JPS59202504A - Control system - Google Patents

Abstract

PURPOSE:To ensure control with cooperative decision by applying a fuzzy inference to a system having a transmission line for information which connects the decision control elements to each other for an interchange system, a production system, etc. CONSTITUTION:The decision elements 11-1, 11-2- corresponding to control stations 21-1, 21-2- decides preponderantly the priority to a local conflict between two trains and between a relevant stations and its adjacent station and decides the arrangement decision of operation. The decision element 12 connected to a train command means 22 gives the arrangement decision of operation centering on the general decision for operation of each train and the train group. The decision element 13 connected to an operation command means 23 performs the decision for alteration of operation centering on the operation for trains and train crew. The decision element 24 connected to a work control means 24 decides the constitutional work schedule. The decision elements 31-1, 32-1- connected to operation deciding means 32-1, 32-2- set on each train decide preponderantly the local priority to a conflict phenomenon to the preceding and following trains in accordance with the situation of each of preceding and following trains, the situations of preceding and following stations, the environmental condition of operation, etc. and then decide the arrangement of operation.

Description

[Detailed Description of the Invention] [Field of Application of the Invention] The present invention provides a judgment control element that monitors the states of a plurality of control objects, judges the behavior of the control objects, and controls the same, and each of these judgments. The present invention relates to a control method for locally and globally controlling a system consisting of information transmission paths interconnecting control elements, depending on the situation.

[Background of the invention]

Complex social systems such as transportation systems and production systems are composed of a large number of diverse subsystems, or control objects, and the relationships between these objects are governed by ambiguous relationships between local and global situations. There is. Conventionally, for such systems, determination of the operation control method and control operations of the system based on the determination results have all been performed using human experience rules. In other words, the advantage of human judgment and control in complex control systems is that it is possible to make optimal judgments that take into account the balance between locality and globality, in response to a variety of changing situations.

When automating such a system, the relationships, constraints, and objective functions between each controlled object are fixed, deterministic, and
Moreover, if the environmental conditions were within a fixed range, it was possible to apply conventional methods to automated systems.

However, in cases where the relationship between multiple controlled objects changes ambiguously depending on local and global situations, as mentioned above, in other words, systems where multiple people are making management/control decisions. In the past, the person in charge of each controlled object listened to the opinions of multiple other controlled objects and made decisions by taking into account the degree of relationship between each controlled object, but this system control evaluation goal In most cases, it has been impossible to formulate it using the prior art.

For example, in the conventional control method used in train operation management systems for rail transportation, all local decisions at each station are made deterministic, and the decisions are sequentially repeated up to the terminal station to evaluate the order of arrival at the terminal station. If the results were unsatisfactory, the method used was to repeat the intermediate judgments. Another method is to control the number of changes in the middle using a change margin up to the end.

However, when it comes to deterministic judgments, there remains a problem with unambiguous judgments around the threshold. The decision logic to reach the terminal station becomes more complex as the number of stages increases. It is necessary to prepare decision logic for each exceptionally handled station along the way. It is almost impossible to formulate a formula that satisfies all the implications for the operation of return trains beyond the terminal station, vehicle operation, crew operation, etc. Furthermore, due to various reasons such as the ambiguity of events near and after these terminal stations, the above methods are not as good as conventional human judgment. There were other problems.

[Purpose of the invention]

The purpose of the present invention is to solve such conventional problems, and to solve the problem of a system that is composed of a plurality of control objects and in which the relationship between each control object changes ambiguously depending on local and global situations. On the other hand, the object is to provide a control method that automates the range of judgment and control that humans have made based on empirical rules, incorporates other related judgment results, and makes it possible to perform cooperative judgment and control.

[Summary of the invention]

In order to achieve the above object, the control method of the present invention includes a judgment element that monitors the states of a plurality of control objects, determines and controls the actions of the control objects, and information that interconnects each judgment element. In a system composed of transmission lines, each of the above-mentioned judgment elements independently judges the control output for an event occurring in the control target of the self-judgment element, and sends the judgment result to the transmission line. a second means for receiving and recording other judgment results related to the judgment from the above, and weighting the other judgment results taken in by the second means according to the degree of relationship between the self-judgment element and each other judgment element; After that, the present invention is characterized by having a third means for obtaining a judgment result by coordinating each result with the judgment result of the first means.

[Embodiments of the invention]

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

FIG. 1 is an overall configuration diagram when the present invention is applied to a train operation management system for track transportation.

The transmission line 1 connects a plurality of transmission control devices 2 in a ring, and each determination element is connected to the transmission control device 2.
It is connected to the. These ground facilities and each train on the track are connected by well-known communication means such as guided radio.

In FIG. 1, 21-1.21-2.21-3 is a control means installed at a control station including route control, etc.; 11-1.
Reference numerals 11-2 and 11-3 are control judgment elements (hereinafter simply referred to as judgment elements) that govern control of each of the control stations. control station 21
-1.21-2.21-3, etc. are control targets, and each judgment element 11-1 corresponding to each control station. ．． 11-2.11
-3 makes timetable rescheduling decisions by focusing on prioritizing local conflicts between two trains between the station and the station that has instant contact with the station.

22 is a train command means, and 12 is a judgment element connected to the train command means, which makes timetable rescheduling decisions centering on global judgments regarding the operation of each train and train group.

Reference numeral 23 denotes a vehicle and crew operation command means, and 13 a judgment element connected to the operation command means 23, which makes operation change decisions mainly regarding vehicle operation and crew operation.

24 is a depot work management means, and 14 is a decision element connected to the work management means 24, which focuses on decisions such as changes in yard work due to traffic rescheduling or changes in operation, or changes in depot schedule due to vehicle breakdowns, etc. to determine the on-site work schedule.

32-1.32-2...32-6 is an operation judgment means provided on each train running on track-L, 31-1
．． 31-2. ...31-6 are judgment elements connected to the operation judgment means, and the status of each train before and after,
By using the conditions of the preceding and preceding stations and the operating environment, etc., to make local priority judgments regarding the phenomenon of competition with preceding and preceding trains, and making decisions on train rescheduling, the operation control means and guidance broadcasting means for the train are activated. provide information on environmental conditions to

Next, each function of typical judgment elements will be explained.

In this case, there are two types of trains, express trains A and trains B that stop at each station, and at stations equipped with sidings, the trains B that stop at each station can shunt the express train A and allow A to precede the train. However, the planned evacuation stations are determined by the train schedule.

First, on-board judgment elements 31-1, 31- of express train A
The functions of No. 6 will be described in detail.

This determination element 31-1 is activated upon receiving information that the preceding train B, which stops at each station, has reached the change determination point (this is referred to as S) of the next shelter station.

The input variables are (&) to (d) below.

(a) Distance from the following train A at point S o (
distance.

Difference from plan ΔSB~A), (b) Vehicle A (7) Delay time, (c) Delay time increase rate of Vehicle A, (d) Delay time of preceding train B.

Each of these human power values is converted into a fuzzy variable (Pect/I/) using a given membership function. Here, fuzzy is a set concept for multi-valued logic systems used for the limits of binary logic systems.

Regarding the interval (distance) between trains A and B at the S0 point, using the difference from the plan △SB~, △SB, -w
Attribution degree function [μ(
The evacuation judgment) △S~□] is shown in FIG. In addition, the train delay time value △t and the degree of membership function regarding the degree of delay (
That is, a function for converting the amount of delay into terms of degree of delay) [μ (degree of delay) Δt] is shown in FIG.

In Figure 2, traveling as planned, that is, the difference △SB, -
, When A is O, evacuate at the designated station, but A, B
When the interval between both trains is shorter than planned, evacuate at the station one station before the designated station, and when the interval between both trains is longer than planned, evacuate at the next station after the designated station, and if it is even longer than the designated station, take shelter at the next station. evacuate each.

It should be noted that FIG. 2 is a function of the degree of attribution of the judgment of evacuation change as well as the degree of deviation of the distance between both trains from the planned timetable.

In FIGS. 2 and 3, the judgment side of this judgment element for the above input is, for example, ``△5B-A is ten small,
If the degree of delay of the car is medium, the rate of increase is on the ground, and the degree of delay of the vehicle in front is small, change the evacuation notice to one station after another. ”, etc. This fuzzy inference is expressed as a four-dimensional matrix.

To illustrate the explanation, an example of the first two-person judgment side is shown in the fourth column.

The contents of the IJ matrix indicate the inference output, and in the above two-person example, the output is as shown in Figure 4.

For example, if the distance from the preceding vehicle is not large and the delay of the vehicle is O or small, the vehicle will evacuate at this station as planned. Furthermore, if the distance from the preceding vehicle is the same as the planned timetable, the vehicle will evacuate at this station as planned, regardless of the degree of delay of the vehicle. If you have to evacuate at the next station, the distance between you and the vehicle in front and the degree of delay will be significant.

This is when it is small or medium, or a combination of medium.

Similarly, an example of the control station determination element for determining whether to change the evacuation of 2 approaching trains is shown in Figure 5, and an example of the 2nd train command determination element
An example of determining whether or not to change the evacuation between trains is shown in FIG. 6A.

On the judgment side based on the judgment elements of each control station in Figure 5, the evacuation order is determined based on the average delay of the approaching train group and the competitive loss value of both trains B and A, regardless of the planned evacuation order. to decide. The loss values (time) of B and A have opposite signs.

Note that this station in Figure 5 is not necessarily the same as the planned station.

The train command judgment factors in Figure 6A are based on the degree of margin for evacuation changes to ensure that the order of arrival at terminal stations does not change from the plan, and the degree of disruption to the current train group operations, such as vehicle operation, etc.
Using the degree of influence on other variables as a variable, the degree to which local decisions such as evacuation changes are allowed is inferred. By elaborating the degree of disruption in the operation of a train group using the amount of delay and the number of order changes, etc., the degree of influence on rolling stock operations, turnaround trains, base operations, etc. is determined using a degree of belonging function. be able to. Figure 6A shows that the ripple effect on others is large.
If the change margin is already negative or 0, local changes are prohibited, and even if the ripple effect is large, if the margin is only medium-sized, changes to one station after another or to the next station are prohibited. This represents the decision-making side, such as allowing up to two changes to the station.

"Degree of spillover to the pond", which is one variable on the judgment side in FIG. 6A, is inferred using the fuzzy judgment side shown in FIGS. 6B and 6C. Note that FIG. 6C is an example in which one output in FIG. 6B further spreads.

In FIG. 6B, the degree of influence on others is output based on a combination of large, medium, and small arrival order change rates and large, medium, and small delay amounts of turnaround trains. The large, medium, and small elements of the queue are determined by (1) platform change at the terminal station, (il) disturbance spreading to the returning train,
011) Indicates the degree of number of operational changes, etc.

In FIG. 6C, the degree of influence on others is output based on the combination of the large, medium, and small number of operation changes and the large, medium, and small values of the delay amount of arriving cars (case X). The value of each element in the matrix (large, medium, small) is the number of changes such as (1) changes in on-site work (in/out vehicle handling), (11) changes in vehicle inspection (maintenance) schedule, 011) changes in operation at the next terminal station, etc. Indicates the degree of

Through each of the above processes, the judgment results (independent judgment results) of each element related to one event are obtained.

Next, a method of obtaining a cooperative result with judgment results of other judgment elements, which is a feature of the present invention, will be explained.

First, the judgment results of other elements are weighted according to the degree of relationship with the own element.

The value of the membership function regarding the output variable y of the judgment result of element P, that is, the membership degree, is μp(y), the weight multiplier is ω, and the weighted membership degree is μp(y)', and the respective relationships are expressed as follows. Set it as follows.

μ (b)'-o, s-ω(0,5-μpke))...
...”'Pp(y) < o, sμ(b)1=0.5
... μphi)-o, 5μke)'-=0.500(
μpke)-o,s)...0.5〈μhi)≦1 For the evacuation change judgment result of a certain control station judgment element, large,
The results of medium and small weighting are shown in FIG.

In Figure 7, the evacuation judgment is the previous station (0,0) and the current station (0,4).
), the next station (0, 9), and the next station (0, 6) are weighted (ω), and when the weight is large, ω = 1. When the weight is medium, ω-2/3 is set, and when the weight is small, ω=l/3, and when the degree of membership μ is determined from the cutting allowance, the values shown in FIG. 7 are obtained.

A cooperation result is obtained from each weighted inference output (a set of variable values indicating the degree of belonging regarding control specification).

Some examples on the collaborative decision side are listed below.

The output of train A, the output of train B, the planned evacuation notice output, the next station output, the train command output, and the coordination result are arranged in this order. Note that the △ mark indicates AND.

(1) Change to the next station as planned △ Change to the previous station, Δ Change to the next station, △ As planned △ If there is a small production, change to the next station.

(II) If it goes as planned, change to △next station, △change to previous station, △change to next station, △low birth, then change to next station.

0i1) Change to the next station △ As planned △ Change to the next station, then change to one station after another △ No change, then it is as planned.

Change to Reijitsu Station, △Change to the next station, Change to 〇Next Station△If it goes as planned, △If there is a small birth, change to the next station.

FIG. 8 is a diagram showing an example of two-person collaborative reasoning between the output of the planned evacuation notice determining element and the output of the train command determining element.

The output of the former (i.e., marked as "current station judgment") is unchanged, i.e., has a large weight, ω=1, and the output of the latter (i.e., marked as "command judgment") has a small weight, that is, ω-
It is calculated as 1/3.

The output of the station element has a maximum value of 0.8 when changing to the next station, while the raw output of the command element has a maximum value of 0.8 when changing to the next station.
The maximum value is 9, and the weighting is later changed to 0.63. The vertical axis (output of command judgment) in FIG. 8 is a control law that outputs unchanged, distorted, moderate, and overeating shown in FIG. 6A.

In Figure 8, the judgment result of this station is that the evacuation is changed to the next station, the judgment result of the train command is unchanged, and the weight of the command judgment is small, but the result of this cooperation side is that the evacuation is unchanged, that is, the evacuation is changed to the next station. The station will be evacuated. This is because the value of 0.63 is valid.

The thick lines in the figure represent the boundaries of regions where the collaboration results have the same language value.

The operation of collaborative reasoning is similar to general 7Ajii reasoning,
The maximum value in each output language value region is selected, and the language value with the maximum value in each region thus obtained becomes the output of collaborative inference. If the values of multiple language values are the same, the one closest to the plan is selected.

In FIG. 8, since the value of the region of the current station is 0.63, which is the maximum value, the output of the cooperative inference is that the current station is evacuated, that is, there is no change.

Next, a method for detecting abnormal elements using the above-mentioned cooperative judgment results will be described.

FIG. 9 is a diagram of abnormality test on the fuzzy cooperation side.

Each judgment element, including its own element, receives the output of each judgment element, which is input for cooperation, using the fuzzy cooperation side shown in Fig. 9.
The degree of abnormality is tested based on the degree of difference in output.

In this method, the collaborative output that is the basis for inference is the collaborative result of several elements weighted according to the strength of the relationship, so unless two or more elements with a strong relationship -E are abnormal, , based on the premise that the output is close to optimal. If this assumption changes, A, B in Figure 9,
This can be dealt with by changing the interpretation of C and D. In addition,
In Figure 9, A means no abnormality, B means no abnormality but requires caution if it does not continue, C means caution (needs to take measures against abnormality),
D is an abnormality (abnormality countermeasures are required).

Next, a method for selectively inputting judgment information according to the degree of relationship when receiving and inputting judgment information transmitted by another judgment element will be explained.

Each judgment element emits information (NL), the degree of relationship between other elements and its own element, and the number of matches between keywords included in the above information and keywords used to judge its own element. The input information (judgment result) is selected using the fuzzy judgment side shown in Fig. 10.The selection criteria are A, B,
This can be dealt with by changing the interpretations of C, D, and E.

Note that A is input unconditionally, B is input when the judgment event occurrence frequency is high, C is input when the judgment event occurrence frequency is medium, D is input when the judgment event occurrence frequency is small, and E is input when the judgment event occurrence frequency is moderate. Each shows unconditional disregard.

FIG. 11 is a flowchart of a process executed by a determination element targeting each control element, and FIGS. 12 to 15 are partial flowcharts thereof.

Each judgment element monitors the state of the controlled object, performs appropriate processing in response to the occurrence of an event, and outputs control output or abnormality countermeasures.It also constantly monitors the transmission line side and outputs information related to its own controlled object or its own element. If there are judgment results of other factors related to the event being judged, they are taken in and used for one's own judgment and evaluation. At the same time, it sends out events to be controlled by itself, independent judgment results, and evaluation results.

The detailed flow of step 100 (object monitoring) in FIG. 11 is steps 101 to 103 in FIG. 12, and the detailed flow of step 130 (self-element individual abnormality determination) in FIG. ~134, and step 300 in FIG. 11 (cooperative judgment with other judgment results)
The detailed flow is steps 301 to 302 in FIG. 14, and the detailed flow of step 400 (evaluation of each independent judgment result) in FIG. 11 is steps 401 to 404 in FIG.
It is.

The present invention provides a first means for determining a control target of its own element;
A second means for receiving (i4%) judgment information of other elements via a transmission path, and a third means for weighting the results obtained by the first and second means to obtain a coordinated judgment result. The method is characterized in that step 130 in FIG. 11 (131 to 134 in FIG. 13) is the first means, steps 200, 210, 211, 220, 221,
270 and 271 are second means, step 300 (
301 and 302) in FIG. 14 are the third means.

In step 133 of the first means, examples of the degree of belonging functions of variables such as those shown in FIGS. 2 and 3 are provided, and in step 134, judgment elements for each human power value as shown in FIGS. One of the judgment sides is applied respectively. Second
The output of step 211 in the means is for cooperative judgment, and the output of step 221 is for activating self-judgment by other machines related to the control target of the own element. Also,
Step 301 in the third means uses the fuzzy weight control law as shown in FIG.
The fuzzy collaborative inference side as shown in the figure is applied respectively.

In addition, step 400 in FIG. 11 (steps 401 to 404 in FIG. 15) is a fourth means, in which a fuzzy evaluation agent as shown in FIG. 9 is applied to step 401, and depending on the result, A determination is made in step 402. Further, steps 250 and 251 are the fifth means, and step 250 is applied with the fuzzy selection side as shown in FIG. environmental state variables in control decisions;
It is used as a constraint condition, etc. to make judgments that are error-free.

Explain the overall flow. Each judgment element is determined in step 10.
0 to 103, the event is accepted, appropriate processing is performed for the occurrence of the event to be controlled, information to be reported to other judgment elements is edited, and the event is analyzed. Next, in step 120, it is determined whether the event is abnormal. In step 130, self-element independent event judgment (fuzzy reasoning 1)
However, in steps 200, 250, and 251, the transmission line side is also constantly monitored, determining whether or not the information is related to the object to be controlled, receiving the information, and incorporating it to make decisions and decisions. Use for evaluation (Use Figure 10, etc.)
. Further, in steps 220 and 221, it is determined whether the event is an event of other related determining factors, and the information is received and taken into step 120.

Further, in steps 210 and 211, if there are judgment results of other judgment elements that flash on the event being judged by the self-judgment element, they are taken in and the process proceeds to step 270. Step 1
In steps 30 to 134, if the event is abnormal, the state variables related to the object are processed, information related to the self-judgment element is taken in, and the global state variables related to the environment are processed. As a result, after fuzzifying the variables using the variable membership functions shown in Figures 2 and 3, 7-agility inference is performed to resolve abnormalities in the state of the controlled object (Figures 4 to 4). (Use diagram 6C).

Then, in steps 270 and 271, if there is not enough time to take in m or more other judgment results and make a judgment, the process immediately proceeds to step 300.

In steps 300 to 302, cooperative judgment with judgments of other elements! In order to perform P1 (fuzzy collaborative inference), first weight the judgment results from other elements (using Fig. 7 etc.),
Step 134 - Perform fuzzy cooperative inference between the output of the element and the output from other elements (using FIG. 8, etc.).

Next, in steps 400 to 404, the cooperative judgment result and the independent judgment result are compared to detect errors (fuzzy comparative inference). In other words, each independent judgment result and cooperative result are
A fuzzy comparison is made using diagrams, etc., and it is determined whether the difference is unexpected or not, and whether the element with a significant difference is the own element. If it is not the own element, the related output is edited and sent. On the other hand, if it is the own element, abnormality processing is activated. In addition, Figure 11~
L in FIG. 15 indicates transmission to the transmission path.

As a result of the above processing, the following matters were clarified.

(1) By applying fuzzy inference, it is possible to make realistic judgments that incorporate the situation of the trains in front and behind when handling areas near the threshold of individual local judgments, and to make the same judgment based on judgment factors of surrounding trains and stations. , by integrating judgments from different standpoints, as well as judgments from the perspective of train dispatchers centered on terminal stations, taking into account the ripple effects on turnaround trains and operations in the future, using a collaborative judgment method, to improve the judgment target. ambiguity,
Possibility of modeling that takes into account the ambiguity of future phenomena. Since such cooperative judgment can be applied to the judgment elements related to each controlled object, all the judgment elements making up the system will be able to make realistic judgments in the same way as human judgments.

(11) Judgment elements on each train car, at each station, each command, etc., incorporate the results of other related judgments and make cooperative judgments, so that even if there is an error in judgment, it is covered, and each individual Abnormal elements can be detected by comparing the judgment results with the above coordination results.

011) Each judgment element uses fuzzy reasoning to select and input information relevant to itself from the information sent by other elements, thereby making judgments related to the above judgment from its own position. In addition to being able to participate in the collaborative judgment of other judgment elements by executing it, the self-judgment element can also make a cooperative judgment using the judgment results of other judgment elements.

[Effects of the Invention] Therefore, according to the present invention, by using fuzzy inference, it is possible to make a realistic judgment by introducing a wide range of ambiguous situations and conditions into judgments near the threshold value, and to improve the accuracy of each judgment. Each element selects and receives the judgment information and judgment results of other elements related to the object outside of self-control, and makes fuzzy cooperative judgments according to the degree of relationship with the self-judgment, thereby making not only local judgments. , in addition to judgments from other standpoints, judgments from a broader perspective can be spread as needed.1171
It becomes possible to make decisions that also take into consideration. Furthermore, by using fuzzy cooperative judgment, one 4C or a small number of judgment errors can be covered, and the error and its degree can be detected. Note that the present invention can be applied to all other systems such as FA and control systems.

[Brief explanation of the drawing]

Figure 1 shows the overall configuration V when the present invention is applied to a train operation management system for rail transport, Figures 2 and 3 are diagrams showing examples of variable membership functions, and Figure 4 is a judgment regarding two-manpower. Fig. 5 is a diagram showing an example of the agent for determining whether to change the evacuation of two approaching trains, and Fig. 6A is a diagram showing an example of the evacuation change determination agent for two approaching trains. Figures 5B and 6C are diagrams showing the criteria for determining whether a two-train evacuation change is necessary and the extent of its influence on others, Figure 7 is a diagram showing the weighting of the evacuation change judgment result, and Figure 8 is a diagram showing the results of a two-man-powered Figure 9 shows an example of cooperative inference, Figure 9 shows abnormality testing using a fuzzy coordinating agent, Figure 1Q shows an example of the fuzzy judgment side, and Figure 11 is executed with a judgment element targeting each control element. The processes 70 to 15 shown in FIGS. 12 to 15 are all partially detailed flowcharts of FIG. 11. 11-1 to 11-3, 31-1 to 31-6, 12-1
4.2 system accommodation■judgment element, 21-1 to 21-52 control station control means, 22: train command means, 23: crew operation command means, 24: depot work management means, 32-1 to 32-
6: Entrainment judgment means on the train, 130: First means, 2
10', 211, 220° 221.270.271:
Second means, 300: Third means, 400.450: Fourth means, 250.251: Fifth means. Patent Applicant: Hitachi, Ltd. - Figure 1 - Figure 2 15 Figure 3 Figure 4 Figure 5 V 6, 1'4'' 1 Figure 6 B Figure 7 Figure 8 Figure 12 Figure 13 Figure 14 Figure 15

Claims (1)

[Claims] α) Consisting of a plurality of control objects, a judgment element that monitors the state of the control objects, judges their behavior, and controls them, and an information transmission path that interconnects each judgment element. In the system, each of the above-mentioned judgment elements independently judges the control output for an event occurring in the controlled object of the self-judgment element, and sends the judgment result to the above-mentioned control object and an information transmission path, and an information transmission path. a second means for receiving and storing other judgment results related to the self-judgment element from the computer; and a second means for receiving and storing other judgment results related to the self-judgment element from C) The first, second, third means are 2. The control method according to claim 1, wherein fuzzy inference is applied to each of the control methods. 0 In addition to the first, @2, and third means, each of the above-mentioned judgment elements includes:
Comparing the results of the first means and the results of other judgment factors obtained from the second means with the results of the third means, respectively,
Detecting the degree of abnormality that requires self-judgment or other judgment @
Claim 1 characterized in that it has the means of 4.
Control method described in section. (4) Each of the above judgment elements is the first, second, third and fourth judgment element.
In addition to the above means, in the second means, when receiving and storing judgment information of other judgment elements, the key words included in the judgment information and the judgment process carried out by the self-judgment element are
The present invention is characterized by having a fifth means for determining whether or not to incorporate information based on the degree of coincidence with the keyword that is transmitted and the degree of relationship between the self-judgment element and other judgment elements that have transmitted the judgment information. A control method according to claim 1. (5) The control system according to claim 1, 3 or culture, wherein the fourth and fifth means apply fuzzy inference, respectively.