JP4727484B2 - Program, train operation adaptive Petri net model generation device, train suppression arrangement support device, train operation adaptive Petri net model generation method and train suppression arrangement support method - Google Patents

Description

  The present invention relates to an apparatus, a program, a method, and the like for generating a Petri net model adapted to train operation.

  A Petri net model is known as one of methods for modeling a discrete event system. This Petri net model is a model that represents a change in state by connecting a place and two types of nodes called transitions by arcs, and a token transitions between places.

An example in which this Petri net model is applied to train diagram simulation is disclosed in Patent Document 1.
JP-A-10-217969

  A train always runs on a track, and the track is basically fixed. In addition, the track is divided by block sections, and only one train can be present in each block section. Therefore, a plurality of trains are not located in the same closed section, and it is impossible to pass the same train as the preceding train and pass the train. In view of these restrictions, it can be said that the train diagram simulation is suitable for the Petri net model.

  However, when a Petri net model is simply generated on the basis of equipment data such as lines, the Petri net model composed of places, transitions, and arcs excluding tokens exactly represents the line wiring itself. For this reason, when a token representing a train is arranged / transitioned to simulate a train operation, a complicated process is required.

  Specifically, for example, when a train that runs on a single track section needs to be controlled to change the token to both up and down, or when a normal train and a fast train run on the same track between stations May need to identify and control tokens for regular trains and high-speed trains.

  The present invention has been made in view of the above-described problems, and an object of the present invention is to generate a Petri net model from the viewpoint of the facility side that simply generates a Petri net model based on facility data. Instead, it is to generate a Petri net model from the viewpoint of the operation side adapted to the operation of the train.

  Another object of the present invention is to generate an appropriate train suppression arrangement plan by applying a Petri net model from the viewpoint of the train operation side to simulation of train suppression arrangement.

  Train deterrence means stopping a train that has been affected by a commander and a train that may be affected at an appropriate location when a safety or train operation is expected to be disrupted due to an accident, etc. The arrangement that is the procedure for moving the train for that purpose is called a train deterrent arrangement.

  There are two main types of train deterrence, one is train deterrence (hereinafter referred to as “emergency deterrence”) for the purpose of ensuring that safety does not allow the train to enter the vicinity of the obstacle, and the other is obstruction. Train deterrence (hereinafter referred to as “rearrangement deterrence”) for smooth operation arrangement after release.

  More specifically, emergency deterrence is an emergency deterrence when an accident or the like occurs and a problem is expected. For this reason, when performing emergency deterrence, giving priority to stopping the train itself.

  On the other hand, the arrangement suppression is performed after emergency suppression or almost simultaneously, and the train is suppressed by determining an appropriate stop point. For example, if the arrangement is suppressed so that the train stops at a station that can be turned back, the train can be smoothly turned back by the driving arrangement.

  Another object of the present invention is to generate an appropriate arrangement plan for making a desired arrangement suppression state from a state where the train is urgently suppressed.

The first invention for solving the above problems is:
Computer
An equipment reference Petri net model is generated on the basis of at least equipment data (for example, equipment data 55 in FIG. 13; inter-station line data 56 and station number line entry / exit condition data 57) in which inter-station lines and station number line entry / exit conditions are defined. Equipment model generation means (for example, step S11 in FIG. 1, step A1 in FIG. 14),
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation means (for example, step S13 in FIG. 1, step A1 in FIG. 14),
Train operation adaptive model generation means for generating the train operation adaptive Petri net model (for example, FIG. 1 step S15, FIG. 14 step A1),
For example, the train operation adaptive model generation program 52 in FIG. 13.

As another invention,
An equipment model generating means for generating an equipment reference Petri net model based on equipment data in which at least the inter-station line and the station number line entry / exit conditions are defined;
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation means;
The train operation adaptive model generation means for generating the train operation adaptive Petri net model by synthesizing the generated operation Petri net model for each type of train by wrapping with a predetermined supplementary place for blockage for each block section,
It is good also as comprising the train operation adaptive Petri net model production | generation apparatus (For example, the train suppression arrangement assistance apparatus 1 of FIG. 13) provided with.

Furthermore,
An equipment model generation step for generating an equipment reference Petri net model based on equipment data in which at least the inter-station line and station number line entry / exit conditions are defined,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation process;
The train operation adaptive model generation step of generating the train operation adaptive Petri net model by synthesizing the generated operation Petri net model for each train type by wrapping with a predetermined supplementary place for blockage for each block section,
The train operation adaptive Petri net model generation method including

  According to these inventions, an operation Petri net model for each train type is generated from an equipment reference Petri net model generated based on the equipment data, and the operation Petri net model for each train type is a predetermined supplementary place for blockage. By being bundled, a train operation adaptive Petri net model is generated. Therefore, the train operation adaptive Petri net model includes a Petri net model corresponding to each type of train, and is combined with a supplementary place for blockage for each block section. Therefore, it is possible to generate a Petri net model suitable for train operation without any contradiction to facility data.

The second invention is the program of the first invention,
Emergency suppression position setting means (for example, step B3 in FIG. 15) for setting the position of the train in the emergency suppression state,
Arrangement suppression desired position setting means (for example, step B5 in FIG. 15) for setting the desired position of the train in the arrangement inhibition state,
A plurality of train restraint arrangements that define a train movement procedure based on the generated train operation adaptive Petri net model, the set emergency restraint state train position, and the set order restraint state desired train position Candidate generating means for generating candidates (for example, step B11 in FIG. 15),
Evaluation criterion setting means (for example, step B1 in FIG. 15) for setting an evaluation criterion firing condition that defines a combination of the firing order and a transition desired to be ignited in that order,
For each train suppression arrangement candidate generated, the combination of the firing order of transitions according to the train movement procedure by the train suppression arrangement candidate and the transitions fired in that order is based on the set evaluation criterion firing conditions. Evaluation processing means for evaluating (for example, step B15 in FIG. 15),
Based on the evaluation result by the evaluation processing means, output means (for example, step B21 in FIG. 15) for determining and outputting a train suppression arrangement candidate to be output among the generated train suppression arrangement candidates.
As a program for causing the computer to function (for example, a train suppression arrangement generating program 53 in FIG. 13).

As another invention,
An equipment model generating means for generating an equipment reference Petri net model based on equipment data in which at least the inter-station line and the station number line entry / exit conditions are defined;
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation means;
The train operation adaptive model generation means for generating the train operation adaptive Petri net model by synthesizing the generated operation Petri net model for each type of train by wrapping with a predetermined supplementary place for blockage for each block section,
Emergency deterrence position setting means for setting the position of the train in an emergency deterrence state;
An arrangement suppression desired position setting means for setting a desired position of an arrangement inhibition state train;
A plurality of train restraint arrangements that define a train movement procedure based on the generated train operation adaptive Petri net model, the set emergency restraint state train position, and the set order restraint state desired train position Candidate generation means for generating candidates;
An evaluation standard setting means for setting an evaluation standard ignition condition that defines a combination of an ignition order and a transition desired to be fired in the order;
For each train suppression arrangement candidate generated, the combination of the firing order of transitions according to the train movement procedure by the train suppression arrangement candidate and the transitions fired in that order is based on the set evaluation criterion firing conditions. An evaluation processing means for evaluating
Based on the evaluation result by the evaluation processing means, among the generated train suppression arrangement candidates, output means for determining and outputting a train suppression arrangement candidate to be output;
It is good also as comprising the train suppression arrangement assistance apparatus (For example, the train suppression arrangement assistance apparatus 1 of FIG. 13) provided with.

Furthermore,
An equipment model generation step for generating an equipment reference Petri net model based on equipment data in which at least the inter-station line and station number line entry / exit conditions are defined,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation process;
The train operation adaptive model generation step of generating the train operation adaptive Petri net model by synthesizing the generated operation Petri net model for each train type by wrapping with a predetermined supplementary place for blockage for each block section,
An emergency deterrence position setting step for setting the position of the train in an emergency deterrence state;
An arrangement control desired position setting step for setting a desired position of the train in an arrangement suppression state;
A plurality of train restraint arrangements that define a train movement procedure based on the generated train operation adaptive Petri net model, the set emergency restraint state train position, and the set order restraint state desired train position A candidate generation step for generating candidates;
An evaluation standard setting step for setting an evaluation standard ignition condition that defines a combination of an ignition order and a transition desired to be fired in the order,
For each train suppression arrangement candidate generated, the combination of the firing order of transitions according to the train movement procedure by the train suppression arrangement candidate and the transitions fired in that order is based on the set evaluation criterion firing conditions. An evaluation process step to evaluate
Based on the evaluation result in the evaluation processing step, among the generated train suppression arrangement candidates, an output step of determining and outputting a train suppression arrangement candidate to be output; and
It is good also as comprising the train suppression arrangement | sequence assistance method containing.

  According to these inventions, train suppression arrangement candidates are generated based on the train operation adaptive Petri net model, the train position in the emergency suppression state, and the desired train position in the organization suppression state. However, the train suppression arrangement candidate to be output is determined based on the evaluation result for the evaluation reference ignition condition. Since the evaluation criterion firing condition is a combination of the firing order and the transition that is desired to fire in that order, for example, the firing order of the transition for moving a train located around the location where the accident occurred is By setting an evaluation standard firing condition that gives priority to the firing order of transitions for moving a train located far from a location, train suppression arrangement candidates that meet the desired evaluation standard firing condition can be highly evaluated. become able to.

The third invention is the program of the first or second invention,
Train number setting means for setting the number of trains in an emergency deterrent state (for example, step B3 in FIG. 15),
Desired movement number setting means (for example, step B1 in FIG. 15) for setting the number of train movements between desired closed sections from the emergency deterrence state to the arrangement deterrence state,
Of the transitions constituting the generated train operation adaptive Petri net model, combination calculation means for obtaining a combination of transitions to be ignited by the number of times of train movement set by the desired movement number setting means (for example, step B11 in FIG. 15) ),
Token increase / decrease value calculating means (for example, step B7 in FIG. 15) for calculating the total increase / decrease value of tokens when each of the transitions constituting the calculated combination is ignited,
If the calculated total increase / decrease value is other than zero, it is impossible to determine that it is impossible to move the train from the emergency deterrence state to the arrangement deterrence state with the set number of train movements. Determination means (for example, step B7 in FIG. 15),
As a program for causing the computer to function.

  Calculation of train suppression arrangements for moving a train in an emergency suppression state to an arrangement suppression state involves a process of repeatedly calculating a large number of train suppression arrangement candidates, so the calculation amount is enormous. Emergency deterrence due to an accident or the like is in a state where there is no waiting, and in order to shift to the arrangement deterrence state as quickly as possible, it is desired to reduce the amount of calculation of train deterrent arrangement. According to the third invention, this calculation amount can be reduced. That is, it can be determined by simple determination whether the total increase / decrease value of the token calculated by the token increase / decrease value calculation means is zero or not that the train suppression arrangement candidate that is repeatedly calculated cannot be executed reliably.

  ADVANTAGE OF THE INVENTION According to this invention, the Petri net model from the viewpoint of the operation side adapted to the operation of a train can be produced | generated. In addition, an appropriate train deterrence arrangement plan for making a desired arrangement deterrence state can be generated from an emergency deterrence state of the train.

1. Principle First, with reference to FIG. 1, the support of train suppression arrangement by generating a train suppression arrangement plan will be described.
Train restraint arrangement support is largely realized by two steps: train operation adaptive model generation (step S1) and train suppression arrangement plan generation (step S3).

1-1. Train Operation Adaptation Model Generation Train operation adaptation model generation is a step of generating a train operation adaptation model using a Petri net model, an equipment reference model generation (step S11), a train type model generation (step S13), and a train type. This is realized by three steps including another model synthesis (step S15). Hereinafter, these three steps will be described in detail.

1-1-1. Equipment Standard Model Generation First, a model called “equipment standard model” is generated. Here, the case where the equipment reference model for the wiring shown in FIG. 2 is generated will be described as an example. The wiring shown in FIG. 2 shows a double-track line consisting of a down line and an up line. One station has two lines, one line and two lines, two stations have one line, two lines, and three lines. There are two number lines, number 1 and number 2, at 3 stations and 3 stations. In addition, each line except the 3rd line of 2 stations can be turned back.

  The equipment reference model is expressed by a Petri net model. The Petri net model is a bipartite directed graph having two types of nodes called “places” and “transitions”.

The place is a node represented by a circle and indicates a condition included in the target system.
In the present embodiment, there are places representing “blocking sections” existing between the stations and places representing “number lines” of the stations. Hereinafter, a place is comprehensively represented by “p”.

  The transition is a node represented by a bar and indicates an event included in the target system. In the present embodiment, as a transition, a transition indicating that the train “enters a closed section between stations from the station line” and a train “enters a next closed section from the closed section between stations” is represented. There are transitions and transitions that indicate that a train “enters a station line from a closed section between stations”. Hereinafter, the transition is generically represented by “t”.

  Then, a point called “token” is arranged in the place, and the change of the state is represented by the transition of the token from place to place. The token indicates that the condition is satisfied, and in principle, the token indicates “train” in the present embodiment. However, in this embodiment, it is assumed that at most one token is arranged in one place. That is, only one train can exist in one “blocked section” or “numbered line”.

  The place and the transition are connected by an arrow called “arc” having a predetermined weight. However, in this embodiment, the weight of all arcs is “1”. Hereinafter, an arc from the place to the transition is referred to as an “input arc”, and an arc from the transition to the place is referred to as an “output arc”. The place where the input arc departs is called “input place”, and the place where the output arc reaches is called “output place”.

  The token placed in the place transitions to another place when the transition is ignited. Specifically, when all the input places of the transition have tokens equal to or greater than the weight set in the input arc, the transition can be fired. However, transitions that can be ignited may or may not ignite.

  When the transition is ignited, tokens are removed from all the input places of the transition by the number of input arc weights, and tokens are arranged at the number of output arc weights at all of the output places of the transition. This indicates that the train has advanced from one closed section (or number line) to the next closed section (or number line).

FIG. 3 is a diagram for explaining the operation of the Petri net model.
FIG. 3 shows a Petri net model including places “p1” to “p4” and transitions “t1” and “t2”. The place and the transition are connected by an arc, and the weight of each arc is “1”.

  The input places of the transition “t1” are the places “p1” and “p2”, and the transition “t1” can be ignited by arranging one or more tokens in each place. However, in FIG. 3A, one token is placed in the place “p1”, but no token is placed in the place “p2”, so the transition “t1” cannot be fired.

  On the other hand, in FIG. 3B, since one token is arranged in each of the places “p1” and “p2”, the transition “t1” can be ignited. Then, by igniting the transition “t1”, one token is removed from each of the places “p1” and “p2”, and one token is placed in the place “p3” that is the output place of the transition “t1”. (FIG. 3C).

  Although the Petri net model has been described, when generating the equipment reference model, first, based on the equipment data 55 relating to the wiring to be generated (here, FIG. 2), the Petri net called the “facility reference temporary model” Generate a model.

FIG. 4 is a diagram showing facility data 55 representing the wiring of FIG.
The facility data 55 includes inter-station line data 56 and station number line entry / exit condition data 57. The inter-station line data 56 is shown in FIG. 4 (a), and the station number line entry / exit condition data 57 is shown in FIG. 4 (b). .

  The inter-station line data 56 is associated with a line 561, a start station 563, a terminal station 565, and an allowable number 567. A line 561 indicates whether the type of the line is “down” or “up”. The start station 563 and the end station 565 indicate the departure station and arrival station of the train, respectively, and here, “1 station” to “3 stations” are set. The allowable number 567 indicates the number of trains allowed to be between the start station 563 and the end station 565. That is, the allowable number 567 represents the number of closed sections existing between stations.

  In the station number line entry / exit condition data 57, a station 571, a number line 573, a line 575, and an entry / exitable section 577 are associated. In the station 571, any one of “1 station” to “3 stations” is set. A number line 573 indicates a number line existing at the station, and here, any one of “number 1” to “number 3” is set.

  The line 575 is the same as the line 561 of the inter-station line data 56. The entry / exit section 577 indicates whether or not the train can advance or enter between the number line 573 and the track 575. When the entry is only possible, “advance” or only the entry is possible. “Enter” and “Enter” are set when both are possible.

  First, it can be seen from the first line L11 of the inter-station line data 56 that “if the train advances from one station on the down line, the next station is two stations”. Further, from the first line L21 of the station number line entry / exit condition data 57, it can be seen that "It is possible to advance from the first line of one station to the down line". By setting a place and a transition based on these two pieces of information and connecting them with an arc, the Petri net model shown in FIG. 5A can be obtained.

  Further, from the fourth line L14 of the inter-station line data 56, it is understood that “if the train enters one station on the up line, the previous station is two stations”. Further, from the second line L22 of the station number line entry / exit condition data 57, it can be seen that "the first line of one station can be entered on the up line". Based on these two pieces of information in addition to the previous two pieces of information, the Petri net model is changed as follows. That is, by adding places and transitions to the Petri net model of FIG. 5A and connecting them with arcs, the Petri net model of FIG. 5B can be obtained.

  Thereafter, the same procedure is repeated, and places and transitions are added based on information obtained from combinations of all the lines of the inter-station line data 56 and the station number line entry / exit condition data 57, and they are connected by arcs. The Petri net model obtained in step 1 is used as a facility-based temporary model. FIG. 6 shows a provisional standard model finally obtained for the wiring of FIG.

  Next, an equipment standard model is generated by modifying the generated equipment standard temporary model. Specifically, based on the allowable number 567 of the inter-station line data 56, each place representing the blocked section included in the facility reference temporary model is divided according to the number of allowable numbers 567.

  Specifically, the number of places representing the blockage section is set as many as the allowable number 567, and one transition is set between adjacent places. Then, the set place and transition are connected by an arc.

For example, from the first line L11 of the inter-station line data 56 in FIG.
It can be seen that there are two trains between stations. Therefore, the place representing the blocked section of the downlink between the first station and the second station is divided into two places.

  Thereafter, the same procedure is repeated, and places representing blocked sections are divided based on the allowable number 567 of all the lines of the inter-station line data 56, and the finally obtained Petri net model is used as the equipment reference model. . FIG. 7 shows an equipment standard model obtained from the equipment standard provisional model of FIG.

  In the equipment standard model of FIG. 7, the parts represented by “N1” to “N4” respectively divide places included in the parts represented by “M1” to “M4” of the equipment standard provisional model of FIG. This is the part obtained in

1-1-2. Train-specific model generation After generating the equipment reference model in step S11, a Petri net model called a "train type-specific model" is generated based on the generated equipment reference model. Hereinafter, for simplicity, a case will be described in which the equipment reference model is represented by a Petri net model as shown in FIG.

  First, the equipment reference model is duplicated for the existing train types. Considering the case where there are two types of train types, “train type 1” and “train type 2”, the equipment reference model in FIG. 8A is duplicated in two.

  Then, for each train type, a portion unnecessary for train operation is deleted from the copied Petri net model, and the resulting model is used as a train type model. For example, if “train type 1” is a regular train and “train type 2” is a rapid train, the regular train may pass either the main line or the secondary line of the station, but the rapid train may be the secondary line of the station. There is a request not to let go through as much as possible.

  Therefore, for “train type 2”, the place representing the number line corresponding to the secondary line of the station is deleted from the copied Petri net model, and the transition adjacent to the place and the arc connecting them are deleted. To do. FIG. 8B shows a train type model obtained from the facility reference model of FIG.

  In FIG. 8B, a place represented by “N5” of the train type model for “train type 1” represents a place that represents a number line corresponding to the sub-main line of the station, and a place adjacent to the place. It is a part to include. And it turns out that the part corresponding to "N5" is deleted in the model according to train classification about "train classification 2".

1-1-3. Model synthesis by train type When a model by train type is generated in step S13, a "train operation adaptive model" is generated by combining the train type models into one. Specifically, the train type model is synthesized by tying places showing the same closed section or line in each train type model with a dummy place called “complement place”.

  FIG. 9 shows a train operation adaptive model obtained by synthesizing the train type model of FIG. 8B. In the train operation adaptation model of FIG. 9, supplementary places “sp1” to “sp6” are placed between places representing the same closed section or line in the train type model for each of “train type 1” and “train type 2”. Is set.

  Here, with reference to FIG. 10, the significance of synthesizing the train type model using the complementary place will be described in detail. In FIGS. 10A to 10C, it is assumed that “P2” and “P5” represent the same closed section.

  FIG. 10A is a diagram illustrating an example of a train type model before synthesis. Now, one token is placed in the place “p1” of the train type model for the upper “train type 1” and the place “p5” of the train type model for the lower “train type 2”. Think if you are. This means that one train exists in the block section represented by the place “p1” and the block section represented by the place “p5”.

  In this case, when the transition “t1” is fired, the token of the place “p1” is removed, and the token is placed in the place “p2”. Then, one token is placed in each of the places “p2” and “p5”. However, since the places “p2” and “p5” represent the same blockage section, Two trains are present. This shouldn't really happen.

  Therefore, the places “p2” and “p5” representing the same blockage section are enclosed by the complementary place “sp1”. Considering the case where one token is placed in each of place “p4” and complementary place “sp1” (FIG. 10B), transition “t3” can be ignited and transition “t3”. Is fired, one token is removed from the place “p4” and the complementary place “sp1” one by one, and one token is placed in the place “p5”. After that, it is assumed that one token is placed in the place “p1” (FIG. 10C).

  Although a token is placed in the place “p1”, but no token is placed in the complementary place “sp1”, the transition “t1” cannot be ignited. Accordingly, no token is placed in the place “p2” in this state.

  Further, in order for a token to be placed at the place “p2”, a token needs to be placed at the complementary place “sp1”, and the transition “t1” needs to be ignitable. However, the token is placed in the complementary place “sp1” after the transition “t4” is fired and the token placed in the place “p5” is removed. From the above, it can be said that it is unlikely that tokens exist simultaneously in both places “p2” and “p5”.

  In this way, by combining the same place of the train type model with the supplementary place, it is possible to avoid the simultaneous presence of tokens in the same place. In practice, the supplementary place also represents the blockage section (or line).

1-2. Train suppression arrangement plan generation Next, the principle of generating a train suppression arrangement plan using the train operation adaptation model generated in step S1 will be described.

  First, for the generated train operation adaptation model, a maximum value of the number of transition firings (hereinafter referred to as “maximum number of firings”) is set. This maximum number of firings represents the desired number of train movements from the emergency deterrent state to the organized deterrence state.

  Moreover, based on the number of trains in the emergency deterrence state and the stop position of each train, the initial state is set by placing tokens in places of the train operation adaptive model. In addition, the final desired state is set by placing tokens in the place of the train operation adaptive model based on the number of trains in the organized suppression state and the desired stop position of each train.

Now, the arrangement of tokens in a place is called marking and is defined by m = [m (1) m (2)... M (M)] ^T. However, “M” is the total number of places included in the train operation adaptation model. The marking in the initial state is represented by “m ₀ ”, and the marking in the final desired state is represented by “m _f ”.

  The element “m (i)” of the marking “m” is “1” when a token is arranged in the place “pi”, and “0” when the token is not arranged. For example, if a token is placed in the place “p1”, “m (1) = 1”, and if not placed, “m (1) = 0”.

Further, the firing order of transitions and the combination of fired transitions are called firing sequences, and are defined as u (k) = [u (k, 1) u (k, 2)... U (k, N)] ^T . However, “N” is the total number of transitions included in the train operation adaptation model.

  The element “u (k, j)” of the firing sequence “u (k)” is “1” when the k-th firing occurs in the transition “tj”, and “0” when it does not occur. For example, when the second ignition occurs in the transition “t4”, “u (2,4) = 1”.

Also, a connection matrix “A” representing the structure of the train operation adaptive model is defined. Here, a method of defining the connection matrix “A” will be described with reference to FIG.
Consider a case where the train operation adaptation model is represented by a simple Petri net model as shown in FIG. However, the token is omitted in FIG.

  In the train operation adaptation model of FIG. 11, when the transition “t1” is fired, one token is placed in the place “p2”. When the transition “t2” is fired, one token is placed in each of the places “p3” and “p4”. A state in which the tokens are arranged is represented by an output connection matrix “A (+)” in the following equation (1).

  In Expression (1), each row of the output connection matrix “A (+)” represents a place “p”, the first row is the place “p1”, the second row is the place “p2”, and the third row is the place. “P3” and the fourth line correspond to the place “p4”, respectively. Each column represents a transition “t”, and the first column corresponds to the transition “t1”, and the second column corresponds to the transition “t2”.

  Then, “1” is set to the component of the place where the token is arranged by firing of the transition, and “0” is set to the component of the place where the token is not arranged. In Equation (1), the (2, 1), (3, 2) and (4, 2) components (where (row, column)) of the output connection matrix “A (+)” are “1”, The other components are “0”.

  Further, in the train operation adaptation model of FIG. 11, when the transition “t1” is fired, one token is removed from each of the places “p1” and “p4”. When the transition “t2” is fired, one token is removed from the place “p2”. A state in which this token is removed is represented by an input connection matrix “A (−)” of the following equation (2).

  Similar to the output connection matrix “A (+)”, each row of the input connection matrix “A (−)” represents a place “p”, and each column represents a transition “t”. Then, “1” is set to the component of the place where the token is removed by the firing of the transition, and “0” is set to the component of the place where the token is not removed. In Expression (2), the (1, 1), (4, 1) and (2, 2) components of the input connection matrix “A (−)” are “1”, and the other components are “0”. .

  Then, using the output connection matrix “A (+)” and the input connection matrix “A (−)”, the connection matrix “A” is defined by the following equation (3).

  As is apparent from Equation (3), the connection matrix “A” is represented by the difference between the output connection matrix “A (+)” and the input connection matrix “A (−)”. This connection matrix “A” represents an increase or decrease in tokens due to the firing of a transition. Therefore, when the connection matrix “A” is operated as an operator on the firing sequence “u (k)”, the number of tokens increased or decreased due to the firing of the transition is obtained.

  Under the above definition, numerical calculation based on a known method is performed using the marking “m” and the connection matrix “A” to obtain the firing sequence “u (k)”. At this time, the following expressions (4) to (6) are given as constraint conditions.

However, “K” is the maximum number of firings, “M” is the number of places, “N” is the number of transitions, “a (i, j)” is the (i, j) component of the connection matrix A, “a ⁻ (i, j) "represents the (i, j) component of the input connection matrix A (-).

  Equation (4) means that any transition always fires in the k-th firing. Equation (5) means that the total increase / decrease number of tokens due to the firing of the transition is always equal to the difference between the number of tokens in the final desired state and the number of tokens in the initial state. Expression (6) means which transition can be ignited at the k-th time in the marking after the k-th time ignited.

  When the firing sequence “u (k)” is obtained under the constraints of the equations (4) to (6), the evaluation value E is calculated according to the following equation (7).

  However, “w (k, j)” is a weight, and a larger value is set for an element of a firing sequence related to a transition to be preferentially fired. For example, if you want to preferentially fire the transition “t5” in the first firing, set a large value (for example, “1”) to “w (1, 5)” and set all other weights small. A value (for example, “0”) may be set.

  Thereafter, the initial state is fixed, the final desired state and the maximum number of firings are changed, and the process of calculating the evaluation value E by repeatedly calculating the firing sequence “u (k)” by numerical calculation is repeated. The series “u (k)” is a train suppression arrangement candidate. And let the operation | movement of the train operation adaptive model represented by the train suppression arrangement candidate with the largest evaluation value E be a train suppression arrangement plan.

  In addition, although calculation of a train suppression arrangement depends on the scale of the train operation adaptation model, it involves a process of repeatedly calculating a large number of train suppression arrangement candidates, so that the calculation amount is enormous. However, there is no waiting for emergency deterrence due to an accident or the like, and it is desirable to reduce the amount of calculation in order to shift to the organized deterrence state as quickly as possible.

Therefore, in the middle of calculating train suppression arrangement candidates, there is a solution that satisfies equation (5) when “Σ _k u (k, j), j = 1, 2,... It is determined whether or not it exists. If it does not exist, the next repetitive calculation is performed. This is because, although not here specifically, _{"Σ k u (k, j)} , j = 1,2, ···, N " If the solution satisfying the equation (5) when regarded as variable does not exist This is because it is apparent that it is impossible to reach the final desired state from the initial state. However, just because “Σ _k u (k, j), j = 1, 2,... It is not guaranteed that the desired state can be reached.

When determining whether there exists a solution satisfying the equation (5) when “Σ _k u (k, j), j = 1, 2,..., N” is regarded as a variable. The determination is made by paying attention only to the number of firings of each transition, and omitting information on the firing order.

Next, the principle of train suppression arrangement plan generation will be described with a specific example.
Consider a case where the train operation adaptation model is represented by a simple Petri net model as shown in FIG. Also, the state where the token is placed only at the place “p1” (FIG. 12B) is set as the initial state, and the state where the token is placed only at the place “p4” (FIG. 12C) is set as the final desired state. And set the maximum number of firings to “2”.

In this case, the marking “m ₀ ” in the initial state is represented by m ₀ = [1000] ^T. The marking “m _f ” in the final desired state is represented by m _f = [0001] ^T.

  The output connection matrix A (+), the input connection matrix A (−), and the connection matrix A are given by the following equations (8) to (10), respectively.

  Then, the firing sequences “u (1)” and “u (2)” are obtained by numerical calculation under the constraints given by the following equations (11) to (15).

  However, although the expressions (11) and (12) are described using the element “u (k, j)” of the firing sequence, the expressions (13) to (15) It is described using the firing sequence “u (k)”, marking “m”, connection matrix “A”, and input connection matrix “A (−)”. Actually, numerical calculation is performed using each element.

  Expression (11) means that any transition always fires in the first firing. Expression (12) means that any transition always fires in the second firing. Equation (13) shows that the sum of the number of tokens increased and decreased by the first firing and the number of tokens increased and decreased by the second firing is the difference between the number of tokens in the final desired state and the number of tokens in the initial state. It shows that they are equal. Expression (14) indicates which transition can be ignited the first time in the initial marking. Expression (15) indicates which transition can be fired the second time in the marking after the first firing.

  When the firing sequences “u (1)” and “u (2)” are obtained by numerical calculation, the evaluation value E is calculated according to the following equation (16).

Thereafter, while changing the final desired state and the maximum number of firings, the process of calculating the evaluation value E by obtaining the firing sequences “u (1)” and “u (2)” by numerical calculation is repeated, and the evaluation value E The train operation adaptation model represented by the maximum firing series “u (1)” and “u (2)” is the final train suppression arrangement plan. In this case, as the firing sequence “u (k)”, for example, u (1) = [0100] ^T and u (2) = [0001] ^T are obtained.

2. Example Next, a train suppression arrangement support device 1 that is a device that generates a train suppression arrangement plan based on the procedure described in “1. Principle” will be described.

2-1. Configuration First, the configuration will be described.
FIG. 13 is a block diagram illustrating a functional configuration of the train suppression arrangement support device 1.
The train suppression arrangement support device 1 includes a CPU (Central Processing Unit) 10, an input unit 20, a display unit 30, a communication unit 40, a hard disk 50, and a RAM (Random Access Memory) 60. The computer system is configured to be connected to each other so as to be capable of data communication.

  The CPU 10 comprehensively controls each unit according to a system program or the like stored in the hard disk 50. Further, the CPU 10 performs main processing according to the main program 51 stored in the hard disk 50 to generate a train operation adaptive model, and generates a train suppression arrangement plan based on the generated train operation adaptive model.

  The input unit 20 is realized by, for example, a keyboard and outputs a pressed key signal to the CPU 10. Various data input, processing instructions, and the like are made by key operations by the input unit 20.

  The display unit 30 is configured by an LCD (Liquid Crystal Display) or the like, and is a display device that performs various displays based on a display signal input from the CPU 10.

  The communication unit 40 is a communication device for exchanging information used inside the device with the outside via a communication network such as the Internet based on the control of the CPU 10.

  The hard disk 50 stores programs, data, and the like for realizing various functions included in the train suppression arrangement support device 1, and is read by the CPU 10 and executed as a main process (see FIG. 14), equipment, and the like. Data 55 is provided.

  The main program 51 includes a train operation adaptation model generation program 52 that is executed as a train operation adaptation model generation process, and a train suppression arrangement plan generation program 53 that is executed as a train suppression arrangement plan generation process (see FIG. 15). It is provided as a subroutine. The facility data 55 includes inter-station line data 56 and station number line entry / exit condition data 57.

  The train operation adaptive model generation process is a process in which the CPU 10 generates a train operation adaptive model based on the procedure described in “1-1. Train operation adaptive model generation”. Specifically, the CPU 10 first generates an equipment reference model based on the equipment data 55. And CPU10 duplicates the produced | generated equipment reference | standard model for every train classification, and produces | generates a model according to train classification by deleting the unnecessary part on train operation. Finally, the CPU 10 generates a train operation adaptive model by combining and synthesizing the train type models in a supplemental place.

  The train suppression arrangement plan generation process is a process in which the CPU 10 generates a train suppression arrangement plan based on the procedure described in “1-2. Train suppression arrangement plan generation”. The train suppression arrangement plan generation process will be described later in detail again using a flowchart.

  The inter-station line data 56 is data in which the types of lines between stations and the allowable number of trains are set, and an example of the data configuration is as shown in FIG. The station number line entry / exit condition data 57 is data in which the number line of each station and the advance / entry of the train are set, and the data configuration example is as shown in FIG.

  The RAM 60 forms a work area for temporarily storing a system program executed by the CPU 10, various processing programs, data being processed in various processes, processing results, and the like. In the RAM 60, for example, the train operation adaptation model data 61, the emergency suppression state data 62, the organization suppression state data 63, the connection matrix data 64, the candidate data 65, the evaluation value data 66, and the train suppression arrangement data 67 are stored. Is memorized.

  The train operation adaptive model data 61 stores the train operation adaptive model generated by the CPU 10 in the train operation adaptive model generation process. The emergency deterrence state data 62 stores information on the stop position of each train in the emergency deterrence completion state. In the train suppression arrangement plan generation process, an initial state is set by the CPU 10 based on the emergency suppression state data 62.

  The arrangement suppression state data 63 stores information on the desired stop position of each train in the arrangement suppression state. In the train suppression arrangement plan generation process, the final desired state is set by the CPU 10 based on the arrangement suppression state data 63.

  The connection matrix data 64 stores an input / output connection matrix “A (−)”, “A (+)”, and a connection matrix “A” representing the structure of the train operation adaptive model. These matrices are calculated simultaneously with the generation of the train operation adaptive model by the CPU 10 in the train operation adaptive model generation process.

  In the candidate data 65, the ignition sequence obtained by the CPU 10 in the train suppression arrangement plan generation process is stored as a train suppression arrangement candidate. The evaluation value data 66 stores the evaluation value E calculated by the CPU 10 based on the formula (7) in the train suppression arrangement plan generation process.

  In the train deterrence arrangement data 67, the train deterrence arrangement candidate (ignition sequence) determined by the CPU 10 as having the maximum evaluation value E in the train deterrent arrangement plan generation process is stored as the final train deterrence arrangement plan. .

2-2. Process Flow Next, the process flow will be described.
FIG. 14 is a flowchart showing a flow of main processing executed in the train suppression arrangement assisting apparatus 1 by the CPU 10 reading and executing the main program 51.

  First, the CPU 10 performs a train operation adaptive model generation process by executing the train operation adaptive model generation program 52 (step A1). Specifically, the CPU 10 first generates an equipment reference model based on the equipment data 55. Next, the generated equipment reference model is duplicated for each train type, and a train type-specific model is generated by deleting a portion unnecessary for train operation. And CPU10 produces | generates a train operation adaptive model by bundle | synthesize | combining and combining the model according to train classification in an auxiliary place. Further, the CPU 10 calculates an input / output connection matrix “A (−)”, “A (+)”, and a connection matrix “A” representing the train operation adaptation model at the same time as generating the train operation adaptation model, and connecting The matrix data 64 is stored in the RAM 60.

  Next, the CPU 10 executes a train suppression arrangement plan generation program 53 to perform a train suppression arrangement plan generation process (step A3), and ends the main process.

FIG. 15 is a flowchart showing a flow of train suppression arrangement plan generation processing.
First, the CPU 10 performs initial setting (step B1). Specifically, the total number of places included in the train operation adaptation model is calculated as “M”, and the total number of transitions is calculated as “N”. Further, the CPU 10 sets a predetermined value for the maximum firing number “K”. This value may be set to “0”, for example, or may be manually input by a person who has a general register.

  Further, the CPU 10 sets an initial value for each element “u (k, j)” of the firing sequence “u (k)”. Furthermore, the CPU 10 sets a threshold value for the maximum number of firings and an upper limit for the number of train suppression arrangement candidates.

Next, the CPU 10 sets an initial state by placing a token at a place of the train operation adaptive model based on the emergency deterrent state data 62 and calculating the marking “m ₀ ” (step B3). Further, the CPU 10 sets a final desired state by placing a token in a place included in the train operation adaptive model based on the arrangement inhibition state data 63 and calculating the marking “m _f ” (step B5).

Next, the CPU 10 performs a feasibility simple determination process (step B7). Specifically, the CPU 10 performs the marking “m ₀ ” in the initial state calculated in step B 3, the marking “m _f ” in the final desired state, the connection matrix “A” included in the connection matrix data 64, and the maximum number of firings “ based on K "," _{Σ k u (k, j)} , j = 1,2, ···, determine whether the solution satisfying the equation (5) when regarded N "and variable exists However, if it is determined that it exists, there is a possibility of execution. Since this operation of the decision is known method, it is omitted, Briefly, under the constraint condition of Equation (5), _{"Σ k u (k, j)} , j = 1,2, · .., N ”is an operation for obtaining a solution when the term represented as a variable is regarded as a variable.

  Next, the CPU 10 determines whether or not there is a possibility of execution (step B9). If it is determined that there is (step B9; Yes), the CPU 10 performs a train suppression arrangement candidate generation process (step B11). Since the calculation of this train suppression arrangement candidate is a known method, the description thereof is omitted, but simply, the firing sequence “u (k)” is obtained under the constraints of the equations (4) to (6). This is the operation to be calculated. Then, the obtained ignition sequence “u (k)” is set as a train suppression arrangement candidate.

  Thereafter, the CPU 10 determines whether or not the firing sequence “u (k)” has been obtained, that is, whether or not the train suppression arrangement candidate has been successfully generated (step B13), and if it is determined that it has been successful (step B13; Yes), arrangement candidate evaluation processing is performed (step B15). Specifically, the CPU 10 calculates the evaluation value E according to the equation (7) using each element “u (k, j)” of the firing sequence “u (k)” obtained in step B11.

  Next, the CPU 10 stores the ignition sequence “u (k)” obtained in step B11 in the RAM 60 as candidate data 65, and stores the evaluation value E obtained in step B13 in the RAM 60 as evaluation value data 66 ( Step B17).

  Then, the CPU 10 determines whether or not the number of train suppression arrangement candidates has reached a predetermined number that is the upper limit set in Step B1 (Step B19). If it is determined that the number has become (Step B19; Yes), the evaluation is performed. The train suppression arrangement candidate with the largest value E is set as a train suppression arrangement plan. And CPU10 memorize | stores a train suppression arrangement plan in RAM60 as the train suppression arrangement data 67, displays it on the display part 30 (step B21), and complete | finishes a train suppression arrangement plan production | generation process.

  On the other hand, when it is determined in step B9 that there is no execution possibility (step B9; No), the CPU 10 determines whether or not the maximum number of firings has reached the threshold set in step B1 (step B23). ). And when it determines with not having reached the threshold value (step B23; No), CPU10 increments the maximum number of times of ignition (step B25), and returns to step B7.

  On the other hand, when it determines with having reached the threshold value in step B23 (step B23; Yes), CPU10 changes the setting of the last desired state (step B27). Then, the CPU 10 resets the maximum number of firings (step B29) and returns to step B7.

  When it is determined in step B19 that the number of train suppression arrangement candidates has not reached the predetermined number (step B19; No), the CPU 10 shifts the process to step B27.

3. Effect According to this embodiment, an equipment standard model is generated based on equipment data including inter-station line data and station number line entry / exit condition data, and the equipment standard model is duplicated for each train type, which is unnecessary for train operation. By deleting such a part, a train type model is generated. Then, the same place of the train type-specific model is synthesized by being bundled by a supplement place, and a train operation adaptive model is generated. Therefore, the train operation adaptation model includes an equipment reference model corresponding to each train type, and is combined and supplemented by a supplementary place for each block section (or number line). Therefore, it is possible to generate a Petri net model suitable for train operation without any contradiction to facility data.

  In addition, a train suppression arrangement plan is generated using the generated train operation adaptive model. Specifically, train suppression arrangement candidates are generated based on the train position in the emergency suppression state and the desired train position in the organization suppression state. And among train suppression arrangement candidates, the train suppression arrangement candidate with the largest evaluation value E is taken as a train suppression arrangement plan. Here, since the evaluation value E is defined by the weighted sum of each element of each ignition series and a given weight, the weight of the element of the ignition series related to the transition to be preferentially ignited is set to be large. The transition can be preferentially ignited.

In addition, the calculation of train suppression arrangement involves a process of repeatedly calculating a large number of train suppression arrangement candidates, so that the calculation amount is enormous. Emergency deterrence due to an accident or the like is in a state where there is no waiting, and in order to shift to the arrangement deterrence state as quickly as possible, it is desired to reduce the amount of calculation of train deterrence arrangements. However, in the present embodiment, _{"Σ k u (k, j)} , j = 1,2, ···, N " whether solutions that satisfies Equation (5) when regarded as variable exists By determining, it is simply determined whether or not it is possible to reach the final desired state from the initial state, and a train suppression arrangement plan is generated only when it is determined that it exists. Therefore, the amount of calculation is reduced.

4). Modified example 4-1. Example of Use of Train Operation Adaptation Model In the present embodiment, the case where train suppression support is supported using the generated train operation adaptation model has been described as an example, but the present invention is not limited thereto. For example, train operation prediction or operation arrangement may be performed using the generated train operation adaptation model.

4-2. Evaluation value E
In the present embodiment, the train suppression arrangement candidate having the maximum evaluation value E is determined as the train suppression arrangement proposal, but this is given priority in the definition formula of the evaluation value E represented by Expression (7). This is because a large value is set for the weight related to the transition to be ignited. Therefore, it is of course possible to set a small value for the weight related to the transition to be preferentially ignited and to determine the train suppression arrangement candidate with the smallest evaluation value E as the train suppression arrangement plan.

The flowchart which shows the flow of train suppression arrangement support. The figure which shows an example of wiring. Explanatory drawing of operation | movement of a Petri net model. The figure which shows the data structural example of track line data between stations, and station number line entrance / exit condition data. The figure which shows the production | generation procedure of an equipment standard temporary model. The figure which shows an example of an equipment standard temporary model. The figure which shows an example of an equipment reference | standard model. The figure which shows the production | generation procedure of the model according to train classification. The figure which shows an example of a train operation adaptation model. Explanatory drawing of model synthesis by train type. Explanatory drawing of a connection matrix. Explanatory drawing of train suppression arrangement plan generation. The block diagram which shows the function structure of a train suppression arrangement assistance apparatus. The flowchart which shows the flow of a main process. The flowchart which shows the flow of a train suppression arrangement plan production | generation process.

Explanation of symbols

1 Train deterrence arrangement support device 10 CPU
DESCRIPTION OF SYMBOLS 20 Input part 30 Display part 40 Communication part 50 Hard disk 51 Main program 52 Train operation adaptive model generation program 53 Train suppression arrangement plan generation program 55 Equipment data 56 Inter-station line data 57 Station number line entry / exit condition data 60 RAM
61 Train operation adaptation model data 62 Emergency deterrence state data 63 Arrangement deterrence state data 64 Connection matrix data 65 Candidate data 66 Evaluation value data 67 Train deterrence arrangement data 70 Bus

Claims (7)

Computer
1) and the inter-station line number defined in block section between stations, 2) based on the facility data and the station track number input and conditions whether train expansion / entry is determined for each track number of each station is defined at least A facility model generation means for generating a facility reference Petri net model,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation means,
A supplementary place that is a place for defining the number of trains for the same blockage section is set between places that represent the same blockage section among the places that constitute each of the generated operation Petri net models for each train type. it is, train operating adaptive model generation means for generating a train operation adaptive Petri net model by synthesizing the generated train type-specific operating Petri net model,
Program to function as. The program according to claim 1,
Emergency deterrence position setting means for setting the position of the train in an emergency deterrence state;
Arrangement deterrence desired position setting means for setting a desired position of an arrangement deterrenced train,
A plurality of train restraint arrangements that define a train movement procedure based on the generated train operation adaptive Petri net model, the set emergency restraint state train position, and the set order restraint state desired train position Candidate generation means for generating candidates,
An evaluation standard setting means for setting an evaluation standard ignition condition that defines a combination of an ignition order and a transition desired to be fired in the order,
For each train suppression arrangement candidate generated, the combination of the firing order of transitions according to the train movement procedure by the train suppression arrangement candidate and the transitions fired in that order is based on the set evaluation criterion firing conditions. Evaluation processing means to evaluate,
Based on the evaluation result by the evaluation processing means, output means for determining and outputting train suppression arrangement candidates to be output among the generated train suppression arrangement candidates,
A program for causing the computer to function as The program according to claim 1 or 2,
Train number setting means for setting the number of trains in an emergency deterrent state,
Desired travel number setting means for setting the desired number of train movements between the blocked sections from the emergency deterrence state to the arrangement deterrence state,
Of each transition constituting the generated train operation adaptive Petri net model, a combination calculation means for obtaining a combination of transitions to be ignited by the number of train movements set by the desired movement number setting means,
Token increase / decrease value calculation means for calculating the total increase / decrease value of tokens when each of the transitions constituting the calculated combination is ignited,
If the calculated total increase / decrease value is other than zero, it is impossible to determine that it is impossible to move the train from the emergency deterrence state to the arrangement deterrence state with the set number of train movements. Determination means,
A program for causing the computer to function as 1) and the inter-station line number defined in block section between stations, 2) based on the facility data and the station track number input and conditions whether train expansion / entry is determined for each track number of each station is defined at least Equipment model generating means for generating equipment reference Petri net model,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation means;
A supplementary place that is a place for defining the number of trains for the same blockage section is set between places that represent the same blockage section among the places that constitute each of the generated operation Petri net models for each train type. it is a train service adaptive model generation means for generating a train operation adaptive Petri net model by synthesizing the generated train type-specific operating Petri net model,
Train operation adaptive Petri net model generator equipped with. 1) and the inter-station line number defined in block section between stations, 2) based on the facility data and the station track number input and conditions whether train expansion / entry is determined for each track number of each station is defined at least Equipment model generating means for generating equipment reference Petri net model,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation means;
A supplementary place that is a place for defining the number of trains for the same blockage section is set between places that represent the same blockage section among the places that constitute each of the generated operation Petri net models for each train type. it is a train service adaptive model generation means for generating a train operation adaptive Petri net model by synthesizing the generated train type-specific operating Petri net model,
Emergency deterrence position setting means for setting the position of the train in an emergency deterrence state;
An arrangement suppression desired position setting means for setting a desired position of an arrangement inhibition state train;
A plurality of train suppressions that define a train moving procedure based on the generated train operation adaptive Petri net model, the set train y position in the emergency suppression state, and the set desired train position in the organization suppression state Candidate generation means for generating arrangement candidates;
An evaluation standard setting means for setting an evaluation standard ignition condition that defines a combination of an ignition order and a transition desired to be fired in the order;
For each train suppression arrangement candidate generated, the combination of the firing order of transitions according to the train movement procedure by the train suppression arrangement candidate and the transitions fired in that order is based on the set evaluation criterion firing conditions. An evaluation processing means for evaluating
Based on the evaluation result by the evaluation processing means, among the generated train suppression arrangement candidates, output means for determining and outputting a train suppression arrangement candidate to be output;
Train deterrence arrangement support device equipped with. 1) and the inter-station line number defined in block section between stations, 2) based on the facility data and the station track number input and conditions whether train expansion / entry is determined for each track number of each station is defined at least An equipment model generation process for generating an equipment reference Petri net model,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation process;
A supplementary place that is a place for defining the number of trains for the same blockage section is set between places that represent the same blockage section among the places that constitute each of the generated operation Petri net models for each train type. it is a train service adaptive model generation step of generating a train operation adaptive Petri net model by synthesizing the generated train type-specific operating Petri net model,
Train operation adaptive Petri net model generation method including. 1) and the inter-station line number defined in block section between stations, 2) based on the facility data and the station track number input and conditions whether train expansion / entry is determined for each track number of each station is defined at least An equipment model generation process for generating an equipment reference Petri net model,
For each type of train to be operated, by correcting the generated equipment-based Petri net model to a Petri net model according to the operation of the type of train, a train type that generates an operation Petri net model for each train type Another model generation process;
A supplementary place that is a place for defining the number of trains for the same blockage section is set between places that represent the same blockage section among the places that constitute each of the generated operation Petri net models for each train type. it is a train service adaptive model generation step of generating a train operation adaptive Petri net model by synthesizing the generated train type-specific operating Petri net model,
An emergency deterrence position setting step for setting the position of the train in an emergency deterrence state;
An arrangement control desired position setting step for setting a desired position of the train in an arrangement suppression state;
A plurality of train restraint arrangements that define a train movement procedure based on the generated train operation adaptive Petri net model, the set emergency restraint state train position, and the set order restraint state desired train position A candidate generation step for generating candidates;
An evaluation standard setting step for setting an evaluation standard ignition condition that defines a combination of an ignition order and a transition desired to be fired in the order,
For each train suppression arrangement candidate generated, the combination of the firing order of transitions according to the train movement procedure by the train suppression arrangement candidate and the transitions fired in that order is based on the set evaluation criterion firing conditions. An evaluation process step to evaluate
Based on the evaluation result in the evaluation processing step, among the generated train suppression arrangement candidates, an output step of determining and outputting a train suppression arrangement candidate to be output; and
Train deterrence arrangement support method.

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