CN113747563A - Synchronous acquisition method and device for power internet of things sensors - Google Patents
Synchronous acquisition method and device for power internet of things sensors Download PDFInfo
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Abstract
The invention discloses a synchronous acquisition method and a synchronous acquisition device for a power Internet of things sensor, wherein the method comprises the following steps: time synchronization is carried out on the control nodes; calculating the transmission delay time of the communication between the control node and the sensor node by taking the control node clock as a reference; considering the transmission delay time, performing time synchronization on the sensor nodes, and issuing data acquisition trigger time; and carrying out synchronous data acquisition and uploading on the sensor nodes. The invention provides a unified time reference for the sensor nodes by predicting the delay time in the actual data transmission process at the control node, so that the sensor time can be synchronous with the satellite time and a unified data acquisition strategy is issued through a broadcast channel, and the synchronous data acquisition of the multi-sensor nodes is completed in a required time.
Description
Technical Field
The invention belongs to the technical field of ubiquitous power Internet of things, and relates to a synchronous acquisition method and device for a power Internet of things sensor.
Background
The basis of the ubiquitous power internet of things is power equipment of a perception layer, and the running state information of the power equipment comprises basic data such as voltage, current, frequency, phase, power and the like; the environment information includes temperature data, infrared data, etc. of the operating environment of the power system. The real-time synchronous acquisition of the running state information and the environmental information of the power equipment on the sensing layer plays an important role in the intelligent management and maintenance of the power internet of things. In the power internet of things, the collection of the running state information and the environmental information of the power equipment is completed by corresponding sensor nodes. The control node collects data information collected by each sensor node, and then accesses the Internet through a base station or the Ethernet to upload data to the cloud. Due to uncertain factors such as sampling objects, sampling modes, sampling periods, sampling post-processing and clock crystal oscillator temperature drift of all the acquisition devices, the sampling data of the sensors of all the nodes of the power equipment of the current power internet of things are asynchronous, and the acquired data cannot truly reflect changes of the real world, so that the stable operation of a power system cannot be guaranteed.
At present, various time synchronization methods are applied to sensor synchronization acquisition in the field of internet of things, such as FTSP (flooding time synchronization protocol), RBS (reference message time synchronization), TPSN (wireless sensor network time synchronization protocol), DMTS (delay measurement time synchronization), and the like, and in general, these researches focus on optimization of three time synchronization models: the first time synchronization model is the simplest model, expresses the sequence relation of event occurrence by logical time, and only maintains the time correlation of collection tasks between sensor nodes. The second time synchronization model is based on relative time, each node maintains an independent clock, the node clocks are asynchronous with each other, and the node only stores relative time information of the node and other nodes in the network, so that the time synchronization task is completed. The third time synchronization model is the most complex model in which each node in the network maintains a clock, and the nodes in the entire network synchronize the clock of a reference node, thereby ensuring global clock synchronization.
The time synchronization model based on the logic time weakens the concept of time synchronization, and only focuses on the occurrence sequence of different sensor node events actually, but cannot synchronize with the absolute time of the real world. The time synchronization model based on relative time and the time synchronization model for setting a global clock align the clocks of the sensor nodes through a time synchronization algorithm, but the signaling overhead for maintaining time synchronization among all the nodes is large. Generally speaking, due to the influence of practical factors such as unequal processing time of different sensor nodes, temperature drift of a clock crystal oscillator and the like, the prior art cannot realize synchronous data acquisition of wireless sensor nodes at a certain required time with high efficiency and low cost.
Disclosure of Invention
The invention aims to provide a synchronous acquisition method and a synchronous acquisition device for a power internet of things sensor, which solve the problem that the traditional time synchronization model cannot process delay caused by practical factors such as sampling difference of different sensor nodes, temperature drift of a clock crystal oscillator and the like at low cost, and simultaneously meet the requirement of a power transmission internet of things wireless sensor network on time self-synchronization.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides a synchronous acquisition method of a power Internet of things sensor, which comprises the following steps:
time synchronization is carried out on the control nodes; the control node is a node which runs the application of the Internet of things in the power Internet of things and communicates with an upper cloud end;
calculating the transmission delay time of the communication between the control node and the sensor node by taking the control node clock as a reference;
according to the transmission delay time, performing time synchronization on the sensor nodes and acquiring data acquisition trigger time;
and carrying out synchronous data acquisition and uploading on the sensor nodes based on the data acquisition trigger time.
Preferably, the method further comprises the following steps:
initializing the control node and the peripheral equipment.
Preferably, the time synchronization of the control node includes:
the NB-IoT communication module adopts a global satellite navigation system/a global positioning system/a Beidou satellite navigation system to carry out satellite time service, and the satellite time service time is used for synchronously controlling the clock of the node;
or, the current network time is acquired through the Ethernet module, and the clock of the control node is synchronized by using the acquired network time.
Preferably, the calculating of the transmission delay time of the communication between the control node and the sensor node includes:
calculating the transmission delay time of the communication between the control node and the sensor node according to the time T1 of the first synchronization signal sent by the control node and the time T2 of the control node receiving the response information of the sensor node:
τ=(T2-T1)/2。
preferably, the time synchronization of the sensor nodes considering the transmission delay time includes:
according to a second synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time of T3, the time for synchronizing the sensor node clock is T3+ tau/2;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
Preferably, the calculating of the transmission delay time of the communication between the control node and the sensor node includes:
training a prediction model of the transmission delay time, converting the prediction model into a TFLite prediction model by using a Tensorflow Lite converter tool, embedding the TFLite prediction model into a control node for operation, and predicting the transmission delay time tau from the control node to a sensor node in real time.
Preferably, the time synchronization of the sensor nodes considering the transmission delay time includes:
according to a synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time T, the time for synchronizing the clock of the sensor node is T + tau;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
Preferably, the control node is capable of communicating with a plurality of sensor nodes in parallel.
Preferably, the training of the prediction model of the transmission delay time includes:
the following operations are cyclically carried out:
the control node transmits the current time T1 through a broadcast channel;
the sensor node returns response information to the control node, and the time T2 when the control node receives the response information of the sensor node is recorded;
calculating the transmission delay time of the communication between the control node and the sensor node based on T1 and T2:
τ=(T2-T1)/2；
and collecting data of the transmission delay time tau of the sensor node and the data influencing the tau as a training set, and training a prediction model of the transmission delay time based on a neural network.
Preferably, the performing of synchronous data acquisition and uploading of the sensor node includes:
when the current time of the sensor node meets the next data acquisition triggering time, driving a sensor corresponding to the sensor node by using a clock, and triggering the sensor to acquire data;
and the sensor node uses the serial port idle interruption to stamp a time stamp T _ set for the data which is acquired and sends the data to the control node.
The invention also provides a synchronous acquisition device of the power internet of things sensor, which comprises:
the first synchronization module is used for carrying out time synchronization on the control nodes; the control node is a node which runs the application of the Internet of things in the power Internet of things and communicates with an upper cloud end;
the first calculation module is used for calculating the transmission delay time of communication between the control node and the sensor node by taking the control node clock as a reference;
the second synchronization module is used for carrying out time synchronization on the sensor nodes according to the transmission delay time and acquiring data acquisition trigger time;
and the number of the first and second groups,
and the acquisition module is used for acquiring and uploading the synchronous data of the sensor nodes based on the data acquisition trigger time.
Preferably, the first calculation module is specifically configured to,
calculating the transmission delay time of the communication between the control node and the sensor node according to the time T1 of the first synchronization signal sent by the control node and the time T2 of the control node receiving the response information of the sensor node:
τ=(T2-T1)/2。
preferably, the second synchronization module is specifically configured to,
according to a second synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time of T3, the time for synchronizing the sensor node clock is T3+ tau/2;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
Preferably, the first calculation module is specifically configured to,
training a prediction model of the transmission delay time, converting the prediction model into a TFLite prediction model by using a Tensorflow Lite converter tool, embedding the TFLite prediction model into a control node for operation, and predicting the transmission delay time tau from the control node to a sensor node in real time.
Preferably, the second synchronization module is specifically configured to,
according to a synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time T, the time for synchronizing the clock of the sensor node is T + tau;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
Preferably, the method further comprises the following steps:
and the initialization module is used for initializing the control node and the peripheral equipment.
Preferably, the first and second liquid crystal materials are,
the peripheral equipment comprises a communication module for controlling the node to carry out time synchronization, a communication module for controlling the node and the sensor to carry out communication and a real-time clock chip configured on the sensor node;
the communication module for controlling the node to perform time synchronization is any one of an NB-IoT communication module or an Ethernet module;
the communication module for communicating the control node and the sensor is any one of an LORA module, a ZigBee module and a low-power Bluetooth module;
the signal source of the real-time clock chip is provided by a temperature drift compensation crystal oscillator of 32.768 KHz.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a synchronous acquisition method of a power Internet of things sensor, which is characterized in that a control node predicts delay time in the actual data transmission process to provide a uniform time reference for a sensor node, so that the sensor time can be synchronized with satellite time and a uniform data acquisition strategy is issued through a broadcast channel, and the synchronous acquisition of data of multiple sensor nodes in a required time is realized.
The method solves the problems that sampling data of each sensor node of the current power internet of things power equipment is asynchronous and the acquired data cannot truly reflect real world changes due to different sampling objects, different sampling modes, various sampling post-processing methods, clock crystal oscillator temperature drift and the like of each acquisition device, and is beneficial to stable operation of the power internet of things system.
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Fig. 1 is a flowchart of a synchronous acquisition method for a power internet of things sensor provided in embodiment 1 of the present invention;
fig. 2 is a flowchart of a synchronous acquisition method of an electric power internet of things sensor based on edge artificial intelligence provided in embodiment 2 of the present invention;
fig. 3 is a schematic diagram of a power equipment monitoring system of the power internet of things in the embodiment of the invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention provides a synchronous acquisition method of a power Internet of things sensor, which comprises the following steps:
initializing a control node and peripheral equipment; the control node is a microcontroller, and the peripheral equipment comprises a communication module and a real-time clock chip;
time synchronization is carried out on the control nodes;
calculating the transmission delay time of the communication between the control node and the sensor node by taking the control node clock as a reference;
taking the transmission delay time into account, and carrying out time synchronization on the sensor nodes;
and carrying out synchronous data acquisition and uploading.
As a preferred embodiment, the control node may receive data of a plurality of sensor nodes in parallel.
As a preferred embodiment, the communication module includes an NB-IoT (narrowband internet of things) communication module and a 4G module (supporting an AT instruction set or DTU (data transmission unit) firmware package) supporting a GNSS/GPS/beidou satellite system, and a ZigBee module, a bluetooth low energy module, and other short-distance wireless communication modules communicating with the sensor node.
In a preferred embodiment, the real time clock RTC signal source is provided by a high precision temperature drift compensation crystal oscillator at 32.768 KHz. The clock of the sensor node is provided by the real-time clock chip.
In a preferred embodiment, all control nodes are time synchronized by GNSS (global navigation satellite system)/GPS (global positioning system)/beidou satellite system time service or ethernet time service. The satellite time service is completed by an NB-IoT communication module and a 4G module (supporting an AT instruction set or DTU firmware) which support a GNSS/GPS/Beidou satellite system.
In a preferred embodiment, the sensor node communicates with the control node through a short-range wireless communication module such as LORA/ZigBee/Bluetooth low energy.
In a preferred embodiment, the calculation of the transmission delay time is performed at the control node to reduce the power consumption of the sensor node and improve the endurance time of the sensor node.
In a preferred embodiment, the sensor node is configured with a timer to trigger data acquisition.
In a preferred embodiment, after the synchronous data acquisition is completed, the control node collects the data synchronously acquired by the plurality of sensors and adds a time stamp to the data in an idle interruption manner.
In a preferred embodiment, the upload data packet includes, but is not limited to, transmission delay time, data acquisition time, crystal temperature drift, and data processing time of the sensor.
As a preferred embodiment, a neural network prediction model of the transmission delay time τ is built using a tensoflow framework and converted into a lightweight TFLite prediction model that can run on the microcontroller of the control node using a tensoflow Lite Converter tool.
Example 1
The embodiment of the invention provides a synchronous acquisition method for a power internet of things sensor, which is shown in fig. 1 and has the following specific implementation processes:
step 101: control node initialization and peripheral initialization.
In this step, the control node refers to an embedded core system module which has certain operational capability and storage space and can store authentication information and run the application of the internet of things, and necessary peripherals connected with the embedded core system module. The initialized peripheral devices include, but are not limited to, NB-IoT/4G communication module supporting GNSS/GPS, real-time clock module, ZigBee/BLE (bluetooth low energy)/LORA (long distance) and other wireless communication modules using unlicensed spectrum resources, ethernet module, memory module, and the like.
Step 102: controlling node clock synchronization
This step can be implemented using in particular one of the following methods:
the first method is as follows: the control node carries out satellite time service through a GNSS/GPS/Beidou satellite navigation system, and the clock of the control node is synchronously controlled by using the satellite time service time.
The second method comprises the following steps: the control node acquires the current network time through the Ethernet module, and synchronizes the clock of the control node by using the acquired network time.
Step 103: the sensor nodes are synchronized for the first time.
In this step, the control node completes time synchronization, and performs time synchronization on the sensor nodes by using the time of the control node as a time reference.
The control node transmits the current time T1 through a broadcast channel and records T1, the sensor node returns response information to the control node, and the time T2 when the control node receives the response information of the sensor node is recordedi(i =1,2, …, n), where n is the number of sensor nodes to which the control node is connected in multiple.
The response message may define 1 bit or more data, for example, 1 bit response message is used, 0xF0 indicates that the clock is synchronized, and if no or an error is recovered, the synchronization command is issued again.
Step 104: and the sensor nodes perform second synchronization.
In this step, the transmission delay time of the communication between the control node and the sensor node is calculated:
τi=(T2i-T1)/2，(i=1,2,…,n)，
the control node issues a corrected time synchronization signal to the sensor node at the second synchronization time T3: t3+ TiA/2 (i =1,2, …, n) and a next acquisition trigger time T _ set,
the sensor node analyzes the instruction issued by the control node, and the time for synchronizing the sensor node clock is T3+ taui/2(i=1,2,…,n)。
Step 105: performing synchronized data acquisition
In this step, when the current time of the sensor node meets T _ now = T _ set, the sensor corresponding to the sensor node is driven by using the clock, and the sensor is triggered to perform data acquisition.
And the sensor node uses the serial port idle interruption to stamp a time stamp T _ set for the data which is acquired and sends the data to the control node.
The trigger time received by all the sensor nodes is the same, and the sensor nodes complete the time synchronization with the control node, so that the synchronous acquisition of the sensor data can be considered to be completed.
Step 106: data uploading
In this step, the control node collects the data packets uploaded by the sensor nodes and uploads the data packets to the cloud end through the internet.
The data packet content includes the data collected and a timestamp of completion of the data collection.
Example 2
By circulating the steps of 101 to 106 in the embodiment 1, the sensor nodes work in a real environment and collect the transmission delay time tau of each sensor node and the data influencing tau as a data set for training a neural network model. Training a prediction model of the transmission delay time tau, converting the trained prediction model into a TFLite prediction model which can run on a resource-limited microcontroller by using a Tensorflow Lite converter tool, embedding the model into a control node to run, and predicting the transmission delay time tau from the control node to each sensor node in real time. On the basis of which the following steps are carried out.
Step 201: control node initialization and peripheral initialization
In this step, the control node is initialized, which includes firmware program initialization operation and TFLite prediction model initialization operation, and the peripheral initialization is the same as step 101.
Step 202: and controlling the clock synchronization of the nodes.
In this step, the clock synchronization method of the control node is the same as that in step 102.
Step 203: and predicting the transmission delay of the sensor node.
In the step, the control node predicts the transmission delay time tau of the sensor node T _ set moment by locally operating the TFLite prediction model, and returns the prediction result to the control node microcontroller.
Step 204: and the sensor nodes carry out time synchronization.
In this step, the control node predicts the transmission delay time τ of the sensor node according to the predicted transmission delay time τiAnd issuing a corrected sensor synchronization time T + tau at a synchronization time Ti(i =1,2, …, n) and an acquisition trigger time T _ set.
And the sensor node analyzes the message sent by the control node and synchronizes the clock time of the sensor node to wait for the arrival of the acquisition triggering moment.
Step 205: and (5) synchronous data acquisition.
In the step, when the current time of the sensor node clock meets T _ now = T _ set, the sensor node starts data acquisition,
and the sensor node uses the serial port idle interruption to stamp a time stamp T _ set for the data which is acquired and sends the data to the control node. The trigger time received by all the sensor nodes is the same, and the sensor nodes complete the time synchronization with the control node, so that the synchronous acquisition of the sensor data can be considered to be completed.
Step 206: and uploading the data.
In this step, the data uploading method is the same as that in step 106.
Example 3
As shown in fig. 2, the power equipment monitoring system based on the internet of things comprises two independent control nodes, a control node 1 is connected to the internet through an NB base station and is in communication with a power equipment monitoring cloud, a control node 2 is connected to the internet through an ethernet connection mode and is in communication with the power equipment monitoring cloud, each control node is in wireless communication with three mutually independent sensor nodes, and the network topology is fixed. The sensor nodes are respectively a three-phase loop full-electric parameter measuring node, a three-phase active electric energy measuring node and a temperature and leakage current measuring node, the sensor nodes are installed on electric equipment in an electric power system to work and collect data information of the electric power system in real time, the control node 1 is communicated with the sensor nodes in a ZigBee wireless communication mode, and the control node 2 is communicated with the sensor nodes through an LORA wireless communication module. The method for implementing the synchronous acquisition of the sensors for the monitoring system comprises the following implementation steps:
step 101: control node initialization and peripheral initialization
The control node 1 initializes a real-time clock chip, an NB-IoT module supporting a GNSS/GPS/Beidou satellite navigation system, a ZigBee wireless communication module and an external storage module. The control node 2 initializes the real time clock chip, the ethernet module, the LORA wireless communication module, and the external storage module.
Step 102: controlling node clock synchronization
The control node 1 carries out satellite time service through a GNSS/GPS/Beidou satellite navigation system, and the clock of the control node is synchronously controlled by using the satellite time service time.
The control node 2 acquires the current network time through the Ethernet module, and synchronizes the clock of the control node by using the network time.
Step 103: sensor node for first synchronization
The control node 1 and the control node 2 are all completed with time synchronization, the control node issues the time T1 of the current control node through a broadcast channel and records T1, the three-phase loop full electric parameter measuring sensor node, the three-phase active electric energy measuring sensor node and the temperature and leakage current measuring sensor node listen and analyze a time synchronization command issued by the broadcast channel, the clock of each sensor node is synchronized to be T1, synchronization success response information is returned to the control node, and the time T2 of the control node receiving the sensor node response information is recordedi，i=1,2,3。
Step 104: sensor node performs second synchronization
Calculating the propagation delay time taui=(T2i-T1)/2(i =1,2,3), the control node issuing a time synchronization T3+ τ to the respective subordinate sensor node at time T3i2(i =1,2,3) and the time T _ set for triggering acquisition, the sensor node analyzes the instruction issued by the control node, and the time for synchronizing the sensor node clock is T3+ taui/2(i=1,2,3)。
Step 105: synchronizing data acquisition
When the current time of the sensor node meets T _ now = T _ set, triggering a three-phase loop full-electric parameter measuring sensor to acquire data such as voltage, current, power, phase angle, power factor and the like of a low-voltage three-phase electric loop; triggering a three-phase active electric energy measuring sensor to acquire electric energy data; the trigger temperature and leakage current measuring sensor collects the operating temperature and leakage current data of the power equipment. The sensor node uses the serial port idle interruption to stamp a time stamp T _ set for the data which are acquired and sends the data to the control node. The acquisition trigger time received by all the sensor nodes is the same, and the sensor nodes complete the time synchronization with the control node, so that the synchronous acquisition of the sensor data can be considered to be completed.
Step 106: data uploading
The control node 1 collects data packets uploaded by each sensor node, and accesses the Internet through the NB base station to transmit data to the power equipment monitoring cloud; the control node 2 collects data packets uploaded by the sensor nodes and accesses the internet through the Ethernet to transmit the data to the power equipment monitoring cloud.
Example 4
By repeating the steps 101 to 106 in the above embodiment 3, the sensor nodes are operated in the power system and the transmission delay time τ of each sensor node and the data related to the transmission delay time τ are collected as the data set for training the neural network model. And (2) establishing a prediction model of a transmission delay time sequence by using a Tensorflow deep learning frame and a related tool kit, converting the trained Tensorflow prediction model into a TFLite prediction model which can run on a microcontroller with limited resources by using a Tensorflow Lite converter tool, embedding the prediction model into a control node and running locally, and predicting the transmission delay time from the control node to each sensor node in real time. On the basis of which the following steps are carried out.
Step 201: control node initialization and peripheral initialization
Initializing a control node 1 and a control node 2, wherein the initialization comprises the initialization of firmware program operation of the control node and the initialization of Tensorflow prediction model operation, and the initialization of peripheral equipment is the same as the step 101.
Step 202: and controlling the clock synchronization of the nodes.
The clock synchronization method of the control node 1 and the control node 2 is the same as step 102.
Step 203: and predicting the transmission delay of the sensor node.
The control node 1 and the control node 2 respectively predict the transmission delay time tau of the three-phase loop full electric parameter measuring sensor node, the three-phase active electric energy measuring sensor node and the temperature and leakage current measuring sensor node at the T _ set moment by locally running a Tensorflow prediction modeli(i =1,2,3), and returns the prediction result to the control sectionAnd (4) point.
Step 204: and carrying out time synchronization on the sensor nodes.
The control node predicts the transmission delay time tau of each sensor node according to the predictioni(i =1,2,3), issuing a sensor synchronization time T + τ at time Ti(i =1,2,3) and an acquisition trigger time T _ set. And the sensor node analyzes the message sent by the control node and synchronizes the clock time of the sensor node to wait for the arrival of the acquisition triggering moment.
Step 205: and carrying out synchronous data acquisition.
When the current time T _ now = T _ set of the sensor node clock, triggering a three-phase loop full-electric parameter measuring sensor to acquire data such as voltage, current, power, phase angle, power factor and the like of a low-voltage three-phase electric loop; triggering a three-phase active electric energy measuring sensor to acquire electric energy data; the trigger temperature and leakage current measuring sensor collects the operating temperature and leakage current data of the power equipment. And the sensor node applies a serial port idle interruption function to stamp a timestamp T _ set for the acquired data and sends the data to the control node. The acquisition trigger time received by all the sensor nodes is the same, and the sensor nodes complete the time synchronization with the control node, so that the synchronous acquisition of the sensor data can be considered to be completed.
Step 206: and uploading the data.
The control node 1 collects data packets uploaded by each sensor node, and accesses the Internet through the NB base station to transmit data to the power equipment monitoring cloud; the control node 2 collects data packets uploaded by the sensor nodes and accesses the internet through the Ethernet to transmit the data to the power equipment monitoring cloud.
The invention also provides a synchronous acquisition device of the power internet of things sensor, which comprises:
the first synchronization module is used for carrying out time synchronization on the control nodes; the control node is a node which runs the application of the Internet of things in the power Internet of things and communicates with an upper cloud end;
the first calculation module is used for calculating the transmission delay time of communication between the control node and the sensor node by taking the control node clock as a reference;
the second synchronization module is used for taking the transmission delay time into consideration, performing time synchronization on the sensor nodes and acquiring data acquisition trigger time;
and the number of the first and second groups,
and the acquisition module is used for acquiring and uploading the synchronous data of the sensor nodes based on the data acquisition trigger time.
As a preferred embodiment, the first calculation module is specifically adapted to,
calculating the transmission delay time of the communication between the control node and the sensor node according to the time T1 of the first synchronization signal sent by the control node and the time T2 of the control node receiving the response information of the sensor node:
τ=(T2-T1)/2。
as a preferred embodiment, the second synchronization module is specifically adapted to,
according to a second synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time of T3, the time for synchronizing the sensor node clock is T3+ tau/2;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
As a preferred embodiment, the first calculation module is specifically adapted to,
training a prediction model of the transmission delay time, converting the prediction model into a TFLite prediction model by using a Tensorflow Lite converter tool, embedding the TFLite prediction model into a control node for operation, and predicting the transmission delay time tau from the control node to a sensor node in real time.
As a preferred embodiment, the second synchronization module is specifically adapted to,
according to a synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time T, the time for synchronizing the clock of the sensor node is T + tau;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
It is to be noted that the apparatus embodiment corresponds to the method embodiment, and the implementation manners of the method embodiment are all applicable to the apparatus embodiment and can achieve the same or similar technical effects, so that the details are not described herein.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, 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 apparatus 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 apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (17)
1. A synchronous acquisition method for a power Internet of things sensor is characterized by comprising the following steps:
time synchronization is carried out on the control nodes; the control node is a node which runs the application of the Internet of things in the power Internet of things and communicates with an upper cloud end;
calculating the transmission delay time of the communication between the control node and the sensor node by taking the control node clock as a reference;
according to the transmission delay time, performing time synchronization on the sensor nodes and acquiring data acquisition trigger time;
and carrying out synchronous data acquisition and uploading on the sensor nodes based on the data acquisition trigger time.
2. The synchronous acquisition method for the sensors of the internet of things in the electric power system according to claim 1, further comprising:
initializing the control node and the peripheral equipment.
3. The synchronous acquisition method for the sensors of the power internet of things according to claim 2, wherein the time synchronization of the control nodes comprises:
a global satellite navigation system/a global positioning system/a Beidou satellite navigation system is adopted to give a satellite, and the satellite time-service time is used for synchronously controlling the clocks of the nodes;
or acquiring the current network time, and synchronizing the clock of the control node by using the acquired network time.
4. The synchronous acquisition method for the sensors of the power internet of things according to claim 1, wherein the calculating of the transmission delay time of the communication between the control node and the sensor node comprises:
calculating the transmission delay time of the communication between the control node and the sensor node according to the time T1 of the first synchronization signal sent by the control node and the time T2 of the control node receiving the response information of the sensor node:
τ=(T2-T1)/2。
5. the synchronous acquisition method of the sensors of the power internet of things as claimed in claim 4, wherein the time synchronization of the sensor nodes in consideration of the transmission delay time comprises:
according to a second synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time of T3, the time for synchronizing the sensor node clock is T3+ tau/2;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
6. The synchronous acquisition method for the sensors of the power internet of things according to claim 1, wherein the calculating of the transmission delay time of the communication between the control node and the sensor node comprises:
training a prediction model of the transmission delay time, converting the prediction model into a TFLite prediction model by using a Tensorflow Lite converter tool, embedding the TFLite prediction model into a control node for operation, and predicting the transmission delay time tau from the control node to a sensor node in real time.
7. The synchronous acquisition method of the sensors of the power internet of things as claimed in claim 6, wherein the time synchronization of the sensor nodes in consideration of the transmission delay time comprises:
according to a synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time T, the time for synchronizing the clock of the sensor node is T + tau;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
8. The synchronous acquisition method for the sensors of the power internet of things as claimed in any one of claims 4 to 7, wherein the control node can communicate with a plurality of sensor nodes in parallel.
9. The synchronous acquisition method for the sensors of the power internet of things as claimed in claim 6, wherein the training of the prediction model of the transmission delay time comprises:
the following operations are cyclically carried out:
the control node transmits the current time T1 through a broadcast channel;
the sensor node returns response information to the control node, and the time T2 when the control node receives the response information of the sensor node is recorded;
calculating the transmission delay time of the communication between the control node and the sensor node based on T1 and T2:
τ=(T2-T1)/2；
and collecting data of the transmission delay time tau of the sensor node and the data influencing the tau as a training set, and training a prediction model of the transmission delay time based on a neural network.
10. The synchronous acquisition method of the sensors of the internet of things in the electric power system according to claim 1, wherein the synchronous data acquisition and uploading of the sensor nodes comprises the following steps:
when the current time of the sensor node meets the next data acquisition triggering time, driving a sensor corresponding to the sensor node by using a clock, and triggering the sensor to acquire data;
and the sensor node uses the serial port idle interruption to stamp a time stamp T _ set for the data which is acquired and sends the data to the control node.
11. The utility model provides a synchronous collection system of electric power thing networking sensor which characterized in that includes:
the first synchronization module is used for carrying out time synchronization on the control nodes; the control node is a node which runs the application of the Internet of things in the power Internet of things and communicates with an upper cloud end;
the first calculation module is used for calculating the transmission delay time of communication between the control node and the sensor node by taking the control node clock as a reference;
the second synchronization module is used for carrying out time synchronization on the sensor nodes according to the transmission delay time and acquiring data acquisition trigger time;
and the number of the first and second groups,
and the acquisition module is used for acquiring and uploading the synchronous data of the sensor nodes based on the data acquisition trigger time.
12. The synchronous acquisition device of the power Internet of things sensor as claimed in claim 11, wherein the first computing module is specifically configured to,
calculating the transmission delay time of the communication between the control node and the sensor node according to the time T1 of the first synchronization signal sent by the control node and the time T2 of the control node receiving the response information of the sensor node:
τ=(T2-T1)/2。
13. the synchronous acquisition device of the power Internet of things sensor as claimed in claim 12, wherein the second synchronization module is specifically configured to,
according to a second synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time of T3, the time for synchronizing the sensor node clock is T3+ tau/2;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
14. The synchronous acquisition device of the power Internet of things sensor as claimed in claim 11, wherein the first computing module is specifically configured to,
training a prediction model of the transmission delay time, converting the prediction model into a TFLite prediction model by using a Tensorflow Lite converter tool, embedding the TFLite prediction model into a control node for operation, and predicting the transmission delay time tau from the control node to a sensor node in real time.
15. The synchronous acquisition device of the power Internet of things sensor as claimed in claim 14, wherein the second synchronization module is specifically configured to,
according to a synchronous signal and transmission delay time which are issued to the sensor node by the control node at the time T, the time for synchronizing the clock of the sensor node is T + tau;
and the number of the first and second groups,
and resolving the next data acquisition trigger time T _ set according to the synchronous signal issued by the control node.
16. The synchronous acquisition device of the power internet of things sensor as claimed in claim 11, further comprising:
and the initialization module is used for initializing the control node and the peripheral equipment.
17. The synchronous acquisition device of the power Internet of things sensor as claimed in claim 16,
the peripheral equipment comprises a communication module for controlling the node to carry out time synchronization, a communication module for controlling the node and the sensor to carry out communication and a real-time clock chip configured on the sensor node;
the communication module for controlling the node to perform time synchronization is any one of an NB-IoT communication module or an Ethernet module;
the communication module for communicating the control node and the sensor is any one of an LORA module, a ZigBee module and a low-power Bluetooth module;
the signal source of the real-time clock chip is provided by a temperature drift compensation crystal oscillator of 32.768 KHz.
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