241 results about "Environmental adaptation" patented technology

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention discloses a forest land ecosystem carbon reserve accounting method and system, and the method comprises the steps: obtaining and screening feature variables, such as optical remote sensing data, vegetation indexes, texture factors, satellite-borne laser radar feature parameters, so as to determine features having the most influence on a target variable, and removing redundant and irrelevant variables; the method comprises the following steps of: screening characteristic variables, reserving variables which contribute to the model prediction effect, improving the precision and stability of the model, constructing a canopy height inversion model by adopting a random forest method based on the screened characteristic variables, realizing accurate estimation of the canopy height, and finally calculating the biomass of vegetation of the forest ecological system by using a different-speed growth equation. And finally, summarizing the biomass of the vegetation of the forest ecological system to obtain the total biomass of the vegetation, and multiplying the total biomass of the vegetation by the carbon-containing coefficient to obtain the vegetation carbon reserve of the forest ecological system, so that wide-area and efficient forest parameter monitoring is realized, the monitoring cost is reduced, the environmental adaptability is improved, and the problems of data continuity and standardization are solved.

The invention relates to the technical field of environment adaptability testing, and discloses an identification key performance comprehensive environment adaptability testing system. The system comprises a multi-environment factor acquisition and processing module for acquiring data and performing normalization preprocessing; the environment scene dynamic modeling module is used for constructing a multi-dimensional association graph and updating a topological structure; the performance index evaluation module evaluates a performance degradation index in a composite environment by means of a spatial-temporal feature fusion algorithm; the failure mode prediction module is used for generating a potential failure mode under multi-environment coupling according to the performance degradation trend and the stress accumulation effect; the environment tolerance analysis module is used for integrating data to establish a virtual model and outputting a tolerance residual error of a specific environment section; the test scheme optimization module is used for generating a self-adaptive test sequence according to the residual error and the map; and the environment scene simulation control module is used for regulating and controlling parameters of the test cabin in combination with multiple information. The system can comprehensively and accurately evaluate the performance and endurance capability of equipment in a composite environment, and has a dynamic adjustment characteristic.

The invention discloses a dexterous hand environment article sensing and modeling algorithm based on body sensing. The algorithm comprises the steps that a mechanical arm drives a dexterous hand to approach an object; tactile-spatial data acquisition: a three-axis force sensor is arranged at the tail end of each finger of the dexterous hand, and the system is in contact with an object through multiple fingers of the dexterous hand to acquire multi-dimensional information of the appearance, surface characteristics and local topology of the object; active exploration sampling, multi-finger movement of the mechanical arm and the dexterous hand, and active penetration of unknown object surface and enhanced sampling of local features through the dexterous hand by the system; the method comprises the following steps: modeling an object attribute, analyzing a mechanical response sequence obtained by tactile-spatial data acquisition and active exploration sampling, modeling a target object in three dimensions of elasticity, hardness and local geometric morphology, and realizing accurate identification and modeling of an unknown object appearance form and an object attribute in an environment. The distribution uniformity of the tactile point cloud and the model integrity are improved, the hardware dependency is reduced, and the environmental adaptability is enhanced.

The invention discloses an autonomous exploration method based on multi-modal data fusion and a large language model, solves the problems of low autonomous exploration efficiency, insufficient decision intelligence, poor environmental adaptability and the like in the prior art, and belongs to the technical field of robots. The autonomous exploration method comprises the following steps: S1, deeply fusing laser radar geometric information and visual semantic information through cooperative work of a three-dimensional laser and a visual sensor, and constructing an accurate semantic map; s2, performing code conversion on the semantic map to enable a large language model to understand and perform reasoning; and S3, based on an output result of the large language model in the S2, selecting semantic-oriented exploration or geometric frontier exploration until the task is completed or all regions are explored. According to the method, accurate and detailed environment perception is realized, the intelligence and task correlation of exploration decision making are improved, and the method has wide application potential.

The invention discloses a nuclear power plant bolt installation method and system based on augmented reality, and belongs to the field of bolt intelligent installation, and the method comprises the steps: collecting nuclear power plant environment data based on an environment monitoring sensor, and obtaining environment adaptation parameters through anti-radiation reinforcement processing; generating an installation guidance sequence containing specifications and torque values based on the environment adaptation parameters and the bolt identification result; bolt image features in the installation guidance sequence are analyzed through an image processing algorithm, position and angle deviation indexes are extracted, and deviation calibration data are obtained; collecting a fastening torque signal based on a torque measurement sensor, and fusing the fastening torque signal and the deviation calibration data to obtain a torque deviation correction value; updating calibration parameters based on the torque deviation correction value, obtaining the corrected mounting position and angle, and determining the accuracy of the bolt in-position state; bolt surface defects and fastening degree characteristics are extracted through image analysis, and a bolt installation quality evaluation result is generated.

The invention relates to the technical field of electronic component reliability engineering and environmental engineering, and discloses an electronic component environmental adaptability reliability prediction and life evaluation system and method, and the system comprises an environmental parameter collection module, a reliability prediction module, a life evaluation module, and a data storage and interaction module. According to the invention, a multi-dimensional sensor array including temperature, humidity, vibration, electromagnetic radiation and salt mist concentration is adopted by an environmental parameter acquisition module, multi-physical field stress parameters are synchronously acquired, and common environmental interference factors of electronic components are covered; a Kalman filtering algorithm is built in the data preprocessing unit to suppress environmental noise, the noise suppression ratio is increased by 40%, then abnormal values are eliminated through the 3 sigma criterion, and the data integrity reaches 99% or above; besides, the stress simulation sub-module realizes single / multi-stress coupling simulation through a temperature control cabin, a vibration table and the like, and ensures that the simulation stress steady-state error is less than or equal to 1% in combination with PID feedback control, so that extreme environment data of a laboratory can be supplemented, and the problem of insufficient field data is solved.

The invention discloses a high-precision indoor and outdoor positioning method based on WiFi FTM distance estimation. The method comprises the following steps: step 1, data preprocessing and coarse correction; step 2, modeling an error system; step 3, constructing a regression model and performing multipath compensation; and step 4, quality control optimization: on the basis of the distance observation value after multipath compensation, new feature engineering and target variables are reconstructed based on new observation data. According to the method, dynamic error compensation and quality control based on machine learning are provided, the problems that in the prior art, multi-path interference errors are difficult to model, the positioning precision is low, and the environmental adaptability is poor are solved, and the precision, usability, reliability and robustness of WiFi FTM indoor and outdoor scene positioning are improved.

The invention belongs to the technical field of intelligent feeding for cow breeding, and discloses a cow feed proportioning optimization method based on artificial intelligence. Body weight, movement, heart rate, temperature and humidity and feed component sensors are arranged in dairy cow individuals and a breeding environment, physiological data and environmental data are collected, and the physiological data and the environmental data are transmitted to edge computing equipment and a cloud platform through a wireless network to be preprocessed and stored. And the artificial intelligence analysis module is used for generating a personalized feed formula of each cow based on a machine learning and / or deep learning model in combination with an optimized objective function, and carrying out environmental adaptability adjustment according to temperature, humidity and feed component changes. The system controls the automatic feeding equipment to perform quantitative feeding according to the heads, and utilizes feedback data of milk amount, body weight and exercise amount to update health state indexes and continuously correct feed proportion. On the premise of meeting individual nutritional requirements of dairy cows, the feed utilization rate and the production performance are improved, the feed cost and the disease risk are reduced, and the feed is suitable for fine management of large-scale pastures.

The invention is suitable for the technical field of electric energy transmission, and provides an SWPT system frequency adaptive regulation and control method and system based on environmental perception, and the method comprises the steps: obtaining the output current data of high-frequency electric energy, and obtaining the first current data; determining an initial frequency disturbance step length in combination with the collected environmental parameters; applying disturbance to the output frequency according to the initial frequency disturbance step length, and collecting output current data after disturbance to obtain second current data; the change trend of the two current data is analyzed, and the deviation direction of the current working frequency and the system resonant frequency is judged; adjusting the output frequency according to a frequency control instruction generated in the deviation direction, and enabling the current working frequency to approach the resonant frequency; current data is continuously updated, and in combination with environment parameters collected in real time, the working frequency tracks resonant frequency drift caused by environment change through iteration; and the environmental adaptability, the energy transmission efficiency and the overall reliability of the single-line electric energy transmission system are effectively improved.

The invention discloses an optimization method for the closure jacking force of an extradosed cable-stayed bridge oriented to environmental adaptability, and belongs to the technical field of jacking force calculation, and the optimization method comprises the steps: constructing a coupling model of an environment temperature field and structural response, collecting temperature gradient and strain data in real time through arranging a distributed temperature and strain sensor array, and calculating the jacking force of the extradosed cable-stayed bridge. Establishing a temperature-strain mapping function; a multi-objective optimization function is defined, the jacking force serves as a decision variable, structural stress, design linear deviation and construction safety risks are comprehensively considered, and multi-objective optimization is achieved by dynamically adjusting weight coefficients; setting constraint conditions including structural stress, elevation deviation and axis error; solving the optimal jacking force by adopting an improved particle swarm optimization algorithm; and outputting an optimal solution set, and selecting the solution with the lowest construction safety risk in the Pareto leading edge as the final jacking force. According to the method, the precision and efficiency of jacking force optimization are improved, the adaptability in a complex environment is enhanced, and a scientific and reliable solution is provided for bridge construction.

The invention discloses an inclinometer calibration method for oil exploration, and relates to the technical field of oil exploration. Comprising the steps of S1, setting up a calibration environment and fixing an inclinometer, S2, collecting data of a multi-attitude sensor, S3, calculating compensation parameters by a calibration algorithm module, S4, writing and preliminarily verifying the compensation parameters, S5, calibrating the adaptability of a temperature and humidity environment, S6, tracking and calibrating long-term stability, S7, compensating and calibrating magnetic field interference, S8, calibrating the precision of a data acquisition module, and S9, calibrating and accepting system joint debugging. Through the steps S1, S2 and S3, a basic calibration system with a stable environment, comprehensive data and accurate calculation is constructed, and the basic measurement accuracy of the inclinometer is remarkably improved; through the steps S5, S7 and S9, a scenarized calibration closed loop of environmental adaptation, interference resistance and actual combat verification is formed, so that the inclinometer can keep stable precision in a temperature zone of-20 DEG C to 60 DEG C, a wet zone of 30% RH-90% RH and a magnetic field interference environment, and the adaptability and reliability of the inclinometer in an actual exploration scene are greatly improved.

The invention discloses a predictive test system and method based on digital twinning and environmental Monte Carlo simulation, and relates to the technical field of equipment environmental adaptability tests.The system comprises a test cabin, an environmental parameter actuator, a climate Monte Carlo engine, an online sensing network and a health state evaluation and feedback control module; the method comprises the steps that an engine generates an initial multi-dimensional environment control parameter sequence according to a target environment model, an actuator applies environment stress according to the initial multi-dimensional environment control parameter sequence, a sensing network acquires internal state parameters of equipment in real time, an evaluation module judges whether the parameters reach a cascade failure threshold value or not, if yes, an engine algorithm is adjusted or the parameters are corrected, and if not, testing continues; and after finishing, constructing an aged digital twin model which can reproduce a multi-environment coupling effect and realize self-adaptive loading, thereby accurately predicting the failure and the service life of the equipment.

The invention relates to the technical field of robot positioning, and discloses an indoor and outdoor robot positioning method based on multi-sensor fusion. According to the method, original data streams generated by multiple sensors in the moving process of the robot are collected, and due to the fact that time of data generation of different sensors is different and is prone to interference, time alignment and filtering processing are carried out on the original data streams to generate a synchronous and denoised sensor data sequence. The motion characteristics of the robot are extracted from the sequence, position change, speed fluctuation, attitude offset and the like are covered, then the time evolution rule of the motion characteristics is analyzed, and the stability index and the environmental fitness of the motion characteristics of the robot are calculated. And according to the stability index and the environmental fitness, multi-sensor data are fused, so that positioning coordinates of the robot are generated. According to the method, the positioning accuracy and the environment adaptability of the robot are effectively improved, and the robot can be stably and accurately positioned in complex indoor and outdoor environments.

The invention discloses a pipeline laying vehicle and a laying method, and belongs to the technical field of communication construction. The invention aims to solve the problems of low laying efficiency, difficulty in positioning and poor environmental adaptability of wires in a pipeline in the prior art. The device provided by the invention adopts a cylindrical shell design, and a transmission system driven by double motors and an auxiliary system comprising a camera, a high beam and a loudspeaker are integrated in the device. The transmission system achieves synchronous rotation of wheels through a driving gear, a driven gear and a wheel fixing ring assembly, and the device can adapt to pipeline environments with different pipe diameters in cooperation with an elastic telescopic mechanism of a wheel support. The camera can monitor the environment in the pipeline in real time, the high beam is automatically triggered to illuminate according to light changes, and the loudspeaker is started to drive living organisms when the living organisms are detected. And the wire fixing nut frame is matched with the wire fixing bolt, so that the wire can be firmly fixed. According to the invention, automatic laying and accurate positioning of wires in the pipeline can be realized, the construction safety and efficiency are obviously improved, and the method is especially suitable for communication construction scenes in a narrow pipeline environment.

An intelligent compensation method for coupling errors of a dynamic gravimeter comprises the following steps: selecting typical environment interference influencing the dynamic gravimeter in a complex measurement environment, and analyzing an influence mechanism of each typical environment interference on the measurement accuracy of the dynamic gravimeter; according to the influence mechanism under each typical environment interference, analyzing a coupling error influence mechanism between the typical environment interferences; based on the coupling error influence mechanism, constructing a coupling error comprehensive compensation model; adding the coupling effect loss into a data-driven loss function, and defining a model coefficient constrained by physical information; and performing model training by using the training data, and calculating the total loss of the verification set on the obtained model to obtain a final coupling error comprehensive compensation model. According to the invention, the coupling error of the dynamic gravimeter in a complex environment is compensated based on coupling error mechanism analysis and a physical constraint long-short-term memory model, and the measurement precision and environmental adaptability of the dynamic gravimeter under complex multi-factor interference conditions are improved.

The invention discloses a water supply and drainage pipe network data acquisition method and system, particularly relates to the technical field of multi-parameter monitoring, and comprises the steps of reference parameter calibration, environment adaptation grade judgment, multi-parameter dynamic screening, node acquisition standard determination and water quality data classification identification. According to the method, the water supply and drainage pipe network design standard parameters and the actual initial collection parameters are obtained, the parameter adaptability is compared and adjusted, and a reliable reference is established for subsequent collection; collecting pipe network surrounding environment parameters, comparing with suitable collection environment requirements, dividing interference levels, avoiding environment interference, and improving the purity of collected data; calling an environment adaptation level and a reference adaptation scheme, starting a sensor to obtain and screen real-time data, and reducing invalid data; the node type is judged, and the acquisition requirement is clarified in combination with a benchmark adaptation scheme, so that the acquisition pertinence is improved; and the acquisition parameters are compared with the node adaptation standard, the acquisition suitability is verified, the process compliance and the result accuracy are ensured, and a closed-loop acquisition process is formed.

The invention discloses a self-leveling anti-seismic field emergency intelligent operating bed, and relates to the technical field of operating beds. Comprising a supporting table and a supporting box fixedly installed at the top of the supporting table, an operating bed body is installed on the top wall of the supporting table, and according to the self-leveling anti-seismic field emergency intelligent operating bed, through arrangement of a leveling assembly, when the operating bed needs to be arranged in the field, firstly, switching of a stable supporting state of a moving state can be rapidly completed; under the self-weight action of the whole device, the pressing seat and the cone are pushed to be inserted into the ground to form primary support, and intelligent self-leveling is conducted on the supporting table through the horizontal control mechanism according to the horizontal state of the whole supporting table, so that the supporting table is rapidly kept in the horizontal supporting state, and the inclination or shaking condition is prevented; and during intelligent leveling, adjustment and adaptation can be carried out according to each fulcrum, especially in the complex field environment, stable leveling can be completed on the uneven ground, and better environmental adaptability is achieved.

The embodiment of the invention discloses salt fog resistant user front equipment suitable for a marine environment. According to one specific embodiment, the salt mist resistant user front equipment comprises a shell, a control assembly and a communication assembly, wherein the control assembly and the communication assembly are both arranged in the shell made of a corrosion resistant material; the shell is provided with a communication interface; a humidity sensor and a temperature sensor which are in communication connection with the control assembly are further arranged in the shell. The control assembly is configured to execute the following steps: controlling the humidity sensor and the temperature sensor to collect environment adaptation parameters according to a preset frequency; performing mean filtering processing on the environment adaptation parameters to obtain filtered environment adaptation parameters; generating a dynamic humidity threshold based on the filtered environment adaptation parameter; and in response to the fact that the relative humidity value is larger than the generated dynamic humidity threshold value, a dehumidification assembly preset in the shell is started. According to the embodiment, corrosion of a high-salt-mist environment to equipment is reduced by presetting the dehumidification assembly. Therefore, the service life of the user front equipment is prolonged.

The invention discloses a method and a device for monitoring the use reliability of information equipment. The method comprises the following steps: acquiring an environment information set and an operation technology state index information set of the information equipment in a use process; the environment information set comprises a measurement value sequence of each type of environment parameters; the operation technology state index information set comprises a measurement state value sequence of each technology index; the measurement value sequence and the measurement state value sequence are acquired according to preset sampling time; the measured value sequence comprises a plurality of measured values; the measurement state value sequence comprises a plurality of measurement state values; performing environment adaptability evaluation processing on the environment information set to obtain an environment factor set; and performing reliability evaluation processing on the environment factor set and the operation technology state index information set to obtain a use reliability value of the information equipment.

This application discloses an underwater wearable physiological parameter monitoring device. A first body-worn monitoring module includes a first body-worn outer shell and a first physiological parameter monitoring component, the first physiological parameter monitoring component being disposed inside the first body-worn outer shell. The outer surface of the first body-worn outer shell has an integrally concave first arc-shaped body-worn surface. A second body-worn monitoring module includes a second body-worn outer shell and a second physiological parameter monitoring component. The outer surface of the second body-worn outer shell has an integrally concave second arc-shaped body-worn surface, and the second physiological parameter monitoring component is mounted on the second arc-shaped body-worn surface. The first and second body-worn monitoring modules are connected by a flexible strap. The wearable assembly enables the first and second body-worn monitoring modules and the flexible strap to together form a closed-loop restraint structure that can wrap around the human limbs. This solution can achieve simultaneous acquisition of multiple physiological parameters of the human body during underwater work, and combines environmental adaptability, wearing comfort, and convenience.

The application discloses a kind of microscale microplastic migration occurrence multi-physical field coupling simulation method, belong to ecological environment field.The method aims at solving the problem that existing microplastic migration model ignores interface adsorption process, is difficult to couple the action of multiple physical fields and is not suitable for the problem of insufficient adaptability to heterogeneous environment.Its main points are: in the Lagrangian framework, by constructing seepage field, and comprehensively calculating the stokes drag, dielectrophoresis force and Brownian force suffered by microplastic particles to track its movement, while explicitly introducing the adsorption and desorption between particles and pore wall, to simulate the migration trajectory and occurrence state of microplastic in heterogeneous porous medium.The application provides a high-precision simulation tool for revealing the environmental fate and health risk of microplastics in groundwater systems.

The invention relates to a dynamic illumination control method, system and device for myopia prevention and treatment and a medium. The method comprises the following steps: generating a multi-dimensional input vector by fusing environmental data such as geographic positions and seasonal characteristics with medical intervention parameters; based on a medical rule model, dynamically mapping the environment variables and the individual myopia characteristics into an illumination change curve conforming to the human body biological rhythm; converting the continuous curve into a sequential control instruction to drive the intelligent lamp; an execution deviation report is generated by comparing the actual illumination parameter with the target value in real time; the medical parameters are dynamically optimized in combination with user vision feedback data, and a closed-loop control mechanism is formed. According to the method, the problems of poor environmental adaptability, weak individual pertinence and lack of medical cooperation in the prior art are solved, dynamic adaptation of the illumination scheme and the natural rhythm, personalized adjustment of myopia intervention and collaborative optimization of the medical terminal and the execution terminal are realized, and the myopia prevention and control effect of children and adolescents is remarkably improved.

The invention discloses a self-adaptive fire early-stage detection method and system based on infrared, visible light and gas concentration information fusion. The method comprises the following steps: synchronously acquiring data through multiple sensors, extracting temperature, vision and gas characteristics, and obtaining environment context information; a three-level model including data-level association, feature-level complementation and decision-level adaptive weighted fusion is adopted for fusion, and the fusion weight is dynamically adjusted according to the environment context; and performing time domain analysis on the fusion result by using a time sequence confidence accumulation algorithm, and realizing hierarchical alarm based on risk integration. The system comprises a corresponding sensing layer, a data layer, a fusion decision-making layer and an application layer. According to the invention, extremely early and high-accuracy detection of flames and smoldering fire is realized, and the method has strong anti-interference capability and environmental adaptability.

The invention provides an intelligent micro-grid multi-module electrochromic collaborative spectrum monitoring device, belongs to the technical field of micro-grid monitoring, and is used for solving the problems of single monitoring dimension, weak collaboration and poor environmental adaptation in related technologies. The device guarantees stable operation through multi-module accurate deployment and structure protection, integrates multi-spectral detection, cross-modal sensing and multi-dimensional environment data, realizes functional complementation and logic linkage of each module through multi-data fusion calculation of intelligent scene recognition and weight optimization in combination with multi-module collaborative scheduling and self-adaptive closed-loop regulation and control, and realizes multi-module cooperative scheduling and self-adaptive closed-loop regulation and control. Finally, the effects of micro-grid global coverage, multi-scene adaptation, accurate fault recognition and real-time response are achieved, and safe and reliable operation and maintenance of the micro-grid are guaranteed.

The invention discloses a multi-task deep learning driven COD and compensation parameter synchronous measurement method, and belongs to the field of water environment monitoring, and the measurement method specifically comprises the following steps: I, synchronously collecting various water quality data through an online water quality sensor, and preprocessing the collected various water quality data into continuous time input; according to the method, the consistency and availability of the multi-source water quality data in the time dimension are remarkably improved, high-fidelity water quality state estimation can be output at any time point, the continuous change characteristic of a real water environment can be better met, the cooperative change and mutual compensation effect of different parameters under different time scales can be dynamically reflected, and the accuracy and the reliability of the system are improved. The limitation of static correlation or artificial experience modeling is overcome, excessive self-confidence error prediction is avoided, and meanwhile high environmental adaptability and long-term operation stability are achieved; therefore, the invention provides a multi-task deep learning driven COD and compensation parameter synchronous measurement method.

The invention discloses a Tibetan costume pattern generation method and system based on big data analysis, and relates to the technical field of Tibetan costume image generation, and the method comprises the steps: obtaining user text description and plateau atmosphere data, and generating candidate patterns through a generative adversarial network; calculating an environment expression vector by combining the atmosphere data, and obtaining an environment expression stability index by adopting a data discrete model; after the stable candidate patterns are screened out, calculating the height difference influence index of each pattern through a visual influence model based on the environment expression vector and the plateau atmosphere data of the stable candidate patterns; and outputting a high-quality pattern after screening again. The problems of color distortion, fuzzy details and the like caused by the fact that a traditional method is difficult to adapt to plateau complex optical conditions are solved, and the environment adaptability and the culture integrating degree of generated patterns are remarkably improved. By quantifying the influence of environmental factors on pattern visual performance, automatic generation and screening of patterns which can keep Tibetan cultural characteristics and have excellent environmental adaptability in a plateau complex environment are realized.

The invention discloses a wildlife prevention and control system suitable for an extreme environment, and the system comprises a monitoring subsystem which is used for collecting image data of a wildlife appearing and disappearing region; the intelligent identification subsystem is used for receiving the image data, identifying and classifying wild animals in the image through a deep learning model, and outputting an identification result; wherein the deep learning model is integrated with an attention module and is subjected to adversarial training; the ion expelling system is used for starting one or more expelling measures according to the identification result; the transmission subsystem is used for transmitting image data and identification results among the monitoring subsystem, the intelligent identification subsystem, the ion driving system and the remote control end; and the control system is used for calculating a threat level based on the identification result and a preset dynamic threat level model, and controlling the ion expelling system to execute corresponding expelling operation. The problems that an existing system is low in recognition rate and poor in environmental adaptability are solved, and automatic, intelligent and active prevention and control of wild animal threats are achieved.

The invention discloses an underwater intelligent bionic detection method, and belongs to the technical field, and the method comprises the steps of bionic signal generation and emission adjustment based on environmental adaptability. According to the WaveGAN-based underwater intelligent bionic detection technology provided by the invention, on one hand, high-similarity bionic signals are constructed; and on the other hand, aiming at the efficiency problem in bionic signal transmission, a combined optimization method of pulse interval adjustment based on a channel transmission time delay characteristic and pulse sound source level adjustment based on a channel propagation loss characteristic is provided by utilizing the coupling characteristic of organisms and the environment, so that the sampling point number after adjustment and optimization is reduced by 45%, and the processing time is reduced by 42%. And meanwhile, the energy consumption of the system is greatly reduced, high-efficiency bionic detection can be realized, and the method can provide important reference for underwater hidden detection and bionic sonar performance optimization.

The application discloses a kind of humanoid robot motor skills training methods based on body-object-environment relationship modeling.First, the reference motion data of target motor skills and interactive object, environment state data are obtained;During the training process, the body-object, body-environment, object-environment relationship characteristics and contact state are calculated based on the robot ontology, object and environment state, and the relationship enhanced state representation is constructed;Input it into the adversarial discriminant network to generate the adversarial reward representing the naturalness of action and the consistency of interaction;Finally, the adversarial reward, task, stability and safety reward are fused, and the control strategy is trained based on reinforcement learning.Compared with existing methods, the application explicitly learns the interaction relationship, reduces the complexity of reward engineering, improves the naturalness of movement, the rationality of interaction and the environmental adaptability, and is suitable for whole-body interaction tasks such as carrying, supporting and obstacle crossing.