CN111650922A - Smart home abnormity detection method and device - Google Patents
Smart home abnormity detection method and device Download PDFInfo
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- CN111650922A CN111650922A CN202010464430.4A CN202010464430A CN111650922A CN 111650922 A CN111650922 A CN 111650922A CN 202010464430 A CN202010464430 A CN 202010464430A CN 111650922 A CN111650922 A CN 111650922A
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/54—Testing for continuity
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/56—Testing of electric apparatus
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Abstract
The invention provides an intelligent home abnormity detection method and device, and relates to the technical field of intelligent home. The method comprises the steps of obtaining real-time operation data of the household equipment, and matching the real-time operation data according to a time sequence to generate a data characteristic curve. And matching the data characteristic curve based on the first state curve, and sending fault state information to the client and the operation and maintenance terminal when the equipment is determined to be in the operation fault state. When the equipment is determined to be in a normal operation state, the operation state of the household equipment is predicted according to the abnormal recognition rule and the data characteristic curve, the accuracy of abnormal operation detection of the equipment is improved, and corresponding abnormal state information is determined. The abnormal state information is transmitted to the client and the operation and maintenance terminal in a targeted manner, and early warning can be provided for the client. Meanwhile, the operation and maintenance terminal can take equipment fault measures in advance, and the system performance of the intelligent home is improved.
Description
Technical Field
The invention relates to the technical field of intelligent home, in particular to an intelligent home abnormity detection method and device.
Background
The smart home is embodied in an internet of things manner under the influence of the internet of things, and various home devices (such as audio and video devices, lighting system devices, curtain control devices, air conditioner control devices, security system devices and the like) in the home are connected together by the smart home through the internet of things technology to form a smart home system. At present, with the continuous development of smart homes, in order to provide better experience for users, home device self-checking methods and apparatuses are developed on the market, and are used for supporting periodic device self-checking state reporting according to device types, and reminding users of replacing damaged devices and handling abnormal states of devices.
However, in the conventional smart home abnormal state detection, the current state of the device is usually determined only according to the current state of the device, and the abnormal state of the device cannot be predicted and accurately determined, which may also cause false alarm and false negative alarm of the abnormal state of the device.
Disclosure of Invention
In view of the above problems, the present invention is proposed to provide a smart home anomaly detection method and apparatus that overcomes or at least partially solves the above problems.
According to a first aspect of the present invention, a smart home anomaly detection method is provided, where the method includes:
acquiring real-time operation data of the household equipment;
analyzing the real-time operation data to determine a data characteristic curve;
matching the data characteristic curve, and determining the running state of the household equipment, wherein the running state comprises the following steps: running in a normal state;
and under the condition that the running state is a normal running state, predicting abnormal state information of the household equipment according to an abnormal recognition rule and the data characteristic curve.
According to a second aspect of the present invention, there is provided an intelligent home abnormality detection apparatus, the apparatus comprising:
the data acquisition module is used for acquiring real-time operation data of the household equipment;
the data analysis module is used for analyzing the real-time operation data and determining a data characteristic curve;
the state matching module is used for matching the data characteristic curve and determining the running state of the household equipment, wherein the running state comprises the following steps: running in a normal state;
and the state prediction module is used for predicting the abnormal state information of the household equipment according to an abnormal recognition rule and the data characteristic curve under the condition that the running state is a normal running state.
According to the scheme, the real-time operation data of the household equipment are obtained, and the real-time operation data are matched according to the time sequence to generate the data characteristic curve. And matching the data characteristic curve based on the first state curve, and sending fault state information to the client and the operation and maintenance terminal when the equipment is determined to be in the operation fault state. When the equipment is determined to be in a normal operation state, the operation state of the household equipment is predicted according to the abnormal recognition rule and the data characteristic curve, the accuracy of abnormal operation detection of the equipment is improved, and corresponding abnormal state information is determined. The abnormal state information is transmitted to the client and the operation and maintenance terminal in a targeted manner, and early warning can be provided for the client. Meanwhile, the operation and maintenance terminal can take equipment fault measures in advance, and the system performance of the intelligent home is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings.
In the drawings:
fig. 1 is a flowchart illustrating steps of a smart home anomaly detection method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of another smart home anomaly detection method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a generated data characteristic curve according to an embodiment of the present invention;
fig. 4 is a block diagram of an intelligent home anomaly detection device according to an embodiment of the present invention;
fig. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring to fig. 1, a flowchart illustrating steps of a smart home anomaly detection method provided by an embodiment of the present invention is shown, where the method may include:
In the embodiment of the invention, the real-time operation data can be acquired from each household device through the server. The detection can be carried out in the running process of the household equipment, so that the physical values of the equipment, such as voltage, current and the like, are obtained and serve as real-time running data, and the real-time running data are uploaded to the server. The real-time operation data refers to real-time data in an operation process of the device, and in this embodiment, the real-time operation data may be various data that may affect a state of the device. The type of the real-time operation data may include a current class, a voltage class, and the corresponding real-time operation data may include a current data, a voltage data, and the like.
And 102, analyzing the real-time operation data to determine a data characteristic curve.
In the embodiment of the invention, each real-time operation data of the household equipment can be matched according to the time sequence to obtain the operation characteristic information capable of reflecting the characteristics of the corresponding operation data, such as a directed line segment, a characteristic vector and the like corresponding to a certain type of real-time operation data. And obtaining a data characteristic curve according to the operation characteristic information. The data characteristic curve refers to a curve that can characterize the specified type of operational data during the operation of the device. For example, the analysis may be performed according to the type of the real-time operation data, for example, the voltage fluctuation information may be analyzed in time sequence to obtain a corresponding voltage variation curve, and the current variation may be analyzed in time sequence to obtain a corresponding current variation curve.
And 103, matching the data characteristic curve to determine whether the running state of the household equipment is a normal running state.
In the embodiment of the present invention, a first characteristic curve may be preset, and the first characteristic curve may be understood as a characteristic curve generated according to operation data when the device operates normally. The first characteristic curve can be generated as a first characteristic curve by collecting the operation data of the household equipment in a normal operation state and analyzing the change of corresponding data in the operation process, the first characteristic curve can be divided into characteristic curves corresponding to various types according to the types of the data, and each characteristic curve can describe the change characteristics of the corresponding type of data in the normal operation of the equipment. In some examples, the change of the curve may not be associated with a specific value, but describe a trend of the change, and the like, and of course, in combination with a specific device, such as a specific model of device, the change may also be determined by associating a value, which may be determined according to a requirement and an actual operation of the device, and this is not limited in the embodiment of the present application.
Therefore, the change states corresponding to various types of operation data in the normal operation state of the equipment can be determined based on the first characteristic curve, the data characteristic curve determined according to the corresponding types of real-time operation data can be matched with the first characteristic curve, and the operation states of the household equipment can be determined, such as normal operation, fault occurrence and the like, wherein the operation states can include an operation normal state or an operation fault state.
If the operation state is the normal operation state, executing step 104; otherwise, step 105 is performed.
104. And predicting abnormal state information of the household equipment according to an abnormal recognition rule and the data characteristic curve.
In the embodiment of the invention, the operation data corresponding to the failed equipment can be collected, historical operation data can be collected according to the types of the operating failure states of the household equipment, such as short circuit, power failure, damage and the like, and then the characteristic curves corresponding to the types of failures are respectively determined, which can be called as second characteristic curves.
After a fault occurs, the equipment may stop operating, and before the equipment stops operating due to the fault, the operation data of the equipment usually has some changes, for example, a short circuit is caused due to excessive current, the current may increase over a certain amplitude within a set time, and the like, so that the operation data before and during the fault occurs are collected and analyzed to obtain a second characteristic curve. Whether the equipment is likely to fail can be predicted by matching with each second characteristic curve based on real-time operation data of the equipment.
And matching a certain second characteristic curve, and determining the corresponding two-point characteristics, wherein one is the fault state type, and the other is the curve corresponding to which data type. Thus, it is determined that the device may fail, and the corresponding fault status type and data type may be obtained. Wherein, the fault state type can be represented by codes, such as characters a, b, c and the like, wherein a represents short circuit of the equipment, b represents power failure of the equipment, c represents damage of the equipment and the like. For example, the voltage class may be represented by the character U and the current class may be represented by the character I.
The information corresponding to the two types can be sent to the client and the operation and maintenance terminal as abnormal state information, early warning can be provided for a user, and meanwhile, the abnormal state information can also be sent to the operation and maintenance terminal, so that the operation and maintenance terminal can take equipment fault countermeasures in advance, and the system performance of the smart home is improved.
And 105, determining that the household equipment is in an operation fault state, and obtaining fault state information.
In the embodiment of the invention, the failure of the household equipment can be determined without matching the corresponding first characteristic curve. Therefore, information representing equipment failure, such as a failure mark, can be sent to the client and the operation and maintenance terminal as failure state information.
In summary, the data characteristic curve is generated by acquiring the real-time operation data of the household equipment and matching the real-time operation data according to the time sequence. And matching the data characteristic curve based on the first state curve, and sending fault state information to the client and the operation and maintenance terminal when the equipment is determined to be in the operation fault state. When the equipment is determined to be in a normal operation state, the operation state of the household equipment is predicted according to the abnormal recognition rule and the data characteristic curve, the accuracy of abnormal operation detection of the equipment is improved, and corresponding abnormal state information is determined. The abnormal state information is transmitted to the client and the operation and maintenance terminal in a targeted manner, and early warning can be provided for the client. Meanwhile, the operation and maintenance terminal can take equipment fault measures in advance, and the system performance of the intelligent home is improved.
Referring to fig. 2, a flowchart illustrating steps of another smart home anomaly detection method provided in an embodiment of the present invention is shown, where the method may include:
In the embodiment of the invention, the real-time operation data can be acquired from each household device through the server. The detection can be carried out in the running process of the household equipment, so that the physical values of the equipment, such as voltage, current and the like, are obtained and serve as real-time running data, and the real-time running data are uploaded to the server. The real-time operation data refers to real-time data in an operation process of the device, and in this embodiment, the real-time operation data may be various data that may affect a state of the device. The type of the real-time operation data may include a current class, a voltage class, and the corresponding real-time operation data may include a current data, a voltage data, and the like.
Analyzing the real-time operation data to determine a data characteristic curve, comprising the following steps: step 202-.
In the embodiment of the invention, each real-time operation data of the household equipment can be matched to obtain the characteristics capable of reflecting the corresponding operation data, such as the change trend of the real-time operation data and the data attribute with the same change trend, wherein the change trend can be the change of the voltage data according to the corresponding size of the time sequence. The data attribute may be understood as a numerical type, such as a start point, an end point, a maximum value, a minimum value, an average value, etc., that describes the state of the N real-time operating data of a single trend segment.
For example, the change trend of each real-time operation data can be determined by presetting the state identification type. If the state identification types can include rising, falling, leveling and the like, after the real-time operation data is obtained, the fluctuation analysis is carried out on the ith-1 and ith corresponding voltage values:
if X (i) -X (i-1) > 0, the ith real-time operation data is ascending.
If X (i) -X (i-1) ═ 0, then the data is leveled for the ith real-time run.
If X (i) -X (i-1) < 0, the data is decreased for the ith real-time operation.
Therefore, each real-time operation data can be matched according to the type of the state identifier, and N real-time operation data continuously marked by the same type are represented by morpheme symbols. Morpheme notation may be understood as a description of the overall trend of the N real-time operating data. The type of morpheme symbol may be a class of numeric symbols such as-2, -1, 0, 1, 2, 3, etc. For example, if 5 voltage data are matched and the corresponding status identifier types are all descending, a morpheme symbol-1 may be used to represent a descending segment corresponding to the voltage data. Accordingly, a morpheme symbol 0 is used to represent a smooth segment corresponding to the voltage data. The morpheme symbol 1 is used to represent a rising segment corresponding to the voltage data. In addition, a morpheme symbol 2 can be used for representing the maximum voltage value in each real-time operation data, and a morpheme symbol-2 can be used for representing the minimum voltage value in each real-time operation data. Meanwhile, in order to expand the error allowable range and improve the flexibility of data matching, a morpheme symbol 3 can be adopted, which is equivalent to any one of morpheme symbols in-2, -1, 0, 1 and 2.
Therefore, according to the morpheme symbol and the data attribute, the feature vector of the corresponding voltage data can be obtained. The expression is Z ═ J, M0, M1, M2. Wherein Z is a feature vector; j is a morpheme symbol; m0, M1,. and Mn-1 may be data attributes corresponding to morpheme values.
And step 203, forming a data characteristic curve by using the characteristic vectors according to the time sequence.
In the embodiment of the present invention, the start point and the end point of each feature vector may be sequentially connected according to a time sequence, so as to form a data feature curve. The data characteristic curve may be a curve characterizing a change in voltage data during operation of the device. For example, the acquired voltage data is analyzed according to a time sequence to obtain the following 4 feature vectors:
ZA＝(-2，M0，M1，...，Mn-1)；ZB＝(1，M0，M1，...，Mn-1)；
ZC＝(0，M0，M1，...，Mn-1)；ZD＝(-1，M0，M1，...，Mn-1)。
to ZA、ZB、ZCAnd ZDConnecting, referring to FIG. 3, an example of a data characteristic curve is shown, resulting in a data characteristic curve from point A to point D.
The data characteristic curve can also form a curve characteristic by only adopting morpheme symbols, and describes the change trend from the point A to the point D, such as (-2, 1, 0, -1). Wherein, -2 represents the wave trough of the A point corresponding curve; 1 represents the ascending section of the curve corresponding to the point A to the point B; 0 represents a smooth segment of the corresponding curve from point B to point C; -1 represents the descending segment of the curve corresponding from point C to point D.
And 204, matching the data characteristic curve according to the operation identification rule, and determining whether the operation state of the household equipment is a normal operation state.
The operation identification rule is determined according to a first characteristic curve, and the first characteristic curve is determined according to historical operation data of the household equipment which normally operates.
In the embodiment of the invention, a first characteristic curve can be preset, and the first characteristic curve can be understood as a characteristic curve generated according to historical voltage operation data when equipment normally operates. The first characteristic curve can be used for collecting voltage data of the household equipment in a normal operation state and analyzing the change of the voltage data of the equipment in an operation process, so that the voltage change curve is used as the first characteristic curve, and each first characteristic curve forms an operation identification rule.
Therefore, the change state corresponding to the voltage data in the normal operation state of the equipment can be determined based on the first characteristic curve, the data characteristic curve determined according to the real-time operation data of the corresponding voltage class can be matched according to the operation identification rule, and the operation state of the household equipment can be determined, such as normal operation, fault and the like, wherein the operation state can comprise an operation normal state or an operation fault state.
For example, the change of the first characteristic curve may not be associated with specific data, but only describe a trend of the change, for example, for a certain household device, the curve change characteristic of the first characteristic curve corresponding to the normal operation of the household device is (-2, 1, 0, 3), and the characteristic vector Z is usedA、ZB、ZCAnd ZDThe curve change characteristics (-2, 1, 0, -1) of the formed data characteristic curve are matched with the data characteristic curve, and the running state of the household equipment can be determined to be a normal running state.
If the operation state is the normal operation state, step 205 is executed; otherwise, step 208 is performed.
And step 205, matching the data characteristic curve with each second characteristic curve in the abnormal recognition rule respectively.
And the second characteristic curve is determined according to historical operating data of the household equipment with the fault.
In the embodiment of the invention, the operation data corresponding to the failed equipment can be collected, historical operation data can be collected according to the types of the operating failure states of the household equipment, such as short circuit, power failure, damage and the like, and then the characteristic curves corresponding to the types of the failures are respectively determined to be used as the second characteristic curves. The reason of each type of fault is different, so the second characteristic curve corresponding to each type may include a plurality of characteristic curves, and each second characteristic curve constitutes an abnormality identification rule.
After a fault occurs, the equipment may stop operating, and before the equipment stops operating due to the fault, the operation data of the equipment usually has some changes, for example, a short circuit is caused due to excessive current, the current may increase over a certain amplitude within a set time, and the like, so that the operation data before and during the fault occurs are collected and analyzed to obtain a second characteristic curve. Whether the equipment is likely to fail can be predicted by matching with each second characteristic curve based on real-time operation data of the equipment.
For example, when the short circuit occurs in the equipment, before the equipment stops operating, the voltage value of the equipment is sharply reduced within a set time, the corresponding current value is sharply increased, data corresponding to two data types before the short circuit fault occurs can be respectively collected, and the second characteristic curves corresponding to different data types can be obtained through analysis. The data profile may be matched to the second profile according to the corresponding data type to predict whether the device will fail.
And step 206, determining the matching degree of the data characteristic curve and the second characteristic curve.
In the embodiment of the invention, for a certain household device, the change is determined by correlating the data corresponding to the real-time operation data, so that the accuracy of predicting the operation state of the household device can be improved. For example, the data characteristic curve may have a partial match with the second characteristic curve, and therefore, when the operation data corresponding to the failed device is collected, the characteristic vectors corresponding to the historical operation data may be generated, and the characteristic vectors may be formed into the second characteristic curve in time series.
In this way, for a certain matched second characteristic curve, a corresponding characteristic vector can be determined, and thus, the characteristic vector can be matched with the characteristic vector corresponding to the data characteristic curve, so that the number of differences between the morpheme symbol J and any one or more of the data attributes M0, M1. According to the difference information, a matching degree of the data characteristic curve and the second characteristic curve is obtained, and the matching degree can be a similarity of each data change in the data characteristic curve and the second characteristic curve, such as 50%, 70% and the like.
And step 207, determining abnormal state information of the household equipment according to the matching degree.
In the embodiment of the invention, after the matching degree is determined, whether the equipment has faults or not can be predicted. For example, the matching degree is 90%, according to the matching degree, it can be determined that the number of difference values between the feature vectors corresponding to the data feature curve and the second feature curve is small, the data change of the corresponding type obtained from the device in real time is high, the similarity with the second feature curve corresponding to the corresponding fault type is high, and it can be predicted that the home device will be in fault.
According to the matched second characteristic curve, the corresponding two-point characteristics can be determined, wherein one is the fault state type, and the other is the curve corresponding to which data type. Therefore, the device is determined to be likely to have a fault, the corresponding fault state type and the data type can be obtained, the information corresponding to the two types can be used as abnormal state information and sent to the client, early warning can be provided for a user, and meanwhile, the abnormal state information can also be sent to the operation and maintenance terminal, so that the operation and maintenance terminal can take device fault countermeasures in advance, and the system performance of the smart home is improved.
In an alternative embodiment, step 207 may comprise the steps of:
s41, determining the matching degree meeting the matching condition, and determining the fault information of the second characteristic curve corresponding to the matching degree.
In the embodiment of the present invention, the matching condition may be preset, and the matching condition may be a limited range of the matching degree, for example, the matching degree is higher than 90%, 70% -90%, and the like. The failure information may understand the probability of a possible operational failure of the device, such as 100%, 85%, etc. Historical data of the running state of the household equipment can be predicted by collecting the second characteristic curves, and a mapping relation between matching conditions and fault information is established. Therefore, under the condition of determining the matching degree, the matching condition met by the matching degree can be found, and the fault information of the corresponding second characteristic curve is determined according to the matching condition, so that the false alarm and the false alarm frequency are reduced conveniently.
And S42, determining abnormal operation information of the household equipment according to the fault information.
In the embodiment of the present invention, the second characteristic curve may be generated by collecting historical operating data corresponding to different data types of the device under different operating fault state types, so that, for a certain second characteristic curve, a corresponding fault state type and a corresponding data type may be determined.
Wherein, the fault state type can be represented by codes, such as characters a, b, c and the like, wherein a represents short circuit of the equipment, b represents power failure of the equipment, c represents damage of the equipment and the like. For example, the voltage class may be represented by the character U and the current class may be represented by the character I.
Therefore, the fault information, the fault state type and the corresponding data type can be used as the abnormal operation information of the household equipment.
For example, the abnormal operation information may be "85%, c, U".
And S43, determining the address information of the home equipment according to the real-time operation data.
And determining the abnormal operation information and the address information as abnormal state information.
In the embodiment of the invention, the server acquires the real-time operation data from each household device, and can determine the address information, such as an IP address and the like, of the corresponding household device according to the acquired real-time operation data. Therefore, according to the address information, the unique household equipment can be determined.
Therefore, the abnormal operation information and the address information can be combined to constitute the abnormal state information. For example, the abnormal state information may be "100.4.5.6, 85%, c, U".
And sending the abnormal state information to the client and the operation and maintenance terminal. Therefore, early warning can be provided for a user, and meanwhile, abnormal state information can be sent to the operation and maintenance terminal, so that the operation and maintenance terminal can take equipment fault countermeasures in advance, and the system performance of the smart home is improved.
And 208, determining that the household equipment is in the operation fault state to obtain fault state information.
In the embodiment of the invention, the failure of the household equipment can be determined without matching the corresponding first characteristic curve. Therefore, information representing equipment failure, such as a failure mark, can be sent to the client and the operation and maintenance terminal as failure state information.
In summary, by acquiring the real-time operation data of the household equipment, the feature vectors that clearly describe the real-time operation data are generated, and the feature vectors are matched according to the time sequence to generate the data feature curve. The data characteristic curves are matched based on the first state curves. And when the operation fault state is determined, sending the fault state information to the client and the operation and maintenance terminal. And when the abnormal state is determined to be the normal operation state, matching the data characteristic curves according to the second characteristic curves in the abnormal recognition rule to determine the matching degree, and determining corresponding abnormal state information according to the matching degree. Based on the method, the abnormal state information is transmitted to the client and the operation and maintenance terminal in a targeted manner, and early warning can be provided for the client. Meanwhile, the operation and maintenance terminal can take equipment fault measures in advance, and the system performance of the intelligent home is improved.
Referring to fig. 4, a smart home anomaly detection apparatus provided in an embodiment of the present invention is shown, where the apparatus may include:
the data acquisition module 401 is configured to acquire real-time operation data of the home equipment.
A data analysis module 402, configured to analyze the real-time operating data to determine a data characteristic curve.
A state matching module 403, configured to match the data characteristic curve and determine an operation state of the home device, where the operation state includes: and operating in a normal state.
And the state prediction module 404 is configured to predict abnormal state information of the home equipment according to an abnormal recognition rule and the data characteristic curve when the operation state is an operation normal state.
In an alternative embodiment, the data analysis module may include:
and the data conversion submodule is used for determining corresponding characteristic vectors according to the real-time operation data.
And the curve generation submodule is used for forming the data characteristic curve by using the characteristic vectors according to the time sequence.
In an alternative embodiment of the invention, the state matching module may include:
and the first characteristic matching submodule is used for matching the data characteristic curve according to the operation identification rule to determine the operation state of the household equipment, the operation identification rule is determined according to a first characteristic curve, and the first characteristic curve is determined according to the historical operation data of the household equipment which normally operates.
In an alternative embodiment, the state prediction module may include:
and the second characteristic matching submodule is used for respectively matching the data characteristic curve with each second characteristic curve in the abnormal recognition rule, and the second characteristic curve is determined according to the historical operating data of the household equipment with faults.
And the matching degree analysis submodule is used for determining the matching degree of the data characteristic curve and the second characteristic curve.
And the state information generating submodule is used for determining the abnormal state information of the household equipment according to the matching degree.
In an optional embodiment of the present invention, the status information generating sub-module may include:
and the fault information determining unit is used for determining the matching degree meeting the matching condition and determining the fault information of the second characteristic curve corresponding to the matching degree.
And the operation information determining unit is used for determining abnormal operation information of the household equipment according to the fault information.
And the address acquisition unit is used for determining the address information of the home equipment according to the real-time operation data.
And the abnormal information generating unit is used for determining the abnormal operation information and the address information as abnormal state information.
In summary, by acquiring the real-time operation data of the household equipment, the feature vectors that clearly describe the real-time operation data are generated, and the feature vectors are matched according to the time sequence to generate the data feature curve. The data characteristic curves are matched based on the first state curves. And when the operation fault state is determined, sending the fault state information to the client and the operation and maintenance terminal. And when the abnormal state is determined to be the normal operation state, matching the data characteristic curves according to the second characteristic curves in the abnormal recognition rule to determine the matching degree, and determining corresponding abnormal state information according to the matching degree. Based on the method, the abnormal state information is transmitted to the client and the operation and maintenance terminal in a targeted manner, and early warning can be provided for the client. Meanwhile, the operation and maintenance terminal can take equipment fault measures in advance, and the system performance of the intelligent home is improved.
Referring to fig. 5, an exemplary system architecture applicable to the smart home anomaly detection method or the smart home anomaly detection apparatus of the present application is shown. The system architecture may include a first household device 501, a second household device 502, a third household device 503, a gateway 504, a cloud platform 505, a client 506, and an operation and maintenance terminal 507.
The first home device 501 may be a curtain control device, the second home device 502 may be an air conditioner control device, the third home device 503 may be a lighting system device, an intelligent home system is formed by the first home device 501, the second home device 502, and the third home device 503, all home devices interact corresponding device data through a gateway 504, for example, an aito (Artificial Intelligence internet of things) gateway, and an aito gateway server preprocesses acquired real-time operation data from each home device, where the preprocessing may include data cleaning, data integration, data conversion, and the like. And then transmitting the real-time operation data to a cloud platform 505, matching a data characteristic curve generated by the real-time operation data through a cloud platform server, and determining the corresponding operation state of the home equipment. If the operation fault state is the operation fault state, the cloud platform 505 sends the fault state information to the client 506 and the operation and maintenance terminal 507. When the normal operation state is determined, the cloud platform 505 matches the data characteristic curve to determine a matching degree, and determines corresponding abnormal state information according to the matching degree. The cloud platform 505 sends the abnormal state information to the client 506 and the operation and maintenance terminal 507.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are mutually referred to
As is readily imaginable to the person skilled in the art: any combination of the above embodiments is possible, and thus any combination between the above embodiments is an embodiment of the present invention, but the present disclosure is not necessarily detailed herein for reasons of space.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
An electronic device, comprising:
one or more processors;
a memory;
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the methods of the embodiments described above.
A computer-readable storage medium storing a computer program for use in conjunction with an electronic device, the computer program being executable by a processor to perform the speech-based input method of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The smart home anomaly detection method and the smart home anomaly detection device provided by the invention are described in detail, specific examples are applied in the text to explain the principle and the implementation mode of the invention, and the description of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (12)
1. The intelligent home abnormity detection method is characterized by comprising the following steps:
acquiring real-time operation data of the household equipment;
analyzing the real-time operation data to determine a data characteristic curve;
matching the data characteristic curve, and determining the running state of the household equipment, wherein the running state comprises the following steps: running in a normal state;
and under the condition that the running state is a normal running state, predicting abnormal state information of the household equipment according to an abnormal recognition rule and the data characteristic curve.
2. The method of claim 1, wherein analyzing the real-time operational data to determine a data profile comprises:
determining corresponding characteristic vectors according to the real-time operation data;
and (5) forming a data characteristic curve by using the characteristic vectors according to the time sequence.
3. The method according to claim 1, wherein the matching the data characteristic curve to determine the operating state of the household device comprises:
and matching the data characteristic curves according to the operation identification rule to determine the operation state of the household equipment, wherein the operation identification rule is determined according to a first characteristic curve, and the first characteristic curve is determined according to the historical operation data of the household equipment which normally operates.
4. The method according to claim 1, wherein the predicting the abnormal state information of the household equipment according to the abnormal recognition rule and the data characteristic curve comprises:
matching the data characteristic curve with each second characteristic curve in the abnormal recognition rule, wherein the second characteristic curve is determined according to historical operation data of the household equipment with faults;
determining the matching degree of the data characteristic curve and the second characteristic curve;
and determining the abnormal state information of the household equipment according to the matching degree.
5. The method according to claim 4, wherein the determining the abnormal state information of the household equipment according to the matching degree comprises:
determining the matching degree meeting the matching condition, and determining the fault information of the second characteristic curve corresponding to the matching degree;
determining abnormal operation information of the household equipment according to the fault information;
determining address information of the home equipment according to the real-time operation data;
and determining the abnormal operation information and the address information as abnormal state information.
6. The utility model provides an unusual detection device of intelligence house, its characterized in that, the device includes:
the data acquisition module is used for acquiring real-time operation data of the household equipment;
the data analysis module is used for analyzing the real-time operation data and determining a data characteristic curve;
the state matching module is used for matching the data characteristic curve and determining the running state of the household equipment, wherein the running state comprises the following steps: running in a normal state;
and the state prediction module is used for predicting the abnormal state information of the household equipment according to an abnormal recognition rule and the data characteristic curve under the condition that the running state is a normal running state.
7. The apparatus of claim 6, wherein the data analysis module comprises:
the data conversion submodule is used for determining corresponding eigenvectors according to the real-time operation data;
and the curve generation submodule is used for forming the data characteristic curve by using the characteristic vectors according to the time sequence.
8. The apparatus of claim 6, wherein the state matching module comprises:
and the first characteristic matching submodule is used for matching the data characteristic curve according to the operation identification rule to determine the operation state of the household equipment, the operation identification rule is determined according to a first characteristic curve, and the first characteristic curve is determined according to the historical operation data of the household equipment which normally operates.
9. The apparatus of claim 6, wherein the state prediction module comprises:
the second characteristic matching submodule is used for respectively matching the data characteristic curve with each second characteristic curve in the abnormal recognition rule, and the second characteristic curve is determined according to the historical operating data of the failed household equipment;
the matching degree analysis sub-module is used for determining the matching degree of the data characteristic curve and the second characteristic curve;
and the state information generating submodule is used for determining the abnormal state information of the household equipment according to the matching degree.
10. The apparatus of claim 9, wherein the status information generation submodule comprises:
the fault information determining unit is used for determining the matching degree meeting the matching condition and determining the fault information of the second characteristic curve corresponding to the matching degree;
the operation information determining unit is used for determining abnormal operation information of the household equipment according to the fault information;
the address acquisition unit is used for determining the address information of the home equipment according to the real-time operation data;
and the abnormal information generating unit is used for determining the abnormal operation information and the address information as abnormal state information.
11. An electronic device, comprising:
one or more processors;
a memory;
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of any of claims 1-5.
12. A computer-readable storage medium storing a computer program for use in conjunction with an electronic device, the computer program being executable by a processor to perform the method of any of claims 1-5.
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