CN113378253A - Adaptive arrangement method for graphs of intelligent control configuration soft PLC system of power grid equipment - Google Patents

Adaptive arrangement method for graphs of intelligent control configuration soft PLC system of power grid equipment Download PDF

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CN113378253A
CN113378253A CN202110768259.0A CN202110768259A CN113378253A CN 113378253 A CN113378253 A CN 113378253A CN 202110768259 A CN202110768259 A CN 202110768259A CN 113378253 A CN113378253 A CN 113378253A
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graph
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张云菊
郭明
史虎军
杨强
张玉罗
邢苗苗
石启宏
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Guizhou Power Grid Co Ltd
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Abstract

The invention discloses a graph self-adaptive arrangement method of a power grid equipment intelligent control configuration soft PLC system, which comprises the steps of carrying out translation transformation in a two-dimensional coordinate system on a graph sequence to be arranged through a graph self-adaptive arrangement algorithm based on coordinate transformation, maintaining the horizontal distance of graphs in a self-adaptive manner, keeping the vertical coordinates of the graphs on the same line equal, and simultaneously ensuring that the graph positions in all the sequences are always in a designated area; the problem of the prior art to the unordered or chaotic figure that the soft PLC system figure of power grid equipment intelligent control configuration exists the adjustment based on the limitation on the variable size figure arrangement problem of the figure arrangement algorithm of grid technique is solved.

Description

Adaptive arrangement method for graphs of intelligent control configuration soft PLC system of power grid equipment
Technical Field
The invention belongs to an automatic design technology, and particularly relates to a graph self-adaptive arrangement method for an intelligent control configuration soft PLC system of power grid equipment.
Background
In computer aided design, designers are often required to draw a variety of graphics in an editing interface. As the number of graphics in the editing interface increases, readability problems due to disordered or chaotic graphic arrangements can severely affect the work efficiency of the designer.
The prior art adopts a manual dragging mode to arrange and align the graphs, and the efficiency is low and the accuracy cannot be ensured. The grid-based graph arrangement algorithm divides an editing view into grids with fixed length and width, the grids are used as the minimum moving unit when graphs are arranged, the final positions of the graphs are determined by calculating the grids, and limitation exists when the graphs with different sizes are processed. The method for tracking the boundary of the object by adopting the level set method has the defects that the boundary of the object needs to be extracted, and detailed information such as a shape boundary cannot be comprehensively expressed, and the adopted lattice automaton method is easy to process in parallel, so that a graphic processor has a higher processing rate. The graph arrangement algorithm based on the table technology solves the limitation of the grid algorithm in processing graphs with different sizes by designing a table type supporting random setting of the occupied size of the graph, but when the size of the graph needing to be arranged changes, the grid division of a view needs to be refined again by resources with more expenditure. One of the main problems of the method for supporting the design, editing and mode retrieval of graphics by adopting a computer automated software engineering tool is that only standard modeling languages such as UML, state diagrams and similar general modeling languages are supported, and the code generation is mostly limited to the mapping of architectural drawings such as static code structures and modules of UML class diagrams. The above method does not solve the problems caused by the inconsistent size of the graphics on the bounded view and the possibility of shape change with the change of the input content of the graphics well.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the method is used for solving the problems that the prior art aims at the limitation of the adjustment of disordered or disordered graphs existing in the graphs of the intelligent control configuration soft PLC system of the power grid equipment on the aspect of processing the graph arrangement problem of variable size based on the graph arrangement algorithm of the grid technology.
The technical scheme of the invention is as follows:
a graph self-adaptive arrangement method of a power grid equipment intelligent control configuration soft PLC system is characterized in that translation transformation in a two-dimensional coordinate system is carried out on a graph sequence to be arranged through a graph self-adaptive arrangement algorithm based on coordinate transformation, the horizontal distance of graphs is maintained in a self-adaptive mode, the vertical coordinates of graphs on the same line are kept equal, and meanwhile, the graph positions in all the sequences are guaranteed to be always in a designated area.
The self-adaptive graph arrangement algorithm based on coordinate transformation is a process of once full arrangement for the first time, and then the algorithm starts from a starting graph of a disordered area and sequentially judges the coordinate change conditions before and after graph translation transformation to determine whether to terminate the arrangement according to the local disorder condition caused by graph increase and decrease, graph sequence exchange or graph size change in a sequence.
The time measure of the complexity of the graph self-adaptive arrangement algorithm based on coordinate transformation is represented by running time.
The calculation formula of the running time is as follows:
t(s)＝caA(s)+csS(s)+cmM(s)+cdD(s)+ccC(s)+ceE(s) (1)
in the formula: ca. cs, cm, cd, cc, ce are respectively the time required by one addition, subtraction, multiplication, division, comparison and exchange operation; the functions a, S, M, D, C and E are the number of addition, subtraction, multiplication, division, comparison and exchange operations in the algorithm, respectively.
Formula (1) is simplified to express:
t(s)＝caA(s)+ccC(s)。
the graph self-adaptive arrangement algorithm based on coordinate transformation comprises the following steps:
step 1, in a plane rectangular coordinate system, any point Op(xp,yp) The translation process of (A) is to point OpA repositioning process moving from one coordinate position to another along a linear path T;
point OpThe coordinate relationship before and after translation is expressed by formula (1):
Figure BDA0003152754530000021
step 2, according to the principle of geometric transformation, OpThe translation transformation of (a) is calculated according to equation (2):
Figure BDA0003152754530000031
step 3, supposing that a plane alpha exists, and the value range of the horizontal coordinate of the plane alpha is [0, X ]max]The value range of the ordinate is [0, + ∞ ]; given with n (n)>0) Set of graphic elements S ═ { O ═ Ok|Ok∈α,k∈(0,n]N k belongs to Z, and a graphic element OkThe occupied width and height are respectively marked as wkAnd hk，wkSatisfies the constraint condition wk<Xmax(ii) a While agreeing on the starting coordinate of the arrangement of the figure elements as P0(x0,y0) Then the goal of the arrangement of graphic elements can be described as: in plane alpha with P0For each element O in the starting pair SkTranslation vector TkThen non-overlapping sequences are formed; if it is not
Figure BDA0003152754530000032
The patterns are arranged in more than one row and the following condition is satisfied: (1) the sum of the widths of each row of graphic elements is not more than Xmax(ii) a (2) The height occupied by each row is the maximum height of the elements in the current row;
step 4, based on the formula (2), using
Figure BDA0003152754530000033
Maximum value, d, representing the height of all graphical objects in the ith rowwRepresenting the lateral distance between adjacent patterns, dhDenotes the longitudinal distance between adjacent rows, let μ ═ x'k-1+wk-1+dw+wk，
Figure BDA0003152754530000034
Get the graph Ok(k>1) According to vector TkThe coordinates after translation are represented by formula (3) and are represented byk-1(k>1) Is translated to obtain
Figure BDA0003152754530000035
Step 5, setting
Figure BDA0003152754530000036
The value range of (1) is {0, 1 };
the piecewise function after simplifying equation (3) is shown in equation (5):
Figure BDA0003152754530000041
when λ ═ 1, i.e., μ ═ x'k-1+wk-1+dw+wk>XmaxWhen, the pattern OkNeed to be arranged in line, hence being called x'k-1+wk-1+dw+wk>XmaxIs a critical condition for graphics line feed; at this time, a pattern O is obtainedk(k>1) The translation transformation of (2) is shown in equation (6):
Figure BDA0003152754530000042
Figure BDA0003152754530000043
in order to translate the transformation matrix a,
Figure BDA0003152754530000044
is a translation vector B;
so for the pattern O1Push button
Figure BDA0003152754530000045
Making translation transformation, for the rest of graph, if graph Ok(k>1) The critical condition of line feed drawing is not satisfied, namely mu epsilon (0, X)max]While drawing the graph Ok(k>1) And (4) performing translation transformation according to A, or performing translation transformation according to B, and finishing the arrangement target.
Figure BDA0003152754530000051
The determination method of the value range {0, 1} is as follows:
arbitrary xk，wkAnd dwAre all less than XmaxThe value range of mu is (0, X) when the real number of (d) is positivemax)；
When μ ∈ (0, X)max]Time mu/X max1, when λ ∈ {0 };
when μ e (X)max，4Xmax]Sometimes:
μ/Xmax＞1
then
Figure BDA0003152754530000052
In this case, λ ∈ {1}, and thus the value range of λ is {0, 1 }.
Vector O is expressed according to equation (3)kAnd Ok-1Making a difference, let t be (1-lambda) (w)k+dw-x0)+x0Equation (4) is obtained:
Figure BDA0003152754530000053
when y'k-y′k-1When h is equal to 0, due toi max+dh≠0，
λ is 0;
x 'at this time'k-x′k-1＝t＝wk+dw＝c0；
For equation (4), when x'k-x0When the value is 0, (1-lambda) x 'is obtained'k-1+t＝x0If 1- λ is 0; at this time have
Figure BDA0003152754530000054
The invention has the beneficial effects that:
the invention is based on the limitation of the graphic arrangement algorithm of the grid technology in processing the problem of variable-size graphic arrangement, establishes the algorithm of the graphic self-adaptive arrangement on the bounded plane based on the geometric transformation thought in order to improve the flexibility of the graphic arrangement, and proves the correctness of the algorithm. A graph self-adaptive arrangement method based on coordinate transformation is provided, the algorithm uses a graph translation transformation mode to replace a grid division method to deploy graph elements on a bounded view, and the time complexity is O (N); finally, the proposed algorithm is realized on a soft PLC system, and the test result shows that: on the premise of achieving the effect of orderly arrangement among the graphs, the CPU occupancy rate is maintained at about 2%, and the algorithm has high running rate and stability and low resource occupancy rate.
The problem of the prior art to the unordered or chaotic figure that the soft PLC system figure of power grid equipment intelligent control configuration exists the adjustment based on the limitation on the variable size figure arrangement problem of the figure arrangement algorithm of grid technique is solved.
Drawings
FIG. 1 is a schematic diagram of the coordinate translation transformation of the present invention;
FIG. 2 is a diagram illustrating a graphical location before a graphical element is translated;
FIG. 3 is a diagram illustrating a position of a graphic element after a translation transformation;
FIG. 4 is a graph of the program run at an add frequency of 2 Hz;
FIG. 5 is a graph of the program run at an add frequency of 5 Hz;
FIG. 6 is a graph of the program run at an add frequency of 10 Hz;
FIG. 7 is a graph of the program run at an add frequency of 20 Hz.
Detailed Description
The invention aims to solve the limitation of a graph arrangement algorithm based on a grid technology in processing the variable-size graph arrangement problem and improve the flexibility of graph arrangement, establishes a mathematical model of graph self-adaptive arrangement on a bounded plane based on a geometric transformation thought, and proves the correctness of the mathematical model. A graph self-adaptive arrangement algorithm based on coordinate transformation is provided, and the algorithm uses a graph translation transformation mode to replace a grid division method to deploy graph elements on a bounded view, wherein the time complexity is O (N). Finally, the proposed algorithm is realized on a soft PLC system, and the test result shows that: on the premise of achieving the effect of orderly arrangement among the graphs, the CPU occupancy rate is maintained at about 2%, and the algorithm has high running rate and stability and low resource occupancy rate.
A graph self-adaptive arrangement algorithm (AGLA-CT) based on coordinate transformation is provided by using coordinate calculation in translation transformation to replace a fixed interface grid division technology.
In a rectangular plane coordinate system, any point Op(xp,yp) The translation process of (A) is to point OpA repositioning process moving from one coordinate position to another along a linear path T.
FIG. 1 is a schematic diagram of a coordinate translation transformation, therefore, point OpThe coordinate relationship before and after translation can be shown by formula (1).
Figure BDA0003152754530000071
According to the principle of geometric transformation, OpThe translation transformation of (2) is operated as in equation (2).
Figure BDA0003152754530000072
Assuming the presence of a plane alpha, the abscissa thereofHas a value range of [0, X ]max]The ordinate is in the range of [0, + ∞ ]. Given with n (n)>0) Set of graphic elements S ═ { O ═ Ok|Ok∈α,k∈(0,n]N k belongs to Z, and a graphic element OkThe occupied width and height are respectively marked as wkAnd hkWherein w iskSatisfies the constraint condition wk<Xmax. While agreeing on the starting coordinate of the arrangement of the figure elements as P0(x0,y0) Then the goal of the arrangement of graphic elements can be described as:
in plane alpha with P0For each element O in the starting pair SkTranslation vector TkNon-overlapping sequences are then formed. If it is not
Figure BDA0003152754530000073
The patterns are arranged in a plurality of rows and the following condition is satisfied: (1) the sum of the widths of each row of graphic elements is not more than Xmax(ii) a (2) The height occupied by each row is the maximum height of the elements in the current row.
Based on equation (2), using hi maxMaximum value, d, representing the height of all graphical objects in the ith rowwRepresenting the lateral distance between adjacent patterns, dhDenotes the longitudinal distance between adjacent rows, let μ ═ x'k-1+wk-1+dw+wk，
Figure BDA0003152754530000074
The graph O satisfying the proposition can be obtainedk(k>1) According to vector TkThe coordinates after translation can be represented by formula (3) from Ok-1(k>1) And translation transformation is carried out.
Figure BDA0003152754530000075
Theorem 1:
Figure BDA0003152754530000081
k belongs to n when y'k-y′k-1When being 0, there is x'k-x′k-1＝c0(c0E.g., R) is true.
And (3) proving that:
vector O is expressed according to equation (3)kAnd Ok-1Making a difference, let t be (1-lambda) (w)k+dw-x0)+x0Equation (4) is obtained.
Figure BDA0003152754530000082
When y'k-y′k-1When h is equal to 0, due toi max+dhNot equal to 0, then
Figure BDA0003152754530000083
X 'at this time'k-x′k-1＝t＝wk+dw＝c0The theory is to obtain the syndrome.
Theorem 2:
Figure BDA0003152754530000084
k belongs to n when x'k-x0When 0, there is y'k-y′k-1＝c1(c1E.g., R) is true.
And (3) proving that:
for equation (4), when x'k-x0When the value is 0, (1-lambda) x 'is obtained'k-1+t＝x0And then 1- λ is 0. At this time, there is y'k-y′k-1＝hi max+dh＝c1The theory is to obtain the syndrome.
Theorem 1 shows that when two adjacent graphs have the same ordinate, their lateral spacing maintains a constant c0. Theorem 2 shows that when two adjacent patterns are different by one line, their longitudinal spacing maintains a constant c1. Thus, equation (3) satisfies theorems 1 and 2, i.e., for arbitrary figure Ok(k>1) Both equations (3) hold.
Theorem 3:
Figure BDA0003152754530000085
the value range of (c) is {0, 1 }.
And (3) proving that:
knowing any x from propositionsk，wkAnd dwAre all less than XmaxThe value range of mu is (0, X) when the real number of (d) is positivemax)。
When μ ∈ (0, X)max]At a time there is
μ/Xmax＝1
At this time, lambda belongs to {0 };
when μ e (X)max，4Xmax]Sometimes there is a mu/Xmax＞1
Then:
Figure BDA0003152754530000091
at this time, λ ∈ {1}, so the value range of λ is {0, 1}, and the theorem proves.
The piecewise function after simplifying equation (3) by theorem 3 is shown in equation (5).
Figure BDA0003152754530000092
Equation (5) indicates that when λ ═ 1, i.e., μ ═ x'k-1+wk-1+dw+wk>XmaxWhen, the pattern OkNeed to be arranged in line, hence being called x'k-1+wk-1+dw+wk>XmaxIs a critical condition for graphics line feed. At this time, a pattern O can be obtainedk(k>1) The translation transformation of (a) is shown in equation (6).
Figure BDA0003152754530000093
Figure BDA0003152754530000094
Is noted as the translation transformation matrix a,
Figure BDA0003152754530000101
is the translation vector B.
So for the pattern O1Push button
Figure BDA0003152754530000102
Making translation transformation, for the rest graphs, if the graph Ok(k>1) The critical condition of line feed drawing is not satisfied, namely mu epsilon (0, X)max]While drawing the graph Ok(k>1) And (4) performing translation transformation according to the A, or performing translation transformation according to the B, and finishing the arrangement target.
The invention adaptively maintains the transverse spacing of the graphs by performing translation transformation in a two-dimensional coordinate system on the graph sequences to be arranged, keeps the vertical coordinates of the graphs on the same line equal, and simultaneously ensures that the graph positions in all the sequences are always in the designated area
The algorithm is executed for the first time in a full arrangement process, and then the local confusion condition caused by the increase and decrease of graphs, the exchange of graph sequences or the change of graph sizes in a sequence is used for determining whether to terminate the arrangement by sequentially judging the coordinate change condition before and after the graph translation transformation from the initial graph of a confusion area, and at the moment, the algorithm does not perform coordinate calculation on all the graphs in the sequence, but a local arrangement process.
In general, a time measure of algorithm complexity can be expressed by the running time t(s) in equation (7).
t(s)＝caA(s)+csS(s)+cmM(s)+cdD(s)+ccC(s)+ceE(s) (7)
In the formula (7), ca, cs, cm, cd, cc and ce are respectively the time required by one addition, subtraction, multiplication, division, comparison and exchange operation; the functions a, S, M, D, C and E are the number of addition, subtraction, multiplication, division, comparison and exchange operations in the algorithm, respectively. For the present algorithm, equation (7) can be abbreviated as t(s) ═ caa(s) + ccc(s). It can be known from the algorithm flow that the process of relocating the coordinates of the graph Ok requires 5 times of addition operation and 3 times of comparison operation, and the best case is that only the arrangement algorithm needs to be executed on the chaotic region when the graph sequence is partially chaotic, and at this time, k e (1, n) times of arrangement is needed, and t(s) ═ k (5ca +3cc) exists; the worst case is the first alignment process of the pattern sequence, where n alignments are required, so t(s) equals n (5ca +3 cc). The temporal complexity of the algorithm is therefore O (N).
In recent years, with the establishment of the international standards of the PLC, the soft PLC technology becomes an emerging technology which breaks the limitations of the conventional PLC. The subject group of authors in this document aims to improve reusability and maintainability of a soft PLC editing module, improve the development efficiency of customized functions of an intelligent control system, and develop a high-stability and high-performance soft PLC system for programming and debugging a control logic ladder diagram. The main functions of the system are: 1) object configuration function: adding entity objects, communicating with hardware, taking values and the like. The object configuration is the basis of the whole system and belongs to the middle layer. 2) Hardware configuration functions: module configuration, module write value/point mapping, port configuration, etc. 3) Control logic and policy making function: and the control logic of the whole system is completed, and the controlled equipment is controlled. 4) Data analysis function: according to the system operation condition, dynamically generating various storage tables of data to be recorded, data query, curve generation, energy efficiency analysis, report printing, equipment information and the like. In a soft PLC system, the ladder diagram editing view is part of the control logic and policy compilation user interface, and the system uses the algorithm presented herein to solve the problem of the arrangement of the graphic instructions in the system. The system is developed on a QT platform by using a C + + language, and the specific environment of the development is as follows: the processor is AMD Athlon 643000 +1.8GHz, the memory is 2G, and the hard disk is 320G.
Since any graph can be represented by a rectangle, the rectangle is used as a basic graph to perform performance test on the arrangement algorithm when the method is implemented, and the coordinate of the upper left corner of the rectangle is used for representing the position of the graph in a coordinate system. Some parameters in the test are given here, the transverse boundaries X of the plane αmax740, lateral spacing d between adjacent patterns w10, longitudinal distance d h10, initial arrangement coordinate P0(x0,y0) Initialization is (50, 10). The 9 types of graphic libraries such as Bit, call, company, Control, Counter, ForBRK, and Translgic in the system are shown in table 1, and the graphics with different specifications used in the test are all randomly selected from the libraries.
Table 19 type graphic element parameter table
Figure BDA0003152754530000111
Figure BDA0003152754530000121
Fig. 2 shows the random distribution of 20 graphic units with different specifications randomly selected from the graphic library in the plane α, and fig. 3 shows the distribution of the 20 graphic units after performing the AGLA-CT once.
As can be seen from FIG. 2, the graphs before translation transformation are distributed disorderly, after the graph sequence is translated by using AGLA-CT, the graph No. 1 is translated to the specified position according to the algorithm rule, and then the rest graphs in the sequence have been self-adaptively adjusted in position, wherein the graphs No. 6, No. 12 and No. 17 have been self-adaptively translated to the starting position of the next line due to the line feed condition in the algorithm being satisfied. In addition, the patterns on the same row are on the same horizontal line, and the patterns are arranged in order.
The conditions of the execution time of the algorithm and the number of the translated graphs are shown after the random graphs are added to the plane alpha which has 100 graphs with different specifications and is arranged in order after 10 times of tests.
It can be seen that the time for execution of the AGLA-CT algorithm is not exactly the same each time, which is determined by the AGLA-CT local translation strategy. Further analysis compares the width of the added pattern to the algorithm run time, which is related to the size of the local chaotic region, and the number of patterns that need to be translated per arrangement, which is related to both the width and position of the added pattern. 4 patterns with different widths are added in the test respectively, the widths of the patterns added in the 2 nd test and the 3 rd test are the same, but the sizes of chaotic areas in the two times are different, so that the number of elements subjected to translation transformation by the arrangement algorithm is different, and the execution time of the arrangement algorithm is also different. The same applies to the subsequent 6 th, 7 th, 8 th and 9 th experiments, which also show the property of AGLA-CT to perform coordinate calculations only for locally cluttered areas. In addition, the execution time of the 10 times of test algorithms is basically controlled within 30ms, and the times of completely arranging all the graphs in 10 times of tests only account for 30% of the experiment times.
Fig. 4 to 7 show the case where new graphics are added to the plane α every 500ms, 200ms, 100ms, and 50ms, and CPU and memory occupancy of the AGLA-CT algorithm are observed every 1000ms, respectively. Analyzing the occupancy rate curve of the CPU 20s before the AGLA-CT algorithm in the graphs of 5-7, it can be seen that the occupancy rate of the CPU is low under the condition of low operation frequency, the fluctuation of the occupancy rate curve is small, and the average occupancy rate is maintained at about 2%. And when the algorithm operation frequency is high, the average occupancy rate curve of the CPU rises to 20% and the fluctuation is large and tends to rise, because the graphics base number inside the region rapidly increases in a short time, when the time for one execution of the algorithm increases. In practice, the operating frequency of a designer is far less than that of a test case, so that the CPU occupancy rate of the algorithm is low in practice. On the other hand, the occupancy rate curves of the memories in fig. 5 to 7 have no fluctuation, and it can be known that the occupancy rate of the memory executed by the algorithm is very low.
From the tests, the AGLA-CT has higher algorithm execution speed and more stable algorithm operation on the premise of ensuring that the effect of orderly arrangement among the graphs is achieved, and meanwhile, the algorithm execution time is reduced due to the characteristic of local arrangement, and the algorithm efficiency is improved. Meanwhile, the instantaneous CPU occupies a lower space, a new memory space is not opened up in the algorithm execution process, and the time efficiency and the space efficiency are higher.

Claims (8)

1. A self-adaptive arrangement method for graphs of a power grid equipment intelligent control configuration soft PLC system is characterized by comprising the following steps: the method comprises the steps of carrying out translation transformation in a two-dimensional coordinate system on a graph sequence to be arranged through a graph self-adaptive arrangement algorithm based on coordinate transformation, maintaining the horizontal distance of graphs in a self-adaptive mode, keeping the vertical coordinates of graphs on the same row equal, and meanwhile ensuring that the graph positions in all sequences are always in a designated area.
2. The adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 1, characterized in that: the self-adaptive graph arrangement algorithm based on coordinate transformation is a process of once full arrangement for the first time, and then the algorithm starts from a starting graph of a disordered area and sequentially judges the coordinate change conditions before and after graph translation transformation to determine whether to terminate the arrangement according to the local disorder condition caused by graph increase and decrease, graph sequence exchange or graph size change in a sequence.
3. The adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 1, characterized in that: the time measure of the complexity of the graph self-adaptive arrangement algorithm based on coordinate transformation is represented by running time.
4. The adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 3, characterized in that: the calculation formula of the running time is as follows:
t(s)＝caA(s)+csS(s)+cmM(s)+cdD(s)+ccC(s)+ceE(s) (1)
in the formula: ca. cs, cm, cd, cc, ce are respectively the time required by one addition, subtraction, multiplication, division, comparison and exchange operation; the functions a, S, M, D, C and E are the number of addition, subtraction, multiplication, division, comparison and exchange operations in the algorithm, respectively.
5. The adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 4, characterized in that: formula (1) is simplified to express:
t(s)＝caA(s)+ccC(s)。
6. the adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 1, characterized in that: the graph self-adaptive arrangement algorithm based on coordinate transformation comprises the following steps:
step 1, in a plane rectangular coordinate system, any point Op(xp,yp) The translation process of (A) is to point OpA repositioning process moving from one coordinate position to another along a linear path T;
point OpThe coordinate relationship before and after translation is expressed by formula (1):
Figure FDA0003152754520000021
step 2, according to the principle of geometric transformation, OpThe translation transformation of (a) is calculated according to equation (2):
Figure FDA0003152754520000022
step 3, supposing that a plane alpha exists, and the value range of the horizontal coordinate of the plane alpha is [0, X ]max]The value range of the ordinate is [0, + ∞ ]; given with n (n)>0) Set of graphic elements S ═ { O ═ Ok|Ok∈α,k∈(0,n]N k belongs to Z, and a graphic element OkThe occupied width and height are respectively marked as wkAnd hk，wkSatisfies the constraint condition wk<Xmax(ii) a While agreeing on the starting coordinate of the arrangement of the figure elements as P0(x0,y0) Then the goal of the arrangement of graphic elements can be described as: in plane alpha with P0For each element O in the starting pair SkTranslation vector TkThen non-overlapping sequences are formed; if it is not
Figure FDA0003152754520000023
The patterns are arranged in more than one row and the following condition is satisfied: (1) the sum of the widths of each row of graphic elements is not more than Xmax(ii) a (2) The height occupied by each row is the maximum height of the elements in the current row;
step 4, based on the formula (2), using
Figure FDA0003152754520000024
Maximum value, d, representing the height of all graphical objects in the ith rowwRepresenting the lateral distance between adjacent patterns, dhDenotes the longitudinal distance between adjacent rows, let μ ═ x'k-1+wk-1+dw+wk，
Figure FDA0003152754520000025
Get the graph Ok(k>1) According to vector TkThe coordinates after translation are represented by formula (3) and are represented byk-1(k>1) Is translated to obtain
Figure FDA0003152754520000026
Step 5, setting
Figure FDA0003152754520000031
The value range of (1) is {0, 1 };
the piecewise function after simplifying equation (3) is shown in equation (5):
Figure FDA0003152754520000032
when λ ═ 1, i.e., μ ═ x'k-1+wk-1+dw+wk>XmaxWhen, the pattern OkNeed to be arranged in line, hence being called x'k-1+wk-1+dw+wk>XmaxIs a critical condition for graphics line feed; at this time, a pattern O is obtainedk(k>1) The translation transformation of (2) is shown in equation (6):
Figure FDA0003152754520000033
Figure FDA0003152754520000034
in order to translate the transformation matrix a,
Figure FDA0003152754520000035
is a translation vector B;
so for the pattern O1Push button
Figure FDA0003152754520000036
Making translation transformation, for the rest of graph, if graph Ok(k>1) The critical condition of line feed drawing is not satisfied, namely mu epsilon (0, X)max]While drawing the graph Ok(k>1) And (4) performing translation transformation according to A, or performing translation transformation according to B, and finishing the arrangement target.
7. The adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 6, wherein the adaptive graphical arrangement method comprises the following steps:
Figure FDA0003152754520000041
the determination method of the value range {0, 1} is as follows:
arbitrary xk，wkAnd dwAre all less than XmaxThe value range of mu is (0, X) when the real number of (d) is positivemax)；
When μ ∈ (0, X)max]Time mu/Xmax1, when λ ∈ {0 };
when μ e (X)max，4Xmax]Sometimes:
μ/Xmax＞1
then
Figure FDA0003152754520000042
In this case, λ ∈ {1}, and thus the value range of λ is {0, 1 }.
8. The adaptive graphical arrangement method for the power grid equipment intelligent control configuration soft PLC system according to claim 6, wherein the adaptive graphical arrangement method comprises the following steps:
vector O is expressed according to equation (3)kAnd Ok-1Making a difference, let t be (1-lambda) (w)k+dw-x0)+x0Equation (4) is obtained:
Figure FDA0003152754520000043
when y'k-y′k-1When it is equal to 0, the reason is that
Figure FDA0003152754520000044
λ is 0;
x 'at this time'k-x′k-1＝t＝wk+dw＝c0；
For equation (4), when x'k-x0When the value is 0, (1-lambda) x 'is obtained'k-1+t＝x0If 1- λ is 0; at this time have
Figure FDA0003152754520000051
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