CN112632067A - System and method for analyzing data of one-time crew operation of locomotive crew member - Google Patents
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Abstract
The invention belongs to the technical field of railway industry locomotive service systems, and relates to a locomotive crew member one-time crew service operation data analysis system and method, which comprises a data source information module, an entity data module, a data generation module and a data processing module; extracting required data from the data source information module according to the form established by the entity data module; and calculating the real-time data in the crew operation data storage entity table, associating the calculation result with the analysis dimension table, simultaneously storing the calculation result and the association result into the fact table, constructing a data analysis model, further extracting data information to be analyzed, and analyzing to obtain the working hours of the locomotive crew. The invention ensures the integrity of operation data, analyzes the overstrain condition of the locomotive crew member, avoids the continuous night shift of the crew member, ensures the energetic state of the locomotive crew member in value riding and ensures the driving safety of the locomotive crew member; the management efficiency of the management department is improved.
Description
Technical Field
The invention belongs to the technical field of railway industry engineering systems, and relates to a system and a method for analyzing data of one-time engineering operation of locomotive operators.
Background
The locomotive crew member is the main technical category of railway transportation production, bears the important responsibility of safe integral operation of driving locomotives and motor train units, and has the working characteristics of multiple lines, independent operation without day and night, long-term high tension state, and the working quality directly related to the life of passengers and the national property safety. Because the data of one-time crew operation of the crew members are dispersed in different service systems, the integrity of the data can not be ensured, and the absence of the data brings inconvenience for effectively analyzing the operation condition of each traffic crew member; and the conditions of average time spent, highest time spent, overrun in the month and continuous night shift of the locomotive crew members have hysteresis, so that real-time control cannot be carried out, overrun operation cannot ensure that the locomotive crew members are full of energy in value, and the locomotive driving safety cannot be ensured.
Disclosure of Invention
In order to solve the problem of locomotive crew operation, the invention provides a locomotive crew member one-time crew operation data analysis system and a locomotive crew operation data analysis method, which ensure the integrity of locomotive crew member operation data of different service systems, obtain the overstrain condition of the locomotive crew member through system analysis, avoid continuous night shift of the crew member, ensure the full energy of the locomotive crew member in value riding and ensure the driving safety of the locomotive crew member; and simultaneously, the management efficiency of a management department is improved.
In order to achieve the purpose, the invention adopts the technical scheme that:
a locomotive crew member one-time crew operation data analysis system comprises a data source information module, an entity data module, a data generation module and a data processing module;
the data source information module: the data storage device is used for storing data of the crew operation of each railway section;
the entity data module: the system comprises a database, a database server and a database server, wherein the database is used for storing a data storage entity table, a fact table and an analysis dimension table of the crew work; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table;
the crew operation data storage entity table: extracting real-time data of one-time riding operation of locomotive crew members in the locomotive running process from the data source information module;
the fact table is as follows: a crew working hour data form which is established according to the crew working data storage entity table and takes a driver as granularity;
the analysis dimension table is as follows: a data form established on the basis of the crew operation data storage entity table by taking the analysis dimension as the granularity;
the data generation module: the data form is used for receiving the data form sent by the entity data module, calculating the real-time data in the crew operation data storage entity table according to the analysis needs of the user, associating the calculation result with the analysis dimension table, and simultaneously storing and updating the calculation result and the association result to the fact table; thereby constructing a fact table data model and an analysis dimension data model;
the data processing module: the method is used for extracting the analysis dimension model to be analyzed by the user from the analysis dimension data model and analyzing by combining the fact table data model to obtain the working hour condition of the locomotive crew member.
Further, the data stored in the data source information module comprises LKJ processing result information data, crew exit and exit information data and locomotive entry and exit information data;
the LKJ processing result information data comprise an engineering section code, an LKJ file ID, a locomotive model, a locomotive number, a vehicle type, a vehicle number, a driver work number, an assistant driver work number, file processing time, file starting time, driving time, file ending time, a starting station, a terminal station, a workshop, a vehicle team and a guide group;
the crew attendance information data comprises crew traffic routes, driver work numbers, planned starting points, locomotive models, locomotive numbers, attendance time, attendance places, attendance time and attendance places;
the locomotive warehouse-in and warehouse-out information data comprises locomotive models, locomotive numbers, warehouse-out time, warehouse-out places, warehouse-in time and warehouse-in places.
Further, the crew operation data storage entity table includes a crew section code, an LKJ file ID, a locomotive model, a locomotive number, a vehicle type, a vehicle number, a driver job number, a file processing time, a file start time, a driving time, a file end time, a start station, an end station, a workshop, a vehicle fleet, a guidance group, a crew cut, a planned driving point, a departure time, a departure place, a warehousing time, a warehousing place, a warehousing time, and a warehousing place.
Further, the fact table comprises an LKJ file ID, a driver key value, a time key value, a crew section key value, a crew traffic route key value, an attendance place key value, a warehousing place key value, an ex-warehouse place key value, an attendance to ex-warehouse time length, an attendance to driving time length, an ex-warehouse to driving time length, an arrival to warehousing time length, an arrival to attendance time length and an attendance to attendance time length.
Further, the analysis dimension comprises a driver dimension, a time dimension, a crew section dimension, a duty traffic dimension, an exit/exit location dimension and an entry/exit location dimension;
the driver dimension table comprises driver key values, driver job numbers and driver names; the time dimension table comprises time key values, years, months, days and hours; the maintenance table of the locomotive depot comprises a locomotive depot key value, a locomotive depot code and a locomotive depot name; the crew traffic route dimension table comprises crew traffic route key values, crew traffic route codes and crew traffic route names; the attendance exit place dimension table comprises an attendance exit place key value, a place code and a place name; the warehouse-in and warehouse-out place dimension table comprises warehouse-in and warehouse-out place key values, place codes and place names.
An analysis method of a locomotive crew member one-time crew operation data analysis system comprises the following steps:
1) collecting data of the crew operation of each railway section, and storing the collected data in a data source information module;
2) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table;
3) transmitting the crew operation data storage entity table filled with the real-time data, the fact table filled with the crew working data and the updated analysis dimension table to the data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a crew operation data storage entity table, associating a calculation result with an analysis dimension table, simultaneously storing the calculation and association results into the fact table to generate a fact table data model, and constructing a data analysis model through the fact table data model and the analysis dimension data model;
4) and extracting the analysis dimension to be analyzed from the analysis dimension data model according to the analysis requirement of the user, and analyzing the crew operation condition in the process of the locomotive crew operation by combining the fact table data model to obtain the working time condition of the locomotive and the crew.
Further, in the step 1),
1.1) splitting, counting or converting formats of LKJ processing result information data, crew attendance and attendance information data and locomotive in-out information data stored in a data source information module;
1.2) carrying out duplication elimination operation on LKJ processing result information data by taking the file start time, the driver number, the locomotive model and the locomotive number as conditions.
Further, the specific process of step 2) is as follows:
2.1) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the user requirements;
2.2) extracting real-time data of one-time crew operation of the locomotive crew member from the data source information module; combining the LKJ processing result information data subjected to the weight removal by using file processing time, file starting time, driver number, locomotive model and locomotive number, storing the combined real-time data into a crew operation data storage entity table, and updating a driver dimension table and a crew section dimension table at the same time;
2.3) extracting six real-time data of the crew traffic routes, planned starting points, attendance time, attendance places, attendance time and attendance place information from the crew attendance and attendance information data by using the locomotive model, the locomotive number, the driver number, the driving time information and the warehousing time information, storing the extracted real-time data into a crew operation data storage entity table, and simultaneously updating a crew traffic route dimension table, a attendance and attendance place dimension table and a time dimension table;
and 2.4) extracting the four real-time data of the ex-warehouse time, the ex-warehouse place, the in-warehouse time and the in-warehouse place information from the locomotive ex-warehouse information data according to the locomotive model, the locomotive number, the driver number, the driving time information, the file ending time information, the attendance time and the attendance place information, storing the extracted real-time data into a crew operation data storage entity table, and updating the ex-warehouse place dimension table.
Further, the merging operation in step 2.2) is performed by: merging records of the same driver number, the same locomotive model, the same locomotive number and the file start time within 12 hours;
and taking the file start time and the driving time in the minimum file start time record as the file start time and the driving time of the current crew operation, and taking the maximum file end time as the file end time of the current crew operation.
Further, the analysis of the work condition of the crew in the step 4) comprises late point analysis of ex-warehouse, travel time analysis and overstrain summary analysis;
the locomotive ex-warehouse later point analysis is to set standard time for locomotive ex-warehouse, combine the data analysis extracted from the dimension data model and the fact table data model in step 3) to obtain the situation of right and later point, the time division at later point, the summary situation of a certain time period and the summary situation of the whole locomotive section for each locomotive section, and parallel out detailed lists;
the travel time analysis result is the travel time standard specified by the locomotive running chart data, the data extracted from the dimension data model and the fact table data model are analyzed in combination with the step 3), if the actual running time of the locomotive exceeds the travel time standard, the statistics of the travel time is overtime, and overtime summary and average conditions of a certain section can be inquired;
the overstrain summary analysis is to analyze data extracted from the dimension data model and the fact table data model according to the crew section and the crew traffic section in combination with the step 3), automatically count the total shift of the crew value, the shift of overstrain, the overstrain accumulated time, the overstrain average time, the overstrain rate, the late point of leaving warehouse overtime, the departure stop overtime, the travel overtime and the arrival stop overtime respectively, and summarize the whole overstrain condition.
The invention has the beneficial effects that:
1. the invention provides a data analysis system for one-time riding operation of a locomotive attendant, which comprises a data source information module, an entity data module, a data generation module and a data processing module; a data source information module: the data storage device is used for storing data of the crew operation of each railway section; an entity data module: the system is used for establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the needs of users; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table; a data generation module: receiving a crew operation data storage entity table filled with real-time data, a fact table of crew working data and an updated analysis dimension table which are sent by an entity data module, calculating the real-time data in the crew operation data storage entity table according to user analysis requirements, associating a calculation result with the analysis dimension table, storing and updating the calculation result and the association result to the fact table, and thus constructing a data analysis model; a data processing module: the data analysis module is used for extracting data information to be analyzed from the data analysis model according to the analysis requirements of the user, analyzing the work of the locomotive crew member to obtain the working hours of the locomotive and the crew member. Through the data analysis system, a large data platform for locomotive crew service data operation can be established, the efficiency of locomotive crew service data management is improved, and the formed large data platform provides data support for subsequent analysis; the crew operation data is effectively managed, and the integrity and accuracy of subsequent analysis results are ensured.
2. The invention provides a method for analyzing data of one-time crew operation of a locomotive crew member, which comprises the following steps: 1) collecting data of the crew operation of each railway section, and storing the collected data in a data source information module; 2) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table; 3) transmitting the crew operation data storage entity table filled with the real-time data, the fact table filled with the crew working data and the updated analysis dimension table to the data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a crew operation data storage entity table, associating a calculation result with an analysis dimension table, simultaneously storing the calculation and association results into the fact table to generate a fact table data model, and constructing a data analysis model through the fact table data model and the analysis dimension data model; 4) and extracting data to be analyzed from the data analysis model according to the analysis requirements of the user, and analyzing the crew operation condition in the process of the crew operation of the locomotive to obtain the working time condition of the locomotive and the crew. Corresponding real-time data are extracted from a data source according to target operation data to be analyzed, the extracted real-time data are calculated, an analysis dimension table is updated in a correlation mode, a data analysis model is built, integrity analysis of crew operation data of different business systems is achieved, operation data information of each cross-route crew can be obtained, and average time-of-use, highest time-of-use, time-of-overuse and month-overuse data information of each cross-route crew are obtained through data processing and analysis, so that continuous night shifts of the crew are avoided, full energy of the crew in the locomotive crew is guaranteed, and driving safety of the locomotive is guaranteed.
Drawings
FIG. 1 is a schematic diagram of a locomotive attendant one-time attendant work data analysis system according to the present invention;
FIG. 2 is a schematic diagram of a crew labor hour fact table and an analysis dimension table according to the present invention;
fig. 3 is a schematic diagram of a locomotive crew member one-time crew operation data analysis system according to the present invention.
Detailed Description
The present invention will now be described in detail with reference to the accompanying drawings and examples.
Example 1
Referring to fig. 1, the data analysis system for one-time crew operation of a locomotive crew member provided by the embodiment includes a data source information module, an entity data module, a data generation module and a data processing module;
the data source information module provided by this embodiment: used for storing data of the crew operation of each railway section.
The entity data module provided by the embodiment: the system comprises a database, a database server and a database server, wherein the database is used for storing a data storage entity table, a fact table and an analysis dimension table of the crew work; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table;
the crew operation data storage entity table provided by the embodiment: extracting real-time data of one-time riding operation of locomotive crew members in the locomotive running process from the data source information module;
the fact table provided in this example: a crew working hour data form which is established according to the crew working data storage entity table and takes a driver as granularity;
the analysis dimension table provided in this embodiment: and the data form is established on the basis of the data storage entity table of the crew operation by taking the analysis dimension as the granularity.
The data generation module provided by this embodiment: the data form is used for receiving the data form sent by the entity data module, calculating the real-time data in the crew operation data storage entity table according to the analysis needs of the user, associating the calculation result with the analysis dimension table, and simultaneously storing and updating the calculation result and the association result to the fact table; thereby constructing a fact table data model and an analysis dimension data model;
the data processing module provided by the embodiment: the method is used for extracting the analysis dimension model to be analyzed by the user from the analysis dimension data model and analyzing by combining the fact table data model to obtain the working hour condition of the locomotive crew member.
The data stored in the data source information module provided by the embodiment comprises LKJ processing result information data, crew exit and exit attendance information data and locomotive in-out information data; the LKJ processing result information data comprise an engineering section code, an LKJ file ID, a locomotive model, a locomotive number, a vehicle type, a vehicle number, a driver work number, an assistant driver work number, file processing time, file starting time, driving time, file ending time, a starting station, a terminal station, a workshop, a vehicle team and a guide group; the crew attendance information data comprises a crew traffic route, a driver work number, a planned starting point, a locomotive model, a locomotive number, attendance time, an attendance place, attendance time and an attendance place; the locomotive warehouse-in and warehouse-out information data comprises locomotive models, locomotive numbers, warehouse-out time, warehouse-out places, warehouse-in time and warehouse-in places.
The crew operation data storage entity table provided by this embodiment includes a crew section code, an LKJ file ID, a locomotive model, a locomotive number, a vehicle type, a vehicle number, a driver job number, a file processing time, a file start time, a departure time, a file end time, a start station, an end station, a workshop, a vehicle fleet, a guidance group, a crew cut, a planned departure point, an attendance time, an attendance place, an attendance time, an attendance place, a warehousing-in time, a warehousing-in place, a warehousing-out time, and a warehousing-out place.
Referring to fig. 2, the fact table provided in this embodiment includes an LKJ file ID, a driver key value, a time key value, a crew section key value, a crew traffic route key value, an attendance location key value, a warehousing location key value, an ex-warehouse location key value, an attendance to ex-warehouse time length, an attendance to driving time length, an outbound to driving time length, an arrival to warehousing time length, an arrival to attendance time length, and an attendance to attendance time length.
Referring to fig. 2, the analysis dimensions provided by the present embodiment include a driver dimension, a time dimension, a crew section dimension, a crew traffic path dimension, an exit/exit location dimension, and an entry/exit location dimension; the driver dimension table comprises driver key values, driver work numbers and driver names; the time dimension table comprises time key values, years, months, days and hours; the maintenance table of the crew section comprises a crew section key value, a crew section code and a crew section name; the crew traffic route dimension table comprises a crew traffic route key value, a crew traffic route code and a crew traffic route name; the attendance exit place dimension table comprises an attendance exit place key value, a place code and a place name; the warehouse-in/out place dimension table comprises warehouse-in/out place key values, place codes and place names.
The data stored in the data source information module provided by this embodiment is data from the crew operation of each railway section, and the data types thereof are relatively complicated, and the specific data types include a relational database, file-type data, standard data, an unstructured database, and a distributed data source; the relational database comprises Oracle, DB2, SQLServer, MySQL and Informix; the file type data comprises Excel, txt files and XML files; the standard data comprises WebService and SOA; unstructured databases, MongoDB and HBASE; distributed data sources include SAP, Hadoop, and Gemfire.
In the embodiment, the data coverage of the data source information module is wide, and the crew operation data is effectively managed; and the data source supports different types of data, and when the data source is implemented, the data of different file types and different formats in the data source can be converted and split according to the requirement of the fact table on the data, so that the intelligent processing of the data is realized, the data meets the requirement of the data format in the fact table, the efficiency of the locomotive crew operation data is improved, the data in the constructed data model is conveniently processed, and the integrity and the accuracy of the subsequent analysis result are ensured.
Example 2
Referring to fig. 3, the method for analyzing data of one-time crew operation of a locomotive crew member according to the present embodiment includes the following steps:
1) collecting data of the crew operation of each railway section, and storing the collected data in a data source information module;
specifically, the step 1) comprises the following steps:
1.1) splitting, counting or converting formats of LKJ processing result information data, crew attendance and attendance information data and locomotive in-out information data stored in a data source information module;
1.2) carrying out duplication elimination operation on LKJ processing result information data by taking the file start time, the driver number, the locomotive model and the locomotive number as conditions;
2) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table;
further, the specific process of step 2) is as follows:
2.1) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the user requirements;
2.2) extracting real-time data of one-time crew operation of the locomotive crew member from the data source information module; combining the LKJ processing result information data subjected to the weight removal by using file processing time, file starting time, driver number, locomotive model and locomotive number, storing the combined real-time data into a crew operation data storage entity table, and updating a driver dimension table and a crew section dimension table at the same time;
2.3) extracting six real-time data of the crew traffic routes, planned starting points, attendance time, attendance places, attendance time and attendance place information from the crew attendance and attendance information data by using the locomotive model, the locomotive number, the driver number, the driving time information and the warehousing time information, storing the extracted real-time data into a crew operation data storage entity table, and simultaneously updating a crew traffic route dimension table, a attendance and attendance place dimension table and a time dimension table;
and 2.4) extracting the four real-time data of the ex-warehouse time, the ex-warehouse place, the in-warehouse time and the in-warehouse place information from the locomotive ex-warehouse information data according to the locomotive model, the locomotive number, the driver number, the driving time information, the file ending time information, the attendance time and the attendance place information, storing the extracted real-time data into a crew operation data storage entity table, and updating the ex-warehouse place dimension table.
Further, the merging operation in step 2.2) is as follows: merging records of the same driver number, the same locomotive model, the same locomotive number and the file start time within 12 hours;
taking the file start time and the driving time in the minimum file start time record as the file start time and the driving time of the current crew operation, and taking the maximum file end time as the file end time of the current crew operation;
3) transmitting the crew operation data storage entity table filled with the real-time data, the fact table filled with the crew working data and the updated analysis dimension table to the data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a crew operation data storage entity table, associating a calculation result with an analysis dimension table, simultaneously storing the calculation and association results into the fact table to generate a fact table data model, and constructing a data analysis model through the fact table data model and the analysis dimension data model;
4) and extracting the analysis dimension to be analyzed from the analysis dimension data model according to the analysis requirement of the user, and analyzing the crew operation condition in the process of the locomotive crew operation by combining the fact table data model to obtain the working time condition of the locomotive and the crew.
Further, the analysis of the riding operation condition in the step 4) comprises late point analysis of ex-warehouse, travel time analysis and overstrain summary analysis;
the locomotive ex-warehouse later point analysis is to set standard time for locomotive ex-warehouse, combine the data analysis extracted from the dimension data model and the fact table data model in step 3) to obtain the situation of right and later point, the time division at later point, the summary situation of a certain time period and the summary situation of the whole locomotive section for each locomotive section, and parallel out detailed lists;
the travel time analysis result is that the data extracted from the dimension data model and the fact table data model are analyzed in combination with the step 3) according to the travel time standard specified by the locomotive running chart data, if the actual running time of the locomotive exceeds the travel time standard, the statistics is that the travel time is overtime, and overtime summary and average conditions of a certain section can be inquired;
the overstrain summary analysis is to analyze data extracted from the dimension data model and the fact table data model according to the crew section and the crew traffic section in combination with the step 3), automatically count the total shift of the crew value, the shift of overstrain, the overstrain accumulated time, the overstrain average time, the overstrain rate, the late point of leaving warehouse overtime, the departure stop overtime, the travel overtime and the arrival stop overtime respectively, and summarize the whole overstrain condition.
In the analysis method provided by the embodiment, when the analysis method is implemented, a fact table with a driver as a granularity is established according to the content information of the entity table stored in the crew operation data, an association analysis dimension table is established, and a labor time data analysis model and an analysis dimension model are further established through data extraction and calculation; the analysis is convenient, the integrity analysis of the crew operation data of different business systems is realized, and a foundation is provided for the subsequent data analysis. According to the user requirements, extracting required analysis dimension tables from 6 analysis dimension tables of the analysis dimension model according to the user requirements, obtaining the locomotive crew fatigue condition by combining the crew fatigue data fact model, and further analyzing the reason of overstrain. For example, after the locomotive delivery delay result and the travel time result are obtained through analysis, if the delay or overtime exists, the reason for the locomotive delivery delay and the reason for the overtime of the travel time can be analyzed by combining the data in the dimension table, so that convenience is brought to the management work of a management department, and the management efficiency is improved; according to the overstrain summary analysis result, whether the locomotive crew member is overstrain operation or not can be judged, if the locomotive crew member is overstrain operation, the locomotive crew member can be reminded to have a rest in time, the energy of the locomotive crew member in riding is ensured to be sufficient, and the locomotive driving safety is ensured; meanwhile, the reason of the overstrain operation of the crew is analyzed, and particularly, factors influencing the overstrain operation such as a locomotive section, a traffic route, time, a locomotive, a driver and the like are analyzed, so that the management work of a management department is facilitated, and guidance is provided for subsequent improvement.
Claims (10)
1. A locomotive crew member one-time crew operation data analysis system is characterized in that: the data analysis system for the primary crew operation of the locomotive crew member comprises a data source information module, an entity data module, a data generation module and a data processing module;
the data source information module: the data storage device is used for storing data of the crew operation of each railway locomotive depot;
the entity data module: the system comprises a database, a database server and a database server, wherein the database is used for storing a data storage entity table, a fact table and an analysis dimension table of the crew work; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table;
the crew operation data storage entity table: extracting real-time data of one-time riding operation of locomotive crew members in the locomotive running process from the data source information module;
the fact table is as follows: a crew working hour data form which is established according to the crew working data storage entity table and takes a driver as granularity;
the analysis dimension table is as follows: a data form established on the basis of the crew operation data storage entity table by taking the analysis dimension as the granularity;
the data generation module: the data form is used for receiving the data form sent by the entity data module, calculating the real-time data in the crew operation data storage entity table according to the analysis needs of the user, associating the calculation result with the analysis dimension table, and simultaneously storing and updating the calculation result and the association result to the fact table; thereby constructing a fact table data model and an analysis dimension data model;
the data processing module: the method is used for extracting the analysis dimension model to be analyzed by the user from the analysis dimension data model and analyzing by combining the fact table data model to obtain the working hour condition of the locomotive crew member.
2. The locomotive attendant one-time attendant work data analysis system of claim 1, wherein: the data stored in the data source information module comprises LKJ processing result information data, crew attendance information data and locomotive in-out information data;
the LKJ processing result information data comprise an engineering section code, an LKJ file ID, a locomotive model, a locomotive number, a vehicle type, a vehicle number, a driver work number, an assistant driver work number, file processing time, file starting time, driving time, file ending time, a starting station, a terminal station, a workshop, a vehicle team and a guide group;
the crew attendance information data comprises crew traffic routes, driver work numbers, planned starting points, locomotive models, locomotive numbers, attendance time, attendance places, attendance time and attendance places;
the locomotive warehouse-in and warehouse-out information data comprises locomotive models, locomotive numbers, warehouse-out time, warehouse-out places, warehouse-in time and warehouse-in places.
3. The locomotive attendant one-time attendant work data analysis system of claim 2, wherein: the crew operation data storage entity table comprises a crew section code, an LKJ file ID, a locomotive model, a locomotive number, a vehicle type, a vehicle number, a driver work number, file processing time, file starting time, driving time, file ending time, a starting station, a terminal station, a workshop, a vehicle team, a guidance group, a crew traffic, a planned driving point, attendance time, an attendance place, an attendance time, an attendance exit place, an attendance return time, an attendance return place, a warehousing-in time, a warehousing-in place, an ex-warehousing time and an ex-warehousing place.
4. The locomotive attendant one-time attendant work data analysis system of claim 3, wherein: the fact table comprises an LKJ file ID, a driver key value, a time key value, a crew section key value, a crew traffic road key value, a attendance place key value, an attendance place key value, a warehousing place key value, an ex-warehouse place key value, an attendance-to-out time period, an attendance-to-driving time period, an ex-warehouse-to-driving time period, an arrival-to-warehousing time period, an arrival-to-attendance-to-returning time period and an attendance-to-returning time period.
5. The locomotive attendant one-time attendant work data analysis system of claim 4, wherein: the analysis dimension comprises a driver dimension, a time dimension, a crew section dimension, a duty traffic path dimension, an exit/exit location dimension and an entry/exit location dimension;
the driver dimension table comprises driver key values, driver job numbers and driver names; the time dimension table comprises time key values, years, months, days and hours; the maintenance table of the locomotive depot comprises a locomotive depot key value, a locomotive depot code and a locomotive depot name; the crew traffic route dimension table comprises crew traffic route key values, crew traffic route codes and crew traffic route names; the attendance exit place dimension table comprises an attendance exit place key value, a place code and a place name; the warehouse-in and warehouse-out place dimension table comprises warehouse-in and warehouse-out place key values, place codes and place names.
6. A method for analyzing a locomotive crew primary crew operation data analysis system according to claim 5, wherein the method comprises the steps of: the analysis method comprises the following steps:
1) collecting data of the crew operation of each railway section, and storing the collected data in a data source information module;
2) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting required real-time data from the data source information module, filling the required real-time data into a corresponding crew operation data storage entity table, and updating the analysis dimension table;
3) transmitting the crew operation data storage entity table filled with the real-time data, the fact table filled with the crew working data and the updated analysis dimension table to the data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a crew operation data storage entity table, associating a calculation result with an analysis dimension table, simultaneously storing the calculation and association results into the fact table to generate a fact table data model, and constructing a data analysis model through the fact table data model and the analysis dimension data model;
4) and extracting the analysis dimension to be analyzed from the analysis dimension data model according to the analysis requirement of the user, and analyzing the crew operation condition in the process of the locomotive crew operation by combining the fact table data model to obtain the working time condition of the locomotive and the crew.
7. The method for analyzing locomotive attendant one-time crew operation data according to claim 6, wherein: in the step 1) described above, the step of,
1.1) splitting, counting or converting formats of LKJ processing result information data, crew attendance and attendance information data and locomotive in-out information data stored in a data source information module;
1.2) carrying out duplication elimination operation on LKJ processing result information data by taking the file start time, the driver number, the locomotive model and the locomotive number as conditions.
8. The method for analyzing locomotive attendant one-time crew operation data according to claim 7, wherein: the specific process of the step 2) is as follows:
2.1) establishing a crew operation data storage entity table, a fact table and an analysis dimension table according to the user requirements;
2.2) extracting real-time data of one-time crew operation of the locomotive crew member from the data source information module; combining the LKJ processing result information data subjected to the weight removal by using file processing time, file starting time, driver number, locomotive model and locomotive number, storing the combined real-time data into a crew operation data storage entity table, and updating a driver dimension table and a crew section dimension table at the same time;
2.3) extracting six real-time data of the crew traffic routes, planned starting points, attendance time, attendance places, attendance time and attendance place information from the crew attendance and attendance information data by using the locomotive model, the locomotive number, the driver number, the driving time information and the warehousing time information, storing the extracted real-time data into a crew operation data storage entity table, and simultaneously updating a crew traffic route dimension table, a attendance and attendance place dimension table and a time dimension table;
and 2.4) extracting the four real-time data of the ex-warehouse time, the ex-warehouse place, the in-warehouse time and the in-warehouse place information from the locomotive ex-warehouse information data according to the locomotive model, the locomotive number, the driver number, the driving time information, the file ending time information, the attendance time and the attendance place information, storing the extracted real-time data into a crew operation data storage entity table, and updating the ex-warehouse place dimension table.
9. The method for analyzing locomotive crew one-time crew operation data according to claim 8, wherein: the merging operation process in the step 2.2) is as follows: merging records of the same driver number, the same locomotive model, the same locomotive number and the file start time within 12 hours;
and taking the file start time and the driving time in the minimum file start time record as the file start time and the driving time of the current crew operation, and taking the maximum file end time as the file end time of the current crew operation.
10. The method for analyzing locomotive attendant one-time crew operation data according to claim 9, wherein: the analysis of the riding operation condition in the step 4) comprises late point analysis, travel time analysis and overstrain summary analysis;
the locomotive ex-warehouse later point analysis is to set standard time for locomotive ex-warehouse, combine the data analysis extracted from the dimension data model and the fact table data model in step 3) to obtain the situation of right and later point, the time division at later point, the summary situation of a certain time period and the summary situation of the whole locomotive section for each locomotive section, and parallel out detailed lists;
the travel time analysis result is the travel time standard specified by the locomotive running chart data, the data extracted from the dimension data model and the fact table data model are analyzed in combination with the step 3), if the actual running time of the locomotive exceeds the travel time standard, the statistics of the travel time is overtime, and overtime summary and average conditions of a certain section can be inquired;
the overstrain summary analysis is to analyze data extracted from the dimension data model and the fact table data model according to the crew section and the crew traffic section in combination with the step 3), automatically count the total shift of the crew value, the shift of overstrain, the overstrain accumulated time, the overstrain average time, the overstrain rate, the late point of leaving warehouse overtime, the departure stop overtime, the travel overtime and the arrival stop overtime respectively, and summarize the whole overstrain condition.
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