A maintenance data integration management system

Abstract

The present application belongs to the technical field of locomotive data management, and provides a locomotive data integration management system. The system includes: a train monitoring module, which is used to collect image information at various positions of the train, and segment the image information at specific time intervals to obtain segmented image information; a database, which is used to store the segmented image information and set metadata for each segment of the segmented image information; an operation monitoring module, which is used to monitor train operation failures and issue warnings based on the fault types of the monitored train operation failures; a processing module, which is used to automatically extract the corresponding segmented image information from the database through the metadata matching according to the time information and location information of the monitored train operation failures when the operation monitoring module issues a warning. The present application can achieve high efficiency of data call.

Description

Technical Field

[0001] The present application belongs to the technical field of maintenance data management, and specifically relates to a maintenance data integration management system. Background Art

[0002] The development of the railway big data era has led to the continuous expansion of data information. Data concentration, timely statistics, and accurate results have become very important, and are also the bottlenecks for the urgent construction of highly integrated data. The computer equipment of the current traditional railway locomotive system has relatively simple functions and a very backward organizational structure. When processing data, due to the non-uniform data format, the information sharing function of the locomotive depot system in various regions is low, and the information utilization rate is also very low, resulting in a waste of information resources. In particular, there are obvious deficiencies in system integration. The operability is not adapted to the current platform, and it cannot be further integrated and compatible upward. Functional expansion and adjustment are difficult, the maintenance cost is very high, and there is a large gap between the application effect and actual needs.

[0003] Through the integrated management of locomotive data, it is possible to establish a basic database of people, trains and equipment and import basic data to form a locomotive data chain. The data of the locomotive data chain is extracted, stored, mined, analyzed, displayed and standardized, so as to achieve data connectivity between different railway operation subsystems. However, the existing locomotive data system only plays the role of data transmission between different subsystems, and the operation processes between subsystems are still independent of each other.

[0004] For the train operation monitoring process, when a train fault is detected, further analysis will be performed by calling the corresponding data in the database. However, this process has a low efficiency in data calling, so the analysis process has a lag. Summary of the invention

[0005] In order to solve the technical problem of how to achieve high efficiency of data calling based on integrated maintenance data, the present application proposes a maintenance data integration management system.

[0006] In a first aspect of the present application, a maintenance data integration management system is provided, comprising:

[0007] The train monitoring module is used to collect image information of each position of the train and segment the image information at specific time intervals to obtain segmented image information;

[0008] A database, used for storing the segmented image information and setting metadata for each segmented image information;

[0009] An operation monitoring module is used to monitor train operation faults and issue warnings according to the fault types of the monitored train operation faults;

[0010] A processing module is used to automatically extract the corresponding segmented image information from the database through the metadata matching according to the time information and location information of the monitored train operation failure when the operation monitoring module issues a warning.

[0011] In some embodiments, the system further includes an image analysis module, and the image analysis module is used to call and analyze the segmented image information according to a preset monitoring strategy.

[0012] In some embodiments, the system further includes a maintenance management module, which is used to obtain the predicted stop time point, stop time period, number of people getting off and number of people getting on at the stop station of the train, form predicted train data and store it in the database;

[0013] The train monitoring module is also used to adjust the monitoring strategy and the specific time interval according to the predicted train data in the database.

[0014] In some embodiments, the monitoring strategy is: performing early warning analysis on the segmented image information through multiple groups of image analysis servers.

[0015] In some embodiments, the monitoring strategy adjustment process is:

[0016] Determining the passenger flow in the parking section according to the predicted train data;

[0017] Comparing the human flow with a preset value;

[0018] If the human flow is greater than the preset value, the number of running image analysis servers is increased to N, and the specific time interval is reduced to M;

[0019] If the human flow is less than or equal to the preset value, the number of running image analysis servers is increased to X, and the specific time interval is reduced to Y;

[0020] Wherein, the running number X of the image analysis server and the specific time interval Y are both preset fixed values; N>X, M>Y.

[0021] In some embodiments, during the train's stop interval [T0-T p ,T0+ΔT+T p ], the expression of the flow of people is:

[0022]

[0023] Among them, Q is the flow of people, T0 is the parking time point, ΔT is the parking time period, T p is the preset time period, n u is the number of people on board, nd is the number of people getting off the bus, f t () is an operating function; the operating function is pre-set according to the operating pressure size set for different passenger flow levels based on the historical operating data of the train.

[0024] In some embodiments, when the flow of people is greater than the preset value, the expression of the running number N of the image analysis server is:

[0025]

[0026] The specific time interval M is expressed as:

[0027]

[0028] Where [] is the rounding symbol, Q is the flow rate, γ is the correction coefficient, Q th is the preset value; ΔQ is the average flow rate.

[0029] In some embodiments, the system also includes a crew management module, which is used to obtain the crew's work status information based on the segmented image information, perform labor time analysis based on the work status information of each crew member, and remind the crew based on the labor time analysis results.

[0030] In some embodiments, the process of labor time analysis includes:

[0031] Identify whether the flight attendant is working according to the flight attendant's working status information;

[0032] If the identification result is working, the attendance time, total working time and longest continuous working time of the crew member will be counted based on the crew member's attendance time point;

[0033] The crew's labor time coefficient is calculated based on the crew's attendance time, total time in working state and longest continuous working time;

[0034] When the crew member's labor time coefficient is greater than the preset threshold, a rest reminder is issued.

[0035] In some embodiments, the expression of the labor time coefficient is:

[0036]

[0037] Where W is the labor time coefficient, t c Attendance time, t w is the total time in working state, t p is the longest continuous working time, θ1 is the first weight coefficient, θ2 is the second weight coefficient, and t0 is the preset time period.

[0038] The second aspect of the present application provides a maintenance data integration management method, comprising:

[0039] Collect image information at each position of the train;

[0040] Segmenting the image information at specific time intervals to obtain segmented image information;

[0041] The segmented image information is stored in a database, and metadata is set for each segmented image information;

[0042] Monitor train operation failures;

[0043] A warning is issued according to the fault type of the monitored train operation fault; and according to the time information and location information of the monitored train operation fault, the corresponding segmented image information is automatically extracted from the database through the metadata matching.

[0044] In some embodiments, the method further comprises:

[0045] Obtain the predicted stop time, stop time period, number of people getting off and number of people getting on at the stop station to form predicted train data;

[0046] Adjusting the monitoring strategy and the specific time interval according to the predicted train data, wherein the monitoring strategy is to perform early warning analysis on the segmented image information through multiple groups of image analysis servers;

[0047] The segmented image information is called and analyzed according to the monitoring strategy.

[0048] In some embodiments, the monitoring strategy adjustment process is:

[0049] Determine the passenger flow of the train in the parking section according to the predicted train data;

[0050] Comparing the human flow with a preset value;

[0051] If the human flow is greater than the preset value, the number of running image analysis servers is increased to N, and the specific time interval is reduced to M;

[0052] If the human flow is less than or equal to the preset value, the number of running image analysis servers is increased to X, and the specific time interval is reduced to Y;

[0053] Wherein, the running number X of the image analysis server and the specific time interval Y are both preset fixed values; N>X, M>Y.

[0054] In some embodiments, during the train's stop interval [T0-T p ,T0+ΔT+Tp ], the expression of the flow of people is:

[0055]

[0056] Among them, Q is the flow of people, T0 is the parking time point, ΔT is the parking time period, T p is the preset time period, n u is the number of people on board, n d is the number of people getting off the bus, f t () is an operating function; the operating function is pre-set according to the operating pressure size set for different passenger flow levels based on the historical operating data of the train.

[0057] In some embodiments, when the flow of people is greater than the preset value, the expression of the running number N of the image analysis server is:

[0058]

[0059] The specific time interval M is expressed as:

[0060]

[0061] Where [] is the rounding symbol, Q is the flow rate, γ is the correction coefficient, Q th is the preset value; ΔQ is the average flow rate.

[0062] In some embodiments, the method further comprises:

[0063] Acquiring the crew member's working status information according to the segmented image information;

[0064] The labor time analysis is carried out based on the work status information of each flight attendant, and reminders are issued to the flight attendants based on the labor time analysis results.

[0065] In some embodiments, the process of labor time analysis includes:

[0066] Identify whether the flight attendant is working according to the flight attendant's working status information;

[0067] If the identification result is working, the attendance time, total working time and longest continuous working time of the crew member will be counted based on the crew member's attendance time point;

[0068] The crew's labor time coefficient is calculated based on the crew's attendance time, total time in working state and longest continuous working time;

[0069] When the crew member's labor time coefficient is greater than the preset threshold, a rest reminder is issued.

[0070] In some embodiments, the expression of the labor time coefficient is:

[0071]

[0072] Where W is the labor time coefficient, t c Attendance time, t w is the total time in working state, t p is the longest continuous working time, θ1 is the first weight coefficient, θ2 is the second weight coefficient, and t0 is the preset time period.

[0073] A third aspect of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors to implement the maintenance data integration management method as described above.

[0074] The fourth aspect of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, the memory and the processor are communicatively connected to each other, and when the computer program is executed by the processor, the maintenance data integration management method as described above is implemented.

[0075] Compared with the prior art, the technical solution of this application has the following advantages or beneficial effects:

[0076] (1) This application uses a processing module to link the operation monitoring module with the database, and can quickly obtain relevant image information of the corresponding position and time point based on metadata, thereby achieving high efficiency of data call.

[0077] (2) The present application can adaptively adjust the monitoring process according to the actual operating conditions of different stopover sites, thereby improving the efficiency of monitoring at sites with excessive operating pressure, thereby improving the timeliness of monitoring, and executing according to the basic monitoring efficiency during time periods with less operating pressure, thereby reducing the pressure of processing and analysis.

[0078] (3) This application combines the longest continuous working time, total working time and cumulative working time of the flight attendants to comprehensively evaluate their fatigue level, more accurately judge the status of the flight attendants, and can more scientifically allocate tasks to the flight attendants to ensure the working status of the flight attendants. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in the relevant field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0080] Figure 1A schematic diagram of the structure of a maintenance data integration management system provided in Example 1 of the present application;

[0081] Figure 2 A schematic diagram of the structure of a maintenance data integration management system provided in Example 2 of the present application;

[0082] Figure 3 A schematic diagram of the structure of a maintenance data integration management system provided in Example 3 of the present application;

[0083] Figure 4 A flowchart of a maintenance data integration management method provided in Embodiment 4 of the present application;

[0084] Figure 5 A flowchart of a maintenance data integration management method provided in Embodiment 4 of the present application;

[0085] Figure 6 A flowchart of a maintenance data integration management method provided in Embodiment 4 of the present application;

[0086] Figure 7 A connection block diagram of an electronic device provided in Example 6 of the present application. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0088] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0089] If similar descriptions of "first\second\third" appear in the application documents, the following instructions are added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0090] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0091] Embodiment 1

[0092] This embodiment provides a maintenance data integration management system, such as Figure 1 As shown, the system of this embodiment includes a train monitoring module 100 , a database 400 , an operation monitoring module 200 and a processing module 300 .

[0093] The train monitoring module 100 is used to collect image information of each position of the train and segment the image information at specific time intervals to obtain segmented image information. The image information segmentation process and metadata setting process are both implemented through existing technologies and will not be described in detail here.

[0094] The database 400 is used to store the segmented image information and set metadata for each segmented image information.

[0095] The operation monitoring module 200 is used to monitor train operation failures and issue warnings according to the types of failures detected. The train operation failures include common problems such as abnormal parking and abnormal noise, and the operation monitoring module 200 monitors the train operation failures through the existing operation monitoring subsystem, which will not be described in detail here.

[0096] The processing module 300 is used to automatically extract the corresponding segmented image information from the database 400 through the metadata matching according to the time information and location information of the monitored train operation failure when the operation monitoring module issues a warning.

[0097] In this embodiment, the collected image information is segmented and stored in the database 400 through the train monitoring module 100; metadata is established for the stored segmented image information through the database 400. Therefore, when the operation monitoring module detects that a train operation failure occurs, the operation monitoring module and the database 400 are linked to each other through the processing module 300, and the segmented image information related to the corresponding position and the corresponding time point can be quickly obtained according to the metadata, thereby achieving high efficiency of data call.

[0098] Embodiment 2

[0099] This embodiment provides a maintenance data integration management system, such as Figure 2 As shown, the difference from the first embodiment is that the system further includes a maintenance management module 600 and an image analysis module 500 .

[0100] The maintenance management module 600 is used to obtain the predicted stop time point, stop time period, number of people getting off and number of people getting on at the stop station of the train, form predicted train data and store it in the database 400. In this embodiment, the maintenance management module 600 is connected with the ticket purchasing system and the dispatching system to obtain the predicted stop time point, stop time period, number of people getting off and number of people getting on at the stop station of the train, and the predicted train data is stored in the database 400 to obtain the predicted train data.

[0101] The image analysis module 500 is used to call and analyze the segmented image information according to a preset monitoring strategy.

[0102] The train monitoring module 100 is further configured to adjust the monitoring strategy and the specific time interval according to the predicted train data in the database 400 .

[0103] In this embodiment, the preset monitoring strategy and specific time interval of the train monitoring module 100 are adjusted by predicting train data, so that the monitoring process can be adaptively adjusted according to the actual operating conditions of different stop sites, thereby improving the monitoring efficiency at sites with excessive operating pressure, thereby improving the timeliness of monitoring, and executing according to the basic monitoring efficiency during time periods with less operating pressure can reduce the pressure of processing and analysis.

[0104] In one embodiment, the monitoring strategy is to perform early warning analysis on segmented image information through multiple groups of image analysis servers.

[0105] Multiple image analysis servers are used to analyze image information at different locations. Some of the analysis is based on the train's own servers, while others are completed by transmitting data to a cloud server. The specific analysis process is implemented through video and image recognition algorithm models in the prior art and will not be described in detail here.

[0106] In one embodiment, the monitoring strategy is adjusted as follows:

[0107] Step S1, determining the passenger flow in the train's parking section based on the predicted train data. p ,T0+ΔT+T p ], the expression of the flow of people is:

[0108]

[0109] Where Q is the flow of people. T0 is the parking time point, ΔT is the parking time period, T p is the preset time period, n u is the number of people on board, n dis the number of people getting off the bus, f t () is an operating function; the operating function is pre-set according to the operating pressure size set for different passenger flow levels based on the historical operating data of the train.

[0110] Step S2, comparing the human flow with a preset value.

[0111] If the flow rate Q is greater than the preset value Q th , then the number of running image analysis servers is increased to N, and the specific time interval is reduced to M.

[0112] If the flow rate Q is less than or equal to the preset value Q th , then the number of running image analysis servers is increased to X, and the specific time interval is reduced to Y.

[0113] Wherein, the running number X of the image analysis server and the specific time interval Y are both preset fixed values; N>X, M>Y.

[0114] This embodiment provides a method for adjusting a preset monitoring strategy and a specific time interval. Specifically, the train parking interval [T0-T p ,T0+ΔT+T p ], and the passenger flow is calculated based on the predicted train data within this interval. Since the running status of the train when it stops is not only related to the passenger flow, but also to the stop time, the expression Obtain the passenger flow Q, and use the passenger flow Q to judge the operating pressure state when the train stops. th When the operating pressure is high, the efficiency is improved by reducing the specific time interval M, and the number of image analysis servers is increased to meet the analysis process, thereby improving the monitoring and analysis efficiency of the train under high operating pressure, and realizing timely warning when abnormalities occur.

[0115] In another embodiment, when the flow of people is greater than the preset value, the expression of the running number N of the image analysis server is:

[0116]

[0117] The specific time interval M is expressed as:

[0118]

[0119] Where [] is the rounding symbol, Q is the flow rate, γ is the correction coefficient, Q th is the preset value; ΔQ is the average flow rate.

[0120] It should be noted that the value Y is much smaller than the specific time interval, so the problem of M being smaller than the specific time interval will not occur in actual application.

[0121] Through the above technical solution, when Q≤Q th When , it means that the operating pressure is small, so setting X and Y to fixed values ​​can meet the monitoring requirements. When the operating pressure is too high, by adjusting the number of image analysis servers running and reducing the specific time interval, the monitoring efficiency can be improved. Specifically, through the expression Calculate N, where [] is the rounding symbol and γ is the correction coefficient. Therefore, the value of N is adaptively adjusted according to the actual operating pressure level, thereby adaptively improving the monitoring capability; through the expression The reduced time interval M is calculated, and the value of M is adaptively adjusted according to the actual operating pressure level, thereby adaptively improving the monitoring efficiency, and then by adjusting the preset monitoring strategy and specific time intervals, the monitoring capability and efficiency are improved.

[0122] Embodiment 3

[0123] This embodiment provides a maintenance data integration management system, such as Figure 3 As shown, the difference from Example 1 is that the system also includes a crew management module 700, and the crew management module 700 is used to obtain the work status information of the crew according to the segmented image information, and perform labor time analysis based on the work status information of each crew member, and remind the crew according to the labor time analysis results.

[0124] This embodiment is also based on data integration, and the acquired image information is used in the work time analysis process of the flight attendants. Through the integrated management of data, the flight attendants who are overworked can be reminded of their information, so as to ensure that the flight attendants receive scientific work arrangements.

[0125] In one embodiment, the process of labor time analysis includes:

[0126] Step S1, identifying whether the crew member is working according to the crew member's working status information.

[0127] If the recognition result is working, the attendance time of the flight attendant, the total time in working state and the longest continuous working time are counted based on the flight attendant's attendance time point.

[0128] Step S2, calculating the crew's labor time coefficient based on the crew's attendance time, the total time in working state and the longest continuous working time.

[0129] When the crew member's labor time coefficient is greater than the preset threshold, a rest reminder is issued.

[0130] In some embodiments, the expression of the labor time coefficient is:

[0131]

[0132] Where W is the labor time coefficient, t c Attendance time, t w is the total time in working state, t p is the longest continuous working time, θ1 is the first weight coefficient, θ2 is the second weight coefficient, and t0 is the preset time period. The preset time period t0 is selected according to the standard working time period of the crew, and the first weight coefficient θ1 and the second weight coefficient θ2 are both selected through test data in the actual working process.

[0133] Conventional methods for analyzing crew labor hours mainly use the crew's cumulative working hours and cumulative rest time to determine. However, in the actual working state of the crew, long-term continuous work is more likely to affect the normal working state of the crew than intermittent work. Therefore, this embodiment obtains the attendance time t c , total time in working state t w And the maximum continuous working time t p , through the expression By calculating the labor time coefficient, the crew's longest continuous working time, total working time and cumulative working time can be combined to comprehensively evaluate the crew's fatigue level, and thus the crew's status can be more accurately judged. Since train crew services are continuous operations, the labor time analysis method in this embodiment can more scientifically allocate tasks to crew members and ensure the crew's working status.

[0134] Embodiment 4

[0135] This embodiment provides a maintenance data integration management method, such as Figure 4 As shown, the system provided in this embodiment includes:

[0136] Step S11, collecting image information of each position of the train.

[0137] Step S12, segmenting the image information at specific time intervals to obtain segmented image information. The image information segmentation process and metadata setting process are both implemented by existing technologies and will not be described in detail here.

[0138] Step S13: storing the segmented image information in a database, and setting metadata for each segmented image information.

[0139] Step S14, monitoring train operation failures. The train operation failures include common problems such as abnormal parking and abnormal noise. The monitoring method for the train operation failures is implemented through the existing operation monitoring subsystem, which will not be described in detail here.

[0140] Step S15, issuing a warning according to the fault type of the monitored train operation fault; and automatically extracting the corresponding segmented image information from the database through the metadata matching according to the time information and location information of the monitored train operation fault.

[0141] In this embodiment, the collected image information is segmented and stored; metadata is established for the stored segmented image information. Therefore, when a train operation failure is detected, the segmented image information related to the corresponding position and the corresponding time point can be quickly obtained according to the metadata, thereby achieving high efficiency of data retrieval.

[0142] In some embodiments, Figure 5 As shown, the method also includes:

[0143] Step S21, obtaining the stop time point, stop time period, number of people getting off and number of people getting on at the stop station of the train, and forming predicted train data. In this embodiment, by connecting data with the ticket purchasing system and the dispatching system, the predicted stop time point, stop time period, number of people getting off and number of people getting on at the stop station of the train are obtained, and the acquisition of predicted train data is realized.

[0144] Step S22, adjusting the monitoring strategy and the specific time interval according to the predicted train data.

[0145] Step S23: calling and analyzing the segmented image information according to the monitoring strategy.

[0146] In this embodiment, the preset monitoring strategy and specific time interval are adjusted by predicting train data, so that the monitoring process can be adaptively adjusted according to the actual operating conditions of different stop sites, thereby improving the monitoring efficiency at sites with excessive operating pressure, thereby improving the timeliness of monitoring, and executing according to the basic monitoring efficiency during time periods with less operating pressure can reduce the pressure of processing and analysis.

[0147] In some embodiments, the monitoring strategy is to perform early warning analysis on the segmented image information through multiple groups of image analysis servers.

[0148] Multiple image analysis servers are used to analyze image information at different locations. Some of the analysis is based on the train's own servers, while others are completed by transmitting data to a cloud server. The specific analysis process is implemented through video and image recognition algorithm models in the prior art and will not be described in detail here.

[0149] In some embodiments, the monitoring strategy adjustment process is:

[0150] Step S221, determining the passenger flow in the train parking section according to the predicted train data.

[0151] Step S222, comparing the human flow with a preset value.

[0152] Step S223: If the human flow is greater than the preset value, the number of running image analysis servers is increased to N, and the specific time interval is reduced to M.

[0153] Step S224: If the human flow is less than or equal to the preset value, the number of image analysis servers in operation is increased to X, and the specific time interval is reduced to Y. The number of image analysis servers in operation X and the specific time interval Y are both preset fixed values; N>X, M>Y.

[0154] In some embodiments, during the train's stop interval [T0-T p ,T0+ΔT+T p ], the expression of the flow of people is:

[0155]

[0156] Among them, Q is the flow of people, T0 is the parking time point, ΔT is the parking time period, T p is the preset time period, n u is the number of people on board, n d is the number of people getting off the bus, f t () is an operating function; the operating function is pre-set according to the operating pressure size set for different passenger flow levels based on the historical operating data of the train.

[0157] This embodiment provides a method for adjusting a preset monitoring strategy and a specific time interval. Specifically, the train parking interval [T0-T p ,T0+ΔT+T p ], and the passenger flow is calculated based on the predicted train data within this interval. Since the running status of the train when it stops is not only related to the passenger flow, but also to the stop time, the expression Obtain the passenger flow Q, and use the passenger flow Q to judge the operating pressure state when the train stops. th When the operating pressure is high, the efficiency is improved by reducing the specific time interval M, and the number of image analysis servers is increased to meet the analysis process, thereby improving the monitoring and analysis efficiency of the train under high operating pressure, and realizing timely warning when abnormalities occur.

[0158] In some embodiments, when the flow of people is greater than the preset value, the expression of the running number N of the image analysis server is:

[0159]

[0160] The specific time interval M is expressed as:

[0161]

[0162] Where [] is the rounding symbol, Q is the flow rate, γ is the correction coefficient, Q th is the preset value; ΔQ is the average flow rate.

[0163] It should be noted that the value Y is much smaller than the specific time interval, so the problem of M being smaller than the specific time interval will not occur in actual application.

[0164] Through the above technical solution, when Q≤Q th When , it means that the operating pressure is small, so setting X and Y to fixed values ​​can meet the monitoring requirements. When the operating pressure is too high, by adjusting the number of image analysis servers running and reducing the specific time interval, the monitoring efficiency can be improved. Specifically, through the expression Calculate N, where [] is the rounding symbol and γ is the correction coefficient. Therefore, the value of N is adaptively adjusted according to the actual operating pressure level, thereby adaptively improving the monitoring capability; through the formula The reduced time interval M is calculated, and the value of M is adaptively adjusted according to the actual operating pressure level, thereby adaptively improving the monitoring efficiency, and then by adjusting the preset monitoring strategy and specific time intervals, the monitoring capability and efficiency are improved.

[0165] In some embodiments, Figure 6 As shown, the method also includes:

[0166] Step S31, obtaining the crew member's working status information according to the segmented image information;

[0167] Step S32, performing a labor time analysis based on the work status information of each crew member, and issuing a reminder to the crew member based on the labor time analysis result.

[0168] This embodiment is also based on data integration, and the acquired image information is used in the work time analysis process of the flight attendants. Through the integrated management of data, the flight attendants who are overworked can be reminded of their information, so as to ensure that the flight attendants receive scientific work arrangements.

[0169] In some embodiments, the process of labor time analysis includes:

[0170] Step S1, identifying whether the crew member is working according to the crew member's working status information.

[0171] Step S2: If the identification result is that the crew is working, the crew's attendance time, the total time in working state and the longest continuous working time are counted based on the crew's attendance time point.

[0172] Step S3, calculating the crew's labor time coefficient based on the crew's attendance time, the total time in working state and the longest continuous working time.

[0173] Step S4: When the crew member's labor time coefficient is greater than a preset threshold, a rest reminder is issued.

[0174] In some embodiments, the expression of the labor time coefficient is:

[0175]

[0176] Where W is the labor time coefficient, t c Attendance time, t w is the total time in working state, t p is the longest continuous working time, θ1 is the first weight coefficient, θ2 is the second weight coefficient, and t0 is the preset time period. The preset time period t0 is selected according to the standard working time period of the crew, and the first weight coefficient θ1 and the second weight coefficient θ2 are both selected through test data in the actual working process.

[0177] Conventional methods for analyzing crew labor hours mainly use the crew's cumulative working hours and cumulative rest time to determine. However, in the actual working state of the crew, long-term continuous work is more likely to affect the normal working state of the crew than intermittent work. Therefore, this embodiment obtains the attendance time t c , total time in working state t w And the maximum continuous working time t p , through the expression By calculating the labor time coefficient, the crew's longest continuous working time, total working time and cumulative working time can be combined to comprehensively evaluate the crew's fatigue level, and thus the crew's status can be more accurately judged. Since train crew services are continuous operations, the labor time analysis method in this embodiment can more scientifically allocate tasks to crew members and ensure the crew's working status.

[0178] Embodiment 5

[0179] This embodiment further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method steps in the above embodiment can be implemented, and this embodiment will not be repeated here.

[0180] Among them, the computer-readable storage medium may also include computer programs, data files, data structures, etc. alone, or include a combination thereof. The computer-readable storage medium or computer program may be specifically designed and understood by a technician in the field of computer software, or the computer-readable storage medium may be known and available to a technician in the field of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and tapes; optical media, such as CDROM disks and DVDs; magneto-optical media, such as optical disks; and hardware devices, specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, app application stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code, which can be executed by a computer using an interpreter. The described hardware device may be configured to be used as one or more software modules to perform the operations and methods described above, and vice versa. In addition, the computer-readable storage medium may be distributed in a networked computer system, and program code or computer programs may be stored and executed in a decentralized manner.

[0181] Embodiment 6

[0182] Figure 7 A connection block diagram of an electronic device provided in an embodiment of the present application, such as Figure 7 As shown, the electronic device 400 may include: one or more processors 410 , a memory 420 , a multimedia component 430 , an input / output (I / O) interface 440 , and a communication component 450 .

[0183] The processor 410 is used to execute all or part of the steps in the method of Embodiment 1. The memory 420 is used to store various types of data, which may include, for example, instructions of any application or method in the electronic device, and data related to the application.

[0184] The processor 410 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the methods in the above embodiments.

[0185] The memory 420 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0186] The multimedia component 430 may include a screen and an audio component, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal may be further stored in a memory or sent via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0187] The I / O interface 440 provides an interface between the processor 410 and other interface modules, which may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.

[0188] The communication component 450 is used for wired or wireless communication between the electronic device 400 and other devices.

[0189] Wired communication includes communication through network ports, serial ports, etc.; wireless communication includes: Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or a combination of them. Therefore, the corresponding communication component 450 may include: Wi-Fi module, Bluetooth module, NFC module.

[0190] It should also be understood that the method or system disclosed in the embodiments provided in the present application may also be implemented in other ways. The method or system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the methods and devices according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a computer program segment, or a portion of a computer program, and the module, computer program segment, or a portion of a computer program includes one or more computer programs for implementing a specified logical function.

[0191] It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the drawings, may actually be performed substantially in parallel, and may sometimes be performed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flow chart, and combinations of blocks in the block diagram and / or flow chart, may be implemented with a dedicated hardware-based system that performs the specified functions or actions, or may be implemented with a combination of dedicated hardware and computer programs.

[0192] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements that are inherent to such process, method, article, or apparatus. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, device or equipment including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features; in the description of this application, unless otherwise specified, the terms "multiple" and "multiple" mean at least two; if there is a description of a server, it should be noted that the server can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server that can provide basic cloud computing services such as cloud servers, cloud databases, cloud storage and CDN; if there is a description of a smart terminal or mobile device in this application, it should be noted that the smart terminal or mobile device can be a mobile phone, a tablet computer, a smart watch, a netbook, a wearable electronic device, a personal digital assistant (PDA), an augmented reality technology device (AR), a virtual reality device (VR), a smart TV, a smart speaker, a personal computer (PC), a tablet computer, a smart phone, a smart TV, a smart speaker, a smart phone ... Computer, PC), etc., but not limited to this, and the present application does not specifically limit the specific form of the smart terminal or mobile device.

[0193] Finally, it should be noted that, in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0194] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary, and the contents described are only embodiments adopted for the convenience of understanding the present application, and are not intended to limit the present application. Any technician in the technical field to which the present application belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be based on the scope defined in the attached claims.

Claims

1. A maintenance data integration management system, characterized in that: include: The train monitoring module is used to collect image information of each position of the train and segment the image information at specific time intervals to obtain segmented image information; A database, used for storing the segmented image information and setting metadata for each segmented image information; An operation monitoring module is used to monitor train operation faults and issue warnings according to the fault types of the monitored train operation faults; A processing module, configured to automatically extract the corresponding segmented image information from the database through the metadata matching according to the time information and location information of the monitored train operation failure when the operation monitoring module issues a warning; The crew management module is used to obtain the crew's work status information according to the segmented image information, and to perform labor time analysis according to the work status information of each crew member, and to remind the crew member according to the labor time analysis result; The process of labor time analysis includes: Identify whether the flight attendant is working according to the flight attendant's working status information; If the identification result is working, the attendance time, total working time and longest continuous working time of the crew member will be counted based on the crew member's attendance time point; The crew's labor time coefficient is calculated based on the crew's attendance time, total time in working state and longest continuous working time; When the crew member's labor time coefficient is greater than the preset threshold, a rest reminder is issued.

2. The maintenance data integration management system according to claim 1, characterized in that: The system further comprises an image analysis module, and the image analysis module is used to call and analyze the segmented image information according to a preset monitoring strategy.

3. The maintenance data integration management system according to claim 2, characterized in that: The system also includes a maintenance management module, which is used to obtain the predicted stop time point, stop time period, number of people getting off and number of people getting on at the stop station, form predicted train data and store it in a database; The train monitoring module is also used to adjust the monitoring strategy and the specific time interval according to the predicted train data in the database.

4. The maintenance data integration management system according to claim 3, characterized in that: The monitoring strategy is: performing early warning analysis on the segmented image information through multiple groups of image analysis servers.

5. The maintenance data integration management system according to claim 4, characterized in that: The adjustment process of the monitoring strategy is: Determine the passenger flow of the train in the parking section according to the predicted train data; Comparing the human flow with a preset value; If the human flow is greater than the preset value, the number of running image analysis servers is increased to N, and the specific time interval is reduced to M; If the human flow is less than or equal to the preset value, the number of running image analysis servers is increased to X, and the specific time interval is reduced to Y; Wherein, the running number X of the image analysis server and the specific time interval Y are both preset fixed values; N>X, M>Y.

6. The maintenance data integration management system according to claim 5, characterized in that: In the train's stopping section [T0-T p ,T0+ΔT+T p ], the expression of the flow of people is: Among them, Q is the flow of people, T0 is the parking time point, ΔT is the parking time period, T p is the preset time period, n u is the number of people on board, n d is the number of people getting off the bus, f t () is an operating function; the operating function is pre-set according to the operating pressure size set for different passenger flow levels based on the historical operating data of the train.

7. The maintenance data integration management system according to claim 6, characterized in that: When the flow of people is greater than the preset value, the expression of the running number N of the image analysis server is: The specific time interval M is expressed as: Where [] is the rounding symbol, Q is the flow rate, γ is the correction coefficient, Q th is the preset value; ΔQ is the average flow rate.

8. The maintenance data integrated management system according to claim 1, characterized in that: The expression of the labor time coefficient is: Where W is the labor time coefficient, t c is the attendance time, t w is the total time in working state, t p is the longest continuous working time, θ1 is the first weight coefficient, θ2 is the second weight coefficient, and t0 is the preset time period.

9. A maintenance data integration management method, characterized in that: include: Collect image information at each position of the train; Segmenting the image information at specific time intervals to obtain segmented image information; The segmented image information is stored in a database, and metadata is set for each segmented image information; Monitor train operation failures; issuing a warning according to the fault type of the monitored train operation fault; and automatically extracting the corresponding segmented image information from the database through the metadata matching according to the time information and location information of the monitored train operation fault; The maintenance data integration management method also includes: Acquiring the crew member's working status information according to the segmented image information; Perform labor time analysis based on each crew member's work status information and issue reminders to the crew members based on the labor time analysis results; The process of labor time analysis includes: Identify whether the flight attendant is working according to the flight attendant's working status information; If the identification result is working, the attendance time, total working time and longest continuous working time of the crew member will be counted based on the crew member's attendance time point; The crew's labor time coefficient is calculated based on the crew's attendance time, total time in working state and longest continuous working time; When the crew member's labor time coefficient is greater than the preset threshold, a rest reminder is issued.

10. The maintenance data integration management method according to claim 9, characterized in that: The method further comprises: Obtain the predicted stop time, stop time period, number of people getting off and number of people getting on at the stop station to form predicted train data; Adjusting the monitoring strategy and the specific time interval according to the predicted train data, wherein the monitoring strategy is to perform early warning analysis on the segmented image information through multiple groups of image analysis servers; The segmented image information is called and analyzed according to the monitoring strategy.

11. The maintenance data integration management method according to claim 10, characterized in that: The adjustment process of the monitoring strategy is: Determine the passenger flow of the train in the parking section according to the predicted train data; Comparing the human flow with a preset value; If the human flow is greater than the preset value, the number of running image analysis servers is increased to N, and the specific time interval is reduced to M; If the human flow is less than or equal to the preset value, the number of running image analysis servers is increased to X, and the specific time interval is reduced to Y; Wherein, the running number X of the image analysis server and the specific time interval Y are both preset fixed values; N>X, M>Y.

12. The maintenance data integration management method according to claim 11, characterized in that: In the train's stopping section [T0-T p ,T0+ΔT+T p ], the expression of the flow of people is: Among them, Q is the flow of people, T0 is the parking time point, ΔT is the parking time period, T p is the preset time period, n u is the number of people on board, n d is the number of people getting off the bus, f t () is an operating function; the operating function is pre-set according to the operating pressure size set for different passenger flow levels based on the historical operating data of the train.

13. The maintenance data integration management method according to claim 12, characterized in that: When the flow of people is greater than the preset value, the expression of the running number N of the image analysis server is: The specific time interval M is expressed as: Where [] is the rounding symbol, Q is the flow rate, γ is the correction coefficient, Q th is the preset value; ΔQ is the average flow rate.

14. The maintenance data integration management method according to claim 9, characterized in that: The expression of the labor time coefficient is: Where W is the labor time coefficient, t c is the attendance time, t w is the total time in working state, t p is the longest continuous working time, θ1 is the first weight coefficient, θ2 is the second weight coefficient, and t0 is the preset time period.

15. A computer-readable storage medium, characterized in that: The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the maintenance data integration management method according to any one of claims 9 to 14.

16. An electronic device, characterized in that: It comprises a memory and one or more processors, the memory stores a computer program, the memory and the processor are communicatively connected with each other, and when the computer program is executed by the processor, the maintenance data integration management method as described in any one of claims 9 to 14 is executed.

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