbing the spatial evolution tensor function, and the formula model is as follows:

[0115] ;

[0116] in, , :respectively for position 、 The disturbance response function is obtained by step S3;

[0117] : Spatial coupling coefficient function, used to reflect the disturbance from Spread to The spatial path coupling strength of:

[0118] ;

[0119] in, : physical distance, in meters;

[0120] : The disturbance response phase difference is defined as:

[0121] ;

[0122] : Spatial propagation attenuation factor, set based on line material and wave speed;

[0123] : Phase response amplification factor, used to highlight the change in disturbance direction;

[0124] The perturbation space evolution tensor function in step S4 has the following usage method:

[0125] Spatial propagation path identification:

[0126] For all Build matrix;

[0127] If Significantly higher than the surrounding area, it can be inferred that the disturbance comes from Towards Clear dissemination;

[0128] It can be used to construct a “disturbance migration path map”;

[0129] Disturbance aggregation and reflection feature recognition:

[0130] If a position There are multiple If it is concentrated, it is the disturbance focus position;

[0131] like , then there is directional disturbance; otherwise, it means that local reflection or attenuation may occur;

[0132] Disturbance spatiotemporal pattern classification assisted feature extraction:

[0133] According to The time series characteristics construct the disturbance propagation vector field;

[0134] Cluster analysis of the spatial propagation patterns of different disturbances, including radial, progressive, and chain conduction types;

[0135] Constructing the perturbation space coupling tensor field and the mean intensity tensor , used to identify disturbance clusters and evolution trend clusters. Unlike the traditional method that relies on signal mutation threshold judgment, this scheme uses tensor field structure to reconstruct the spatial distribution characteristics of disturbances, which can reveal the structural evolution law in the disturbance conduction process. It is particularly suitable for disturbance cluster identification and evolution analysis in multi-source interference resonance scenarios.

[0136] S5. Invert and classify the disturbance source position according to the coupled disturbance structure;

[0137] In step S5, based on the disturbance space evolution tensor field model, the disturbance aggregation trend function is used to reconstruct the model The disturbance space is quantitatively inverted, and the reconstruction formula is:

[0138] ;

[0139] in, , reflecting the disturbance segment The average spatial coupling strength of

[0140] The disturbance source location result is determined by the following formula:

[0141] ;

[0142] That is, in the sliding window The line segment with the largest coupling disturbance trend , which is determined to be the disturbance source location;

[0143] In step S5, the structural discrimination capability of disturbance recognition is improved by introducing a normalized disturbance function:

[0144] ;

[0145] And based on its first-order and second-order time derivatives, a set of disturbance morphology identification rules is constructed:

[0146] Sudden disturbance:

[0147] When the absolute value of the first-order derivative of the disturbance function is significantly greater than the criterion threshold , and its second-order derivative is positive, that is:

[0148] ,and ;

[0149] Slowly varying disturbances:

[0150] When the second-order derivative of the disturbance function is less than zero and is accompanied by obvious response lag characteristics, that is:

[0151] , and the response time is delayed significant;

[0152] Continuous disturbance:

[0153] When the integral value of the disturbance function within a certain time window is significantly greater than the criterion threshold ,Right now:

[0154] .

[0155] S6. Use the disturbance source distribution structure for dynamic threshold warning;

[0156] When using this method to perform dynamic threshold warning, the disturbance level mapping function and the regional warning response model are used, including:

[0157] Construction of disturbance level mapping function family

[0158] First, introduce the disturbance level mapping function , which is expressed as:

[0159] ;

[0160] in, : Perturbation trend response function, output by step S5;

[0161] : Regional average response tensor, output by step S4;

[0162] : Response function, output by step S3;

[0163] : nonlinear mapping function, used to uniformly convert disturbance evolution trend, intensity density and evolution response into disturbance level index;

[0164] Construct a nonlinear perturbation level expression to achieve quantifiable analytical capabilities of the mapping function:

[0165] ;

[0166] in:

[0167] 、 is the nonlinear amplification coefficient and suppression scale factor of the disturbance level;

[0168] Control the mapping compression of the response function under sudden intensity changes;

[0169] Exponential amplification and power suppression constitute an asymmetric disturbance response adjustment mechanism;

[0170] Construction of regional early warning response model

[0171] Constructing regional cluster disturbance weight function , used for regional spatial warning fusion output:

[0172] ;

[0173] in:

[0174] : Indicates the area number;

[0175] :area The set of monitored optical fiber sub-segments included;

[0176] : normalized coupling coefficient in disturbance space;

[0177] Characterize the overall disturbance level intensity of the region;

[0178] When using the disturbance level mapping function and regional warning response model:

[0179] when Exceeding the preset regional response threshold When the system triggers a regional warning;

[0180] in accordance with Set up three levels of regional warning, including routine, concern, and emergency;

[0181] Combine The change gradient of the region is used to predict the future evolution trend of the region;

[0182] Constructing a perturbation level mapping function and regional early warning response model , realizing the dynamic conversion from disturbance trend information to spatial warning level output. Different from the traditional warning mechanism based on fixed thresholds or simple classification rules, this method introduces a tensor-driven warning index function, and constructs a high-dimensional mapping relationship by coupling trend, intensity and response, so that the warning system has evolutionary adaptability and regional fusion capabilities, significantly improving the accuracy of warnings and response precision.

[0183] Example 2:

[0184] Reference Figures 1 to 3, which is the second embodiment of the present invention, provides a power transmission line monitoring and early warning system based on distributed optical fiber sensing. The power transmission line monitoring and early warning system based on distributed optical fiber sensing includes the following functional modules:

[0185] Fiber optic sensing data fusion analysis module: responsible for accessing the Raman / Brillouin signals obtained by the distributed fiber optic sensing system and constructing the multi-dimensional disturbance characteristic matrix of the line segment ;

[0186] Spatial disturbance propagation modeling module: Constructs disturbance propagation function based on the output of fiber optic sensing data fusion analysis module , to achieve the analysis of disturbance spatial propagation relations and identify interference transmission paths;

[0187] Spatial coupling tensor modeling and response function building blocks: Introducing the perturbation evolution tensor With the response function , establish a disturbance source coupling analysis model to achieve nonlinear correlation characterization;

[0188] Coupled perturbation structure tensor field reconstruction module: Use S3 results to construct the perturbation space coupling tensor field With the mean intensity tensor , extract evolution trends and disturbance clusters;

[0189] Perturbation inversion and type identification module: Based on the output of the coupled perturbation structure tensor field reconstruction module, using the trend function Invert the disturbance source location and combine it with the response derivative criterion to complete the disturbance type classification;

[0190] Dynamic threshold warning module: Based on the disturbance classification and evolution trend quantification results, it constructs a disturbance level index, generates warning outputs that can be directly called by the scheduling system, and forms a regional disturbance warning label mapping map.

[0191] The transmission line monitoring and early warning system based on distributed fiber optic sensing includes the following functional modules: fiber optic sensing data fusion and analysis module, spatial disturbance propagation modeling module, spatial coupling tensor modeling and response function construction module, coupled disturbance structure tensor field reconstruction module, disturbance inversion and type identification module and dynamic threshold early warning module.

[0192] Example 3:

[0193] This embodiment also provides a computer device, which is suitable for a transmission line monitoring and early warning system and method based on distributed optical fiber sensing, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a transmission line monitoring and early warning system and method based on distributed optical fiber sensing as proposed in the above embodiment.

[0194] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, a power transmission line monitoring and early warning system and method based on distributed optical fiber sensing as proposed in the above embodiment is implemented.

[0195] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0196] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0197] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0198] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0199] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0200] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A transmission line monitoring and early warning method based on distributed optical fiber sensing, characterized in that: The following steps are involved: S1. Establish a distributed optical fiber sensing transmission channel and a segmented reference coordinate system; S2, temperature-strain coupled frequency shift feature extraction based on reference coordinate mapping matrix; S3. Inversion of the temporal disturbance intensity based on the coupled frequency shift characteristics; S4. Analysis of the coupling rate of disturbances between adjacent segments based on the disturbance intensity evolution sequence; S5. Invert and classify the disturbance source position according to the coupled disturbance structure; S6. Use the disturbance source distribution structure for dynamic threshold warning.

2. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 1, characterized in that: In step S1, the optical fiber is arranged as a continuous sensing medium along the transmission line to form a one-dimensional sensing path. ,in For the total length of the line, a segmented reference coordinate system is constructed synchronously to divide the line into monitoring segments of equal length. , each segment length is ,satisfy ; The initial Brillouin frequency reference value matrix is ​​established in the step: 。 3. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 1, characterized in that: In step S2, the Brillouin frequency at the current moment is Perform differential mapping to extract the comprehensive frequency shift characteristics of temperature and strain: ； According to temperature and strain The influence of Brillouin frequency shift is established by establishing a joint characteristic equation: 。 4. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 1, characterized in that: In step S3, a disturbance response function mapping model is constructed to further identify and classify the disturbance wave structure characteristics. The disturbance response function mapping model is: ； in, : first-order disturbance response term, corresponding to the traditional disturbance intensity; : The second-order perturbation curvature term reflects the perturbation acceleration and sharp turning characteristics; : Adjust the parameters to adjust the mapping proportion of different disturbance dimensions to the overall response; : Local micro-magnitude structure factor, defined as: ； in, is the average frequency shift value of the local window; is the disturbance point in the local micro interval.

5. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 4, characterized in that: The disturbance response function mapping model in step S3 has the following usage method: Extraction of local disturbance field evolution trend; Auxiliary features for identifying abnormal disturbance categories; Construction of feature space for clustering of dynamic disturbance patterns.

6. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 1, characterized in that: In step S4, the spatial propagation mode and structural coupling characteristics are expressed by perturbing the spatial evolution tensor function, and the formula model is as follows: ； in, , :respectively for position 、 The disturbance response function is obtained by step S3; : Spatial coupling coefficient function, used to reflect the disturbance from Spread to The spatial path coupling strength of: ； in, : physical distance, in meters; : The disturbance response phase difference is defined as: ； : Spatial propagation attenuation factor, set based on line material and wave speed; : Phase response amplification factor, used to highlight the change in disturbance direction.

7. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 6, characterized in that: The perturbation space evolution tensor function in step S4 has the following usage method: Spatial propagation path identification; disturbance aggregation and reflection feature recognition; Classification of disturbed spatiotemporal patterns aided feature extraction.

8. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 1, characterized in that: In step S5, based on the disturbance space evolution tensor field model, the disturbance aggregation trend function is used to reconstruct the model The disturbance space is quantitatively inverted, and the reconstruction formula is: ； in, , reflecting the disturbance segment The average spatial coupling strength of The disturbance source location result is determined by the following formula: ； That is, in the sliding window The line segment with the largest coupling disturbance trend , which is determined to be the disturbance source location.

9. The method for monitoring and early warning of power transmission lines based on distributed optical fiber sensing according to claim 8, characterized in that: In step S5, the structural discrimination capability of disturbance recognition is improved by introducing a normalized disturbance function: ； And based on its first-order and second-order time derivatives, a set of disturbance morphology identification rules is constructed: Sudden disturbance: When the absolute value of the first-order derivative of the disturbance function is significantly greater than the criterion threshold , and its second-order derivative is positive, that is: ,and ; Slowly varying disturbances: When the second-order derivative of the disturbance function is less than zero and is accompanied by obvious response lag characteristics, that is: , and the response time is delayed significant; Continuous disturbance: When the integral value of the disturbance function within a certain time window is significantly greater than the criterion threshold ,Right now: 。 10. The power transmission line monitoring and early warning system based on distributed optical fiber sensing according to claim 1, characterized in that: The transmission line monitoring and early warning system based on distributed fiber optic sensing includes the following functional modules: fiber optic sensing data fusion and analysis module, spatial disturbance propagation modeling module, spatial coupling tensor modeling and response function construction module, coupled disturbance structure tensor field reconstruction module, disturbance inversion and type identification module and dynamic threshold early warning module.

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