Power transmission line defect detection method and system based on deep learning

Abstract

According to the deep learning-based power transmission line defect detection method and system provided by the invention, the plurality of first defect circuit information sets are determined from the first line setting directory, and then the influence vectors among the first defect circuit information sets are determined through the hidden vectors of the first defect circuit information sets; since the first influence vector of each first defective circuit information set is determined based on the hidden vector of each first defective circuit information set, the influence vectors among the first defective circuit information sets cover hidden information in the first part of power transmission line data; the process of optimizing the first defect description attribute by using the first influence vector is equivalent to optimizing the first defect description attribute of each first defect circuit information set based on hidden information in the first power transmission line data; therefore, influence data related to hiding and contained in the first part of power transmission line data caused by hiding can be weakened, and the accuracy of data detection is improved.

Description

Technical Field

[0001] The present application relates to the technical field of defect detection, and specifically, to a method and system for power transmission line defect detection based on deep learning. Background Art

[0002] The transmission line is realized by using a transformer to step up the voltage of the electric energy generated by the generator, and then connecting it to the transmission line through control equipment such as circuit breakers. In terms of structural form, the transmission line is divided into overhead transmission lines and cable lines. This process is to ensure the normal use of electrical equipment, which can not only ensure the normal use of electricity in the hospital, but also ensure the normal operation of important equipment, and effectively protect people's quality of life.

[0003] However, if there are defects in the transmission line, various problems may occur, but there is no quick way to detect them. As a result, it takes a certain amount of time to restore power. Therefore, there is an urgent need for a transmission line defect detection technology to overcome the above technical problems. Summary of the invention

[0004] In order to improve the technical problems existing in the related technologies, the present application provides a transmission line defect detection method and system based on deep learning.

[0005] In a first aspect, a transmission line defect detection method based on deep learning is provided, the method at least comprising: obtaining partial knowledge fragments of a first line setting directory in the transmission line data to be detected; determining multiple first defect circuit information sets from the first line setting directory through the obtained partial knowledge fragments, and selecting the first defect description attribute of each first defect circuit information set; determining the hidden core hidden in the first line setting directory, and determining the first hidden vector hidden by each first defect circuit information set through the detected hidden core; obtaining the first influence vector between each first defect circuit information set detected by the first hidden vector obtained by statistics; optimizing each first defect description attribute through the obtained first influence vector, comparing the optimized first defect description attribute with the second defect description attribute, and obtaining the defect detection result of the first line setting directory, wherein the second defect description attribute is: the key content obtained by optimizing the defect description attribute of each second defect circuit information set through each second influence vector, and each second defect circuit information set is: the knowledge fragment corresponding to each first defect circuit information set in the second line setting directory set in the partial transmission line data.

[0006] In the present application, the method of comparing the optimized first defect description attribute with the second defect description attribute to obtain the defect detection result of the first circuit setting directory includes: determining the similarity knowledge fragments between the optimized first defect description attributes of each first defect circuit information set and the corresponding second defect description attributes, and determining them as the similarity knowledge fragments corresponding to each first defect circuit information set; determining the trust weights of the first defect description attributes of each first defect circuit information set for the characteristics of the first circuit setting directory based on the first hidden vectors of each first defect circuit information set; integrating the similarity knowledge fragments corresponding to each first defect circuit information set through the detected trust weights to obtain the integrated processing results, and determining them as the similarity knowledge fragments between the first circuit setting directory and the second circuit setting directory; and determining the defect detection result of the first circuit setting directory through the obtained similarity knowledge fragments.

[0007] In the present application, the method of determining the trusted weight of the first defect description attribute of each first defect circuit information set for the characteristics of the first line setting directory based on the first hidden vector of each first defect circuit information set includes: determining the trusted weight of the first defect description attribute of each first defect circuit information set for the characteristics of the first line setting directory based on the first hidden vector of each first defect circuit information set and the second hidden vector of the corresponding second defect circuit information set.

[0008] In the present application, the optimized first defect description attribute is compared with the second defect description attribute to obtain the defect detection result of the first circuit setting directory, including: combining the first defect description attributes of each first defect circuit information set through the first hidden vector of each first defect circuit information set to obtain a first combined processing vector, and combining the second defect description attributes of each second defect circuit information set through the second hidden vector of each second defect circuit information set to obtain a second combined processing vector; determining the similarity knowledge fragment between the first combined processing vector and the second combined processing vector; and determining the defect detection result of the first circuit setting directory by determining the obtained similarity knowledge fragment.

[0009] In the present application, the first impact vector is determined according to the following expression: Effect Pro = max (hidden vector effect vector, threshold) x (1-hidden vector effect vector), wherein Effect Pro is the first impact vector of the first defective circuit information set Defect Electron on the first defective circuit information set effect vector, the hidden vector effect vector represents the hidden vector of the first defective circuit information set effect vector, and threshold is the set threshold.

[0010] In the present application, the method of optimizing each first defect description attribute by acquiring the first influence vector and comparing the optimized first defect description attribute with the second defect description attribute to obtain the defect detection result of the first line setting directory includes: loading each first defect description attribute, the first influence vector between each first defect circuit information set, the defect description attribute of each second defect circuit information set, and the second influence vector between each second defect circuit information set into a pre-trained defect detection network, so that the defect detection network optimizes each first defect description attribute based on each first influence vector to obtain the optimized first defect description attribute, and optimizes the defect description attribute of each second defect circuit information set based on each second influence vector to obtain the second defect description attribute, and compares the optimized first defect description attribute with the second defect description attribute to output the defect detection result; and obtaining the defect detection result output by the defect detection network.

[0011] In the present application, the method of determining multiple first defective circuit information sets from the first line setting directory through the acquired partial knowledge fragments includes: determining the knowledge fragment category to which each partial knowledge fragment belongs through the key category of the acquired partial knowledge fragment and the association relationship between the set partial knowledge fragment category and the knowledge fragment category of the first defective circuit information set; for each knowledge fragment category, based on the position information of the partial knowledge fragment belonging to the knowledge fragment category, obtaining the standard position of the defective circuit information set belonging to the knowledge fragment category, and determining the knowledge fragment difference between the standard position of the defective circuit information set belonging to the knowledge fragment category and the standard position of the adjacent defective circuit information set, and determining the knowledge fragment vector of the defective circuit information set belonging to the knowledge fragment category through the detected knowledge fragment difference; determining the first defective circuit information set to which each standard position belongs based on the acquired standard position and the determined knowledge fragment vector.

[0012] In the present application, the selection of partial description content in each first defective circuit information set includes: selecting a global knowledge vector set of the first line setting directory in the transmission line data to be detected; determining the knowledge fragments corresponding to each first defective circuit information set in the global knowledge vector set based on the position of each first defective circuit information set in the first line setting directory; debugging the knowledge fragments of each first defective circuit information set according to the set knowledge vector set, and generating a vector that is a knowledge vector of the set knowledge vector set; determining the partial description content corresponding to the knowledge vector of each first defective circuit information set, and determining it as the partial description content in each first defective circuit information set.

[0013] In a second aspect, a deep learning-based transmission line defect detection system is provided, comprising a processor and a memory that communicate with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0014] A deep learning-based transmission line defect detection method and system provided in an embodiment of the present application determines multiple first defect circuit information sets from a first line setting directory, and then determines the influence vectors between each first defect circuit information set through the hidden vectors of each first defect circuit information set. Since the first influence vector of each first defect circuit information set is obtained based on the hidden vector of each first defect circuit information set, the influence vector between the first defect circuit information sets covers the hidden information in the first part of the transmission line data. The process of optimizing the first defect description attribute using the first influence vector is equivalent to optimizing the first defect description attribute of each first defect circuit information set based on the hidden information in the first transmission line data, thereby weakening the hidden-related influence data covered in the first part of the transmission line data caused by hiding, thereby improving the accuracy of data detection.

[0015] Furthermore, the influence vectors between the first defective circuit information sets are determined through the hidden vectors of the first defective circuit information sets, thereby realizing the analysis of the relationship between the first defective circuit information sets, and then using the analyzed relationship between the knowledge fragments to realize data processing, further improving the accuracy of data detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1A flowchart of a power transmission line defect detection method based on deep learning provided in an embodiment of the present application.

[0018] Figure 2 A block diagram of a power transmission line defect detection device based on deep learning provided in an embodiment of the present application.

[0019] Figure 3 An architectural diagram of a power transmission line defect detection system based on deep learning provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0021] See also Figure 1 , shows a method for detecting power transmission line defects based on deep learning, which may include the technical solutions described in the following steps S101-S105.

[0022] S101: Acquire a partial knowledge fragment of a first line setting directory in the power transmission line data to be detected.

[0023] S102: Determine a plurality of first defective circuit information sets from a first circuit setting catalog using the acquired partial knowledge fragments, and select a first defect description attribute of each first defective circuit information set.

[0024] S103: Determine the hidden core hidden in the first circuit setting directory, and determine the first hidden vector hidden in each first defective circuit information set through the detected hidden core.

[0025] S104: Obtain a first influence vector between first defective circuit information sets detected by the first hidden vector obtained by statistics.

[0026] S105: Optimize each first defect description attribute through the obtained first influence vector, compare the optimized first defect description attribute with the second defect description attribute, and obtain the defect detection result of the first line setting directory, wherein the second defect description attribute is: the key content obtained by optimizing the defect description attribute of each second defect circuit information set through each second influence vector, and each second defect circuit information set is: the knowledge fragment corresponding to each first defect circuit information set in the second line setting directory set in the partial transmission line data.

[0027] The disclosed embodiment provides a transmission line defect detection method based on deep learning, wherein a plurality of first defect circuit information sets are determined from a first line setting directory, and then influence vectors between the first defect circuit information sets are determined through hidden vectors of the first defect circuit information sets. Since the first influence vectors of the first defect circuit information sets are obtained based on the hidden vectors of the first defect circuit information sets, the influence vectors between the first defect circuit information sets cover the hidden information in the first part of the transmission line data. The process of optimizing the first defect description attribute using the first influence vector is equivalent to optimizing the first defect description attribute of each first defect circuit information set based on the hidden information in the first transmission line data, thereby weakening the influence data related to the hiding caused by the hiding, and improving the accuracy of data detection.

[0028] Furthermore, similar knowledge fragments between each first defective circuit information set are determined through the hidden vectors of each first defective circuit information set, thereby realizing the analysis of the relationship between each first defective circuit information set, and then using the analyzed relationship between the knowledge fragments to realize data processing, further improving the accuracy of data detection.

[0029] For the second line setting directory in the partial transmission line data, the second defect circuit information set, the defect description attributes of the second defect circuit information set, the second influence vectors between the second defect circuit information sets, and the second defect description attributes obtained by optimizing the defect description attributes based on the second influence vectors can be detected in advance.

[0030] Optionally, the data passing through part of the transmission line is processed according to steps S101-S104 to obtain a second defect description attribute of a second defect circuit information set of a second line setting directory in the data passing through part of the transmission line.

[0031] In one possible implementation embodiment, for each first defect circuit information set, the second defect circuit information set corresponding to the first defect circuit information set may be: a second defect circuit information set whose position in the second circuit setting directory is the same as the position of the first defect circuit information set in the first circuit setting directory, or a second defect circuit information set whose knowledge fragment category is the same as the knowledge fragment category of the first defect circuit information set, or a second defect circuit information set that meets both of the above requirements.

[0032] When the knowledge segment category of the first defective circuit information set is a local knowledge segment category, the second defective circuit information set corresponding to the first defective circuit information set may be: a second defective circuit information set whose knowledge segment category in the second line setting directory is a local knowledge segment category.

[0033] In one embodiment of the present disclosure, the above-mentioned step S105 can be implemented based on a pre-trained defect detection network, and specifically can include the following steps: loading each first defect description attribute, each first influence vector between the first defect circuit information sets, each defect description attribute of the second defect circuit information set, and each second influence vector between the second defect circuit information sets into the pre-trained defect detection network, so that the defect detection network optimizes each first defect description attribute based on each first influence vector to obtain an optimized first defect description attribute, and optimizes the defect description attribute of each second defect circuit information set based on each second influence vector to obtain a second defect description attribute, and compares the optimized first defect description attribute with the second defect description attribute to output a defect detection result; and obtains the defect detection result output by the defect detection network.

[0034] Furthermore, the first defect description attribute of each first defective circuit information set, the defect description attribute of each second defective circuit information set, and an adjacent data set recording the first influence vectors between the first defective circuit information sets and the second influence vectors between the second defective circuit information sets are regarded as inputs of the defect detection network.

[0035] The embodiment of the present disclosure also provides a method for detecting power transmission line defects based on deep learning, and the implementation of step S105 may specifically include the following contents.

[0036] S201: Determine similarity knowledge segments between the optimized first defect description attributes of each first defective circuit information set and the corresponding second defect description attributes, and determine them as similarity knowledge segments corresponding to each first defective circuit information set.

[0037] In this step, the similarity knowledge segments between the optimized first defect description attribute and the second defect description attribute can be determined by the knowledge segment queues corresponding to the first defect description attribute and the second defect description attribute. By determining the cosine similarity knowledge segments between the knowledge segment queue corresponding to the first defect description attribute and the knowledge segment queue corresponding to the second defect description attribute, the similarity knowledge segments between the optimized first defect description attribute and the corresponding second defect description attribute are determined. Optionally, the larger the cosine similarity knowledge segments, the higher the similarity knowledge segments.

[0038] S202: Determine, based on the first hidden vector of each first defective circuit information set, a trust weight of a first defect description attribute of each first defective circuit information set for a feature of a first circuit setting catalog.

[0039] In this step, the larger the first hidden vector is, the more influencing data is covered in the first defect description attribute of the first defect circuit information set, and the larger the first hidden vector is, the smaller the trust weight of the first defect description attribute of the first defect circuit information set for the characteristics of the first line setting directory is.

[0040] Optionally, in one implementation, an association relationship between hidden vectors and trusted weights may be established first, and after determining the first hidden vectors of each first defective circuit information set, the trusted weights corresponding to the first hidden vectors of each first defective circuit information set may be determined based on the association relationship.

[0041] Optionally, in another implementation, each defective circuit information set may be evaluated based on the first hidden vector of each defective circuit information set, and the trust weight of the evaluation score of each defective circuit information set may be regarded as the trust weight of each first defective circuit information set.

[0042] Optionally, in another implementation, the trust weight of the first defect description attribute of each first defect circuit information set for the feature of the first line setting directory can also be determined based on the first hidden vector of each first defect circuit information set and the second hidden vector of the corresponding second defect circuit information set.

[0043] S203: integrating the similarity knowledge segments corresponding to the first defective circuit information sets according to the detected trust weights to obtain an integration result, which is determined as the similarity knowledge segments of the first circuit setting catalog and the second circuit setting catalog.

[0044] S204: Determine the defect detection result of the first line setting catalog by using the obtained similarity knowledge fragments.

[0045] In this step, a similarity knowledge segment threshold may be set. When the obtained similarity knowledge segment is greater than the similarity knowledge segment threshold, it is determined that the user information of the first line setting directory is the same as the user information of the second line setting directory, otherwise, they are different.

[0046] A transmission line defect detection method based on deep learning provided in an embodiment of the present disclosure is the basis for the beneficial effects of a transmission line defect detection method based on deep learning, and provides a technical solution for comparing an optimized first defect description attribute with a second defect description attribute. Since the similarity knowledge fragments of each first defect description attribute are determined separately, the workload of each detection can be reduced.

[0047] On the premise of a power transmission line defect detection method based on deep learning, the embodiment of the present disclosure also provides a power transmission line defect detection method based on deep learning, and the implementation of step S105 may specifically include the following contents.

[0048] S301: Combining and processing the first defect description attributes of each first defect circuit information set through the first hidden vector of each first defect circuit information set to obtain a first combined processing vector, and combining and processing the second defect description attributes of each second defect circuit information set through the second hidden vector of each second defect circuit information set to obtain a second combined processing vector.

[0049] The second defect description attributes of each second defective circuit information set are combined with the first defect description attributes of each first defective circuit information set.

[0050] S302: Determine similar knowledge segments between the first combined processing vector and the second combined processing vector.

[0051] S303: Determine the defect detection result of the first line setting catalog by determining the obtained similarity knowledge fragments.

[0052] The disclosed embodiment provides a transmission line defect detection method based on deep learning, a basis for the beneficial effects of a transmission line defect detection method based on deep learning, and provides a technical solution for comparing an optimized first defect description attribute with a second defect description attribute. Since the processing vectors are combined first and then the similarity knowledge fragments of the features are determined, the determination steps can be simplified and the efficiency of the combined processing can be improved.

[0053] On the premise of the above-mentioned deep learning-based transmission line defect detection method, the embodiment of the present disclosure also provides a deep learning-based transmission line defect detection method to achieve the determination of the first defective circuit information set, which may specifically include the following steps.

[0054] S401: Determine the knowledge segment category to which each partial knowledge segment belongs through the key category of the acquired partial knowledge segment and the association relationship between the set partial knowledge segment category and the knowledge segment category of the first defective circuit information set.

[0055] S402: For each knowledge fragment category, based on the position information of some knowledge fragments belonging to the knowledge fragment category, the standard position of the defective circuit information set belonging to the knowledge fragment category is obtained, and the difference between the standard position of the defective circuit information set belonging to the knowledge fragment category and the standard position of the adjacent defective circuit information set is determined. Through the detected difference between the knowledge fragments, the knowledge fragment vector of the defective circuit information set belonging to the knowledge fragment category is determined.

[0056] S403: Based on the acquired standard positions and the determined knowledge fragment vectors, determine the first defective circuit information set to which each standard position belongs.

[0057] On the premise of the above-mentioned deep learning-based power transmission line defect detection method, the embodiment of the present disclosure also provides a deep learning-based power transmission line defect detection method to achieve the selection of the first defect description attribute, which may specifically include the following steps.

[0058] S501: Selecting a global knowledge vector set of a first line setting directory in the power transmission line data to be detected.

[0059] S502: Determine, based on the position of each first defective circuit information set in the first circuit setting directory, a knowledge segment corresponding to each first defective circuit information set in the global knowledge vector set.

[0060] In this step, each pixel point in the first circuit setting directory exists at a corresponding position in the global knowledge vector set. Therefore, the knowledge fragments mapped to each first defective circuit information set on the global knowledge vector set can be determined through the association relationship between the first circuit setting directory and the global knowledge vector set.

[0061] S503: According to the set knowledge vector set, the knowledge segments of each first defective circuit information set are debugged to generate a vector which is a knowledge vector of the set knowledge vector set.

[0062] S504: Determine the partial description content corresponding to the knowledge vector of each first defective circuit information set, and determine it as the partial description content in each first defective circuit information set.

[0063] In this step, the knowledge vectors acquired in step S503 may be further processed to obtain partial description content in each first defective circuit information set.

[0064] Under the above premise, please refer to Figure 2 , a power transmission line defect detection device 200 based on deep learning is provided, which is applied to a power transmission line defect detection system based on deep learning, and the device comprises: The attribute selection module 210 is used to obtain partial knowledge fragments of the first line setting directory in the transmission line data to be detected; determine multiple first defective circuit information sets from the first line setting directory through the obtained partial knowledge fragments, and select the first defect description attribute of each first defective circuit information set; A vector determination module 220, configured to determine a hidden core hidden in the first circuit setting directory, and determine a first hidden vector hidden in each first defective circuit information set through the detected hidden core; A vector influence module 230, configured to obtain a first influence vector between first defective circuit information sets detected by a first hidden vector obtained by statistics; The result detection module 240 is used to optimize each first defect description attribute through the obtained first influence vector, compare the optimized first defect description attribute with the second defect description attribute, and obtain the defect detection result of the first line setting directory, wherein the second defect description attribute is: the key content obtained by optimizing the defect description attribute of each second defect circuit information set through each second influence vector, and each second defect circuit information set is: the knowledge fragment corresponding to each first defect circuit information set in the second line setting directory set in the partial transmission line data.

[0065] Under the above premise, please refer to Figure 3 , shows a deep learning-based transmission line defect detection system 300, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0066] Under the above premise, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0067] In summary, based on the above scheme, multiple first defect circuit information sets are determined from the first line setting directory, and then the influence vectors between the first defect circuit information sets are determined through the hidden vectors of each first defect circuit information set. Since the first influence vectors of each first defect circuit information set are obtained based on the hidden vectors of each first defect circuit information set, the influence vectors between the first defect circuit information sets cover the hidden information in the first part of the transmission line data. The process of optimizing the first defect description attribute using the first influence vector is equivalent to optimizing the first defect description attribute of each first defect circuit information set based on the hidden information in the first transmission line data, thereby weakening the hidden-related influence data covered in the first part of the transmission line data caused by hiding, thereby improving the accuracy of data detection.

[0068] Furthermore, the influence vectors between the first defective circuit information sets are determined through the hidden vectors of the first defective circuit information sets, thereby realizing the analysis of the relationship between the first defective circuit information sets, and then using the analyzed relationship between the knowledge fragments to realize data processing, further improving the accuracy of data detection.

[0069] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by processors of various types, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0070] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or may be any other beneficial effects that may be obtained.

[0071] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only determined as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0072] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0073] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0074] A computer storage medium may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0075] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or determined as an independent software package to run on the user's computer, or partially run on the user's computer and partially run on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or determined as a service such as software as a service (SaaS).

[0076] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0077] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus facilitate the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this application, multiple features are sometimes grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the subject matter of this application requires more features than those mentioned in the claims. In fact, the features of the embodiments are fewer than the global features of the single embodiment disclosed above.

[0078] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0079] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0080] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, the alternative configurations of the embodiments of the present application, which are determined as examples rather than limitations, may be considered consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0081] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A power transmission line defect detection method based on deep learning, characterized in that: The method at least comprises: Acquire partial knowledge fragments of a first line setting directory in the transmission line data to be detected; determine multiple first defective circuit information sets from the first line setting directory through the acquired partial knowledge fragments, and select a first defect description attribute of each first defective circuit information set; Determine a hidden core hidden in the first circuit setting directory, and determine a first hidden vector hidden in each first defective circuit information set through the detected hidden core; Obtaining a first influence vector between each first defective circuit information set detected by a first hidden vector obtained by statistics; Each first defect description attribute is optimized by the obtained first influence vector, and the optimized first defect description attribute is compared with the second defect description attribute to obtain the defect detection result of the first line setting directory, wherein the second defect description attribute is: the key content obtained by optimizing the defect description attribute of each second defect circuit information set by each second influence vector, and each second defect circuit information set is: the knowledge fragment corresponding to each first defect circuit information set in the second line setting directory set in the partial transmission line data.

2. The method according to claim 1, characterized in that The step of comparing the optimized first defect description attribute with the second defect description attribute to obtain the defect detection result of the first line setting directory includes: Determine similarity knowledge segments between the optimized first defect description attributes of each first defective circuit information set and the corresponding second defect description attributes, and determine them as similarity knowledge segments corresponding to each first defective circuit information set; Determining, based on the first hidden vectors of the respective first defective circuit information sets, a trust weight of the first defect description attribute of the respective first defective circuit information sets for the features of the first line setting catalog; By using the detected trust weights, integrating the similarity knowledge fragments corresponding to the first defective circuit information sets, obtaining an integration result, and determining the similarity knowledge fragments of the first circuit setting catalog and the second circuit setting catalog; The defect detection result of the first line setting catalog is determined by using the obtained similarity knowledge fragments.

3. The method according to claim 2, characterized in that The method of determining the trusted weight of the first defect description attribute of each first defect circuit information set for the characteristics of the first line setting directory based on the first hidden vector of each first defect circuit information set includes: determining the trusted weight of the first defect description attribute of each first defect circuit information set for the characteristics of the first line setting directory based on the first hidden vector of each first defect circuit information set and the second hidden vector of the corresponding second defect circuit information set.

4. The method according to claim 1, characterized in that The step of comparing the optimized first defect description attribute with the second defect description attribute to obtain the defect detection result of the first line setting directory includes: Combining and processing the first defect description attributes of each first defect circuit information set through the first hidden vector of each first defect circuit information set to obtain a first combined processing vector, and combining and processing the second defect description attributes of each second defect circuit information set through the second hidden vector of each second defect circuit information set to obtain a second combined processing vector; determining similarity knowledge segments between the first combined processing vector and the second combined processing vector; By determining the obtained similarity knowledge fragments, the defect detection result of the first line setting catalog is determined.

5. The method according to any one of claims 1 to 4, characterized in that The first impact vector is determined according to the following expression: Effect Pro = max(hidden vector effect vector, threshold) x(1-hidden vector effectvector), wherein Effect Pro is the first impact vector of the first defective circuit information set Defect Electron on the first defective circuit information set effect vector, the hidden vector effect vector represents the hidden vector of the first defective circuit information set effectvector, and threshold is the set threshold.

6. The method according to claim 1, characterized in that The step of optimizing each first defect description attribute by using the obtained first influence vector and comparing the optimized first defect description attribute with the second defect description attribute to obtain the defect detection result of the first line setting directory includes: Load each first defect description attribute, each first influence vector between each first defect circuit information set, each defect description attribute of each second defect circuit information set, and each second influence vector between each second defect circuit information set into a pre-trained defect detection network, so that the defect detection network optimizes each first defect description attribute based on each first influence vector to obtain an optimized first defect description attribute, and optimizes each second defect description attribute of each second defect circuit information set based on each second influence vector to obtain a second defect description attribute, and compares the optimized first defect description attribute with the second defect description attribute to output a defect detection result; and obtains the defect detection result output by the defect detection network.

7. The method according to claim 1, characterized in that The step of determining a plurality of first defective circuit information sets from the first circuit setting catalog by using the acquired partial knowledge fragments includes: Determine the knowledge segment category to which each partial knowledge segment belongs through the key category of the acquired partial knowledge segment and the association relationship between the set partial knowledge segment category and the knowledge segment category of the first defective circuit information set; For each knowledge segment category, based on the position information of some knowledge segments belonging to the knowledge segment category, the standard position of the defective circuit information set belonging to the knowledge segment category is obtained, and the difference between the standard position of the defective circuit information set belonging to the knowledge segment category and the standard position of the adjacent defective circuit information set is determined, and the knowledge segment vector of the defective circuit information set belonging to the knowledge segment category is determined through the detected difference between the knowledge segments; Based on the acquired standard positions and the determined knowledge fragment vectors, the first defective circuit information set to which each standard position belongs is determined.

8. The method according to claim 1, characterized in that The selecting of part of the description content in each first defective circuit information set includes: Selecting a global knowledge vector set of a first line setting directory in the power transmission line data to be detected; Determine, based on the position of each first defective circuit information set in the first line setting directory, a knowledge segment corresponding to each first defective circuit information set in the global knowledge vector set; According to the set knowledge vector set, the knowledge fragments of each first defective circuit information set are debugged to generate a vector which is a knowledge vector of the set knowledge vector set; A partial description content corresponding to the knowledge vector of each first defective circuit information set is determined as the partial description content in each first defective circuit information set.

9. A power transmission line defect detection system based on deep learning, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 8.

Images