CN112067632A - Power equipment detection cloud platform and detection method - Google Patents
Power equipment detection cloud platform and detection method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
- G01N2291/0289—Internal structure, e.g. defects, grain size, texture
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/26—Scanned objects
- G01N2291/269—Various geometry objects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/26—Scanned objects
- G01N2291/269—Various geometry objects
- G01N2291/2697—Wafer or (micro)electronic parts
Abstract
The invention discloses a power equipment defect detection cloud platform and a detection method, wherein the detection cloud platform comprises the following steps: the device comprises an input module, a classification module, a detection module and an output module. Data of the power equipment is input to the cloud platform through the input module; the classification module identifies data input to the cloud platform and classifies the data according to a defect detection method corresponding to the data; the detection module simultaneously detects the data classified by the classification module according to a corresponding defect detection method and generates a corresponding detection result; and the output module receives the detection result and outputs the detection result. Through using above-mentioned cloud platform to detect power equipment's defect, can carry out multiple detection to power equipment simultaneously, detection cycle is short and detect the strong reliability.
Description
Technical Field
The invention relates to the field of equipment detection, in particular to a power equipment detection cloud platform and a detection method.
Background
In recent years, research on power equipment defect identification and detection technology has been greatly advanced, and various defect detection methods for power equipment have been developed at home and abroad, such as an X-ray detection method (scanning power equipment with X-rays, and determining a defect type and a defect position according to a scanning result), an infrared detection method (scanning power equipment with an infrared camera, and determining a defect type and a defect position according to a scanning result), a voiceprint detection method (scanning power equipment with a voiceprint acquisition device, and determining a defect type and a defect position according to a scanning result), and the like.
Although there are many existing power equipment defect detection technologies, each detection method is independent, that is, when the same power equipment needs to be subjected to X-ray detection, infrared detection and voiceprint detection, an operator needs to use three sets of equipment to respectively detect the same power equipment, and the period of comprehensive detection is long. And with the increase of the number of the electric power equipment, the data generated in the defect identification and detection process of the electric power equipment increases geometrically, and the traditional electric power detection system is difficult to process increasingly huge data.
Disclosure of Invention
The invention aims to provide a power equipment detection cloud platform and a detection method.
In order to achieve the above object, the present invention discloses a power equipment defect detection cloud platform, which includes: the device comprises an input module, a classification module, a detection module and an output module. Data of the power equipment is input to the cloud platform through the input module; the classification module identifies data input to the cloud platform and classifies the data according to a defect detection method corresponding to the data; the detection module simultaneously detects the data classified by the classification module according to a corresponding defect detection method and generates a corresponding detection result; and the output module receives the detection result of the detection module and outputs the detection result.
Preferably, the cloud platform further comprises a platform supporting module, and the platform supporting module ensures that the input module, the classification module, the detection module and the output module run stably.
Preferably, the classification module comprises a first classification unit for classifying the data input by the operator through the input module manually and a second classification unit for classifying the data input by other devices through the input module.
Preferably, the first classification unit divides the data input by the operator into first X-ray data, first infrared data and first voiceprint data; and the second classification unit divides the data input by the other equipment into second X-ray data, second infrared data and second voiceprint data.
Preferably, the first classification unit comprises first storage means storing classification rules for use by the first classification unit; the second classification unit comprises second storage means storing classification rules for use by the second classification unit.
Preferably, the detection module includes an X-ray detection unit that detects the first X-ray data and the second X-ray data; an infrared detection unit that detects the first infrared data and the second infrared data; and a voiceprint detection unit that detects the first voiceprint data and the second voiceprint data.
Preferably, the X-ray detection unit includes a first detection means for performing X-ray fault detection and fault localization on the first X-ray data and the second X-ray data; the infrared detection unit comprises a second detection component for performing infrared fault detection and fault positioning on the first infrared data and the second infrared data; the voiceprint detection unit includes a third detection means that performs voiceprint fault detection and fault localization on the first voiceprint data and the second voiceprint data.
Preferably, the detection module comprises at least two of the X-ray detection units, at least two of the infrared detection units, and at least two of the voiceprint detection units.
Preferably, the platform support module distributes the X-ray data input into the detection module to at least one X-ray detection unit according to the configuration and the load rate of each X-ray detection unit; the platform supporting module distributes the infrared data input into the detection module to at least one infrared detection unit according to the configuration and the load rate of each infrared detection unit; the platform support module distributes voiceprint data input into the detection module to at least one voiceprint detection unit according to the configuration and the load rate of each voiceprint detection unit.
The embodiment of the invention also provides a power equipment defect detection method for detecting the power equipment by using the cloud platform, which comprises the following steps: s1, inputting data of the power equipment into the cloud platform through the input module; s2, the classification module identifies the data input into the cloud platform and classifies the data according to a corresponding defect detection method; s3, the detection module simultaneously detects the data classified by the classification module according to a corresponding defect detection method and generates a corresponding detection result; s4, and the output module receives the detection result and outputs the detection result.
Preferably, the following steps are further included between the steps S2 and S3: configuring higher weight for the detection unit with high load and low load in the detection module to process more data; allocating lower weight to the detection unit with low configuration and high load in the detection module to process a small amount of data; and in order to ensure load balance, the formula needs to be satisfied
Wherein c(s) is the total number of connections of the selected detecting units in the detecting module, w(s) is the weight assigned to the selected detecting units in the detecting module, c (sn) is the total number of connections of the non-selected detecting units in the detecting module, and w (sn) is the weight assigned to the non-selected detecting units in the detecting module.
Preferably, the step S2 is specifically: if the detection data of the power equipment is manually input into the input module by an operator, classifying the detection data of the power equipment by adopting a classification rule stored in a first storage component of the classification module; if the detection data of the electric power equipment is input into the input module by the detection equipment, classifying the detection data of the electric power equipment according to the classification rule stored in the second storage component of the classification module.
Preferably, the step S3 is specifically: an X-ray detection unit in the detection module detects first X-ray data manually input by an operator and second X-ray data input by detection equipment, an infrared detection unit in the detection module detects first infrared data manually input by the operator and second infrared data input by the detection equipment, and/or a voiceprint detection unit in the detection module detects first voiceprint data manually input by the operator and second voiceprint data input by the detection equipment;
the X-ray detection unit, the infrared detection unit and the voiceprint detection unit respectively analyze and process the first X-ray data and the second X-ray data, the first infrared data and the second infrared data, and the first voiceprint data and the second voiceprint data through a convolutional neural network to obtain a fault type and a fault position, so that a detection result is obtained.
By adopting the technical scheme, the invention mainly has the following technical effects:
1. the detection unit simultaneously performs X-ray detection, infrared detection and voiceprint detection, so that the comprehensive detection period of the power equipment can be obviously shortened;
2. the cloud platform can classify data of the electric power equipment manually input into the cloud platform according to manual operation of an operator or automatically classify data of the electric power equipment input into the cloud platform by other equipment, the cloud platform meets the working mode of automatic operation or manual operation, and the reliability of the cloud platform is high;
3. the data of the power equipment are reasonably distributed according to the load condition in the detection unit, the running reliability of the cloud platform is improved, and the situation that the cloud platform is broken down due to the fact that too much data need to be processed by a certain detection unit of the cloud platform is avoided.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a power device detection cloud platform according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a classification module and a detection module according to an embodiment of the invention;
fig. 3 is a flowchart of a detection method according to an embodiment of the invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, the present embodiment discloses a power device detection cloud platform, which includes an input module, a classification module, a detection module, and an output module. The data of the power equipment is input to the cloud platform through the input module; the classification module identifies data input to the cloud platform and classifies the data according to a corresponding defect detection method; the detection module simultaneously detects the data classified by the classification module by a corresponding defect detection method and generates a corresponding detection result; the output module receives the detection result and outputs the detection result.
In order to ensure the stable operation of the cloud platform, the cloud platform in this embodiment further includes a platform support module, and the platform support module ensures the stable operation of the input module, the classification module, the detection module and the output module. The platform support module in this embodiment has functions of service fusing, service monitoring, and the like. In the embodiment, since the modules in the cloud platform are deployed in a distributed cluster manner, service fusing refers to that when one or more of the input module, the classification module, the detection module and the output module fails, the platform support module closes the failed module so as to ensure normal operation of other modules, that is, to prevent an avalanche effect; the service monitoring means that the platform supporting module monitors the working states of the input module, the classification module, the detection module and the output module, and ensures the normal operation of the input module, the classification module, the detection module and the output module.
Referring to fig. 1 and 2, the data input into the cloud platform includes data manually input into the cloud platform by an operator and data input into the cloud platform by other devices (e.g., infrared images input by the inspection robot, video images captured by an external camera, related data returned by a voiceprint sensor, etc.). For making things convenient for the classification module to classify to the data of different sources, the classification module includes first classification unit and second classification unit, and wherein, first classification unit is categorised through the data of input module input to the artifical operating personnel, and the second classification unit is categorised through the data of input module input to other equipment. The first classification unit comprises a first storage component, classification rules used by the first classification unit are stored in the first storage component, and the first classification unit classifies data manually input by an operator through the input module according to the stored classification rules and divides the data into first X-ray data, first infrared data and first voiceprint data; similarly, the second classification unit comprises a second storage component, the second storage component stores the identification rule of the second classification unit, and the second classification unit classifies the data input by other equipment through the input module according to the stored classification rule and divides the data into second X-ray data, second infrared data and second voiceprint data. The classification rules stored in the first storage means and the classification rules stored in the second storage means are not limited herein. Preferably, the classification rule stored in the first storage means in the present embodiment is selected by an operator, that is, when the operator inputs the data of the electric power equipment, the data of the electric power equipment is classified manually; the classification rule stored in the second storage component is to classify according to the serial number of the device, for example, the serial number of the inspection robot, the camera and other devices acquiring the infrared image is set to 1, the serial number of the inspection robot, the camera and other devices acquiring the X-ray image is set to 2, the serial number of the voiceprint data acquisition device, such as the voiceprint sensor, is set to 3, the classification module divides the data input by the device with the serial number of 1 into the second infrared data according to the data source, divides the data input by the device with the serial number of 2 into the second X-ray data and divides the data input by the device with the serial number of 3 into the second voiceprint data.
In order to detect the classified data, the detection module of the cloud platform comprises an X-ray detection unit, an infrared detection unit and a voiceprint detection unit. The X-ray detection unit detects first X-ray data and second X-ray data, the infrared detection unit detects first infrared data and second infrared data, and the voiceprint detection unit detects first voiceprint data and second voiceprint data. The X-ray detection unit comprises a first detection component, and the first detection component carries out X-ray fault detection and fault location on the first X-ray data and the second X-ray data; the infrared detection unit comprises a second detection component, and the second detection component carries out infrared fault detection and fault positioning on the first infrared data and the second infrared data; the voiceprint detection unit comprises a third detection component, and the third detection component carries out voiceprint fault detection and fault location on the first voiceprint data and the second voiceprint data. The working modes of the first detection component, the second detection component, and the third detection component are not limited, and in this embodiment, the first detection component, the second detection component, and the third detection component all analyze and process data through a convolutional neural network to obtain the fault location and the fault type of the device.
In order to improve the detection efficiency of the cloud platform and ensure that the cloud platform can process data of multiple pieces of power equipment at the same time, the detection module in this embodiment includes at least two X-ray detection units, at least two infrared detection units and at least two voiceprint detection units. Through the arrangement, the detection module can simultaneously process data of multiple power devices, and when the data volume input into the cloud platform is large, the cloud platform can rapidly obtain a detection result, so that the detection efficiency of the cloud platform is improved.
In order to avoid the cloud platform from being broken down or even collapsed due to the fact that a certain X-ray detection unit, a certain infrared detection unit and/or a certain voiceprint detection unit in the cloud platform are overloaded, the platform supporting module needs to reasonably classify data into each detection unit. The platform support module in this embodiment distributes the X-ray data in the detection module to at least one X-ray detection unit according to the configuration and the load rate of each X-ray detection unit, that is, preferentially inputs the X-ray data into the X-ray detection units with high configuration and low load rate; the platform supporting module distributes the infrared data in the detection module to at least one infrared detection unit according to the configuration and the load rate of each infrared detection unit, namely, preferentially inputs the infrared data into the infrared detection units with high configuration and low load rate; the platform support module distributes the voiceprint data in the detection module to at least one voiceprint detection unit according to the configuration and the load rate of each voiceprint detection unit, namely, the voiceprint data are preferentially input into the voiceprint detection units with high configuration and low load rate. Through the arrangement, the flexibility and the data processing capacity of the cloud platform are improved.
In this embodiment, the output module outputs the detection result in the form of a report, that is, the output module generates a detection report including the contents of the image after the framing identification, the defect type, the defect number statistics, and the like, and an operator overhauls the power equipment according to the detection report.
To better understand the working principle of the power equipment detection Cloud platform in this embodiment, the Cloud platform of this embodiment may be abstracted as a six-element set Cloud, and Cloud ═ Ms, Mb, Input, Fault, Cnn, Kb }. Wherein Ms is the set of the classification module, the detection module and the output module, Ms is { Msg, Mai, Mse }, Msg is the entrance of the classification module, Mai includes a first classification unit, a second classification unit, a first detection member and a second detection member, Mse is the output module, a detection report is generated and output according to the detection result, and the triple of Ms represents data and enters the classification module for classification and is detected by the detection module to obtain the detection result and form the detection report to be output by the output module. Mb is a platform support module, the platform support module guarantees normal operation of each module of the cloud platform, the platform support module comprises a service registration center, all detection units in the detection module are registered in the service registration center to form a service list, and specifically, the service registration center generates registration information including a service name, a service request address, a port number and the like according to relevant information of each detection unit and stores the registration information in the form of the service list. For example { "ser _ name": "extracted _ ser", "ser _ address": "192.168.1.1", "ser _ prot": "5002", … ", where ser _ name refers to the service name of the registered detection unit, ser _ address refers to the service request address of the registered detection unit, and ser _ prot refers to the port number of the registered detection unit. And the Msg compares the obtained detection request information with the service list to obtain the address and the port of the corresponding detection unit, and inputs the data of the electric power equipment into the detection unit for detection in a gateway routing mode. The Input is an Input module, data of the power equipment are Input into the Ms through the Input module, and the data in the Input comprises data manually Input into the cloud platform by an operator and data Input into the cloud platform by the detection equipment. The Fault is a detection result obtained by the Ms, and the Fault comprises a Fault position and a defect type of the power equipment. Cnn is a set of convolutional neural networks Cnnj, Cnnj represents the convolutional neural network analyzed for the jth fault detection. Kb is an event rule repository, which includes a first storage component and a second storage component, the cloud platform classifies the input detection data of the power equipment according to the classification rule stored in Kb, for example, the serial number of the inspection robot, the camera, and other equipment for acquiring the infrared image is set to 1, the serial number of the inspection robot, the camera, and other equipment for acquiring the X-ray image is set to 2, and the serial number of the detection equipment for acquiring the voiceprint is set to 3, and the request mode for acquiring the corresponding detection unit through the equipment serial number is specifically shown in the following table.
TABLE 1 Kb mode of operation
The Ser _ name in the above table is the classification rule, and the Req _ data is the classification result.
Referring to fig. 3, the embodiment further includes a method for detecting an electrical device by using a cloud platform, which includes the following steps: s1, inputting the data of the power equipment into the cloud platform through the input module; s2, the classification module identifies the data input into the cloud platform and classifies the data according to the corresponding defect detection method; s3, the detection module simultaneously detects the data classified by the classification module according to the corresponding defect detection method and generates a corresponding detection result; and S4, the output module receives the detection result and outputs the detection result.
The specific step of inputting the data of the power device into the cloud platform through the input module in step S1 is as follows: an operator manually inputs data of the power equipment into the Input and/or the detection equipment (equipment for detecting the power equipment such as a patrol robot, an external camera and a monitoring sensor) inputs detection data of the power equipment into the Input, and the Input transmits the data into the Ms in a wireless network bridge mode.
In step S2, the classification module identifies the data input to the cloud platform and classifies the data according to the corresponding defect detection method specifically as follows: the cloud platform classifies the data Input in the Input according to the classification rules stored in the Kb, that is, if the detection data of the electrical equipment is manually Input into the Input module by an operator, the detection data of the electrical equipment is classified by using the classification rules stored in the first storage component and/or if the detection data of the electrical equipment is Input into the Input module by the detection equipment, the detection data of the electrical equipment is classified according to the classification rules stored in the second storage component.
In step S3, the detecting module detects the data classified by the classifying module according to the corresponding defect detecting method and generates the corresponding detecting result specifically as follows: the method further includes detecting the first X-ray data and the second X-ray data using an X-ray detection unit, detecting the first infrared data and the second infrared data using an infrared detection unit, and/or detecting the first voiceprint data and the second voiceprint data using a voiceprint detection unit.
Because the configuration and/or the load factor of every X-ray detection unit, every infrared detection unit and every voiceprint detection unit are all different in the cloud platform, consequently need rationally with power equipment's detection data transport to every X-ray detection unit, every infrared detection unit and every voiceprint detection unit in, adopt load balancing strategy rational distribution data in this embodiment, specifically do: configuring a higher weight for the detection unit with high configuration and low load to process more data; the detection units with low configuration and high load are assigned with lower weights and are allowed to process a small amount of data. In addition, to ensure load balancing, the formula needs to be satisfied
Wherein c(s) is the total number of connections of the selected detecting units in the detecting module, w(s) is the weight assigned to the selected detecting units in the detecting module, c (sn) is the total number of connections of the non-selected detecting units in the detecting module, and w (sn) is the weight assigned to the non-selected detecting units in the detecting module.
The step S3, in which the detecting module simultaneously performs corresponding method detection on the data classified by the classifying module and generates a corresponding detection result, further includes: the first detection component, the second detection component and the third detection component analyze and process the X-ray data, the infrared data and the voiceprint data through a convolutional neural network. More specifically, after receiving the classified data, Mai calls Cnn to analyze the data, Cnn generates device image data, Cnn deeply analyzes the device image data to obtain a Fault type and a Fault position, so as to obtain a detection result Fault, and inputs the detection result Fault into Mse to generate a detection report. If the X-ray data defect identification is carried out, extracting an equipment image data characteristic diagram through a convolutional layer of a convolutional neural network, outputting a target characteristic diagram through a pooling layer of a defect identification model, sending the target characteristic diagram to a deep convolutional neural network to obtain an equipment defect type, and finally obtaining the accurate position of a detection frame by adopting frame regression processing to obtain a judgment result of the power equipment defect identification.
The step S4, where the receiving and outputting of the detection result by the output module to the cloud platform specifically includes: and the Mse receives the detection result Fault, automatically generates a detection report including the information of the image after the framing identification, the defect type, the defect quantity statistics and the like, and outputs the detection report for reference when an operator maintains or replaces the electric power equipment.
Finally, it should be noted that: the embodiment of the present invention is disclosed only as a preferred embodiment of the present invention, which is only used for illustrating the technical solutions of the present invention and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A power equipment defect detection cloud platform, comprising:
the data of the power equipment are input to the cloud platform through the input module;
the classification module is used for identifying the data input into the cloud platform and classifying the data according to a defect detection method corresponding to the data;
the detection module is used for simultaneously detecting a plurality of data classified by the classification module according to corresponding defect detection methods; and
and the output module receives the detection result of the detection module and outputs the detection result.
2. The cloud platform of claim 1, wherein:
the cloud platform further comprises a platform supporting module, and the platform supporting module guarantees stable operation of the input module, the classification module, the detection module and the output module.
3. The cloud platform of claim 1 or 2, wherein:
the classification module comprises a first classification unit and a second classification unit, wherein the first classification unit is used for classifying data input by an operator through the input module manually, and the second classification unit is used for classifying data input by the detection equipment through the input module;
the first classification unit comprises a first storage component, the first storage component stores classification rules used by the first classification unit, and the first classification unit divides data input by the operator into first X-ray data, first infrared data and first voiceprint data;
the second classification unit comprises a second storage component, the second storage component stores classification rules used by the second classification unit, and the second classification unit divides data input by the electronic detection equipment into second X-ray data, second infrared data and second acoustic data.
4. The cloud platform of claim 3, wherein:
the detection module comprises:
an X-ray detection unit that detects the first X-ray data and the second X-ray data;
an infrared detection unit that detects the first infrared data and the second infrared data; and
a voiceprint detection unit that detects the first voiceprint data and the second voiceprint data;
the number of the X-ray detection units, the number of the infrared detection units and the number of the voiceprint detection units are at least two.
5. The cloud platform of claim 4, wherein:
the X-ray detection unit comprises a first detection component, and the first detection component carries out X-ray fault detection and fault location on the first X-ray data and the second X-ray data;
the infrared detection unit comprises a second detection component, and the second detection component carries out infrared fault detection and fault location on the first infrared data and the second infrared data;
the voiceprint detection unit comprises a third detection component, and the third detection component carries out voiceprint fault detection and fault location on the first voiceprint data and the second voiceprint data.
6. The cloud platform of claim 4 or 5, wherein:
the platform supporting module distributes X-ray data input into the detection module to at least one X-ray detection unit according to the configuration and the load rate of each X-ray detection unit;
the platform supporting module distributes infrared data input into the detection module to at least one infrared detection unit according to the configuration and the load rate of each infrared detection unit;
the platform supporting module distributes the voiceprint data input into the detecting module to at least one voiceprint detecting unit according to the configuration and the load rate of each voiceprint detecting unit.
7. A cloud platform-based power equipment defect detection method is characterized by comprising the following steps:
s1, inputting the detection data of the power equipment into the cloud platform through an input module;
s2, the classification module identifies the data input into the cloud platform and classifies the data according to the defect detection method corresponding to the data;
s3, the detection module simultaneously detects the data classified by the classification module according to the corresponding defect detection method and generates the corresponding detection result; and
and S4, the output module receives the detection result and outputs the detection result.
8. The method of claim 7, further comprising the following steps between the steps S2 and S3: configuring higher weight for the detection unit with high load and low load in the detection module to process more data; allocating lower weight to the detection unit with low configuration and high load in the detection module to process a small amount of data; and in order to ensure load balance, the formula needs to be satisfied
Wherein c(s) is the total number of connections of the selected detecting units in the detecting module, w(s) is the weight assigned to the selected detecting units in the detecting module, c (sn) is the total number of connections of the non-selected detecting units in the detecting module, and w (sn) is the weight assigned to the non-selected detecting units in the detecting module.
9. The method according to claim 7 or 8, wherein the step S2 specifically includes: if the detection data of the power equipment is manually input into the input module by an operator, classifying the detection data of the power equipment by adopting a classification rule stored in a first storage component of the classification module; if the detection data of the electric power equipment is input into the input module by the detection equipment, classifying the detection data of the electric power equipment according to the classification rule stored in the second storage component of the classification module.
10. The method according to claim 7 or 8, characterized in that: the step S3 specifically includes: an X-ray detection unit in the detection module detects first X-ray data manually input by an operator and second X-ray data input by detection equipment, an infrared detection unit in the detection module detects first infrared data manually input by the operator and second infrared data input by the detection equipment, and/or a voiceprint detection unit in the detection module detects first voiceprint data manually input by the operator and second voiceprint data input by the detection equipment;
the X-ray detection unit, the infrared detection unit and the voiceprint detection unit respectively analyze and process the first X-ray data and the second X-ray data, the first infrared data and the second infrared data, and the first voiceprint data and the second voiceprint data through a convolutional neural network to obtain a fault type and a fault position, so that a detection result is obtained.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113537517A (en) * | 2021-09-16 | 2021-10-22 | 深圳市信润富联数字科技有限公司 | Defect detection model training method, device, equipment and storage medium |
| CN117705816A (en) * | 2024-02-06 | 2024-03-15 | 西南交通大学 | Unmanned aerial vehicle-based steel rail surface defect detection method, system, equipment and medium |
| CN117705816B (en) * | 2024-02-06 | 2024-04-26 | 西南交通大学 | Unmanned aerial vehicle-based steel rail surface defect detection method, system, equipment and medium |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070168461A1 (en) * | 2005-02-01 | 2007-07-19 | Moore James F | Syndicating surgical data in a healthcare environment |
| WO2008057385A1 (en) * | 2006-11-02 | 2008-05-15 | The Boeing Company | Remote nondestructive inspection systems and methods |
| US20140207601A1 (en) * | 2013-01-22 | 2014-07-24 | General Electric Company | Systems and methods for a non-destructive testing ecosystem |
| CN104937554A (en) * | 2013-01-22 | 2015-09-23 | 通用电气公司 | Systems and methods for analyzing data in a non-destructive testing system |
| US20160034809A1 (en) * | 2014-06-10 | 2016-02-04 | Sightline Innovation Inc. | System and method for network based application development and implementation |
| CN105447507A (en) * | 2015-11-09 | 2016-03-30 | 南京信息工程大学 | Cloud platform and core extreme learning machine-based electrical appliance status detection method |
| CN108737591A (en) * | 2018-05-22 | 2018-11-02 | 华为技术有限公司 | A kind of method and device of service configuration |
| CN109154896A (en) * | 2016-04-29 | 2019-01-04 | 思杰系统有限公司 | System and method for service chaining load balance |
| CN109978052A (en) * | 2019-03-25 | 2019-07-05 | 北京快电科技有限公司 | A kind of user side energy device wisdom repair method |
| CN209085657U (en) * | 2017-08-02 | 2019-07-09 | 强力物联网投资组合2016有限公司 | For data gathering system related or industrial environment with chemical production technology |
| CN110544247A (en) * | 2019-09-03 | 2019-12-06 | 东莞德福得精密五金制品有限公司 | method for inspecting and monitoring operation defects of artificial intelligent cloud computing multi-path equipment system |
| CN110995859A (en) * | 2019-12-17 | 2020-04-10 | 四川赛康智能科技股份有限公司 | Intelligent transformer substation supporting platform system based on ubiquitous Internet of things |
| CN111142003A (en) * | 2020-02-04 | 2020-05-12 | 北方民族大学 | System and method for power capacitor state monitoring and reliability analysis |
-
2020
- 2020-06-04 CN CN202010498546.XA patent/CN112067632A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070168461A1 (en) * | 2005-02-01 | 2007-07-19 | Moore James F | Syndicating surgical data in a healthcare environment |
| WO2008057385A1 (en) * | 2006-11-02 | 2008-05-15 | The Boeing Company | Remote nondestructive inspection systems and methods |
| US20140207601A1 (en) * | 2013-01-22 | 2014-07-24 | General Electric Company | Systems and methods for a non-destructive testing ecosystem |
| CN104937554A (en) * | 2013-01-22 | 2015-09-23 | 通用电气公司 | Systems and methods for analyzing data in a non-destructive testing system |
| US20160034809A1 (en) * | 2014-06-10 | 2016-02-04 | Sightline Innovation Inc. | System and method for network based application development and implementation |
| CN105447507A (en) * | 2015-11-09 | 2016-03-30 | 南京信息工程大学 | Cloud platform and core extreme learning machine-based electrical appliance status detection method |
| CN109154896A (en) * | 2016-04-29 | 2019-01-04 | 思杰系统有限公司 | System and method for service chaining load balance |
| CN209085657U (en) * | 2017-08-02 | 2019-07-09 | 强力物联网投资组合2016有限公司 | For data gathering system related or industrial environment with chemical production technology |
| CN108737591A (en) * | 2018-05-22 | 2018-11-02 | 华为技术有限公司 | A kind of method and device of service configuration |
| CN109978052A (en) * | 2019-03-25 | 2019-07-05 | 北京快电科技有限公司 | A kind of user side energy device wisdom repair method |
| CN110544247A (en) * | 2019-09-03 | 2019-12-06 | 东莞德福得精密五金制品有限公司 | method for inspecting and monitoring operation defects of artificial intelligent cloud computing multi-path equipment system |
| CN110995859A (en) * | 2019-12-17 | 2020-04-10 | 四川赛康智能科技股份有限公司 | Intelligent transformer substation supporting platform system based on ubiquitous Internet of things |
| CN111142003A (en) * | 2020-02-04 | 2020-05-12 | 北方民族大学 | System and method for power capacitor state monitoring and reliability analysis |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113537517A (en) * | 2021-09-16 | 2021-10-22 | 深圳市信润富联数字科技有限公司 | Defect detection model training method, device, equipment and storage medium |
| CN117705816A (en) * | 2024-02-06 | 2024-03-15 | 西南交通大学 | Unmanned aerial vehicle-based steel rail surface defect detection method, system, equipment and medium |
| CN117705816B (en) * | 2024-02-06 | 2024-04-26 | 西南交通大学 | Unmanned aerial vehicle-based steel rail surface defect detection method, system, equipment and medium |
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