CN116629715A - Simulation training comprehensive evaluation method and system based on-the-fly event and emergency event - Google Patents
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Abstract
The invention relates to the technical field of data processing, and discloses a simulation training comprehensive evaluation method and system based on an on-the-fly event and an emergency event, which are used for improving the accuracy and efficiency in operation evaluation of students. Comprising the following steps: real-time monitoring of the on-line events is carried out on the training unit, and when the real-time monitoring result is that the on-line events exist, first operation data are collected and evaluation index data extraction is carried out to obtain a first evaluation index data set; performing index data real-time monitoring to obtain an index data set, performing state combination value analysis, determining a target state combination value, performing event type matching, and determining a target event type; collecting second operation data of a target student, extracting evaluation index data from the second operation data to obtain a second evaluation index data set, and carrying out evaluation index weight analysis to obtain an evaluation index weight set; and (3) performing evaluation matrix calculation to obtain a target evaluation matrix, performing operation evaluation analysis on a target student, and generating a student operation evaluation result.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a simulation training comprehensive evaluation method and system based on an on-the-fly event and an emergency event.
Background
The simulation training is a training mode capable of simulating working environment, working process and equipment working state, the simulation training comprehensive evaluation system can record the operation flow of a student, reasonably evaluate the operation of the student, detect training effect, timely find errors, correct the errors, analyze and solve the problems, and remarkably improve the scientificity of training.
The ship data receiving and transmitting subsystem is used as an important component of a ship communication system, bears the transmission and interaction tasks of information such as information, electricity, information data and the like between the inside and the outside of the ship, and is a foundation for implementing command, control and information transmission. With the development of modern communication technology, the types of equipment used for receiving and transmitting ship data are increasingly expanded, the model is complex, and higher requirements are put forward on the training level of ship communication personnel. In the current ship data receiving and transmitting simulation training system, the training effect evaluation elements are simple, and the whole training process cannot be effectively covered. The main aspects are as follows:
Firstly, training an operation response of an evaluation index system without considering a temporary event; secondly, lack of time-based analysis and evaluation of emergency treatment of students; thirdly, the injected temporary events and emergency events are not distinguished, and the comprehensive and detailed analysis training process cannot be achieved. The accuracy rate of evaluating the operation flow of the learner in the existing scheme is low.
Disclosure of Invention
Therefore, the embodiment of the invention provides a simulation training comprehensive evaluation method and a simulation training comprehensive evaluation system based on an on-the-fly event and an emergency, which solve the technical problems of lower accuracy and efficiency in the process of evaluating the operation flow of a learner.
The invention provides a simulation training comprehensive evaluation method based on an on-the-fly event and an emergency event, which comprises the following steps: real-time monitoring of the on-line events is carried out on the training unit, a real-time monitoring result is determined, when the real-time monitoring result is that the on-line events exist, first operation data of a target student are collected, evaluation index data extraction is carried out on the first operation data, and a first evaluation index data set corresponding to the first operation data is obtained; the method comprises the steps of monitoring index data of a training unit in real time to obtain an index data set corresponding to the training unit, wherein the index data set comprises network link state, working voltage and heartbeat message data; analyzing the state combination value of the index data set, determining a target state combination value, and performing event type matching through the target state combination value to determine a target event type; based on the target event type, collecting second operation data of a target student, and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data; performing evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set; and based on the evaluation index weight set, performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set to obtain a target evaluation matrix, and performing operation evaluation analysis on the target scholars through the target evaluation matrix to generate a scholars operation evaluation result.
In the present invention, the step of analyzing the state combination value of the index data set, determining a target state combination value, and performing event type matching through the target state combination value, and determining a target event type includes: performing first index value matching on the network link state to determine a first index value; performing second index value matching on the working voltage to determine a second index value; performing third index value matching on the heartbeat message data to determine a third index value; performing state combination value analysis on the first index value, the second index value and the third index value to determine a target state combination value; and carrying out event type matching on the target state combination value based on a preset event type mapping table, and determining a target event type.
In the present invention, the step of collecting second operation data of a target learner based on the target event type and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data includes: collecting second operation data of a target student based on the target event type; performing data segmentation on the second operation data to obtain a plurality of sub second operation data; and extracting evaluation index data from the second operation data through the plurality of sub second operation data to obtain a second evaluation index data set.
In the present invention, the step of extracting the evaluation index data from the second operation data by using a plurality of sub second operation data to obtain the second evaluation index data set includes: performing primary evaluation index extraction on the plurality of sub second operation data to obtain a primary evaluation index data set, wherein the primary evaluation index data set comprises data transceiving adaptability data, data transceiving timeliness data and data transceiving emergency timeliness data; and carrying out secondary evaluation index data extraction on the primary evaluation index data set to obtain a secondary evaluation index data set, and constructing the second evaluation index data set through the secondary evaluation index data set.
In the present invention, the step of performing an evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set includes: extracting initial weights of each evaluation index data in the first evaluation index data set and the second evaluation index data set, and determining initial weights corresponding to each evaluation index data; performing weight data adjustment on the initial weight corresponding to each piece of evaluation index data based on the target state combination value, and determining candidate weights corresponding to each piece of evaluation index data; and carrying out weight data correction on the candidate weights corresponding to each evaluation index data to obtain the evaluation index weight set.
In the present invention, the step of performing weight data correction on the candidate weights corresponding to each piece of evaluation index data to obtain the evaluation index weight set includes:
and carrying out weight data correction on candidate weight data corresponding to each piece of evaluation index data based on a preset weight data correction formula to obtain an evaluation index weight set, wherein the evaluation index data are as follows:
wherein ,is->Candidate weight data corresponding to the respective evaluation index data, +.>For the evaluation index weight set +.>Evaluation index weight corresponding to the individual evaluation index data,/->N is the number of evaluation index data, which is a positive integer.
In the present invention, the step of calculating the evaluation matrix of the first evaluation index data set and the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix, and performing operation evaluation analysis on the target student through the target evaluation matrix to generate a student operation evaluation result includes: extracting matrix row elements from the first evaluation index data set and the second evaluation index data set, and determining matrix row element data; performing matrix element extraction on the evaluation index data set, and determining matrix element data; performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set based on the matrix row element data and the matrix column element data to obtain a target evaluation matrix; performing operation score mapping through the target evaluation matrix, and determining a target operation score; and performing operation evaluation analysis on the target trainee based on the target operation score to generate a trainee operation evaluation result.
The invention also provides a simulation training comprehensive evaluation system based on the temporary event and the emergency event, which comprises the following steps:
the system comprises an acquisition module, a training unit, a first evaluation index data set and a second evaluation index data set, wherein the acquisition module is used for carrying out real-time monitoring on a temporary event of the training unit, determining a real-time monitoring result, acquiring first operation data of a target student when the real-time monitoring result is that the temporary event exists, and extracting evaluation index data of the first operation data to obtain the first evaluation index data set corresponding to the first operation data;
the monitoring module is used for monitoring the index data of the practical training unit in real time to obtain an index data set corresponding to the practical training unit, wherein the index data set comprises network link state, working voltage and heartbeat message data;
the matching module is used for carrying out state combination value analysis on the index data set, determining a target state combination value, carrying out event type matching through the target state combination value and determining a target event type;
the extraction module is used for collecting second operation data of a target student based on the target event type, and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data;
The analysis module is used for carrying out evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set;
the generating module is used for carrying out evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix, carrying out operation evaluation analysis on the target student through the target evaluation matrix and generating a student operation evaluation result.
In the technical scheme provided by the application, the training unit is subjected to real-time monitoring of the temporary event, and when the real-time monitoring result is that the temporary event exists, first operation data are collected and evaluation index data extraction is carried out to obtain a first evaluation index data set; performing index data real-time monitoring to obtain an index data set, performing state combination value analysis, determining a target state combination value, performing event type matching, and determining a target event type; collecting second operation data of a target student, extracting evaluation index data from the second operation data to obtain a second evaluation index data set, and carrying out evaluation index weight analysis to obtain an evaluation index weight set; and (3) performing evaluation matrix calculation to obtain a target evaluation matrix, performing operation evaluation analysis on a target student, and generating a student operation evaluation result. In the application, the index data of the network link state, the working voltage, the heartbeat message and the like can be timely obtained by carrying out the real-time monitoring of the index data on the training unit, and the real-time data support is provided for subsequent evaluation and analysis. This helps to timely understand the status and performance of the training unit and to make further data analysis and decisions. By performing a state combination value analysis on the index data set, a target state combination value can be extracted from the plurality of index data. This helps to comprehensively evaluate the overall state of the training unit and determine the target event type by event type matching. Therefore, the state of the training unit can be known more accurately, the specific event type can be identified, and a more targeted basis is provided for subsequent operation evaluation. And collecting second operation data of the target trainee according to the type of the target event, and extracting a second evaluation index data set from the second operation data. This helps translate the student's operational behavior into specific assessment metrics such as accuracy, speed, etc. for subsequent evaluation and analysis. By extracting the evaluation index, the operation performance of the learner can be objectively evaluated, and basis is provided for targeted feedback and guidance. By weight analysis of the second evaluation index data set, the relative importance of each evaluation index can be determined. This helps to ensure that the evaluation process is more accurate and reasonable, and avoids the influence of subjective factors on the evaluation result. Through weight analysis, a weighing basis can be provided for subsequent evaluation matrix calculation, so that the evaluation process is more objective. And performing evaluation matrix calculation on the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix. The operation evaluation analysis is carried out on the target trainees through the target evaluation matrix, the operation performance of the trainees can be objectively evaluated, and the operation evaluation results of the trainees are generated, so that the accuracy and the efficiency of the operation flow evaluation of the trainees are further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a simulation training comprehensive evaluation method based on an on-the-fly event and an emergency event in an embodiment of the invention.
FIG. 2 is a flowchart of performing evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a simulation training comprehensive evaluation system based on an on-the-fly event and an emergency event according to an embodiment of the present invention.
Reference numerals:
301. an acquisition module; 302. a monitoring module; 303. a matching module; 304. an extraction module; 305. an analysis module; 306. and generating a module.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. 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.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
For easy understanding, the following describes a specific flow of the embodiment of the present invention, referring to fig. 1, fig. 1 is a flowchart of a simulation training comprehensive evaluation method based on an occurrence and an emergency, and as shown in fig. 1, the method includes the following steps:
s101, performing real-time monitoring on an on-line event of a training unit, determining a real-time monitoring result, collecting first operation data of a target student when the real-time monitoring result is that the on-line event exists, and extracting evaluation index data of the first operation data to obtain a first evaluation index data set corresponding to the first operation data;
S102, monitoring index data of a practical training unit in real time to obtain an index data set corresponding to the practical training unit, wherein the index data set comprises network link state, working voltage and heartbeat message data;
s103, analyzing the state combination value of the index data set, determining a target state combination value, and performing event type matching through the target state combination value to determine a target event type;
s104, based on the target event type, collecting second operation data of a target student, and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data;
s105, carrying out evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set;
s106, based on the evaluation index weight set, performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set to obtain a target evaluation matrix, and performing operation evaluation analysis on a target student through the target evaluation matrix to generate a student operation evaluation result.
In the embodiment of the invention, the training unit is subjected to real-time monitoring of the on-machine event, the real-time monitoring result is determined, when the real-time monitoring result is that the on-machine event exists, first operation data of a target student are collected, evaluation index data extraction is carried out on the first operation data, a first evaluation index data set corresponding to the first operation data is obtained, and then the special IPMC module and the simulation training comprehensive evaluation system are respectively initialized; afterwards, the special IPMC system monitors three indexes of the practical training unit in real time: the network link state, the working voltage, the heartbeat message receiving and judging the current emergency type according to the combined value of the three states; because the special IPMC circuit is powered by a special UPS, the whole process of generating the emergency can be ensured to be detectable; after the comprehensive evaluation system loads the crash event, the single monitoring index is actually 1 because of simulating the fault event, and the crash event and the emergency event type can be distinguished.
The IPMC monitors network link state, working voltage and heartbeat message data in the training unit. For example, IPMC may detect whether the network connection is normal, whether the voltage is stable, the frequency and timing of heartbeat messages, etc. Constructing an index data set: and (5) sorting and recording the index data monitored in real time. For example, the network link state (normal, failure), the operating voltage value (V), the heartbeat message data (frequency, timing) and the like at each point in time are recorded. The IPMC monitors network link state, working voltage and heartbeat message data in the training unit. For example, IPMC may detect whether the network connection is normal, whether the voltage is stable, the frequency and timing of heartbeat messages, etc. And (5) sorting and recording the index data monitored in real time. For example, the network link state (normal, failure), the operating voltage value (V), the heartbeat message data (frequency, timing) and the like at each point in time are recorded. And carrying out state combination value analysis on the index data set to determine a target state combination value. For example, the network link state and the working voltage are comprehensively considered to obtain a state combination value which represents the overall state of the training unit. And performing event type matching according to the target state combination value. For example, if the status combination value indicates a network link failure and voltage anomaly, then one "network failure and voltage anomaly" event type may be matched. During the simulated training, second operational data of the target trainee is monitored. For example, emergency disposal measures taken by a learner in the face of network failure and voltage abnormality, such as power outage, use of backup power, and the like, are recorded. Evaluation index data is extracted from second operation data of the trainee. For example, index data such as operation time, effectiveness of treatment measures, response speed, and the like are extracted. The weight of the evaluation index is determined by expert review or statistical analysis. For example, the expert may give higher weight to the time of operation, considering the response speed as the most important indicator in emergency treatment. Based on the set of evaluation index weights, an evaluation matrix of the evaluation index data is calculated. For example, after normalizing the operation time, weighting and summing are performed according to the weights, so as to obtain a comprehensive evaluation value: and performing evaluation analysis on the operation of the target trainee by using the evaluation matrix. For example, the emergency treatment ability of a student is classified into excellent, good, and general grades according to the comprehensive evaluation value, and an evaluation result of the student operation is generated.
In the embodiment of the application, the training unit is monitored in real time for the on-line event, and when the on-line event exists as a real-time monitoring result, first operation data are collected and evaluation index data are extracted to obtain a first evaluation index data set; performing index data real-time monitoring to obtain an index data set, performing state combination value analysis, determining a target state combination value, performing event type matching, and determining a target event type; collecting second operation data of a target student, extracting evaluation index data from the second operation data to obtain a second evaluation index data set, and carrying out evaluation index weight analysis to obtain an evaluation index weight set; and (3) performing evaluation matrix calculation to obtain a target evaluation matrix, performing operation evaluation analysis on a target student, and generating a student operation evaluation result. In the application, the index data of the network link state, the working voltage, the heartbeat message and the like can be timely obtained by carrying out the real-time monitoring of the index data on the training unit, and the real-time data support is provided for subsequent evaluation and analysis. This helps to timely understand the status and performance of the training unit and to make further data analysis and decisions. By performing a state combination value analysis on the index data set, a target state combination value can be extracted from the plurality of index data. This helps to comprehensively evaluate the overall state of the training unit and determine the target event type by event type matching. Therefore, the state of the training unit can be known more accurately, the specific event type can be identified, and a more targeted basis is provided for subsequent operation evaluation. And collecting second operation data of the target trainee according to the type of the target event, and extracting a second evaluation index data set from the second operation data. This helps translate the student's operational behavior into specific assessment metrics such as accuracy, speed, etc. for subsequent evaluation and analysis. By extracting the evaluation index, the operation performance of the learner can be objectively evaluated, and basis is provided for targeted feedback and guidance. By weight analysis of the second evaluation index data set, the relative importance of each evaluation index can be determined. This helps to ensure that the evaluation process is more accurate and reasonable, and avoids the influence of subjective factors on the evaluation result. Through weight analysis, a weighing basis can be provided for subsequent evaluation matrix calculation, so that the evaluation process is more objective. And performing evaluation matrix calculation on the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix. The operation evaluation analysis is carried out on the target trainees through the target evaluation matrix, the operation performance of the trainees can be objectively evaluated, and the operation evaluation results of the trainees are generated, so that the accuracy and the efficiency of the operation flow evaluation of the trainees are further improved.
In a specific embodiment, the process of executing step S103 may specifically include the following steps:
(1) Performing first index value matching on the network link state, and determining a first index value;
(2) Performing second index value matching on the working voltage to determine a second index value;
(3) Performing third index value matching on the heartbeat message data to determine a third index value;
(4) Performing state combination value analysis on the first index value, the second index value and the third index value to determine a target state combination value;
(5) And carrying out event type matching on the target state combination value based on a preset event type mapping table, and determining the target event type.
It should be noted that, performing first index value matching on the network link state, determining that the first index value matches the working voltage with a second index value, and determining the second index value; and (3) carrying out third index value matching on the heartbeat message data, determining a third index value, and assuming that when the first index value is 0, the second index value is 1 and the third index value is 1, obtaining a state combination value (0, 1), indicating that the communication link is abnormal, when the first index value is 0, the second index value is 0 and the third index value is 0, obtaining a state combination value (0, 0), indicating that the packaging equipment is in fault or dead, and when the first index value is 1, the second index value is 1 and the third index value is 0, obtaining a state combination value (1, 0), indicating that the software is blocked or exited.
In a specific embodiment, the process of executing step S104 may specifically include the following steps:
(1) Collecting second operation data of a target student based on the target event type;
(2) Performing data segmentation on the second operation data to obtain a plurality of sub second operation data;
(3) And extracting evaluation index data from the second operation data through the plurality of sub second operation data to obtain a second evaluation index data set.
In a specific embodiment, the process of performing the step of extracting the evaluation index data from the second operation data by using the plurality of sub-second operation data to obtain the second evaluation index data set may specifically include the following steps:
(1) Performing primary evaluation index extraction on the plurality of sub second operation data to obtain a primary evaluation index data set, wherein the primary evaluation index data set comprises data receiving and transmitting suitability data, data receiving and transmitting timeliness data and data receiving and transmitting emergency timeliness data;
(2) And carrying out secondary evaluation index data extraction on the primary evaluation index data set to obtain a secondary evaluation index data set, and constructing a second evaluation index data set through the secondary evaluation index data set.
Specifically, it should be noted that the first-level evaluation index includes data transmit-receive suitability Time-efficient data transmission and receptionEmergency timeliness of data receiving and transmitting>Wherein, in data transmit-receive suitability +.>The category of the system also comprises secondary evaluation indexes such as communication means accuracy, communication quality, message length accuracy and the like, the category of data receiving and transmitting timeliness also comprises secondary evaluation indexes such as data input correction time, transmission processing time, copying processing time and the like,in the embodiment of the invention, based on the target event type, the second operation data of the target trainee is collected, the second operation data is subjected to data segmentation to obtain a plurality of sub second operation data, the second operation data is subjected to evaluation index data extraction through a plurality of sub second operation data to obtain a second evaluation index data set, and simultaneously, the plurality of sub second operation data is subjected to primary evaluation index extraction to obtain a primary evaluation index data set, wherein the primary evaluation index data set comprises data transmission and reception suitability data, data transmission and reception timeliness data and data transmission and reception timeliness data, the primary evaluation index data set is subjected to secondary evaluation index data extraction to obtain a secondary evaluation index data set, the second evaluation index data set is constructed through the secondary evaluation index data set, and the index data can be acquired through a channel such as a communication sub analog terminal interface, a message return value is obtained through a special channel, a channel rate, a channel error rate in a message is calculated, and a message is input after a message is calculated, a channel error rate and a channel error rate is calculated, a message is input to a message is calculated, and a time is calculated after a message is input to a message is received, and a message is subjected to a time-down time is calculated, and a channel error rate is calculated, and a time is calculated after a message is calculated and a message is input to a time is subjected to a time-down, the method comprises the steps of (1) replacing starting and ending time of a practical training unit data processing card returned by a special IPMC, calculating the reconstruction time of a practical training unit module and the reconstruction time of a practical training unit whole machine returned by the special IPMC, and evaluating the starting and ending time of the recovery work of the special module returned by software.
In a specific embodiment, as shown in fig. 2, the process of performing step S105 may specifically include the following steps:
s201, extracting initial weights of each evaluation index data in a first evaluation index data set and a second evaluation index data set, and determining initial weights corresponding to each evaluation index data;
s202, adjusting weight data of initial weights corresponding to each piece of evaluation index data based on a target state combination value, and determining candidate weights corresponding to each piece of evaluation index data;
and S203, carrying out weight data correction on the candidate weights corresponding to each piece of evaluation index data to obtain an evaluation index weight set.
It should be noted that, first, extracting an initial weight of each evaluation index data in the first evaluation index data set and the second evaluation index data set, and determining an initial weight corresponding to each evaluation index dataFurther, the simulation training comprehensive evaluation system receives the return value of the special IPMC module in real time, the return value is generated if the state return of the real training unit working voltage and the heartbeat message according to the monitored { network link state }, if so, the return value is generatedx=1; return value if not generatedx=0; furthermore, according to- >First-class index set->Dedicated IPMC return valuexThe index weight of the self-body is adjusted in real time>The calculation formula is as follows:
wherein ,
in a specific embodiment, the process of executing step S203 may specifically include the following steps:
(1) And carrying out weight data correction on candidate weight data corresponding to each evaluation index data based on a preset weight data correction formula to obtain an evaluation index weight set, wherein the evaluation index data are as follows:
wherein ,is->Candidate weight data corresponding to the respective evaluation index data, +.>For the evaluation index weight set +.>Evaluation index weight corresponding to the individual evaluation index data,/->N is the number of evaluation index data, which is a positive integer.
In the embodiment of the application, the initial weights of all N index sets of the simulation training comprehensive evaluation system are obtained through expert experience，/>Representing the initial weight of the ith state evaluation index, wherein i is more than or equal to 1 and less than or equal to N; the simulation training comprehensive evaluation system receives a return value of a special IPMC module in real time, the return value is x=1 according to the monitored state of { network link state, practical training unit working voltage and heartbeat message }; return value x=0 if not generated; according to- >The first level index set {>,/> ,/>The special IPMC returns a value x, and the index weight of the IPMC is adjusted in real time>Wherein, in the embodiment of the application, the weight data is calculated as follows:
weight data calculation table
In a specific embodiment, the process of step S106 is performed, including the following steps:
(1) Extracting matrix row elements from the first evaluation index data set and the second evaluation index data set, and determining matrix row element data;
(2) Performing matrix element extraction on the evaluation index data set, and determining matrix element data;
(3) Performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set based on matrix row element data and matrix column element data to obtain a target evaluation matrix;
(4) Performing operation score mapping through a target evaluation matrix, and determining a target operation score;
(5) And performing operation evaluation analysis on the target trainee based on the target operation score to generate a trainee operation evaluation result.
The index to be the matrix row element is selected based on the evaluation index data set. For example, an index of operation time, treatment measure effectiveness, or the like is selected as row element data of the matrix. An index to be a matrix element is selected based on the evaluation index data set. For example, the indices in the set of evaluation index weights are selected as column element data of the matrix. And calculating an evaluation matrix by using the matrix row element data and the matrix column element data. For example, according to the evaluation index data and the weights, the row elements and the column elements are multiplied or other corresponding operations are performed to obtain the target evaluation matrix. And determining the mapping rule of the operation scores according to the target evaluation matrix. For example, different value ranges in the evaluation matrix are mapped to different levels or scores of the operational score. And performing evaluation analysis on the operation of the target trainee according to the target operation score. For example, the operation score of the learner is compared with the set evaluation standard to determine the operation evaluation result of the learner, such as excellent, good, general grade.
The embodiment of the invention also provides a simulation training comprehensive evaluation system based on the temporary event and the emergency, as shown in fig. 3, the simulation training comprehensive evaluation system based on the temporary event and the emergency specifically comprises:
the acquisition module 301 is configured to perform real-time monitoring on an on-line event on the training unit, determine a real-time monitoring result, acquire first operation data of a target learner when the real-time monitoring result is that the on-line event exists, and extract evaluation index data of the first operation data to obtain a first evaluation index data set corresponding to the first operation data;
the monitoring module 302 is configured to monitor the index data of the training unit in real time, so as to obtain an index data set corresponding to the training unit, where the index data set includes network link state, working voltage and heartbeat message data;
the matching module 303 is configured to perform state combination value analysis on the index data set, determine a target state combination value, and perform event type matching according to the target state combination value, and determine a target event type;
the extracting module 304 is configured to collect second operation data of a target learner based on the target event type, and extract evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data;
The analysis module 305 is configured to perform an evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set;
the generating module 306 is configured to calculate an evaluation matrix for the first evaluation index data set and the second evaluation index data set based on the evaluation index weight set, obtain a target evaluation matrix, and perform operation evaluation analysis on the target student through the target evaluation matrix, so as to generate a student operation evaluation result.
Through the cooperation of the modules, the training unit is monitored in real time for the temporary event, and when the temporary event exists as a real-time monitoring result, first operation data are collected and evaluation index data are extracted to obtain a first evaluation index data set; performing index data real-time monitoring to obtain an index data set, performing state combination value analysis, determining a target state combination value, performing event type matching, and determining a target event type; collecting second operation data of a target student, extracting evaluation index data from the second operation data to obtain a second evaluation index data set, and carrying out evaluation index weight analysis to obtain an evaluation index weight set; and (3) performing evaluation matrix calculation to obtain a target evaluation matrix, performing operation evaluation analysis on a target student, and generating a student operation evaluation result. In the application, the index data of the network link state, the working voltage, the heartbeat message and the like can be timely obtained by carrying out the real-time monitoring of the index data on the training unit, and the real-time data support is provided for subsequent evaluation and analysis. This helps to timely understand the status and performance of the training unit and to make further data analysis and decisions. By performing a state combination value analysis on the index data set, a target state combination value can be extracted from the plurality of index data. This helps to comprehensively evaluate the overall state of the training unit and determine the target event type by event type matching. Therefore, the state of the training unit can be known more accurately, the specific event type can be identified, and a more targeted basis is provided for subsequent operation evaluation. And collecting second operation data of the target trainee according to the type of the target event, and extracting a second evaluation index data set from the second operation data. This helps translate the student's operational behavior into specific assessment metrics such as accuracy, speed, etc. for subsequent evaluation and analysis. By extracting the evaluation index, the operation performance of the learner can be objectively evaluated, and basis is provided for targeted feedback and guidance. By weight analysis of the second evaluation index data set, the relative importance of each evaluation index can be determined. This helps to ensure that the evaluation process is more accurate and reasonable, and avoids the influence of subjective factors on the evaluation result. Through weight analysis, a weighing basis can be provided for subsequent evaluation matrix calculation, so that the evaluation process is more objective. And performing evaluation matrix calculation on the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix. The operation evaluation analysis is carried out on the target trainees through the target evaluation matrix, the operation performance of the trainees can be objectively evaluated, and the operation evaluation results of the trainees are generated, so that the accuracy and the efficiency of the operation flow evaluation of the trainees are further improved.
The above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the scope of the claims.
Claims (8)
1. A simulation training comprehensive evaluation method based on an on-the-fly event and an emergency event is characterized by comprising the following steps:
real-time monitoring of the on-line events is carried out on the training unit, a real-time monitoring result is determined, when the real-time monitoring result is that the on-line events exist, first operation data of a target student are collected, evaluation index data extraction is carried out on the first operation data, and a first evaluation index data set corresponding to the first operation data is obtained;
the method comprises the steps of monitoring index data of a training unit in real time to obtain an index data set corresponding to the training unit, wherein the index data set comprises network link state, working voltage and heartbeat message data;
analyzing the state combination value of the index data set, determining a target state combination value, and performing event type matching through the target state combination value to determine a target event type;
Based on the target event type, collecting second operation data of a target student, and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data;
performing evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set;
and based on the evaluation index weight set, performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set to obtain a target evaluation matrix, and performing operation evaluation analysis on the target scholars through the target evaluation matrix to generate a scholars operation evaluation result.
2. The method for comprehensive evaluation of simulation training based on an on-the-fly event and an emergency event according to claim 1, wherein the step of analyzing the state combination value of the index data set, determining a target state combination value, performing event type matching through the target state combination value, and determining a target event type comprises the steps of:
performing first index value matching on the network link state to determine a first index value;
performing second index value matching on the working voltage to determine a second index value;
Performing third index value matching on the heartbeat message data to determine a third index value;
performing state combination value analysis on the first index value, the second index value and the third index value to determine a target state combination value;
and carrying out event type matching on the target state combination value based on a preset event type mapping table, and determining a target event type.
3. The comprehensive evaluation method for simulation training based on the on-the-fly event and the emergency event according to claim 1, wherein the step of collecting second operation data of a target student based on the target event type and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data comprises the following steps:
collecting second operation data of a target student based on the target event type;
performing data segmentation on the second operation data to obtain a plurality of sub second operation data;
and extracting evaluation index data from the second operation data through the plurality of sub second operation data to obtain a second evaluation index data set.
4. The method for comprehensive evaluation of simulation training based on an on-the-fly event and emergency event according to claim 3, wherein the step of extracting the evaluation index data of the second operation data by a plurality of sub-second operation data to obtain the second evaluation index data set comprises:
Performing primary evaluation index extraction on the plurality of sub second operation data to obtain a primary evaluation index data set, wherein the primary evaluation index data set comprises data transceiving adaptability data, data transceiving timeliness data and data transceiving emergency timeliness data;
and carrying out secondary evaluation index data extraction on the primary evaluation index data set to obtain a secondary evaluation index data set, and constructing the second evaluation index data set through the secondary evaluation index data set.
5. The method for comprehensively evaluating simulation training based on the occurrence and emergency according to claim 1, wherein the step of performing evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set comprises the following steps:
extracting initial weights of each evaluation index data in the first evaluation index data set and the second evaluation index data set, and determining initial weights corresponding to each evaluation index data;
performing weight data adjustment on the initial weight corresponding to each piece of evaluation index data based on the target state combination value, and determining candidate weights corresponding to each piece of evaluation index data;
And carrying out weight data correction on the candidate weights corresponding to each evaluation index data to obtain the evaluation index weight set.
6. The comprehensive evaluation method for simulation training based on the temporary event and the emergency according to claim 5, wherein the step of performing weight data correction on the candidate weights corresponding to each evaluation index data to obtain the evaluation index weight set comprises the following steps:
and carrying out weight data correction on candidate weight data corresponding to each piece of evaluation index data based on a preset weight data correction formula to obtain an evaluation index weight set, wherein the evaluation index data are as follows:
wherein ,is->Candidate weight data corresponding to the respective evaluation index data, +.>For the evaluation index weight set +.>Individual evaluation fingerEvaluation index weight corresponding to the target data, +.>N is the number of evaluation index data, which is a positive integer.
7. The comprehensive evaluation method for simulation training based on the on-the-fly event and the emergency event according to claim 1, wherein the step of performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix, performing operation evaluation analysis on the target trainee through the target evaluation matrix, and generating a trainee operation evaluation result comprises the following steps:
Extracting matrix row elements from the first evaluation index data set and the second evaluation index data set, and determining matrix row element data;
performing matrix element extraction on the evaluation index data set, and determining matrix element data;
performing evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set based on the matrix row element data and the matrix column element data to obtain a target evaluation matrix;
performing operation score mapping through the target evaluation matrix, and determining a target operation score;
and performing operation evaluation analysis on the target trainee based on the target operation score to generate a trainee operation evaluation result.
8. A simulated training comprehensive assessment system based on an occurrence and an emergency event for executing the simulated training comprehensive assessment method based on an occurrence and an emergency event as claimed in any one of claims 1 to 7, comprising:
the system comprises an acquisition module, a training unit, a first evaluation index data set and a second evaluation index data set, wherein the acquisition module is used for carrying out real-time monitoring on a temporary event of the training unit, determining a real-time monitoring result, acquiring first operation data of a target student when the real-time monitoring result is that the temporary event exists, and extracting evaluation index data of the first operation data to obtain the first evaluation index data set corresponding to the first operation data;
The monitoring module is used for monitoring the index data of the practical training unit in real time to obtain an index data set corresponding to the practical training unit, wherein the index data set comprises network link state, working voltage and heartbeat message data;
the matching module is used for carrying out state combination value analysis on the index data set, determining a target state combination value, carrying out event type matching through the target state combination value and determining a target event type;
the extraction module is used for collecting second operation data of a target student based on the target event type, and extracting evaluation index data of the second operation data to obtain a second evaluation index data set corresponding to the second operation data;
the analysis module is used for carrying out evaluation index weight analysis on the first evaluation index data set and the second evaluation index data set to obtain an evaluation index weight set;
the generating module is used for carrying out evaluation matrix calculation on the first evaluation index data set and the second evaluation index data set based on the evaluation index weight set to obtain a target evaluation matrix, carrying out operation evaluation analysis on the target student through the target evaluation matrix and generating a student operation evaluation result.
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