CN113902598A - Rail transit multiplex simulation training intelligent processing method and system - Google Patents

Rail transit multiplex simulation training intelligent processing method and system Download PDF

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CN113902598A
CN113902598A CN202111514300.8A CN202111514300A CN113902598A CN 113902598 A CN113902598 A CN 113902598A CN 202111514300 A CN202111514300 A CN 202111514300A CN 113902598 A CN113902598 A CN 113902598A
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training
course
representing
assessment
voice
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李姝欣
黄成周
杨杨
唐艳
马顺强
章磊
徐建君
曾理
方代利
杨智彬
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Chengdu Yunda Technology Co Ltd
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Abstract

The invention discloses an intelligent processing method and system for rail transit multiplex simulation training, which relates to the technical field of rail transit simulation training and has the technical scheme that: establishing a course system for multi-work scene training; establishing an assessment rule and an autonomous assessment degree according to a course system; setting virtual roles according to the number of the training personnel and the work type requirement of the scene course, and carrying out practical training on the training personnel to obtain an assessment result; separating the voice call response training results of the participants from the assessment results based on the course scene implementation and call response proportion, and analyzing the proportion of the voice response operation in the total course system to obtain a voice response operation training plan and a voice response operation expected training assessment target of the corresponding training course; and correcting the training plan and recommending a new training plan according to the recommended training index until the course system, the independent evaluation degree and the virtual role are optimized, so that the independent intellectualization of the full-professional multi-variety full-process training and examination is realized.

Description

Rail transit multiplex simulation training intelligent processing method and system
Technical Field
The invention relates to the technical field of rail transit simulation training, in particular to an intelligent processing method and system for rail transit multiple work type simulation training.
Background
The rail transit simulation training system is mainly a comprehensive training system constructed by multiple core operation types such as scheduling, stations, crew services and the like of rail transit, and can meet the independent training requirements of the core operation types and the collaborative combined drilling training requirements among the multiple types.
In the traditional rail transit training process, all the participating roles need to be online to realize multi-type implementation and call response training, the implementation part of a student can be evaluated based on rules, the call response part of the student needs to be subjectively evaluated by a teacher, and the training and evaluation efficiency is low and is not standard; in addition, the reasonability and complexity of the training course setting cannot be measured, and further the condition that the existing rail transit simulation training cannot carry out autonomous intelligent optimization training assessment on the training process of the personnel involved in training is caused.
Therefore, the research and design of an intelligent processing method and system for rail transit multiplex simulation training, which can overcome the defects, is a problem which is urgently needed to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide an intelligent processing method and system for rail transit multiple-work-type simulation training, which are used for carrying out virtual-real interaction and automatic call response training and examination by applying virtual character complement to any real character which does not participate in rail transit simulation training, so that single-work-type, full-flow and multi-professional autonomous intelligent training and examination are realized.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, an intelligent processing method for rail transit multiplex simulation training is provided, which comprises the following steps:
establishing a course system for multi-variety scene training according to the multi-variety capability model, the field device operation and the event handling process;
establishing an assessment and evaluation rule and an autonomous evaluation degree according to a course system, and performing auxiliary training on a scene course with high autonomous evaluation degree by applying a voice behavior recognition and operation guidance system;
setting virtual roles according to the number of the participants and the work type requirement of the scene course, generating a corresponding training plan and an expected training assessment target by combining the training requirements of the participants, and carrying out training on the participants to obtain assessment results;
separating the voice call response training results of the participants from the assessment results based on the course scene implementation and call response proportion, and analyzing the proportion of the voice response operation in the total course system to obtain a voice response operation training plan and a voice response operation expected training assessment target of the corresponding training course;
and comparing the voice call response training result with a call response expected training target to obtain a recommended training index of a related capability item, correcting the training plan according to the recommended training index, recommending a new training plan, and performing round-robin training on the personnel to be trained through the new training plan until the course system, the autonomous evaluation degree and the virtual role are optimized.
Further, the establishing expression of the course system is specifically as follows:
Figure 452236DEST_PATH_IMAGE001
wherein,
Figure 385688DEST_PATH_IMAGE002
representing a course system;
Figure 962163DEST_PATH_IMAGE003
representing a training specific gravity;
Figure 29476DEST_PATH_IMAGE004
representing a multi-station capability model of rail transit;
Figure 499641DEST_PATH_IMAGE005
wherein,
Figure 118841DEST_PATH_IMAGE007
to indicate rail traffic
Figure 792399DEST_PATH_IMAGE008
Item capability level one
Figure 522458DEST_PATH_IMAGE009
The training course proportion of the individual work category and the requirement
Figure 634680DEST_PATH_IMAGE010
；
Figure 300148DEST_PATH_IMAGE011
Wherein,
Figure 975849DEST_PATH_IMAGE012
to represent
Figure 509598DEST_PATH_IMAGE008
Individual capability level correspondence
Figure 393503DEST_PATH_IMAGE013
Specific gravity of the item ability and satisfy
Figure 495451DEST_PATH_IMAGE014
，
Figure 392869DEST_PATH_IMAGE015
Training specific gravity for all levels of each ability; f is an intermediate amount, and has no specific meaning;
Figure 87899DEST_PATH_IMAGE016
wherein,
Figure 403474DEST_PATH_IMAGE018
is shown as
Figure 223794DEST_PATH_IMAGE013
Item capability item first
Figure 405245DEST_PATH_IMAGE009
The capability demand degree of each work type is met
Figure 156163DEST_PATH_IMAGE019
，
Figure 450879DEST_PATH_IMAGE020
Is as follows
Figure 376853DEST_PATH_IMAGE009
All capability items of a single work category.
Further, the calculation formula established by the autonomous evaluation degree is specifically as follows:
Figure 999595DEST_PATH_IMAGE021
wherein,
Figure 413259DEST_PATH_IMAGE022
to representSelf-evaluation degree;
Figure 687114DEST_PATH_IMAGE002
representing a course system;
Figure 301767DEST_PATH_IMAGE023
express assessment and evaluation rule correspondence
Figure 270860DEST_PATH_IMAGE009
Individual work species
Figure 114313DEST_PATH_IMAGE008
Difficulty factors of individual capability levels;
Figure 258986DEST_PATH_IMAGE024
and expressing assessment and evaluation rules.
Specifically, the evaluation rules are examined
Figure 903594DEST_PATH_IMAGE025
Aiming at scene training course system
Figure 750197DEST_PATH_IMAGE026
Making a specific course, wherein
Figure 505663DEST_PATH_IMAGE027
Is shown as
Figure 973684DEST_PATH_IMAGE009
The evaluation of the individual race in the course is in proportion. The self-evaluation degree reflects the achievable degree of the event handling standard process proportion and the objective evaluation in the course, the course with high self-evaluation degree guides the participant to learn by using a voice recognition mode, the course event handling process is pushed to the participant according to the scene process, the course event handling state is updated, and the highest teaching efficiency can be achieved.
Further, the calculation formula for obtaining the assessment results is specifically as follows:
Figure 677942DEST_PATH_IMAGE028
wherein,
Figure 887206DEST_PATH_IMAGE029
showing the assessment results;
Figure 321730DEST_PATH_IMAGE022
representing the degree of autonomous evaluation;
Figure 34471DEST_PATH_IMAGE030
representing a training plan;
Figure 145515DEST_PATH_IMAGE031
representing the training proportion of the real role in the course;
Figure 451863DEST_PATH_IMAGE032
indicating the use of the trained personnel
Figure 814711DEST_PATH_IMAGE030
For the first after training plan
Figure 273637DEST_PATH_IMAGE008
Individual level of capability, the
Figure 165369DEST_PATH_IMAGE013
Training results for individual capability items.
In particular, training plans
Figure 224592DEST_PATH_IMAGE030
The expression is as follows:
Figure 515765DEST_PATH_IMAGE033
wherein,
Figure 937519DEST_PATH_IMAGE034
indicating the kind of work of the personnel involved in the training
Figure 141098DEST_PATH_IMAGE008
The individual capacity level,
Figure 169841DEST_PATH_IMAGE013
Training plan of individual ability item, and satisfy
Figure 874492DEST_PATH_IMAGE035
，
Figure 557277DEST_PATH_IMAGE036
Expressed as a training plan
Figure 56391DEST_PATH_IMAGE030
Middle level of capability
Figure 808316DEST_PATH_IMAGE008
Is complete.
Further, the calculation formula for separating the voice call response training result specifically comprises:
Figure 192024DEST_PATH_IMAGE037
wherein,
Figure 588370DEST_PATH_IMAGE038
representing a voice call response training result;
Figure 150063DEST_PATH_IMAGE022
representing the degree of autonomous evaluation;
Figure 405595DEST_PATH_IMAGE039
representing the proportion of voice response operation in the total course system;
Figure 452049DEST_PATH_IMAGE029
showing the assessment results;
Figure 93115DEST_PATH_IMAGE040
representing a participantAfter training, the member is right
Figure 934032DEST_PATH_IMAGE008
Individual level of capability, the
Figure 411280DEST_PATH_IMAGE013
Training results of voice response operations of individual capability items.
In particular, the proportion of voice response operations in the overall course hierarchy
Figure 884594DEST_PATH_IMAGE039
The expression of (a) is as follows:
Figure 255532DEST_PATH_IMAGE041
wherein,
Figure 142717DEST_PATH_IMAGE042
to represent
Figure 231896DEST_PATH_IMAGE008
The individual capacity level,
Figure 10365DEST_PATH_IMAGE013
The voice response part in the course of each ability item accounts for the proportion of the total course and needs to satisfy
Figure 376755DEST_PATH_IMAGE043
The capability item expressed as each hierarchy is composed of an actual operation part in addition to the voice response;
Figure 716732DEST_PATH_IMAGE044
representing the transpose of the matrix.
Further, the calculation formula obtained by the voice response operation training plan is specifically as follows:
Figure 434152DEST_PATH_IMAGE045
wherein,
Figure 360520DEST_PATH_IMAGE046
representing a voice response operation training plan;
Figure 830684DEST_PATH_IMAGE022
representing the degree of autonomous evaluation;
Figure 449884DEST_PATH_IMAGE039
representing the proportion of voice response operation in the total course system;
Figure 123442DEST_PATH_IMAGE030
representing a training plan;
Figure 853501DEST_PATH_IMAGE047
to represent
Figure 676707DEST_PATH_IMAGE008
The individual capacity level,
Figure 342175DEST_PATH_IMAGE013
Voice call answering part of a training program in a course of individual capacity points and satisfaction
Figure 893242DEST_PATH_IMAGE048
，
Figure 286046DEST_PATH_IMAGE049
Representing the level of competency in a voice response operation training plan
Figure 340589DEST_PATH_IMAGE050
The voice response part capability item of (1) is complete.
Further, the calculation formula obtained by the expected training assessment target of the voice response operation is specifically as follows:
Figure 442538DEST_PATH_IMAGE051
wherein,
Figure 575841DEST_PATH_IMAGE052
representing expected training assessment targets of voice response operation;
Figure 913281DEST_PATH_IMAGE022
representing the degree of autonomous evaluation;
Figure 228856DEST_PATH_IMAGE039
representing the proportion of voice response operation in the total course system;
Figure 219815DEST_PATH_IMAGE053
representing expected training assessment targets;
Figure 479895DEST_PATH_IMAGE054
to represent
Figure 965234DEST_PATH_IMAGE008
The individual capacity level,
Figure 259949DEST_PATH_IMAGE013
The voice call answering portion of the curriculum of individual capability items trains the target.
Specifically, the expression of the expected training assessment objective is as follows:
Figure 920344DEST_PATH_IMAGE055
wherein,
Figure 667720DEST_PATH_IMAGE056
is as follows
Figure 222330DEST_PATH_IMAGE008
At the level of individual capability
Figure 230606DEST_PATH_IMAGE013
Expected training goals for training planning of individual capacity points.
Further, the recommended training index of the related ability item is determined according to the similarity between the voice call response training result and the voice response operation expected training assessment target.
Further, a similarity calculation formula between the voice call response training result and the voice response operation expected training assessment target is specifically as follows:
Figure 704312DEST_PATH_IMAGE057
wherein,
Figure 814351DEST_PATH_IMAGE058
、
Figure 766126DEST_PATH_IMAGE059
respectively representing the actual voice operation training result and the expected voice operation training target of the personnel participating in the same course;
Figure 395953DEST_PATH_IMAGE060
representing a training result sequence number;
Figure 915927DEST_PATH_IMAGE061
and representing the total number of the training results of each level and each training item point.
In a second aspect, an intelligent processing system for rail transit multiplex simulation training is provided, which includes:
the system establishing module is used for establishing a course system of multi-work scene training according to the multi-work capability model, the field device operation and the event handling process;
the evaluation establishing module is used for establishing an evaluation rule and an autonomous evaluation degree according to a course system and performing auxiliary training on a scene course with high autonomous evaluation degree by applying a voice behavior recognition and operation guidance system;
the role supplementing module is used for setting virtual roles according to the number of the participants and the work type requirements of the scene courses, generating a corresponding training plan and an expected training assessment target by combining the training requirements of the participants, and carrying out training on the participants to obtain assessment results;
the result analysis module is used for separating the voice call response training results of the participants from the assessment results based on the course scene implementation and call response proportion, and analyzing the proportion of the voice response operation in the total course system to obtain the voice response operation training plan and the voice response operation expected training assessment target of the corresponding training course;
and the correction optimization module is used for comparing the voice call response training result with a call response expected training target to obtain a recommended training index of a related capability item, correcting the training plan according to the recommended training index, recommending a new training plan, and performing round-robin training on the training personnel through the new training plan until the course system, the autonomous evaluation degree and the virtual role are optimized.
Compared with the prior art, the invention has the following beneficial effects:
1. aiming at the low autonomy of scene process propulsion of the traditional training course, the intelligent processing method for rail transit multiple work type simulation training provided by the invention helps to consistently finish the course training process and improve the autonomy of examination training on one hand by constructing the virtual role in the training course system; on the other hand, the virtual roles are used for voice behavior recognition, so that the subjectivity of human factors for judging voice call response operation events is avoided, and the intelligence of assessment and evaluation is improved;
2. aiming at the characteristics of multiple types of rail transit, based on a multiple-type scene course training system, the virtual role is used for replacing other types of work which are not trained, and the virtual role is matched with the real role in voice call response and event triggering operation, so that the training staff can better master the skill required by training the types of work; according to the work types and the levels of plan training of the personnel involved in training, the virtual roles can be correspondingly changed and adjusted, the coordination and cooperation among the work types are improved, and the course difficulty degrees of different capability levels are rationalized;
3. aiming at the voice response operation of the participants, the course training system, the assessment and evaluation target and the assessment and evaluation result of the participants are separated into two parts, namely event triggering operation and voice response operation, based on the voice behavior recognition method, so that the intellectualization degree of the assessment and evaluation of the voice response operation part is improved, and the necessity of a teacher in the course process is reduced; and according to the target and result of the part of the participating personnel, adjusting a course training system and realizing a closed loop optimization process of the multi-class training process of the voice behavior recognition rail transit.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart in an embodiment of the invention;
fig. 2 is a block diagram of a system in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1: an intelligent processing method for rail transit multiplex simulation training is shown in fig. 1 and is specifically realized by the following steps.
A rail transit multi-station autonomous intelligent assessment and evaluation method based on voice behavior recognition is used for training high-speed railway vehicle service stations, such as a traffic dispatcher station.
The high-speed railway has eight types of 'car, machine and electric vehicle supply passenger' in common, each type of capability item is divided into three types of basic theory, professional knowledge and actual operation content, and the situations of the above types of capability items are simply called as
Figure 372316DEST_PATH_IMAGE062
Correspondingly constructed high-speed railway work type capability model
Figure 252417DEST_PATH_IMAGE063
The following were used:
Figure 376230DEST_PATH_IMAGE064
wherein,
Figure 67106DEST_PATH_IMAGE065
and the method represents three types of capability items of basic theory, professional knowledge and actual operation content which completely cover each work type.
The capacity training proportion of the high-speed railway with three types of elementary theory, professional knowledge and practical operation content corresponding to the initial, middle and high single capacity levels of various work types
Figure 922977DEST_PATH_IMAGE066
The following were used:
Figure 216555DEST_PATH_IMAGE067
。
a rail transit multi-station scene course system is constructed by combining field equipment operation and event handling process nodes of a high-speed railway, and eight high-speed railway major categories correspond to course systems of three capability levels, namely a first capability level, a middle capability level and a high capability level
Figure 991613DEST_PATH_IMAGE068
The method comprises the following specific steps:
Figure 978023DEST_PATH_IMAGE069
。
in course system
Figure 284371DEST_PATH_IMAGE068
Middle selection course
Figure 647219DEST_PATH_IMAGE070
Making course evaluation rules, and making eight kinds of work in course
Figure 840565DEST_PATH_IMAGE070
Ratio of (1) to (2)
Figure 138823DEST_PATH_IMAGE071
The following were used:
Figure 57100DEST_PATH_IMAGE072
。
evaluation rules
Figure 82694DEST_PATH_IMAGE073
Difficulty coefficient corresponding to 3 capability levels of eight major engineering types
Figure 770027DEST_PATH_IMAGE074
The following were used:
Figure 973606DEST_PATH_IMAGE075
。
calculating the self-evaluation degree of the course by combining a scene course system, evaluation rules and potential knowledge and capability levels of the personnel involved in training
Figure 267928DEST_PATH_IMAGE076
The following were used:
Figure 113524DEST_PATH_IMAGE078
and pushing the courses with high self-evaluation degrees to the trainees by using a voice recognition mode according to the scene flow, updating the handling state of the course events, and guiding the trainees to learn the operation of the event handling flow.
Use course
Figure 920943DEST_PATH_IMAGE070
When the driving dispatcher is trained, other related characters are replaced by the virtual characters, and the training proportion of the virtual characters in courses
Figure 544692DEST_PATH_IMAGE079
The following were used:
Figure 312928DEST_PATH_IMAGE080
。
the training plan is made according to the training requirements of the driving dispatcher, and the training requirements of the first-level driving dispatcher, the middle-level driving dispatcher and the high-level driving dispatcher on basic theories, professional knowledge and practical operation contents are covered, so that the planning plan is used for
Figure 244106DEST_PATH_IMAGE081
The following were used:
Figure 46977DEST_PATH_IMAGE082
。
making an expected training target of a driving dispatcher for an expected training target of basic theories, professional knowledge and actual operation contents of three types of capacity items of the first, middle and high-level levels after the driving dispatcher uses a training plan for training
Figure 841626DEST_PATH_IMAGE083
：
Figure 956213DEST_PATH_IMAGE084
。
Train operation dispatcher use training plan
Figure 878032DEST_PATH_IMAGE085
Training, the use of the vehicle dispatcher as a real character in the training
Figure 394464DEST_PATH_IMAGE086
Presentation, training results
Figure 124129DEST_PATH_IMAGE087
The following were used:
Figure 866957DEST_PATH_IMAGE088
。
voice response operation training target for vehicle dispatcher
Figure 841736DEST_PATH_IMAGE089
Comprises the following steps:
Figure 212674DEST_PATH_IMAGE090
。
voice response operation training result of vehicle dispatcher
Figure 365438DEST_PATH_IMAGE091
Comprises the following steps:
Figure 189037DEST_PATH_IMAGE092
。
training results of voice response operation
Figure 468971DEST_PATH_IMAGE093
Operating training target with voice response
Figure 835362DEST_PATH_IMAGE094
Similarity calculation
Figure 408294DEST_PATH_IMAGE095
Representing a training plan
Figure 984769DEST_PATH_IMAGE097
The training result is similar to the expected target value, the training effect is better by adopting the training plan, and the training plan can be continuously corrected and the recommended training plan can be given
Figure 786503DEST_PATH_IMAGE098
Round optimization itinerant training is carried out, and the similarity between an actual training result and an expected training target is improved
Figure 132034DEST_PATH_IMAGE100
And the optimal training effect is achieved.
Example 2: an intelligent processing system for rail transit multiple-work-type simulation training is shown in fig. 2 and comprises a system establishing module, an evaluation establishing module, a role supplementing module, a result analyzing module and a correction optimizing module.
The system establishing module is used for establishing a course system for multi-work scene training according to the multi-work capability model, the field device operation and the event handling process. And the evaluation establishing module is used for establishing an evaluation rule and an autonomous evaluation degree according to the course system and performing auxiliary training on the scene courses with high autonomous evaluation degree by applying the voice behavior recognition and operation guidance system. And the role supplementing module is used for setting virtual roles according to the number of the personnel participating in the scene course and the work type requirement, generating a corresponding training plan and an expected training assessment target by combining the training requirements of the personnel participating in the scene course, and carrying out practical training on the personnel participating in the scene course to obtain assessment results. And the result analysis module is used for separating the voice call response training results of the participants from the assessment results based on the course scene implementation and call response proportion, and analyzing the proportion of the voice response operation in the total course system to obtain the voice response operation training plan and the voice response operation expected training assessment target of the corresponding training course. And the correction optimization module is used for comparing the voice call response training result with a call response expected training target to obtain a recommended training index of a related capability item, correcting the training plan according to the recommended training index, recommending a new training plan, and performing round-robin training on the training personnel through the new training plan until the course system, the autonomous evaluation degree and the virtual role are optimized.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. An intelligent processing method for rail transit multiplex simulation training is characterized by comprising the following steps:
establishing a course system for multi-variety scene training according to the multi-variety capability model, the field device operation and the event handling process;
establishing an assessment and evaluation rule and an autonomous evaluation degree according to a course system, and performing auxiliary training on a scene course with high autonomous evaluation degree by applying a voice behavior recognition and operation guidance system;
setting virtual roles according to the number of the participants and the work type requirement of the scene course, generating a corresponding training plan and an expected training assessment target by combining the training requirements of the participants, and carrying out training on the participants to obtain assessment results;
separating the voice call response training results of the participants from the assessment results based on the course scene implementation and call response proportion, and analyzing the proportion of the voice response operation in the total course system to obtain a voice response operation training plan and a voice response operation expected training assessment target of the corresponding training course;
and comparing the voice call response training result with a call response expected training target to obtain a recommended training index of a related capability item, correcting the training plan according to the recommended training index, recommending a new training plan, and performing round-robin training on the personnel to be trained through the new training plan until the course system, the autonomous evaluation degree and the virtual role are optimized.
2. The rail transit multiplex simulation training intelligent processing method as claimed in claim 1, wherein the course system establishing expression specifically comprises:
Figure 286117DEST_PATH_IMAGE001
wherein,
Figure 840726DEST_PATH_IMAGE002
representing a course system;
Figure 596805DEST_PATH_IMAGE003
expressing cultureTraining specific gravity;
Figure 70512DEST_PATH_IMAGE004
representing a multi-station capability model of rail transit;
Figure 914971DEST_PATH_IMAGE005
wherein,
Figure 132326DEST_PATH_IMAGE006
to indicate rail traffic
Figure 11420DEST_PATH_IMAGE007
Item capability level one
Figure 531394DEST_PATH_IMAGE008
The training course proportion of the individual work category and the requirement
Figure 253363DEST_PATH_IMAGE009
；
Figure 884195DEST_PATH_IMAGE010
Wherein,
Figure 742430DEST_PATH_IMAGE011
to represent
Figure 698885DEST_PATH_IMAGE007
Individual capability level correspondence
Figure 252357DEST_PATH_IMAGE012
Specific gravity of the item ability and satisfy
Figure 811514DEST_PATH_IMAGE013
，
Figure 665201DEST_PATH_IMAGE014
Training specific gravity for all levels of each ability;
Figure 789627DEST_PATH_IMAGE015
wherein,
Figure 486188DEST_PATH_IMAGE016
is shown as
Figure 458823DEST_PATH_IMAGE012
Item capability item first
Figure 167016DEST_PATH_IMAGE008
The capability demand degree of each work type is met
Figure 324328DEST_PATH_IMAGE017
，
Figure 383550DEST_PATH_IMAGE018
Is as follows
Figure 691035DEST_PATH_IMAGE008
All capability items of a single work category.
3. The rail transit multiplex simulation training intelligent processing method as claimed in claim 1, wherein the calculation formula established by the autonomous evaluation degree is specifically as follows:
Figure 253735DEST_PATH_IMAGE019
wherein,
Figure 847527DEST_PATH_IMAGE020
representing the degree of autonomous evaluation;
Figure 862887DEST_PATH_IMAGE002
representing a course system;
Figure 442904DEST_PATH_IMAGE021
express assessment and evaluation rule correspondence
Figure 984744DEST_PATH_IMAGE022
Individual work species
Figure 621874DEST_PATH_IMAGE023
Difficulty factors of individual capability levels;
Figure 249165DEST_PATH_IMAGE024
and expressing assessment and evaluation rules.
4. The rail transit multiplex simulation training intelligent processing method as claimed in claim 1, wherein the calculation formula for obtaining the assessment results is specifically as follows:
Figure 632873DEST_PATH_IMAGE025
wherein,
Figure 170165DEST_PATH_IMAGE026
showing the assessment results;
Figure 105760DEST_PATH_IMAGE027
representing the degree of autonomous evaluation;
Figure 830133DEST_PATH_IMAGE028
representing a training plan;
Figure 876586DEST_PATH_IMAGE029
representing true anglesTraining proportion of color in courses;
Figure 533964DEST_PATH_IMAGE030
indicating the use of the trained personnel
Figure 250247DEST_PATH_IMAGE028
For the first after training plan
Figure 852130DEST_PATH_IMAGE023
Individual level of capability, the
Figure 312061DEST_PATH_IMAGE031
Training results for individual capability items.
5. The rail transit multiplex simulation training intelligent processing method as claimed in claim 1, wherein the calculation formula for separating the voice call response training result is specifically as follows:
Figure 683000DEST_PATH_IMAGE032
wherein,
Figure 570184DEST_PATH_IMAGE033
representing a voice call response training result;
Figure 659363DEST_PATH_IMAGE020
representing the degree of autonomous evaluation;
Figure 943493DEST_PATH_IMAGE034
representing the proportion of voice response operation in the total course system;
Figure 309883DEST_PATH_IMAGE035
showing the assessment results;
Figure 492603DEST_PATH_IMAGE036
showing the trained personnel to the second
Figure 944444DEST_PATH_IMAGE007
Individual level of capability, the
Figure 870812DEST_PATH_IMAGE012
Training results of voice response operations of individual capability items.
6. The rail transit multiplex simulation training intelligent processing method as claimed in claim 1, wherein the calculation formula obtained by the voice response operation training plan is specifically as follows:
Figure 91709DEST_PATH_IMAGE037
wherein,
Figure 586275DEST_PATH_IMAGE038
representing a voice response operation training plan;
Figure 728674DEST_PATH_IMAGE020
representing the degree of autonomous evaluation;
Figure 724312DEST_PATH_IMAGE034
representing the proportion of voice response operation in the total course system;
Figure 65295DEST_PATH_IMAGE039
representing a training plan;
Figure 399937DEST_PATH_IMAGE040
to represent
Figure 951004DEST_PATH_IMAGE007
The individual capacity level,
Figure 625699DEST_PATH_IMAGE012
Voice call answering part of a training program in a course of individual capacity points and satisfaction
Figure 352346DEST_PATH_IMAGE041
，
Figure 608622DEST_PATH_IMAGE042
Representing the level of competency in a voice response operation training plan
Figure 460034DEST_PATH_IMAGE043
The voice response part capability item of (1) is complete.
7. The rail transit multiplex simulation training intelligent processing method as claimed in claim 1, wherein the calculation formula obtained by the expected training assessment target of the voice response operation is specifically as follows:
Figure 531896DEST_PATH_IMAGE044
wherein,
Figure 113050DEST_PATH_IMAGE045
representing expected training assessment targets of voice response operation;
Figure 651478DEST_PATH_IMAGE020
representing the degree of autonomous evaluation;
Figure 459029DEST_PATH_IMAGE034
representing the proportion of voice response operation in the total course system;
Figure 3755DEST_PATH_IMAGE046
representing expected training assessment targets;
Figure 908257DEST_PATH_IMAGE047
to represent
Figure 211062DEST_PATH_IMAGE007
The individual capacity level,
Figure 833805DEST_PATH_IMAGE012
The voice call answering portion of the curriculum of individual capability items trains the target.
8. The intelligent processing method for rail transit multiplex simulation training as claimed in claim 1, wherein the recommended training index of the related ability item is determined according to the similarity between the voice call response training result and the voice response operation expected training assessment target.
9. The rail transit multiplex simulation training intelligent processing method as claimed in claim 8, wherein a similarity calculation formula between the voice call response training result and the voice response operation expected training assessment target is specifically as follows:
Figure 122835DEST_PATH_IMAGE048
wherein,
Figure DEST_PATH_IMAGE049
、
Figure 678581DEST_PATH_IMAGE050
respectively representing the actual voice operation training result and the expected voice operation training target of the personnel participating in the same course;
Figure DEST_PATH_IMAGE051
representing a training result sequence number;
Figure 824391DEST_PATH_IMAGE052
and representing the total number of the training results of each level and each training item point.
10. The utility model provides a rail transit multiplex simulation training intelligent processing system which characterized by includes:
the system establishing module is used for establishing a course system of multi-work scene training according to the multi-work capability model, the field device operation and the event handling process;
the evaluation establishing module is used for establishing an evaluation rule and an autonomous evaluation degree according to a course system and performing auxiliary training on a scene course with high autonomous evaluation degree by applying a voice behavior recognition and operation guidance system;
the role supplementing module is used for setting virtual roles according to the number of the participants and the work type requirements of the scene courses, generating a corresponding training plan and an expected training assessment target by combining the training requirements of the participants, and carrying out training on the participants to obtain assessment results;
the result analysis module is used for separating the voice call response training results of the participants from the assessment results based on the course scene implementation and call response proportion, and analyzing the proportion of the voice response operation in the total course system to obtain the voice response operation training plan and the voice response operation expected training assessment target of the corresponding training course;
and the correction optimization module is used for comparing the voice call response training result with a call response expected training target to obtain a recommended training index of a related capability item, correcting the training plan according to the recommended training index, recommending a new training plan, and performing round-robin training on the training personnel through the new training plan until the course system, the autonomous evaluation degree and the virtual role are optimized.
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