Environment simulation system for training exercise

Abstract

The invention relates to the technical field of teaching training, and discloses an environment simulation system for training exercise. Comprising the steps of extracting request features from comprehensive input data, identifying feature attributes of the request features, dividing the request features into environment features and personality features, simulating a personality exercise environment according to the environment features and the personality features, calculating an environment simulation index, and comparing the environment simulation index with an environment simulation safety value. Abnormal point locations are identified from the individual exercise environment; compared with the prior art, the method has the advantages that a large amount of useless data can be removed, the application range of simulation data is narrowed, and personalized special training exercise requirements of the user side are met through the constructed personalized exercise environment, so that the matching degree of the personalized exercise environment and the requirements of the user side is improved; the effect of environment simulation analysis and evaluation of the environment simulation system is achieved, and the abnormal point location is tracked and positioned according to the evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching and training, and more particularly to an environment simulation system for training exercises. Background Art

[0002] As the troops' requirements for training exercises become increasingly stringent, how to effectively improve the troops' combat capabilities and ability to respond to emergencies has become an urgent problem to be solved. At the same time, in order to reduce the investment cost of training exercises and improve the convenience of training exercises, environmental simulation systems are usually used at this stage to simulate different environments for training exercises to meet the use requirements of training exercises, thereby providing convenience for the troops' training exercises.

[0003] The patent application with reference publication number CN116110266A discloses a training system and method based on multi-mode disaster environment linkage simulation, including a disaster scenario simulation module for using real coal mine data; a disaster setting module for calling pre-stored environmental parameters of different disaster types; a disaster type judgment module for judging the current multiple disaster situations; a trigger module for judging the disaster type and the corresponding environmental parameters; a rescue best operation generation module for generating the best rescue operation based on the final environmental parameters; and an exercise personnel operation module for the exercise personnel to perform corresponding rescue operations after receiving the alarm signal.

[0004] When simulating a training exercise environment, the existing environmental simulation system usually analyzes all the user's training and exercise needs one by one, and simulates all the user's needs in an overall environment. For example, in the above-mentioned patent application, it is used to simulate the scene and the corresponding environmental parameters based on real coal mine data, so as to achieve a real overall simulation effect of the coal mine underground. Although the above method can achieve a real simulation effect of the training environment, the overall unified simulation of all training exercise needs will cause the simulated training exercise environment to lack specificity, and cannot accurately simulate the user's special and personalized training exercise needs, thereby reducing the simulation effect of the training exercise environment.

[0005] In view of this, the present invention proposes an environment simulation system for training exercises to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: an environmental simulation system for training exercises, applied to an environmental simulator, comprising:

[0007] A feature extraction module, used for receiving comprehensive input data from the user end and extracting request features from the comprehensive input data;

[0008] A feature classification module is used to identify the feature attributes of the request features, which include basic attributes and optimized attributes, and to classify the request features into environmental features and individual features based on feature differentiation criteria;

[0009] The environment simulation module is used to simulate the basic exercise environment based on the environmental characteristics as the simulation standard, and optimize the basic exercise environment into a personalized exercise environment based on the individual characteristics as the optimization standard;

[0010] The data calculation module is used to collect comprehensive simulation data of the individual exercise environment under the effective simulation state, the comprehensive simulation data includes the explosion point trigger delay value, the eruption rate difference and the sound intensity difference, and calculate the environment simulation index;

[0011] A comparison and determination module is used to compare the environmental simulation index with the environmental simulation safety value and determine whether to perform an environmental anomaly tracking operation;

[0012] The point recognition module is used to identify optimized data from comprehensive simulation data and identify abnormal points from personalized exercise environments.

[0013] Furthermore, the method for extracting the request feature includes:

[0014] The data semantics of the comprehensive input data are identified one by one through natural language processing technology, and the data semantics are divided into text phrases and digital phrases according to different semantic types;

[0015] Record the data semantics that do not contain both text phrases and numeric phrases as useless semantics, and remove the comprehensive input data corresponding to the useless semantics;

[0016] Combining the text phrases and the number phrases in the remaining comprehensive input data in sequence to generate comprehensive features;

[0017] The state values ​​of the comprehensive features are queried one by one, and the comprehensive features with a state value of 001, 010 or 100 are recorded as request features, to obtain A request features.

[0018] Furthermore, the feature differentiation criteria are: the text phrases of the environmental features must contain the standard phrases, and the numerical phrases must be less than or equal to the standard values;

[0019] The methods of dividing environmental characteristics and personality characteristics include:

[0020] Compare the text phrases in the A request features with the standard phrases pre-stored in the database one by one;

[0021] Record the request feature containing any standard phrase in the text phrase as the feature to be verified, and record the numeric phrase in the feature to be verified as the feature value, to obtain B feature values;

[0022] Mark the text parts of the standard values ​​pre-stored in the database one by one, and record the standard values ​​whose text parts are consistent with the text phrases in the feature to be verified as comparison values, to obtain B comparison values;

[0023] The features to be verified whose feature values ​​are less than or equal to the comparison value are recorded as environmental features, and C environmental features are obtained. The remaining request features and features to be verified are recorded as personality features, and D personality features are obtained.

[0024] Furthermore, the simulation method of the basic exercise environment includes:

[0025] The environmental semantics of C environmental features are identified one by one through natural language processing technology, and the environmental features with environmental semantics of explosion point, smoke, sound and light are recorded as the first exercise element, the second exercise element, the third exercise element and the fourth exercise element respectively;

[0026] The simulation positions corresponding to the explosion point, smoke, sound and light are marked on the environmental simulator respectively, and are recorded as the first simulation position, the second simulation position, the third simulation position and the fourth simulation position;

[0027] The simulation values ​​of the first simulation bit, the second simulation bit, the third simulation bit and the fourth simulation bit are respectively adjusted to be consistent with the characteristic values ​​in the first exercise element, the second exercise element, the third exercise element and the fourth exercise element to obtain a basic exercise environment.

[0028] Further, the optimization method of the personalized exercise environment includes:

[0029] Mark D independent optimization positions in the basic exercise environment, and number the D optimization positions in ascending order according to the order of marking;

[0030] Two cells are established in the D optimized positions respectively, which are recorded as the first cell and the second cell;

[0031] According to the order of numbering from small to large, the same words as the word phrases in the D personality characteristics are annotated in the first cells of the D optimization positions, and the same numbers as the number phrases in the D personality characteristics are annotated in the second cells of the D optimization positions, to obtain the optimized D optimization positions;

[0032] An interactive channel is established between two adjacent optimized positions to obtain D-1 interactive channels, and simulation cycles with the same duration are set on the D-1 interactive channels to generate a personalized exercise environment.

[0033] Furthermore, the method for obtaining the explosion point trigger delay value includes:

[0034] Mark the E explosion points in the personalized exercise environment one by one, and record the time when the human infrared sensor configured at the explosion point receives the human infrared signal for the first time as the starting time, and obtain E starting times;

[0035] The time when the explosion operation is performed at the E explosion points is queried one by one through the data concentrator to obtain E explosion times, and the time between the E starting times and the E explosion times is recorded as the trigger time to obtain E trigger time;

[0036] After subtracting the E trigger durations from the preset standard durations, E sub-delay values ​​are obtained. After removing the maximum and minimum sub-delay values, the remaining E-2 sub-delay values ​​are accumulated and averaged to obtain the explosion point trigger delay value;

[0037] The expression of the explosion point trigger delay value is:

[0038]

[0039] In the formula, ZD ys is the delay value of the explosion point trigger, CF sce is the e-th sub-delay value, BZ sc The preset standard duration.

[0040] Furthermore, the method for obtaining the eruption rate difference includes:

[0041] Mark the F smoke points in the personalized exercise environment one by one, and record the smoke ejectors configured at the smoke points as target ejectors, and obtain F target ejectors;

[0042] According to the order of marking, the initial rates of smoke emission of F target emitters are queried in sequence through the data concentrator to obtain F real-time rates;

[0043] Subtract the preset standard rate from the F real-time rates in turn to obtain F sub-rate differences, and then add up the F sub-rate differences and average them to obtain the eruption rate difference;

[0044] The expression for the difference in eruption rate is:

[0045]

[0046] In the formula, PF sl is the eruption rate difference, BZ sl is the preset standard rate, SS slf is the fth real-time rate.

[0047] Furthermore, the method for obtaining the sound intensity difference includes:

[0048] The maximum decibels of the sounds at E explosion points at E explosion times are queried one by one through the data concentrator to obtain E real-time decibels;

[0049] Subtract the E real-time decibels from the preset standard decibels in turn and take the absolute value to obtain E sub-intensity difference values, and then accumulate and average the E sub-intensity difference values ​​to obtain the sound intensity difference value;

[0050] The expression of the sound intensity difference is:

[0051]

[0052] In the formula, ST qd is the sound intensity difference, SS qde is the e-th real-time decibel, BZ qd It is the preset standard decibel.

[0053] Furthermore, the expression of the environmental simulation index is:

[0054]

[0055] In the formula, MN zs is the environmental simulation index, ρ1, ρ2, and ρ3 are all proportional coefficients greater than 0;

[0056] The determination method of whether to perform the environmental anomaly tracking operation includes:

[0057] Environmental simulation index MN zs Simulate safety value MN with the environment aq Compare;

[0058] When MN zs Greater than or equal to MN aq When the abnormality tracking operation of the execution environment is determined;

[0059] When MN zs Less than MN aq When the environment abnormality tracking operation is determined not to be performed.

[0060] Further, the identification method of the optimized data includes:

[0061] In the personalized exercise environment, the trigger duration that is greater than the preset standard duration is recorded as the first abnormal data, and the number of the first abnormal data is counted to obtain the first abnormal value;

[0062] One half of the trigger duration is recorded as the first safety value. When the first abnormal value is greater than the first safety value, the explosion point trigger delay value is recorded as the optimization data.

[0063] Recording the real-time rate that is less than the preset standard rate as the second abnormal data, and counting the number of the second abnormal data to obtain a second abnormal value;

[0064] One half of the real-time rate is recorded as the second safety value, and when the second abnormal value is greater than the second safety value, the eruption rate difference is recorded as the optimized data;

[0065] Recording the real-time decibel that is less than or greater than the preset standard decibel as third abnormal data, and counting the number of the third abnormal data to obtain a third abnormal value;

[0066] One half of the real-time decibel value is recorded as the third safety value, and when the third abnormal value is greater than the third safety value, the sound intensity difference is recorded as the optimization data.

[0067] The technical effects and advantages of the environmental simulation system for training exercises of the present invention are as follows:

[0068] The present invention receives comprehensive input data from a user end and extracts request features from the comprehensive input data, thereby simplifying the representation of complex and lengthy comprehensive input data and eliminating a large amount of useless data, thereby narrowing the use scope of simulation data. The present invention identifies the characteristic attributes of the request features and divides the request features into environmental features and individual features based on a characteristic distinction criterion. The basic exercise environment is simulated using the environmental features as a simulation standard, and the basic exercise environment is optimized into an individual exercise environment using the individual features as an optimization standard, so that the environmental features and the individual features can respectively represent the conventional needs and individual needs of training exercises, and the user end's personalized special training exercises are satisfied through the constructed individual exercise environment. The needs are met, thereby improving the matching degree between the personalized exercise environment and the user-side needs. Under the effective simulation state, the comprehensive simulation data of the personalized exercise environment is collected, and the environmental simulation index is calculated. The environmental simulation index is compared with the environmental simulation safety value, and it is determined whether to perform the environmental anomaly tracking operation. The optimization data is identified from the comprehensive simulation data, and the abnormal points are identified from the personalized exercise environment. Through the collection and calculation of the comprehensive simulation data, the stability and reliability of the personalized exercise environment can be accurately calculated, and the effect of environmental simulation analysis and evaluation of the environmental simulation system can be achieved. According to the evaluation results, the abnormal points are tracked and located, thereby providing a basis for the subsequent optimization and adjustment to improve the stability and reliability of the environmental simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of an environment simulation system for training exercises provided in Embodiment 1 of the present invention;

[0070] Figure 2 A schematic diagram of an environment simulation method for training exercises provided in Embodiment 2 of the present invention;

[0071] Figure 3A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention;

[0072] Figure 4 A schematic diagram of the structure of a computer-readable storage medium provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Example 1: Please refer to Figure 1 As shown, the environment simulation system for training exercises described in this embodiment is applied to an environment simulator, including:

[0075] A feature extraction module receives comprehensive input data from the user end and extracts request features from the comprehensive input data;

[0076] The user end refers to the operating user who uses the environmental simulation system to conduct training exercises and can input original user data into the environmental simulator. At this time, the original user data is recorded as comprehensive input data, so that all training exercise data input by the user end to the environmental simulator can be represented as a whole, and the comprehensive input data can also be used as the data basis for subsequent simulations;

[0077] Request features refer to the key information in the comprehensive input data that can affect the success of the training exercise environment simulation. They can also accurately simplify the complex and lengthy comprehensive input data and serve as a reference for subsequent training exercise environment simulation.

[0078] The methods for extracting request features include:

[0079] The data semantics of the comprehensive input data are identified one by one through natural language processing technology, and the data semantics are divided into text phrases and numeric phrases according to different semantic types; data semantics is used to concisely express the true meaning expressed in the comprehensive input data, and data semantics includes but is not limited to multiple types of data such as text, numbers, and graphics, while text phrases and numeric phrases are the basic units that constitute data semantics and are used to express the text and numbers in the comprehensive input data;

[0080] Record the data semantics that do not contain both text phrases and numeric phrases as useless semantics, and remove the comprehensive input data corresponding to the useless semantics;

[0081] Combining the text phrases and the number phrases in the remaining comprehensive input data in sequence to generate comprehensive features;

[0082] The status values ​​of the comprehensive features are queried one by one, and the comprehensive features with status values ​​of 001, 010 or 100 are recorded as request features to obtain A request features. The status value is a numerical representation of whether the comprehensive feature is of the request type. Specifically, the status values ​​include 001, 010, 100, 110, 101 and 011, among which, when the status value is 110, 101 and 011, it indicates that the comprehensive feature is not a request feature.

[0083] It should be noted that the request features obtained are used to represent the direct features of the training exercise environment simulation in the comprehensive input data, so that it can directly determine whether the training exercise environment can be successfully simulated. Therefore, the request features at this time will contain multi-dimensional and multi-style data of the simulated training exercise environment, so that the number of request features is not unique.

[0084] A feature classification module identifies the feature attributes of the request feature and classifies the request feature into environmental features and personality features based on feature classification criteria;

[0085] Feature attributes are used to represent the specific meaning of request features in the simulation of the training exercise environment, which can effectively and accurately distinguish request features with different meanings, thereby effectively distinguishing mixed request features, facilitating subsequent efficient and accurate simulation operations of the training exercise environment;

[0086] Feature attributes include basic attributes and optimized attributes; specifically, basic attributes refer to the basic data of the environment simulation in the training exercise environment simulation of the requested feature, which is used to ensure that the framework of the training exercise environment can be initialized and simulated, and provide a basis for subsequent optimization and supplementation; optimized attributes refer to the optimized data of the environment simulation in the training exercise environment simulation of the requested feature, which is used to ensure that the training exercise environment is more in line with the training exercise usage requirements of the user end and improve the pertinence of the training exercise environment;

[0087] After identifying the basic attributes and optimization attributes, it is necessary to distinguish the request features according to different feature attributes, so that the request features can be divided into environmental features and individual features. At this time, the environmental features are used to represent the basic framework of the training and exercise environment, and the individual features are used to represent the user needs of the training and exercise environment.

[0088] In order to provide accuracy in the division of environmental features and personality features, it is necessary to divide the request features under the constraints of feature differentiation criteria;

[0089] The feature differentiation criteria are: the text phrases of the environmental features must contain the standard phrases, and the digital phrases must be less than or equal to the standard values; this ensures that the divided environmental features can meet the simulation requirements of the general and conventional training and exercise environment, thereby ensuring that the initial training and exercise environment framework can be quickly simulated;

[0090] The methods of dividing environmental characteristics and personality characteristics include:

[0091] Compare the text phrases in the A request features one by one with the standard phrases pre-stored in the database; the pre-stored standard phrases refer to the text phrases corresponding to the environmental features pre-stored in the database, so as to serve as the basis for subsequent text phrase comparison;

[0092] Record the request feature containing any standard phrase in the text phrase as the feature to be verified, and record the numeric phrase in the feature to be verified as the feature value, to obtain B feature values;

[0093] Mark the text parts of the standard values ​​pre-stored in the database one by one, and record the standard values ​​whose text parts are consistent with the text phrases in the feature to be verified as comparison values, and obtain B comparison values; the pre-stored standard values ​​refer to the digital phrases corresponding to the environmental features pre-stored in the database, so as to serve as the basis for subsequent digital phrase comparison;

[0094] The features to be verified whose feature values ​​are less than or equal to the comparison value are recorded as environmental features, and C environmental features are obtained. The remaining request features and features to be verified are recorded as personality features, and D personality features are obtained.

[0095] It should be noted that the sum of the number of environmental features and the number of individual features is the number of requested features, and the number of environmental features is usually greater than the number of individual features, which can ensure that the requested features can be completely divided and that there are enough environmental features to simulate the initial training exercise environment.

[0096] The environment simulation module uses environmental characteristics as the simulation standard to simulate the basic exercise environment, and uses individual characteristics as the optimization standard to optimize the basic exercise environment into an individual exercise environment;

[0097] The basic exercise environment is a training exercise environment simulated according to environmental characteristics, so that the basic exercise environment can meet other training exercise requirements of the user side except for personalized requirements, and provide a good foundation for the accurate simulation of subsequent personalized exercise environments;

[0098] When simulating the basic exercise environment, it is necessary to base it on the environmental characteristics so that the simulated basic exercise environment can be consistent with and adapt to the environmental characteristics;

[0099] The simulation methods of the basic exercise environment include:

[0100] The environmental semantics of C environmental features are identified one by one through natural language processing technology, and the environmental features with environmental semantics of explosion point, smoke, sound and light are recorded as the first exercise element, the second exercise element, the third exercise element and the fourth exercise element respectively;

[0101] The simulation positions corresponding to the explosion point, smoke, sound and light are marked on the environmental simulator, which are recorded as the first simulation position, the second simulation position, the third simulation position and the fourth simulation position; the simulation position refers to the position in the environmental simulator used to import the environmental characteristics corresponding to the conventional training and exercise requirements of the user end, so as to ensure that the conventional training and exercise requirements of the user end can be effectively simulated;

[0102] The simulation values ​​of the first simulation bit, the second simulation bit, the third simulation bit and the fourth simulation bit are adjusted to be consistent with the characteristic values ​​of the first exercise element, the second exercise element, the third exercise element and the fourth exercise element, respectively, to obtain the basic exercise environment. The simulation value is used to represent the specific numerical value on the simulation bit, that is, to represent the size and quantity of the corresponding explosion point, smoke, sound and light required by the user's conventional training exercise.

[0103] After the basic exercise environment is simulated, the basic exercise environment can only represent the basic training exercise needs of the user end, and cannot meet the personalized training exercise needs of the user end. Therefore, it is necessary to optimize the basic exercise environment into a personalized exercise environment based on the basic exercise environment and take the individual characteristics as the optimization standard, so that the personalized exercise environment can more accurately meet the actual training exercise needs of the user end;

[0104] Methods for optimizing the personalized exercise environment include:

[0105] Mark D independent optimization positions in the basic exercise environment, and number the D optimization positions in ascending order according to the order of marking; the optimization position refers to the position in the environment simulator used to import the personality characteristics corresponding to the personality training exercise requirements of the user end, so as to ensure that the personality training exercise requirements of the user end can be effectively simulated;

[0106] Two cells are established in the D optimized positions respectively, which are recorded as the first cell and the second cell;

[0107] According to the order of numbering from small to large, the same words as the word phrases in the D personality characteristics are annotated in the first cells of the D optimization positions, and the same numbers as the number phrases in the D personality characteristics are annotated in the second cells of the D optimization positions, to obtain the optimized D optimization positions;

[0108] An interactive channel is established between two adjacent optimized positions to obtain D-1 interactive channels, and a simulation cycle with the same duration is set on each of the D-1 interactive channels to generate a personalized exercise environment. The interactive channel is used to transmit data between two adjacent optimized positions, which can ensure that the simulated data in the personalized exercise environment can flow to each other. The simulation cycle refers to the duration of the collection and analysis of the simulated data in the interactive channel, which can ensure that the personalized exercise environment can effectively simulate the operation and achieve the effect of timed data collection.

[0109] It should be noted that when the basic exercise environment is optimized to a personalized optimized environment, the personalized exercise environment at this time can meet the personalized training and exercise needs of the user end, ensuring that the user end can perform real and effective training and exercise operations in the simulated personalized exercise environment.

[0110] The data calculation module collects comprehensive simulation data of the individual exercise environment under effective simulation conditions and calculates the environmental simulation index; the comprehensive simulation data includes the explosion point trigger delay value, the eruption rate difference and the sound intensity difference;

[0111] The effective simulation state refers to the state in which the personalized exercise environment is in the simulation cycle, ensuring that the relevant data in the personalized exercise environment at this time can be effectively collected and analyzed, avoiding omissions and losses of relevant data, and ensuring the accuracy of the subsequent analysis of the simulation effect of the personalized exercise environment. On the contrary, when the personalized exercise environment is not in the simulation cycle, the relevant data in the personalized exercise environment at this time cannot be effectively and accurately collected;

[0112] Comprehensive simulation data is the relevant change data of the personalized exercise environment when conducting training exercises under effective simulation state, which can provide the basis for analyzing and evaluating the actual simulation stability and reliability of the personalized exercise environment, and thus be used to evaluate the simulation effect of the personalized exercise environment;

[0113] Comprehensive simulation data include explosion point trigger delay value, eruption rate difference and sound intensity difference;

[0114] The explosion point trigger delay value refers to the difference between the time from the explosion point receiving the trigger signal to the explosion point executing the explosion operation in the personalized exercise environment and the standard time, which can represent the explosion point trigger time in the personalized exercise environment. The larger the explosion point trigger delay value, the worse the simulation stability of the personalized exercise environment, and the larger the environment simulation index;

[0115] Methods for obtaining the explosion point trigger delay value include:

[0116] Mark the E explosion points in the personalized exercise environment one by one, and record the time when the human infrared sensor configured at the explosion point receives the human infrared signal for the first time as the starting time, and obtain E starting times;

[0117] The time when the explosion operation is performed at the E explosion points is queried one by one through the data concentrator to obtain E explosion times, and the time between the E starting times and the E explosion times is recorded as the trigger time to obtain E trigger time;

[0118] After subtracting the E trigger durations from the preset standard durations, E sub-delay values ​​are obtained. After removing the maximum and minimum sub-delay values, the remaining E-2 sub-delay values ​​are accumulated and averaged to obtain the explosion point trigger delay value. The preset standard duration refers to the maximum value of the trigger duration in the individual exercise environment under high stability and high reliability conditions, so that the actual trigger duration can be accurately determined, thereby improving the calculation accuracy of the explosion point trigger delay value.

[0119] The expression of the explosion point trigger delay value is:

[0120]

[0121] In the formula, ZD ys is the delay value of the explosion point trigger, CF sce is the e-th sub-delay value, BZ sc The preset standard duration.

[0122] The difference in eruption rate refers to the difference between the rate at which the smoke point in the personalized exercise environment performs the smoke eruption operation and the standard rate, which can represent the eruption rate of the smoke point in the personalized exercise environment. The larger the difference in eruption rate, the worse the simulation stability of the personalized exercise environment, and the larger the environmental simulation index;

[0123] Methods for obtaining the eruption rate difference include:

[0124] Mark the F smoke points in the personalized exercise environment one by one, and record the smoke ejectors configured at the smoke points as target ejectors, and obtain F target ejectors;

[0125] According to the order of marking, the initial rates of smoke emission of F target emitters are queried in sequence through the data concentrator to obtain F real-time rates;

[0126] After subtracting the preset standard rate from the F real-time rates in turn, F sub-rate differences are obtained, and the F sub-rate differences are accumulated and averaged to obtain the eruption rate difference; the preset standard rate refers to the minimum value of the real-time rate under the high stability and high reliability state of the individual exercise environment, so that the actual real-time rate size can be accurately determined, and the calculation accuracy of the eruption rate difference is improved;

[0127] The expression for the difference in eruption rate is:

[0128]

[0129] In the formula, PF sl is the eruption rate difference, BZ sl is the preset standard rate, SS slf is the fth real-time rate.

[0130] The sound intensity difference refers to the difference between the sound decibel emitted by the sonic boom point in the personalized exercise environment and the standard decibel, which can be used to represent the sound intensity in the personalized exercise environment. The larger the sound intensity difference, the worse the simulation stability of the personalized exercise environment, and the larger the environmental simulation index;

[0131] Methods for obtaining the sound intensity difference include:

[0132] The maximum decibels of the sounds at E explosion points at E explosion times are queried one by one through the data concentrator to obtain E real-time decibels;

[0133] Subtract the E real-time decibels from the preset standard decibels in turn and take the absolute value to obtain E sub-intensity difference values, and then add up the E sub-intensity difference values ​​and average them to obtain the sound intensity difference value; the preset standard decibel refers to the constant value of the real-time decibel in the individual exercise environment under the state of high stability and high reliability, so that the actual real-time decibel size can be accurately determined, and the calculation accuracy of the sound intensity difference value can be improved;

[0134] The expression of the sound intensity difference is:

[0135]

[0136] In the formula, SY qd is the sound intensity difference, SS qde is the e-th real-time decibel, BZ qd It is the preset standard decibel.

[0137] When the explosion point trigger delay value, the eruption rate difference and the sound intensity difference are obtained, the environment simulation index can be calculated according to the explosion point trigger delay value, the eruption rate difference and the sound intensity difference, so that the environment simulation index can numerically represent the simulation stability and reliability of the personalized exercise environment, and serve as a basis for judging whether the personalized exercise environment can meet the user's requirements for high stability and high reliability of training exercises;

[0138] The expression of environmental simulation index is:

[0139]

[0140] In the formula, MN zs is the environmental simulation index, ρ1, ρ2, and ρ3 are all proportional coefficients greater than 0;

[0141] Among them, ρ1+ρ2+ρ3=1, and the settings of ρ1, ρ2, and ρ3 are to adjust the proportion of the explosion point trigger delay value, the eruption rate difference, and the sound intensity difference in the environmental simulation index, so as to ensure that the explosion point trigger delay value, the eruption rate difference, and the sound intensity difference can cause corresponding changes in the environmental simulation index when they change, thereby improving the calculation accuracy of the environmental simulation index.

[0142] A comparison and determination module compares the environmental simulation index with the environmental simulation safety value and determines whether to perform an environmental anomaly tracking operation;

[0143] After the environmental simulation index is calculated, the environmental simulation index can be compared with the preset environmental simulation safety value, and based on the comparison result, it is decided whether to perform the environmental anomaly tracking operation. The environmental anomaly tracking operation refers to the tracing operation of the data and factors that cause the low stability and reliability when the stability and reliability of the individual exercise environment are poor, so as to achieve the abnormal tracking effect of the environmental simulation system;

[0144] The determination method of whether to perform the environmental anomaly tracking operation includes:

[0145] Environmental simulation index MN zs Simulate safety value MN with the environment aq Comparison; The environmental simulation safety value refers to the maximum value of the environmental simulation index of the individual exercise environment under high stability and high reliability, which can provide a basis for comparison of whether the real-time environmental simulation index is within the normal range, so as to accurately determine whether to perform environmental abnormality tracking operations;

[0146] When MN zs Greater than or equal to MN aq When , it means that the environmental simulation index of the personalized exercise environment exceeds the maximum value of the environmental simulation index under the conditions of high stability and high reliability. At this time, the stability and reliability of the personalized exercise environment are abnormal, and it is determined that the environmental abnormality tracking operation is executed;

[0147] When MN zs Less than MN aq When , it indicates that the environmental simulation index of the personalized exercise environment does not exceed the maximum value of the environmental simulation index under high stability and high reliability. At this time, there is no abnormality in the stability and reliability of the personalized exercise environment, and it is determined that the environmental abnormality tracking operation will not be performed.

[0148] The point recognition module identifies optimized data from comprehensive simulation data and identifies abnormal points from the personalized exercise environment based on the optimized data;

[0149] Optimization data refers to specific comprehensive simulation data that can have a negative impact on the high stability and high reliability of the individual exercise environment, and serves as the basis for subsequent optimization and adjustment of the environmental simulation system, thereby improving the simulation stability and reliability of the environmental simulation system for the training exercise environment;

[0150] Identification methods for optimizing data include:

[0151] In the personalized exercise environment, the trigger duration that is greater than the preset standard duration is recorded as the first abnormal data, and the number of the first abnormal data is counted to obtain the first abnormal value;

[0152] One half of the trigger duration is recorded as the first safety value. When the first abnormal value is greater than the first safety value, it means that the trigger duration exceeds the preset standard duration by a large number, and the explosion point trigger delay value is recorded as the optimization data.

[0153] Recording the real-time rate that is less than the preset standard rate as the second abnormal data, and counting the number of the second abnormal data to obtain a second abnormal value;

[0154] One half of the real-time rate is recorded as the second safety value. When the second abnormal value is greater than the second safety value, it means that the real-time rate is lower than the preset standard rate by a large number, and the difference in the eruption rate is recorded as the optimization data.

[0155] Recording the real-time decibel that is less than or greater than the preset standard decibel as third abnormal data, and counting the number of the third abnormal data to obtain a third abnormal value;

[0156] One half of the real-time decibel number is recorded as the third safety value. When the third abnormal value is greater than the third safety value, it means that the real-time decibel is greater than or less than the preset standard decibel by a large amount, and the sound intensity difference is recorded as optimization data.

[0157] Abnormal points refer to explosion points or smoke points that cause low stability and reliability of the individual exercise environment. At this time, the abnormal points can be used as corresponding points for subsequent optimization and adjustment of the environmental simulation system;

[0158] Specifically, when the optimized data is the explosion point trigger delay value, the abnormal points are concentrated on the explosion points. At this time, all explosion points whose trigger duration is longer than the preset standard duration are recorded as abnormal points. When the optimized data is the eruption rate difference, the abnormal points are concentrated on the smoke points. At this time, all smoke points whose real-time rate is less than the preset standard rate are recorded as abnormal points. When the optimized data is the sound intensity difference, the abnormal points are concentrated on the explosion points. At this time, all explosion points whose real-time decibels are greater than or less than the preset standard decibels are recorded as abnormal points.

[0159] It should be noted that after the abnormal points are identified, corresponding optimization and adjustment operations need to be performed on the abnormal points to improve the stability and reliability of the environmental simulation system for the personalized exercise environment simulation of the user end. For example, when the optimization data is the explosion point trigger delay value, the human infrared sensor, explosion point controller and other related equipment at the abnormal point need to be inspected or replaced. When the optimization data is the eruption rate difference, the smoke sprayer and other related equipment at the abnormal point need to be inspected or replaced. When the optimization data is the sound intensity difference, the amount of explosives and the installation position of the explosives at the abnormal point need to be inspected or replaced.

[0160] In this embodiment, by receiving the comprehensive input data of the user end and extracting the request features from the comprehensive input data, it is possible to simplify the representation of the complex and lengthy comprehensive input data, and at the same time eliminate a large amount of useless data, thereby narrowing the scope of use of the simulation data. By identifying the characteristic attributes of the request features and based on the characteristic distinction criteria, the request features are divided into environmental features and individual features. The basic exercise environment is simulated with the environmental features as the simulation standard, and the basic exercise environment is optimized into the individual exercise environment with the individual features as the optimization standard, so that the environmental features and the individual features can respectively represent the conventional needs and individual needs of the training exercises, and the constructed individual exercise environment can meet the personalized special training exercises of the user end. Learning needs are met, thereby improving the matching degree between the personalized exercise environment and the user-side needs. Under the effective simulation state, the comprehensive simulation data of the personalized exercise environment is collected, and the environmental simulation index is calculated. The environmental simulation index is compared with the environmental simulation safety value, and it is determined whether to perform the environmental anomaly tracking operation, and the optimization data is identified from the comprehensive simulation data, and the abnormal points are identified from the personalized exercise environment. Through the collection and calculation of the comprehensive simulation data, the stability and reliability of the personalized exercise environment can be accurately calculated, and the effect of environmental simulation analysis and evaluation of the environmental simulation system can be achieved. According to the evaluation results, the abnormal points are tracked and located, thereby providing a basis for the subsequent optimization and adjustment to improve the stability and reliability of the environmental simulation.

[0161] Example 2: Please refer to Figure 2As shown, the part not described in detail in this embodiment is described in the first embodiment, and an environment simulation method for training exercises is provided, which is applied to an environment simulator and is implemented based on an environment simulation system for training exercises, including:

[0162] S1: receiving comprehensive input data from the user end and extracting request features from the comprehensive input data;

[0163] S2: identifying characteristic attributes of the request features, which include basic attributes and optimized attributes, and dividing the request features into environmental features and individual features based on the characteristic distinction criteria;

[0164] S3: Taking environmental characteristics as simulation standards, simulate the basic exercise environment, and taking individual characteristics as optimization standards, optimize the basic exercise environment into an individual exercise environment;

[0165] S4: Under the effective simulation state, collect the comprehensive simulation data of the individual exercise environment, the comprehensive simulation data includes the explosion point trigger delay value, the eruption rate difference and the sound intensity difference, and calculate the environment simulation index;

[0166] S5: Compare the environment simulation index with the environment simulation safety value, and determine whether to perform an environment anomaly tracking operation;

[0167] S6: If an environmental anomaly tracking operation is performed, the optimized data is identified from the comprehensive simulation data, and the abnormal points are identified from the personalized exercise environment.

[0168] Example 3: Please refer to Figure 3 As shown, this embodiment discloses an electronic device, including a processor and a memory;

[0169] Wherein, the memory stores a computer program that can be called by the processor;

[0170] The processor executes the method for simulating an environment for training exercises by calling the computer program stored in the memory.

[0171] Since the electronic device introduced in this embodiment is an electronic device used to implement an environmental simulation method for training exercises in Example 2 of the present application, based on the environmental simulation method for training exercises introduced in the embodiment of the present application, a person skilled in the art can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as a person skilled in the art implements an electronic device used in an environmental simulation method for training exercises in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0172] Example 4: Please refer to Figure 4 As shown, this embodiment discloses a computer-readable storage medium on which a rewritable computer program is stored;

[0173] When the computer program is executed, the method for simulating an environment for training exercises is implemented.

[0174] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An environmental simulation system for training exercises, applied to an environmental simulator, characterized in that: include: A feature extraction module, used for receiving comprehensive input data from the user end and extracting request features from the comprehensive input data; A feature classification module is used to identify the feature attributes of the request features, which include basic attributes and optimized attributes, and to classify the request features into environmental features and individual features based on feature differentiation criteria; The environment simulation module is used to simulate the basic exercise environment based on the environmental characteristics as the simulation standard, and optimize the basic exercise environment into a personalized exercise environment based on the individual characteristics as the optimization standard; The data calculation module is used to collect comprehensive simulation data of the individual exercise environment under the effective simulation state, the comprehensive simulation data includes the explosion point trigger delay value, the eruption rate difference and the sound intensity difference, and calculate the environment simulation index; A comparison and determination module is used to compare the environmental simulation index with the environmental simulation safety value and determine whether to perform an environmental anomaly tracking operation; The point recognition module is used to identify optimized data from comprehensive simulation data and identify abnormal points from personalized exercise environments.

2. The environmental simulation system for training exercises according to claim 1, characterized in that: The methods for extracting request features include: The data semantics of the comprehensive input data are identified one by one through natural language processing technology, and the data semantics are divided into text phrases and digital phrases according to different semantic types; Record the data semantics that do not contain both text phrases and numeric phrases as useless semantics, and remove the comprehensive input data corresponding to the useless semantics; Combining the text phrases and the number phrases in the remaining comprehensive input data in sequence to generate comprehensive features; The state values ​​of the comprehensive features are queried one by one, and the comprehensive features with a state value of 001, 010 or 100 are recorded as request features, to obtain A request features.

3. The environmental simulation system for training exercises according to claim 2, characterized in that: Feature differentiation criteria The text phrases of environmental characteristics must contain the standard phrases, and the numerical phrases must be less than or equal to the standard values; The methods of dividing environmental characteristics and personality characteristics include: Compare the text phrases in the A request features with the standard phrases pre-stored in the database one by one; Record the request feature containing any standard phrase in the text phrase as the feature to be verified, and record the numeric phrase in the feature to be verified as the feature value, to obtain B feature values; Mark the text parts of the standard values ​​pre-stored in the database one by one, and record the standard values ​​whose text parts are consistent with the text phrases in the feature to be verified as comparison values, to obtain B comparison values; The features to be verified whose feature values ​​are less than or equal to the comparison value are recorded as environmental features, and C environmental features are obtained. The remaining request features and features to be verified are recorded as personality features, and D personality features are obtained.

4. The environmental simulation system for training exercises according to claim 3, characterized in that: The simulation methods of the basic exercise environment include: The environmental semantics of C environmental features are identified one by one through natural language processing technology, and the environmental features with environmental semantics of explosion point, smoke, sound and light are recorded as the first exercise element, the second exercise element, the third exercise element and the fourth exercise element respectively; The simulation positions corresponding to the explosion point, smoke, sound and light are marked on the environmental simulator respectively, and are recorded as the first simulation position, the second simulation position, the third simulation position and the fourth simulation position; The simulation values ​​of the first simulation bit, the second simulation bit, the third simulation bit and the fourth simulation bit are respectively adjusted to be consistent with the characteristic values ​​in the first exercise element, the second exercise element, the third exercise element and the fourth exercise element to obtain a basic exercise environment.

5. The environmental simulation system for training exercises according to claim 4, characterized in that: Methods for optimizing the personalized exercise environment include: Mark D independent optimization positions in the basic exercise environment, and number the D optimization positions in ascending order according to the order of marking; Two cells are established in the D optimized positions respectively, which are recorded as the first cell and the second cell; According to the order of numbering from small to large, the same words as the word phrases in the D personality characteristics are annotated in the first cells of the D optimization positions, and the same numbers as the number phrases in the D personality characteristics are annotated in the second cells of the D optimization positions, to obtain the optimized D optimization positions; An interactive channel is established between two adjacent optimized positions to obtain D-1 interactive channels, and simulation cycles with the same duration are set on the D-1 interactive channels to generate a personalized exercise environment.

6. The environmental simulation system for training exercises according to claim 5, characterized in that: The methods for obtaining the explosion point trigger delay value include: Mark the E explosion points in the personalized exercise environment one by one, and record the time when the human infrared sensor configured at the explosion point receives the human infrared signal for the first time as the starting time, and obtain E starting times; The time when the explosion operation is performed at each of the E explosion points is queried one by one through the data concentrator to obtain E explosion times, and the time between the E starting times and the E explosion times is recorded as the trigger time, to obtain e trigger time; After subtracting the E trigger durations from the preset standard durations, E sub-delay values ​​are obtained. After removing the maximum and minimum sub-delay values, the remaining E-2 sub-delay values ​​are accumulated and averaged to obtain the explosion point trigger delay value; The expression of the explosion point trigger delay value is: In the formula, ZD ys is the delay value of the explosion point trigger, CF sce is the e-th sub-delay value, BZ sc The preset standard duration.

7. The environmental simulation system for training exercises according to claim 6, characterized in that: Methods for obtaining the eruption rate difference include: Mark the F smoke points in the personalized exercise environment one by one, and record the smoke ejectors configured at the smoke points as target ejectors, and obtain F target ejectors; According to the order of marking, the initial rates of smoke emission of F target emitters are queried in sequence through the data concentrator to obtain F real-time rates; Subtract the preset standard rate from the F real-time rates in turn to obtain F sub-rate differences, and then add up the F sub-rate differences and average them to obtain the eruption rate difference; The expression for the difference in eruption rate is: In the formula, PF sl is the eruption rate difference, BZ sl is the preset standard rate, SS slf is the fth real-time rate.

8. The environmental simulation system for training exercises according to claim 7, characterized in that: Methods for obtaining the sound intensity difference include: The maximum decibels of the sounds at E explosion points at E explosion times are queried one by one through the data concentrator to obtain E real-time decibels; Subtract the E real-time decibels from the preset standard decibels in turn and take the absolute value to obtain E sub-intensity difference values, and then accumulate and average the E sub-intensity difference values ​​to obtain the sound intensity difference value; The expression of the sound intensity difference is: In the formula, SY qd is the sound intensity difference, SS qde is the e-th real-time decibel, BZ qd It is the preset standard decibel.

9. The environmental simulation system for training exercises according to claim 8, characterized in that: The expression of environmental simulation index is: In the formula, MN zs is the environmental simulation index, ρ1, ρ2, and ρ3 are all proportional coefficients greater than 0; The determination method of whether to perform the environmental anomaly tracking operation includes: Environmental simulation index MN zs Simulate safety value MN with the environment aq Compare; When MN zs Greater than or equal to MN aq When the abnormality tracking operation of the execution environment is determined; When MN zs Less than MN aq When the environment abnormality tracking operation is determined not to be performed.

10. The environment simulation system for training exercises according to claim 9, characterized in that: Identification methods for optimizing data include: In the personalized exercise environment, the trigger duration that is greater than the preset standard duration is recorded as the first abnormal data, and the number of the first abnormal data is counted to obtain the first abnormal value; One half of the trigger duration is recorded as the first safety value. When the first abnormal value is greater than the first safety value, the explosion point trigger delay value is recorded as the optimization data. Recording the real-time rate that is less than the preset standard rate as the second abnormal data, and counting the number of the second abnormal data to obtain a second abnormal value; One half of the real-time rate is recorded as the second safety value, and when the second abnormal value is greater than the second safety value, the eruption rate difference is recorded as the optimized data; Recording the real-time decibel that is less than or greater than the preset standard decibel as third abnormal data, and counting the number of the third abnormal data to obtain a third abnormal value; One half of the real-time decibel value is recorded as the third safety value, and when the third abnormal value is greater than the third safety value, the sound intensity difference is recorded as the optimization data.

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