CN117499335B - Calorimeter data acquisition and communication method - Google Patents
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- H—ELECTRICITY
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- H04L47/50—Queue scheduling
- H04L47/62—Queue scheduling characterised by scheduling criteria
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/50—Queue scheduling
- H04L47/62—Queue scheduling characterised by scheduling criteria
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Abstract
The invention provides a heat meter data acquisition and communication method, which relates to the technical field of data communication, wherein each heat meter and a control center establish communication handshake to acquire data streams corresponding to channels between the control center and each heat meter data acquisition terminal; priority information of each channel between the control center and the calorimeter data acquisition terminal is obtained, and the data stream occurrence rate of each priority is predicted; dynamically adjusting the length of the queue in each period according to the data stream occurrence rate of each priority; the data stream transmission time of each channel is distributed in time slices, and the data stream transmission time of each time slice is calculated; scheduling data transmission according to the data stream transmission time of each time segment; the intelligent distribution of the calorimeter channels in the access network equipment is carried out, the rapid data transmission is realized within the limited transmission time, the transmission efficiency is increased, the parameters of channel transmission are monitored, the real-time transmission scheduling is carried out, and the congestion data is responded in real time.
Description
Technical Field
The invention relates to the technical field of data communication, in particular to a heat meter data acquisition and communication method.
Background
In the current society, the related technology of the Internet of things is rapidly developed and widely applied in the fields of public utilities, smart cities, intelligent transportation and the like. With the development of the internet of things technology, more and more newly built buildings adopt a wireless communication mode to collect heat meter data. In the prior art, heat data of a heat meter are acquired through a wireless sharing meter reading device. However, in this technical scheme, because some heat meters are far away from the wireless sharing meter collector, it is difficult to accurately and efficiently transmit the heat data of the heat meters to the wireless sharing meter collector. Meanwhile, the intelligent calorimeter of the single-phase internet of things, which is applicable to apartments and rents, and the intelligent calorimeter of the three-phase internet of things, which is applicable to occasions such as base station heat management, enterprise energy efficiency markets and the like, appear in the field of heat energy metering. The heat meter of the type not only realizes the functions of metering, charging, local fee control and the like of the traditional heat meter, but also realizes the remote communication connection function of the heat meter by using the communication technology of the Internet of things. The calorimeters are connected to the cloud platform or the master station through technologies such as NB-IOT, WIFI and the like, so that electricity consumption conditions can be monitored in real time, and more value-added services can be realized based on the electricity consumption conditions.
However, as the running time of the intelligent heat meter is long, software faults are inevitably generated in the using process, communication errors occur between the control center and the intelligent heat meter, and heat cannot be written normally, so that the intelligent heat meter management platform needs to repair the intelligent heat meter at intervals; however, the existing repair method of the intelligent calorimeter cannot finish the accurate repair of the intelligent calorimeter, so that the efficiency of data acquisition of the calorimeter is affected.
Disclosure of Invention
In order to solve the technical problems, the invention provides a heat meter data acquisition and communication method, which comprises the following steps:
step S1, establishing a communication handshake between each heat meter and a control center, and acquiring channel data streams of the control center and each heat meter;
s2, predicting the data stream occurrence rate of each channel priority between the control center and the calorimeter;
step S3, dynamically adjusting the length of a data flow queue in each period according to the data flow occurrence rate of each channel priority;
s4, distributing time slices for data stream transmission of each channel, and calculating data stream sending time of each time slice;
and S5, carrying out data transmission scheduling according to the data stream transmission time of each time segment.
Further, in step S2, let Q represent the size of the data stream to be transmitted, Q i Representing the buffer queue length corresponding to the ith channel in the N channels with different priorities, and the CM represents that each buffer queue occupies the total storage space:
；
let each buffer queue length be equal to a fixed length Q Ci And dynamic column length Q Vi The composition is as follows:
Q i =Q Ci +Q Vi 。
further, when the data stream passes through the time period T, the data stream occurrence rate P of each priority level in the time period T is calculated by the following formula b {X=i}：
P b {X=i}=B i /B；
Wherein B is the total amount of channel data streams in the time period T, B i Data traffic with priority i in the time period T;
assuming that n time periods T exist, the data traffic with the priority of i in the jth time period is B ij The total amount of channel data streams in the jth time period is B j The occurrence rate P { x=i, y=j } of the data stream with priority i in the j-th period can be obtained by:
P{X=i,Y=j}=B ij /B j 。
further, in step S3, the congestion condition of each priority queue in the jth time period is determined, and Q is set yi For the length of the occupied queue of the ith channel, calculating a buffer queue Q i Unoccupied queue length Q u When Q u Below the threshold Cong, then the queue is about to be congested:
Q u =Q i -Q yi ﹤Cong；
Cong=P{X=i,Y=j}×B j ；
q which will have been detected to meet congestion requirements i Queue space adjustment is performed from high priority to low priority, and each adjustment queue is amplifiedColumn dynamic column length Q Vi 。
Further, in step S4, the total time period is divided into equal-length time segments, each unit time segment has a length of DeltaT, and the total waiting delay D of M buffer queues to :
；
Average waiting delay D per buffer queue av The formula is as follows:
。
further, the time segment length is set to be the transmission time of the data stream plus the maximum propagation delay, and the segment length Δt includes two parts:
；
wherein T is tx Representing data stream transmission time;indicating the maximum propagation delay.
Further, data stream transmission time T tx The following formula is calculated:
；
wherein Q represents the data stream size; s is(s) r Indicating the transmission rate.
Further, in step S5, M buffer queues f are provided 1 、f 2 …、f J …、f M Data are respectively transmitted to the control center, and for each buffer queue f J The transmission period T of each data stream is calculated by the following formula J Scheduling period T t ：
T J =Df J /V J ；
T t =LCM(T l ,T 2 ,T 3 …,T J ,…T M )；
Wherein LCM is the scheduling function.
The expression of the scheduling function LCM is:
LCM(T l ,T 2 ,T 3 …,T J ,…T M )= ；
wherein Df is J Representing the frame length of the data stream of each buffer queue, V J Representing the transmission speed of the data stream, LCM is a scheduling function.
Compared with the prior art, the invention has the following beneficial technical effects:
establishing a communication handshake between each heat meter and a control center, and acquiring data streams corresponding to channels between the control center and each heat meter data acquisition terminal; priority information of each channel between the control center and the calorimeter data acquisition terminal is obtained, and the data stream occurrence rate of each priority is predicted; dynamically adjusting the length of the queue in each period according to the data stream occurrence rate of each priority; the data stream transmission time of each channel is distributed in time slices, and the data stream transmission time of each time slice is calculated; the data transmission is arranged according to the data stream transmission time of each time segment. The invention aims to intelligently allocate the calorimeter channels in the access network equipment, realize quick data transmission within limited transmission time, increase transmission efficiency, monitor parameters of channel transmission, transmit and schedule in real time and respond to congestion data in real time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a calorimeter data acquisition and communication method of the present invention;
FIG. 2 is a schematic diagram of the present invention dividing a total time period into M time segments;
fig. 3 is a schematic diagram of a heat meter data acquisition and communication system according to the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of 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 apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principle of each element in the system, the connection relationship of each part in the device is represented, but only the relative positional relationship between each element is clearly distinguished, and the limitations on the signal transmission direction, connection sequence and the structure size, dimension and shape of each part in the element or structure cannot be constructed.
As shown in fig. 1, a flow chart of the heat meter data acquisition and communication method of the present invention,
s1, establishing communication handshakes between each heat meter and the control center, and acquiring data streams corresponding to channels between the control center and each heat meter data acquisition terminal.
Establishing communication handshake time between each heat meter and the control center to obtain communication response time between each heat meter and the control center; after receiving the instruction of acquiring the heat data of the heat meters issued by the server, the control center sends a heat data acquisition instruction to all the heat meters, and the heat meters send heat data to the control center; and the communication module of the control center collects heat data of all heat meters and adopts low-speed communication frequency to transmit the data through a channel.
S2, acquiring priority information of each channel between the control center and the calorimeter data acquisition terminal, and predicting the data stream occurrence rate of each priority.
When the control center detects a communication connection establishment request, a queue dynamic control method is adopted to sense the transmission quantity of each priority in the data stream, and the data transmission quantity of each channel is predicted through the change trend of the data quantity in a certain time, so that the dynamic adjustment of the queue is realized, and the reliability of data transmission is ensured.
Wherein Q represents the data stream to be transmitted, Q i (i=1, 2, … …, N) represents the buffer queue length corresponding to the i-th channel of the N different priority channels, the queue space length is dynamically variable, CM represents that each buffer queue occupies the total storage space, and the specific calculation formula is as follows:
；
the multi-priority data scheduling algorithm is based on a multi-queue service structure, divides channel data into multiple priorities, and establishes multiple buffer queue lengths Q corresponding to multiple classes of data respectively by a queue dynamic control method i ，Q max Representing the maximum length of each buffer queue, each buffer queue is composed of a fixed column length Q Ci And dynamic column length Q Vi The composition is as follows:
Q i =Q Ci +Q Vi ；
let Q yi For the length of the occupied queue of the ith channel, in the initial state of the network, Q yi The value is zero, and the buffer queue length Q corresponding to each channel i i And a fixed column length Q Ci The equality is M, the initial state of the queue is expressed as follows, M is a constant, and the initial state of the queue is expressed as a fixed column length set value.
；
In the data transmission process, the data streams of each channel are classified according to the priority order, and in order to ensure the real-time transmission of the data, the rate prediction algorithm is adopted to predict the occurrence rate of the data streams of each priority.
As the data flows throughWhen the time period T passes, calculating the occurrence rate P of each priority data stream in the time period by the following method b { X=i }, where B is the total amount of channel data streams in the T period, B i Is the data traffic with priority i in the time period.
P b {X=i}=B i /B；
Assuming that there are n time periods of time T, the data traffic with priority i in the jth time period is B ij The total amount of channel data streams in the jth time period is B j The occurrence rate P { x=i, y=j } of the data stream with priority i in the j-th period can be obtained by:
P{X=i,Y=j}=B ij /B j ；
then, the following formula is executed, and the occurrence rate of each priority data stream in n time periods is given an initial value:
；
a rate prediction algorithm is executed to predict the occurrence rate P { x=i } of the data stream with the priority of i, μ being a prediction weight coefficient, as shown in the following formula:
；
s3, dynamically adjusting the length of the buffer queue in each period according to the data stream occurrence rate of each priority.
The dynamic adjustment of the length of the queue can save the resource expenditure of the queue, lighten the data processing pressure of the server, adjust the length of the queue according to the data flow in the time period and avoid the problem of network congestion caused by overlarge burst data flow.
Judging congestion of each priority queue in the jth time period, firstly calculating a buffer queue Q i Unoccupied queue length Q u When Q u Below the threshold Cong, the buffer queue is considered to be congested, as follows:
Q u =Q i -Q yi ﹤Cong；
Cong=P{X=i,Y=j}×B j ；
q which is detected to meet congestion requirements by queue dynamic control method i Queue space adjustment is carried out from high priority to low priority, and dynamic queue length Q of each adjustment queue is amplified Vi 。
In the j-th time period, when the buffer queue corresponding to the channel i is empty and the buffer queue length Q corresponding to the channel i is not longer than the buffer queue length Q corresponding to the channel i i The ratio R is higher than the threshold L and the predicted occurrence rate P { x=i } is always lower than the adjustable rate threshold Pr and R>Pr, the buffer queue length is reduced, and the buffer queue length reduction value V is calculated by the following formula:
V=(Q Ci +Q Vi )×L×(1-pr)；
calculating a dynamic column length after dynamic adjustmentThe following formula:
；
adjusted buffer queue length Q i The following formula:
；
therefore, when the transmission quantity of the data of each priority is reduced, the queue dynamic control method adjusts the buffer queue space of each priority in real time, dynamically manages the buffer space and saves memory resources; when the burst property of each priority data transmission quantity is increased, the model enlarges the buffer queue length in real time, adapts to the requirement of large data quantity buffer, reduces the transmission delay of data packets, and meets the QoS transmission requirement of differentiation of each priority data.
S4, time slice distribution is carried out on the data stream transmission time of each channel, and the data stream transmission time of each time slice is calculated.
Dividing the data stream transmission time into equal-length time slices, and selecting the corresponding time slices for data stream transmission when the task terminal of the heat meter has data stream transmission.
If the time slice division is not reasonable, too long time slices can cause no data stream transmission in part of the time slices, which can lead to idle channels and increase of end-to-end delay, and too short time slices can cause time slice encroachment.
The time segment distribution is a key for ensuring that task terminals of the heat meter transmit in a collision-free manner and improving the utilization rate of the time segments, and distributes the time segments for the task terminals of the heat meter, so that the waiting delay of the task terminals of the heat meter is reduced and the utilization rate of channels is improved.
Assuming that the total time period is arranged with M buffer queues, the total time period is divided into M time slices, and the allocation is as shown in fig. 2.
The total time period is divided into equal-length time segments, each unit time segment has the length of DeltaT, and the total waiting delay D of M buffer queues to The following formula is shown:
；
average waiting delay D per buffer queue av The formula is as follows:
；
the average waiting delay of the buffer queues is mainly dependent on the total number M of arranged buffer queues and the fragment length per unit time Δt.
Setting the time slice length as the transmission time of the data stream plus the maximum propagation delay, the unit time slice length Δt comprises two parts:
；
wherein T is tx The data stream transmission time is represented in ms;indicating maximum propagation delayThe unit is us.
Delaying the maximum propagationUp-rounding and then incorporating T tx The transmission time, the unit time segment length DeltaT mainly considers the data stream transmission time T tx 。
Data stream transmission time T tx The following formula is calculated:
；
wherein Q represents the length of the data stream, and the unit is bit; s is(s) r Indicating the transmission rate, the unit is bps.
S5, carrying out data transmission scheduling according to the data stream sending time of each time segment.
There are M buffer queues f known 1 、f 2 …、f J …、f M Data are respectively transmitted to the control center, and for each buffer queue f J (j=1, 2,3 … M), assuming that the length thereof is fixed, the data period is transmitted, and the transmission period T of each data stream is calculated by the following formula J Scheduling period T t Wherein Df J Representing the frame length of the data stream of each buffer queue, V J Representing the transmission speed of the data stream.
T J =Df J /V J ；
T t =LCM(T l ,T 2 ,T 3 …,T J ,…T M )。
Wherein LCM is the scheduling function.
The expression of the scheduling function LCM is:
LCM(T l ,T 2 ,T 3 …,T J ,…T M )= ；
i.e. select (T) l ,T 2 ,T 3 …,T J ,…T M ) Scheduling period T with intermediate value in sequence as output t 。
The present embodiment preferably employs a time slice rotation method, which is an algorithm that schedules according to time slices. It divides the total time period into several time segments and assigns each time segment to a data stream that is running or waiting to run. When a data stream runs out of its allocated time slots, the system suspends it and allocates time resources to the next data stream waiting to run. The time slice rotation method can avoid the phenomenon of rabbit starvation, and can ensure that all running or waiting-to-run data streams can obtain time resources. However, if the time slice is too small, the process is frequently switched, and the system performance is affected; if the time slice is too large, a long job waiting time results.
Setting the number of time slices in a scheduling period, N s That is, representing the number of time slices in a scheduling period, allocating a fixed time length to each time slice, initializing all time slices to be idle, arranging the time slices in sequence according to the sequence, and calculating the time length L allocated by the available time slices by the following formula st :
；
The time period from the beginning to the end of the transmission of each queue is called the transmission window of the queue, and w is used J And (3) representing. In the initial state, each buffer queue transmits a window w J Duration and time segment Length L st Equal, for channels of different priorities, equal time segment lengths are allocated, but high priority data is sent preferentially, and the initial condition is set as follows:
；
each channel has a data stream waiting for transmission, and at this time, each buffer queue transmits a window w J Value and L st The values are equal, as follows: w (w) J =L st 。
In a preferred embodiment, the task terminal of the calorimeter selects the task to be uploadedData flow task T i The calculations are performed locally or using the wireless access point load into a buffer queue.
As shown in fig. 3, which is a schematic diagram of a heat meter data acquisition and communication system, after receiving a command for acquiring heat data of a heat meter issued by a server, a control center sends a heat data acquisition command to all heat meters, and the heat meters send heat data to the control center; and the communication module of the control center collects heat data of all heat meters and adopts low-speed communication frequency to transmit the data through a channel.
The channel gain between task terminal I and the wireless access point is time-varying, usingTo represent the instantaneous channel gain g between the task terminal I and the wireless access point Ip Wherein d is Ip Indicating the distance between task terminal I and the wireless access point,/->Is the path loss index,/-, and>is a fading factor. Because of the time-varying nature of the channel gain +.>And also over time.
When the task terminal I selects the data stream task T to be uploaded i Loading the data into a buffer queue to execute calculation processing, and then uploading the data to the task terminal IThe method comprises the following steps:
；
wherein W is p Representing transmission channel bandwidth of wireless access point, P I Representing the transmission power of the task terminal I, g Ip Representing task terminal I to wireless access pointThe instantaneous channel gain between them,indicated is background noise.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (5)
1. The heat meter data acquisition and communication method is characterized by comprising the following steps:
step S1, establishing a communication handshake between each heat meter and a control center, and acquiring channel data streams of the control center and each heat meter;
s2, predicting the data stream occurrence rate of each channel priority between the control center and the calorimeter;
let Q denote the size of the data stream to be transmitted, Q i Representing the buffer queue length corresponding to the ith channel in the N channels with different priorities, and the CM represents that each buffer queue occupies the total storage space:
；
let each buffer queue length be equal to a fixed length Q Ci And dynamic column length Q Vi The composition is as follows:
Q i =Q Ci +Q Vi ；
when the data stream passes through the time period T, the data stream occurrence rate P of each priority level in the time period T is calculated by the following formula b {X=i}：
P b {X=i}=B i /B；
Wherein B is the total amount of channel data streams in the time period T, B i Data traffic with priority i in the time period T;
assuming that n time periods T exist, the data traffic with the priority of i in the jth time period is B ij The total amount of channel data streams in the jth time period is B j The occurrence rate P { x=i, y=j } of the data stream with priority i in the j-th period can be obtained by:
P{X=i,Y=j}=B ij /B j ；
step S3, dynamically adjusting the length of a data flow queue in each period according to the data flow occurrence rate of each channel priority;
judging congestion conditions of each priority queue in the jth time period, and setting Q yi For the length of the occupied queue of the ith channel, calculating a buffer queue Q i Unoccupied queue length Q u When Q u Below the threshold Cong, then the queue is about to be congested:
Q u =Q i -Q yi ﹤Cong；
Cong=P{X=i,Y=j}×B j ；
q which will have been detected to meet congestion requirements i Queuing with priority from high to lowSpace adjustment, amplifying dynamic column length Q of each adjustment queue Vi ；
S4, distributing time slices for data stream transmission of each channel, and calculating data stream sending time of each time slice;
and S5, carrying out data transmission scheduling according to the data stream transmission time of each time segment.
2. The method of claim 1, wherein in step S4, the total time period is divided into equal-length time segments, each unit time segment has a length of Δt, and the total waiting delays D of the M buffer queues to :
；
Average waiting delay D per buffer queue av The formula is as follows:
。
3. the calorimeter data acquisition and communication method of claim 2, wherein the time segment length is set to be the transmission time of the data stream plus the maximum propagation delay, and the segment length Δt comprises two parts:
；
wherein T is tx Representing data stream transmission time;indicating the maximum propagation delay.
4. A calorimeter data acquisition and communication method as claimed in claim 3, wherein the data stream transmission time T tx The following formula is calculated:
；
wherein Q represents the data stream size; s is(s) r Indicating the transmission rate.
5. The heat meter data collection and communication method according to claim 1, wherein in step S5, M buffer queues f are provided 1 、f 2 …、f J …、f M Data are respectively transmitted to the control center, and for each buffer queue f J The transmission period T of each data stream is calculated by the following formula J Scheduling period T t ：
T J =Df J /V J ；
T t =LCM(T l ,T 2 ,T 3 …,T J ,…T M )；
Wherein LCM is a scheduling function;
the expression of the scheduling function LCM is:
LCM(T l ,T 2 ,T 3 …,T J ,…T M )= ；
wherein Df is J Representing the frame length of the data stream of each buffer queue, V J Representing the transmission speed of the data stream, LCM is a scheduling function.
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