CN112632067B - Locomotive crewmember primary operation data analysis system and analysis method - Google Patents
Locomotive crewmember primary operation data analysis system and analysis method Download PDFInfo
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Abstract
The invention belongs to the technical field of railway industry machine service systems, and relates to a locomotive crew one-time operation data analysis system and an analysis method, wherein the analysis system 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 real-time data in the real-time data storage entity table of the passenger operation data, correlating the calculation result with the analysis dimension table, storing the calculation result and the correlation result into the fact table, constructing a data analysis model, extracting data information to be analyzed, and analyzing to obtain the time spent condition of the locomotive crew. The invention ensures the integrity of operation data, analyzes and obtains the overstrain condition of the locomotive crew, avoids continuous night shifts of the crew, ensures the energy of the locomotive crew in the duty and ensures the running safety of the locomotive crew; and the management efficiency of the management department is improved.
Description
Technical Field
The invention belongs to the technical field of railway industry machine service systems, and relates to a locomotive crew one-time passenger operation data analysis system and analysis method.
Background
The locomotive crews are the main technical work of railway transportation production and bear the important duty of driving locomotives and trains of motor train units to run safely and all the time, and the working characteristics are that the operation is long in multiple lines, not divided into day and night and independently operated, and the working quality is directly related to the life and national property safety of passengers in a highly stressed state for a long time. Because the data of one-time operation of the crews are scattered in different service systems, the data integrity cannot be ensured, and the loss of the data brings inconvenience to the effective analysis of the operation conditions of each road crews; moreover, the device has hysteresis for the average time of work, the highest time of work, the overtime of work, the month overtime of work and the continuous night shift of the crew, and cannot be controlled in real time, the overtime operation cannot ensure the energy of the crew on duty of the locomotive, and the driving safety of the locomotive cannot be ensured.
Disclosure of Invention
In order to solve the problem of locomotive crewmember operation, the invention provides a locomotive crewmember primary crewmember operation data analysis system and an analysis method, which ensure the integrity of locomotive crewmember operation data of different service systems, obtain the overstrain condition of the locomotive crewmember through system analysis, avoid continuous night shift of the crewmember, ensure the energy in the operation of the locomotive crewmember and ensure the safety of the locomotive driving; and meanwhile, the management efficiency of the management department is improved.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The 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 system is used for storing the data of the passenger operation of each railway section;
The entity data module: the system comprises a data storage entity table, a fact table and an analysis dimension table, wherein the data storage entity table, the fact table and the analysis dimension table are used for establishing associated multiplication operation data according to the needs of users; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables;
The multiplication job data storage entity table: the real-time data of one-time passenger operation of the locomotive crews are extracted from the data source information module in the locomotive running process;
The fact table: a crew man-hour data form which is established by the entity table according to the crew operation data storage and takes a driver as granularity;
the analytical dimension table: a data form established by taking the analysis dimension as granularity on the basis of a multiplication operation data storage entity table;
The data generation module: the system comprises a data table, a data storage module and a data storage module, wherein the data table is used for receiving the data table sent by the entity data module, calculating real-time data in the entity table according to user analysis requirements, correlating a calculation result with an analysis dimension table, and simultaneously storing and updating the calculation result and the correlation 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 an analysis dimension model to be analyzed by a user from the analysis dimension data model, and analyzing the analysis dimension model by combining the fact table data model to obtain the time-consuming condition of the locomotive crew.
Further, the data stored in the data source information module comprises LKJ processing result information data, crewmember attendance information data and locomotive warehouse entry information data;
The LKJ processing result information data comprises a machine service section code, an LKJ file ID, a locomotive model number, a locomotive number, a train number, a driver work number, an auxiliary driver work number, file processing time, file starting time, driving time, file ending time, a starting station, a terminal station, a workshop, a train and a guidance group;
the crewmember attendance information data comprise a passenger traffic road, a driver work number, a planned starting point, a locomotive model, a locomotive number, attendance time, attendance places, attendance time and attendance places;
the locomotive ex-warehouse information data comprise locomotive models, locomotive numbers, ex-warehouse time, ex-warehouse places, warehouse time and warehouse places.
Further, the passenger service data storage entity table comprises a machine service section code, an LKJ file ID, a locomotive model number, a locomotive number, a train type, a train number, a driver work number, a file processing time, a file starting time, a driving time, a file ending time, a starting station, a terminal station, a workshop, a train, a guidance group, a passenger service delivery path, a planned starting point, a attendance time, an attendance place, an attendance time, an attendance place, a warehousing time, a warehousing place, a ex-warehouse time and an ex-warehouse place.
Further, the fact table includes LKJ file ID, driver key value, time key value, machine section key value, passenger service route key value, attendance place key value, warehouse place key value, attendance to and ex time duration, attendance to and start time duration, ex to and start time duration, arrival to and warehouse time duration, arrival to and ex time duration.
Further, the analysis dimension comprises a driver dimension, a time dimension, a machine section dimension, a passenger traffic dimension, an attendance and withdrawal place dimension and an warehouse-in place dimension;
The driver dimension table comprises a driver key value, a driver work number and a driver name; the time dimension table comprises a time key value, a year, a month, a day and an hour; the machine section dimension table comprises a machine section key value, a machine section code and a machine section name; the multiplication service path dimension table comprises a multiplication service path key value, a multiplication service path code and a multiplication service path name; the attendance place dimension table comprises attendance place key values, place codes and place names; the access location dimension table comprises an access location key value, a location code and a location name.
An analysis method of a locomotive attendant primary passenger operation data analysis system comprises the following steps:
1) Collecting data of each railway section passenger service operation, and storing the collected data in a data source information module;
2) Establishing a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables;
3) Transmitting the real-time data-filled passenger operation data storage entity table, the real-time data-filled crew labor-time data fact table and the updated analysis dimension table to a data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a real-time table of the multiplication operation data storage entity, correlating a calculation result with an analysis dimension table, storing the calculation result and the correlation result into a 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) Extracting analysis dimensions to be analyzed from the analysis dimension data model according to user analysis requirements, and analyzing the service operation conditions of locomotive crews in the service operation process by combining the fact table data model to obtain the time-consuming conditions of the locomotive and the crews.
Further, in the step 1),
1.1 Splitting, counting or converting the LKJ processing result information data, the crewmember attendance information data and the locomotive warehouse-in and warehouse-out information data stored by the data source information module;
1.2 And (3) performing duplication removal operation on the LKJ processing result information data by taking the file starting time, the driver number, the locomotive model number and the locomotive number as conditions.
Further, the specific process of the step 2) is as follows:
2.1 Building a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user;
2.2 Extracting real-time data of one-time passenger operation of locomotive crews from the data source information module; combining the LKJ processing result information data subjected to de-duplication according to file processing time, file starting time, driver number, locomotive model and locomotive number, storing the combined real-time data into a passenger operation data storage entity table, and simultaneously updating a driver dimension table and a locomotive section dimension table;
2.3 Extracting six real-time data including a passenger traffic route, a planned starting point, a attendance time, an attendance place, an attendance time and attendance place information from the attendance information data of the crews by using the model number, the driver number, the driving time information and the warehousing time information of the locomotives, storing the extracted real-time data into a passenger work data storage entity table, and simultaneously updating a passenger traffic route dimension table, an attendance place dimension table and a time dimension table;
2.4 Extracting four real-time data of ex-warehouse time, ex-warehouse place, warehouse-in time and warehouse-in place information from the information data of the ex-warehouse of the locomotive by using the model number, the number of the locomotive, the driver number, the driving time information, the file end time information, the attendance time and the attendance place information of the locomotive, storing the extracted real-time data into a passenger operation data storage entity table, and simultaneously updating the dimension table of the ex-warehouse place.
Further, the merging operation in the step 2.2) is as follows: merging records with the same driver number, the same locomotive model number, the same locomotive number and file starting time within 12 hours;
Taking the file starting time and the driving time in the minimum file starting time record as the file starting time and the driving time of the current multiplication operation, and taking the maximum file ending time as the file ending time of the current multiplication operation.
Further, the passenger operation condition analysis in the step 4) comprises warehouse-out late analysis, travel time analysis and overwork summarization analysis;
The locomotive delivery late analysis is to set locomotive delivery standard time, combine the data analysis extracted from the dimension data model and the fact table data model in the step 3) to obtain the delivery time, the late time, the time period summarizing condition and the whole locomotive segment summarizing condition of a certain locomotive in each locomotive segment, and list detailed sheets;
The travel time analysis result is according to the travel time standard specified by 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 is that the travel time is overtime, and the overtime summary and average condition of a certain section can be inquired;
the overtime summarization analysis is to analyze the data extracted from the dimension data model and the fact table data model according to the machine service section and the riding service traffic section and by combining the step 3), respectively and automatically count the riding service value multiplied total shift, the overtime occurrence shift of the overtime, the overtime time, the overtime average time, the overtime rate, the overtime of the delivery stop, the overtime of the travel time and the overtime of the arrival stop, and summarize the whole overtime condition.
The beneficial effects of the invention are as follows:
1. the invention provides a locomotive crewmember primary passenger operation data analysis system, which comprises a locomotive crewmember primary passenger operation data analysis system, a data processing module and a data analysis system, wherein the locomotive crewmember primary passenger operation data analysis system comprises a data source information module, an entity data module, a data generation module and a data processing module; and the data source information module is used for: the system is used for storing the data of the passenger operation of each railway section; entity data module: the system comprises a data storage entity table, a fact table and an analysis dimension table, wherein the data storage entity table is used for establishing a passenger operation data according to the needs of a user; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables; and a data generation module: receiving a real-time data of the passenger operation data storage entity table filled with real-time data, a fact table of the time-consuming data of the crew and an updated analysis dimension table, which are sent by an entity data module, calculating the real-time data in the real-time data of the passenger operation data storage entity table according to the analysis requirement of a user, correlating a calculation result with the analysis dimension table, and storing and updating the calculation result and the correlation result to the fact table so as to construct a data analysis model; and a data processing module: the method is used for extracting data information to be analyzed from the data analysis model according to the analysis requirement of a user, and analyzing the operation of the crew of the locomotive to obtain the time consuming conditions of the locomotive and the crew of the locomotive. The large data platform for the operation of the locomotive crew member service data can be established through the data analysis system, the efficiency of the operation of the locomotive crew member service data is improved, and the formed large data platform provides data support for subsequent analysis; and the passenger operation data is effectively managed, so that the integrity and the accuracy of a subsequent analysis result are ensured.
2. The invention provides a locomotive crewmember primary service operation data analysis method which comprises the following steps: 1) Collecting data of each railway section passenger service operation, and storing the collected data in a data source information module; 2) Establishing a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables; 3) Transmitting the real-time data-filled passenger operation data storage entity table, the real-time data-filled crew labor-time data fact table and the updated analysis dimension table to a data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a real-time table of the multiplication operation data storage entity, correlating a calculation result with an analysis dimension table, storing the calculation result and the correlation result into a 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 user analysis requirements, and analyzing the service operation conditions of locomotive crews in the service operation process to obtain the time-consuming conditions of the locomotive and the crews. According to the target operation data to be analyzed, corresponding real-time data are extracted from a data source, the extracted real-time data are calculated, an analysis dimension table is related and updated, a data analysis model is built, the integrity analysis of the operation data of the crews of different service systems is realized, the operation data information of each road crews can be obtained, the average time spent, the highest time spent, the time spent and the month spent data information of each road crews are obtained through data processing analysis, continuous night shifts of the crews are avoided, energy in the multiplication of locomotive crews is guaranteed, and the driving safety of the locomotive is ensured.
Drawings
FIG. 1 is a schematic diagram of a system for analyzing primary crew operation data of a locomotive crew member;
FIG. 2 is a representation of the time-of-flight facts table and analysis dimensions provided by the present invention;
FIG. 3 is a schematic diagram of a system for analyzing primary crew operation data of a locomotive crew member according to the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and examples.
Example 1
Referring to fig. 1, the locomotive attendant primary passenger operation data analysis system provided in the present 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 in this embodiment: and the system is used for storing the data of the passenger operation of each railway section.
The entity data module provided in this embodiment: the system comprises a data storage entity table, a fact table and an analysis dimension table, wherein the data storage entity table, the fact table and the analysis dimension table are used for establishing associated multiplication operation data according to the needs of users; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables;
The multiplication job data storage entity table provided in this embodiment: the real-time data of one-time passenger operation of the locomotive crews are extracted from the data source information module in the locomotive running process;
The facts table provided in this embodiment: a crew man-hour data form which is established by the entity table according to the crew operation data storage and takes a driver as granularity;
The analysis dimension table provided in this embodiment: and establishing a data form with the analysis dimension as granularity on the basis of the multiplication job data storage entity table.
The data generation module provided in this embodiment: the system comprises a data table, a data storage module and a data storage module, wherein the data table is used for receiving the data table sent by the entity data module, calculating real-time data in the entity table according to user analysis requirements, correlating a calculation result with an analysis dimension table, and simultaneously storing and updating the calculation result and the correlation result to the fact table; thereby constructing a fact table data model and an analysis dimension data model;
The data processing module provided in this embodiment: the method is used for extracting an analysis dimension model to be analyzed by a user from the analysis dimension data model, and analyzing the analysis dimension model by combining the fact table data model to obtain the time-consuming condition of the locomotive crew.
The data stored in the data source information module provided by the embodiment comprises LKJ processing result information data, crewmember attendance information data and locomotive warehouse entry information data; the LKJ processing result information data comprises a locomotive service section code, an LKJ file ID, a locomotive model number, a locomotive number, a train number, a driver work number, an auxiliary driver work number, a file processing time, a file starting time, a driving time, a file ending time, a starting station, a terminal station, a workshop, a train and a guidance group; the crewmember attendance information data comprises a passenger traffic road, a driver work number, a planned starting point, a locomotive model, a locomotive number, attendance time, attendance places, attendance time and attendance places; the locomotive warehouse-in and warehouse-out information data comprise locomotive models, locomotive numbers, warehouse-out time, warehouse-out places, warehouse-in time and warehouse-in places.
The passenger service operation data storage entity table provided by the embodiment comprises a machine service section code, an LKJ file ID, a locomotive model, a locomotive number, a train type, a train number, a driver work number, a file processing time, a file starting time, a driving time, a file ending time, a starting station, a terminal station, a workshop, a train team, a guidance group, a passenger service delivery path, a planned starting point, a attendance time, an attendance place, a warehousing time, a warehousing place, a ex-warehouse time and an ex-warehouse place.
Referring to fig. 2, the facts table provided in the present embodiment includes LKJ file ID, driver key value, time key value, machine section key value, passenger service route key value, attendance place key value, warehouse entry place key value, attendance to ex-warehouse time period, attendance to on-duty time period, ex-warehouse to on-duty time period, arrival to in-warehouse time period, arrival to attendance-withdrawal time period, and attendance to attendance-withdrawal time period.
Referring to fig. 2, the analysis dimensions provided in the present embodiment include a driver dimension, a time dimension, a crew section dimension, a crew traffic path dimension, an attendance place dimension, and an warehouse entry place dimension; the driver dimension table comprises a driver key value, a driver work number and a driver name; the time dimension table comprises a time key value, a year, a month, a day and an hour; the machine section dimension table comprises a machine section key value, a machine section code and a machine section name; the multiplication service route dimension table comprises a multiplication service route key value, a multiplication service route code and a multiplication service route name; the attendance place dimension table comprises attendance place key values, place codes and place names; the ex-warehouse location dimension table comprises an ex-warehouse location key value, a location code and a location name.
The data stored in the data source information module provided by the embodiment is data from the passenger operation of each railway section, the data type is complex, and the specific data type comprises a relational database, file type data, standard data, unstructured database and a distributed data source; relational databases include Oracle, DB2, SQLSERVER, MYSQL, and Informix; the file data comprises Excel, txt files and XML files; standard data includes WebService and SOA; unstructured databases MongoDB and HBASE; distributed data sources include SAP, hadoop, and Gemfire.
In the embodiment, the data in the data source information module has wide coverage, and the data of the passenger operation is effectively managed; and the data source supports different types of data, when in implementation, the data of different file types and different formats in the data source can be converted and split according to the requirements of the fact table, so that the data meets the requirements of the data format in the fact table, the efficiency of locomotive crew working management data is improved, the data in a constructed data model is also 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 primary passenger operation data of a locomotive crew provided in the present embodiment includes the following steps:
1) Collecting data of each railway section passenger service operation, and storing the collected data in a data source information module;
Specifically, step 1) includes:
1.1 Splitting, counting or converting the LKJ processing result information data, the crewmember attendance information data and the locomotive warehouse-in and warehouse-out information data stored by the data source information module;
1.2 Performing duplication removal operation on LKJ processing result information data by taking the file starting time, the driver number, the locomotive model number and the locomotive number as conditions;
2) Establishing a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables;
further, the specific process of the step 2) is as follows:
2.1 Building a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user;
2.2 Extracting real-time data of one-time passenger operation of locomotive crews from the data source information module; combining the LKJ processing result information data subjected to de-duplication according to file processing time, file starting time, driver number, locomotive model and locomotive number, storing the combined real-time data into a passenger operation data storage entity table, and simultaneously updating a driver dimension table and a locomotive section dimension table;
2.3 Extracting six real-time data including a passenger traffic route, a planned starting point, a attendance time, an attendance place, an attendance time and attendance place information from the attendance information data of the crews by using the model number, the driver number, the driving time information and the warehousing time information of the locomotives, storing the extracted real-time data into a passenger work data storage entity table, and simultaneously updating a passenger traffic route dimension table, an attendance place dimension table and a time dimension table;
2.4 Extracting four real-time data of ex-warehouse time, ex-warehouse place, warehouse-in time and warehouse-in place information from the information data of the ex-warehouse of the locomotive by using the model number, the number of the locomotive, the driver number, the driving time information, the file end time information, the attendance time and the attendance place information of the locomotive, storing the extracted real-time data into a passenger operation data storage entity table, and simultaneously updating the dimension table of the ex-warehouse place.
Further, the merging operation in step 2.2) is as follows: merging records with the same driver number, the same locomotive model number, the same locomotive number and file starting time within 12 hours;
Taking the file starting time and the driving time in the minimum file starting time record as the file starting time and the driving time of the current multiplication operation, and taking the maximum file ending time as the file ending time of the current multiplication operation;
3) Transmitting the real-time data-filled passenger operation data storage entity table, the real-time data-filled crew labor-time data fact table and the updated analysis dimension table to a data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a real-time table of the multiplication operation data storage entity, correlating a calculation result with an analysis dimension table, storing the calculation result and the correlation result into a 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) Extracting analysis dimensions to be analyzed from the analysis dimension data model according to user analysis requirements, and analyzing the service operation conditions of locomotive crews in the service operation process by combining the fact table data model to obtain the time-consuming conditions of the locomotive and the crews.
Further, the passenger operation condition analysis in the step 4) comprises warehouse-out late analysis, travel time analysis and overfatigue summarization analysis;
The locomotive ex-warehouse late analysis is to set locomotive ex-warehouse standard time, combine the data analysis extracted from the dimension data model and the fact table data model in the step 3) to obtain the ex-warehouse late condition, the late time, the time period summarizing condition and the whole locomotive segment summarizing condition of a certain locomotive in each locomotive segment, and list detailed sheets;
The travel time analysis result is according to 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 is that the travel time is overtime, and the overtime summarization and average condition of a certain section can be inquired;
The overtime summarization analysis is to analyze the data extracted from the dimension data model and the fact table data model according to the machine service section and the traffic service traffic section and by combining the step 3), respectively and automatically count the total number of times of the traffic service value multiplication, the number of times of overtime occurrence, the overtime time of overtime, the overtime average time, the overtime rate, the overtime of the warehouse-out late time, the overtime of the outbound stop, the overtime of the travel and the overtime of the arrival stop, and summarize the whole overtime condition.
According to the analysis method provided by the embodiment, a fact table with a driver as granularity is established according to the content information of the entity table stored by the passenger operation data, and an associated analysis dimension table is established, and further, a time-consuming data analysis model and an analysis dimension model are established through data extraction and calculation; the analysis is convenient, the integrity analysis of the crew operation data of different service systems is realized, and a foundation is provided for the subsequent data analysis. According to the user requirements, extracting required analysis dimension tables from the 6 analysis dimension tables of the analysis dimension model, combining with a crew time-consuming data fact model to obtain the locomotive crew time-consuming situation, and further analyzing the reason of overfatigue. For example, after the locomotive delivery late results and the travel time results are obtained through analysis, if the locomotive delivery late results and the travel time results are late or overtime, the reasons of the locomotive delivery late results and the travel time overtime can be analyzed by combining the data in the dimension table, so that convenience is brought to management work of a management department, and the management efficiency is improved; according to the overfatigue summarization analysis result, whether the locomotive crews are overfatigue operation can be judged, if the locomotive crews are overfatigue operation, the locomotive crews can be reminded of taking a rest in time, the locomotive crews are full of energy in the duty, and the safety of the locomotive crews in driving is ensured; meanwhile, the reason of the overwork operation of the crews is analyzed, and the factors influencing the overwork operation, such as a machine service section, a road crossing, time, a locomotive, a driver and the like, are particularly included, so that the management work of a management department is facilitated, and guidance is provided for subsequent improvement.
Claims (7)
1. A locomotive crewmember primary service operation data analysis system is characterized in that: the locomotive crewmember primary passenger 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 system is used for storing the data of the passenger operation of each railway machine section;
The data stored in the data source information module comprises LKJ processing result information data, crewmember attendance information data and locomotive warehouse-in and warehouse-out information data;
The LKJ processing result information data comprises a machine service section code, an LKJ file ID, a locomotive model number, a locomotive number, a train number, a driver work number, an auxiliary driver work number, file processing time, file starting time, driving time, file ending time, a starting station, a terminal station, a workshop, a train and a guidance group;
the crewmember attendance information data comprise a passenger traffic road, a driver work number, a planned starting point, a locomotive model, a locomotive number, attendance time, attendance places, attendance time and attendance places;
The locomotive ex-warehouse information data comprise locomotive models, locomotive numbers, ex-warehouse time, ex-warehouse places, warehouse time and warehouse places;
The entity data module: the system comprises a data storage entity table, a fact table and an analysis dimension table, wherein the data storage entity table, the fact table and the analysis dimension table are used for establishing associated multiplication operation data according to the needs of users; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables;
The multiplication job data storage entity table: the real-time data of one-time passenger operation of the locomotive crews are extracted from the data source information module in the locomotive running process;
The fact table: a crew man-hour data form which is established by the entity table according to the crew operation data storage and takes a driver as granularity; the fact table comprises LKJ file ID, driver key value, time key value, machine section key value, passenger traffic path key value, attendance place key value, warehouse place key value, attendance to and ex time duration, attendance to and start time duration, ex to and start time duration, arrival to and warehouse time duration, arrival to and ex time duration;
the analytical dimension table: a data form established by taking the analysis dimension as granularity on the basis of a multiplication operation data storage entity table;
the analysis dimension comprises a driver dimension, a time dimension, a machine service section dimension, a passenger service road dimension, an attendance place dimension and an warehouse place dimension;
the driver dimension table comprises a driver key value, a driver work number and a driver name; the time dimension table comprises a time key value, a year, a month, a day and an hour; the machine section dimension table comprises a machine section key value, a machine section code and a machine section name; the multiplication service path dimension table comprises a multiplication service path key value, a multiplication service path code and a multiplication service path name; the attendance place dimension table comprises attendance place key values, place codes and place names; the warehouse-in and warehouse-out place dimension table comprises a warehouse-in and warehouse-out place key value, a place code and a place name;
The data generation module: the system comprises a data table, a data storage module and a data storage module, wherein the data table is used for receiving the data table sent by the entity data module, calculating real-time data in the entity table according to user analysis requirements, correlating a calculation result with an analysis dimension table, and simultaneously storing and updating the calculation result and the correlation 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 an analysis dimension model to be analyzed by a user from the analysis dimension data model, and analyzing the analysis dimension model by combining the fact table data model to obtain the time-consuming condition of the locomotive crew.
2. The locomotive attendant primary ride operation data analysis system of claim 1, wherein: the passenger service data storage entity table comprises a machine service section code, an LKJ file ID, a locomotive model number, a locomotive number, a train type, a train number, a driver work number, a file processing time, a file starting time, a driving time, a file ending time, a starting station, a terminal station, a workshop, a train, a guidance group, a passenger service delivery path, a planned starting point, a attendance time, an attendance place, a warehouse time, a warehouse place, an attendance time and an ex-warehouse place.
3. An analysis method based on the locomotive attendant primary passenger operation data analysis system as claimed in claim 2, characterized by comprising the following steps: the analysis method comprises the following steps:
1) Collecting data of each railway section passenger service operation, and storing the collected data in a data source information module;
2) Establishing a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user; extracting needed real-time data from the data source information module, filling the real-time data into corresponding multiplication operation data storage entity tables, and updating the analysis dimension tables;
3) Transmitting the real-time data-filled passenger operation data storage entity table, the real-time data-filled crew labor-time data fact table and the updated analysis dimension table to a data generation module, and generating an analysis dimension model by the updated analysis dimension table; calculating real-time data in a real-time table of the multiplication operation data storage entity, correlating a calculation result with an analysis dimension table, storing the calculation result and the correlation result into a 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) Extracting analysis dimensions to be analyzed from the analysis dimension data model according to user analysis requirements, and analyzing the service operation conditions of locomotive crews in the service operation process by combining the fact table data model to obtain the time-consuming conditions of the locomotive and the crews.
4. A method of analyzing data of a primary crew operation of a locomotive crew member according to claim 3, wherein: in the step (1) of the above-mentioned process,
1.1 Splitting, counting or converting the LKJ processing result information data, the crewmember attendance information data and the locomotive warehouse-in and warehouse-out information data stored by the data source information module;
1.2 And (3) performing duplication removal operation on the LKJ processing result information data by taking the file starting time, the driver number, the locomotive model number and the locomotive number as conditions.
5. The locomotive attendant one-time ride operation data analysis method of claim 4, wherein: the specific process of the step 2) is as follows:
2.1 Building a multiplication operation data storage entity table, a fact table and an analysis dimension table according to the needs of a user;
2.2 Extracting real-time data of one-time passenger operation of locomotive crews from the data source information module; combining the LKJ processing result information data subjected to de-duplication according to file processing time, file starting time, driver number, locomotive model and locomotive number, storing the combined real-time data into a passenger operation data storage entity table, and simultaneously updating a driver dimension table and a locomotive section dimension table;
2.3 Extracting six real-time data including a passenger traffic route, a planned starting point, a attendance time, an attendance place, an attendance time and attendance place information from the attendance information data of the crews by using the model number, the driver number, the driving time information and the warehousing time information of the locomotives, storing the extracted real-time data into a passenger work data storage entity table, and simultaneously updating a passenger traffic route dimension table, an attendance place dimension table and a time dimension table;
2.4 Extracting four real-time data of ex-warehouse time, ex-warehouse place, warehouse-in time and warehouse-in place information from the information data of the ex-warehouse of the locomotive by using the model number, the number of the locomotive, the driver number, the driving time information, the file end time information, the attendance time and the attendance place information of the locomotive, storing the extracted real-time data into a passenger operation data storage entity table, and simultaneously updating the dimension table of the ex-warehouse place.
6. The locomotive attendant one-time ride operation data analysis method of claim 5, wherein: the merging operation in the step 2.2) comprises the following steps: merging records with the same driver number, the same locomotive model number, the same locomotive number and file starting time within 12 hours;
Taking the file starting time and the driving time in the minimum file starting time record as the file starting time and the driving time of the current multiplication operation, and taking the maximum file ending time as the file ending time of the current multiplication operation.
7. The locomotive attendant one-time ride operation data analysis method of claim 6, wherein: the passenger operation condition analysis in the step 4) comprises warehouse-out late analysis, travel time analysis and overfatigue summarization analysis;
The locomotive delivery late analysis is to set locomotive delivery standard time, combine the data analysis extracted from the dimension data model and the fact table data model in the step 3) to obtain the delivery time, the late time, the time period summarizing condition and the whole locomotive segment summarizing condition of a certain locomotive in each locomotive segment, and list detailed sheets;
The travel time analysis result is according to the travel time standard specified by 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 is that the travel time is overtime, and the overtime summary and average condition of a certain section can be inquired;
the overtime summarization analysis is to analyze the data extracted from the dimension data model and the fact table data model according to the machine service section and the riding service traffic section and by combining the step 3), respectively and automatically count the riding service value multiplied total shift, the overtime occurrence shift of the overtime, the overtime time, the overtime average time, the overtime rate, the overtime of the delivery stop, the overtime of the travel time and the overtime of the arrival stop, and summarize the whole overtime condition.
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