CN114675977A - Distributed monitoring and transportation and management system based on power internet of things - Google Patents
Distributed monitoring and transportation and management system based on power internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of data processing of an electric power Internet of things, in particular to a distributed monitoring and operation and management system based on the electric power Internet of things, which is a data processing system specially suitable for management and supervision purposes and is used for realizing the following steps: determining an executive program target function according to the time complexity and the number of associated controllers of each executive program of the micro data processing center and the running state to be determined of each executive program; solving the objective function of the executive program according to the load rate of the micro data processing center at the current moment and the program load value of each executive program to obtain the running state of each executive program, and finally determining the first type of sensing data information processed locally and the second type of sensing data information needing to be transmitted to a cloud computing end for processing. Under the condition of high load, the method ensures the stable operation of each micro data processing center, has the characteristic of low delay, and is favorable for realizing the management and supervision of the power internet of things.
Description
Technical Field
The invention relates to the technical field of data processing of an electric power Internet of things, in particular to a distributed monitoring and transportation and management system based on the electric power Internet of things.
Background
With the development of ubiquitous power internet of things, distributed power equipment sensor signal data are continuously increased, the requirement on computing capacity is higher and higher, a micro data processing center is established at the edge side of equipment at present to perform edge computing, so that the consumption of computing resources of cloud computing and bandwidth pressure are reduced, and the abnormal monitoring and processing speed of the equipment is higher due to the fact that the edge computing is closer to the equipment and delay is low.
Due to the fact that the processing capacity of the micro data processing center is limited, signals of the sensors of the power equipment are excessive, when the data volume of the micro data processing center exceeds the upper limit of the computing capacity of the micro data processing center, data can be blocked or unstable, uploading of data to a cloud end is affected, data processing of the micro data processing center can be delayed, early warning of a monitoring and transportation management system is not timely enough, and monitoring risks of the equipment are increased.
When the computing capacity of the micro data processing center is regulated, the existing computation unloading strategy cuts a program, and performs collaborative computation by means of the distributed advantages of the micro data processing center, but because the whole number of the power equipment sensors is increased, the computation burden of each micro data processing center is increased, and if the unloading strategy is not reasonable, the operation stability of each micro data processing center is poor.
Disclosure of Invention
The invention aims to provide a distributed monitoring and operation management system based on an electric power internet of things, which is used for solving the problem that the existing micro data processing center is poor in operation stability.
In order to solve the technical problem, the invention provides a distributed monitoring and transportation and management system based on an electric power internet of things, which comprises a micro data processing center module, wherein the micro data processing center module is used for realizing the following steps:
acquiring various sensing data information of the monitored power equipment at the current moment, time complexity and number of associated controllers of each execution program of a micro data processing center of the monitored power equipment and load rate of the micro data processing center at the current moment;
determining the program load value of each execution program according to the time complexity and the number of associated controllers of each execution program of the micro data processing center of the monitored power equipment;
determining an executive program target function at the current moment according to the time complexity and the program load value of each executive program and the running state to be determined of each executive program;
solving the executive program objective function at the current moment according to the load rate of the micro data processing center at the current moment and the program load value of each executive program to obtain the running state of each executive program;
and distributing various sensing data information at the current moment according to the running state of each execution program to obtain first type sensing data information processed locally and second type sensing data information needing to be transmitted to a cloud computing end for processing.
Further, the solving the objective function of the executive program at the current moment to obtain the running state of each executive program includes:
determining a program load value curve, an initial target program load value on the program load value curve and a program change difference value corresponding to the initial target program load value according to the program load value of each execution program at the current moment of the micro data processing center;
determining an offset corresponding to the initial target program load value according to the load rate of the micro data processing center at the current moment, the initial target program load value, the program change difference value corresponding to the initial target program load value and the initial adjustment factor;
determining an initial actual target program load value on a program load value curve according to the initial target program load value and an offset corresponding to the initial target program load value;
determining a final actual target program load value on the program load value curve according to the initial actual target program load value on the program load value curve, the executing program target function at the current moment and the program load value curve;
and determining the running state of each execution program according to the final actual target program load value on the program load value curve and the program load value curve.
Further, the determining a program load value curve, an initial target program load value on the program load value curve, and a program change difference value corresponding to the initial target program load value includes:
arranging the program load values of the execution programs in the descending order according to the program load values of the execution programs at the current moment of the micro data processing center, and then performing curve fitting by taking the program load values of the execution programs as vertical coordinates and the arrangement serial numbers corresponding to the program load values of the execution programs as horizontal coordinates so as to obtain a program load value curve;
determining a median of program load values which are arranged from big to small, and taking the median as an initial target program load value on a program load value curve;
determining a slope value corresponding to the initial target program load value on the program load value curve, and taking the absolute value of the slope value as a program change difference value corresponding to the initial target program load value.
Further, the calculation formula for determining the offset corresponding to the initial target program load value is as follows:
wherein the content of the first and second substances,
an offset corresponding to the initial target program load value,
is the initial adjustment factor for the adjustment of the power factor,
is the current time of the micro data processing centertThe load factor of (a) is,
is a piecewise function of
When the threshold value is larger than the set threshold value, then
And if not, the step (B),
，
the program change difference value corresponding to the initial target program load value.
Further, the determining a final actual target program load value on the program load value curve includes:
determining the initial running state of each execution program according to the initial actual target program load value on the program load value curve and the program load value curve;
determining target program load value of the next round on the program load value curve and program change difference values corresponding to the target program load value of the next round according to the initial running state of each execution program, the execution program target function at the current moment and the program load value curve;
adjusting the initial adjustment factor according to the target program load value and the initial actual target program load value of the next round to obtain the adjustment factor of the next round;
determining the offset corresponding to the target program load value of the next round according to the load rate of the micro data processing center at the current moment, the target program load value of the next round, the program change difference value corresponding to the target program load value of the next round and the adjustment factor of the next round;
determining the actual target program load value of the next round on the program load value curve according to the target program load value of the next round and the offset corresponding to the target program load value of the next round;
and determining the running state of the next round of each execution program according to the actual target program load value of the next round on the program load value curve and the program load value curve, further determining the target program load value of the next round on the program load value curve and the program change difference value corresponding to the target program load value of the next round according to the running state of the next round of each execution program, the execution program target function of the current moment and the program load value curve, continuously repeating the process until the set round number is reached, and taking the target program load value of the last round on the program load value curve as the final actual target program load value on the program load value curve.
Further, the calculation formula corresponding to the adjustment factor of the next round is obtained as follows:
wherein the content of the first and second substances,
is a firstiThe adjustment factors of the number of rounds are adjusted,
is as followsi-a factor for adjustment of 1 round,
is as followsi-an abscissa corresponding to the actual target program load value for 1 round,
is as followsiAnd the abscissa corresponding to the target program load value of the round.
Further, the determining the running state of the next round of each execution program includes:
and setting the running state of each execution program corresponding to the abscissa on the program load value curve, which is smaller than the abscissa corresponding to the actual target program load value of the next round, as 1 and setting the running states of the other execution programs as 0 according to the actual target program load value of the next round on the program load value curve and the program load value curve.
Further, the calculation formula for determining the program load value of each execution program is as follows:
wherein the content of the first and second substances,
is a firstiThe value of the program load of an executing program,
is as followsiAn execution programAccording to the degree of importance ofiThe number of associated controllers of each execution program is obtained through normalization processing,
is a firstiThe time complexity after normalization of the individual executing programs,
is a hyper-parameter.
Further, the calculation formula corresponding to the objective function of the executive program at the current time is determined as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the current timetIs to execute the program object function of (a),
is as followsiThe time complexity after normalization of the individual executing programs,
is as followsiThe running state to be determined of each execution program,
or a combination of the values of 0,
is a firstiThe value of the program load of each executing program,Iis the total number of programs executed.
Further, the allocating various sensing data information at the current moment to obtain the first type of sensing data information processed locally and the second type of sensing data information to be transmitted to the cloud computing side for processing includes:
determining all sensing data information corresponding to each executive program required to be operated in the various sensing data information of the monitored electric equipment at the current moment according to the operation state of each executive program, taking all the sensing data information corresponding to each executive program required to be operated as first-class sensing data information processed locally, and taking all the sensing data information except the first-class sensing data information in the various sensing data information of the monitored electric equipment at the current moment as second-class sensing data information required to be transmitted to a cloud computing terminal for processing.
The invention has the following beneficial effects: the invention provides a distributed monitoring and managing system based on an electric power internet of things, which is a data processing system specially suitable for management and supervision purposes, and solves the executive program objective function at the current moment by determining the program load value of each executive program of a micro data processing center, constructing a reasonable executive program objective function and combining the load rate of the micro data processing center at the current moment and the program load value of each executive program to obtain the running state of each executive program so as to realize the secondary classification of each sensing data information at the micro data processing center, namely determining the first type of sensing data information which is locally processed and the second type of sensing data information which needs to be transmitted to a cloud computing end for processing in each sensing data information, ensuring that important data of each micro data processing center has low delay under high load, the computing power of each micro-processing center can be kept in a stable state, and management and supervision of the power Internet of things are facilitated.
Drawings
FIG. 1 is a flow chart of a method for implementing a distributed monitoring and transportation management system based on the Internet of things of electric power according to the invention;
FIG. 2 is a flow chart of the present invention for obtaining the operating status of each executive;
FIG. 3 is a schematic diagram of a program load value curve according to the present invention;
fig. 4 is a diagram comparing the occupancy rate of the micro-processing center in the present embodiment with the occupancy rate of the micro-processing center in the prior art.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the technical solutions according to the present invention will be given with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Compared with the conventional coordination among all micro data processing centers, the distributed monitoring and operation management system based on the power internet of things reasonably establishes the objective function to more reasonably determine whether the executive program of each micro data processing center needs to operate or not, further realizes the classification of each sensor data, can ensure the stable operation of each micro data processing center under the condition of high load, and has the characteristic of low delay. In addition, the system can achieve the purpose of obtaining an approximate optimal solution more quickly by initializing the solving parameters more reasonably so as to complete the calculation unloading strategy and further ensure the stable operation of each micro data processing center.
Specifically, the distributed monitoring and operation system based on the power internet of things comprises one or more micro data processing center modules and monitoring modules such as sensors of power equipment, wherein each micro data processing center module is connected with the sensors of the power equipment in a wireless mode to transmit data. Meanwhile, each micro data processing center is connected with the cloud computing end in a wireless mode so as to send the processed data to the cloud computing end for further processing, and therefore the monitoring data needed by the monitoring and managing system are obtained.
As shown in fig. 1, each micro data processing center module of the distributed monitoring and management system based on the internet of things of electric power can implement the following steps:
(1) the method comprises the steps of obtaining various sensing data information of the monitored power equipment at the current moment, time complexity and the number of associated controllers of various execution programs of a micro data processing center of the monitored power equipment, and load rate of the micro data processing center at the current moment.
Wherein, in the electric power distribution room, each monitored power equipment can be provided with multiple sensor, and these sensors include sensors such as temperature sensor, humidity transducer, voltage sensor, current sensor, electric field sensor, and these sensors can gather corresponding sensory data information in real time to send its little data processing center module that corresponds through wireless transmission's mode. The micro data processing center module carries out unified processing on different types of sensing data information through a heterogeneous data unified module carried by the micro data processing center module to obtain various sensing data information after real-time unified processing.
After the micro data processing center module obtains various sensing data information after real-time unified processing, under the condition of high computing performance load, the micro data processing center can send the various sensing data information to the cloud computing end for processing and analysis, but the delay is high, and network bandwidth congestion is easily caused, so that the system is unstable, therefore, in order to ensure that the micro data processing center can quickly respond to the abnormity of the power equipment and make intelligent control of abnormal conditions, local computing needs to be adopted for part of important data under the condition of high load, and part of less important sensing data is transmitted to the cloud computing end for data analysis and processing.
Therefore, in order to facilitate the subsequent distinction of part of important data and part of less important data in various sensing data information, that is, to determine the first type of sensing data information processed locally and the second type of sensing data information which needs to be transmitted to the cloud computing terminal for processing, the time complexity and the number of associated controllers of each execution program of the micro data processing center and the load rate of the micro data processing center at the current moment need to be obtained. Each execution program herein refers to an execution program that performs corresponding processing on various kinds of sensed data information after the unified processing. Since each implementation of the execution degree corresponds to a certain time complexity, the time complexity of each execution program of the micro data processing center can be obtained. Since each execution program can be associated with a plurality of controllers, that is, the execution result of one execution program may require a plurality of controllers to perform corresponding control simultaneously or in sequence, the number of associated controllers of each execution program of the micro data processing center can be obtained, and when the number of associated controllers is larger, the importance of the execution program is increased. Because the operation of each execution program of the micro data processing center occupies a certain memory, the load rate of the micro data processing center at the current time can be obtained according to the execution state of each execution program of the micro data processing center at the current time.
(2) The method comprises the steps of determining the program load value of each execution program according to the time complexity and the number of associated controllers of each execution program of the micro data processing center of the monitored power equipment, wherein the program load value refers to the load value of the execution program, the program load value is related to the importance degree and the time complexity of the execution program, when the importance degree of the execution program is larger and the time complexity is smaller, the corresponding program load value is higher, and the execution program is more likely to be executed.
After the time complexity and the number of the associated controllers of each execution program of the micro data processing center are obtained on the basis of the step (1), the time complexity of each execution program is normalized by adopting a maximum and minimum normalization method, and the normalized value is used as the final time complexity of the corresponding execution program. Meanwhile, the number of the associated controllers of each executive program is normalized by adopting a maximum value and minimum value normalization method, and the normalized value is used as the importance degree of the corresponding executive program. Then, according to the time complexity and the importance degree after each execution program is normalized, the program load value of each execution program can be determined, and the corresponding calculation formula is as follows:
wherein the content of the first and second substances,
is a firstiThe value of the program load of an executing program,
is a firstiThe degree of importance of the executed program is determined according toiThe number of associated controllers of each execution program is obtained through normalization processing,
is as followsiThe time complexity after normalization of the individual executing programs,
is a hyper-parameter.
As can be seen from the above-mentioned formula for calculating the program load value of each executed program, the program load value of the execution degree is greater when the importance degree of the execution degree is greater and the time complexity thereof is smaller. In the formula for this calculation, the calculation,
is to prevent
When 0, the hyper-parameter
Failure, over-parameter
The method is used for adjusting the weight of the time complexity in the program load value, an implementer can adjust the time complexity according to a specific implementation scene during specific implementation, and the setting of the embodiment
=0.2。
(3) And determining the executive program objective function at the current moment according to the time complexity and the program load value of each executive program and the to-be-determined running state of each executive program.
On the basis of the step (2), in order to enable the data processed by the current micro data processing center to have high value under different load rates, an executive program objective function at the current time needs to be constructed, where the current time is referred to as time t, and then the executive program objective function at the current time, that is, the corresponding calculation formula of the executive program objective function at time t, is as follows:
wherein the content of the first and second substances,
as the current timetIs to execute the program object function of (a),
is as followsiThe time complexity after normalization of the individual executing programs,
is as followsiThe running state to be determined of each execution program,
or a combination of the values of 0,
is as followsiThe value of the program load of an executing program,Iis the total number of programs executed.
In the above-mentioned calculation formula of the executive program objective function at the current time, that is, the executive program objective function at the time t,
characterize the nextThe calculation value of the time micro data processing center when processing data,
the larger the value of (A) is, the higher the calculation value of the data processed by the micro data processing center at the next moment under different load rates is, so that the high value of the calculation value of the micro data processing center at the next moment is obtained by determining how each execution program is distributed under different load rates. I.e. the embodiment is expected to obtain the maximum value
The operation state corresponding to each corresponding executive program
The data processed by the micro data processing center at the next moment is guaranteed to have high value under the high load rate, and therefore the problem of how to distribute the execution programs under the high load rate is solved.
(4) And solving the executive program objective function at the current moment according to the load rate of the micro data processing center at the current moment and the program load value of each executive program to obtain the running state of each executive program.
Obtaining the object function of the executive program at the current moment through the step (3)
Then, in order to execute the program objective function
The solution needs to initialize the running state of each executive program, that is, each executive program needs to be given an initial program running state value
，
Take 0 or 1. In thatThe running state of each executive program is initialized, and the conventional simulated annealing algorithm is random initialization, but under the condition of the random initialization, the iterative solution efficiency cannot be guaranteed, and because the computing capacity of the micro data processing center is limited, a solution as good as possible needs to be found under the limited iteration times. Therefore, in the present embodiment, when initializing the operating state of each execution program, the program load values of the execution programs are first ranked from high to low according to the program load values of the execution programs at the current time of the micro data processing center, and then a polynomial curve fitting is performed in advance to obtain the program load value curves of all the programs. After the program load value curve is obtained, the program load values of the execution programs are subjected to secondary classification to obtain an initial target program load value, a program change difference value corresponding to the initial target program load value is determined, then an offset corresponding to the initial target program load value is determined by combining the current load rate of the micro data processing center and the program change difference value corresponding to the initial target program load value, so that an initial actual target program load value is determined, and finally the initialization process of the running state of each execution program is realized. According to the initialization result of the running state of each executive program, solving the executive program objective function at the current moment to finally obtain the running state of each executive program, as shown in fig. 2, the specific implementation process comprises the following steps:
(4-1) determining a program load value curve, an initial target program load value on the program load value curve and a program change difference value corresponding to the initial target program load value according to the program load value of each execution program of the micro data processing center at the current moment, specifically comprising:
(4-1-1) arranging the program load values of the execution programs in descending order according to the program load values of the execution programs at the current time of the micro data processing center, and then performing curve fitting by using the program load values of the execution programs as vertical coordinates and the arrangement numbers corresponding to the program load values of the execution programs as horizontal coordinates to obtain a program load value curve.
The program load values of the execution programs at the current moment of the micro data processing center are sorted from high to low, and then the program load values of the execution programs are used as ordinate and the arrangement serial numbers corresponding to the program load values of the execution programs are used as abscissa, and the value curves of all the programs are obtained by performing polynomial curve fitting in advance. In this embodiment, a schematic diagram corresponding to the program load value curve is shown in fig. 3, where a black dot in the diagram refers to a data point corresponding to the program load value of the executed program.
(4-1-2) determining a median of the program load values arranged in descending order, and using the median as an initial target program load value on the program load value curve.
After the program load values of the execution programs at the current time of the micro data processing center are sorted from high to low, the median of the sorted program load values is used as a demarcation point of the program load values, namely as an initial target program load value, and the coordinate position of the initial target program load value on a program load value curve is obtained.
(4-1-3) determining a slope value corresponding to the initial target program load value on the program load value curve, and taking an absolute value of the slope value as a program change difference value corresponding to the initial target program load value.
After acquiring the coordinate position of the initial target program load value on the program load value curve, the absolute value of the slope value at the coordinate position is obtained by using the existing derivative function
And the absolute value of the slope value is calculated
The program change difference value corresponding to the initial target program load value is used.
(4-2) determining an offset corresponding to the initial target program load value according to the load rate of the micro data processing center at the current moment, the initial target program load value, the program change difference value corresponding to the initial target program load value and the initial adjustment factor, wherein the corresponding calculation formula is as follows:
wherein the content of the first and second substances,
an offset corresponding to the initial target program load value,
is the initial adjustment factor for the adjustment of the power factor,
for the current time of the micro data processing centertThe load factor of (a) is,
is a piecewise function whose effect is on
Make a judgment when
When the threshold value is larger than the set threshold value, then
And if not, the step (B),
，
the program change difference value corresponding to the initial target program load value.
The following explanation is made with respect to the above-described calculation formula of the offset amount corresponding to the initial target program load value:
load factor of micro data processing center at present
As a weight, make the load rate
The higher the demarcation point, i.e., the closer the initial target program load value is to the high program load value.
Regulating factor
Has an initialization value of 1, i.e.
In order to obtain a more optimal solution more quickly in the following process, the current solution process needs to be performed according to the position where the last approximate optimal solution appears
And updating and adjusting the value.
Program change difference value corresponding to initial target program load value
The absolute value of the slope at the demarcation point, that is, the position where the initial target program load value is located, the larger the value of the absolute value, the larger the difference of the program load values at the current position is, the lower the possessed offset value, because the absolute value is likely to be the position where the optimal solution is located.
And (4-3) determining an initial actual target program load value on the program load value curve according to the initial target program load value and the offset corresponding to the initial target program load value.
After the offset corresponding to the initial target program load value is obtained, adding the abscissa corresponding to the initial target program load value on the program load value curve and the offset corresponding to the initial target program load value, wherein the ordinate corresponding to the added value is the initial actual target program load value on the program load value curve, and then determining the position of the initial actual demarcation point according to the added value and the ordinate corresponding to the added value.
(4-4) determining a final actual target program load value on the program load value curve according to the initial actual target program load value on the program load value curve, the executing program target function at the current moment and the program load value curve, wherein the specific implementation steps comprise:
(4-4-1) determining an initial operating state of each of the execution programs based on the initial actual target program load value on the program load value curve and the program load value curve.
After the initial actual target program load value on the program load value curve is determined, that is, after the position of the initial actual demarcation point is determined, the running state of the execution program positioned on the left side of the initial actual demarcation point is initialized to 1, and the running state of the execution program positioned on the right side of the demarcation point is initialized to 0, so that the initial running state of each execution program is obtained.
And (4-4-2) determining the target program load value of the next round on the program load value curve and the program change difference value corresponding to the target program load value of the next round according to the initial running state of each execution program, the execution program target function at the current moment and the program load value curve.
After the initial operation state of each executive program is obtained through the step (4-4-1), the initial operation state of each executive program is substituted into the executive program objective function at the current time, and the value of the executive program objective function at the current time is obtained
. Then based on the value of the executive program objective function at the current moment
And obtaining the optimal approximate solution through a simulated annealing algorithm. Specifically, random disturbance is generated on the ordinate of the actual demarcation point on the program load value curve, the running state of the executive program on the left side of the ordinate obtained after disturbance is set to be 1, and the running state of the executive program on the right side of the ordinate obtained after disturbance is set to be 0, so that a new executive program at the current moment after random disturbance is generated can be obtainedValue of the objective function
. Will be provided withF t (0) AndF t (1) and performing difference making, if the difference value is less than 0, updating the ordinate of the actual demarcation point, if not less than 0, judging whether the ordinate of the actual demarcation point needs to be updated by adopting a Monte Carlo criterion until the iteration frequency is reached, setting the iteration frequency to be I, and setting the I to be the number of all executed programs, finally obtaining the ordinate of the actual demarcation point reaching the iteration frequency, wherein the ordinate of the actual demarcation point reaching the iteration frequency is the current optimal approximate solution, and taking the ordinate of the actual demarcation point reaching the iteration frequency as the target program load value of the next round on the program load value curve.
After obtaining the optimal approximate solution when the iteration times are reached through the simulated annealing algorithm, namely obtaining the target program load value of the next round on the program load value curve, calculating the absolute value of the slope value of the program load value curve at the target program load value position of the next round, and taking the absolute value of the slope value as the program change difference value corresponding to the target program load value of the next round.
And (4-4-3) adjusting the initial adjustment factor according to the target program load value and the initial actual target program load value of the next round to obtain the adjustment factor of the next round.
In order to obtain the optimal approximate solution more quickly in the next time, according to the abscissa corresponding to the initial actual target program load value on the program load value curve and the abscissa corresponding to the target program load value of the next round on the program load value curve, the adjustment factor is adjusted
Adjusting and updating to obtain the adjustment factor of the next round, wherein the corresponding formula is as follows:
wherein the content of the first and second substances,
for the adjusted adjustment factor at the current time t, i.e. the firstiThe adjustment factors of the number of rounds are adjusted,
for the pre-adjustment factor at the current time t, i.e. the firsti-a regulatory factor for 1 round of operation,
is as followsi-an abscissa corresponding to the actual target program load value for 1 round,
is as followsiAnd the abscissa corresponding to the target program load value of the round.
And (4-4-4) determining the offset corresponding to the target program load value of the next round according to the load rate of the micro data processing center at the current moment, the target program load value of the next round, the program change difference value corresponding to the target program load value of the next round and the adjustment factor of the next round.
After the target program load value of the next round, the program change difference value corresponding to the target program load value of the next round, and the adjustment factor of the next round are obtained, the offset corresponding to the target program load value of the next round can be obtained by referring to the calculation formula for determining the offset in the step (4-2) in combination with the load rate of the micro data processing center at the current time t, and the corresponding calculation formula is as follows:
wherein the content of the first and second substances,
is as followsiThe offset corresponding to the target program load value for a round,
is as followsiThe adjustment factors of the number of rounds are adjusted,
is the current time of the micro data processing centertThe load factor of (a) is,
is a piecewise function whose effect is on
Make a judgment when
When the threshold value is larger than the set threshold value, then
And if not, the step (B),
，
is as followsiAnd program change difference values corresponding to target program load values of the turns.
And (4-4-5) determining the actual target program load value of the next round on the program load value curve according to the target program load value of the next round and the offset corresponding to the target program load value of the next round.
When the offset corresponding to the target program load value of the next round and the target program load value of the next round are known, the actual target program load value of the next round on the program load value curve can be determined by combining the method of determining the initial actual target program load value on the program load value curve in the step (4-3).
(4-4-6) determining the running state of the next round of each execution program according to the actual target program load value of the next round on the program load value curve and the program load value curve, further determining the target program load value of the next round on the program load value curve and the program change difference value corresponding to the target program load value of the next round according to the running state of the next round of each execution program, the execution program target function of the current moment and the program load value curve, continuously repeating the process until the set round is reached, and taking the target program load value of the last round on the program load value curve as the final actual target program load value on the program load value curve.
After the actual target program load value of the next round on the program load value curve is known, referring to the manner of determining the initial operating state of each execution program in the step (4-4-1), that is, according to the actual target program load value of the next round on the program load value curve and the program load value curve, setting the operating state of each execution program corresponding to the abscissa on the program load value curve, which is smaller than the abscissa corresponding to the actual target program load value of the next round, to 1, and setting the operating states of the remaining execution programs to 0, so that the operating state of the next round of each execution program can be determined. And then, based on the running state of the next round of each executive program, determining the value of the objective function of the executive program at the current moment again, obtaining the optimal approximate solution of the next round by a simulated annealing algorithm, and continuously repeating the steps (4-4-1) - (4-4-6) until the preset set round number is reached. And taking the optimal approximate solution obtained in the last round as the final actual target program load value on the program load value curve.
And (4-5) determining the running state of each execution program according to the final actual target program load value on the program load value curve and the program load value curve.
After the final actual target program load value on the program load value curve is obtained through the step (4-4), referring to the manner of determining the initial operating state of each execution program in the step (4-4-1), the operating state of the execution program located on the left side of the final actual target program load value on the program load value curve is initialized to 1, and the operating state of the execution program located on the right side of the final actual target program load value is initialized to 0, so that the operating state of each execution program is finally obtained.
In step (4) above, after determining the initial actual target program load value on the program load value curve, the initialization of the actual target program load value is completed. The conventional initialization means is a means for finding distribution characteristics through clustering, but because the micro data processing center has limited computing capacity, a simple, quick and effective initialization means is needed. In the embodiment, the median of the program load value of each execution program is selected as an initial demarcation point, and then an initial solution, namely an initial actual target program load value, is quickly obtained through a piecewise function, a slope and a current load rate. The method comprises the steps that a piecewise function can determine the offset direction, the slope can describe the change of a program load value at the current initial solution, the current offset brought by the load rate is considered, and then the load rate requirement is fitted better, after the offset is obtained, due to the limitation of the iteration times, the current approximate optimal solution which is obtained at present cannot be guaranteed to be the optimal solution, the offset basis when the target function is solved at each time is obtained through adjusting factors, the initial solution can be better approximated to the optimal solution by calculating the difference between the initial solution and the final solution, and the offset corresponding to the next initial solution is better corrected according to the difference between the offsets corresponding to the previous initial solutions, so that the final solution can be obtained more quickly.
When the classification state of each program is searched, the classification state of each program can be obtained by solving through a simulated annealing optimization algorithm. When the simulated annealing algorithm is used for solving the objective function, an initial classification state needs to be randomly allocated to the classification states of all current execution programs in advance, then the classification states of some programs are replaced by adding random disturbance to obtain a new objective function value, the objective function value is updated approximately, the program classification state corresponding to the maximum objective function value is reserved, and the iteration is stopped when the objective function value meets the expected requirement or the maximum iteration number is reached. And acquiring the program classification state corresponding to the maximum objective function value in the whole iteration process as the classification state of the current program.
Therefore, in the whole iteration process, even if the iteration number is set to be too small, if the initial program classification state and the optimal program classification state have too large deviation, the obtained program classification state may still have great deviation with the optimal program classification state after the iteration is completed. If the iteration number is set to be too large, although the optimal program classification state can be obtained, the required iteration number is too large, so that the solution time is too long. If a random solution method is adopted, each program can be in two conditions of local calculation or cloud calculation, and if n programs exist, the classification states of the n programs are shared
States of different combinations.
Therefore, when the program classification state is solved by the simulated annealing optimization algorithm, the initialization is adjusted, random initialization is not adopted, and the program load value is adopted
Sort because of high
The program has more local computing value and low cost
The program is more suitable for cloud computing and selection
The program corresponding to the median of (1) is used as the initial demarcation point because of the high
The program to the left of the initial cut point is set to 1, indicating that it is used for local computation. But will be
The program corresponding to the median is used as an initial demarcation point and is suitable for all load conditions, so that the offset of the median demarcation point is further obtained, and all load conditions are better fitted. And taking the offset difference value between the position of the final solution in each iteration solving process and the position of the solution which is initialized as an adjusting factor of the offset of the median demarcation point during initialization.
In accordance with program load value
After sorting, selecting
If there are n programs, the programs have n solutions. When the classification state of the initialization program is adjusted through the offset of the median demarcation point, although n solutions are still total, the offset of the median demarcation point is closer to the optimal solution, so that the effectiveness of the final solution is ensured, namely the final solution is closer to the optimal solution of the objective function, and then the offset is adjusted according to the adjusting factor, so that the iteration times can be properly reduced while the effectiveness of the solution is further ensured, namely in each iteration process, when the offset is smaller, the difference between the load value of the objective program and the load value of the actual objective program in the current iteration process is smaller, redundant iteration is not needed, the iteration times in the solution process can be properly reduced, the purpose of further shortening the iteration time is achieved, and the solution speed of the objective function is improved.
(5) And distributing various sensing data information at the current moment according to the running state of each execution program to obtain first type sensing data information processed locally and second type sensing data information needing to be transmitted to a cloud computing end for processing.
Determining each executive program with the running state of 1 according to the running state of each executive program, wherein each executive program with the running state of 1 is each executive program required to run, further determining all sensing data information corresponding to each executive program required to run, taking all sensing data information corresponding to each executive program required to run as first type sensing data information processed locally, and taking all sensing data information except the first type sensing data information in various sensing data information of the monitored power equipment at the current moment as second type sensing data information required to be transmitted to a cloud computing terminal for processing. That is to say, according to the running state of each executive program, all the sensor data information after the unified processing corresponding to each executive program with a running state value of 1 is retained in the micro data processing center for local calculation, and all the sensor data information after the unified processing corresponding to each executive program with a running state value of 0 is transmitted to the cloud computing terminal for calculation, so that calculation and unloading are completed, the data processing pressure of the micro data processing center is reduced, the controller in the power distribution room can still have the characteristic of low delay, and monitoring and management of the power equipment are realized. In order to verify the beneficial effect of the method in the scheme, a fluctuation curve of the cpu occupancy of the micro data processing center along with time is introduced as an evaluation model to evaluate the operation stability of the micro data processing center. As shown in fig. 4, a curve 1 is a fluctuation curve of cpu occupancy of a micro data processing center with time when random slicing processing is performed on an execution program in the prior art, and since a load is excessively high, a cpu is unstable and congested in operation, and is easily stuck, and a high-value program cannot be calculated and executed. The method of the scheme is used for value classification of the execution programs, local calculation is preferentially executed according to the program with high program load value, the program with low value is sent to the cloud computing end to be executed, queuing occupation of the cpu of the micro-processing center is reduced, so that the cpu occupation can keep a stable load, and the curve 2 is a fluctuation curve of the cpu occupation rate of the micro-data processing center along with time after program distribution is carried out by the method of the scheme. By comparing the curve 1 with the curve 2, the method can effectively improve the operation stability of the micro data processing center.
According to the method, the program load value of each executive program of the micro data processing center is determined, a reasonable executive program objective function is constructed, the current moment load rate of the micro data processing center and the program load value of each executive program are combined, the current moment executive program objective function is solved, the running state of each executive program is obtained, the second classification of each sensing data information at the micro data processing center is achieved, namely the first type of sensing data information which is processed locally and the second type of sensing data information which needs to be transmitted to the cloud computing end to be processed in each sensing data information are determined, and the micro data processing centers can be guaranteed to be stable in self computing under high load, and meanwhile the low delay characteristic of each micro data processing center is guaranteed. And the invention determines the offset corresponding to the initial target program load value by selecting the median of the program load value of each executive program as the initial demarcation point, namely the initial target program load value, then combining the piecewise function, the slope and the current load rate, corrects the initial demarcation point by adopting the offset, and determines the initial actual target program load value, thereby completing the initialization of the actual target program load value, and optimizing the adjustment in the optimizing process, thereby ensuring the optimal actual target program load value to be determined more quickly, and improving the rapidity of the optimizing process.
It should be noted that: the above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. The utility model provides a distributed control fortune pipe system based on electric power thing networking which characterized in that, includes little data processing center module, little data processing center module is used for realizing following step:
acquiring various sensing data information of the monitored power equipment at the current moment, time complexity and number of associated controllers of each execution program of a micro data processing center of the monitored power equipment and load rate of the micro data processing center at the current moment;
determining the program load value of each execution program according to the time complexity and the number of associated controllers of each execution program of the micro data processing center of the monitored power equipment;
determining an executive program target function at the current moment according to the time complexity and the program load value of each executive program and the running state to be determined of each executive program;
solving the executive program objective function at the current moment according to the load rate of the micro data processing center at the current moment and the program load value of each executive program to obtain the running state of each executive program;
and distributing various sensing data information at the current moment according to the running state of each execution program to obtain first type sensing data information processed locally and second type sensing data information needing to be transmitted to a cloud computing end for processing.
2. The distributed monitoring and operation and management system based on the power internet of things as claimed in claim 1, wherein the step of solving the objective function of the executive program at the current moment to obtain the running state of each executive program comprises the following steps:
determining a program load value curve, an initial target program load value on the program load value curve and a program change difference value corresponding to the initial target program load value according to the program load value of each execution program at the current moment of the micro data processing center;
determining an offset corresponding to the initial target program load value according to the load rate of the micro data processing center at the current moment, the initial target program load value, the program change difference value corresponding to the initial target program load value and the initial adjustment factor;
determining an initial actual target program load value on a program load value curve according to the initial target program load value and the offset corresponding to the initial target program load value;
determining a final actual target program load value on the program load value curve according to the initial actual target program load value on the program load value curve, the executing program target function at the current moment and the program load value curve;
and determining the running state of each execution program according to the final actual target program load value on the program load value curve and the program load value curve.
3. The power internet of things-based distributed monitoring and transportation and management system according to claim 2, wherein the determining of the program load value curve, the initial target program load value on the program load value curve, and the program change difference value corresponding to the initial target program load value comprises:
arranging the program load values of the execution programs in the descending order according to the program load values of the execution programs at the current moment of the micro data processing center, and then performing curve fitting by taking the program load values of the execution programs as vertical coordinates and the arrangement serial numbers corresponding to the program load values of the execution programs as horizontal coordinates so as to obtain a program load value curve;
determining a median of program load values which are arranged from big to small, and taking the median as an initial target program load value on a program load value curve;
and determining a slope value corresponding to the initial target program load value on the program load value curve, and taking the absolute value of the slope value as a program change difference value corresponding to the initial target program load value.
4. The distributed monitoring and operation and management system based on the power internet of things as claimed in claim 3, wherein the calculation formula for determining the offset corresponding to the initial target program load value is as follows:
wherein the content of the first and second substances,
an offset corresponding to the initial target program load value,
is the initial adjustment factor for the adjustment of the power factor,
is the current time of the micro data processing centertThe load factor of (a) is,
is a piecewise function of
When the threshold value is larger than the set threshold value, then
If not, then,
，
the program change difference value corresponding to the initial target program load value.
5. The power internet of things-based distributed monitoring and management system according to claim 2, wherein the determining of the final actual target program load value on the program load value curve comprises:
determining the initial running state of each execution program according to the initial actual target program load value on the program load value curve and the program load value curve;
determining a target program load value of the next round on the program load value curve and a program change difference value corresponding to the target program load value of the next round according to the initial running state of each execution program, the execution program target function at the current moment and the program load value curve;
adjusting the initial adjustment factor according to the target program load value and the initial actual target program load value of the next round to obtain the adjustment factor of the next round;
determining the offset corresponding to the target program load value of the next round according to the load rate of the micro data processing center at the current moment, the target program load value of the next round, the program change difference value corresponding to the target program load value of the next round and the adjustment factor of the next round;
determining the actual target program load value of the next round on the program load value curve according to the target program load value of the next round and the offset corresponding to the target program load value of the next round;
and determining the running state of the next round of each execution program according to the actual target program load value of the next round on the program load value curve and the program load value curve, further determining the target program load value of the next round on the program load value curve and the program change difference value corresponding to the target program load value of the next round according to the running state of the next round of each execution program, the execution program target function of the current moment and the program load value curve, continuously repeating the process until the set round number is reached, and taking the target program load value of the last round on the program load value curve as the final actual target program load value on the program load value curve.
6. The distributed monitoring and transportation and management system based on the power internet of things as claimed in claim 5, wherein the calculation formula corresponding to the adjustment factor of the next round is obtained as follows:
wherein, the first and the second end of the pipe are connected with each other,
is as followsiThe adjustment factors of the number of rounds are adjusted,
is a firsti-a factor for adjustment of 1 round,
is a firsti-an abscissa corresponding to the actual target program load value for 1 round,
is as followsiAnd the abscissa corresponding to the target program load value of the round.
7. The electric power internet of things-based distributed monitoring and transportation and management system according to claim 5, wherein the determining the operation state of the next round of each execution program comprises:
and setting the running state of each execution program corresponding to the abscissa on the program load value curve, which is smaller than the abscissa corresponding to the actual target program load value of the next round, as 1 and setting the running states of the other execution programs as 0 according to the actual target program load value of the next round on the program load value curve and the program load value curve.
8. The distributed monitoring and transportation and management system based on the power internet of things as claimed in claim 1, wherein the calculation formula for determining the program load value of each execution program is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is as followsiThe value of the program load of an executing program,
is as followsiThe degree of importance of the executed program is determined according toiThe number of associated controllers of each execution program is obtained through normalization processing,
is as followsiThe time complexity after normalization of the individual executing programs,
is a hyper-parameter.
9. The distributed monitoring and operation and management system based on the power internet of things as claimed in claim 1, wherein the calculation formula corresponding to the objective function of the executive program at the current moment is determined as follows:
wherein the content of the first and second substances,
as the current timetIs to execute the program object function of (a),
is as followsiThe time complexity after normalization of the individual executing programs,
is as followsiThe running state to be determined of each execution program,
or a combination of the values of 0,
is as followsiThe value of the program load of an executing program,Iis the total number of programs executed.
10. The distributed monitoring operation and management system based on the power internet of things as claimed in claim 1, wherein the step of distributing various sensing data information at the current moment to obtain a first type of sensing data information processed locally and a second type of sensing data information to be transmitted to a cloud computing end for processing comprises the steps of:
determining all sensing data information corresponding to each executive program required to be operated in the various sensing data information of the monitored electric equipment at the current moment according to the operation state of each executive program, taking all the sensing data information corresponding to each executive program required to be operated as first-class sensing data information processed locally, and taking all the sensing data information except the first-class sensing data information in the various sensing data information of the monitored electric equipment at the current moment as second-class sensing data information required to be transmitted to a cloud computing terminal for processing.
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