CN115296424A - Distributed power supply comprehensive monitoring system and method based on fusion terminal - Google Patents

Distributed power supply comprehensive monitoring system and method based on fusion terminal Download PDF

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CN115296424A
CN115296424A CN202211230968.4A CN202211230968A CN115296424A CN 115296424 A CN115296424 A CN 115296424A CN 202211230968 A CN202211230968 A CN 202211230968A CN 115296424 A CN115296424 A CN 115296424A
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data
building
power supply
power
new area
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CN115296424B (en
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阮佳阳
杨兆静
陈操
艾丽娜
张嗣勇
陈万喜
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Beijing Zhimeng Ict 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
    • 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
    • H02J13/00006—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 information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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  • Power Engineering (AREA)
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Abstract

The invention discloses a distributed power supply comprehensive monitoring system and method based on a fusion terminal, and belongs to the technical field of distributed power supply monitoring. The system comprises an electric power utilization data processing module, a new region prediction module, a threshold analysis module and a distributed power supply monitoring module; the output end of the power usage data processing module is connected with the input end of the new region prediction module; the output end of the new region prediction module is connected with the input end of the threshold analysis module; and the output end of the threshold analysis module is connected with the input end of the distributed power supply monitoring module. The invention also provides a comprehensive monitoring method of the distributed power supply based on the fusion terminal, which can accurately detect the power use data in the development of a new region, reduce the urban power grid pressure through a mechanism of the distributed power supply, provide time early warning analysis of the power use data under the condition that buildings are continuously added in the new region, and improve the power dispatching level.

Description

Distributed power supply comprehensive monitoring system and method based on fusion terminal
Technical Field
The invention relates to the technical field of distributed power supply monitoring, in particular to a distributed power supply comprehensive monitoring system and method based on a fusion terminal.
Background
The intelligent integrated terminal is a user terminal on the periphery of a computer network, and in the prior art, the intelligent integrated terminal has the functions of information acquisition, internet of things agent and edge calculation, and supports marketing, power distribution and emerging services. The intelligent integration terminal device integrates the functions of power supply and power information acquisition of a power distribution station area, data collection of each acquisition terminal or electric energy meter, equipment state monitoring and communication networking, local analysis decision, cooperative calculation and the like. The system is generally deployed at the cloud, online management and remote operation and maintenance of various types of edge Internet of things agents and intelligent terminals are achieved, various types of acquisition terminals are managed in a unified mode, various types of acquisition sensing data are gathered according to a unified Internet of things information model, and model conversion and data preprocessing are conducted. By constructing the power distribution internet of things, the topology of the power supply relation at the low-voltage 0.4kV side can be monitored on line, and the branch switch, the line state, the metering box and the electric energy meter can be monitored comprehensively; by sensing the Internet of things of the transformer area, meter reading, line loss calculation, power quality management, topology identification, fault first-aid repair and the like can be realized; meanwhile, the power supply reliability and the high-quality service level can be improved, the marketing and distribution service fusion is realized, and the visual management of 'full networking', 'full online' and 'full monitoring' of the distribution network assets is achieved.
The distributed power supply is distributed at a user end and is connected with a power grid with a voltage level of 35kV or below so as to be consumed on site. The energy-saving power generation system comprises solar energy, natural gas, biomass energy, wind energy, water energy, hydrogen energy, geothermal energy, ocean energy, resource comprehensive utilization power generation (including coal mine gas power generation), energy storage and the like. These power sources are owned by the power department, the power consumer, or the 3 rd party to meet power system and consumer specific requirements. Such as peak regulation, power supply for remote users or commercial districts and residential districts, power transmission and transformation investment saving, power supply reliability improvement and the like.
Nowadays, the application of distributed power supplies in development areas is emerging, the development areas are generally in the uncovered areas of urban power grids, the power transmission and transformation investment can be greatly reduced and the power supply reliability can be controlled by using the distributed power supplies, however, the load of the distributed power supplies is continuously increased due to the continuous development of the development areas, and no effective supervision means exists at present.
Disclosure of Invention
The invention aims to provide a distributed power supply comprehensive monitoring system and method based on a fusion terminal, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a distributed power supply comprehensive monitoring method based on a fusion terminal comprises the following steps:
the method includes the steps that S1, electric power use data collected by a new area are obtained from a fusion terminal according to preset collection frequency, the new area refers to a development area in a city, the development area refers to various development areas which are approved by state couriers, provinces, autonomous regions, and national government of the direct municipality to set up in a city planning area and carry out national specific preferential policies, such as economic technology development areas, bonded areas, high and new technology industry development areas, national tourism and vacation areas, the development areas are special mechanisms set by local governments for promoting regional economy to rapidly develop, the development areas refer to undeveloped places, and the places have economic or human environment potential. The connotation of a development area mainly comprises two layers: first, a newly reclaimed land resource area; and secondly, excavating and discovering areas with economic potential. Generally refers to a new area which has not exerted resources and economic advantages and needs to be artificially developed so as to achieve the purpose that the social limited resources generate the maximum social benefit through reasonable allocation. The power usage data comprises distributed power supply power data and urban power grid power data;
s2, classifying the collected power utilization data according to the type of the power utilization data, wherein the type of the power utilization data comprises industrial production power consumption of each building in the new area and commercial life power consumption of each building in the new area, and power utilization information of each building in the new area is obtained; the buildings comprise office buildings, shopping malls, factories and the like, wherein the power utilization of the factories is recorded as industrial production power utilization; the electricity consumption of business life is recorded in office buildings, shopping malls and the like;
s3, constructing a first building prediction model of the new area, generating the time and the type of newly added buildings in the new area in a preset period, constructing a second building prediction model of the new area, and generating predicted power use data changes of buildings in the new area in the preset period;
and S4, acquiring threshold information of the power utilization data according to output results of the first building prediction model and the second building prediction model, generating time early warning information when the predicted power utilization data in the new area exceed the threshold, and outputting the time early warning information to an administrator port.
According to the technical scheme, the distributed power supply power data refer to power use data provided for each building by a distributed power supply in a new region; the urban power grid power data refers to power utilization data provided by the urban power grid for each building in the new area. Under the current technical means, four to six can be achieved generally, namely, the distributed power supply can provide 40% of electric quantity, and the rest 60% is provided by a city power grid.
According to the above technical solution, the first building prediction model includes:
constructing an initial training set, and acquiring the time of the same newly added building in a new area, wherein the newly added buildings comprise two buildings, one is a building using industrial production electricity, and the other is a building using commercial life electricity;
establishing an initial training set according to historical time data of the same newly added building;
setting the mean square error function as the loss function, noted
Figure 803563DEST_PATH_IMAGE001
Wherein
Figure 592527DEST_PATH_IMAGE002
Which represents the output of the computer system,
Figure 678164DEST_PATH_IMAGE003
setting the maximum iteration times for the first building prediction model, and recording as T;
at the loss function
Figure 185369DEST_PATH_IMAGE001
When the minimum value is taken, the weak learner is initialized and recorded as
Figure 991651DEST_PATH_IMAGE004
；
Performing iterative training on the initialized weak learner, and calculating the negative gradient of each data sample i in the initial training set
Figure 41646DEST_PATH_IMAGE005
：
Figure 907971DEST_PATH_IMAGE006
Wherein, the first and the second end of the pipe are connected with each other,
Figure 27106DEST_PATH_IMAGE007
is the value of the data sample i,
Figure 637079DEST_PATH_IMAGE008
is composed of
Figure 400635DEST_PATH_IMAGE007
A corresponding loss function; in the formula
Figure 313228DEST_PATH_IMAGE009
Adopts a strong learning device in the previous round
Figure 60604DEST_PATH_IMAGE010
A first building prediction model of; t represents the number of iterations;
Figure 333322DEST_PATH_IMAGE011
a differential is indicated;
using acquired negative gradients
Figure 216964DEST_PATH_IMAGE005
Fitting a regression tree, recording as the t-th regression tree, and recording as the corresponding leaf node region
Figure 566037DEST_PATH_IMAGE012
Calculating the best fit value
Figure 269551DEST_PATH_IMAGE013
：
Figure 221327DEST_PATH_IMAGE014
Wherein i and j are labels; c is a constant;
the best fit value refers to the output value that minimizes the loss function in the samples in each leaf node region, i.e., the best output value that fits the leaf node region;
and adding the weak learner of each round into the trained model to obtain a new strong learner:
Figure 84109DEST_PATH_IMAGE015
wherein the content of the first and second substances,
Figure 463138DEST_PATH_IMAGE016
representing a leaf region;
Figure 794893DEST_PATH_IMAGE017
representing a strong learner obtained by the t-th iteration; i represents the value of best fit
Figure 550360DEST_PATH_IMAGE013
Combining, representing the decision tree fitting function of the current round;
when T = T, ending the iterative process and obtaining the final strong learner
Figure 143015DEST_PATH_IMAGE018
And as an output first building prediction model, generating two groups of time data of newly added buildings by using the first building prediction model respectively, wherein a newly added time data set of the buildings using industrial production electricity is recorded as:
Figure 83158DEST_PATH_IMAGE019
(ii) a The newly added time data set of the building using the commercial life electricity is recorded as:
Figure 761264DEST_PATH_IMAGE020
(ii) a Wherein m and n are constants related to the preset period and satisfy
Figure 930208DEST_PATH_IMAGE021
、
Figure 642950DEST_PATH_IMAGE022
The time point is less than or equal to the end time of the preset period, and
Figure 363781DEST_PATH_IMAGE023
、
Figure 919396DEST_PATH_IMAGE024
at a time point greater than the end time of the preset period.
According to the above technical solution, the second building prediction model includes:
respectively acquiring classified electric power use data information, and taking historical information of electric power use data under the same building as a second training set;
respectively establishing a horizontal smooth equation, a trend smooth equation and a season smooth equation to predict the season and the trend of the data;
wherein the horizontal smoothing equation is:
Figure 16665DEST_PATH_IMAGE025
the trend smoothing equation is:
Figure 193700DEST_PATH_IMAGE026
the seasonal smoothing equation is:
Figure 351011DEST_PATH_IMAGE027
the second building prediction model is constructed as follows:
Figure 393923DEST_PATH_IMAGE028
wherein u is the current power usage data; v is the cycle length;
Figure 763724DEST_PATH_IMAGE029
a smoothing parameter that is horizontal;
Figure 451057DEST_PATH_IMAGE030
a smoothing parameter that is a trend;
Figure 654637DEST_PATH_IMAGE031
a smoothing parameter for the season;
Figure 529052DEST_PATH_IMAGE032
is a first
Figure 358336DEST_PATH_IMAGE033
A predicted value of the period, i.e., power usage data at the h-th period;
Figure 900176DEST_PATH_IMAGE034
is the actual value of the u-th period, i.e., the power usage data at the u-th period;
Figure 133711DEST_PATH_IMAGE035
is the estimated level of phase u;
Figure 636368DEST_PATH_IMAGE036
is the predicted trend of the u-th stage;
Figure 347972DEST_PATH_IMAGE037
a season smoothing prediction for the u-th stage;
and respectively generating power use data change predicted values of two buildings in the new area by using a second building prediction model, wherein the power use data change predicted values of the buildings using industrial production power are integrated as:
Figure 134531DEST_PATH_IMAGE038
(ii) a The set of predicted values of the change of the electricity utilization data of the building using the electricity of the commercial life is recorded as:
Figure 804547DEST_PATH_IMAGE039
(ii) a Wherein
Figure 653554DEST_PATH_IMAGE040
、
Figure 309795DEST_PATH_IMAGE041
All represent constant values.
According to the above technical solution, the generating time early warning information includes:
constructing a time early warning model:
Figure 826227DEST_PATH_IMAGE042
wherein, the first and the second end of the pipe are connected with each other,
Figure 526199DEST_PATH_IMAGE043
at a time point
Figure 128081DEST_PATH_IMAGE044
Power usage data of the time;
Figure 447067DEST_PATH_IMAGE045
represents rounding up;
Figure 693372DEST_PATH_IMAGE046
representing a data set for using industrial production electricity when selecting electricity use data;
Figure 705190DEST_PATH_IMAGE047
representing a data set for using commercial life electricity when selecting electricity usage data;
Figure 387844DEST_PATH_IMAGE048
represents a serial number;
Figure 776100DEST_PATH_IMAGE049
representing the time point
Figure 876911DEST_PATH_IMAGE044
The total number of buildings using industrial production electricity is newly added in the new area;
Figure 794052DEST_PATH_IMAGE050
representing the time point
Figure 370527DEST_PATH_IMAGE044
The total number of buildings using electricity for commercial life is newly added in the new region;
Figure 155949DEST_PATH_IMAGE051
representing the time point of the newly added building;
Figure 235900DEST_PATH_IMAGE052
representing a period duration between data in the second building prediction model;
Figure 730467DEST_PATH_IMAGE053
at the time point
Figure 263079DEST_PATH_IMAGE044
A predicted value of a change in electricity usage data of a building that uses electricity for industrial production;
Figure 727559DEST_PATH_IMAGE054
at the time point
Figure 52230DEST_PATH_IMAGE044
The total number of buildings using electricity in the original industrial production;
Figure 576752DEST_PATH_IMAGE055
at the time point
Figure 472027DEST_PATH_IMAGE044
A predicted value of a change in electricity usage data of a structure using electricity for business life;
Figure 740197DEST_PATH_IMAGE056
at the time point
Figure 794741DEST_PATH_IMAGE044
The total number of buildings powered by the original commercial life;
in the above formula, consideration is given to the difference between the change of the power usage data of the newly added building and the change of the power usage data of the existing building, and refinement is performed.
Wherein the content of the first and second substances,
Figure 614798DEST_PATH_IMAGE057
by taking the time point
Figure 387582DEST_PATH_IMAGE044
According to the calculation, selecting and calculating in the sets A and B; e.g. at time point k1, satisfy
Figure 334809DEST_PATH_IMAGE058
Then get
Figure 978280DEST_PATH_IMAGE049
Is 9; can be obtained by the same principle
Figure 234818DEST_PATH_IMAGE050
。
Obtaining the threshold value of the power supply of the distributed power supply, and recording as
Figure 963740DEST_PATH_IMAGE059
；
Obtaining
Figure 839292DEST_PATH_IMAGE060
Is greater than or equal to for the first time
Figure 478215DEST_PATH_IMAGE059
Time point of time
Figure 515441DEST_PATH_IMAGE044
And acquiring the average time required for constructing a new distributed power supply station by indicating that the currently required power data exceeds the load value of the distributed power supply
Figure 387451DEST_PATH_IMAGE061
Generating time warning information at
Figure 535535DEST_PATH_IMAGE062
The time point of the power consumption is output to an administrator port, and the administrator is reminded to restrict the power consumption planning in a new area or construct a new distributed power supply station; wherein
Figure 419178DEST_PATH_IMAGE063
Represents the average value of the proportion of the supplied power of the distributed power supply and the urban power grid.
A distributed power supply comprehensive monitoring system based on a convergence terminal comprises: the system comprises an electric power usage data processing module, a new region prediction module, a threshold analysis module and a distributed power supply monitoring module;
the electric power usage data processing module is used for acquiring electric power usage data acquired by a new area from the fusion terminal according to a preset acquisition frequency, wherein the new area refers to a development area in a city, and the electric power usage data comprises distributed power supply electric power data and urban power grid electric power data; classifying the collected power usage data according to the type of the power usage data, wherein the type of the power usage data comprises industrial production power consumption of each building in the new area and commercial life power consumption of each building in the new area, and power usage information of each building in the new area is obtained; the new area prediction module is used for constructing a first building prediction model of a new area, generating the time and the type of newly added buildings in the new area in a preset period, constructing a second building prediction model of the new area, and generating predicted power use data change of each building in the new area in the preset period; the threshold analysis module is used for generating a predicted sharing proportion value of the distributed power supply according to proportion data of the electric quantity provided by the urban power grid and the distributed power supply; the distributed power supply monitoring module is used for acquiring a prediction sharing proportion value of the distributed power supply as threshold information of power utilization data according to output results of the first building prediction model and the second building prediction model, generating time early warning information when the predicted power utilization data in a new area exceed a threshold value, and outputting the time early warning information to an administrator port;
the output end of the power usage data processing module is connected with the input end of the new region prediction module; the output end of the new region prediction module is connected with the input end of the threshold analysis module; and the output end of the threshold analysis module is connected with the input end of the distributed power supply monitoring module.
According to the technical scheme, the power usage data processing module comprises a power usage data acquisition unit and a power usage data classification unit;
the electric power usage data acquisition unit is used for acquiring electric power usage data acquired in a new area from the fusion terminal according to a preset acquisition frequency; the electric power usage data classification unit is used for classifying the collected electric power usage data according to the type of the electric power usage data.
According to the technical scheme, the new area prediction module comprises a first building prediction unit and a second building prediction unit;
the first building prediction unit is used for constructing a first building prediction model of the new area and generating the time and the type of newly added buildings in the new area under a preset period; and the second building prediction unit is used for constructing a second building prediction model of the new area and generating predicted power use data change of each building of the new area under a preset period.
According to the technical scheme, the threshold analysis module comprises a historical data acquisition unit and an output unit;
the historical data acquisition unit is used for acquiring the proportion data of the electric quantity provided by the urban power grid and the distributed power supply; and the output unit is used for selecting an average value to generate a predicted sharing proportion value of the distributed power supply according to historical proportion data of the electric quantity provided by the urban power grid and the distributed power supply.
According to the technical scheme, the distributed power supply monitoring module comprises a threshold selecting unit and a time early warning unit;
the threshold selecting unit acquires a predicted sharing proportion value of the distributed power supply as threshold information of the power use data; and the time early warning unit is used for generating time early warning information according to the output results of the first building prediction model and the second building prediction model when the predicted power utilization data in the new region exceeds a threshold value, and outputting the time early warning information to an administrator port.
Compared with the prior art, the invention has the following beneficial effects:
the method can accurately detect the power use data in the development of a new region, reduces the urban power grid pressure through a mechanism of the distributed power supply, provides time early warning analysis for the power use data under the condition that buildings are continuously added in the new region, can provide time points for constructing the distributed power supply station to relieve the urban power grid pressure, and improves the power dispatching level.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow diagram of a distributed power supply comprehensive monitoring system and method based on a convergence terminal according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, in the first embodiment: selecting a new economic development area in a city as a new area, acquiring power usage data acquired by the new area from a fusion terminal according to preset acquisition frequency, and classifying the acquired power usage data according to the type of the power usage data, wherein the type of the power usage data comprises industrial production power consumption of each building in the new area and commercial life power consumption of each building in the new area, so as to obtain power usage information of each building in the new area; constructing a first building prediction model of the new area, generating the time and the type of newly added buildings in the new area under a preset period,
the first building prediction model comprises:
constructing an initial training set, and acquiring the time of the same newly-added building in a new area, wherein the newly-added buildings comprise two types, one type is a building using industrial production electricity, and the other type is a building using commercial life electricity;
establishing an initial training set according to historical time data of the same newly added building;
for example, the same newly added building is marked as a building for commercial life electricity, taking an office building as an example, and recording the newly added time as follows: 30 days, 22 days, 12 days, 20 days;
setting the mean square error function as the loss function, and recording as
Figure 237092DEST_PATH_IMAGE001
Wherein
Figure 206185DEST_PATH_IMAGE002
Which represents the output of the computer system,
Figure 548174DEST_PATH_IMAGE003
setting the maximum iteration times for the first building prediction model, and recording as T;
at the loss function
Figure 20743DEST_PATH_IMAGE001
When the minimum value is taken, initializing the weak learner and recording the result as
Figure 665351DEST_PATH_IMAGE004
；
Performing iterative training on the initialized weak learner, and respectively calculating the negative gradient of each data sample i in the initial training set
Figure 731527DEST_PATH_IMAGE005
：
Figure 486994DEST_PATH_IMAGE006
Wherein the content of the first and second substances,
Figure 469862DEST_PATH_IMAGE007
for the value of the data sample i,
Figure 19792DEST_PATH_IMAGE008
is composed of
Figure 697898DEST_PATH_IMAGE007
A corresponding loss function; in the formula
Figure 132422DEST_PATH_IMAGE009
Adopts the strong learning device of the previous round
Figure 579584DEST_PATH_IMAGE010
A first building prediction model of; t represents the number of iterations;
Figure 690628DEST_PATH_IMAGE011
means differentiation;
using acquired negative gradients
Figure 856030DEST_PATH_IMAGE005
Fitting a regression tree, recording as the t-th regression tree, and recording as the corresponding leaf node region
Figure 953299DEST_PATH_IMAGE012
Calculating the best fit value
Figure 395913DEST_PATH_IMAGE013
：
Figure 287645DEST_PATH_IMAGE064
Wherein i and j are labels; c is a constant;
the best fit value refers to the output value that minimizes the loss function in the samples in each leaf node region, i.e., the best output value that fits the leaf node region;
and adding the weak learner of each round into the trained model to obtain a new strong learner:
Figure 64977DEST_PATH_IMAGE065
wherein, the first and the second end of the pipe are connected with each other,
Figure 965937DEST_PATH_IMAGE016
representing a leaf region;
Figure 528637DEST_PATH_IMAGE017
representing a strong learner obtained by the t-th iteration; i represents the value of the best fit
Figure 591271DEST_PATH_IMAGE013
Combining, representing the decision tree fitting function of the current round;
continuously fitting and calculating by using MATLAB, and finishing the iterative process when T = T to obtain the final strong learner
Figure 731265DEST_PATH_IMAGE018
And as the output first building prediction model, generating two groups of time data of the newly added building by using the first building prediction model respectively, wherein a newly added time data set of the building using industrial production electricity is recorded as:
Figure 29391DEST_PATH_IMAGE019
(ii) a The newly added time data set of the building using the commercial life electricity is recorded as:
Figure 571231DEST_PATH_IMAGE020
(ii) a Wherein m and n are constants related to the preset period and satisfy
Figure 945712DEST_PATH_IMAGE021
、
Figure 307423DEST_PATH_IMAGE022
The time point is less than or equal to the end time of the preset period, and
Figure 550185DEST_PATH_IMAGE023
、
Figure 336745DEST_PATH_IMAGE024
the time point is greater than the end time of the preset period.
Constructing a second building prediction model of the new area, and generating predicted power use data changes of buildings in the new area in a preset period;
the main change points are the commercial living directions, such as the business buildings are continuously moved into new enterprises, the residential buildings are continuously provided with new users to live in, and the like, and the increase of the electric power caused by the change points is obviously in a trend;
respectively acquiring classified electric power use data information, and taking historical information of electric power use data under the same building as a second training set;
respectively establishing a horizontal smooth equation, a trend smooth equation and a season smooth equation to predict the season and the trend of the data;
wherein the horizontal smoothing equation is:
Figure 741181DEST_PATH_IMAGE025
the trend smoothing equation is:
Figure 465555DEST_PATH_IMAGE026
the seasonal smoothing equation is:
Figure 246429DEST_PATH_IMAGE027
constructing a second building prediction model as follows:
Figure 762861DEST_PATH_IMAGE028
wherein u is the current period power usage data; v is the cycle length;
Figure 728412DEST_PATH_IMAGE029
a smoothing parameter that is horizontal;
Figure 799136DEST_PATH_IMAGE030
a smoothing parameter that is a trend;
Figure 259067DEST_PATH_IMAGE031
a smoothing parameter for the season;
Figure 630006DEST_PATH_IMAGE032
is as follows
Figure 376245DEST_PATH_IMAGE033
A predicted value of the period, i.e., power usage data at the h-th period;
Figure 590057DEST_PATH_IMAGE034
actual value for the u-th period, i.e., power usage data for the u-th period;
Figure 447155DEST_PATH_IMAGE035
is the estimated level of phase u;
Figure 813545DEST_PATH_IMAGE036
is the predicted trend of the u-th stage;
Figure 996265DEST_PATH_IMAGE037
a season smoothing prediction for the u-th stage;
and respectively generating power utilization data change predicted values of two buildings in the new area by using a second building prediction model, wherein the power utilization data change predicted values of the buildings using industrial production power are integrated as:
Figure 41581DEST_PATH_IMAGE038
(ii) a The set of predicted values of the change of the electricity utilization data of the building using the electricity of the commercial life is recorded as:
Figure 92583DEST_PATH_IMAGE039
(ii) a Wherein
Figure 438114DEST_PATH_IMAGE040
、
Figure 401522DEST_PATH_IMAGE041
All represent constant values.
The change curve of the power use data of the building using the industrial production power is generally gentle, and the average of the industrial production power is reflected; the change curve of the electricity usage data of the building using the electricity for business life generally fluctuates greatly and tends to be in a rising trend, for example, in an office building in a new area, the electricity consumption is gradually increased due to the continuous entering of merchants and enterprises, so in the second building prediction model, the continuous change condition of the electricity for business life is mainly considered.
The generating of the time warning information includes:
constructing a time early warning model:
Figure 199713DEST_PATH_IMAGE066
wherein, the first and the second end of the pipe are connected with each other,
Figure 54406DEST_PATH_IMAGE043
at a point of time
Figure 988864DEST_PATH_IMAGE044
Power usage data of the time;
Figure 513386DEST_PATH_IMAGE045
represents rounding up;
Figure 408661DEST_PATH_IMAGE046
representing a data set for using industrial production electricity when selecting electricity use data;
Figure 942410DEST_PATH_IMAGE047
representing a data set for using commercial life electricity when selecting electricity usage data;
Figure 121588DEST_PATH_IMAGE048
represents a serial number;
Figure 551432DEST_PATH_IMAGE049
represents the time point
Figure 324216DEST_PATH_IMAGE044
The total number of buildings using industrial production electricity is newly added in the new area;
Figure 271443DEST_PATH_IMAGE050
representing the time point
Figure 914914DEST_PATH_IMAGE044
The total number of buildings using electricity for commercial life is newly added in the new region;
Figure 171452DEST_PATH_IMAGE051
representing the time point of the newly added building;
Figure 165953DEST_PATH_IMAGE052
representing a period duration between data in the second building prediction model;
Figure 775926DEST_PATH_IMAGE053
at the time point
Figure 414849DEST_PATH_IMAGE044
A predicted value of a change in electricity usage data of a building using electricity for industrial production;
Figure 717654DEST_PATH_IMAGE054
at the time point
Figure 58506DEST_PATH_IMAGE044
The total number of buildings using electricity in the original industrial production;
Figure 472169DEST_PATH_IMAGE055
at the time point
Figure 90233DEST_PATH_IMAGE044
A predicted value of a change in electricity usage data of a structure using electricity for business life;
Figure 439305DEST_PATH_IMAGE056
at the time point
Figure 408398DEST_PATH_IMAGE044
The total number of buildings consumed by the original commercial life;
in the above formula, consideration is given to the difference between the change of the power usage data of the newly added building and the change of the power usage data of the original building, and refinement is performed.
Wherein the content of the first and second substances,
Figure 484808DEST_PATH_IMAGE057
by taking the time point
Figure 222957DEST_PATH_IMAGE044
According to the calculation, selecting and calculating in the sets A and B; e.g. at time point k1, satisfy
Figure 601985DEST_PATH_IMAGE058
Then get
Figure 933741DEST_PATH_IMAGE049
Is 9; the same can be obtained
Figure 689207DEST_PATH_IMAGE050
。
Obtaining a threshold value of the power supply of the distributed power supply, and recording as
Figure 695513DEST_PATH_IMAGE059
；
Obtaining
Figure 979864DEST_PATH_IMAGE060
Is greater than or equal to for the first time
Figure 798915DEST_PATH_IMAGE059
Time point of time
Figure 92493DEST_PATH_IMAGE044
And acquiring the average time required for constructing a new distributed power supply station by indicating that the currently required power data exceeds the load value of the distributed power supply
Figure 805234DEST_PATH_IMAGE061
Generating time warning information at
Figure 650699DEST_PATH_IMAGE062
The time point of the power consumption is output to an administrator port, and the administrator is reminded to restrict the power consumption planning in a new area or construct a new distributed power supply station; wherein
Figure 816102DEST_PATH_IMAGE063
Represents the average value of the proportion of the supplied power of the distributed power supply and the urban power grid.
In the second embodiment, a distributed power supply comprehensive monitoring system based on a convergence terminal is provided, where the system includes: the system comprises an electric power use data processing module, a new region prediction module, a threshold analysis module and a distributed power supply monitoring module;
the electric power usage data processing module is used for acquiring electric power usage data acquired from a new area from the fusion terminal according to a preset acquisition frequency, wherein the new area refers to a development area in a city, and the electric power usage data comprises distributed power supply electric power data and city power grid electric power data; classifying the collected power use data according to the type of the power use data, wherein the type of the power use data comprises industrial production power consumption of each building in the new area and commercial life power consumption of each building in the new area, and power use information of each building in the new area is obtained; the new area prediction module is used for constructing a first building prediction model of a new area, generating the time and the type of newly added buildings in the new area in a preset period, constructing a second building prediction model of the new area, and generating predicted power use data change of each building in the new area in the preset period; the threshold analysis module is used for generating a predicted sharing proportion value of the distributed power supply according to proportion data of the electric quantity provided by the urban power grid and the distributed power supply; the distributed power supply monitoring module is used for acquiring a prediction sharing proportion value of the distributed power supply as threshold information of power utilization data according to output results of the first building prediction model and the second building prediction model, generating time early warning information when the predicted power utilization data in a new area exceed a threshold value, and outputting the time early warning information to an administrator port;
the output end of the power usage data processing module is connected with the input end of the new region prediction module; the output end of the new region prediction module is connected with the input end of the threshold analysis module; and the output end of the threshold analysis module is connected with the input end of the distributed power supply monitoring module.
The electric power usage data processing module comprises an electric power usage data acquisition unit and an electric power usage data classification unit;
the electric power usage data acquisition unit is used for acquiring electric power usage data acquired in a new area from the fusion terminal according to a preset acquisition frequency; the electric power usage data classification unit is used for classifying the collected electric power usage data according to the type of the electric power usage data.
The new area prediction module comprises a first building prediction unit and a second building prediction unit;
the first building prediction unit is used for constructing a first building prediction model of the new area and generating the time and the type of newly added buildings in the new area under a preset period; the second building prediction unit is used for constructing a second building prediction model of the new area and generating predicted power use data changes of buildings in the new area in a preset period.
The threshold analysis module comprises a historical data acquisition unit and an output unit;
the historical data acquisition unit is used for acquiring the proportion data of the electric quantity provided by the urban power grid and the distributed power supply; and the output unit is used for selecting an average value to generate a predicted sharing proportion value of the distributed power supply according to historical proportion data of the electric quantity provided by the urban power grid and the distributed power supply.
The distributed power supply monitoring module comprises a threshold selecting unit and a time early warning unit;
the threshold selecting unit acquires a predicted sharing proportion value of the distributed power supply as threshold information of power use data; and the time early warning unit is used for generating time early warning information according to the output results of the first building prediction model and the second building prediction model when the predicted power utilization data in the new region exceeds a threshold value, and outputting the time early warning information to an administrator port.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. 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. A distributed power supply comprehensive monitoring method based on a fusion terminal is characterized in that: the method comprises the following steps:
the method includes the steps that S1, electric power usage data collected in a new area are obtained from a fusion terminal according to a preset collection frequency, the new area refers to a development area in a city, and the electric power usage data comprise distributed power supply electric power data and city power grid electric power data;
s2, classifying the collected power utilization data according to the type of the power utilization data, wherein the type of the power utilization data comprises industrial production power consumption of each building in the new area and commercial life power consumption of each building in the new area, and power utilization information of each building in the new area is obtained;
s3, constructing a first building prediction model of the new area, generating the time and the type of newly added buildings in the new area in a preset period, constructing a second building prediction model of the new area, and generating predicted power use data changes of buildings in the new area in the preset period;
and S4, acquiring threshold information of the power utilization data according to output results of the first building prediction model and the second building prediction model, generating time early warning information when the power utilization data predicted in the new area exceeds the threshold, and outputting the time early warning information to an administrator port.
2. The integrated monitoring method for the distributed power supply based on the converged terminal according to claim 1, wherein the integrated monitoring method comprises the following steps: the distributed power supply power data refers to power usage data provided by distributed power supplies in a new area to each building; the urban power grid power data refers to power utilization data provided by the urban power grid for each building in the new area.
3. The integrated monitoring method for the distributed power supply based on the converged terminal according to claim 2, wherein the integrated monitoring method comprises the following steps: the first building prediction model comprises:
constructing an initial training set, and acquiring the time of the same newly added building in a new area, wherein the newly added buildings comprise two buildings, one is a building using industrial production electricity, and the other is a building using commercial life electricity;
establishing an initial training set according to historical time data of the same newly added building;
setting the mean square error function as the loss function, and recording as
Figure 849072DEST_PATH_IMAGE001
In which
Figure 172737DEST_PATH_IMAGE002
Which represents the output of the optical fiber,
Figure 483632DEST_PATH_IMAGE003
setting the maximum iteration times for the first building prediction model, and recording as T;
at the loss function
Figure 534634DEST_PATH_IMAGE001
When the minimum value is taken, the weak learner is initialized and recorded as
Figure 614585DEST_PATH_IMAGE004
；
Performing iterative training on the initialized weak learner, and respectively calculating the negative gradient of each data sample i in the initial training set
Figure 968206DEST_PATH_IMAGE005
：
Figure 641764DEST_PATH_IMAGE006
Wherein, the first and the second end of the pipe are connected with each other,
Figure 106244DEST_PATH_IMAGE007
is the value of the data sample i,
Figure 430915DEST_PATH_IMAGE008
is composed of
Figure 955437DEST_PATH_IMAGE007
A corresponding loss function; in the formula
Figure 975345DEST_PATH_IMAGE009
Adopts the strong learning device of the previous round
Figure 118882DEST_PATH_IMAGE010
A first building prediction model of; t represents the number of iterations;
Figure 907846DEST_PATH_IMAGE011
a differential is indicated;
using acquired negative gradients
Figure 993483DEST_PATH_IMAGE005
Fitting a regression tree, recording as the t-th regression tree, and recording as the corresponding leaf node region
Figure 766267DEST_PATH_IMAGE012
Calculating the best fit value
Figure 447915DEST_PATH_IMAGE013
：
Figure 356965DEST_PATH_IMAGE014
Wherein i and j are labels; c is a constant;
and adding the weak learner of each round into the trained model to obtain a new strong learner:
Figure 223290DEST_PATH_IMAGE015
wherein the content of the first and second substances,
Figure 342425DEST_PATH_IMAGE016
representing a leaf area;
Figure 952398DEST_PATH_IMAGE017
representing a strong learner obtained by the t-th iteration; i represents the value of the best fit
Figure 856900DEST_PATH_IMAGE013
Combining, representing the decision tree fitting function of the current round;
when T = T, ending the iterative process and obtaining the final strong learner
Figure 894126DEST_PATH_IMAGE018
And as an output first building prediction model, generating two groups of time data of newly added buildings by using the first building prediction model respectively, wherein a newly added time data set of the buildings using industrial production electricity is recorded as:
Figure 375923DEST_PATH_IMAGE019
(ii) a The newly added time data set of the building using the commercial life electricity is recorded as:
Figure 914220DEST_PATH_IMAGE020
(ii) a Wherein m and n are constants related to the preset period and satisfy
Figure 532283DEST_PATH_IMAGE021
、
Figure 881356DEST_PATH_IMAGE022
The time point is less than or equal to the end time of the preset period, and
Figure 584870DEST_PATH_IMAGE023
、
Figure 536646DEST_PATH_IMAGE024
at a time point greater than the end time of the preset period.
4. The integrated monitoring method for the distributed power supply based on the converged terminal according to claim 3, wherein: the second building prediction model comprises:
respectively acquiring classified electric power use data information, and taking historical information of electric power use data under the same building as a second training set;
respectively establishing a horizontal smoothing equation, a trend smoothing equation and a season smoothing equation to predict the seasons and the trends of the data;
wherein the horizontal smoothing equation is:
Figure 665008DEST_PATH_IMAGE025
the trend smoothing equation is:
Figure 44036DEST_PATH_IMAGE026
the seasonal smoothing equation is:
Figure 375792DEST_PATH_IMAGE027
constructing a second building prediction model as follows:
Figure 131258DEST_PATH_IMAGE028
wherein u is the current period power usage data; v is the cycle length;
Figure 458334DEST_PATH_IMAGE029
a smoothing parameter that is horizontal;
Figure 398477DEST_PATH_IMAGE030
a smoothing parameter that is a trend;
Figure 76583DEST_PATH_IMAGE031
a smoothing parameter for the season;
Figure 511107DEST_PATH_IMAGE032
is a first
Figure 223848DEST_PATH_IMAGE033
A predicted value of the period, i.e., power usage data at the h-th period;
Figure 679100DEST_PATH_IMAGE034
actual value for the u-th period, i.e., power usage data for the u-th period;
Figure 234715DEST_PATH_IMAGE035
is the estimated level of the u-th stage;
Figure 66405DEST_PATH_IMAGE036
is the predicted trend of the u-th stage;
Figure 774598DEST_PATH_IMAGE037
seasonal smooth prediction for the u-th stage;
and respectively generating power utilization data change predicted values of two buildings in the new area by using a second building prediction model, wherein the power utilization data change predicted values of the buildings using industrial production power are integrated as:
Figure 666330DEST_PATH_IMAGE038
(ii) a The set of predicted values of the change of the electricity utilization data of the building using the electricity of the commercial life is recorded as:
Figure 319029DEST_PATH_IMAGE039
(ii) a Wherein
Figure 344622DEST_PATH_IMAGE040
、
Figure 500797DEST_PATH_IMAGE041
All represent constant values.
5. The integrated monitoring method for the distributed power supply based on the converged terminal according to claim 4, wherein: the generating time early warning information comprises:
constructing a time early warning model:
Figure 704377DEST_PATH_IMAGE042
wherein the content of the first and second substances,
Figure 578792DEST_PATH_IMAGE043
at a time point
Figure 283442DEST_PATH_IMAGE044
Power usage data of the time;
Figure 949916DEST_PATH_IMAGE045
represents rounding up;
Figure 183451DEST_PATH_IMAGE046
representing a data set for using industrial production electricity when selecting electricity use data;
Figure 686108DEST_PATH_IMAGE047
representing a data set for using commercial life electricity when selecting electricity use data;
Figure 663291DEST_PATH_IMAGE048
represents a serial number;
Figure 59637DEST_PATH_IMAGE049
representing the time point
Figure 854287DEST_PATH_IMAGE044
The total number of buildings using industrial production electricity is newly added in the new area;
Figure 703294DEST_PATH_IMAGE050
representing the time point
Figure 359535DEST_PATH_IMAGE044
The total number of buildings using electricity for commercial life is newly added in the new region;
Figure 875967DEST_PATH_IMAGE051
representing the time point of the newly added building;
Figure 841518DEST_PATH_IMAGE052
representing a cycle duration between data in the second building prediction model;
Figure 177821DEST_PATH_IMAGE053
at the time point
Figure 762386DEST_PATH_IMAGE044
A predicted value of a change in electricity usage data of a building using electricity for industrial production;
Figure 743112DEST_PATH_IMAGE054
at the time point
Figure 754930DEST_PATH_IMAGE044
The total number of buildings using electricity in the original industrial production;
Figure 703163DEST_PATH_IMAGE055
at the time point
Figure 91419DEST_PATH_IMAGE044
A predicted value of a change in electricity usage data of a structure using electricity for business life;
Figure 51285DEST_PATH_IMAGE056
at the time point
Figure 109371DEST_PATH_IMAGE044
The total number of buildings powered by the original commercial life;
wherein the content of the first and second substances,
Figure 420266DEST_PATH_IMAGE057
by taking the time point
Figure 471268DEST_PATH_IMAGE044
According to the calculation, selecting and calculating in the sets A and B; obtaining the threshold value of the power supply of the distributed power supply, and recording as
Figure 551219DEST_PATH_IMAGE058
；
Obtaining
Figure 904840DEST_PATH_IMAGE059
Is greater than or equal to for the first time
Figure 578398DEST_PATH_IMAGE058
Time point of time
Figure 42878DEST_PATH_IMAGE044
Obtaining an average time required to build a new distributed power station
Figure 101969DEST_PATH_IMAGE060
Generating time warning information at
Figure 892071DEST_PATH_IMAGE061
To the administrator port at the time point of (c); wherein
Figure 52925DEST_PATH_IMAGE062
Represents the average value of the proportion of the supplied power of the distributed power supply and the urban power grid.
6. A distributed power supply comprehensive monitoring system based on a fusion terminal is characterized in that: the system comprises: the system comprises an electric power usage data processing module, a new region prediction module, a threshold analysis module and a distributed power supply monitoring module;
the electric power usage data processing module is used for acquiring electric power usage data acquired from a new area from the fusion terminal according to a preset acquisition frequency, wherein the new area refers to a development area in a city, and the electric power usage data comprises distributed power supply electric power data and city power grid electric power data; classifying the collected power use data according to the type of the power use data, wherein the type of the power use data comprises industrial production power consumption of each building in the new area and commercial life power consumption of each building in the new area, and power use information of each building in the new area is obtained; the new area prediction module is used for constructing a first building prediction model of a new area, generating the time and the type of a newly added building in the new area under a preset period, constructing a second building prediction model of the new area, and generating predicted power use data change of each building in the new area under the preset period; the threshold analysis module is used for generating a predicted sharing proportion value of the distributed power supply according to proportion data of the electric quantity provided by the urban power grid and the distributed power supply; the distributed power supply monitoring module is used for acquiring a predicted sharing proportion value of the distributed power supply as threshold information of power utilization data according to output results of the first building prediction model and the second building prediction model, generating time early warning information when the predicted power utilization data in the new area exceed the threshold, and outputting the time early warning information to an administrator port;
the output end of the power usage data processing module is connected with the input end of the new region prediction module; the output end of the new region prediction module is connected with the input end of the threshold analysis module; and the output end of the threshold analysis module is connected with the input end of the distributed power supply monitoring module.
7. The integrated monitoring system for the distributed power supply based on the converged terminal according to claim 6, wherein: the electric power usage data processing module comprises an electric power usage data acquisition unit and an electric power usage data classification unit;
the electric power usage data acquisition unit is used for acquiring electric power usage data acquired in a new area from the fusion terminal according to a preset acquisition frequency; the power usage data classification unit is used for classifying the collected power usage data according to the type of the power usage data.
8. The integrated monitoring system for the distributed power supply based on the converged terminal according to claim 6, wherein: the new area prediction module comprises a first building prediction unit and a second building prediction unit;
the first building prediction unit is used for constructing a first building prediction model of the new area and generating the time and the type of newly added buildings in the new area under a preset period; the second building prediction unit is used for constructing a second building prediction model of the new area and generating predicted power use data changes of buildings in the new area in a preset period.
9. The integrated monitoring system for the distributed power supply based on the converged terminal according to claim 6, wherein: the threshold analysis module comprises a historical data acquisition unit and an output unit;
the historical data acquisition unit is used for acquiring the proportion data of the electric quantity provided by the urban power grid and the distributed power supply; and the output unit is used for selecting an average value to generate a predicted sharing proportion value of the distributed power supply according to historical proportion data of the electric quantity provided by the urban power grid and the distributed power supply.
10. The integrated monitoring system for the distributed power supply based on the converged terminal according to claim 6, wherein: the distributed power supply monitoring module comprises a threshold selecting unit and a time early warning unit;
the threshold selecting unit acquires a predicted sharing proportion value of the distributed power supply as threshold information of power use data; and the time early warning unit is used for generating time early warning information according to the output results of the first building prediction model and the second building prediction model when the predicted power utilization data in the new region exceed a threshold value, and outputting the time early warning information to the administrator port.
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