Cable fault prediction system and method based on cable full life cycle data

Abstract

The embodiment of the invention discloses a cable fault prediction system and method based on cable full life cycle data, and the method comprises the steps: determining the fault type and production type batch of a first cable when the fault of the first cable is monitored, first full life cycle data of the first cable and second full life cycle data of a second cable with the same batch as the production type are obtained, the first full life cycle data and the second full life cycle data comprise a plurality of sub life cycles and corresponding cycle parameter data, and the associated life cycle of the fault type is determined; and respectively determining cycle parameter data corresponding to the sub-life cycle same as the associated life cycle in the first full life cycle data and the second full life cycle data as first comparison data and second comparison data, performing similarity comparison on the first comparison data and the second comparison data, and predicting the second cable according to a comparison result to obtain a predicted fault cable. According to the scheme, multi-dimensional cable fault prediction under more possibilities can be realized by monitoring the cable in the whole life cycle.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of cable monitoring technology, and in particular to a cable fault prediction system and method based on cable full life cycle data. Background Art

[0002] As an important carrier of power transmission and communication, the safety and stability of cables are directly related to people's production, life and the normal operation of society. Cable fault prediction is one of the key measures to ensure the reliable operation of the power system. Cable fault prediction technology can timely discover potential fault hazards and take measures in advance for repair and replacement, which can avoid power outages and power restrictions caused by cable faults and ensure the stable operation of the power system. At the same time, by quickly handling cable faults, the economic losses caused by power outages can be reduced and the operating efficiency of the power system can be improved.

[0003] However, most of the existing cable monitoring technologies monitor the cable usage process after the cable is installed, and the prediction of cable faults is mostly based on the data collected during the cable usage process. However, the correlation of some cable faults may be based on other cable life cycles, such as cable design, cable manufacturing, cable transportation, etc. The fault prediction method based only on the data collected during the cable usage process has fewer reference information dimensions, resulting in incomplete considerations and low accuracy of cable fault prediction. Summary of the invention

[0004] The embodiments of the present invention provide a cable fault prediction system and method based on cable full life cycle data, which solves the problem that the existing cable fault prediction technology has fewer reference information dimensions, resulting in incomplete consideration of factors and low accuracy of cable fault prediction. It can realize cable fault prediction under more possibilities in multiple dimensions by monitoring the entire life cycle of the cable.

[0005] In a first aspect, an embodiment of the present invention provides a cable fault prediction method based on cable life cycle data, including:

[0006] In the case where it is monitored that the first cable fails during use, determining the failure type and production type batch of the first cable;

[0007] Obtain first life cycle data of the first cable and second life cycle data of a second cable of the same production type batch, wherein the first life cycle data and the second life cycle data include multiple sub-life cycles and corresponding cycle parameter data;

[0008] Determine one or more associated life cycles of the fault type, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data as the first comparison data, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data as the second comparison data, perform a similarity comparison between the first comparison data and the second comparison data, and predict the second cable based on the comparison result to obtain a predicted faulty cable.

[0009] Optionally, the cycle parameter data includes a plurality of cycle parameters, and the determining of one or more associated life cycles of the fault type includes:

[0010] A preset impact parameter mapping table is queried according to the fault type to obtain one or more corresponding impact parameters, and a sub-lifecycle corresponding to a cycle parameter identical to the impact parameter is determined as an associated lifecycle of the fault type.

[0011] Optionally, the periodic parameter data further includes periodic parameter values ​​corresponding to a plurality of periodic parameters, and the performing similarity comparison between the first comparison data and the second comparison data includes:

[0012] Calculating parameter similarity of period parameter values ​​of the same period parameter in the first comparison data and the second comparison data, determining fault influencing parameter similarity based on the parameter similarity of the period parameter that is the same as the influencing parameter, and determining overall similarity between the first comparison data and the second comparison data based on the parameter similarity;

[0013] The fault influencing parameter similarity is compared with a first preset similarity, and the overall similarity is compared with a second preset similarity.

[0014] Optionally, the determining the fault influencing parameter similarity based on the parameter similarity of the periodic parameter that is the same as the influencing parameter includes:

[0015] In the case where there are multiple influencing parameters, a first preset weight of the periodic parameter that is the same as the influencing parameter under the fault type is determined, and the first preset weight is multiplied and superimposed with the corresponding parameter similarity to obtain the fault influencing parameter similarity.

[0016] Optionally, determining the overall similarity between the first comparison data and the second comparison data based on the parameter similarity includes:

[0017] Calculate the average parameter similarity of the periodic parameters in the associated life cycle. When there are multiple associated life cycles, determine the second preset weight of the associated life cycle under the fault type. Multiply the second preset weight with the corresponding average parameter similarity and superimpose them to obtain the overall similarity between the first comparison data and the second comparison data.

[0018] Optionally, predicting the second cable according to the comparison result to obtain a predicted faulty cable includes:

[0019] It is determined whether the comparison result meets the early warning condition. If the early warning condition is met, the second cable corresponding to the fault influencing parameter similarity and the overall similarity is determined as the predicted fault cable.

[0020] Optionally, determining whether the comparison result meets a warning condition includes:

[0021] When the fault influencing parameter similarity is greater than the first preset similarity and the overall similarity is greater than the second preset similarity, it is determined that the warning condition is met; otherwise, it is determined that the warning condition is not met, wherein the first preset similarity is greater than the second preset similarity.

[0022] In a second aspect, an embodiment of the present invention further provides a cable fault prediction system based on cable life cycle data, including:

[0023] A type batch determination module, used to determine the fault type and production type batch of the first cable when a fault occurs during use of the first cable;

[0024] A data acquisition module, used to acquire first life cycle data of the first cable and second life cycle data of a second cable of the same production type batch, wherein the first life cycle data and the second life cycle data both include multiple sub-life cycles and corresponding cycle parameter data;

[0025] An associated period determination module, used to determine one or more associated life cycles of the fault type;

[0026] a comparison data determination module, configured to determine the period parameter data corresponding to the sub-lifecycle that is the same as the associated lifecycle in the first full lifecycle data as the first comparison data, and to determine the period parameter data corresponding to the sub-lifecycle that is the same as the associated lifecycle in the second full lifecycle data as the second comparison data;

[0027] A similarity comparison module, used for performing a similarity comparison between the first comparison data and the second comparison data;

[0028] The prediction module is used to predict the second cable according to the comparison result to obtain a predicted faulty cable.

[0029] In a third aspect, an embodiment of the present invention further provides a cable fault prediction device based on cable life cycle data, the device comprising:

[0030] one or more processors;

[0031] a storage device for storing one or more programs,

[0032] When the one or more programs are executed by the one or more processors, the one or more processors implement the cable fault prediction method based on cable life cycle data described in an embodiment of the present invention.

[0033] In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer executable instructions, which, when executed by a computer processor, are used to execute the cable fault prediction method based on cable life cycle data described in an embodiment of the present invention.

[0034] In an embodiment of the present invention, when it is monitored that a first cable fails during use, the fault type and production type batch of the first cable are determined, the first life cycle data of the first cable and the second life cycle data of the second cable with the same production type batch are obtained, the first life cycle data and the second life cycle data include multiple sub-life cycles and corresponding cycle parameter data, one or more associated life cycles of the fault type are determined, the cycle parameter data corresponding to the sub-life cycle with the same associated life cycle in the first life cycle data is determined as the first comparison data, the cycle parameter data corresponding to the sub-life cycle with the same associated life cycle in the second life cycle data is determined as the second comparison data, the first comparison data and the second comparison data are compared for similarity, and the second cable is predicted according to the comparison result to obtain a predicted faulty cable. This solution predicts faults by using the full life cycle data of the cable, solves the problem that the reference information dimension is small in the existing cable fault prediction technology, resulting in incomplete consideration of factors and low accuracy of cable fault prediction, and can realize cable fault prediction under more possibilities in multiple dimensions by monitoring the cable under the full life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a cable fault prediction method based on cable life cycle data provided by an embodiment of the present invention;

[0036] Figure 2 A flowchart of another cable fault prediction method based on cable life cycle data provided by an embodiment of the present invention;

[0037] Figure 3 A flowchart of another cable fault prediction method based on cable life cycle data provided by an embodiment of the present invention;

[0038] Figure 4 A module structure block diagram of a cable fault prediction system based on cable life cycle data provided by an embodiment of the present invention;

[0039] Figure 5 A schematic diagram of the structure of a cable fault prediction device based on cable full life cycle data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, rather than to limit the embodiments of the present invention. It is also necessary to explain that, for ease of description, only parts related to the embodiments of the present invention are shown in the accompanying drawings, rather than all structures.

[0041] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and or or" in the specification and claims represents at least one of the connected objects, and the character "or" generally indicates that the objects associated before and after are in an "or" relationship.

[0042] The cable fault prediction method based on the cable life cycle data provided in the embodiment of the present application can be applied to the prediction scenario of cable faults. For example, if a fault is detected during the use of the cable, the fault prediction of other cables can be performed by the prediction method provided in the embodiment of the application. The cable fault prediction method based on the cable life cycle data provided in the embodiment of the present application, the execution subject of each step can be a computer device, which refers to any electronic device with data calculation, processing and storage capabilities, such as mobile phones, PCs (Personal Computers), tablet computers and other terminal devices, and can also be servers and other devices, which are not limited in the embodiment of the present application.

[0043] Figure 1 A flowchart of a cable fault prediction method based on cable life cycle data provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, specifically including:

[0044] Step S101: when it is monitored that a first cable fails during use, determine the failure type and production type batch of the first cable.

[0045] Among them, the first cable can be used to characterize the cable that is monitored to have a fault during use. The fault type is used to characterize the classification of the fault that occurs in the cable, which can be a fault type such as an over-humidity fault type and an over-temperature fault type. The production type batch can be used to characterize the coding identification of cables with the same production time and production line and the same classification according to certain rules. In one embodiment, the system distinguishes between the cables by different labels associated with each cable. When it is monitored that the first cable has a fault during use, the fault type of the monitored first cable is obtained, and the corresponding production type batch is queried according to the label of the first cable. In another embodiment, when it is monitored that the first cable has a fault during use, the fault parameter type of the first cable is obtained, the fault type of the first cable is determined according to the fault parameter type, and the identification code on the first cable is scanned to obtain the corresponding production type batch.

[0046] Step S102, obtain the first life cycle data of the first cable and the second life cycle data of the second cable of the same production type batch, the first life cycle data and the second life cycle data include multiple sub-life cycles and corresponding cycle parameter data.

[0047] Among them, the full life cycle of the cable can be the entire process from the design, production, transportation, installation to the use of the cable. The design, production, transportation, installation, and use are each a sub-life cycle in the full life cycle, and different parameter data will be recorded during the execution of each sub-life cycle. The first full life cycle data is used to characterize the cycle parameter data of the first cable recorded in each sub-life cycle in the full life cycle, and the second full life cycle data is used to characterize the data of various parameters recorded by the cable during the execution of the sub-life cycle. After determining the production type batch of the first cable, the first full life cycle data of the first cable and the second full life cycle data of the second cable with the same production type batch are obtained. An exemplary example can be that the production type batch of the first cable is A001, and the recorded data of the first cable in the design cycle, production cycle, transportation cycle, installation cycle, and use cycle are obtained. It is determined that the second cable with the same production type batch as A001 is cable a, cable b, and cable c, and the recorded data of cable a, cable b, and cable c in the design cycle, production cycle, transportation cycle, installation cycle, and use cycle are obtained.

[0048] Step S103, determine one or more associated life cycles of the fault type, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data as the first comparison data, and determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data as the second comparison data.

[0049] Among them, the associated life cycle is used to characterize the life cycle of the cable fault that may cause the fault type. For example, the fault type is softening of the insulation layer. The associated life cycle of the softening of the insulation layer is the design cycle, the production cycle, and the use cycle. Unreasonable design of the insulation layer, abnormalities in the production process, and harsh use environment may all cause the softening of the insulation layer. In one embodiment, a comparison table of pre-set fault types and associated life cycles is queried to obtain one or more corresponding associated life cycles, and the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data is determined as the first comparison data, and the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data is determined as the second comparison data. Optionally, the cycle parameter data includes multiple cycle parameters. A method for determining the associated life cycle may be to query the preset influence parameter mapping table according to the fault type to obtain one or more corresponding influence parameters, and determine the sub-life cycle corresponding to the cycle parameter that is the same as the influence parameter as the associated life cycle of the fault type. Determining the corresponding associated life cycle by the parameter that affects the fault type can improve the accuracy and rationality of the associated life cycle. An illustrative example may be that the fault type is softening of the insulation layer. According to the softening of the insulation layer, a preset influencing parameter mapping table is queried to obtain the corresponding influencing parameters, which are the high temperature resistance design parameters of the insulation layer, the high temperature resistance production parameters of the insulation layer, and the operating environment temperature parameters. The sub-life cycle of the high temperature resistance design parameters of the insulation layer included in the cycle parameter data is the design cycle, the sub-life cycle of the high temperature resistance production parameters of the insulation layer included in the cycle parameter data is the production cycle, and the sub-life cycle of the operating environment temperature parameters included in the cycle parameter data is the usage cycle, that is, the associated life cycles of the softening of the insulation layer are determined to be the design cycle, the production cycle, and the usage cycle.

[0050] Step S104: perform a similarity comparison between the first comparison data and the second comparison data, and predict the second cable according to the comparison result to obtain a predicted faulty cable.

[0051] Among them, the predicted fault cable is used to characterize the cable that is predicted to be likely to fail. In one embodiment, after determining the first comparison data and the second comparison data, the first comparison data and the second comparison data are input into a trained similarity comparison model, and the similarity value between the first comparison data and the second comparison data is outputted. When the similarity value between the first comparison data and the second comparison data is greater than or equal to the preset similarity, the second cable corresponding to the second comparison data is determined as the predicted fault cable. Optionally, the cycle parameter data also includes cycle parameter values ​​corresponding to multiple cycle parameters. A similarity comparison method may be: calculating the parameter similarity of the cycle parameter values ​​of the same cycle parameters in the first comparison data and the second comparison data, determining the fault influencing parameter similarity based on the parameter similarity of the cycle parameters that are the same as the influencing parameters, determining the overall similarity between the first comparison data and the second comparison data based on the parameter similarity, comparing the fault influencing parameter similarity with the first preset similarity, and comparing the overall similarity with the second preset similarity. The similarity of the fault influencing parameters of the first cable and the second cable and the overall similarity of the first comparison data and the second comparison data are calculated, and the similarity of the fault influencing parameters and the overall similarity are compared with the corresponding preset values ​​respectively, thereby improving the dimension of the reference information and the accuracy of the fault prediction.

[0052] As can be seen from the above, when it is monitored that the first cable fails during use, the fault type and production type batch of the first cable are determined, the first full life cycle data of the first cable and the second full life cycle data of the second cable with the same production type batch are obtained, the first full life cycle data and the second full life cycle data include multiple sub-life cycles and corresponding cycle parameter data, one or more associated life cycles of the fault type are determined, the cycle parameter data corresponding to the sub-life cycle with the same associated life cycle in the first full life cycle data is determined as the first comparison data, the cycle parameter data corresponding to the sub-life cycle with the same associated life cycle in the second full life cycle data is determined as the second comparison data, the first comparison data and the second comparison data are compared for similarity, and the second cable is predicted according to the comparison result to obtain a predicted faulty cable. This scheme predicts faults through the full life cycle data of the cable, solves the problem that the reference information dimension is small in the existing cable fault prediction technology, resulting in incomplete consideration of factors and low accuracy of cable fault prediction, and can realize cable fault prediction under more possibilities in multiple dimensions by monitoring the cable under the full life cycle.

[0053] Figure 2 A flowchart of another cable fault prediction method based on cable life cycle data provided by an embodiment of the present invention provides a specific process of an optional similarity comparison, such as Figure 2 As shown, specifically including:

[0054] Step S201: when it is monitored that a first cable fails during use, determine the failure type and production type batch of the first cable.

[0055] Step S202, obtain the first full life cycle data of the first cable and the second full life cycle data of the second cable of the same production type batch, the first full life cycle data and the second full life cycle data include multiple sub-life cycles and corresponding cycle parameter data.

[0056] Step S203, determine one or more associated life cycles of the fault type, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data as the first comparison data, and determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data as the second comparison data.

[0057] Step S204: Calculate parameter similarity of period parameter values ​​of the same period parameter in the first comparison data and the second comparison data, and determine fault influencing parameter similarity based on the parameter similarity of the period parameter that is the same as the influencing parameter.

[0058] Among them, the fault influencing parameter similarity is used to characterize the similarity of the periodic parameters of the cable fault that affects the fault type. The parameter similarity of the periodic parameter values ​​of the same periodic parameter in the first comparison data and the second comparison data is calculated, and the parameter similarity of the periodic parameter that is the same as the influencing parameter is determined as the fault influencing parameter similarity. Optionally, in the case of multiple influencing parameters, the first preset weight of the periodic parameter that is the same as the influencing parameter under the fault type is determined, and the first preset weight is multiplied and superimposed with the corresponding parameter similarity to obtain the fault influencing parameter similarity. An exemplary example may be that the influencing parameters of the insulation layer softening fault type are the insulation layer high temperature resistance design parameters, the insulation layer high temperature resistance production parameters, and the use environment temperature parameters. The similarities of the insulation layer high temperature resistance design parameter values, the insulation layer high temperature resistance production parameter values, and the use environment temperature parameter values ​​in the first comparison data and the second comparison data are calculated to be 90%, 80%, and 95% respectively. The pre-set weights of the insulation layer high temperature resistance design parameters, the insulation layer high temperature resistance production parameters, and the use environment temperature parameters under the insulation layer softening fault type are queried to be 0.4, 0.4, and 0.2 respectively. Each weight is multiplied by the corresponding similarity and superimposed to obtain a fault influencing parameter similarity of 87% (90%*0.4+80%*0.4+95%*0.2). In another embodiment, when there are multiple influencing parameters, the average value of the parameter similarity of each period parameter that is the same as the influencing parameter is calculated and determined as the fault influencing parameter similarity.

[0059] Step S205: determine the overall similarity between the first comparison data and the second comparison data based on the parameter similarity, compare the fault influencing parameter similarity with the first preset similarity, and compare the overall similarity with the second preset similarity, and predict the second cable according to the comparison result to obtain a predicted faulty cable.

[0060] Among them, the overall similarity is used to characterize the similarity between the first comparison data and the second comparison data. The overall similarity between the first comparison data and the second comparison data is determined based on the parameter similarity, the fault impact parameter similarity is compared with the first preset similarity, and the overall similarity is compared with the second preset similarity, and the second cable is predicted according to the comparison result to obtain a predicted faulty cable. Optionally, a method for determining the overall similarity can be: calculating the average parameter similarity of the periodic parameters in the associated life cycle, and when there are multiple associated life cycles, determining the second preset weight of the associated life cycle under the fault type, multiplying the second preset weight with the corresponding parameter similarity average and superimposing them to obtain the overall similarity between the first comparison data and the second comparison data. An exemplary example may be that the associated life cycle of the insulation layer softening fault type is the design cycle, the production cycle, and the use cycle. The design cycle includes the insulation layer heat resistance design parameters, the insulation layer tensile strength design parameters, the conductivity design parameters, and the sheath thickness design parameters. The corresponding parameter similarities are 90%, 85%, 90%, and 95%, respectively. The average parameter similarity of the design cycle is 90%. The production cycle includes the insulation layer heat resistance production parameters, the insulation layer tensile strength production parameters, the conductivity production parameters, and the sheath thickness production parameters. The corresponding parameter similarities are 95%, 90%, 100%, 95%, then the average parameter similarity of the production cycle is 95%, the use cycle includes temperature parameters and humidity parameters, and the corresponding parameter similarities are 80% and 90% respectively, then the average parameter similarity of the use cycle is 85%, the second preset weights of the design cycle, production cycle, and use cycle under the insulation layer softening fault type are 0.5, 0.3, and 0.2 respectively, then the overall similarity of the first comparison data and the second comparison data is 90.5% (90%*0.5+95%*0.3+85%*0.2). In another embodiment, the average value of all parameter similarities corresponding to the first comparison data and the second comparison data is calculated and determined as the overall similarity of the first comparison data and the second comparison data.

[0061] As can be seen from the above, the parameter similarity of the periodic parameter values ​​of the same periodic parameters in the first comparison data and the second comparison data is calculated, the fault influencing parameter similarity is determined based on the parameter similarity of the periodic parameters that are the same as the influencing parameters, the overall similarity of the first comparison data and the second comparison data is determined based on the parameter similarity, the fault influencing parameter similarity is compared with the first preset similarity, and the overall similarity is compared with the second preset similarity, and the second cable is predicted according to the comparison result to obtain a predicted faulty cable. This solution improves the dimension of reference information and the accuracy of fault prediction by calculating the fault influencing parameter similarity and overall similarity of the first cable and the second cable, and comparing them with the corresponding preset values ​​respectively.

[0062] Figure 3 A flowchart of another cable fault prediction method based on cable life cycle data provided by an embodiment of the present invention provides an optional specific process for predicting the second cable, such as Figure 3 As shown, specifically including:

[0063] Step S301: when it is monitored that a first cable fails during use, determine the failure type and production type batch of the first cable.

[0064] Step S302, obtain the first life cycle data of the first cable and the second life cycle data of the second cable of the same production type batch, the first life cycle data and the second life cycle data include multiple sub-life cycles and corresponding cycle parameter data.

[0065] Step S303, determine one or more associated life cycles of the fault type, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data as the first comparison data, and determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data as the second comparison data.

[0066] Step S304: perform a similarity comparison between the first comparison data and the second comparison data to determine whether the comparison result meets the warning condition. If the warning condition is met, determine the second cable corresponding to the fault influencing parameter similarity and the overall similarity as the predicted fault cable.

[0067] Among them, the early warning condition can be used to characterize the conditions for judging that the cable may fail. After comparing the fault influencing parameter similarity with the first preset similarity and comparing the overall similarity with the second preset similarity, it is determined whether the comparison result meets the early warning condition. If the comparison result meets the early warning condition, the second cable corresponding to the fault influencing parameter similarity and the overall similarity is determined as the predicted fault cable. Optionally, if the fault influencing parameter similarity is greater than the first preset similarity and the overall similarity is greater than the second preset similarity, it is determined that the early warning condition is met, otherwise, it is determined that the early warning condition is not met, wherein the first preset similarity is greater than the second preset similarity. An exemplary example may be that the first preset similarity is 90%, the second preset similarity is 80%, the second cable includes cable a, cable b and cable c, the fault influence parameter similarity between cable a and the first cable is 95%, and the overall similarity is 90%, the fault influence parameter similarity between cable b and the first cable is 60%, and the overall similarity is 65%, the fault influence parameter similarity between cable c and the first cable is 70%, and the overall similarity is 85%, the fault influence parameter similarity corresponding to cable a is greater than the first preset similarity and the overall similarity is greater than the second preset similarity, satisfying the early warning condition, the fault influence parameter similarity corresponding to cable b is less than the first preset similarity and the overall similarity is less than the second preset similarity, and does not satisfy the early warning condition, the fault influence parameter similarity corresponding to cable c is less than the first preset similarity, and does not satisfy the early warning condition, that is, cable a is a predicted fault cable. In another embodiment, preset weights corresponding to the fault influencing parameter similarity and the overall similarity are determined, the fault influencing parameter similarity and the overall similarity are multiplied by the corresponding preset weights and superimposed to obtain the conditional judgment similarity, and when the conditional judgment similarity is greater than a preset threshold, the second cable corresponding to the conditional judgment similarity is determined as the predicted fault cable.

[0068] As can be seen from the above, the first comparison data and the second comparison data are compared for similarity to determine whether the comparison result meets the warning condition. If the warning condition is met, the second cable corresponding to the similarity of the fault influencing parameters and the overall similarity is determined as the predicted fault cable. This solution predicts faults by comparing the similarity between the associated life cycle data of the faulty cable and the associated life cycle data of the same production type batch of cables. This can improve the accuracy of cable fault prediction and timely perform cable maintenance to avoid cable failures that affect the operation of the power system.

[0069] Figure 4 The module structure block diagram of a cable fault prediction system based on cable life cycle data provided by an embodiment of the present invention is used to execute the cable fault prediction method based on cable life cycle data provided by the above embodiment, and has the corresponding functional modules and beneficial effects of the execution method. Figure 4As shown, the system specifically includes:

[0070] The type batch determination module 101 is used to determine the fault type and production type batch of the first cable when it is detected that the first cable fails during use;

[0071] A data acquisition module 102 is used to acquire first life cycle data of the first cable and second life cycle data of a second cable of the same production type batch, wherein the first life cycle data and the second life cycle data both include multiple sub-life cycles and corresponding cycle parameter data;

[0072] An associated period determination module 103, configured to determine one or more associated life cycles of the fault type;

[0073] A comparison data determination module 104 is used to determine the period parameter data corresponding to the sub-lifecycle that is the same as the associated lifecycle in the first full lifecycle data as the first comparison data, and determine the period parameter data corresponding to the sub-lifecycle that is the same as the associated lifecycle in the second full lifecycle data as the second comparison data;

[0074] A similarity comparison module 105, configured to perform a similarity comparison between the first comparison data and the second comparison data;

[0075] The prediction module 106 is used to predict the second cable according to the comparison result to obtain a predicted faulty cable.

[0076] It can be seen from the above scheme that when it is monitored that the first cable fails during use, the fault type and production type batch of the first cable are determined, the first full life cycle data of the first cable and the second full life cycle data of the second cable with the same production type batch are obtained, the first full life cycle data and the second full life cycle data include multiple sub-life cycles and corresponding cycle parameter data, one or more associated life cycles of the fault type are determined, the cycle parameter data corresponding to the sub-life cycle with the same associated life cycle in the first full life cycle data is determined as the first comparison data, the cycle parameter data corresponding to the sub-life cycle with the same associated life cycle in the second full life cycle data is determined as the second comparison data, the first comparison data and the second comparison data are compared for similarity, and the second cable is predicted according to the comparison result to obtain a predicted faulty cable. This scheme predicts faults through the full life cycle data of the cable, solves the problem that the reference information dimension is small in the existing cable fault prediction technology, resulting in incomplete consideration of factors and low accuracy of cable fault prediction, and can realize cable fault prediction under more possibilities in multiple dimensions by monitoring the cable under the full life cycle.

[0077] In a possible embodiment, the association period determination module 103 is specifically configured to:

[0078] A preset impact parameter mapping table is queried according to the fault type to obtain one or more corresponding impact parameters, and a sub-lifecycle corresponding to a cycle parameter identical to the impact parameter is determined as an associated lifecycle of the fault type.

[0079] In a possible embodiment, the similarity comparison module 105 is specifically used to:

[0080] Calculating parameter similarity of period parameter values ​​of the same period parameter in the first comparison data and the second comparison data, determining fault influencing parameter similarity based on the parameter similarity of the period parameter that is the same as the influencing parameter, and determining overall similarity between the first comparison data and the second comparison data based on the parameter similarity;

[0081] The fault influencing parameter similarity is compared with a first preset similarity, and the overall similarity is compared with a second preset similarity.

[0082] In a possible embodiment, the similarity comparison module 105 is further used for:

[0083] In the case where there are multiple influencing parameters, a first preset weight of the periodic parameter that is the same as the influencing parameter under the fault type is determined, and the first preset weight is multiplied and superimposed with the corresponding parameter similarity to obtain the fault influencing parameter similarity.

[0084] In a possible embodiment, the similarity comparison module 105 is further used for:

[0085] Calculate the average parameter similarity of the periodic parameters in the associated life cycle. When there are multiple associated life cycles, determine the second preset weight of the associated life cycle under the fault type. Multiply the second preset weight with the corresponding average parameter similarity and superimpose them to obtain the overall similarity between the first comparison data and the second comparison data.

[0086] In a possible embodiment, the prediction module 106 is specifically configured to:

[0087] It is determined whether the comparison result meets the early warning condition. If the early warning condition is met, the second cable corresponding to the fault influencing parameter similarity and the overall similarity is determined as the predicted fault cable.

[0088] In a possible embodiment, the prediction module 106 is further configured to:

[0089] When the fault influencing parameter similarity is greater than the first preset similarity and the overall similarity is greater than the second preset similarity, it is determined that the warning condition is met; otherwise, it is determined that the warning condition is not met, wherein the first preset similarity is greater than the second preset similarity.

[0090] Figure 5 A schematic diagram of the structure of a cable fault prediction device based on cable life cycle data provided by an embodiment of the present invention, such as Figure 5 As shown, the device includes a processor 201, a memory 202, an input device 203 and an output device 204; the number of processors 201 in the device can be one or more. Figure 5 A processor 201 is taken as an example; the processor 201, memory 202, input device 203 and output device 204 in the device can be connected by a bus or other means. Figure 5 The example of connecting through a bus is taken. The memory 202, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions or modules corresponding to the cable fault prediction method based on the cable life cycle data in the embodiment of the present invention. The processor 201 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 202, that is, realizes the above-mentioned cable fault prediction method based on the cable life cycle data. The input device 203 can be used to receive input digital or character information, and generate key signal input related to the user settings and function control of the device. The output device 204 may include a display device such as a display screen.

[0091] An embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions are used to execute a cable fault prediction method based on cable life cycle data when executed by a computer processor, the method comprising:

[0092] In the case where it is monitored that the first cable fails during use, determining the failure type and production type batch of the first cable;

[0093] Obtain first life cycle data of the first cable and second life cycle data of a second cable of the same production type batch, wherein the first life cycle data and the second life cycle data include multiple sub-life cycles and corresponding cycle parameter data;

[0094] Determine one or more associated life cycles of the fault type, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data as the first comparison data, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data as the second comparison data, perform a similarity comparison between the first comparison data and the second comparison data, and predict the second cable based on the comparison result to obtain a predicted faulty cable.

[0095] It is worth noting that in the above-mentioned embodiment of the cable fault prediction method system based on cable life cycle data, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0096] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the embodiments of the present invention are not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the embodiments of the present invention. Therefore, although the embodiments of the present invention are described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the embodiments of the present invention, and the scope of the embodiments of the present invention is determined by the scope of the appended claims.

Claims

1. A cable fault prediction method based on cable life cycle data, characterized in that: include: In the case where it is monitored that the first cable fails during use, determining the failure type and production type batch of the first cable; Obtain first life cycle data of the first cable and second life cycle data of a second cable of the same production type batch, wherein the first life cycle data and the second life cycle data include multiple sub-life cycles and corresponding cycle parameter data; Determine one or more associated life cycles of the fault type, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the first full life cycle data as the first comparison data, determine the cycle parameter data corresponding to the sub-life cycle that is the same as the associated life cycle in the second full life cycle data as the second comparison data, perform a similarity comparison between the first comparison data and the second comparison data, and predict the second cable based on the comparison result to obtain a predicted faulty cable.

2. The cable fault prediction method based on cable life cycle data according to claim 1 is characterized in that: The cycle parameter data includes a plurality of cycle parameters, and the determining of one or more associated life cycles of the fault type includes: A preset impact parameter mapping table is queried according to the fault type to obtain one or more corresponding impact parameters, and a sub-lifecycle corresponding to a cycle parameter identical to the impact parameter is determined as an associated lifecycle of the fault type.

3. The cable fault prediction method based on cable life cycle data according to claim 1 is characterized in that: The periodic parameter data also includes periodic parameter values ​​corresponding to a plurality of periodic parameters, and the performing similarity comparison between the first comparison data and the second comparison data includes: Calculating parameter similarity of period parameter values ​​of the same period parameter in the first comparison data and the second comparison data, determining fault influencing parameter similarity based on the parameter similarity of the period parameter that is the same as the influencing parameter, and determining overall similarity between the first comparison data and the second comparison data based on the parameter similarity; The fault influencing parameter similarity is compared with a first preset similarity, and the overall similarity is compared with a second preset similarity.

4. The cable fault prediction method based on cable life cycle data according to claim 3 is characterized in that: The determining of the fault influencing parameter similarity based on the parameter similarity of the periodic parameter which is the same as the influencing parameter comprises: In the case where there are multiple influencing parameters, a first preset weight of the periodic parameter that is the same as the influencing parameter under the fault type is determined, and the first preset weight is multiplied and superimposed with the corresponding parameter similarity to obtain the fault influencing parameter similarity.

5. The cable fault prediction method based on cable life cycle data according to claim 3 is characterized in that: The determining the overall similarity between the first comparison data and the second comparison data based on the parameter similarity includes: Calculate the average parameter similarity of the periodic parameters in the associated life cycle. When there are multiple associated life cycles, determine the second preset weight of the associated life cycle under the fault type. Multiply the second preset weight with the corresponding average parameter similarity and superimpose them to obtain the overall similarity between the first comparison data and the second comparison data.

6. The cable fault prediction method based on cable life cycle data according to any one of claims 1 to 5, characterized in that: The step of predicting the second cable according to the comparison result to obtain a predicted faulty cable includes: It is determined whether the comparison result meets the early warning condition. If the early warning condition is met, the second cable corresponding to the fault influencing parameter similarity and the overall similarity is determined as the predicted fault cable.

7. The cable fault prediction method based on cable life cycle data according to claim 6 is characterized in that: The determining whether the comparison result meets the warning condition includes: When the fault influencing parameter similarity is greater than the first preset similarity and the overall similarity is greater than the second preset similarity, it is determined that the warning condition is met; otherwise, it is determined that the warning condition is not met, wherein the first preset similarity is greater than the second preset similarity.

8. The cable fault prediction system based on the cable life cycle data is characterized by: include: A type batch determination module, used to determine the fault type and production type batch of the first cable when a fault occurs during use of the first cable; A data acquisition module, used to acquire first life cycle data of the first cable and second life cycle data of a second cable of the same production type batch, wherein the first life cycle data and the second life cycle data both include multiple sub-life cycles and corresponding cycle parameter data; An associated period determination module, used to determine one or more associated life cycles of the fault type; a comparison data determination module, configured to determine the period parameter data corresponding to the sub-lifecycle that is the same as the associated lifecycle in the first full lifecycle data as the first comparison data, and to determine the period parameter data corresponding to the sub-lifecycle that is the same as the associated lifecycle in the second full lifecycle data as the second comparison data; A similarity comparison module, used for performing a similarity comparison between the first comparison data and the second comparison data; The prediction module is used to predict the second cable according to the comparison result to obtain a predicted faulty cable.

9. A cable fault prediction device based on cable life cycle data, the device comprising: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the cable fault prediction method based on cable life cycle data as described in any one of claims 1-7.

10. A storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute the cable fault prediction method based on cable life cycle data as described in any one of claims 1 to 7.

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