CN114386715A - Prediction method, system, equipment and medium for main braking air path pressure leakage - Google Patents

Abstract

The invention discloses a method, a system, equipment and a medium for predicting pressure leakage of a main braking air circuit, wherein the prediction method comprises the following steps: acquiring running state data of a target subway train in a target time period; screening parking state data corresponding to the longest parking time period of the target subway train from the running state data according to the parking confirmation parameters; calculating and obtaining effective data of the pressure of the main air path in the longest parking time period based on the parking state data; inputting the effective data of the main air path pressure into the leakage model, and outputting the leakage state of the main air path pressure of the braking of the target subway train in the target time period. According to the method, effective data of the pressure of the main air path is obtained through calculation under the condition that the accuracy of the sensor is limited; the reliability and effectiveness of pressure leakage prediction data are improved, and the efficiency and accuracy of main air path pressure prediction of the subway train are enhanced; the maintenance work of the main air path of the subway train is guided in time, and the maintenance cost is reduced.

Description

Method, system, equipment and medium for predicting pressure leakage of main air circuit of brake

technical field

The invention relates to the technical field of train braking main air gas path pressure detection, in particular to a method, system, equipment and medium for predicting pressure leakage of the main brake air air path.

Background technique

In winter, due to thermal expansion and cold contraction, the tightness of the pipe joints is poor, resulting in more air leakage failures in the pipeline. In the traditional vehicle maintenance process, the detection methods for air tightness leaks mainly rely on hearing, manual detection, soapy water detection and other methods. However, this detection method is inefficient and it is difficult to find small leaks.

With the continuous deepening of the level of intelligent operation and maintenance of subways and the rapid development of technologies such as sensors, embedded systems, network communication, and big data, subway trains generally use on-board data transmission devices to transmit faults and key traffic problems during vehicle operation. The parameters are sent to the ground data center to monitor the status of the fleet in real time. However, due to the limitation of communication capacity and high cost of sensors, there are few types of air pressure signals transmitted by vehicles, the accuracy of pressure data is not high enough, and the accuracy of leakage prediction is low; additional sensors are required, which is costly and difficult to promote.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a method, system, equipment and medium for predicting the pressure leakage of the main brake air circuit in order to overcome the defects of low efficiency and poor accuracy of the brake main air circuit pressure prediction in the prior art.

The present invention solves the above-mentioned technical problems through the following technical solutions:

In a first aspect, the present invention provides a method for predicting the pressure leakage of the main air circuit of the brake, the predicting method comprising:

Obtain the running state data of the target subway train in the target period; the running state data includes the parking confirmation parameter;

According to the parking confirmation parameter, filter out the parking state data corresponding to the longest parking time period of the target subway train from the running state data;

Calculating and obtaining valid data of the main air circuit pressure in the longest parking time period based on the parking state data;

Input the effective data of the main air circuit pressure into the leakage model, and output the leakage state of the brake main air circuit pressure of the target subway train in the target time period; the leakage model is a mathematical model constructed by a two-dimensional threshold array and a two-dimensional attribute array. model, each threshold value in the two-dimensional threshold value array represents the retention time threshold value of the main air circuit pressure at the set temperature and the set pressure, and the attribute values of the two-dimensional attribute array include true values and false values, and the two The dimensions of the dimensional threshold array include temperature and pressure.

Preferably, the parking confirmation parameters include train speed, cab activation coefficient and air compressor working coefficient;

The step of filtering out the parking state data corresponding to the longest parking time period of the target subway train from the running state data according to the parking confirmation parameter includes:

intercepting at least one alternative parking time period from the target period according to the train speed, the driver's cab activation coefficient and the air compressor operating coefficient;

or,

The operating state data further includes a time parameter and a reference parameter, the reference parameter includes the brake main air pressure; the parking confirmation parameter includes the train speed, the activation coefficient of the driver's cab and the working coefficient of the air compressor;

intercepting at least one primary parking time period from the target period according to the train speed, the driver's cab activation coefficient and the air compressor operating coefficient;

Comparing every two adjacent air pressure change values in the pressure of the main brake air circuit with the first threshold value, and determining at least one alternative parking time period from the preliminary selected parking time period according to the comparison result;

Screening out the longest time period from the alternative parking time periods as the longest parking time period;

The parking state data corresponding to the longest parking time period is intercepted from the running state data.

Preferably, the step of calculating and obtaining the valid data of the main air passage pressure in the longest parking time period based on the parking state data includes:

Extracting a plurality of different air pressure values in the parking state data, and deleting the maximum and minimum air pressure values to generate an initial air pressure array;

Eliminate the air pressure value corresponding to the sampling time interval greater than the second threshold from the initial air pressure array, and generate the pressure maintenance set and timeout judgment information;

generating an alternative pressure maintaining sample set and pressure maintaining preset judgment information according to the pressure maintaining set and the parking state data;

comparing the number of samples in the alternative pressure holding sample set with a preset number;

If the number of samples is not greater than the preset number, the candidate pressure maintenance sample set is directly used as the effective pressure maintenance sample set, and the candidate pressure maintenance sample judgment information and the effective data of the main air gas path pressure are generated;

If the number of samples is greater than the preset number, after sorting the samples in the candidate pressure maintenance set in descending order of the pressure maintenance duration, filter to obtain effective pressure maintenance that meets the preset number sample set, and generate the judgment information of the candidate pressure retention sample and the valid data of the main air circuit pressure;

Wherein, the effective pressure maintaining sample set includes at least one of a pressure maintaining value, a maintaining temperature value, a pressure maintaining duration and a starting time point.

Preferably, the step of generating an alternative pressure holding sample set according to the pressure holding set and the parking state data includes:

selecting at least one target pressure hold value from the set of pressure hold values;

After comparing the difference between the air pressure value and the target pressure holding value in the parking state data with a third threshold value, intercept a plurality of candidate target pressure holding periods from the longest parking period according to the comparison result;

Screen out the longest target pressure holding period from the candidate target pressure periods, and determine whether the longest target pressure holding period satisfies a preset condition;

If so, generating the candidate pressure maintaining sample set according to the target pressure maintaining state data corresponding to the longest target pressure maintaining period intercepted from the parking state data;

If not, the step of selecting at least one target pressure holding value from the pressure holding value set is performed again until all pressure holding values in the pressure holding value set are traversed.

Preferably, the prediction method is trained to obtain the leakage model through the following steps, including:

Collecting the pressure holding data of multiple normal main air circuit pressures of subway trains in a historical period;

The pressure retention data is input into an initial model for training to obtain the leak model.

Preferably, the pressure maintenance data includes standard parameters, and the step of inputting the pressure maintenance data into an initial model for training to obtain the leakage model includes:

Selecting the pressure maintaining data with the standard parameter within the set range as the effective pressure maintaining data;

Based on the pressure holding time in the effective pressure holding data, the pressure holding time threshold of the two-dimensional threshold array in the initial model is corrected to generate the leakage model.

Preferably, after obtaining the running state data of the target subway train in the target time period, the prediction method further includes:

Preprocessing the operating status data; the preprocessing includes performing integrity detection and abnormal data correction on the operating status data, and generating preprocessing judgment information;

Wherein, the effective data of the main air circuit pressure includes the effective pressure maintenance sample set, the preprocessing judgment information, the timeout judgment information, the pressure maintenance preset judgment information, and the alternative pressure maintenance sample judgment information .

In a second aspect, the present invention provides a system for predicting pressure leakage of a brake main air circuit, the predicting system comprising:

an acquisition module, used for acquiring the running state data of the target subway train in the target period; the running state data includes the parking confirmation parameter;

a screening module, configured to screen out the parking status data corresponding to the longest parking time period of the target subway train from the running status data according to the parking confirmation parameter;

a calculation module, configured to calculate and obtain the effective data of the main air gas path pressure in the longest parking time period based on the parking state data;

The prediction module is used to input the effective data of the main air gas path pressure into the leakage model, and output the leakage state of the brake main air gas path pressure of the target subway train in the target period; the leakage model is a two-dimensional threshold array and a two-dimensional Mathematical model constructed by an attribute array, each threshold in the two-dimensional threshold array represents the retention time threshold of the main air gas path pressure at the set temperature and the set pressure, and the attribute values of the two-dimensional attribute array include true and false value, the dimensions of the two-dimensional threshold array include temperature and pressure.

Preferably, the parking confirmation parameters include train speed, cab activation coefficient and air compressor working coefficient; the screening module includes:

a first intercepting unit, configured to intercept at least one alternative parking time period from the target time period according to the train speed, the driver's cab activation coefficient and the air compressor operating coefficient;

or,

The operating state data further includes a time parameter and a reference parameter, the reference parameter includes the brake main air path pressure, and the parking confirmation parameter includes the train speed, the driver's cab activation coefficient and the air compressor working coefficient; the screening module, include:

a second intercepting unit, configured to intercept at least one primary parking time period from the target time period according to the train speed, the driver's cab activation coefficient and the air compressor operating coefficient;

a first comparison unit, configured to compare every two adjacent air pressure change values in the pressure of the main brake air circuit with a first threshold value, and determine at least one alternative parking time from the preliminary selected parking period according to the comparison result part;

a first screening unit, configured to screen out the longest time period from the alternative parking time periods as the longest parking time period;

A third intercepting unit, configured to intercept the parking state data corresponding to the longest parking time period from the running state data.

Preferably, the computing module includes:

an extraction unit, configured to extract a plurality of different air pressure values in the parking state data, delete the maximum and minimum air pressure values, and generate an initial air pressure array;

A removal unit, configured to remove the air pressure value corresponding to the sampling time interval greater than the second threshold value from the initial air pressure array, and generate a pressure holding set and timeout judgment information;

a generating unit, configured to generate an alternative pressure maintaining sample set and pressure maintaining preset judgment information according to the pressure maintaining set and the parking state data;

a second comparison unit, configured to compare the number of samples in the candidate pressure maintenance sample set with a preset number;

a first processing unit, configured to directly use the candidate pressure maintenance sample set as an effective pressure maintenance sample set if the number of samples is not greater than the preset number, and generate candidate pressure maintenance sample judgment information and the Valid data of main air circuit pressure;

The second processing unit is configured to, if the number of samples is greater than the preset number, sort the samples in the candidate pressure holding set in descending order of pressure holding time, and filter out the samples that meet the requirements of the pressure holding time. a preset number of effective pressure holding sample sets, and generating the candidate pressure holding sample judgment information and the effective data of the main air path pressure;

Wherein, the effective pressure maintaining sample set includes at least one of a pressure maintaining value, a maintaining temperature value, a pressure maintaining duration and a starting time point.

Preferably, the generating unit is specifically used for:

selecting at least one target pressure hold value from the set of pressure hold values;

After comparing the difference between the air pressure value and the target pressure holding value in the parking state data with a third threshold value, intercept a plurality of candidate target pressure holding periods from the longest parking period according to the comparison result;

Screen out the longest target pressure holding period from the candidate target pressure periods, and determine whether the longest target pressure holding period satisfies a preset condition;

If so, generating the candidate pressure maintaining sample set according to the target pressure maintaining state data corresponding to the longest target pressure maintaining period intercepted from the parking state data;

If not, the step of selecting at least one target pressure holding value from the pressure holding value set is performed again until all pressure holding values in the pressure holding value set are traversed.

Preferably, the prediction system obtains the leakage model by training the following modules, including:

The acquisition module is used to collect the pressure maintenance data of multiple normal main air circuit pressures of subway trains in a historical period;

A training module, configured to input the pressure maintenance data into an initial model for training to obtain the leakage model.

Preferably, the pressure maintenance data includes standard parameters, and the training module includes:

a selection unit, configured to select the pressure maintaining data whose standard parameters are within a set range as the effective pressure maintaining data;

A correction unit, configured to correct the pressure holding time threshold of the two-dimensional threshold array in the initial model based on the pressure holding time in the effective pressure holding data to generate the leakage model.

The forecasting system also includes:

a preprocessing module, configured to preprocess the operating status data; the preprocessing includes performing integrity detection and abnormal data correction on the operating status data, and generating preprocessing judgment information;

Wherein, the effective data of the main air gas path pressure includes the effective pressure maintaining sample set, the preprocessing judgment information, the timeout judgment information, the pressure maintaining preset judgment information and the alternative pressure maintaining sample judgment information.

In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implementing the computer program described in the first aspect when the processor executes the computer program Prediction method of brake main air circuit pressure leakage.

In a fourth aspect, the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the brake main air pressure of the first aspect is realized Leak prediction methods.

The positive and progressive effect of the present invention is to provide a method, system, equipment and medium for predicting the pressure leakage of the braking main air path. The running state data of the train is calculated to obtain the effective data of the pressure of the main air circuit, and the effective data of the pressure of the main air circuit is input into the leakage model to predict the air pressure leakage of the train. Under the condition of limited sensor accuracy, the invention calculates and obtains effective data of the main air gas path pressure; improves the reliability and validity of the pressure leakage prediction data, and enhances the efficiency and accuracy of the main air air path pressure prediction of subway trains; It guides the maintenance work of the main air road of subway trains and reduces the cost of maintenance.

Description of drawings

FIG. 1 is a flowchart of a method for predicting the pressure leakage of the brake main air passage according to Embodiment 1 of the present invention.

FIG. 2 is a flowchart of step S3 of the method for predicting the pressure leakage of the main air passage of the brake according to Embodiment 1 of the present invention.

FIG. 3 is a flowchart of step S4 of the method for predicting the pressure leakage of the main air passage of the brake according to Embodiment 1 of the present invention.

FIG. 4 is a flowchart of step S43 of the method for predicting the pressure leakage of the main air passage of the brake according to Embodiment 1 of the present invention.

FIG. 5 is a schematic block diagram of a system for predicting the pressure leakage of the brake main air circuit according to Embodiment 2 of the present invention.

FIG. 6 is a schematic diagram showing the results of initial training of the leakage model of the system for predicting the pressure leakage of the main air circuit of the brake according to Embodiment 2 of the present invention.

FIG. 7 is a schematic diagram showing the result of the completion of the training of the leakage model of the system for predicting the pressure leakage of the main air passage of the brake according to Embodiment 2 of the present invention.

FIG. 8 is a schematic diagram of a hardware structure of an electronic device according to Embodiment 3 of the present invention.

Detailed ways

The present invention is further described below by way of examples, but the present invention is not limited to the scope of the described examples.

Example 1

As shown in FIG. 1 , this embodiment provides a method for predicting the pressure leakage of the main air circuit of the brake, and the predicting method includes the following steps:

S1. Acquire the running state data of the target subway train in the target period; the running state data includes parking confirmation parameters.

S2. Preprocessing the running state data; the preprocessing includes performing integrity detection on the running state data and correcting abnormal data; and generating preprocessing judgment information.

S3. According to the parking confirmation parameter, filter out the parking state data corresponding to the longest parking time period of the target subway train from the running state data.

S4, based on the parking state data, calculate and obtain valid data on the pressure of the main air circuit in the longest parking time period.

S5. Input the effective data of the main air gas path pressure into the leakage model, and output the leakage state of the brake main air air path pressure of the target subway train in the target period. The leakage model is a mathematical model constructed by a two-dimensional threshold array and a two-dimensional attribute array. The dimensions of the two-dimensional threshold array include temperature and pressure. Each threshold in the two-dimensional threshold array represents the pressure of the main air passage at the set temperature and set pressure. Below the hold time threshold, the attribute values of the two-dimensional attribute array include true and false values.

In step S1, the running data related to the train operation collected by electronic devices such as sensors on the target subway train are received regularly. Obtain the operating state data related to the pressure of the main air circuit during the target period (early morning on December 1, 2021 to 24:00 on December 2, 2021) from the driving data. The operating state data may include the current time, the pressure value of the main air circuit, the ambient temperature value outside the vehicle, and the parking confirmation parameters, and the like.

In step S2, first check the integrity of the running status data, if the data is complete, further check whether the data format meets the preset requirements (for example, pkl or csv file), if it meets the requirements of the data format, finally determine the Whether the data volume meets the minimum data volume limit. If any of the above checks fails, a prompt message indicating that data acquisition failed can be returned. Identify abnormal data points corresponding to the main air path pressure in the operating state data, for example, data points with a single sudden change and data points beyond a preset range (for example, the preset range is 7.5bar-9.0bar). The abnormal data points are corrected by means of neighbor value substitution, average value correction, default value correction, etc. If the correction fails, you can return a prompt message that the correction failed.

In step S3, a plurality of parking time periods are selected from the running state data according to the parking confirmation parameter, and the longest parking time period is selected from the plurality of parking time periods. Further, the parking state data corresponding to the longest parking time period is filtered out from the running state data.

It can be understood that the parking confirmation parameter can be used as the screening condition 1. In order to ensure the stability of the pressure change, the pressure change interval of the main air circuit can be less than or equal to 0.1 bar as the screening condition 2, so as to determine the target subway train in the target period. Maximum parking time period.

In step S4, the maximum pressure value and the minimum pressure value are removed according to the pressure value corresponding to the non-repetitive main air path pressure appearing in the parking state data, and finally invalid data such as the main air path pressure corresponding to the abnormal sampling time are removed. Sieve out to generate a set of pressure hold values.

Each time a target pressure hold value (eg, 8.5 bar) is selected from the set of pressure hold values, the parked state data is traversed to select at least one candidate pressure hold value greater than or equal to 8.5 bar and less than 8.6 bar. Further judge whether the sampling period corresponding to the target pressure holding value and the alternative pressure holding value is reasonable. If it is reasonable, select the longest sampling period as the target subway train in the target period. How long the pressure stays on. By analogy, the next target pressure holding value (for example, 8.6 bar) is selected from the pressure holding set, and the pressure holding time period corresponding to the main air passage pressure holding value of 8.6 bar of the target subway train in the target period is determined according to the above method. , until all the pressure holding values in the pressure holding set have been traversed, to generate valid data on the pressure of the main air circuit.

In step S5, the effective data of the pressure of the main air passage is input into the pre-trained leakage model, and the leakage state result of the pressure of the target subway train in the target time period is output. It can be understood that the data status result of the valid data of the main air passage pressure can also be output. For example, the data status results may include data integrity analysis, preprocessing analysis, parking time existence analysis, time-out point existence analysis, and pressure holding duration analysis, and the like.

In an implementable solution, as shown in FIG. 2 , the parking confirmation parameters include train speed, cab activation coefficient and air compressor working coefficient; step S3 specifically includes:

S31. Intercept at least one alternative parking time period from the target time period according to the train speed, the activation coefficient of the driver's cab and the working coefficient of the air compressor.

S34. Screen out the longest time period from the alternative parking time periods as the longest parking time period.

S35. Intercept the parking state data corresponding to the longest parking time period from the running state data.

In step S31 , the time period when the train speed of each time period in the running state data is zero, the activation coefficients of the front and rear cabs are both zero, and the working coefficient of the air compressor is zero is taken as the alternative parking time period of the target subway train.

In another feasible solution, as shown in FIG. 2 , the running state data further includes a time parameter and a reference parameter, and the reference parameter includes the brake main air pressure; the parking confirmation parameter includes the train speed, the driver's cab activation coefficient and the air pressure. machine operating coefficient; step S3 specifically includes:

S32. Intercept at least one primary parking time period from the target period according to the train speed, the activation coefficient of the driver's cab and the working coefficient of the air compressor.

S33. Compare every two adjacent air pressure change values in the pressure of the main brake air air path with the first threshold value, and determine at least one alternative parking time period from the primary selection parking time period according to the comparison result.

S34. Screen out the longest time period from the alternative parking time periods as the longest parking time period.

S35. Intercept the parking state data corresponding to the longest parking time period from the running state data.

In step S32, the time period when the train speed in each time period in the running state data is zero, the activation coefficients of the front and rear cabs are both zero, and the air compressor operating coefficient is zero is used as the primary parking time period of the target subway train.

In step S33-step S35, preset data processing is performed on the pressure of the main brake air passage corresponding to the plurality of preliminary selected parking time periods. Specifically, after removing the invalid primary selection parking time period in which the difference between adjacent air pressure changes in the pressure of the main brake air circuit is greater than the first threshold (0.1 bar), the remaining primary selection parking time period is used as the alternative parking time period, The longest parking time period is filtered out from the alternative parking time periods, and the parking status data corresponding to the longest parking time period is further obtained. If all the primary parking time periods do not meet the conditions, a prompt message of failure to obtain the parking status data may be returned.

In an implementable solution, as shown in FIG. 3 , step S4 specifically includes:

S41 , extracting multiple different air pressure values in the parking state data, and deleting the maximum and minimum air pressure values to generate an initial air pressure array.

S42 , remove the air pressure value corresponding to the sampling time interval greater than the second threshold from the initial air pressure array, and generate a pressure holding set and timeout judgment information.

S43. Generate an alternative pressure maintenance sample set and pressure maintenance preset judgment information according to the pressure maintenance set and the parking state data.

S44. Compare the number of samples in the candidate pressure maintenance sample set with the preset number. If the number of samples is not greater than the preset number, step S45 is performed, and if the number of samples is greater than the preset number, step S46 is performed.

S45. Directly use the candidate pressure holding sample set as the effective pressure holding sample set, and generate the judgment information of the candidate pressure holding sample and the valid data of the pressure of the main air circuit.

S46 , after sorting the samples in the candidate pressure maintenance set in descending order of the pressure maintenance duration, filter to obtain a set of valid pressure maintenance samples that meet the preset number, and generate the judgment information and main atmosphere of the candidate pressure maintenance samples Road pressure valid data.

Wherein, the effective pressure maintaining sample set includes at least one of a pressure maintaining value, a maintaining temperature value, a pressure maintaining duration and a starting time point.

In step S41, the air pressure values of all non-repetitive main air air path pressures in the parking state data are counted. The non-repeated air pressure values are [8.6bar, 8.7bar, 8.8bar, 8.9bar], the maximum value of the non-repeated air pressure values is 8.9bar and the minimum value is 8.6bar, and the initial air pressure array is generated as [8.7bar, 8.8 bar].

In step S42, after removing two air pressure values whose sampling time interval is greater than the second threshold (for example, 10 min) from the initial air pressure array, the pressure holding set is [8.7bar, 8.8bar] and timeout information is generated. The time-out information may include whether there are abnormal data points with time intervals in the initial air pressure array, that is, time-out data air pressure points.

In step S43-step S46, the number of samples in the candidate pressure maintenance set samples is compared with the preset number N, the effective pressure maintenance sample set is determined according to the comparison result, and the main atmosphere is further generated according to the effective pressure maintenance sample set Road pressure valid data and alternative pressure hold sample judgment information. The alternative pressure-holding sample judgment information includes information on whether there is an excessive number of samples, an excessively small number of samples, or a reasonable number of samples in the number of samples.

It should be noted that the valid data of the main air path pressure also includes preprocessing judgment information, the timeout judgment information, the pressure maintenance preset judgment information, and the alternative pressure maintenance sample judgment information.

In an implementable solution, as shown in FIG. 4 , step S43 specifically includes:

S431. Select at least one target pressure maintaining value from the pressure maintaining value set.

S432. After comparing the difference between the air pressure value and the target pressure holding value in the parking state data with the third threshold, intercept multiple candidate target pressure holding periods from the longest parking period according to the comparison result.

S433. Screen out the longest target pressure holding period from the candidate target pressure periods, and determine whether the longest target pressure holding period satisfies a preset condition. If satisfied, go to step S434, if not, go to step S431 until all pressure holding values in the pressure holding value set are traversed.

S434 , according to the target pressure maintaining state data corresponding to the longest target pressure maintaining period intercepted from the parking state data, generate a set of candidate pressure maintaining samples.

In step S431, a target pressure holding value (eg, 8.7 bar) can be selected from the pressure holding set at a time, and in step S432, a sensitivity unit greater than or equal to 8.7 bar and no more than one sensitive unit (the first one is screened out from the parking state data) Multiple alternative target pressure holding values for three thresholds, eg, 0.1 bar).

In step S433, after the longest target pressure holding period is selected from each candidate target pressure holding period, it is determined whether the sampling time corresponding to the longest target pressure holding period starts from the parking time of the target subway train or ends at the target subway train The stop time of the train. If yes, then remove the longest target pressure holding period, if not, continue to judge whether the length of the pressure holding period corresponding to the longest target pressure holding period is less than 1 minute, if so, then the longest target pressure holding period If not, then keep the longest target pressure holding period.

In an implementable solution, the prediction method is trained to obtain a leakage model through the following steps, including:

The pressure retention data of multiple normal main air circuit pressures of subway trains in a historical period are collected.

The pressure-holding data is fed into the initial model for training, resulting in a leak model.

Specifically, during the period from December 21, 2018 to February 28, 2019 and from June 29, 2019 to July 9, 2019, a subway train in a healthy state (no leakage or a small amount of reasonable leakage) was collected. The pressure holding data of the main air circuit pressure, the pressure holding data of the two periods are input into the initial model for training in units of days. It can be understood that the initial state threshold array of the initial model is all set to 0, and the attribute array is all set to false.

The pressure holding data includes standard parameters, and the steps of inputting the pressure holding data into the initial model for training to obtain the leakage model include:

Select the pressure holding data with the standard parameters within the set range as the effective pressure holding data;

Based on the pressure holding time in the effective pressure holding data, the pressure holding time threshold of the two-dimensional threshold array in the initial model is corrected to generate a leak model.

Specifically, in the case where the standard parameters can include temperature and pressure, the preset temperature change range is -10-50°C, the interval is 1°C, and the pressure change range is set to 7.5-9bar, and the interval is 0.1bar; keep the pressure data The data that meet the above standard parameter setting conditions at the same time are regarded as the effective pressure holding data.

The multiple pressure holding times under the set temperature and set pressure conditions in the effective pressure holding data are compared with the pressure holding time thresholds in the two-dimensional threshold array (an array of corresponding temperature and pressure) in the initial model in turn. Update the minimum pressure holding time under the set pressure and set temperature to the pressure holding time threshold of the corresponding two-dimensional threshold array, and update the attribute value of the corresponding two-dimensional attribute array to true. According to the above training method, the pressure holding time under all the remaining temperature and pressure conditions in the effective pressure holding data is compared with that in the initial model, the model training is completed, and the leakage model is generated.

The present embodiment provides a method for predicting the pressure leakage of the braking main air path. The leakage model is obtained by training based on the relevant parameters of the subway train braking main air path, and the effective data of the main air path pressure is calculated based on the running state data of the subway train. Input the effective data of the main air path pressure into the leakage model to predict the air pressure leakage of the train. Under the condition of limited sensor accuracy, the invention calculates and obtains effective data of the main air gas path pressure; improves the reliability and validity of the pressure leakage prediction data, and enhances the efficiency and accuracy of the main air air path pressure prediction of subway trains; It guides the maintenance work of the main air road of subway trains and reduces the cost of maintenance.

Example 2

As shown in FIG. 5 , this embodiment provides a system for predicting the pressure leakage of the brake main air circuit. The prediction system includes an acquisition module 210 , a preprocessing module 220 , a screening module 230 , a calculation module 240 and a prediction module 250 .

Wherein, the obtaining module 210 is used for obtaining the running state data of the target subway train in the target period; the running state data includes the parking confirmation parameter.

The preprocessing module 220 is used for preprocessing the operating status data; the preprocessing includes performing integrity detection and abnormal data correction on the operating status data; and generating preprocessing judgment information.

The screening module 230 is configured to screen out the parking status data corresponding to the longest parking time period of the target subway train from the running status data according to the parking confirmation parameter.

The calculation module 240 is configured to calculate and obtain valid data of the pressure of the main air circuit during the longest parking period based on the parking state data.

The prediction module 250 is configured to input the effective data of the main air passage pressure into the leakage model, and output the leakage state of the braking main air passage pressure of the target subway train in the target period. The leakage model is a mathematical model constructed by a two-dimensional threshold array and a two-dimensional attribute array. The dimensions of the two-dimensional threshold array include temperature and pressure. Each threshold in the two-dimensional threshold array represents the pressure of the main air passage at the set temperature and set pressure. Below the hold time threshold, the attribute values of the two-dimensional attribute array include true and false values.

The acquisition module 210 regularly receives the running data related to the train operation collected by electronic devices such as sensors on the target subway train. The obtaining module 210 obtains, from the driving data, the operating state data related to the pressure of the main air passage during the target period (early morning on December 1, 2021 to 24:00 on December 2, 2021). The operating state data may include the current time, the pressure value of the main air circuit, the ambient temperature value outside the vehicle, and the parking confirmation parameters, and the like.

The preprocessing module 220 first checks the integrity of the running status data, and if the data is complete, further detects whether the data format meets the preset requirements (for example, pkl or csv file), and if it meets the requirements of the data format, finally determines the data Whether the amount meets the minimum data amount limit. If any of the above checks fails, a prompt message indicating that data acquisition failed can be returned. Identify abnormal data points corresponding to the pressure of the main air circuit in the operating state data, for example, data points with a single sudden change and data points beyond a preset range (for example, the preset range is 7.5bar-9.0bar). The preprocessing module 220 corrects the abnormal data points by means of neighbor value substitution, average value correction, default value correction, and the like. If the correction fails, you can return the prompt message that the correction failed.

According to the parking confirmation parameter, a plurality of parking time periods are selected from the running state data, and the longest parking time period is selected from the plurality of parking time periods. The further screening module 230 screens out the parking status data corresponding to the longest parking time period from the running status data.

It can be understood that the parking confirmation parameter can be used as the screening condition 1. In order to ensure the stability of the pressure change, the pressure change interval of the main air circuit can be less than or equal to 0.1 bar as the screening condition 2, so as to determine the target subway train in the target period. Maximum parking time period.

According to the pressure value corresponding to the non-repetitive main air circuit pressure in the parking state data, the maximum pressure value and the minimum pressure value are removed. Finally, the calculation module 240 filters out invalid data such as the main air circuit pressure corresponding to the abnormal sampling time. off, a set of pressure hold values is generated.

Each time a target pressure hold value (eg, 8.5 bar) is selected from the set of pressure hold values, the parked state data is traversed to select at least one candidate pressure hold value greater than or equal to 8.5 bar and less than 8.6 bar. Further judge whether the sampling period corresponding to the target pressure holding value and the alternative pressure holding value is reasonable. If it is reasonable, select the longest sampling period as the target subway train in the target period. How long the pressure stays on. By analogy, the next target pressure holding value (for example, 8.6 bar) is selected from the pressure holding set, and the pressure holding time period corresponding to the main air passage pressure holding value of 8.6 bar of the target subway train in the target period is determined according to the above method. , until all the pressure holding values in the pressure holding set have been traversed, to generate valid data on the pressure of the main air circuit.

The effective data of the main air passage pressure is input into the pre-trained leakage model, and the prediction module 250 outputs the leakage state result of the pressure of the target subway train in the target period. It can be understood that the prediction module 250 can also output the data status result of the valid data of the main air circuit pressure. For example, the data status results may include data integrity analysis, preprocessing analysis, parking time existence analysis, time-out point existence analysis, and pressure holding duration analysis, and the like.

In an implementable solution, the parking confirmation parameters include train speed, cab activation coefficient and air compressor working coefficient; the screening module 230 includes:

The first intercepting unit 231 is configured to intercept at least one alternative parking time period from the target time period according to the train speed, the driver's cab activation coefficient and the air compressor operating coefficient.

The first screening unit 234 is configured to screen out the longest time period from the candidate parking time periods as the longest parking time period.

The third intercepting unit 235 is configured to intercept the parking state data corresponding to the longest parking time period from the running state data.

The first intercepting unit 231 takes the time period when the train speed of each time period in the running state data is zero, the activation coefficients of the front and rear cabs are both zero, and the working coefficient of the air compressor is zero, as the alternative parking time period of the target subway train.

In another implementable solution, as shown in FIG. 5 , the running state data further includes a time parameter and a reference parameter, and the reference parameter includes the brake main air pressure; the parking confirmation parameter includes the train speed, the driver's cab activation coefficient and the air pressure. machine operating coefficient; the screening module 230 includes:

The second intercepting unit 232 is configured to intercept at least one primary parking time period from the target time period according to the train speed, the driver's cab activation coefficient and the air compressor operating coefficient.

The first comparison unit 233 is configured to compare every two adjacent air pressure change values in the pressure of the main brake air circuit with the first threshold value, and determine at least one alternative parking time period from the preliminary selected parking time period according to the comparison result.

The first screening unit 234 is configured to screen out the longest time period from the candidate parking time periods as the longest parking time period.

The third intercepting unit 235 is configured to intercept the parking state data corresponding to the longest parking time period from the running state data.

The second intercepting unit 232 takes the time period when the train speed of each time period in the running state data is zero, the activation coefficients of the front and rear cabs are zero, and the working coefficient of the air compressor is zero, as the primary parking time period of the target subway train.

Preset data processing is performed on the pressure of the main brake air circuit corresponding to the plurality of preliminary selected parking time periods. Specifically, the first comparison unit 233 removes the invalid primary selection parking time period in which the difference between adjacent air pressure changes in the brake main air pressure is greater than the first threshold (0.1 bar), and uses the remaining primary selection parking time period as a backup Select a parking time period, filter out the longest parking time period from the alternative parking time periods, and further obtain the parking status data corresponding to the longest parking time period. If all the primary parking time periods do not meet the conditions, a prompt message of failure to obtain the parking status data may be returned.

In one possible implementation, the computing module 240 includes:

The extraction unit 241 is configured to extract a plurality of different air pressure values in the parking state data, delete the maximum and minimum air pressure values, and generate an initial air pressure array.

The removing unit 242 is configured to remove the air pressure value corresponding to the sampling time interval greater than the second threshold value from the initial air pressure array, and generate the pressure holding set and the timeout judgment information.

The generating unit 243 is configured to generate an alternative pressure maintaining sample set and pressure maintaining preset judgment information according to the pressure maintaining set and the parking state data.

The second comparison unit 244 is configured to compare the number of samples in the candidate pressure maintenance sample set with the preset number.

The first processing unit 245 is configured to directly use the candidate pressure holding sample set as the effective pressure holding sample set if the number of samples is not greater than the preset number, and generate the judgment information of the candidate pressure holding sample and the valid data of the main air path pressure.

The second processing unit 246 is configured to, if the number of samples is greater than the preset number, sort the samples in the candidate pressure maintaining set in descending order of the pressure maintaining duration, and then filter to obtain valid pressure maintaining conforming to the preset number Sample collection, and generate alternative pressure retention sample judgment information and main air circuit pressure valid data.

Wherein, the effective pressure maintaining sample set includes at least one of a pressure maintaining value, a maintaining temperature value, a pressure maintaining duration and a starting time point.

The extraction unit 241 counts the air pressure values of all non-repetitive main air air path pressures in the parking state data. The non-repeated air pressure values are [8.6bar, 8.7bar, 8.8bar, 8.9bar], the maximum value of 8.9bar and the minimum value of 8.6bar in the non-repeated air pressure values are removed, and the initial air pressure array is generated as [8.7bar, 8.8bar] .

After removing two air pressure values whose sampling time interval is greater than the second threshold (for example, 10 min) from the initial air pressure array, the culling unit 242 generates a pressure holding set of [8.7bar, 8.8bar] and timeout information. The time-out information may include whether there are abnormal data points with time intervals in the initial air pressure array, that is, time-out data air pressure points.

The number of samples in the candidate pressure holding set samples is compared with the preset number N, and the effective pressure holding sample set is determined according to the comparison result, and the first processing unit 245 or the second processing unit 246 further maintains the sample set according to the effective pressure. Generate the valid data of the main air circuit pressure and the judgment information of the alternative pressure holding samples. The alternative pressure-holding sample judgment information includes information on whether there is an excessive number of samples, an excessively small number of samples, or a reasonable number of samples in the number of samples.

It should be noted that the valid data of the main air path pressure also includes preprocessing judgment information, the timeout judgment information, the pressure maintenance preset judgment information, and the alternative pressure maintenance sample judgment information.

In an implementable solution, the generating unit 243 is specifically configured to:

Select at least one target pressure hold value from a set of pressure hold values.

After comparing the difference between the air pressure value and the target pressure holding value in the parking state data with the third threshold, a plurality of candidate target pressure holding periods are intercepted from the longest parking period according to the comparison result.

The longest target pressure holding period is selected from the candidate target pressure periods, and it is judged whether the longest target pressure holding period satisfies the preset condition.

If the target pressure maintaining state data corresponding to the longest target pressure maintaining period intercepted from the parking state data is satisfied, a set of candidate pressure maintaining samples is generated.

If not, the step of selecting at least one target pressure holding value from the pressure holding value set is performed again until all pressure holding values in the pressure holding value set are traversed.

The generating unit 243 may select a target pressure maintaining value (for example, 8.7 bar) from the pressure maintaining set each time, and in step S432, filter out the parking state data that is greater than or equal to 8.7 bar and not more than one sensitive unit (third threshold, eg, 0.1 bar) multiple alternative target pressure hold values.

The generating unit 243 also determines whether the sampling time corresponding to the longest target pressure holding period begins at the start of the parking time of the target subway train or ends at the target subway train after screening out the longest target pressure holding period in each candidate target pressure holding period. the end of the parking time. If yes, then remove the longest target pressure holding period, if not, continue to judge whether the length of the pressure holding period corresponding to the longest target pressure holding period is less than 1 minute, if so, then the longest target pressure holding period If not, then keep the longest target pressure holding period.

In an implementable solution, the prediction system is trained to obtain a leakage model through the following modules, including:

The collection module is used to collect the pressure maintenance data of multiple normal main air path pressures of the subway train in a historical period.

The training module is used to input the pressure holding data into the initial model for training to obtain the leakage model.

Specifically, the collection module collects a subway train in a healthy state (no leakage or a small amount of reasonable leakage) during the period from December 21, 2018 to February 28, 2019 and from June 29, 2019 to July 9, 2019. ) of the main air circuit pressure holding data. The pressure maintenance data of the two periods is in days, and is input into the initial model through the training module for training. It can be understood that the initial state threshold array of the initial model is all set to 0, and the attribute array is all set to false.

The pressure holding data includes standard parameters, and the training module includes:

The selection unit is used to select the pressure holding data whose standard parameters are within the set range as the effective pressure holding data.

The correction unit is used for correcting the pressure holding time threshold of the two-dimensional threshold array in the initial model based on the pressure holding time in the effective pressure holding data to generate a leakage model.

Specifically, in the case where the standard parameters can include temperature and pressure, the preset temperature change range is -10-50°C, the interval is 1°C, and the pressure change range is set to 7.5-9bar, and the interval is 0.1bar; The data in the holding data that also meets the above standard parameter setting conditions are regarded as the effective pressure holding data.

Compare the multiple pressure holding times under the set temperature and set pressure conditions in the effective pressure holding data with the pressure holdings in the two-dimensional threshold array (an array of corresponding temperatures and pressures) in the initial model (as shown in Figure 6) The time thresholds are compared in turn. The correction unit updates the minimum pressure holding time under the set pressure and set temperature to the pressure holding time threshold of the corresponding two-dimensional threshold array, and updates the attribute value of the corresponding two-dimensional attribute array to true. According to the above training method, compare the pressure holding time under all other temperature and pressure conditions in the effective pressure holding data with the initial model, complete the model training (as shown in Figure 7), and generate a leak model.

This embodiment provides a system for predicting the pressure leakage of the main air path of the braking system. The leakage model is obtained by training based on the relevant parameters of the main air path for braking the subway train. The prediction module inputs the effective data of the main air air path pressure into the leakage model to predict the air pressure leakage of the train. Under the condition of limited sensor accuracy, the invention calculates and obtains effective data of the main air gas path pressure; improves the reliability and validity of the pressure leakage prediction data, and enhances the efficiency and accuracy of the main air air path pressure prediction of subway trains; It guides the maintenance work of the main air road of subway trains and reduces the cost of maintenance.

Example 3

FIG. 8 is a schematic structural diagram of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the method for predicting the pressure leakage of the brake main air passage according to Embodiment 1 when the processor executes the program, The electronic device 60 shown in FIG. 8 is only an example, and should not impose any limitation on the function and scope of use of the embodiments of the present invention.

The electronic device 60 may take the form of a general-purpose computing device, which may be, for example, a server device. The components of the electronic device 60 may include, but are not limited to: the above-mentioned at least one processor 61 , the above-mentioned at least one memory 62 , and a bus 63 connecting different system components (including the memory 62 and the processor 61 ).

The bus 63 includes a data bus, an address bus, and a control bus.

Memory 62 may include volatile memory, such as random access memory (RAM) 621 and/or cache memory 622 , and may further include read only memory (ROM) 623 .

The memory 62 may also include a program/utility 625 having a set (at least one) of program modules 624 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, which An implementation of a network environment may be included in each or some combination of the examples.

The processor 61 executes various functional applications and data processing by running the computer program stored in the memory 62, such as the method for predicting the pressure leakage of the main air circuit of the brake according to Embodiment 1 of the present invention.

The electronic device 60 may also communicate with one or more external devices 64 (eg, keyboards, pointing devices, etc.). Such communication may take place through input/output (I/O) interface 65 . Also, the model-generating device 630 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN), and/or a public network such as the Internet) through a network adapter 66 . As shown, the network adapter 66 communicates with other modules of the model generation device 60 via the bus 63 . It should be understood that, although not shown, other hardware and/or software modules may be used in conjunction with the model-generated device 60, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk) array) systems, tape drives, and data backup storage systems.

It should be noted that although several units/modules or sub-units/modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into multiple units/modules to be embodied.

Example 4

This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps of the method for predicting the pressure leakage of the brake main air path according to Embodiment 1.

Wherein, the readable storage media may include, but are not limited to, portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any of the above suitable combination.

In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program codes, when the program product runs on a terminal device, the program code is used to cause the terminal device to execute the implementation The steps of the method for predicting the pressure leakage of the main air circuit of the brake according to the first embodiment.

Wherein, the program code for executing the present invention can be written in any combination of one or more programming languages, and the program code can be completely executed on the user equipment, partially executed on the user equipment, as an independent The software package executes on the user's device, partly on the user's device, partly on the remote device, or entirely on the remote device.

Although the specific embodiments of the present invention are described above, those skilled in the art should understand that this is only an illustration, and the protection scope of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims (16)

1. A prediction method for pressure leakage of a main brake air path is characterized by comprising the following steps:

acquiring running state data of a target subway train in a target time period; the operating state data comprises parking confirmation parameters;

screening parking state data corresponding to the longest parking time period of the target subway train from the running state data according to the parking confirmation parameters;

calculating and obtaining the effective pressure data of the main air path in the longest parking time period based on the parking state data;

inputting the effective data of the main air path pressure into a leakage model, and outputting the leakage state of the main air path pressure of the braking of the target subway train in a target time period; the leakage model is a mathematical model constructed by a two-dimensional threshold value array and a two-dimensional attribute array, each threshold value in the two-dimensional threshold value array represents a retention time threshold value of the pressure of the main wind gas circuit at a set temperature and a set pressure, the attribute values of the two-dimensional attribute array comprise a true value and a false value, and the dimensionality of the two-dimensional threshold value array comprises the temperature and the pressure.

2. The method of predicting main brake air path pressure leak of claim 1, wherein the parking validation parameters include train speed, cab activation coefficient, and air compressor operating coefficient;

the step of screening out the parking state data corresponding to the longest parking time period of the target subway train from the running state data according to the parking confirmation parameters comprises the following steps:

intercepting at least one alternative parking time period from the target time period according to the train speed, the cab activation coefficient and the air compressor working coefficient;

or the like, or, alternatively,

the running state data further comprises a time parameter and a reference parameter, wherein the reference parameter comprises the pressure of a main braking air path; the parking confirmation parameters comprise train speed, cab activation coefficients and air compressor working coefficients;

intercepting at least one primary parking time period from the target time period according to the train speed, the cab activation coefficient and the air compressor working coefficient;

comparing every two adjacent air pressure change values in the main braking air path pressure with a first threshold value, and determining at least one alternative parking time period from the primary parking time period according to a comparison result;

screening out the longest time period from the alternative parking time periods as the longest parking time period;

and intercepting the parking state data corresponding to the longest parking time period from the running state data.

3. The method of predicting main brake air path pressure leak of claim 1, wherein said step of calculating available main air path pressure data for the longest parking time period based on the parking status data comprises:

extracting a plurality of different air pressure values in the parking state data, deleting the maximum and minimum air pressure values, and generating an initial air pressure array;

eliminating the air pressure value corresponding to the sampling time interval larger than a second threshold value from the initial air pressure array, and generating a pressure holding set and overtime judgment information;

generating an alternative pressure holding sample set and pressure holding preset judgment information according to the pressure holding set and the parking state data;

comparing the number of samples in the alternative pressure maintaining sample set with a preset number;

if the number of the samples is not larger than the preset number, directly taking the alternative pressure maintaining sample set as an effective pressure maintaining sample set, and generating alternative pressure maintaining sample judgment information and main air path pressure effective data;

if the number of the samples is larger than the preset number, sorting the samples in the alternative pressure holding set according to the sequence of the pressure holding duration from large to small, screening to obtain an effective pressure holding sample set according with the preset number, and generating alternative pressure holding sample judgment information and main air path pressure effective data;

wherein the set of effective pressure-hold samples comprises at least one of a pressure-hold value, a hold temperature value, a pressure-hold duration, and a start time point.

4. The method of predicting a main brake air circuit pressure leak of claim 3, wherein the step of generating a set of alternative pressure maintenance samples from the set of pressure maintenance and the parked state data comprises:

selecting at least one target pressure maintenance value from the set of pressure maintenance values;

comparing the difference value between the air pressure value and the target pressure holding value in the parking state data with a third threshold value, and then intercepting a plurality of candidate target pressure holding time periods from the longest parking time period according to the comparison result;

screening out the longest target pressure maintaining time period from the alternative target pressure time periods, and judging whether the longest target pressure maintaining time period meets a preset condition;

if so, generating the alternative pressure holding sample set according to the target pressure holding state data corresponding to the longest target pressure holding time period intercepted from the parking state data;

if not, then the step of selecting at least one target pressure maintenance value from the set of pressure maintenance values is performed until all of the pressure maintenance values in the set of pressure maintenance values have been traversed.

5. The method of predicting main brake air path pressure leakage according to claim 1, wherein the prediction method is trained to obtain the leakage model by the steps of:

collecting pressure maintenance data of a plurality of normal main air circuit pressures of a subway train in a historical period;

and inputting the pressure maintenance data into an initial model for training to obtain the leakage model.

6. The method of predicting a main brake air path pressure leak of claim 5, wherein the pressure maintenance data includes standard parameters, and the step of inputting the pressure maintenance data into an initial model for training to obtain the leak model includes:

selecting the pressure maintenance data in which the standard parameter is within a set range as effective pressure maintenance data;

and correcting the pressure holding time threshold of the two-dimensional threshold array in the initial model based on the pressure holding time in the effective pressure holding data to generate the leakage model.

7. The method for predicting main brake air circuit pressure leakage according to claim 4, wherein after the operation state data of the target subway train in the target time period is obtained, the method further comprises the following steps:

preprocessing the running state data; the preprocessing comprises integrity detection and abnormal data correction of the running state data and generating preprocessing judgment information;

the main air path pressure effective data comprises the effective pressure holding sample set, the preprocessing judgment information, the overtime judgment information, the pressure holding preset judgment information and the alternative pressure holding sample judgment information.

8. A system for predicting brake main duct pressure leakage, the system comprising:

the acquisition module is used for acquiring the running state data of the target subway train in a target time period; the operating state data comprises parking confirmation parameters;

the screening module is used for screening the parking state data corresponding to the longest parking time period of the target subway train from the running state data according to the parking confirmation parameters;

the calculation module is used for calculating and obtaining the effective pressure data of the main air path in the longest parking time period based on the parking state data;

the prediction module is used for inputting the effective data of the main air path pressure into a leakage model and outputting the leakage state of the main air path pressure of the braking of the target subway train in a target time interval; the leakage model is a mathematical model constructed by a two-dimensional threshold value array and a two-dimensional attribute array, each threshold value in the two-dimensional threshold value array represents a retention time threshold value of the pressure of the main wind gas circuit at a set temperature and a set pressure, the attribute values of the two-dimensional attribute array comprise a true value and a false value, and the dimensionality of the two-dimensional threshold value array comprises the temperature and the pressure.

9. The system of claim 8, wherein the parking validation parameters include train speed, cab activation coefficient, and air compressor operating coefficient; the screening module includes:

the first intercepting unit is used for intercepting at least one alternative parking time period from the target time period according to the train speed, the cab activation coefficient and the air compressor working coefficient;

or the like, or, alternatively,

the running state data further comprises a time parameter and a reference parameter, the reference parameter comprises the pressure of a main braking air circuit, and the parking confirmation parameter comprises the train speed, the cab activation coefficient and the working coefficient of an air compressor; the screening module includes:

the second intercepting unit is used for intercepting at least one primary parking time period from the target time period according to the train speed, the cab activation coefficient and the air compressor working coefficient;

the first comparison unit is used for comparing every two adjacent air pressure change values in the main braking air path pressure with a first threshold value and determining at least one alternative parking time period from the primary parking time period according to a comparison result;

the first screening unit is used for screening out the longest time period from the alternative parking time periods to be used as the longest parking time period;

and the third intercepting unit is used for intercepting the parking state data corresponding to the longest parking time period from the running state data.

10. The system of claim 8, wherein the calculation module comprises:

the extraction unit is used for extracting a plurality of different air pressure values in the parking state data, deleting the maximum and minimum air pressure values and generating an initial air pressure array;

the removing unit is used for removing the air pressure value corresponding to the sampling time interval larger than a second threshold value from the initial air pressure array, and generating a pressure holding set and overtime judgment information;

the generating unit is used for generating an alternative pressure maintaining sample set and pressure maintaining preset judgment information according to the pressure maintaining set and the parking state data;

the second comparison unit is used for comparing the number of the samples in the alternative pressure maintaining sample set with a preset number;

the first processing unit is used for directly taking the alternative pressure maintaining sample set as an effective pressure maintaining sample set and generating alternative pressure maintaining sample judgment information and main air path pressure effective data if the number of the samples is not greater than the preset number;

the second processing unit is used for sorting the samples in the alternative pressure maintaining set according to the sequence of the pressure maintaining duration from large to small if the number of the samples is larger than the preset number, screening to obtain an effective pressure maintaining sample set which accords with the preset number, and generating alternative pressure maintaining sample judgment information and main air path pressure effective data;

wherein the set of effective pressure-hold samples comprises at least one of a pressure-hold value, a hold temperature value, a pressure-hold duration, and a start time point.

11. The system for predicting main brake air path pressure leak of claim 10, wherein the generating unit is specifically configured to:

selecting at least one target pressure maintenance value from the set of pressure maintenance values;

comparing the difference value between the air pressure value and the target pressure holding value in the parking state data with a third threshold value, and then intercepting a plurality of candidate target pressure holding time periods from the longest parking time period according to the comparison result;

screening out the longest target pressure maintaining time period from the alternative target pressure time periods, and judging whether the longest target pressure maintaining time period meets a preset condition;

if so, generating the alternative pressure holding sample set according to the target pressure holding state data corresponding to the longest target pressure holding time period intercepted from the parking state data;

if not, then the step of selecting at least one target pressure maintenance value from the set of pressure maintenance values is performed until all of the pressure maintenance values in the set of pressure maintenance values have been traversed.

12. The system of claim 8, wherein the prediction system is trained to derive the leak model by:

the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring pressure maintenance data of a plurality of normal main air circuit pressures of the subway train in a historical time period;

and the training module is used for inputting the pressure maintaining data into an initial model for training so as to obtain the leakage model.

13. The system of claim 12, wherein the pressure maintenance data includes standard parameters, the training module including:

a selection unit configured to select the pressure maintenance data in which the standard parameter is within a set range as effective pressure maintenance data;

and the correcting unit is used for correcting the pressure holding time threshold of the two-dimensional threshold array in the initial model based on the pressure holding time in the effective pressure holding data to generate the leakage model.

14. The system for predicting a main brake air path pressure leak of claim 10, further comprising:

the preprocessing module is used for preprocessing the running state data; the preprocessing comprises integrity detection and abnormal data correction of the running state data and generating preprocessing judgment information;

the main air path pressure effective data comprises the effective pressure maintaining sample set, the preprocessing judgment information, overtime judgment information, pressure maintaining preset judgment information and alternative pressure maintaining sample judgment information.

15. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for predicting a main brake air path pressure leak of any of claims 1-7 when executing the computer program.

16. A computer-readable storage medium, having stored therein a computer program which, when executed by a processor, implements the method of predicting main brake wind path pressure leak of any of claims 1-7.

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