8results about How to "Enhanced confrontation ability" patented technology

The application relates to the technical field of artificial intelligence, and provides a method and device for training a battle strategy model of a multi-player battle game, a computer device, a storage medium and a computer program product. The method comprises the following steps: obtaining each historical state from battle data corresponding to a virtual object lineup for each virtual object lineup, determining a game target corresponding to each historical state, determining a battle strategy type, training a game target prediction model based on the historical state, the battle strategy type and the reward data, inputting a response state corresponding to each battle action of each virtual object lineup based on an initial battle strategy model and a battle strategy type corresponding to the corresponding virtual object lineup into the game target prediction model to obtain a predicted game target, and training the initial battle strategy model based on reward data corresponding to each predicted game target to obtain a battle strategy model, so that the confrontation ability of the virtual object lineup can be effectively improved.

The invention relates to a low-detectability active and passive composite blinding system for an urban combat environment, and belongs to the technical field of electronic countermeasures. Comprising an active blinding module for emitting a directional energy beam to cause saturation, interference or permanent damage to an enemy optical, infrared or laser sensor; the passive blinding module is used under the condition that the electromagnetic energy is not actively emitted; the low detectability platform is used for bearing the active blinding module and the passive blinding module; and a cooperative control unit. According to the invention, active blinding, passive blinding and low detectability technologies are unprecedentedly deeply fused, and unified scheduling is carried out by an intelligent cooperative control unit, so that the risk of detection and countering by enemies is reduced to the greatest extent, and the characteristics of short sight distance, complex background and diversified threat sources of urban combat are fully considered. Active accurate blinding and passive hidden interference can be effectively combined, and meanwhile the method has the advantages of being high in survivability, high in confrontation efficiency and high in tactical flexibility.

The invention discloses an adaptive drilling Agent system and method based on a large language model and a storage medium, and the system comprises an instruction receiving and contextualization module which is used for receiving an unstructured natural language instruction of a performer and generating structured prompt information; the large language model reasoning core is used for outputting semantic reply oriented to participants and a structured action sequence for simulation execution based on the structured prompt information; the simulation and constraint engine is used for performing physical feasibility verification and logic / authority verification on the structured action sequence based on a physical evolution model and an environment logic rule and outputting a verification result; and the action verification and feedback module is used for generating a structured failure message when the verification result is failure and recharging the structured failure message to the big language model reasoning core as a hard constraint to trigger the regeneration of an alternative action sequence. And when the verification result is that the structured action sequence passes the verification, sending the structured action sequence passing the verification to a simulation and constraint engine for execution, and outputting an execution result.

This invention belongs to the field of artificial intelligence algorithm technology and discloses an intelligent transfer adversarial method and system for electromagnetic signal recognition under incomplete information. First, multiple models of alternative target networks with incomplete information are constructed in parallel, and these alternative models are trained using an evolutionary strategy based on a prior knowledge matrix. Then, an ensemble network model is generated based on the trained alternative models, and a fully connected layer and a layer with feature-level weighted fusion are designed. Finally, an optimized adversarial method for electromagnetic spatial signal recognition tasks is designed, generating adversarial samples under the ensemble model, and transferring these adversarial samples to the target network to achieve adversarial capabilities against the target network. This invention can effectively achieve adversarial capabilities against the target network when the target network information is unknown, and it still exhibits good transfer adversarial performance under the same signal recognition task and different unknown target network conditions.

The invention provides an industrial control equipment-oriented flow trip line generation method and system based on honey points, and relates to the technical field of network security. The method provided by the invention comprises the following steps: generating an instruction based on a configuration file and a tripwire strategy; obtaining an original load sequence, identifying sensitive fields in the original load sequence based on a flow trip line load generation model, and replacing the sensitive fields with mask marks to form a mask load sequence; predicting a mask mark position in the mask load sequence to generate bait data; filling the bait data to a mask mark position, and combining the bait data with a non-sensitive field in the mask load sequence to generate camouflage load data; embedding the camouflage load data into a standard protocol frame based on the instruction, constructing a flow trip line data packet, and embedding the address information of the honey point into the flow trip line data packet; and when the generation time arrives, actively sending the traffic trip line data packet to the outside according to the sending interval. According to the invention, active trapping perception and dynamic cheating of potential attacks are realized by actively sending analog data traffic.

The application provides an adversarial training method, device and equipment based on adaptive group sample perturbation constraint, which comprises the following steps: inputting a training image into an initial network model to obtain a network output vector corresponding to the training image and a predicted category; if it is determined that the classification result of the training image is wrong based on the predicted category and an actual category of the training image, the training image is determined as a natural sample image; if it is determined that the classification result of the training image is correct based on the predicted category and the actual category, a target adaptive group corresponding to the training image is determined based on the network output vector; a target perturbation vector corresponding to the training image is determined based on a target sample perturbation constraint corresponding to the target adaptive group, and a perturbation sample image is generated based on the target perturbation vector and the training image; and the initial network model is trained based on the natural sample image and the perturbation sample image to obtain a target network model. Through the technical scheme of the application, the anti-interference ability of the target network model to attack samples is significantly improved.

The utility model discloses a driving hammer type fighting toy car, which comprises a push shovel, a car body, an attack hammer, a driving hammer power device and a magnetic attraction armor, the push shovel is arranged at the front end of the car body, the driving hammer power device is arranged on the car head of the car body, and the end part of a hammer handle of the attack hammer is arranged on the driving hammer power device. The attack hammer and the corresponding driving hammer power device are added on the basis of an original push shovel, and the attack hammer is driven by the driving hammer power device to turn over, withdraw or swing, so that the toy car has two attack modes of shoveling and hammering, the toy car is more aggressive in battle, playability of the toy car is remarkably improved, and the toy car is worthy of popularization and application. The interactivity and the confrontation among the toy cars are also increased, players are attracted to put more energy to participate in fight, and the interestingness and the irritation in wartime are greatly improved; and when the impact force of the magnetic armor is large enough, a magnetic armor falling mechanism can be triggered, so that an intuitive and clear victory or defeat judgment standard is provided for fight, and joyful feeling and fight passion during playing can be improved.

The application provides an underwater unmanned underwater vehicle formation dynamic area search system and relates to the field of unmanned underwater vehicle cooperative operation, and comprises the following steps: generating a virtual marine environment according to seabed depth information and land obstacle distribution information; arranging virtual particles in a preset search area to form a gravitational field, and giving the virtual particles an initial mass; constructing a detection pressure field according to the detection activities of the unmanned underwater vehicle formation, determining the probability distribution of the target appearance position based on the reverse distribution of the detection pressure field, and generating a counter target according to the probability distribution; dynamically updating the mass value of the virtual particles according to the detection coverage range of the unmanned underwater vehicle formation; and calculating the gravity and repulsion that each unmanned underwater vehicle in the unmanned underwater vehicle formation receives according to the mass distribution of the virtual particles in the gravitational field, and updating the position and heading of each unmanned underwater vehicle based on the gravity and repulsion. The application improves the efficiency and authenticity of dynamic target search.