CN113794537A - Sensing network in power Internet of things and self-adaptive dynamic sampling method and device thereof - Google Patents
Sensing network in power Internet of things and self-adaptive dynamic sampling method and device thereof Download PDFInfo
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Abstract
The application discloses a sensing network in an electric power Internet of things and a self-adaptive dynamic adoption method and device thereof, wherein the method and device are applied to the sensing network of the electric power Internet of things, and particularly control the switching between the normal working state and the abnormal working state of sensing nodes of the sensing network; and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold. By the scheme, the sampling frequency and the uploading frequency can be adaptively adjusted, the communication and calculation resources of the sensing network are reduced, and the requirements on the sampling frequency and the transmission frequency under abnormal working conditions can be met, so that the system can accurately capture the signal characteristics in time.
Description
Technical Field
The application relates to the technical field of Internet of things, in particular to a sensing network in an electric Internet of things and a self-adaptive dynamic adoption method and device thereof.
Background
Under the scene of the power internet of things, a plurality of sensing nodes in a sensing network send acquired data to an edge internet of things agent for analysis and processing, and the method is specifically shown in fig. 1. According to the type of sensing data of the sensing nodes, the sensing nodes in the existing sensing network are divided into environment sensing nodes and electric sensing nodes, and under the normal working state, each sensing node collects data according to fixed frequency and uploads the data to the edge Internet of things agent through the sensing network.
The inventor of the application discovers in practice, in order to meet the requirement for parameter acquisition, massive sensing equipment is arranged in the power internet of things, data transmission amount and calculation amount are large, the conventional sensing network usually adopts fixed sampling frequency or transmission frequency during sensing data acquisition and transmission, and thus the problem is caused.
Disclosure of Invention
In view of this, the application provides a sensing network in an electric power internet of things, and a self-adaptive dynamic sampling method and device thereof, which are used for avoiding the problem that the signal characteristics cannot be accurately and timely captured due to the adoption of a fixed sampling frequency and a fixed transmission frequency in the existing sensing network.
In order to achieve the above object, the following solutions are proposed:
a self-adaptive dynamic sampling method is applied to a sensing network of an electric power Internet of things, and comprises the following steps:
controlling the switching between the normal working state and the abnormal working state of a sensing node of a sensing network;
and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold.
Optionally, the controlling the switching between the normal working state and the abnormal working state of the sensing node of the sensing network includes:
when the environment sensing node is abnormal, the environment sensing node and the electric sensing node in the abnormal occurrence range are controlled to switch the working state;
or when the electrical sensing node is abnormal, the environmental sensing node and the electrical sensing node in the abnormal occurrence range are controlled to switch the working state.
Optionally, the abnormal occurrence range when the environmental sensing node is abnormal includes all the environmental sensing nodes and the electrical sensing nodes in the sensing space of the abnormal environmental sensing node.
Optionally, the abnormal occurrence range when the electrical sensing node is abnormal includes all the environmental sensing nodes whose sensing space covers the abnormal electrical sensing node, and the electrical sensing nodes upstream and downstream of the abnormal electrical sensing node.
Optionally, the controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold includes:
in a normal working state, controlling the sensing node to dynamically adjust the sampling frequency in a sampling time interval value interval in the normal working state and dynamically adjust the uploading threshold value in an uploading threshold value interval in the normal working state;
or, in an abnormal working state, controlling the sensing node to dynamically adjust the sampling frequency in the sampling time interval value interval of the abnormal working state and dynamically adjust the uploading threshold value in the uploading threshold value interval of the abnormal working state.
Optionally, the sampling time interval value interval in the normal operating state and the sampling time interval value interval in the abnormal operating state may be different;
or the uploading threshold value interval in the normal working state and the uploading threshold value interval in the abnormal working state can be different.
Optionally, the sampling frequency is dynamically adjusted within the sampling time interval value range according to the change slope of the sampling value of the sensing node.
Optionally, the sampling frequency is dynamically adjusted within an uploading threshold value range according to whether the sampling value of the sensing node is an extreme value or not.
The utility model provides an adaptive dynamic sampling device, is applied to the sensing network of electric power thing networking, adaptive dynamic sampling device includes:
the first control module is configured to control the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network;
a second control module configured to control the sensing node to dynamically adjust a sampling frequency and an upload threshold.
Optionally, the sensing node is an environmental sensing node or an electrical sensing node, and the first control module is configured to control switching between a normal working state and an abnormal working state of the sensing node of the sensing network, and includes:
when the environment sensing node is abnormal, the environment sensing node and the electric sensing node in the abnormal occurrence range are controlled to switch the working state;
or when the electrical sensing node is abnormal, the environmental sensing node and the electrical sensing node in the abnormal occurrence range are controlled to switch the working state.
Optionally, the abnormal occurrence range when the environmental sensing node is abnormal includes all the environmental sensing nodes and the electrical sensing nodes in the sensing space of the abnormal environmental sensing node.
Optionally, the abnormal occurrence range when the electrical sensing node is abnormal includes all the environmental sensing nodes whose sensing space covers the abnormal electrical sensing node, and the electrical sensing nodes upstream and downstream of the abnormal electrical sensing node.
Optionally, the second control module is configured to control the sensing node to dynamically adjust a sampling frequency and an upload threshold, and includes:
in a normal working state, controlling the sensing node to dynamically adjust the sampling frequency in a sampling time interval value interval in the normal working state and dynamically adjust the uploading threshold value in an uploading threshold value interval in the normal working state;
or, in an abnormal working state, controlling the sensing node to dynamically adjust the sampling frequency in the sampling time interval value interval of the abnormal working state and dynamically adjust the uploading threshold value in the uploading threshold value interval of the abnormal working state.
Optionally, the sampling time interval value interval in the normal operating state and the sampling time interval value interval in the abnormal operating state may be different;
or the uploading threshold value interval in the normal working state and the uploading threshold value interval in the abnormal working state can be different.
Optionally, the sampling frequency is dynamically adjusted within the sampling time interval value range according to the change slope of the sampling value of the sensing node.
Optionally, the sampling frequency is dynamically adjusted within an uploading threshold value range according to whether the sampling value of the sensing node is an extreme value or not.
The utility model provides a sensing network of electric power thing networking is provided with as above adaptive sampling device.
A sensor network of an electric power internet of things, the sensor network comprising an edge internet of things agent comprising at least one processor and a memory connected to the processor, wherein:
the memory is for storing a computer program or instructions;
the processor is configured to execute the computer program or instructions to cause the edge agent to perform the adaptive dynamic sampling method as described above.
According to the technical scheme, the method and the device are applied to the sensing network of the power internet of things, and particularly control switching between a normal working state and an abnormal working state of a sensing node of the sensing network; and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold. By the scheme, the sampling frequency and the uploading frequency can be adaptively adjusted, the communication and calculation resources of the sensing network are reduced, and the requirements on the sampling frequency and the transmission frequency under abnormal working conditions are met, so that the system can accurately capture the signal characteristics in time.
Drawings
In order to more clearly illustrate the embodiments of the present application 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, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a sensing network architecture of an electric power Internet of things;
fig. 2 is a flowchart of an adaptive dynamic sampling method according to an embodiment of the present application;
fig. 3 is a flow chart of switching the working state of the sensor network according to the embodiment of the present application;
FIG. 4 is a flow chart of dynamic sampling of a sensor network according to an embodiment of the present application;
fig. 5 is a block diagram of an adaptive dynamic sampling apparatus according to an embodiment of the present application;
fig. 6 is a block diagram of an edge internet of things proxy of a sensor network according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
Example one
Fig. 2 is a flowchart of an adaptive dynamic sampling method according to an embodiment of the present application.
As shown in fig. 2, the adaptive dynamic sampling method provided in this embodiment is applied to a sensing network of an electric power internet of things, and in particular, is applied to an edge internet of things agent and a sensing node of the sensing network, where the sensing network includes an environmental sensing node that detects and samples an environmental parameter of each node and an electrical sensing node that detects and samples an electrical parameter of each node, and the adaptive dynamic sampling method includes the following steps:
and S1, controlling the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network.
Each sensing node of the sensing network has two working states, namely a normal working state and an abnormal working state, the default working state is the normal working state, and the edge Internet of things agent controls each sensing node to switch the working states.
Specifically, the switching process of the working state of the sensor network is as follows, as shown in fig. 3:
1. each sensing node in the sensing network defaults to be in a normal working state, dynamically adjusts sampling frequency and uploading threshold value in a sampling time interval and an uploading threshold value interval of the sensing node in the normal working state, and uploads a sampling result to the edge Internet of things agent.
2. When any sensing node in the sensing network acquires abnormal data and reports the abnormal data to the edge internet of things agent, the sensing node is marked as a trigger sensing node, the edge internet of things agent controls the trigger sensing node and the related sensing node to be switched into an abnormal working state, the sampling frequency and the uploading threshold value of the corresponding sensing node are dynamically adjusted in a sampling time interval and an uploading threshold value interval under the abnormal working state, and the sampling result is uploaded to the edge internet of things agent.
Because the sensing nodes related by the application comprise the environment sensing nodes and the electric sensing nodes, the selection of the related sensing nodes is different as follows according to the types of the triggering sensing nodes:
when the triggering sensing node is an environment sensing node, the related sensing nodes sense all the environment sensing nodes and electric sensing nodes in the space for the environment sensing node.
When the trigger sensing node is an electrical environment node, the related sensing nodes are all environment sensing nodes of which the sensing space contains the electrical sensing node, and upstream and downstream electrical sensing nodes of a power line where the electrical sensing node is located.
3. When the data collected by the trigger sensing node and the related sensing nodes are recovered to be normal, the edge internet of things agent controls the working state of the trigger sensing node and the related sensing nodes to be switched to be a normal working state, the sampling frequency and the uploading threshold value of the corresponding sensing nodes are dynamically adjusted in a sampling time interval and an uploading threshold value interval under the normal working state, and the sampling result is uploaded to the edge internet of things agent.
And S2, controlling the sensing nodes of the sensing network to dynamically adjust the sampling frequency and the uploading threshold.
Specifically, the following dynamic sampling strategies are adopted for the normal working state and the abnormal working state of each sensing node in the sensing network, as shown in fig. 4:
taking any certain sensing node in the sensing network as an example, the symbols and their meanings in the dynamic sampling strategy are as follows:
△M: default upload threshold
△′M: uploading threshold value at extreme value point, generally delta'M≤△M，△M、△′MIn the upload threshold interval ΔMmin,△Mmax]Internal value
τM: default sampling time interval
Wherein, M is a mark bit of a normal working state and an abnormal working state, the normal working state is 0, and the abnormal working state is 1; the sampling time interval and the uploading threshold interval in the normal working state and the sampling time interval and the uploading threshold interval in the abnormal working state can be different, and the uploading threshold, the uploading threshold at the extreme point and the sampling time interval in the normal working state are not less than the uploading threshold, the uploading threshold at the extreme point and the sampling time interval in the abnormal working state under the general condition.
xn: up-value of nth sampling period
specifically, the flow of the ith sampling in the nth sampling period is as follows, as shown in fig. 4:
1. sense nodes to
Sampling time interval data acquisition, data acquisition
2. Judging whether to dynamically adjust the next sampling time interval: in accordance withAccording to
Value, if necessary, to dynamically adjust the next sampling interval, e.g. if
If the sampling interval is less than 1, the sampling interval is increased, if the sampling interval is more than 1, the sampling interval is decreased, and the time interval value interval is [ tau ]Mmin,τMmax]；
3. Judging whether to adjust an uploading threshold value: sampling variation value
And
whether the same number is the same number, the same number is a non-extreme value, the different number is an extreme value, and when the extreme value appears, the uploading threshold value is adjusted to be delta'M；
4. Judging whether to upload data: in the non-extreme state of the state,
when the material is not sent upwards,
time-on-time delivery, under extreme condition, judgment
Whether is less than delta'MWhen less than the time, not send upward
When greater than the threshold, the liquid is sent upwards
Judgment of
Whether is less than delta'MWhen is less than that, notUpward feeding
When greater than the threshold, the liquid is sent upwards
5. Repeating the step 1, and restarting the (i + 1) th sampling in the nth sampling period (without sending up
Time) or start the 1 st sampling (up-sending) of the (n + 1) th sampling period
Time)
According to the technical scheme, the embodiment provides the self-adaptive dynamic adoption method, which is applied to the sensing network of the power internet of things, and particularly controls the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network; and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold. By the scheme, the sampling frequency and the uploading frequency can be adaptively adjusted, the communication and calculation resources of the sensing network are reduced, and the requirements on the sampling frequency and the transmission frequency under abnormal working conditions are met, so that the system can accurately capture the signal characteristics in time.
Example two
Fig. 5 is a block diagram of an adaptive dynamic sampling apparatus according to an embodiment of the present application.
As shown in fig. 5, the adaptive dynamic sampling apparatus provided in this embodiment is applied to a sensing network of an electric power internet of things, and in particular, is applied to an edge internet of things proxy of the sensing network, where the sensing network includes an environment sensing node that detects and samples an environment parameter of each node and an electrical sensing node that detects and samples an electrical parameter of each node, and the adaptive dynamic sampling apparatus includes a first control module 10 and a second control module 20.
The first control module is used for controlling the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network.
Each sensing node of the sensing network has two working states, namely a normal working state and an abnormal working state, the default working state is the normal working state, and the edge Internet of things agent controls each sensing node to switch the working states.
Specifically, the switching process of the working state of the sensor network is as follows, as shown in fig. 3:
1. each sensing node in the sensing network defaults to be in a normal working state, dynamically adjusts sampling frequency and uploading threshold value in a sampling time interval and an uploading threshold value interval of the sensing node in the normal working state, and uploads a sampling result to the edge Internet of things agent.
2. When any sensing node in the sensing network acquires abnormal data and reports the abnormal data to the edge internet of things agent, the sensing node is marked as a trigger sensing node, the edge internet of things agent controls the trigger sensing node and the related sensing node to be switched into an abnormal working state, the sampling frequency and the uploading threshold value of the corresponding sensing node are dynamically adjusted in a sampling time interval and an uploading threshold value interval under the abnormal working state, and the sampling result is uploaded to the edge internet of things agent.
Because the sensing nodes related by the application comprise the environment sensing nodes and the electric sensing nodes, the selection of the related sensing nodes is different as follows according to the types of the triggering sensing nodes:
when the triggering sensing node is an environment sensing node, the related sensing nodes sense all the environment sensing nodes and electric sensing nodes in the space for the environment sensing node.
When the trigger sensing node is an electrical environment node, the related sensing nodes are all environment sensing nodes of which the sensing space contains the electrical sensing node, and upstream and downstream electrical sensing nodes of a power line where the electrical sensing node is located.
3. When the data collected by the trigger sensing node and the related sensing nodes are recovered to be normal, the edge internet of things agent controls the working state of the trigger sensing node and the related sensing nodes to be switched to be a normal working state, the sampling frequency and the uploading threshold value of the corresponding sensing nodes are dynamically adjusted in a sampling time interval and an uploading threshold value interval under the normal working state, and the sampling result is uploaded to the edge internet of things agent.
The second control module is used for controlling the sensing nodes of the sensing network to dynamically adjust the sampling frequency and the uploading threshold.
Specifically, the following dynamic sampling strategies are adopted for the normal working state and the abnormal working state of each sensing node in the sensing network, as shown in fig. 4:
taking any certain sensing node in the sensing network as an example, the symbols and their meanings in the dynamic sampling strategy are as follows:
△M: default upload threshold
△′M: uploading threshold value at extreme value point, generally delta'M≤△M，△M、△′MIn the upload threshold interval ΔMmin,△Mmax]Internal value
τM: default sampling time interval
Wherein, M is a mark bit of a normal working state and an abnormal working state, the normal working state is 0, and the abnormal working state is 1; the sampling time interval and the uploading threshold interval in the normal working state and the sampling time interval and the uploading threshold interval in the abnormal working state can be different, and the uploading threshold, the uploading threshold at the extreme point and the sampling time interval in the normal working state are not less than the uploading threshold, the uploading threshold at the extreme point and the sampling time interval in the abnormal working state under the general condition.
xn: up-value of nth sampling period
specifically, the flow of the ith sampling in the nth sampling period is as follows, as shown in fig. 4:
1. sense nodes to
Sampling time interval data acquisition, data acquisition
2. Judging whether to dynamically adjust the next sampling time interval: according to
Value, if necessary, to dynamically adjust the next sampling interval, e.g. if
If the sampling interval is less than 1, the sampling interval is increased, if the sampling interval is more than 1, the sampling interval is decreased, and the time interval value interval is [ tau ]Mmin,τMmax]；
3. Judging whether to adjust an uploading threshold value: sampling variation value
And
whether the same number is the same number, the same number is a non-extreme value, the different number is an extreme value, and when the extreme value appears, the uploading threshold value is adjusted to be delta'M；
4. Judging whether to upload data: in the non-extreme state of the state,
when the material is not sent upwards,
time-on-time delivery, under extreme condition, judgment
Whether is less than delta'MWhen less than the time, not send upward
When greater than the threshold, the liquid is sent upwards
Judgment of
Whether is less than delta'MWhen less than the time, not send upward
When greater than the threshold, the liquid is sent upwards
5. Repeating the step 1, and restarting the (i + 1) th sampling in the nth sampling period (without sending up
Time) or start the 1 st sampling (up-sending) of the (n + 1) th sampling period
Time)
According to the technical scheme, the self-adaptive dynamic adoption device is applied to the sensing network of the power internet of things, and particularly controls the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network; and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold. By the scheme, the sampling frequency and the uploading frequency can be adaptively adjusted, the communication and calculation resources of the sensing network are reduced, and the requirements on the sampling frequency and the transmission frequency under abnormal working conditions are met, so that the system can accurately capture the signal characteristics in time.
EXAMPLE III
The embodiment provides a sensing network of an electric power internet of things, and the sensing network is provided with the self-adaptive dynamic sampling device provided by the embodiment. The device is used for controlling the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network; and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold. . By the scheme, the sampling frequency and the uploading frequency can be adaptively adjusted, the communication and calculation resources of the sensing network are reduced, and the requirements on the sampling frequency and the transmission frequency under abnormal working conditions are met, so that the system can accurately capture the signal characteristics in time.
Example four
Fig. 6 is a block diagram of an edge internet of things proxy of a sensor network according to an embodiment of the present application.
The embodiment provides a sensing network of an electric power internet of things, which comprises an edge internet of things agent. The edge agent comprises at least one processor 101 and a memory 102, both connected by a data bus 103, as shown in particular in fig. 6. The memory is used for storing corresponding computer programs or instructions, and the processor is used for executing the corresponding computer programs or instructions, so that the edge internet of things agent of the sensing network realizes the self-adaptive dynamic sampling method provided by the embodiment.
The self-adaptive dynamic sampling method is characterized by specifically controlling the switching between the normal working state and the abnormal working state of a sensing node of a sensing network; and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold. By the scheme, the sampling frequency and the uploading frequency can be adaptively adjusted, the communication and calculation resources of the sensing network are reduced, and the requirements on the sampling frequency and the transmission frequency under abnormal working conditions are met, so that the system can accurately capture the signal characteristics in time.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be 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 terminal 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 terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The technical solutions provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the descriptions of the above examples are only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (18)
1. A self-adaptive dynamic sampling method is applied to a sensing network of an electric power Internet of things, and is characterized by comprising the following steps:
controlling the switching between the normal working state and the abnormal working state of a sensing node of a sensing network;
and controlling the sensing node to dynamically adjust the sampling frequency and the uploading threshold.
2. The adaptive sampling method according to claim 1, wherein the controlling of the switching between the normal operating state and the abnormal operating state of the sensing nodes of the sensing network comprises:
when the environment sensing node is abnormal, the environment sensing node and the electric sensing node in the abnormal occurrence range are controlled to switch the working state;
or when the electrical sensing node is abnormal, the environmental sensing node and the electrical sensing node in the abnormal occurrence range are controlled to switch the working state.
3. The adaptive sampling method according to claim 2, wherein the abnormal occurrence range of the environmental sensing nodes includes all the environmental sensing nodes and the electrical sensing nodes in the sensing space of the abnormal environmental sensing nodes.
4. The adaptive sampling method according to claim 2, wherein the abnormal occurrence range of the electrical sensing node includes all environmental sensing nodes whose sensing space covers the abnormal electrical sensing node and electrical sensing nodes upstream and downstream of the abnormal electrical sensing node.
5. The adaptive sampling method of claim 1, wherein the controlling the sensing node to dynamically adjust a sampling frequency and an upload threshold comprises:
in a normal working state, controlling the sensing node to dynamically adjust the sampling frequency in a sampling time interval value interval in the normal working state and dynamically adjust the uploading threshold value in an uploading threshold value interval in the normal working state;
or, in an abnormal working state, controlling the sensing node to dynamically adjust the sampling frequency in the sampling time interval value interval of the abnormal working state and dynamically adjust the uploading threshold value in the uploading threshold value interval of the abnormal working state.
6. The adaptive sampling method of claim 5, wherein the normal operating state sampling time interval value interval and the abnormal operating state sampling time interval value interval may be different;
or the uploading threshold value interval in the normal working state and the uploading threshold value interval in the abnormal working state can be different.
7. The adaptive sampling method of claim 5, wherein the sampling frequency is dynamically adjusted over a sampling interval based on a slope of change of the sampling value at the sensing node.
8. The adaptive sampling method of claim 5, wherein the sampling frequency is dynamically adjusted within an upper threshold value interval according to whether the sensing node sampling value is extreme.
9. The utility model provides an adaptive dynamic sampling device, is applied to the sensing network of electric power thing networking, its characterized in that, adaptive dynamic sampling device includes:
the first control module is configured to control the switching between the normal working state and the abnormal working state of the sensing nodes of the sensing network;
a second control module configured to control the sensing node to dynamically adjust a sampling frequency and an upload threshold.
10. The adaptive sampling device according to claim 9, wherein the sensing node is an environmental sensing node or an electrical sensing node, and the first control module is configured to control switching between a normal operating state and an abnormal operating state of the sensing node of the sensing network, and includes:
when the environment sensing node is abnormal, the environment sensing node and the electric sensing node in the abnormal occurrence range are controlled to switch the working state;
or when the electrical sensing node is abnormal, the environmental sensing node and the electrical sensing node in the abnormal occurrence range are controlled to switch the working state.
11. The adaptive sampling device of claim 10, wherein the abnormal-time abnormality occurrence range of the environmental sensing node includes all environmental sensing nodes and electrical sensing nodes in a sensing space of the abnormal environmental sensing node.
12. The adaptive sampling device according to claim 10, wherein the abnormal occurrence range of the electrical sensing node includes all environmental sensing nodes whose sensing space covers the abnormal electrical sensing node and electrical sensing nodes upstream and downstream of the abnormal electrical sensing node.
13. The adaptive sampling device of claim 9, wherein the second control module is configured to control the sensing node to dynamically adjust a sampling frequency and an upload threshold, and comprises:
in a normal working state, controlling the sensing node to dynamically adjust the sampling frequency in a sampling time interval value interval in the normal working state and dynamically adjust the uploading threshold value in an uploading threshold value interval in the normal working state;
or, in an abnormal working state, controlling the sensing node to dynamically adjust the sampling frequency in the sampling time interval value interval of the abnormal working state and dynamically adjust the uploading threshold value in the uploading threshold value interval of the abnormal working state.
14. The adaptive sampling device of claim 9, wherein the normal operating state sampling time interval span can be different from the abnormal operating state sampling time interval span;
or the uploading threshold value interval in the normal working state and the uploading threshold value interval in the abnormal working state can be different.
15. The adaptive sampling device of claim 9, wherein the sampling frequency is dynamically adjusted over a sampling interval based on a slope of change of the sampling value at the sensing node.
16. The adaptive sampling method of claim 9, wherein the sampling frequency is dynamically adjusted within an upper threshold value interval according to whether the sensing node sampling value is extreme.
17. A sensing network of an electric power Internet of things is characterized in that the self-adaptive sampling device is provided according to any one of claims 9-16.
18. A sensor network of an electric power internet of things, the sensor network comprising an edge internet of things agent, the edge internet of things agent comprising at least one processor and a memory connected with the processor, wherein:
the memory is for storing a computer program or instructions;
the processor is configured to execute the computer program or instructions to cause the edge agent to perform the adaptive dynamic sampling method according to any one of claims 1 to 8.
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