Method and device for train control, electronic equipment, and storage medium

Abstract

The present application relates to the technical field of automatic train driving. The embodiments of the present application provide a method, device, electronic device, and storage medium for train control, wherein the method includes: obtaining the actual speed of the current train and the actual distance between the current train and the preceding train; comparing the actual speed with the preset expected speed to obtain the actual speed deviation, and comparing the actual distance with the preset expected distance to obtain the actual distance deviation; performing a table lookup operation according to the actual speed deviation and the actual distance deviation to obtain the actual optimal speed weight interval and the actual optimal distance weight interval; obtaining a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval, respectively; obtaining the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; obtaining a level signal according to the acceleration; and controlling the current train according to the level signal to stably control the train.

Description

Technical Field

[0001] The present application relates to the technical field of automatic train driving, and in particular, to a method and device, electronic equipment, and storage medium for train control. Background Art

[0002] With the vigorous development of my country's urban rail transit industry, automatic train driving is becoming an inevitable trend in the future. Most mainstream control algorithms use model predictive control algorithms, but using this algorithm alone has certain disadvantages. Compared with traditional control algorithms such as PID, the model predictive control algorithm has more control parameters and is not easy to determine. If the parameters are set unreasonably, it is easy to cause poor vehicle control effect and fail to meet the control requirements. Most of the parameters of the model predictive control algorithm used in the prior art are fixed, that is, after the parameters are set, they are applicable throughout the process. However, in the actual vehicle control process, different vehicle operating states will appear. Therefore, the use of a single set parameter is likely to lead to poor vehicle control effect and it is difficult to achieve the purpose of stable vehicle control. However, traditional model predictive control algorithms are difficult to achieve stable vehicle control under different vehicle operating states.

[0003] Therefore, achieving stable vehicle control under different vehicle operating conditions is a technical problem that technical personnel in this field urgently need to solve.

[0004] The above information disclosed in the background section is only for enhancing understanding of the background of the present application and therefore it may contain information that does not form the prior art known to a person of ordinary skill in the art. Summary of the invention

[0005] To solve one of the above technical problems, an embodiment of the present application provides a method and device for train control, an electronic device, and a storage medium.

[0006] According to the first aspect of the embodiment of the present application, a method for train control is provided, comprising: obtaining an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes speed deviation intervals and distance deviation intervals under different vehicle operating conditions, and the output fuzzy rule table includes speed weight intervals and distance weight intervals under different vehicle operating conditions; testing the train according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and optimal distance interval of the train under different operating conditions; determining the fuzzy rule table according to the input fuzzy rule table, the output fuzzy rule table, each of the optimal speed weight intervals and each of the optimal distance intervals; obtaining the current train Actual vehicle speed, actual distance between the current train and the preceding vehicle; comparing the actual vehicle speed with a preset expected vehicle speed to obtain an actual speed deviation, and comparing the actual distance with a preset expected distance to obtain an actual distance deviation; performing a table lookup operation using the actual speed deviation and the actual distance deviation according to the fuzzy rule table to obtain an actual optimal speed weight interval and an actual optimal distance weight interval; obtaining a speed weight parameter and a distance weight parameter respectively according to the actual optimal speed weight interval and the actual optimal distance weight interval; obtaining an acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; obtaining a level signal according to the acceleration; and controlling the current train according to the level signal.

[0007] According to a second aspect of an embodiment of the present application, a device for train control is provided, comprising: a first acquisition module, configured to acquire an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes a speed deviation interval and a distance deviation interval under different vehicle operating conditions, and the output fuzzy rule table includes a speed weight interval and a distance weight interval under different vehicle operating conditions; a test module, configured to test a train according to the input fuzzy rule table and the output fuzzy rule table, and obtain an optimal speed weight interval and an optimal distance interval for the train under different operating conditions; a determination module, configured to determine a fuzzy rule table according to the input fuzzy rule table, the output fuzzy rule table, each of the optimal speed weight intervals and each of the optimal distance intervals; a second acquisition module, configured to acquire the actual speed of the current train, the current train and the optimal distance interval. the actual distance between the preceding vehicles; a comparison module, configured to compare the actual vehicle speed with a preset expected vehicle speed to obtain an actual speed deviation, and to compare the actual distance with a preset expected distance to obtain an actual distance deviation; a query module, configured to perform a table lookup operation using the actual speed deviation and the actual distance deviation according to the fuzzy rule table to obtain an actual optimal speed weight interval and an actual optimal distance weight interval; a third acquisition module, configured to respectively obtain a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval; a fourth acquisition module, configured to obtain an acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; a fifth acquisition module, configured to obtain a level signal according to the acceleration; a control module, configured to control the current train according to the level signal.

[0008] According to a third aspect of an embodiment of the present application, there is provided an electronic device comprising a processor and a memory storing program instructions, wherein the processor is configured to execute the above-mentioned method for train control when running the program instructions.

[0009] According to a fourth aspect of an embodiment of the present application, a storage medium is provided, storing program instructions, which, when run, execute the above-mentioned method for train control.

[0010] Since the embodiments of the present application adopt the above technical scheme, they have the following technical effects: by determining the fuzzy rule table, the actual speed and actual spacing of the current train are obtained, and the actual speed and actual spacing are compared with the expected speed and expected spacing respectively to obtain the actual speed deviation and the actual distance deviation. According to the fuzzy rule table, the actual speed deviation and the actual distance deviation are used to perform a table lookup operation, so that the speed weight parameter and the distance weight parameter corresponding to the current train can be determined, and the acceleration can be obtained according to the speed weight parameter and the distance weight parameter, so as to obtain the level signal according to the acceleration, and the current train can be controlled according to the level signal. In this way, the current train can be controlled according to the actual speed and actual spacing in different vehicle operating states, so as to achieve stable vehicle control and avoid the problem of poor vehicle control effect caused by using a single set parameter to control the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0012] Figure 1 A schematic diagram of a method for train control provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of a method for obtaining a fuzzy rule table provided in an embodiment of the present application;

[0014] Figure 3 A schematic diagram of an application of a method for train control provided in an embodiment of the present application;

[0015] Figure 4 A schematic diagram of a method for train control provided in an embodiment of the present application;

[0016] Figure 5 A schematic diagram of a device for train control provided in an embodiment of the present application;

[0017] Figure 6 A schematic diagram of an electronic device provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0019] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0020] Unless otherwise stated, the term "plurality" means two or more.

[0021] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0022] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0023] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0024] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for performing train control, comprising:

[0025] Step S101, obtaining an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes speed deviation intervals and distance deviation intervals under different vehicle operating conditions, and the output fuzzy rule table includes speed weight intervals and distance weight intervals under different vehicle operating conditions.

[0026] Step S102, testing the train according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance interval of the train under different operating conditions.

[0027] Step S103, determining a fuzzy rule table according to the input quantity fuzzy rule table, the output quantity fuzzy rule table, each optimal speed weight interval and each optimal distance interval.

[0028] Step S104, obtaining the actual speed of the current train and the actual distance between the current train and the preceding train.

[0029] Step S105 , comparing the actual vehicle speed with the preset expected vehicle speed to obtain an actual speed deviation, and comparing the actual distance with the preset expected distance to obtain an actual distance deviation.

[0030] Step S106, performing a table lookup operation using the actual speed deviation and the actual distance deviation according to the fuzzy rule table to obtain an actual optimal speed weight interval and an actual optimal distance weight interval.

[0031] Step S107, obtaining a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval respectively.

[0032] Step S108, obtaining the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter.

[0033] Step S109, obtaining a level signal according to the acceleration.

[0034] Step S110, controlling the current train according to the level signal.

[0035] By adopting the method for train control provided by the embodiment of the present disclosure, the actual speed and actual spacing of the current train are obtained by determining a fuzzy rule table, and the actual speed and actual spacing are compared with the expected speed and expected spacing respectively to obtain the actual speed deviation and the actual distance deviation. The actual speed deviation and the actual distance deviation are used to perform a table lookup operation according to the fuzzy rule table, so that the speed weight parameter and the distance weight parameter corresponding to the current train can be determined, and the acceleration can be obtained according to the speed weight parameter and the distance weight parameter, so as to obtain the level signal according to the acceleration, and the current train can be controlled according to the level signal. In this way, the current train can be controlled according to the actual speed and actual spacing in different vehicle operating states, so as to achieve stable vehicle control and avoid the problem of poor vehicle control effect caused by using a single set parameter to control the vehicle.

[0036] In some embodiments, the speed deviation intervals under different vehicle operating conditions include: HE (extremely small, -500 to -300), HC (small, -300 to -100), GD (zero, -100 to 100), FC (large, 100 to 300) and FE (extremely large, 300 to 500), in units of cm / s.

[0037] In some embodiments, the distance deviation intervals under different vehicle operating conditions include: HE' (extremely small, -4000 to -2000), HC' (small, -2000 to -500), GD' (zero, -500 to 500), FC' (large, 500 to 2000) and FE' (extremely large, 2000 to 4000), in cm.

[0038] In some embodiments, speed weight intervals under different vehicle operating states include: gd (0-0.25), fc (0.25-0.5), fw (0.5-0.75) and fe (0.75-1).

[0039] In some embodiments, the distance weight intervals under different vehicle operating conditions are: gd' (0-0.25), fc' (0.25-0.5), fw' (0.5-0.75) and fe' (0.75-1).

[0040] Optionally, the train is tested according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance interval under different operating conditions of the train, including: arranging and combining each speed deviation interval and each distance deviation interval to obtain multiple groups of interval combinations, and arranging and combining each speed weight value in the speed weight interval and each distance weight value in the distance weight interval to obtain multiple groups of weight value combinations; obtaining the intermediate value of each interval combination; testing the train according to each intermediate value and each weight value combination to obtain the test speed deviation and the test distance deviation under different weight value combinations; determining the speed deviation interval corresponding to the test speed deviation within the first preset range as the first target interval range, and determining the distance deviation interval corresponding to the test distance deviation within the second preset range as the second target interval range; determining the speed weight interval where the weight values ​​corresponding to each first target interval range are located as the optimal speed weight interval, and determining the distance weight interval where the weight values ​​corresponding to the second target interval range are located as the optimal distance weight interval.

[0041] Optionally, the first preset range and the second preset range are both parameter ranges with better train running conditions, that is, the test speed deviation is in a lower parameter range, and the test distance deviation is in a lower parameter range. In this way, by screening out the test speed deviation and test distance deviation with better running conditions, the optimal speed weight interval can be determined from multiple speed weight intervals, and the optimal distance weight interval can be determined from multiple distance weight intervals, thereby facilitating the acquisition of the fuzzy rule table.

[0042] In some embodiments, each speed deviation interval includes 5 groups, each distance deviation interval includes 5 groups, and each speed deviation interval and each distance deviation interval are arranged and combined to obtain 25 groups of interval combinations.

[0043] In some embodiments, each speed weight interval includes a total of 100 speed weight values, each distance weight interval includes a total of 100 distance weight values, and each speed weight value and each distance weight value are arranged and combined to obtain 10,000 sets of weight value combinations.

[0044] In some embodiments, when the interval combination is "speed deviation interval is HC (small, -300 to -100), distance deviation interval is GD' (zero, -500 to 500)", the middle value of the interval combination is "speed deviation is -200, distance deviation is 0".

[0045] Optionally, the train is tested according to each combination of intermediate values ​​and weight values, including: testing the running state of the train under each combination of intermediate values ​​and weight values; optionally, the running state includes a test speed deviation and a test distance deviation.

[0046] In some embodiments, after determining the intermediate value, the speed weight value is fixed to 0.01, the distance weight value is traversed from 0.01 to 1, the speed weight value is fixed to 0.02, the distance weight value is traversed from 0.01 to 1, the speed weight value is fixed to 0.03, the distance weight value is traversed from 0.01 to 1,... the speed weight value is fixed to 1, and the distance weight value is traversed from 0.01 to 1; the running status of the train is tested under 10,000 different combinations of weight values; optionally, the running status includes testing speed deviation and testing distance deviation.

[0047] Optionally, the fuzzy rule table includes a speed weight rule table and a distance weight rule table, and the actual speed deviation and the actual distance deviation are used to perform a table lookup operation according to the fuzzy rule table to obtain the actual optimal speed weight interval and the actual optimal distance weight interval, including: finding the actual optimal speed weight interval corresponding to the actual speed deviation and the actual distance deviation from the speed weight rule table, and finding the actual optimal distance weight interval corresponding to the actual speed deviation and the actual distance deviation from the distance weight rule table. In this way, the actual optimal speed weight interval and the actual optimal distance weight interval can be determined from the obtained fuzzy rule table, and the method of fuzzy reasoning weight is realized. The optimal parameters obtained by the previous training can be used to perform fuzzy reasoning under different new car states, so as to obtain the actual optimal speed weight interval and the actual optimal distance weight interval, which is convenient for subsequent defuzzification, and the speed weight parameters and distance weight parameters of the current train are obtained, which are more reasonable than the controller parameters of the automatic train driving formulated according to manual experience.

[0048] Optionally, the fuzzy rule is determined according to the corresponding relationship between the input quantity and the output quantity.

[0049] In some embodiments, the fuzzy rule includes: If HE and HC', Qv==gd, Qd==dg'. That is, when the speed deviation is in the HE interval and the distance deviation is in the HC' interval, the actual optimal speed weight interval is gd, and the actual optimal distance weight interval is gd'.

[0050] In some embodiments, the speed weight rule table is shown in Table 1, which is a schematic diagram of a speed weight rule table provided in an embodiment of the present disclosure.

[0051]

[0052] Table 1

[0053] In some embodiments, when the speed deviation is in the HE interval and the distance deviation is in the HC' interval, the actual optimal speed weight interval is gd; when the speed deviation is in the GD interval and the distance deviation is in the GD' interval, the actual optimal speed weight interval is fw.

[0054] In some embodiments, the speed weight rule table is shown in Table 2, which is a schematic diagram of a distance weight rule table provided in an embodiment of the present disclosure.

[0055]

[0056] Table 2

[0057] In some embodiments, when the speed deviation is in the HE interval and the distance deviation is in the HC' interval, the actual optimal distance weight interval is gd'; when the speed deviation is in the GD interval and the distance deviation is in the GD' interval, the actual optimal speed weight interval is gd'.

[0058] Optionally, the actual vehicle speed is compared with a preset expected vehicle speed to obtain an actual speed deviation, and the actual distance is compared with a preset expected distance to obtain an actual distance deviation, including: subtracting the preset expected vehicle speed from the actual vehicle speed to obtain the actual speed deviation, and subtracting the preset expected distance from the actual distance to obtain the actual distance deviation.

[0059] Combination Figure 2 As shown, the embodiment of the present disclosure provides a method for obtaining a fuzzy rule table, comprising:

[0060] Step S201, obtaining an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes speed deviation intervals and distance deviation intervals under different vehicle operating conditions, and the output fuzzy rule table includes speed weight intervals and distance weight intervals under different vehicle operating conditions.

[0061] Step S202, testing the train according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance interval of the train under different operating conditions.

[0062] Step S203, determining a fuzzy rule table according to the input quantity fuzzy rule table, the output quantity fuzzy rule table, each optimal speed weight interval and each optimal distance interval.

[0063] By adopting the method for obtaining the fuzzy rule table provided in the embodiment of the present disclosure, the train is tested to obtain the optimal speed weight interval and the optimal distance interval of the train under different operating conditions by obtaining the input fuzzy rule table and the output fuzzy rule table, and the fuzzy rule table is determined according to the input fuzzy rule table, the output fuzzy rule table, each optimal speed weight interval and each optimal distance interval. In this way, the fuzzy rule table can be determined, which is convenient for obtaining the optimal weight parameters of the train under different conditions by using the fuzzy rule table, which is more reasonable than the method of formulating controller parameters by manual experience.

[0064] Combination Figure 3 As shown, Figure 3 It is an application diagram of the method for train control provided by an embodiment of the present disclosure; a fuzzy rule table is obtained through offline training, fuzzy rules are determined according to the corresponding relationship between input quantities and output quantities in the fuzzy rule table, and fuzzy reasoning is performed using the fuzzy rules to obtain optimal weight parameters; the actual speed and actual distance of the current train collected are fuzzified to obtain an actual optimal speed weight interval and an actual optimal distance weight interval; and the actual optimal speed weight interval and the actual optimal distance weight interval are defuzzified using the centroid method to obtain speed weight parameters and distance weight parameters; the speed weight parameters and distance weight parameters are sent to the MPC The controller obtains acceleration and sends the acceleration to the level converter to obtain a level signal, the level signal includes a level value and a level symbol, and sends the level value and the level symbol to the controller of the current train to control the current train; the actual speed and actual distance of the current train are collected, the actual speed is compared with the expected speed to obtain the speed deviation, and the actual distance is compared with the expected distance to obtain the distance deviation; the speed weight parameter and distance weight parameter of the current train are re-obtained according to the obtained speed deviation and distance deviation, so as to realize dynamic adjustment of the optimal weight parameters of the current train and achieve the goal of stable vehicle control under different driving conditions.

[0065] Optionally, the speed weight parameter and the distance weight parameter are obtained respectively according to the actual optimal speed weight interval and the actual optimal distance weight interval, including: using the center of gravity method to defuzzify the actual optimal speed weight interval and the actual optimal distance weight interval respectively, to obtain a first center of gravity corresponding to the actual optimal speed weight interval and a second center of gravity corresponding to the actual optimal distance weight interval; the weight value corresponding to the first center of gravity is determined as the speed weight parameter, and the weight value corresponding to the second center of gravity is determined as the distance weight parameter.

[0066] In some embodiments, the first centroid of the area enclosed by the membership function curve corresponding to the optimal speed weight interval and the horizontal coordinate is taken, and the numerical value corresponding to the first centroid is determined as the final output value of the fuzzy reasoning, that is, the speed weight parameter; the second centroid of the area enclosed by the membership function curve corresponding to the optimal distance weight interval and the horizontal coordinate is taken, and the numerical value corresponding to the second centroid is determined as the final output value of the fuzzy reasoning, that is, the distance weight parameter.

[0067] Optionally, obtaining the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter includes: inputting the speed weight parameter and the distance weight parameter into a preset controller to obtain the acceleration corresponding to the current train.

[0068] Optionally, the preset controller is an MPC (Model Predictive Control) controller.

[0069] In some embodiments, after obtaining the speed weight parameter and the distance weight parameter, the speed weight parameter and the distance weight parameter are sent to the MPC controller, and the MPC controller solves the speed weight parameter and the distance weight parameter to obtain the optimal control acceleration, that is, the acceleration corresponding to the current train.

[0070] Optionally, the level signal includes a level value and a level symbol, and obtaining the level signal according to the acceleration includes: inputting the acceleration into a preset level converter to obtain the level value and level symbol corresponding to the current train. In this way, by converting the acceleration into a level signal that can be received by the current train, it is convenient to control the current train.

[0071] Optionally, the level symbols include: traction, coasting and braking. Optionally, the level value is used to represent the throttle size.

[0072] In some embodiments, the acceleration is input into a preset level converter, and when the acceleration is less than 0, the level symbol output by the level converter is "braking"; the maximum traction acceleration of the current speed of the current train is obtained, and the braking ratio is obtained based on the maximum traction acceleration of the current speed; the braking level value is obtained based on the braking ratio, and the braking level value and the level symbol are output as "braking".

[0073] In some embodiments, the acceleration is input into a preset level converter, and when the acceleration is greater than 0, the level symbol output by the level converter is "traction"; the maximum traction acceleration of the current speed of the current train is obtained, and the traction ratio is obtained based on the maximum traction acceleration of the current speed; the traction level value is obtained based on the traction ratio, and the traction level value and the level symbol are output as "traction".

[0074] In some embodiments, the acceleration is input into a preset level converter. When the acceleration is equal to 0, the level symbol output by the level converter is "idling", and the output inertia level value is 0.

[0075] Optionally, after controlling the current train according to the level signal, it also includes: recollecting the actual speed of the current train and the actual distance between the current train and the preceding vehicle; comparing the recollected actual speed and actual distance with the expected speed and expected distance respectively; when the comparison result is that the recollected actual speed is different from the expected speed, and / or the recollected actual distance is different from the expected distance, controlling the current train according to the recollected actual speed and actual distance until the speed of the current train is the same as the expected speed, and the distance between the current train and the preceding vehicle is the same as the expected distance. In this way, by continuing to collect the actual speed of the current train and the actual distance between the current train and the preceding vehicle after controlling the current train according to the level signal, and comparing the recollected actual speed and actual distance with the expected speed and expected distance respectively, the closed-loop control of the current train is realized, so that the control parameters of the current train can be dynamically adjusted, thereby realizing stable vehicle control, and enabling the vehicle to obtain the optimal speed weight parameter and the optimal distance weight parameter at different times and in different states, so as to achieve the purpose of stable vehicle control.

[0076] Optionally, after controlling the current train according to the level signal, it also includes: recollecting the actual speed of the current train and the actual distance between the current train and the preceding train; comparing the recollected actual speed and actual distance with the expected speed and expected distance respectively; when the comparison result is that the speed deviation between the recollected actual speed and the expected speed is not within the preset speed deviation range, and / or the distance deviation between the recollected actual distance and the expected distance is not within the preset distance deviation range, dynamically adjusting the speed weight parameter and distance weight parameter of the current train according to the speed deviation and distance deviation. In this way, dynamic closed-loop adjustment parameters of the current train can be realized, thereby enabling stable vehicle control.

[0077] Combination Figure 4 As shown, the embodiment of the present disclosure provides a method for train control, comprising:

[0078] Step S401, obtaining an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes speed deviation intervals and distance deviation intervals under different vehicle operating conditions, and the output fuzzy rule table includes speed weight intervals and distance weight intervals under different vehicle operating conditions.

[0079] Step S402, testing the train according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance interval of the train under different operating conditions.

[0080] Step S403, determining a fuzzy rule table according to the input fuzzy rule table, the output fuzzy rule table, each optimal speed weight interval and each optimal distance interval.

[0081] Step S404, obtaining the actual speed of the current train and the actual distance between the current train and the preceding train.

[0082] Step S405 , comparing the actual vehicle speed with the preset expected vehicle speed to obtain an actual speed deviation, and comparing the actual distance with the preset expected distance to obtain an actual distance deviation.

[0083] Step S406, performing a table lookup operation using the actual speed deviation and the actual distance deviation according to the fuzzy rule table to obtain an actual optimal speed weight interval and an actual optimal distance weight interval.

[0084] Step S407, obtaining a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval respectively.

[0085] Step S408, obtaining the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter.

[0086] Step S409, obtaining a level signal according to the acceleration.

[0087] Step S410, controlling the current train according to the level signal; returning to step S404, until the comparison result is that the speed of the current train is the same as the expected speed, and the distance between the current train and the preceding train is the same as the expected distance.

[0088] By adopting the method for train control provided by the embodiment of the present disclosure, after controlling the current train according to the level signal, the actual speed of the current train and the actual distance between the current train and the preceding train are continuously collected, and the re-collected actual speed and actual distance are respectively compared with the expected speed and expected distance, so as to realize closed-loop control of the current train. In this way, the control parameters of the current train can be dynamically adjusted, thereby realizing stable train control, and enabling the vehicle to obtain the optimal speed weight parameter and the optimal distance weight parameter at different times and under different conditions, thereby realizing the purpose of stable train control.

[0089] Combination Figure 5As shown, the embodiment of the present disclosure provides a device for train control, including: a first acquisition module 501, a test module 502, a determination module 503, a second acquisition module 504, a comparison module 505, a query module 506, a third acquisition module 507, a fourth acquisition module 508, a fifth acquisition module 509 and a control module 510; the first acquisition module 501 is configured to acquire an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes a speed deviation interval and a distance deviation interval under different vehicle running states, and the output fuzzy rule table includes a speed weight interval and a distance weight interval under different vehicle running states; and the input fuzzy rule table and the output fuzzy rule table are sent to the test module 502, the test module 503, the determination module 504, the comparison module 505, the query module 506, the third acquisition module 507, the fourth acquisition module 508, the fifth acquisition module 509 and the control module 510; the first acquisition module 501 is configured to acquire an input fuzzy rule table and an output fuzzy rule table; the input fuzzy rule table includes a speed deviation interval and a distance deviation interval under different vehicle running states, and the output fuzzy rule table includes a speed weight interval and a distance weight interval under different vehicle running states; and the input fuzzy rule table and the output fuzzy rule table are sent to the test module 502, the test module 503, the determination module 503, the second acquisition module 504, the comparison module 505, the query module 506, the third acquisition module 507, the fourth acquisition module 508, the fifth acquisition module 509 and the control module 510; The test module 502 is configured to receive the input fuzzy rule table and the output fuzzy rule table sent by the first acquisition module; the train is tested according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance interval of the train under different operating conditions; and each optimal speed weight interval and each optimal distance interval are sent to the determination module; the determination module 503 is configured to receive each optimal speed weight interval and each optimal distance interval sent by the test module; the fuzzy rule table is determined according to the input fuzzy rule table, the output fuzzy rule table, each optimal speed weight interval and each optimal distance interval; and the fuzzy rule table is sent to the query module; the second acquisition module 504 is configured to The actual speed of the current train and the actual distance between the current train and the preceding train are obtained; and the actual speed and the actual distance are sent to the comparison module; the comparison module 505 is configured to receive the actual speed and the actual distance sent by the second acquisition module; the actual speed is compared with the preset expected speed to obtain the actual speed deviation, and the actual distance is compared with the preset expected distance to obtain the actual distance deviation; and the actual speed deviation and the actual distance deviation are sent to the query module; the query module 506 is configured to receive the fuzzy rule table sent by the determination module and the actual speed deviation and the actual distance deviation sent by the comparison module; according to the fuzzy rule table, the actual speed deviation and the actual distance deviation are used to perform a table lookup operation to obtain the actual optimal speed weight interval and the actual The actual optimal distance weight interval; and the actual optimal speed weight interval and the actual optimal distance weight interval are sent to the third acquisition module; the third acquisition module 507 is configured to receive the actual optimal speed weight interval and the actual optimal distance weight interval sent by the query module; obtain the speed weight parameter and the distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval respectively; and send the speed weight parameter and the distance weight parameter to the fourth acquisition module; the fourth acquisition module 508 is configured to receive the speed weight parameter and the distance weight parameter sent by the third acquisition module; obtain the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; and send the acceleration to the fifth acquisition module; the fifth acquisition module 509 is configured to receive the acceleration sent by the fourth acquisition module;Acquire the level signal according to the acceleration; and send the level signal to the control module; the control module 510 is configured to receive the level signal sent by the fifth acquisition module; and control the current train according to the level signal. ;

[0090] By adopting the method for train control provided by the embodiment of the present disclosure, the actual speed and actual spacing of the current train are obtained by determining a fuzzy rule table, and the actual speed and actual spacing are compared with the expected speed and expected spacing respectively to obtain the actual speed deviation and the actual distance deviation. The actual speed deviation and the actual distance deviation are used to perform a table lookup operation according to the fuzzy rule table, so that the speed weight parameter and the distance weight parameter corresponding to the current train can be determined, and the acceleration can be obtained according to the speed weight parameter and the distance weight parameter, so as to obtain the level signal according to the acceleration, and the current train can be controlled according to the level signal. In this way, the current train can be controlled according to the actual speed and actual spacing in different vehicle operating states, so as to achieve stable vehicle control and avoid the problem of poor vehicle control effect caused by using a single set parameter to control the vehicle.

[0091] Optionally, the test module is configured to test the train according to the input fuzzy rule table and the output fuzzy rule table in the following manner to obtain the optimal speed weight interval and the optimal distance interval under different operating conditions of the train: arrange and combine each speed deviation interval and each distance deviation interval to obtain multiple groups of interval combinations, and arrange and combine each speed weight value in the speed weight interval and each distance weight value in the distance weight interval to obtain multiple groups of weight value combinations; obtain the intermediate value of each interval combination; test the train according to each intermediate value and each weight value combination to obtain the test speed deviation and test distance deviation under different weight value combinations; determine the speed deviation interval corresponding to the test speed deviation within the first preset range as the first target interval range, and determine the distance deviation interval corresponding to the test distance deviation within the second preset range as the second target interval range; determine the speed weight interval where the weight values ​​corresponding to each first target interval range are located as the optimal speed weight interval, and determine the distance weight interval where the weight values ​​corresponding to the second target interval range are located as the optimal distance weight interval.

[0092] Optionally, the fuzzy rule table includes a speed weight rule table and a distance weight rule table, and the query module is configured to perform a table lookup operation according to the fuzzy rule table using the actual speed deviation and the actual distance deviation to obtain the actual optimal speed weight interval and the actual optimal distance weight interval, including: searching the speed weight rule table for the actual optimal speed weight interval corresponding to the actual speed deviation and the actual distance deviation, and searching the distance weight rule table for the actual optimal distance weight interval corresponding to the actual speed deviation and the actual distance deviation.

[0093] Optionally, the third acquisition module is configured to respectively obtain the speed weight parameter and the distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval: use the center of gravity method to defuzzify the actual optimal speed weight interval and the actual optimal distance weight interval, respectively, to obtain the first center of gravity corresponding to the actual optimal speed weight interval and the second center of gravity corresponding to the actual optimal distance weight interval; determine the weight value corresponding to the first center of gravity as the speed weight parameter, and determine the weight value corresponding to the second center of gravity as the distance weight parameter.

[0094] Optionally, the fourth acquisition module is configured to acquire the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter: the speed weight parameter and the distance weight parameter are input into a preset controller to obtain the acceleration corresponding to the current train.

[0095] Optionally, the fifth acquisition module is configured so that the level signal includes a level value and a level symbol, and the level signal is acquired according to the acceleration: the acceleration is input into a preset level converter to obtain the level value and level symbol corresponding to the current train.

[0096] Optionally, after controlling the current train according to the level signal, it also includes: re-collecting the actual speed of the current train and the actual distance between the current train and the preceding train; comparing the re-collected actual speed and actual distance with the expected speed and expected distance, respectively; when the comparison result is that the re-collected actual speed is different from the expected speed, and / or the re-collected actual distance is different from the expected distance, controlling the current train according to the re-collected actual speed and actual distance until the speed of the current train is the same as the expected speed, and the distance between the current train and the preceding train is the same as the expected distance.

[0097] Combination Figure 6 As shown, an embodiment of the present disclosure provides an electronic device, including a processor (processor) 600 and a memory (memory) 601. Optionally, the electronic device may also include a communication interface (CommunicationInterface) 602 and a bus 603. Among them, the processor 600, the communication interface 602, and the memory 601 can communicate with each other through the bus 603. The communication interface 602 can be used for information transmission. The processor 600 can call the logic instructions in the memory 601 to execute the method for train control of the above embodiment.

[0098] By using the electronic device provided by the embodiment of the present disclosure, the actual speed and actual spacing of the current train are obtained by determining a fuzzy rule table, and the actual speed and actual spacing are compared with the expected speed and expected spacing respectively to obtain the actual speed deviation and the actual distance deviation. The actual speed deviation and the actual distance deviation are used to perform a table lookup operation according to the fuzzy rule table, so that the speed weight parameter and the distance weight parameter corresponding to the current train can be determined, and the acceleration can be obtained according to the speed weight parameter and the distance weight parameter, so as to obtain the level signal according to the acceleration, and the current train can be controlled according to the level signal. In this way, the current train can be controlled according to the actual speed and actual spacing in different vehicle operating states, so as to achieve stable vehicle control and avoid the problem of poor vehicle control effect caused by using a single set parameter to control the vehicle.

[0099] In addition, the logic instructions in the memory 601 described above can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0100] The memory 601 is a computer-readable storage medium that can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 600 executes the function application and data processing by running the program instructions / modules stored in the memory 601, that is, implementing the method for train control in the above embodiment.

[0101] The memory 601 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 601 may include a high-speed random access memory and may also include a non-volatile memory.

[0102] Optionally, the electronic device includes a server or a computer, etc.

[0103] An embodiment of the present disclosure provides a storage medium storing program instructions, which execute the above-mentioned method for train control when running.

[0104] An embodiment of the present disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above-mentioned method for train control.

[0105] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0106] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0107] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0108] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for train control, characterized in that: include: Obtaining an input quantity fuzzy rule table and an output quantity fuzzy rule table; The input quantity fuzzy rule table includes speed deviation intervals and distance deviation intervals under different vehicle operation states, and the output quantity fuzzy rule table includes speed weight intervals and distance weight intervals under different vehicle operation states; Testing the train according to the input quantity fuzzy rule table and the output quantity fuzzy rule table to obtain the optimal speed weight interval and the optimal distance weight interval of the train under different operating conditions; Determine a fuzzy rule table according to the input fuzzy rule table, the output fuzzy rule table, each of the optimal speed weight intervals and each of the optimal distance weight intervals; Get the actual speed of the current train and the actual distance between the current train and the preceding train; Comparing the actual vehicle speed with a preset expected vehicle speed to obtain an actual speed deviation, and comparing the actual distance with a preset expected distance to obtain an actual distance deviation; According to the fuzzy rule table, a table lookup operation is performed using the actual speed deviation and the actual distance deviation to obtain an actual optimal speed weight interval and an actual optimal distance weight interval; Acquire a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval respectively; Acquire the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; Acquire a level signal according to the acceleration; Controlling the current train according to the level signal; Testing the train according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance weight interval of the train under different operating conditions, including: Arrange and combine the speed deviation intervals and the distance deviation intervals to obtain multiple interval combinations, and arrange and combine each speed weight value in the speed weight interval and each distance weight value in the distance weight interval to obtain multiple weight value combinations; Obtaining the middle value of each of the interval combinations; Testing the train according to each of the intermediate values ​​and each of the weight value combinations to obtain a test speed deviation and a test distance deviation under different weight value combinations; The speed deviation interval corresponding to the test speed deviation within the first preset range is determined as the first target interval range, and the distance deviation interval corresponding to the test distance deviation within the second preset range is determined as the second target interval range; the first preset range is a parameter range in which the test speed deviation is at a lower range, and the second preset range is a parameter range in which the test distance deviation is at a lower range; The speed weight intervals in which the weight values ​​corresponding to the first target intervals are located are determined as the optimal speed weight intervals, and the distance weight intervals in which the weight values ​​corresponding to the second target intervals are located are determined as the optimal distance weight intervals.

2. The method according to claim 1, characterized in that The fuzzy rule table includes a speed weight rule table and a distance weight rule table. According to the fuzzy rule table, the actual speed deviation and the actual distance deviation are used to perform a table lookup operation to obtain an actual optimal speed weight interval and an actual optimal distance weight interval, including: The actual optimal speed weight interval corresponding to the actual speed deviation and the actual distance deviation is found from the speed weight rule table, and the actual optimal distance weight interval corresponding to the actual speed deviation and the actual distance deviation is found from the distance weight rule table.

3. The method according to claim 1, characterized in that Acquiring a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval respectively includes: Defuzzifying the actual optimal speed weight interval and the actual optimal distance weight interval respectively by using the center of gravity method to obtain a first center of gravity corresponding to the actual optimal speed weight interval and a second center of gravity corresponding to the actual optimal distance weight interval; The weight value corresponding to the first center of gravity is determined as the speed weight parameter, and the weight value corresponding to the second center of gravity is determined as the distance weight parameter.

4. The method according to claim 1, characterized in that: Acquiring the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter, including: The speed weight parameter and the distance weight parameter are input into a preset controller to obtain the acceleration corresponding to the current train.

5. The method according to claim 1, characterized in that The level signal includes a level value and a level symbol, and obtaining the level signal according to the acceleration includes: The acceleration is input into a preset level converter to obtain the level value and level symbol corresponding to the current train.

6. The method according to claim 1, characterized in that After the current train is controlled according to the level signal, the method further includes: Re-collecting the actual speed of the current train and the actual distance between the current train and the preceding train; Comparing the re-collected actual vehicle speed and actual distance with the expected vehicle speed and the expected distance respectively; When the comparison result is that the re-collected actual speed is different from the expected speed, and / or the re-collected actual distance is different from the expected distance, the current train is controlled according to the re-collected actual speed and actual distance until the speed of the current train is the same as the expected speed and the distance between the current train and the preceding train is the same as the expected distance.

7. A device for train control, characterized in that: include: A first acquisition module is configured to acquire an input quantity fuzzy rule table and an output quantity fuzzy rule table; The input quantity fuzzy rule table includes speed deviation intervals and distance deviation intervals under different vehicle operation states, and the output quantity fuzzy rule table includes speed weight intervals and distance weight intervals under different vehicle operation states; A testing module is configured to test the train according to the input fuzzy rule table and the output fuzzy rule table to obtain the optimal speed weight interval and the optimal distance weight interval of the train under different operating conditions; A determination module is configured to determine a fuzzy rule table according to the input fuzzy rule table, the output fuzzy rule table, each of the optimal speed weight intervals and each of the optimal distance weight intervals; The second acquisition module is configured to acquire the actual speed of the current train and the actual distance between the current train and the preceding train; A comparison module is configured to compare the actual vehicle speed with a preset expected vehicle speed to obtain an actual speed deviation, and compare the actual distance with a preset expected distance to obtain an actual distance deviation; A query module is configured to perform a table lookup operation using the actual speed deviation and the actual distance deviation according to the fuzzy rule table to obtain an actual optimal speed weight interval and an actual optimal distance weight interval; A third acquisition module is configured to respectively acquire a speed weight parameter and a distance weight parameter according to the actual optimal speed weight interval and the actual optimal distance weight interval; a fourth acquisition module, configured to acquire the acceleration corresponding to the current train according to the speed weight parameter and the distance weight parameter; a fifth acquisition module, configured to acquire a level signal according to the acceleration; a control module, configured to control the current train according to the level signal; The test module is configured to arrange and combine each speed deviation interval and each distance deviation interval to obtain multiple groups of interval combinations, and arrange and combine each speed weight value in the speed weight interval and each distance weight value in the distance weight interval to obtain multiple groups of weight value combinations; obtain the intermediate value of each interval combination; test the train according to each intermediate value and each weight value combination to obtain the test speed deviation and test distance deviation under different weight value combinations; determine the speed deviation interval corresponding to the test speed deviation within the first preset range as the first target interval range, and determine the distance deviation interval corresponding to the test distance deviation within the second preset range as the second target interval range; the first preset range is a parameter range in which the test speed deviation is in a lower range, and the second preset range is a parameter range in which the test distance deviation is in a lower range; determine the speed weight interval in which the weight values ​​corresponding to each first target interval range are located as the optimal speed weight interval, and determine the distance weight interval in which the weight values ​​corresponding to the second target interval range are located as the optimal distance weight interval.

8. An electronic device comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for train control according to any one of claims 1 to 6 when running the program instructions.

9. A storage medium storing program instructions, characterized in that: When the program instructions are executed, the method for train control according to any one of claims 1 to 6 is executed.

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