CN112036449A - Fault analysis decision platform and fault analysis method based on intelligent cable - Google Patents
Fault analysis decision platform and fault analysis method based on intelligent cable Download PDFInfo
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Abstract
The embodiment of the application discloses a fault analysis decision platform, a fault analysis method and a storage medium based on an intelligent cable. According to the technical scheme provided by the embodiment of the application, the operation monitoring data of the intelligent cable is subjected to matching identification by pre-constructing a fault identification model and a fault strategy model; the fault analysis server analyzes and matches the operation monitoring data collected by the data collection cluster to obtain corresponding fault data, and obtains a corresponding fault solution strategy according to the fault data matching to provide for operation maintenance personnel, so that the fault identification efficiency is greatly improved. According to the scheme of the embodiment of the application, the fault reason and the corresponding solution strategy are determined by comprehensively analyzing various input data, so that operators are helped to maintain the intelligent cable better, and a safer cable use environment is constructed.
Description
Technical Field
The embodiment of the application relates to the technical field of cables, in particular to a fault analysis decision platform and a fault analysis method based on an intelligent cable.
Background
Modern electric energy is related to various aspects of daily life, production and the like of people, so once an electric accident happens, serious consequences are very likely to be caused, and the fault needs to be quickly positioned and repaired. At present, in order to better monitor the running state of the cable in real time and realize better operation and maintenance effects on the cable, the operation and maintenance setting of the cable tends to be more and more intelligent. The intelligent cable can realize real-time monitoring on parameters such as voltage, current, local current and the like of the cable through detection setting of relevant operation states, and even can monitor cable faults, so that operation management on the cable can be well realized, and the operation and maintenance effects of the cable are optimized.
However, most of the existing intelligent cable fault analysis methods adopt a data statistics method to display contents after a fault is found, and cannot accurately analyze the fault and provide corresponding countermeasures. Manually analyzing the displayed data content to finally determine the fault type, and then manually determining the decision type; therefore, designing a platform and a method capable of performing fault analysis decision becomes a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The embodiment of the application provides a fault analysis decision platform, a fault analysis method and a storage medium based on an intelligent cable, which can match and identify operation monitoring data of the intelligent cable by pre-constructing a neural network model, obtain corresponding fault data according to the current monitoring data, obtain a corresponding fault solution strategy according to the fault data matching and give an operation maintenance worker, and help the operation worker to realize better maintenance on the intelligent cable by comprehensively analyzing various input data to determine fault reasons and corresponding solution strategies.
In a first aspect, an embodiment of the present application provides a fault analysis and decision platform based on a smart cable, including:
the data acquisition cluster is used for acquiring operation monitoring data of the intelligent cable and transmitting the operation monitoring data to the fault analysis server;
the fault analysis server comprises a data conversion unit, a data analysis unit, a data matching unit and a data transmission unit, wherein the data conversion unit is used for converting received operation monitoring data into digital signals and outputting the digital signals to the data analysis unit, the data analysis unit is used for inputting the operation monitoring data converted into the digital signals into a preset fault recognition model to perform fault analysis on corresponding intelligent cables and outputting corresponding fault data to the data matching unit and the data transmission unit, and the fault recognition model is built by adopting a neural network model;
the data matching unit compares the fault data with a pre-stored fault strategy model to obtain a corresponding fault solution strategy, and transmits the fault solution strategy to the data transmission unit, and the data transmission unit is used for transmitting the fault data and the fault solution strategy to corresponding operation and maintenance personnel.
Further, the fault data comprises a fault type and a fault size;
the fault analysis server further comprises an early warning unit, early warning fault data are stored in the early warning unit in advance, and when the fault type and the fault size are judged to be matched with the pre-stored fault characteristic parameters, early warning operation is conducted on the current state of the intelligent cable.
Further, the neural network model includes any one of a BP neural network model or a radial basis function neural network model or a convolutional neural network model.
Further, the data acquisition cluster includes monitoring module and gateway unit that corresponds with the smart cable, the gateway unit with the monitoring module electricity is connected, the gateway unit is used for assembling the operation monitoring data of each monitoring module transmission, and will operation monitoring data transmits to the fault analysis server.
Further, the monitoring module includes that the distributing type sets up in temperature monitoring module, current monitoring module, partial discharge monitoring module and the vibration monitoring module of each position of smart cable.
Furthermore, the monitoring module also comprises a camera module, and the camera module is used for acquiring image data corresponding to the intelligent cable;
the fault analysis server further comprises an aging detection unit, and the aging detection unit is used for determining the aging condition and the service life state of the corresponding intelligent cable according to the image data.
Further, the data acquisition cluster still includes the shielding unit with partial discharge monitoring module electric connection, the shielding unit is used for right partial discharge monitoring module shields the protection with the filtering environmental noise to the interference of the partial discharge signal of the monitoring of partial discharge monitoring module.
In a second aspect, an embodiment of the present application provides a fault analysis method based on a smart cable, including:
receiving first operation monitoring data collected by a data collection cluster at an intelligent cable;
converting the first operation monitoring data into a digital signal to obtain second operation monitoring data;
transmitting the second operation monitoring data to an input end of a preset fault recognition model, and obtaining corresponding fault data at an output end of the fault recognition model; the fault identification model is constructed by adopting a neural network model;
transmitting the fault data to a data matching module to perform data matching with a pre-stored fault strategy model so as to obtain a corresponding fault solution strategy;
and transmitting the fault data and the fault resolution strategy to corresponding operation and maintenance personnel.
Further, the neural network model includes any one of a BP neural network model, a radial basis function neural network model or a convolutional neural network model;
the fault identification model is constructed by the following steps:
obtaining marked historical monitoring data;
collecting the obtained marked historical monitoring data to generate a corresponding monitoring training set;
and constructing a fault recognition model based on the convolutional neural network, and recognizing and training the fault recognition model by taking the monitoring training set as input and the fault data as output until the training requirements are met.
Further, after the transmitting the fault data and the fault resolution policy to the corresponding operation and maintenance personnel, the method further includes:
and receiving the repair data of the intelligent cable fed back by the operation and maintenance personnel, and storing the repair data.
Further, after the data storing the repair data, the method further includes:
and after the repair data and the fault data are marked, adding the repair data and the fault data into a monitoring training set to optimize the fault recognition model.
Further, the first operation monitoring data includes one or more of temperature data, current data, vibration data, partial discharge data, and image data.
In a third aspect, embodiments of the present application provide a storage medium containing computer-executable instructions for performing the smart cable-based fault analysis method according to the second aspect when executed by a computer processor.
The fault analysis decision platform based on the intelligent cable carries out matching identification on the operation monitoring data of the intelligent cable by pre-constructing a fault identification model and a fault strategy model; the fault analysis server analyzes and matches the operation monitoring data collected by the data collection cluster to obtain corresponding fault data, and obtains a corresponding fault solution strategy according to the fault data matching to provide for operation maintenance personnel, so that the fault identification efficiency is greatly improved. According to the scheme of the embodiment of the application, the fault reason and the corresponding solution strategy are determined by comprehensively analyzing various input data, so that operators are helped to maintain the intelligent cable better, and a safer cable use environment is constructed.
Drawings
Fig. 1 is a block diagram of a fault analysis and decision platform based on a smart cable according to an embodiment of the present application;
fig. 2 is a flowchart of a fault analysis method based on a smart cable according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a fault analysis model construction provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a data acquisition cluster provided in an embodiment of the present application;
fig. 5 is an architecture diagram of a BP neural network model provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
At present, after faults are found in most intelligent cable fault analysis, contents are displayed in a data statistics mode, and the faults cannot be accurately analyzed and corresponding countermeasures cannot be given. And the displayed data content needs to be analyzed manually to finally determine the fault type, and then the decision type is determined manually. Based on the above, the fault analysis decision platform based on the intelligent cable performs matching identification on the operation monitoring data of the intelligent cable by pre-constructing a fault identification model and a fault strategy model; the fault analysis server analyzes and matches the operation monitoring data collected by the data collection cluster to obtain corresponding fault data, and obtains a corresponding fault solution strategy according to the fault data matching to provide for operation maintenance personnel, so that the fault identification efficiency is greatly improved. According to the scheme of the embodiment of the application, the fault reason and the corresponding solution strategy are determined by comprehensively analyzing various input data, so that operators are helped to maintain the intelligent cable better, and a safer cable use environment is constructed.
Fig. 1 is a block diagram of a structure of a fault analysis decision platform based on an intelligent cable according to an embodiment of the present disclosure, and as shown in fig. 1, the embodiment of the present disclosure provides a fault analysis decision platform based on an intelligent cable, including:
the data acquisition cluster is used for acquiring operation monitoring data of the intelligent cable and transmitting the operation monitoring data to the fault analysis server;
the fault analysis server comprises a data conversion unit, a data analysis unit, a data matching unit and a data transmission unit, wherein the data conversion unit is used for converting received operation monitoring data into digital signals and outputting the digital signals to the data analysis unit, the data analysis unit is used for inputting the operation monitoring data converted into the digital signals into a preset fault recognition model to perform fault analysis on corresponding intelligent cables and outputting corresponding fault data to the data matching unit and the data transmission unit, and the fault recognition model is built by adopting a neural network model;
the data matching unit compares the fault data with a pre-stored fault strategy model to obtain a corresponding fault solution strategy, and transmits the fault solution strategy to the data transmission unit, and the data transmission unit is used for transmitting the fault data and the fault solution strategy to corresponding operation and maintenance personnel.
Specifically, in this application embodiment, the data collection cluster is disposed at the front end and used for collecting operation monitoring data of each intelligent cable line, wherein the operation monitoring data can be periodically collected and uploaded to the fault analysis server by the data collection cluster according to a set data collection period. Or the fault analysis server periodically issues a data acquisition request to acquire the operation monitoring data cached in the data acquisition cluster. It should be noted that the operation monitoring data collected by the data collection cluster needs to include the line number information of the intelligent cable corresponding to the operation monitoring data, so as to ascertain the intelligent cable line to which the corresponding operation monitoring data belongs when performing the storage and analysis of the operation monitoring data subsequently.
And the fault analysis server carries out operation fault monitoring analysis based on the received operation monitoring data and the operation monitoring data in real time. And carrying out fault analysis by using a fault analysis model which is constructed in advance through a neural network model to obtain corresponding fault data. The fault data are sent to a data matching unit for strategy matching, and corresponding fault solving strategies are obtained according to the fault data matching and are provided for operation and maintenance personnel, so that the fault identification efficiency is greatly improved.
Further, fig. 2 is a flowchart of a fault analysis method based on a smart cable according to an embodiment of the present disclosure, where the fault analysis method based on a smart cable according to the present disclosure may be executed by a fault analysis device based on a smart cable, and the fault analysis device based on a smart cable may be implemented in a software and/or hardware manner, and the fault analysis device based on a smart cable may be formed by two or more physical entities or may be formed by one physical entity. Generally, the fault analysis device based on the smart cable can be a computer, a mobile phone, a tablet or a fault analysis decision platform, etc.
The following description will be given by taking a fault analysis decision platform as an example of equipment for executing a fault analysis method based on an intelligent cable. Referring to fig. 2, the fault analysis method based on the smart cable specifically includes:
s101: and receiving first operation monitoring data collected by the data collection cluster at the intelligent cable.
In the step, basic data of intelligent cable operation are mainly collected, and then the data are further identified and processed to confirm the fault condition. When the intelligent cable breaks down, various corresponding monitoring data at the intelligent cable are changed. For example, common faults of cable lines include mechanical damage, insulation moisture, insulation aging deterioration, overvoltage, cable overheating fault and the like. When the fault occurs, the temperature data, the current data and the vibration data nearby the fault can be known to be correspondingly transformed; by identifying the data, the corresponding failure condition can be confirmed.
More preferably, the first operation monitoring data includes one or more of temperature data, current data, vibration data, partial discharge data, and image data. Besides detection for some physical conditions, diagnosis can be performed by combining image data, particularly for some cable conditions, such as aging or mechanical damage and the like which can be directly seen through appearance, an image recognition model is constructed to determine the aging condition of the cable, and corresponding temperature data, current data, vibration data and partial discharge data are combined to perform fault diagnosis, so that more dimensional data recognition contents can be provided by adding the image recognition method.
More preferably, the data collection cluster further includes a shielding unit electrically connected to the partial discharge monitoring module, and the shielding unit is configured to shield and protect the partial discharge monitoring module to filter interference of the partial discharge signal monitored by the partial discharge monitoring module due to environmental noise. Because when adopting partial discharge monitoring module to carry out data monitoring, receive environmental noise to its influence easily, in order to further carry out noise filtering interference to the data that obtains, adopted the shielding module to carry out the shielding of data in this application embodiment and handled, and then filtering environmental noise is right the interference of the partial discharge signal of the monitoring of partial discharge monitoring module improves the accuracy of data monitoring.
S102: and converting the first operation monitoring data into a digital signal to obtain second operation monitoring data.
The step is mainly to convert the data of the analog signal part in the first operation monitoring data into a corresponding digital signal for subsequent transmission and data processing. Besides the conversion of digital signals, the data can be uniformly processed and marked according to a certain rule, so that the input of the data into the recognition model is facilitated.
S103: transmitting the second operation monitoring data to an input end of a preset fault recognition model, and obtaining corresponding fault data at an output end of the fault recognition model; and the fault identification model is constructed by adopting a neural network model.
And inputting the processed operation monitoring data into a preset fault recognition model for recognition processing so as to determine which fault the corresponding fault belongs to. The fault identification model in the embodiment of the application adopts a neural network model to construct data.
More preferably, the neural network model includes any one of a BP neural network model, a radial basis function neural network model, or a convolutional neural network model.
Specifically, fig. 3 is a schematic flow chart of the fault analysis model construction provided in the embodiment of the present application, and as shown in fig. 3, the fault identification model is constructed through the following steps:
s103 a: obtaining marked historical monitoring data;
s103 b: collecting the obtained marked historical monitoring data to generate a corresponding monitoring training set;
s103 c: and constructing a fault recognition model based on the convolutional neural network, and recognizing and training the fault recognition model by taking the monitoring training set as input and the fault data as output until the training requirements are met.
In the above mode of adopting the convolutional neural network to construct the model, when data is constructed, the obtained historical monitoring data needs to be marked, the historical monitoring data is the data monitored when a fault occurs, various monitoring index data are obtained, then fault confirmation marking is carried out manually, and after the marking is completed, a corresponding training set is generated in a combined manner; and then, in the neural network model, the fault recognition model is recognized and trained by taking the monitoring training set as input and the fault data as output until the training requirement is met. And when the identification accuracy reaches the set target, determining that the corresponding fault identification model can be put into use when meeting the standard requirement.
In addition to the convolutional neural network model for constructing the fault analysis model, in the embodiment of the present application, a BP neural network model may be used for constructing the analysis model.
Specifically, fig. 5 is a schematic diagram of an architecture of a BP neural network model provided in an embodiment of the present application, and as shown in fig. 5, in a modeling process of the BP neural network model, a data sample, that is, input/output sample data of a system, is loaded first, then a 3-layer forward neural network is created, a weight and a threshold of the model are trained by using a training sample, the trained model is tested, and an output of the model is compared with a detection value of the system to check accuracy of the model. Because the initial weight coefficient is a small random nonzero value, and the convergence rate of the BP algorithm is related to the selection of the initial weight, a good training result can be obtained by training the neural network for multiple times.
Specifically, in the present embodiment, classification of the resistance pattern of the fault point is implemented for the measured values of the 5 detection point potentials, and a neural network is used as a classifier for the relationship between the detection point potentials and the resistance pattern of the fault point. The neural network classifier has a structure that three layers of BP neural networks input 5 rows and 6 columns of vectors, an output layer is a 2-dimensional output vector, and corresponding fault modes are divided into three types, namely single-phase metallic ground fault, two-phase ground fault and three-phase ground fault. The relation between the detection value and the fault type is found through continuous learning and training of the neural network model, and then corresponding model construction is completed, so that when fault identification is carried out subsequently, the corresponding fault type can be determined directly through identifying corresponding data.
S104: and transmitting the fault data to a data matching module to perform data matching with a pre-stored fault strategy model so as to obtain a corresponding fault solution strategy.
This step is mainly to confirm the matching problem between the specific fault type and the fault resolution strategy, because even if the corresponding fault type is identified, if the corresponding resolution strategy is not provided, the maintenance management of the manager is not convenient, and the auxiliary function is not obvious enough.
Some faults and corresponding measures for handling the faults are listed in the embodiment of the application, but the specific implementation is not limited to the following ways:
1. cable line ground fault and handling measures: the underground moving soil is damaged, the insulation is damaged, the ground can be dug, and the insulation can be repaired; the artificial grounding is not removed, and the grounding wire needs to be removed; the load is too large, the temperature is too high, the insulation is aged, the load is adjusted, the temperature is reduced, the aged insulation is replaced, and some cables which are seriously aged are replaced; the sleeve is dirty, and discharge is generated due to cracks, and the dirty sleeve needs to be cleaned and replaced by the cracked sleeve.
2. Cable short circuit burn-out fault and treatment measure; the cause of the short-circuit failure may be as follows: the cable selection is unscientific, the thermal stability is not enough, the insulation damage occurs, the short circuit is cracked, the cable load is reduced after the repair is needed, and the line continues to operate; the multiphase grounding or grounding wire and the short circuit wire are not removed, the grounding point needs to be found out, and the fault or grounding wire and the short circuit wire are removed; aging of interphase insulation and mechanical damage; the cable head joint is not hard up, overheated, and the ground connection collapses to burn, and the cable head joint of will fastening is avoided becoming flexible.
3. Cable interphase insulation breakdown short circuit or relative ground insulation breakdown fault and treatment measures: the cable itself is mechanically damaged to cause insulation damage; when the insulation is affected with moisture due to various reasons, the insulation strength is reduced and the insulation is broken down; cable insulation aging; the corrosion of the protective layer and the lead ladle causes the damage of the insulating layer to be punctured; breakdown is caused by overvoltage; the cable has too high operating temperature, which causes insulation breakdown and breakdown. After a failure, the battery is reconnected or replaced with a new one.
4. Inter-phase insulation breakdown or phase-to-ground insulation breakdown of the intermediate joint to ground short circuit fault and treatment measures: the intermediate junction box has defects, such as loose connection of all parts when assembled, insulation damp breakdown caused by moisture immersion due to poor sealing after washing and filling of the insulating agent, and qualified intermediate junction boxes and reworked intermediate joints need to be selected and made. The lead connecting joint is in poor contact, and the insulation breakdown is caused by local heating, so that the heating reason is found out and corresponding measures are taken. The wiring has sand holes or cracks, so that moisture and humidity enter the box, insulation is damped and punctured, defects need to be eliminated, and the quality of the wiring box is improved. The middle joint is not properly manufactured, for example, the wire core and the joint are not uniformly connected, and the local insulation reduces the breakdown; the cable glue is poured unevenly, and the dielectric medium is dissociated under the action of an electric field, so that the insulation is damaged and broken down. The intermediate joint manufacturing process is to be observed. The breakdown caused by poor insulating material is to configure and select the insulating material with good quality.
5. The method for preventing the secondary fault of the cable caused by the overvoltage comprises the following steps: different types of faults can often occur to the cable due to overload, poor management and the like, and the occurrence of the faults often causes overvoltage to cause secondary faults of the cable. The breakdown of the cable middle joint caused by the cable ground fault, the cable breakdown caused by the three-phase interphase short circuit of the line and the like. In the event of a single-phase metallic ground fault, the non-fault phase-to-ground voltage may rise to three times the rated voltage, and through the fault to the arc resistance ground, intermittent arcs of arc extinction and re-ignition may occur. These fault conditions can cause the circuit to resonate, produce the overvoltage in fault phase and non-fault phase, the time that this overvoltage lasts is usually crossed, the danger of overvoltage is also very big, it can accelerate the cable insulation aging, break down the cable in some insulating weak links. This phenomenon occurs more in cables insulated with impregnated paper. In order to avoid the secondary fault of the cable caused by overvoltage, the following methods are adopted: mechanical damage to the cable is reduced as much as possible during cable erection and construction; carrying out a voltage withstand test on the cable regularly to eliminate hidden dangers; the manufacturing quality of the cable terminal and the intermediate joint is improved.
The implementation matching of the corresponding content is performed by setting a data matching model for the specific fault type and the corresponding fault solution strategy.
S105: and transmitting the fault data and the fault resolution strategy to corresponding operation and maintenance personnel.
The method mainly comprises the steps of transmitting corresponding fault data and a fault solving strategy to corresponding maintenance personnel, transmitting the two data to the maintenance personnel instead of transmitting the solving strategy to technical personnel, enabling the maintenance personnel to further know the actual situation on the basis of knowing the actual situation, and not knowing the solving content singly. By providing the above strategy to the operation and maintenance manager, the operation and maintenance manager can know various conditions of the intelligent cable and then find out a proper solution strategy. After the corresponding policy is selected, data storage is needed to know the actual processing situation.
In addition to the foregoing embodiment, it is further preferable that, after the transmitting the fault data and the fault resolution policy to the corresponding operation and maintenance personnel, the method further includes:
and receiving the repair data of the intelligent cable fed back by the operation and maintenance personnel, and storing the repair data.
Because the cable laying amount is large, the situations encountered in the actual process are more various, and therefore, data fed back by operation and maintenance personnel in the actual process need to be further stored, and then the data are summarized. Therefore, in the embodiment of the application, the repair data fed back by the operation and maintenance personnel is received, and the corresponding repair data comprises a fault repair strategy, fault data and the like.
More preferably, after the data storing the repair data, the method further includes:
and after the repair data and the fault data are marked, adding the repair data and the fault data into a monitoring training set to optimize the fault recognition model.
The fault recognition model is further optimized by continuously collecting and marking data and inputting the data into the fault recognition model for training, and when the fault recognition model is specifically implemented, the data can be input into the fault recognition model for optimization and can also be input into a fault strategy model for strategy matching, so that more various and more accurate fault solution strategies are provided for subsequent operation and maintenance personnel.
More preferably, the fault data includes a fault type and a fault size;
the fault analysis server further comprises an early warning unit, early warning fault data are stored in the early warning unit in advance, and when the fault type and the fault size are judged to be matched with the pre-stored fault characteristic parameters, early warning operation is conducted on the current state of the intelligent cable.
The early warning operation is carried out on the faults by setting the early warning module, the early warning is only carried out when the specific fault type and the fault size are met, the early warning is a higher-level prompt, and the condition of operation and maintenance personnel is prompted to be urgently needed to be processed.
More preferably in this embodiment, fig. 4 is a schematic structural diagram of the data collection cluster provided in this embodiment, and as shown in fig. 4, the data collection cluster includes a monitoring module and a gateway unit corresponding to the smart cable, the gateway unit is electrically connected to the monitoring module, and the gateway unit is configured to collect operation monitoring data transmitted by each monitoring module and transmit the operation monitoring data to the failure analysis server. And the data is summarized and collected by arranging a gateway unit.
More preferably, the monitoring module includes that the distributing type sets up in temperature monitoring module, current monitoring module, partial discharge monitoring module and the vibration monitoring module of each position of smart cable.
Furthermore, when the temperature monitoring module, the current monitoring module, the partial discharge monitoring module and the vibration monitoring module are used for collecting operation monitoring data, the distributed setting mode is adopted to set the operation monitoring data at each position of the intelligent cable, and the quantity of each type of monitoring module is set according to the collection requirement of the operation monitoring data of the intelligent cable. Furthermore, it should be noted that when various monitoring modules upload operation monitoring data, the corresponding monitoring module numbers of the monitoring modules need to be attached to the operation monitoring data, so that when the operation monitoring data are uploaded to a fault analysis server for analysis and storage, it can be known that the operation monitoring data come from a certain monitoring module of a certain line intelligent cable. Therefore, different monitoring module numbers and the set geographic positions are bound, and a certain position of the intelligent cable from which certain operation monitoring data comes can be confirmed. In one embodiment, the current monitoring module position can be determined by using GPS positioning, and the operation monitoring data is uploaded together with the current GPS positioning information. Specifically, when various monitoring modules of this application are gathering operation monitoring data to the gateway, each monitoring module can establish communication link with the gateway one by one through wired or wireless mode to gather operation monitoring data to the gateway. In one embodiment, the data can be uploaded in a data hopping mode. For example, a plurality of temperature monitoring modules are correspondingly arranged on one intelligent cable, the temperature monitoring modules are distributed on the line of the intelligent cable according to set intervals, and one or more host nodes are selected as data transmission host nodes. Furthermore, after each temperature monitoring module collects temperature data, the temperature data is subjected to skip transmission to the adjacent temperature monitoring modules, and by analogy, the temperature data is subjected to skip transmission on the temperature monitoring modules on the intelligent cable line and is gathered to the nearest main node, and the main node uploads the temperature data to the gateway, so that data skip transmission is completed. The monitoring modules can adopt communication modules such as Bluetooth or ZigBee and the like to realize communication among the monitoring modules. Compared with the remote transmission of monitoring data to a gateway, the embodiment of the application carries out data skip transmission and collection through Bluetooth or ZigBee, so that the interference in the data transmission process can be reduced, and the communication charge is reduced.
More preferably, in addition to the detection data, the monitoring module further includes a camera module, and the camera module is configured to obtain image data corresponding to the smart cable;
the fault analysis server further comprises an aging detection unit, and the aging detection unit is used for determining the aging condition and the service life state of the corresponding intelligent cable according to the image data.
The intelligent cable aging detection method has the advantages that the image data which is more visual is obtained through the image data to carry out aging detection, when the cable is aged to a certain degree, operation and maintenance personnel are reminded through sending information to help the operation and maintenance personnel to carry out protection in advance so as to cause more serious cable accidents due to follow-up improper operation.
According to the scheme of the embodiment of the application, a neural network model is constructed through machine learning, and data analyzed by the model comprises parameters related to various intelligent cables; and inputting the acquired various cable parameters into the neural network model in real time, outputting related accurate fault data by the model, automatically matching the most appropriate solution strategy and providing the most appropriate solution strategy for a manager. In addition, according to the scheme of the embodiment of the application, various input data are comprehensively analyzed and processed through the trained neural network model to obtain the fault reason and determine the corresponding solving strategy, so that the problems that most platforms in the prior art are statistical display of data, the occurring faults cannot be accurately analyzed, and corresponding countermeasures cannot be given are solved.
The fault analysis decision platform based on the intelligent cable carries out matching identification on the operation monitoring data of the intelligent cable by pre-constructing a fault identification model and a fault strategy model; the fault analysis server analyzes and matches the operation monitoring data collected by the data collection cluster to obtain corresponding fault data, and obtains a corresponding fault solution strategy according to the fault data matching to provide for operation maintenance personnel, so that the fault identification efficiency is greatly improved. According to the scheme of the embodiment of the application, the fault reason and the corresponding solution strategy are determined by comprehensively analyzing various input data, so that operators are helped to maintain the intelligent cable better, and a safer cable use environment is constructed.
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor 31, are configured to perform a smart cable-based fault analysis method, including:
receiving first operation monitoring data collected by a data collection cluster at an intelligent cable;
converting the first operation monitoring data into a digital signal to obtain second operation monitoring data;
transmitting the second operation monitoring data to an input end of a preset fault recognition model, and obtaining corresponding fault data at an output end of the fault recognition model; the fault identification model is constructed by adopting a neural network model;
transmitting the fault data to a data matching module to perform data matching with a pre-stored fault strategy model so as to obtain a corresponding fault solution strategy;
and transmitting the fault data and the fault resolution strategy to corresponding operation and maintenance personnel.
The fault analysis decision platform based on the intelligent cable carries out matching identification on the operation monitoring data of the intelligent cable by pre-constructing a fault identification model and a fault strategy model; the fault analysis server analyzes and matches the operation monitoring data collected by the data collection cluster to obtain corresponding fault data, and obtains a corresponding fault solution strategy according to the fault data matching to provide for operation maintenance personnel, so that the fault identification efficiency is greatly improved. According to the scheme of the embodiment of the application, the fault reason and the corresponding solution strategy are determined by comprehensively analyzing various input data, so that operators are helped to maintain the intelligent cable better, and a safer cable use environment is constructed.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations, e.g., in different computer systems connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors 31.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the smart cable-based fault analysis method described above, and may also perform related operations in the smart cable-based fault analysis method provided in any embodiment of the present application.
The smart cable-based fault analysis apparatus, the storage medium, and the electronic device provided in the foregoing embodiments may perform the smart cable-based fault analysis method provided in any embodiment of the present application, and reference may be made to the smart cable-based fault analysis method provided in any embodiment of the present application without detailed technical details described in the foregoing embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (12)
1. A fault analysis decision platform based on intelligent cables is characterized by comprising:
the data acquisition cluster is used for acquiring operation monitoring data of the intelligent cable and transmitting the operation monitoring data to the fault analysis server;
the fault analysis server comprises a data conversion unit, a data analysis unit, a data matching unit and a data transmission unit, wherein the data conversion unit is used for converting received operation monitoring data into digital signals and outputting the digital signals to the data analysis unit, the data analysis unit is used for inputting the operation monitoring data converted into the digital signals into a preset fault recognition model to perform fault analysis on corresponding intelligent cables and outputting corresponding fault data to the data matching unit and the data transmission unit, and the fault recognition model is built by adopting a neural network model;
the data matching unit compares the fault data with a pre-stored fault strategy model to obtain a corresponding fault solution strategy, and transmits the fault solution strategy to the data transmission unit, and the data transmission unit is used for transmitting the fault data and the fault solution strategy to corresponding operation and maintenance personnel.
2. The smart cable-based fault analysis decision platform of claim 1, wherein the fault data comprises a fault type and a fault size;
the fault analysis server further comprises an early warning unit, early warning fault data are stored in the early warning unit in advance, and when the fault type and the fault size are judged to be matched with the pre-stored fault characteristic parameters, early warning operation is conducted on the current state of the intelligent cable.
3. A smart cable-based fault analysis decision platform as claimed in claim 1 wherein the neural network model comprises any one of a BP neural network model or a radial basis neural network model or a convolutional neural network model.
4. The smart cable-based fault analysis and decision platform as claimed in claim 1, wherein the data collection cluster comprises monitoring modules corresponding to smart cables and a gateway unit, the gateway unit is electrically connected to the monitoring modules, and the gateway unit is configured to collect operation monitoring data transmitted by each monitoring module and transmit the operation monitoring data to the fault analysis server.
5. The smart cable-based fault analysis and decision platform of claim 4, wherein the monitoring modules comprise a temperature monitoring module, a current monitoring module, a partial discharge monitoring module and a vibration monitoring module which are distributed at various positions of the smart cable.
6. The smart cable-based fault analysis decision platform of claim 5, wherein the monitoring module further comprises a camera module for obtaining image data corresponding to a smart cable;
the fault analysis server further comprises an aging detection unit, and the aging detection unit is used for determining the aging condition and the service life state of the corresponding intelligent cable according to the image data.
7. The smart cable-based fault analysis and decision platform as claimed in claim 5, wherein the data collection cluster further comprises a shielding unit electrically connected to the partial discharge monitoring module, and the shielding unit is configured to shield and protect the partial discharge monitoring module to filter interference of environmental noise on the partial discharge signal monitored by the partial discharge monitoring module.
8. A fault analysis method based on an intelligent cable is characterized by comprising the following steps:
receiving first operation monitoring data collected by a data collection cluster at an intelligent cable;
converting the first operation monitoring data into a digital signal to obtain second operation monitoring data;
transmitting the second operation monitoring data to an input end of a preset fault recognition model, and obtaining corresponding fault data at an output end of the fault recognition model; the fault identification model is constructed by adopting a neural network model;
transmitting the fault data to a data matching module to perform data matching with a pre-stored fault strategy model so as to obtain a corresponding fault solution strategy;
and transmitting the fault data and the fault resolution strategy to corresponding operation and maintenance personnel.
9. A smart cable-based fault analysis method as claimed in claim 8, wherein the neural network model comprises any one of a BP neural network model or a radial basis neural network model or a convolutional neural network model;
the fault identification model is constructed by the following steps:
obtaining marked historical monitoring data;
collecting the obtained marked historical monitoring data to generate a corresponding monitoring training set;
and constructing a fault recognition model based on the convolutional neural network, and recognizing and training the fault recognition model by taking the monitoring training set as input and the fault data as output until the training requirements are met.
10. A smart cable-based fault analysis method as recited in claim 9, further comprising, after said transmitting the fault data and the fault resolution policy to respective operation and maintenance personnel:
and receiving the repair data of the intelligent cable fed back by the operation and maintenance personnel, and storing the repair data.
11. A smart cable-based fault analysis method as recited in claim 10, further comprising, after said data storing said repair data:
and after the repair data and the fault data are marked, adding the repair data and the fault data into a monitoring training set to optimize the fault recognition model.
12. A smart cable-based fault analysis method according to any of claims 8-11, wherein the first operational monitoring data comprises one or more of temperature data, current data, vibration data, partial discharge data, and image data.
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