CN116052503A - Virtual simulation training method and system for medium-low nuclear discharge waste liquid cement curing production line - Google Patents
Virtual simulation training method and system for medium-low nuclear discharge waste liquid cement curing production line Download PDFInfo
- Publication number
- CN116052503A CN116052503A CN202310198240.6A CN202310198240A CN116052503A CN 116052503 A CN116052503 A CN 116052503A CN 202310198240 A CN202310198240 A CN 202310198240A CN 116052503 A CN116052503 A CN 116052503A
- Authority
- CN
- China
- Prior art keywords
- training
- sequences
- personnel
- waste liquid
- production line
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012549 training Methods 0.000 title claims abstract description 219
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 39
- 239000004568 cement Substances 0.000 title claims abstract description 34
- 239000007788 liquid Substances 0.000 title claims abstract description 32
- 239000002699 waste material Substances 0.000 title claims abstract description 28
- 238000012937 correction Methods 0.000 claims abstract description 30
- 230000009471 action Effects 0.000 claims abstract description 23
- 238000012163 sequencing technique Methods 0.000 claims abstract description 13
- 230000008569 process Effects 0.000 claims description 14
- 230000008439 repair process Effects 0.000 claims description 10
- 238000000354 decomposition reaction Methods 0.000 claims description 8
- 239000013598 vector Substances 0.000 claims description 8
- 230000002194 synthesizing effect Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 5
- 230000005855 radiation Effects 0.000 claims description 4
- 230000001960 triggered effect Effects 0.000 claims description 4
- 239000002925 low-level radioactive waste Substances 0.000 claims description 3
- 239000000463 material Substances 0.000 claims description 3
- 238000007711 solidification Methods 0.000 claims 1
- 230000008023 solidification Effects 0.000 claims 1
- 230000002650 habitual effect Effects 0.000 abstract description 9
- 238000004088 simulation Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 5
- 238000003860 storage Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000004308 accommodation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 206010052804 Drug tolerance Diseases 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000004397 blinking Effects 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000026781 habituation Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000002285 radioactive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B9/00—Simulators for teaching or training purposes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
Abstract
The invention discloses a method and a system for virtual simulation training of a medium-low nuclear discharge waste liquid cement curing production line, which relate to the technical field of computer virtual simulation and comprise the steps of obtaining examination data of training staff in various training projects; classifying the assessment data according to the relation compactness among training projects; sequencing the assessment data in each class to generate a plurality of groups of first sequences, and comparing the similarity of each group of first sequences with the corresponding standard sequences to obtain a comparison result; correcting according to the comparison result and referring to the historical data of the current training personnel to obtain a correction score; and combining the correction scores corresponding to the training items of the training personnel, and outputting the final scores as training results of the training personnel. According to the method and the system, the training items are classified in a tightness mode and the historical data of the training personnel are referred to for correction, so that the finally calculated training result can be considered in terms of similarity and habitual aspects of the assessment actions of the training personnel.
Description
Technical Field
The invention relates to the technical field of computer virtual simulation, in particular to a method and a system for virtual simulation training of a medium-low nuclear discharge waste liquid cement curing production line.
Background
At present, more virtual simulation systems, especially virtual simulation systems aiming at personnel skill training, such as driving simulation systems, climbing simulation systems, building simulation systems and the like, are developed through virtual reality technology. The simulation system enables the user to perform simulation experience and perform specific operation in the virtual environment, so that the experience purpose is achieved. The training assessment is generally to judge whether the assessment of the link is passed or not by continuously collecting the action node time and action accuracy of the user, and the training assessment is relatively suitable for simulating the virtual assessment situation of a general environment.
For the environment with radioactivity possibility, if the virtual simulation is to check by simply collecting the action node time and action accuracy of the user, the check result is easy to be distorted, for example, the operation time of the operator can be greatly compressed on the premise that the operator can bear limited irradiation, so that the check action of the operator can be similar and habitual, especially between similar check links, if the check is carried out according to the unified check standard, the check result is inevitably distorted, and the check system with the general standard is not suitable for the simulation training and evaluation operation of the operator.
In view of this, the present application is specifically proposed.
Disclosure of Invention
The invention aims to provide a virtual simulation training method and a virtual simulation training system for a middle-low nuclear waste liquid cement curing production line.
Embodiments of the present invention are implemented as follows:
in a first aspect, a method for virtual simulation training of a cement curing production line of medium-low nuclear discharge waste liquid comprises the following steps: acquiring assessment data of training staff in various training projects, wherein the training projects refer to projects involved in the comprehensive operation skill virtual simulation training of the production staff of the cement curing production line for completing the medium-low nuclear discharge waste liquid; classifying the assessment data according to the relation compactness among training projects; sequencing the assessment data in each category to generate a plurality of groups of first sequences, and comparing the similarity of each group of first sequences with the corresponding standard sequences to obtain a comparison result, wherein the standard sequences refer to operation sequences which are pre-configured according to the operation process of the corresponding training items; correcting according to the comparison result and referring to the historical data of the current training personnel to obtain a correction score; and combining the correction scores corresponding to the training items of the training personnel, and outputting the final scores as training results of the training personnel.
In an alternative embodiment, the standard sequence is determined as follows: collecting operation nodes which need to be completed for each type of training project, and logically judging each operation node; if the operation nodes with the specific logic sequence are combined into a multi-node group, otherwise, the operation nodes without the specific logic sequence are taken as a single-node group; and randomly sequencing and combining the multi-node group and the single-node group, and taking the sequencing and combining result as a standard sequence.
In an alternative embodiment, the similarity alignment of each set of first sequences with the corresponding standard sequences comprises the steps of: determining the ordering point positions of all the check data nodes in the first sequence, determining the ordering point positions of all the operation nodes in the standard sequence, and sequentially comparing all the ordering point positions in the first sequence with all the ordering point positions in the standard sequence according to the same principle of point position attribute, wherein an obtained sequence comparison result is used as a basis for calculating the comparison result.
In an alternative embodiment, the similarity alignment of each set of first sequences with the corresponding standard sequences further comprises the steps of: and matching all the sorting points in the first sequence with all the sorting points in the standard sequence according to the point attribute similarity principle, and taking the matching result as a correction coefficient of the sequence comparison result to calculate the comparison result.
In an alternative embodiment, categorizing the assessment data according to the affinity between training programs includes the steps of: in each training project, display information fed back by wearing equipment by training staff is collected; counting each form of display information appearing in each training program; performing pairwise comparison on all training items, and if the similarity of the counting results of various display information of the two training items exceeds a preset threshold value, classifying the two training items into one type, wherein the classified training items are not subjected to pairwise comparison with the rest training items; wherein, the display information includes: reversing, rotating, changing color, stretching, moving pictures, changing light and shadow, changing materials or generating special effects.
In an alternative embodiment, when the display information occurs, action information of the current training personnel for control is used as check data.
In an alternative embodiment, the on-display information is used as a basis for generating standard sequence nodes.
In an alternative embodiment, the correcting the historical data of the current training personnel according to the comparison result comprises the following steps: acquiring historical training assessment data of at least one group of training personnel; analyzing the operation characteristics of the training personnel according to the historical training assessment data; repairing the operating characteristic; when the display information can be triggered in a standard way through the repaired operation, the repair factors in the process are obtained; the repair factor is used as the basis for correction calculation.
In an alternative embodiment, repairing the operational characteristic includes the steps of: decomposing the operation characteristic to obtain at least two decomposition vectors, and repairing and synthesizing the at least two decomposition vectors, wherein the repairing and synthesizing direction is determined according to the display information distribution characteristics of the training items and the background environment of the training items, and the background environment comprises radiation, high temperature or noise factors.
In a second aspect, a virtual simulation training system for a medium-low nuclear waste liquid cement curing production line includes: the collecting module is used for acquiring the examination data of the training personnel in various training projects, wherein the training projects refer to projects involved in the comprehensive operation skill virtual simulation training of the production personnel of the finished medium-low-level waste liquid cement curing production line; the classification module is used for classifying the examination data according to the link compactness among the training projects; the comparison module is used for sequencing the assessment data in each category to generate a plurality of groups of first sequences, and carrying out similarity comparison on each group of first sequences and corresponding standard sequences to obtain comparison results, wherein the standard sequences are operation sequences which are pre-configured according to the operation process of corresponding training projects; the correction module is used for correcting according to the comparison result and referring to the historical data of the current training personnel to obtain a correction score; the output module is used for combining the correction scores corresponding to the training items of the training personnel and outputting the final scores as training results of the training personnel.
The embodiment of the invention has the beneficial effects that:
according to the virtual simulation training method and system for the medium-low level waste liquid cement curing production line, the assessment data in each project of the training staff are obtained, the training projects closely related to the training projects are classified into one type for comprehensive assessment, the method and system can be suitable for the situation that the training staff make comprehensive assessment on the premise of time reduction on similarity actions, and after preliminary comparison, the training staff are corrected by referring to historical data of the training staff so as to adapt to habitual actions of the training staff in assessment, so that compared with the traditional unified assessment list standard, particularly in a mode of singly assessing each training project, the training assessment method and system are more suitable for training assessment of real scenes, the situation that on the premise that the safety of personnel under irradiation is first is fully considered, the appropriateness of the assessment action standard is reduced, the results obtained by restoring the real assessment scenes are more convenient, and especially for the personnel with multiple simulation references, the result distortion is smaller.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the main steps of a training method provided by an embodiment of the present invention;
FIG. 2 is a flow chart of sub-steps of one step S200 of the main steps shown in FIG. 1;
FIG. 3 is a flow chart of sub-steps of one step S300 of the main steps shown in FIG. 1;
FIG. 4 is a flow chart of sub-steps of one step S400 of the main steps shown in FIG. 1;
FIG. 5 is an exemplary block diagram of a training system provided by an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is to be understood that the terms "system," "apparatus," and/or "module" as used herein are intended to be one way of distinguishing between different components, elements, parts, portions, or assemblies of different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used herein and in the claims, the terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly dictates otherwise. Generally, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in the present invention to describe the operations performed by the system according to embodiments of the present application. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Examples
Because the prior virtual simulation system adopts a unified standard for judging each assessment link, assessment personnel often show more skill and speed for assessment actions on the premise of time urgency, for example, actions in similar training projects show consistency, actions in specific training projects show habituation and the like. If the same single standard is adopted for identification to output the assessment result, the result distortion is easy to occur, namely the simulation situation often has larger deviation for the operation under the real situation, namely the assessment action of the training personnel through training the assessment is not high in consistency with the assessment action in the actual operation in the simulation, especially in the irradiation environment, the operation time is limited by the upper limit of the irradiation bearing of the human body, so that the addition of a plurality of special assessment methods suitable for the situations is helpful for obtaining the result which is closer to the real result. Therefore, the embodiment provides a virtual simulation training method for the cement curing production line of the medium-low nuclear discharge waste liquid.
Referring to fig. 1, the method for virtual simulation training of a cement curing production line for low-and-medium nuclear waste liquid provided by the embodiment includes the following steps:
s100: acquiring assessment data of training staff in various training projects, wherein the training projects refer to projects involved in the comprehensive operation skill virtual simulation training of the production staff of the cement curing production line for completing the medium-low nuclear discharge waste liquid; after the training personnel carry out simulation training, various examination data are generated in the training system, wherein the examination data are operation data corresponding to training items in comprehensive operation skill training of the production personnel of the medium-low nuclear waste liquid cement curing production line, such as empty bucket receiving, feed liquid system, cement system, curing bucket system, residual liquid cement curing, curing body transferring, cement curing line stopping, auxiliary system and the like.
The above training items are introduced into the system and modeled in advance to wait for the training personnel to operate through the connection of the wearable equipment, a great amount of interaction information is generated in the operation process, and the interaction information is used as check data to enter step S200: classifying the assessment data according to the relation compactness among the training projects; the step represents classifying similar training projects, such as engineering truck start and stop projects, production system start and stop projects and the like, and the assessment mode is scientific by classifying assessment data generated in a plurality of training projects in the class and then uniformly judging by adopting comprehensive assessment standards, so that the method can be suitable for carrying out actual assessment on similarity operations generated in different equipment operation processes in a limited operation time under a radioactive environment, and compared with the mode of independent assessment according to different standards, the method can be more close to reality, thereby avoiding great difference between simulation assessment and actual operation results.
In this embodiment, according to the rule of the relationship closeness, for example, the similar types and the similar amounts are presented according to the checking actions, specifically referring to fig. 2, classifying the checking data according to the relationship closeness between training items includes the following steps:
s210: in each training project, training staff wears display information fed back by equipment; the step of extracting the checking action can be realized through feedback display information of the wearable device, wherein the display information comprises the following steps: reversing, rotating, changing color, stretching, animation, light and shadow change, material change or special effect occur, namely, the action of the training personnel in the virtual scene can be fed back on the state and position change of the wearing equipment, namely, the operation information of the training personnel at the moment can be judged by capturing the display information.
S220: counting each form of display information appearing in each training program; this step represents that all display information appearing in one training program is counted, for example, 5 reversals, 2 rotations, 3 retractions, 1 animation, etc., and represents the actual operation of the training staff in the training program, and then step S230 is performed: performing pairwise comparison on all training items, and if the similarity of the counting results of various display information of the two training items exceeds a preset threshold value, classifying the two training items into one type, wherein the classified training items are not subjected to pairwise comparison with the rest training items; the step of comparing the information of the actual project assessment of the training personnel, finding the similarity project operated by the training personnel to classify, and if the similarity exceeds a preset standard, the classifying is used as a class, and the classifying is not performed any more, so that the classifying is only performed once. When the display information occurs, the action information of the current training personnel for operation is used as the examination data, so that the convenience and accuracy of data extraction can be achieved.
S300: and ordering the assessment data in each category to generate a plurality of groups of first sequences, namely, how many categories have the first sequences. Performing similarity comparison on each group of first sequences and corresponding standard sequences to obtain comparison results, wherein the standard sequences are operation sequences which are pre-configured according to the operation process of corresponding training projects; the step shows that the assessment data is subjected to preliminary assessment, and the standard of the assessment is a standard sequence configured by the data which is input in advance, so that the matching degree relationship between the assessment data and the standard sequence is judged, the higher the matching degree is, the better the training assessment effect is proved, and the worse the training assessment effect is otherwise. Of course, in some embodiments, the display information may also be used as a basis for generating the standard sequence node, that is, the standard sequence may use various display information as a node basis constructed by itself, and the final purpose of node ordering according to logic is achieved according to the ordering rule of the features.
Specifically, the standard sequence is determined as follows: collecting operation nodes which need to be completed for each type of training project, and logically judging each operation node; if the operation nodes with the specific logic sequence are combined into a multi-node group, otherwise, the operation nodes without the specific logic sequence are taken as a single-node group; and randomly sequencing and combining the multi-node group and the single-node group, and taking the sequencing and combining result as the standard sequence. Through the steps, the standard sequence can be constructed based on the node attribute and the node arrangement sequence of each training project, wherein the combination of the operation nodes with the specific logic sequence exists as a multi-node group, which means that the two nodes are required to be completed according to the sequence, the operation nodes are not limited by the specific logic sequence, the operation nodes are also required to be completed according to the sequence, the standard sequence is obtained after all the groups are randomly ordered, and the standard sequence has a plurality of possible forms.
Referring to fig. 3, the similarity comparison between each group of the first sequences and the corresponding standard sequences includes the following steps:
s310: determining the ordering point positions of all check data nodes in the first sequence; s320: and determining the ordering point positions of all the operation nodes in the standard sequence. The two steps indicate that the positions of the nodes of the first sequence and the positions of the nodes of the standard sequence need to be determined. Then, step S330 is performed: and sequentially comparing all the sequencing points in the first sequence with all the sequencing points in the standard sequence according to the principle of identical point position attributes, wherein the principle of identical point position attributes indicates that operation attributes represented by two nodes are identical, for example, display information fed back to the wearable device is identical. According to the determined sequence, a comparison is performed, and it should be noted that, because there are multiple possibilities of the standard sequences, when the comparison is performed, the first sequence needs to be compared with all the standard sequences one by one, and a standard sequence with the highest similarity is found as a comparison basis, where the highest similarity refers to the fact that the point location attributes are the same and the most. S340: the obtained sequence comparison result is used as a basis for calculating the comparison result, and the basis of the comparison result participates in the subsequent evaluation basis for the round of examination of the training staff.
Based on the above technical solution, considering that the comparison result may be too strict due to a bit of error between data comparison according to the same comparison of the attributes, so that the similarity determination result tends to be strict and the acceptance is not strong, in this embodiment, performing similarity comparison between each group of the first sequences and the corresponding standard sequences further includes the following step S350: and matching all the sorting points in the first sequence with all the sorting points in the standard sequence according to the principle of similar point attributes, and calculating the comparison result by taking the matching result as a correction coefficient of the sequential comparison result. The step shows that the comparison standard is properly relaxed, namely the principle that the point location attribute is similar is lower than the comparison standard of the principle that the point location attribute is the same, for example, repeated operation is more than or equal to three units of dislocation between corresponding nodes, so that the final comparison result has stronger accommodation, and misoperation conditions which do not influence the final result possibly exist under the premise of time urgency.
Through the above technical solution, adaptability and accommodation are considered from the aspect of similarity of examination actions of the training staff, but for the examination staff who do not pass the examination of multiple times of participation in training or the training staff with great working age, habitual actions are very likely to exist, and these habitual actions do not affect the final operation result, and need to be considered in the training system more closely to the actual situation, namely step S400 is performed: correcting according to the comparison result and referring to the historical data of the current training personnel to obtain a correction score; the step represents that the historical data of the training personnel participating in training before is called, whether the habitual action of the training personnel has a substantial influence on the final comparison result is judged based on the characteristics displayed in the historical data, and the purpose of evaluating the training personnel more close to the actual situation is achieved by carrying out the necessary grading correction.
In this embodiment, referring to fig. 4, the correction according to the comparison result by referring to the history data of the current training personnel includes the following steps:
s410: acquiring historical training assessment data of at least one group of training personnel; the step shows that the historical data of the training personnel need to be called, if a plurality of groups can be called for analysis, at least one group is called in consideration of the calculation load of data analysis, and if the training personnel do not participate in the examination before, the examination result of the time is used as the historical data. Then, step S420 is performed: analyzing the operation characteristics of the training personnel according to the historical training assessment data; this step represents finding out the operational characteristics of the training person, i.e. habitual actions, by examining the data. The analysis of the habitual operation may be performed by means of machine learning, but it is necessary to determine the habitual operation without substantially affecting the overall operation result.
In this embodiment, for example, the operation characteristic is decomposed to obtain at least two decomposition vectors, that is, one operation decomposes into multiple dimensions, and the dimensions may be fed back through the wearable device, such as direction, color, displacement, blinking time, and the like. At least two decomposition vectors are patched, i.e. according to the operational characteristics that should be present, i.e. according to the absence of the corresponding dimension. The repair synthesis direction is determined according to the display information distribution characteristics of the training items and the background environments of the training items, wherein the background environments comprise radiation, high temperature or noise factors, and the purpose is that the actual operation is influenced due to the fact that the backgrounds of the non-communication training items are different under the irradiation environment, and the auxiliary consideration is carried out through the backgrounds while the display information distribution characteristics (such as rotation and reversing are mainly distributed) of the training items are considered, so that the purpose of truly restoring the operation characteristics is achieved.
S430: repairing the operating characteristic; when the display information can be triggered in a standard way through the repaired operation, the repair factors in the process are obtained; this step represents a comparison between the operation characteristic and the standard operation, and a difference portion in the comparison is obtained, for example, the difference portion may be one more action, one action is not standard, or the order of actions is fixed, and the like, and the difference portion is quantized into readable data, and then step S440 is performed: the repair factor is used as the basis of the correction calculation, namely, the readable repair factor is used as a weight or a value to adjust the final scoring result, so that the purpose of judging in a more similar way to a real scene is achieved.
Step S500 is performed after the final score performed by each training program is corrected: and combining the correction scores corresponding to the training items of the training personnel, and outputting the final score as the training result of the training personnel, wherein the combination is summation or integration combination, and the excessive limitation is avoided.
In this embodiment, a virtual simulation training system 600 for a low-and-medium nuclear waste liquid cement curing production line is further provided, please refer to a modularized schematic diagram of the virtual simulation training system 600 for the low-and-medium nuclear waste liquid cement curing production line in fig. 5, which is mainly used for dividing functional modules of the virtual simulation training system 600 for the low-and-medium nuclear waste liquid cement curing production line according to the embodiment of the method. For example, each functional module may be divided, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, the division of the modules in the present invention is illustrative, and is merely a logic function division, and other division manners may be implemented in practice. For example, in the case of dividing the respective functional modules with the respective functions, fig. 5 shows only a schematic diagram of a system/apparatus, wherein the medium-low nuclear waste cement curing line virtual simulation training system 600 may include a collection module 610, a classification module 620, a comparison module 630, a correction module 640, and an output module 650. The functions of the respective unit modules are explained below.
The collection module 610 is configured to obtain assessment data of a training person in various training projects, where a training project refers to a project involved in comprehensive operation skill virtual simulation training of a production person of the complete medium-low level nuclear waste liquid cement curing production line.
A classification module 620 for classifying the assessment data according to the affinity between the training programs. The classification module 620 is further configured to collect display information fed back by the training personnel wearing equipment in each training project; counting each form of display information appearing in each training program; and (3) carrying out pairwise comparison on all training items, and if the similarity of the counting results of various display information of the two training items exceeds a preset threshold value, classifying the two training items into one type, wherein the classified training items are not subjected to pairwise comparison with the rest training items.
The comparison module 630 is configured to sort the assessment data in each category to generate a plurality of groups of first sequences, and perform similarity comparison on each group of first sequences and corresponding standard sequences to obtain a comparison result, where the standard sequences are operation sequences configured in advance according to an operation process of a corresponding training project. The comparison module 630 is further configured to determine a sorting point location of each check data node in the first sequence, determine a sorting point location of each operation node in the standard sequence, and sequentially compare all sorting points in the first sequence with all sorting points in the standard sequence according to the same principle of point location attribute, where an obtained sequence comparison result is used as a basis for calculating the comparison result; and matching all the sorting points in the first sequence with all the sorting points in the standard sequence according to the principle of similar point attributes, and calculating the comparison result by taking the matching result as a correction coefficient of the sequential comparison result.
The correction module 640 is configured to correct the current training personnel according to the comparison result by referring to the historical data of the current training personnel, so as to obtain a correction score; the correction module 640 is further configured to obtain historical training assessment data of at least one group of the training personnel; analyzing the operation characteristics of the training personnel according to the historical training assessment data; repairing the operating characteristic; when the display information can be triggered in a standard way through the repaired operation, the repair factors in the process are obtained; taking the repair factor as a basis for the correction calculation; decomposing the operation characteristic to obtain at least two decomposition vectors, and repairing and synthesizing the at least two decomposition vectors, wherein the repairing and synthesizing direction is determined according to the display information distribution characteristics of the training items and the background environment of the training items, and the background environment comprises radiation, high temperature or noise factors.
And the output module 650 is used for combining the correction scores corresponding to the training items of the training personnel and outputting the final scores as the training results of the training personnel.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, optical fiber), or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to encompass such modifications and variations.
Claims (10)
1. The virtual simulation training method for the medium-low nuclear discharge waste liquid cement curing production line is characterized by comprising the following steps of:
acquiring assessment data of training staff in various training projects, wherein the training projects refer to projects involved in the comprehensive operation skill virtual simulation training of the production staff of the cement curing production line for completing the medium-low nuclear discharge waste liquid;
classifying the assessment data according to the relation compactness among the training projects;
sequencing the assessment data in each category to generate a plurality of groups of first sequences, and comparing the similarity of each group of first sequences with the corresponding standard sequences to obtain a comparison result, wherein the standard sequences are operation sequences which are pre-configured according to the operation process of the corresponding training items;
correcting according to the comparison result and referring to the historical data of the current training personnel to obtain a correction score;
and combining the correction scores corresponding to the training items of the training personnel, and outputting the final scores as training results of the training personnel.
2. The method for virtually simulating and training the cement curing production line of the medium-low nuclear waste liquid according to claim 1, wherein the standard sequence is determined as follows:
collecting operation nodes which need to be completed for each type of training project, and logically judging each operation node; if the operation nodes with the specific logic sequence are combined into a multi-node group, otherwise, the operation nodes without the specific logic sequence are taken as a single-node group; and randomly sequencing and combining the multi-node group and the single-node group, and taking the sequencing and combining result as the standard sequence.
3. The method for virtually simulating and training the cement curing production line with the medium-low nuclear discharge waste liquid according to claim 2, wherein the step of comparing the similarity between each group of the first sequences and the corresponding standard sequences comprises the following steps:
determining the ordering point positions of all the check data nodes in the first sequence, determining the ordering point positions of all the operation nodes in the standard sequence, and sequentially comparing all the ordering point positions in the first sequence with all the ordering point positions in the standard sequence according to the principle of identical point position attributes, wherein an obtained sequence comparison result is used as a basis for calculating the comparison result.
4. The method for virtually simulating training a cement curing production line with low-medium level nuclear waste liquid according to claim 3, wherein the step of comparing each group of the first sequences with the corresponding standard sequences in similarity further comprises the steps of:
and matching all the sorting points in the first sequence with all the sorting points in the standard sequence according to the principle of similar point attributes, and calculating the comparison result by taking the matching result as a correction coefficient of the sequential comparison result.
5. The method for virtually simulating training a medium-low nuclear waste liquid cement curing production line according to any one of claims 1-4, wherein the categorizing the assessment data according to the tightness of the association between the training projects comprises the steps of:
in each training project, display information fed back by wearing equipment by training staff is collected; counting each form of display information appearing in each training program; performing pairwise comparison on all training items, and if the similarity of the counting results of various display information of the two training items exceeds a preset threshold value, classifying the two training items into one type, wherein the classified training items are not subjected to pairwise comparison with the rest training items; wherein, the display information includes: reversing, rotating, changing color, stretching, moving pictures, changing light and shadow, changing materials or generating special effects.
6. The virtual simulation training method for the medium-low nuclear waste liquid cement curing production line according to claim 5, wherein when the display information occurs, action information of the current training personnel for control is used as the assessment data.
7. The method for virtually simulating training a cement curing production line with low-medium level nuclear waste liquid according to claim 6, wherein the display information is used as a basis for generating the standard sequence nodes.
8. The virtual simulation training method of the medium-low nuclear waste liquid cement curing production line according to claim 5, wherein the correcting according to the comparison result by referring to the historical data of the current training personnel comprises the following steps:
acquiring historical training assessment data of at least one group of training personnel; analyzing the operation characteristics of the training personnel according to the historical training assessment data; repairing the operating characteristic; when the display information can be triggered in a standard way through the repaired operation, the repair factors in the process are obtained; the repair factor is used as a basis for the correction calculation.
9. The method for virtually simulating training a cement curing production line with low-and medium-level nuclear waste liquid according to claim 8, wherein the repairing the operation characteristic comprises the following steps:
decomposing the operation characteristic to obtain at least two decomposition vectors, and repairing and synthesizing the at least two decomposition vectors, wherein the repairing and synthesizing direction is determined according to the display information distribution characteristics of the training items and the background environment of the training items, and the background environment comprises radiation, high temperature or noise factors.
10. The utility model provides a well low nuclear waste liquid cement solidification production line virtual simulation training system which characterized in that includes:
the collecting module is used for acquiring the examination data of the training personnel in various training projects, wherein the training projects refer to projects involved in the comprehensive operation skill virtual simulation training of the production personnel of the finished medium-low-level waste liquid cement curing production line;
the classification module is used for classifying the assessment data according to the relationship compactness among the training projects;
the comparison module is used for sequencing the assessment data in each category to generate a plurality of groups of first sequences, and carrying out similarity comparison on each group of first sequences and corresponding standard sequences to obtain comparison results, wherein the standard sequences are operation sequences which are pre-configured according to the operation process of corresponding training projects;
the correction module is used for correcting according to the comparison result and referring to the historical data of the current training personnel to obtain a correction score;
the output module is used for combining the correction scores corresponding to the training items of the training personnel and outputting the final scores as training results of the training personnel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310198240.6A CN116052503B (en) | 2023-03-03 | 2023-03-03 | Virtual simulation training method and system for medium-low nuclear discharge waste liquid cement curing production line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310198240.6A CN116052503B (en) | 2023-03-03 | 2023-03-03 | Virtual simulation training method and system for medium-low nuclear discharge waste liquid cement curing production line |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116052503A true CN116052503A (en) | 2023-05-02 |
| CN116052503B CN116052503B (en) | 2023-11-10 |
Family
ID=86120228
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310198240.6A Active CN116052503B (en) | 2023-03-03 | 2023-03-03 | Virtual simulation training method and system for medium-low nuclear discharge waste liquid cement curing production line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116052503B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070224586A1 (en) * | 2006-03-24 | 2007-09-27 | Robert K. MASSIE | Method and system for evaluating and matching educational content to a user |
| US20160225283A1 (en) * | 2015-01-29 | 2016-08-04 | Accenture Global Services Limited | Automated training and evaluation of employees |
| CN109754663A (en) * | 2017-11-02 | 2019-05-14 | 贵州电网有限责任公司培训与评价中心 | Implementation method and device are evaluated in power transformation O&M operation Whole Process Simulation training |
| CN111028581A (en) * | 2019-12-25 | 2020-04-17 | 青岛黄海学院 | Teaching training platform based on virtual reality |
| CN111709396A (en) * | 2020-07-08 | 2020-09-25 | 六盘水达安驾驶培训有限公司 | Driving skill subject two and three examination auxiliary evaluation method based on human body posture |
| CN112331004A (en) * | 2020-09-30 | 2021-02-05 | 核动力运行研究所 | Training and checking system for full operation value of nuclear power plant |
| US20210073703A1 (en) * | 2019-09-09 | 2021-03-11 | Yandex Europe Ag | Method and system for determining productivity rate of user in computer-implemented crowd-sourced environment |
| CN112489513A (en) * | 2020-11-11 | 2021-03-12 | 贵州电网有限责任公司 | System and method for virtual training of equipment in power transformation industry |
| CN215007048U (en) * | 2021-06-30 | 2021-12-03 | 郑州捷安高科股份有限公司 | Simulation principle system and device of train braking system |
| CN115222300A (en) * | 2022-09-20 | 2022-10-21 | 珠海翔翼航空技术有限公司 | Flight simulator retraining duration distribution system and method based on intelligent evaluation algorithm |
| CN115631664A (en) * | 2022-09-06 | 2023-01-20 | 郑州电力高等专科学校 | Electric power training simulation method and system |
-
2023
- 2023-03-03 CN CN202310198240.6A patent/CN116052503B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070224586A1 (en) * | 2006-03-24 | 2007-09-27 | Robert K. MASSIE | Method and system for evaluating and matching educational content to a user |
| US20160225283A1 (en) * | 2015-01-29 | 2016-08-04 | Accenture Global Services Limited | Automated training and evaluation of employees |
| CN109754663A (en) * | 2017-11-02 | 2019-05-14 | 贵州电网有限责任公司培训与评价中心 | Implementation method and device are evaluated in power transformation O&M operation Whole Process Simulation training |
| US20210073703A1 (en) * | 2019-09-09 | 2021-03-11 | Yandex Europe Ag | Method and system for determining productivity rate of user in computer-implemented crowd-sourced environment |
| CN111028581A (en) * | 2019-12-25 | 2020-04-17 | 青岛黄海学院 | Teaching training platform based on virtual reality |
| CN111709396A (en) * | 2020-07-08 | 2020-09-25 | 六盘水达安驾驶培训有限公司 | Driving skill subject two and three examination auxiliary evaluation method based on human body posture |
| CN112331004A (en) * | 2020-09-30 | 2021-02-05 | 核动力运行研究所 | Training and checking system for full operation value of nuclear power plant |
| CN112489513A (en) * | 2020-11-11 | 2021-03-12 | 贵州电网有限责任公司 | System and method for virtual training of equipment in power transformation industry |
| CN215007048U (en) * | 2021-06-30 | 2021-12-03 | 郑州捷安高科股份有限公司 | Simulation principle system and device of train braking system |
| CN115631664A (en) * | 2022-09-06 | 2023-01-20 | 郑州电力高等专科学校 | Electric power training simulation method and system |
| CN115222300A (en) * | 2022-09-20 | 2022-10-21 | 珠海翔翼航空技术有限公司 | Flight simulator retraining duration distribution system and method based on intelligent evaluation algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116052503B (en) | 2023-11-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110825644B (en) | Cross-project software defect prediction method and system | |
| CN110610193A (en) | Method and device for processing labeled data | |
| CN111339072B (en) | User behavior-based change value analysis method and device, electronic equipment and medium | |
| JP2019028834A (en) | Abnormal value diagnostic device, abnormal value diagnostic method, and program | |
| CN113544604A (en) | Assembly error correction for a flow line | |
| CN111062925A (en) | Intelligent cloth defect identification method based on deep learning | |
| CN113421192A (en) | Training method of object statistical model, and statistical method and device of target object | |
| CN111612149A (en) | Main network line state detection method, system and medium based on decision tree | |
| CN116052503B (en) | Virtual simulation training method and system for medium-low nuclear discharge waste liquid cement curing production line | |
| CN112700112A (en) | RPA flow adjusting method, device, electronic equipment and storage medium | |
| CN116166967B (en) | Data processing method, equipment and storage medium based on meta learning and residual error network | |
| CN109977030B (en) | Method and device for testing deep random forest program | |
| CN113822379B (en) | Process process anomaly analysis method and device, electronic equipment and storage medium | |
| CN113743461B (en) | Unmanned aerial vehicle cluster health degree assessment method and device | |
| CN115829925A (en) | Appearance defect detection method and device, computer equipment and storage medium | |
| CN114968816A (en) | Data simulation-based strategy testing method, device, equipment and storage medium | |
| Chernyshev et al. | Integration of building information modeling and artificial intelligence systems to create a digital twin of the construction site | |
| CN110991841B (en) | Analysis method for nonstandard behaviors in bidding process based on AI technology | |
| Tóth et al. | Adding process metrics to enhance modification complexity prediction | |
| CN113111804A (en) | Face detection method and device, electronic equipment and storage medium | |
| CN112434648A (en) | Wall shape change detection method and system | |
| US20230316718A1 (en) | Learning model generating method and inspection device | |
| CN117289667B (en) | Component size quality control system and method | |
| Chernyshev et al. | Digital Object Detection of Construction Site Based on Building Information Modeling and Artificial Intelligence Systems. | |
| CN113035299B (en) | Sub-center recommendation method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |