Comprehensive adaptive simulator for crews of weapons, military and special equipment

Abstract

FIELD: integrated simulator.SUBSTANCE: integrated adaptive simulator for use of weapons, military and special equipment (WMSE) is designed as a frame body and contains at least four workstations for trainees, including the driver's mechanic's workstation, the workstation of a crew member performing work outside the vehicle's body, the commander's workstation, the gunner's workstation, signal collection and processing devices (SCPD), a video recorder control device (VRCD), a simulation system (SS), and a control and prediction device (CPD). Each workstation is equipped with video monitors, controls with sensors that determine their position, and a student remote control. The SCPD consists of a reprogrammable memory device (RPM), a digital input device, a controller, a digital output module, and a switching control module. The VRCD consists of a frame number register for the beginning of a video fragment, a frame number register for the end of a video fragment, two digital comparators, a logic circuit, a film start sensor, and key cascades. The SS contains a digital input device, an RPM controller, and a digital output module. The control unit includes a switching and measurement unit (SMU), a memory unit (MU), a weighting coefficient unit, a unit for calculating the probabilistic characteristics of a priori information (CU1), a unit for calculating conditional a posteriori probabilities (CU2), a results correction unit (RCU), an indication unit (IU) and a management unit (MU).EFFECT: expansion of the functional capabilities of integrated simulators and a reduction in crew training time.1 cl, 5 dwg, 8 tbl

Description

[0001] Field of technology to which the invention relates

[0002] The proposed invention relates to technical means for training military specialists operating weapons, military and special equipment (VVST), which is intended to implement adaptive training, which involves automatic monitoring of the actions of the student, predicting the likelihood that he is able to learn the selected exercise or fulfill the standard in the allotted time, as well as the time that the student actually needs to learn the selected exercise or fulfill the standard, taking into account his personal pace of study and the formation of recommendations for a further strategy for his training.

[0003] Currently, when training crews in algorithms for operating complex weapons and equipment, complex simulators are used that do not have devices capable of automatically monitoring the trainee's actions, predicting the likelihood that he or she will be able to learn the selected exercise or complete the standard within the allotted time, as well as the time that the trainee actually needs to learn the selected exercise or complete the standard, taking into account his or her personal learning pace, and formulating recommendations for optimizing the training process. This generally leads to difficulties in distributing lesson time for learning these exercises and practicing standards among trainees in a group of specialists, and to a delay in training time due to the fact that the level of knowledge, skills, and abilities of the trainee may not correspond to the complexity of the material being studied or the stage of training, which generally leads to an increase in the time required for training specialists.

[0004] Interpretation of terms used in the application

[0005] An adaptive simulator is a simulator for a human-machine system (HMS) operator, providing automatic optimization of the HMS operator training process, taking into account the results of the operator's performance of training tasks. [GOST 26387-84. Human-machine system. Terms and definitions. - Moscow: Standartinform, 2006. - 6 p.].

[0006] Crew - the primary (lowest) unit in the armed forces of many countries, directly operating an aircraft (airplane, helicopter, etc.), tank, infantry fighting vehicle, airborne combat vehicle, and other self-propelled (including remotely controlled) equipment. [https: / / bigenc.ra / / The Great Russian Encyclopedia].

[0007] Technology Level

[0008] Description of an analogue of the claimed device

[0009] A “Simulator for training artillery crews” is known, described in the patent of the Russian Federation No. 65640, IPC F41G 3 / 26 (published 10.08.2007, Bulletin 22), containing a simulator of a launch device and a unit for calculating a training and information model, connected to a simulator of a laser target designator-rangefinder, a simulator of a launch object, a simulator of a visual situation, a simulator of a commander’s console and a simulator of a computer of firing installations, connected to the unit for calculating a training and information model and to each other.

[0010] The disadvantage of this invention is that it does not include devices that provide automatic control of the actions of students, predict the likelihood that he is able to study the selected exercise and standard in the time specified by the training program, as well as the time that is actually necessary for the student to study the selected exercise and standard, taking into account his personal pace of study, as a result of which the instructor does not have the opportunity to promptly stop the training process in cases where the level of knowledge, skills and abilities of the student is not sufficient to study the selected exercise and standard, and it is necessary to study the theoretical material in more depth, or the pace of the student is above average and it is possible, without waiting for the end of the time allocated for training, to move on to studying the next exercise and standard, in connection with which the total training time of a group of specialists increases.

[0011] The closest in technical essence to the proposed invention and accepted as a prototype is a device from the group of inventions "Method of automated training and assessment of the level of training of crews and a simulator for its implementation", described in Russian patent No. 2063651, IPC G09B 9 / 00 (published on July 10, 1996). The device consists of an automated training and control system (ATCS), position sensors for working elements, video monitors at each crew member's workstation and video recorders (VR) with recorded correct actions of crew members in performing various training tasks. Control of the VR and monitoring of the position of the working elements is carried out using a signal collection and processing device (SAD) and an instructor's console (IC). The SAD is a reprogrammable memory device (RPM) consisting of a switching control module and a digital output module, each of which is connected to a controller.The search for the desired hint fragment is performed on the VM using a control unit assembled from key stages, frame start and end number registers, digital comparators, and a logic circuit. The device simulating control instrument readings and the sound of operating systems consists of a digital input device and a controller with an output through a scale amplifier to the simulator's control panel and through a digital output module to the speaker system.

[0012] The disadvantage of the said invention is that it does not include devices that provide automatic control of the actions of students, predict the likelihood that he is able to study the selected exercise and standard in the time specified by the training program, as well as the time that is actually necessary for the student to study the selected exercise and standard, taking into account his personal pace of study, as a result of which the instructor does not have the opportunity to promptly stop the training process in cases where the level of knowledge, skills and abilities of the student is not sufficient to study the selected exercise and standard, and it is necessary to study the theoretical material in more depth, or the pace of the student is above average and it is possible, without waiting for the end of the time allocated for training, to move on to studying the next exercise and standard, in connection with which the total training time of a group of specialists increases.

[0013] Disclosure of the essence of the invention

[0014] Technical result which the invention is aimed at achieving

[0015] The objective of the claimed invention is to create a complex adaptive training device (CAT) capable of automatically monitoring the actions of a student, predicting the likelihood that he will be able to learn the selected exercise and standard in the allotted time, as well as the time that is actually necessary for the student to learn the selected exercise and standard, taking into account his personal pace of study, and issuing recommendations to the instructor for optimizing the process of training specialists.

[0016] The technical result of the claimed invention is the expansion of the functional capabilities of complex simulators and the reduction of the training time for military and military equipment crews.

[0017] The essence of the invention is explained with the help of drawings.

[0018] Fig. 1 - diagram of the complex adaptive simulator;

[0019] Fig. 2 - device for collecting and processing signals;

[0020] Fig. 3 - VCR control device;

[0021] Fig. 4 - simulation system;

[0022] Fig. 5 - control and forecasting device (CPD).

[0023] The KAT is a frame housing containing the main assemblies that determine the functions of the controlled object. These assemblies are supplied with power from an external source.

[0024] The following trainee stations are provided in the CAT: regular crew stations and stations for training crew members performing work outside the hull. A trainee console 4 and a video monitor 2 are installed at each trainee's workstation. Each control, which the trainee operates, is equipped with control position sensors 3, which are connected to the USOS 5 and, through the USOS 5, to the UKP 12. A demonstration of the correct actions of the crew members with the corresponding announcer's accompaniment is recorded on VM 6. VM 6, via the USOS 5, are connected to video monitors 2 and trainee consoles 4. The AVSK 10 includes PI 8, which is connected with the trainee stations via the USOS 5 and, through the video recorder control devices 7 (VCR) with VM 6. PI 8 is a computer with a set of peripheral devices.Since some of the components in the proposed CAT are non-functional, the CAT requires a system to simulate external factors affecting these systems, such as sound, and generate signals for the control instruments. This simulation system is connected to the USOS-5 and the crew member's control instruments.

[0025] Description of drawings

[0026] The simulator shown in Fig. 1 consists of 4 trainee workstations 1: workstation No. 1 for the driver, workstation No. 2 for the crew member performing work outside the tank hull, workstation No. 3 for the commander, and workstation No. 4 for the gunner. Workstations No. 1, 3, and 4 consist of the corresponding regular workstations 1 for the tank crew members. Each workstation (including workstation No. 2) is equipped with video monitors 2 located within the trainee's field of vision. All controls operated by the trainees are equipped with sensors 3 that determine their position, the information outputs of which are connected to the simulation system 9. Within easy reach of each trainee's workstation, trainee consoles 4 are installed and connected to the USOS 5.

[0027] USOS 5 is also connected to the simulation system 9, helmet telephones 11, UKP 12, VM 6, AVSK 10 and PI 8 with ASOK 13, connected to the UVM7 device.

[0028] The digital input device (see Fig. 2) consists of the following modules: digital input device 14, controller 15, digital output module 16, switching control module 17 and ROM 18. The digital input device operates as follows. Signals from the student's console, sensors and the UCP are fed to the digital input device 14, where they are converted into logical signals compatible in level with the signals of the bus line of the controller 15. The digital output module 16 converts logical digital signals of a given level, transmitted to the system for simulating the readings of control devices and sound 9, Fig. 1. The switching control module 17, Fig. 2 ensures the switching of input and output signals (visual and audio channels) in accordance with the control signals from the controller 15. The ROM 18 ensures the storage of the operating program of the controller 15 in the absence of power, as well as the output of this program to the controller 15 during its operation.Controller 15 provides input, output, switching and processing of signals from the student's workstation, as well as the formation and transmission of information to the PI during the training process. In addition, it ensures the reception of control signals from the PI. The operation of controller 15 is controlled by a program stored in ROM 18. Controller 15 in Fig. 2 is a peripheral controller 15, i.e., a device that connects peripheral equipment (in this case, digital input device 14, digital output module 16 and switching control module 17) to the central processor (in this case, the PI), relieving the processor of direct control of the operation of this equipment. Controller 15 operates under the control of a program stored in ROM 18. With a cyclic frequency determined by the program, controller 15 polls digital input device 14 and transmits information received from digital input device 14 to the PI.Upon receiving control signals from the PI, the controller writes the specified information to digital output module 16 or switching control module 17. The UOS is assembled from standard, industrially available blocks, such as blocks from the microprocessor control system of the Periphery special-purpose technological and testing equipment.

[0029] The block diagram of the VCR device is shown in Fig. 3 and consists of: a video fragment start frame number register 19, a video fragment end frame number register 20, two digital comparators 21, a logic circuit 22, a film start sensor 23 and key cascades 24. The fragment start frame number register 19 and the fragment end frame number register 20 are intended for recording and storing 15-bit codes of the fragment start and end frame numbers. Recording is performed using a strobe sync pulse. The proposed VCR device implements the following operating algorithm of the logic circuit 20. When the power is turned on, the logic circuit generates a control signal to the VCR "Forward rewind". When the film start marker is found (a signal from the film start sensor), the logic circuit sends a signal "Stop" to the VCR, as well as a signal "Ready" going to the PI.Depending on the signals at the output of the digital comparator, which compares the current frame number with the frame start number, the logic circuit generates the following control signals: ">" - "Forward rewind", "<" "Rewind", "=" "Play". In the presence of the "=" signal from the digital comparator, which compares the current frame number with the frame end number, a "Stop" signal is generated for the CM and a "Ready" signal is generated for the PI. The fragment start and end registers can be implemented on 555IR23 integrated circuits (eight-bit latch register). Digital comparators perform a bit-by-bit comparison of two 15-bit words (frame start numbers Nнк and frame end numbers Nкк). Based on the comparison results, an appropriate signal is generated, which is used to control the CM. The digital comparator unit can be built using 555SP1 microcircuits (4-bit digital comparators).Logic circuit 20 generates control signals for the key cascades based on a comparison of the frame start and end codes with the current video frame number, generates a signal to reset the circuit elements to their initial state upon power-on, and generates a "Ready" signal indicating readiness to receive information from the instructor's console for film fragment search. The circuit is implemented using 155LAZ and 155TM microcircuits. The film start sensor is designed to detect the film start. It generates a "Stop" and "Reset" signal, which resets the VM frame counter to its initial (zero) state. The sensor is a photosensor and an amplifier for the signal from the photosensor. The amplifier is implemented using a common emitter circuit. An AL107 LED and an FD256 photodiode can be used as a photosensor. Key cascades 22 are designed to supply control signals to the VM control system. The cascade can be implemented using a common emitter circuit.

[0030] Fig. 4 shows a block diagram of the system for simulating the readings of control instruments and the sound associated with the operation of the engine and heater. The system includes: digital input device 25, controller 26, ROM 27, digital output module 28. Controller 26 is a device for controlling external devices (in this case, digital input device 25, digital-to-analog converter (DAC) 29, timer 31, and digital output module 28. Controller 26 operates under the control of a program stored in ROM 27 and sequentially performs the following actions: 1 - reading information from digital input device 25; 2 - comparing information read from digital input device 25 with data stored in ROM 27; 3 - in accordance with the conditions set forth in the program, depending on the comparison results, controller 26 issues control signals to DAC 29, timer 31, digital output module 28; 4 - transition to point 1.The four units listed above completely replicate the structure of the USOS 5 described above and correspond to it in purpose. In addition to the modules listed above, the simulation system also includes a DAC 29 and a scaling amplifier 30, designed to convert the digital code received from controller 26 into an analog signal (in this case, voltage) and amplify it accordingly. The number of DAC modules 29 and scaling amplifiers 30 is determined by the number of continuously changing analog signals being simulated (e.g., coolant temperature, oil temperature, etc.). The signal from scaling amplifier 30 is fed directly to the device located on the driver-mechanic's instrument panel. The "Elektronika MS8205" module can be used as DAC 29.To generate a pulse signal of a given frequency required for simulating control devices that require such a signal (for example, a TE-48 tachometer), the device includes a timer 31 and a three-phase sine wave signal generating unit 32. The following module can be used as a timer: Elektronika MS4402. To simulate the sound of an operating engine and heater, the device has a noise generator 33, a modulator 34, a power amplifier 35 and an acoustic system 36. The signal comes to the noise generator 33 from the digital output module 28, the signal from the noise generator 33 goes to the modulator 34. The signal from the three-phase signal generating unit 32 also goes to the modulator 34. From the modulator 34, the signal goes to the acoustic system 36. For alarms that require a discrete signal (for example, an engine oil pressure alarm), the signal comes directly from the digital output device 28.

[0031] UKP 12 (Fig. 5) is a well-known device [Technical diagnostic tools: Handbook / V.V. Klyuev, P.P. Parkhomenko, V.E. Abramchuk et al.; Under the general editorship of V.V. Klyuev. - M.: Mashinostroenie, 1989. - 672 p., ill., pp. 158-159 fig. 17], which includes a switching and measurement unit (SMU) 12.1, a memory unit (MU) 12.2, a weighting coefficient unit (WCU) 12.3, a unit for calculating the probabilistic characteristics of a priori information (BV1) 12.4, a unit for calculating conditional a posteriori probabilities (BV2) 12.5, a results correction unit (RCU) 12.6, indication units (IU) 12.7 and control units (CU) 12.8, connected in accordance with the diagram shown in Fig. 5.

[0032] Implementation of the invention

[0033] CAT provides for crew training in the following modes: training, practice and testing.

[0034] When training in training mode, the device operates as follows. Students occupy their workstations. The instructor, using PI 8, registers them in ASOK 13, which stores a list of exercises required for each student to complete at their workstation. Once the student is ready to complete an exercise, the instructor launches ASOK 13, which assigns each student the training task selected by the instructor. Signals about the selected exercises are sent from ASOK 13 to the UVM 7 device and, via AVSK 10 and USOS 5, to BKI 12.1 UKP 12, which transmits the signal to BU 12.8.

[0035] The UVM 7 devices generate control signals to search for and display a video fragment based on a predetermined list of training questions, recorded on tapes and installed in the VM. The desired video fragment is specified by the frame numbers of the beginning and end of the fragment. Based on a comparison of these numbers with the current frame number of the video, the control commands "Forward Rewind," "Rewind," "Play," and "Stop" are generated. The audio and visual signals from the VM 6 are sent to the USOS 5 and then to the visual signals on the video monitors 2, while the audio signals (narration accompanying the video) are sent to the students' helmet-mounted headsets 11. After viewing the desired fragment, the student can, at their discretion, request a repeat viewing of the video fragment by pressing the key on the student's remote control 4. In this case, the signal from PI 8 goes to USOS 5, then to PI 8, ASOK 13, UVM device 7, VM 6 and to video monitor 2 and student’s helmet phone 11.In this way, the student watches the video demonstration of the completion of the educational task again.

[0036] Upon receipt of a signal from USOS 5 in UKP 12, BU 12.8 generates and transmits signals to BKI 12.1 to measure signals corresponding to time T тек from the beginning of the study of the exercise, the number of correct actions on the controls, the number of M круг repetitions of the exercise being studied, time t повторi each repetition of the selected exercise, the number n of incorrect actions on the controls and their transmission to the BKR 12.6.

[0037] BP 12.2, storing information on statistics R общ students' completion of this exercise within the given time, personal statistics R лич the student, corresponding to the probability of the student learning an exercise of the chosen complexity, information about the values ​​of the number N доп permissible errors when studying this exercise, the number of M эталонcorrect impacts on the controls when studying the selected exercise, the number of M повтор repetitions that the student must do to master the selected exercise, time T общ , allocated for studying the selected exercise, the value of the lower P кн and the upper P вн the probability limits for students to learn the selected exercise in a given time, the smoothing coefficient α and the damping coefficient β, transmit signals corresponding to the values ​​of P общ , α and β лич , in BV1 12.4.

[0038] BV1 12.4 receives signals from BP 12.2 corresponding to the values ​​of P общ , α and P лич , and calculates the P value апр a priori probability of learning the selected exercise by a given student in a given time, and transmits this value as a signal to BP 12.2.

[0039] In BKR 12.6, based on signals coming from BKI 12.2 and BP 2.2, the number of M is calculated текrepetitions of the exercise performed during time T тек from the beginning of studying the exercise, coefficient K ош quality of learning an exercise, characterizing the percentage of mistakes made by the student out of the possible number, coefficient K темп the speed of learning of the selected exercise by the student, characterizing how quickly the student completes the learning of the exercise at time T тек , coefficient K прогр progress, characterizing the number of correctly executed actions on the controls during time T тек from the beginning of studying the exercise, coefficient K план lag characterizing the proportion of the volume of the studied exercise from the planned value at time T тек from the beginning of studying the exercise, coefficient K треидdynamics of the speed of learning an exercise, characterizing how much the student accelerates or slows down after the next repetition of the exercise, calculated after receiving a signal in the form of the value of the number of M тек repetitions of the exercise are more than two, and the predicted value of time T конец , necessary for students to study the exercise taking into account the signals corresponding to the number N доп permissible errors when studying this exercise, the number of M эталон correct impacts on the controls, the number of M повтор repetitions that the student needs to do to learn the selected exercise and time T общ , allocated for studying the selected exercise, and the damping coefficient P, coming from BP 12.2, and transmits these values ​​in the form of signals to BV2 12.3.

[0040] BVK 12.3. stores two variants of values ​​of weighting coefficients w ош ,, w тепм ,, w план , w трcharacterizing the significance of the coefficients K ош quality of learning the exercise, K темп , the speed of learning the selected exercise by the student, K план lags and K тренд dynamics of the speed of learning the exercise, respectively, and transmits them to BV2 12.3 depending on the value of the signal of the number of M тек repetitions of the exercise performed during time T тек from the beginning of studying the exercise coming from BKI 12.2.

[0041] BV2 12.5 calculates P усп a posteriori probability of students learning the selected exercise in a given time, taking into account the values ​​of P апр a priori probability of the student learning the selected exercise in a given time, received in the form of signals from BP 12.2, and signals corresponding to the values ​​of the coefficients K план , TO темп , TO ош , TO тренд , progress coming from BKR 12.2, and the values ​​of the weighting coefficients w ош , w mепм , w план , w трендreceived in the form of signals from BVK 12.3, and transmits this value in the form of a signal to BI 12.7.

[0042] BI 12.7 generates signals in the form of data with P values апр a priori probability of the student learning the selected exercise in a given time, quality coefficients K ош study of the exercise, speed K темп the student's study of the selected exercise, K план lags and dynamics of K тренд changes in the speed of learning the exercise and the predicted time T конец , necessary to complete the study of the selected exercise.

[0043] BU 12.8 compares the signals corresponding to the received values ​​of P усп a posteriori probability of the student learning the selected exercise in a given time, with signals corresponding to the critical lower P кн and the upper P внthe limits of the probability of the trainee learning the selected exercise in a given time, coming from BP 12.2, and generates signals with data in the form of recommendations for continuing the learning process or changing it.

[0044] If the signal with the value P усп less signal with value P кн then BU 12.8 generates and transmits via BKI 12.1, USOS 5 and AVSK 10 to the PI 8 monitor a signal with the data "Lagging behind! Stop studying this exercise, proceed to additional training." If the value of signal P усп greater than the value of the signal P вн then BU 12.8 generates and transmits via BKI 12.1, USOS 5 and AVSK 10 to the PI 8 monitor a signal with the data "Confidently ahead! Stop studying this exercise, go to the next exercise." If the signal with the value P усп between the values ​​of signals P кн and R вн then BU 12.8 generates and transmits via BKI 12.1, USOS 5 and AVSK 10 to the PI 8 monitor a signal with the data “Average temp. Continue study”.

[0045] Before changing an exercise, the instructor can review the information about why the student needs to pause the study of the selected exercise using the data with the time values ​​T тек from the beginning of the study of the exercise, quality factors K ош study of the exercise, speed K темп the student's study of the selected exercise, K план lags and dynamics of K тренд changes in the speed of learning an exercise, a posteriori P усп the probability that the student will learn the selected exercise in a given time and the predicted time T конец , necessary for completing the study of the selected exercise, received in the form of signals on BI 12.7, and without waiting for the end of time T общ , allocated for studying the selected exercise, with the help of PI 8, selects the exercise required for study and launches ASOK 13, due to which the student’s preparation time is reduced.

[0046] Signals about the selected exercise are sent to the UVM 7 device, and through the AVSK 10 and USOS 5 to the BKI 12.1 UKP 12.

[0047] The training mode is designed to develop students' stable skills in performing exercises that are assessed by standards from a collection of standards that correspond to the probability of the student's timely and error-free completion of the selected exercise.

[0048] After registering students using PI 8, the instructor selects the training mode and in ASOK 13 selects the exercise that the student has learned and starts the training.

[0049] In training mode, the CAT functions in a similar way, except that the trainee does not have the opportunity to watch a video film and calculate the quality factors K ош study of the exercise, speed K темп the student's study of the selected exercise, K план lags and dynamics of K трендchanges in the speed of learning the exercise required by BV2 12.8 UKP 12 to predict the values ​​of P усп and T конец , signals are received from BP 12.2 to BKR 12.6, the number of which is N доп permissible errors when training this exercise, the number of M эталон correct impacts on the controls, the number of M повтор repetitions that the student needs to do to practice the selected exercise and time T общ , allocated for training the selected exercise, and BU 12.8, based on the results of calculating P уаь , compares this value with the critical lower P кн and the upper P вн the limits of the probability of timely and error-free fulfillment of the standard, generates and transmits via BKI 12.1, USOS 5 and AVSK 10 to the PI 8 monitor a signal with data on the need to continue training or suspend the fulfillment of the standard and change the training program for this student.

[0050] Before changing the standard, the instructor can review the information about why the student needs to pause training of the selected exercise using the data with the time values ​​T тек from the beginning of the training exercise, quality factors K ош training exercises, speed K темп training of the students in the selected exercise, K план lags and dynamics of K тренд changes in the speed of exercise training and the predicted time T конец , required to complete the training of the selected exercise, a posteriori P усп the probability of timely and error-free fulfillment of the standard received in the form of signals and displayed on BI 12.7, and without waiting for the end of time T общ , allocated for training the selected exercise, with the help of PI 8 selects the exercise required for training and launches ASOK 13, due to which the training time of the trainee is reduced.

[0051] In the offset mode, CAT works in a similar way, except that based on the calculation results of P усп a posteriori probability of timely and error-free fulfillment of the selected standard, BU 12.8 compares this value only with the critical lower limit P кн the probability of timely and error-free completion of the standard, the corresponding grade of "unsatisfactory", meaning that it is necessary to stop the test, since the student is not able to complete it with a positive grade and he needs to move on to training for this standard, all other values ​​of P усп The a posteriori probability of timely and error-free fulfillment of the selected standard is received in the form of signals through BKI 12.1, USOS 5 and AVSK 10 in ASOK 13, where it is converted into a score.

[0052] In all CAT operating modes, after completing the exercise, standard training or test, ASOK 13 automatically assigns a score to the student in points corresponding to the value of the signal Pусп, coming from BU 12.8 UKP 12 through BKI 12.1, USOS 5 and AVSK 10.

[0053] Causal relationship between the device features and the technical result.

[0054] Thanks to a new set of essential features, due to the additionally introduced UCP and new material connections, the functional capabilities of the prototype device have been expanded to automatically monitor the student's actions and predict the likelihood that he is able to study the selected exercise and standard in the allotted time, as well as the time that the student actually needs to study the selected exercise and standard, taking into account his personal pace of study, and to generate recommendations for optimizing the process of his preparation, as a result of which the instructor has the opportunity to stop the training process in cases where the level of knowledge, skills and abilities of the student is not sufficient to study the selected exercise and standard, and it is necessary to study the theoretical material in more depth, or the pace of the student is above average and it is possible, without waiting for the end of the time allocated for training, to move on to studying the next exercise and standard,as a result, the overall time for training a group of specialists is reduced.

[0055] Evidence of compliance of the claimed invention with the patentability conditions of “novelty” and “inventive step”.

[0056] The applicant's analysis of the prior art revealed that there are no existing analogs characterized by sets of features identical to all the features of the claimed device. Consequently, the claimed invention meets the patentability requirement of "Novelty."

[0057] A search of prior art in this and related fields of technology to identify features matching the distinctive features of the claimed invention from prior art revealed that these features are not clearly evident from the prior art. The applicant's prior art analysis does not reveal any prior art knowledge of the impact of the essential features of the claimed invention on achieving the stated technical result. Consequently, the claimed invention meets the patentability requirement of "inventive step."

[0058] Possibility of obtaining the technical result specified by the applicant.

[0059] The possibility of achieving the stated technical result was verified during an experiment.

[0060] To conduct the experiment, a mock-up of the UKP was created and connected to the operating 2X51 "Bunkerovka" simulator for the crew of the self-propelled artillery gun SAO 2S19 [see Artillery Journal No. 2. St. Petersburg: MVAA. 2022 - pp. 34-43 ISSN 2949-1266].

[0061] In accordance with the design of the experiment, two groups of three trainees were formed, who were to study the algorithm of a crew member’s work when transferring the self-propelled gun to a combat position.

[0062] The characteristics of the exercise being studied are given in Table 1.

[0063]

[0064] Two series of experiments were conducted during the experiment. In the first series, training took place without the use of the UCP, and in the second series, with it.

[0065] The distribution of students during the experiment is shown in Table 2.

[0066]

[0067]

[0068] During the first series of experiments, students #1, #2, and #3 sequentially studied the algorithm for operating a self-propelled aerial vehicle (SAOV) crew member. The instructor allocated time T общ Each student was assigned to study the selected exercise. Students were assessed based on the relative number of errors they made using the formula:

[0069]

[0070] where n i - the number of mistakes made by the i-th student;

[0071] N доп - the number of acceptable errors in the selected exercise (from table 1).

[0072] Next is the value N ош was translated into points on the scale given in Table 3.

[0073]

[0074] The results of the first series of experiments are shown in Table 4.

[0075]

[0076] The total time it took three students to study the selected exercise was 60 minutes.

[0077] Analysis of the data presented in Table 4 indicates that the students varied significantly in their knowledge, skills, and abilities acquired prior to studying the exercise. For example, Student #1 completed the exercise with only one error. Student #2's error rate was within the acceptable range, while Student #3's significantly exceeded it. However, the instructor lacked data reflecting the progress of the exercise's learning process to facilitate appropriate control actions. This would terminate the exercise for Student #3, who required preparatory training, and for Student #1, who could have proceeded to studying the exercise without waiting for the allotted time to expire and receive a grade.

[0078] When summing up the results of the first series of experiments, it was established that student No. 1 knew the theoretical part of the exercise very well, but student No. 3 was completely unprepared.

[0079] When conducting experiments using the control unit in the power supply unit, the following parameters were entered as requirements: общ =20 min, N доб .=6, M повтор =5, data according to statistics P общ =0.6 students' study of this exercise in a given time, personal statistics of each student P лич =0.45, the values ​​of the critical lower limit of P кн =0.4 and the upper limit of P вн =0.8 probability of the student to master the selected exercise in a given time, smoothing coefficient α=0.7 and damping coefficient β=0.5, and the weighting coefficients w are entered into the BVK план =0.2, w тепм =0.5, w ош =0.3.

[0080] During the study of exercises by students No. 4, No. 5 and No. 6 at the BI UCP at time T тек =12 min the data presented in Table 5 were recorded.

[0081]

[0082] Analysis of the predicted data shown in Table 5 indicates that the UKP at time Tтек =12 min issued a recommendation to stop student #4 from studying the selected exercise, as they are significantly behind schedule, and to stop student #6, as they are well ahead of the target time. Meanwhile, student #5 was advised to continue studying the exercise, as their learning pace is within the average (planned) timeframe.

[0083] The physical meaning of the calculated and predicted parameters of the UKP, given in Table 5, is explained using the example of the data of student No. 4. Thus, the probability that he will learn the selected exercise in the allotted time is P усп =0.09, the student spent 60% of the allocated time (K вр =60%), studied 36% of the required volume (K прогр =0.36), the rate of learning the exercise from repetition to repetition remains low and does not change (K темп =0.66), and the predicted value of the time required to complete the exercise is T конеч =31.2 min.

[0084] To verify the reliability of the values ​​calculated and predicted by the UCP during the experiment, the time spent studying the exercise by students No. 4, No. 5 and No. 6, the number of repetitions, the time of each repetition and the number of errors they made were measured and recorded, and then the intermediate and final results were manually recalculated, similar to the calculations carried out by the UCP.

[0085] The following are calculations for checking the calculated and predicted UKP values ​​for student No. 4.

[0086] Statistical data for student No. 4 at the time of forecasting T тек are given in Table 6.

[0087]

[0088] 1. Checking the calculations made in BV1.

[0089] Calculation of the prior probability P апр Student No. 4 will learn the selected exercise within the allotted time using the formula:

[0090]

[0091] where R общ- statistics of students' study of this exercise for a given period of time (the signal comes from the BP);

[0092] R лич - personal statistics of student No. 4 (the signal comes from the BP);

[0093] α - smoothing coefficient (signal comes from the power supply).

[0094] 2. Verification of calculations made in the BKR.

[0095] 2.1. The number of repetitions of the selected exercise completed by the student is calculated using the formula:

[0096]

[0097] where M кр - the number of full repetitions of the selected exercise (the signal comes from the BKI);

[0098] t1 - the number of actions in the i-th repetition during forecasting (the signal comes from the BKI).

[0099] 2.3. Calculating the value of the coefficient K ош the quality of the student's learning of the selected exercise, which corresponds to the proportion of errors that the student can still make out of the possible ones, according to the formula:

[0100]

[0101] n ош - the number of errors made by the student when studying the selected exercise (the signal comes from the BKI);

[0102] N доп - the number of acceptable errors when studying an exercise (the signal comes from the BP).

[0103] 2.4. Calculation of the K coefficient темп The pace of learning the selected exercise, which indicates the percentage by which the student's learning speed for the selected exercise lags behind or advances the planned value. It is calculated in several stages.

[0104] First, the planned time allocated for the i-th repetition is calculated based on the signals coming from the BP corresponding to the values ​​of T о6щ and M повтор , according to the formula:

[0105]

[0106] To calculate the time of stable learning of the selected exercise, which characterizes the average repetition time of the selected exercise, the formula is used:

[0107]

[0108] where t повторi - current time of the i-th repetition of the exercise (the signal comes from the BKI).

[0109] Next, the coefficient K is calculated. темп the pace of studying the selected exercise according to the formula:

[0110]

[0111] K темп =0.66 shows that the average speed of learning the selected exercise by the student is 34% slower than the planned value.

[0112] 2.4. Calculation of the K coefficient прогр progress, which corresponds to the proportion of repetitions of the exercise completed by the student from the number required according to the formula:

[0113]

[0114] 2.5 Calculation of the coefficient of time spent on studying the exercise using the formula:

[0115]

[0116] 2.6 Calculation of the discrepancy coefficient between the proportion of repetitions of an exercise completed by a student and the time spent on studying it using the formula:

[0117]

[0118] 2.7 Calculation of the coefficient of lag from the schedule using the formula:

[0119]

[0120] 2.8 At the final stage, the forecast value of time T is calculated конец , necessary for the student to complete the exercise.

[0121] To do this, the number of repetitions that the student needs to complete is calculated using the formula:

[0122]

[0123] Next, the calculation of the predicted value of time T is carried out. конец , required by the student to complete the exercise, according to the formula:

[0124]

[0125] where T тек- current time of studying the exercise (the signal comes from the BKI).

[0126] T value конец =1812 sec or 31.2 minutes indicates that student #4 will finish studying the exercise 11.2 minutes later than the allotted time.

[0127] 3. Checking the calculations made in BV2.

[0128] In BV2, the probability that a student will be able to master a selected exercise in a given time is calculated using the formula:

[0129]

[0130] where R априор - a priori probability of the student learning the selected exercise within a given time (the signal comes from the BP);

[0131] w1=0.5, w2=0.3, w3=0.2 - the value of the weighting coefficients (signals come from the BVK);

[0132] signals K план , TO темп and K ош come from the BKR.

[0133] 4. BU compares the obtained values ​​of P априор =0.33 with a critical lower limit P кн=0.4 and generates a signal with the data "Lagging behind! Stop studying this exercise, move on to additional training."

[0134] Next is the value of P усп can be converted into points. The conversion scale is given in Table 7 [Zhukov G.P., Vikulov S.F. Military-economic analysis and operations research: textbook for universities. - Moscow: Voenizdat.1987. - P. 316].

[0135]

[0136] Thus, the reliability of the values ​​calculated by the UKP is confirmed.

[0137] The reliability of the predicted values ​​was determined by comparing the time values ​​predicted by the training course program with the actual time spent by students studying the exercise. The comparison data between the actual and predicted exercise study times is presented in Table 8.

[0138]

[0139] The mean absolute forecast error is:

[0140]

[0141] Thus, the reliability of the values ​​of time required for a student to study an exercise, predicted by the UKP, has been proven.

[0142] To prove the possibility of achieving the stated technical result, a comparison was made of the time spent by students on studying the exercise without the use of the training program and with its use.

[0143] The calculation of the time value for studying an exercise without using the UCP is based on the fact that all students spent 20 minutes on training according to the formula:

[0144]

[0145] The calculation of the time value for studying the exercise using the UCP is based on the fact that student No. 5 spent 20 minutes on studying, and students No. 4 and No. 6 spent 12 minutes each, since the instructor could suspend this process in accordance with the recommendations generated by the UCP, according to the formula:

[0146]

[0147] The value of time reduced by using the UCP is calculated using the formula:

[0148]

[0149] Which is 26% of the total time allocated to studying the exercise by three students.

[0150] Thus, the possibility of achieving the stated technical result, which consists of expanding the functional capabilities of the prototype device and reducing the overall training time for specialists, has been proven.

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