CN105629947A - Household equipment monitoring method, household equipment monitoring device and terminal - Google Patents
Household equipment monitoring method, household equipment monitoring device and terminal Download PDFInfo
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- CN105629947A CN105629947A CN201510863954.XA CN201510863954A CN105629947A CN 105629947 A CN105629947 A CN 105629947A CN 201510863954 A CN201510863954 A CN 201510863954A CN 105629947 A CN105629947 A CN 105629947A
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- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000012544 monitoring process Methods 0.000 title claims abstract description 23
- 238000012806 monitoring device Methods 0.000 title claims abstract description 16
- 230000008569 process Effects 0.000 claims description 12
- 238000001816 cooling Methods 0.000 claims description 10
- 238000010606 normalization Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 abstract description 23
- 230000002650 habitual effect Effects 0.000 abstract 1
- 230000006399 behavior Effects 0.000 description 16
- 238000001514 detection method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000010801 machine learning Methods 0.000 description 5
- 238000003909 pattern recognition Methods 0.000 description 5
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- 238000004378 air conditioning Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 235000012364 Peperomia pellucida Nutrition 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05B23/0243—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 model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
- G05B23/027—Alarm generation, e.g. communication protocol; Forms of alarm
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0286—Modifications to the monitored process, e.g. stopping operation or adapting control
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- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Engineering & Computer Science (AREA)
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Abstract
The invention provides a household equipment monitoring method, a household equipment monitoring device and a terminal. The household equipment monitoring method includes the following steps that: the real-time operating information of household equipment is acquired; a characteristic model is established for the household equipment according to the real-time operating information; the characteristic model is compared with a security presetting model of the household equipment through a Bayesian classifier, so that whether the real-time operating information is abnormal can be determined. With the technical schemes of the invention adopted, different usage behavior and habitual patterns of users can be distinguished; when the characteristic model is not matched with the security presetting model, the abnormal real-time operating information of the household equipment can be timely recognized, and security and convenience in the use of the household equipment of the users can be improved, and the adjustment of the operating state of the household equipment according to the actual habits of the users can be facilitated.
Description
Technical field
The present invention relates to field of terminal technology, in particular to a kind of home equipment monitoring method, a kind of home equipment monitoring device and a kind of terminal.
Background technology
At present, Smart Home (smarthome) is a branch of technology of Internet of things, Smart Home passes through technology of Internet of things by the various equipment in subscriber household, such as, the control of video system, illuminator, curtain, airconditioning control, safety-protection system, network home appliance etc. connect together, it is provided that several functions and the means such as home wiring control, Lighting control, remote control using telephone, indoor and outdoor remote control, burglar alarm, environmental monitoring, HVAC control, infrared forwarding and programmable Timer control.
The intelligent domestic system that presently, there are is confined to man-to-man control action mostly, or uses the mobile terminal remote control to Smart Home, lacks an effective risk assessment based on user behavior or use inertia and trusted feedback system. Additionally, a lot of preset programs are all factory settings, the actual demand of each family can not be met very well.
It is thus desirable to a kind of new technical scheme, it is possible to promote user further and use safety and the convenience of home equipment.
Summary of the invention
The present invention is based on the problems referred to above, it is proposed that a kind of new technical scheme, it is possible to promotes user further and uses safety and the convenience of home equipment.
In view of this, an aspect of of the present present invention proposes a kind of home equipment monitoring method, including: gather the real-time traffic information of home equipment; According to described real-time traffic information, set up characteristic model for described home equipment; By Bayes classifier, the preset model of safety of described characteristic model Yu described home equipment is compared, to determine whether described real-time traffic information exception occurs.
In this technical scheme, characteristic model is set up by the real-time traffic information for home equipment, and model preset with the safety of home equipment mates, different usage behaviors and the inertia mode of user can be distinguished, the abnormal real-time traffic information of home equipment is identified in time when this characteristic model is with the preset unmatched models of safety, improve user and use safety and the convenience of home equipment, also allow for the actual custom according to user and adjust the running status of home equipment. Additionally; what abnormal information assessment and detection adopted is the Bayes classifier of minimum risk; the Bayes classifier Bayesian network for classifying; the standard evaluating this grader is average risk; its average risk is minimum; by this grader, household is controlled in real time, the use safety of home equipment can be protected, reduce risk.
In technique scheme, it is preferable that described according to described real-time traffic information, set up characteristic model for described home equipment, specifically include: every kind described, real-time traffic information sets up a corresponding n dimensional vector n for each described home equipment; According to the described result setting up a corresponding n dimensional vector n, set up characteristic of correspondence model for described every kind of real-time traffic information.
In this technical scheme, it is possible to all set up characteristic of correspondence model for every kind of real-time traffic information, in each characteristic model, each home equipment has a n dimensional vector n of correspondence. Specifically, pattern recognition and machine learning can be used to build characteristic model when normally using of user, exception for jointly predicting home equipment with the preset model of safety uses information, for every kind of real-time traffic information, a n dimensional vector n is used to represent different home equipments, such as, for using duration, because the use duration of the distinct device of each family is inconsistent, there is use habit, the impact of the variance factors such as rule of life, when therefore use duration being modeled, if vector P_order={p0, p1..., pi..., pN-1, pN, wherein, 0��i < N, N is the number of home equipment, piFor the use duration of each home equipment, so namely characteristic vector is established for every kind of real-time traffic information, thus obtaining characteristic model.
In any of the above-described technical scheme, it is preferable that before the real-time traffic information of described collection home equipment, also include: gather the initial run information of described home equipment; According to described initial run information, set up the preset model of described safety for described home equipment.
In this technical scheme, need to gather the initial run information of home equipment, to determine usage behavior and the inertia mode of user, and set up the preset model of safety with this, the characteristic model obtained by the real-time traffic information of user is determined whether real-time traffic information meets the inertia mode of user compared with the preset model of safety, can be judged as when not meeting occurring extremely, now to send prompting and warn, or directly stop the running of this home equipment. By this technical scheme, it is simple to identify the abnormal real-time traffic information of home equipment further when characteristic model is with the preset unmatched models of safety in time, improve user and use the safety of home equipment.
In any of the above-described technical scheme, it is preferable that described according to described real-time traffic information, before setting up characteristic model for described home equipment, also include: the described real-time traffic information collected is carried out pretreatment, and described pretreatment includes abandoning invalid data; Pretreated described real-time traffic information is normalized.
In this technical scheme, before modeling, data prediction can be carried out, to remove the invalid information in real-time traffic information, retain effective information, subsequent step promotes treatment effeciency. It addition, pretreated real-time traffic information is normalized, namely according to the type of real-time traffic information, each home equipment, pretreated real-time traffic information is classified, in order to promote modeling efficiency.
In any of the above-described technical scheme, preferably, described real-time traffic information includes at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment.
In this technical scheme, real-time traffic information includes but not limited at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment. By this technical scheme, diversified real-time traffic information is modeled, it is possible to identify the accuracy of the abnormal real-time traffic information of home equipment, and ensure that user uses the safety of home equipment.
Another aspect of the present invention proposes a kind of home equipment monitoring device, including: the first collecting unit, gather the real-time traffic information of home equipment; Modeling unit, according to described real-time traffic information, sets up characteristic model for described home equipment; Comparing unit, is compared the preset model of safety of described characteristic model Yu described home equipment by Bayes classifier, to determine whether described real-time traffic information exception occurs.
In this technical scheme, characteristic model is set up by the real-time traffic information for home equipment, and model preset with the safety of home equipment mates, different usage behaviors and the inertia mode of user can be distinguished, the abnormal real-time traffic information of home equipment is identified in time when this characteristic model is with the preset unmatched models of safety, improve user and use safety and the convenience of home equipment, also allow for the actual custom according to user and adjust the running status of home equipment. Additionally; what abnormal information assessment and detection adopted is the Bayes classifier of minimum risk; the Bayes classifier Bayesian network for classifying; the standard evaluating this grader is average risk; its average risk is minimum; by this grader, household is controlled in real time, the use safety of home equipment can be protected, reduce risk.
In technique scheme, preferably, described modeling unit specifically for: every kind described, real-time traffic information is that each described home equipment sets up a corresponding n dimensional vector n, and according to the described result setting up a corresponding n dimensional vector n, set up characteristic of correspondence model for described every kind of real-time traffic information.
In this technical scheme, it is possible to all set up characteristic of correspondence model for every kind of real-time traffic information, in each characteristic model, each home equipment has a n dimensional vector n of correspondence. Specifically, pattern recognition and machine learning can be used to build characteristic model when normally using of user, exception for jointly predicting home equipment with the preset model of safety uses information, for every kind of real-time traffic information, a n dimensional vector n is used to represent different home equipments, such as, for using duration, because the use duration of the distinct device of each family is inconsistent, there is use habit, the impact of the variance factors such as rule of life, when therefore use duration being modeled, if vector P_order={p0, p1..., pi..., pN-1, pN, wherein, 0��i < N, N is the number of home equipment, piFor the use duration of each home equipment, so namely characteristic vector is established for every kind of real-time traffic information, thus obtaining characteristic model.
In any of the above-described technical scheme, it is preferable that also include: the second collecting unit, before the real-time traffic information of described collection home equipment, gather the initial run information of described home equipment; Initial modeling unit, according to described initial run information, sets up the preset model of described safety for described home equipment.
In this technical scheme, need to gather the initial run information of home equipment, to determine usage behavior and the inertia mode of user, and set up the preset model of safety with this, the characteristic model obtained by the real-time traffic information of user is determined whether real-time traffic information meets the inertia mode of user compared with the preset model of safety, can be judged as when not meeting occurring extremely, now to send prompting and warn, or directly stop the running of this home equipment. By this technical scheme, it is simple to identify the abnormal real-time traffic information of home equipment further when characteristic model is with the preset unmatched models of safety in time, improve user and use the safety of home equipment.
In any of the above-described technical scheme, it is preferable that also include: pretreatment unit, described according to described real-time traffic information, before setting up characteristic model for described home equipment, the described real-time traffic information collected being carried out pretreatment, described pretreatment includes abandoning invalid data; Normalization unit, is normalized pretreated described real-time traffic information.
In this technical scheme, before modeling, data prediction can be carried out, to remove the invalid information in real-time traffic information, retain effective information, subsequent step promotes treatment effeciency. It addition, pretreated real-time traffic information is normalized, namely according to the type of real-time traffic information, each home equipment, pretreated real-time traffic information is classified, in order to promote modeling efficiency.
In any of the above-described technical scheme, preferably, described real-time traffic information includes at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment.
In this technical scheme, real-time traffic information includes but not limited at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment. By this technical scheme, diversified real-time traffic information is modeled, it is possible to identify the accuracy of the abnormal real-time traffic information of home equipment, and ensure that user uses the safety of home equipment.
Another aspect of the invention proposes a kind of terminal, device is monitored including the home equipment according to any one of technique scheme, therefore, this terminal has monitors, with the home equipment according to any one of technique scheme, the technique effect that device is identical, does not repeat them here.
By above technical scheme, different usage behaviors and the inertia mode of user can be distinguished, the abnormal real-time traffic information of home equipment is identified in time when this characteristic model is with the preset unmatched models of safety, improve user and use safety and the convenience of home equipment, also allow for the actual custom according to user and adjust the running status of home equipment.
Accompanying drawing explanation
Fig. 1 illustrates the flow chart of home equipment monitoring method according to an embodiment of the invention;
Fig. 2 illustrates the block diagram of home equipment according to an embodiment of the invention monitoring device;
Fig. 3 illustrates the block diagram of terminal according to an embodiment of the invention;
Fig. 4 illustrates the flow chart of home equipment monitoring method according to another embodiment of the invention;
Fig. 5 illustrates the block diagram of home equipment monitoring device according to another embodiment of the invention.
Detailed description of the invention
In order to be more clearly understood that the above-mentioned purpose of the present invention, feature and advantage, below in conjunction with the drawings and specific embodiments, the present invention is further described in detail. It should be noted that when not conflicting, embodiments herein and the feature in embodiment can be mutually combined.
Elaborate a lot of detail in the following description so that fully understanding the present invention; but; the present invention can also adopt other to be different from other modes described here to implement, and therefore, protection scope of the present invention is by the restriction of following public specific embodiment.
Fig. 1 illustrates the flow chart of home equipment monitoring method according to an embodiment of the invention.
As it is shown in figure 1, home equipment monitoring method according to an embodiment of the invention, including:
Step 102, gathers the real-time traffic information of home equipment;
Step 104, according to described real-time traffic information, sets up characteristic model for described home equipment;
Step 106, is compared the preset model of safety of characteristic model Yu described home equipment by Bayes classifier, to determine whether described real-time traffic information exception occurs.
In this technical scheme, characteristic model is set up by the real-time traffic information for home equipment, and model preset with the safety of home equipment mates, different usage behaviors and the inertia mode of user can be distinguished, the abnormal real-time traffic information of home equipment is identified in time when this characteristic model is with the preset unmatched models of safety, improve user and use safety and the convenience of home equipment, also allow for the actual custom according to user and adjust the running status of home equipment. Additionally; what abnormal information assessment and detection adopted is the Bayes classifier of minimum risk; the Bayes classifier Bayesian network for classifying; the standard evaluating this grader is average risk; its average risk is minimum; by this grader, household is controlled in real time, the use safety of home equipment can be protected, reduce risk.
In technique scheme, it is preferable that step 104 specifically includes: every kind described, real-time traffic information sets up a corresponding n dimensional vector n for each described home equipment; According to the described result setting up a corresponding n dimensional vector n, set up characteristic of correspondence model for described every kind of real-time traffic information.
In this technical scheme, it is possible to all set up characteristic of correspondence model for every kind of real-time traffic information, in each characteristic model, each home equipment has a n dimensional vector n of correspondence. Specifically, pattern recognition and machine learning can be used to build characteristic model when normally using of user, exception for jointly predicting home equipment with the preset model of safety uses information, for every kind of real-time traffic information, a n dimensional vector n is used to represent different home equipments, such as, for using duration, because the use duration of the distinct device of each family is inconsistent, there is use habit, the impact of the variance factors such as rule of life, when therefore use duration being modeled, if vector P_order={p0, p1..., pi..., pN-1, pN, wherein, 0��i < N, N is the number of home equipment, piFor the use duration of each home equipment, so namely characteristic vector is established for every kind of real-time traffic information, thus obtaining characteristic model.
In any of the above-described technical scheme, it is preferable that also included before step 102: gather the initial run information of described home equipment; According to described initial run information, set up the preset model of described safety for described home equipment.
In this technical scheme, need to gather the initial run information of home equipment, to determine usage behavior and the inertia mode of user, and set up the preset model of safety with this, the characteristic model obtained by the real-time traffic information of user is determined whether real-time traffic information meets the inertia mode of user compared with the preset model of safety, can be judged as when not meeting occurring extremely, now to send prompting and warn, or directly stop the running of this home equipment. By this technical scheme, it is simple to identify the abnormal real-time traffic information of home equipment further when characteristic model is with the preset unmatched models of safety in time, improve user and use the safety of home equipment.
In any of the above-described technical scheme, it is preferable that also included before step 104: the described real-time traffic information collected is carried out pretreatment, and described pretreatment includes abandoning invalid data; Pretreated described real-time traffic information is normalized.
In this technical scheme, before modeling, data prediction can be carried out, to remove the invalid information in real-time traffic information, retain effective information, subsequent step promotes treatment effeciency. It addition, pretreated real-time traffic information is normalized, namely according to the type of real-time traffic information, each home equipment, pretreated real-time traffic information is classified, in order to promote modeling efficiency.
In any of the above-described technical scheme, preferably, described real-time traffic information includes at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment.
In this technical scheme, real-time traffic information includes but not limited at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment. By this technical scheme, diversified real-time traffic information is modeled, it is possible to identify the accuracy of the abnormal real-time traffic information of home equipment, and ensure that user uses the safety of home equipment.
Fig. 2 illustrates the block diagram of home equipment according to an embodiment of the invention monitoring device.
As in figure 2 it is shown, home equipment according to an embodiment of the invention monitoring device 200, including: the first collecting unit 202, modeling unit 204 and comparing unit 206.
Wherein, the first collecting unit 202 is for gathering the real-time traffic information of home equipment; Modeling unit 204 is for according to real-time traffic information, setting up characteristic model for home equipment; Comparing unit 206 is for comparing the preset model of safety of characteristic model Yu home equipment by Bayes classifier, to determine whether real-time traffic information exception occurs.
In this technical scheme, characteristic model is set up by the real-time traffic information for home equipment, and model preset with the safety of home equipment mates, different usage behaviors and the inertia mode of user can be distinguished, the abnormal real-time traffic information of home equipment is identified in time when this characteristic model is with the preset unmatched models of safety, improve user and use safety and the convenience of home equipment, also allow for the actual custom according to user and adjust the running status of home equipment. Additionally; what abnormal information assessment and detection adopted is the Bayes classifier of minimum risk; the Bayes classifier Bayesian network for classifying; the standard evaluating this grader is average risk; its average risk is minimum; by this grader, household is controlled in real time, the use safety of home equipment can be protected, reduce risk.
In technique scheme, preferably, modeling unit 204 specifically for: be that each home equipment sets up a corresponding n dimensional vector n in every kind of real-time traffic information, and according to the described result setting up a corresponding n dimensional vector n, set up characteristic of correspondence model for every kind of real-time traffic information.
In this technical scheme, it is possible to all set up characteristic of correspondence model for every kind of real-time traffic information, in each characteristic model, each home equipment has a n dimensional vector n of correspondence. Specifically, pattern recognition and machine learning can be used to build characteristic model when normally using of user, exception for jointly predicting home equipment with the preset model of safety uses information, for every kind of real-time traffic information, a n dimensional vector n is used to represent different home equipments, such as, for using duration, because the use duration of the distinct device of each family is inconsistent, there is use habit, the impact of the variance factors such as rule of life, when therefore use duration being modeled, if vector P_order={p0, p1..., pi..., pN-1, pN, wherein, 0��i < N, N is the number of home equipment, piFor the use duration of each home equipment, so namely characteristic vector is established for every kind of real-time traffic information, thus obtaining characteristic model.
In any of the above-described technical scheme, it is preferable that also include: the second collecting unit 208 and initial modeling unit 210.
Wherein, the second collecting unit 208 is for, before gathering the real-time traffic information of home equipment, gathering the initial run information of home equipment; Initial modeling unit 210 is for according to initial run information, setting up the preset model of safety for home equipment.
In this technical scheme, need to gather the initial run information of home equipment, to determine usage behavior and the inertia mode of user, and set up the preset model of safety with this, the characteristic model obtained by the real-time traffic information of user is determined whether real-time traffic information meets the inertia mode of user compared with the preset model of safety, can be judged as when not meeting occurring extremely, now to send prompting and warn, or directly stop the running of this home equipment. By this technical scheme, it is simple to identify the abnormal real-time traffic information of home equipment further when characteristic model is with the preset unmatched models of safety in time, improve user and use the safety of home equipment.
In any of the above-described technical scheme, it is preferable that also include: pretreatment unit 212 and normalization unit 214.
Wherein, pretreatment unit 212 is for according to real-time traffic information, before setting up characteristic model for home equipment, carrying out pretreatment to the real-time traffic information collected, and pretreatment includes abandoning invalid data; Normalization unit 214 is for being normalized pretreated real-time traffic information.
In this technical scheme, before modeling, data prediction can be carried out, to remove the invalid information in real-time traffic information, retain effective information, subsequent step promotes treatment effeciency. It addition, pretreated real-time traffic information is normalized, namely according to the type of real-time traffic information, each home equipment, pretreated real-time traffic information is classified, in order to promote modeling efficiency.
In any of the above-described technical scheme, preferably, real-time traffic information includes at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment.
In this technical scheme, real-time traffic information includes but not limited at least one of or its combination: the time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment. By this technical scheme, diversified real-time traffic information is modeled, it is possible to identify the accuracy of the abnormal real-time traffic information of home equipment, and ensure that user uses the safety of home equipment.
Fig. 3 illustrates the block diagram of terminal according to an embodiment of the invention.
As it is shown on figure 3, terminal 300 according to an embodiment of the invention, monitoring device 200 including the home equipment shown in Fig. 2, therefore, this terminal 300 has monitors, with the home equipment shown in Fig. 2, the technique effect that device 200 is identical, does not repeat them here.
Fig. 4 illustrates the flow chart of home equipment monitoring method according to another embodiment of the invention.
As shown in Figure 4, home equipment monitoring method according to another embodiment of the invention, including:
Step 402, by the real-time traffic information of data collecting module collected home equipment. Such as, whether detection air-conditioning is opened, and whether curtain is pulled on, and whether door closes, and whether washing machine operates, Indoor Temperature angle value, humidity value etc.
Step 404, carries out information pre-processing for home equipment.
Step 406, is modeled according to pretreated real-time traffic information.
By Bayes classifier, step 408, determines whether real-time traffic information exception occurs, when judged result is for being, enter step 410, otherwise, returns step 402.
Step 410, sends warning and/or fitment is carried out security control.
Specifically, as it is shown in figure 5, in combination with the step described in the embodiment of Fig. 4, home equipment monitoring system 500 includes data analysis module 502 and data acquisition module 504.
Wherein, data analysis module 502 receives the data of uploading that data acquisition module 504 obtains from home equipment 600 and is analyzed, and carries out Modeling analysis, and user uses the characteristic model 506 of foundation in real time to mate with the preset model 508 of built-in safety. The result of analysis is sent to data feedback module 510 by data analysis module 502.
Wherein, the behavioral pattern of user includes: set the time, the time namely home equipment set; Setup parameter, the parameter that namely user often sets; Setting order, i.e. the operation order of the operating process of arbitrary home equipment and single home equipment; Use duration, namely Various Seasonal, the time period use duration to different home equipments; Action type, including mobile terminal operation, home computer operation, cloud platform operation etc.; Operation device parameter, such as, the operation device parameter of mobile terminal includes operation unit type, producer address, identification code address etc., and the operation device parameter of cloud platform operation includes browser type, version etc.
It addition, home equipment monitoring system 500 also includes data preprocessing module, it is used for processing collection data, retains useful data, abandon hash, carry out data normalization process.
Home equipment monitoring system 500 also includes characteristic vector generation module, and it can all set up characteristic of correspondence model for every kind of real-time traffic information, and in each characteristic model, each home equipment has a n dimensional vector n of correspondence. Specifically, pattern recognition and machine learning can be used to build characteristic model when normally using of user, exception for jointly predicting home equipment with the preset model of safety uses information, for every kind of real-time traffic information, a n dimensional vector n is used to represent different home equipments, such as, for using duration, because the use duration of the distinct device of each family is inconsistent, there is use habit, the impact of the variance factors such as rule of life, when therefore use duration being modeled, if vector P_order={p0, p1..., pi..., pN-1, pN, wherein, 0��i < N, N is the number of home equipment, piFor the use duration of each home equipment, so namely characteristic vector is established for every kind of real-time traffic information, thus obtaining characteristic model.
Home equipment monitoring system 500 also includes Bayes classifier, and the normal of home equipment and abnormal state information are fed back to user. Home equipment as required, can be carried out real-time operation by user. Wherein abnormal information assessment generally adopts Least risk Bayes grader with detection. Bayes classifier is the Bayesian network for classifying, and should comprise class node C in this network, and wherein the value of C comes from class set { c1, c2..., cm, also comprise one group of node X={X1, X2.., Xn, represent the feature for classifying. For Bayes classifier, if a certain sample D to be sorted, its characteristic of division value is x={x1, x2..., xn, then sample D belongs to classification ciProbability P (C=ci|X1=x1, X2={ x2..., Xn=xn, i=1,2 ..., m} should meet following formula:
P (C=ci| X=x)=ax{P (C=c1| X=x), P (C=c2| X=x) ..., P (C=cm| X=x) }
Due to the complexity of objective things, and the Different Effects of extraneous factor, risk when various judgement made by Bayes classifier grader is different. Such as, Deviant Behavior is mistaken for the risk of normal behaviour just than normal behaviour being mistaken for having a big risk of Deviant Behavior. Therefore, Bayes classifier introduces the concept of risk, need to determine as the case may be the size of various risk in actual applications, typically by a system number CijRepresent, CijIdentified sample classification is �� by presentation class devicei, and the real classification of this sample is ��jTime risk. The basic thought of design minimum risk grader is: calculate by posterior probability and x is categorized as ��iThe more each R of conditional riskiX classification that the size of () is corresponding with minima is the result classified. The standard evaluating this grader is average risk, and its average risk is minimum.
Home equipment monitoring system 500 also includes abnormality warnings and controls module 512; abnormality warnings and control module 512 are according to abstract characteristics; set up user behavior vector, modeling and point out abnormal use information with detecting step to user 700 by Bayes classifier assessment; receive the instruction of user 700; household is controlled in real time; thus protecting the use safety of Smart Home, reduce risk.
Technical scheme is described in detail above in association with accompanying drawing, pass through technical scheme, different usage behaviors and the inertia mode of user can be distinguished, the abnormal real-time traffic information of home equipment is identified in time when this characteristic model is with the preset unmatched models of safety, improve user and use safety and the convenience of home equipment, also allow for the actual custom according to user and adjust the running status of home equipment.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations. All within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (11)
1. a home equipment monitoring method, it is characterised in that including:
Gather the real-time traffic information of home equipment;
According to described real-time traffic information, set up characteristic model for described home equipment;
By Bayes classifier, the preset model of safety of characteristic model Yu described home equipment is compared, to determine whether described real-time traffic information exception occurs.
2. home equipment monitoring method according to claim 1, it is characterised in that described according to described real-time traffic information, sets up characteristic model for described home equipment, specifically includes:
Every kind described, real-time traffic information is that each described home equipment sets up a corresponding n dimensional vector n;
According to the described result setting up a corresponding n dimensional vector n, set up characteristic of correspondence model for described every kind of real-time traffic information.
3. home equipment monitoring method according to claim 1 and 2, it is characterised in that before the real-time traffic information of described collection home equipment, also includes:
Gather the initial run information of described home equipment;
According to described initial run information, set up the preset model of described safety for described home equipment.
4. home equipment monitoring method according to claim 3, it is characterised in that described according to described real-time traffic information, before setting up characteristic model for described home equipment, also includes:
The described real-time traffic information collected is carried out pretreatment, and described pretreatment includes abandoning invalid data;
Pretreated described real-time traffic information is normalized.
5. home equipment monitoring method according to claim 4, it is characterised in that described real-time traffic information includes at least one of or its combination:
The time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment.
6. a home equipment monitoring device, it is characterised in that including:
First collecting unit, gathers the real-time traffic information of home equipment;
Modeling unit, according to described real-time traffic information, sets up characteristic model for described home equipment;
Comparing unit, is compared the preset model of safety of described characteristic model Yu described home equipment by Bayes classifier, to determine whether described real-time traffic information exception occurs.
7. home equipment according to claim 6 monitoring device, it is characterised in that described modeling unit specifically for:
Every kind described, real-time traffic information is that each described home equipment sets up a corresponding n dimensional vector n, and according to the described result setting up a corresponding n dimensional vector n, sets up characteristic of correspondence model for described every kind of real-time traffic information.
8. the home equipment monitoring device according to claim 6 or 7, it is characterised in that also include:
Second collecting unit, before the real-time traffic information of described collection home equipment, gathers the initial run information of described home equipment;
Initial modeling unit, according to described initial run information, sets up the preset model of described safety for described home equipment.
9. home equipment according to claim 8 monitoring device, it is characterised in that also include:
Pretreatment unit, described according to described real-time traffic information, before setting up characteristic model for described home equipment, carries out pretreatment to the described real-time traffic information collected, and described pretreatment includes abandoning invalid data;
Normalization unit, is normalized pretreated described real-time traffic information.
10. home equipment according to claim 9 monitoring device, it is characterised in that described real-time traffic information includes at least one of or its combination:
The time of running, use duration, operational factor, the operating process of arbitrary home equipment, the operation order of multiple home equipment, operation Control Cooling, for operating the parameter of terminal controlling home equipment.
11. a terminal, it is characterised in that include the home equipment monitoring device as according to any one of claim 6 to 10.
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