Integrated system for assessment and evaluation of operational data based on real-time system simulation

Abstract

This invention provides an integrated system for combat joint debugging data judgment and evaluation based on real-time system simulation, belonging to the field of simulation combat technology. The system structure 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. In the entire system structure, data is collected and integrated for various capability parameters of reconnaissance, command and control, and combat equipment. After analyzing and integrating the collected data with a deep neural network, an optimized solution 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 the deep neural network, we can more comprehensively and accurately evaluate the various capability parameters of reconnaissance equipment, command and control equipment, and combat equipment, providing strong support for decision support and performance optimization.

Description

Technical Field

[0001] This invention relates to the field of simulation warfare technology, specifically to an integrated system for judging and evaluating operational data based on real-time system simulation. Background Technology

[0002] According to Chinese Patent No. CN112749496B, a method and system for evaluating the combat effectiveness of an equipment system based on a time-series combat loop includes: acquiring the equipment and performance indicators of both the red and blue sides during combat, as well as the connection relationships between the equipment; classifying the equipment into four types: reconnaissance equipment (S), decision-making equipment (D), strike equipment (A), and target equipment (T); abstracting each piece of equipment as a node; analyzing the correlation relationships between different equipment nodes and their existence time; constructing a time-series combat loop in the equipment system; constructing a dynamic network structure of the equipment system based on the time-series combat loop; and evaluating the combat effectiveness of the equipment system based on the dynamic network structure. This invention takes into account the dynamic nature of the equipment system, extending the time-series combat loop based on the static combat loop, and then calculating the system's combat effectiveness based on the dynamic network structure. It analyzes the evolution of the system's combat effectiveness over time, and with the dynamic network description closely reflecting actual combat, its evaluation effect is more accurate.

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

[0004] Problem 1: When using combat simulation evaluation, the coverage of parameters collected from all parties involved in the combat is insufficient, resulting in inaccurate collection of basic data and thus low accuracy of the output results.

[0005] The second issue is that during simulation training, the collection and analysis of parameters for different combat environments are insufficient, which leads to the inability to generate corresponding plans in a timely manner or insufficient post-war evaluation and analysis, hindering the unified analysis and use of actual joint debugging data. Summary of the Invention

[0006] Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides an integrated system for judging and evaluating operational coordination data based on real-time system simulation, solving the following problems:

[0008] 1. Problems with inaccurate data collection and insufficient coverage;

[0009] 2. Problems include insufficient simulation analysis and poor environmental adaptability.

[0010] Technical solution

[0011] To achieve the above objectives, the present invention provides the following technical solution: an integrated system for combat joint debugging data judgment and evaluation based on real-time system simulation, wherein the system comprises 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 is used to detect, locate, and identify enemy invading targets, and to test the system's reconnaissance capabilities. Data collection and simulation settings are performed to enhance reconnaissance capabilities. Included detection range Terrain shielding Scanning speed Signal-to-noise ratio Resolution False alarm rate and detection probability ;

[0013] The command simulation system integrates information obtained from the reconnaissance and earthquake preparedness system, analyzes the combat situation, issues combat orders, and monitors the system's command and control capabilities. To collect data and set up simulations, and to enhance command and control capabilities. Including data processing speed Concurrency processing capability Latency ;

[0014] The combat simulation system receives combat instructions from the command simulation system, destroys enemy invading targets, and assesses the combat capabilities within the system. To collect data and set up simulations, combat capabilities Includes firepower strike capabilities Mobility Survival ability , communication ability ;

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

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

[0017] Preferably, the reconnaissance simulation system has the following reconnaissance capabilities. The calculation further includes the following:

[0018] Regarding the detection range The calculation is shown in the following formula:

[0019]

[0020] in, For transmission power, For receiving sensitivity, For antenna gain, For the target reflective area, As the loss factor;

[0021] Terrain shielding The calculation is shown in the following formula:

[0022]

[0023] in, The detection range for low-altitude missile targets, expressed in kilometers. This is the shading angle correction factor, with a value of:

[0024]

[0025] in, For the main defensive fan-shaped shielding angle, Height above the ground For radar at an altitude of Maximum detection range;

[0026] Regarding scanning speed The calculation is shown in the following formula:

[0027]

[0028] in, Size of the scan area For scan rate;

[0029] Regarding signal-to-noise ratio The calculation is shown in the following formula:

[0030]

[0031] in, For the target signal power, Background noise power;

[0032] For resolution The calculation is shown in the following formula:

[0033]

[0034] in, For wavelength, This refers to the antenna aperture.

[0035] Preferably, the command simulation system has the following reconnaissance capabilities. The calculation further includes the following:

[0036] Regarding data processing speed The calculation is shown in the following formula:

[0037]

[0038] in, The total amount of data processed. For processing completion time;

[0039] Regarding latency The calculation is shown in the following formula:

[0040]

[0041] in, To process the completion time point, Enter the time point for the information.

[0042] Preferably, the combat simulation system has the following reconnaissance capabilities. The calculation further includes the following:

[0043] Targeting firepower capabilities The calculation is shown in the following formula:

[0044]

[0045] in, For weapon reaction time, For the kill zone indicator group, For the firepower intensity index group, This represents the single-shot kill probability. This is a correction factor;

[0046] against The mathematical model for the kill zone indicator group is:

[0047]

[0048] in, , , , , , These are the kill zone upper limit altitude, lower limit altitude, far limit slant range, near limit slant range, maximum elevation angle, and maximum course angle, respectively.

[0049] against The mathematical model for the firepower intensity index is:

[0050]

[0051] in, For the target capacity, For the number of launching devices, For the number of missiles to be assembled, For a single target, the number of shots is single. For missile loading time;

[0052] Regarding mobility The calculation is shown in the following formula:

[0053]

[0054]

[0055] Among them, For engine power, For vehicle quality, The drag coefficient, For the target speed, The initial velocity, This is the average acceleration.

[0056] Preferably, the combat simulation system has the following reconnaissance capabilities. The calculation further includes the following:

[0057] Regarding communication capabilities The calculation is shown in the following formula:

[0058]

[0059] in, For transmission power, For receiving sensitivity, For antenna gain, Path loss

[0060] Preferably, the data processing system has four databases, three of which are connected to the command simulation system, the combat simulation system, and the reconnaissance simulation system, and the other database integrates, analyzes, and processes the other three databases.

[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 combat plans. The evaluation system evaluates the combat plan data, including the 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 ends.

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

[0063] Beneficial effects

[0064] This invention provides an integrated system for evaluating and assessing operational coordination data based on real-time system simulation. It offers the following advantages:

[0065] 1. This invention employs a system structure comprising a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system, and an intelligent judgment and evaluation system. Within this system structure, data is collected and integrated on various capability parameters of reconnaissance, command and control, and combat equipment. The collected data is then analyzed and integrated with a deep neural network to provide optimized solutions. By modifying and adjusting the data, the neural network within the system is trained and learned, improving 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 reconnaissance, command and control, and combat equipment, providing strong support for decision support and performance optimization.

[0066] 2. This invention adopts a system that uses the capability parameters of reconnaissance, command and control, and combat equipment as a benchmark when evaluating and judging based on simulated combat data. Compared with the traditional method of collecting data in a single way, the data collection of the entire system is more accurate and better meets the needs of combat, thus resulting in higher accuracy. The coverage of multiple parameter features allows the entire system to perform real-time data analysis and evaluation on different combat environments and multilateral combat conditions, making it highly adaptable and flexible.

[0067] 3. This invention employs a system structure comprising a reconnaissance simulation system, a command simulation system, a combat simulation system, a data processing system, and an intelligent judgment and evaluation system. The reconnaissance, command, and combat simulation systems, after being standardized by the data processing system, provide the data foundation for the intelligent judgment and evaluation system. Through continuous adjustment of weights and parameters, and repeated training simulations, the TPN deep network model is trained to perform layer-by-layer transmission and calculation of network data, resulting in an integrated output. This minimizes prediction errors, improves accuracy and intelligence, and provides a more precise and appropriate evaluation scheme and analysis for the combat environment. Attached Figure Description

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

[0069] Figure 2 This is a diagram of the TPN deep neural network structure of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1:

[0072] like Figure 1-2 As shown, the integrated system for combat joint debugging data judgment and evaluation based on real-time system 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, wherein:

[0073] The reconnaissance simulation system is used to detect, locate, and identify enemy invading targets, and to test the system's reconnaissance capabilities. Data collection and simulation settings are performed to enhance reconnaissance capabilities. Included detection range Terrain shielding Scanning speed Signal-to-noise ratio Resolution False alarm rate and detection probability ;

[0074] The command simulation system integrates information obtained from the reconnaissance and earthquake preparedness system, analyzes the combat situation, issues combat orders, and monitors the system's command and control capabilities. To collect data and set up simulations, and to enhance command and control capabilities. Including data processing speed Concurrency processing capability Latency ;

[0075] The combat simulation system receives combat instructions from the command simulation system, destroys enemy targets, and assesses the combat capabilities within the system. To collect data and set up simulations, combat capabilities Includes firepower strike capabilities Mobility Survival ability , communication ability ;

[0076] The data processing system receives and stores data from 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 data from the reconnaissance simulation system, command simulation system, and combat simulation system in real time, uses the TPN deep neural network model to determine parameters, provides optimization solutions, and conducts evaluations from all parties after the operation.

[0078] In the entire system architecture, data is collected and integrated for various capability parameters of reconnaissance, command and control, and combat equipment. After analyzing and integrating the collected data with deep neural networks, an optimized solution is provided. By modifying 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, providing strong support for decision support and performance optimization. Specific Implementation Example 2:

[0080] like Figure 1-2 As shown, the entire reconnaissance simulation system has the following reconnaissance capabilities. The calculation methods and contents of each parameter are as follows:

[0081] Investigative simulation system investigative capabilities The calculation further includes the following:

[0082] Regarding the detection range The calculation is shown in the following formula:

[0083]

[0084] in, For transmission power, For receiving sensitivity, For antenna gain, For the target reflective area, As the loss factor;

[0085] Terrain shielding The calculation is shown in the following formula:

[0086]

[0087] in, The detection range for low-altitude missile targets, expressed in kilometers. This is the shading angle correction factor, with a value of:

[0088]

[0089] in, For the main defensive fan-shaped shielding angle, Height above the ground For radar at an altitude of Maximum detection range;

[0090] Regarding scanning speed The calculation is shown in the following formula:

[0091]

[0092] in, Size of the scan area For scan rate;

[0093] Regarding signal-to-noise ratio The calculation is shown in the following formula:

[0094]

[0095] in, For the target signal power, Background noise power;

[0096] For resolution The calculation is shown in the following formula:

[0097]

[0098] in, For wavelength, This refers to the antenna aperture.

[0099] Regarding investigative capabilities Corresponding false alarm rate and detection probability These estimates are usually based on statistical models, taking into account factors such as equipment performance, environmental noise, and target characteristics. These formulas are not usually calculated directly, but are obtained through the analysis of a large amount of test data. They have fixed values ​​for specific environments. When using them, you can directly input the values ​​and then normalize them. Specific Implementation Example 3:

[0101] like Figure 1-2 As shown, the command and control capabilities of the entire command simulation system The calculation methods and contents of each parameter are as follows:

[0102] Command simulation system reconnaissance capabilities The calculation further includes the following:

[0103] Regarding data processing speed The calculation is shown in the following formula:

[0104]

[0105] in, The total amount of data processed. For processing completion time;

[0106] Regarding latency The calculation is shown in the following formula:

[0107]

[0108] in, To process the completion time point, Enter the time point for the information.

[0109] For concurrent processing capabilities The calculation 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] Meanwhile, the command and control capabilities of the command simulation system In addition to the three parameters mentioned above, resource utilization rate and error rate can also be calculated and statistically analyzed. These two parameters can generally be obtained directly from the system without additional calculation. Specific Implementation Example 4:

[0112] like Figure 1-2 As shown, the command and control capabilities of the entire combat simulation system The calculation methods and contents of each parameter are as follows:

[0113] combat simulation system reconnaissance capabilities The calculation further includes the following:

[0114] Targeting firepower capabilities The calculation is shown in the following formula:

[0115]

[0116] in, For weapon reaction time, For the kill zone indicator group, For the firepower intensity index group, This represents the single-shot kill probability. This is a correction factor;

[0117] against The mathematical model for the kill zone indicator group is:

[0118]

[0119] in, , , , , , These are the kill zone upper limit altitude, lower limit altitude, far limit slant range, near limit slant range, maximum elevation angle, and maximum course angle, respectively.

[0120] against The mathematical model for the firepower intensity index is:

[0121]

[0122] in, For the target capacity, For the number of launching devices, For the number of missiles to be assembled, For a single target, the number of shots is single. For missile loading time;

[0123] Regarding mobility The calculation is shown in the following formula:

[0124]

[0125]

[0126] Among them, For engine power, For vehicle quality, The drag coefficient, For the target speed, The initial velocity, This is the average acceleration.

[0127] Regarding communication capabilities The calculation is shown in the following formula:

[0128]

[0129] in, For transmission power, For receiving sensitivity, For antenna gain, This is the path loss.

[0130] In terms of the combat capabilities of combat simulation systems When calculating, the strike capability is taken into account. Primarily focused on shooting capabilities, mainly used for calculating shooting performance, and communication capabilities. The calculation also includes communication reliability calculations, where assessors typically consider parameters such as bit error rate, packet loss rate, and survivability. With defensive capabilities and reconnaissance capabilities The main focus is on defensive capabilities. This mainly includes the detection range Resolution and the scope of investigation Data, including detection distance The calculation method can refer to the detection distance in the reconnaissance simulation system. The calculation formulas for resolution and detection range are as follows:

[0131] For resolution:

[0132]

[0133] Where k is a constant and λ is the probe beamwidth.

[0134] Regarding the scope of the investigation:

[0135]

[0136] in, The calculation formula and same. Specific Implementation Example 5:

[0138] like Figure 1-2 As shown, the data processing system has four databases. Three of them are connected to the command simulation system, the combat simulation system, and the reconnaissance simulation system, while the other one integrates, analyzes, and processes the other three databases.

[0139] Both the data processing system and the intelligent judgment and evaluation system operate based on the TPN deep neural network. When the data processing system receives data from the reconnaissance simulation system, command and control simulation system, and combat simulation system, it first performs data preprocessing, cleaning the data, removing data with too many duplicate, erroneous, or missing values, and converting data from different devices or with different parameters to the same scale to facilitate neural network processing. It then performs normalization or standardization, encodes classified or textual data, and performs feature extraction. The TPN deep neural network can automatically remove useful features. Finally, it performs data fusion, fusing low-level, intermediate, and top-level data, ready for input into the intelligent judgment and evaluation system. Specific Implementation Example Six:

[0141] like Figure 1-2As shown, the intelligent judgment and evaluation system includes a judgment system and an evaluation system. The judgment system makes judgments based on data and provides combat plans. The evaluation system evaluates the combat plan data, including the 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. The fused data uses a multi-input network structure, adding fusion layers at different levels of the network to integrate information from different devices. Then, training and optimization are carried out, using transfer learning methods and using network weights pre-trained on similar tasks as initial weights to accelerate the training process. Finally, after evaluation and adjustment, cross-validation is used to evaluate the model, resulting in the final device information results from multiple devices.

[0142] The TPN deep neural network performs convolution operations and data transmission in three channels. Two channels use group convolution, and one channel uses the traditional ResNet convolution structure of dimensionality reduction followed by dimensionality increase. The entire TPN deep neural network is obtained by connecting the DPN network and the ResNet module in parallel. 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 modules on the main path of the model use both the traditional ResNet dimensionality reduction followed by dimensionality increase convolution structure and the group convolution operation in ResNext, which further enhances the network's ability to extract features.

[0143] In practical use, each module in the TPN deep neural network divides the input data x after receiving it. The data is then divided into two parts: data_o1, data_o2, and data_o3. Data_o1 is assigned to the ResNext structure, data_o2 to the DenseNet structure, and data_o3 to ResNet. Next, 1×1, 3×3, and 1×1 convolution operations are performed on the data. The 3×3 convolution still uses the group convolution from ResNext. The results of the convolutions are also divided to obtain data_n1 and data_n2 corresponding to data_o1 and data_o2. ta_o3 undergoes individual 1×1 convolution, 3×3 convolution, and 1×1 convolution operations, resulting in data_n3. The convolution operations follow the execution order of BN, ReLU, and CONV in ResNet. The values ​​of data_o1 and data_n1 are added together to obtain the result data_res1, similar to the addition operation in ResNet. The channels of data_o2 and data_2 are merged to obtain the result data_dense. The values ​​of data_o3 and data_n3 are added together to obtain the result data_res3. Therefore, the TPN module returns three sets of data: data_res1, data_dense, and data_res3.

[0144] Furthermore, when the TPN module is not the first module in the network, the channels of the three sets of data need to be merged before the partitioning, so that the output channels of the previous TPN module can be reorganized. The system structure 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. After the reconnaissance simulation system, command simulation system, and combat simulation system are standardized by the data processing system, they provide the data foundation for the intelligent judgment and evaluation system. By continuously adjusting the weights and parameters, repeated training simulations are carried out. Through training the TPN deep network model, the network data is transmitted and calculated layer by layer to obtain the integrated output results, so as to minimize the prediction error, improve the accuracy and intelligence of the system, and provide a more accurate and appropriate evaluation scheme and analysis for the combat environment. Specific Implementation Example 7:

[0146] like Figure 1-2 As shown, the entire device is mainly suitable for use in simulated sandy warfare or simulated ocean warfare. For use in air warfare, due to the large size of the air environment and the high speed of flight, it is not easy to unify with sandy warfare and ocean warfare. Therefore, it needs to be used separately in air warfare. It can be applied to both sandy warfare and ocean warfare at the same time.

[0147] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

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

Claims

1. An integrated system for judging and evaluating operational data based on real-time system simulation, characterized in that: The system comprises 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 is used to detect, locate, and identify enemy invading targets, and to test the system's reconnaissance capabilities. Data collection and simulation settings are performed to enhance reconnaissance capabilities. Included detection range Terrain shielding Scanning speed Signal-to-noise ratio Resolution False alarm rate and detection probability ; The reconnaissance simulation system has reconnaissance capabilities. The calculation further includes the following: Regarding the detection range The calculation is shown in the following formula: ； in, For transmission power, For receiving sensitivity, For antenna gain, For the target reflective area, As the loss factor; Terrain shielding The calculation is shown in the following formula: ； in, The detection range for low-altitude missile targets, expressed in kilometers. This is the shading angle correction factor, with a value of: ； in, For the main defensive fan-shaped shielding angle, Height above the ground For radar at an altitude of Maximum detection range; Regarding scanning speed The calculation is shown in the following formula: ； in, Size of the scan area For scan rate; Regarding signal-to-noise ratio The calculation is shown in the following formula: ； in, For the target signal power, Background noise power; For resolution The calculation is shown in the following formula: ； in, For wavelength, Antenna aperture; The command simulation system integrates information obtained from the reconnaissance and earthquake preparedness system, analyzes the combat situation, issues combat orders, and monitors the system's command and control capabilities. To collect data and set up simulations, and to enhance command and control capabilities. Including data processing speed Concurrency processing capability Latency ; The command simulation system's reconnaissance capabilities The calculation further includes the following: Regarding data processing speed The calculation is shown in the following formula: ； in, The total amount of data processed. For processing completion time; Regarding latency The calculation is shown in the following formula: ； in, To process the completion time point, Enter the time point for the information; The combat simulation system receives combat instructions from the command simulation system, destroys enemy invading targets, and assesses the combat capabilities within the system. To collect data and set up simulations, combat capabilities Includes firepower strike capabilities Mobility Survival ability , communication ability ; The reconnaissance capability of the combat simulation system The calculation further includes the following: Targeting firepower capabilities The calculation is shown in the following formula: ； in, For weapon reaction time, For the kill zone indicator group, For the firepower intensity index group, This represents the single-shot kill probability. This is a correction factor; against The mathematical model for the kill zone indicator group is: ； in, , , , , , These are the kill zone upper limit altitude, lower limit altitude, far limit slant range, near limit slant range, maximum elevation angle, and maximum course angle, respectively. against The mathematical model for the firepower intensity index is: ； in, For the target capacity, For the number of launching devices, For the number of missiles to be assembled, For a single target, the number of shots is single. For missile loading time; Regarding mobility The calculation is shown in the following formula: ； ； Among them, For engine power, For vehicle quality, The drag coefficient, For the target speed, The initial velocity, The average acceleration; the reconnaissance capability of the combat simulation system. The calculation further includes the following: Regarding communication capabilities The calculation is shown in the following formula: ； in, For transmission power, For receiving sensitivity, For antenna gain, This is the path loss; The data processing system receives and stores data from the reconnaissance simulation system, command simulation system, and combat simulation system, and normalizes and standardizes the data. The intelligent judgment and evaluation system synchronizes data from the reconnaissance simulation system, command simulation system, and combat simulation system in real time, uses the TPN deep neural network model to determine parameters, provides optimization solutions, and conducts evaluations from all parties after the operation.

2. The integrated system for judging and evaluating operational joint debugging data based on real-time system simulation as described in claim 1, characterized in that: The data processing system has four databases, three of which are connected to the command simulation system, the combat simulation system, and the reconnaissance simulation system, while the other database integrates, analyzes, and processes the other three databases.

3. The integrated system for judging and evaluating operational joint debugging data based on real-time system simulation as described in claim 1, 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 combat plans. The evaluation system evaluates the combat plan data, including the 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 ends.

4. The integrated system for judging and evaluating operational joint debugging data based on real-time system simulation as described in claim 1, characterized in that: The TPN deep neural network performs convolution operations and data transmission in three channels, with two channels using group convolution and one channel using the traditional ResNet convolution structure of first reducing dimensionality and then increasing dimensionality.

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