CN107085153B - Electricity abnormal fire early warning method and system - Google Patents
Electricity abnormal fire early warning method and system Download PDFInfo
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- CN107085153B CN107085153B CN201710289490.5A CN201710289490A CN107085153B CN 107085153 B CN107085153 B CN 107085153B CN 201710289490 A CN201710289490 A CN 201710289490A CN 107085153 B CN107085153 B CN 107085153B
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Abstract
The invention discloses a fire early warning method and system for electricity utilization abnormity. The method comprises the following steps: monitoring and acquiring real-time power utilization data in a power utilization loop to be detected; judging whether the abnormal use condition of the electric appliance exists in the power utilization loop to be detected or not according to the acquired real-time power utilization data and a preset normal power utilization model of the user; and when the abnormal use condition of the electric appliance in the electric loop to be detected is confirmed, sending out corresponding fire early warning to a user. The power utilization abnormal fire early warning method provided by the invention can be used for quickly and accurately predicting the abnormal use condition of the electric appliance in the power utilization loop to be detected, and early warning the fire caused by the abnormal use of the electric appliance in advance, so that the loss of life and property of people caused by the fire caused by the abnormal use of the electric appliance can be effectively avoided; the condition that only overcurrent protection is needed as the existing circuit breaker is avoided, and early warning can not be carried out at the initial stage of fire or when the fire does not occur yet.
Description
Technical Field
The invention relates to the technical field of electric fire early warning, in particular to a method and a system for early warning of abnormal electricity utilization fire.
Background
With the development of science and technology and the improvement of the living standard of people, electric energy is widely applied as an important energy source. Meanwhile, many unsafe factors of electricity utilization cause continuous occurrence of electrical fire, at present, no good detection technology is available for effective prevention of fire caused by electrical short circuit, circuit aging and current overload, and the prior art mainly relies on a circuit breaker or a fuse of a power distribution system to achieve a preset fixed breaking condition after the current exceeds a certain value and is maintained for a period of time, so as to cut off a fault line and avoid formation and occurrence of fire. However, in order to avoid frequent false operations, the overcurrent protection point of a circuit breaker or a fuse in a power distribution system is usually high, which cannot accurately protect an electric appliance from a spark phenomenon caused by local short circuit and insulation aging of an electric line, and further prevent an accident of fire. In order to reduce the hidden danger of electrical fire and ensure the safety of people's life and property, it is necessary to carry out intelligent early warning on the fire in advance.
Disclosure of Invention
In order to solve the problem that the existing circuit fire cannot be early warned in advance, the embodiment of the invention provides a method and a system for early warning of abnormal fire by using electricity. The technical scheme is as follows:
on one hand, the embodiment of the invention provides a fire early warning method for abnormal electricity utilization, which comprises the following steps:
monitoring and acquiring real-time power utilization data in a power utilization loop to be detected;
judging whether an abnormal use condition of the electric appliance exists in the power utilization loop to be detected or not according to the acquired real-time power utilization data and a preset user power utilization model;
and when the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed, sending out corresponding fire early warning to a user.
In the above method for early warning of fire due to abnormal power consumption in an embodiment of the present invention, the power consumption model of the user is obtained based on analysis of normal power consumption behavior of the user within a preset time period.
In the method for early warning of a fire disaster with abnormal power consumption according to the embodiment of the present invention, the determining whether an abnormal use condition of an electrical appliance exists in the power consumption loop to be tested according to the acquired real-time power consumption data and a preset user power consumption model includes:
dividing a time period of normal power utilization behavior analysis of a user into n judgment cycles, wherein n is a positive integer greater than 1;
in any one or more judgment periods, if at least one of the following judgment conditions is met, the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed to occur:
Irms>An；
Wpk>K2*Wnsur；
when t ispk<tsurWhen, Ipk>K1*an；
Wherein, Irms: real-time effective current of the power circuit to be measured, AnMaximum effective current value, W, of the circuit to be tested in any judgment period obtained by analyzing normal electricity utilization behavior of userspk: real time total energy, W, of the power supply in the loop during the period of occurrence of the inrush currentnsur: maximum energy value, t, of power supply in the loop in the generation period of the surge current obtained by analyzing normal power utilization behaviors of userspkThe holding time t of the real-time surge current in the power loop to be measurednsur: maintenance time of surge current, I, obtained by analyzing normal electricity utilization behavior of userpk: real-time peak current, a, of the power circuit to be measurednMaximum peak current value of the power utilization circuit to be tested in any judgment period, obtained through user behavior analysis, K1: maximum effective power error coefficient set by normal electricity usage behavior analysis of the user, K2: and analyzing the set maximum energy error coefficient through the normal electricity utilization behavior of the user.
In the method for early warning of the abnormal fire by electricity, provided by the embodiment of the invention, the value range of K1 is 1.5-10, and the value range of K2 is 1.5-10.
In the above-mentioned fire early warning method for abnormal power consumption in an embodiment of the present invention, the method further includes:
and continuously correcting the normal power utilization model of the user according to the feedback of the user on the fire early warning judgment result and by combining machine learning.
On the other hand, the embodiment of the invention provides a fire early warning system for abnormal electricity utilization, which comprises:
the acquisition module is used for monitoring and acquiring real-time power utilization data in the power utilization loop to be detected;
the judging module is used for judging whether an abnormal use condition of an electric appliance exists in the power utilization loop to be detected or not according to the acquired real-time power utilization data and a preset user power utilization model;
and the processing module is used for sending out corresponding fire early warning to a user when the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed.
In the above fire early warning system for abnormal power consumption in an embodiment of the present invention, the power consumption model for the user is obtained based on analysis of normal power consumption behavior of the user within a preset time period.
In the above-described fire early warning system for abnormal power consumption according to an embodiment of the present invention, the determining module includes:
the dividing unit is used for dividing the time period of normal power utilization behavior analysis of the user into n judgment cycles, wherein n is a positive integer greater than 1;
the judging unit is used for confirming that the abnormal use condition of the electric appliance occurs in the electric loop to be tested if at least one of the following judging conditions is met in any one or more judging periods:
Irms>An；
Wpk>K2*Wnsur；
when t ispk<tsurWhen, Ipk>K1*an；
Wherein, Irms: real-time effective current of the power circuit to be measured, AnMaximum effective current value, W, of the circuit to be tested in any judgment period obtained by analyzing normal electricity utilization behavior of userspk: real time total energy, W, of the power supply in the loop during the period of occurrence of the inrush currentnsur: maximum energy value, t, of power supply in the loop in the generation period of the surge current obtained by analyzing normal power utilization behaviors of userspkThe holding time t of the real-time surge current in the power loop to be measurednsur: maintenance time of surge current, I, obtained by analyzing normal electricity utilization behavior of userpk: real-time peak current, a, of the power circuit to be measurednThe maximum peak current value of the power utilization loop to be tested in any judgment period is obtained through the analysis of the normal power utilization behavior of the user, K1: maximum effective power error coefficient set by normal electricity usage behavior analysis of the user, K2: and analyzing the set maximum energy error coefficient through the normal electricity utilization behavior of the user.
In the fire early warning system for abnormal electricity consumption in the embodiment of the invention, the value range of K1 is 1.5-10, and the value range of K2 is 1.5-10.
In the above-mentioned fire early warning system for abnormal power consumption in an embodiment of the present invention, the system further includes:
and the correction learning module is used for continuously correcting the normal power utilization model of the user according to the feedback of the user on the fire early warning judgment result and by combining machine learning.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the method comprises the steps of monitoring and acquiring real-time power utilization data in a power utilization loop to be detected, judging whether an electrical appliance abnormal use condition exists in the power utilization loop to be detected according to the acquired real-time power utilization data and a preset user normal power utilization model, and finally sending out corresponding fire early warning to a user when the electrical appliance abnormal use condition exists in the power utilization loop to be detected. The power utilization abnormal fire early warning method can quickly and accurately predict the abnormal use condition of the electric appliance in the power utilization loop to be detected, and early warn the fire caused by the abnormal use of the electric appliance, so that the loss of life and property of people caused by the fire caused by the abnormal use of the electric appliance can be effectively avoided; the condition that only overcurrent protection can be carried out as the existing circuit breaker is avoided, and early warning can not be carried out at the initial stage of fire or when the fire does not occur yet.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a fire early warning method for abnormal power consumption according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an abnormal power consumption fire warning device according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a determining module according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example one
The embodiment of the invention provides a fire early warning method for electricity utilization abnormity, which can be applied to an intelligent distribution box and can comprise the following steps:
and step S11, monitoring and acquiring real-time power utilization data in the power utilization loop to be tested.
It should be noted that the reasons for fire generally include two reasons, one is caused by an electrical short circuit and an overload current caused by abnormal use of an electrical appliance; the second is caused by wire aging or circuit aging.
In the embodiment, in order to avoid a fire caused by abnormal use of an electrical appliance, the power utilization abnormal fire early warning method performs big data analysis on normal power utilization behaviors of a user to obtain a power utilization model of the user, so as to identify abnormal use conditions of the electrical appliance in a power utilization loop and further perform fire early warning.
And step S12, judging whether the abnormal use condition of the electric appliance exists in the power utilization loop to be detected according to the acquired real-time power utilization data and the preset user power utilization model.
In this embodiment, a large amount of collected normal power consumption behavior data of a user are analyzed by a big data analysis technology to obtain a power consumption model of the user, that is, the power consumption model of the user is obtained by analyzing the normal power consumption behavior of the user within a preset time period, and then whether an abnormal use condition of an electric appliance exists in a power consumption loop to be detected is judged by judging whether surge current appearing in the power consumption loop to be detected is caused by an abnormal power consumption behavior of the user, so as to perform targeted fire early warning.
Specifically, the step S12 can be implemented as follows:
a, dividing a time period of normal power utilization behavior analysis of a user into n judgment cycles, wherein n is a positive integer greater than 1.
In this embodiment, the user electricity usage model is evaluated in a piecewise function form based on the evaluation period, and according to the difference of the electricity usage behaviors of the user at different time points, a time period including various electricity usage behaviors of the user is divided into n evaluation periods, and parameters of the user electricity usage model in each evaluation period are not completely the same. Wherein, the time period can be any one of day, week, month and season. Of course, the working time can be divided into working days and non-working days.
b, in any one or more judgment periods, if at least one of the following judgment conditions is met, determining that the surge current occurring in the electric loop to be tested is caused by the abnormal use condition of the electric appliance:
Irms>An；
Wpk>K2*Wnsur；
when t ispk<tsurWhen, Ipk>K1*an；
Wherein, Irms: real-time effective current of power circuit to be measured, AnMaximum effective current value, W, of the circuit to be tested in any judgment period obtained by analyzing normal electricity utilization behavior of userspk: real time total energy, W, of the power supply in the loop during the inrush current generation period (i.e., the current hold time of each inrush current)nsur: maximum energy value, t, of power supply in the loop in the generation period of the surge current obtained by analyzing normal power utilization behaviors of userspkReal-time surge current holding time, t, in the power circuit to be testednsur: maintenance time of surge current, I, obtained by analyzing normal electricity utilization behavior of userpk: real-time peak current, a, of the power circuit to be measurednMaximum peak current value of the power utilization circuit to be tested in any judgment period, obtained through user behavior analysis, K1: maximum effective power error coefficient set by normal electricity usage behavior analysis of the user, K2: and analyzing the set maximum energy error coefficient through the normal electricity utilization behavior of the user.
In this embodiment, the three conditions may be determined in any one determination period, or the three conditions may be determined in a plurality of determination periods, and of course, the more the determination times are, the more accurate the corresponding determination result is, and the determination efficiency will be correspondingly reduced.
Furthermore, the value range of K1 is 1.5-10, and the value range of K2 is 1.5-10. Different K1, K2 can regard as the compensation to the error in the user normal power consumption module, can discern the electrical apparatus use abnormal conditions effectively, strengthen the accuracy of judging.
And step S13, when the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed, sending out corresponding fire early warning to the user.
In this embodiment, when it is determined that an abnormal use condition of an electrical appliance exists in an electrical loop to be tested, a corresponding fire alarm is sent to a user according to the predicted probability, abnormal severity level and other analysis results, for example: and the APP of the user is sent out that measures such as abnormal use of electric appliances possibly existing in the power utilization loop and searching for professional electricians are required, or the automatic power supply cut-off is directly taken.
And step S14, continuously correcting the normal power utilization model of the user according to the feedback of the user on the fire early warning judgment result and by combining machine learning.
In this embodiment, in order to further optimize the accuracy of fire early warning in the power consumption abnormality fire early warning method, parameters in a normal power consumption model of a user are continuously corrected according to feedback of the user on a fire early warning judgment result and by combining machine learning, for example: and correcting the effective value of the current, the peak value of the current, the total energy in the surge current maintaining time and the like so as to continuously improve the prediction accuracy of the normal electricity utilization model of the user.
The embodiment of the invention monitors and acquires real-time power utilization data in the power utilization loop to be detected, judges whether the abnormal use condition of the electric appliance exists in the power utilization loop to be detected according to the acquired real-time power utilization data and a preset user power utilization model, and finally sends out corresponding fire early warning to a user when the abnormal use condition of the electric appliance exists in the power utilization loop to be detected. According to the power utilization abnormal fire early warning method, the abnormal use condition of the electric appliance in the power utilization loop to be detected can be rapidly and accurately predicted, the fire caused by the abnormal use of the electric appliance can be early warned in advance, and further the loss of life and property of people caused by the fire caused by the abnormal use of the electric appliance can be effectively avoided; the condition that only overcurrent protection is needed as the existing circuit breaker is avoided, and early warning can not be carried out at the initial stage of fire or when the fire does not occur yet. In addition, the power utilization abnormity fire early warning method can automatically perform corresponding fire protection measures while performing fire early warning, and can perform machine learning according to the feedback of the user to the early warning result, so that the accuracy of fire early warning is continuously improved, the intelligent degree is high, and reliable fire early warning guarantee can be brought to the user.
Example two
The embodiment of the invention provides a fire early warning system for abnormal electricity utilization, which realizes the method in the first embodiment, and referring to fig. 2, the system can comprise: the device comprises an acquisition module 100, a judgment module 200 and a processing module 300.
The acquiring module 100 is configured to monitor and acquire real-time power consumption data in a power consumption loop to be detected.
It should be noted that the reasons for fire generally include two reasons, one is caused by an electrical short circuit and an overload current caused by abnormal use of an electrical appliance; the second is caused by wire aging or circuit aging.
In the embodiment, in order to avoid a fire caused by abnormal use of an electrical appliance, the power utilization abnormal fire early warning method performs big data analysis on normal power utilization behaviors of a user to obtain a power utilization model of the user, so as to identify abnormal use conditions of the electrical appliance in a power utilization loop and further perform fire early warning.
And the judging module 200 is used for judging whether the abnormal use condition of the electric appliance exists in the power utilization loop to be detected according to the acquired real-time power utilization data and the preset normal power utilization model of the user.
In this embodiment, a large amount of collected normal power consumption behavior data of a user are analyzed by a big data analysis technology to obtain a power consumption model of the user, that is, the power consumption model of the user is obtained by analyzing the normal power consumption behavior of the user within a preset time period, and then whether an abnormal use condition of an electric appliance exists in a power consumption loop to be detected is judged by judging whether surge current appearing in the power consumption loop to be detected is caused by an abnormal power consumption behavior of the user, so as to perform targeted fire early warning.
And the processing module 300 is configured to send a corresponding fire warning to the user when it is determined that the electrical appliance is in an abnormal use condition in the electrical loop to be tested.
In this embodiment, when it is determined that an abnormal use condition of an electrical appliance exists in an electrical loop to be tested, a corresponding fire alarm is sent to a user according to the predicted probability, abnormal severity level and other analysis results, for example: and the APP of the user is sent out that measures such as abnormal use of electric appliances possibly existing in the power utilization loop and searching for professional electricians are required, or the automatic power supply cut-off is directly taken.
Specifically, referring to fig. 3, the determining module 200 may include: a dividing unit 201 and a judging unit 202.
The dividing unit 201 is configured to divide a time period of normal power consumption behavior analysis of a user into n determination cycles, where n is a positive integer greater than 1.
In this embodiment, the normal power utilization model of the user is evaluated in a piecewise function form based on the evaluation period, and according to the difference of power utilization behaviors of the user at different time points, a time period including various power utilization behaviors of the user is divided into n evaluation periods, and parameters of the normal power utilization model of the user in each evaluation period are not completely the same. Wherein, the time period can be any one of day, week, month and season. Of course, the working time can be divided into working days and non-working days.
The judging unit 202 is configured to, in any one or more judging cycles, determine that an inrush current occurring in the power loop to be tested is caused by an abnormal use condition of the electrical appliance if at least one of the following judging conditions is satisfied:
Irms>An；
Wpk>K2*Wnsur；
when t ispk<tsurWhen, Ipk>K1*an；
Wherein, Irms: real-time effective current of power circuit to be measured, AnMaximum effective current value, W, of the circuit to be tested in any judgment period obtained by analyzing normal electricity utilization behavior of userspk: real time total energy, W, of the power supply in the loop during the inrush current generation period (i.e., the current hold time of each inrush current)nsur: maximum energy value, t, of power supply in the loop in the generation period of the surge current obtained by analyzing normal power utilization behaviors of userspkReal-time surge current holding time, t, in the power circuit to be testednsur: maintenance time of surge current, I, obtained by analyzing normal electricity utilization behavior of userpk: real-time peak current, a, of the power circuit to be measurednMaximum peak current value of the power utilization circuit to be tested in any judgment period, obtained through user behavior analysis, K1: maximum effective power error coefficient set by normal electricity usage behavior analysis of the user, K2: and the maximum energy error coefficient is set for analysis through the normal electricity utilization of the user row.
In this embodiment, the three conditions may be determined in any one determination period, or the three conditions may be determined in a plurality of determination periods, and of course, the more the determination times are, the more accurate the corresponding determination result is, and the determination efficiency will be correspondingly reduced.
Furthermore, the value range of K1 is 1.5-10, and the value range of K2 is 1.5-10. Different K1, K2 can regard as the compensation to the error in the user normal power consumption module, can discern the electrical apparatus use abnormal conditions effectively, strengthen the accuracy of judging.
Optionally, referring to fig. 2, the system may further include: the learning module 400 is revised.
And the correction learning module 400 is used for continuously correcting the normal power utilization model of the user according to the feedback of the user on the fire early warning judgment result and by combining machine learning.
In this embodiment, in order to further optimize the accuracy of fire early warning in the power consumption abnormality fire early warning method, parameters in a normal power consumption model of a user are continuously corrected according to feedback of the user on a fire early warning judgment result and by combining machine learning, for example: and correcting the effective value of the current, the peak value of the current, the total energy in the surge current maintaining time and the like so as to continuously improve the prediction accuracy of the normal electricity utilization model of the user.
The embodiment of the invention monitors and acquires real-time power utilization data in the power utilization loop to be detected, judges whether the abnormal use condition of the electric appliance exists in the power utilization loop to be detected according to the acquired real-time power utilization data and a preset normal power utilization model of a user, and finally sends out corresponding fire early warning to the user when the abnormal use condition of the electric appliance exists in the power utilization loop to be detected. The power utilization abnormal fire early warning device can quickly and accurately predict the abnormal use condition of the electric appliance in the power utilization loop to be detected, and early warn the fire caused by the abnormal use of the electric appliance in advance, so that the loss of life and property of people caused by the fire caused by the abnormal use of the electric appliance can be effectively avoided; like the existing circuit breaker, only overcurrent protection is provided, and early warning cannot be carried out at the initial stage of fire or before the fire happens. In addition, this abnormal fire early warning device of power consumption can also carry out corresponding conflagration safeguard measure voluntarily when carrying out the conflagration early warning to according to the feedback of user to the early warning result, carry out machine learning, constantly improve the accuracy of conflagration early warning, intelligent degree is high, can bring reliable conflagration early warning guarantee for the user.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
It should be noted that: when the electricity abnormal fire early warning system provided by the embodiment implements the electricity abnormal fire early warning method, the division of the functional modules is only used for illustration, and in practical application, the function distribution can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the power consumption abnormal fire early warning system and the power consumption abnormal fire early warning method provided by the embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment and is not described again.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A fire early warning method for abnormal electricity utilization is characterized by comprising the following steps:
monitoring and acquiring real-time power utilization data in a power utilization loop to be detected;
judging whether an abnormal use condition of the electric appliance exists in the power utilization loop to be tested according to the acquired real-time power utilization data and a preset user power utilization model, wherein the user power utilization model is obtained based on normal power utilization behavior analysis of a user in a preset time period;
when the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed, corresponding fire early warning is sent to a user,
the method comprises the following steps of judging whether the abnormal use condition of the electric appliance exists in the power utilization loop to be detected according to the acquired real-time power utilization data and a preset user power utilization model, and comprising the following steps:
dividing a time period of normal power utilization behavior analysis of a user into n judgment cycles, wherein n is a positive integer greater than 1;
in any one or more judgment periods, if the following judgment conditions are simultaneously met, the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed to occur:
Irms>An；
Wpk>K2*Wnsur；
when t ispk<tsurWhen, Ipk>K1*an；
Wherein, Irms: real-time effective current of the power circuit to be measured, AnThe maximum effective current value of the circuit to be tested in any judgment period is obtained by analyzing the normal electricity utilization behavior of the user,Wpk: real time total energy, W, of the power supply in the loop during the period of occurrence of the inrush currentnsur: maximum energy value, t, of power supply in the loop in the generation period of the surge current obtained by analyzing normal power utilization behaviors of userspkThe holding time t of the real-time surge current in the power loop to be measurednsur: maintenance time of surge current, I, obtained by analyzing normal electricity utilization behavior of userpk: real-time peak current, a, of the power circuit to be measurednMaximum peak current value of the power utilization circuit to be tested in any judgment period, obtained through user behavior analysis, K1: maximum effective power error coefficient set by normal electricity usage behavior analysis of the user, K2: and analyzing the set maximum energy error coefficient through the normal electricity utilization behavior of the user.
2. The method of claim 1, wherein K1 is selected from the range of 1.5 to 10, and K2 is selected from the range of 1.5 to 10.
3. The method of claim 1 or 2, further comprising:
and continuously correcting the power utilization model of the user according to the feedback of the user on the fire early warning judgment result and by combining machine learning.
4. An electricity abnormal fire early warning system, comprising:
the acquisition module is used for monitoring and acquiring real-time power utilization data in the power utilization loop to be detected;
the judging module is used for judging whether an abnormal use condition of an electric appliance exists in the power utilization loop to be detected according to the acquired real-time power utilization data and a preset user power utilization model, and the user power utilization model is obtained based on normal power utilization behavior analysis of a user in a preset time period;
the processing module is used for sending out corresponding fire early warning to a user when the abnormal use condition of the electric appliance in the electric loop to be tested is confirmed,
the judging module comprises:
the dividing unit is used for dividing the time period of normal power utilization behavior analysis of the user into n judgment cycles, wherein n is a positive integer greater than 1;
the judging unit is used for confirming that the abnormal use condition of the electric appliance occurs in the electric loop to be detected if the following judging conditions are simultaneously met in any one or more judging periods:
Irms>An；
Wpk>K2*Wnsur；
when t ispk<tsurWhen, Ipk>K1*an；
Wherein, Irms: real-time effective current of the power circuit to be measured, AnMaximum effective current value, W, of the circuit to be tested in any judgment period obtained by analyzing normal electricity utilization behavior of userspk: real time total energy, W, of the power supply in the loop during the period of occurrence of the inrush currentnsur: maximum energy value, t, of power supply in the loop in the generation period of the surge current obtained by analyzing normal power utilization behaviors of userspkThe holding time t of the real-time surge current in the power loop to be measurednsur: maintenance time of surge current, I, obtained by analyzing normal electricity utilization behavior of userpk: real-time peak current, a, of the power circuit to be measurednThe maximum peak current value of the power utilization loop to be tested in any judgment period is obtained through the analysis of the normal power utilization behavior of the user, K1: maximum effective power error coefficient set by normal electricity usage behavior analysis of the user, K2: and analyzing the set maximum energy error coefficient through the normal electricity utilization behavior of the user.
5. The system of claim 4, wherein K1 is selected from the range of 1.5 to 10 and K2 is selected from the range of 1.5 to 10.
6. The system of claim 4 or 5, further comprising:
and the correction learning module is used for continuously correcting the normal power utilization model of the user according to the feedback of the user on the fire early warning judgment result and by combining machine learning.
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| CN107730395B (en) * | 2017-09-13 | 2021-05-18 | 国网天津市电力公司电力科学研究院 | Power consumption abnormity detection method based on power consumption deviation rate for low-voltage users |
| CN111554046A (en) * | 2020-04-24 | 2020-08-18 | 成都中科慧源科技有限公司 | Method for realizing safe gas utilization of Internet of things gas meter by limiting gas utilization in intervals |
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| CN112255480A (en) * | 2020-10-12 | 2021-01-22 | 浙江长元科技有限公司 | Hall effect based electric appliance characteristic identification method and safety early warning system thereof |
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