Combat joint debugging data judgment and evaluation integrated system based on system real-time simulation

Abstract

The invention provides a combat joint debugging data judgment and evaluation integrated system based on system real-time simulation, and relates to the technical field of simulation combat, a system structure is composed of a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system, data acquisition and integration are performed on various capability parameters of investigation, command control and combat equipment, the acquired data and a deep neural network are analyzed and integrated, an optimized scheme is given, and the neural network in the system is trained and learned by changing and adjusting the data, so that the accuracy of an output result is improved, and the accuracy of the system is improved. Through confluence analysis of the deep neural network, various capability parameters of investigation equipment, command and control equipment and combat equipment can be evaluated more comprehensively and accurately, and powerful support is provided for decision support and performance optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of combat simulation, and in particular to a combat joint debugging data determination and evaluation integrated system based on system real-time simulation. Background Art

[0002] According to a method and system for evaluating the combat effectiveness of an equipment system based on a sequential combat ring disclosed by Chinese patent number "CN112749496B", the method includes obtaining the equipment of the red and blue sides and their performance indicators and the connection relationship between the equipment during the combat process; dividing the equipment into four types: reconnaissance equipment S, decision-making equipment D, strike equipment A, and target equipment T, abstracting each equipment into a node, analyzing the association relationship between different equipment nodes and the time of their existence, and constructing a sequential combat ring in the equipment system; constructing a dynamic network structure of the equipment system based on the sequential combat ring; and evaluating the combat effectiveness of the equipment system on the dynamic network structure of the equipment system. The present invention takes into account the dynamic nature of the equipment system, expands the sequential combat ring on the basis of the static combat ring, and then calculates the combat effectiveness of the system based on the dynamic network structure of the system, analyzes the evolution of the combat effectiveness of the system over time, and its evaluation effect is more accurate when the dynamic network description is close to the combat reality.

[0003] The above patent documents and prior art have the following technical problems when used:

[0004] Problem 1: When used for combat simulation evaluation, the collection coverage of the parameters of the combat parties is insufficient, resulting in inaccurate collection of basic data, which in turn leads to low accuracy of the output effect.

[0005] The second problem is that during simulation training, the collection and analysis of different combat environment parameters are insufficient, resulting in the inability to generate corresponding plans in a timely manner or insufficient post-war evaluation and analysis, which is not conducive to the unified analysis and use of actual joint debugging data. Summary of the invention

[0006] Technical issues solved

[0007] In view of the shortcomings of the prior art, the present invention provides an integrated system for determining and evaluating combat joint debugging data based on real-time system simulation, which solves the following problems:

[0008] 1. The collected data is not accurate and the coverage is insufficient;

[0009] 2. Insufficient simulation analysis and poor environmental adaptability.

[0010] Technical Solution

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: an integrated system for combat joint commissioning data judgment and evaluation based on system real-time simulation, the system consists of a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system, wherein:

[0012] The reconnaissance simulation system detects, locates and identifies enemy attack targets, and collects data and simulates the system's reconnaissance capability A, which includes detection distance a1, terrain shielding a2, scanning speed a3, signal-to-noise ratio a4, resolution a5, false alarm rate a6 and detection probability a7;

[0013] The command simulation system integrates the information obtained from the reconnaissance and earthquake prevention system, analyzes the combat situation, issues combat orders, and collects data and simulates the command capability B in the system. The command capability B includes data processing rate b1, concurrent processing capability b2, and delay rate b3.

[0014] The combat simulation system receives the combat instructions of the command simulation system, destroys the enemy's attack targets, and collects data and performs simulation settings for the combat capability C in the system. The combat capability C includes firepower strike capability c1, mobility capability c2, survivability c3, and communication capability c4;

[0015] The data processing system receives and stores data from the reconnaissance simulation system, the command simulation system, and the combat simulation system, and normalizes and standardizes the data;

[0016] The intelligent judgment and evaluation system synchronizes the data in the reconnaissance simulation system, command simulation system, and combat simulation system in real time, and uses the TPN deep neural network model to judge the parameters, provide optimization solutions, and conduct evaluations of all parties after the operation.

[0017] Preferably, the calculation of the reconnaissance capability A of the reconnaissance simulation system further includes the following contents:

[0018] The calculation for the detection distance a1 is as follows:

[0019]

[0020] Among them, P is the transmit power, β is the receiving sensitivity, α is the antenna gain, S is the target reflection area, and ρ is the loss factor;

[0021] The calculation of terrain shielding a2 is as follows:

[0022]

[0023] Among them, R Mis the detection distance of low-altitude missile targets, in kilometers, and K1 is the shielding angle correction coefficient, which is:

[0024]

[0025] Among them, θ ZB is the shielding angle of the main defense sector, H P is the height above the ground, R P The radar is at a height of H P The maximum detection distance;

[0026] The calculation for the scanning speed a3 is as follows:

[0027]

[0028] Where S is the size of the scanning area, and V is the scanning rate;

[0029] The calculation for the signal-to-noise ratio a4 is as follows:

[0030]

[0031] Among them, P s is the target signal power, P o is the background noise power;

[0032] The calculation for resolution a5 is as follows:

[0033]

[0034] Where λ is the wavelength and D is the antenna aperture.

[0035] Preferably, the calculation of the reconnaissance capability B of the command simulation system further includes the following contents:

[0036] The calculation for the data processing rate b1 is as follows:

[0037]

[0038] Among them, W1 is the total amount of processed data, T1 is the processing completion time;

[0039] The calculation of the delay rate b3 is as follows:

[0040]

[0041] Among them, T2 is the time point when the processing is completed, and T3 is the time point when the information is input.

[0042] Preferably, the calculation of the reconnaissance capability C of the combat simulation system further includes the following contents:

[0043] The calculation of firepower strike capability c1 is as follows:

[0044]

[0045] Among them, c 11 is the weapon reaction time, c 12 is the kill zone indicator group, c 13 is the fire intensity index group, c 14 is the single-shot kill probability, K1 is the correction factor;

[0046] For c 12 The mathematical model of the kill zone indicator group is:

[0047]

[0048] Among them, c 121 , c 122 , c 123 , c 124 , c 125 , c 126 They are the upper boundary altitude, lower boundary altitude, far boundary slant distance, near boundary slant distance, maximum height and low angle, and maximum route angle of the kill zone;

[0049] For c 13 The mathematical model of the firepower intensity index is:

[0050]

[0051] Among them, c 131 is the target capacity, c 132 is the number of launchers, c 133 is the number of missiles installed, c 134 is a single target shot singular, c 135 Reload time for missiles;

[0052] The calculation of mobility c2 is as follows:

[0053]

[0054]

[0055] Among them, W is the engine power, M is the vehicle mass, f is the drag coefficient, V1 is the target speed, V2 is the initial speed, and a is the average acceleration.

[0056] Preferably, the calculation of the reconnaissance capability C of the combat simulation system further includes the following contents:

[0057] The calculation for the communication capacity c4 is as follows:

[0058]

[0059] Among them, P is the transmit power, β is the receiving sensitivity, α is the antenna gain, and L is the path loss

[0060] Preferably, the data processing system is internally provided with four groups of databases, three of which are connected to the command simulation system, the combat simulation system and the reconnaissance simulation system, and the other one integrates, analyzes and processes the remaining three groups.

[0061] Preferably, the intelligent judgment and evaluation system includes a judgment system and an evaluation system. The judgment system makes judgments based on data and provides a combat plan. The evaluation system evaluates the combat plan data, including an evaluation of combat effectiveness and combat risk indicators. The evaluation system also performs a secondary evaluation and summary of the entire combat data after the operation is completed.

[0062] Preferably, the TPN deep neural network performs convolution operations and data transmission on three channels, two of which use group convolution, and one channel uses the convolution structure of first reducing dimension and then increasing dimension in the traditional ResNet.

[0063] Beneficial Effects

[0064] The present invention provides an integrated system for determining and evaluating combat joint debugging data based on real-time system simulation. It has the following beneficial effects:

[0065] 1. The present invention adopts a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system to form a system structure. In the entire system structure, data collection and integration are performed on various capability parameters of reconnaissance, command and control and combat equipment, and the collected data is analyzed and integrated with a deep neural network to provide an optimization plan. By changing and adjusting the data, the neural network in the system is trained and learned to improve the accuracy of the output results. Through the integrated analysis of the deep neural network, we can more comprehensively and accurately evaluate the various capability parameters of the reconnaissance equipment, command and control equipment and combat equipment, and provide strong support for decision support and performance optimization.

[0066] 2. The present invention uses the entire system to evaluate and judge based on the simulated combat data, with the corresponding capability parameter characteristics of the reconnaissance, command and combat equipment as the benchmark. Compared with the traditional single data collection method, the data collection of the entire system is more accurate and more in line with combat needs, so that the final accuracy is higher. The coverage of multi-parameter characteristics enables the entire system to perform real-time data analysis and evaluation of different combat environments and multilateral combat conditions when in use, with high adaptability and flexibility.

[0067] 3. The present invention adopts a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system to form a system structure. After the reconnaissance simulation system, the command simulation system and the combat simulation system are standardized by the data processing system, a data basis is provided for the intelligent judgment and evaluation system. By continuously adjusting the weights and parameters, repeated training simulations are performed. By training the TPN deep network model, the network data is transmitted and calculated layer by layer to obtain the integrated output result, so as to minimize the prediction error, improve the accuracy and intelligent system, and provide a more accurate and appropriate evaluation plan and analysis for the combat environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a system structure diagram of the present invention;

[0069] Figure 2 This is a structural diagram of the TPN deep neural network of the present invention. DETAILED DESCRIPTION

[0070] 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. Specific embodiment one:

[0072] like Figure 1-2 As shown in the figure, the integrated system of combat joint commissioning data judgment and evaluation based on system real-time simulation consists of a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system, among which:

[0073] The reconnaissance simulation system detects, locates and identifies enemy invading targets, and collects data and simulates the system's reconnaissance capability A, which includes detection distance a1, terrain shielding a2, scanning speed a3, signal-to-noise ratio a4, resolution a5, false alarm rate a6 and detection probability a7;

[0074] The command simulation system integrates the information obtained from the reconnaissance and anti-seismic system, analyzes the combat situation, issues combat orders, and collects data and simulates the command capability B in the system. The command capability B includes data processing rate b1, concurrent processing capability b2, and delay rate b3.

[0075] The combat simulation system receives the combat instructions of the command simulation system, destroys the enemy's attack targets, and collects data and simulates the combat capability C in the system. The combat capability C includes firepower strike capability c1, mobility capability c2, survivability c3, and communication capability c4;

[0076] The data processing system receives and stores the data of the reconnaissance simulation system, command simulation system, and combat simulation system, and normalizes and standardizes the data;

[0077] The intelligent judgment and evaluation system synchronizes the data in the reconnaissance simulation system, command simulation system, and combat simulation system in real time, and uses the TPN deep neural network model to judge the parameters, provide optimization solutions, and conduct evaluations of all parties after the operation.

[0078] In the entire system structure, data is collected and integrated for various capability parameters of reconnaissance, command and control, and combat equipment. After the collected data is analyzed and integrated with the deep neural network, an optimization plan is given. By changing and adjusting the data, the neural network in the system is trained and learned to improve the accuracy of the output results. Through the integrated analysis of deep neural networks, we can more comprehensively and accurately evaluate the various capability parameters of reconnaissance equipment, command and control equipment, and combat equipment, and provide strong support for decision support and performance optimization. Specific embodiment 2:

[0080] like Figure 1-2 As shown, the calculation method and content of various parameters of the reconnaissance capability A of the entire reconnaissance simulation system are as follows:

[0081] The calculation of the reconnaissance capability A of the reconnaissance simulation system further includes the following contents:

[0082] The calculation for the detection distance a1 is as follows:

[0083]

[0084] Among them, P is the transmit power, β is the receiving sensitivity, α is the antenna gain, S is the target reflection area, and ρ is the loss factor;

[0085] The calculation of terrain shielding a2 is as follows:

[0086]

[0087] Among them, R M is the detection distance of low-altitude missile targets, in kilometers, and K1 is the shielding angle correction coefficient, which is:

[0088]

[0089] Among them, θZB is the shielding angle of the main defense sector, H P is the height above the ground, R P The radar is at a height of H P The maximum detection distance;

[0090] The calculation for the scanning speed a3 is as follows:

[0091]

[0092] Where S is the size of the scanning area, and V is the scanning rate;

[0093] The calculation for the signal-to-noise ratio a4 is as follows:

[0094]

[0095] Among them, P s is the target signal power, P o is the background noise power;

[0096] The calculation for resolution a5 is as follows:

[0097]

[0098] Where λ is the wavelength and D is the antenna aperture.

[0099] The false alarm rate a6 and detection probability a7 corresponding to the detection capability A are usually estimated based on statistical models, taking into account factors such as equipment performance, environmental noise and target characteristics. These formulas are usually not directly calculated through formulas, but are obtained through analysis of a large amount of test data. They have fixed values ​​for specific environments and can be directly brought in and normalized when used. Specific embodiment three:

[0101] like Figure 1-2 As shown, the calculation method and content of various parameters of the command and control capability B of the entire command simulation system are as follows:

[0102] The calculation of the command simulation system reconnaissance capability B further includes the following:

[0103] The calculation for the data processing rate b1 is as follows:

[0104]

[0105] Among them, W1 is the total amount of processed data, T1 is the processing completion time;

[0106] The calculation of the delay rate b3 is as follows:

[0107] b3=T2-T3(8)

[0108] Among them, T2 is the time point when the processing is completed, and T3 is the time point when the information is input.

[0109] The calculation of concurrent processing capability b2 is usually measured by the number of concurrent tasks or data streams supported by the device. In actual use, it is determined by the amount of tasks received by the command simulation system.

[0110] At the same time, in addition to the above three parameters, the command and control capability B of the command simulation system can also calculate and count resource utilization, error rate, etc. These two parts can generally be obtained directly through the system without additional calculation. Specific embodiment four:

[0112] like Figure 1-2 As shown, the calculation method and content of various parameters of the command and control capability B of the entire combat simulation system are as follows:

[0113] The calculation of the combat simulation system reconnaissance capability C further includes the following:

[0114] The calculation of firepower strike capability c1 is as follows:

[0115]

[0116] Among them, c 11 is the weapon reaction time, c 12 is the kill zone indicator group, c 13 is the fire intensity index group, c 14 is the single-shot kill probability, K1 is the correction factor;

[0117] For c 12 The mathematical model of the kill zone indicator group is:

[0118]

[0119] Among them, c 121 , c 122 , c 123 , c 124 , c 125 , c 126 They are the upper boundary altitude, lower boundary altitude, far boundary slant distance, near boundary slant distance, maximum height and low angle, and maximum route angle of the kill zone;

[0120] For c 13 The mathematical model of the firepower intensity index is:

[0121]

[0122] Among them, c 131 is the target capacity, c 132 is the number of launchers, c133 is the number of missiles installed, c 134 is a single target shot singular, c 135 Reload time for missiles;

[0123] The calculation of mobility c2 is as follows:

[0124]

[0125]

[0126] Among them, W is the engine power, M is the vehicle mass, f is the drag coefficient, V1 is the target speed, V2 is the initial speed, and a is the average acceleration.

[0127] The calculation for the communication capacity c4 is as follows:

[0128]

[0129] Among them, P is the transmit power, β is the receive sensitivity, α is the antenna gain, and L is the path loss.

[0130] When calculating the combat capability C of the combat simulation system, the strike capability c1 is mainly based on the shooting capability, which is mainly used for the calculation of the shooting capability. The calculation of the communication capability c4 also includes the calculation of the communication reliability. The assessor usually considers parameters such as bit error rate and packet loss rate. The survivability c3 is based on the defense capability c4. 31 and reconnaissance capabilities 32 Mainly, the defensive ability is c 31 , mainly including the detection distance c 311 , resolution c 312 and detection range c 313 The data of detection distance c 311 The calculation method can refer to the calculation formula of the detection distance a1 in the reconnaissance simulation system. The calculation formula for resolution and detection range is as follows:

[0131] For resolution:

[0132]

[0133] Where k is a constant and λ is the detection beam width.

[0134] Regarding the scope of investigation:

[0135]

[0136] Among them, c 311 The calculation formula is the same as a1. Specific embodiment five:

[0138] like Figure 1-2 As shown, there are four groups of databases inside the data processing system, three of which are connected to the command simulation system, the combat simulation system and the reconnaissance simulation system, and the other one integrates, analyzes and processes the remaining three groups.

[0139] Both the data processing system and the intelligent judgment and evaluation system are operated and used based on the TPN deep neural network. When the data processing system receives data from the reconnaissance simulation system, the command and control simulation system, and the combat simulation system, it first pre-processes the data, cleans the data, removes data with too many duplicates, errors, or missing values, converts data from different devices or different parameters to the same scale for neural network processing, normalizes or standardizes it, encodes the classified data or text data, and then extracts features. The TPN deep neural network can automatically eliminate useful features, and then performs data fusion, and performs fusion of the low, middle and top layers, waiting to be input into the intelligent judgment and evaluation system. Specific embodiment six:

[0141] like Figure 1-2 As shown, the intelligent judgment and evaluation system includes a judgment system and an evaluation system. The judgment system makes judgments based on data and gives a combat plan. The evaluation system evaluates the combat plan data, including the evaluation of combat effectiveness and combat risk indicators. The evaluation system also conducts a secondary evaluation and summary of the entire combat data after the operation. The fused data uses a multi-input network structure, and fusion layers are added to different levels of the network to integrate information from different equipment ratios. Then, training and optimization are carried out, and the transfer learning method is used. The network weights pre-trained on similar tasks are used as the initial weights to accelerate the training process. Finally, after evaluation and adjustment, the cross-validation method is used to evaluate the model, and the final device information result of multiple device information is obtained.

[0142] The TPN deep neural network performs convolution operations and data transmission on three channels, two of which use group convolution, and one channel uses the traditional ResNet convolution structure of first reducing dimension and then increasing dimension. The entire TPN deep neural network is obtained by connecting the DPN network and the ResNet module in parallel, and the DPN network is obtained by connecting the ResNext module and the DenesNet module in parallel. The TPN structure is equivalent to connecting the ResNet, ResNext, and DenseNet networks in parallel. Moreover, the convolution module on the main path of the model not only uses the traditional ResNet convolution structure of first reducing dimension and then increasing dimension, but also uses the group convolution operation in ResNext, which further enhances the network's ability to extract features.

[0143] When the entire TPN deep neural network is actually used, each module in TPN will also divide the data after obtaining the input data x. At this time, the data needs to be divided into two parts, data_o1, data_o2 and data_o3, where data_o1 is assigned to the ResNext structure, data_o2 is assigned to the DenseNet structure, and data_o3 is assigned to ResNet. Then, 1×1 convolution, 3×3 convolution, and 1×1 convolution operations are applied to the data. The 3×3 convolution still uses the group convolution in ResNext. The results of the convolution are also divided to obtain data_n1 and data_n2 corresponding to data_o1 and data_o2, and da ta_o3 performs separate 1×1 convolution, 3×3 convolution, and 1×1 convolution operations, and the result is recorded as data_n3. The convolution operation follows the execution order of BN, ReLU, and CONV in ResNet. The values ​​of data_o1 and data_n1 are added to obtain the result data_res1. Similar to the addition operation in ResNet, data_o2 and data_2 are merged to obtain the result data_dense. The values ​​of data_o3 and data_n3 are added to obtain the result data_res3. Therefore, the TPN module returns three sets of data data_res1, data_dense, and data_res3.

[0144] In addition, when the TPN module is not the first module in the network, the channels of the three groups of data need to be merged before division, so that the output channels of the previous TPN module can be reorganized. The system structure is composed of a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system. After the reconnaissance simulation system, the command simulation system and the combat simulation system are standardized by the data processing system, a data basis is provided for the intelligent judgment and evaluation system. By continuously adjusting the weights and parameters, repeated training simulations are carried out. Through the training of the TPN deep network model, the network data is transmitted and calculated layer by layer to obtain the integrated output result, so as to minimize the prediction error, improve the accuracy and intelligent system, and provide a more accurate and appropriate evaluation plan and analysis for the combat environment. Specific embodiment seven:

[0146] like Figure 1-2 As shown, the entire device is mainly suitable for sand combat simulation or ocean combat simulation during actual use. As for air combat, due to the large air environment and high flight speed, it is not easy to unify sand combat and ocean combat. Therefore, it needs to be used alone in air combat. It can be used in sand combat and ocean combat at the same time.

[0147] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0148] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An integrated system for determining and evaluating combat joint debugging data based on real-time system simulation, characterized by: The system is composed of a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system and an intelligent judgment and evaluation system, wherein: The reconnaissance simulation system detects, locates and identifies enemy attack targets, and collects data and simulates the system's reconnaissance capability A, which includes detection distance a1, terrain shielding a2, scanning speed a3, signal-to-noise ratio a4, resolution a5, false alarm rate a6 and detection probability a7; The command simulation system integrates the information obtained from the reconnaissance and earthquake prevention system, analyzes the combat situation, issues combat orders, and collects data and simulates the command capability B in the system. The command capability B includes data processing rate b1, concurrent processing capability b2, and delay rate b3. The combat simulation system receives the combat instructions of the command simulation system, destroys the enemy's attack targets, and collects data and performs simulation settings for the combat capability C in the system. The combat capability C includes firepower strike capability c1, mobility capability c2, survivability c3, and communication capability c4; The data processing system receives and stores data from the reconnaissance simulation system, the command simulation system, and the combat simulation system, and normalizes and standardizes the data; The intelligent judgment and evaluation system synchronizes the data in the reconnaissance simulation system, command simulation system, and combat simulation system in real time, and uses the TPN deep neural network model to judge the parameters, provide optimization solutions, and conduct evaluations of all parties after the operation.

2. The integrated system for determining and evaluating combat coordination data based on real-time system simulation according to claim 1 is characterized in that: The calculation of the reconnaissance capability A of the reconnaissance simulation system further includes the following contents: The calculation for the detection distance a1 is as follows: Among them, P is the transmit power, β is the receiving sensitivity, α is the antenna gain, S is the target reflection area, and ρ is the loss factor; The calculation of terrain shielding a2 is as follows: Among them, R M is the detection distance of low-altitude missile targets, in kilometers, and K1 is the shielding angle correction coefficient, which is: Among them, θ zB is the shielding angle of the main defense sector, H P is the height above the ground, R P The radar is at a height of H P The maximum detection distance; The calculation for the scanning speed a3 is as follows: Where S is the size of the scanning area, and V is the scanning rate; The calculation for the signal-to-noise ratio a4 is as follows: Among them, P s is the target signal power, P o is the background noise power; The calculation for resolution a5 is as follows: Where λ is the wavelength and D is the antenna aperture.

3. The integrated system for determining and evaluating combat coordination data based on real-time system simulation according to claim 1 is characterized in that: The calculation of the reconnaissance capability B of the command simulation system further includes the following contents: The calculation for the data processing rate b1 is as follows: Among them, W1 is the total amount of processed data, T1 is the processing completion time; The calculation of the delay rate b3 is as follows: b3=T2-T3(8) Among them, T2 is the time point when the processing is completed, and T3 is the time point when the information is input.

4. The integrated system for determining and evaluating combat coordination data based on real-time system simulation according to claim 1 is characterized in that: The calculation of the combat simulation system reconnaissance capability C further includes the following contents: The calculation of firepower strike capability c1 is as follows: Among them, c 11 is the weapon reaction time, c 12 is the kill zone indicator group, c 13 is the fire intensity index group, c 14 is the single-shot kill probability, K1 is the correction factor; For c 12 The mathematical model of the kill zone indicator group is: Among them, c 121 , c 122 , c 123 , c 124 , c 125 , c 126 They are the upper boundary altitude, lower boundary altitude, far boundary slant distance, near boundary slant distance, maximum height and low angle, and maximum route angle of the kill zone; For c 13 The mathematical model of the firepower intensity index is: Among them, c 131 is the target capacity, c 132 is the number of launchers, c 133 is the number of missiles installed, c 134 is a single target shot singular, c 135 Reload time for missiles; The calculation of mobility c2 is as follows: Among them, W is the engine power, M is the vehicle mass, f is the drag coefficient, V1 is the target speed, V2 is the initial speed, and a is the average acceleration.

5. The integrated system for determining and evaluating combat joint debugging data based on real-time system simulation according to claim 1 is characterized in that: The calculation of the combat simulation system reconnaissance capability C further includes the following contents: The calculation for the communication capacity c4 is as follows: Among them, P is the transmit power, β is the receive sensitivity, α is the antenna gain, and L is the path loss.

6. The integrated system for determining and evaluating combat joint debugging data based on real-time system simulation according to claim 1 is characterized in that: The data processing system has four groups of databases inside, three of which are connected to the command simulation system, the combat simulation system and the reconnaissance simulation system, and the other one integrates, analyzes and processes the remaining three groups.

7. The integrated system for determining and evaluating combat coordination data based on real-time system simulation according to claim 1 is characterized in that: The intelligent judgment and evaluation system includes a judgment system and an evaluation system. The judgment system makes judgments based on data and provides a combat plan. The evaluation system evaluates the combat plan data, including an evaluation of combat effectiveness and combat risk indicators. The evaluation system also performs a secondary evaluation and summary of the entire combat data after the operation is completed.

8. The integrated system for determining and evaluating combat coordination data based on real-time system simulation according to claim 1 is characterized in that: The TPN deep neural network performs convolution operations and data transmission on three channels, two of which use group convolution, and one channel uses the traditional ResNet convolution structure of first reducing the dimension and then increasing the dimension.

Images