CN112508100A - Method and device for monitoring running state of intelligent community public equipment - Google Patents
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Abstract
The invention provides a method and a device for monitoring the running state of public equipment in an intelligent community, which comprises the following steps: fitting historical power data according to Gaussian distribution to obtain an abnormal probability model of the running state of the public equipment; acquiring historical power data of the public equipment in a normal running state, and determining an identification threshold value of the abnormal running state based on an abnormal probability model; acquiring real-time power data of the public equipment, and calculating the probability of abnormal operation of the current public equipment according to the real-time power data and the abnormal probability model; and comparing the identification threshold with the calculation result, and analyzing the running state of the public equipment according to the comparison result. Based on the abnormal probability model and the determined identification threshold value, the real-time power data are analyzed, the data utilization efficiency is improved, meanwhile, the power operation and maintenance efficiency of the community is fully improved, and personal and property accidents caused by public power utilization safety problems can be effectively avoided.
Description
Technical Field
The invention belongs to the field of power utilization monitoring, and particularly relates to a method and a device for monitoring the running state of public equipment in an intelligent community.
Background
The intelligent community is the future development direction of the Chinese community, and aims to provide a safe, comfortable and convenient modern living and working environment for community users by using a new-generation information technology such as the Internet of things. Since 2014 house and urban and rural construction department release the trial documents of the intelligent community construction guide, the intelligent community construction of China has achieved remarkable achievements, various monitoring service applications facing the intelligent community emerge in the market, the service range of the intelligent community monitoring service application can cover various aspects such as entrance guard, parking space, property cost and smart home, and great convenience is provided for community management work.
Most public equipment in current wisdom community only relies on staff's periodic maintenance, and staff is difficult to in time notice when the equipment appears unusually, and this has increased the risk that the accident took place, not only influences the life of equipment itself, still causes the life inconvenience to the resident in the wisdom community.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an operation state monitoring method of intelligent community public equipment, which comprises the following steps:
fitting historical power data according to Gaussian distribution to obtain an abnormal probability model of the running state of the public equipment;
acquiring historical power data of the public equipment in a normal running state, and determining an identification threshold value of the abnormal running state based on an abnormal probability model;
acquiring real-time power data of the public equipment, and calculating the probability of abnormal operation of the current public equipment according to the real-time power data and the abnormal probability model;
and comparing the identification threshold with the calculation result, and analyzing the running state of the public equipment according to the comparison result.
Optionally, the fitting the historical power data according to gaussian distribution to obtain an abnormal probability model of the operating state of the public device includes:
constructing probability density distribution f (p) based on formula I;
wherein f (p) represents the probability that the power data is p, mu is the mean value of p, and sigma is the standard deviation of p; the value ranges of p, mu and sigma are positive numbers, and the value range of f (p) is a positive number not more than 1;
constructing an abnormal probability model F (p) based on formula twoi)；
Wherein, F (p)i) For historical power data as piProbability of a temporal running anomaly, piFor the acquired historical power data, the value range of i is a positive integer from 1 to N, N is the total data number of the historical power data, and f (p)i) For power data as piThe probability of (d); p is a radical ofi、pkHas a value range of positive integer, F (p)i) The value range of (a) is a positive number not greater than 1, and the value range of N, k is a positive integer.
Optionally, the obtaining historical power data of the public device in the normal operating state and determining the identification threshold of the abnormal operating state based on the abnormal probability model include:
obtaining an abnormal probability corresponding to the historical power data through an abnormal probability model;
calculating an identification threshold value A of the abnormal operation state based on a formula III;
A＝1-min{F(p1),F(p2),F(p3),…,F(pN) 1, equation three;
F(p1),F(p2),F(p3),…,F(pN) The abnormal probabilities corresponding to the historical power data are respectively, N is the total data number of the historical power data, the value range of A is a positive number smaller than 1, and the value range of N is a positive integer.
Optionally, the obtaining real-time power data of the public device, and calculating the probability of the current abnormal operation of the public device according to the real-time power data and the abnormal probability model includes:
acquiring real-time electric power data of public equipment through a sensor deployed at a preset monitoring point;
determining the window size to be w based on a preset state analysis period and a sensor acquisition period;
carrying out windowing interception on the collected real-time electric power data according to the determined window to obtain the tth time sequence T of the real-time electric power datat＝{pt1,pt2,pt3,…,ptwH, converting the time sequence TtInputting the abnormal probability model to obtain an output result F (p)tk)；
Calculating the probability F (T) of the abnormal operation of the current public equipment based on the formula IV;
determining the value of J according to a formula five;
ptkis a time sequence TtOf (1) a k-th timing value, ptkHas a positive value, F (T), F (p)tk) The value ranges of t, k and w are positive integers.
Optionally, the comparing the identification threshold with the calculation result, and analyzing the operating state of the public device according to the comparison result includes:
if the absolute value of the calculation result does not exceed the identification threshold, the current running state of the public equipment is normal;
if the calculation result is larger than the identification threshold, the current operation state of the public equipment is power excess;
and if the calculation result is smaller than the inverse number of the identification threshold, the current operation state of the public equipment is insufficient power.
The invention also provides a device for monitoring the running state of the intelligent community public equipment based on the same thought. The method comprises the following steps:
a modeling unit: the abnormal probability model is used for fitting the historical power data according to Gaussian distribution to obtain an abnormal probability model of the running state of the public equipment;
a threshold calculation unit: the method comprises the steps of acquiring historical power data of the public equipment in a normal running state, and determining an identification threshold value of the abnormal running state based on an abnormal probability model;
a real-time monitoring unit: the system comprises a real-time power data acquisition module, an abnormal probability model calculation module and a fault probability calculation module, wherein the real-time power data acquisition module is used for acquiring real-time power data of public equipment and calculating the probability of abnormal operation of the current public equipment according to the real-time power data and the abnormal probability;
an analysis unit: and the system is used for comparing the identification threshold with the calculation result and analyzing the running state of the public equipment according to the comparison result.
Optionally, the modeling unit is specifically configured to:
constructing probability density distribution f (p) based on formula I;
wherein f (p) represents the probability that the power data is p, mu is the mean value of p, and sigma is the standard deviation of p; the value ranges of p, mu and sigma are positive numbers, and the value range of f (p) is a positive number not more than 1;
constructing an abnormal probability model F (p) based on formula twoi)；
Wherein, F (p)i) For historical power data as piProbability of a temporal running anomaly, piFor the acquired historical power data, the value range of i is a positive integer from 1 to N, N is the total data number of the historical power data, and f (p)i) For power data as piThe probability of (d); p is a radical ofi、pkHas a value range of positive integer, F (p)i) The value range of (a) is a positive number not greater than 1, and the value range of N, k is a positive integer.
Optionally, the threshold calculation unit is specifically configured to:
obtaining an abnormal probability corresponding to the historical power data through an abnormal probability model;
calculating an identification threshold value A of the abnormal operation state based on a formula III;
A＝1-min{F(p1),F(p2),F(p3),…,F(pN) 1, equation three;
F(p1),F(p2),F(p3),…,F(pN) Respectively corresponding abnormal probability of each historical power data, N is the total data number of the historical power data, and A is a positive number with the value range less than 1And the value range of N is a positive integer.
Optionally, the real-time monitoring unit is specifically configured to:
acquiring real-time electric power data of public equipment through a sensor deployed at a preset monitoring point;
determining the window size to be w based on a preset state analysis period and a sensor acquisition period;
carrying out windowing interception on the collected real-time electric power data according to the determined window to obtain the tth time sequence T of the real-time electric power datat＝{pt1,pt2,pt3,…,ptwH, converting the time sequence TtInputting the abnormal probability model to obtain an output result F (p)tk)；
Calculating the probability F (T) of the abnormal operation of the current public equipment based on the formula IV;
determining the value of J according to a formula five;
ptkis a time sequence TtOf (1) a k-th timing value, ptkHas a positive value, F (T), F (p)tk) The value ranges of t, k and w are positive integers.
Optionally, the analysis unit is specifically configured to:
if the absolute value of the calculation result does not exceed the identification threshold, the current running state of the public equipment is normal;
if the calculation result is larger than the identification threshold, the current operation state of the public equipment is power excess;
and if the calculation result is smaller than the inverse number of the identification threshold, the current operation state of the public equipment is insufficient power.
The technical scheme provided by the invention has the beneficial effects that:
based on the abnormal probability model and the determined identification threshold value, the real-time power data are analyzed, the inherent value of the historical power data of the equipment is mined, the data utilization efficiency is improved, the power operation and maintenance efficiency of the community is fully improved, and personal and property accidents caused by public power utilization safety problems can be effectively avoided.
Drawings
In order to more clearly illustrate the technical solution 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 the drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a method for monitoring an operating status of a public device in an intelligent community according to the present invention;
fig. 2 is a block diagram of an operation status monitoring apparatus for smart community public equipment according to the present invention.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present invention provides a method for monitoring an operation state of a public device in an intelligent community, including:
s1: and fitting the historical power data according to the Gaussian distribution to obtain an abnormal probability model of the running state of the public equipment.
And (3) constructing probability density distribution f (p) based on a formula I, wherein the probability density distribution f (p) follows Gaussian distribution, accords with the distribution rule of the running state and the electric power data of the public equipment, and improves the scientificity of the abnormal probability model.
Wherein f (p) represents the probability that the power data is p, mu is the mean value of p, and sigma is the standard deviation of p; the value ranges of p, mu and sigma are positive numbers, and the value range of f (p) is a positive number not more than 1.
Constructing an abnormal probability model F (p) based on formula twoi)；
Wherein, F (p)i) For historical power data as piProbability of a temporal running anomaly, piFor the acquired historical power data, the value range of i is a positive integer from 1 to N, N is the total data number of the historical power data, and f (p)i) For power data as piThe probability of (d); p is a radical ofi、pkHas a value range of positive integer, F (p)i) The value range of (a) is a positive number not greater than 1, and the value range of N, k is a positive integer.
Because the power data of the public equipment is complex and various and is influenced by various factors in the actual operation process, the operation state of the public equipment is judged to be too complete only according to whether the power data exceeds the normal range, and an inaccurate result is easily obtained. The operation state monitoring method provided by the embodiment judges on the basis of the abnormal probability, scientifically constructs the Gaussian distribution relation among the power data, the operation state and the abnormal probability, and can improve the accuracy of the monitoring result.
S2: historical power data of the public equipment in a normal running state are obtained, and an identification threshold value of the running state abnormity is determined based on an abnormity probability model.
Obtaining an abnormal probability corresponding to the historical power data through an abnormal probability model;
calculating an identification threshold value A of the abnormal operation state based on a formula III;
A＝1-min{F(p1),F(p2),F(p3),…,F(pN) 1, equation three;
F(p1),F(p2),F(p3),…,F(pN) Respectively for each historical powerAnd (3) the abnormal probability corresponding to the data, wherein N is the total number of the historical power data, the value range of A is a positive number smaller than 1, and the value range of N is a positive integer.
The traditional running state monitoring method is often analyzed according to the artificial setting of thresholds experienced, and is easily influenced by subjective factors, so that the thresholds are unreasonable to set, and the monitoring effect is further influenced. The operation state monitoring method provided by the embodiment calculates the identification threshold based on the abnormal probability model in the step S1, and is used for identifying the abnormal operation state of the public equipment, thereby overcoming the limitation of subjective factors.
S3: and acquiring real-time power data of the public equipment, and calculating the probability of the current abnormal operation of the public equipment by combining the real-time power data with the abnormal probability model.
And acquiring real-time power data of the public equipment through a sensor deployed at a preset monitoring point. In the embodiment, the real-time power data can be displayed on a real-time monitoring page, so that real-time visual display of each monitoring point of the smart community is realized.
And the top of the real-time monitoring page is a comprehensive statistical module for displaying the total number of the current monitoring points, the number of the normal and abnormal public equipment and the latest prompt information of the abnormal equipment state. The middle part of the real-time monitoring page is provided with a data display module which displays real-time electric power data of each monitoring point, and the display mode comprises numerical value display and curve display. For single-phase equipment, the data types which can be selectively displayed comprise voltage effective values, current effective values, active power, reactive power, apparent power, power factors, residual current effective values, voltage harmonic distortion rates and current harmonic distortion rates; for a three-phase device, the corresponding voltage virtual, current virtual, active, reactive, apparent, power factor, and total active, reactive, power factor, three-phase imbalance of voltage and current, etc. may be shown A, B, C. A monitoring point selection button, monitoring point name character display and public equipment type character display are arranged above each monitoring point display area; the lower part comprises a 3 x 3 numerical value display array, a data curve display frame and two corresponding refresh buttons. The numerical value display box in the table supports the selection of data types by using a pull-down menu, and the numerical value or curve display can be manually refreshed by clicking a refresh button. And displaying the analysis result of the power data on the right side of the real-time monitoring page, and summarizing and displaying the name and the state information of each monitoring point in a table form. The operation state information of the public equipment comprises an operation state, a harmonic distortion state and an electric leakage condition, and for the three-phase equipment, the operation state information further comprises a three-phase imbalance state, and the state information can be updated according to a state analysis period preset by a user.
The analysis of the harmonic distortion state of the public equipment is mainly realized by a method of threshold value judgment, the threshold value is manually set by a user on a system, when the average value of the harmonic distortion rate of the current waveform or the voltage waveform of the public equipment in a state analysis period exceeds the threshold value, the harmonic distortion state is 'current/voltage serious distortion', and for three-phase equipment, the harmonic distortion state is 'certain phase current/voltage serious distortion'.
Similarly, the three-phase unbalanced state analysis of the three-phase device is also realized by using a threshold judgment method, the threshold is also manually set on the system by a user, and when the average value of the three-phase unbalanced degree of the current/voltage of the device in one state analysis period exceeds the corresponding threshold, the corresponding state information is 'three-phase current/voltage serious unbalance'.
The leakage condition detection function of the device is also realized by using a threshold judgment method, for example, the preset leakage threshold is 50mA, and when the average value of the residual current in one state analysis period is greater than 50mA, the corresponding leakage condition information is displayed as "leakage danger".
The following describes an analysis process of the operation state information:
determining the window size to be w based on the preset state analysis period and the acquisition period of the sensor, carrying out windowing interception on the acquired real-time electric power data according to the determined window, and obtaining the T-th time sequence T of the real-time electric power datat＝{pt1,pt2,pt3,…,ptw}. For example, if the sampling period is 0.5s and the state analysis period preset by the user is 10s, then w is 10/0.5-20, that is, the sampling period is 0.5s, that is, the value isThe time series sequence has 20 sequence values. Will time sequence TtInputting the abnormal probability model to obtain an output result F (p)tk)。
Calculating the probability F (T) of the abnormal operation of the current public equipment based on the formula IV;
determining the value of J according to a formula five;
ptkis a time sequence TtOf (1) a k-th timing value, ptkHas a positive value, F (T), F (p)tk) The value ranges of t, k and w are positive integers.
S4: and comparing the identification threshold with the calculation result, and analyzing the running state of the public equipment according to the comparison result.
If the absolute value of the calculation result does not exceed the identification threshold, namely when | F (T) | is less than or equal to A, the current running state of the public equipment is normal;
if the calculation result is larger than the identification threshold, namely when F (T) > A, the current operation state of the public equipment is power excess;
and if the calculation result is smaller than the opposite number of the identification threshold value, namely when F (T) < -A, the current operation state of the public equipment is insufficient power.
Example two
As shown in fig. 2, the present invention provides an operation status monitoring apparatus 5 for public equipment in smart community, comprising:
the modeling unit 51: and the abnormal probability model is used for fitting the historical power data according to the Gaussian distribution to obtain the abnormal probability model of the running state of the public equipment. The method is specifically used for:
and (3) constructing probability density distribution f (p) based on a formula I, wherein the probability density distribution f (p) follows Gaussian distribution, accords with the distribution rule of the running state and the electric power data of the public equipment, and improves the scientificity of the abnormal probability model.
Wherein f (p) represents the probability that the power data is p, mu is the mean value of p, and sigma is the standard deviation of p; the value ranges of p, mu and sigma are positive numbers, and the value range of f (p) is a positive number not more than 1.
Constructing an abnormal probability model F (p) based on formula twoi)；
Wherein, F (p)i) For historical power data as piProbability of a temporal running anomaly, piFor the acquired historical power data, the value range of i is a positive integer from 1 to N, N is the total data number of the historical power data, and f (p)i) For power data as piThe probability of (d); p is a radical ofi、pkHas a value range of positive integer, F (p)i) The value range of (a) is a positive number not greater than 1, and the value range of N, k is a positive integer.
Because the power data of the public equipment is complex and various and is influenced by various factors in the actual operation process, the operation state of the public equipment is judged to be too complete only according to whether the power data exceeds the normal range, and an inaccurate result is easily obtained. The operation state monitoring method provided by the embodiment judges on the basis of the abnormal probability, scientifically constructs the Gaussian distribution relation among the power data, the operation state and the abnormal probability, and can improve the accuracy of the monitoring result.
Threshold value calculation unit 52: the method is used for acquiring historical power data of the public equipment when the running state is normal and determining the identification threshold value of the running state abnormality based on the abnormality probability model. The method is specifically used for:
obtaining an abnormal probability corresponding to the historical power data through an abnormal probability model;
calculating an identification threshold value A of the abnormal operation state based on a formula III;
A＝1-min{F(p1),F(p2),F(p3),…,F(pN) 1, equation three;
F(p1),F(p2),F(p3),…,F(pN) The abnormal probabilities corresponding to the historical power data are respectively, N is the total data number of the historical power data, the value range of A is a positive number smaller than 1, and the value range of N is a positive integer.
The traditional running state monitoring method is often analyzed according to the artificial setting of thresholds experienced, and is easily influenced by subjective factors, so that the thresholds are unreasonable to set, and the monitoring effect is further influenced. The operation state monitoring method provided by the embodiment calculates the identification threshold value based on the abnormal probability model in the modeling unit 51, and is used for identifying the abnormal operation state of the public equipment, so that the limitation of subjective factors is overcome.
The real-time monitoring unit 53: the method is used for acquiring real-time power data of the public equipment and calculating the probability of the current abnormal operation of the public equipment by combining the real-time power data with the abnormal probability model. The method is specifically used for:
and acquiring real-time power data of the public equipment through a sensor deployed at a preset monitoring point. In the embodiment, the real-time power data can be displayed on a real-time monitoring page, so that real-time visual display of each monitoring point of the smart community is realized.
And the top of the real-time monitoring page is a comprehensive statistical module for displaying the total number of the current monitoring points, the number of the normal and abnormal public equipment and the latest prompt information of the abnormal equipment state. The middle part of the real-time monitoring page is provided with a data display module which displays real-time electric power data of each monitoring point, and the display mode comprises numerical value display and curve display. For single-phase equipment, the data types which can be selectively displayed comprise voltage effective values, current effective values, active power, reactive power, apparent power, power factors, residual current effective values, voltage harmonic distortion rates and current harmonic distortion rates; for a three-phase device, the corresponding voltage virtual, current virtual, active, reactive, apparent, power factor, and total active, reactive, power factor, three-phase imbalance of voltage and current, etc. may be shown A, B, C. A monitoring point selection button, monitoring point name character display and public equipment type character display are arranged above each monitoring point display area; the lower part comprises a 3 x 3 numerical value display array, a data curve display frame and two corresponding refresh buttons. The numerical value display box in the table supports the selection of data types by using a pull-down menu, and the numerical value or curve display can be manually refreshed by clicking a refresh button. And displaying the analysis result of the power data on the right side of the real-time monitoring page, and summarizing and displaying the name and the state information of each monitoring point in a table form. The operation state information of the public equipment comprises an operation state, a harmonic distortion state and an electric leakage condition, and for the three-phase equipment, the operation state information further comprises a three-phase imbalance state, and the state information can be updated according to a state analysis period preset by a user.
The analysis of the harmonic distortion state of the public equipment is mainly realized by a method of threshold value judgment, the threshold value is manually set by a user on a system, when the average value of the harmonic distortion rate of the current waveform or the voltage waveform of the public equipment in a state analysis period exceeds the threshold value, the harmonic distortion state is 'current/voltage serious distortion', and for three-phase equipment, the harmonic distortion state is 'certain phase current/voltage serious distortion'.
Similarly, the three-phase unbalanced state analysis of the three-phase device is also realized by using a threshold judgment method, the threshold is also manually set on the system by a user, and when the average value of the three-phase unbalanced degree of the current/voltage of the device in one state analysis period exceeds the corresponding threshold, the corresponding state information is 'three-phase current/voltage serious unbalance'.
The leakage condition detection function of the device is also realized by using a threshold judgment method, for example, the preset leakage threshold is 50mA, and when the average value of the residual current in one state analysis period is greater than 50mA, the corresponding leakage condition information is displayed as "leakage danger".
The following describes an analysis process of the operation state information:
determining the window size to be w based on the preset state analysis period and the acquisition period of the sensor, carrying out windowing interception on the acquired real-time electric power data according to the determined window, and obtaining the T-th time sequence T of the real-time electric power datat＝{pt1,pt2,pt3,…,ptw}. For example, if the sampling period is 0.5s and the state analysis period preset by the user is 10s, w is 10/0.5-20, i.e., the sequence has 20 sequence values. Will time sequence TtInputting the abnormal probability model to obtain an output result F (p)tk)。
Calculating the probability F (T) of the abnormal operation of the current public equipment based on the formula IV;
determining the value of J according to a formula five;
ptkis a time sequence TtOf (1) a k-th timing value, ptkHas a positive value, F (T), F (p)tk) The value ranges of t, k and w are positive integers.
The analyzing unit 54: and the system is used for comparing the identification threshold with the calculation result and analyzing the running state of the public equipment according to the comparison result. The method is specifically used for:
if the absolute value of the calculation result does not exceed the identification threshold, namely when | F (T) | is less than or equal to A, the current running state of the public equipment is normal;
if the calculation result is larger than the identification threshold, namely when F (T) > A, the current operation state of the public equipment is power excess;
and if the calculation result is smaller than the opposite number of the identification threshold value, namely when F (T) < -A, the current operation state of the public equipment is insufficient power.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An operation state monitoring method of intelligent community public equipment is characterized by comprising the following steps:
fitting historical power data according to Gaussian distribution to obtain an abnormal probability model of the running state of the public equipment;
acquiring historical power data of the public equipment in a normal running state, and determining an identification threshold value of the abnormal running state based on an abnormal probability model;
acquiring real-time power data of the public equipment, and calculating the probability of abnormal operation of the current public equipment according to the real-time power data and the abnormal probability model;
and comparing the identification threshold with the calculation result, and analyzing the running state of the public equipment according to the comparison result.
2. The method for monitoring the operating state of the intelligent community public equipment according to claim 1, wherein the step of fitting the historical power data according to Gaussian distribution to obtain an abnormal probability model of the operating state of the public equipment comprises the following steps:
constructing probability density distribution f (p) based on formula I;
wherein f (p) represents the probability that the power data is p, mu is the mean value of p, and sigma is the standard deviation of p; the value ranges of p, mu and sigma are positive numbers, and the value range of f (p) is a positive number not more than 1;
constructing an abnormal probability model F (p) based on formula twoi)；
Wherein, F (p)i) For historical power data as piProbability of a temporal running anomaly, piFor the acquired historical power data, the value range of i is a positive integer from 1 to N, N is the total data number of the historical power data, and f (p)i) For power data as piThe probability of (d); p is a radical ofi、pkHas a value range of positive integer, F (p)i) The value range of (a) is a positive number not greater than 1, and the value range of N, k is a positive integer.
3. The method for monitoring the operating state of the intelligent community public equipment according to claim 1, wherein the step of acquiring historical power data of the public equipment in a normal operating state and determining the identification threshold of the abnormal operating state based on an abnormal probability model comprises the following steps:
obtaining an abnormal probability corresponding to the historical power data through an abnormal probability model;
calculating an identification threshold value A of the abnormal operation state based on a formula III;
A＝1-min{F(p1),F(p2),F(p3),…,F(pN) 1, equation three;
F(p1),F(p2),F(p3),…,F(pN) The abnormal probabilities corresponding to the historical power data are respectively, N is the total data number of the historical power data, the value range of A is a positive number smaller than 1, and the value range of N is a positive integer.
4. The method for monitoring the operation state of the intelligent community public equipment according to claim 1, wherein the step of acquiring real-time power data of the public equipment and calculating the probability of the current abnormal operation of the public equipment by combining the real-time power data with an abnormal probability model comprises the following steps:
acquiring real-time electric power data of public equipment through a sensor deployed at a preset monitoring point;
determining the window size to be w based on a preset state analysis period and a sensor acquisition period;
carrying out windowing interception on the collected real-time electric power data according to the determined window to obtain the tth time sequence T of the real-time electric power datat＝{pt1,pt2,pt3,…,ptwH, converting the time sequence TtInputting the abnormal probability model to obtain an output result F (p)tk)；
Calculating the probability F (T) of the abnormal operation of the current public equipment based on the formula IV;
determining the value of J according to a formula five;
ptkis a time sequence TtOf (1) a k-th timing value, ptkHas a positive value, F (T), F (p)tk) The value ranges of t, k and w are positive integers.
5. The method as claimed in claim 1, wherein the comparing the recognition threshold with the calculation result and analyzing the operation status of the public device according to the comparison result comprises:
if the absolute value of the calculation result does not exceed the identification threshold, the current running state of the public equipment is normal;
if the calculation result is larger than the identification threshold, the current operation state of the public equipment is power excess;
and if the calculation result is smaller than the inverse number of the identification threshold, the current operation state of the public equipment is insufficient power.
6. The utility model provides an operating condition monitoring device of public equipment of wisdom community which characterized in that, operating condition monitoring device includes:
a modeling unit: the abnormal probability model is used for fitting the historical power data according to Gaussian distribution to obtain an abnormal probability model of the running state of the public equipment;
a threshold calculation unit: the method comprises the steps of acquiring historical power data of the public equipment in a normal running state, and determining an identification threshold value of the abnormal running state based on an abnormal probability model;
a real-time monitoring unit: the system comprises a real-time power data acquisition module, an abnormal probability model calculation module and a fault probability calculation module, wherein the real-time power data acquisition module is used for acquiring real-time power data of public equipment and calculating the probability of abnormal operation of the current public equipment according to the real-time power data and the abnormal probability;
an analysis unit: and the system is used for comparing the identification threshold with the calculation result and analyzing the running state of the public equipment according to the comparison result.
7. The device for monitoring the operating state of the intelligent community public equipment according to claim 6, wherein the modeling unit is specifically configured to:
constructing probability density distribution f (p) based on formula I;
wherein f (p) represents the probability that the power data is p, mu is the mean value of p, and sigma is the standard deviation of p; the value ranges of p, mu and sigma are positive numbers, and the value range of f (p) is a positive number not more than 1;
constructing an abnormal probability model F (p) based on formula twoi)；
Wherein, F (p)i) For historical power data as piProbability of a temporal running anomaly, piFor history of acquisitionThe value range of i is a positive integer from 1 to N, N is the total data number of the historical power data, and f (p)i) For power data as piThe probability of (d); p is a radical ofi、pkHas a value range of positive integer, F (p)i) The value range of (a) is a positive number not greater than 1, and the value range of N, k is a positive integer.
8. The device for monitoring the operating status of the intelligent community public equipment as claimed in claim 6, wherein the threshold calculation unit is specifically configured to:
obtaining an abnormal probability corresponding to the historical power data through an abnormal probability model;
calculating an identification threshold value A of the abnormal operation state based on a formula III;
A＝1-min{F(p1),F(p2),F(p3),…,F(pN) 1, equation three;
F(p1),F(p2),F(p3),…,F(pN) The abnormal probabilities corresponding to the historical power data are respectively, N is the total data number of the historical power data, the value range of A is a positive number smaller than 1, and the value range of N is a positive integer.
9. The device for monitoring the operating state of the intelligent community public equipment according to claim 6, wherein the real-time monitoring unit is specifically configured to:
acquiring real-time electric power data of public equipment through a sensor deployed at a preset monitoring point;
determining the window size to be w based on a preset state analysis period and a sensor acquisition period;
carrying out windowing interception on the collected real-time electric power data according to the determined window to obtain the tth time sequence T of the real-time electric power datat＝{pt1,pt2,pt3,…,ptwH, converting the time sequence TtInputting the abnormal probability model to obtain an output result F (p)tk)；
Calculating the probability F (T) of the abnormal operation of the current public equipment based on the formula IV;
determining the value of J according to a formula five;
ptkis a time sequence TtOf (1) a k-th timing value, ptkHas a positive value, F (T), F (p)tk) The value ranges of t, k and w are positive integers.
10. The device for monitoring the operating status of the intelligent community public equipment as claimed in claim 6, wherein the analysis unit is specifically configured to:
if the absolute value of the calculation result does not exceed the identification threshold, the current running state of the public equipment is normal;
if the calculation result is larger than the identification threshold, the current operation state of the public equipment is power excess;
and if the calculation result is smaller than the inverse number of the identification threshold, the current operation state of the public equipment is insufficient power.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114841081A (en) * | 2022-06-21 | 2022-08-02 | 国网河南省电力公司郑州供电公司 | Method and system for controlling abnormal accidents of power equipment |
| CN114884849A (en) * | 2022-04-01 | 2022-08-09 | 开源网安物联网技术(武汉)有限公司 | CAN bus abnormity detection method and system based on Adaboost |
| CN115115352A (en) * | 2022-08-29 | 2022-09-27 | 山东中鲁实业有限公司 | Public equipment operation control system based on digital city operation management service |
| CN117372969A (en) * | 2023-12-08 | 2024-01-09 | 暗物智能科技(广州)有限公司 | Monitoring scene-oriented abnormal event detection method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114884849A (en) * | 2022-04-01 | 2022-08-09 | 开源网安物联网技术(武汉)有限公司 | CAN bus abnormity detection method and system based on Adaboost |
| CN114884849B (en) * | 2022-04-01 | 2023-10-13 | 开源网安物联网技术(武汉)有限公司 | CAN bus abnormality detection method and system based on Adaboost |
| CN114841081A (en) * | 2022-06-21 | 2022-08-02 | 国网河南省电力公司郑州供电公司 | Method and system for controlling abnormal accidents of power equipment |
| CN115115352A (en) * | 2022-08-29 | 2022-09-27 | 山东中鲁实业有限公司 | Public equipment operation control system based on digital city operation management service |
| CN117372969A (en) * | 2023-12-08 | 2024-01-09 | 暗物智能科技(广州)有限公司 | Monitoring scene-oriented abnormal event detection method |
| CN117372969B (en) * | 2023-12-08 | 2024-05-10 | 暗物智能科技(广州)有限公司 | Monitoring scene-oriented abnormal event detection method |
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