CN116111727A - Comprehensive distribution box abnormity monitoring method based on dynamic temperature threshold - Google Patents

Comprehensive distribution box abnormity monitoring method based on dynamic temperature threshold Download PDF

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CN116111727A
CN116111727A CN202310390718.5A CN202310390718A CN116111727A CN 116111727 A CN116111727 A CN 116111727A CN 202310390718 A CN202310390718 A CN 202310390718A CN 116111727 A CN116111727 A CN 116111727A
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CN116111727B (en
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叶盛鑫
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Shengfeng Electric Power Technology Co ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
    • H02J13/00036—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving switches, relays or circuit breakers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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  • Testing And Monitoring For Control Systems (AREA)

Abstract

The invention relates to the technical field of data processing, and provides a comprehensive distribution box abnormality monitoring method based on a dynamic temperature threshold, which comprises the following steps: collecting real-time data of the comprehensive distribution box and acquiring big data; dividing the big data into a plurality of first dividing periods and second dividing periods according to the data change of the big data, and acquiring first environment hysteresis of each first dividing period and first load hysteresis of each second dividing period; acquiring a plurality of third duration time periods according to the first environment hysteresis and the first load hysteresis, and acquiring an internal temperature sequence, an environment temperature average value and a load average value of each third duration time period; acquiring each third duration and real-time aging degree, and clustering to acquire a plurality of reference internal temperature sequences; and setting a dynamic temperature threshold range for the real-time internal temperature data according to the reference internal temperature sequence, and completing the anomaly monitoring of the comprehensive distribution box. The present invention aims to accurately monitor anomalies by setting a dynamic temperature threshold in conjunction with temperature hysteresis.

Description

Comprehensive distribution box abnormity monitoring method based on dynamic temperature threshold
Technical Field
The invention relates to the technical field of data processing, in particular to a comprehensive distribution box abnormality monitoring method based on a dynamic temperature threshold.
Background
The comprehensive distribution box is a kind of power equipment, and is a kind of control equipment formed by assembling power switch equipment, measuring instruments, protection appliances and the like in a metal cabinet; in the whole power system, temperature monitoring is an important parameter for judging normal operation of power equipment, and with the continuous increase of the power equipment and long-term use of the equipment, fire caused by heating of the equipment becomes a serious hazard for safe production, so that automatic temperature monitoring for an integrated distribution box is an important measure for reducing loss, and a temperature sensor is arranged in the integrated distribution box to collect temperature data for automatic monitoring.
In the temperature monitoring process, a fixed temperature threshold is set for monitoring, and if the internal temperature of the distribution box at the current moment is greater than the set temperature threshold, temperature early warning is carried out; however, the setting of the fixed temperature threshold needs a large amount of test acquisition, meanwhile, the operation of the power equipment in the comprehensive distribution box changes along with time, and the environment where the comprehensive distribution box is located has temperature change, which can cause the temperature inside the comprehensive distribution box to change, so that the fixed temperature threshold cannot accurately obtain an abnormal monitoring result; meanwhile, the temperature change caused by the power equipment and the temperature change caused by the environment temperature have hysteresis influence, so that the hysteresis of the temperature is combined to set dynamic temperature thresholds at different times of the comprehensive distribution box, and the accuracy of an abnormal monitoring result is improved.
Disclosure of Invention
The invention provides a comprehensive distribution box abnormality monitoring method based on a dynamic temperature threshold, which aims to solve the problem that an abnormality monitoring result is inaccurate due to the fact that the existing fixed temperature threshold cannot be combined with temperature change and temperature hysteresis, and adopts the following technical scheme:
one embodiment of the invention provides a comprehensive distribution box abnormality monitoring method based on a dynamic temperature threshold, which comprises the following steps:
acquiring real-time internal temperature data, environment temperature data, load data, current data and voltage data of the comprehensive distribution box; collecting historical data of a plurality of comprehensive distribution boxes of the same model in a long time period to form internal temperature big data, environment temperature big data, load big data, current big data and voltage big data;
dividing a plurality of long time periods into a plurality of first divided time periods and a plurality of second divided time periods according to the ambient temperature big data and the load big data respectively, and acquiring the similar time period of each first divided time period and the similar time period of each second divided time period except the first divided time period and the first second divided time period in each long time period respectively through preset intervals; respectively acquiring initial environmental hysteresis and initial load hysteresis of each long time period according to the first divided time period and the similar time period, the second divided time period and the similar time period and the internal temperature big data, acquiring first environmental hysteresis of each first divided time period according to the initial environmental hysteresis and the environmental temperature big data, and acquiring first load hysteresis of each second divided time period according to the initial load hysteresis and the load big data;
The method comprises the steps of respectively endowing first environmental hysteresis and first load hysteresis to each time point in each long time period, acquiring segmentation degree of each time point according to the first environmental hysteresis and the first load hysteresis of each time point, acquiring segmentation time points according to the segmentation degree, dividing a plurality of long time periods to obtain a plurality of third duration time periods, and acquiring an internal temperature sequence, an environmental temperature average value and a load average value of each third duration time period from big data;
obtaining the aging degree and the real-time aging degree of each third duration according to the current big data, the voltage big data and the real-time current data and the voltage data, and clustering the aging degree and the real-time environment temperature data, the load average value and the real-time aging degree according to the environment temperature average value, the load average value and the aging degree of each third duration to obtain a plurality of reference internal temperature sequences of the real-time internal temperature data;
and setting a dynamic temperature threshold range for the real-time internal temperature data according to the reference internal temperature sequence, and completing the anomaly monitoring of the comprehensive distribution box.
Optionally, the dividing the plurality of long time periods according to the ambient temperature big data and the load big data to obtain a plurality of first divided time periods and a plurality of second divided time periods respectively includes the following specific methods:
Acquiring historical environmental temperature data processed by any one comprehensive distribution box in the environmental temperature big data, and taking the ratio of the difference value of each environmental temperature data and the environmental temperature data at the previous moment to the sampling frequency as the change slope of each environmental temperature data;
obtaining the absolute value of the difference between the change slope of each environmental temperature data and the change slope of the environmental temperature data at the previous moment, normalizing all the absolute values of the difference, marking the obtained result as a normalized slope difference of each environmental temperature data, and marking the time point corresponding to the environmental temperature data with the normalized slope difference larger than a preset first threshold value as a first dividing time point; acquiring all first dividing time points in each long time period, dividing a plurality of long time periods into a plurality of time periods through the first dividing time points, and recording each time period as a first dividing time period;
and carrying out normalized slope difference calculation on each load data of the historical load data processed by each comprehensive distribution box in the load big data, marking a time point corresponding to the load data with the normalized slope difference larger than a preset first threshold value as a second dividing time point, dividing a plurality of long time periods into a plurality of time periods through the second dividing time point, and marking each time period as a second dividing time period.
Optionally, the method for acquiring the similar time period of each first divided time period and the similar time period of each second divided time period except the first divided time period and the first second divided time period in each long time period through a preset interval includes the following specific steps:
acquiring any one first dividing period as a target first dividing period, acquiring a starting time point and an ending time point of the target first dividing period, and recording a period from a preset interval before the starting time point to a preset interval after the ending time point as a similar period of the target first dividing period;
acquiring a similar time period of each first divided time period and a similar time period of each second divided time period except the first divided time period and the first second divided time period in each long time period; a plurality of second divided periods in the similar period of each first divided period are acquired, and a plurality of first divided periods in the similar period of each second divided period are acquired.
Optionally, the method for respectively obtaining the initial environmental hysteresis and the initial load hysteresis of each long time period includes the following specific steps:
acquiring any one long time period as a target long time period, acquiring the absolute value of the difference between the internal temperature data average value of each first divided time period and the internal temperature data average value of the previous adjacent first divided time period except the first divided time period in the target long time period, taking any one first divided time period as the target first divided time period, acquiring the time difference between the starting time point of the target first divided time period and the starting time point of the first second divided time period in the similar time period of the target first divided time period, marking the ratio of the absolute value of the difference obtained by the target first divided time period to the time difference as a first ratio, acquiring the first ratio of each first divided time period except the first divided time period in the target long time period, and taking the average value of all the first ratios as the initial environmental hysteresis of the target long time period;
Acquiring the absolute value of the difference between the internal temperature data mean value of each second divided period and the internal temperature data mean value of the previous adjacent second divided period except for the first second divided period in the target long period, taking any one of the second divided periods as the target second divided period, acquiring the time difference between the starting time point of the target second divided period and the starting time point of the first divided period in the similar period of the target second divided period, marking the ratio of the absolute value of the difference obtained by the target second divided period to the time difference as a second ratio, obtaining the second ratio of each second divided period except for the first second divided period in the target long period, and taking the mean value of all the second ratios as the initial load hysteresis of the target long period;
initial environmental hysteresis and initial load hysteresis for each long period of time are obtained.
Optionally, the method for acquiring the first environmental hysteresis of each first divided period according to the initial environmental hysteresis and the environmental temperature big data includes the following specific steps:
taking any one long time period as a target long time period, acquiring the average value of all the environmental temperature data in the target long time period, acquiring the environmental temperature data average value of each first dividing time period in the target long time period, acquiring the absolute value of the difference value of the environmental temperature data average value of each first dividing time period in the target long time period and all the environmental temperature data average value, recording the absolute value as the first difference of each first dividing time period, recording the ratio of the first difference of each first dividing time period in the target long time period to the maximum value of the first difference in the target long time period as the first ratio of each first dividing time period, and taking the product of the first ratio and the initial environmental hysteresis of the target long time period as the first environmental hysteresis of each first dividing time period in the target long time period; the first environmental hysteresis of each first divided period in each long period is acquired.
Optionally, the method for obtaining the first load hysteresis of each second divided period according to the initial load hysteresis and the load big data includes the following specific steps:
taking any one long time period as a target long time period, acquiring the average value of all load data in the target long time period, acquiring the load data average value of each second division time period in the target long time period, acquiring the absolute value of the difference value of the load data average value of each second division time period in the target long time period and all load data average value, recording the absolute value as the second difference of each second division time period, recording the ratio of the second difference of each second division time period in the target long time period to the maximum value of the second difference in the target long time period as the second ratio of each second division time period, and taking the product of the second ratio and the initial load hysteresis of the target long time period as the first load hysteresis of each second division time period in the target long time period; the first load hysteresis of each second divided period in each long period is acquired.
Optionally, the obtaining the segmentation degree of each time point according to the first environmental hysteresis and the first load hysteresis of each time point includes the following specific methods:
Any one long time period is taken as a target long time period, and the first time period in the target long time period
Figure SMS_1
Degree of segmentation at each time point
Figure SMS_2
The calculation method of (1) is as follows:
Figure SMS_3
wherein ,
Figure SMS_6
representing the first time period of the target
Figure SMS_9
The maximum in the hysteresis of the first environment for each point in time and the immediately preceding adjacent point in time,
Figure SMS_11
representing the first time period of the target
Figure SMS_5
The minimum in the first environmental hysteresis of a point in time and the immediately preceding adjacent point in time,
Figure SMS_8
representing the first time period of the target
Figure SMS_12
The maximum in the first load hysteresis for each point in time and the immediately preceding adjacent point in time,
Figure SMS_13
representing the first time period of the target
Figure SMS_4
The minimum in the first load hysteresis for one point in time and the immediately preceding adjacent point in time,
Figure SMS_7
and (3) with
Figure SMS_10
Respectively represent a first environmental hysteresis and a first negativeInfluence weight of hysteresis.
Optionally, the method for obtaining the aging degree and the real-time aging degree of each third duration according to the current big data, the voltage big data and the real-time current data and the voltage data includes the following specific steps:
any one long time period is taken as a target long time period, and the first time period in the target long time period
Figure SMS_14
Degree of aging of the third duration
Figure SMS_15
The calculation method of (1) is as follows:
Figure SMS_16
wherein ,
Figure SMS_19
Representing the first time period of the target
Figure SMS_21
The degree of aging for a third duration,
Figure SMS_26
representing the first time period of the target
Figure SMS_18
The number of time points of the third duration,
Figure SMS_22
representing the first time period of the target
Figure SMS_25
Third duration of time
Figure SMS_29
The voltage data at the point in time is,
Figure SMS_17
representing the first time period of the target
Figure SMS_23
Third duration of time
Figure SMS_27
The current data at the time points are used,
Figure SMS_30
representing the first time period of the target
Figure SMS_20
The voltage data average for the third duration,
Figure SMS_24
representing the first time period of the target
Figure SMS_28
The current data means for a third duration,
Figure SMS_31
representing absolute value;
acquiring a time point of acquired data of the current time on the same day, dividing the time point into a plurality of third duration time periods of the current time, acquiring the third duration time period of the current time, and acquiring the aging degree of each third duration time period except the third duration time period of the current time on the same day;
Figure SMS_32
wherein ,
Figure SMS_33
indicating the degree of ageing in real time,
Figure SMS_34
indicating the degree of ageing of the third duration preceding the third duration to which the current moment belongs,
Figure SMS_35
representing the voltage data in real-time and,
Figure SMS_36
representing the current data in real-time and,
Figure SMS_37
represents the average value of the voltage data which is acquired in the third duration period to which the current moment belongs,
Figure SMS_38
Indicating the current data mean value which is acquired in the third duration period to which the current moment belongs,
Figure SMS_39
representing absolute values.
The beneficial effects of the invention are as follows: according to the invention, the influence of the environmental temperature and the load on the internal temperature is respectively obtained through the historical data of a plurality of comprehensive distribution boxes with the same model and the distribution boxes to be monitored, the aging degree is obtained through the combination of the voltage data and the current data according to the data change in the historical data, a plurality of reference internal temperature sequences of the real-time internal temperature data are further obtained, and the dynamic temperature threshold is set according to the reference internal temperature sequences, so that the setting of the dynamic temperature threshold can be adaptively changed along with the environmental temperature change and the load change and the aging degree brought by the working time; in the process of determining the reference internal temperature sequence, comprehensively considering the influence caused by environmental hysteresis and load hysteresis, further accurately acquiring a plurality of third duration time periods with the same influence of the environmental hysteresis and the load hysteresis, and clustering by combining the aging degree; meanwhile, the dynamic temperature threshold value is set in a range, so that the fluctuation range of the internal temperature in real time is ensured, and the defect of inaccurate abnormal monitoring results caused by the fact that the fixed temperature threshold value cannot be adaptively changed along with time is avoided; therefore, the accuracy of anomaly monitoring of the comprehensive distribution box through the internal temperature is ensured through the dynamic temperature threshold.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of a comprehensive distribution box anomaly monitoring method based on a dynamic temperature threshold according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for monitoring an abnormality of an integrated distribution box based on a dynamic temperature threshold according to an embodiment of the invention is shown, the method includes the following steps:
And S001, acquiring real-time internal temperature data, environment temperature data, load data, current data and voltage data of the comprehensive distribution box, and acquiring internal temperature big data, environment temperature big data, load big data, current big data and voltage big data.
The purpose of this embodiment is to perform anomaly monitoring according to real-time internal temperature data of the comprehensive distribution box, where the internal temperature data is affected by ambient temperature data and load data, and where the load data is related to circuit power in the comprehensive distribution box, current data and voltage data need to be obtained; the real-time acquisition of the internal temperature data and the environment temperature data is respectively carried out through the temperature sensor in the comprehensive distribution box and the temperature sensor in the external environment, wherein the sampling frequency is acquired once in 1 second; for current data and voltage data, collecting real-time voltage data and current data of the comprehensive distribution box at the same sampling frequency, wherein the voltage data is the working voltage of the comprehensive distribution box, the current data is the passing current of the comprehensive distribution box, and the product of the voltage data and the current data is taken as real-time load data, wherein a sensor adopted by the current and voltage data is not particularly limited, and a temperature sensor is not particularly arranged; it should be noted that, the real-time data of the comprehensive distribution box to be monitored is continuously acquired, each day is taken as a working period, and the real-time data acquisition of one day is finished and then the historical data is incorporated, and the data of the new day is taken as the new real-time data.
Further, acquiring historical internal temperature data, historical environmental temperature data, historical load data, historical current data and historical voltage data of a plurality of comprehensive distribution boxes of the same model, which are acquired in a long period of time at the same sampling frequency, and forming the historical data of all the comprehensive distribution boxes of the same model into big data, wherein the embodiment acquires all the historical data of the comprehensive distribution boxes of 9 same models and the comprehensive distribution boxes to be monitored currently to form big data, and acquiring the internal temperature big data, the environmental temperature big data, the load big data, the current big data and the voltage big data in nearly 180 days in the long period of time; the number of the comprehensive distribution boxes with the same model and the time length for collection, which are required by big data, can be set and adjusted by an implementer according to actual conditions; because various historical data are all time sequence data for a long time, the embodiment adopts a Savitzky-Golay method to carry out smooth denoising treatment on each historical data of each comprehensive distribution box, wherein the Savitzky-Golay method is a known technology, the embodiment is not repeated, the window size is 5, and a three-time least square method is adopted to complete smooth denoising treatment, and the various processed historical data respectively form corresponding big data.
Thus, real-time internal temperature data, environment temperature data, load data, current data and voltage data of the comprehensive distribution box are obtained, and meanwhile, internal temperature big data, environment temperature big data, load big data, current big data and voltage big data are obtained.
Step S002, carrying out time period division on the environmental temperature big data and the load big data according to the data change to obtain a plurality of first division time periods and second division time periods, obtaining initial environmental hysteresis and initial load hysteresis according to the data change time difference of the first division time periods and the second division time periods which are similar in time, and obtaining the first environmental hysteresis of each first division time period and the first load hysteresis of each second division time period by combining the big data.
It should be noted that, the environmental temperature big data and the load big data are all composed of the environmental temperature data or the load data of a plurality of devices in a long period according to the time sequence relationship, and the environmental temperature and the load have hysteresis influence on the internal temperature of the comprehensive distribution box; therefore, firstly, the abrupt change time of the environmental temperature big data and the load big data, namely the time point when the environmental temperature or the load changes greatly, is needed to be obtained, and the environmental temperature big data and the load big data are respectively divided into time periods through the abrupt change time; according to the dividing period according to the environment temperature and the dividing period according to the load under the similar time, combining the data change and the time difference to obtain a part which has single influence on the internal temperature, thereby completing the initial quantification of the environment hysteresis or the load hysteresis of each dividing period; meanwhile, as the load is larger, the internal temperature can be increased, and the influence of corresponding load hysteresis can be changed due to the heat conductivity of heat, so that the longer the influence time on the internal temperature is; the environmental temperature and the internal temperature are the same as each other in heat conduction, and the larger the temperature difference is, the longer the influence time is, and the larger the corresponding hysteresis is.
Specifically, taking environmental temperature big data as an example, acquiring historical environmental temperature data processed by any one comprehensive distribution box, taking the ratio of the difference value of each environmental temperature data and the environmental temperature data at the previous moment to sampling frequency as the change slope of each environmental temperature data, acquiring the absolute value of the difference value of the change slope of each environmental temperature data and the change slope of the environmental temperature data at the previous moment, carrying out linear normalization on all the absolute values of the difference values in the historical environmental temperature data, marking the obtained result as the normalized slope difference value of each environmental temperature data, giving a preset first threshold value for judging the abrupt change relation, calculating the preset first threshold value by adopting 0.51, and marking the time point corresponding to the environmental temperature data with the normalized slope difference value larger than the preset first threshold value as a first dividing time point; acquiring all first dividing time points in each long time period according to the method, dividing a plurality of long time periods into a plurality of time periods through the first dividing time points, and recording each time period as a first dividing time period; it should be noted that, in the historical ambient temperature data processed by each comprehensive distribution box, in the process of performing normalized slope interpolation calculation on the first ambient temperature data, data complement is performed by a linear interpolation method so as to complete calculation.
Further, according to the method, normalized slope difference value calculation of each load data is performed on the historical load data processed by each comprehensive distribution box in the load big data, a time point corresponding to the load data with the normalized slope difference value larger than a preset first threshold value is marked as a second dividing time point, a plurality of long time periods are divided into a plurality of time periods through the second dividing time point, and each time period is marked as a second dividing time period.
It should be further noted that, for the first divided period, the environmental temperature data in each period is similar, and the corresponding internal temperature data is mainly affected by the change of the load data; for the second divided period, the load data in each period is similar, and the corresponding internal temperature data is mainly influenced by the change of the environmental temperature data; thus, by the first and second divided periods of similar time and the data change, the single effect result of environmental hysteresis or load hysteresis is quantified.
Specifically, by the first
Figure SMS_40
For example, the second divided period is obtained by obtaining the starting time point and the ending time point of the second divided period, and a preset time is obtained before the starting time pointThe time interval of the preset interval after the ending time point is recorded as the similar time interval of the second divided time interval, wherein the preset interval is calculated by adopting 360 seconds in the embodiment, namely the number of the time points contained in the similar time interval is 720 more than that of the second divided time interval; acquiring a first divided period in a similar period and acquiring a starting time point of the first divided period, wherein the starting time point of the first divided period may not be in the similar period; acquiring the similar time periods of each second divided time period except the first second divided time period in each long time period according to the method, and acquiring the starting time point of the first divided time period in the similar time period of each second divided time period; initial load hysteresis for any one long period of time
Figure SMS_41
The specific calculation method of (a) is as follows:
Figure SMS_42
wherein ,
Figure SMS_43
representing the number of second divided periods in any one long period,
Figure SMS_46
representing the first
Figure SMS_48
An internal temperature data average value within the second divided period,
Figure SMS_45
representing the first
Figure SMS_47
An internal temperature data average value within the second divided period,
Figure SMS_49
representing the first
Figure SMS_50
Start time point and the first time point of the second divided period
Figure SMS_44
The time difference of the initial time point of the first divided period in the similar period of the second divided period is obtained by a large value reduction value, and the obtained result is a positive value; the initial load hysteresis is quantified by combining the difference of the internal temperature data average values of adjacent second divided periods and the time difference of the first divided periods and the second divided periods under similar periods, the larger the difference of the internal temperature data average values is, the larger the influence caused by load change is, the larger the hysteresis influence caused by corresponding load change is, the larger the time difference is, the larger the influence caused by environmental temperature change is, the smaller the hysteresis influence caused by corresponding load change is, and the initial load hysteresis of the long period is obtained by averaging the load hysteresis of all the second divided periods; acquiring initial load hysteresis of each long time period in the load big data according to the method; the long time period is the time period corresponding to each comprehensive distribution box obtained historical load data, and the number of comprehensive distribution boxes in the load big data is the number of the long time period.
Further, acquiring a similar time period of each first divided time period except for a first divided time period in each long time period according to the method, and acquiring a starting time point of a first second divided time period in the similar time period of each first divided time period, wherein the preset interval for acquiring the similar time period is still calculated by adopting 360 seconds; initial environmental hysteresis for any one long period of time
Figure SMS_51
The calculation method of (1) is as follows:
Figure SMS_52
wherein ,
Figure SMS_55
represents the number of first divided periods in any one long period,
Figure SMS_57
representing the first
Figure SMS_58
An internal temperature data average value within the first divided period,
Figure SMS_53
representing the first
Figure SMS_56
An internal temperature data average value within the first divided period,
Figure SMS_59
representing the first
Figure SMS_60
Start time point and the first time period of the first division
Figure SMS_54
The time difference of the starting time points of the first and second divided time periods in the similar time periods of the first divided time periods is obtained by a large-value reduced value, and the obtained result is a positive value; the initial environmental hysteresis is quantified by combining the difference of the internal temperature data average values of adjacent first division periods and the time difference of the first division periods and the second division periods under similar periods, the larger the difference of the internal temperature data average values is, the larger the influence caused by the environmental temperature change is, the larger the hysteresis influence caused by the corresponding environmental temperature change is, the larger the time difference is, the larger the influence caused by the load change is, the smaller the hysteresis influence caused by the corresponding environmental temperature change is, and the initial environmental hysteresis of the long period is obtained by averaging the environmental hysteresis of all the first division periods; the initial environmental hysteresis for each long period of time in the ambient temperature profile is obtained as described above.
It should be further noted that, taking the environmental temperature big data as an example, the difference between the environmental temperature data of different first division periods is larger, the hysteresis effect of the environmental temperature data of different first division periods in the same long period on the internal temperature data is different, and the load data of different second division periods in the load big data is the same; it is therefore necessary to acquire the first environmental hysteresis of each first divided period from the environmental temperature data of different first divided periods and the first load hysteresis of each second divided period from the load data of different second divided periods.
Specifically, by the first
Figure SMS_61
The first time period
Figure SMS_62
For example, the first period of time is the first environmental hysteresis
Figure SMS_63
The calculation method of (1) is as follows:
Figure SMS_64
wherein ,
Figure SMS_66
represent the first
Figure SMS_72
Initial environmental hysteresis for a long period of time,
Figure SMS_73
represent the first
Figure SMS_68
The average of all ambient temperature data over a long period of time,
Figure SMS_70
represent the first
Figure SMS_75
The first time period
Figure SMS_77
The ambient temperature data means within the first divided period,
Figure SMS_65
represent the first
Figure SMS_69
The maximum value of the average value of the ambient temperature data in all the first divided periods in the long period,
Figure SMS_74
represent the first
Figure SMS_76
The minimum value of the average value of the ambient temperature data in all the first divided periods in the long period,
Figure SMS_67
The representation is to take the absolute value,
Figure SMS_71
representing maximum value; quantifying the first environmental hysteresis of the first division period by the difference between the average value of the environmental temperature data in the first division period and the average value of all the environmental temperature data in the long time period, wherein the larger the difference is, the larger the influence of the environmental temperature on the internal temperature is, and the longer the corresponding influence time is, the larger the environmental hysteresis is; the first environmental hysteresis of each first divided period in each long period is acquired in the above-described manner.
Further, in the first step
Figure SMS_78
The first time period
Figure SMS_79
For example, the second divided period has a first load hysteresis
Figure SMS_80
The calculation method of (1) is as follows:
Figure SMS_81
wherein ,
Figure SMS_83
represent the first
Figure SMS_86
Initial load hysteresis for a long period of time,
Figure SMS_92
represent the first
Figure SMS_84
The average of all load data over a long period of time,
Figure SMS_87
represent the first
Figure SMS_90
The first time period
Figure SMS_94
The load data average value in the second divided period,
Figure SMS_82
represent the first
Figure SMS_88
The maximum value of the load data average value in all the second divided periods in the long period,
Figure SMS_91
represent the first
Figure SMS_93
The minimum value of the load data average value in all the second divided periods in the long period,
Figure SMS_85
the representation is to take the absolute value,
Figure SMS_89
representing maximum value; quantifying the difference between the average value of the load data in the second divided period and the average value of all the load data in the long period The first load hysteresis of the two divided time periods shows that the larger the difference is, the larger the influence of the load on the internal temperature is, and the longer the corresponding influence time is, the larger the load hysteresis is; the first load hysteresis of each second divided period in each long period is acquired in the above-described manner.
Thus, the first environmental hysteresis of each first divided period in the environmental temperature big data and the first load hysteresis of each second divided period in the load big data are obtained.
Step S003, according to the first environmental hysteresis of each first divided period and the first load hysteresis of each second divided period, a plurality of third duration periods are obtained, an internal temperature sequence of each third duration period is obtained from the internal temperature big data, and meanwhile, the environmental temperature average value and the load average value of each third duration period are obtained.
It should be noted that, after the first environmental hysteresis of each first divided period and the first load hysteresis of each second divided period are obtained, a period in which the first environmental hysteresis and the first load hysteresis change are smaller in each long period needs to be obtained, that is, the long periods are re-divided according to the first environmental hysteresis and the first load hysteresis, so as to obtain a plurality of third duration periods, and further obtain a relevant data composition sequence of each third duration period, so that a reference is provided for subsequent anomaly monitoring of real-time internal temperature data.
Specifically, the first environmental hysteresis of each first divided period is respectively given to each time point in each long period, that is, the first environmental hysteresis of the first divided period to which each time point belongs is given to each time point; the first load hysteresis of each second divided period is respectively given to each time point of each long period; each time point in each long time period has corresponding first environmental hysteresis and first load hysteresis, and the segmentation time point is required to be obtained according to the difference of the adjacent time points in the first environmental hysteresis and the first load hysteresis; in any one of long time periods
Figure SMS_95
For example, the degree of segmentation at a time point
Figure SMS_96
The calculation method of (1) is as follows:
Figure SMS_97
wherein ,
Figure SMS_99
representing the first time period in any one of the long time periods
Figure SMS_104
The maximum in the hysteresis of the first environment for each point in time and the immediately preceding adjacent point in time,
Figure SMS_107
represent the first
Figure SMS_100
The minimum in the first environmental hysteresis of a point in time and the immediately preceding adjacent point in time,
Figure SMS_102
represent the first
Figure SMS_106
The maximum in the first load hysteresis for each point in time and the immediately preceding adjacent point in time,
Figure SMS_109
represent the first
Figure SMS_98
Minimum value in first load hysteresis of time point and previous adjacent time point, wherein
Figure SMS_103
，
Figure SMS_105
And (3) with
Figure SMS_108
The present embodiment considers both as important, and therefore adopts
Figure SMS_101
Calculating; judging the segmentation degree through the difference of the first environmental hysteresis and the first load hysteresis of each time point and the previous adjacent time point, wherein if the two time points are in the same first dividing period, the ratio of the first environmental hysteresis is 1, if the two time points are in the same second dividing period, the ratio of the first load hysteresis is 1, and the ratio is obtained through the large value to the small value of the two time points, so that if the two time points are inconsistent in one of the first environmental hysteresis and the first load hysteresis, the segmentation degree is larger than 1; according to the method, the segmentation degree of each time point in each long time period is obtained, a preset second threshold value is set for judging the segmentation time points, the preset second threshold value is calculated by 1, the time points with the segmentation degree larger than the preset second threshold value are used as segmentation time points, each long time period is divided through the segmentation time points, a plurality of newly divided time periods are obtained, and the newly divided time periods are recorded as third duration periods.
Further, acquiring a plurality of internal temperature data corresponding to each third duration from the internal temperature big data, and arranging the plurality of internal temperature data corresponding to each third duration according to time sequence to obtain an internal temperature sequence of each third duration; simultaneously acquiring a plurality of environmental temperature data corresponding to each third duration from the environmental temperature big data, and calculating the average value to obtain the environmental temperature average value of each third duration; and acquiring a plurality of load data corresponding to each third duration from the load big data, and calculating the average value to obtain the load average value of each third duration.
So far, a plurality of third duration periods are obtained according to the changes of the first environmental hysteresis and the first load hysteresis, and an internal temperature sequence, an environmental temperature average value and a load average value of each third duration period are obtained.
Step S004, the aging degree and the real-time aging degree of each third duration period are obtained according to the current big data, the voltage big data and the real-time current data and the voltage data, and clustering is carried out according to the ambient temperature average value, the load average value and the aging degree of each third duration period, the real-time ambient temperature data, the load data and the aging degree, so that a plurality of reference internal temperature sequences of the real-time internal temperature data are obtained.
It should be noted that, the comprehensive distribution box is retired along with the working time, the circuit and the equipment are aged, the resistance inside the equipment is gradually increased, and the corresponding current data and voltage data are changed, so that the aging degree is obtained through the current data and the voltage data, clustering is performed according to the aging degree, the environmental temperature data and the load data, a plurality of third duration time periods which can be referred to in real time at the current moment are obtained, and a plurality of reference internal temperature sequences are obtained.
Specifically, a plurality of current data corresponding to each third duration period are obtained from the current big data, and a plurality of voltage data of each third duration period are obtained from the voltage big data, so that for any one long time period, the first time period is selected
Figure SMS_110
A third duration of time, the degree of aging
Figure SMS_111
The calculation method of (1) is as follows:
Figure SMS_112
wherein ,
Figure SMS_114
indicating the first time period in the long period
Figure SMS_118
The degree of aging for a third duration,
Figure SMS_122
representing the first
Figure SMS_115
The number of time points of the third duration,
Figure SMS_119
represent the first
Figure SMS_121
Third duration of time
Figure SMS_125
The voltage data at the point in time is,
Figure SMS_113
represent the first
Figure SMS_120
Third duration of time
Figure SMS_123
The current data at the time points are used,
Figure SMS_126
represent the first
Figure SMS_116
The voltage data average for the third duration,
Figure SMS_117
Represent the first
Figure SMS_124
The current data means for a third duration,
Figure SMS_127
representing absolute value; the aging degree is quantified by obtaining the resistance according to the ratio of the voltage to the current, and the aging degree is larger as the resistance change is larger; it should be noted that, for the aging degree of the first third duration in any one long period, the previous third duration does not exist, and the corresponding aging degree of the previous third duration is calculated by adopting 0; each of the first is obtained according to the above methodAging degree for three duration.
Furthermore, for real-time aging degree calculation, because the data of the comprehensive distribution box are collected in real time, environmental temperature data, load data, internal temperature data, current data and voltage data can be obtained for a period of time, and the period of time in the embodiment is the data collected on the same day; dividing the time point of the acquired data of the current day according to the dividing method of the third duration to obtain the third duration to which the current time belongs; it should be noted that, the starting time point of the first third duration of the day is not necessarily the first time point of the day, may be a certain time point of the previous day, and is specifically determined according to the change of the environmental temperature data and the load data, so that the aging degree of the third duration of the day needs to be calculated on the basis of the aging degree of each third duration in the historical data; the aging degree of each third duration except the third duration to which the current moment belongs on the same day is obtained according to the method, and then the aging degree in real time is obtained
Figure SMS_128
The calculation method of (1) is as follows:
Figure SMS_129
wherein ,
Figure SMS_130
indicating the degree of ageing of the third duration preceding the third duration to which the current moment belongs,
Figure SMS_131
representing the voltage data in real-time and,
Figure SMS_132
representing the current data in real-time and,
Figure SMS_133
indicating that the voltage data which has been acquired in the third duration period to which the current time belongs are allThe value of the sum of the values,
Figure SMS_134
the current data average value which is acquired in the third duration time to which the current moment belongs is represented; the resistor is obtained through the ratio of the voltage to the current, and the aging degree is quantified according to the change of the resistor, so that the real-time aging degree is obtained.
Further, setting up a three-dimensional coordinate system by taking the environmental temperature data as an x-axis, the load data as a y-axis and the aging degree as a z-axis, and placing the real-time environmental temperature data, the load data and the aging degree in the three-dimensional coordinate system to obtain a real-time coordinate point; placing the average value of the ambient temperature, the average value of the load and the aging degree of each third duration period in a three-dimensional coordinate system to obtain a coordinate point of each third duration period; performing DBSCAN clustering on all coordinate points, wherein the clustering distance is the spatial distance between the coordinate points, namely the Euclidean distance, the minimum point number in the clustering parameters is set to be 10, the DBSCAN clustering is a known technology, and a plurality of clusters are obtained without repeated description in the embodiment; and taking a third duration time period corresponding to other coordinate points in the cluster where the real-time coordinate point is located as a reference time period of the current time, and taking an internal temperature sequence corresponding to each reference time period as a reference internal temperature sequence of the current time, so that a plurality of reference internal temperature sequences of the real-time internal temperature data are obtained.
And quantifying each third duration time and real-time aging degree through the current data and the voltage data, and obtaining a plurality of reference internal temperature sequences of the real-time internal temperature data through clustering.
And S005, setting a dynamic temperature threshold range for the real-time internal temperature data according to the reference internal temperature sequence, and completing the anomaly monitoring of the comprehensive distribution box.
After the plurality of reference internal temperature sequences are obtained, the third duration time periods corresponding to the reference internal temperature sequences are similar to the real-time ambient temperature average value, the load average value and the aging degree in terms of the ambient temperature average value, the load average value and the aging degree, so that the internal temperature sequences corresponding to the third duration time periods can be used as the reference internal temperature sequences of the real-time internal temperature data, and further, the real-time dynamic temperature threshold range setting is realized, and the abnormal monitoring of the comprehensive distribution box is completed.
Specifically, all internal temperature data in all reference internal temperature sequences are acquired and recorded as reference internal temperature data; taking the maximum value of all the reference internal temperature data as the temperature upper boundary threshold value of the dynamic temperature threshold value range set by the internal temperature data at the current moment, and taking the minimum value of all the reference internal temperature data as the temperature lower boundary threshold value of the dynamic temperature threshold value range; it should be noted that, the range from the lower temperature threshold to the upper temperature threshold is a dynamic temperature threshold range, so as to provide a reasonable fluctuation range for the internal temperature data at the current moment; the real-time internal temperature data at the current moment indicates that the comprehensive distribution box at the current moment works normally if the internal temperature data is in the dynamic temperature threshold range, and the working state of the comprehensive distribution box at the current moment is abnormal if the internal temperature data is not in the dynamic temperature threshold range, so that abnormal early warning is carried out; it should be noted that, the real-time dynamic temperature threshold range does not need to be changed in real time along with the sampling frequency for 1 second, when the real-time environmental temperature data or the load data presents a sudden change state, that is, when the normalized slope difference between the change slope of the environmental temperature data (load data) and the change slope of the environmental temperature data (load data) at the previous moment is greater than the preset first threshold, the real-time internal temperature data needs to acquire the reference internal temperature sequence again, so as to obtain a new dynamic temperature threshold range and complete anomaly monitoring; if the normalized slope difference value is smaller than or equal to a preset first threshold value, the real-time internal temperature data is still in the same state, and the dynamic temperature threshold value range is not required to be acquired again; wherein a sudden change in one of the ambient temperature data or the load data requires re-acquisition of the dynamic temperature threshold range.
So far, the self-adaptive dynamic temperature threshold range is set for the internal temperature data at the current moment of the comprehensive distribution box through the internal temperature big data, the environment temperature big data, the load big data and the current voltage big data generated by the historical work of a plurality of equipment with the same type, and then the real-time abnormality monitoring of the comprehensive distribution box is completed according to the dynamic temperature threshold range.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims (8)

1. The comprehensive distribution box abnormality monitoring method based on the dynamic temperature threshold is characterized by comprising the following steps of:
acquiring real-time internal temperature data, environment temperature data, load data, current data and voltage data of the comprehensive distribution box; collecting historical data of a plurality of comprehensive distribution boxes of the same model in a long time period to form internal temperature big data, environment temperature big data, load big data, current big data and voltage big data;
dividing a plurality of long time periods into a plurality of first divided time periods and a plurality of second divided time periods according to the ambient temperature big data and the load big data respectively, and acquiring the similar time period of each first divided time period and the similar time period of each second divided time period except the first divided time period and the first second divided time period in each long time period respectively through preset intervals; respectively acquiring initial environmental hysteresis and initial load hysteresis of each long time period according to the first divided time period and the similar time period, the second divided time period and the similar time period and the internal temperature big data, acquiring first environmental hysteresis of each first divided time period according to the initial environmental hysteresis and the environmental temperature big data, and acquiring first load hysteresis of each second divided time period according to the initial load hysteresis and the load big data;
The method comprises the steps of respectively endowing first environmental hysteresis and first load hysteresis to each time point in each long time period, acquiring segmentation degree of each time point according to the first environmental hysteresis and the first load hysteresis of each time point, acquiring segmentation time points according to the segmentation degree, dividing a plurality of long time periods to obtain a plurality of third duration time periods, and acquiring an internal temperature sequence, an environmental temperature average value and a load average value of each third duration time period from big data;
obtaining the aging degree and the real-time aging degree of each third duration according to the current big data, the voltage big data and the real-time current data and the voltage data, and clustering the aging degree and the real-time environment temperature data, the load average value and the real-time aging degree according to the environment temperature average value, the load average value and the aging degree of each third duration to obtain a plurality of reference internal temperature sequences of the real-time internal temperature data;
and setting a dynamic temperature threshold range for the real-time internal temperature data according to the reference internal temperature sequence, and completing the anomaly monitoring of the comprehensive distribution box.
2. The method for monitoring the abnormality of the comprehensive distribution box based on the dynamic temperature threshold according to claim 1, wherein the method for dividing the plurality of long time periods into a plurality of first divided time periods and a plurality of second divided time periods according to the ambient temperature big data and the load big data respectively comprises the following specific steps:
Acquiring historical environmental temperature data processed by any one comprehensive distribution box in the environmental temperature big data, and taking the ratio of the difference value of each environmental temperature data and the environmental temperature data at the previous moment to the sampling frequency as the change slope of each environmental temperature data;
obtaining the absolute value of the difference between the change slope of each environmental temperature data and the change slope of the environmental temperature data at the previous moment, normalizing all the absolute values of the difference, marking the obtained result as a normalized slope difference of each environmental temperature data, and marking the time point corresponding to the environmental temperature data with the normalized slope difference larger than a preset first threshold value as a first dividing time point; acquiring all first dividing time points in each long time period, dividing a plurality of long time periods into a plurality of time periods through the first dividing time points, and recording each time period as a first dividing time period;
and carrying out normalized slope difference calculation on each load data of the historical load data processed by each comprehensive distribution box in the load big data, marking a time point corresponding to the load data with the normalized slope difference larger than a preset first threshold value as a second dividing time point, dividing a plurality of long time periods into a plurality of time periods through the second dividing time point, and marking each time period as a second dividing time period.
3. The method for monitoring abnormal conditions of an integrated distribution box based on a dynamic temperature threshold according to claim 1, wherein the method for acquiring the similar time period of each first divided time period and the similar time period of each second divided time period except the first divided time period and the first second divided time period in each long time period respectively through a preset interval comprises the following specific steps:
acquiring any one first dividing period as a target first dividing period, acquiring a starting time point and an ending time point of the target first dividing period, and recording a period from a preset interval before the starting time point to a preset interval after the ending time point as a similar period of the target first dividing period;
acquiring a similar time period of each first divided time period and a similar time period of each second divided time period except the first divided time period and the first second divided time period in each long time period; a plurality of second divided periods in the similar period of each first divided period are acquired, and a plurality of first divided periods in the similar period of each second divided period are acquired.
4. The method for monitoring the abnormality of the comprehensive distribution box based on the dynamic temperature threshold according to claim 3, wherein the method for respectively obtaining the initial environmental hysteresis and the initial load hysteresis of each long time period comprises the following specific steps:
Acquiring any one long time period as a target long time period, acquiring the absolute value of the difference between the internal temperature data average value of each first divided time period and the internal temperature data average value of the previous adjacent first divided time period except the first divided time period in the target long time period, taking any one first divided time period as the target first divided time period, acquiring the time difference between the starting time point of the target first divided time period and the starting time point of the first second divided time period in the similar time period of the target first divided time period, marking the ratio of the absolute value of the difference obtained by the target first divided time period to the time difference as a first ratio, acquiring the first ratio of each first divided time period except the first divided time period in the target long time period, and taking the average value of all the first ratios as the initial environmental hysteresis of the target long time period;
acquiring the absolute value of the difference between the internal temperature data mean value of each second divided period and the internal temperature data mean value of the previous adjacent second divided period except for the first second divided period in the target long period, taking any one of the second divided periods as the target second divided period, acquiring the time difference between the starting time point of the target second divided period and the starting time point of the first divided period in the similar period of the target second divided period, marking the ratio of the absolute value of the difference obtained by the target second divided period to the time difference as a second ratio, obtaining the second ratio of each second divided period except for the first second divided period in the target long period, and taking the mean value of all the second ratios as the initial load hysteresis of the target long period;
Initial environmental hysteresis and initial load hysteresis for each long period of time are obtained.
5. The method for monitoring the abnormality of the comprehensive distribution box based on the dynamic temperature threshold according to claim 1, wherein the method for acquiring the first environmental hysteresis of each first divided period according to the initial environmental hysteresis and the environmental temperature big data comprises the following specific steps:
taking any one long time period as a target long time period, acquiring the average value of all the environmental temperature data in the target long time period, acquiring the environmental temperature data average value of each first dividing time period in the target long time period, acquiring the absolute value of the difference value of the environmental temperature data average value of each first dividing time period in the target long time period and all the environmental temperature data average value, recording the absolute value as the first difference of each first dividing time period, recording the ratio of the first difference of each first dividing time period in the target long time period to the maximum value of the first difference in the target long time period as the first ratio of each first dividing time period, and taking the product of the first ratio and the initial environmental hysteresis of the target long time period as the first environmental hysteresis of each first dividing time period in the target long time period; the first environmental hysteresis of each first divided period in each long period is acquired.
6. The method for monitoring abnormal conditions of the comprehensive distribution box based on the dynamic temperature threshold according to claim 1, wherein the method for obtaining the first load hysteresis of each second divided period according to the initial load hysteresis and the load big data comprises the following specific steps:
taking any one long time period as a target long time period, acquiring the average value of all load data in the target long time period, acquiring the load data average value of each second division time period in the target long time period, acquiring the absolute value of the difference value of the load data average value of each second division time period in the target long time period and all load data average value, recording the absolute value as the second difference of each second division time period, recording the ratio of the second difference of each second division time period in the target long time period to the maximum value of the second difference in the target long time period as the second ratio of each second division time period, and taking the product of the second ratio and the initial load hysteresis of the target long time period as the first load hysteresis of each second division time period in the target long time period; the first load hysteresis of each second divided period in each long period is acquired.
7. The method for monitoring abnormal conditions of an integrated distribution box based on a dynamic temperature threshold according to claim 1, wherein the step of obtaining the segmentation degree of each time point according to the first environmental hysteresis and the first load hysteresis of each time point comprises the following specific steps:
Any one long time period is taken as a target long time period, and the first time period in the target long time period
Figure QLYQS_1
Degree of segmentation at each time point
Figure QLYQS_2
The calculation method of (1) is as follows:
Figure QLYQS_3
wherein ,
Figure QLYQS_5
indicating +.>
Figure QLYQS_10
Maximum value of the hysteresis of the first environment between the time point and the immediately preceding adjacent time point,/>
Figure QLYQS_12
Indicating +.>
Figure QLYQS_6
Minimum value of hysteresis of the first environment between the time point and the immediately preceding adjacent time point,/for the first environment>
Figure QLYQS_8
Indicating +.>
Figure QLYQS_11
Maximum value in the first load hysteresis of the time point and the immediately preceding adjacent time point,/->
Figure QLYQS_13
Indicating +.>
Figure QLYQS_4
Minimum value of the first load hysteresis between the time point and the immediately preceding adjacent time point,/->
Figure QLYQS_7
And->
Figure QLYQS_9
The impact weights of the first environmental hysteresis and the first load hysteresis are represented, respectively.
8. The method for monitoring the abnormality of the comprehensive distribution box based on the dynamic temperature threshold according to claim 1, wherein the method for obtaining the aging degree and the real-time aging degree of each third duration according to the current big data, the voltage big data and the real-time current data and the voltage data comprises the following specific steps:
any one long time period is taken as a target long time period, and the first time period in the target long time period
Figure QLYQS_14
Degree of aging for the third duration +.>
Figure QLYQS_15
The calculation method of (1) is as follows:
Figure QLYQS_16
wherein ,
Figure QLYQS_17
indicating +.>
Figure QLYQS_23
Degree of aging for a third duration, +.>
Figure QLYQS_27
Indicating +.>
Figure QLYQS_19
The number of time points of the third duration, +.>
Figure QLYQS_22
Indicating +.>
Figure QLYQS_26
The third duration is +.>
Figure QLYQS_30
Voltage data for each time point, +.>
Figure QLYQS_18
Indicating +.>
Figure QLYQS_24
The third duration is +.>
Figure QLYQS_25
Current data for each time point, +.>
Figure QLYQS_29
Indicating +.>
Figure QLYQS_20
A third duration of voltage data mean, < >>
Figure QLYQS_21
Indicating +.>
Figure QLYQS_28
A third duration of current data mean, < >>
Figure QLYQS_31
Representing absolute value;
acquiring a time point of acquired data of the current time on the same day, dividing the time point into a plurality of third duration time periods of the current time, acquiring the third duration time period of the current time, and acquiring the aging degree of each third duration time period except the third duration time period of the current time on the same day;
Figure QLYQS_32
wherein ,
Figure QLYQS_33
indicating the degree of ageing in real time->
Figure QLYQS_34
Indicating the ageing degree of the third duration preceding the third duration to which the current moment belongs,/->
Figure QLYQS_35
Representing real-time voltage data, ">
Figure QLYQS_36
Representing real-time current data +. >
Figure QLYQS_37
Representing the mean value of the voltage data already acquired in the third duration to which the current time belongs, +.>
Figure QLYQS_38
Indicating the mean value of the current data which has been acquired in the third duration to which the current time belongs, +.>
Figure QLYQS_39
Representing absolute values. />
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CN117470380A (en) * 2023-12-26 2024-01-30 西安艺琳农业发展有限公司 Wisdom is raised pigs and is monitored and early warning system of multisensor
CN118018882A (en) * 2024-04-09 2024-05-10 苏州元澄科技股份有限公司 Internet of things data acquisition and storage method

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CN116304956B (en) * 2023-05-15 2023-08-15 济宁市质量计量检验检测研究院(济宁半导体及显示产品质量监督检验中心、济宁市纤维质量监测中心) Chip temperature anomaly online detection method
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CN116772285B (en) * 2023-08-28 2023-11-07 山东国能智能科技有限公司 Intelligent building heating load safety real-time monitoring method
CN117040137A (en) * 2023-10-09 2023-11-10 国网山东省电力公司聊城供电公司 Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data
CN117040137B (en) * 2023-10-09 2024-05-07 国网山东省电力公司聊城供电公司 Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data
CN117470380A (en) * 2023-12-26 2024-01-30 西安艺琳农业发展有限公司 Wisdom is raised pigs and is monitored and early warning system of multisensor
CN117470380B (en) * 2023-12-26 2024-03-22 西安艺琳农业发展有限公司 Wisdom is raised pigs and is monitored and early warning system of multisensor
CN118018882A (en) * 2024-04-09 2024-05-10 苏州元澄科技股份有限公司 Internet of things data acquisition and storage method

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