449 results about "Local environment" patented technology

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention relates to a refined energy consumption analysis and optimization management method and system based on rail transit, and solves the problems that a regulation and control mode based on overall energy consumption data cannot accurately adapt to actual energy consumption requirements of all regions, and energy waste or reduction of local environment comfort is easily caused. The method comprises the following steps: according to the optimal illumination brightness and air conditioner output combination of each micro-area obtained by a multi-target particle swarm optimization algorithm, sending a control instruction to related equipment of each micro-area; acquiring energy consumption data of each micro-area, and analyzing and determining whether the energy consumption of the micro-area is higher than an expected range or not according to a preset energy consumption reference value and an expected fluctuation range of each micro-area; if not, maintaining the original setting; and if yes, adjusting the weight of the energy consumption objective function in the multi-objective particle swarm optimization algorithm according to a preset weight adjustment strategy. The method has the advantages that the overall energy consumption of the rail transit is effectively reduced, and fine management is achieved.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention provides a collaborative decision-making system based on multi-agent reinforcement learning, and the system comprises a global decision-making coordination module, an agent local decision-making module, a state information sharing module, a conflict detection and solution module, and a security and privacy protection module. Each agent realizes autonomous decision making and real-time learning in a local environment, the dependence of the system on centralized control is reduced, the communication burden of the system during data interaction is effectively reduced in a distributed manner, the agents can directly respond based on local data, and the data interaction efficiency is improved. The state information of each agent is collected and analyzed in real time through a global decision coordination module, and the overall strategy of the system is adjusted based on a reinforcement learning algorithm, so that global target implementation in a complex environment is ensured, and the limitation that a traditional system cannot be adaptively adjusted in a dynamic environment is effectively avoided through global optimization; and the global optimization of the multi-agent cooperative system on the basis of each agent target is ensured.

The invention relates to the technical field of battery thermal management, and provides a dynamic thermal management method and system for a battery module, and the method comprises the following steps: carrying out the local environment heat dissipation capability evaluation when a preset triggering condition is satisfied, and generating an evaluation result; generating a heat dissipation adjustment parameter of the heat dissipation system based on the evaluation result and the current thermal state of the local battery; carrying out data monitoring and recording, and generating reported data information; the reported data information comprises data related to local environment heat dissipation capability evaluation, evaluation result data, heat dissipation adjustment parameter data of a heat dissipation system and battery thermal state change data after the heat dissipation system executes adjustment; and uploading the reported data information to a cloud platform, so that the cloud platform executes continuous learning and optimization operation of the thermal management strategy based on the reported data information. The method has the advantages that the adaptability and safety of heat management are improved, and continuous optimization of a cloud strategy is supported.

The invention discloses an airport unmanned luggage tractor path planning method and system, and belongs to the technical field of unmanned vehicle autonomous navigation, and the method comprises the steps: obtaining an initial path and a final path of an airport unmanned luggage tractor; matching the global electronic map of the airport and the path information of the final path with the actual environment information, and judging whether abnormity exists or not; a local environment in an airport is divided into a plurality of two-dimensional grid units, various obstacles are identified in the two-dimensional grid units, potential energy values are given to the grid units corresponding to the obstacles respectively, when the potential energy values are larger than a preset potential energy maximum value, it is judged that abnormity occurs, and a driving path of the unmanned luggage tractor is re-planned and serves as a final path. The operation of judging whether the abnormity exists or not is executed again, and when the abnormity does not exist, the driving path of the unmanned luggage tractor is generated. The method can avoid local path planning of sudden dynamic obstacles.

The invention provides a control method and system for automatic carrying equipment of a special transportation line. The method comprises the following steps: constructing a local environment model based on micro-gridding to collect and transmit equipment state information; performing task allocation and path planning on the automation equipment according to the micro-grid environment model; dynamically optimizing task allocation and path planning in a global range according to the task allocation and path planning in each micro-grid; the execution conditions of optimized task allocation and path planning are monitored in real time, the execution sequence of tasks and path planning are dynamically adjusted through an intelligent scheduling algorithm, and flexible adaptive adjustment is made according to resource states and environment changes. Through combination of the micro-grid network and the ant colony algorithm, the problems of single-point fault risk, poor path planning adaptability, inflexible task scheduling and the like in a traditional automatic transportation system are solved, and a brand new solution is provided for efficient, reliable and flexible operation of an automatic transportation special line.

The invention relates to the technical field of robot control, in particular to a robot motion control method, a robot and a system. The method comprises the following steps: acquiring depth information and color information of a current scene and visual textures and geometric features of a target object through an RGB-D camera, and generating a local environment map; introducing a motor current sampling value, correcting the material category of the target object, and determining an initial set value of the clamping force; the corrected joint torque estimated value is converted into a tail end force corrected value through a mechanical arm jacobian matrix; and on the basis of the force control weight and the position control weight, position control output and impedance force control output are calculated respectively, and a final control instruction is generated in combination with the clamping force initial set value and the tooth space compensation item. A harmonic compensation function based on the joint position, the speed and the current is constructed, periodic torque disturbance caused by the cogging effect of the servo motor is effectively restrained, the tail end force estimation is closer to the real acting force, and the clamping control stability is improved.

The invention relates to the technical field of substation operation and maintenance, in particular to a robot inspection path optimization method and system applied to an extra-high voltage substation, and the method comprises the steps: triggering the dynamic optimization of an inspection path in response to the detected equipment operation state change or inspection environment change of the extra-high voltage substation; based on a preset transformer substation environment space model and real-time equipment states and environment parameters collected in real time, a first target inspection path is generated through path cost analysis, and simulation verification and fine adjustment are carried out; when interference risks, positioning deviation overrun and / or multi-machine conflicts exist in execution of the first target inspection path, path planning is updated based on local environment information collected by the robot in real time, and a second target inspection path is generated; and executing the second target inspection path. According to the invention, the equipment state and the environment change can be responded in real time, the inspection path can be automatically adjusted, key inspection tasks are prevented from being omitted, and the continuity of the inspection process is ensured.

The invention provides an unknown target collection-oriented mobile robot motion planning method. The method comprises the following steps of S1, synchronously constructing a grid map in a moving process of a mobile robot; s2, extracting a boundary region and a passable region; s3, the mobile robot continuously explores the environment; s4, the exploration degree of the local environment is judged according to the balance function; s5, if the exploration of the local environment is completed, the mobile robot collects targets in the task area; s6, the mobile robot completes ascertainment of the sub-regions and region coverage collection; step S7, when all unknown environments are detected and all task area targets are collected, the unknown target collection task is completed; the unknown target collection motion planning method of the mobile robot is good in performance, the mobile robot can rapidly complete exploration of an unknown environment and collection of targets in a task area, and task requirements can be effectively met.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention provides an unmanned ship intelligent hunting control method, device and equipment, and relates to the technical field of unmanned ship intelligent control, and the method comprises the steps: obtaining the state information of a current unmanned ship and the local environment data sensed by the current unmanned ship, and obtaining the sensing state information corresponding to the current unmanned ship; based on the sensing state information, constructing a structured state input vector by adopting a distance sorting and filling cutting mechanism; inputting the structured state input vector into a hierarchical strategy network, generating a task role and behavior decision by a high layer, and generating a navigation speed and direction control instruction by a low layer in combination with the high-layer output and structured state input vector; wherein the hierarchical policy network is obtained by optimizing a preset policy network by adopting reinforcement learning through a structured reward mechanism; and controlling the current unmanned ship to execute a hunting task on the target ship according to the navigation speed and direction control instruction. According to the scheme, the stability and flexibility of a multi-boat cooperation strategy and the success rate of a hunting task are effectively improved.

The invention relates to the technical field of automatic agricultural equipment, in particular to an orchard plant protection robot cluster operation system which comprises a central scheduling module, a cluster communication module, an environment modeling module and an updating module. The central scheduling module communicates with a robot through a communication technology, and the cluster communication module constructs a robot cluster communication network by using a communication network configuration strategy; the environment modeling module evaluates the complexity of a local environment through a sensor assembly, and generates a local map containing orchard terrain, vegetation density and obstacle information. The updating module splices the local maps to generate an operation map, updates a communication network according to the environmental complexity, and optimizes task allocation and path planning of the robot cluster; the system has the functions of task scheduling, communication network optimization and map updating, and efficient collaborative operation of a robot cluster in a complex orchard environment is achieved.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention discloses an office chair cushion temperature and humidity multi-stage regulation and control method and system. The method comprises the steps that the contact state of a cushion and a human body contact interface, the local environment, human body physiological response and external environment multi-dimensional parameters are collected in real time; the parameters are matched with a dynamic reference model constructed by multi-user historical comfort data, the matching degree is calculated, and multi-level discomfort levels of all contact areas of the cushion are divided; in combination with the discomfort level and the environmental condition, generating a personalized strategy corresponding to the multi-level regulation intensity through a multi-objective optimization algorithm; and the temperature and humidity adjusting device is driven to execute multi-stage cooperative regulation according to a strategy, and feedback data is collected to update the model. The system integrates a multi-parameter acquisition module, a data processing module, an execution regulation module and the like, and can realize automatic damp-proof management of the seat cushion after the seat is separated from the seat. The problems that existing regulation and control are single, the dynamic comfort requirement cannot be met, and the hidden danger of moisture after the cushion is separated from the seat is solved, and the comfort, sanitation and durability of the cushion are remarkably improved through full-period and multi-stage precise regulation and control.

The invention relates to the technical field of video recognition, in particular to a construction safety real-time high-precision detection method and system based on environmental characteristics. Arranging a plurality of sensors in a to-be-detected area to collect field environment data in real time; analyzing the illumination change of the to-be-detected area through the light and shadow analysis model, calculating a light-safety misjudgment index, and identifying a potential light misjudgment area; a visual processing model is utilized to analyze the worker video, human skeleton key points are recognized in real time, personnel behavior safety indexes are obtained, and potential dangerous behaviors are detected; detecting local wind speed and turbulence conditions and vibration and resonance phenomena of a to-be-detected area in real time through a local breeze and vibration detection model, and evaluating a local environment stability index; and the safety level of the to-be-detected area is calculated in real time by using the light-safety misjudgment index, the personnel behavior safety index and the local environment stability index, so that multi-dimensional and real-time construction safety risk assessment is realized.

The invention provides a multi-modal fire-fighting robot cluster cooperation method and system, a medium and a product, and relates to the technical field of fire-fighting robots, and the method comprises the steps: obtaining own multi-modal sensing data collected by a fire-fighting robot cluster in real time, and generating a corresponding local environment model for each robot in the cluster; the working state of the current master control fire-fighting robot is monitored, if abnormity is found, the master control performance of a non-master control robot cluster (except the current master control robot) is analyzed according to the local environment models, and a new master control fire-fighting robot is determined; acquiring associated multi-mode sensing data collected by a new master controller from the local environment model of the slave robot cluster through a wireless ad hoc network, and generating a new global collaborative operation strategy according to the associated multi-mode sensing data; and distributing the new global collaborative operation strategy to each robot of the non-master-control robot cluster, and controlling the robots to carry out fire-fighting operation according to the strategy. The technical problem that a fire-fighting robot cluster is difficult to collaboratively carry out fire-fighting operation under the condition that a master control node fails in the prior art is solved.

The invention discloses a multi-unmanned aerial vehicle cooperative formation method in a complex dynamic environment based on an MASAC algorithm. The method comprises the following steps: firstly, establishing a mathematical model of multi-unmanned aerial vehicle cooperative formation, and initializing model parameters; then calculating a dynamic target of each unmanned aerial vehicle by adopting a multi-unmanned aerial vehicle cooperative control method combining a dynamic virtual target and pilot following, and interacting with the environment to move to a new position; converting a multi-unmanned aerial vehicle cooperative formation problem into a distributed partially observable Markov model, and training the model by using an MASAC algorithm to obtain a convergent strategy model; and finally, carrying out a multi-unmanned aerial vehicle cooperative formation task by using the trained strategy model. According to the invention, the accuracy of autonomous navigation and adjustment of the multi-unmanned aerial vehicle formation in a complex environment is improved, the adaptability of the multi-unmanned aerial vehicle task is improved, the unmanned aerial vehicles can quickly respond to the change of a local environment without depending on a central node, and the efficiency and safety of task execution of the multi-unmanned aerial vehicle cooperative formation are improved.

The invention relates to the technical field of dust concentration detection, in particular to an online detection method and system based on limited space dust concentration, and the method comprises the following steps: based on each detection area, analyzing scattered light signals, classifying the scattered light signals into different particle size categories according to angles and coordinates, and performing signal compensation in combination with real-time humidity; adjusting the irradiation direction of the LED array to optimize illumination distribution, comparing light intensity differences, dynamically scheduling uniform illumination areas, subdividing detection points, analyzing layering signals, and obtaining particle size layering concentration data. According to the invention, distribution mapping with distinct particle size category difference is realized by using a spatial multi-dimensional layered structure, signal adaptability is improved by fusing a humidity dynamic response mechanism, particle size, humidity and illumination multi-source data are dynamically fused, local environment interference is eliminated through layering and compensation linkage, and the accuracy of the system is improved. The continuity of data expression and the response speed of concentration monitoring are improved, and continuous dynamic tracking of the dust state in a limited space complex environment is supported.

The invention provides an unmanned ship cluster confrontation game method and system based on situation information. The survival ability and combat efficiency of an unmanned ship cluster in a complex confrontation environment are improved. The method comprises the following steps: firstly, collecting threat data of the enemy unmanned surface vehicle and quantitatively evaluating the threat degree, collecting performance data of the our unmanned surface vehicle and quantitatively evaluating the adaptability, and fusing the threat data and the performance data by using an analytic hierarchy process to obtain a comprehensive situation evaluation result; then, sub-task targets are determined according to the comprehensive situation assessment and the overall task target, and tasks are reasonably allocated to the unmanned surface vehicles by adopting an improved Hungary algorithm; and finally, performing local path planning on each unmanned ship by adopting an improved A * algorithm in combination with a local environment, and performing global path planning based on multi-agent collaborative consideration of cluster collaborative requirements and enemy threat distribution. According to the method, comprehensive situation assessment, reasonable task planning and scientific path planning are realized, and the unmanned ship cluster confrontation capability is improved.

The invention provides a multilevel backtracking unmanned aerial vehicle path planning method based on a neighborhood extension algorithm, aiming at the problems that the traditional A * algorithm is low in efficiency and easy to fall into dead angles in unmanned aerial vehicle path planning. The method comprises the following steps: firstly, generating a random two-dimensional grid map simulation task environment; secondly, grids in eight neighborhoods of the current position of the unmanned aerial vehicle are scanned in real time, and local environment information is dynamically updated; and finally, a neighborhood extension planning algorithm is adopted, the unmanned aerial vehicle is allowed to perform diagonal motion, the planning flexibility and efficiency are improved, and redundant nodes and unnecessary curves are reduced. According to the method, a multi-stage backtracking strategy is designed, and a key backtracking point is preset in a path planning process, so that the unmanned aerial vehicle can quickly identify and return to a preset optimal backtracking point when entering a dead angle due to the limitation of a local environment, thereby quickly returning to a safe area and re-planning a path. According to the strategy, local optimal solution and infinite loop search are effectively avoided, and the task execution efficiency is remarkably improved in a complex environment containing a large number of dead angles.

The invention discloses a vehicle track prediction method and system for a complex traffic scene, and the method comprises the following steps: S1, obtaining the historical track information of a target vehicle and surrounding vehicles in the traffic scene, and extracting the track time sequence characteristics of a prediction target and the historical track information of the surrounding vehicles through a hierarchical graph neural network; s2, acquiring environment information in a traffic scene where the target vehicle is located, and extracting environment multi-scale features in global and local environment information around the target vehicle by using a hierarchical graph neural network and an attention feature fusion module; s3, performing interactive fusion on the vehicle track time sequence features and the environment features by using multiple attention blocks to obtain interactive features between the scene and the vehicle; and S4, decoding the fusion features of the scene and the vehicle, and outputting a multi-modal future trajectory of the prediction target. The method can ensure that the model can adapt to a complex and changeable traffic environment while the prediction accuracy of the model is improved and the complexity is not increased.

The present invention proposes a control method and system for automated handling equipment on a dedicated transport line. The method includes: constructing a microgrid-based local environment model to collect and transmit equipment status information; performing task assignment and path planning for automated equipment based on the microgrid environment model; dynamically optimizing global task assignment and path planning based on task assignment and path planning within each microgrid; and monitoring the execution of the optimized task assignment and path planning in real time, dynamically adjusting the execution order of tasks and path planning through an intelligent scheduling algorithm, and making flexible adaptive adjustments based on resource status and environmental changes. By combining a microgrid network with an ant colony algorithm, the present invention solves the problems of single-point failure risk, poor path planning adaptability, and inflexible task scheduling in traditional automated transport systems, providing a new solution for the efficient, reliable, and flexible operation of automated transport lines.

The invention discloses a cloud native non-consistency fault detection method for a distributed application, and the method comprises the steps: deploying a Kubernetes cloud native platform in a local environment, deploying a to-be-detected distributed application on the Kubernetes cloud native platform as a detection object, and operating a work load in the detection object; injecting a plurality of cloud native non-consistency fault modes into the detection object, wherein the modes comprise unstable IP address distribution, DNS service delay and SIGTERM signal termination application program; based on a cluster health monitoring algorithm and test judgment logic of different fault modes, whether a fault exists or not is judged by monitoring the number and the state of Pod. The method is a testing tool for the cloud native non-consistency fault in the distributed application, the cloud native non-consistency fault can be detected in an end-to-end mode, and the stability and reliability of the distributed application are ensured.

The present application provides a method for generating a user interface and a method for controlling avatar movement through a user interface. An overhead view, a navigation mesh map and a local environment map that are generated and a main view constitute the user interface. Controlling avatars through the user interface allows users to control the avatars intuitively and immersively while allowing operators to control the freedom of limb movements of the avatars.

The invention discloses a vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning. The method comprises the following steps: independently executing detection according to local observation information and a strategy generation model through an intelligent agent deployed at an edge node; the strategy generation model is constructed based on a decentralized partial observable Markov decision process, and is obtained through cloud centralized training and global shared reward function optimization. When the agent obtains a forward reward, packaging a corresponding vulnerability context and related strategy parameters into an experience package; and the experience packet is diffused in the cooperative detection network in a mode of randomly selecting neighbor nodes and forwarding step by step. And after each agent receives the data, analyzing the content of the experience package and comparing the content with the local environment so as to dynamically adjust local strategy parameters and realize collaborative optimization. According to the vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning provided by the invention, the problems that network security vulnerability detection is poor in expandability and lacks knowledge generalization ability are solved.

The invention provides a heat stress activation state threshold regulation and control method and system based on poultry group behavior feedback, and relates to the technical field of intelligent management and control of livestock and poultry breeding environment, and the method comprises the steps: recognizing the number of poultry heads in a heat wheezing state and the total number of the poultry heads based on poultry head images, and obtaining the real-time heat wheezing proportion of poultry groups; based on a distributed environment real-time acquisition network, obtaining an in-cage environment temperature and humidity index; aiming at each region, integrating data pairs synchronized by a time window, fitting a piecewise linear regression relationship of the real-time heat wheezing proportion of the poultry group along with the change of the temperature and humidity index of the environment in the cage, dynamically generating a heat stress grading threshold value of the region based on a characteristic turning point, generating a house-level regulation and control instruction and a region-level regulation and control instruction, and carrying out region-level regulation and control on the region-level regulation and control instruction. The local environment of the corresponding area is adjusted and alarmed, and a heat stress activation state threshold regulation and control result is obtained. The problems that poultry group heat stress is difficult to accurately and dynamically assess and poultry house environment is difficult to finely and hierarchically control in different production scenes are solved.

The invention discloses a basement ventilation and smoke exhaust system and method based on digital management, and the method comprises the steps: constructing a digital twin architecture comprising a terminal layer, an edge layer and a cloud layer, and carrying out the real-time mapping and interaction of a physical system for basement ventilation and smoke exhaust and a virtual model; carrying out integrated monitoring on the terminal layer by utilizing multiple sensors, and collecting relevant data parameters of ventilation and smoke exhaust of the basement; and a high-integration-level edge computing gateway is adopted to preprocess the collected data. The regional controller adopts a dynamic Bayesian network to fuse the preprocessed multi-source data, and constructs a local environment state model; the edge layer utilizes a deep reinforcement learning algorithm and a digital twinborn model to simulate multiple scenes to carry out state real-time prediction, and generates an emergency plan library; and the cloud layer central server operates a double-track decision engine and performs adaptive adjustment in combination with a deterministic rule base and an adaptive deep reinforcement learning model, and the technical scheme of the invention realizes efficient and accurate ventilation and smoke exhaust cooperative control.

The invention relates to a confidential group psychological test system and a data processing method thereof, and belongs to the field of psychological test data processing. The system disclosed by the invention is particularly suitable for an environment with a high confidentiality requirement, and can ensure that individual privacy is fully protected in each step from data collection to report generation in the whole test process. The system supports quick test starting through a QR code, the operation process is greatly simplified, sensitive data is ensured to be processed and accessed only in an authorized local environment through localized data processing and decryption, and data leakage and unauthorized access are effectively prevented. The improvements not only improve the operation efficiency, but also strengthen the security of the system and the credibility of the user.

The embodiment of the invention discloses a robot autonomous navigation and obstacle avoidance method and system based on multi-sensor fusion. The method comprises the following steps: acquiring multi-dimensional data; fusing multi-dimensional data to generate a robot pose and a local environment point cloud; dividing a front sensing area into a high-reliability green area and a low-reliability red area according to the positions of the red and green light spots, and respectively distributing sensing weights; projecting the local environment point cloud into an occupied grid, performing obstacle detection according to the point cloud density in each grid unit, and multiplying the detection result by the corresponding sensing weight to obtain a sensing energy consumption cost value of the unit; according to the perceived energy consumption cost value, generating a target attraction direction and an obstacle rejection direction on the occupied grid; and generating a motion path based on the attraction direction and the rejection direction, and controlling the robot to advance according to the motion path. According to the invention, deep collaborative optimization of energy consumption cost and motion planning is realized.