JP3040852B2 - Process operation support system - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system for supporting the operation of a process such as a plant, and more particularly to a system which can provide information representing the state of a process in an expression which can be easily understood by an operator. It relates to a process operation support system.

[0002]

2. Description of the Related Art In plants such as chemical plants,
Because process fluctuations in the plant have a significant effect on the final product, monitoring of process trends and changes, monitoring of the abnormalities of the instruments and sensors that make up the control loop, and whether the process is operating in accordance with the operation pattern Monitoring is required. Normally, a system for monitoring the operation of a plant is configured to output an alarm when there is an abnormality in process data as compared with a predetermined reference. As an element for outputting an alarm, for example, upper and lower limits, a change rate, a deviation, and the like regarding data representing a process state are used. The plant operator performs an operation on the plant by monitoring them or by generating an instantaneous alarm in control cycle units.

[0003] However, in many cases, these alarms are already slow after they are generated. for that reason,
It is necessary to catch signs of plant abnormalities and take measures to avoid abnormal situations and accidents in advance. For this monitoring, for example, it is necessary to accurately grasp the characteristics of the time-series data representing the state of the process. Therefore, conventionally, in operation of the plant, in addition to the alarm processing, a trend graph of various process data is displayed on a display device in parallel with the alarm processing, and the operator monitors the trend graph. That is, the operator operates the plant by constantly monitoring these trend graphs to detect trends and changes in the process. This trend graph monitoring is especially useful when starting up a plant.
This is a very important task for the operator when the state of the plant changes greatly, such as at the time of a grade change or shutdown.

[0004] On the other hand, plant operation is performed based on a plant operation plan that has been previously considered as an operator's personal experience and design knowledge. Also, if the current operating condition of the plant becomes abnormal, the operator recalls similar cases or searches for records in the past cases that he or she has experienced, and employs the situation at that time. Driving with reference to the operation performed.

[0005]

By the way, in order to catch a precursor of a plant abnormality, it is necessary to read from the trend graph what kind of process change has occurred in the past. In this case, whether or not a change in a process on the trend is determined to be an abnormal factor depends on individual differences among operators. However, with the conventional technology,
There was no consideration on this point, and the reading of a change that was a precursor of an abnormality from the trend graph was judged by the subjective personality of the operator. For this reason, since the judgment is influenced by the skill of the operator, attention, physical condition, and the like, the problem that the precursor phenomenon is overlooked or mistaken occurs.

[0006] In addition, the operation performed by the operator on the plant and the response of the plant to the operation have conventionally been stored only as an operator's personal experience or recorded as a daily operation report. .

On the other hand, in the case where the state of the process is to be changed from the current state, conventionally, a target value is set and given to the plant to change the operation amount, thereby changing the state of the process. . This state change operation is the same even when a small change is commanded. That is, even if the operator wants to slightly change the current state of the process, the operator must follow the above-described procedure, which is troublesome. In the operation of the process, for example, an operation that is expressed in an ambiguous amount such as “I want to raise the temperature a little more than the current state” or “I want to make the rising curve a little smaller” is actually required. . However, even in this case, it is necessary to issue a command in the same procedure as a general manual operation to change the process state. But,
Indicating ambiguous changes is not always easy. If the instruction is not appropriate, the fluctuation may be too large. Further, the ambiguity differs depending on the operator.
Therefore, conventionally, it has been difficult to accept such ambiguous instructions.

A first object of the present invention is to extract characteristics of information indicating a state of a process without being influenced by personal conditions of a monitoring operator, and to accurately grasp the state of a process. It is to provide a process operation support system.

A second object of the present invention is to provide a method for changing the state of a process by a small amount or an opaque amount.
An object of the present invention is to provide a process operation support system capable of accepting an ambiguous expression of an operator as it is and instructing an operation on a plant.

[0010]

According to a first aspect of the present invention, to attain the first object, data representing a state of a process is fetched and converted into data which can be processed by a subsequent processing means. Process data capture processing means for processing, symbol processing means for generating encoded data symbolically representing characteristics of data representing the state of the process, and encoded data storage means for accumulating the encoded data;
Display processing means for extracting data in a specified range from the symbolized data storage means and outputting the data as display data;
A process operation support system is provided that includes an input / output unit that displays the display data on a screen and receives an external instruction.

The symbolization processing means generates a vector sequence by linearly approximating the input data and a dictionary for symbolization processing having standard vector data and symbol expression data for symbolically expressing the characteristics of the standard vector data. Then, for this vector series, using a dictionary for symbolization processing, search for similar standard vector series, extract symbolic expression data corresponding to the most similar standard vector, and perform symbolization processing with a symbolization processing unit. Can be provided.

As the dictionary for symbolization processing, a dictionary having symbol expression data in which the characteristics of standard vector data are expressed by characters and / or graphics is preferably used. These are preferably those which, at first glance, indicate a specific meaning without any misunderstanding by the operator. For example, a character string indicating a specific concept such as "rise", "stationary", or "fall", or a figure such as an arrow or a diagram can be used.

The encoded data storage means can be constituted by having an encoded data file for storing the encoded data, and an encoded data storage processing unit for storing the encoded data in the encoded data file. That is,
Anything that can construct a database may be used. The encoded data file and the encoded data storage processing unit store the encoded data and the process data (numerical data) on which the encoded data is based in a time series. Is preferred.

The display processing means extracts the designated range of the encoded data from the encoded data file, extracts the process data on which the encoded data is based from the encoded data file, and decodes the data. And output it as display data. In this case, it is preferable that the display processing means displays the process data as a trend graph and displays the corresponding symbolized data along the time axis of the trend graph.

According to a second aspect of the present invention, in order to achieve the second object, in addition to the first aspect, a preset operation instruction represented in a symbolic manner is provided for the operation target. A process operation support system is provided, comprising: a symbolized operation instruction receiving unit that receives a symbolized operation instruction; and a symbol inverse conversion processing unit that converts the received symbolized operation instruction into numerical data.

[0016] The symbolized operation instruction receiving means can use, as the symbolically expressed operation instruction, symbol expression data that symbolically expresses the characteristics of the standard vector data.

In the second aspect, control processing means for outputting control data for the operation target of the process can be further provided. The control processing means calculates and outputs control data for the operation target based on the process data fetched by the process data fetching processing means, and receives an output signal of the symbol inversion processing section and converts the corresponding control data. It can be configured to output.

[0018]

According to the present invention, data representing the state of a process is fetched by the process data fetching means.
The data is converted into data that can be processed by the symbol processing means in the subsequent stage. The symbolization processing means generates symbolized data that symbolically represents the characteristics of the data representing the state of the process. In this symbolization processing means, for example,
Generate a vector series by linear approximation using a filter,
Using a dictionary, this vector sequence is integrated into a vector sequence composed of fewer vectors, and the sequentially obtained vector sequences are compared with standard vector sequences registered in the dictionary, respectively, and a name of a similar standard vector sequence is obtained. Extract as a symbol.

The encoded data is accumulated as an encoded data string in an encoded data accumulating means. For example, since many plant data changes in a time series, this encoded data string is accumulated in a time series, for example. Of course, spatially distributed data
For example, they can be stored in a spatial sequence such as a tree structure.

In the stored data, a designated range is taken out from the symbolized data storage means by the display processing means and output as display data. The range to be taken out can be set arbitrarily. For example, data of one sampling cycle including the present, data of one or several sampling cycles including a certain date and time in the past, and the like can be designated. Further, the latest data may be automatically updated and output.

The input / output means issues an instruction to output the encoded data, an instruction to search the encoded data, an instruction to operate the process, and the like, and displays the display data on a screen.
Since the data displayed here is, for example, symbolized data such as a character string, the operator can easily and easily change the state of the process based on the meaning of the symbol.
It can be grasped accurately without causing individual differences.

Further, by using the symbolized operation instruction, the ambiguous instruction of the operator can be applied to the process operation without individual difference.

[0023]

Embodiments of the present invention will be described below with reference to the drawings.

One of the features of the present invention is that the features of process data from a plant or the like can be shown to an operator using symbolic expressions. Therefore, prior to the description of the embodiment relating to the process operation support system, a symbol processing system used as a symbol processing means in the support system will be described. Here, the encoding processing system will be described as an independent encoding device, but it goes without saying that it can be configured as one component of a process operation support system described later.

The first embodiment of the encoding apparatus according to the present invention will be described in detail with reference to FIGS.

FIG. 1 is a block diagram showing the configuration of the encoding apparatus. In the figure, reference numeral 110 denotes a communication device, which receives an identification number of a pattern data to be processed, a sampling cycle, a processing target section, a conversion coefficient, and the like through a communication network. Reference numeral 120 denotes a data storage device which stores information indicating a state of a process. As information representing the state of the process, for example, in the field of process control such as a power plant, a chemical plant, and a water treatment plant, time-series data such as temperature, pressure, and flow rate of each part of the process or data of a spatial distribution pattern. However, in the process of processing data in fields such as finance, securities, and distribution,
Time-series data or pattern data such as economic indicators, sales information, and management information are stored.

An input processing unit 130 is activated by a signal from the communication device 110. Input processing unit 13
0, based on the information of the identification number, the sampling period, and the processing section sent from the communication device 110, the corresponding data is extracted from the processing target section at a given sampling period from the data storage device 120, and Send to 140.

The polygonal line approximation processing unit 150 includes a memory 140
Is converted into a vector series of line segments constituting the polygonal line, and the result is sent to the integration processing unit 160. 155 is a filter storage unit,
A feature extraction filter used in the polygonal line approximation processing unit 150 is stored.

Reference numeral 160 denotes an integration processing unit, which integrates a series of broken line vectors obtained by the broken line approximation processing unit 150 and sends it to the dictionary search processing unit 170. Reference numeral 165 stores integration parameters used in the integration processing unit 160.

The dictionary search processing unit 170 includes the integrated processing unit 16
0 and the standard pattern storage unit 1
75 with the vector sequence of events stored in
Search for a vector series of correspondence candidates. Reference numeral 175 denotes a standard pattern storage unit which stores a standard pattern corresponding to an event name for identifying an input pattern as a vector sequence approximated by a broken line and a name for each vector sequence. The standard pattern and its corresponding name are prepared in advance by the system. The user may register an arbitrary name. Also, an arbitrary pattern may be registered together with its name as a standard pattern.

Numeral 180 denotes a similarity calculation processing unit which calculates the similarity and the scale between the vector series of the correspondence candidate obtained by the dictionary search processing unit 170 and the vector series of events stored in the standard pattern storage unit 175. The calculation is performed, and the event name, the similarity, the scale, the section of the corresponding data, and the like of the standard pattern whose scale is within a certain range and the similarity is a certain value or more are sent to the memory 190.

The processing result stored in the memory 190 is transmitted to another information processing system or the like via the communication device 110.

The encoding device of the present embodiment includes a central processing unit,
It is configured by a computer system having a main storage device, an auxiliary storage device, various interface devices, and the like. Various functions of the encoding device of the present embodiment are realized by the central processing unit executing a program stored in the main storage device, and the following operations are executed. As a hardware system for realizing a symbol or a device, for example, a system as shown in FIG. 19 can be used.

Next, the operation of each section of the encoding apparatus based on the above configuration will be described in detail. FIG. 2 is a flowchart showing the operation of the polygonal line approximation processing unit 150. When roughly classified, the pattern f (t) of the input data and the feature extraction filter W
2 (x), a pattern division process 210 for calculating the unevenness of the pattern and calculating a point τp at which the pattern is divided into polygonal lines, and a feature extraction filter W0 (x).
Find the average value of the data near the division point of the pattern,
An estimation process 220 for obtaining the estimated value fe (τp) of the data at the division point, an end estimation process 230 for obtaining the end values from the regression lines at both ends of the data section, and a pattern using the division point and the estimated values at both ends And a vector sequence calculation process 240 for calculating a vector sequence when the is approximated by a polygonal line.

In the pattern division processing 210, first, the input data is multiplied by the conversion coefficient sent through the communication network to normalize the data, and the processed data f (t) (t
= 0, 1,..., T). Next, for the input pattern data f (t) (t = 0, 1,..., T), the feature extraction filter W2 stored in the filter storage unit 155 is used.
Sum of products calculation using (x)

[0036]

(Equation 1)

Thus, the characteristic amount g2 (t) indicating the degree of unevenness
Is calculated. Here, a is a constant indicating the spread of the filter. With respect to the example of the input pattern shown in FIG.
FIG. 3B shows the result of the product-sum calculation performed by the feature extraction filter W2 (x) shown in FIG. Calculating the division point τp (p = 1, 2,..., K) of the input pattern by extracting points having positive and negative extreme values whose absolute values are larger than appropriate constants from the calculation results shown in Equation 1. Can be. In the example of FIG. 3B, τp (p = 1, 2,..., 9) is obtained as a division point.

In the estimating process 220, at the division point τp obtained by the pattern dividing process 210, the feature extraction filter W 0 stored in the filter storage unit 155 is stored.
Sum of products calculation using (x)

[0039]

(Equation 2)

As a result, the characteristic value g0 (τp) representing the average value
Is calculated. FIG. 5B shows an example of the feature extraction filter W0 (x). Estimated value fe of pattern at division point τp
(τp) is calculated by using a feature amount g2 (τp) indicating unevenness and a feature amount g0 (τp) indicating an average value.

[0041]

[Mathematical formula-see original document] fe ([tau] p) = W0 (0) * g0 ([tau] p) + W2 (0) * g2 ([tau] p) (Expression 3)

In the both ends estimation process 230, the estimated values of both ends of the input pattern are obtained. The estimated values (τ1, fe (τ1)) of the division points obtained in the estimation process 220 and (τ
k, fe (τk))

[0043]

(Equation 4)

[0044]

(Equation 5)

Is calculated by

Equations 4 and 5 show that t = 0 and t = T, respectively.
, The estimated values f at both ends of the input pattern
e (0) (= h1 (0)) and fe (T) (= h2 (T)) are obtained.

In the vector series calculation process 240,
The point sequence (0, fe (0)) and (τp, fe (τp)) (p = 1,
2,..., K) and (T, fe (T)), a vector sequence Ai = (Pi, Qi) (i = 1, 2,.
k + 1),

[0048]

(Equation 6)

Is determined by An example of the obtained vector sequence is shown in FIG. Each vector is given the name of the closest standard vector (slope gradient) among the standard vectors shown in FIG. That is, the inner product of each vector and the standard vector is determined, the standard vector having the largest inner product is selected as the closest standard vector, and the name is given. Each of the named vectors is shown in FIG.
The data is transmitted to the integration processing unit 160 as data as shown in (d).

Next, the details of the processing in the integration processing section 160 will be described. The integration processing is performed when the processing result of the polygonal line approximation processing unit 150 includes two or more vectors. The process includes three steps as shown in FIG. 710 is a step of integrating adjacent vectors having the same name, 720 is a step of integrating short “balanced” vectors, and 730 is a step of integrating short vectors other than “balanced”. Hereinafter, the processing in each step will be described in detail.

(1) Step 710: Combine adjacent vectors having the same name. For example, in the case of FIG.
Two consecutive "balanced" vectors are combined into one "balanced" vector.

(2) Step 720: A vector given the name "balance" and having a length equal to or less than a predetermined value is integrated with other vectors.

(2-1) When a vector having a change width equal to or smaller than a certain value stored in the integrated parameter storage unit 165 is before and after, the vector is integrated with the vector. For example, FIG.
In case (b), the short "equilibrium" vector is preceded by the short "descent" vector, so that both are integrated to generate a new "descent" vector.

(2-2) If the angle between the preceding and succeeding vectors is equal to or smaller than a certain value stored in the integrated parameter storage unit 165, the vectors are integrated. For example, in the case of FIG. 8C, since cos (θ1)> cos (θ2), the “equilibrium” vector is integrated with the previous “descent” vector to form a new “descent” vector.

(3) Step 730: A vector having a name other than "equilibrium" and having a small change width is integrated with other vectors.

(3-1) When a short vector whose change width is equal to or smaller than a predetermined value stored in the integrated parameter storage unit 165 is before and after, the vector is integrated with the vector. For example,
In the case of FIG. 9A, a short “fall” vector and a short “rise” vector are integrated to generate a new “balanced” vector.

(3-2) If the angle between the preceding and succeeding vectors is equal to or smaller than a certain value stored in the integrated parameter storage unit 165, the vectors are integrated. For example,
In the case of FIG. 9B, since cos (θ1)> cos (θ2), the short “rise” vector is integrated with the previous “fall” vector to generate a new “balanced” vector.

In the above cases (2) and (3), the name of the newly generated vector is changed to the name of the standard vector (FIG. 6) having the smallest angle with the vector. FIG. 4A illustrates the vector sequence after integration. This vector sequence is sent to the dictionary search processing unit 170 as data as shown in FIG.

Next, the operation of the dictionary search processing section 170 will be described. FIG. 10 is a flowchart showing the operation of the dictionary search processing unit 170. As shown in FIG. 11, the standard pattern storage unit 175 is provided with a number for each event name to be identified, and is stored in numerical order. Further, each vector of the vector series constituting the event is constituted by a vector having the same inclination as the standard vector in FIG. 6, and the name of the corresponding standard vector is given. FIG. 11 shows the standard pattern storage unit 175.
Shows an example of the event name stored in the event name. In FIG. 11, the event name “stepwise rise” is stored as number 1,
The event name “fall from rise” is stored as number 2.

The details of the processing of the dictionary search processing section 170 shown in FIG. 10 will be described. First, in step 1010, a vector sequence Ai (i =
1, 2,..., M) are input from the integration processing unit 160. Next, in step 1020,
The number N of the event name to be searched is set to 1, and the search is started. In the search process 1030, if a vector sequence similar to the event in the standard pattern dictionary is selected from the vector sequence of the input pattern as a correspondence candidate, the process proceeds to step 10
By 40, the flow of the process proceeds to the transmission process 1050, where the number N of the event name and the vector sequence of the correspondence candidate are sent to the similarity calculation processing unit 180. If there is no corresponding candidate, it is determined in step 1060 whether or not the search for all event names has been completed (the number of standard patterns is Nmax). If the search for all the standard patterns has not been completed, in step 1070, the event name number is updated “N = N + 1”, and the search for the next standard pattern is continued by the search processing 1030.

FIG. 12 is a flowchart for explaining the details of the processing in the search processing 1030. Where Ai
(I = 1, 2,..., M) is a vector sequence that forms the input pattern and is sent from the integration processing unit 160, Vj (j = j
1, 2,..., N) are vector series of standard patterns of events stored in the standard pattern storage unit 175. First, “is = 1” is set in step 1210 to search for a vector of the standard pattern corresponding to the first vector of the input pattern. Next, step 12
20, "j = 1, i = is" is set, and the search is started. The vector number of the standard pattern being searched is m
If it is +1, the search ends at step 1230. The name of the vector Ai of the input pattern and the name of the vector Vj of the standard pattern are compared in step 1240.
A comparison with the next input pattern vector is made. If they match, the pair of Ai and Vj is stored in the temporary storage 1255 in step 1250. In step 1260, it is determined whether the search for all the vectors of the standard pattern has been completed. If j ≠ n, step 1270
, "I = i + 1, j = j + 1", and the search for the next vector is continued. If j = n, the process “is = ik + 1” in step 1280 is performed, and
Search processing for a standard pattern is performed from the s-th input vector. Here, ik is the number of the vector of the latest input pattern in which the name of the vector Vj of the standard pattern matches, and among the correspondence candidate strings stored in the temporary storage, the vector of the latest input pattern corresponding to Vj Number.

In the similarity calculation processing section 180, FIG.
2 according to the contents of the temporary storage 1255, the similarity S and scale K between the input pattern and the standard pattern are

[0063]

(Equation 7)

[0064]

(Equation 8)

Is calculated. Here, Vi (i =
1, 2,..., M) are vector series of standard patterns, Ai
j is a correspondence candidate of the vector series of the input pattern.
i and Aij (i = 1, 2,..., ni) are correspondence candidates. S in Equation 7 is for calculating a correlation coefficient between vector sequences, and is 1.0 if the patterns are completely similar. Equation 8 is a scale (scale) when the vector series of the input pattern most closely matches the vector series of the standard pattern. A certain range in which the similarity and the scale are determined for the standard pattern obtained by the similarity calculation processing unit 180

[0066]

The event names of the standard patterns satisfying Smax ≦ S, Kmin ≦ K ≦ Kmax (Expression 9) are arranged in ascending order of similarity and stored in the memory 190. FIG.
(C) shows an example of the encoding result.

The encoding result (event name, similarity, scale, section of matched data) stored in the memory 190 is transmitted to another information processing system or the like via the communication device 110.

In the encoding apparatus according to the present embodiment, a search for a candidate portion of an input pattern that matches a standard pattern can be performed by using the name of a vector. There is an effect that a small number of candidates can be searched. Furthermore, since the correlation coefficient is calculated between the vector sequences and the degree of similarity is calculated, similar patterns can be searched, and the number of event patterns stored in the standard pattern storage unit 175 can be reduced. ing.

A second embodiment of the encoding apparatus according to the present invention will be described with reference to FIGS. The first embodiment is an embodiment in which pattern data stored in a database or the like is collectively converted into a symbol, whereas the second embodiment is a process in which data obtained from a process is momentarily converted into a symbol. This is an embodiment in the case of conversion. In this case, since the data up to one data before the current one in the sampling period has already been converted into the vector sequence, the processing based on the addition of one data may be performed for each sampling period.

FIG. 13 shows the configuration of the encoding apparatus of this embodiment. In the figure, reference numeral 1310 denotes a communication device similar to 110, which receives an identification number of pattern data to be processed through a communication network. Reference numeral 1320 denotes a data storage device, which stores together with process data a vector sequence, an event name, and the like, which are processing results up to the previous sampling cycle. The input processing unit 1330 is activated by a signal from the communication device 1310,
Data information corresponding to the identification number (processing results up to the previous sampling data and the latest sampling data) is read from 320 and sent to the memory 1340.

Reference numeral 1350 denotes a broken line approximation processing unit. 13
Reference numeral 55 denotes a filter storage unit, and a polygonal line approximation processing unit 135
A feature extraction filter used at 0 is stored. In this embodiment, a feature filter as shown in FIG. 15 is used as the real-time processing in order to perform processing from the latest data to the past data. The polygonal line approximation processing unit 1350 performs feature extraction from a portion including the latest data of the input pattern stored in the memory 1340, approximates the pattern data with a polygonal line, and converts the data into a vector sequence of line segments constituting the polygonal line.

Reference numeral 1360 denotes an integration processing unit, which integrates the series of broken line vectors obtained by the broken line approximation processing unit 1350 and sends it to the dictionary search processing unit 1370. 1365
Stores integration parameters used in the integration processing unit 1360.

The dictionary search processing unit 1370 compares the vector sequence of the input pattern sent from the polygonal line approximation processing unit 1350 of FIG. 13 with the vector sequence of the standard pattern stored in the standard pattern dictionary 1375, and Search for a vector series. Standard pattern storage unit 1375
As in the first embodiment, an event name, a vector sequence thereof, and a name of each vector are stored as shown in FIG. 1380 is a similarity calculation processing unit,
The similarity and scale between the vector series of the correspondence candidate obtained by the dictionary search processing unit 1370 and the vector series of the standard pattern are calculated. If the obtained event name is different from the previous sampling time and the scale is within a certain range and the similarity is a certain value or more, the standard pattern number, the corresponding input pattern number and the similarity , And the measure are stored in the memory 1390. The result stored in the memory 1390 is transmitted to another information processing system or the like via the communication device 1310.

Next, the operation of the encoding apparatus based on the above configuration will be described in detail.

FIG. 14 shows the broken line approximation processing unit 1350
6 is a flowchart showing the operation of the first embodiment. When roughly classified, a pattern division process 1410 for calculating the unevenness of the pattern by the product sum calculation of the input pattern f (t) and the feature extraction filter W2 (x) and obtaining the point τp at which the pattern is divided into polygonal lines 1410
And feature extraction filters W0 (x), W1 (x), and W2
By using (x), the average value of the data near the division point of the pattern is obtained, and the estimated value fe (τ
p), an end-point estimation process 1450 for obtaining an end-point estimated value from a regression line at the end of the data section, and a vector series obtained when the pattern is approximated by a polygonal line using the division points and the end-point estimated values. And a vector series calculation process 1460 for calculation.

First, in the pattern division processing 1410, the input pattern data f (t) (t = −T, −T +
1,..., 0) for the product-sum calculation using the feature extraction filter W2 (x) stored in the filter storage unit 1355

[0077]

(Equation 10)

As a result, a feature amount g2 (0) indicating the degree of unevenness near the current time is calculated. Here, a is a constant indicating the spread of the filter. As a result of performing a product-sum calculation on the example of the input pattern shown in FIG. 16A using the feature extraction filter W2 (x) shown in FIG.

[0079]

[Equation 11]

When the condition is satisfied, t = -b (b is a constant value determined by the filter) is a new division point. If a new dividing point is not generated by the processing step 1420, the process proceeds to a processing step 1450, and if a new dividing point is generated, the process proceeds to a processing step 1430.

In the estimation processing 1430, the feature extraction filter Wk (x) (k = 0, 1, 2) stored in the filter storage unit 1355 is used at the division point τp obtained by the pattern division processing 1410. Sum of products calculation

[0082]

(Equation 12)

As a result, the characteristic amount g0 (τ
p), a feature amount g1 (τp) representing the inclination, and a feature amount g2 (τp) representing the unevenness. FIG. 15 shows an example of the feature extraction filters W0 (x), W1 (x), and W2 (x). The estimated value fe (τp) of the pattern at the division point τp is

[0084]

(Equation 13)

Is calculated.

Next, in the termination estimation processing 1440,
Find the estimated value at the end of the input pattern. Estimation process 143
0 (τp, fe (τ
p)) through a regression line

[0087]

[Equation 14]

Is calculated.

By substituting t = 0 into Equation 14, an estimated value fe (0) (= h) at the end (t = 0) of the input pattern is obtained.
(0)) is required.

In the vector series calculation processing 1440, the sequence of points (τp, fe (τp)), (0, fe (0)) obtained in the estimation processing 1430 and the termination estimation processing 1430
From the vector sequence Aj = (P
j, Qj),

[0091]

(Equation 15)

[0092] FIG. 16B shows a vector sequence obtained from an example of a point sequence when a new division point τp is generated. Each of these vectors is given the name of the closest (similar to the gradient) vector among the standard vectors shown in FIG. 6 and sent to the integration processing unit 1360.

Next, details of the processing in the integration processing unit 1360 will be described. The integration process is performed by the polygonal line approximation processing unit 135.
As a result of 0, when the number of vectors is three or more, the processing is performed on the vector two before the latest vector. The content of the processing is the same as the processing shown in FIG. FIG. 16C shows an example of the processing result.

The operation of dictionary search processing section 1370 is the same as dictionary search processing section 170 in FIG.
The operation of 380 is the same as the similarity 180 of FIG. FIG. 17 shows an example of the obtained symbols.

The symbolization result (event name, similarity, scale, section of matched data) stored in memory 1390 is
The information is transmitted to another information processing system or the like via the communication device 1310.

In the encoding apparatus according to the present embodiment, not only the same effects as in the first embodiment, but also the processing is simple,
Since a symbolic representation of data can be obtained at high speed, a large amount of process data can be processed by an inexpensive processing device.

In order to better understand the embodiments of the symbolizer and to evaluate its inherent possibilities and advantages, the following provides a brief mathematical background and basis for the techniques used to implement the embodiments. Present. The expressions introduced also provide insight into the functionality of the embodiment.

The feature extraction filters W0 (x), W1 (x), and W2 (x) stored in the filter storage unit 155 or 1355 and used in the polygonal line approximation processing unit 150 or 1350 are as follows. A filter for obtaining an average value, W1 (x) is a filter for obtaining an average inclination, and W2 (x) is a filter for obtaining an average unevenness of a pattern. Below, W0 (x), W1 (x), and W2
The method of obtaining (x) is organized and shown.

As a method for developing pattern data,

[0100]

(Equation 16)

The polynomial Hm (x) defined by Where E (x) is a suitable differentiable function, a and b
Is a constant indicating a defined section. At this time,

[0102]

[Equation 17]

In other words,

[0104]

(Equation 18)

Constitutes an orthonormal function system.

When the time series data f (t) is expanded near time t using the orthonormal function system ψm (x) as it is,
In order to obtain a good approximation of the original data, it is necessary to obtain higher-order terms. In order to avoid this, the time series data is

[0107]

[Equation 19]

Will be handled after conversion.

By expanding the equation 19 using the orthogonal function system of the equation 18,

[0110]

(Equation 20)

Is obtained. Here, the expansion coefficient am (t) (m =
0,1, ..., ∞)

[0112]

(Equation 21)

[0113] From Equations 19 and 20, the time-series data f (t) is near the time t,

[0114]

(Equation 22)

This is expanded.

The differentiable function E (x) is given by

[0117]

(Equation 23)

If b = −a and the exponential function is represented by the following equation, Hm (x) is an m-order Hermite polynomial from equation (16). In addition, according to Equation 22, the time series data f (t) is expanded into a polynomial in the vicinity of the time t. At this time, the expansion filter Wm (x) is obtained by adding Expression 21 and Expression 18 to Expression 22.
By substituting

[0119]

(Equation 24)

From the above,

[0121]

(Equation 25)

Are represented as follows.

The time series data estimation value fe (t) at time t is obtained by using the result of the polynomial expansion by the feature extraction filter represented by Expression 25. By substituting x = 0 into Equation 22, fe (t) becomes

[0124]

(Equation 26)

Is obtained.

A quadratic term a2 (t) of the expansion coefficient according to Equation 26
Indicates a state of unevenness of the time-series data near time t. Therefore, for the time series data f (t),
The time at which the gradient of the time-series data changes can be obtained by obtaining the expansion coefficient a2 (t) of the quadratic polynomial according to 4 and extracting the time having the extreme value.

Hereinafter, a method of obtaining the similarity and the scale will be described. A vector sequence of one symbol stored in the standard pattern storage unit 175 or 1375 is represented by Vi (i =
1, 2,..., M), and the vector series of the corresponding candidates included in the time-series data is Ai (i = 1, 2,..., M). When the vector sequence stored in the standard pattern storage unit 175 or 1375 is multiplied by K, the difference between the two vector sequences is 2
The squared error E is

[0128]

[Equation 27]

Are represented as follows. First, a scale K that minimizes the square error E is obtained. Expanding Equation 27 gives

[0130]

[Equation 28]

Is obtained. Here, (,) indicates the inner product of the vectors. Equation 28 is a quadratic equation in which the quadratic coefficient relating to K is a positive quadratic equation, and the scale K that gives the minimum value of the square error E is

[0132]

(Equation 29)

From the above,

[0134]

[Equation 30]

Is given. The square error E at that time is obtained by substituting Equation 30 into Equation 28,

[0136]

(Equation 31)

Is given by The square error E in Equation 31
Is normalized with respect to the vector series of the time series pattern,

[0138]

(Equation 32)

Is obtained. Therefore, an evaluation index of the degree of correspondence based on the square error E normalized by the scale K is defined by the similarity S using the square root of the second term of Expression 32.

[0140]

[Equation 33]

In Equation 32, the square error E is the same regardless of whether the scale K according to Equation 30 is positive or negative. But,
When a symbolic name is given to the time-series data, the similarity must be reduced if they are inverted and match. Number 33
Is given by considering the square root of the second term on the right side of Equation 32.
An appropriate similarity can be obtained. The similarity S represented by Expression 32 satisfies −1.0 ≦ S ≦ 1.0, has a form of an expression very similar to a normal correlation coefficient, and is considered as a correlation coefficient between vector sequences. be able to.

As described above, the equations of the similarity S and the scale K were determined on the assumption that the vector series of the time series data and the vector series of the standard pattern corresponded one to one. In general, one vector may correspond to a plurality of vectors between the vector sequence of the standard pattern and the vector sequence of the time-series data. In this case, the similarity S and the scale K shown in Equations 33 and 30 are calculated.

[0143]

(Equation 34)

[0144]

(Equation 35)

It is used after being modified. Here, the vector series of pattern data corresponding to the vector Vj of the standard pattern is Aij (j = 1, 2,..., Ni).

The symbolizing method according to the present embodiment is characterized in that, by converting time-series data into a vector series and handling it, it is possible to perform collation in consideration of pattern displacement and changes in shape.

Hereinafter, embodiments of the process operation support system of the present invention will be described in detail with reference to the drawings.

FIG. 18 shows the configuration of an embodiment of the process operation support system according to the present invention. This embodiment is an example applied to the process operation of a plant.

The process operation support system shown in FIG. 18 includes a process data capture processing unit 1 which captures process data as time data from various sensors Sn and the like of a plant to be monitored in a time series, and symbolically describes characteristics of the captured data. , A symbolization processing dictionary 3 used for the symbolization process, and the symbolized data stored in a time series together with the corresponding process data (numerical data). Data historical file 5 to be encoded, a symbol data storage processor 4 for performing storage processing therefor, and a data composite for displaying the symbol data stored in the symbol data historical file 5 in combination with the corresponding process data. A display processing unit for detecting a change in the encoded data and outputting an alarm signal An alarm output processing unit 20, an alarm suppression processing unit 16 for performing suppression processing so that alarms are not chained in a certain case, and an information output for the composite display, alarm output and the like, and an instruction for the system and the like. And a man-machine device 10.

Further, the process operation support system of the present embodiment provides a symbolic operation instructing unit (instructing a plant control device (to be described later) to control the plant in an expression including ambiguity, which is slightly changed from the current state. Since the change of the amount including the ambiguity is instructed by a button operation, it may be expressed as "a little more button") 8 and a symbolization operation instruction reception process for receiving an instruction from the symbolization operation instruction unit 8 It includes a unit 9, a symbolization inverse conversion processing unit 11 for inversely converting the symbolization operation instruction data into numerical data, and a manual operation reception processing unit 19 for receiving a manual operation of an operator using the man-machine device 10.

Further, the manual operation symbolizing processing section 18 for symbolizing the contents of the manual operation, the above-mentioned symbolized operation instructions and the symbolized manual operation contents are stored with time, and the operation contents and the state of the process are stored. Operation history file 1 for storing data indicating a causal relationship with the encoded data indicating
3 and when the manual operation is performed, the coded data having a causal relationship with the operation content is searched, and compared with the coded data representing the current state of the process. A manual operation consideration diagnosing unit 14 that determines whether the operation is caused by an operation and outputs an alarm suppression signal to the alarm suppression processing unit 16, and encodes the encoded data having a pattern similar to the designated encoded data. A similar pattern search processing unit 7 that searches stored data from present to past data, and a time-series data support inference unit 15 that starts inference in knowledge processing using the symbolized data obtained in this embodiment. And

In the present embodiment, the control operation processing unit 17 for calculating the operation amount for controlling the process based on the process data, the control data is sent to the operation object based on the operation result, and the symbol A control data output processing unit 1 that sends control data based on the output of the inverse conversion processing unit 11 or the output of the manual operation reception processing unit 19 to the corresponding operation target
2 is provided. These can be provided separately as a controller separately from the support system. In a plant operation system having a controller,
The controller may be provided with a control operation processing unit 17 and a control data output processing unit 12. Further, the process operation support system of the present embodiment may be incorporated in a control system such as a plant controller.

The symbol processing section 2 and the symbol dictionary form a symbol processing means, which is configured in the same manner as the system shown in FIG. Here, it is built not in an independent system but in an operation support system.

The man-machine device 10 includes a CRT display 10a, a touch panel 10b, and a keyboard 10c.
And The man-machine device 10 and the symbolizing operation instructing unit 8 constitute an input / output unit.

FIG. 19 shows an outline of a system configuration of hardware constituting the process operation support system shown in FIG. As shown in FIG. 19, in the present embodiment, a man-machine device 10, a CPU (central processing unit) 20 for executing various processes to realize various means of the present invention, a symbolizing dictionary 3, A file device 21 for storing data such as a digitized data historical file 5 and a symbolization operation history file 13 and a program for the CPU 20,
And a process input / output device 22 for inputting and outputting data to and from the plant as hardware. File device 2
1 comprises a main storage device and an auxiliary storage device.

Next, the operation of this embodiment will be described for each function.

(1) Symbolization Processing The data from the various sensors Sn of the plant is captured by the process data capture processor 1. The process data acquisition processing unit 1 collects data at a fixed sampling cycle and stores data for a fixed period. Here, the process data is time-series data,
It is input to the symbol processing unit 2. The symbolization processing unit 2 generates vector information of the input data by the broken line approximation processing in the above-described symbolization apparatus shown in FIG. 13, and uses the generated vector information as a standard pattern in many dictionaries for symbolization processing. 3, and outputs coded data having the highest similarity.

By the way, examples of symbols are ascending, descending,
There are a step rise, a rapid rise, a gentle rise, a pulse, and the like, but any name (symbol) may be given. In other words, it is only necessary that a human can understand the meaning of the symbol here by listening / seeing the symbol.

Further, the timepiece for symbol processing 3 can store a character string as the symbol data directly in the form of a table. Alternatively, a configuration may be adopted in which a character string is simply stored as a code, and a table for associating the code with the encoded data is separately provided.

When converting time-series data, which is input data for the symbolization processing, into vector information, two parameters such as a time scale and a data size as shown in FIG. Becomes In other words, since the type of process data (for example, temperature data, flow rate data, pressure data, etc.) has different reactions and response speeds, those with slow response speeds (such as those that do not change easily) such as temperature data require a larger time scale. ,
Since the vector information is made sparse with respect to the time scale, and the flow rate and pressure data have a high response speed, the time scale is made small and the vector information is made dense with respect to the time scale so that the change is slow (less). It is used to give accurate symbols to data and fast-changing (many) data.

FIG. 21 shows an application example of the parameter for symbol conversion. In the case of FIG. 7A, since the time scale is small, even a slight change (movement) appears as vector information. On the other hand, in the case of FIG. 3B, since the time scale is large, even if the data fluctuates considerably, the change is small as vector information. As described above, an appropriate symbol is output by determining this parameter according to the nature of the data to be monitored.

(2) Alarm Output Processing The encoded data obtained by the above processing (1) flows to the alarm output processing section 20. Alarm output processing unit 2
If the symbolized data corresponding to various input data is different from the symbolic data obtained at the previous collection timing, it is determined that a change has occurred in the plant state, the flow is sent to the alarm suppression processing unit 16, and finally, 0 is output. The operator is notified by the machine device 10.

Next, the details of the alarm processing and the data structure will be described with reference to FIG. The processing shown in FIG. 22A is periodically executed, and becomes one processing by looping for the number of conversions of the encoded data. First, the process of extracting the latest encoded data for each input data,
A process of extracting the previous encoded data obtained from the result of the previous process (one time before) is performed (steps 2201 to 220).
3). Thereafter, the former is compared with the latter (step 2).
204), only when the former and latter encoded data are different, information is passed to the alarm suppression processing unit 16 in order to suppress the chain occurrence of alarms, and the latest captured encoded data is set as the previous value in the table (FIG. b)) (steps 2205 and 2206).

Here, an example of alarm display in the alarm output processing will be described with reference to FIG.

(A) The data name (F1024) and the symbol data (hunting) are displayed as characters on the display 10a.

(B) The symbol data is represented by a pictorial display (symbol), and the occurrence time and the data name (F1011, Q3111...) Are displayed below the symbol on the display 10a. All symbols registered in the dictionary are prepared in advance, and the corresponding symbol and data name are displayed when an alarm occurs.

(C) When an alarm occurs, a symbol indicating the occurrence of the alarm is pointed to a blinking symbol, and a window showing the alarm occurrence status in detail is displayed. In the window, symbols meaning symbol data and generated data names arranged in chronological order for each symbol data are displayed. The pointing may be performed, for example, by touching a corresponding portion of the touch panel 10b with a finger.

(D) The latest alarms are displayed in order of (a) in chronological order.

By this processing, the operator can know the change in the plant state without constantly monitoring the detailed data of the input data in the man-machine device 10. This is because the conventional system only detects abnormalities such as the upper and lower alarms of process data.
It is said that the information service has been greatly improved compared to what was notified to the operator.

(3) Construction of Database of Encoded Data The encoded data output by the encoding process of (1) is stored in the encoded historical file 5. The symbolized historical file 5 adds input data (numerical data) information to the symbolized data, and collects and stores the input numerical data and the symbolized data at that time as a time-series data file. This storage processing is executed by the symbolized data storage processing unit 4 as described below.

The processing of the historical file collection procedure shown in FIG. 24 is periodically executed. First, a loop is performed for the number of input data (step 2401), and for each input data type, the encoded data and numerical data at that time (at the time of executing this processing) are extracted (step 2402, 2403) and stored in the encoded historical file 5 (Step 240
4).

As described above, by storing the encoded data and the numerical data in the encoded data historical file 5 by the encoded data storage processing unit 4, various functions described below can be realized.

FIG. 26 shows an example of the structure of the encoded data historical file. In the example shown in the figure, a pair of numerical values and encoded data are stored in a time series at one-minute intervals.

(4) Composite display function of plant data and symbolized data The data composite display processing section 6 outputs the symbolized historical file information to the man-machine device 10. This display example is shown in FIG. By displaying in this way, at the time of registration of the symbolization processing dictionary 3, since the waveform of the actual data and the result of the symbolization processing can be checked at a glance, it can function as a parameter tuning screen for symbolization processing dictionary registration. .

Normally, during operation, not only the trend graph but also symbols (words)
, The situation recognition rate of the operator is improved.

The operation of the data composite display processing section 6 will be described with reference to FIG. The processing unit 6 is activated by pressing or pointing a function key of the keyboard 10c of the man-machine device 10 or a soft function key in the screen. When activated, first, a screen for prompting the operator to input a data name to be displayed is displayed on the display 10a, and an input from the operator is received (step 2701). If the input data is registered in the encoded data file 5 in advance, a screen for inputting the display start time (step 2)
701 may be displayed on the display 10a and an input from the operator is accepted (step 2).
702). When the display start time is input, the encoded data at the corresponding time is stored in the encoded data historical file 5.
, The time-series data (numerical value) of a displayable section (specified time) is displayed as a trend graph on the display 10a, and further, the encoded data attached to this time is displayed. (Steps 2703, 2
704). When the execution key is pressed without inputting the time, the time-series data and the symbol data are displayed by going back to a past displayable time such that the current time is at the right end of the graph.

FIG. 28 is an example of this display. By displaying the time-series data and the encoded data in this way, it is possible to refer to a plurality of data on the same time axis. For this reason, the state between data types can be easily grasped.

Further, a mode may be provided in which the coded data within a certain time range including the present time is updated and displayed at regular intervals on the screen. When operated in this mode, near past data including the latest data is automatically displayed.
In this case, the operation of this mode is canceled if there is a special instruction, and after the processing based on the instruction of the operator is completed,
The return may be made automatically or by an instruction.

(5) Similar Pattern Search Processing Similar pattern search processing is performed by the similar pattern search processing unit 7, as shown in FIG. First, by the above-described composite display processing, the display 10a of the man-machine device 10 is caused to output a composite display screen of plant data and symbolized data serving as base data to be searched. Then, the similar pattern search processing unit 7 operates by requesting a similar pattern search from the keyboard 10c of the man-machine device 10 (step 2901).

The similar pattern search processing section 7 collects the currently displayed data name and the coded data of the currently displayed section time (step 2902). If the display shown in FIG. 25 is taken as an example, "up"-"down"-
The continuous symbol data of "rapid rise"-"constant"-"rapid fall" corresponds. The continuous encoded data is searched sequentially for the same data in the encoded historical file 5 and in the past from the present to the past (step 2903). The search of the encoded historical file 5 is continued until continuous symbol data of the search source is found, and the same continuous encoded data is searched for (step 290).
4).

If the search data exists, the start time and the end time of the continuous symbol data and the numerical data of the section are read from the symbolized historical file 5 in order to display the trend graph at that time (step 2905),
The data is further overwritten on the plant data / symbolized data composite display screen (step 2906). This display example is shown in FIG.

When a search result exists in a plurality of sections, data closest to the search source data is displayed according to the similarity and the time factor of each continuous symbol data in the plurality of sections. For example, as shown in FIG. 31, when the continuous symbol data of the search source is “up” − “down” − “rapid up”, the similarity at this time is 0.8, 0.5, 0.7 and time Assuming that the factor is the start time ts and the end time is te, the continuous symbol data of the search result is, of course, "rise"-"fall"-"rapid rise", but the similarity of the result 1 is 0.5, 0.5. 3,0.
2. If the similarity of the result 2 is 0.7, 0.4, 0.8, the sum of the absolute values of the differences from the similarity of the search source data, that is, in this example, the result 1 = 10.8 −0.51 + 10.5−0.31 + 10.
7−0.21 = 1.0 Result 2 = 10.8−0.71 + 10.5−0.41 + 10.
7−0.81 = 0.3, and result 2 has priority.

If the calculated value is within the allowable range, the priority is determined by the time factor. in this case,
The section time of result 1 and the section time of result 2
The one closest to -ts is chosen.

Processing when there are a plurality of similar patterns is performed as shown in FIG. That is, the similar pattern search processing unit 7 collects the display data name, the display continuous symbol data, the display start time and the display end time (step 3201), and also collects the symbolized historical file search / determination information (step 3202). ). here,
It is determined whether there are a plurality of similar patterns (step 3
203).

If there are a plurality of search results, it is compared with the search source similarity to determine whether or not it is equal to or more than an allowable range (step 3204). If it is within the allowable range, a result close to the search source end time-start time is selected (step 3205). Then, the selected one is output to the man-machine device 10 (step 3206).

In step 3203, if there are a plurality of search results, and if it is more than the allowable range in step 3204, the process jumps to step 3206 and outputs the results to the man-machine device 10.

Next, FIG. 33 shows an application example of similar pattern search display. In FIG. 33, the search source data having the data name F1000 and its symbolized data are superimposed on the time-series data of the search result and its symbolized data, and are displayed in the upper graph on the screen of the display 10a. In the second row and below, a plurality of highly relevant time-series data having a causal relationship with F1000 are displayed. In this method, when searching for past data similar to the search source data, related data corresponding to the time of the search result is also searched and displayed. That is, on the screen, for the data name F1000, the search source data and the searched similar data, and the time series data of the related data (F1111, F1112, F1113 ...) at the same time as the similar data are displayed.

The result searched in this processing is
Displayed by the man-machine device 10. In this case, by superimposing the section information of the search section + α as the search result on the screen at the time of the search request, it is possible to easily predict the trend tendency of the search source data.

(6) Function of Symbolizing Operation Instruction (A Little More Button) The function of the button 8 and the symbolizing operation instruction receiving process 9 will be described with reference to FIG. The button 8 is positioned on the operation unit of the man-machine device 10. This operation unit may be, for example, a mechanical push button of the keyboard 10c or a soft key displayed on the display 10c. In other words, by operating a button, the request may be input to the symbolization operation instruction reception processing unit 9 as a request.

A little more button 8 is displayed, for example, as shown in FIG.
, There are 8a to 8f corresponding to "rapid rise", "slight increase", "step rise", and so on. These buttons 8a to 8f indicate operation functions for certain data.
For example, when the "rapid rise" button 8a is used for the temperature set value of T1001, a function of giving an instruction "Temperature rise a little more" is provided.

FIG. 34 shows an example of the operation of each of the buttons 8a to 8f and the movement of the target operation data at that time. In the upper part of FIG. 34, the types of the buttons 8a to 8f operated by the man-machine device 10 are written.
(A), (b), and (c) show examples when the target data is a temperature set value, a flow set value, and a flow control output value. here,
One type of button does not indicate that a plurality of data are simultaneously operated. This figure shows that, even with the same button, if the target data is different, the meaning of the symbol changes and the waveform is different.

The operation process of the button 8 is realized by the symbol reverse conversion processing unit 11. The symbol inversion processing section 11 performs the reverse operation of the symbol processing section 2 and outputs time series data to an output by giving symbolized data to an input. At the time of the inverse conversion processing, the symbolization processing dictionary 3 is used. That is, using symbol and vector information, time scale and data size, the symbol data is converted into vector information,
Vector information can be converted to time-series data. That is, for example, by giving the symbolized data “rapid rise” and the data name F1201MV to the symbol inverse conversion processing unit 11, the “rapid rise” of the corresponding data can be replaced with time-series data.

Next, with reference to FIG. 35, the procedure of the symbol inversion processing will be described. Here, data name F1201
The following describes an example in which the button 8b of “slightly ascend” shown in FIG. 34 is pressed a little more.

The symbol inversion processing unit 11 first extracts vector information from the symbolized data “slightly rising”,
From F1201, various parameters (data size, time scale) are extracted from the dictionary (step 3501). The extracted data is processed as shown in the figure, and the amount of change (= data size × standard value) and operation cycle (= time scale × standard cycle) are obtained based on the vector information (step 3).
502, 3503).

The first operation on the target data is the result of adding the above change amount to the current value at the time of execution of this processing (step 3504). Next, a waiting process is performed until the calculated value (time) elapses as the operation cycle. Next, as the second operation, the current value at the time when the first operation cycle time has elapsed is taken out, the amount of change is added, and the target data is operated, whereby the second operation is completed. This is performed for the number of vectors of the vector information (step 3505).

By supplying the replaced time-series data to the control data output processing unit 12 according to the time scale, the same behavior as the time-series data obtained by the conversion process can be provided to the target process. In addition, as long as the forward conversion by the symbol processing unit 2 is guaranteed for an erroneous operation with respect to the plant output, since the output value is within the guaranteed range, the operator does not need to worry about the degree of the operation. Thus, plant operation becomes possible.

In the above example, the description has been given by giving two parameters of the button type and the data name.
By using the soft keys displayed on the display 10a of the man-machine device 10, the operation of the data name and a little more button can be reduced by operation.

A more detailed example of the man-machine operation processing of the button will be described.

(I) With a little more button by software, a little more button
Is displayed in the display 10a. The buttons are displayed as a group of buttons 8g in the window 10w within the screen. The buttons 8 displayed here are registered in the symbolized data dictionary 3 in advance, and can be used by the symbol reverse conversion processing unit 11 to generate reverse conversion data. The screen base screen 10d is a real-time trend screen capable of displaying the state of process data in real time.

The operation of the button 8 by the software is performed as follows. First, window 1
A little more button 8 displayed in 0w is activated by a pointing operation. The operation target is the data displayed on the base screen 10d on which the window 10w is displayed, and as the operation type, the button function operates a little more with respect to the operation amount or the set value. It is necessary to select in advance which of the operation amount and the set value should be operated at the time of the screen display request. The effect of the button 8 can be confirmed by the instantaneous value of the data type.

FIG. 36 shows that the button 8i is operated a little more with respect to the MV (operation amount) of the data of F1000.
This shows a state in which the effect is confirmed by PV (instantaneous value).

(Ii) As for the slightly more hardware buttons, the more slightly buttons are assigned to function keys in the keyboard 10c. This button should also be operated in combination with a tuning screen as shown in FIG. 34 to prevent erroneous operation. Data to be operated is displayed as a tuning screen, and a function key in the keyboard 10c, that is, a button is operated a little more. The functions are the same as those of the above-mentioned software buttons.

(7) Manual Operation Consideration Diagnosis When the operator makes an operation request to the plant via the man-machine device 10, the manual operation consideration diagnosis unit 14 uses the information at that time as symbolized data as the symbolized operation history file 13. It is a function realized by storing as.
As means for requesting an operation from the plant by the operator, there are two kinds of methods, namely, the operation by the button 8 described above and the same means as the conventional one by changing the numerical value. In the latter case of the plant operation by changing the numerical value, the process is changed in a time series in an analog manner. Therefore, if the time series data is used as it is, the data amount increases. For this reason, by considering how the manual operation reception processing unit 19 and the manual operation encoding processing unit 18 convert the operation performed by the operator into encoded data, and handling the encoded data after conversion, The aim is to reduce the amount. The former slightly more button 8 handles coded data in advance, so that it can be used without any particular problem.

The storage of the symbolizing operation information for the plant of the operator obtained in this way is separated by providing a symbolizing operation history file 13 as a new database in order to avoid mixing of input data with the symbolizing information. I'm trying.

FIG. 3 shows the manual operation symbolization processing unit 18.
This will be described with reference to FIG. This process is started by the execution of an interrupt by the manual operation reception processing unit 19. Now, after collecting the name of the data executing the operation operation (step 3701), it is monitored whether or not the operation operation of the target data is being executed (step 3702).
The monitoring is continued until the time-series data collection of the corresponding data, the encoding process, and the operation operation are completed.

FIG. 38 shows an example of the symbolization operation log file (manual data historical file) 13 obtained by the manual operation symbolization processing unit 18 and the symbolization operation instruction reception processing unit 9. This file 13 has a chronological structure,
The contents of the coded data obtained from the manual operation coding processing section 18 and the coding operation instruction reception processing section 9 are stored. In this example, the F1201SV "rises" at 0:05
Let me. The rest is no operation.

FIG. 39 shows a procedure of a diagnosis process considering manual operation. By registering the causal relationship between the operator operation and the input data in advance, the manual operation consideration diagnosing unit 14 can not report a state change of specific input data when a certain operation is performed. For example, when the SV of F1201 is “rising”, “slowly rising”, or “rapidly rising” as the symbol data of the operator operation information, the PV or MV whose symbol data of the input data is F1201 is “rising” or “slowly rising”. If it is "rapid rise", the status is not reported. The registration of the causal relationship is performed, for example, by storing the result in the encoding operation history file 13. This registration is performed in the encoded data historical file 5
May be used. Alternatively, a dedicated file, for example, a manual operation cause-and-effect file may be prepared and performed on the file.

Next, the manual operation consideration diagnosing unit 14 is configured as shown in FIG.
As shown in (1), first, coded data indicating the current state of the plant is fetched from the coding processing unit 2, and a change in the state of the plant is detected (step 3901).

Next, it is determined whether or not there is a data obtained by symbolizing the current manual operation and a manual operation related to the data (step 3902). That is, the symbolized data of the current manual operation and the symbolized data of the plant information having a causal relationship with the symbolized operation history file 13 are read out, and the symbolized data of the data currently input from the plant are read. Compare. If the current data change pattern has a causal relationship to manual operation, the current state change of the plant is expected,
In order to omit the report of the state change, the alarm suppression processing unit 16 is instructed to suppress the alarm.

On the other hand, if the change in the coded data representing the current state of the plant does not match the change in the data having a causal relationship with the manual operation, this state change is reported (step 3903). That is, no command is issued to suppress the alarm.

FIG. 40 shows an example of man-machine output of the diagnostic result of the manual operation-considered diagnostic section 14. However, the result of the manual operation consideration diagnosis is stored in the alarm suppression processing unit 16.
And is reported to the operator as alarm information. FIG. 40 shows an example in which the input data used in the manual operation-considered diagnosis is displayed as a composite of a trend graph and symbolized data. By using this example, it is possible to confirm at a glance where a time (state) that does not need to be reported to the operator has occurred.

This manual operation consideration diagnosis can be performed in combination with the similar pattern search described above. In other words, by making the search target the two of the symbolized historical file 5 and the symbolized operation history file 13, it becomes possible to search and teach similar data accompanied by an operator operation. This can provide past operation case information as an operation guide to the operator in addition to prediction of future data.

This function can be used by inexperienced operators,
It can be said that it is very effective for an operator having a large monitoring range in a large-scale plant or the like.

(8) Time Series Data Support Inference The time series data support inference section 15 supports the activation of inference in knowledge processing as shown in FIG. In the conventional inference starting method, inference starting can be performed only when the numerical value of input data exceeds a certain specified value (in the figure, an upper limit, for example). However, according to this conventional method, the number of types of specified values is limited by performing comparison and judgment processing using numerical data and specified values. The specified values include, for example, an upper limit, an upper limit, a rate of change, a deviation, a lower limit, a lower limit, and the like.

Therefore, the present embodiment is provided with a time-series support inference function of this function in order to start inference based on a change state of the encoded data. As a result, inference can be started in the state of change of symbols and symbols, so that it is possible to execute inferences corresponding to various situations, and it is possible to assume a situation in which the process state is narrowed down in the inference contents. The amount can be reduced.

FIGS. 42 and 43 show an inference activation table and an inference activation monitoring procedure. For example, in the inference activation table in FIG. 42, the data “F1201PV” is “constant”.
When the coded data changes from "" to "rapid rise", the inference start factor is used. In that case, inference No. 1 is started. The inference here includes various inferences. For example, there is a chain occurrence alarm suppression inference described below. Although not specifically disclosed in the present embodiment, fault diagnosis inference, prediction inference, scheduling, and the like can be given.

The monitoring of the inference start in FIG. 43 is started by an interruption due to a symbol change. It is checked whether or not the interrupted symbol change pattern matches the change pattern of the encoded data in the registration table (step 430).
1) If they match, it is monitored whether the data names match (step 4302). If the data names are the same, an inference start request is issued (step 4303).

Further, in this embodiment, the state of the plant and the contents of the manual operation are respectively encoded, and the symbolized historical file 5 and the symbolized operation history file 13 are encoded.
Therefore, such information can be collected in the process of inference in knowledge processing, and the symbolic data can be used as it is in the rule as it is in the rule.

As for the rule expression in knowledge, for example, 1F (＠measured value of P1001 is rapidly rising) (＠measured value of F1001 is divergent) THEN valve 10 is abnormal.

Or Thus, the symbol of the symbolized historical data is described in the 1F condition term.

In this way, compared to the conventional rule description method, since the waveform recognition of historical data is pattern recognition, words that are generally recognized by humans can be used as symbolized data, and the symbols can be used in the rules. Directly.

Since the conventional mathematical numerical analysis is not used,
The troublesome tuning of the analysis parameters becomes unnecessary.

(9) Chain occurrence alarm suppression processing The alarm suppression processing unit 16
Alarms that are inevitably issued by operation,
Various alarms that occur in a chain due to plant operation knowledge are suppressed while monitoring the encoded data. There are two ways to realize such alarms.

First, as shown in FIG. 44, a method for suppressing an alarm when a condition is met while referring to encoded data in inference using knowledge processing. Here, it is assumed that the following rules using encoded data are registered as rule knowledge in the knowledge base.

1F (＠Measured value of P1001 is increasing) (＠Measured value of F1002 is increasing) (（SV operation of F1002 is available) (経 過 SV operation elapsed time of F1002 is 30 or less The upper limit alarm suppression of THEN T1002.

This rule uses the status of change in process data and data operation information by the operator. Of the encoded data converted in real time,
Referring to the trend change waveforms 1001 and F1002, both of the data are ascending waveforms. For F1002, if the SV value has been changed by an operator operation within 30 seconds, the data of T1002 is
Because there is a risk of rising and an upper limit alarm being generated,
This is to stop the alarm based on the upper limit alarm set value of 002.

As described above, the change in the process data expected from the conventional experience is described in the 1F condition term in the rule. The data that can be described include process data for performing the symbolizing process in the symbolizing processor 2 and the symbol data, operator operation information and elapsed time from the operation, instantaneous values of plant data, and the like. In the THEN item, a process data name, an alarm type, and a processing mode such as suppression / recovery are described.

The THEN portion of the rule performs the processing of the described content by executing the rule, but actually turns on the alarm sub-less function of the process data definition information table of the control arithmetic unit. It is to be.

The second is the monitoring by the combination of the coded data by the decision tables shown in FIGS. 45 and 46.

FIG. 45 is a diagnosis table based on a diagnosis example of the feedback loop. In the case of a feedback loop, there is a causal relationship among the three data of the set value, the manipulated variable, and the measured value. Although the relationship between the three data in Cases 1 to 3 shown in FIG. 7 is normal, Cases 4 and 5 are relationships that do not exist even in view of the principle of feedback control. As described above, by storing the data values (encoded data) in a table, when a set value is changed or an operation amount is changed, an alarm generated due to a transition of another data is suppressed. .

The same applies to the diagnosis based on the plant configuration shown in FIG. However, in this case, case 1
The normal operation as in No. 3 and the abnormal operation in Cases 4 and 5 will be described. Thereby, the alarm can be suppressed in the case of the normal operation, and the abnormal alarm can be output in the case of the abnormal operation.

By performing the alarm suppression in this manner, normal operation can be continued without notifying the operator of an alarm that may occur or a change in state, thereby stably operating the plant and reducing the burden on the operator. Can be realized simultaneously.

Next, another embodiment of the present invention will be described. This embodiment relates to an example in which a distributed processing type process operation support system is applied to plant operation support. In the embodiment described above, the embodiment of the function centralized process operation support system for one computer has been described. However, the present invention is not limited to this. For example,
A process operation support system using a distributed CPU as shown in FIG. 47 may be used. Next, this example will be described.

In the system shown in FIG. 47, a plurality of control devices 24 mainly performing control processing, and a man-machine control device 2 mainly performing man-machine processing.
5 are connected by a network 23. The main purpose of a distributed system is to reduce CPU load by distributing functions,
That is, there is an increase in the processing scale due to a reduction in the CPU load.

FIG. 48 shows an example of the configuration of a distributed processing type plant operation support system. In the example of the figure, one control device 24 and a man-machine control device 25
And are connected.

In the figure, the control device includes a process data fetch processing section 1 and a control arithmetic processing section 17.
A control data output processing unit 12, a symbolization processing unit 2,
The dictionary for symbolization processing 3, the symbolized data storage unit 4, the symbol inverse transformation processing unit 11, and the time series data support inference unit 15
And

Further, the man-machine control device 25
Is a symbolized data historical file 5, a data composite display processing unit 6, a similar pattern search processing unit 7, an alarm output processing unit 20, an alarm suppression processing unit 16, a symbolizing operation instruction reception processing unit 9, Manual operation reception processing unit 19
, A manual operation symbolization processing section 18, a symbolization operation history file 13, and a manual operation consideration diagnosis section 14. This man-machine control device 25 includes the man-machine device 1
0 and a symbolizing operation instruction section (a little more button) 8 are connected.

The components of the control device 24 and the man-machine control device 25 are the same as those of FIG.
8 is similar to the system shown in FIG. Therefore, the description of the configuration and operation of them will be omitted.

Data is exchanged between the control device 24 and the man-machine control device 25 by communication means (network 23), whereby the system functions as a plant monitoring system as a whole. In the conventional system, the communication data is mainly a numerical value or a character string. However, in the system according to the present embodiment, this is changed to encoded data. By using the symbol data, the communication data amount can be compressed according to the meaning given to the symbol data. That is, even if one piece of symbol data “rising” is taken, “rising” has a chronological meaning and a meaning that data goes up every moment. Numerical data equivalent to this is 1.5 seconds after 1 second, 3.0 seconds after 3 seconds, 4.5 after 4 seconds, 6.0 after 4 seconds, and so on. Two or more numerical data are required. Therefore, in the present embodiment, the amount of communication data between the control device 24 and the man-machine device 25 can be reduced.

In this embodiment, the process information of a plurality of control devices is processed by one man-machine control device, so that it is suitable for centrally managing a large amount of process information. Also, since the information can be processed in a distributed manner, the burden on the man-machine control device is reduced.

In each of the above embodiments of the present invention, an example in which plant process data is used as input data has been described.
The present invention is not limited to this. In the case of plant data, for example, data such as temperature, flow rate, pressure, etc. are used, but the field of data processing process operation for grasping events in processes other than plants, for example, finance, securities, distribution, etc. In, time series data such as economic indicators, sales information, management information, inventory management information, etc. can be used as input data. By providing the data such as the inventory management information to the symbol processing device, it is possible to recognize the trend situation and the like of these events in a symbolized expression. Information such as sales information can be easily obtained from a device such as a sales input terminal, for example. These terminals can be considered to correspond to sensors in a plant, for example.

Further, in each of the above embodiments, data involving a time factor called time-series data is handled, but it goes without saying that the present invention can be applied to data other than time-series data. For example, the present invention can also be applied to data in which input data is expressed in two or more dimensions such as a temperature distribution, a density distribution, and an array. Specifically, the temperature distribution is represented by T (I). T (temperature) is represented by a function determined by I (location / position). Thereby, the state of the temperature distribution is, for example, "the temperature distribution is dense",
It can be provided to the user in a symbolic expression such as "sparse temperature distribution".

Further, in each of the above embodiments, the symbol processing device shown in FIG. 13 is used as the symbol processing means. However, the present invention is not limited to this, and the symbol processing device shown in FIG. Symbolization can be performed. In this case, process data can be temporarily stored.

Further, in the above-described embodiment, an example in which the symbolized data is represented by a character string has been described. However, the present invention is not limited to this. Any symbolic expression that can be understood by an operator may be used. It can also be expressed with emoticons.

In addition, in each of the embodiments described above, the example in which the coded data is displayed on the display has been described, but the coded data can be expressed by voice if it can be called. In this case, if the notification by voice is performed together with the display on the display, the effect of alerting the operator is further increased. It should be noted that the notification by voice may be troublesome if it occurs too frequently. Therefore, only an alarm may be selectively output, such as only for the coded data designated by the operator.

In the above-described various embodiments, examples having various functions have been described. However, the present invention can be selected by the user according to the necessity. In addition, other functions can be added or a link with another system can be performed.

[0247]

As described above, according to the present invention,
The characteristics of the information indicating the status of the process can be extracted without being affected by the personal conditions of the operator who monitors the process, and the status of the process can be accurately grasped.

Further, according to the present invention, by symbolizing the operation on the process, it is possible to instruct the operation on the plant by receiving a small change in the process and an instruction by an ambiguous expression of the operator.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an embodiment of a symbolizing device used to convert process data into a symbolic expression in the present invention.

FIG. 2 is a flowchart showing an operation of a broken line approximation processing unit in the encoding apparatus.

FIG. 3 is an explanatory diagram (part 1) illustrating a process of symbolization in the symbolizing device.

FIG. 4 is an explanatory view (part 2) for explaining the process of symbolization in the symbolizing device;

FIG. 5 is an explanatory diagram showing an example of a feature extraction filter used in the encoding device.

FIG. 6 is an explanatory diagram showing an example of a standard vector used in the encoding device.

FIG. 7 is a flowchart illustrating processing in an integration processing unit in the encoding device.

FIG. 8 is an explanatory diagram illustrating an example of an integration process in an integration processing unit.

FIG. 9 is an explanatory diagram illustrating an example of an integration process in an integration processing unit.

FIG. 10 is a flowchart illustrating processing in a dictionary search processing unit of the symbolizing device.

FIG. 11 is an explanatory diagram showing an example of a standard pattern dictionary used in the dictionary search processing unit.

FIG. 12 is a flowchart illustrating a procedure of a search process in a dictionary search processing unit.

FIG. 13 is a block diagram showing the configuration of another embodiment of a symbolizing device used to convert process data into a symbolic expression in the present invention.

FIG. 14 is a flowchart showing the operation of a broken line approximation processing unit in the symbolizing device.

FIG. 15 is an explanatory diagram showing an example of a feature extraction filter used in the polygonal line approximation processing unit.

FIG. 16 is an explanatory diagram for explaining a process of symbolization in a broken line approximation processing unit;

FIG. 17 is an explanatory diagram for explaining the result of symbolization.

FIG. 18 is a block diagram illustrating an outline of a configuration of an embodiment of a process operation support system according to the present invention.

FIG. 19 is a hardware configuration diagram of a process operation support system of the present invention.

FIG. 20 is an explanatory diagram showing symbol conversion parameters used in the symbol processing.

FIG. 21 is an explanatory diagram showing an application example of a symbol conversion parameter.

FIG. 22A is a flowchart illustrating a procedure of an alarm output process, and FIG. 22B is an explanatory diagram illustrating an example of a data structure of previously collected encoded data used for performing an alarm process.

FIG. 23 is an explanatory diagram showing an example of alarm display in the alarm output process.

FIG. 24 is a flowchart showing a procedure for collecting a symbolized historical file.

FIG. 25 is an explanatory diagram showing an example of parallel display of time-series data and encoded data.

FIG. 26 is an explanatory diagram showing an example of the configuration of a symbolized data historical file.

FIG. 27 is a flowchart showing a display operation procedure in data composite display processing.

FIG. 28 is an explanatory diagram showing an example of a plurality of displays of time-series data and encoded data.

FIG. 29 is a flowchart showing a display procedure in a similar pattern search process.

FIG. 30 is an explanatory diagram showing an example of a symbolized database similar pattern search result.

FIG. 31 is an explanatory diagram showing an example of data of a plurality of similar pattern results.

FIG. 32 is a flowchart showing a processing procedure when there are a plurality of search results in the similar pattern search processing.

FIG. 33 is an explanatory diagram showing a display example when there are a plurality of similar pattern search processing results.

FIG. 34 is an explanatory diagram showing an example of a reception state of a chat button;

FIG. 35 is a flowchart showing a symbol reverse conversion processing procedure when an operation command is issued by a little more button.

FIG. 36 is an explanatory diagram showing an example of a process output tuning screen.

FIG. 37 is a flowchart illustrating an example of a processing procedure in a manual operation symbolizing process.

FIG. 38 is an explanatory diagram showing an example of the configuration of a manual operation data historical file (encoding operation history file).

FIG. 39 is a flowchart illustrating an example of a procedure of a manual operation consideration diagnosis process.

FIG. 40 is an explanatory diagram showing an example of a manual operation-considered diagnosis and a plant data manual operation symbolized composite display.

FIG. 41 is an explanatory diagram showing an example of inference activation of knowledge processing using encoded data.

FIG. 42 is an explanatory diagram showing an example of an inference activation table.

FIG. 43 is a flowchart showing a procedure of time-series data support inference activation monitoring.

FIG. 44 is an explanatory diagram showing a description example of a chain occurrence alarm suppression rule.

FIG. 45 is an explanatory diagram showing an example of a diagnosis table based on diagnosis of a feedback loop.

FIG. 46 is an explanatory diagram showing an example of a diagnosis table based on a diagnosis based on a plant configuration.

FIG. 47 is a hardware configuration diagram of a system according to another embodiment of the present invention.

FIG. 48 is a block diagram showing an outline of the configuration of another embodiment of the present invention.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Process data taking-in processing part, 2 ... Symbolization processing part, 3 ... Symbolization processing dictionary, 4 ... Symbolized data storage processing part, 5 ... Symbolized data historical file, 6 ... Data composite display processing part, 7 ... Similar pattern search processing unit, 8 ...
Symbolization operation instruction section (other button), 9: Symbolization operation instruction reception processing section, 10: Man-machine device, 10a ...
Display, 10c: Keyboard, 11: Symbol inverse conversion processing unit, 12: Control data output processing unit, 13: Symbolized operation history file, 14: Manual operation consideration diagnosis unit, 15: Time series data support promotion unit, 16: Alarm Suppression processing section,
17 control arithmetic processing section, 18 manual operation symbolization processing section,
19: Manual operation reception processing unit, 20: Alarm output processing unit, 110: Communication device, 120, 1320: Data storage device, 130, 1330: Input processing unit, 140, 139
0: memory, 150, 1350: broken line approximation processing unit, 1
55, 1355 ... filter storage unit, 160, 1360 ...
Integrated processing unit, 165, 1365: Integrated parameter storage unit, 170, 1370: Dictionary search processing unit, 175, 13
75 ... standard pattern storage unit, 180, 1380 ... similarity calculation processing unit, 190, 1390 ... memory.

Continuation of the front page (56) References JP-A-56-124911 (JP, A) JP-A-50-39151 (JP, A) JP-A-62-98404 (JP, A) JP-A-4-268413 (JP) , A) (58) Fields investigated (Int. Cl. ⁷ , DB name) G01D 21/00 G01D 7/00 301 G05B 23/02 301

Claims (6)

(57) [Claims] 1. A method for acquiring data representing a state of a process.
To convert the data into data that can be processed by the subsequent processing means.
Process data capture processing means and process status
Representation of process status symbolically based on data
Encoding processing means for generating encoded data to be encoded, and encoding
Means for storing encoded data, and a designated
The data in the specified range from the symbolized data storage means
And display processing means for outputting as display data,
Display data on the screen and receive external instructions.
Input / output means for accepting the input data , the symbolization processing means comprising: a symbolization processing dictionary having standard vector data and symbol expression data for symbolically expressing the characteristics of the standard vector data; A vector series is generated by using the dictionary for symbolization processing, and a similar standard vector series is searched for the vector series, and symbol expression data corresponding to the most similar standard vector is extracted to perform symbolization processing. A process operation support system comprising a symbol processing unit. 2. An encoded data file according to claim 1, wherein the encoded data storage means includes: an encoded data file for storing the encoded data;
Having a symbolized data storage processing unit for storing and processing the symbolized data in the symbolized data file, wherein the symbolized data file and the symbolized data storage processing unit are:
A process operation support system in which the process data (numerical data) on which the encoded data is based is stored in chronological order in association with the process data. 3. Upon receipt of an instruction from the input / output means according to claim 1, the encoded data storage means is searched for data of a pattern similar to the display data currently displayed on the input / output means, and A process operation support system further comprising a similar pattern search processing means for displaying data when it exists. 4. An operation instruction unit for instructing a process to control by a symbolic expression including ambiguity, and an operation instruction reception processing unit for receiving an operation on the operation instruction unit according to an operation target of the process. A symbolizing operation instruction receiving unit, a dictionary for symbolizing processing having standard vector data and symbolic expression data for symbolically expressing the characteristics of the standard vector data, and a received symbolized operation instruction for symbolizing processing. A process operation support system comprising: a symbol reverse conversion processing unit that converts a reference to a dictionary to numerical data. 5. A process for capturing data representing a state of a process.
To convert the data into data that can be processed by the subsequent processing means.
Process data capture processing means and process status
Representation of process status symbolically based on data
Encoding processing means for generating encoded data to be encoded, and encoding
Means for storing encoded data, and a designated
The data in the specified range from the symbolized data storage means
And display processing means for outputting as display data,
Display data on the screen and receive external instructions.
Input / output means for receiving, and the encoded data accumulating means comprises:
Process data (numerical data) on which data is based
Are stored in chronological order, and the display processing means
Process data on which the symbolized data of
These data are retrieved from the symbolized data storage
And output as display data by combining
Process operation support system. 6. The display processing device according to claim 5, wherein
Display the trend data as a trend graph.
Displays the corresponding symbolized data along the time axis of the command graph.
Process operation support system to be shown.