Alarm situation actual combat handling drill intelligent software control system based on AI confrontation simulation

Abstract

The invention discloses an AI confrontation simulation-based intelligent software control system for alarm condition actual combat disposal drilling, and relates to the technical field of police affair intelligent software control, the AI confrontation simulation-based intelligent software control system comprises an alarm condition scene construction module, an AI confrontation simulation module, a drilling monitoring and evaluation module, an intelligent feedback learning module and a data management and storage module, and the alarm condition scene construction is used for generating a vivid scene by combining a GAN with spatio-temporal data; aI confrontation simulation enables intelligent agents to confront by means of deep reinforcement learning and meta learning; the drill monitoring evaluation is fused with the multi-modal data evaluation; intelligent feedback learning provides feedback and learning paths by means of the knowledge graph; data management and storage use a distributed technology to guarantee data security and efficient processing. According to the invention, the scene is vivid, the immersion is enhanced, the ability of people to cope with a complex environment is exercised, the AI confrontation improves the decision strain, scientific evaluation feedback assists people in growth, intelligent learning meets individual demands, data management promotes experience sharing, multi-module cooperation is realized, and the alarm handling drilling effect and the police actual combat level are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of police intelligent software control, and in particular to an intelligent software control system for police situation actual combat disposal drills based on AI confrontation simulation. Background Art

[0002] With the rapid development of technologies such as artificial intelligence and big data, some simple simulation software has begun to be applied in the field of police situation drills. However, most of these software have single functions and cannot achieve highly realistic police situation simulations. Their construction of police situation scenarios is often based on fixed templates, lacking dynamics and randomness, and it is difficult to reflect the diversity and complexity of real-life police situations. During the simulation process, the simulation of personnel behavior and event development is also relatively simple, and it is impossible to accurately simulate the real reactions of different roles in complex situations. For example, in the simulation of a hostage-taking incident, the behavior patterns and psychological changes of the suspect cannot be truly reflected, resulting in a significant reduction in the effectiveness of the drill.

[0003] In addition, existing drill systems generally lack intelligent evaluation and feedback mechanisms. After the drill, the evaluation of the performance of the drill personnel often relies on manual subjective judgment and lacks scientific and objective quantitative indicators. This may lead to deviations in the evaluation results and cannot accurately reflect the true level and existing problems of the drill personnel. At the same time, due to the lack of intelligent feedback, it is difficult for the drill personnel to obtain targeted improvement suggestions and learning resources, which is not conducive to the improvement of their capabilities. Moreover, the drill data between different regions and different departments are often independent of each other, unable to achieve sharing and collaborative analysis, and it is difficult to summarize general lessons, restricting the overall improvement of police actual combat capabilities. Summary of the Invention

[0004] An intelligent software control system for police situation actual combat disposal drills based on AI confrontation simulation proposed by the present invention is used to solve the problems mentioned in the above prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: An intelligent software control system for police situation actual combat disposal drills based on AI confrontation simulation includes the following modules: Police situation scenario construction module: Use a generative adversarial network combined with spatio-temporal data analysis technology to construct a police situation scenario, and use geographic information system and digital twin technology to map the real world to a virtual space. The scene complexity evaluation formula is: , where C, , , are the complexities of the scene, geographical environment, personnel activities, and event development respectively, is the weight coefficient; AI Adversarial Simulation Module: Adopts a multi-agent adversarial architecture that combines deep reinforcement learning and meta-learning. The state transition probability and the reward function are adjusted according to the police situation scenario. The opponent modeling technology is introduced to construct an opponent model. The formula is , where is the opponent's action sequence, is the scenario state sequence, and f is the modeling function, which is implemented using a neural network; Drill Process Monitoring and Evaluation Module: Utilizes multi-modal data fusion technology to integrate data, monitor the drill process. The evaluation system is based on the fuzzy comprehensive evaluation method. The evaluation score formula is , where n is the number of evaluation index levels, m is the number of hierarchical indicators, w i is the weight of the i-th level, v ij is the weight of the j-th indicator in the i-th level, and r ij is the score of the j-th indicator in the i-th level; Intelligent Feedback and Learning Module: With the help of knowledge graphs and reinforcement learning algorithms, provides feedback to the drill personnel. The customized learning path formula is , where is the evaluation result, is the knowledge graph, and g is the path planning function, which is implemented using a heuristic search algorithm; Data Management and Storage Module: Manages data using a distributed hash table combined with encrypted storage technology. The data consistency maintenance algorithm is through a distributed consensus mechanism. The formula is , where is the data consistency index, N is the number of data replicas, d i is the i-th data replica, is the function to judge the consistency between the replica and the main data.

[0006] Furthermore, it also includes a multi-modal interaction module. This module uses emotion computing technology to recognize the emotions of the drill personnel, adjusts the interaction method through an adaptive interaction strategy, uses virtual image generation technology to generate virtual roles of personnel, and provides an immersive experience for the personnel through VR / AR technology.

[0007] Furthermore, it also includes a real-time communication and collaboration module. This module uses software-defined network technology to optimize the communication network topology, uses blockchain technology to construct a distributed collaboration ledger to record the collaboration behaviors and task assignments in the drill, introduces intelligent contract technology to automatically execute the assignment and reward and punishment mechanisms of collaboration tasks, and ensures communication security through real-time communication encryption technology to support large-scale collaborative drills.

[0008] Furthermore, the police situation scenario construction module also has the functions of dynamic risk assessment and early warning. It uses machine learning algorithms to predict scenario risks, and through the risk propagation model, simulates the diffusion process of risks in the scenario. The risk assessment formula is: , where R is the risk value, p k is the probability of the kth type of risk occurring, d k is the loss degree after the kth type of risk occurs, o is the number of risk types. When the risk value exceeds the threshold, the system issues an early warning to prompt the drill personnel.

[0009] Furthermore, the agents in the AI confrontation simulation module adopt transfer learning technology, transfer the experience learned in similar police situation scenarios to new scenarios, and through the multi-agent cooperation strategy optimization algorithm, the agents automatically negotiate cooperation tasks, and use the method combining reinforcement learning and evolutionary algorithm to optimize the agent strategy.

[0010] Furthermore, the drill process monitoring and evaluation module uses federated learning technology, adopts unsupervised learning algorithms to mine potential patterns and abnormal behaviors in the drill data, and adjusts the drill process and difficulty through the real-time evaluation result feedback mechanism.

[0011] Furthermore, the intelligent feedback and learning module adopts knowledge push technology, pushes relevant knowledge according to the needs of the drill personnel, provides question answers using an intelligent tutoring system, and predicts the learning effect through a learning effect prediction model. The formula is: , where is the evaluation result, is the execution situation of the learning path, is the degree of knowledge mastery, and h is the prediction function, which is implemented using a deep learning model.

[0012] Furthermore, the data management and storage module uses big data analysis technology to discover patterns and trends, visually displays data characteristics and analysis results through data visualization technology, and processes personal sensitive information data using data desensitization technology.

[0013] Furthermore, the system also has an emergency response ability improvement module. When a police situation occurs, the system switches to the emergency response mode, uses experience to provide decision support for the commanders, evaluates and simulates the police situation through real-time data access and analysis, formulates plans, and allocates police forces and resources using intelligent scheduling algorithms.

[0014] Furthermore, the system provides a multi-language support module, switches the languages of the system interface and prompt messages according to the needs of the drill personnel, supports the use of multiple languages, and optimizes language translation and interaction effects through language adaptive learning algorithms.

[0015] Compared with the existing technologies, the beneficial effects of the present invention are: In terms of scenario simulation, the highly realistic and dynamically changing police scenarios constructed using advanced technology allow trainees to experience the complexity and uncertainty of real police situations firsthand, greatly enhancing the realism and immersion of the drills, and effectively training trainees' ability to respond in complex environments.

[0016] In the AI ​​confrontation simulation, the autonomous decision-making and confrontation mechanism of multiple agents simulated the real reactions and strategies of different roles such as suspects and victims, providing a very challenging confrontation environment for the trainees, prompting them to continuously optimize their handling strategies and improve their response and decision-making capabilities in actual combat.

[0017] The drill process monitoring and evaluation module achieves a comprehensive, objective, and quantitative evaluation of the drill through multimodal data fusion and a scientific evaluation system. This not only accurately reflects the performance of the drill personnel, but also provides them with detailed feedback and targeted improvement suggestions to help them quickly improve their capabilities.

[0018] The intelligent feedback and learning module accurately pushes learning resources and customizes learning paths to drill personnel based on drill data, meeting personalized learning needs and effectively improving learning efficiency. In addition, the data management and storage module realizes the secure storage, efficient retrieval and in-depth analysis of data, promotes experience sharing and coordinated development among different regions and departments, and helps to improve the practical level of the entire police system.

[0019] In addition, the multimodal interaction module allows drill participants to interact with virtual scenes in a natural and diverse way, which enhances the fun and participation of the drill; the real-time communication and collaboration module ensures efficient coordination of cross-regional and cross-departmental drills and improves team collaboration capabilities; the emergency response capability enhancement module can quickly provide decision-making support and optimize resource allocation when a real police situation occurs, playing an important role in ensuring social security. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a schematic block diagram of an intelligent software control system for police situation actual combat handling drills based on AI confrontation simulation proposed by the present invention; Figure 2 A bar chart comparing decision-making times in different exercise scenarios proposed by the present invention; Figure 3 This is a line graph showing changes in teamwork scores during multiple drills proposed by the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0023] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined. In addition, the terms "mounted", "connected" and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. The present invention will be further described in detail below with reference to the drawings.

[0024] Refer to Figures 1 to 3 : An intelligent software control system for police situation actual combat disposal drills based on AI confrontation simulation, including the following modules: Police Situation Scenario Construction Module: This module is the foundation of the system and provides a realistic virtual scenario for drills. First, various real police situation cases are collected through big data analysis technology, covering different geographical environments, weather conditions, personnel distributions, and event types. Accurate geographical data is obtained using Geographic Information System (GIS), and virtual geographical scenarios are created in combination with 3D modeling technology. At the same time, Generative Adversarial Network (GAN) combined with spatio-temporal data analysis technology is used to mine spatio-temporal features from massive multi-source data (such as surveillance videos, social media, and police databases), making the scenario more dynamic and realistic. To achieve dynamic adjustment of the scenario, an adaptive scenario generation algorithm is adopted to dynamically adjust scenario parameters according to drill requirements and real-time data. The scenario complexity evaluation formula is: , where C represents the scenario complexity, represents the geographical environment complexity, is the personnel activity complexity, is the event development complexity, is the weight coefficient, which is dynamically adjusted according to the drill type, ranging from 0 to 1 and . For example, in an urban anti-terrorism drill, can be set to 0.3, can be set to 0.4, can be set to 0.3 to highlight the complexity of personnel activities and event development.

[0025] AI Adversarial Simulation Module: This module is the core of the system and is responsible for simulating the adversarial process in police situations. A multi-agent adversarial architecture combining deep reinforcement learning and meta-learning is adopted, and each agent has an independent reinforcement learning policy network and value network. Through the meta-learning mechanism, the agent can quickly adapt to different police situation scenarios and opponent strategies. At the same time, using the attention mechanism, the agent can focus on key information and improve decision-making efficiency. During the simulation, the agent makes decisions based on the Markov Decision Process (MDP), and the state transition probability and the reward function are dynamically adjusted according to the police situation scenario. The opponent modeling technology is introduced, and the agent analyzes the opponent's strategy in real time and constructs an opponent model. The formula is , where is the opponent model, is the opponent's action sequence, is the scenario state sequence, and f is the modeling function, which is implemented using a neural network. For example, in the simulation of a hostage-taking incident, the agent can construct a behavior model of the suspect based on the suspect's action sequence and the current scenario state, so as to formulate more effective countermeasures.

[0026] Drill Process Monitoring and Evaluation Module: This module is used to monitor and evaluate the drill process in real time. By using multi-modal data fusion technology, it integrates data such as videos, audios, and motion captures to comprehensively monitor the behaviors of drill personnel. It analyzes the behaviors of drill personnel by combining the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), and analyzes the dialogue content through semantic understanding technology. The evaluation system is based on the fuzzy comprehensive evaluation method to construct a multi-level evaluation index system. The formula for calculating the evaluation score is: , where E is the drill evaluation score, n is the number of levels of evaluation indicators, m is the number of indicators at each level, w i is the weight of the i-th level, v ij is the weight of the j-th indicator at the i-th level, r ij is the score of the j-th indicator at the i-th level, and the score range is 0 - 100 points. For example, for an emergency rescue drill, the number of levels of evaluation indicators n = 3 can be set, namely action execution, decision-making ability, and teamwork; the number of indicators at each level m = 3. For example, the action execution level can include the arrival time at the scene, rescue operation specifications, material use efficiency, etc. By calculating the scores of each indicator, the final drill evaluation score can be obtained.

[0027] Intelligent Feedback and Learning Module: This module provides personalized feedback and learning suggestions for drill personnel based on the drill evaluation results. With the help of the knowledge graph and reinforcement learning algorithms, it provides real-time and personalized feedback for drill personnel. Updates the knowledge graph according to the drill data, mines knowledge associations, and optimizes the feedback strategy through reinforcement learning. Adopts the Case-Based Reasoning (CBR) technology to retrieve similar cases from the historical drill case library to provide solution references for drill personnel. The personalized learning path planning algorithm generates a customized learning path according to the ability level and weak links of drill personnel, and the formula is , where P is the learning path, is the drill evaluation result, is the knowledge graph, and g is the path planning function, which is implemented by using the heuristic search algorithm. For example, if the drill personnel score low in communication and cooperation, the system will screen relevant knowledge from the knowledge graph and combine historical cases to generate a targeted learning path for them, such as recommending communication skills training courses and team cooperation simulation drills.

[0028] Data Management and Storage Module: This module is responsible for the management and storage of various types of data in the system. It uses a distributed hash table (DHT) combined with encrypted storage technology to manage data, ensuring data security and reliability. Erasure coding technology is used for data storage to improve the fault tolerance of data storage. Data retrieval utilizes an inverted index and a distributed query optimization algorithm to enhance retrieval efficiency. Homomorphic encryption technology is applied to sensitive data for data analysis and processing in an encrypted state. The data consistency maintenance algorithm uses a distributed consensus mechanism to ensure data consistency when stored on multiple nodes. The formula is , where is the data consistency metric, N is the number of data replicas, and d i is the i-th data replica, is a function to judge the consistency between the replica and the main data, with 1 for consistency and 0 for inconsistency. For example, when there are 5 data replicas in the system and 4 of them are consistent with the main data, the data consistency metric .

[0029] In the present invention, there is also a multimodal interaction module. This module uses emotion computing technology to deeply analyze the facial micro-expressions, voice fluctuations, and physiological data of the drill personnel, accurately identifying emotional states such as tension and anxiety. According to the adaptive interaction strategy, it flexibly adjusts the interaction method, such as using a softer voice prompt to relieve tension. At the human-computer interaction level, it integrates gesture recognition, speech recognition, and eye movement tracking technologies, enabling the drill personnel to interact with the system conveniently through simple gestures, daily conversations, or even eye movements, greatly improving the interaction efficiency. Through virtual image generation technology, it real-time collects the actions and expressions of the drill personnel and quickly generates vivid virtual characters, comprehensively enhancing the immersion of the drill. Then, with the help of virtual reality (VR) / augmented reality (AR) technology, it creates an immersive experience, closely integrating the virtual scene with the real environment, significantly improving the authenticity and participation of the drill, and bringing better effects to the drill.

[0030] In the present invention, there is also a real-time communication and collaboration module. With the help of software-defined network (SDN) technology, this module can flexibly and rapidly optimize the communication network topology according to the dynamic requirements of real-time communication. Whether it is a sudden surge in data traffic or a temporary change in network nodes, it can ensure the stability of real-time communication, minimize latency, and ensure the timeliness of information transmission. In terms of collaboration management, blockchain technology is used to build a distributed collaboration ledger, which details the collaboration behaviors and task assignments during the drill. This makes the entire collaboration process completely transparent, every link is traceable, and the responsibilities are clear. At the same time, intelligent contract technology is introduced to automatically execute the assignment of collaboration tasks according to preset rules, and reward excellent teams or individuals and punish those who do not meet the standards. In addition, real-time communication encryption technology comprehensively protects the security of communication content and prevents information leakage. This module strongly supports large-scale collaborative drills across regions and departments, enabling different units to break through spatial and organizational boundaries, achieve efficient collaboration, and greatly improve the drill effect.

[0031] In the present invention, the police situation scenario construction module also has a dynamic risk assessment and early warning function. With the help of machine learning algorithms, this function can accurately predict potential risks that may occur in the police situation scenario, and these risks cover multiple key aspects such as casualties and property losses. To better understand the diffusion law of risks in the police situation scenario, a risk propagation model is introduced into the system. Through this model, the diffusion process of risks in the scenario can be carefully simulated, so that relevant personnel can plan in advance and formulate effective countermeasures. In terms of risk assessment, a scientific calculation formula is adopted: , where R is the risk value, p k is the probability of the kth risk occurring, d k is the loss degree after the kth risk occurs, and o is the number of risk types. When the risk value calculated according to the above formula exceeds the pre-set threshold, the system will quickly and automatically send out a warning message. This warning message will be promptly transmitted to the drill personnel, prompting them to immediately take corresponding measures to effectively respond to possible risks and reduce losses.

[0032] In the present invention, the agents in the AI confrontation simulation module adopt transfer learning technology. The agents can skillfully transfer the learning experience accumulated in similar police situation scenarios to a brand-new police situation scenario. This process greatly accelerates its learning process in the new environment, enabling it to adapt and respond more quickly. In addition, with the help of the multi-agent cooperation strategy optimization algorithm, intelligent interaction is achieved among the agents. They are no longer isolated individuals, but can automatically conduct negotiations based on the algorithm and reasonably allocate cooperation tasks. This automatic negotiation mechanism avoids the blindness and unreasonableness of task allocation, greatly improves the cooperation efficiency among the agents, and enables them to cooperate more tacitly when dealing with police situations. In terms of enhancing the capabilities of the agents themselves, an innovative method combining reinforcement learning and evolutionary algorithms is adopted. Reinforcement learning continuously gives the agents positive or negative feedback to guide them to adjust their strategies; the evolutionary algorithm simulates the natural evolution process to optimize the strategies of the agents through continuous iteration. Through this two-pronged approach, the adaptability and decision-making ability of the agents in the complex and changeable police situation environment are effectively enhanced, enabling them to make more reasonable and effective decisions when facing various difficult situations.

[0033] In the present invention, the drill process monitoring and evaluation module uses federated learning technology to achieve collaborative analysis and evaluation of multi-source data while protecting the data privacy of each participating unit. To analyze the drill data more deeply, the module adopts unsupervised learning algorithms. This algorithm can autonomously discover the latent patterns and abnormal behaviors in the drill data. It does not require pre-set labels for guidance, but relies on its own powerful learning ability to discover the subtle laws and problems from the massive data, opening up a more comprehensive and in-depth perspective for drill evaluation, so that the evaluation is no longer limited to surface phenomena, but can reach the deep information behind the data. Moreover, the module also constructs a real-time evaluation result feedback mechanism. During the drill, the system continuously evaluates the drill situation and quickly feeds back the real-time evaluation results. Based on these timely feedback messages, relevant personnel can accurately judge the current state of the drill and flexibly adjust the drill process and difficulty accordingly. In this way, the drill can not only always maintain a certain level of challenge to stimulate the participants to continuously improve their response capabilities, but also be more targeted at cultivating the actual combat skills of the participants in specific scenarios in close accordance with actual needs.

[0034] In the present invention, the intelligent feedback and learning module adopts personalized knowledge push technology to accurately push relevant knowledge such as laws, regulations, and tactical skills according to the learning history and current needs of the drill personnel. Using an intelligent tutoring system, it provides real-time question answering and guidance for the drill personnel. Through a learning effect prediction model, it predicts the learning effect of the drill personnel in advance and adjusts the learning plan in a timely manner. The learning effect prediction formula is: , where L is the predicted value of the learning effect, is the drill evaluation result, is the execution of the learning path, is the degree of knowledge mastery, and h is the prediction function, which is implemented using a deep learning model.

[0035] In the present invention, the data management and storage module utilizes big data analysis technology to conduct in-depth and meticulous mining of a large amount of historical drill data. During this process, by integrating, sorting, and analyzing various types of data, the rules and trends hidden behind the data can be keenly captured. These rules and trends cover many aspects such as the rationality of the drill process, the change in the proficiency of personnel operations, and the frequency trend of various risks, providing extremely valuable reference basis for subsequent drill optimization. To more efficiently transmit the data value to the decision-making level, this module introduces data visualization technology. With the help of this technology, data is no longer presented in the form of complex numbers and texts, but is displayed in the form of intuitive and clear charts and graphs to show data characteristics and analysis results. Whether it is a bar chart presenting the time consumption comparison of different drill links or a line chart reflecting the change trend of drill effects over time, relevant personnel can clearly obtain key information at a glance, thereby providing strong support for drill decision-making and making the decision-making process more scientific and accurate. In terms of data security protection, the data management and storage module adopts data desensitization technology. For data involving personal privacy and sensitive information, this technology will be processed according to strict rules and procedures. By means of replacement, obfuscation, etc., the data that may originally disclose personal privacy or pose a security risk is transformed into a form that cannot directly identify specific individuals or sensitive content, thereby ensuring the security of data in all aspects during the use and storage of data and effectively preventing the risks brought by data leakage.

[0036] In the present invention, the system also has an emergency response capability improvement module. When a real police situation occurs, the system can quickly switch to the emergency response mode, utilize the experience and data accumulated from drills to provide decision-making support for the command personnel. Through real-time data access and analysis, quickly evaluate and simulate the real police situation, and formulate the optimal disposal plan. Use intelligent scheduling algorithms to reasonably allocate police forces and resources to improve the emergency response efficiency.

[0037] In the present invention, the system provides a multilingual support module which can accurately perceive the needs of the drill personnel and automatically and smoothly switch the languages of the system interface and the prompt information. The supported languages cover a wide range, including not only mainstream languages such as English, Chinese, French, Spanish, etc. which are widely used globally, but also many ethnic minority languages. This feature fully takes into account the usage needs of personnel from different regions and cultural backgrounds, enabling drill personnel from all over the world with diverse backgrounds to operate and understand the system without language barriers. In addition, the module is equipped with a language adaptive learning algorithm. This algorithm continuously conducts in-depth analysis on the language data in the system and, based on the actual usage feedback of users, continuously optimizes the accuracy of language translation and the interaction effect. As time goes by and the usage frequency increases, the language expression of the system will become more natural and accurate, greatly enhancing the usability and versatility of the system and enabling every user to obtain a high-quality operation experience.

[0038] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation, characterized in that: Includes the following modules: Police situation scenario construction module: Generative adversarial networks are used in combination with spatiotemporal data analysis technology to construct police situation scenarios. Geographic information systems and digital twin technology are used to map reality into virtual space. The scenario complexity evaluation formula is: , among which C, , , They are the complexity of the scene, geographical environment, personnel activities, and event development. is the weight coefficient; AI confrontation simulation module: a multi-agent confrontation architecture that uses deep reinforcement learning combined with meta-learning, state transition probability And the reward function According to the police situation scenario adjustment, the opponent modeling technology is introduced to build the opponent model. The formula is: ,in is the opponent's action sequence, is the scene state sequence, f is the modeling function, which is implemented using a neural network; Exercise process monitoring and evaluation module: Use multimodal data fusion technology to integrate data and monitor the exercise process. The evaluation system is based on fuzzy comprehensive evaluation method. The evaluation score formula is: , where n is the number of evaluation index levels, m is the number of level indicators, and w i is the weight of the i-th level, v ij is the weight of the jth indicator at the i-th level, r ij is the score of the jth indicator at the i-th level; Intelligent feedback and learning module: With the help of knowledge graph and reinforcement learning algorithm, feedback is provided to drill personnel. The formula for customized learning path is: ,in is the evaluation result, is the knowledge graph, g is the path planning function, which is implemented using a heuristic search algorithm; Data management and storage module: Distributed hash tables are used in combination with encrypted storage technology to manage data. The data consistency maintenance algorithm uses a distributed consensus mechanism. The formula is: ,in is the data consistency indicator, N is the number of data copies, and d i is the i-th data copy, It is a function to determine the consistency between the replica and the primary data.

2. According to claim 1, the intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation is characterized in that: It also includes a multimodal interaction module, which uses emotional computing technology to identify the emotions of drill personnel, adjusts the interaction method through adaptive interaction strategies, uses virtual image generation technology to generate virtual characters for personnel, and provides an immersive experience for personnel through VR / AR technology.

3. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: It also includes a real-time communication and collaboration module, which uses software-defined network technology to optimize the communication network topology, uses blockchain technology to build a distributed collaborative ledger, records collaborative behaviors and task allocation in drills, introduces smart contract technology, and automatically executes the allocation and reward and punishment mechanism of collaborative tasks. It uses real-time communication encryption technology to ensure communication security and support large-scale collaborative drills.

4. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The alarm scenario construction module also has dynamic risk assessment and early warning functions. It uses machine learning algorithms to predict scenario risks and simulates the risk diffusion process in the scenario through risk propagation models. The risk assessment formula is: , where R is the risk value, p k is the probability of the kth risk occurring, d k is the degree of loss after the kth risk occurs, o is the number of risk types, and when the risk value exceeds the threshold, the system issues an early warning to the drill personnel.

5. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The intelligent agents in the AI ​​adversarial simulation module use transfer learning technology to transfer the experience learned in similar police scenarios to new scenarios. Through the multi-agent collaborative strategy optimization algorithm, the intelligent agents automatically negotiate collaborative tasks and optimize the agent strategies by combining reinforcement learning with evolutionary algorithms.

6. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The drill process monitoring and evaluation module uses federated learning technology and unsupervised learning algorithms to mine potential patterns and abnormal behaviors in drill data, and adjusts the drill progress and difficulty through a real-time evaluation result feedback mechanism.

7. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The intelligent feedback and learning module uses knowledge push technology to push relevant knowledge according to the needs of the drill personnel, uses the intelligent tutoring system to provide answers to questions, and predicts the learning effect through the learning effect prediction model. The formula is: ,in is the evaluation result, is the learning path execution status, is the degree of knowledge mastery, h is the prediction function, and it is implemented using a deep learning model.

8. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The data management and storage module uses big data analysis technology to discover patterns and trends, uses data visualization technology to display data characteristics and analysis results in an intuitive form, and uses data desensitization technology to process personal sensitive information data.

9. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The system also has an emergency response capability enhancement module. When an emergency occurs, the system switches to emergency response mode and uses experience to provide decision-making support for commanders. Through real-time data access and analysis, it evaluates and simulates the emergency situation, develops plans, and uses intelligent scheduling algorithms to allocate police force and resources.

10. The intelligent software control system for police situation actual combat handling drill based on AI confrontation simulation according to claim 1 is characterized in that: The system provides a multi-language support module, which switches the language of the system interface and prompt information according to the needs of the drill personnel, supports the use of multiple languages, and optimizes language translation and interaction effects through language adaptive learning algorithms.

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