CN119329568B - Parking control method, device and system for urban rail train - Google Patents
Parking control method, device and system for urban rail train Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61C—LOCOMOTIVES; MOTOR RAILCARS
- B61C17/00—Arrangement or disposition of parts; Details or accessories not otherwise provided for; Use of control gear and control systems
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T13/00—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems
- B60T13/74—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0062—On-board target speed calculation or supervision
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
- B61L25/02—Indicating or recording positions or identities of vehicles or trains
- B61L25/021—Measuring and recording of train speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
- B61L25/02—Indicating or recording positions or identities of vehicles or trains
- B61L25/028—Determination of vehicle position and orientation within a train consist, e.g. serialisation
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Abstract
The application relates to the technical field of urban rail train control, in particular to a parking control method, a control device and a system of an urban rail train, wherein the control method comprises the following steps: based on the difference of speed data between each carriage and all the rest carriages at the current moment and the difference of all the historical speed data of each carriage between the current moment and the adjacent previous acquisition moment, determining the speed consistency coefficient of each carriage at the current moment, determining the data reliability coefficient of each carriage at the current moment by combining the speed accuracy coefficient and the speed rule coefficient, and carrying out parking control on the urban rail train by combining the speed data of all the carriages at the current moment and the actual position of the urban rail train. The application comprehensively analyzes the difference of speed data in different carriages and different times and improves the accuracy of urban rail train parking control.
Description
Technical Field
The application relates to the technical field of urban rail train control, in particular to a parking control method, a parking control device and a parking control system for an urban rail train.
Background
With the development of urban rail trains, urban rail train control of urban rail traffic gradually adopts ATO (Automatic tarin operation, urban rail train automatic operation system) to realize automatic driving of urban rail trains. The ATO system mainly relies on data acquisition equipment such as transponders and sensors in a track to acquire related data of the urban rail train, and continuously adjusts traction force and braking force of the urban rail train according to the distance, speed, acceleration and line information from the urban rail train to a stop point and a speed curve generated by the system, so that proper running speed is output, and automatic stop of the urban rail train is realized.
However, in the actual urban rail train running process, due to the influence of the environment and equipment, a certain error may exist in the acquired urban rail train speed (such as the influence of vibration and noise on measurement data in the urban rail train high-speed running process or the measurement error generated in the speed sensor mounting and application process), so that the braking force applied by the urban rail train is inconsistent with the target value, and the final urban rail train braking distance is inconsistent with the urban rail train running distance, thereby reducing the automatic stopping accuracy of the urban rail train.
Disclosure of Invention
In a first aspect, the embodiment of the application provides a parking control method of urban rail trains, which comprises the following steps that in each urban rail train, speed data of each carriage at the current moment of each day and the actual position of the urban rail train are obtained, and speed data of all acquisition moments of each carriage in a preset time before the current moment are used as historical speed data of each carriage at the current moment;
Determining a speed normal coefficient of each carriage at the current time based on the average distribution level of the similarity of the historical speed data between each carriage and all the rest of the carriages at the current time and the difference between the discrete degree of all the elements in the first-order differential sequence of all the historical speed data of each carriage and the discrete degree of all the elements in one-order differential sequence of all the historical speed data of all the rest of the respective carriages;
Acquiring mutation speed data in all historical speed data of each carriage at the current time by adopting a mutation point detection algorithm, analyzing the average distribution condition of differences of corresponding acquisition moments of all speed mutation data between each carriage and all other carriages, determining the interval gentle coefficient of each carriage at the current time, and determining the speed accurate coefficient of each carriage at the current time by combining the speed normal coefficients;
Determining a speed rule coefficient of each carriage at the moment of the day based on the difference of speed data of each carriage between the current moment and the same moment of the previous day and the difference of variation trend of all historical speed data;
determining a speed consistency coefficient of each carriage at the current time based on the difference of speed data between each carriage and all the rest carriages at the current time and the difference of all historical speed data of each carriage between the current time and the adjacent previous acquisition time, and determining a data reliability coefficient of each carriage at the current time by combining the speed accuracy coefficient and the speed rule coefficient;
And based on the data reliability coefficient, combining the speed data of all carriages at the current moment and the actual position of the urban rail train, and carrying out parking control on the urban rail train.
Preferably, the method for determining the normal speed coefficient of each carriage at the current time comprises the following steps:
In each urban rail train, analyzing the similarity of all historical speed data between each carriage and other carriages at the current moment, and taking the average level of the similarity between each carriage and other carriages as the speed similarity of each carriage at the current moment;
Analyzing the discrete degree of all elements in a first-order differential sequence of all historical speed data of each carriage at the current time, and taking the accumulated result of the difference between the discrete degree of each carriage and the discrete degree of all the rest carriages as the speed difference coefficient of each carriage at the current time;
the normal speed coefficient of each carriage at the current time is the ratio of the speed similarity of each carriage at the current time to the speed difference coefficient.
Preferably, the method for determining the gentle coefficient of the interval of each carriage at the current time comprises the following steps:
analyzing the difference of the acquisition time corresponding to all the abrupt change speed data between each carriage and other carriages at the current time, and taking the difference of the acquisition time between each carriage and other carriages as an average value to be used as a time difference coefficient of each carriage at the current time;
Analyzing the difference of all mutation speed data between each carriage and other carriages at the current time, and taking the average value of the difference of the mutation speed data between each carriage and other carriages as a mutation difference coefficient of each carriage at the current time;
And taking the interval gentle coefficient of each carriage at the current moment as the sum value of the time difference coefficient and the abrupt change difference coefficient of each carriage at the current moment to obtain the reciprocal.
Preferably, the expression of the speed accuracy coefficient of each carriage at the current time is:; The speed accuracy coefficient of the lower carriage j at the current moment is represented; the normal speed coefficient of the carriage j at the current moment is represented; representing the interval flattening coefficient of the lower compartment j at the current time.
Preferably, the method for determining the speed law coefficient of each carriage at the moment of the present day comprises the following steps:
analyzing the difference of the speed data of each carriage between the current moment and the same moment of the previous day, and taking the difference as the speed difference of each carriage at the current moment;
taking all historical speed data of each carriage at the current time as input of a trend checking algorithm, outputting trend intensity, and analyzing the difference of the trend intensity of each carriage between the current time and the same time of the previous day as the trend difference of each carriage at the current time;
Velocity law coefficient of carriage j at current moment The expression of (2) is: In the formula (I), in the formula (II), Representing the speed difference of the lower carriage j at the current moment; The trend difference of the lower carriage j at the current moment is represented; Indicating that a constant greater than 0 is preset.
Preferably, the method for determining the speed consistency coefficient of each carriage at the current time comprises the following steps:
Calculating the average value of the difference of the speed data between each carriage and all the rest carriages at the current moment, and taking the average value as the workshop speed difference of each carriage at the current moment;
Calculating the absolute value of the sum of the difference values of all the speed data of each carriage between the current moment and the adjacent previous acquisition moment, and taking the absolute value as the consistency index of each carriage at the current moment;
The speed consistency coefficient of each carriage at the current time is the ratio of the consistency index of each carriage at the current time to the speed difference of the workshop.
Preferably, the expression of the data reliability coefficient of each carriage at the current time is: In the formula (I), in the formula (II), The data reliability coefficient of the lower carriage j at the current moment is represented; a speed law coefficient of a lower carriage j at the current moment is represented; and the speed consistency coefficient of the lower carriage j at the current moment is represented.
Preferably, the parking control for the urban rail train comprises:
taking the data reliability coefficients of all carriages at the current moment as the input of a threshold segmentation algorithm, outputting a segmentation threshold, and taking the average value of the speed data which is greater than or equal to the segmentation threshold in the speed data of all carriages at the current moment as the actual speed data of the urban rail train at the current moment;
And taking the distance between the preset ideal stopping position and the actual position of the urban rail train at the current moment and the difference value between 0 and the actual speed data of the urban rail train at the current moment as the input of the PID controller, and outputting a control signal to control the urban rail train to stop.
In a second aspect, an embodiment of the present application further provides a parking control device for a urban rail train, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of the parking control method for a urban rail train according to any one of the foregoing when executing the computer program.
In a third aspect, an embodiment of the present application provides a parking control system for a urban rail train, the control system comprising:
the speed data acquisition module is used for acquiring the speed data of each carriage at the current moment every day and the actual position of the urban rail train in each urban rail train, and acquiring the speed data of all the acquisition moments of each carriage in a preset time before the current moment, and taking the speed data as the historical speed data of each carriage at the current moment;
The speed weight acquisition module is used for determining the speed normal coefficient of each carriage at the current moment based on the similarity of all the historical speed data between each carriage and other carriages at the current moment and the discrete degree of all elements in the first-order differential sequence of all the historical speed data of each carriage;
determining a gentle interval coefficient of each carriage at the current moment based on the difference of abrupt change conditions of all historical speed data between each carriage and other carriages at the current moment, and determining a speed accuracy coefficient of each carriage at the current moment by combining the speed normal coefficients;
Determining a speed rule coefficient of each carriage at the moment of the day based on the difference of speed data of each carriage between the current moment and the same moment of the previous day and the difference of variation trend of all historical speed data;
determining a speed consistency coefficient of each carriage at the current time based on the difference of speed data between each carriage and all the rest carriages at the current time and the difference of all historical speed data of each carriage between the current time and the adjacent previous acquisition time, and determining a data reliability coefficient of each carriage at the current time by combining the speed accuracy coefficient and the speed rule coefficient;
And the control parking module is used for carrying out parking control on the urban rail train based on the data reliability coefficient and combining the speed data of all carriages at the current moment and the actual position of the urban rail train.
As can be seen from the above embodiments, the parking control method for the urban rail train provided by the embodiment of the present application has at least the following beneficial effects:
The method has the beneficial effects that the influence of environmental vibration and noise on the measurement accuracy of the speed sensor can be eliminated, the authenticity and the accuracy of the acquired urban rail train speed data are ensured, and the accuracy and the safety of parking control are improved;
The method and the device have the advantages that the accuracy and the reliability of the speed data can be comprehensively evaluated by comprehensively considering the speed normal coefficient and the interval gentle coefficient, and the speed change during normal operation and abnormal data caused by the error of the measuring force of the speed sensor can be distinguished, so that the accuracy and the safety of parking control are improved;
The method and the system have the beneficial effects that the speed rule coefficient of each carriage at the current moment is determined based on the difference of the speed data of each carriage between each day and the same time of the previous day and the difference of the change trend of all historical speed data, so that the speed and braking of the urban rail train can be accurately regulated when the urban rail train encounters resistance, the inaccuracy of parking caused by a data error is avoided, and the accuracy and the safety of parking control are improved;
Based on the difference of speed data between each carriage and all the rest carriages at the current moment and the difference of all the historical speed data of each carriage between the current moment and the adjacent previous acquisition moment, determining the speed consistency coefficient of each carriage at the current moment, combining the speed accuracy coefficient and the speed rule coefficient, determining the data reliability coefficient of each carriage at the current moment, and carrying out parking control on the urban rail train based on the data reliability coefficient and combining the speed data of all the carriages at the current moment and the actual position of the urban rail train.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a step flowchart of a parking control method of a urban rail train according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a data reliability coefficient extraction process according to an embodiment of the present application;
Fig. 3 is a block diagram of a parking control system of a urban rail train according to an embodiment of the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the application to achieve the preset aim, the following description refers to the specific implementation, structure, characteristics and effects of the parking control method, control device and system for urban rail trains according to the application in detail by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The application provides a parking control method, a parking control device and a parking control system for urban rail trains.
Referring to fig. 1, a step flow chart of a parking control method for a urban rail train according to an embodiment of the application is shown, the control method includes the following steps:
And step S1, acquiring speed data of each carriage at the current moment every day and the actual position of the urban rail train in each urban rail train, and acquiring speed data of all acquisition moments of each carriage in a preset time before the current moment, and taking the speed data as historical speed data of each carriage at the current moment.
The method comprises the steps of installing speed sensors on all carriages of each urban rail train, starting to stop running from each urban rail train running every day, acquiring speed data of all the acquisition moments of each carriage in a preset time period T before the current moment and speed data of each carriage at the current moment, setting a data sampling interval as T, taking the speed data of all the acquisition moments of each carriage in the preset time period before the current moment as historical speed data of each carriage at the current moment, and acquiring the actual position of the urban rail train at the current moment in an ATO system of the urban rail train.
It should be noted that, the preset time period T and the value of the data sampling interval T are set manually, the preset time period T in this embodiment is 5min, the value of the data sampling interval T is 0.5s, and the implementer can also set by himself in combination with specific situations, and the embodiment is not limited in particular.
And step S2, determining the normal speed coefficient of each carriage at the current time based on the similarity of all the historical speed data between each carriage and the rest carriages at the current time and the discrete degree of all elements in the first-order differential sequence of all the historical speed data of each carriage.
When parking control is needed in the high-speed running process of the urban rail train, the speed of the urban rail train is usually required to be frequently determined, and the parking braking force of the urban rail train is adjusted according to actual conditions. When the urban rail train starts braking, the urban rail train pipe starts to decompress and exhaust, the pressure of the urban rail train brake cylinder rises, the braking force of the front part of the urban rail train is larger than that of the rear part of the urban rail train, the rear carriage is congested forward, the pressure of the urban rail train head brake cylinder rises to the maximum along with the progress of braking, and then the braking force of each carriage slowly rises to the same level until the urban rail train stops running. In the process, the speed of the urban rail train head begins to continuously decline at first, the urban rail train speed of the rest carriages immediately follows, and the speed decline speed tends to be the same after a period of time until the speed is 0.
The speed data of the urban rail train is mainly represented as the speed data of the urban rail train head to start to be reduced, the other carriages start to descend the urban rail train head to descend at a relatively high speed after a small time interval, the other carriages descend relatively slowly, the descending speed is gradually increased, the descending speed is the same after a period of time, the overall descending trend is similar, and the descending amplitude is similar. When the speed measurement is not on time (such as the urban rail train can generate environmental vibration and noise in the high-speed running process, and further the measurement accuracy of a speed sensor is affected), the collected speed data of the urban rail train can possibly generate irregular change, the change speed is faster, the duration is shorter, the abrupt change trend is presented on the speed data of the urban rail train, further the speed decline trend of a carriage is enabled to generate larger change, the fluctuation is enhanced, and the decline degree in a short period is greatly different.
Therefore, based on the similarity of all the historical speed data between each carriage and the rest carriages at the current time and the discrete degree of all elements in the first-order differential sequence of all the historical speed data of each carriage, the speed normal coefficient of each carriage at the current time is determined so as to eliminate the influence of the environmental vibration and noise generated by the urban rail train in the high-speed running process on the measurement accuracy of the speed sensor, and the method specifically comprises the following steps:
(1) In each urban rail train, analyzing the similarity of all historical speed data between each carriage and other carriages at the current moment, and taking the average level of the similarity between each carriage and other carriages as the speed similarity of each carriage at the current moment;
It should be noted that, there are many methods for measuring the similarity between data sets, in this embodiment, the cosine similarity of all the historical speed data between each car and each other car at the current time is calculated to measure the similarity between the speeds of different cars in the same urban rail train, and an implementer may also use other methods for measuring the similarity between data sets, such as the reciprocal of the euclidean distance, and the method for measuring the similarity between data sets is not limited in this embodiment.
The cosine similarity calculation process is a known technology, and specific calculation steps thereof are not described in detail.
In addition, it should be understood that there are many methods for measuring the average level of a set of data, in this embodiment, the average level of the similarity between each car and all the rest of the cars is measured by calculating the average value of the similarity between each car and all the rest of the cars, and the practitioner may also use other methods such as geometric mean, and the method for measuring the average level of data is not particularly limited in this embodiment.
(2) Further, analyzing the discrete degree of all elements in the first-order differential sequence of all the historical speed data of each carriage at the current time, and taking the accumulated result of the difference between the discrete degree of each carriage and the discrete degree of all the rest carriages as the speed difference coefficient of each carriage at the current time;
It should be noted that, there are many methods for measuring the discrete degree of a set of data, in this embodiment, the discrete degree of all elements in the first-order differential sequence is measured by calculating the variances of all elements in the first-order differential sequence of all historical speed data of each carriage, and an embodiment may also use other methods for measuring the discrete degree of data, such as discrete coefficients or standard deviations, and the method for measuring the discrete degree of a set of data is not limited in particular.
In addition, it should be understood that there are many methods for measuring the difference between the data, in this embodiment, the difference condition of the discrete degrees between the different cars is measured by calculating the absolute value of the difference between the discrete degrees of each car and the discrete degrees of all the other cars, and the implementer may also use other methods for measuring the difference, such as a ratio, and the choice of the method for measuring the difference is not limited in particular in this embodiment.
The method for acquiring the first-order differential sequence is a known technology, and the specific acquisition process is not described in detail in this embodiment.
(3) Further, based on the speed similarity and the speed difference coefficient, determining a speed normal coefficient of each carriage at the current moment, wherein the speed normal coefficient of each carriage at the current moment is the ratio of the speed similarity of each carriage at the current moment to the speed difference coefficient.
Further, it can be understood according to the speed normal coefficient of each carriage in each urban rail train at the current time, if the speed data collected by the current carriage at the current time is the accurate data collected normally, the larger the average value of the similarity between the historical speed data of the urban rail train and the historical speed data of the urban rail trains of the rest carriages is, namely the larger the speed similarity is, the smaller the variation gap of the speed data is, the smaller the difference between the discrete degree of the current carriage and the discrete degree of the rest carriages is, namely the smaller the speed difference coefficient is, the larger the speed normal coefficient is, which means that the speed data of the urban rail train collected at the current time has lower possibility of speed measurement errors, and the more normal speed data is;
Otherwise, if the speed data collected by the current carriage at the current moment is abnormal, the smaller the average value of the similarity between the historical speed data of the urban rail train and the historical speed data of the urban rail trains of the rest carriages is, namely the smaller the speed similarity is, the larger the variation gap of the speed data is, the larger the difference between the discrete degree of the current carriage and the discrete degree of the rest carriages is, namely the larger the speed difference coefficient is, the smaller the speed normal coefficient is, and the higher the possibility that the speed measurement error exists in the speed data of the urban rail trains collected at the current moment is indicated.
And step S3, determining a gentle interval coefficient of each carriage at the current time based on the difference of abrupt change conditions of all historical speed data between each carriage and other carriages at the current time, and determining a speed accurate coefficient of each carriage at the current time by combining the speed normal coefficients.
After the urban rail train is braked for a period of time, the descending speeds of all carriages tend to be consistent, the speed data of the urban rail train also gradually tend to be the same, in the process, because a certain difference exists in the initial braking force of each carriage (such as the braking force is larger when the head carriage of the urban rail train is braked, the speed of the urban rail train starts to decline at first, and the rest carriages decline gradually), so that a certain time interval exists between the starting time of the speed data change of each carriage, the integral trend of the speed data of the urban rail train is similar, the starting time of the speed data change is different, and the similar interval exists between the starting time. When the speed measurement of the carriage is inaccurate, the speed change starting time of a carriage may be changed, and due to the randomness of the measurement error, a certain difference exists between the speed data of the urban rail train measured by the current carriage and the speed data of the rest carriages when the error occurs.
Therefore, based on the difference of abrupt change conditions of all historical speed data between each carriage and other carriages at the current moment, the interval gentle coefficient of each carriage at the current moment is determined, and the speed accurate coefficient of each carriage at the current moment is determined by combining the speed normal coefficient, so as to judge whether abnormal data caused by the measurement error of a speed sensor exist in the speed data, specifically:
(1) Taking all historical speed data of each carriage at the current moment as input of a mutation point detection algorithm, and outputting all mutation speed data;
analyzing the difference of the acquisition time corresponding to all the abrupt change speed data between each carriage and other carriages at the current time, and taking the difference of the acquisition time between each carriage and other carriages as an average value to be used as a time difference coefficient of each carriage at the current time;
It should be noted that, there are many common mutation point detection methods, in this embodiment, a Pettitt mutation point detection algorithm is adopted to obtain mutation speed data, and an implementer may also adopt other mutation point detection methods such as a random forest algorithm, and the selection of the mutation point detection method is not particularly limited in this embodiment.
Wherein Pettitt mutation point detection algorithm is a formula technology, and the specific process of detecting mutation data is not described again.
(2) Further, analyzing the difference of all the abrupt change speed data between each carriage and other carriages at the current time, and taking the average value of the difference of the abrupt change speed data between each carriage and other carriages as the abrupt change difference coefficient of each carriage at the current time;
It should be noted that there are many methods for measuring the difference between the data, in this embodiment, the difference between the mutation speed data corresponding to the collection time of different carriages is measured by calculating the absolute value of the difference between the sampling time corresponding to all the mutation speed data between each carriage and the rest of each carriage at the current time, the difference between the mutation speed data between different carriages is measured by calculating the absolute value of the difference between all the mutation speed data between each carriage and the rest of each carriage at any sampling time, and the implementer may also use other methods for measuring the difference such as the ratio, etc. other methods for measuring the difference between the mutation speed data are not limited in particular to this embodiment.
(3) Further, based on the time difference coefficient and the abrupt change difference coefficient, the interval gentle coefficient of each carriage at the current time is determined, specifically:
And taking the interval gentle coefficient of each carriage at the current moment as the sum value of the time difference coefficient and the abrupt change difference coefficient of each carriage at the current moment to obtain the reciprocal.
Further, it can be understood from the gentle coefficient of the interval of each carriage in each urban rail train at the current moment, if the speed data of the urban rail train collected at the current moment is the speed change data in the normal running process of the urban rail train, the time interval of the speed data of the urban rail trains of each carriage is approximate, the smaller the difference between the speed data is, namely the smaller the time difference coefficient of the carriages is, the smaller the abrupt change difference coefficient is, the larger the interval gentle coefficient is, the urban rail train speed data collected at the current moment is the normal speed data, otherwise, if the speed data of the urban rail trains collected at the current moment is abnormal, the time interval difference of the speed data of the urban rail trains of each carriage is larger, the larger the difference between the speed data is, namely the time difference coefficient of the carriages is, the larger the abrupt change coefficient is, the smaller the interval gentle coefficient is, and the possibility that the speed data of the urban rail trains collected at the current moment is abnormal is indicated.
(4) Further, based on the speed normal coefficient and the interval gentle coefficient, determining the speed accurate coefficient of each carriage in each urban rail train at the current moment, specifically:
accurate speed coefficient of carriage j at current moment The expression of (2) is:; the normal speed coefficient of the carriage j at the current moment is represented; representing the interval flattening coefficient of the lower compartment j at the current time.
According to the accurate speed coefficient of each carriage in each urban rail train at the current moment, it can be understood that in the running process of the urban rail train, if the speed data of the urban rail train collected by the speed sensor at a single moment is the speed data of the urban rail train in normal running, the trend similarity of the speed data of each carriage is larger, the change degree of the speed data is more similar, the normal speed coefficient is larger, the time interval when the speed change of the carriages occurs is more similar, namely the interval flat coefficient is larger, the accurate speed coefficient is larger, the possibility of error occurrence of the currently collected speed data of the urban rail train is smaller, otherwise, in the running process of the urban rail train, if the speed data of the urban rail train collected by the speed sensor at a single moment is the speed data of the urban rail train in running under the influence of vibration or environment, the trend similarity of the speed data of each carriage is smaller, the change degree of the speed data is larger, the normal speed coefficient is smaller, the similarity of time interval when the speed change occurs, namely the interval flat coefficient is smaller, the error occurrence of the speed is larger, and the possibility of error occurrence of the current error occurrence of the urban rail train is larger.
And S4, determining the speed rule coefficient of each carriage at the moment of the current day based on the difference of the speed data of each carriage at the moment of the current day and the same moment of the previous day and the difference of the change trend of all the historical speed data.
When the conditions of ascending, descending and the like occur in the running process of the urban rail train, the resistance of the urban rail train can change instantaneously, and then the speed data of the urban rail train can change to a certain degree rapidly. The speed change condition of the urban rail train is similar to the speed data characteristics when errors occur in speed measurement, and the speed change caused by resistance and the speed data when errors occur in measurement are difficult to distinguish when calculation is performed only by the method, so that further analysis is needed.
Under the normal condition, the daily running routes of the same urban rail train are consistent, the running speeds of the urban rail trains at the same moment are similar under the condition of normal standard points, and the times of the urban rail trains encountering resistance are also similar, so that the speed change of the urban rail trains due to the resistance change has certain regularity under the normal condition. The method is characterized in that urban rail train speed data is similar to urban rail train speed data at the same time of the previous day, and the change trend is similar.
Therefore, based on the difference in the speed data of each car between the present time and the same time of the previous day, and the difference in the trend of variation of all the historical speed data, the speed law coefficient of each car at the present time is determined to distinguish between the normal speed variation due to the resistance and the abnormal data variation due to the speed sensor measurement error, specifically:
(1) In each urban rail train, analyzing the difference of speed data of each carriage between the current moment and the same moment of the previous day, and taking the difference as the speed difference of each carriage at the current moment;
(2) Taking all historical speed data of each carriage at the current time as input of a trend checking algorithm, outputting trend intensity, and analyzing the difference of the trend intensity of each carriage between the current time and the same time of the previous day as the trend difference of each carriage at the current time;
It should be noted that, there are many trend test algorithms in common use, in this embodiment, the Mann-Kendall trend test algorithm is adopted to obtain the trend intensity of all the historical speed data of each carriage at the current moment, and the implementer may also adopt other trend test algorithms such as Kendall rank correlation coefficient algorithm, so that the choice of the trend test algorithm is not limited in particular.
The Mann-Kendall trend test algorithm is a known technology, and the specific principle thereof is not described in detail.
(3) Further, based on the speed difference and the trend difference, determining a speed rule coefficient of each carriage at the current time, specifically:
Velocity law coefficient of carriage j at current moment The expression of (2) is: In the formula (I), in the formula (II), Representing the speed difference of the lower carriage j at the current moment; The trend difference of the lower carriage j at the current moment is represented; Representing a constant preset to be greater than 0 for preventing the denominator from being 0, wherein The value of (2) is set manually, in this embodimentThe value of (2) is 0.01, and the implementer can set by himself in combination with specific situations on the premise of ensuring that the denominator is not 0 and the calculation result is not excessively influenced, and the embodiment is not particularly limited.
According to the speed rule coefficient of each carriage at the current moment, it can be understood that if the urban rail train speed data acquired at the current moment is the change caused by the resistance change in the running process of the normal urban rail train, the urban rail train speed data is relatively similar to the urban rail train speed data at the same moment of the previous day in size, namely, the smaller the speed difference is, and the change trend of the urban rail train speed data at the same moment of the previous day is relatively similar, namely, the smaller the trend difference is, the larger the speed rule coefficient is, the more likely the urban rail train speed data acquired at the current moment is the speed data caused by the resistance change, otherwise, if the urban rail train speed data acquired at the current moment is the change caused by the resistance change in the running process of the normal urban rail train, the urban rail train speed data is relatively large in size compared with the urban rail train speed data at the same moment of the previous day, namely, the larger the speed difference is, and the trend of the current moment and the urban rail train speed data at the same moment of the previous day is relatively similar, namely, the larger the trend is, the greater the speed coefficient is, the urban rail train speed data acquired at the current moment is relatively small, and the urban rail train speed data acquired at the current moment is the more likely to be the speed error.
And S5, determining a speed consistency coefficient of each carriage at the current moment based on the difference of speed data between each carriage and all the rest carriages at the current moment and the difference of all historical speed data of each carriage between the current moment and the adjacent previous acquisition moment, and determining a data reliability coefficient of each carriage at the current moment by combining the speed accuracy coefficient and the speed rule coefficient.
If the urban rail train is in motion, the speed data collected by the speed sensor of each carriage of the urban rail train is changed due to the change of the resistance, and the changing direction is consistent (namely, the speed data of the urban rail train is larger or smaller), and the absolute value of the sum of the data collected by all the speed sensors at a single moment and the difference value at the last moment is larger. If the speed data of the urban rail train changes due to speed measurement errors, only the speed data of one or more carriages may change, and the change amounts have large differences.
Therefore, based on the difference of the speed data between each carriage and all the rest carriages at the current time and the difference of all the historical speed data of each carriage between the current time and the adjacent previous acquisition time, the speed consistency coefficient of each carriage at the current time is determined, and the normal speed change caused by resistance and the abnormal speed change caused by the measurement error of the speed sensor are further distinguished, specifically:
(1) Calculating the average value of the difference of the speed data between each carriage and all the rest carriages at the current moment, and taking the average value as the workshop speed difference of each carriage at the current moment;
(2) Further, calculating the absolute value of the sum of the difference values of all the speed data of each carriage between the current moment and the adjacent previous acquisition moment, and taking the absolute value as the consistency index of each carriage at the current moment;
(3) The speed consistency coefficient of each carriage at the current time is the ratio of the consistency index of each carriage at the current time to the speed difference of the workshop.
According to the speed consistency coefficient of each carriage at the current moment, if the speed data of the urban rail train collected by a single sensor at a single moment is a change caused by resistance, the smaller the speed data of the urban rail train is, namely the smaller the data difference between the speed data of the urban rail train and the speed data collected by the rest sensors is, namely the smaller the workshop speed difference is, the more consistent the data change direction is, namely the larger the consistency index is, the larger the speed consistency coefficient is, the more likely to be the speed change data caused by the resistance change of the urban rail train, otherwise, if the speed data of the urban rail train collected by the single sensor at the single moment is a change caused by a measurement error, the larger the speed data of the urban rail train is, namely the larger the speed data difference between the urban rail train and the speed data collected by the rest sensors is, namely the more inconsistent the data change direction is, namely the smaller the speed consistency index is, the smaller the speed consistency coefficient is, and the speed data is the more likely to be the speed change data caused by the measurement error.
Further, based on the speed consistency coefficient of each carriage at the current time, and combining the speed accuracy coefficient and the speed rule coefficient, determining the data reliability coefficient of each carriage at the current time, specifically:
data reliability coefficient of carriage j under current moment The expression of (2) is: In the formula (I), in the formula (II), A speed law coefficient of a lower carriage j at the current moment is represented; and the speed consistency coefficient of the lower carriage j at the current moment is represented.
According to the data reliability coefficient of each carriage at the current moment, it can be understood that if the urban rail train speed data acquired by the speed sensor at the single moment is the conventional speed change condition generated by the resistance change of the urban rail train, the higher the change regularity of the data is, namely the higher the speed rule coefficient is, and the higher the consistency of the change in each sensor is, namely the greater the speed consistency coefficient is, the higher the accuracy degree of the speed data is, namely the higher the speed accuracy coefficient is, the higher the data reliability coefficient is, and at the moment, if the urban rail train needs to be stopped, the acquired data can be combined for control, otherwise, if the change regularity of the speed data at the single moment is lower, namely the speed rule coefficient is lower, and the smaller the consistency of the change in each sensor is, namely the speed consistency coefficient is, the greater the higher the speed uniformity coefficient is, namely the greater the possibility that the urban rail train speed data is abnormal change caused by the speed sensor measurement error of the urban rail train speed is, and the lower the accuracy degree of the speed data is, namely the lower the speed coefficient is.
Preferably, a schematic diagram of the data reliability coefficient extraction process provided in this embodiment is shown in fig. 2.
And step S6, based on the data reliability coefficient of each carriage at the current moment, combining the speed data of all carriages at the current moment and the actual position of the urban rail train, and carrying out parking control on the urban rail train.
Taking the data reliability coefficients of all carriages at the current moment as the input of a threshold segmentation algorithm, outputting a segmentation threshold, removing the speed data smaller than the segmentation threshold from the speed data of all carriages at the current moment, and taking the average value of the speed data of the rest carriages as the actual speed data of the urban rail train at the current moment;
And taking the distance between the preset ideal stopping position and the actual position of the urban rail train at the current moment and the difference value between 0 and the actual speed data of the urban rail train at the current moment as the input of the PID controller, and outputting a control signal to control the urban rail train to stop. Wherein, 0 represents a speed of 0, and a speed of 0 represents that the urban rail train stops.
It should be noted that, there are many common threshold segmentation algorithms, in this embodiment, an oxford threshold segmentation algorithm is adopted, and an implementer may also adopt other threshold segmentation algorithms, and the choice of the threshold segmentation algorithm is not limited in particular in this embodiment.
The principles of the oxford threshold segmentation algorithm and the PID controller are known in the art, and the specific implementation process thereof is not described in detail.
Based on the same inventive concept as the control method, the embodiment of the application also provides a parking control system of the urban rail train, which comprises the following steps:
the speed data acquisition module is used for acquiring the speed data of each carriage at the current moment every day and the actual position of the urban rail train in each urban rail train, and acquiring the speed data of all the acquisition moments of each carriage in a preset time before the current moment, and taking the speed data as the historical speed data of each carriage at the current moment;
The speed weight acquisition module is used for determining the speed normal coefficient of each carriage at the current moment based on the similarity of all the historical speed data between each carriage and other carriages at the current moment and the discrete degree of all elements in the first-order differential sequence of all the historical speed data of each carriage;
determining a gentle interval coefficient of each carriage at the current moment based on the difference of abrupt change conditions of all historical speed data between each carriage and other carriages at the current moment, and determining a speed accuracy coefficient of each carriage at the current moment by combining the speed normal coefficients;
Determining a speed rule coefficient of each carriage at the moment of the day based on the difference of speed data of each carriage between the current moment and the same moment of the previous day and the difference of variation trend of all historical speed data;
determining a speed consistency coefficient of each carriage at the current time based on the difference of speed data between each carriage and all the rest carriages at the current time and the difference of all historical speed data of each carriage between the current time and the adjacent previous acquisition time, and determining a data reliability coefficient of each carriage at the current time by combining the speed accuracy coefficient and the speed rule coefficient;
And the control parking module is used for carrying out parking control on the urban rail train based on the data reliability coefficient and combining the speed data of all carriages at the current moment and the actual position of the urban rail train.
The block diagram of the parking control system of the urban rail train provided by the embodiment of the application is shown in fig. 3.
Based on the same inventive concept as the control method, the embodiment of the application also provides parking equipment of the urban rail train, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the control methods of the urban rail train when executing the computer program.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present application are intended to be included within the scope of the present application.
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| CN115993257A (en) * | 2023-03-23 | 2023-04-21 | 禾多科技（北京）有限公司 | Reliability determination method for automatic driving system |
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| WO2016035597A1 (en) * | 2014-09-05 | 2016-03-10 | 三菱電機株式会社 | Automated train operation system |
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| CN119117850B (en) * | 2024-10-11 | 2025-04-01 | 广东华凯电梯有限公司 | Elevator operation fault detection alarm system based on artificial intelligence |
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| CN115993257A (en) * | 2023-03-23 | 2023-04-21 | 禾多科技（北京）有限公司 | Reliability determination method for automatic driving system |
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