CN110579695B - Arc detection model detection method, detection device, storage medium and processor - Google Patents
Arc detection model detection method, detection device, storage medium and processor Download PDFInfo
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- CN110579695B CN110579695B CN201910990252.6A CN201910990252A CN110579695B CN 110579695 B CN110579695 B CN 110579695B CN 201910990252 A CN201910990252 A CN 201910990252A CN 110579695 B CN110579695 B CN 110579695B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
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Abstract
The application provides a detection method, a detection device, a storage medium and a processor of an arc detection model. The detection method comprises the following steps: detecting the action frequency of the intelligent socket corresponding to each load in a preset area by adopting an arc detection model; calculating the sum of the action frequencies corresponding to the loads of the same kind in the preset area; and determining whether the arc detection model is suitable for the corresponding load according to the sum. Firstly, detecting the operating frequency of the intelligent socket corresponding to each load in a preset area; secondly, calculating the sum of the action frequencies corresponding to the loads of the same type in the preset area; and finally, determining whether the arc detection model is suitable for the corresponding load according to the sum. According to the method, the sum of the action frequencies of the intelligent sockets with the same load in a certain area is calculated, and whether the arc detection model is suitable for the corresponding load or not is determined according to the sum, so that the accuracy of the arc detection model is guaranteed.
Description
Technical Field
The present application relates to the field of arc detection, and in particular, to a detection method, a detection apparatus, a storage medium, and a processor for an arc detection model.
Background
Parameters of an arc detection model of a general smart socket are fixed, and the fixed model parameters often cause false detection of an arc, so that the accuracy of the arc detection model needs to be improved in a mode of upgrading the arc detection model.
The power grid conditions of each region are different, the conditions of fault arcs are different, and the probability that certain users have fault arcs under the same load is different, so that the stability and the detection accuracy of the fault arc detection model can be accurately known by judging the applicability of the arc detection model in the region.
The above information disclosed in this background section is only for enhancement of understanding of the background of the technology described herein and, therefore, certain information may be included in the background that does not form the prior art that is already known in this country to a person of ordinary skill in the art.
Disclosure of Invention
The application mainly aims to provide a detection method, a detection device, a storage medium and a processor of an arc detection model, so as to solve the problem that the accuracy of detection of a fault arc detection model is difficult to determine in the prior art.
In order to achieve the above object, according to an aspect of the present application, there is provided a detection method of an arc detection model, including: detecting the action frequency of the intelligent socket corresponding to each load in a preset area by adopting an arc detection model; calculating a sum of the operation frequencies corresponding to the loads of the same kind in the predetermined area; and determining whether the arc detection model is suitable for the corresponding load according to the sum.
Further, determining whether the arc detection model is applicable to the corresponding load according to the sum includes: determining that the arc detection model is not applicable to the corresponding load if the sum is greater than the predetermined frequency.
Further, before calculating the sum of the action frequencies corresponding to the loads of the same kind in the predetermined area, the detection method further includes: determining a category of each of the loads within the predetermined area.
Further, determining a category of each of the loads within the predetermined area includes: acquiring a current waveform and/or a voltage waveform of the load; determining the type of the load if the coincidence of the current waveform with a predetermined current waveform is greater than a first predetermined coincidence, and/or if the coincidence of the voltage waveform with a predetermined voltage waveform is greater than a second predetermined coincidence.
Further, detecting the operating frequency of the smart socket of each of the loads in a predetermined area using an arc detection model, comprising: and updating the action frequency of the intelligent socket corresponding to the load when the arc detection model detects fault arc.
Further, in a case where it is determined that the arc detection model is not applicable to the corresponding load, the detection method further includes: updating the arc detection model.
Further, the predetermined area includes a plurality of users, each of the users includes a plurality of the loads, and each of the loads corresponds to each of the smart sockets.
According to another aspect of the present application, there is provided a detection apparatus of an arc detection model, including: the detection unit is used for detecting the action frequency of the intelligent socket corresponding to each load in the preset area by adopting an arc detection model; a calculation unit that calculates a sum of the operation frequencies corresponding to the loads of the same kind in the predetermined area; and the determining unit is used for determining whether the arc detection model is applicable to the corresponding load according to the sum.
According to still another aspect of the present application, there is provided a storage medium including a stored program, wherein the program executes any one of the detection methods.
According to still another aspect of the present application, there is provided a processor for executing a program, wherein the program executes any one of the detection methods.
By the technical scheme, whether the arc detection model is suitable for the corresponding load or not is judged by counting the sum of the action frequencies of the intelligent sockets with the same load in a certain area, so that the accuracy of the arc detection model is guaranteed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 shows a flow diagram of a detection method of an arc detection model according to an embodiment of the present application;
FIG. 2 shows a schematic view of a detection apparatus of an arc detection model according to an embodiment of the present application;
FIG. 3 illustrates an arc fault detection flow diagram according to an embodiment of the present application;
FIG. 4 illustrates a flow chart of arc fault discrimination according to an embodiment of the present application;
FIG. 5 is a statistical schematic diagram of load operating frequency according to an embodiment of the present application; and
FIG. 6 shows a flow diagram of arc detection model detection according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. Also, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element or "connected" to the other element through a third element.
According to the introduction in the background art, parameters of an arc detection model of a general smart socket in the prior art are fixed, and the fixed model parameters often cause false detection of an arc, which causes a problem that it is difficult to determine the accuracy of fault arc detection model detection. In order to solve the technical problem that the accuracy of the fault arc detection model detection is difficult to determine, according to an exemplary embodiment of the present application, a method for detecting an arc detection model is provided.
Fig. 1 is a flow chart of a detection method of an arc detection model according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S101, detecting the action frequency of the intelligent socket corresponding to each load in a preset area by adopting an arc detection model;
step S102 of calculating a sum of the operating frequencies corresponding to the loads of the same type in the predetermined area;
step S103, determining whether the arc detection model is applied to the corresponding load according to the sum.
In the scheme, firstly, the action frequency of the intelligent socket corresponding to each load in a preset area is detected; secondly, calculating the sum of the action frequencies corresponding to the loads of the same type in the preset area; and finally, determining whether the arc detection model is suitable for the corresponding load according to the sum. According to the method, the sum of the action frequencies of the intelligent sockets with the same load in a certain area is calculated, and whether the arc detection model is suitable for the corresponding load or not is determined according to the sum, so that the accuracy of the fault arc detection model is determined.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
In an embodiment of the present application, determining whether the arc detection model is suitable for the corresponding load according to the sum includes: in the case where the sum is greater than the predetermined frequency, it is determined that the arc detection model is not applicable to the corresponding load. The arc detection models corresponding to each load are different due to different performances of each load, and after multiple tests, whether the arc detection model corresponding to each load is suitable for the corresponding load can be obtained.
Specifically, the predetermined frequency of the present application may be determined according to a suitable method, and in an embodiment of the present application, the setting of the predetermined frequency may be implemented by debugging a certain type of air conditioner for many times, for example, obtaining an average value of the operating frequencies of the smart sockets of the air conditioner, calculating an average value of the operating frequencies of the smart sockets of a plurality of air conditioners, and then adding the average values to obtain the predetermined frequency. When the sum of the tested frequencies is greater than the predetermined frequency, it is determined that the arc detection model is not applicable at this time, i.e., the model error is large.
In an embodiment of the present application, before calculating a sum of the operating frequencies corresponding to the loads of the same type in the predetermined area, the detecting method further includes: and determining the type of each load in the predetermined area, namely determining the type of the load first and then determining the sum of the operating frequencies corresponding to the same type of load. Specifically, the types of the loads may include household appliances such as an air conditioner, an electric fan, a water heater, a humidifier, a range hood, and/or a microwave oven, and may also include equipment in a factory that needs to test an arc, and may also include any household appliance and equipment in a factory that can obtain an operation frequency of the smart socket. Of course, the type of load may be obtained in advance in other manners, and is not necessarily a step in the detection process.
In one embodiment of the present application, determining a type of each of the loads in the predetermined area includes: acquiring a current waveform and/or a voltage waveform of the load; and determining the type of the load when the coincidence degree of the current waveform and the preset current waveform is greater than a first preset coincidence degree and/or when the coincidence degree of the voltage waveform and the preset voltage waveform is greater than a second preset coincidence degree. Specifically, before each load leaves the factory, the current waveform and/or the voltage waveform corresponding to the load is tested, and then the tested current waveform and/or voltage waveform is stored in a database as a predetermined current waveform and/or a predetermined voltage waveform for comparison in subsequent actual tests. The coincidence degree of the current waveform and/or the voltage waveform can be measured by using the error energy, and in the case that the coincidence degree satisfies the above condition, the kind of the load can be determined, specifically, in practical application, the current waveform and the predetermined current waveform have the same time axis, and/or the voltage waveform and the predetermined voltage waveform have the same time axis, that is, the obtained current waveform and the predetermined current waveform can be ensured to be at the same time, and/or the voltage waveform and the predetermined voltage waveform can be ensured to be at the same time. For example, if the database stores a certain current at time T after power-up, then the current collected at time T after power-up is compared. It is of practical significance to ensure that the coincidence of the current waveform and/or the voltage waveform is achieved only when the above conditions are met.
It should be noted that the manner of obtaining the load type is not limited to the manner of obtaining the load type through the current waveform and/or the voltage waveform, and the load type may also be obtained through the power parameter, and a person skilled in the art may select an appropriate manner to obtain the load type.
In an embodiment of the present application, detecting an operating frequency of a smart socket of each of the above-mentioned loads in a predetermined area by using an arc detection model includes: the method for updating the operating frequency of the intelligent socket comprises the steps that when the arc detection model detects a fault arc, the operating frequency of the intelligent socket corresponding to the load is updated, the mode for updating the operating frequency of the intelligent socket corresponding to the load can be achieved through a counter, specifically, when the fault arc is generated, the intelligent socket can generate corresponding protection action, the operating frequency of the intelligent socket can be automatically updated, the updating method is that 1 is automatically added to bytes on corresponding units of an intelligent socket storage module, the frequency of the operating frequency and the type of the corresponding load have a corresponding relation, and the intelligent socket uploads a data packet (the type of the load and the operating frequency of the intelligent socket) to a server. In addition, the operating frequency of the smart socket may be obtained in other manners.
In an embodiment of the present application, in a case where it is determined that the arc detection model is not applicable to the corresponding load, the detection method further includes: and updating the arc detection model. The accuracy and applicability of the arc detection model can be ensured by updating the arc detection model.
In an embodiment of the present application, the predetermined area includes a plurality of users, each of the users includes a plurality of the loads, and the loads correspond to the smart sockets one to one. The predetermined area can be a certain cell, a certain unit, a certain factory building, a certain school and the like, and the applicability and the accuracy of the arc detection model can be detected by calculating the action frequency of the intelligent socket with a plurality of loads in a certain area.
In an exemplary embodiment of the present application, a detection apparatus of an arc detection model is provided, and fig. 2 is a schematic diagram of the detection apparatus according to the embodiment of the present application. As shown in fig. 2, the apparatus includes:
a detection unit 10 which detects the operating frequency of the smart socket corresponding to each load in a predetermined area by using an arc detection model;
a calculation unit 20 that calculates a sum of the operating frequencies corresponding to the loads of the same type in the predetermined region;
the first determination unit 30 determines whether the arc detection model is applied to the corresponding load based on the sum.
By adopting the detection device, the detection unit detects the action frequency of the intelligent socket corresponding to each load in the preset area; secondly, the calculating unit calculates the sum of the action frequencies corresponding to the loads of the same type in the preset area; the first determining unit determines whether the arc detection model is applied to the corresponding load according to the sum. In the device, the sum of the action frequencies of the intelligent sockets with the same load in a certain area is calculated through the calculating unit, and whether the arc detection model is suitable for the corresponding load is determined according to the sum through the first determining unit, so that the accuracy of the fault arc detection model is determined.
In an embodiment of the application, the first determining unit is adapted to determine that the arc detection model is not applicable to the corresponding load if the sum is greater than the predetermined frequency. The arc detection models corresponding to each load are different due to different performances of each load, and after multiple tests, whether the arc detection model corresponding to each load is suitable for the corresponding load can be obtained.
Specifically, the predetermined frequency of the present application may be determined according to a suitable method, and in an embodiment of the present application, the setting of the predetermined frequency may be implemented by debugging a certain type of air conditioner for many times, for example, obtaining an average value of the operating frequencies of the smart sockets of the air conditioner, calculating an average value of the operating frequencies of the smart sockets of a plurality of air conditioners, and then adding the average values to obtain the predetermined frequency. When the sum of the tested frequencies is greater than the predetermined frequency, it is determined that the arc detection model is not applicable at this time, i.e., the model error is large.
In an embodiment of the present application, the detection apparatus further includes a second determination unit configured to determine a type of each of the loads in the predetermined area before calculating a sum of the operating frequencies corresponding to the loads of the same type in the predetermined area, that is, determine the type of the load first and then determine the sum of the operating frequencies corresponding to the loads of the same type. Specifically, the types of the loads may include household appliances such as an air conditioner, an electric fan, a water heater, a humidifier, a range hood, and/or a microwave oven, and may also include equipment in a factory that needs to test an arc, and may also include any household appliance and equipment in a factory that can obtain an operation frequency of the smart socket. Of course, the type of load may not be acquired by the detection device, and may be acquired in advance by another method.
In an embodiment of the application, the second determining unit includes an obtaining module and a determining module, the obtaining module is configured to obtain a current waveform and/or a voltage waveform of the load, and the determining module is configured to determine the type of the load when a degree of coincidence between the current waveform and a predetermined current waveform is greater than a first predetermined degree of coincidence and/or when a degree of coincidence between the voltage waveform and a predetermined voltage waveform is greater than a second predetermined degree of coincidence. Specifically, before each load leaves the factory, the current waveform and/or the voltage waveform corresponding to the load is tested, and then the tested current waveform and/or voltage waveform is stored in a database as a predetermined current waveform and/or a predetermined voltage waveform for comparison in subsequent actual tests. The coincidence degree of the current waveform and/or the voltage waveform can be measured by using error energy, and in the case that the coincidence degree meets the above condition, the kind of the load can be determined.
It should be noted that the obtaining module is not limited to obtain the current waveform and/or the voltage waveform, and may also obtain the power parameter to obtain the kind of the load, and a person skilled in the art may select a suitable obtaining module to obtain the kind of the load.
In an embodiment of the application, the detection unit further includes an update module, where the update module is configured to update the action frequency of the smart socket corresponding to the load when the arc detection model detects a fault arc, and the mode of updating the action frequency of the smart socket corresponding to the load may be implemented by a counter, specifically, when a fault arc occurs, the smart socket may generate a corresponding protection action, and when the smart socket generates a protection action, the action frequency of the smart socket may be automatically updated, and the update mode is to automatically add 1 to a byte in a corresponding unit of the smart socket storage module, where the number of the action frequency and the type of the corresponding load have a corresponding relationship, and the smart socket uploads a data packet (the type of the load and the number of the actions of the smart socket) to the server. In addition, the operating frequency of the smart socket may be obtained in other manners.
In an embodiment of the application, the detection apparatus further includes an updating unit, and the updating unit is configured to update the arc detection model when it is determined that the arc detection model is not applicable to the corresponding load. The accuracy and applicability of the arc detection model can be ensured by updating the arc detection model.
In an embodiment of the present application, the predetermined area includes a plurality of users, each of the users includes a plurality of the loads, and the loads correspond to the smart sockets one to one. The predetermined area can be a certain cell, a certain unit, a certain factory building, a certain school and the like, and the applicability and the accuracy of the arc detection model can be detected by calculating the action frequency of the intelligent socket with a plurality of loads in a certain area.
An embodiment of the present invention provides a storage medium on which a program is stored, the program implementing the above-described detection method when executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the detection method is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein when the processor executes the program, at least the following steps are realized:
step S101, detecting the action frequency of the intelligent socket corresponding to each load in a preset area by adopting an arc detection model;
step S102 of calculating a sum of the operating frequencies corresponding to the loads of the same type in the predetermined area;
step S103, determining whether the arc detection model is applied to the corresponding load according to the sum.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program of initializing at least the following method steps when executed on a data processing device:
step S101, detecting the action frequency of the intelligent socket corresponding to each load in a preset area by adopting an arc detection model;
step S102 of calculating a sum of the operating frequencies corresponding to the loads of the same type in the predetermined area;
step S103, determining whether the arc detection model is applied to the corresponding load according to the sum.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Examples
In this embodiment, the predetermined area is a cell, the cell includes a plurality of users, each user corresponds to a home, each home has n loads, each load corresponds to a smart socket, each user corresponds to a server, the cell has a total server, the total server communicates with the servers, where n is a positive integer, the cell has a plurality of loads, and each load has a plurality of loads.
The detection method of the arc detection model in the embodiment includes:
step S201, determining the types of the loads in the cell;
specifically, the step S201 includes:
step A1, a user starts a load, and after the load is started for a few minutes, the intelligent socket collects current or voltage waveforms for a certain time;
step A2, uploading the acquired current or voltage waveform to a server;
step a2 specifically includes that the database on the server maintains a load category matching table, where the matching table mainly includes a voltage or current waveform when the load is normally started and a corresponding load type under the waveform. The method comprises the steps of comparing waveforms uploaded to a server with waveforms in a database, if the coincidence degree of the waveforms and the waveforms in the database is within a certain threshold value (the coincidence degree is high), indicating that a load in operation has a corresponding matching item in the database, sending the type of the load to an intelligent socket by the server, and storing the corresponding type of the load on a storage module by the intelligent socket. If the coincidence degree of the waveform and the waveform of the database exceeds a preset threshold value, the server does not have a corresponding load category, and the intelligent socket does not record the relevant running state of the load.
Step S202, detecting the action frequency of the intelligent socket corresponding to each load in the cell by adopting an arc detection model;
step S203, the arc detection model detects whether a fault arc exists;
specifically, fig. 3 shows the arc fault detection flowchart, and fig. 4 shows an arc fault discrimination flowchart. The method specifically comprises the following steps:
step B1, as shown in fig. 5, the smart socket first collects N current data in one period, and records the current data as I0、I1、...IN-1While expressing the normal current data of the load as IO0、IO1、...ION-1Calculating current dataAverage value of (1)aveAnd counting the number N of zero-value currentszeroThe specific calculation method is as follows:
Iave＝(I0+I1+…IN-1)/N
Nzero＝Z1+Z2+…+Zk(k＝1、2…N)
Zkthere are two cases, ① when Ik|<a|IO|maxWhen Z isk1(a is a coefficient less than 1) ②, whereas Zk＝0
Sum＝|I0-IO0|+|I1-IO1|+|I2-IO2|+…|IN-1-ION-1|
Step B2, comparing the current data I collected in the current period0、I1、...IN-1Obtaining the maximum value Imax of the collected current data, and comparing the normal current data IO in the current period0、IO1、...ION-1Obtaining the maximum value IOmax of the normal current data, and calculating the change situation of the current peak value in the current period: c ═ Imax-IOmax |;
step B3, calculating a change situation of a maximum difference value of the current data in the current period, where Δ Ik is Ik-1, representing a difference value of two adjacent current data, comparing the difference values to obtain a maximum value D of the difference values Δ Ik (where k is 1, 2 … N), obtaining a maximum value DO of normal current data in the current period, and obtaining a change situation Δ D of the maximum difference value, where Δ D is | D-DO |;
step B4, after the calculation from step B1 to step B3, performing logic judgment on the relevant parameters, wherein the specific judgment logic is as shown in fig. 6, firstly judging whether the Sum value is greater than the predetermined threshold value 1, and when the Sum value is less than the predetermined threshold value, refreshing the IO again0、IO1、...ION-1On the contrary, the length of the flat shoulder (i.e. the number N of zero-value currents) is judgedzero) When the length of the flat shoulder is larger than a preset threshold value 2, the change condition C of the current data peak value in the current period is judged, and when the change condition C is larger than a preset threshold value 3, the fault arc is indicatedGenerating; updating IO when the length of the flat shoulder is less than a predetermined threshold 20、IO1、...ION-1The value of (c). When the change condition C is less than a predetermined threshold value 3, updating the IO0、IO1、...ION-1And judging △ D of the change condition of the maximum difference value obtained between the normal current data and the collected current data in the current period, wherein when the change condition △ D of the maximum difference value is larger than a preset threshold value 4, the occurrence of a fault arc is indicated, otherwise, IO is updated again0、IO1、...ION-1The value of (c).
Step S204 of updating the operating frequency of the smart jack corresponding to the load when the arc detection model detects a fault arc;
step S205 of calculating a sum of the operating frequencies corresponding to the loads of the same type in the predetermined area;
step S206, determining whether the arc detection model is suitable for the corresponding load according to the sum;
step S206 further includes uploading the load type and the sum of the operation frequencies to a main server, and comparing data in a database of the main server to determine whether the arc detection model is suitable for the corresponding load;
in step S207, when it is determined that the arc detection model is not applicable to the corresponding load, the arc detection model is updated.
By adopting the detection method of the arc detection model in the embodiment, the accuracy and the applicability of the arc detection model can be ensured.
As shown in fig. 5, which is a specific statistical diagram of load operating frequencies, the first smart socket corresponds to a first load, the second smart socket corresponds to a second load …, the smart socket n corresponds to a load n, and f1, f2, f3 … fn correspond to the operating frequencies of the first smart socket to n during the verification period T; after the corresponding load types are confirmed and obtained, the smart socket counts the number of times of actions of the smart socket in the verification period T, as shown in fig. 3, if the load detected by the smart socket is a first load, the fault arc detection model on the smart socket detects whether a fault arc is generated, if the fault arc is generated, the action frequency f1 in the verification period T is updated, and so on, if the load detected by the smart socket is a second load, the fault arc detection model on the smart socket detects whether a fault arc is generated, and if the fault arc is generated, the action frequency f12 in the verification period T is updated.
As shown in fig. 6, a flow chart of a specific arc detection model detection is shown, where f1, f2, f3 … fn represent an operating frequency of the smart socket C1 during a verification period T corresponding to a first load in a first user, an operating frequency of the smart socket C2 during a verification period T corresponding to a first load in a second user, … an operating frequency of the smart socket C2 during a verification period T corresponding to a first load in an nth user, respectively, where the first server corresponds to the first user, the second three-way server corresponds to the second user, and the third server corresponds to the nth user …, and contents of the first server, the second server, and the third server … nth server are uploaded to a total server, where the first server corresponds to the first user, the second server corresponds to the second user, the third server corresponds to a third user …, the nth server corresponds to an nth user, the total server stores and analyzes the action frequencies of the same load type and load, superposes the action frequencies of the same load, calculates the action frequency sum f of the same load in the verification period T by using the formula action frequency f (f 1+ f2+ f3+ … + fn), wherein f1, f2 and f3 … fn respectively represent the action frequency of the intelligent socket corresponding to the first load in the first user and the action of the intelligent socket corresponding to the first load in the second user, and the sum f of the action frequencies of the same load in the verification period T is to be equal to f1+ f2+ f3+ fnPreset ofMaking a comparison if f is greater than fPreset ofIt shows that the applicability of the firmware of the smart socket corresponding to the same load in the area is low, and the parameters of the arc detection model in the firmware need to be readjusted. In practical application, the first server, the second server, and the third server … may be omitted as long as the performance of the total server is good enough, and the same load type and load can be directly usedThe frequency of action is passed to the master server (equivalent to the master server described above).
From the above description, it can be seen that the above-described embodiments of the present application achieve the following technical effects:
1) the control method comprises the steps of firstly, detecting the action frequency of the intelligent socket corresponding to each load in a preset area; secondly, calculating the sum of the action frequencies corresponding to the loads of the same type in the preset area; and finally, determining whether the arc detection model is suitable for the corresponding load according to the sum. According to the method, the sum of the action frequencies of the intelligent sockets with the same load in a certain area is calculated, and whether the arc detection model is suitable for the corresponding load or not is determined according to the sum, so that the accuracy of the arc detection model is guaranteed.
2) First, a detection unit detects the operating frequency of the smart socket corresponding to each load in a predetermined area; secondly, the calculating unit calculates the sum of the action frequencies corresponding to the loads of the same type in the preset area; finally, the first determining unit determines whether the arc detection model is applicable to the corresponding load according to the sum. In the device, the sum of the action frequencies of the intelligent sockets with the same load in a certain area is calculated through the calculating unit, and whether the arc detection model is suitable for the corresponding load is determined according to the sum through the first determining unit, so that the accuracy of the arc detection model is ensured.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (9)
1. A method for detecting an arc detection model, comprising:
detecting the action frequency of the intelligent socket corresponding to each load in a preset area by adopting an arc detection model;
calculating a sum of the operation frequencies corresponding to the loads of the same kind in the predetermined area;
determining from the sum whether the arc detection model is applicable for the corresponding load,
determining from the sum whether the arc detection model is applicable to the corresponding load, comprising:
determining that the arc detection model is not applicable to the corresponding load if the sum is greater than a predetermined frequency.
2. The detection method according to claim 1, wherein before calculating the sum of the operation frequencies corresponding to the loads of the same kind in the predetermined area, the detection method further comprises:
determining a category of each of the loads within the predetermined area.
3. The method of claim 2, wherein determining the class of each of the loads within the predetermined area comprises:
acquiring a current waveform and/or a voltage waveform of the load;
determining the type of the load if the coincidence of the current waveform with a predetermined current waveform is greater than a first predetermined coincidence, and/or if the coincidence of the voltage waveform with a predetermined voltage waveform is greater than a second predetermined coincidence.
4. The method of claim 1, wherein detecting the operating frequency of the smart socket of each of the loads in a predetermined area using an arc detection model comprises:
and updating the action frequency of the intelligent socket corresponding to the load when the arc detection model detects fault arc.
5. The detection method of claim 1, wherein in the event that it is determined that the arc detection model is not applicable to the corresponding load, the detection method further comprises:
updating the arc detection model.
6. The method of claim 1, wherein the predetermined area comprises a plurality of users, each of the users comprising a plurality of the loads, wherein the loads correspond to the smart sockets one-to-one.
7. A detection apparatus of an arc detection model, comprising:
the detection unit is used for detecting the action frequency of the intelligent socket corresponding to each load in the preset area by adopting an arc detection model;
a calculation unit that calculates a sum of the operation frequencies corresponding to the loads of the same kind in the predetermined area;
a first determination unit that determines whether the arc detection model is applicable to the corresponding load according to the sum,
the first determination unit is further configured to determine that the arc detection model is not applicable to the corresponding load if the sum is greater than a predetermined frequency.
8. A storage medium characterized in that the storage medium includes a stored program, wherein the program executes the detection method according to any one of claims 1 to 6.
9. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the detection method according to any one of claims 1 to 6 when running.
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