CN110908844A

CN110908844A - Arc fault detection method and device, computer readable storage medium and socket

Info

Publication number: CN110908844A
Application number: CN201911016795.4A
Authority: CN
Inventors: 吴斌; 周永志; 杨泽
Original assignee: Gree Electric Appliances Inc of Zhuhai
Current assignee: Gree Electric Appliances Inc of Zhuhai
Priority date: 2019-10-24
Filing date: 2019-10-24
Publication date: 2020-03-24
Anticipated expiration: 2039-10-24
Also published as: CN110908844B

Abstract

The invention discloses an arc fault detection method, an arc fault detection device, a computer readable storage medium and a socket, wherein the method comprises the following steps: determining characteristic information of a load to be subjected to arc fault detection; according to the characteristic information of the load, matching a detection model for carrying out arc fault detection on the load; and according to the detection model obtained by matching, carrying out arc fault detection on the load. According to the scheme provided by the invention, the problem that the detection precision of the intelligent socket in arc fault detection on different loads is influenced due to different current and voltage waveforms in the process of running different loads can be solved, and the effect of improving the detection precision of the intelligent socket in arc fault detection on different loads is achieved.

Description

Arc fault detection method and device, computer readable storage medium and socket

Technical Field

The invention belongs to the technical field of arc fault detection, and particularly relates to an arc fault detection method, an arc fault detection device, a computer readable storage medium and a socket, in particular to an arc fault detection interaction method, an arc fault detection interaction device, a computer readable storage medium and a socket.

Background

In a household user, the waveforms of current and voltage of each electric device in operation are different, and when the intelligent socket with arc fault detection is connected with different loads, the current or voltage waveforms of the arc and other faults are different due to the difference of the loads, so that the accuracy of arc detection is influenced.

The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.

Disclosure of Invention

The invention aims to provide an arc fault detection method, an arc fault detection device, a computer-readable storage medium and an arc fault detection socket, aiming at the defects, so as to solve the problem that the detection precision of the intelligent socket in arc fault detection of different loads is influenced due to different current and voltage waveforms in the process of running of different loads, and achieve the effect of improving the detection precision of the intelligent socket in arc fault detection of different loads.

The invention provides an arc fault detection method, which comprises the following steps: determining characteristic information of a load to be subjected to arc fault detection; according to the characteristic information of the load, matching a detection model for carrying out arc fault detection on the load; and according to the detection model obtained by matching, carrying out arc fault detection on the load.

Optionally, the characteristic information of the load includes: the type of load, or the start waveform data of the load; determining characteristic information of a load to be arc fault detected, comprising: acquiring the type of a load to be subjected to arc fault detection, and taking the type of the load as characteristic information of the load; or, acquiring starting waveform data in the load starting process to take the starting waveform data of the load as the characteristic information of the load; the method for acquiring starting waveform data in the load starting process comprises the following steps: in the process of starting the load, the waveform data of the load is collected by taking more than first set time as one collection time, and the waveform data obtained in the collection time is used as one starting waveform data of the load.

Optionally, matching a detection model for arc fault detection of the load comprises: when the characteristic information of the load includes the type of the load, determining a set detection model corresponding to a set type which is the same as the type of the load in the correspondence as a detection model of the load corresponding to the type of the load according to the correspondence between the set type and the set detection model; and, under the condition that there is a detection model of the load in a preset first model base, directly calling the detection model of the load from the first model base; or under the condition that the preset first model base does not have the detection model of the load, updating the detection model of the load into the preset first model base in a local loading or remote upgrading mode, and then calling the detection model of the load from the first model base; or, when the characteristic information of the load includes start waveform data of the load, performing waveform matching processing on the start waveform data of the load, and performing firmware upgrade according to a matching result obtained by the waveform matching processing to obtain a detection model of the load.

Optionally, the performing a waveform matching process on the start waveform data of the load includes: preprocessing starting waveform data of the load to obtain first waveform data; calculating an absolute error value of the first waveform data and standard waveform data acquired at the same time point in a preset waveform database; accumulating absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points to obtain a total absolute error value obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points within one acquisition time; recording the accumulation times of absolute error values of different time points in the total absolute error value when the total absolute error value is greater than a preset error threshold under the condition that the total absolute error value is greater than the preset error threshold; comparing the accumulated times, and determining the first waveform data corresponding to the maximum accumulated time in the accumulated times as a candidate matching waveform of the load; or under the condition that the total absolute error value is less than or equal to the preset error threshold, continuously accumulating the absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points, and continuously recording the accumulation times of the absolute error values at different time points in the total absolute error value when the total absolute error value is greater than the preset error threshold.

Optionally, upgrading the firmware according to a matching result obtained by the waveform matching process includes: determining whether the load is in an operating state; and if the load is not in a working state, under the condition that the socket body is kept powered on and the load is powered off, upgrading the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset upgrading mode.

Optionally, the invoking of the detection model of the load from the first model library specifically includes: determining, by the socket body, a detection model of the load corresponding to the type of the load on a local side, and retrieving the determined detection model of the load from the first model library; or the socket body uploads the load type to the server, and then receives a detection model of the load fed back to the socket body after the processing of the server; the processing of the server comprises: determining a detection model of the load corresponding to the type of the load, calling the determined detection model of the load from a first model library, and feeding back the detection model of the load to the socket body; the socket body is used for carrying out arc fault detection on the load by utilizing the detection model of the load after the detection model of the load is called from the first model library; and/or performing waveform matching processing on the starting waveform data of the load, specifically: carrying out waveform matching processing on the starting waveform data of the load at the local side by the local part of the socket; or the socket body uploads the waveform data of the load to the server, receives the waveform matching processing result fed back to the socket body after the server performs waveform matching processing on the starting waveform data of the load; and/or upgrading firmware according to a matching result obtained by the waveform matching processing, specifically: upgrading the firmware of the matching result obtained according to the waveform matching processing at the local side by the local part of the socket to obtain a detection model of the load after the firmware is upgraded; or, the socket body firstly sends a no-working state instruction of which the load is not in a working state to the server, and then sends an upgrading instruction to the server after receiving a request upgrading instruction fed back by the server based on the no-working state instruction; then, under the condition that the socket body is kept powered on and the load is kept powered off, the server updates the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset updating mode, and feeds back the detection model of the load after the firmware is updated to the socket body; the socket body is used for displaying an upgrading process when firmware is upgraded; and arc fault detection is carried out on the load by utilizing the detection model of the load after the firmware is upgraded.

In accordance with the above method, another aspect of the present invention provides an arc fault detection apparatus, comprising: the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for determining characteristic information of a load to be subjected to arc fault detection; the determining unit is further used for matching a detection model for arc fault detection of the load according to the characteristic information of the load; and the detection unit is used for carrying out arc fault detection on the load according to the detection model obtained by matching.

Optionally, the characteristic information of the load includes: the type of load, or the start waveform data of the load; the determining unit determines characteristic information of a load to be subjected to arc fault detection, including: acquiring the type of a load to be subjected to arc fault detection, and taking the type of the load as characteristic information of the load; or, acquiring starting waveform data in the load starting process to take the starting waveform data of the load as the characteristic information of the load; the determining unit acquires starting waveform data in a load starting process, and the determining unit comprises the following steps: in the process of starting the load, the waveform data of the load is collected by taking more than first set time as one collection time, and the waveform data obtained in the collection time is used as one starting waveform data of the load.

Optionally, the determining unit matches a detection model for arc fault detection of the load, including: when the characteristic information of the load includes the type of the load, determining a set detection model corresponding to a set type which is the same as the type of the load in the correspondence as a detection model of the load corresponding to the type of the load according to the correspondence between the set type and the set detection model; and, under the condition that there is a detection model of the load in a preset first model base, directly calling the detection model of the load from the first model base; or under the condition that the preset first model base does not have the detection model of the load, updating the detection model of the load into the preset first model base in a local loading or remote upgrading mode, and then calling the detection model of the load from the first model base; or, when the characteristic information of the load includes start waveform data of the load, performing waveform matching processing on the start waveform data of the load, and performing firmware upgrade according to a matching result obtained by the waveform matching processing to obtain a detection model of the load.

Optionally, the determining unit performs waveform matching processing on the start waveform data of the load, including: preprocessing starting waveform data of the load to obtain first waveform data; calculating an absolute error value of the first waveform data and standard waveform data acquired at the same time point in a preset waveform database; accumulating absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points to obtain a total absolute error value obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points within one acquisition time; recording the accumulation times of absolute error values of different time points in the total absolute error value when the total absolute error value is greater than a preset error threshold under the condition that the total absolute error value is greater than the preset error threshold; comparing the accumulated times, and determining the first waveform data corresponding to the maximum accumulated time in the accumulated times as a candidate matching waveform of the load; or under the condition that the total absolute error value is less than or equal to the preset error threshold, continuously accumulating the absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points, and continuously recording the accumulation times of the absolute error values at different time points in the total absolute error value when the total absolute error value is greater than the preset error threshold.

Optionally, the upgrading the firmware by the determining unit according to a matching result obtained by the waveform matching process includes: determining whether the load is in an operating state; and if the load is not in a working state, under the condition that the socket body is kept powered on and the load is powered off, upgrading the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset upgrading mode.

Optionally, the determining unit retrieves the detection model of the load from the first model library, specifically: determining, by the socket body, a detection model of the load corresponding to the type of the load on a local side, and retrieving the determined detection model of the load from the first model library; or the socket body uploads the load type to the server, and then receives a detection model of the load fed back to the socket body after the processing of the server; the processing of the server comprises: determining a detection model of the load corresponding to the type of the load, calling the determined detection model of the load from a first model library, and feeding back the detection model of the load to the socket body; the socket body is used for carrying out arc fault detection on the load by utilizing the detection model of the load after the detection model of the load is called from the first model library; and/or, the determining unit performs waveform matching processing on the start waveform data of the load, specifically: carrying out waveform matching processing on the starting waveform data of the load at the local side by the local part of the socket; or the socket body uploads the waveform data of the load to the server, receives the waveform matching processing result fed back to the socket body after the server performs waveform matching processing on the starting waveform data of the load; and/or the determining unit upgrades the firmware according to a matching result obtained by the waveform matching processing, specifically: upgrading the firmware of the matching result obtained according to the waveform matching processing at the local side by the local part of the socket to obtain a detection model of the load after the firmware is upgraded; or, the socket body firstly sends a no-working state instruction of which the load is not in a working state to the server, and then sends an upgrading instruction to the server after receiving a request upgrading instruction fed back by the server based on the no-working state instruction; then, under the condition that the socket body is kept powered on and the load is kept powered off, the server updates the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset updating mode, and feeds back the detection model of the load after the firmware is updated to the socket body; the socket body is used for displaying an upgrading process when firmware is upgraded; and arc fault detection is carried out on the load by utilizing the detection model of the load after the firmware is upgraded.

In accordance with another aspect of the present invention, there is provided a socket including: the arc fault detection device described above.

In accordance with the above method, a further aspect of the present invention provides a computer-readable storage medium, comprising: the computer readable storage medium having stored therein a plurality of instructions; the plurality of instructions for being loaded by a processor and performing the arc fault detection method described above.

In accordance with the above method, a socket is provided in accordance with another aspect of the present invention, comprising: a processor for executing a plurality of instructions; a memory to store a plurality of instructions; wherein the plurality of instructions are configured to be stored by the memory and loaded by the processor and to perform the arc fault detection method described above.

According to the scheme provided by the invention, the online updating of the socket firmware is completed according to the types of the loads, so that the accuracy of arc fault detection of the socket for each type of load can be improved.

Furthermore, the scheme of the invention realizes the online updating of the firmware by using the remote online loading firmware, and the updating is convenient and reliable.

Furthermore, the scheme of the invention realizes the on-line updating of the firmware by remote service and remote on-line upgrading, can determine the corresponding specific detection model for each load, and is favorable for improving the accuracy of arc fault detection for each load.

Further, according to the scheme of the invention, different firmware is selected for detection according to different types of loads, so that the arc fault detection precision aiming at different loads can be improved, and the protection reliability when arc fault protection is carried out aiming at different loads is improved.

Furthermore, according to the scheme of the invention, each load corresponds to a specific detection model, so that the detection precision can be improved, and the load operation safety can be improved.

Therefore, according to the scheme provided by the invention, the online updating of the socket firmware is completed according to the types of the loads, the problem that the detection precision of the intelligent socket in arc fault detection on different loads is influenced due to different waveforms of current and voltage when different loads operate is solved, and the effect of improving the detection precision of the intelligent socket in arc fault detection on different loads is achieved.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

FIG. 1 is a schematic flow chart diagram illustrating one embodiment of an arc fault detection method of the present invention;

FIG. 2 is a flowchart illustrating an embodiment of a detection model for matching loads according to the types of loads in the method of the present invention;

FIG. 3 is a flowchart illustrating an embodiment of a waveform matching process performed on the start waveform data of the load according to the method of the present invention;

FIG. 4 is a flowchart illustrating an embodiment of firmware upgrade according to a matching result obtained by a waveform matching process in the method of the present invention;

FIG. 5 is a schematic structural diagram of an embodiment of the arc fault detection apparatus of the present invention;

FIG. 6 is a general flow diagram of arc fault detection for one embodiment of the receptacle of the present invention;

FIG. 7 is a schematic diagram illustrating a server waveform matching process according to an embodiment of the present invention;

FIG. 8 is a schematic diagram illustrating a firmware upgrade process of an embodiment of the socket of the present invention;

FIG. 9 is a schematic diagram of an arc fault detection process for one embodiment of a receptacle of the present invention;

FIG. 10 is a schematic diagram of a fault cycle determination process of an embodiment of the receptacle of the present invention;

FIG. 11 is a flow chart illustrating arc fault determination for one embodiment of the receptacle of the present invention.

The reference numbers in the embodiments of the present invention are as follows, in combination with the accompanying drawings:

102-a determination unit; 104-detection unit.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.

According to an embodiment of the present invention, an arc fault detection method is provided, as shown in fig. 1, which is a schematic flow chart of an embodiment of the method of the present invention. The arc fault detection method may include: step S110 to step S130.

At step S110, characteristic information of the load to be arc fault detected is determined. The load may be a load to which the smart socket is currently connected, or a load to which the smart socket is to be connected for arc fault detection. The characteristic information may be the kind of the load, or other information that can characterize the identity of the load, such as start waveform data.

The characteristic information of the load may include: the kind of load, or the start waveform data of the load.

Alternatively, the determination of the characteristic information of the load to be subjected to the arc fault detection in step S110 may include any one of the following determination processes.

The first determination process: in the case that the characteristic information of the load may include a type of the load, a manner of directly acquiring the type of the load may specifically include: the method comprises the steps of obtaining the type of a load to be subjected to arc fault detection, and using the type of the load as characteristic information of the load. For example: in the case where the kind of the load can be directly determined, the kind of the load can be directly acquired.

The second determination process: in the case where the characteristic information of the load may include start waveform data of the load, the start waveform data of the load may be directly acquired to use the start waveform data of the load as the characteristic information of the load.

Of course, the determining the type corresponding to the start waveform data as the type of the load based on the start waveform data of the load and the corresponding relationship between the set start waveform data and the set type may specifically include: and acquiring starting waveform data in the load starting process to take the starting waveform data of the load as the characteristic information of the load. Specifically, the start waveform data may be start voltage waveform data, start current waveform data, or start power waveform data during load start.

The obtaining of the start waveform data in the load start process may include: in the process of starting the load, the waveform data of the load is collected by taking more than first set time as one collection time, and the waveform data obtained in the collection time is used as one starting waveform data of the load.

For example: when the load is started, the load generates a start waveform (such as start waveform data), and the smart socket collects the start waveform (such as start waveform data) of the load in the load starting process, wherein the collection time can be more than 1 minute.

Therefore, the convenience and flexibility of matching the corresponding detection model based on the characteristic information of the load are improved by the characteristic information of the load in various forms.

At step S120, a detection model that can be used for arc fault detection for the load is matched based on the characteristic information of the load.

For example: different firmware can be selected according to different types of loads for detection, corresponding detection is carried out according to different load conditions, and for example, each type of load corresponds to a specific detection model, so that the detection precision is improved.

For example: and remote online upgrade is carried out through remote service, namely, the firmware is remotely loaded online, so that online update of the firmware is realized.

Optionally, the matching of the detection model in step S120, which can be used for arc fault detection of the load, may include any one of the following matching processes.

The first matching process: the process of matching the detection model of the load according to the kind of the load may be specifically as follows.

The following further describes a specific process of matching the detection model of the load according to the type of the load, with reference to a flowchart of an embodiment of matching the detection model of the load according to the type of the load in the method of the present invention shown in fig. 2, which may include: step S210 to step S230.

In step S210, when the characteristic information of the load may include the type of the load, the set detection model corresponding to the same set type as the type of the load in the correspondence relationship is determined as the detection model of the load corresponding to the type of the load, based on the correspondence relationship between the set type and the set detection model. And the number of the first and second groups,

in step S220, when there is a detection model of the load in a preset first model library, the detection model of the load is directly retrieved from the first model library, so as to perform arc fault detection on the load by using the directly retrieved detection model of the load.

Or step S230, when there is no detection model of the load in the preset first model library, updating the detection model of the load into the preset first model library by means of local loading or remote upgrade, and then retrieving the detection model of the load from the first model library, so as to perform arc fault detection on the load by using the updated retrieved detection model of the load.

For example: and the online updating of the socket firmware is completed according to the type of the load, so that the accuracy of the socket arc fault detection is improved.

Thus, by setting the correspondence between the type of the load and the detection model, the detection model can be determined based on the type of the load when the type of the load is determined, and the determination of the detection model of the load can be made accurately and easily.

The second matching process: the process of matching the detection model of the load according to the start waveform data of the load may specifically include: and under the condition that the characteristic information of the load can comprise starting waveform data of the load, carrying out waveform matching processing on the starting waveform data of the load, and upgrading firmware according to a matching result obtained by the waveform matching processing to obtain a detection model of the load.

Or, optionally, the process of matching the detection model of the load according to the start waveform data of the load may specifically be as follows:

in the case where the characteristic information of the load may include start waveform data of the load, the set detection model corresponding to the set waveform data that is the same as the start waveform data of the load in the correspondence relationship is determined as the detection model of the load corresponding to the start waveform data of the load, based on the correspondence relationship between the set waveform data and the set detection model. And when the detection model of the load exists in a preset second model base, directly calling the detection model of the load from the second model base, and carrying out arc fault detection on the load by using the directly called detection model of the load. Or under the condition that the preset second model base does not have the detection model of the load, updating the detection model of the load into the preset second model base in a local loading or remote upgrading mode, and calling the detection model of the load from the second model base so as to detect the arc fault of the load by using the updated and called detection model of the load.

Therefore, the detection model of the load is determined based on the starting waveform data of the load, so that the determination of the detection model of the load is more accurate and reliable.

More optionally, the waveform matching process is performed on the start waveform data of the load, which can be seen in the following exemplary description.

With reference to the flowchart of fig. 3 showing an embodiment of performing waveform matching processing on the start waveform data of the load in the method of the present invention, a specific process of performing waveform matching processing on the start waveform data of the load is further described, which may include: step S310 to step S340.

Step S310, preprocessing the start waveform data of the load to obtain first waveform data. Wherein, the pretreatment may include: and (5) noise reduction processing.

For example: the intelligent socket uploads the waveform collected when the load is started to the server, and the starting waveform of the load is compared with a waveform database of the server for judgment. That is to say, when the load is started, the intelligent socket acquires current or voltage waveform when the load is started, and uploads the current or voltage waveform to the server; the server takes the current or voltage waveforms and matches them to standard waveforms in a database.

For example: the intelligent socket acquires a current waveform or a power waveform when the load starts (the waveform collecting time is from T1 moment to T2 moment when the load starts to start), the intelligent socket uploads the waveform to the server, and the server carries out preprocessing on the uploaded waveform, wherein the preprocessing is mainly used for noise reduction. The start waveform of the load is actually composed of data, the smart socket collects the start waveform of the load and then generates a waveform, and a certain amount of data can be collected at a certain frequency from time T1 to time T2. For example: the intelligent socket starts to collect waveforms at the T1 moment when the load is started; when the load starts to run to time T2, the smart socket uploads the acquired waveform to the server at this time.

Step S320, calculating an absolute error value of the first waveform data and standard waveform data acquired at the same time point in a preset waveform database; and accumulating absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points to obtain a total absolute error value obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points within one acquisition time. The standard waveform data is waveform data collected under a preset standard working condition when a load leaves a factory. For example: the waveform database on the server is recorded by a manufacturer before the product leaves a factory; the waveform database is mainly related to starting waveforms corresponding to different loads.

For example: the server may match the preprocessed waveform with a primary current value or a power value of a pre-stored waveform in the waveform library (i.e., the waveform database) at the same time to obtain an absolute error value. Here, the time axes of the two waveforms for obtaining the absolute error value are the same, for example, the two waveforms are collected at the same time of 1s at the time of starting, and then data comparison is performed.

For example: matching the waveforms preprocessed by the server, and sequentially solving the absolute error value Q of the acquired waveforms and the primary current values or power values of the waveforms in each time in the waveform library_nAccumulating the absolute error values to obtain Q, and comparing and judging Q with a preset threshold Q_{Preset of}。

Step S330, recording the accumulation times of the absolute error values of different time points in the absolute error total value when the absolute error total value is greater than the preset error threshold value under the condition that the absolute error total value is greater than the preset error threshold value; and comparing the accumulation times, and determining the first waveform data corresponding to the maximum accumulation time in the accumulation times as the candidate matching waveform of the load.

For example: and accumulating the times of the measured value of the waveform to be matched exceeding a preset threshold value at each time, recording the current accumulation times, and selecting the maximum accumulation times, wherein the waveform is represented as a candidate matching waveform. For example: when Q is greater than Q_{Preset of}At the time, the number of times k of accumulation at this time is recorded_p. Comparison k₁、k₂、k₃…k_pThe maximum k value is selected, and the corresponding waveform is the matched waveform. Wherein k is₁、k₂、k₃…k_pIs the accumulated number of times at different times.

Or step S340, when the total absolute error value is less than or equal to the preset error threshold, continuing to accumulate absolute error values obtained based on the first waveform data collected at different time points and the standard waveform data at corresponding time points, and continuing to record the number of times of accumulating the absolute error values at different time points in the total absolute error value when the total absolute error value is greater than the preset error threshold, so as to determine the candidate matching waveform of the load when the corresponding total absolute error value is greater than the preset error threshold.

For example: the absolute errors of the current values or power values corresponding to the respective times are continuously accumulated, and the number of accumulations is continuously increased.

Therefore, the absolute error value is obtained by the starting waveform data collected in a plurality of collection time and the standard waveform data collected at the same time point, and the total absolute error value obtained by accumulating the absolute error values obtained by the first waveform data collected at different time points and the standard waveform data corresponding to the time points is compared with the preset error threshold value to determine the candidate matching waveform of the load, so that the waveform matching result of the starting waveform data of the load is accurate and reliable.

More optionally, firmware upgrade is performed according to a matching result obtained by the waveform matching process, which may be referred to as an exemplary description below.

With reference to the flowchart of fig. 4 showing an embodiment of firmware upgrade according to a matching result obtained by waveform matching processing in the method of the present invention, a specific process of firmware upgrade according to a matching result obtained by waveform matching processing is further described, which may include: step S410 and step S420.

Step S410, in case of networking, determines whether the load is in an operating state.

For example: and verifying whether the networking is successful or not, and if so, acquiring the working state information from the intelligent socket by the server, which is equivalent to whether the load works or not. Such as: the server acquires the working state of the intelligent socket, and the intelligent socket acquires current or voltage data to judge whether the load is in the working state.

Step S420, if the load is not in the working state, under the condition that the socket body is kept powered on and the load is powered off, upgrading the firmware of the detection model of the load according to the candidate matching waveform of the load and in a preset upgrading mode.

For example: and the server interacts with the intelligent socket according to the matching result, and performs remote firmware upgrade on the successfully matched firmware after the information of the two parties is confirmed to be successful. Specifically, after the server is successfully matched, the server sends a matching result to the intelligent socket, if the intelligent socket receives information of successful matching, the socket can send an online upgrade command to the server in an idle state, and the server obtains the online upgrade command sent by the socket to start firmware upgrade.

Therefore, under the condition that the load is not in a working state, the socket body is kept powered on, the load is guaranteed to be powered off, the firmware of the detection module of the load is upgraded based on the candidate matching waveform of the load obtained by waveform matching, the detection module corresponds to the load, and therefore more reliable and accurate detection is achieved during arc fault detection.

More alternatively, the execution subject of the determination of the detection model of the load may be the body side of the socket or the server side, and specifically, refer to the following exemplary descriptions of several scenarios.

The first case: the method for detecting the load comprises the following steps of calling a detection model of the load from the first model library, specifically: determining, by the socket body, a detection model of the load corresponding to the type of the load on a local side, and retrieving the determined detection model of the load from the first model library; or the socket body uploads the load type to the server, and then receives a detection model of the load fed back to the socket body after the processing of the server; the processing of the server may include: the load detection model corresponding to the type of the load is determined, and after the determined load detection model is retrieved from the first model library, the load detection model is fed back to the socket body.

Specifically, the method comprises the following steps: after the type of the load is uploaded to the server by the socket body, the detection model of the load fed back to the socket body by the server is received. The server determines a detection model of the load corresponding to the type of the load, calls the detection model of the load from the first model library, and feeds back the detection model of the load to the socket body. Or, after the socket body uploads the load type to the server, the detection model of the load corresponding to the load type determined by the server is received, and the determined detection model of the load is retrieved from the first model library and then fed back to the socket body.

The socket body can be used for carrying out arc fault detection on the load by utilizing the detection model of the load after the detection model of the load is called from the first model library. That is, the main body of the outlet is a main body of the outlet which can be used for calling the detection model of the load from the first model library and then detecting the arc fault of the load by using the detection model of the load.

The second case: and performing waveform matching processing on the starting waveform data of the load, specifically: carrying out waveform matching processing on the starting waveform data of the load at the local side by the local part of the socket; or the socket body uploads the waveform data of the load to the server, receives the waveform matching processing result fed back to the socket body after the server performs waveform matching processing on the starting waveform data of the load.

The third situation: upgrading the firmware according to a matching result obtained by the waveform matching processing, which specifically comprises the following steps: and upgrading the firmware of the matching result obtained according to the waveform matching processing at the local side by the local part of the socket to obtain the detection model of the load after the firmware is upgraded. Or, the socket body firstly sends a no-working state instruction of which the load is not in a working state to the server, and then sends an upgrading instruction to the server after receiving a request upgrading instruction fed back by the server based on the no-working state instruction; and then, under the condition that the socket body is kept powered on and the load is kept powered off, the server upgrades the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset upgrading mode, and then the detection model of the load after firmware upgrading is fed back to the socket body.

The socket body can be used for displaying an upgrading process when firmware is upgraded; and arc fault detection is carried out on the load by utilizing the detection model of the load after the firmware is upgraded. Namely, the socket body can be used for displaying the upgrading process when the firmware is upgraded; and the socket body is used for carrying out arc fault detection on the load by utilizing the detection model of the load after the firmware is upgraded.

For example: the intelligent socket sends a no-work state instruction to the server S1, and the server sends a request upgrading instruction after receiving the no-work state instruction S2. If the load is not opened at the moment, the intelligent socket sends a no-working-state command S1 to the server, and the server sends a request upgrading command S2 after receiving S1. The intelligent socket receives the instruction S2 and sends an upgradable instruction S3 to the server, the server starts firmware upgrade after receiving the instruction S3, and the intelligent socket is not allowed to be powered off and the load is not powered on in the firmware upgrade process; and after receiving the S2, the smart socket feeds back a corresponding upgradable instruction S3, the server starts firmware upgrade, in the firmware upgrade process, the smart socket should be kept in a power-on state, and the load is in a power-off state to wait for the server to estimate that the upgrade is finished. Judging whether the intelligent socket receives a firmware upgrading completion instruction S4 sent by the server; and judging whether the firmware of the waiting server is upgraded or not, and finishing the firmware upgrade if the intelligent socket receives an upgrade finishing instruction S4.

Wherein, during the server carries out firmware upgrading, the socket can not be in operating condition to after the server upgrading succeeds, send the successful instruction of upgrading to smart jack, close the upgrade request after smart jack obtains the successful instruction of upgrading, and open normal safeguard function, for example: the normal fault arc protection function is switched on, and with respect to the normal fault arc protection function, reference can be made to the examples shown in fig. 9 and 10.

Alternatively, the fourth scenario: the detection model of the load is called from the second model library, and specifically, the detection model may also be executed by the socket local side or fed back after being executed by the server side, which may specifically refer to the following description.

The detection model of the load corresponding to the type of the load is determined on the local side by the body of the socket, and the determined detection model of the load is retrieved from the second model library. Or the socket body uploads the load type to the server, and then receives a detection model of the load fed back to the socket body after the processing of the server; the processing of the server may include: and after the detection model of the load corresponding to the type of the load is determined and the determined detection model of the load is called from the second model library, the detection model of the load is fed back to the socket body. Specifically, the type of the load may be uploaded to the server by the outlet body, and then the detection model of the load fed back to the outlet body by the server may be received. The server determines a detection model of the load corresponding to the type of the load, retrieves the detection model of the load from the second model library, and feeds back the detection model of the load to the socket body. Of course, the socket body may upload the load type to the server, receive the detection model of the load corresponding to the load type specified by the server, retrieve the specified detection model of the load from the second model library, and feed back the detection model of the load to the socket body.

The socket body can be used for carrying out arc fault detection on the load by utilizing the detection model of the load after the detection model of the load is called from the second model library. That is, the main body of the outlet is a main body of the outlet which can be used for calling the detection model of the load from the second model library and then detecting the arc fault of the load by using the detection model of the load.

Therefore, the convenience of processing can be improved by processing at the body end of the socket, and the communication cost is low; or the processing is carried out at the server side, so that the processing efficiency can be improved.

At step S130, arc fault detection is performed on the load according to the detection model obtained by matching.

For example: the corresponding detection can be carried out according to different load conditions, for example, each load corresponds to a specific detection model, and the detection precision is improved.

Therefore, the detection model used for arc fault detection is determined based on the characteristic information of the load, and then arc fault detection is carried out on the load according to the determined detection model, so that the corresponding detection model can be selected for arc fault detection aiming at the characteristic information of the load, and the detection result of the arc fault detection of the load is more accurate.

Through a large number of tests, the technical scheme of the embodiment is adopted, and the socket firmware is updated on line according to the types of the loads, so that the arc fault detection precision of the socket for each type of load can be improved.

According to an embodiment of the present invention, there is also provided an arc fault detection apparatus corresponding to the arc fault detection method. Referring to fig. 5, a schematic diagram of an embodiment of the apparatus of the present invention is shown. The arc fault detection apparatus may include: a determination unit 102 and a detection unit 104.

In an alternative example, the determination unit 102 may be configured to determine characteristic information of a load to be arc fault detected. The load may be a load to which the smart socket is currently connected, or a load to which the smart socket is to be connected for arc fault detection. The characteristic information may be the kind of the load, or other information that can characterize the identity of the load, such as start waveform data. The specific function and processing of the determination unit 102 are referred to in step S110.

The determining unit 102 determines characteristic information of the load to be arc fault detected, and may include any one of the following determination processes.

The first determination process: in the case that the characteristic information of the load may include a type of the load, a manner of directly acquiring the type of the load may specifically include: the determining unit 102 may be further configured to obtain a type of a load to be subjected to arc fault detection, and use the type of the load as the characteristic information of the load. For example: in the case where the kind of the load can be directly determined, the kind of the load can be directly acquired.

Of course, the determining the type corresponding to the start waveform data as the type of the load based on the start waveform data of the load and the corresponding relationship between the set start waveform data and the set type may specifically include: the determining unit 102 may be further configured to obtain start waveform data in a load start process, so as to use the start waveform data of the load as the characteristic information of the load. Specifically, the start waveform data may be start voltage waveform data, start current waveform data, or start power waveform data during load start.

The determining unit 102 obtains start waveform data in a load start process, and may include: the determining unit 102 may be further configured to, in a process of starting the load, acquire the waveform data of the load by using more than a first set time as one acquisition time, and use the waveform data obtained in the one acquisition time as one starting waveform data of the load.

In an optional example, the determining unit 102 may be further configured to match a detection model that may be used for arc fault detection of the load according to the characteristic information of the load. The specific function and processing of the determination unit 102 are also referred to in step S120.

Optionally, the determining unit 102 matches a detection model that can be used for arc fault detection of the load, and may include any one of the following matching processes.

The first matching process: the process of matching the detection model of the load according to the type of the load may specifically be as follows:

specifically, when the characteristic information of the load may include the type of the load, the determining unit 102 may be further configured to determine, as the detection model of the load corresponding to the type of the load, a setting detection model corresponding to a setting type that is the same as the type of the load in the correspondence relationship, based on the correspondence relationship between the setting type and the setting detection model. The specific function and processing of the determination unit 102 are also referred to in step S210. And the number of the first and second groups,

the determining unit 102 may be further configured to, when there is a detection model of the load in a preset first model library, directly retrieve the detection model of the load from the first model library, and perform arc fault detection on the load by using the directly retrieved detection model of the load. The specific function and processing of the determination unit 102 are also referred to in step S220.

The determining unit 102 may be further specifically configured to, or in a case that the preset first model library does not have the detection model of the load, update the detection model of the load into the preset first model library in a local loading or remote upgrading manner, and then retrieve the detection model of the load from the first model library, so as to perform arc fault detection on the load by using the updated retrieved detection model of the load. The specific function and processing of the determination unit 102 are also referred to in step S230.

The second matching process: the process of matching the detection model of the load according to the start waveform data of the load may specifically be as follows:

the determining unit 102 may be further configured to, in a case that the characteristic information of the load may include start waveform data of the load, perform waveform matching processing on the start waveform data of the load, and perform firmware upgrade according to a matching result obtained by the waveform matching processing, so as to obtain a detection model of the load.

More optionally, the determining unit 102 performs waveform matching processing on the start waveform data of the load, and may include:

the determining unit 102 may be further configured to perform preprocessing on the start waveform data of the load to obtain first waveform data. Wherein, the pretreatment may include: and (5) noise reduction processing. The specific function and processing of the determination unit 102 are also referred to in step S310.

The determining unit 102 may be further configured to specifically calculate an absolute error value from the first waveform data and standard waveform data acquired at the same time point in a preset waveform database; and accumulating absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points to obtain a total absolute error value obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points within one acquisition time. The standard waveform data is waveform data collected under a preset standard working condition when a load leaves a factory. The specific function and processing of the determination unit 102 are also referred to in step S320. For example: the waveform database on the server is recorded by a manufacturer before the product leaves a factory; the waveform database is mainly related to starting waveforms corresponding to different loads.

The determining unit 102 may be further configured to record, when the total absolute error value is greater than a preset error threshold, the number of times of accumulating absolute error values at different time points in the total absolute error value when the total absolute error value is greater than the preset error threshold; and comparing the accumulation times, and determining the first waveform data corresponding to the maximum accumulation time in the accumulation times as the candidate matching waveform of the load. The specific function and processing of the determination unit 102 are also referred to in step S330.

The determining unit 102 may be further specifically configured to, or when the total absolute error value is less than or equal to a preset error threshold, continue to accumulate absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data at corresponding time points, and continue to record the number of times of accumulation of the absolute error values at different time points in the total absolute error value when the total absolute error value is greater than the preset error threshold, so as to determine the candidate matching waveform of the load when the corresponding total absolute error value is greater than the preset error threshold. The specific function and processing of the determination unit 102 are also referred to in step S340.

More optionally, the determining unit 102 performs firmware upgrade according to a matching result obtained by the waveform matching process, which may be referred to as the following exemplary description:

the determining unit 102 may be further configured to determine whether the load is in an operating state in a networking situation. The specific function and processing of the determination unit 102 are also referred to in step S410.

The determining unit 102 may be further specifically configured to, if the load is not in the working state, perform firmware upgrade on the detection model of the load according to a preset upgrade manner according to the candidate matching waveform of the load under the condition that the socket body is kept powered on and the load is powered off. The specific function and processing of the determination unit 102 are also referred to step S420.

The first case: the determining unit 102 retrieves the detection model of the load from the first model library, specifically: the detection model of the load corresponding to the type of the load is determined on the local side by the body of the socket, and the determined detection model of the load is retrieved from the first model library. Or the socket body uploads the load type to the server, and then receives a detection model of the load fed back to the socket body after the processing of the server; the processing of the server may include: the load detection model corresponding to the type of the load is determined, and after the determined load detection model is retrieved from the first model library, the load detection model is fed back to the socket body.

The second case: the determining unit 102 performs waveform matching processing on the start waveform data of the load, specifically: the start waveform data of the load is subjected to waveform matching processing at the local side by the local of the socket. Or the socket body uploads the waveform data of the load to the server, receives the waveform matching processing result fed back to the socket body after the server performs waveform matching processing on the starting waveform data of the load.

The third situation: the determining unit 102 upgrades the firmware according to the matching result obtained by the waveform matching process, specifically: and upgrading the firmware of the matching result obtained according to the waveform matching processing at the local side by the local part of the socket to obtain the detection model of the load after the firmware is upgraded. Or, the socket body firstly sends a no-working state instruction of which the load is not in a working state to the server, and then sends an upgrading instruction to the server after receiving a request upgrading instruction fed back by the server based on the no-working state instruction; and then, under the condition that the socket body is kept powered on and the load is kept powered off, the server upgrades the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset upgrading mode, and then the detection model of the load after firmware upgrading is fed back to the socket body.

In an alternative example, the detection unit 104 may be configured to perform arc fault detection on the load according to the matched detection model. The specific function and processing of the detection unit 104 are also referred to in step S130. For example: the corresponding detection can be carried out according to different load conditions, for example, each load corresponds to a specific detection model, and the detection precision is improved.

Since the processes and functions implemented by the apparatus of this embodiment substantially correspond to the embodiments, principles and examples of the method shown in fig. 1 to 4, the description of this embodiment is not detailed, and reference may be made to the related descriptions in the foregoing embodiments, which are not repeated herein.

Through a large number of tests, the technical scheme of the invention realizes on-line updating of the firmware by using remote on-line loading of the firmware, and the updating is convenient and reliable.

There is also provided, in accordance with an embodiment of the present invention, a receptacle corresponding to an arc fault detection apparatus. The socket may include: the arc fault detection device described above.

In an alternative embodiment, it is contemplated that: certain electrical safety accidents can be caused by overcurrent or overvoltage of the load, some components are burnt out if the load is light, and some fire accidents are caused if the load is heavy; the operating power and the operating voltage of each load have certain difference, which affects the accuracy of the intelligent socket for arc detection of faults such as arc and the like; then, if the arc detection is inaccurate, the overcurrent or overvoltage cannot be accurately protected. Therefore, the scheme of the invention considers how to complete the online updating of the socket firmware according to the type of the load, and is important for improving the accuracy of socket arc fault detection. Therefore, the scheme of the invention provides an interactive scheme for arc fault detection, so that the socket firmware can be updated on line according to the type of the load, and the accuracy of the arc fault detection of the socket is improved.

Optionally, in the solution of the present invention, remote online upgrade may be performed through a remote service, that is, the firmware is loaded remotely online, so as to update the firmware online.

Further optionally, in the scheme of the present invention, corresponding detection may be performed for different load conditions, for example, each load corresponds to a specific detection model, so as to improve the detection accuracy. That is, the scheme of the invention can select different firmware for detection according to different types of loads, thereby improving the precision.

In an alternative embodiment, a specific implementation process of the scheme of the present invention can be exemplarily described with reference to the examples shown in fig. 6 to 9.

Generally, home appliances or household appliances are subjected to product performance and quality detection when manufacturers leave a factory, and therefore each load has a starting waveform during operation. The waveform database on the server related in the scheme of the invention is recorded by a manufacturer before the product leaves a factory; the waveform database is mainly related to starting waveforms corresponding to different loads.

Fig. 6 may show an interactive flow of arc fault detection. The interactive flow of arc fault detection of aspects of the present invention may be illustrated with reference to the example shown in fig. 6.

As shown in fig. 6, an interactive method for arc fault detection provided by an aspect of the present invention may include:

step 11, when the load is started, the load generates a start waveform (e.g., start waveform data), and the smart socket collects the start waveform (e.g., start waveform data) of the load during the start of the load, where the collection time may be more than 1 minute.

And step 12, uploading the waveform collected when the load is started to a server by the intelligent socket, and comparing and judging the starting waveform of the load with a waveform database of the server.

That is to say, when the load is started, the intelligent socket acquires current or voltage waveform when the load is started, and uploads the current or voltage waveform to the server; the server takes the current or voltage waveforms and matches them to standard waveforms in a database.

Because the waveforms in the database are collected by a manufacturer when the load is delivered from a factory, the waveforms are used as standard waveforms of the database (the standard waveforms are collected under the working condition of normal standard); the waveform obtained by the intelligent socket when the load is started is the running waveform of the load, and the waveform is influenced by the factors of the family environment of the user and possibly different from the standard waveform, so the two waveforms need to be compared, namely the waveform obtained by the intelligent socket when the load is started is matched with the standard waveform in the database.

The database mainly comprises a plurality of specific waveforms in a load running state, and the specific waveforms simultaneously correspond to corresponding firmware information. The specific waveform is actually a waveform of the load in different operation states or operation modes, for example, an air conditioner is in an air supply state in a cooling mode when the air conditioner normally operates, and the waveform acquired under the standard working condition is the specific waveform of the operation state. The firmware information mainly relates to the principle of arc detection, and because different operating states affect various parameters of an arc detection model, the firmware information of each load under different operating states is also different.

Fig. 7 may show a server waveform matching process. The following may refer to the example shown in fig. 7, and an exemplary description is given of the process of server waveform matching in step 12.

As shown in fig. 7, the process of server waveform matching may include:

step 21, the smart socket acquires a current waveform or a power waveform when the load starts (the waveform collection time is from the time T1 to the time T2 when the load starts to start), the smart socket uploads the waveform to the server, and the server performs preprocessing on the uploaded waveform, wherein the preprocessing is mainly used for noise reduction.

The start waveform of the load is actually composed of data, the smart socket collects the start waveform of the load and then generates a waveform, and a certain amount of data can be collected at a certain frequency from time T1 to time T2. For example: the intelligent socket starts to collect waveforms at the T1 moment when the load is started; when the load starts to run to time T2, the smart socket uploads the acquired waveform to the server at this time.

Step 22, the server matches the preprocessed waveform, which may be to calculate an absolute error value from the preprocessed waveform and a primary current value or a power value of a pre-stored waveform in the same time in a waveform library (i.e. a waveform database). Here, the time axes of the two waveforms for obtaining the absolute error value are the same, for example, the two waveforms are collected at the same time of 1s at the time of starting, and then data comparison is performed.

Specifically, waveforms preprocessed by the server are matched, and absolute error values Q are sequentially obtained from the acquired waveforms and primary current values or power values of the waveforms in each time in a waveform library_nAccumulating the absolute error values to obtain Q, and comparing and judging Q andpreset threshold value Q_{Preset of}。

For example: the server receives the waveform to be matched, preprocesses the waveform to be matched, and sequentially calculates the absolute error value Q of the current value or the power value of the preprocessed waveform and the waveform of the waveform database at the same moment_nAnd accumulating the absolute error value of each current value or power value to obtain Q ═ Q₁+Q₂+…+Q_n(n is a natural number), and n represents the nth accumulation; comparing Q and Q_{Preset of}The value of (c). Wherein Q is an accumulated value of absolute error values of current values or power values n times, Q_{Preset of}Is a preset value of the absolute error value of the current value or the power value.

Step 23, judging whether Q is more than Q_{Preset of}(ii) a If so, go to step 24, otherwise, go to step 25.

And 24, accumulating the times of the waveform to be matched exceeding a preset threshold value at each time measurement value, recording the current accumulation times, and selecting the maximum accumulation times, wherein the waveform is represented as a candidate matching waveform. Wherein, the accumulation times are the fitting degree of the waveform curve and the preset waveform curve during the operation, if the comparison error of each time point is smaller, the Q is required to be more than Q_{Preset of}The more times, the more matched the curve, the maximum number of accumulations over a certain period.

For example: when Q is greater than Q_{Preset of}At the time, the number of times k of accumulation at this time is recorded_p. Comparison k₁、k₂、k₃…k_pThe maximum k value is selected, and the corresponding waveform is the matched waveform. Wherein k is₁、k₂、k₃…k_pIs the accumulated number of times at different times. Wherein, each operation is automatically added with one to know Q>Q_{Preset of}At that time, the value of k is calculated, e.g., Q ═ Q₁+Q₂+Q₃At this time Q>Q_{Preset of}Then, it means that the value of k is 3, and if Q ═ Q₁+Q₂+Q₃+Q₄When is, Q>Q_{Preset of}Then, k is 4.

And 25, continuously accumulating the absolute errors of the current values or the power values corresponding to the respective times, and continuously increasing the accumulation times.

And step 13, the server interacts with the intelligent socket according to the matching result, and after the information of the two parties is confirmed to be successful, the successfully matched firmware is subjected to remote firmware upgrade.

Specifically, after the server is successfully matched, the server sends a matching result to the intelligent socket, if the intelligent socket receives information of successful matching, the socket can send an online upgrade command to the server in an idle state, and the server obtains the online upgrade command sent by the socket to start firmware upgrade.

Fig. 8 may show a firmware upgrade flow. The following describes an exemplary flow of firmware upgrade in step 13 with reference to the example shown in fig. 8.

As shown in fig. 8, the firmware upgrade procedure may include:

step 31, determining whether networking is successful; if yes, go to step 32; otherwise, step 36 is executed, i.e. the network is reconfigured.

For example: the smart jack connection network may be in network communication with a server.

And step 32, the server acquires the working state of the intelligent socket, and the intelligent socket acquires current or voltage data to judge whether the load is in the working state. If yes, go to step 33; otherwise, the process returns to step 31, i.e. the distribution network is re-authenticated.

For example: the server acquires the working state of the intelligent socket, the working state indicates whether the load works at the moment, and the firmware upgrading and other operations can be carried out only if the socket does not work.

For example: the intelligent socket judges whether the load is in a working state or not according to the current or the voltage of the load, and can judge the running condition of the load only by judging the magnitude of the current and capturing the information of the current or the voltage.

Specifically, whether networking is successful or not is verified, and if networking is successful, the server acquires working state information from the intelligent socket, which is equivalent to whether the load works or not.

And step 33, the intelligent socket sends a no-work state instruction to the server S1, and the server sends a request upgrading instruction after receiving the no-work state instruction S2.

Specifically, if the load is not turned on at this time, the smart socket sends a no-working-state command S1 to the server, and the server sends a request upgrading command S2 after receiving S1.

And step 34, the intelligent socket receives the instruction S2 and sends an upgradable instruction S3 to the server, the server starts firmware upgrade after receiving the instruction S3, and the intelligent socket is not allowed to be powered off and the load is not powered on in the firmware upgrade process.

Specifically, after receiving S2, the smart socket feeds back a corresponding upgradable command S3, the server starts firmware upgrade, during the firmware upgrade, the smart socket should remain in a power-on state, and the load is in a power-off state to wait for the server to estimate that the upgrade is completed.

For example: the server initiates a firmware upgrade, which may include: the server stores corresponding firmware under different waveforms, the running state of the load can be determined when the waveforms are matched, if the running state of the load is refrigeration according to the waveform matching, related refrigeration firmware is loaded, and the firmware loading is to communicate by using a network channel of the server and the intelligent socket.

Step 35, judging whether the intelligent socket receives a firmware upgrading completion instruction S4 sent by the server; if yes, ending the current firmware upgrading process; otherwise, return to step 31.

Specifically, whether the firmware of the waiting server is upgraded is judged, and if the intelligent socket receives the upgrade completion instruction S4, the firmware upgrade is completed.

During the firmware upgrade of the server shown in step 34, the socket cannot be in a working state, and after the server is successfully upgraded, an upgrade success instruction is sent to the smart socket, and after the smart socket shown in step 35 obtains the upgrade success instruction, the upgrade request is closed, and a normal protection function is turned on, for example: the normal fault arc protection function is switched on, and with respect to the normal fault arc protection function, reference can be made to the examples shown in fig. 9 and 10.

Optionally, during the socket firmware upgrade in step 34, the user may be notified in a user prompting or other screen display manner that the socket is upgrading the firmware at the time, the user prompting may be in a manner of prompting through mobile phone APP information, and the like, and at the same time, the socket may also send a signal of upgrading the firmware to the load, and after the upgrade is completed, the load clears the socket firmware upgrade signal, and the load starts to operate.

And when the socket firmware upgrading fails, the socket can resend the online upgrading instruction and resume the firmware upgrading process. For example: the distribution network may be re-authenticated to resume the firmware upgrade process as shown in step 31 of fig. 8.

Optionally, after the firmware is upgraded, when a normal protection function is started, when a load is abnormal (for example, a fault arc occurs in the load), the socket sends abnormal data to the server, the server stores the abnormal data, a specific data abnormal library under the waveform is established, a background server worker can analyze the data abnormal library, analyze possible causes of the abnormality, for example, data abnormality caused by generally low power grid quality of the area or other power grid conditions, and adjust model parameters of the firmware according to the analyzed causes and local actual conditions.

The background server personnel can analyze the data abnormal library, generally intelligently analyze the data abnormal library, and the analysis can be realized by a statistical method.

When the normal protection function is turned on, the detection of the fault arc can be referred to the arc fault detection and arc fault discrimination flow shown in fig. 9 and 10.

Fig. 9 may show an arc fault detection process.

In fig. 9, the smart socket first collects N current data in one cycle and records the data as I₀、I₁、….I_N-1(ii) a While expressing the normal current data of the load as IO₀、IO₁...IO_N-1Calculating the average value I of the current data_aveAnd counting the number N of zero-value currents_zeroN is naturalAnd (4) counting.

As shown in fig. 9, the fault arc detection process may include:

and step 41, collecting current data of one period.

Step 42, calculating the average value I of the current data_aveAnd counting the number N of zero-value currents_zero。

Wherein the average value I of the current data is calculated_aveAnd counting the number N of zero-value currents_zeroThe specific method of (3) can be shown as the following formula:

I_ave＝(I₀+I₁+…I_N-1)/N (1)；

N_zero＝Z₁+Z₂+…+Z_k(k＝1、2…N) (2)。

Z_kthere are two cases: when I_k|<a|IO|_maxWhen Z is_k1(a is a coefficient less than 1); conversely, Z_k＝0。

Wherein Z is_kIs the flag bit of the current data (zero current) around 0 value, a can be a very small value less than 1, we can count a | IO |_maxWhen a value is made closer to a value of 0, when I_k|<a|IO|_maxWhen there is a current data around 0, then Z is used_kSince 1 is expressed, the formula (2) can count current data around 0.

And 43, calculating a current peak value C of the current period and a change difference value △ D of the maximum change rate of the current period.

The Sum of the differences between the N normal current data and the N collected current data within the current period, Sum ═ I₀-IO₀|+|I₁-IO₁|+|I₂-IO₂|+…|I_N-1-IO_N-1|。

Comparing the current I of the current cycle₀、I₁、….I_N-1Maximum value of (1)_maxComparing the current period normal current IO₀、IO₁...IO_N-1Maximum value of IO_maxCalculating the change condition of the current peak value C of the current period：C＝|I_max-IO_max|。

Calculating the change condition of the maximum change rate of the current in the current period, wherein △ I_k＝I_k-I_k-1Representing the difference between two adjacent current values, and comparing the maximum values of these differences △ I_kThe maximum value D (where k is 1, 2 … N) in the current period is obtained, and the change difference △ D of the maximum rate of change DO of the normal current in the current period is | D-DO |.

And step 44, judging the threshold value of the calculated numerical value.

Specifically, after the above calculation, logical judgment, that is, arc fault judgment may be performed on the relevant parameters.

Fig. 10 and 11 may show an arc fault determination process.

As shown in fig. 10 and 11, the process of determining the arc fault may include:

step 51, firstly, judging whether the value of the Sum of the differences between the N normal current data and the N collected current data in the current period is greater than a predetermined first threshold value: if yes, go to step 52; otherwise, step 55 is executed, i.e. when the value of Sum is smaller than the predetermined first threshold, the IO is refreshed again₀、IO₁...IO_N-1The value of (c).

Step 52, judging the length of the flat shoulder, such as judging the number N of zero-value currents_zeroWhether greater than a predetermined second threshold: if yes, go to step 53; otherwise, step 56 is executed, i.e. when the length of the flat shoulder (e.g. the number of zero current N)_zero) Update IO when less than a predetermined second threshold₀、IO₁...IO_N-1The value of (c).

Step 53, when the length of the shoulder is flat (e.g. the number N of zero-value current)_zero) When the current value is larger than the preset second threshold value, starting to judge the change condition of the current peak value C in the current period, and if the current peak value C in the current period is larger than a third threshold value: if yes, go to step 56, i.e. when the variation is greater than the predetermined third threshold (e.g. the current cycle current peak value C is greater than the third threshold), it indicates that a fault arc is generated; otherwise, step 54 is performed.

Wherein, the number N of zero-value currents is counted_zeroThe number of zero-valued currents is also compared with the length of the flat shoulder.

Step 54, when the variation is smaller than a predetermined third threshold (e.g. the current period current peak value C is smaller than the third threshold), the IO is updated again₀、IO₁...IO_N-1If yes, step 56 is executed, namely, when the maximum change rate △ D of the normal current in the current period is larger than the preset fourth threshold value, the generation of the fault arc is indicated, otherwise, the IO arc is renewed₀、IO₁...IO_N-1The value of (c).

And step 45, counting the number of the fault cycles.

For example: the flat shoulder refers to the number of values of a plurality of current data in a fixed period around 0 in the process of arc detection. For example, if the number of values of the continuously detected current value in the vicinity of 0 is 10, it can be said that there is a flat shoulder, and the flat shoulder threshold referred to later is the number of values of the set zero-value current. Failure cycles, for example: a failure cycle if the condition in fig. 10 is satisfied.

Since the processes and functions implemented by the socket of this embodiment substantially correspond to the embodiments, principles and examples of the apparatus shown in fig. 5, the description of this embodiment is not given in detail, and reference may be made to the related descriptions in the foregoing embodiments, which are not described herein again.

Through a large number of tests, the technical scheme of the invention is adopted to carry out remote online upgrade through remote service, realize online firmware update, determine a corresponding specific detection model for each load, and be beneficial to improving the accuracy of arc fault detection for each load.

There is also provided, in accordance with an embodiment of the present invention, a computer-readable storage medium corresponding to an arc fault detection method. The computer-readable storage medium may include: the computer readable storage medium having stored therein a plurality of instructions; the plurality of instructions for being loaded by a processor and performing the arc fault detection method described above.

Since the processes and functions implemented by the computer-readable storage medium of this embodiment substantially correspond to the embodiments, principles, and examples of the method shown in fig. 1 to fig. 4, reference may be made to the related descriptions in the foregoing embodiments for details which are not described in detail in the description of this embodiment, and thus are not described herein again.

Through a large number of tests, the technical scheme provided by the invention can improve the arc fault detection precision aiming at different loads and improve the protection reliability when arc fault protection is carried out aiming at different loads by selecting different firmware for detection according to different types of loads.

There is also provided, in accordance with an embodiment of the present invention, a receptacle corresponding to a method of arc fault detection. The socket may include: a processor for executing a plurality of instructions; a memory to store a plurality of instructions; wherein the plurality of instructions are configured to be stored by the memory and loaded by the processor and to perform the arc fault detection method described above.

Since the processes and functions implemented by the socket of this embodiment substantially correspond to the embodiments, principles and examples of the methods shown in fig. 1 to 4, the description of this embodiment is not detailed, and reference may be made to the related descriptions in the foregoing embodiments, which are not repeated herein.

Through a large number of tests, the technical scheme of the invention is adopted, and each load corresponds to a specific detection model, so that the detection precision can be improved, and the load operation safety can be improved.

In summary, it is readily understood by those skilled in the art that the advantageous modes described above can be freely combined and superimposed without conflict.

The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. An arc fault detection method, comprising:

determining characteristic information of a load to be subjected to arc fault detection;

according to the characteristic information of the load, matching a detection model for carrying out arc fault detection on the load;

and according to the detection model obtained by matching, carrying out arc fault detection on the load.

2. The method of claim 1, wherein the characteristic information of the load comprises: the type of load, or the start waveform data of the load;

determining characteristic information of a load to be arc fault detected, comprising:

acquiring the type of a load to be subjected to arc fault detection, and taking the type of the load as characteristic information of the load;

alternatively, the first and second electrodes may be,

acquiring starting waveform data in a load starting process to take the starting waveform data of the load as characteristic information of the load;

the method for acquiring starting waveform data in the load starting process comprises the following steps: in the process of starting the load, the waveform data of the load is collected by taking more than first set time as one collection time, and the waveform data obtained in the collection time is used as one starting waveform data of the load.

3. The method of claim 1, wherein matching a detection model for arc fault detection of the load comprises:

when the characteristic information of the load includes the type of the load, determining a set detection model corresponding to a set type which is the same as the type of the load in the correspondence as a detection model of the load corresponding to the type of the load according to the correspondence between the set type and the set detection model; and the number of the first and second groups,

under the condition that a preset first model base has a detection model of the load, directly calling the detection model of the load from the first model base;

or under the condition that the preset first model base does not have the detection model of the load, updating the detection model of the load into the preset first model base in a local loading or remote upgrading mode, and then calling the detection model of the load from the first model base;

alternatively, the first and second electrodes may be,

and under the condition that the characteristic information of the load comprises starting waveform data of the load, carrying out waveform matching processing on the starting waveform data of the load, and upgrading firmware according to a matching result obtained by the waveform matching processing to obtain a detection model of the load.

4. The method of claim 3, wherein performing waveform matching on the start-up waveform data of the load comprises:

preprocessing starting waveform data of the load to obtain first waveform data;

calculating an absolute error value of the first waveform data and standard waveform data acquired at the same time point in a preset waveform database; accumulating absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points to obtain a total absolute error value obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points within one acquisition time;

recording the accumulation times of absolute error values of different time points in the total absolute error value when the total absolute error value is greater than a preset error threshold under the condition that the total absolute error value is greater than the preset error threshold; comparing the accumulated times, and determining the first waveform data corresponding to the maximum accumulated time in the accumulated times as a candidate matching waveform of the load;

or under the condition that the total absolute error value is less than or equal to the preset error threshold, continuously accumulating the absolute error values obtained based on the first waveform data acquired at different time points and the standard waveform data corresponding to the time points, and continuously recording the accumulation times of the absolute error values at different time points in the total absolute error value when the total absolute error value is greater than the preset error threshold.

5. The method of claim 3, wherein upgrading firmware according to the matching result obtained from the waveform matching process comprises:

determining whether the load is in an operating state;

and if the load is not in a working state, under the condition that the socket body is kept powered on and the load is powered off, upgrading the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset upgrading mode.

6. The method according to any one of claims 3 to 5, wherein,

the method for detecting the load comprises the following steps of calling a detection model of the load from the first model library, specifically:

determining, by the socket body, a detection model of the load corresponding to the type of the load on a local side, and retrieving the determined detection model of the load from the first model library; alternatively, the first and second electrodes may be,

the socket body uploads the load type to a server, and then receives a detection model of the load fed back to the socket body after the detection model is processed by the server; the processing of the server comprises: determining a detection model of the load corresponding to the type of the load, calling the determined detection model of the load from a first model library, and feeding back the detection model of the load to the socket body;

the socket body is used for carrying out arc fault detection on the load by utilizing the detection model of the load after the detection model of the load is called from the first model library;

and/or the presence of a gas in the gas,

and performing waveform matching processing on the starting waveform data of the load, specifically:

carrying out waveform matching processing on the starting waveform data of the load at the local side by the local part of the socket; alternatively, the first and second electrodes may be,

the method comprises the steps that waveform data of a load are uploaded to a server by a socket body, then waveform matching processing results fed back to the socket body after the server performs waveform matching processing on starting waveform data of the load are received;

and/or the presence of a gas in the gas,

upgrading the firmware according to a matching result obtained by the waveform matching processing, which specifically comprises the following steps:

upgrading the firmware of the matching result obtained according to the waveform matching processing at the local side by the local part of the socket to obtain a detection model of the load after the firmware is upgraded; alternatively, the first and second electrodes may be,

the socket body firstly sends a no-working-state instruction of which the load is not in a working state to a server, and then sends an upgrading instruction to the server after receiving a request upgrading instruction fed back by the server based on the no-working-state instruction; then, under the condition that the socket body is kept powered on and the load is kept powered off, the server updates the firmware of the detection model of the load according to the candidate matching waveform of the load and a preset updating mode, and feeds back the detection model of the load after the firmware is updated to the socket body;

the socket body is used for displaying an upgrading process when firmware is upgraded; and arc fault detection is carried out on the load by utilizing the detection model of the load after the firmware is upgraded.

7. An arc fault detection device, comprising:

the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for determining characteristic information of a load to be subjected to arc fault detection;

the determining unit is further used for matching a detection model for arc fault detection of the load according to the characteristic information of the load;

and the detection unit is used for carrying out arc fault detection on the load according to the detection model obtained by matching.

8. The apparatus of claim 7, wherein the characteristic information of the load comprises: the type of load, or the start waveform data of the load;

the determining unit determines characteristic information of a load to be subjected to arc fault detection, including:

alternatively, the first and second electrodes may be,

the determining unit acquires starting waveform data in a load starting process, and the determining unit comprises the following steps: in the process of starting the load, the waveform data of the load is collected by taking more than first set time as one collection time, and the waveform data obtained in the collection time is used as one starting waveform data of the load.

9. The apparatus of claim 7, wherein the determining unit matches a detection model for arc fault detection of the load, comprising:

alternatively, the first and second electrodes may be,

10. The apparatus of claim 9, wherein the determining unit performs waveform matching processing on the start waveform data of the load, and comprises:

preprocessing starting waveform data of the load to obtain first waveform data;

11. The apparatus according to claim 9, wherein the determining unit performs firmware upgrade according to a matching result obtained by the waveform matching process, and includes:

determining whether the load is in an operating state;

12. The apparatus of any one of claims 9 to 11, wherein,

the determining unit calls the detection model of the load from the first model library, specifically:

and/or the presence of a gas in the gas,

the determining unit performs waveform matching processing on the starting waveform data of the load, specifically:

and/or the presence of a gas in the gas,

the determining unit upgrades the firmware according to the matching result obtained by the waveform matching processing, and specifically comprises the following steps:

13. A socket, comprising: the arc fault detection device of any of claims 7-12;

alternatively, the first and second electrodes may be,

the method comprises the following steps:

a processor for executing a plurality of instructions;

a memory to store a plurality of instructions;

wherein the plurality of instructions for loading and executing by the processor the arc fault detection method of any of claims 1-6.

14. A computer-readable storage medium having a plurality of instructions stored therein; the plurality of instructions for being loaded by a processor and for performing the arc fault detection method of any of claims 1-6.