Urban rail transit practical training system based on cloud computing and use method

Abstract

The invention discloses an urban rail transit practical training system based on cloud computing and a use method, and relates to the technical field of urban rail transit real-time systems, and the system comprises a data collection module, an instruction similarity coefficient generation module, a working data analysis module, a practical training time length acquisition module and a time length adjustment module. The technical problem that in the practical training process, the practical training duration cannot be adjusted according to the practical situation of each trainee, and then some skilled trainees need to spend longer time for practical training is solved. Through combining and analyzing the proficiency coefficient corresponding to each trainee and the instruction similarity corresponding to the practical training instruction, the practical training duration corresponding to the practical training instruction of each trainee is obtained, according to the practical training duration corresponding to each trainee, unnecessary repeated training or overlong practical training time is avoided, and the training efficiency is improved. Waste of practical training time is reduced, and practical training time and practical training resources are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rail transit training systems, and in particular to an urban rail transit training system based on cloud computing and a use method thereof. Background Art

[0002] As an important mode of transportation in modern cities, urban rail transit has put forward higher requirements for the safety training and skills improvement of practitioners. Traditional training methods are limited in time and space, and are costly. Therefore, in the existing urban rail transit training, cloud computing technology is used to simulate the actual rail transit scene in a virtual environment, and real-time data analysis and feedback are provided, so that practitioners can conduct simulation training in various scenarios and improve their ability to respond to emergencies and operational skills. Combining real-time data analysis, simulation and virtual reality technologies, it provides a more realistic, efficient and safe training environment for rail transit practitioners.

[0003] However, in the process of training each personnel, since the proficiency and operating skills of each trainee are different, but the training time of each trainee is a fixed time set by system 1 during the training process, it is impossible to adjust the training time according to the actual situation of each trainee, which makes some skilled trainees need to spend longer time for training, resulting in unnecessary repeated training or too long training time for the trainees, wasting training resources and reducing training efficiency. Based on this, a real-time urban rail transit system and usage method based on cloud computing are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time urban rail transit system based on cloud computing and a method of use, which solves the technical problem that the training time of each trainee in the training process is a fixed time set by the system, resulting in the inability to adjust the training time according to the actual situation of each trainee, thereby causing some skilled trainees to spend longer time on training.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The cloud computing-based urban rail transit real-time system includes:

[0007] The instruction similarity coefficient generation module digitally converts the old and new operation procedures corresponding to the training instructions to generate the old instruction set and the new instruction set, performs similarity analysis on the old instruction set and the new instruction set, and then obtains the instruction similarity of the training instructions;

[0008] The work data analysis module is used to analyze the historical operation times and operation duration of the training instructions corresponding to the trainees, as well as the working hours of the trainees in rail transit work, and then obtain the proficiency coefficient corresponding to each trainee;

[0009] The training duration acquisition module calculates the training duration corresponding to each trainee according to the proficiency coefficient corresponding to each trainee and the instruction similarity of the training instructions;

[0010] The duration adjustment module adjusts the training duration of each trainee for the training instructions according to the training duration corresponding to each trainee.

[0011] As a further solution of the present invention: the instruction similarity coefficient generation module includes an instruction conversion unit and a similarity analysis unit. The instruction conversion unit is used to digitally convert the old version operation process and the new version operation process corresponding to the practical training instructions, thereby forming an old version instruction set and a new version instruction set. The similarity analysis unit is used to perform similarity analysis on the old version instruction set and the new version instruction set, thereby obtaining the instruction similarity corresponding to the practical training instructions.

[0012] As a further solution of the present invention: the specific method of digitally converting the old version operation flow and the new version operation flow of the practical training instruction is as follows:

[0013] S1: Representing each operation instruction in the old version of the operation flow in sequence by numbers, where each number represents a corresponding operation instruction, and sorting the numbers corresponding to each operation instruction according to the order corresponding to each operation instruction in the old version of the operation flow, thereby obtaining an old version instruction set corresponding to the old version of the operation flow;

[0014] S2: Represent each operation instruction in the new version of the operation flow by numbers in the same way as in S1, represent the operation instructions in the new version of the operation flow that are the same as those in the old version of the operation flow by using the same numbers as those in the old version of the operation flow, and represent the operation instructions in the new version of the operation flow that are different from those in the old version of the operation flow by using numbers different from those in the operation instructions of the old version of the operation flow in sequence, thereby obtaining a new version of the operation instruction set corresponding to the new version of the operation flow.

[0015] As a further solution of the present invention: after obtaining the new version of the operation instruction set, when the number of operation instructions contained in the new version of the operation flow is equal to the number of operation instructions contained in the old version of the operation flow, no processing is performed; when the number is not equal, the shorter operation instruction set is padded so that the length of the new version of the operation instruction set and the old version of the operation instruction set are equal.

[0016] As a further solution of the present invention: the specific method of filling the shorter operation instruction set is:

[0017] The old version operation instruction set and the new version operation instruction set are aligned from the beginning, and the tail of the shorter operation instruction set is filled with 0 until the length of the old version operation instruction set is consistent with that of the new version operation instruction set.

[0018] As a further solution of the present invention: the specific method of obtaining instruction similarity is:

[0019] The numbers in the same position of the old version operation instruction set and the new version operation instruction set are compared in order from front to back, and the number of the same numbers in the same position in the two is obtained and the ratio between it and the sum of the numbers included in the old version operation instruction set and the new version operation instruction set is marked as the instruction similarity coefficient X.

[0020] As a further solution of the present invention: the specific method of obtaining the proficiency coefficient corresponding to each trainee is:

[0021] The sum of the products of each trainee’s working hours, historical number of training instruction operations and operation time and their corresponding preset proportional coefficients is marked as the proficiency coefficient Li of each trainee, where i represents the corresponding number of trainees.

[0022] As a further solution of the present invention: the specific method of obtaining the practical training time corresponding to each trainee is:

[0023] According to the formula MT-{1-[(Li+X)×α1]}=Ki, the training time Ki corresponding to each trainee is calculated, where MT is the preset training time, α1 is the preset proportional coefficient, and here α1=0.3786.

[0024] The method for using the urban rail transit real-time system based on cloud computing includes the following steps:

[0025] Step 1: Obtain the number of training instruction operations and operation duration of the trainees and the working hours of each trainee;

[0026] Step 2: Digitally convert the old and new version operation procedures corresponding to the training instructions to generate the old and new version instruction sets;

[0027] Step 3: Perform similarity analysis on the old version instruction set and the new version instruction set, and then obtain the instruction similarity of the training instructions;

[0028] Step 4: Analyze the historical operation times and operation duration of the training instructions corresponding to the trainees, as well as the working hours of the trainees in rail transit, and then obtain the proficiency coefficient corresponding to each trainee;

[0029] Step 5: Calculate the training duration corresponding to each trainee according to the proficiency coefficient corresponding to each trainee and the instruction similarity of the training instructions;

[0030] Step 6: According to the training duration corresponding to each trainee, adjust the training duration of each trainee for the training instructions accordingly.

[0031] Beneficial effects of the present invention:

[0032] The present invention digitally converts and analyzes the old version of the operation process and the new version of the operation process corresponding to the training instruction respectively, thereby obtaining the instruction similarity corresponding to the training instruction, combines and analyzes the proficiency coefficient corresponding to each trainee with the instruction similarity corresponding to the training instruction, thereby obtaining the training time corresponding to the training instruction of each trainee, and makes corresponding adjustments to the training time corresponding to the training instruction of each trainee according to the training time corresponding to each trainee, so as to reasonably arrange the training time of each trainee according to the proficiency coefficient of the trainee and the instruction similarity, avoid unnecessary repeated training or excessive training time, reduce the waste of training time, save training time and training resources, and improve the training efficiency of each trainee. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0035] Figure 2 It is a schematic diagram of the structure of the method of use of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1

[0038] See also Figure 1 As shown, the present invention is a real-time urban rail transit system based on cloud computing, including a data collection module, an instruction similarity coefficient generation module, a work data analysis module, a training duration acquisition module and a duration adjustment module:

[0039] The data collection module is used to collect the historical operation data of the training instructions of the trainees, and to obtain the working hours of the trainees. The historical operation data includes the number of operations and operation time of the trainees on the training instructions. The working time refers to the working time of the trainees engaged in rail transit. The number of operations refers to the number of operations corresponding to the time from the first operation of the trainees on the training instructions to the current time of obtaining the data. The operation time refers to the length of time corresponding to the time from the first operation of the trainees on the training instructions to the current time of obtaining the data. The historical operation data can be collected by recording the operation conditions of the trainees in actual work, and the working time can be obtained by querying the personnel files of the trainees or letting the trainees fill in the information by themselves. It should be noted that the training instructions refer to the corresponding instructions that the trainees need to train, such as opening the door, starting the train and adjusting the speed.

[0040] An instruction similarity coefficient generation module is used to digitally convert the old version operation process and the new version operation process corresponding to the practical training instruction respectively, thereby forming an old version instruction set and a new version instruction set, and then perform similarity analysis on the old version instruction set and the new version instruction set to obtain the instruction similarity corresponding to the practical training instruction;

[0041] The instruction similarity coefficient generation module includes an instruction conversion unit and a similarity analysis unit. The instruction conversion unit is used to digitally convert the old version operation process and the new version operation process corresponding to the practical training instruction respectively, thereby forming an old version instruction set and a new version instruction set. The specific method of digitally converting the old version operation process and the new version operation process of the practical training instruction is as follows:

[0042] S1: Representing each operation instruction in the old version of the operation flow in sequence by numbers, where each number represents a corresponding operation instruction, and sorting the numbers corresponding to each operation instruction according to the order corresponding to each operation instruction in the old version of the operation flow, thereby obtaining an old version instruction set corresponding to the old version of the operation flow;

[0043] For example, the old version operation flow contains multiple operation instructions, and the multiple operation instructions are represented in sequence by numbers as 1, 2, 3, ..., 7, where 7 refers to the number of operation instructions contained in the old version operation flow, and then the numbers corresponding to the operation instructions are sorted according to the order of the operation instructions in the old version operation flow, so that the old version instruction set 1234567 corresponding to the old version operation flow can be obtained;

[0044] S2: each operation instruction in the new version of the operation flow is represented by a number in the same manner as in S1, the operation instructions in the new version of the operation flow that are the same as those in the old version of the operation flow are represented by the same numbers as those in the old version of the operation flow, and the operation instructions in the new version of the operation flow that are different from those in the old version of the operation flow are represented in sequence by numbers different from those in the operation instructions in the old version of the operation flow, thereby obtaining a new version of the operation instruction set corresponding to the new version of the operation flow;

[0045] When the number of operation instructions included in the new version of the operation flow is equal to the number of operation instructions included in the old version of the operation flow, the same operation instructions in the new version of the operation flow as those in the old version of the operation flow are represented by the same numbers as those in the old version of the operation flow, and the different operation instructions in the new version of the operation flow are represented in sequence by numbers different from those in the operation instructions of the old version of the operation flow, thereby obtaining a new version of the operation instruction set corresponding to the new version of the operation flow;

[0046] When the number of operation instructions included in the new version of the operation flow is not equal to the number of operation instructions included in the old version of the operation flow, the same numbers as those in the old version of the operation flow are used to represent the same operation instructions in the new version of the operation flow as in the old version of the operation flow, and at the same time, different numbers from those in the operation instructions of the old version of the operation flow are used to represent the different operation instructions in the new version of the operation flow in sequence, and then the old version of the operation instruction set and the new version of the operation instruction set are aligned from the beginning, and the tail of the shorter operation instruction set is padded with 0 until the length of the old version of the operation instruction set is consistent with that of the new version of the operation instruction set, thereby obtaining the old version of the operation instruction set and the new version of the operation instruction set;

[0047] For example, a new version of the operation flow contains multiple operation instructions, and when the multiple operation instructions are represented in sequence by numbers, when the number of operation instructions contained in the new version of the operation flow is equal to the number of operation instructions contained in the old version of the operation flow, and the second and fifth operation instructions of the new version of the operation flow are different from the operation instructions in the old version of the operation flow, and the first operation instruction is the same as the last operation instruction in the old version of the operation flow, the old version of the instruction set is 1234567, and the new version of the operation instruction set is 7834967; when the number of operation instructions contained in the new version of the operation flow is not equal to the number of operation instructions contained in the old version of the operation flow, for example, the old version of the operation instruction set is 1234567, and the new version of the operation instruction set is 78349, then the tail of the new version of the operation instruction set is padded with 0, and the padded new version of the operation instruction set is 7834900;

[0048] The similarity analysis unit is used to perform similarity analysis on the old version instruction set and the new version instruction set, and then obtain the instruction similarity corresponding to the training instruction. The specific method of obtaining the instruction similarity is:

[0049] Compare the numbers in the same position of the old version operation instruction set and the new version operation instruction set in order from front to back, obtain the number of the same numbers in the same position in the two and mark it as T, and then mark the ratio of T to the sum of the numbers included in the old version operation instruction set and the new version operation instruction set as the instruction similarity coefficient X, that is, T / Z=X, where Z is the sum of the numbers included in the old version operation instruction set and the new version operation instruction set;

[0050] The work data analysis module is used to obtain and analyze the working hours corresponding to each trainee and the historical operation data corresponding to the training instructions of the trainee, and then obtain the proficiency coefficient corresponding to each trainee. The specific method of obtaining the proficiency coefficient corresponding to each trainee is as follows:

[0051] The working hours corresponding to each trainee are marked as T1 i, and the number of operations and operation time of each trainee on the practical training instruction operation are marked as C1 i and Z1 i respectively. The proficiency coefficient Li corresponding to each trainee is calculated by the formula T1 i×β1+C1 i×β2+Z1 i×β3=Li, where i refers to the number of corresponding trainees, i≥1, β1, β2 and β3 are all preset proportional coefficients, and the specific values ​​are formulated by relevant personnel according to actual needs. Here, β1=0.768, β2=0.967 and β3=0.897;

[0052] The proficiency coefficient can be used to evaluate the proficiency of each trainee in the practical training instructions. The larger the value of the proficiency coefficient Li, the higher the proficiency of the corresponding trainee in the practical training instructions, and vice versa;

[0053] The training duration acquisition module is used to analyze and obtain the training duration corresponding to each trainee according to the proficiency coefficient corresponding to each trainee and the instruction similarity corresponding to the training instruction. The specific method of obtaining the training duration corresponding to each trainee is as follows:

[0054] According to the formula MT-{1-[(L i+X)×α1]}=Ki, the training time Ki corresponding to each trainee is calculated, where MT is the preset training time, α1 is the preset proportional coefficient, and the specific value is determined by relevant personnel based on experience, where α1=0.3786;

[0055] The duration adjustment module is used to obtain the training duration corresponding to each trainee, and adjust the training duration corresponding to the training instructions of each trainee according to the training duration corresponding to each trainee;

[0056] By analyzing the historical operation data of the trainees corresponding to the training instructions and the working hours of the trainees, the proficiency coefficient corresponding to each trainee is obtained. After the old version operation process and the new version operation process corresponding to the training instructions are digitally converted respectively, the old version instruction set and the new version instruction set corresponding to the training instructions are obtained. The old version instruction set and the new version instruction are compared and analyzed to obtain the instruction similarity corresponding to the training instructions. The proficiency coefficient corresponding to each trainee and the instruction similarity corresponding to the training instructions are combined and analyzed to obtain the training time corresponding to the training instructions of each trainee. The training time corresponding to the training instructions of each trainee is adjusted accordingly through the time adjustment module according to the training time corresponding to each trainee, so as to reasonably arrange the training time of each trainee according to the proficiency coefficient of the trainees and the instruction similarity, avoid unnecessary repeated training or excessive training time, reduce the waste of training time, save training time and training resources, and improve the training efficiency of each trainee.

[0057] Embodiment 2

[0058] As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes a distinguishing instruction marking module;

[0059] A distinguishing instruction marking module is used to compare and analyze the old version operation instruction set with the new version operation instruction set, obtain the positions of different numbers in the same position between the new version operation instruction set and the old version operation instruction set, obtain the corresponding operation instructions in the new version operation instruction set, mark them as distinguishing instructions and output them to the display module;

[0060] The display module is used to display the new version of the operating process and the corresponding difference instructions, which is helpful for the trainees to intuitively understand the difference between the instructions of the new version of the operating process and the old version of the operating process, and is convenient for the trainees to train on the difference instructions, thereby improving the training efficiency of the trainees.

[0061] Embodiment 3

[0062] See also Figure 2 As shown, the method for using the urban rail transit real-time system based on cloud computing includes the following steps:

[0063] Step 1: Obtain the number of training instruction operations and operation duration of the trainees and the working hours of each trainee;

[0064] Step 2: Digitally convert the old and new version operation procedures corresponding to the training instructions to generate the old and new version instruction sets;

[0065] Step 3: Perform similarity analysis on the old version instruction set and the new version instruction set, and then obtain the instruction similarity of the training instructions;

[0066] Step 4: Analyze the historical operation times and operation duration of the training instructions corresponding to the trainees, as well as the working hours of the trainees in rail transit, and then obtain the proficiency coefficient corresponding to each trainee;

[0067] Step 5: Calculate the training duration corresponding to each trainee according to the proficiency coefficient corresponding to each trainee and the instruction similarity of the training instructions;

[0068] Step 6: According to the training duration corresponding to each trainee, adjust the training duration of each trainee for the training instructions accordingly.

[0069] Embodiment 4

[0070] As the fourth embodiment of the present invention, when the present application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the first, second and third embodiments.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0072] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time urban rail transit system based on cloud computing and a method of using the system, characterized in that: include: The instruction similarity coefficient generation module digitally converts the old and new operation procedures corresponding to the training instructions to generate the old instruction set and the new instruction set, performs similarity analysis on the old instruction set and the new instruction set, and then obtains the instruction similarity of the training instructions; The work data analysis module is used to analyze the historical operation times and operation duration of the training instructions corresponding to the trainees, as well as the working hours of the trainees in rail transit work, and then obtain the proficiency coefficient corresponding to each trainee; The training duration acquisition module calculates the training duration corresponding to each trainee according to the proficiency coefficient corresponding to each trainee and the instruction similarity of the training instructions; The duration adjustment module adjusts the training duration of each trainee for the training instructions according to the training duration corresponding to each trainee.

2. The real-time urban rail transit system and method of use based on cloud computing according to claim 1 is characterized in that: The instruction similarity coefficient generation module includes an instruction conversion unit and a similarity analysis unit. The instruction conversion unit is used to digitally convert the old version operation process and the new version operation process corresponding to the practical training instructions, thereby forming an old version instruction set and a new version instruction set. The similarity analysis unit is used to perform similarity analysis on the old version instruction set and the new version instruction set, thereby obtaining the instruction similarity corresponding to the practical training instructions.

3. The real-time urban rail transit system and method of use based on cloud computing according to claim 2 is characterized in that: The specific methods for digitally converting the old and new versions of the operation procedures of the practical training instructions are as follows: S1: Representing each operation instruction in the old version of the operation flow in sequence by numbers, where each number represents a corresponding operation instruction, and sorting the numbers corresponding to each operation instruction according to the order corresponding to each operation instruction in the old version of the operation flow, thereby obtaining an old version instruction set corresponding to the old version of the operation flow; S2: Represent each operation instruction in the new version of the operation flow by numbers in the same way as in S1, represent the operation instructions in the new version of the operation flow that are the same as those in the old version of the operation flow by using the same numbers as those in the old version of the operation flow, and represent the operation instructions in the new version of the operation flow that are different from those in the old version of the operation flow by using numbers different from those in the operation instructions of the old version of the operation flow in sequence, thereby obtaining a new version of the operation instruction set corresponding to the new version of the operation flow.

4. The real-time urban rail transit system and method of use based on cloud computing according to claim 3 is characterized in that: After obtaining the new version of the operation instruction set, when the number of operation instructions contained in the new version of the operation process is equal to the number of operation instructions contained in the old version of the operation process, no processing is performed; when the number is not equal, the shorter operation instruction set is padded so that the length of the new version of the operation instruction set is equal to that of the old version of the operation instruction set.

5. The real-time urban rail transit system and method of use based on cloud computing according to claim 4 is characterized in that: The specific method of filling the shorter operation instruction set is: The old version operation instruction set and the new version operation instruction set are aligned from the beginning, and the tail of the shorter operation instruction set is filled with 0 until the length of the old version operation instruction set is consistent with that of the new version operation instruction set.

6. The real-time urban rail transit system and method of use based on cloud computing according to claim 5 is characterized in that: The specific method of obtaining instruction similarity is: The numbers in the same position of the old version operation instruction set and the new version operation instruction set are compared in order from front to back, and the number of the same numbers in the same position in the two is obtained and the ratio between it and the sum of the numbers included in the old version operation instruction set and the new version operation instruction set is marked as the instruction similarity coefficient X.

7. The real-time urban rail transit system and method of use based on cloud computing according to claim 6 is characterized in that: The specific method of obtaining the proficiency coefficient corresponding to each trainee is as follows: The sum of the products of each trainee’s working hours, historical number of training instruction operations and operation time and their corresponding preset proportional coefficients is marked as the proficiency coefficient Li of each trainee, where i represents the corresponding number of trainees.

8. The real-time urban rail transit system and method of use based on cloud computing according to claim 7 is characterized in that: The specific method to obtain the training duration corresponding to each trainee is as follows: According to the formula MT-{1-[(Li+X)×α1]}=Ki, the training time Ki corresponding to each trainee is calculated, where MT is the preset training time, α1 is the preset proportional coefficient, and here α1=0.3786.

9. A method for using a real-time urban rail transit system based on cloud computing, characterized in that: The following steps are involved: Step 1: Obtain the number of training instruction operations and operation duration of the trainees and the working hours of each trainee; Step 2: Digitally convert the old and new operating procedures corresponding to the training instructions to generate old and new instruction sets; Step 3: Perform similarity analysis on the old version instruction set and the new version instruction set, and then obtain the instruction similarity of the training instructions; Step 4: Analyze the historical operation times and operation duration of the training instructions corresponding to the trainees, as well as the working hours of the trainees in rail transit, and then obtain the proficiency coefficient corresponding to each trainee; Step 5: Calculate the training duration corresponding to each trainee according to the proficiency coefficient corresponding to each trainee and the instruction similarity of the training instructions; Step 6: According to the training duration corresponding to each trainee, adjust the training duration of each trainee for the training instructions accordingly.

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