Environment perception and command control intelligent deduction platform based on bionic unmanned equipment

Abstract

The invention discloses an environment perception and command control intelligent deduction platform based on bionic unmanned equipment, and relates to the field of intelligent deduction, and the platform comprises the steps: collecting environment data through a multi-mode sensor, encrypting the environment data through an anti-interference communication relay module, and transmitting the encrypted environment data to a central control platform; the data fusion preprocessing module is used for processing multi-source data and outputting a structured data stream, and the environment intelligent modeling module is combined with a geographic information system to construct a three-dimensional dynamic environment model; an intelligent deduction module carries out multi-scene deduction based on the model, and a self-adaptive control strategy generation module converts a decision tree into an anti-disturbance control sequence; and the man-machine collaborative decision-making module fuses operator intervention to generate a final instruction, and the instruction distribution and equipment collaborative control module issues the instruction to the bionic unmanned equipment to realize intelligent collaborative control. The method has the advantages that bionic perception, anti-interference communication and intelligent deduction are integrated, efficient cooperation and adaptive control of unmanned equipment in a complex environment are achieved, and the intelligence and robustness of task execution are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent reasoning, in particular to an environment perception and command control intelligent reasoning platform based on bionic unmanned equipment. BACKGROUND

[0002] The environment perception and command control intelligent reasoning platform based on bionic unmanned equipment emerged in the context of rapid development of modern science and technology. Inspired by the biological perception and cooperative behavior in nature, the platform integrates advanced artificial intelligence, sensor technology and big data analysis, aiming to realize efficient perception, autonomous decision-making and cooperative coordination of unmanned equipment in complex environments. This technology not only promotes the development of unmanned and intelligent equipment, but also is widely used in emergency rescue and disaster monitoring, providing important support for national security and social stability.

[0003] Current platforms on the market rely on single or limited sensor types for environmental perception, making it difficult to comprehensively collect terrain, weather and biological signal data in complex environments, and the perception accuracy and breadth are insufficient. Secondly, the communication system generally lacks neural synapse adaptive frequency hopping protocol and advanced encryption technology, resulting in data transmission being easily disturbed in electromagnetic interference or complex environments, with low security and stability. In addition, the data processing capacity of existing platforms is weak, lacking efficient data fusion preprocessing and dynamic three-dimensional modeling functions, making it difficult to generate high-precision environmental models in real time, limiting the accuracy of intelligent reasoning. In terms of decision-making and control, market platforms mostly use static or preset strategies, lack real-time reasoning capabilities in multiple threads and multiple scenarios, are difficult to cope with dynamic environmental changes, and the human-machine collaboration mechanism is not perfect, with limited flexibility for operator intervention. Finally, the efficiency of command distribution and equipment cooperative control is low, making it difficult to achieve efficient cooperation and dynamic task adjustment of multiple equipment. SUMMARY

[0004] In order to improve the existing platform, an environment perception and command control intelligent reasoning platform based on bionic unmanned equipment is provided, which integrates bionic perception, anti-interference communication, dynamic modeling and intelligent reasoning to realize efficient cooperation and adaptive control of unmanned equipment in complex environments, significantly improving the intelligence and robustness of task execution.

[0005] To achieve the above purpose, the technical solution adopted by the present application is: The environment perception and command control intelligent reasoning platform based on bionic unmanned equipment comprises: Bionic robot cluster module: the module contains multiple types of bionic unmanned equipment, integrates a multi-modal sensor array, and is used to collect terrain, weather, biological signal and obstacle data; Anti-interference communication relay module: the module uses a neural synapse adaptive frequency hopping protocol to encrypt and transmit real-time data collected by the bionic robot cluster module to the central control platform; Data fusion preprocessing module: the module is deployed in the central control platform, used for receiving and processing in real time multi-source heterogeneous environment perception data from the anti-interference communication relay module, pre-processing the data, and outputting a structured environment perception data stream; Environment intelligent modeling module: the module is deployed in the central control platform, based on the structured environment perception data stream output by the data fusion preprocessing module, combined with geographic information system basic data, a three-dimensional dynamic environment model is constructed through deep learning and probabilistic graph model; Intelligent deduction module: the module is deployed in the central control platform, based on the three-dimensional dynamic environment model constructed by the environment intelligent modeling module, combined with the built-in physical rule engine and behavior model library, real-time dynamic deduction is carried out in the virtual environment in multi-thread, multi-scene and multi-strategy; Adaptive control strategy generation module: the module is deployed in the central control platform, the deduction decision tree is converted into executable control instructions, embedded in the behavior model library, and an anti-disturbance intelligent control sequence is generated; Man-machine collaborative decision module: the module is deployed in the central control platform, based on the obtained intelligent control sequence, the final decision instruction of man-machine intelligent fusion is generated by inputting intervention instructions, adjusting deduction parameters and selecting execution strategies of the operator; Instruction distribution and equipment cooperative control module: the module is deployed in the central control platform, the final command and control strategy confirmed by the man-machine collaborative decision module is decomposed into instruction sequence, and is real-time issued to each individual in the bionic unmanned equipment perception cluster.

[0006] Preferably, the bionic robot cluster module specifically comprises: Sensor unit: the unit includes visual, sonar, infrared, and vibration sensors; Simulator unit: the unit includes an olfactory simulator, simulating the biological olfactory system to analyze the chemical composition in the air; Communication cooperation unit: the unit realizes wireless communication between robots in the cluster, ensuring task cooperation and data sharing; Power control unit: the module provides energy management and motion control, ensuring flexible movement and long-time operation of the robot.

[0007] Preferably, the anti-interference communication relay module specifically comprises: Signal processing and frequency hopping control unit: the unit monitors the channel environment in real time through spectrum sensing, identifies the interference frequency band, simulates the adaptive learning ability of neural network through neural-like synapse algorithm, predicts and selects the best frequency hopping sequence according to the historical interference mode, processes the frequency hopping sequence through high-speed digital signal processor to generate and switch logic, and covers a wide frequency spectrum range; Breakpoint resume and cache management unit: the unit stores the data packets that have not been transmitted by a high-capacity cache area, records real-time state data, marks the transmission breakpoint, automatically resumes transmission after communication recovery, and adjusts the storage space based on data priority for dynamic cache allocation; Data encryption transmission unit: the unit encrypts the data through the AES-256 encryption algorithm and dynamic key update and distribution, and transmits it to the central control platform through an end-to-end encrypted secure channel.

[0008] Preferably, the data fusion preprocessing module specifically includes: Feature extraction unit: the unit integrates feature extraction algorithms and extracts environmental perception features from the cleaned and aligned data according to task requirements; Primary fusion unit: the unit performs primary fusion on the extracted features through a multi-sensor fusion algorithm to generate a structured environmental perception data stream containing unified format environmental perception data; Output and interface unit: the unit is used to output the structured environmental perception data stream to the subsequent processing module of the central control platform.

[0009] Preferably, the environmental intelligent modeling module specifically includes: Three-dimensional environmental modeling unit: the unit extracts spatial geometric features from the structured environmental perception data stream output by the data fusion preprocessing module based on convolutional neural networks and point cloud processing networks, constructs the three-dimensional structure of the environment, and generates a three-dimensional environmental grid through stereoscopic geometry and SLAM technology; Probabilistic graph modeling unit: the unit models the state and mutual relationship of environmental entities through a probabilistic graph model, predicts the dynamic change trend of entity state through time series analysis of historical data, and obtains the causal relationship and potential interaction between environmental entities; Entity state and attribute unit: the unit identifies key entities in the environment, associates entity attributes to corresponding objects in the three-dimensional model through extraction, and assigns semantic labels to environmental entities.

[0010] Preferably, the intelligent deduction module specifically includes: Physical rules unit: the unit has a built-in physical rules library covering kinematics, dynamics, and environmental constraints, and simulates the physical behavior of unmanned equipment in the three-dimensional environment through real-time simulation; Behavior model library unit: the unit contains multiple predefined behavior patterns, including cooperative search, formation marching, and target tracking, and adjusts the behavior strategy based on task requirements; Multi-threaded deduction unit: The unit supports parallel processing of multiple deduction scenarios through a multi-threaded computing framework, automatically generates diversified test scenarios based on a three-dimensional environment model, dynamically adjusts the deduction strategy through an integrated strategy switching mechanism to adapt to environmental changes, and adjusts the data simulation parameters in the deduction process based on real-time changes in the three-dimensional environment model; AI decision unit: The unit implements intelligent decision-making through the integration of reinforcement learning, evolutionary algorithms, and imitation learning, including autonomous collaborative decision-making, path planning, task allocation, risk avoidance, and performance evaluation; The path planning and task allocation generate optimal paths that meet environmental constraints through the integration of path planning algorithms, and allocate tasks based on task priority, equipment capability, and environmental state; The risk avoidance and performance evaluation predict collision and communication interruption risks based on a probabilistic graph model, and quantify the performance of each deduction scheme through an efficiency evaluation index system; Deduction decision tree unit: The unit structures the deduction results into a tree structure, displays the decision path and branches, and outputs the decision tree data to other modules of the central control platform Preferably, the adaptive control strategy generation module specifically includes: Control instruction conversion unit: The unit maps decision tree nodes to specific control instructions, including motion control, sensor adjustment, and communication coordination, generates standardized instruction formats for each control instruction, and adapts to the control interfaces of different equipment; Behavior model embedding unit: The unit associates control instructions with behavior model parameters by calling predefined behavior templates matching the task scenario, forms complete execution logic, and obtains structured behavior sequences; Anti-disturbance control sequence generation unit: The unit optimizes structured behavior sequences through robust control algorithms to generate anti-disturbance control sequences.

[0011] Preferably, the human-machine collaborative decision-making module specifically includes: Operator intervention unit: The unit converts operator intervention instructions into a format recognizable by the system, provides a parameter adjustment interface, allows operators to modify deduction parameters, generates multiple deduction strategies, and allows operators to compare the advantages and disadvantages of different strategies; Human-machine intelligence fusion unit: The unit fuses operator intervention instructions, adjustment parameters, and intelligent control sequences through a fusion algorithm to generate final decision instructions.

[0012] Preferably, the instruction distribution and equipment cooperative control module specifically includes: Instruction sequence decomposition unit: The unit decomposes control strategies into individual instructions based on equipment capabilities, task requirements, and environmental constraints, covering motion, perception, and communication task dimensions; Communication link unit: The unit integrates multi-channel communication protocols, responds to electromagnetic interference through frequency switching and signal encryption, and dynamically adjusts the link based on communication quality, switching transmission channels in real time; Command distribution and scheduling unit: The unit performs parallel distribution of commands to multiple pieces of equipment based on equipment priority, task urgency, and communication status, and provides distribution log recording; Equipment Coordination and Control Unit: The unit is used to coordinate the autonomous coordinated movement and task execution of the equipment cluster, dynamically adjust the equipment's sensing tasks according to environmental changes and task requirements, and process the dynamic interaction between the equipment and the environment based on sensing data.

[0013] Compared with the prior art, the advantages of the present invention are: By integrating multimodal sensors through a biomimetic robot swarm module, the platform accurately collects data from complex environments. Combined with a neural synapse-like frequency hopping protocol and AES-256 encryption technology in the anti-interference communication relay module, secure and efficient data transmission is ensured. The data fusion preprocessing module and the intelligent environmental modeling module utilize deep learning and probabilistic graphical models to construct a high-precision 3D dynamic environment model, providing a reliable foundation for intelligent simulation. The intelligent simulation module rapidly generates diverse strategies through multi-threaded, multi-scenario simulation and reinforcement learning technologies, optimizing path planning and task allocation to effectively address dynamic environmental challenges. The adaptive control strategy generation module transforms decisions into disturbance-resistant control sequences, enhancing the robustness of equipment execution. The human-machine collaborative decision-making module achieves deep integration of operators and AI, balancing autonomy and human intervention to enhance decision-making flexibility. The command distribution and equipment collaborative control module ensures efficient command issuance and swarm collaboration, improving task execution efficiency. This platform integrates advanced sensing, communication, modeling, and decision-making technologies, significantly improving the adaptability, collaboration, and intelligence of unmanned equipment in complex environments, providing strong support for efficiently completing diverse tasks, and demonstrating a perfect combination of technological innovation and application value. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the system proposed in this invention; Figure 2 This is a schematic diagram of the biomimetic robot cluster module proposed in this invention; Figure 3 This is a diagram of the anti-interference communication relay module proposed in this invention; Figure 4 This is a diagram of the data fusion preprocessing module proposed in this invention; Figure 5 This is a diagram of the intelligent environmental modeling module proposed in this invention; Figure 6 This is a diagram of the intelligent inference module proposed in this invention; Figure 7 A module diagram for generating the adaptive control strategy proposed in this invention; Figure 8 This is a diagram of the human-machine collaborative decision-making module proposed in this invention; Figure 9 This is a diagram of the instruction distribution and equipment collaborative control module proposed in this invention. Detailed Implementation

[0015] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0016] See Figure 1 As shown, the intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment includes: Bionic robot swarm module: The module contains multiple types of bionic unmanned devices and integrates a multimodal sensor array for collecting terrain, meteorological, biological signals and obstacle data; Anti-interference communication relay module: The module adopts a neural synapse-like adaptive frequency hopping protocol to encrypt and transmit the real-time data collected by the bionic robot cluster module to the central control platform; Data fusion preprocessing module: The module is deployed on the central control platform and is used to receive and process multi-source heterogeneous environmental perception data from the anti-interference communication relay module in real time, preprocess the data, and output a structured environmental perception data stream. Environmental intelligent modeling module: The module is deployed on the central control platform. Based on the structured environmental perception data stream output by the data fusion preprocessing module, combined with the basic data of the geographic information system, it constructs a three-dimensional dynamic environment model through deep learning and probabilistic graphical models. Intelligent simulation module: The module is deployed on the central control platform. Based on the three-dimensional dynamic environment model built by the intelligent environmental modeling module, combined with the built-in physical rule engine and behavior model library, it performs real-time dynamic simulation in the virtual environment with multiple threads, multiple scenarios, and multiple strategies. Adaptive control strategy generation module: The module is deployed on the central control platform, which transforms the deduction decision tree into executable control instructions, embeds them into the behavior model library, and generates disturbance-resistant intelligent control sequences; Human-machine collaborative decision-making module: The module is deployed on the central control platform. Based on the acquired intelligent control sequence, it generates the final decision command of human-machine intelligent integration by having the operator input intervention commands, adjust the inference parameters and select the execution strategy. Command distribution and equipment collaborative control module: The module is deployed on the central control platform. It decomposes the final command and control strategy confirmed by the human-machine collaborative decision-making module into a sequence of commands and distributes them in real time to each individual in the bionic unmanned equipment perception cluster.

[0017] See Figure 2As shown, the bionic robot swarm module specifically includes: Sensor unit: The unit includes vision, sonar, infrared, and vibration sensors; Simulator Unit: The unit includes an olfactory simulator that simulates the biological olfactory system to analyze the chemical components in the air; Communication and coordination unit: This unit enables wireless communication between robots within the cluster, ensuring task coordination and data sharing; Power control unit: This module provides energy management and motion control, ensuring that the robot can move flexibly and operate for extended periods.

[0018] Specifically, when realizing wireless communication between robot clusters, a point-to-point or point-to-multipoint communication network within the cluster is established through a wireless communication protocol, a unique identifier is assigned to each robot, and the network topology is recorded. Pack sensor data into data frames, use Shannon's theorem to ensure communication bandwidth, calculate the maximum channel capacity, and transmit the data frames to the neighboring robot. The bionic robot swarm module transmits multi-source data collected through the communication coordination unit to the anti-interference communication relay module, and receives control commands from the instruction distribution and equipment coordination control module to adjust robot behavior, movement or perception tasks. The power control unit and communication coordination unit within the module ensure the coordinated action and data sharing of the robot swarm.

[0019] See Figure 3 As shown, the anti-interference communication relay module specifically includes: Signal processing and frequency hopping control unit: The unit monitors the channel environment in real time through spectrum sensing, identifies interference frequency bands, simulates the adaptive learning ability of neural networks through a neural synapse-like algorithm, predicts and selects the best frequency hopping sequence based on historical interference patterns, processes the frequency hopping sequence generation and switching logic through a high-speed digital signal processor, and covers a wide spectrum range. Resume Transmission and Cache Management Unit: The unit stores uncompleted data packets in a high-capacity cache, records real-time status data, marks transmission interruption points, automatically resumes transmission after communication is restored, and dynamically allocates and adjusts storage space based on data priority. Data encryption transmission unit: The unit manages data encryption through AES-256 encryption algorithm and dynamic key update and distribution, and transmits the data to the central control platform through an end-to-end encrypted secure channel.

[0020] Specifically, during spectrum sensing and interference identification, a spectrum analyzer is used to monitor the channel environment in real time, obtain spectral power density, identify interference frequency bands, and calculate interference signal strength; historical interference pattern data is collected, and a neural network-like algorithm is used to simulate a neural network to calculate the frequency band availability probability. The optimal frequency hopping sequence is selected based on the frequency band availability probability; frequency hopping sequence control signals are generated through a DSP, the switching time interval is calculated, and a wide spectrum range is covered to ensure that the sequence meets bandwidth requirements. Communication continuity is maintained by switching frequencies in real time. When resuming interrupted transmission, the uncompleted data packets are stored in a high-capacity buffer and the buffer usage is calculated. The communication status is monitored in real time. If interrupted, the interrupted data packets and transmission progress are recorded, and real-time status data is saved. After communication is restored, transmission is resumed based on the real-time status data, and transmission resumes from the interrupted data packets. The buffer allocation is dynamically adjusted according to the data priority, and the allocation weight is calculated. When transmitting data in encrypted form, the data packet is encrypted using the AES-256 algorithm to generate ciphertext. The key is updated according to the time interval or event trigger, a new key is generated, and it is distributed to the receiving end through a secure channel. The data fusion preprocessing module receives encrypted data from the anti-interference communication relay module and outputs a structured data stream to the environmental intelligent modeling module. Through the feature extraction unit and the primary fusion unit, it provides high-quality data in a unified format for subsequent modeling.

[0021] See Figure 4 As shown, the data fusion preprocessing module specifically includes: Feature extraction unit: The unit integrates feature extraction algorithms to extract environmental awareness features from the cleaned and aligned data according to task requirements; Primary fusion unit: The unit performs primary fusion of the extracted features through a multi-sensor fusion algorithm to generate a structured environmental perception data stream, which contains environmental perception data in a unified format; Output and Interface Unit: The unit is used to output the structured environment perception data stream to the subsequent processing module of the central control platform.

[0022] Specifically, select a feature extraction algorithm based on task requirements to extract feature vectors from the cleaned data, such as edge features. The formula is as follows: in, For edge feature values, For gradient operators, For the cleaned data, The partial derivative in the x-direction, The partial derivative in the y-direction; Based on task relevance, calculate feature importance, select highly relevant features, construct feature subsets, and output feature subsets to the primary fusion unit; The fused features of the feature subset are calculated using a fusion algorithm. For Kalman filtering, the state update formula is: in, As a feature of fusion, To predict the state, For Kalman gain, For feature subset, The observation matrix; Format the fusion results into a unified structure; The data fusion preprocessing module receives encrypted data from the anti-interference communication relay module and outputs a structured data stream to the environmental intelligent modeling module. Through the feature extraction unit and the primary fusion unit, it provides high-quality data in a unified format for subsequent modeling.

[0023] See Figure 5 As shown, the environmental intelligent modeling module specifically includes: 3D Environment Modeling Unit: The unit extracts spatial geometric features from the structured environment perception data stream output by the data fusion preprocessing module based on convolutional neural networks and point cloud processing networks, constructs the 3D structure of the environment, and generates a 3D environment mesh through stereo geometry and SLAM technology. Probabilistic graphical modeling unit: The unit models the state and interrelationships of environmental entities through probabilistic graphical models, predicts the dynamic change trend of entity states by performing time-series analysis on historical data, and obtains the causal relationships and potential interactions between environmental entities; Entity State and Attribute Unit: The unit identifies key entities in the environment, extracts entity attributes to associate them with corresponding objects in the 3D model, and assigns semantic labels to environmental entities.

[0024] Specifically, the system receives structured environment perception data streams, processes visual data using convolutional neural networks, extracts spatial geometric features, uses a point cloud processing network on point cloud data to calculate point cloud feature vectors, fuses geometric features and point cloud features to calculate comprehensive features, uses stereo geometry principles to calculate object surface normal vectors, constructs an initial 3D structure, generates a point cloud mesh, applies simultaneous localization and mapping (SLAM) technology to optimize robot position, updates the 3D environment mesh, calculates mesh resolution, and outputs the optimized 3D environment mesh. The system extracts a set of entities from a 3D environment mesh, with each entity containing various states. A probabilistic graphical model is constructed, defining nodes as entity states and edges as relationships between entities, and calculating the joint probability distribution. Historical state data is collected, and a time-series analysis model, such as a Hidden Markov Model, is used to predict the state at the next moment and update the probabilistic graph, optimizing conditional probabilities to reflect the dynamic changes in entity states. Based on the relationships between entities, causal strength is calculated, potential interactions are identified, an interaction matrix is ​​constructed, high-intensity interactions are filtered, and a probabilistic graph containing entities, relationships, and probability distributions is output. Candidate entities are extracted from the 3D environment mesh and probabilistic graph. Based on feature saliency, a clustering algorithm is used to divide the entities into key entity sets. For each key entity, an attribute set is extracted, attribute feature vectors are calculated, attributes are associated with 3D model objects, and a mapping matrix is ​​calculated. Based on the attributes and probabilistic graph, a classification algorithm is used to assign semantic labels, output the entity state and attribute set, and associate them with the 3D model. The intelligent environmental modeling module receives structured data streams from the data fusion preprocessing module and transmits the 3D dynamic environment model to the intelligent inference module. Through the 3D environment modeling unit, probabilistic graphical modeling unit, and entity state and attribute unit, it generates a comprehensive environment model that includes spatial geometry, entity state, and semantic labels.

[0025] See Figure 6 As shown, the intelligent inference module specifically includes: Physical rule unit: The unit has a built-in physical rule library that covers kinematics, dynamics and environmental constraints, and simulates the physical behavior of unmanned equipment in a three-dimensional environment through real-time simulation; Behavior Model Library Unit: This unit contains a variety of predefined behavior patterns, including cooperative search, formation movement, and target tracking, and adjusts the behavior strategies based on task requirements; Multi-threaded simulation unit: The unit supports parallel processing of multiple simulation scenarios through a multi-threaded computing framework, automatically generates diverse test scenarios based on a 3D environment model, dynamically adjusts the simulation strategy to adapt to environmental changes through an integrated strategy switching mechanism, and adjusts the data simulation parameters during the simulation process based on the real-time changes of the 3D environment model. AI Decision-Making Unit: The unit achieves intelligent decision-making by integrating reinforcement learning, evolutionary algorithms and imitation learning, including autonomous collaborative decision-making, path planning, task allocation, risk avoidance and performance evaluation; The path planning and task allocation generate optimal paths that meet environmental constraints through an integrated path planning algorithm, and allocate tasks based on task priority, equipment capabilities, and environmental conditions. The risk avoidance and performance evaluation are based on probabilistic graphical models to predict collision and communication interruption risks, and the performance of each simulation scheme is quantified through a performance evaluation index system. Decision Tree Deduction Unit: This unit structures the deduction results into a tree structure, displays the decision path and branches, and outputs the decision tree data to other modules of the central control platform.

[0026] Specifically, environmental constraints are extracted based on the 3D environment model and entity state, behavioral parameters are adjusted, and adjusted behavioral strategies are generated. The adjusted behavioral strategies are formatted into control commands, which are then output to the multi-threaded inference unit and AI decision-making unit. Environmental variables are extracted from the 3D environment model and probability map, and diverse test scenario sets are generated using random sampling. Threads are allocated to each test scenario, the inference state is initialized, simulations are executed in parallel, inference results are calculated, inference performance is evaluated, and scenario scores are calculated. Real-time changes in the 3D environment model are monitored, environmental parameters are updated, and the inference result set is updated by adjusting simulation parameters. Using path planning algorithms, such as the A* algorithm, the optimal path is calculated. Based on task priority, equipment capability, and environmental status, tasks are allocated, the allocation function is optimized, and the path and task allocation results are output. Based on the probabilistic graphical model, collision risk and communication interruption risk are predicted, the path and actions are adjusted, the risk avoidance target is optimized, an effectiveness evaluation index system is constructed, the performance of the scheme is quantified, and the output is sent to the inference decision tree unit. The system obtains a set of inference results from the multi-threaded inference unit, and obtains decision results and path task results from the AI ​​decision-making unit. It then constructs a decision tree, where nodes represent states and decisions, and edges represent decision branches, calculating branch probabilities. The system traverses the decision tree, calculates the cumulative efficiency of each path, selects the optimal path, marks key decision nodes, verifies path feasibility, and ensures that environmental constraints and task requirements are met. The intelligent simulation module receives a 3D dynamic environment model from the intelligent environmental modeling module, transmits the simulation decision tree to the adaptive control strategy generation module, and simulates the behavior of unmanned equipment and optimizes decisions through the physical rule unit, behavior model library unit, multi-threaded simulation unit and AI decision unit.

[0027] See Figure 7 As shown, the adaptive control strategy generation module specifically includes: Control command conversion unit: The unit maps decision tree nodes to specific control commands, including motion control, sensor adjustment, and communication coordination, and generates standardized command formats for each control command to adapt to the control interfaces of different equipment; Behavior model embedding unit: The unit associates control instructions with behavior model parameters by calling a predefined behavior template that matches the task scenario, forming a complete execution logic and obtaining a structured behavior sequence; Disturbance-resistant control sequence generation unit: The unit optimizes the structured behavior sequence using a robust control algorithm to generate an disturbance-resistant control sequence.

[0028] Specifically, based on the decision tree data output by the inference decision tree unit, key decision nodes are analyzed, and state, decision, path and task results are extracted. For motion control, speed and orientation commands are generated; for sensor adjustment, sensor parameters are calculated; and for communication coordination, communication commands are generated. Control commands are integrated into a unified format and adapted to the command format according to the equipment control interface protocol. Based on the received standardized control commands and task scenario information, a matching template is selected from the behavior model library, the matching degree is calculated, and behavior template parameters such as speed range, steering angle, and execution order are extracted. The control commands are mapped to the behavior parameters, the correlation vector is calculated, and the behavior template and correlation vector are combined to generate a behavior sequence. By optimizing the sequence execution efficiency, the sequence cost is calculated. Disturbance factors, such as wind speed, terrain changes, and sensor noise, are extracted from the 3D environment model and probability map. The impact of disturbances is modeled, and the intensity of disturbances is calculated. The formatted and optimized behavior sequence is used as an anti-disturbance control sequence. The control sequence is encapsulated into a transmission packet and transmitted to the central control platform through a secure channel. The adaptive control strategy generation module receives the inference decision tree from the intelligent inference module, transmits the anti-disturbance control sequence to the human-machine collaborative decision module, and generates control commands adapted to different equipment through the control command conversion unit, behavior model embedding unit and anti-disturbance control sequence generation unit.

[0029] See Figure 8 As shown, the human-machine collaborative decision-making module specifically includes: Operator Intervention Unit: The unit converts the operator's intervention commands into a system-recognizable format, provides a parameter adjustment interface, allows the operator to modify the simulation parameters, generates multiple simulation strategies, and allows the operator to compare the advantages and disadvantages of different strategies; Human-machine intelligent fusion unit: The unit integrates operator intervention commands, adjustment parameters and intelligent control sequences through a fusion algorithm to generate final decision commands.

[0030] Specifically, the system receives intervention commands from operators, including target modification, path adjustment, and priority change. The commands are in the form of natural language or structured input. Through natural language processing or rule mapping, the intervention commands are converted into a system-recognizable format. A parameter adjustment interface is generated, displaying the set of simulation parameters, allowing operators to modify parameters through the interface and generate an adjustment parameter set. Based on the adjustment parameters and conversion commands, a multi-threaded simulation unit is invoked to generate multiple simulation strategies, evaluate the advantages and disadvantages of the strategies, construct an evaluation index system, calculate a comprehensive score, and display the scores, path visualization, and risk prediction of each strategy through a comparison interface. The system receives the candidate strategy set, adjustment parameters, and conversion instructions output by the operator intervention unit, as well as the anti-disturbance control sequence output by the anti-disturbance control sequence generation unit. It integrates the input data into a unified feature vector and calculates the completeness of the features. The system then transforms the fused control sequence into a final decision instruction, encapsulates the final decision instruction into a transmission packet, and transmits it to the central control platform through a secure channel. The human-machine collaborative decision-making module receives control sequences from the adaptive control strategy generation module and intervention instructions from the operator, and transmits the final decision instructions to the instruction distribution and equipment collaborative control module. Through the operator intervention unit and the human-machine intelligent fusion unit, human-machine collaborative optimization decision-making is realized.

[0031] See Figure 9 As shown, the command distribution and equipment coordination control module specifically includes: Command sequence decomposition unit: Based on equipment capabilities, mission requirements and environmental constraints, the unit breaks down the control strategy into individual commands, covering the mission dimensions of motion, perception and communication; Communication link unit: The unit integrates multi-channel communication protocols, responds to electromagnetic interference through frequency switching and signal encryption, and dynamically adjusts the link based on communication quality, switching transmission channels in real time; Command distribution and scheduling unit: The unit performs parallel distribution of commands to multiple pieces of equipment based on equipment priority, task urgency, and communication status, and provides distribution log recording; Equipment Coordination and Control Unit: The unit is used to coordinate the autonomous coordinated movement and task execution of the equipment cluster, dynamically adjust the equipment's sensing tasks according to environmental changes and task requirements, and process the dynamic interaction between the equipment and the environment based on sensing data.

[0032] Specifically, the process involves initializing a multi-channel communication protocol, including protocol type, frequency range, and encryption algorithm; configuring a frequency hopping strategy; generating a frequency hopping sequence based on a pseudo-random function; applying signal encryption to generate encrypted data; and dynamically switching transmission channels based on link quality to generate a switching strategy. The system receives individual instruction sets, equipment priorities, mission urgency, and communication status, calculates distribution priorities, and sorts them to generate a priority queue. Based on the priority queue, it allocates instructions to equipment, generates a distribution plan, and optimizes distribution efficiency by calculating scheduling costs. The equipment coordination control unit is used to coordinate the autonomous coordinated movement and task execution of equipment clusters. It dynamically adjusts the equipment's sensing tasks according to environmental changes and task requirements, and processes the dynamic interaction between the equipment and the environment based on sensing data. The instruction distribution and equipment collaborative control module receives the final decision instruction from the human-machine collaborative decision-making module and distributes the instruction sequence to the bionic robot cluster module. Through the instruction sequence decomposition unit, communication link unit, instruction distribution and scheduling unit, and equipment collaborative control unit, it ensures efficient instruction distribution and equipment collaborative execution.

[0033] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0034] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment, characterized in that: include: Bionic robot swarm module: The module contains multiple types of bionic unmanned devices and integrates a multimodal sensor array for collecting terrain, meteorological, biological signals and obstacle data; Anti-interference communication relay module: The module adopts a neural synapse-like adaptive frequency hopping protocol to encrypt and transmit the real-time data collected by the bionic robot cluster module to the central control platform; Data fusion preprocessing module: The module is deployed on the central control platform and is used to receive and process multi-source heterogeneous environmental perception data from the anti-interference communication relay module in real time, preprocess the data, and output a structured environmental perception data stream. Environmental intelligent modeling module: The module is deployed on the central control platform. Based on the structured environmental perception data stream output by the data fusion preprocessing module, combined with the basic data of the geographic information system, it constructs a three-dimensional dynamic environment model through deep learning and probabilistic graphical models. Intelligent simulation module: The module is deployed on the central control platform. Based on the three-dimensional dynamic environment model built by the intelligent environmental modeling module, combined with the built-in physical rule engine and behavior model library, it performs real-time dynamic simulation in the virtual environment with multiple threads, multiple scenarios, and multiple strategies. Adaptive control strategy generation module: The module is deployed on the central control platform, which transforms the deduction decision tree into executable control instructions, embeds them into the behavior model library, and generates disturbance-resistant intelligent control sequences; Human-machine collaborative decision-making module: The module is deployed on the central control platform. Based on the acquired intelligent control sequence, it generates the final decision command of human-machine intelligent integration by having the operator input intervention commands, adjust the inference parameters and select the execution strategy. Command distribution and equipment collaborative control module: The module is deployed on the central control platform. It decomposes the final command and control strategy confirmed by the human-machine collaborative decision-making module into a sequence of commands and distributes them in real time to each individual in the bionic unmanned equipment perception cluster.

2. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The biomimetic robot cluster module specifically includes: Sensor unit: The unit includes vision, sonar, infrared, and vibration sensors; Simulator Unit: The unit includes an olfactory simulator that simulates the biological olfactory system to analyze the chemical components in the air; Communication and coordination unit: This unit enables wireless communication between robots within the cluster, ensuring task coordination and data sharing; Power control unit: This module provides energy management and motion control, ensuring that the robot can move flexibly and operate for extended periods.

3. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The anti-interference communication relay module specifically includes: Signal processing and frequency hopping control unit: The unit monitors the channel environment in real time through spectrum sensing, identifies interference frequency bands, simulates the adaptive learning ability of neural networks through a neural synapse-like algorithm, predicts and selects the best frequency hopping sequence based on historical interference patterns, processes the frequency hopping sequence generation and switching logic through a high-speed digital signal processor, and covers a wide spectrum range. Resume Transmission and Cache Management Unit: The unit stores uncompleted data packets in a high-capacity cache, records real-time status data, marks transmission interruption points, automatically resumes transmission after communication is restored, and dynamically allocates and adjusts storage space based on data priority. Data encryption transmission unit: The unit manages data encryption through AES-256 encryption algorithm and dynamic key update and distribution, and transmits the data to the central control platform through an end-to-end encrypted secure channel.

4. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The data fusion preprocessing module specifically includes: Feature extraction unit: The unit integrates feature extraction algorithms to extract environmental awareness features from the cleaned and aligned data according to task requirements; Primary fusion unit: The unit performs primary fusion of the extracted features through a multi-sensor fusion algorithm to generate a structured environmental perception data stream, which contains environmental perception data in a unified format; Output and Interface Unit: The unit is used to output the structured environment perception data stream to the subsequent processing module of the central control platform.

5. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The intelligent environmental modeling module specifically includes: 3D Environment Modeling Unit: The unit extracts spatial geometric features from the structured environment perception data stream output by the data fusion preprocessing module based on convolutional neural networks and point cloud processing networks, constructs the 3D structure of the environment, and generates a 3D environment mesh through stereo geometry and SLAM technology. Probabilistic graphical modeling unit: The unit models the state and interrelationships of environmental entities through probabilistic graphical models, predicts the dynamic change trend of entity states by performing time-series analysis on historical data, and obtains the causal relationships and potential interactions between environmental entities; Entity State and Attribute Unit: The unit identifies key entities in the environment, extracts entity attributes to associate them with corresponding objects in the 3D model, and assigns semantic labels to environmental entities.

6. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The intelligent deduction module specifically includes: Physical rule unit: The unit has a built-in physical rule library that covers kinematics, dynamics and environmental constraints, and simulates the physical behavior of unmanned equipment in a three-dimensional environment through real-time simulation; Behavior Model Library Unit: This unit contains a variety of predefined behavior patterns, including cooperative search, formation movement, and target tracking, and adjusts the behavior strategies based on task requirements; Multi-threaded simulation unit: The unit supports parallel processing of multiple simulation scenarios through a multi-threaded computing framework, automatically generates diverse test scenarios based on a 3D environment model, dynamically adjusts the simulation strategy to adapt to environmental changes through an integrated strategy switching mechanism, and adjusts the data simulation parameters during the simulation process based on the real-time changes of the 3D environment model. AI Decision-Making Unit: The unit achieves intelligent decision-making by integrating reinforcement learning, evolutionary algorithms and imitation learning, including autonomous collaborative decision-making, path planning, task allocation, risk avoidance and performance evaluation; The path planning and task allocation generate optimal paths that meet environmental constraints through an integrated path planning algorithm, and allocate tasks based on task priority, equipment capabilities, and environmental conditions. The risk avoidance and performance evaluation are based on probabilistic graphical models to predict collision and communication interruption risks, and the performance of each simulation scheme is quantified through a performance evaluation index system. Decision Tree Deduction Unit: This unit structures the deduction results into a tree structure, displays the decision path and branches, and outputs the decision tree data to other modules of the central control platform.

7. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The adaptive control strategy generation module specifically includes: Control command conversion unit: The unit maps decision tree nodes to specific control commands, including motion control, sensor adjustment, and communication coordination, and generates standardized command formats for each control command to adapt to the control interfaces of different equipment; Behavior model embedding unit: The unit associates control instructions with behavior model parameters by calling a predefined behavior template that matches the task scenario, forming a complete execution logic and obtaining a structured behavior sequence; Disturbance-resistant control sequence generation unit: The unit optimizes the structured behavior sequence using a robust control algorithm to generate an disturbance-resistant control sequence.

8. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The human-machine collaborative decision-making module specifically includes: Operator Intervention Unit: The unit converts the operator's intervention commands into a system-recognizable format, provides a parameter adjustment interface, allows the operator to modify the simulation parameters, generates multiple simulation strategies, and allows the operator to compare the advantages and disadvantages of different strategies; Human-machine intelligent fusion unit: The unit integrates operator intervention commands, adjustment parameters and intelligent control sequences through a fusion algorithm to generate final decision commands.

9. The intelligent simulation platform for environmental perception and command and control based on biomimetic unmanned equipment according to claim 1, characterized in that, The instruction distribution and equipment coordination control module specifically includes: Command sequence decomposition unit: Based on equipment capabilities, mission requirements and environmental constraints, the unit breaks down the control strategy into individual commands, covering the mission dimensions of motion, perception and communication; Communication link unit: The unit integrates multi-channel communication protocols, responds to electromagnetic interference through frequency switching and signal encryption, and dynamically adjusts the link based on communication quality, switching transmission channels in real time; Command distribution and scheduling unit: The unit performs parallel distribution of commands to multiple pieces of equipment based on equipment priority, task urgency, and communication status, and provides distribution log recording; Equipment Coordination and Control Unit: The unit is used to coordinate the autonomous coordinated movement and task execution of the equipment cluster, dynamically adjust the equipment's sensing tasks according to environmental changes and task requirements, and process the dynamic interaction between the equipment and the environment based on sensing data.

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