Distribution cable fault intelligent detection method and system

Abstract

The invention relates to a distribution cable fault intelligent detection method and system, and belongs to the technical field of data detection. According to the method, a to-be-analyzed and to-be-determined important fault description mode is described when a fault data set matrix is associated with distribution cable abnormal feature data content on the premise of division; then, the feature attribute data of the to-be-analyzed to-be-determined important fault description mode is associated with the key fault data feature vector to describe the to-be-determined important fault description mode in the distribution cable abnormal feature data association fault monitoring model, so that the matrix can be better associated with the corresponding preset feature vector; and a user can conveniently know the detection condition of the abnormal characteristic data of the distribution cable and the working condition of the fault monitoring model at any time, multiple times of detection data association are not needed, and the cable fault monitoring precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data detection, and in particular relates to a method and system for intelligent detection of distribution cable faults. Background Art

[0002] Wires and cables are wire products used to transmit electrical (magnetic) energy, information and realize electromagnetic energy conversion. Wires and cables in a broad sense are also referred to as cables. Cables in a narrow sense refer to insulated cables, which can be defined as: a collection of the following parts; one or more insulated cores, and their respective coatings, total protective layers and outer sheaths. Cables may also have additional uninsulated conductors.

[0003] Cable is an indispensable infrastructure in life. Therefore, if a cable fails, it will greatly affect people's lives. In order to promptly repair the cables that may fail, an intelligent cable fault detection method is urgently needed to overcome the above problems. Summary of the invention

[0004] The object of the present invention is to provide a method and system for intelligent detection of distribution cable faults.

[0005] To achieve the above object, the technical solution of the present invention is: a distribution cable fault intelligent detection method, comprising:

[0006] Obtain important fault description methods for abnormal characteristic data of distribution cables in fault monitoring models, and debug the abnormal characteristic data content of distribution cables in fault detection data set matrix;

[0007] Obtain the division basis of the fault detection data set matrix for the abnormal characteristic data content of the distribution cable, detect the associated division of the abnormal characteristic data of the distribution cable, and describe the description method of the important faults to be analyzed and determined when the fault detection data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable on the premise of division;

[0008] In the description method of the important faults to be analyzed, the characteristic attribute data of the abnormal characteristic data of the distribution cable is described as being associated with the key fault data feature vector, and in the characteristic attribute data associated with the key fault data feature vector, the description method of the important faults to be determined in the fault monitoring model of the abnormal characteristic data of the distribution cable is described.

[0009] In one embodiment of the present invention, a description method of important faults to be analyzed and determined when the fault detection data set matrix is ​​associated with abnormal characteristic data content of the distribution cable on the premise of division is described, including:

[0010] Describe the important fault description mode to be analyzed and determined obtained by associating the fault detection data set matrix with the abnormal characteristic data of the distribution cable based on the division premise, using the first associated data description mode as the associated description mode;

[0011] Correspondingly, in the feature attribute data associated key fault data feature vector, the description method of the pending important faults in the distribution cable abnormal feature data associated fault monitoring model is described, including:

[0012] In the characteristic attribute data associated key fault data feature vector, the third associated data description method is used to describe the pending important fault description method in the distribution cable abnormal characteristic data associated fault monitoring model.

[0013] In one embodiment of the present invention, the description method of the important faults to be analyzed and determined includes:

[0014] A first partial pending important fault description method corresponding to the abnormal characteristic data of the distribution cable, and a second partial pending important fault description method corresponding to the abnormal characteristic data of the distribution cable;

[0015] In the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data characteristic vector, including:

[0016] In the first local pending important fault description method corresponding to the abnormal characteristic data of the distribution cable, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data feature vector.

[0017] In one embodiment of the present invention, in the feature attribute data associated with the key fault data feature vector, the description method of the pending important fault in the distribution cable abnormal feature data associated fault monitoring model includes:

[0018] In the characteristic attribute data associated key fault data characteristic vector, the association situation corresponding to the characteristic attribute data content of the distribution cable abnormal characteristic data is used to describe the pending important fault description method in the distribution cable abnormal characteristic data associated fault monitoring model; wherein the characteristic attribute data content includes at least one of the following: the main description content and potential description content of the distribution cable abnormal characteristic data;

[0019] Among them, the description method of pending important faults in the distribution cable abnormal characteristic data associated fault monitoring model is described by using the association situation corresponding to the characteristic attribute data of the distribution cable abnormal characteristic data, including:

[0020] When the characteristic attribute data content is potential description content, the sine value of the potential description content of the abnormal characteristic data of the distribution cable is obtained;

[0021] When the sine value of the potential description content belongs to the range of the sine value of the pending potential description content, the description method of the pending important fault in the distribution cable abnormal feature data association fault monitoring model is described by using the preliminary association situation;

[0022] Wherein, using the association situation corresponding to the characteristic attribute data of the abnormal characteristic data of the distribution cable, describing the pending important fault description method in the distribution cable abnormal characteristic data associated fault monitoring model, it also includes:

[0023] When the characteristic attribute data content is the main description content, the sine value of the main description content of the abnormal characteristic data of the distribution cable is obtained;

[0024] When the sine value of the main description content belongs to the range of the sine value of the pending main description content, the preset association situation is used to describe the pending important fault description method in the distribution cable abnormal characteristic data association fault monitoring model.

[0025] In one embodiment of the present invention, the method further includes:

[0026] In the description method of the important faults to be analyzed and determined, a sample data set of example data for the characteristic attribute data associated with the characteristic vector of the key fault data is described;

[0027] Accordingly, in the description mode of the important fault to be analyzed and determined, after the characteristic attribute data describing the abnormal characteristic data of the power distribution cable is associated with the key fault data feature vector, the method further comprises: receiving a key fault data feature vector fixed basis for the characteristic attribute data associated with the key fault data feature vector triggered based on the example sample data set;

[0028] A fixed basis for key fault data feature vectors is obtained, and the feature attribute data of the fixed description of the abnormal feature data of the distribution cable is associated with the key fault data feature vectors.

[0029] In one embodiment of the present invention, the method further includes:

[0030] In the description mode of the important faults to be analyzed and determined, the key fault data feature vector expansion identification is described for the key fault data feature vector associated with the corresponding feature attribute data;

[0031] Correspondingly, after the characteristic attribute data of the abnormal characteristic data of the power distribution cable described in a fixed manner is associated with the key fault data characteristic vector, the method further comprises: receiving a key fault data characteristic vector expansion basis for the characteristic attribute data associated with the key fault data characteristic vector triggered by the key fault data characteristic vector expansion identification;

[0032] The key fault data feature vector expansion basis is obtained, and the feature attribute data describing the fixed distribution cable abnormal feature data is associated with the key fault data feature vector.

[0033] In one embodiment of the present invention, the method further includes:

[0034] The trigger division of the characteristic attribute data associated with the key fault data feature vector is obtained, and the description method of the important faults to be analyzed when the fault data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable is converted into the description method of the important faults to be analyzed in the distribution cable abnormal characteristic data associated fault monitoring model.

[0035] In one embodiment of the present invention, after the conversion into a pending important fault description mode in the distribution cable abnormal characteristic data associated fault monitoring model, the method further includes:

[0036] A local pending important fault description method is described that is independent of the pending important fault description method, and in the local pending important fault description method, a pending important fault description method corresponding to the distribution cable abnormal characteristic data when the fault data set matrix is ​​associated with the distribution cable abnormal characteristic data content is described.

[0037] In one embodiment of the present invention, the method further includes:

[0038] Describe the association identification of the key fault data feature vector associated with the corresponding feature attribute data; obtain the trigger division for the association identification, and the situation of detecting the association identification is the start situation;

[0039] Correspondingly, in the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data characteristic vector, including: when the associated identification situation is the startup situation, in the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data characteristic vector;

[0040] Wherein, the method further comprises:

[0041] Obtaining a sine value of a characteristic attribute data content corresponding to a characteristic attribute data content of the abnormal characteristic data of the distribution cable; wherein the characteristic attribute data content includes at least one of the following: a main description content and a potential description content of the abnormal characteristic data of the distribution cable;

[0042] When the sine value of the characteristic attribute data content is lower than the content cosine threshold, the fault detection data set matrix is ​​suspended from the distribution cable abnormal characteristic data content, and the characteristic attribute data of the distribution cable abnormal characteristic data described is terminated to associate with the key fault data feature vector;

[0043] Wherein, the method further comprises:

[0044] In the description mode of the important faults to be analyzed and determined, the description corresponds to the suspended identification of abnormal characteristic data of the distribution cable;

[0045] Correspondingly, after the characteristic attribute data of the distribution cable abnormal characteristic data is associated with the key fault data feature vector, the method also includes: obtaining a trigger partition for pause identification, pausing the fault detection data set matrix from the distribution cable abnormal characteristic data content, and terminating the description of the characteristic attribute data of the distribution cable abnormal characteristic data associated with the key fault data feature vector.

[0046] The present invention also provides a distribution cable fault intelligent detection system, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above-mentioned method.

[0047] Compared with the prior art, the present invention has the following beneficial effects: an intelligent detection method and system for distribution cable faults provided in the embodiment of the present application, by describing the division premise, the description method of the important faults to be analyzed and determined when associating the fault data set matrix with the abnormal characteristic data content of the distribution cable, and then describing the important fault description method to be determined in the distribution cable abnormal characteristic data associated fault monitoring model by associating the characteristic attribute data of the important fault description method to be analyzed and determined with the key fault data feature vector, which can not only better associate with the corresponding preset feature vector, but also facilitate users to understand the detection status of the distribution cable abnormal characteristic data itself and the working status of the fault monitoring model at any time, and there is no need to perform detection data association multiple times, thereby improving the accuracy of cable fault monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The present invention provides a flow chart of a method for intelligently detecting power distribution cable faults. DETAILED DESCRIPTION

[0049] 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. The embodiments of the present application and the technical features in the embodiments can be combined with each other without conflict.

[0050] See also Figure 1 , shows a method for intelligent detection of distribution cable faults, which may include the technical solutions described in the following steps 100-300.

[0051] Step 100, obtaining an important fault description method for abnormal characteristic data of a distribution cable in a fault monitoring model, and debugging the abnormal characteristic data content of the distribution cable by using a fault detection data set matrix.

[0052] Furthermore, the distribution cable abnormal characteristic data content represents a virtual important fault description method after association.

[0053] Step 200, obtain the division basis of the fault detection data set matrix for the abnormal characteristic data of the distribution cable, detect the associated division situation of the abnormal characteristic data of the distribution cable, and describe the important fault description method to be analyzed and determined when the fault detection data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable on the premise of division.

[0054] Exemplarily, the division basis represents a set formed by dividing the relevant detection data of each association.

[0055] Furthermore, the association division situation indicates the expression form of the relevant detection data on devices such as computers and mobile phones that can describe the detection data.

[0056] Step 300, in the description method of the important fault to be analyzed and to be determined, describes the characteristic attribute data of the abnormal characteristic data of the distribution cable associated with the key fault data feature vector, and in the characteristic attribute data associated with the key fault data feature vector, describes the description method of the abnormal characteristic data of the distribution cable associated with the important fault to be determined in the fault monitoring model.

[0057] Exemplarily, the pending important fault description method indicates related detection data that has been associated.

[0058] It can be understood that when executing the technical solution described in the above steps 100-300, by describing the division premise and associating the fault data set matrix with the distribution cable abnormal characteristic data content, and then associating the key fault data feature vector with the characteristic attribute data of the important fault description method to be analyzed, the distribution cable abnormal characteristic data is associated with the to-be-determined important fault description method in the fault monitoring model. This can not only better associate the matrix with the corresponding preset feature vector, but also facilitate users to understand the detection status of the distribution cable abnormal characteristic data itself and the working status of the fault monitoring model at any time, without the need to perform detection data association multiple times, thereby improving the accuracy of cable fault monitoring.

[0059] Further, when describing the description method of the important faults to be analyzed and determined when the fault data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable on the premise of division, there is a technical problem of uncertain division situation, which makes it difficult to accurately obtain the description method of the important faults to be analyzed and determined. In order to improve the above technical problem, the steps described in step 200 of describing the description method of the important faults to be analyzed and determined when the fault data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable on the premise of division can specifically include the technical solutions described in the following steps q1 and q2.

[0060] Step q1 describes that on the premise of division, when the fault data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable, the description method of the important fault to be analyzed and determined is obtained by associating with the first associated data description method as the associated description method.

[0061] Step q2, accordingly, describes the pending important fault description method of the abnormal characteristic data of the distribution cable associated with the fault monitoring model in the characteristic attribute data associated key fault data feature vector, including: in the characteristic attribute data associated key fault data feature vector, uses a third associated data description method to describe the pending important fault description method of the abnormal characteristic data of the distribution cable associated with the fault monitoring model.

[0062] It can be understood that when executing the technical solutions described in the above steps q1 and q2, when the fault data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable, the description method of the important faults to be analyzed and determined is avoided, and the technical problem of uncertain division of the situation is avoided, so that the description method of the important faults to be analyzed and determined can be accurately obtained.

[0063] In an alternative embodiment, the inventors found that the method for describing the important faults to be analyzed and determined specifically includes the technical solutions described in the following steps w1 and w2.

[0064] Step w1, a first partial pending important fault description method corresponding to the abnormal characteristic data content of the distribution cable, and a second partial pending important fault description method corresponding to the abnormal characteristic data of the distribution cable.

[0065] Step w2, in the description method of the important fault to be analyzed and determined, describing the characteristic attribute data of the abnormal characteristic data of the distribution cable and associating it with the key fault data feature vector, includes: in the first local description method of the important fault to be determined corresponding to the content of the abnormal characteristic data of the distribution cable, describing the characteristic attribute data of the abnormal characteristic data of the distribution cable and associating it with the key fault data feature vector.

[0066] It can be understood that when executing the technical solutions described in the above steps w1 and w2, the accuracy of processing the abnormal characteristic data content of the distribution cable is improved by accurately analyzing the abnormal characteristic data content of the distribution cable.

[0067] In an alternative embodiment, the inventors discovered that when the characteristic attribute data is associated with the key fault data feature vector, there is a problem of incorrect association, making it difficult to accurately describe the manner in which the abnormal characteristic data of the distribution cable is associated with the pending important fault description method in the fault monitoring model. In order to improve the above technical problem, the step q2 described in the characteristic attribute data associated with the key fault data feature vector describes the manner in which the abnormal characteristic data of the distribution cable is associated with the pending important fault description method in the fault monitoring model, can specifically include the technical solution described in the following step q2a1.

[0068] Step q2a1, in the characteristic attribute data associated with the key fault data feature vector, using the association situation corresponding to the characteristic attribute data content of the distribution cable abnormal characteristic data, describes the distribution cable abnormal characteristic data associated with the pending important fault description method in the fault monitoring model.

[0069] Exemplarily, the characteristic attribute data content includes at least one of the following: main description content and potential description content of the distribution cable abnormal characteristic data.

[0070] It can be understood that when executing the technical solution described in the above step q2a1, when the characteristic attribute data is associated with the key fault data feature vector, the problem of incorrect association is avoided, so that the distribution cable abnormal characteristic data can be accurately described in terms of the pending important fault description method associated with the fault monitoring model.

[0071] In an alternative embodiment, the inventors have discovered that when utilizing the association conditions corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable, there is a problem of unclear potential description content, making it difficult to accurately describe the manner in which the abnormal characteristic data of the distribution cable is associated with the pending important fault description manner in the fault monitoring model. In order to improve the above technical problem, the step q2a1 described in the step of utilizing the association conditions corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable to describe the manner in which the abnormal characteristic data of the distribution cable is associated with the pending important fault description manner in the fault monitoring model may specifically include the technical solutions described in the following steps w1 and w2.

[0072] Step w1, when the characteristic attribute data content is potential description content, obtain the potential description content sine value of the distribution cable abnormal characteristic data.

[0073] Step w2, when the potential description content sine value belongs to the range of potential description content sine values ​​to be determined, using the preliminary association situation, describe the distribution cable abnormal feature data and associate it with the pending important fault description method in the fault monitoring model.

[0074] It can be understood that when executing the technical solutions described in the above steps w1 and w2, by utilizing the association situation corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable, the problem of unclear potential description content is avoided, thereby being able to accurately describe the association of the abnormal characteristic data of the distribution cable with the pending important fault description method in the fault monitoring model.

[0075] In an alternative embodiment, the inventors discovered that when utilizing the association conditions corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable, there is a problem of inaccurate range of sinusoidal values ​​of the pending main description content, making it difficult to accurately describe the manner in which the abnormal characteristic data of the distribution cable is associated with the pending important fault description in the fault monitoring model. In order to improve the above technical problem, the step q2a1 described in the step of utilizing the association conditions corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable to describe the manner in which the abnormal characteristic data of the distribution cable is associated with the pending important fault description in the fault monitoring model may specifically include the technical solutions described in the following steps e1 and e2.

[0076] Step e1, when the characteristic attribute data content is the main description content, obtain the sine value of the main description content of the distribution cable abnormal characteristic data.

[0077] Step e2, when the sine value of the main description content belongs to the range of sine values ​​of the main description content to be determined, using the preset association situation, describing the distribution cable abnormal feature data and associating it with the to-be-determined important fault description method in the fault monitoring model.

[0078] It can be understood that when executing the technical solutions described in the above steps e1 and e2, by utilizing the association conditions corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable, the problem of inaccurate sine value range of the pending main description content can be avoided, thereby being able to accurately describe the association of the abnormal characteristic data of the distribution cable with the pending important fault description method in the fault monitoring model.

[0079] Based on the above foundation, the technical solution described in the following steps r1 to r3 may also be included.

[0080] Step r1, in the description method of the important faults to be analyzed and determined, describes a set of example sample data for the characteristic attribute data associated with the key fault data feature vector.

[0081] Step r2, accordingly, in the description method of the important fault to be analyzed and determined, after describing the characteristic attribute data associated with the key fault data feature vector of the abnormal characteristic data of the distribution cable, the method also includes: receiving a fixed basis for the key fault data feature vector associated with the characteristic attribute data based on the example sample data set triggered.

[0082] Step r3, obtaining a fixed basis for the key fault data feature vector, and associating the feature attribute data of the distribution cable abnormal feature data with the key fault data feature vector.

[0083] It can be understood that when executing the technical solutions described in the above steps r1 to r3, by accurately obtaining a set of example sample data, it is possible to accurately associate the characteristic attribute data of the abnormal characteristic data of the distribution cable with the key fault data characteristic vector.

[0084] Based on the above foundation, the technical solution described in steps t1 to t3 may also be included.

[0085] Step t1, in the description mode of the important faults to be analyzed and determined, describes the expansion and identification of the key fault data feature vectors corresponding to the key fault data feature vectors associated with the characteristic attribute data.

[0086] Step t2, accordingly, after the characteristic attribute data of the fixedly described abnormal characteristic data of the distribution cable is associated with the key fault data feature vector, the method further includes: receiving a key fault data feature vector expansion basis for the key fault data feature vector associated with the characteristic attribute data, which is triggered based on the key fault data feature vector expansion identification.

[0087] Step t3, obtaining the key fault data feature vector expansion basis, describing the fixed characteristic attribute data of the distribution cable abnormal characteristic data associated with the key fault data feature vector.

[0088] It can be understood that when executing the technical solution described in the above steps t1 to t3, accurate identification is performed through the key fault data feature vector, thereby improving the accuracy of associating the fixed characteristic attribute data of the distribution cable abnormal characteristic data with the key fault data feature vector.

[0089] Based on the above foundation, the technical solution described in the following step y1 may also be included.

[0090] Step y1, obtain the trigger division for the key fault data feature vector associated with the characteristic attribute data, and convert the description method of the important faults to be analyzed and determined when the fault data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable into the description method of the important faults to be determined in the fault monitoring model associated with the abnormal characteristic data of the distribution cable.

[0091] It can be understood that when executing the technical solution described in the above step y1, the completeness of the description method of the pending important faults is improved through the triggering division of the key fault data feature vectors.

[0092] Based on the above foundation, after converting the abnormal characteristic data of the distribution cable into the description method of the pending important fault in the fault monitoring model, the technical solution described in the following step u1 may also be included.

[0093] Step u1, describes a local pending important fault description method that is independent of the pending important fault description method, and in the local pending important fault description method, describes the pending important fault description method to be analyzed corresponding to the distribution cable abnormal characteristic data when the fault data set matrix is ​​associated with the distribution cable abnormal characteristic data content.

[0094] It can be understood that when executing the technical solution described in the above step u1, the relevant local terms can be effectively and accurately analyzed through the local pending important fault description method independent of the pending important fault description method, so that the pending important fault description method to be analyzed can be accurately obtained.

[0095] Based on the above foundation, the technical solution described in steps o1 and o2 may also be included.

[0096] Step o1, describes the association identification of the characteristic vector of the key fault data associated with the characteristic attribute data; obtains the trigger division for the association identification, and detects the situation of the association identification as the start situation.

[0097] Step o2, accordingly, in the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data feature vector, including: when the situation identified by the association is a startup situation, in the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data feature vector.

[0098] It can be understood that when executing the technical solutions described in the above steps o1 and o2, the accuracy of the associated identification is improved, thereby improving the accuracy of the key fault data feature vector associated with the feature attribute data of the abnormal feature data of the distribution cable.

[0099] Based on the above foundation, the technical solutions described in steps a1 and a2 may also be included.

[0100] Step a1, obtaining a sine value of a characteristic attribute data content corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable.

[0101] Exemplarily, the characteristic attribute data content includes at least one of the following: main description content and potential description content of the distribution cable abnormal characteristic data.

[0102] Step a2, when the sine value of the characteristic attribute data content is lower than the content cosine threshold, the fault data set matrix is ​​detected to pause from the distribution cable abnormal characteristic data content, and the characteristic attribute data associated with the key fault data feature vector describing the distribution cable abnormal characteristic data is terminated.

[0103] It can be understood that when executing the technical solutions described in the above steps a1 and a2, by accurately obtaining the sine value of the characteristic attribute data content, errors in the characteristic attribute data association with the key fault data characteristic vector of the distribution cable abnormal characteristic data can be avoided.

[0104] Based on the above foundation, the technical solutions described in the following steps s1 and s2 may also be included.

[0105] Step s1, in the description method of the important fault to be analyzed and determined, describes the suspension identification corresponding to the abnormal characteristic data of the distribution cable.

[0106] Step s2, accordingly, after the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data feature vector, the method also includes: obtaining a trigger division for the pause identification, detecting that the fault data set matrix is ​​paused from the content of the abnormal characteristic data of the distribution cable, and terminating the description of the characteristic attribute data describing the abnormal characteristic data of the distribution cable and associating the key fault data feature vector.

[0107] It can be understood that when executing the technical solutions described in the above steps s1 and s2, by pausing the identification of the abnormal characteristic data of the distribution cable, the risk of the characteristic attribute data of the abnormal characteristic data of the distribution cable being associated with the characteristic vector of the key fault data is reduced.

[0108] In a possible embodiment, the inventors found that when obtaining an important fault description method for abnormal characteristic data of distribution cables in a fault monitoring model, there is a problem of recognition error, which makes it difficult to reliably debug the abnormal characteristic data content of the distribution cables by using the fault detection data set matrix. In order to improve the above technical problems, the step 100 of obtaining an important fault description method for abnormal characteristic data of distribution cables in a fault monitoring model and the step of debugging the abnormal characteristic data content of the distribution cables by using the fault detection data set matrix may specifically include the technical solutions described in the following steps d1 and d2.

[0109] Step d1, describes the identification of abnormal characteristic data content used for debugging the distribution cable.

[0110] Step d2, obtaining an important fault description method for the abnormal characteristic data of the distribution cable in the fault monitoring model based on the trigger for identification, and debugging the abnormal characteristic data content of the distribution cable by the fault detection data set matrix.

[0111] It can be understood that when executing the technical solutions described in the above steps d1 and d2, when obtaining the important fault description method for the abnormal characteristic data of the distribution cable in the fault monitoring model, the problem of recognition error is avoided, so that the fault detection data set matrix can reliably debug the abnormal characteristic data content of the distribution cable.

[0112] Based on the above foundation, the technical solution described in step f1 may also be included.

[0113] Step f1, describing the content division data corresponding to the characteristic attribute data content of the abnormal characteristic data of the distribution cable in the characteristic attribute data associated key fault data characteristic vector.

[0114] Exemplarily, the characteristic attribute data content includes at least one of the following: main description content and potential description content of the distribution cable abnormal characteristic data; the content division data is used to divide the reading conditions corresponding to the characteristic attribute data content.

[0115] It can be understood that when executing the technical solution described in the above step f1, accurate analysis is performed by associating the characteristic attribute data with the key fault data characteristic vector, so as to accurately describe the content division data corresponding to the characteristic attribute data content of the distribution cable abnormal characteristic data.

[0116] In summary, based on the above scheme, by describing the division premise, the description method of the important faults to be analyzed and determined when associating the fault data set matrix with the abnormal feature data content of the distribution cable, and then describing the distribution cable abnormal feature data associated fault monitoring model by associating the key fault data feature vector with the characteristic attribute data of the important fault description method to be analyzed, the description method of the important faults to be determined in the fault monitoring model can be described. This can not only make the matrix better associated with the corresponding preset feature vector, but also facilitate users to understand the detection status of the distribution cable abnormal feature data itself and the working status of the fault monitoring model at any time, and there is no need to perform detection data association multiple times, thereby improving the accuracy of cable fault monitoring.

[0117] 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 electrical local 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 electrical local 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 various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0118] 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 any other beneficial effects that may be obtained.

[0119] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for 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.

[0120] 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.

[0121] 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.

[0122] A computer storage medium may include a propagated data signal containing computer program code, for example, 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.

[0123] The computer program code 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 run on the user's computer as an independent software package, or run partially on the user's computer and partially on the remote computer, or run entirely on the remote computer or server. On the latter premise, 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 used as a service such as software as a service (SaaS).

[0124] In addition, unless explicitly stated in the claims, the order of processing elements and matrices, the use of alphanumeric characters, or the use of other names described in the present application are not intended to limit the order of the processes and methods of the present 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 are consistent with the essence and scope of the embodiments of the present 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.

[0125] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0126] 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.

[0127] 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.

[0128] 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, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be 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.

[0129] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for intelligent detection of distribution cable faults, characterized in that: include: Obtain important fault description methods for abnormal characteristic data of distribution cables in fault monitoring models, and debug the abnormal characteristic data content of distribution cables in the fault detection data set matrix; Obtain the division basis of the fault detection data set matrix for the abnormal characteristic data content of the distribution cable, detect the associated division of the abnormal characteristic data of the distribution cable, and describe the description method of the important faults to be analyzed and determined when the fault detection data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable on the premise of division; In the description method of the important faults to be analyzed, the characteristic attribute data of the abnormal characteristic data of the distribution cable is described in association with the key fault data feature vector, and in the characteristic attribute data associated with the key fault data feature vector, the description method of the important faults to be determined in the fault monitoring model of the abnormal characteristic data of the distribution cable is described.

2. A distribution cable fault intelligent detection method according to claim 1, characterized in that: Describe the important fault description methods to be analyzed and determined when the fault detection data set matrix is ​​associated with the abnormal characteristic data content of the distribution cable on the premise of division, including: Describe the important fault description mode to be analyzed and determined obtained by associating the first associated data description mode as the associated description mode when the fault detection data set matrix is ​​associated with the abnormal characteristic data of the distribution cable on the premise of division; Correspondingly, in the feature attribute data associated key fault data feature vector, the description method of the pending important faults in the distribution cable abnormal feature data associated fault monitoring model is described, including: In the characteristic attribute data associated key fault data feature vector, the third associated data description method is used to describe the pending important fault description method in the distribution cable abnormal characteristic data associated fault monitoring model.

3. The method for intelligent detection of power distribution cable faults according to claim 1, characterized in that: The description methods of important faults to be analyzed and determined include: A first partial pending important fault description method corresponding to the abnormal characteristic data of the distribution cable, and a second partial pending important fault description method corresponding to the abnormal characteristic data of the distribution cable; In the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data characteristic vector, including: In the first local pending important fault description method corresponding to the abnormal characteristic data of the distribution cable, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data feature vector.

4. The method for intelligent detection of power distribution cable faults according to claim 1, characterized in that: In the feature attribute data associated key fault data feature vector, the description method of pending important faults in the distribution cable abnormal feature data associated fault monitoring model is described, including: In the characteristic attribute data associated key fault data characteristic vector, the association situation corresponding to the characteristic attribute data content of the distribution cable abnormal characteristic data is used to describe the pending important fault description method in the distribution cable abnormal characteristic data associated fault monitoring model; wherein the characteristic attribute data content includes at least one of the following: the main description content and potential description content of the distribution cable abnormal characteristic data; Among them, the description method of pending important faults in the distribution cable abnormal characteristic data associated fault monitoring model is described by using the association situation corresponding to the characteristic attribute data of the distribution cable abnormal characteristic data, including: When the characteristic attribute data content is potential description content, the sine value of the potential description content of the abnormal characteristic data of the distribution cable is obtained; When the sine value of the potential description content belongs to the range of the sine value of the pending potential description content, the description method of the pending important fault in the distribution cable abnormal feature data association fault monitoring model is described by using the preliminary association situation; Among them, using the association situation corresponding to the characteristic attribute data of the abnormal characteristic data of the distribution cable, describing the pending important fault description method in the distribution cable abnormal characteristic data associated fault monitoring model, also includes: When the characteristic attribute data content is the main description content, the sine value of the main description content of the abnormal characteristic data of the distribution cable is obtained; When the sine value of the main description content belongs to the range of the sine value of the pending main description content, the preset association situation is used to describe the pending important fault description method in the distribution cable abnormal characteristic data association fault monitoring model.

5. The method for intelligent detection of distribution cable faults according to claim 1, characterized in that: The method further comprises: In the description method of the important faults to be analyzed and determined, a sample data set of example data for the characteristic attribute data associated with the characteristic vector of the key fault data is described; Correspondingly, in the description mode of the important fault to be analyzed and determined, after the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data feature vector, the method further comprises: receiving a key fault data feature vector fixed basis for the characteristic attribute data associated with the key fault data feature vector triggered based on the example sample data set; A fixed basis for key fault data feature vectors is obtained, and the feature attribute data of the fixed description of the abnormal feature data of the distribution cable is associated with the key fault data feature vectors.

6. A distribution cable fault intelligent detection method according to claim 5, characterized in that: The method further comprises: In the description mode of the important faults to be analyzed and determined, the key fault data feature vector expansion identification is described for the key fault data feature vector associated with the corresponding feature attribute data; Correspondingly, after the characteristic attribute data of the abnormal characteristic data of the power distribution cable described in a fixed manner is associated with the key fault data characteristic vector, the method further comprises: receiving a key fault data characteristic vector expansion basis for the characteristic attribute data associated with the key fault data characteristic vector triggered by the key fault data characteristic vector expansion identification; The key fault data feature vector expansion basis is obtained, and the feature attribute data describing the fixed distribution cable abnormal feature data is associated with the key fault data feature vector.

7. A distribution cable fault intelligent detection method according to claim 1, characterized in that: The method further comprises: The trigger division of the characteristic attribute data associated with the key fault data feature vector is obtained, and the description method of the important faults to be analyzed and determined when the fault data set matrix described is associated with the abnormal characteristic data content of the distribution cable is converted into the description method of the important faults to be determined in the distribution cable abnormal characteristic data associated fault monitoring model.

8. A distribution cable fault intelligent detection method according to claim 7, characterized in that: After the conversion into the pending important fault description mode in the distribution cable abnormal characteristic data associated fault monitoring model, the method further includes: A local pending important fault description method is described that is independent of the pending important fault description method, and in the local pending important fault description method, a pending important fault description method corresponding to the distribution cable abnormal characteristic data when the fault data set matrix is ​​associated with the distribution cable abnormal characteristic data content is described.

9. The intelligent detection method for distribution cable fault according to claim 1, characterized in that: The method further comprises: Describe the association identification of the key fault data feature vector associated with the corresponding feature attribute data; obtain the trigger division for the association identification, and the situation of detecting the association identification is the start situation; Correspondingly, in the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data characteristic vector, including: when the associated identification situation is the startup situation, in the description method of the important faults to be analyzed and determined, the characteristic attribute data describing the abnormal characteristic data of the distribution cable is associated with the key fault data characteristic vector; Wherein, the method further comprises: Obtaining a sine value of a characteristic attribute data content corresponding to a characteristic attribute data content of the abnormal characteristic data of the distribution cable; wherein the characteristic attribute data content includes at least one of the following: a main description content and a potential description content of the abnormal characteristic data of the distribution cable; When the sine value of the characteristic attribute data content is lower than the content cosine threshold, the fault detection data set matrix is ​​suspended from the distribution cable abnormal characteristic data content, and the characteristic attribute data of the distribution cable abnormal characteristic data described is terminated to associate with the key fault data feature vector; Wherein, the method further comprises: In the description mode of the important faults to be analyzed and determined, the description corresponds to the suspended identification of abnormal characteristic data of the distribution cable; Correspondingly, after the characteristic attribute data of the distribution cable abnormal characteristic data is associated with the key fault data feature vector, the method also includes: obtaining a trigger partition for pause identification, pausing the fault detection data set matrix from the distribution cable abnormal characteristic data content, and terminating the description of the characteristic attribute data of the distribution cable abnormal characteristic data associated with the key fault data feature vector.

10. An intelligent detection system for power distribution cable faults, 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 9.

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