A train speed adjustment method and system using a train communication network

Abstract

The present application relates to the technical field of train speed adjustment, and specifically to a train speed adjustment method and system using a train communication network, the method comprising: obtaining the pressure of each monitoring point in the train at each time, the track humidity at each time, and the train speed, lateral acceleration, longitudinal acceleration and vertical acceleration at each time when the train is running from the train communication network; determining the weight imbalance of the train at each time; determining the lateral energy, longitudinal energy and vertical energy at each time; determining the energy synthesis index at each time; determining the running risk, humidity influence and slip influence of the train at each time; determining the proportional factor of the fuzzy control algorithm at each time; combining the fuzzy control algorithm, determining the acceleration adjustment amount and adjusting the speed of the train. The present application improves the accuracy and timeliness of train speed adjustment, and avoids the side slip and wheel slip when the train is running on a track with high humidity.

Description

Technical Field

[0001] The present application relates to the technical field of train speed adjustment, and in particular to a method and system for adjusting train speed using a train communication network. Background Art

[0002] The train communication network is a system used for data transmission and information exchange between trains and ground control centers. It can realize the real-time sharing of multiple key information such as train operation status, fault information, passenger data, etc. The train communication network is used to adjust the train speed because it can obtain the position, speed and other information of the front and rear vehicles in a timely manner. With this information, the train control system can more accurately calculate the safe driving speed and the best acceleration and deceleration time, thereby improving the efficiency and safety of train operation.

[0003] Based on the gap between the expected speed and the actual speed in the train communication network, the fuzzy control algorithm is used to achieve real-time control of the speed. However, in the traditional fuzzy control algorithm, the proportional factor usually uses a fixed value. When the humidity on the track is high or there is water accumulation, it will cause insufficient accuracy in train speed control. Excessive speed may cause skidding, affecting operational safety and efficiency. Summary of the invention

[0004] In order to solve the above technical problems, a train speed adjustment method and system using a train communication network are provided to solve the existing problems.

[0005] The solution to the technical problem of the present application is to provide a train speed adjustment method and system using a train communication network, comprising the following steps:

[0006] In a first aspect, an embodiment of the present application provides a method for adjusting train speed using a train communication network, the method comprising the following steps:

[0007] Obtain the pressure at each monitoring point in the train at each time, the track humidity at each time, and the train speed, lateral acceleration, longitudinal acceleration and vertical acceleration at each time when the train is running from the train communication network;

[0008] Determine the weight imbalance of the train at each moment according to the discrete distribution of pressure at all monitoring points in the train at the same moment;

[0009] Determine the lateral energy, longitudinal energy, and vertical energy at each moment according to the energy distribution of the lateral acceleration, longitudinal acceleration, and vertical acceleration in the frequency domain in the local time period at each moment; determine the energy synthesis index at each moment based on the lateral energy, the longitudinal energy, and the vertical energy;

[0010] Based on the weight imbalance and the energy synthesis index, determining the running risk of the train at each time; according to the track humidity at each time, combined with the running risk, determining the humidity influence of the train at each time;

[0011] According to the difference in track humidity at adjacent moments and the degree of deviation between the train speed at each moment and the expected running speed of the train, the slip influence degree of the train at each moment is determined; based on the humidity influence degree and the slip influence degree, the proportional factor of the fuzzy control algorithm at each moment is determined; in combination with the fuzzy control algorithm, the acceleration adjustment amount is determined to adjust the speed of the train.

[0012] Preferably, determining the weight imbalance of the train at each moment includes:

[0013] Calculate the discrete degree of pressure at all monitoring points on the train at each moment;

[0014] The sum of the pressures of all monitoring points on the train at the same time is calculated; and the product of the sum and the discrete degree is used as the weight imbalance of the train at each time.

[0015] Preferably, the determining of the lateral energy, longitudinal energy, and vertical energy at each moment includes:

[0016] Any moment and a preset number of moments before the any moment constitute a local window of the any moment;

[0017] Performing frequency domain analysis on the lateral acceleration, longitudinal acceleration, and vertical acceleration at all times within the local window, respectively, to obtain frequency spectra corresponding to the lateral, longitudinal, and vertical directions;

[0018] The mean values ​​of the energies of all frequency components in the spectrum diagram corresponding to the horizontal, vertical and vertical directions are respectively taken as the horizontal energy, vertical energy and vertical energy at each moment.

[0019] The energy synthesis index at each moment is the square root of the cumulative sum of the square of the transverse energy, the square of the longitudinal energy and the square of the vertical energy.

[0020] Preferably, the operating risk of the train at each moment is the product of the weight imbalance and the energy synthesis index.

[0021] Preferably, the humidity impact of the train at each moment is the ratio of the track humidity at each moment to the operation risk.

[0022] Preferably, determining the slip impact degree of the train at each time comprises:

[0023] The difference between the orbital humidity at each time and the orbital humidity at the adjacent time is recorded as the humidity difference;

[0024] The expected speed of the train is obtained in real time from the train communication network; the difference between the train speed at each moment and the expected speed is recorded as the speed deviation;

[0025] The inverse of the product of the humidity difference and the speed deviation is used as the slip influence degree of the train at each moment.

[0026] Preferably, the proportional factor of the fuzzy control algorithm at each moment is the normalized result of the product of the humidity influence degree and the slip influence degree.

[0027] Preferably, the step of determining the adjustment amount of acceleration includes:

[0028] The difference between the speed deviation at each moment and the speed deviation at the previous moment is taken as the rate of change of the speed deviation;

[0029] The speed deviation and the speed deviation change rate at each moment are used as the input of the fuzzy control algorithm, fuzzy rules are defined, fuzzy reasoning is performed, and the fuzzy linguistic variables of the acceleration obtained by reasoning are defuzzified; the product of the defuzzification result and the proportional factor is used as the acceleration adjustment amount.

[0030] In the second aspect, an embodiment of the present application also provides a train speed adjustment system using a train communication network, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements the steps of any one of the above-mentioned methods for adjusting the train speed using a train communication network.

[0031] This application has at least the following beneficial effects:

[0032] The present application determines the weight imbalance of the train at each moment according to the discrete distribution of pressure at all monitoring points in the train at the same moment. The beneficial effect is that the distribution of the weight carried by the train during operation is taken into account to reflect the possibility of danger during the operation of the train, so as to adjust the speed of the train later. The lateral energy, longitudinal energy and vertical energy at each moment are determined according to the energy distribution of the lateral acceleration, longitudinal acceleration and vertical acceleration in the frequency domain in the local time period at each moment. The energy synthesis index at each moment is determined based on the lateral energy, the longitudinal energy and the vertical energy. The beneficial effect is that the frequency domain distribution of acceleration in different directions when the train is running is taken into account to reflect the shaking of the train and the unevenness of the track when it is running. The operation risk of the train at each moment is determined based on the weight imbalance and the energy synthesis index. The beneficial effect is that the risk of the train during operation is reflected in combination with the distribution of the train weight and its acceleration. The humidity influence of the train at each moment is determined according to the track humidity at each moment and in combination with the operation risk. The beneficial effect is that the track humidity is combined with the running risk of the train itself to reflect the influence of the track humidity on the train operation, so as to control and adjust the train speed later; according to the difference of track humidity at adjacent moments and the degree of deviation between the train speed at each moment and the expected running speed of the train, the slip influence degree of the train at each moment is determined, and its beneficial effect is that the change of track humidity at adjacent moments and the deviation between the train speed and the target expected speed are taken into account, so as to reflect the degree of slippage during the operation of the train, so as to adjust the speed of the train later to avoid slippage; based on the humidity influence degree and the slip influence degree, the proportional factor of the fuzzy control algorithm at each moment is determined; combined with the fuzzy control algorithm, the acceleration adjustment amount is determined, and the speed of the train is adjusted, and its beneficial effect is that the influence of track humidity on the train and the possibility of train slippage are taken into account, so as to adjust the proportional factor in real time, and then control the train speed in real time, improve the accuracy and timeliness of the train speed adjustment, and avoid the side slip and wheel slippage when the train runs on the track with high humidity. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The following is a further detailed description of a train speed adjustment method using a train communication network of the present application in conjunction with the accompanying drawings.

[0034] Figure 1 A flowchart of a method for adjusting train speed using a train communication network provided in an embodiment of the present application;

[0035] Figure 2 A flowchart of the steps of a method for obtaining the humidity impact of a train at various times provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the following is a further detailed description of a train speed adjustment method and system using a train communication network proposed in the present application in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0038] See also Figure 1 , which shows a flow chart of a method for adjusting train speed using a train communication network provided by an embodiment of the present application, the method comprising the following steps:

[0039] Step 1: Obtain the pressure of each monitoring point in the train at each moment, the track humidity at each moment, and the train speed, lateral acceleration, longitudinal acceleration and vertical acceleration at each moment when the train is running from the train communication network.

[0040] On an urban rail transit line, a subway train has multiple carriages. A monitoring point is set at a distance of L above the head of each carriage. A pressure sensor is installed at each monitoring point to collect the pressure at each monitoring point. A humidity sensor and a speed sensor are installed at the head of the train to measure the humidity of the running track and the speed of the train. Secondly, three-axis acceleration sensors are installed at key positions of the train, such as the bottom of the front, middle and rear of the train. Each three-axis acceleration sensor measures the acceleration of the train in the lateral, longitudinal and vertical directions. The sampling frequency of all sensors is set to f. The average value of the acceleration of the train in the lateral, longitudinal and vertical directions measured by all three-axis acceleration sensors at the same time is used as the lateral acceleration, longitudinal acceleration and vertical acceleration at each time. The missing values ​​of the collected data are filled in according to the collection time sequence, and the pressure of each monitoring point in each carriage of the train at each time, the track humidity at each time, and the speed, lateral acceleration, longitudinal acceleration and vertical acceleration of the train at each time are obtained in real time.

[0041] Preferably, in this embodiment, it is assumed that a train has 8 carriages, and the length of each carriage is 25 meters. A monitoring point is set every 5 meters starting from the top of the head of each carriage, and a pressure sensor is installed at each monitoring point; the sampling frequency of all sensors is set to 50 Hz. As other implementation modes, the implementers can set it according to actual conditions; secondly, the mean interpolation method is used for filling, wherein the mean interpolation method is a well-known technology and will not be repeated here. As other implementation modes, the implementers can adopt other methods of the prior art, such as majority filling, linear interpolation, etc., and this embodiment does not impose any special restrictions on this.

[0042] At this point, the pressure at each monitoring point in the train at each moment, the track humidity at each moment, and the train speed, lateral acceleration, longitudinal acceleration, and vertical acceleration at each moment when the train is running are obtained.

[0043] Step 2: Determine the weight imbalance of the train at each moment according to the discrete distribution of the pressure of all monitoring points in the train at the same moment.

[0044] The train communication network refers to the system used to realize data transmission and information exchange during train operation. It covers a variety of communication technologies and protocols, and connects the subsystems and equipment on the train and the train with the ground control center to form an efficient data exchange network. It can realize real-time data transmission, obtain key information of the train, and make speed adjustment strategies based on this information to avoid collisions and congestion between trains and improve the transportation efficiency and safety of trains.

[0045] Fuzzy control is an intelligent control method based on fuzzy set theory, fuzzy linguistic variables and fuzzy logic reasoning. Due to the many uncertain factors in the operation of trains, it is difficult to use precise mathematical models to accurately control the speed of trains. The information collected through the train communication network can use fuzzy control algorithms to make reasonable judgments and control decisions on these inaccurate and fuzzy information to adapt to different operating conditions and working conditions of the train.

[0046] In the fuzzy control algorithm, the proportional factor is an important parameter. It is a coefficient used to convert the fuzzy quantity output by the fuzzy controller into the actual precise control quantity. The fuzzy set output by the fuzzy controller is scaled to obtain the precise control quantity that can ultimately act on the controlled object.

[0047] Therefore, the size of the proportional factor in the fuzzy control algorithm has a significant impact on adjusting the train speed. The larger the proportional factor, the faster and more violent the train's response to the input signal, that is, when the train speed is detected to be different from the expected speed, the stronger the control effect, so that the train can adjust the speed faster to approach the expected speed. However, too large a proportional factor may cause the train speed to be adjusted too aggressively, resulting in frequent rapid acceleration and deceleration; the smaller the proportional factor, the slower the train's response to the input signal, which may cause the train to be slow to react when adjusting the speed, and it will take a long time to reach the expected speed, affecting the operating efficiency.

[0048] Based on the above analysis, data is collected through the train communication network, and the appropriate proportional factor is determined according to the operating conditions during the train operation, so that the train can adapt to the complex operating environment and more accurately control and adjust the train's operating speed.

[0049] In the subway tracks, on the one hand, although the underground construction is waterproof, according to the specifications, cracks are allowed in the concrete. When the groundwater level is too high, it may leak in, causing excessive humidity on the tracks or even water accumulation. On the other hand, because the trains have air conditioning, some of the water is condensed from the air conditioner, which may also cause increased humidity and water accumulation.

[0050] When the humidity on the track is too high or even water accumulates, the water forms a lubricating layer between the wheel and the track surface, changing the friction between the wheel and the track from dry friction to wet friction. According to the principle of tribology, the wet friction coefficient is much smaller than the dry friction coefficient, which leads to a significant reduction in the friction between the wheel and the track. Train acceleration requires traction to overcome the train's own inertia and resistance. Due to the reduction in friction, the power output by the traction motor cannot be effectively transmitted to the track through the wheels, resulting in insufficient traction for the train. Insufficient traction will reduce the acceleration of the train, so it will take longer to reach the desired speed. Therefore, when the humidity is higher, the proportional factor should be increased to avoid taking longer to adjust the speed.

[0051] However, when the proportional factor is too large, the train speed will be adjusted quickly, which may cause the train to skid on tracks with high humidity, especially for trains with unbalanced weight distribution. The uneven distribution of passengers in different carriages on the train may make skidding more likely when adjusting the speed, so the proportional factor should be reduced at this time.

[0052] Based on the above analysis, the weight imbalance is determined by analyzing the pressure of the train compartments to reflect the weight distribution in the train, specifically:

[0053] Calculate the discrete degree of pressure at all monitoring points on the train at each moment;

[0054] Calculating the sum of the pressures of all monitoring points on the train at the same time; multiplying the sum by the discrete degree as the weight imbalance of the train at each time;

[0055] Preferably, in this embodiment, the coefficient of variation of the pressure at all monitoring points on the train at each time is calculated, wherein the calculation of the coefficient of variation is a well-known technology and will not be repeated here. As other implementation methods, the implementer may adopt other methods of the prior art to measure the degree of discreteness, such as variance, information entropy, etc. This embodiment does not impose any special restrictions on this.

[0056] It should be noted that, the larger the sum value is, the larger the weight of the train is at this time, and the smoother the train should be when braking at the next station; the larger the degree of dispersion is, the more uneven the weight distribution in the train is, that is, the more unbalanced the weight of the train is at this time, and the braking acceleration should be reduced to avoid danger; the larger the obtained weight imbalance is, the more the train should reduce the acceleration, that is, the proportional factor in the fuzzy control algorithm should be smaller; the smaller the obtained weight imbalance is, the smaller the weight on the train is, and the train can speed up the running acceleration, so that the train can reach the desired speed faster, and the braking acceleration can be increased when stopping at the station to improve the operating efficiency, that is, the proportional factor in the fuzzy control algorithm should be larger.

[0057] Step 3, according to the energy distribution of lateral acceleration, longitudinal acceleration and vertical acceleration in the frequency domain in the local time period at each moment, determine the lateral energy, longitudinal energy and vertical energy at each moment; based on the lateral energy, the longitudinal energy and the vertical energy, determine the energy synthesis index at each moment; based on the weight imbalance and the energy synthesis index, determine the operation risk of the train at each moment; according to the track humidity at each moment, combined with the operation risk, determine the humidity influence of the train at each moment.

[0058] Furthermore, when the train is running, the greater the acceleration of the train, the more likely it is to skid when the weight distribution of the train is uneven; therefore, the acceleration of the train in different directions and the humidity of the track are analyzed to determine the humidity impact to reflect the risk of the train running on tracks with high humidity or water accumulation.

[0059] Further, the step flow chart of the method for obtaining the humidity influence degree of a train at each time provided in the embodiment of the present application is as follows: Figure 2 shown.

[0060] First, the acceleration in different directions within a local period is analyzed in the frequency domain, specifically:

[0061] Any moment and a preset number of moments before the any moment constitute a local window of the any moment;

[0062] Preferably, in this embodiment, any moment and all moments within 2 seconds before any moment constitute a local window of any moment. As other implementation methods, implementers can set them according to actual conditions.

[0063] Performing frequency domain analysis on the lateral acceleration, longitudinal acceleration, and vertical acceleration at all times within the local window, respectively, to obtain frequency spectra corresponding to the lateral, longitudinal, and vertical directions;

[0064] Preferably, in this embodiment, discrete Fourier transform is used to perform frequency domain analysis to obtain frequency spectrum diagrams corresponding to the horizontal, longitudinal and vertical directions, wherein discrete Fourier transform is a well-known technology and will not be described in detail here. As other implementation methods, implementers may adopt other methods of the prior art, such as fast Fourier transform, etc., and this embodiment does not impose any special restrictions on this.

[0065] The mean value of the energy of all frequency components in the spectrum corresponding to the horizontal direction is taken as the horizontal energy at each moment;

[0066] The mean value of the energy of all frequency components in the spectrum corresponding to the longitudinal direction is taken as the longitudinal energy at each moment;

[0067] The mean value of the energy of all frequency components in the spectrum corresponding to the vertical direction is taken as the vertical energy at each moment;

[0068] It should be noted that in the spectrum diagram, the horizontal axis represents frequency and the vertical axis represents energy. Therefore, each frequency corresponds to an energy value. By recording each frequency as a frequency component, the energy corresponding to each frequency component can be obtained.

[0069] The square root of the cumulative sum of the square of the transverse energy, the square of the longitudinal energy and the square of the vertical energy is used as the energy synthesis index at each moment;

[0070] Calculating the product of the weight imbalance degree and the energy synthesis index as the running risk degree of the train at each time;

[0071] The ratio of the track humidity at each time to the running risk is used as the humidity impact of the train at each time;

[0072] Preferably, in this embodiment, the calculation formula for the humidity influence of the train at each time is: ,in, For the train The humidity at the time affects the For the Track humidity at the time, For the train The weight imbalance at the moment, For the The horizontal energy of the moment, For the The vertical energy of the moment, For the The vertical energy of the moment; secondly, is the energy synthesis index; secondly, The degree of operational risk.

[0073] It should be noted that during the travel of the train, the longitudinal acceleration along the track direction comes from the acceleration and deceleration process of the train, that is, the changes in traction and braking force. The lateral acceleration perpendicular to the track usually comes from the centrifugal force generated when the train is traveling on a curve, as well as the unevenness of the left and right tracks or the imbalance of the weight of the train, which causes the train to sway left and right. The vertical acceleration in the vertical direction may be caused by the unevenness of the track or the elastic deformation of the track. The larger the energy synthesis index is, the greater the impact of the train during travel and the more serious the wear on the track, and the more the acceleration should be reduced at this time. The greater the weight imbalance is, the more the train should reduce its acceleration, and the greater the resulting operating risk is. The greater the humidity impact is, the worse the travel conditions of the train are, that is, the greater the risk of sideslip is at this time, and the speed should be adjusted slowly to avoid sideslip. The phenomenon that the proportional factor of the fuzzy control algorithm should be smaller; on the contrary, the greater the humidity, the insufficient traction of the train, the reduced acceleration of the train, and thus it takes longer to reach the expected speed. In order to avoid the speed adjustment taking longer, the proportional factor should be increased. Therefore, when the humidity is greater, the smaller the operation risk is, the greater the humidity influence is, the proportional factor can be increased to reduce the time of speed adjustment, so that the speed of the train can quickly reach the expected speed. It should be noted that the expected speed refers to the ideal speed that the train should reach when the train communication network controls the train operation. Therefore, the expected speed of the train is obtained in real time from the train communication network. In the actual operation of the train, the actual speed of the train at each moment may deviate from the expected speed that the train communication network hopes the train should travel.

[0074] So far, the humidity impact of the train at each time is obtained.

[0075] Step 4: Determine the slip impact of the train at each moment according to the difference in track humidity at adjacent moments and the degree of deviation between the train speed at each moment and the expected running speed of the train.

[0076] Furthermore, when the humidity is too high or water accumulates on the track, there is not only the risk of skidding, but the train may also slip when accelerating or decelerating due to the reduction in friction. If there is a difference between the current speed of the train and the expected speed, traction or braking force will be applied to the train when the speed is adjusted. Due to the reduction in friction, the traction motor and brakes may over-output, causing the wheels to idle on the track and unable to effectively transfer power to the track, thereby delaying the operation of the train and causing the wheels to slip.

[0077] Therefore, when the track humidity changes greatly, that is, when entering a flooded area from a dry area of ​​the track, or when entering a dry area from a flooded area of ​​the track, when the train's current speed is greatly different from the expected speed, if the proportional factor of the fuzzy control algorithm is too large and the speed is adjusted too quickly, the wheels are more likely to slip.

[0078] Based on this analysis, the slip impact is determined by analyzing the difference between the current speed and the expected speed, as well as the difference in track humidity between adjacent times, to reflect the risk of slipping at the current speed of the train. Specifically, it is:

[0079] The difference between the orbital humidity at each moment and the orbital humidity at the previous moment is recorded as the humidity difference;

[0080] Preferably, in this embodiment, the absolute value of the difference between the track humidity at each moment and the track humidity at the previous moment is recorded as the humidity difference.

[0081] The expected speed of the train is obtained in real time from the train communication network; the difference between the train speed at each moment and the expected speed is recorded as the speed deviation;

[0082] Preferably, in this embodiment, the absolute value of the difference between the train speed at each moment and the expected speed is recorded as the speed deviation.

[0083] It should be noted that the expected speed is the ideal speed that the train should reach when the train communication network controls the train operation.

[0084] The inverse of the product of the humidity difference and the speed deviation is used as the slip influence degree of the train at each moment;

[0085] Preferably, in this embodiment, the calculation formula of the slip influence degree of the train at each moment is: ,in, For the train The impact of slippage at the moment, For the Track humidity at the time, For the Track humidity at the time, For the The train speed at the time, is the expected speed of the train, To preset a value greater than 0 to avoid the denominator being 0, in this embodiment, The value is 0.1. As other implementation methods, the implementer can set it according to the actual situation.

[0086] It should be noted that the greater the speed deviation, the greater the traction or braking force output when adjusting the speed in order to reach the expected speed as quickly as possible. Therefore, under the condition of greater humidity changes, that is, the greater the humidity difference, the greater the risk of wheel slippage. In order to avoid accidents caused by wheel slippage, the proportional factor should be reduced at this time; the smaller the slippage effect, the greater the difference between the actual running speed and the expected speed of the train when the humidity changes greatly, and there will be greater traction or braking force when adjusting the train speed. In order to avoid wheel slippage, the proportional factor should be reduced; conversely, the greater the slippage effect, the smaller the track humidity change, or the difference between the current actual speed of the train and the expected speed is small, and the output traction or braking force is also smaller, so the risk of wheel slippage is smaller, and the proportional factor can be increased to reduce the time for train speed adjustment.

[0087] So far, the slip influence of the train at each time is obtained.

[0088] Step 5: Based on the humidity influence and the slip influence, determine the proportional factor of the fuzzy control algorithm at each moment; combine the fuzzy control algorithm to determine the acceleration adjustment amount and adjust the speed of the train.

[0089] Further, based on the humidity influence degree and the slip influence degree, a proportional factor of the fuzzy control algorithm is determined, specifically:

[0090] The normalized result of the product of the humidity influence degree and the slip influence degree is used as the proportional factor of the fuzzy control algorithm at each moment;

[0091] Preferably, in this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the prior art, such as the tanh function for normalization processing, and this embodiment does not impose any special restrictions on this.

[0092] It should be noted that, the larger the proportional factor is, the faster the train speed is adjusted, and the less time is spent on adjusting the train speed, so that the train speed at this moment quickly reaches the desired speed.

[0093] Furthermore, based on the proportional factor and in combination with the fuzzy control algorithm, the train speed is adjusted, specifically:

[0094] The speed deviation at each moment and the speed deviation change rate are used as the input of the fuzzy control algorithm, fuzzy rules are defined, fuzzy reasoning is performed, and the fuzzy linguistic variables of the acceleration obtained by reasoning are defuzzified; the product of the defuzzification result and the proportional factor is used as the acceleration adjustment amount;

[0095] It should be noted that the fuzzy control algorithm is a well-known technology and will not be described in detail here. The main steps of the fuzzy control algorithm are as follows:

[0096] The speed deviation at each moment and the speed deviation change rate are used as input variables of the fuzzy control algorithm, where the speed deviation change rate is the difference between the speed deviation at each moment and the speed deviation at the previous moment;

[0097] The domain of input and output variables is represented as , the fuzzy subset is ,Right now , linguistic variables for input variables and output variables (negative large) uses Z-shaped membership function, linguistic variable PB (positive large) chooses S-shaped membership function, and the remaining linguistic variables choose triangular membership function;

[0098] According to the above settings, fuzzy rules are designed, Mamdani algorithm is used for fuzzy reasoning, and the center of gravity method is used to clarify the fuzzy linguistic variables of acceleration obtained by reasoning. The product of the defuzzification result and the proportional factor is used as the acceleration adjustment amount, thereby controlling and adjusting the speed of the train.

[0099] It can be understood that in this embodiment, the Mamdani reasoning method is used for fuzzy reasoning, and the centroid method is used for defuzzification. Fuzzy reasoning and defuzzification are well-known technologies and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the prior art, for example, the Sugeno reasoning method is used for fuzzy reasoning, and the maximum membership method is used for defuzzification. This embodiment does not impose any special restrictions on this.

[0100] Based on the same inventive concept as the above method, an embodiment of the present application also provides a train speed adjustment system using a train communication network, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned methods for adjusting the train speed using a train communication network are implemented.

[0101] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0102] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A train speed adjustment method using a train communication network, characterized in that: The method comprises the following steps: Obtain the pressure at each monitoring point in the train at each time, the track humidity at each time, and the train speed, lateral acceleration, longitudinal acceleration and vertical acceleration at each time when the train is running from the train communication network; Determine the weight imbalance of the train at each moment according to the discrete distribution of pressure at all monitoring points in the train at the same moment; Determine the lateral energy, longitudinal energy, and vertical energy at each moment according to the energy distribution of the lateral acceleration, longitudinal acceleration, and vertical acceleration in the frequency domain in the local time period at each moment; determine the energy synthesis index at each moment based on the lateral energy, the longitudinal energy, and the vertical energy; Based on the weight imbalance and the energy synthesis index, determining the running risk of the train at each time; according to the track humidity at each time, combined with the running risk, determining the humidity influence of the train at each time; According to the difference in track humidity at adjacent moments and the degree of deviation between the train speed at each moment and the expected running speed of the train, the slip influence degree of the train at each moment is determined; based on the humidity influence degree and the slip influence degree, the proportional factor of the fuzzy control algorithm at each moment is determined; in combination with the fuzzy control algorithm, the acceleration adjustment amount is determined to adjust the speed of the train.

2. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: Determining the weight imbalance of the train at each time comprises: Calculate the discrete degree of pressure at all monitoring points on the train at each moment; The sum of the pressures of all monitoring points on the train at the same time is calculated; and the product of the sum and the discrete degree is used as the weight imbalance of the train at each time.

3. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: The determining of the lateral energy, longitudinal energy, and vertical energy at each moment includes: Any moment and a preset number of moments before the any moment constitute a local window of the any moment; Performing frequency domain analysis on the lateral acceleration, longitudinal acceleration, and vertical acceleration at all times within the local window, respectively, to obtain frequency spectra corresponding to the lateral, longitudinal, and vertical directions; The mean values ​​of the energies of all frequency components in the spectrum diagram corresponding to the horizontal, vertical and vertical directions are respectively taken as the horizontal energy, vertical energy and vertical energy at each moment.

4. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: The energy synthesis index at each moment is the square root of the cumulative sum of the square of the transverse energy, the square of the longitudinal energy and the square of the vertical energy.

5. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: The running risk of the train at each moment is the product of the weight imbalance and the energy synthesis index.

6. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: The humidity impact of the train at each time is the ratio of the track humidity at each time to the operation risk.

7. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: Determining the slip impact of the train at each time comprises: The difference between the orbital humidity at each time and the orbital humidity at the adjacent time is recorded as the humidity difference; The expected speed of the train is obtained in real time from the train communication network; the difference between the train speed at each moment and the expected speed is recorded as the speed deviation; The inverse of the product of the humidity difference and the speed deviation is used as the slip influence degree of the train at each moment.

8. A train speed adjustment method using a train communication network as claimed in claim 1, characterized in that: The proportional factor of the fuzzy control algorithm at each moment is the normalized result of the product of the humidity influence degree and the slip influence degree.

9. A train speed adjustment method using a train communication network as claimed in claim 7, characterized in that: The step of determining the adjustment amount of acceleration includes: The difference between the speed deviation at each moment and the speed deviation at the previous moment is taken as the rate of change of the speed deviation; The speed deviation and the speed deviation change rate at each moment are used as the input of the fuzzy control algorithm, fuzzy rules are defined, fuzzy reasoning is performed, and the fuzzy linguistic variables of the acceleration obtained by reasoning are defuzzified; the product of the defuzzification result and the proportional factor is used as the acceleration adjustment amount.

10. A train speed adjustment system using a train communication network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for adjusting train speed using a train communication network as described in any one of claims 1 to 9 are implemented.

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