A machine learning-based warhead fragment distribution prediction system and method
By integrating multi-source data and utilizing machine learning systems for real-time adjustments and optimizations, the accuracy of warhead fragmentation distribution prediction was solved, improving the accuracy and efficiency of military decision-making and providing detailed analysis reports and optimization suggestions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING UNIV OF SCI & TECH
- Filing Date
- 2024-11-05
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies struggle to achieve real-time and accurate prediction of warhead fragment distribution in complex battlefield environments, resulting in insufficient accuracy and effectiveness in military decision-making.
By integrating multi-source data, including experimental data, sensor data, and historical data, through machine learning-based systems, data processing and feature extraction are performed to build and optimize models, adjust parameters in real time, generate high-precision fragment distribution predictions, and provide decision support and optimization suggestions.
It enables high-precision fragmentation distribution prediction in rapidly changing battlefield environments, improving the accuracy and efficiency of military decision-making, avoiding operational errors caused by inaccurate predictions, and providing detailed analysis reports and optimization suggestions.
Smart Images
Figure CN122490968A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of military technology and next-generation information technology, and in particular to a warhead fragmentation distribution prediction system and method based on machine learning. Background Technology
[0002] In modern warfare, the explosive effects of warheads directly impact the success of military missions. Traditional warhead design and explosive effect assessment typically rely on physical experiments and limited historical data. However, with the increasing complexity of operational environments, single data sources and static analysis methods are no longer sufficient to meet the battlefield's demands for real-time prediction and accurate assessment.
[0003] Current research, while incorporating simulation techniques and mathematical models to predict fragmentation distribution, often suffers from limitations such as relying on a single data source, insufficient model flexibility, and difficulty in responding to real-time battlefield changes. Therefore, integrating multiple data sources, combining environmental parameters, historical data, and real-time sensor data, and employing advanced machine learning algorithms to achieve real-time and accurate prediction of warhead fragmentation distribution has become a crucial issue for improving military decision-making.
[0004] This invention proposes a warhead fragment distribution prediction system and method based on machine learning. By fusing multi-source data and dynamically adjusting model parameters, it generates high-precision fragment distribution predictions and optimization suggestions in real time, providing strong support for military decision-makers and thus effectively improving combat effectiveness. Summary of the Invention
[0005] This invention provides a machine learning-based warhead fragment distribution prediction system to address the problem of how to perform real-time and accurate prediction of warhead fragment distribution based on multi-source data, thereby improving the accuracy and effectiveness of military decision-making.
[0006] To address the aforementioned issues, this invention provides a machine learning-based warhead fragment distribution prediction system, comprising: a data acquisition module, a data processing module, a feature extraction and selection module, a model building and optimization module, a real-time prediction and feedback module, a decision support and report generation module, a user interaction and visualization module, and a system monitoring and adaptive module.
[0007] Furthermore, the data acquisition module is used to acquire data related to the warhead explosion from multiple data sources, including experimental data, sensor data, and historical data.
[0008] Specifically, high-precision sensors are used to collect key parameters during the explosion process in real time, such as explosion energy, fragment velocity, and environmental conditions (temperature, humidity, wind speed, etc.), and to ensure the breadth and validity of the data.
[0009] Furthermore, the data processing module is used to perform preliminary processing on the collected data, including data denoising, missing value imputation, and data standardization, to ensure data consistency and accuracy.
[0010] Furthermore, the processed data will be stored in a high-efficiency database for subsequent analysis and modeling. The feature extraction and selection module extracts key features from the processed data that can effectively predict the distribution of warhead fragments, and uses machine learning algorithms to filter these features, ensuring that the data input into the model has high predictive value.
[0011] Furthermore, the model building and optimization module constructs and optimizes the machine learning model based on the extracted features. During the model training and optimization process, a large amount of historical and experimental data is used for repeated iterations to improve the model's prediction accuracy and reliability.
[0012] Furthermore, the real-time prediction and feedback module receives real-time data during the warhead explosion process and uses a trained model to dynamically predict fragment distribution. The system adjusts model parameters based on real-time environmental changes to generate accurate fragment distribution prediction results, and continuously optimizes the model through a closed-loop feedback mechanism.
[0013] Furthermore, the decision support and report generation module provides optimized warhead design suggestions and tactical deployment plans based on model prediction results, and generates detailed analysis reports to assist military decision-makers in making effective judgments and decisions.
[0014] Furthermore, the user interaction and visualization module displays the system's prediction results and analysis data through an intuitive user interface, supports multi-scenario simulation and interactive operation, helps users quickly understand and utilize the information generated by the system, and improves decision-making efficiency.
[0015] Furthermore, the system monitoring and adaptation module continuously monitors the system's operating status and optimizes model parameters and system settings through adaptive learning to ensure that the system operates stably and efficiently in various complex environments.
[0016] The key innovations of this invention include:
[0017] (1) Multi-source data integration and processing: Through adaptive filtering and multiple verification techniques, the accuracy and consistency of sensor data, environmental parameters and historical data are ensured, providing high-quality data for model input.
[0018] (2) Real-time dynamic prediction: Using a trained machine learning model, dynamic adjustments and predictions are made based on real-time data to ensure high-precision predictions even when the battlefield environment changes.
[0019] (3) Adaptive optimization: The system can automatically adjust the model parameters and switch to the optimal model, and combine actual feedback to perform adaptive learning and continuously optimize the prediction results.
[0020] (4) Intelligent decision support: Provides an intuitive user interface and decision support tools, generates detailed analysis reports and optimization suggestions to assist military commanders in making scientific decisions.
[0021] The following are its main beneficial effects:
[0022] (1) Improve prediction accuracy: By dynamically adjusting model parameters in real time and utilizing multi-source data, this invention can provide high-precision fragment distribution prediction, which significantly improves the accuracy of military decision-making and avoids operational errors that may result from inaccurate prediction.
[0023] (2) Real-time response capability: The present invention employs complex mathematical formulas, including partial differential equation solving, multivariate regression analysis and high-dimensional space mapping, which can complete the real-time prediction of the fragment distribution after the warhead explodes within milliseconds, ensuring that reliable decision support is provided rapidly in the ever-changing battlefield environment.
[0024] (3) Model adaptive optimization: Through automated model optimization and adaptive learning mechanism, the present invention can continuously optimize the prediction model according to real-time battlefield feedback and environmental changes, ensuring that the system always maintains the best performance under various complex combat conditions, avoiding the problems exhibited by traditional models when dealing with complex environments.
[0025] (4) Enhanced decision support: The high-precision prediction results and optimization suggestion reports generated by this invention can intuitively show the actual effects of the warhead explosion and improvement measures, helping military decision-makers to quickly assess the effectiveness of the warhead design and optimize deployment strategies, thereby improving combat effectiveness. Attached Figure Description
[0026] Figure 1 This is a block diagram of a machine learning-based warhead fragment distribution prediction system provided in an embodiment of the present invention. Detailed Implementation
[0027] This invention discloses a machine learning-based warhead fragment distribution prediction system and method. The system achieves real-time and accurate prediction of warhead fragment distribution after explosion through steps including data acquisition and processing, model construction and optimization, real-time data input and dynamic prediction, feedback mechanisms, and adaptive optimization. The system integrates multi-source data, including experimental data, sensor data, and historical data, and dynamically adjusts the prediction results through a machine learning model, providing an intuitive user interface and optimization suggestions for military decision-making, thereby improving the accuracy and effectiveness of military decisions.
[0028] The invention provides a machine learning-based warhead fragment distribution prediction system and method, which is mainly used to solve the technical problem of "how to make real-time and accurate predictions of warhead fragment distribution based on multi-source data in order to improve the accuracy and effectiveness of military decision-making".
[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0030] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.
[0031] Example 1:
[0032] like Figure 1 As shown in the figure, an embodiment of the present invention provides a warhead fragment distribution prediction system based on machine learning. The system includes a data acquisition module 10, a data processing module 20, a feature extraction and selection module 30, a model building and optimization module 40, a real-time prediction and feedback module 50, a decision support and report generation module 60, a user interaction and visualization module 70, and a system monitoring and adaptive module 80.
[0033] Furthermore, the data acquisition module 10 is used to acquire data related to the warhead explosion from multiple data sources, including experimental data, sensor data, and historical data.
[0034] Specifically, high-precision sensors are used to collect key parameters during the explosion process in real time, such as explosion energy, fragment velocity, and environmental conditions (temperature, humidity, wind speed, etc.), and to ensure the breadth and validity of the data.
[0035] Furthermore, the data processing module 20 is used to perform preliminary processing on the collected data, including data denoising, missing value imputation and data standardization, to ensure the consistency and accuracy of the data.
[0036] Furthermore, the processed data will be stored in a high-efficiency database for subsequent analysis and modeling. The feature extraction and selection module 30 extracts key features from the processed data that can effectively predict the distribution of warhead fragments, and uses machine learning algorithms to filter the features to ensure that the data input into the model has high predictive value.
[0037] Furthermore, the model building and optimization module 40 builds and optimizes the machine learning model based on the extracted features. During the training and optimization process, a large amount of historical and experimental data is used for repeated iterations to improve the model's prediction accuracy and reliability.
[0038] Furthermore, the real-time prediction and feedback module 50 receives real-time data during the warhead explosion process and uses a trained model to dynamically predict fragment distribution. The system adjusts model parameters based on real-time environmental changes to generate accurate fragment distribution prediction results and continuously optimizes the model through a closed-loop feedback mechanism.
[0039] Furthermore, the decision support and report generation module 60 provides optimized warhead design suggestions and tactical deployment plans based on model prediction results, and generates detailed analysis reports to assist military decision-makers in making effective judgments and decisions.
[0040] Furthermore, the user interaction and visualization module 70 displays the system's prediction results and analysis data through an intuitive user interface, supports multi-scenario simulation and interactive operation, helps users quickly understand and utilize the information generated by the system, and improves decision-making efficiency.
[0041] Furthermore, the system monitoring and adaptation module 80 continuously monitors the system's operating status and optimizes model parameters and system settings through adaptive learning to ensure that the system operates stably and efficiently in various complex environments.
[0042] Example 2:
[0043] Embodiment 2 of the present invention provides a method for predicting the distribution of warhead fragments based on machine learning, which may include at least steps S100-S400:
[0044] S100 collects and cleans data through automated data processing and builds and optimizes predictive models based on machine learning technology.
[0045] S200. Dynamically predict the distribution of warhead fragments using the constructed model.
[0046] The S300 system is deployed to integrate military hardware and software systems.
[0047] The S400 provides military decision-makers with an intuitive user interface and decision support tools, displaying forecast results and offering optimization suggestions.
[0048] Furthermore, step S100 includes steps S110-S130:
[0049] S110. Collect information related to the warhead explosion from the data source and perform preliminary processing. Specifically:
[0050] The data sources are: the raw data (explosion pressure, fragment velocity) of the j-th dimension collected by the i-th sensor at time t, and the environmental parameters (temperature, humidity, wind speed, etc.) obtained under experimental condition k at time t.
[0051] The data source also includes the relevant explosion data (fragment distribution density, shock wave parameters, etc.) of the m-th record in the historical dataset under the n-th condition.
[0052] The preliminary processing includes noise reduction. Specifically, through convolution operations using an adaptive filter, noise interference in the data is effectively eliminated, generating denoised data. In the explosion shock wave data acquired by the sensor, this process can filter out high-frequency interference while retaining crucial pressure change information.
[0053] Furthermore, the denoised data undergoes consistency verification and correction. Utilizing the functional relationship between environmental parameters and sensor data, the system automatically detects and corrects potential consistency issues in the data, ensuring that data from different data sources can be reasonably matched under the same conditions. For data collected under different temperature conditions, the system corrects for the impact of temperature on the explosion effect, ensuring data accuracy.
[0054] Furthermore, the validated and corrected data is integrated with historical data. This process utilizes historical data to smooth the current data over a given time interval, generating consistent and high-quality data input. This integrated data will serve as the foundational input for subsequent model training, ensuring the model fully considers the influence of historical data and improves prediction accuracy.
[0055] S120. The collected data is processed by an automated data cleaning subsystem, which performs noise reduction, missing value imputation, and standardization. Specifically:
[0056] First, through adaptive wavelet transform, the system can effectively eliminate high-frequency noise in the data and generate denoised data.
[0057] Secondly, for the detected missing values, by combining environmental parameters and historical data, as well as the second derivative term based on time series, the missing data can be accurately predicted and filled. For the missing explosion energy data points, reasonable estimation and filling can be made by the changing trend of the data points before and after and the relevant environmental parameters.
[0058] Next, the denoised and imputed data is standardized to convert it to the same scale. This step ensures that data from different experimental conditions can be reasonably compared, eliminating the impact of dimensional differences on model training. Through standardization, the system can unify the outputs of different sensors to the same range, improving the model's training performance.
[0059] Finally, all the data that has undergone denoising, missing value imputation, and standardization will serve as the final high-quality input data for subsequent model training. This data ensures the model's accurate predictive capability under various combat environments, providing a reliable foundation for high-precision prediction of warhead fragmentation distribution.
[0060] S130. Extract key features from the cleaned data and perform automated selection. Specifically:
[0061] First, each feature is generated by combining the dynamic changes in sensor data, the influence of environmental parameters, and patterns from historical data. Specifically, the combination of the temporal variation of the explosion shock wave and ambient temperature generates a new feature used to predict fragment distribution patterns under specific conditions.
[0062] Then, a functional optimization formula is used to evaluate the contribution of each feature to the prediction model, while controlling the complexity of the features. This process ensures that the selected features not only improve the model's prediction accuracy but also avoid overfitting and enhance the model's generalization ability.
[0063] Secondly, during the feature selection process, by calculating the topological distance and correlation between features, the system combines features with strong correlation and complementarity to enhance the overall performance of the model. Through topology optimization, the system can combine similar features under different environmental conditions into a comprehensive feature, reducing redundancy and improving the efficiency of the model.
[0064] The final feature set, after feature generation, selection, and optimization, will be stored and transmitted to the subsequent model training module. The system records detailed parameters of the entire feature extraction and selection process to ensure transparency and traceability of the feature processing. The final generated feature set provides the model with optimal input data, ensuring high accuracy and reliability in warhead fragment distribution prediction.
[0065] Furthermore, step S200 includes at least steps S210-S230:
[0066] S210 receives and processes real-time data from the battlefield environment, uses a pre-trained machine learning model to perform dynamic predictions, and generates real-time predictions of the fragmentation distribution after the warhead explosion. Specifically:
[0067] At the moment the warhead is about to explode or is exploding, environmental data is collected in real time by various sensors deployed on the battlefield. These sensors include high-precision pressure sensors, accelerometers, infrared imaging devices, and temperature sensors, capable of capturing key parameters such as the intensity of the blast shock wave, the initial velocity of fragments, ambient temperature, humidity, and wind speed. The collected real-time data undergoes basic preprocessing, including data format conversion, timestamp synchronization, noise filtering, and preliminary outlier detection and correction. The preprocessed data ensures its consistency and reliability, becoming effective input for model predictions.
[0068] Furthermore, upon receiving the pre-processed real-time data, a pre-trained machine learning model is loaded. This model has been thoroughly trained and optimized using a large amount of historical and experimental data. The model's parameters are initialized and adjusted based on real-time data and environmental characteristics to ensure its predictive capabilities can adapt to current battlefield conditions in a timely manner.
[0069] Furthermore, during an actual warhead explosion, environmental factors can change rapidly. Sudden changes in wind speed or terrain effects can significantly alter fragmentation patterns. The system dynamically extracts and adjusts these real-time environmental features, combining them with preprocessed data to generate a feature set for model input. These features not only include the physical parameters of the explosion but also incorporate the dynamic changes in the current environment, thereby improving the model's predictive accuracy. Based on the dynamically adjusted feature set, the system calculates fragmentation distribution predictions within a very short time (typically milliseconds) using a loaded machine learning model. This process involves complex mathematical operations, including multivariate regression analysis, partial differential equation solving, and high-dimensional spatial mapping of the data. The system then uses these calculations to predict the density, velocity, and orientation distribution of fragments at different times and spatial locations.
[0070] Furthermore, the real-time prediction results are rapidly processed and output for use by battlefield command systems or other military decision support systems. The system visualizes these predictions, such as generating heatmaps of fragment distribution, dynamic curves of shock wave propagation, and fragment density distribution maps of the target area. These visualizations not only intuitively demonstrate the immediate impact of the explosion but also provide commanders with decision-making support. Simultaneously, the system compares the predictions with actual observation data, continuously adjusting model parameters through a feedback mechanism. If a significant deviation is found between the actual fragment distribution and the prediction, the system automatically corrects the model and optimizes the next prediction. This closed-loop feedback mechanism ensures that the model maintains high accuracy and stability in the constantly changing battlefield environment.
[0071] S220. Based on the difference between the actual fragment distribution and the predicted results, the model parameters are automatically adjusted for adaptive optimization to improve the system's prediction accuracy. Specifically:
[0072] After the warhead explodes, the system continues to collect real-time data on the distribution of actual fragments generated by the explosion using sensors deployed on the battlefield. Key data includes the distribution of fragment landing points, flight speeds, and the range of the shockwave.
[0073] The observed actual fragment distribution data is compared in detail with the fragment distribution predicted by the machine learning model. During the comparison, the system calculates the error Δ between the actual and predicted data. This error analysis helps the system identify the accuracy of the model's predictions and discover under what conditions the model's predictions deviate.
[0074] Furthermore, based on the error Δ analysis, the system identifies the prediction deviation of the model under different parameter settings. If the error is large under certain environmental conditions (such as high humidity or strong wind speed), the system will focus on analyzing the influencing factors of these parameters. The system uses partial differential analysis or gradient descent algorithm to calculate the sensitivity of each model parameter to the error and generates corresponding adjustment strategies.
[0075] Furthermore, the system may contain multiple machine learning models trained on different algorithms or datasets, each with varying strengths and weaknesses under different operational conditions. The system evaluates the performance of each model in real time and selects the most suitable model for the next prediction based on the current operational environment and error analysis results. If the currently used model performs poorly under specific conditions, the system will automatically switch to another model more suitable for the current conditions based on the deviation between the actual data and the prediction results. When wind speed and humidity significantly affect fragment distribution, the system may switch to a model trained under similar environmental conditions. In addition, the system can employ ensemble learning methods to weighted average or vote-based integrate the prediction results of multiple models to improve the robustness and accuracy of the overall prediction.
[0076] During each model prediction and feedback optimization process, the system not only adjusts the parameters but also updates the model's training dataset. By continuously introducing new observation data and error analysis results, the system can perform adaptive learning, thereby making the model perform more accurately in future predictions.
[0077] If the system detects a continuous decline in the performance of the current model, it may trigger a retraining process. Retraining is based on the latest collected data and an updated feature set to ensure the model's continued adaptability. Through this mechanism, the system can continuously improve its prediction accuracy and adaptability while operating without interruption.
[0078] S230: When environmental conditions undergo significant changes or model performance deteriorates, the model is triggered to update in real time, generating real-time suggestions for warhead design optimization and tactical deployment to assist decision-makers in making optimal choices. Specifically:
[0079] The system monitors key indicators of the battlefield environment in real time through sensors, such as temperature, humidity, wind speed, and terrain changes. When a significant change in these environmental parameters is detected that may have a significant impact on the distribution of warhead fragments, the system will mark the change as one of the trigger conditions. A sudden increase in wind speed or a change in terrain conditions may significantly affect the flight trajectory and distribution pattern of fragments. The system also monitors the model's prediction accuracy and other performance indicators (such as error rate and recall rate) in real time. When the model's performance indicators are detected to be continuously declining and reaching a certain threshold, the system will determine that the current model may no longer be suitable for prediction tasks under the current environmental conditions, and will trigger a model update process.
[0080] Furthermore, when triggering conditions such as environmental change or model performance degradation are met, the system immediately initiates the model update process. At this time, the system extracts the latest feature set from the real-time data stream and historical datasets, and rapidly retrains or fine-tunes the model using a predefined algorithm. The system utilizes the latest environmental data and observed actual fragment distribution data to retrain the model online. This process typically involves adjusting the parameters of the existing model or introducing new data points to enhance the model's adaptability. The system may use methods such as gradient descent or Bayesian optimization to quickly update the model's weights and parameters to improve the model's predictive ability under new conditions. After completing the model update, the system first performs a rapid validation of the updated model in a simulated environment to ensure its performance has improved under the new environmental conditions. If the validation passes, the system switches to the new model for real-time prediction; otherwise, it continues optimization or selects another model.
[0081] Furthermore, based on the updated model, the system re-evaluates the warhead's design parameters (such as explosive type, explosive energy, and warhead casing material) and generates design optimization suggestions. The system may suggest using different types of explosives under specific environmental conditions to optimize fragmentation coverage or explosive effects. These optimization suggestions are generated by analyzing the match between model predictions and actual operational requirements and provided to the warhead design team in the form of a suggestion report. In addition to design optimization, the system also generates tactical deployment suggestions based on the latest predictions. The system may suggest adjusting the warhead's deployment location or detonation timing to maximize operational effectiveness or reduce collateral damage. These suggestions, combined with the current battlefield situation and prediction results, are provided to commanders to assist in decision-making. The system generates multiple options and prioritizes them based on the reliability of model predictions to ensure commanders can select the optimal course of action.
[0082] Furthermore, after the implementation of new models and recommended solutions, the system continues to monitor actual combat effects and compare them with predicted results. Any new feedback data is recorded by the system and used to further optimize the model and generate more accurate recommendations. Through continuous updates and iterations, the system accumulates data on the relationship between environmental changes, model performance, and actual effects; this data is used for continuous adaptive learning. Through this continuous optimization, the system's predictive capabilities and the accuracy of its generated recommendations will continuously improve over time, ensuring the system's efficiency and reliability in various complex combat environments.
[0083] Furthermore, step S300 includes at least steps S310-S330:
[0084] S310 integrates the prediction system into existing military hardware and software infrastructure, and debugs and configures it according to different battlefield environments and operational requirements. Specifically:
[0085] During the hardware integration phase, the system first needs to establish physical connections with various sensor devices deployed on the battlefield. These sensor devices include high-precision pressure sensors, accelerometers, infrared imaging devices, and temperature sensors, which are capable of collecting critical data on the environment and the explosion process in real time.
[0086] In terms of software integration, the system needs to interface with existing military command and control (C2) systems, data processing platforms, and communication networks. The system will interact with these platforms via APIs or message queues to ensure seamless data transmission and processing. Sensor data is transmitted to a central server via data relay stations or edge computing nodes, where it is processed and predicted in real time. The system also needs to ensure compatibility with existing security protocols and encryption mechanisms to guarantee data integrity and confidentiality.
[0087] Furthermore, after successful system integration, initial configuration is required. This includes configuring the sensor data acquisition frequency, data format conversion rules, and communication protocols with existing systems. The system will configure initial parameters based on specific operational environments (such as terrain conditions, weather conditions, etc.). In high-humidity battlefield environments, the system may increase the sensor data acquisition frequency to cope with potential data fluctuations. After configuration, the system will undergo preliminary functional testing to ensure all modules operate normally.
[0088] Furthermore, the system undergoes adaptive adjustments based on changes in the actual battlefield environment. By monitoring environmental data in real time (such as temperature, humidity, and wind speed), the system dynamically adjusts its internal parameters and algorithms. The system may adjust filter parameters in the data processing pipeline based on sensor feedback, or adjust the sampling rate to cope with sudden increases in data volume. This adjustment process ensures that the system can operate stably under different battlefield conditions and maintain efficient predictive capabilities in dynamic environments.
[0089] Furthermore, after the system integration and initial debugging are completed, comprehensive functional verification is required. This includes end-to-end testing under simulated battlefield conditions, from data acquisition and transmission to model prediction and result feedback. The system will conduct multiple rounds of testing using historical and simulated data to verify the correctness of data transmission and processing between modules, as well as the accuracy of model predictions. The system will compare the results of historical data and real-time prediction data to ensure that the output of the prediction system is highly consistent with the actual situation. To verify the system's stability under high-intensity combat conditions, stress tests will be conducted. This includes increasing data traffic, shortening sampling intervals, and simulating various contingencies (such as sensor failures, network latency, etc.). Through these tests, the system will identify potential bottlenecks or failure points and perform targeted optimizations to ensure stable operation under various extreme conditions.
[0090] Furthermore, during the debugging process, the system continuously accumulates environmental data and debugging parameters, optimizing system configuration through a feedback mechanism. If certain parameter configurations perform poorly under specific conditions, the system records this information and adjusts it in the next configuration. The system also utilizes machine learning algorithms to automatically adjust some parameters to cope with different operational environments. All system configurations and debugging records are automatically stored and managed through a version control system. This ensures that the system's configuration can be quickly reused in different operational missions, and that any debugging and optimization can be traced and reviewed. The system also provides backup and recovery functions to ensure that in extreme situations, the system can quickly recover to a verified stable state.
[0091] The S320 incorporates multi-layered security and fault-tolerance mechanisms to ensure the system can withstand interference or attacks in battlefield environments and continue stable operation or safely recover in the event of a failure. Specifically:
[0092] During data acquisition and transmission, the system employs advanced encryption standards (such as AES-256) to encrypt sensor data, ensuring that data is not intercepted or tampered with during transmission. The data includes environmental parameters and explosion data collected in real-time by sensors, such as blast shock wave intensity, fragment velocity, and ambient temperature. All data undergoes rigorous encryption before entering the processing pipeline, ensuring effective protection of data integrity and confidentiality even in hostile environments. All access operations within the system require multi-factor authentication mechanisms, including two-factor authentication and role-based access control (RBAC). Only authorized users and system components can access core data storage and model computation units. This measure ensures that even within the internal system, only operators or subsystems with appropriate permissions can read data and perform model operations, avoiding security risks caused by internal oversights or malicious operations. The system deploys a real-time threat monitoring and intrusion detection system (IDS) capable of monitoring and identifying abnormal activities in network traffic, such as malicious attacks, data breach attempts, or unusual access behavior. By combining machine learning algorithms, the system can identify potential threats based on historical data and real-time traffic, and take timely countermeasures, such as isolating infected subsystems or initiating emergency response procedures.
[0093] Furthermore, during the data acquisition phase, all sensor data is backed up in real time to multiple independent storage nodes, ensuring rapid data recovery from other nodes should any node fail. Data backup covers not only the raw sensor data but also preprocessed intermediate data and the final prediction results. This design ensures data integrity and availability even in the event of partial system failure. The system employs a distributed computing architecture, distributing the computational tasks of the prediction model across multiple computing nodes. If a node fails, the system automatically detects this and transfers the computational tasks to other available nodes. This failover mechanism is based on real-time health checks, analyzing node response times and computational load to ensure the system maintains normal operation under high load or sudden failures. During model prediction, if the system detects abnormal deviations in the model output, it activates a model fault tolerance mechanism. First, the system reverts to a previously validated model version to ensure reliable prediction results in the current environment. Simultaneously, the system triggers model retraining or switching, readjusting model parameters or selecting a more suitable model using the latest environmental and historical data.
[0094] Furthermore, the system incorporates an automatic recovery mechanism that can rapidly initiate emergency recovery procedures upon detecting a major system failure or attack. If the sensor network is interrupted or some computing nodes fail, the system will automatically switch to a backup network or activate backup nodes to ensure uninterrupted prediction. The automatic recovery mechanism, through pre-set recovery strategies and backup plans, ensures the system can quickly recover to an available state under extreme conditions. For extreme situations (such as complete network paralysis or data center damage), the system is designed with disaster recovery and reconstruction schemes. All critical data and model versions are backed up off-site in geographically isolated, secure data centers. After a disaster, the system can rebuild core components from backups and redeploy them to the battlefield environment through secure channels. This mechanism ensures that even in the worst-case scenario, the system can continue to provide core functionality through off-site recovery.
[0095] Furthermore, the system continuously monitors the security status and analyzes environmental data. Access logs and security event recordings are used to continuously evaluate the effectiveness of current security measures. The system dynamically adjusts security policies based on actual conditions, strengthening access control for certain nodes or adjusting the update frequency of encryption keys to address newly discovered threats. The system integrates an adaptive security learning mechanism, which continuously updates its security protection strategies based on emerging security events and threat intelligence. Through machine learning and big data analytics, the system can identify potential security risks and proactively deploy defensive measures. This adaptive learning mechanism not only enhances system security but also minimizes the impact of security threats on system stability without affecting core functionality.
[0096] The S330 system enables rapid deployment and remote upgrades, and achieves real-time updates via network connectivity to maintain optimal system performance. Specifically:
[0097] To enable rapid system deployment, the system was pre-configured for different battlefield environments during the design phase. This pre-configuration includes initial settings for sensor data acquisition, data format conversion rules, and compatibility testing with existing military infrastructure. This pre-configuration significantly reduces deployment time on the battlefield. The system's automated deployment tool can automatically generate the necessary configuration files based on the pre-configured template and deploy the system to computing nodes or devices on the battlefield via remote connection. The system employs a distributed deployment architecture, supporting the distribution of core computing and data processing modules across multiple battlefield nodes. Each node can independently complete data acquisition, processing, and preliminary prediction tasks, thereby improving the overall system's response speed and reliability. On the front lines, the system can assign data preprocessing and preliminary analysis tasks to frontline nodes, while handling complex prediction calculations in the rear data center. This distributed deployment strategy not only accelerates system response but also reduces the risk of single points of failure.
[0098] Furthermore, the system adopts a modular design, with each functional module (such as data acquisition, feature extraction, and model prediction) being an independent subsystem, supporting hot-swappable upgrades. During remote upgrades, the system only needs to update specific modules without requiring a complete restart. When new sensor data processing algorithms or improved model parameters become available, the system can replace modules without interrupting existing tasks, achieving seamless upgrades. Through this mechanism, the system can quickly integrate the latest technological advancements and tactical requirements. To improve the efficiency of remote upgrades, the system supports incremental upgrades and differential packet transmission. The incremental upgrade mechanism only transmits and updates files and data that have changed since the last upgrade, reducing transmission time and bandwidth consumption. This is particularly important when battlefield network conditions are limited. The system can update only the model's weight parameters or newly added environmental feature processing modules without retransmitting the entire model file. Differential packet transmission further reduces the resources required for upgrades by comparing version differences and transmitting only the differing parts. The system supports receiving upgrade commands in real time through a secure network channel and executing upgrade tasks in the background. To ensure the security and stability of upgrades, the system automatically creates backup points before each upgrade. If any issues arise during the upgrade process, the system will automatically roll back to the previous stable version, ensuring continued system availability. Furthermore, the system has an automatic verification mechanism that performs functional and stability checks immediately after the upgrade is complete, ensuring the updated system functions correctly.
[0099] Furthermore, the system integrates a version control mechanism to record and manage the changes made in each upgrade in detail. All configuration files, model parameters, algorithm code, etc., are managed through the version control system. The version control system can track the specific content, time, and executor of each upgrade, and supports reverting to any historical version. This mechanism not only ensures the transparency and traceability of the upgrade process, but also facilitates system rollback or comparative analysis when needed. The system has designed a multi-layered upgrade management strategy based on different battlefield conditions and combat missions. For frontline combat missions, the system prioritizes pushing critical security patches and performance optimization updates, while postponing updates to non-critical functions to later stages. The rear command center can receive comprehensive system upgrades, including algorithm optimizations and new feature extraction methods. Through this differentiated upgrade management strategy, the system can ensure continuous improvement in overall performance without affecting critical missions.
[0100] Furthermore, during remote deployment and upgrades, the system employs stringent encryption and authentication mechanisms to ensure data transmission security. The system uses Public Key Infrastructure (PKI) to sign and verify upgrade packages, preventing unauthorized code injection. Simultaneously, upgrade packages are encrypted during transmission to prevent interception or tampering. These security measures ensure the system is not threatened by adversary network attacks during remote upgrades. When performing remote upgrade operations, personnel are required to authenticate themselves using multi-factor authentication (such as passwords, fingerprints, and temporary dynamic passwords). Only authorized personnel and devices can perform remote deployment and upgrade operations. In addition, the system employs a role-based access control policy, ensuring that each operator can only perform operations within their authorized scope, further enhancing system security.
[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and all such variations should be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A machine learning based warhead fragment distribution prediction system, characterized by, It includes modules for data acquisition, data processing, feature extraction and selection, model building and optimization, real-time prediction and feedback, decision support and report generation, user interaction and visualization, and system monitoring and adaptation.
2. The system of claim 1, wherein, The data acquisition module is used to acquire data related to the warhead explosion from multiple data sources, including experimental data, sensor data, and historical data.
3. The system of claim 1, wherein, The data processing module is used to perform preliminary processing on the collected data, including data denoising, missing value imputation, and data standardization, to ensure data consistency and accuracy.
4. The system of claim 1, wherein, The feature extraction and selection module extracts key features from the processed data that can effectively predict the distribution of warhead fragments, and uses machine learning algorithms to filter the features to ensure that the data input into the model has high predictive value.
5. The system of claim 1, wherein, The model building and optimization module constructs and optimizes machine learning models based on extracted features. During the training and optimization process, a large amount of historical and experimental data is used for repeated iterations to improve the model's prediction accuracy and reliability.
6. The system of claim 1, wherein, The real-time prediction and feedback module receives real-time data during the warhead explosion process and uses a trained model to dynamically predict the fragment distribution. The system adjusts model parameters based on real-time environmental changes to generate accurate fragment distribution prediction results, and continuously optimizes the model through a closed-loop feedback mechanism.
7. The system of claim 1, wherein, The decision support and report generation module provides optimized warhead design suggestions and tactical deployment plans based on model prediction results, and generates detailed analysis reports to assist military decision-makers in making effective judgments and decisions.
8. The system of claim 1, wherein, The user interaction and visualization module displays the system's prediction results and analysis data through an intuitive user interface, supports multi-scenario simulation and interactive operation, helps users quickly understand and utilize the information generated by the system, and improves decision-making efficiency.
9. The system of claim 1, wherein, The system monitoring and adaptive module continuously monitors the system's operating status and optimizes model parameters and system settings through adaptive learning to ensure that the system operates stably and efficiently in various complex environments.
10. A method for predicting warhead fragment distribution based on machine learning, the method comprising at least steps S100-S400: S100 collects and cleans data through automated data processing and builds and optimizes predictive models based on machine learning technology. S200. Dynamic prediction of warhead fragment distribution using the constructed model. The S300 system is deployed to integrate military hardware and software systems. The S400 provides military decision-makers with an intuitive user interface and decision support tools, displaying forecast results and offering optimization suggestions.