CN113848430A - Electric energy fault monitoring method and device - Google Patents
Electric energy fault monitoring method and device Download PDFInfo
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Abstract
The invention discloses an electric energy fault monitoring method and device, which are applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the method comprises the following steps: acquiring actual electric energy data acquired by each power line node according to a preset period; respectively generating undetermined fitting curves corresponding to each power line node according to the actual electric energy data; and determining an adjustment strategy of the power line nodes according to the correlation between the to-be-determined fitting curve and the preset standard fitting curve, so that the regular inspection of each power line node is performed more efficiently, and the resource consumption cost is reduced on the premise of ensuring the timeliness.
Description
Technical Field
The invention relates to the technical field of monitoring, in particular to a method and a device for monitoring an electric energy fault.
Background
With the continuous development of modernization, electric energy becomes the most indispensable energy source of human society, and the quality of electric energy is closely related to people's life. In the actual construction of power supply networks, the transmission of electrical energy is usually carried out by laying power lines. However, power lines may be laid in field environments such as suburbs and mountainous areas, and are susceptible to environmental influences. Therefore, in order to ensure stable operation of the power supply network, each power line needs to be periodically inspected.
For this reason, the conventional power line inspection method is usually implemented in a manual inspection or unmanned aerial vehicle inspection mode, for example, an inspection data terminal is configured at each electric energy monitoring node to be inspected in a manual mode; the inspection data terminal acquires the operation data information of the electric energy equipment, the operation information of the power line and the point inspection process of inspection personnel; the method comprises the steps that an inspection worker configures a mobile terminal and sends inspection information to a server, and the server acquires inspection data; or the unmanned aerial vehicle inspection method is used, elements such as inspection site state information, equipment information and personnel information are considered, and equipment, personnel, time and lines needing to be inspected are scheduled through the scheduling terminal, so that an inspection task is completed.
But with the arrival of intelligence thing networking era, monitoring node increases with explosion formula speed, and equipment monitoring information data is magnanimity growth mode, and the time and the resource that above-mentioned prior art consumed are more, and are difficult to guarantee the ageing, can't satisfy the equipment and patrol and examine efficiency demand.
Disclosure of Invention
The invention provides a method and a device for monitoring an electric energy fault, which solve the technical problems that in the prior art, time and resources are consumed in the process of power line inspection, timeliness is difficult to guarantee, and the requirement of equipment inspection efficiency cannot be met.
The invention provides an electric energy fault monitoring method which is applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the method comprises the following steps:
acquiring actual electric energy data acquired by each power line node according to a preset period;
respectively generating undetermined fitting curves corresponding to the power line nodes according to the actual electric energy data;
and determining an adjustment strategy of the power line node according to the correlation between the to-be-determined fitting curve and a preset standard fitting curve.
Optionally, the method further comprises:
acquiring historical electric energy data collected by each power line node in a preset historical time period;
and respectively constructing the standard fitting curve corresponding to each power line node according to the historical electric energy data.
Optionally, the actual power data comprises voltage data and current data; the step of respectively generating a to-be-determined fitting curve corresponding to each power line node according to the actual electric energy data comprises the following steps of:
generating corresponding power data by adopting the voltage data and the current data respectively corresponding to each power line node;
and according to the power data and the preset period, performing curve fitting by adopting a least square method to obtain a to-be-fitted curve corresponding to each power line node.
Optionally, the step of determining an adjustment strategy of the power line node according to a correlation between the to-be-fitted curve and the standard fitted curve includes:
calculating the correlation between the to-be-determined fitting curve and the standard fitting curve by adopting a preset cosine correlation calculation formula;
if the correlation degree is larger than or equal to a first preset threshold value, skipping to execute the step of acquiring the actual electric energy data acquired by each power line node according to a preset period;
and if the correlation degree is smaller than the first preset threshold value, acquiring the position information of the power line node, generating alarm information and outputting the alarm information.
Optionally, if the correlation is smaller than the first preset threshold, the step of obtaining the location information of the power line node, generating alarm information, and outputting includes:
if the correlation degree is smaller than the first preset threshold and larger than a second preset threshold, outputting the alarm information through a task common Internet of things slice;
and if the correlation degree is smaller than or equal to the second preset threshold value, switching the power line associated with the power line node to a preset standby line, and outputting the alarm information through a task key Internet of things slice.
The invention also provides an electric energy fault monitoring device, which is applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the device comprises:
the actual electric energy data acquisition module is used for acquiring actual electric energy data acquired by each electric power line node according to a preset period;
the first curve fitting module is used for respectively generating a to-be-fitted curve corresponding to each power line node according to the actual electric energy data;
and the adjustment strategy determining module is used for determining the adjustment strategy of the power line node according to the correlation between the to-be-determined fitting curve and a preset standard fitting curve.
Optionally, the method further comprises:
the historical electric energy data acquisition module is used for acquiring historical electric energy data acquired by each power line node in a preset historical time period;
and the second curve fitting module is used for respectively constructing the standard fitting curve corresponding to each power line node according to the historical electric energy data.
Optionally, the actual power data comprises voltage data and current data; the first curve fitting module comprises:
the power data generation submodule is used for generating corresponding power data by adopting the voltage data and the current data which respectively correspond to each power line node;
and the curve fitting submodule is used for performing curve fitting by adopting a least square method according to the power data and the preset period to obtain a to-be-fitted curve corresponding to each power line node.
Optionally, the adjustment policy determining module includes:
the correlation degree operator module is used for calculating the correlation degree between the to-be-determined fitting curve and the standard fitting curve by adopting a preset cosine correlation degree calculation formula;
the first judgment submodule is used for skipping to execute the step of acquiring the actual electric energy data collected by each power line node according to the preset period if the correlation degree is greater than or equal to a first preset threshold;
and the second judging submodule is used for acquiring the position information of the power line node, generating alarm information and outputting the alarm information if the correlation degree is smaller than the first preset threshold value.
Optionally, the second determining module includes:
the first slice information output unit is used for outputting the alarm information through a task ordinary Internet of things slice if the correlation degree is smaller than the first preset threshold value and larger than a second preset threshold value;
and the second slice information output unit is used for switching the power line associated with the power line node to a preset standby line if the correlation degree is less than or equal to the second preset threshold value, and outputting the alarm information through a mission critical internet of things slice.
According to the technical scheme, the invention has the following advantages:
according to the invention, the cloud server acquires corresponding actual electric energy data from each electric power line node according to a preset period, respectively generates undetermined fitting curves corresponding to each electric power line node based on each actual electric energy data, calculates the correlation based on the undetermined fitting curves and the standard fitting curves corresponding to each electric power line node, and finally determines respective adjustment strategies according to the correlation of each electric power line node. Therefore, the technical problems that time and resources consumed in the power line inspection process are more, timeliness is difficult to guarantee, and the requirement for equipment inspection efficiency cannot be met in the prior art are solved, regular inspection of each power line node is carried out more efficiently, and resource consumption cost is reduced on the premise that timeliness is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating steps of a power failure monitoring method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of a power failure monitoring method according to a second embodiment of the present invention;
fig. 3 is a block diagram of a power failure monitoring method according to a third embodiment of the present invention.
Detailed Description
The embodiment of the invention provides an electric energy fault monitoring method and device, which are used for solving the technical problems that time and resources are consumed in the power line inspection process, timeliness is difficult to guarantee, and the requirement of equipment inspection efficiency cannot be met in the prior art.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a power failure monitoring method according to an embodiment of the present invention.
The invention provides an electric energy fault monitoring method which is applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the method comprises the following steps:
the cloud server (ECS) is capable of providing an infrastructure Service based on the internet, and flexibly configuring and adjusting each managed node through a centralized remote management platform, so that a simple, efficient, safe and reliable computing Service with elastically scalable processing capability can be provided.
A power line node refers to an active electronic device connected to a network, meaning a power line connected node or a terminal device in a power supply network, capable of transmitting, receiving, collecting or forwarding information over a communication channel.
In the embodiment of the application, the cloud server may obtain actual electric energy data of each power line node in a predetermined period from each power line node, or each power line node reports the actual electric energy data to the cloud server at regular time, so as to obtain a data basis for subsequent electric energy fault monitoring.
Wherein the actual power data includes voltage data and current data.
102, respectively generating undetermined fitting curves corresponding to each power line node according to actual electric energy data;
after the actual electric energy data collected by each electric power line node is obtained, the use trend of the electric power line node cannot be directly obtained because the actual electric energy data are discrete. At this time, according to the actual electric energy data respectively corresponding to each electric power line node, a corresponding to-be-determined fitting curve is respectively fitted and generated for each electric power line node, so that the use trend of each electric power line node in a predetermined period is obtained.
And 103, determining an adjustment strategy of the power line node according to the correlation between the to-be-fitted curve and a preset standard fitted curve.
In specific implementation, a to-be-determined fitting curve and a preset standard fitting curve can be adopted, the correlation degree between the two fitting curves is calculated, and an adjustment strategy for each power line node is determined based on the correlation degree.
In the embodiment of the application, the cloud server acquires corresponding actual electric energy data from each electric power line node according to a preset period, respectively generates undetermined fitting curves corresponding to each electric power line node based on each actual electric energy data, calculates the correlation degree based on the undetermined fitting curves and a standard fitting curve corresponding to each electric power line node, and finally determines respective adjustment strategies according to the correlation degree of each electric power line node. Therefore, the technical problems that time and resources consumed in the power line inspection process are more, timeliness is difficult to guarantee, and the requirement for equipment inspection efficiency cannot be met in the prior art are solved, regular inspection of each power line node is carried out more efficiently, and resource consumption cost is reduced on the premise that timeliness is guaranteed.
Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a power failure monitoring method according to a second embodiment of the present invention.
The invention provides an electric energy fault monitoring method which is applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the method comprises the following steps:
the cloud server (ECS) is capable of providing an infrastructure Service based on the internet, and flexibly configuring and adjusting each managed node through a centralized remote management platform, so that a simple, efficient, safe and reliable computing Service with elastically scalable processing capability can be provided.
A power line node refers to an active electronic device connected to a network, meaning a power line connected node or a terminal device in a power supply network, capable of transmitting, receiving, collecting or forwarding information over a communication channel.
In the embodiment of the application, the cloud server may obtain actual electric energy data of each power line node in a predetermined period from each power line node, or each power line node reports the actual electric energy data to the cloud server at regular time, so as to obtain a data basis for subsequent electric energy fault monitoring.
Wherein the actual power data includes voltage data and current data.
It is worth mentioning that the cloud server may include a plurality of cloud servers, and each cloud server may associate a plurality of power line nodes in a certain area and establish a communication connection.
alternatively, the actual power data includes voltage data and current data, and the step 202 may include the following sub-steps S11-S12:
s11, generating corresponding power data by adopting the voltage data and the current data respectively corresponding to each power line node;
and S12, according to the power data and the preset period, performing curve fitting by adopting a least square method to obtain a to-be-fitted curve corresponding to each power line node.
In this embodiment of the application, the actual electric energy data collected at each power line node may include voltage data and current data, and the voltage value reflected by the voltage data and the current value reflected by the current data are multiplied to generate a power value corresponding to each power line node as power data. And performing curve fitting by adopting a least square method within a time period specified by a preset period according to each power data to obtain a curve to be fitted corresponding to each power line node.
The fitting procedure to generate the curve to be fitted may use the following formula:
y＝k*x+b
wherein, each point slope k in the fitting curve is:
y is a power value, x is a time value,
is the average of all time values x,
the intercept b can be determined by using the undetermined coefficient method for the average of all power values y.
It is worth mentioning that the power line node may be an intra-cell power transmission node or other type of node managed by the cloud server, and the actual power data is transmitted to the cloud server through the 5G.
in the embodiment of the application, historical electric energy data collected by each power line node in a preset historical time period can be obtained, for example, data collected in the historical time period can be obtained for multiple times when each power line node is in a normal operation state, and the average value is used as the historical electric energy data; and respectively constructing a standard fitting curve corresponding to each electric power precedent node according to the historical electric energy data.
The process of fitting the standard curve may refer to the above steps S11-S12, which is not described herein again.
It should be noted that the standard fitting curve may be updated according to a predetermined period to ensure the correctness of the curve, and the preset historical time period may be one day, one week, one hour, and the like, which is not limited in this embodiment of the application.
And step 205, determining an adjustment strategy of the power line node according to the correlation between the to-be-fitted curve and the standard fitted curve.
In one example of the present application, step 205 may include the following sub-steps S21-S23:
s21, calculating the correlation between the to-be-determined fitting curve and the standard fitting curve by adopting a preset cosine correlation calculation formula;
in an embodiment, a preset cosine correlation calculation formula may be adopted to calculate the correlation between the to-be-fitted curve and the standard fitted curve.
The process of calculating the correlation cos θ by using the cosine correlation calculation formula may be as follows:
wherein, A is a curve to be fitted and B is a standard fitting curve.
S22, if the correlation degree is larger than or equal to a first preset threshold value, skipping to execute the step of acquiring actual electric energy data collected by each power line node according to a preset period;
and S23, if the correlation degree is smaller than a first preset threshold value, acquiring the position information of the power line node, generating alarm information and outputting the alarm information.
And after the correlation degrees of the two curves are obtained, comparing the correlation degrees with a first preset threshold, if the correlation degrees are greater than or equal to the first preset threshold, indicating that no fault occurs and no fault risk exists, and directly skipping to execute the step 201 to realize periodic inspection.
If the correlation degree is smaller than the first preset threshold value, it is indicated that a fault has occurred or a fault risk exists, at this time, respective position information can be directly acquired from each power line node, and alarm information output is generated, so that maintenance personnel can conveniently perform fixed-point maintenance.
The position information may include longitude and latitude of the power line node, the alarm information may be output to a control interface built in the cloud server platform or a mobile terminal of a maintenance worker, and the first preset threshold may be set to 80% or 90%.
In another example of the present application, step S23 may include the following sub-steps S231-S232:
s231, if the correlation degree is smaller than a first preset threshold and larger than a second preset threshold, outputting alarm information through the task universality Internet of things slice;
and S232, if the correlation degree is smaller than or equal to a second preset threshold value, switching the power line associated with the power line node to a preset standby line, and outputting alarm information through the mission critical Internet of things slice.
In the optional embodiment of this application, if the relevancy is less than first predetermined threshold and is greater than the second predetermined threshold, can further judge that current power line node only has the trouble risk, for dividing the grade that trouble was salvageed, can pass through task universality thing networking section output alarm information.
If the relevance is smaller than or equal to the second preset threshold, the power line node is indicated to have a fault at the current moment, the power line related to the power line node can be switched to a standby line at the moment to ensure normal use of a user, and meanwhile, alarm information is output through the mission critical internet of things slice to inform maintenance personnel of timely maintenance.
The second preset threshold may be set to 70%, 60%, 50%, or the like, which is not limited in this embodiment of the application.
The mission-critical Internet of things slice refers to a 5G network slice mainly applied to the fields of unmanned driving, automatic factories, smart power grids and the like, and is mainly characterized by ultra-low time delay and high reliability. And the task universality Internet of things slice refers to a 5G network slice with a slightly lower grade than the task criticality Internet of things slice. In order to minimize the end-to-end delay, the core network function and the related server of the original network are sunk to the edge cloud, and different slices are adopted for transmission for different fault risk levels.
To implement Network slicing, Network Function Virtualization (NFV) is a prerequisite. Essentially, NFV is to transfer the software and hardware functions of the dedicated devices in the network (such as MME, S/P-GW and PCRF in the core network, digital unit DU in the radio access network, etc.) to Virtual hosts (VMs). These virtual hosts are industry standard-based commercial servers, which are COTS commercial off-the-shelf products, low cost, and simple to install. Briefly, industry standard based servers, storage and network devices are used to replace dedicated network element devices in a network. After the network is virtualized in function, the radio access network part is called Edge Cloud (Edge Cloud), and the Core network part is called Core Cloud (Core Cloud). VMs in the edge cloud and VMs in the core cloud are interconnected and intercommunicated through an SDN (software defined network).
In the embodiment of the application, the cloud server acquires corresponding actual electric energy data from each electric power line node according to a preset period, respectively generates undetermined fitting curves corresponding to each electric power line node based on each actual electric energy data, calculates the correlation degree based on the undetermined fitting curves and a standard fitting curve corresponding to each electric power line node, and finally determines respective adjustment strategies according to the correlation degree of each electric power line node. Therefore, the technical problems that time and resources consumed in the power line inspection process are more, timeliness is difficult to guarantee, and the requirement for equipment inspection efficiency cannot be met in the prior art are solved, regular inspection of each power line node is carried out more efficiently, and resource consumption cost is reduced on the premise that timeliness is guaranteed.
Referring to fig. 3, fig. 3 is a block diagram of an electrical energy fault monitoring apparatus according to a third embodiment of the present invention.
The embodiment of the invention provides an electric energy fault monitoring device which is applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the device comprises:
an actual electric energy data obtaining module 301, configured to obtain actual electric energy data collected by each power line node according to a predetermined period;
the first curve fitting module 302 is configured to generate to-be-fitted curves corresponding to each power line node according to actual electric energy data;
and an adjusting strategy determining module 303, configured to determine an adjusting strategy of the power line node according to a correlation between the to-be-fitted curve and a preset standard fitted curve.
Optionally, the method further comprises:
the historical electric energy data acquisition module is used for acquiring historical electric energy data acquired by each power line node in a preset historical time period;
and the second curve fitting module is used for respectively constructing a standard fitting curve corresponding to each power line node according to the historical electric energy data.
Optionally, the actual power data comprises voltage data and current data; the first curve fitting module comprises:
the power data generation submodule is used for generating corresponding power data by adopting voltage data and current data respectively corresponding to each power line node;
and the curve fitting submodule is used for performing curve fitting by adopting a least square method according to each power data and a preset period to obtain a to-be-fitted curve corresponding to each power line node.
Optionally, the adjustment policy determining module includes:
the correlation degree operator module is used for calculating the correlation degree between the to-be-determined fitting curve and the standard fitting curve by adopting a preset cosine correlation degree calculation formula;
the first judgment submodule is used for skipping to execute the step of acquiring the actual electric energy data collected by each power line node according to a preset period if the correlation degree is greater than or equal to a first preset threshold;
and the second judgment sub-module is used for acquiring the position information of the power line node, generating alarm information and outputting the alarm information if the correlation degree is smaller than the first preset threshold value.
Optionally, the second decision module includes:
the first slice information output unit is used for outputting alarm information through the task ordinary Internet of things slice if the correlation degree is smaller than a first preset threshold value and larger than a second preset threshold value;
and the second slice information output unit is used for switching the power line associated with the power line node to a preset standby line if the correlation degree is less than or equal to a second preset threshold value, and outputting alarm information through a task key Internet of things slice.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The electric energy fault monitoring method is applied to a cloud server, wherein the cloud server is in communication connection with a plurality of preset power line nodes, and the method comprises the following steps:
acquiring actual electric energy data acquired by each power line node according to a preset period;
respectively generating undetermined fitting curves corresponding to the power line nodes according to the actual electric energy data;
and determining an adjustment strategy of the power line node according to the correlation between the to-be-determined fitting curve and a preset standard fitting curve.
2. The method of claim 1, further comprising:
acquiring historical electric energy data collected by each power line node in a preset historical time period;
and respectively constructing the standard fitting curve corresponding to each power line node according to the historical electric energy data.
3. The method of claim 1, wherein the actual power data comprises voltage data and current data; the step of respectively generating a to-be-determined fitting curve corresponding to each power line node according to the actual electric energy data comprises the following steps of:
generating corresponding power data by adopting the voltage data and the current data respectively corresponding to each power line node;
and according to the power data and the preset period, performing curve fitting by adopting a least square method to obtain a to-be-fitted curve corresponding to each power line node.
4. The method of claim 1, wherein said step of determining an adjustment strategy for said power line node based on a correlation between said curve to be fit and said curve to be fit comprises:
calculating the correlation between the to-be-determined fitting curve and the standard fitting curve by adopting a preset cosine correlation calculation formula;
if the correlation degree is larger than or equal to a first preset threshold value, skipping to execute the step of acquiring the actual electric energy data acquired by each power line node according to a preset period;
and if the correlation degree is smaller than the first preset threshold value, acquiring the position information of the power line node, generating alarm information and outputting the alarm information.
5. The method according to claim 4, wherein the step of acquiring the position information of the power line node, generating alarm information and outputting the alarm information if the correlation degree is smaller than the first preset threshold value comprises:
if the correlation degree is smaller than the first preset threshold and larger than a second preset threshold, outputting the alarm information through a task common Internet of things slice;
and if the correlation degree is smaller than or equal to the second preset threshold value, switching the power line associated with the power line node to a preset standby line, and outputting the alarm information through a task key Internet of things slice.
6. The utility model provides an electric energy fault monitoring device which characterized in that is applied to cloud ware, cloud ware and a plurality of predetermined power line node communication connection, the device includes:
the actual electric energy data acquisition module is used for acquiring actual electric energy data acquired by each electric power line node according to a preset period;
the first curve fitting module is used for respectively generating a to-be-fitted curve corresponding to each power line node according to the actual electric energy data;
and the adjustment strategy determining module is used for determining the adjustment strategy of the power line node according to the correlation between the to-be-determined fitting curve and a preset standard fitting curve.
7. The apparatus of claim 6, further comprising:
the historical electric energy data acquisition module is used for acquiring historical electric energy data acquired by each power line node in a preset historical time period;
and the second curve fitting module is used for respectively constructing the standard fitting curve corresponding to each power line node according to the historical electric energy data.
8. The apparatus of claim 6, wherein the actual power data comprises voltage data and current data; the first curve fitting module comprises:
the power data generation submodule is used for generating corresponding power data by adopting the voltage data and the current data which respectively correspond to each power line node;
and the curve fitting submodule is used for performing curve fitting by adopting a least square method according to the power data and the preset period to obtain a to-be-fitted curve corresponding to each power line node.
9. The apparatus of claim 6, wherein the adjustment policy determination module comprises:
the correlation degree operator module is used for calculating the correlation degree between the to-be-determined fitting curve and the standard fitting curve by adopting a preset cosine correlation degree calculation formula;
the first judgment submodule is used for skipping to execute the step of acquiring the actual electric energy data collected by each power line node according to the preset period if the correlation degree is greater than or equal to a first preset threshold;
and the second judging submodule is used for acquiring the position information of the power line node, generating alarm information and outputting the alarm information if the correlation degree is smaller than the first preset threshold value.
10. The apparatus of claim 9, wherein the second decision module comprises:
the first slice information output unit is used for outputting the alarm information through a task ordinary Internet of things slice if the correlation degree is smaller than the first preset threshold value and larger than a second preset threshold value;
and the second slice information output unit is used for switching the power line associated with the power line node to a preset standby line if the correlation degree is less than or equal to the second preset threshold value, and outputting the alarm information through a mission critical internet of things slice.
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