515 results about "Environmental adaptation" patented technology

The invention provides an intelligent factory production scheduling method and system based on digital twinning, and the method comprises the steps: firstly obtaining a real-time monitoring data set, including equipment operation states, material flow tracks and environment sensing data, of a physical workshop; generating a three-dimensional digital twinborn model containing an equipment dynamic operation topology, a material real-time distribution thermodynamic diagram and an environment state simulation layer, then performing operation state correlation analysis on the three-dimensional digital twinborn model, and generating a feature set of equipment operation efficiency, material flow bottleneck, environment interference and the like; generating a dynamic scheduling parameter set containing equipment load balancing, material priority distribution and environment adaptation compensation parameters, and finally calling a preset scheduling strategy optimization algorithm to perform multi-objective optimization processing on the dynamic scheduling parameter set to generate an equipment scheduling, material distribution and environment regulation and control instruction set. And synchronizing to a physical workshop to execute real-time scheduling operation, thereby realizing intelligent and efficient production scheduling.

The invention relates to the technical field of slope protection, in particular to a slope protection intelligent detection system based on deep learning. According to the technical scheme, the system comprises a multi-source heterogeneous data sensing module, a data fusion and feature extraction module, a slope state intelligent diagnosis and early warning module, an edge-cloud collaborative computing architecture and a system optimization module. Registration and feature complementation of multi-source heterogeneous data are realized through a multi-modal detection network, an overfitting phenomenon is effectively inhibited through a physical information neural network architecture, risk quantitative evaluation is realized through construction of a dynamic risk evaluation model, early warning response time is shortened in cooperation with a four-level early warning strategy, the false alarm rate is reduced, and the early warning efficiency is improved. Besides, the detection precision of the system in an extreme scene is improved through a physical constraint adversarial training method, so that the environmental adaptability of the system is improved, continuous updating and evolution of the model are realized through an online incremental learning module, and the problem of performance degradation of a traditional system caused by change of geological conditions is solved.

The invention discloses a multi-dimensional anti-bird intelligent recognition method and system based on thermal imaging, and relates to the technical field of intelligent monitoring and ecological protection. Multi-modal data is collected through thermal imaging, visible light, millimeter wave radar and a voiceprint sensor, after PTP protocol synchronization and Kalman filtering preprocessing, 3-5-second tracks of birds are predicted by using an LSTM network, and the anti-bird intelligent recognition method and system based on the thermal imaging are obtained. And the cross-modal features are fused through a Transform architecture, so that 95% of classification accuracy is realized. A bird repelling strategy is generated in real time through edge calculation, and the model is updated through cloud federal learning. The system integrates an oil-electric hybrid unmanned aerial vehicle and a ground device, supports dynamic path planning based on a thermodynamic diagram and differentiated repelling of directional sound waves, laser stroboflash and the like, has the night recognition accuracy rate of 92% and the bird repelling response time of 0.8 second, and is suitable for scenes of electric power, airports and the like. Through multi-dimensional perception, dynamic modeling and eco-friendly expelling, the problems of poor environmental adaptability, single strategy and the like of a traditional scheme are solved, and the anti-bird efficiency and the ecological safety are improved.

The invention discloses a humanoid robot somatosensory control system and method based on deep learning. The method comprises the steps that physiological data and environment information of a user are collected through a wearable device and an environment sensor, and full-modal synchronous collection is achieved in combination with audio and visual data. The system carries out noise reduction processing on the collected sound, extracts a voice instruction and recognizes non-language sound features to judge the physiological state of the user; and analyzing the posture and gesture intention of the user through a human body detection and posture recognition algorithm. And multi-modal data is further fused, the intention of a user is accurately judged in combination with scene context and a security verification loop, a multi-dimensional state space and an atomic action library are constructed, and a composite reward function is designed to realize intelligent decision and execution of robot actions. The system comprises a multi-modal data acquisition module, a user intention understanding module, a dynamic action planning module, a safety monitoring and exception handling module and a continuous learning and collaborative optimization module, and can effectively improve the intention recognition precision, environmental adaptability and task execution efficiency of the robot in a complex home scene. And intelligent and personalized family service support is provided for the elderly.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention relates to a multi-sensor fusion positioning method and device based on environmental characteristics. The method comprises the following steps: respectively acquiring RTK data, laser radar data and IMU data; converting the RTK data into RTK positioning data, converting the laser radar data into laser radar positioning data, and obtaining IMU positioning data according to IMU output; monitoring the quality state of the output signal of each sensor in the current environment, and calculating to obtain a corresponding quality score; constructing and forming an input matrix according to the quality score of each sensor, mapping the input matrix to a high-dimensional feature space through an embedded layer and adding a position code, inputting the input matrix into a Transform model for time sequence modeling and extracting environment dynamic features, and finally outputting a real-time fusion weight of each sensor; and performing weighted fusion by using the real-time fusion weight to obtain final pose output. The method has the advantages of being simple in implementation method, high in positioning precision and stability, high in environmental adaptability and robustness and the like.

The invention relates to the technical field of dynamic correction and intelligent compensation of measured data of a current sensor, and discloses a self-adaptive data compensation method for the current sensor, which comprises the following steps of: acquiring an original output signal and environmental parameters in real time, identifying multi-dimensional error sources such as temperature drift, nonlinear error and electromagnetic coupling error, establishing an influence factor model, and calculating the dynamic correction and intelligent compensation of the measured data of the current sensor. Dynamic compensation is realized by fusing directivity judgment of each error source and a weighting mechanism, and online learning and model parameter updating are carried out by introducing a long-short-term memory network structure, so that the problems of poor environmental adaptability, limited compensation precision and lagging model updating in the prior art are effectively solved.

The invention discloses a quantum encryption handheld laser gas monitoring method and system, and relates to the technical field of handheld laser gas monitoring. The quantum encryption handheld laser gas monitoring method and system comprise the following steps: S1, collecting environmental parameter information, storing environmental monitoring data, and establishing an environmental monitoring database; s2, performing laser spectrum monitoring on the target gas, and performing data quality evaluation based on a laser spectrum monitoring result; s3, executing quantum key analysis on the laser spectrum monitoring data qualified in data quality evaluation; and S4, performing environmental adaptability analysis by integrating the evaluation results of the environmental monitoring data and the laser spectrum monitoring data and the quantum key analysis result. The problems that an existing handheld gas monitoring device is unstable in operation under the complex environment conditions of high temperature, low temperature, high humidity, dust interference, air pressure fluctuation and the like, and the measurement precision is difficult to guarantee are solved.

The invention relates to the technical field of detection, and provides a non-contact thickness measurement detection method and detection equipment, a set of multi-dimensional, high-precision and self-adaptive thickness measurement system is constructed by fusing two non-contact detection technologies of laser and ultrasonic, a complete closed loop is formed from signal acquisition and processing to result fusion compensation, and the thickness measurement accuracy is improved. The method effectively breaks through the thickness measurement limitation of a single technology, adapts to complex and variable industrial measurement scenes, realizes accurate and stable measurement of the thickness of a measured object, gives consideration to dynamic compensation and environment adaptation, provides reliable technical support for the fields of high-end manufacturing, precision detection and the like, assists in improving the product quality control level, and has a wide application prospect. And the non-contact thickness measurement technology is promoted to develop to intelligence and high precision.

The invention is suitable for the field of tracking rotary tables, and provides a follow-up identification adjustment control method and system for a tracking rotary table, and the method comprises the steps: collecting the peripheral data of the tracking rotary table in a multi-modal manner; acquiring and fusing the acquired surrounding data of the tracking turntable; establishing a three-dimensional space error model; generating a prediction control strategy based on the three-dimensional space error model; and generating an intelligent feedback optimization result based on a prediction control strategy, and the method has the beneficial effects that by means of deep fusion and accurate analysis of multi-modal data, the tracking turntable is endowed with excellent environmental adaptability and target tracking capability. Complex environment interference such as atmospheric refraction, illumination change and shielding can be effectively handled, the tracking precision is improved, online self-adaptive adjustment can be achieved through an intelligent feedback optimization mechanism and a control strategy, the stability, reliability and intelligence of the system are remarkably enhanced, and an advanced and efficient technical solution is provided for target tracking application in multiple fields.

The invention provides a karst geological environment monitoring method and system, and relates to the technical field of geological environment monitoring. According to the method, a sensor array is deployed at a preset monitoring node of a target karst area, and multi-source data including geological, environmental and chemical sensing data are collected. The monitoring nodes are equipped with edge computing devices, and real-time noise reduction and feature extraction are carried out on data to form a sensing feature set. And uploading the feature set to a cloud, and carrying out dynamic risk assessment to predict the surface collapse probability. And establishing an adaptive early warning mechanism according to the probability, and generating a comprehensive monitoring report. Through technical innovation and multidisciplinary fusion, the defects of an existing karst monitoring system in the aspects of real-time performance, comprehensiveness, early warning precision and environmental adaptability are overcome, technical indexes are remarkably improved, and efficient and reliable technical support is provided for prevention and treatment of geological disasters in the karst area.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent disease and pest monitoring and prevention system which comprises an environment self-adaption module, a spectrum collection module, a dynamic threshold value analysis module, a prevention decision module, an execution terminal and a data storage module. A polarization filter is adopted to suppress reflection of light on the blade surface to obtain a spectrum fusion image; the dynamic threshold value analysis module is used for establishing a dynamic proportion threshold value of the insect spot area and the medicinal material biomass by adopting a U-Net network in combination with a growth cycle recognition algorithm so as to obtain graded insect pest evaluation parameters; and the prevention and control decision module is used for combining the graded insect pest evaluation parameters with the environmental parameter set and generating a prevention and control scheme by adopting a pesticide application decision tree model based on northern climate characteristics. According to the invention, through dynamic environment adaptive adjustment and northern climate optimization pesticide application decision, efficient and accurate prevention and control of diseases and insect pests in a complex agricultural environment are realized.

The invention discloses a geological disaster real-time monitoring method and system based on a ground sensing network, and belongs to the technical field of geophysical exploration, and the method comprises the steps: obtaining an acceleration parameter and a moisture content parameter of each sensor node; according to the change rate of the acceleration parameter and the moisture content parameter, identifying an environment sudden change event, generating an environment trigger signal to activate an emergency sampling mode of a target node and an adjacent node, and constructing a dynamic monitoring cluster; requesting a plurality of nodes in the dynamic monitoring cluster to synchronously measure similar parameters, and comparing measurement results to generate a compressed alarm packet; adjusting weight factors of different monitoring parameters by using the compressed alarm packet and pre-acquired real-time environment parameters, and generating an environment calibration risk index; and when the environment calibration risk index exceeds a preset risk threshold value, outputting a geological disaster early warning instruction. According to the method, multi-parameter cooperative triggering, dynamic cluster response, weight adaptive evaluation and compression transmission technologies are adopted, and the monitoring precision, timeliness and environmental adaptability can be comprehensively improved.

The invention relates to the technical field of intelligent manufacturing, and particularly provides an intelligent ultra-high performance concrete preparation system and a control system thereof.The preparation system comprises an environment adaptability adjusting subsystem used for automatically adjusting preparation operation parameters of equipment according to external environment temperature and humidity changes of an ultra-high performance concrete mixture; the dynamic optimization proportioning subsystem is used for sensing real-time states of viscosity, fluidity and the like of the ultra-high performance concrete mixture in the preparation process through a built-in sensor array; adjusting a water-cement ratio and an aggregate proportion according to real-time states of preparation operation parameters, viscosity, fluidity and the like; and the operation energy efficiency management subsystem is used for adjusting the working mode of the equipment according to the preparation operation parameters. The control system comprises an equipment layout assembly, an equipment communication assembly and a program execution assembly. While the quality of the ultra-high performance concrete is ensured, the preparation efficiency and the environmental adaptability are improved, the energy consumption is reduced, and green and intelligent production of the ultra-high performance concrete is realized.

The invention discloses an intelligent production and quality monitoring system and method for premixed concrete, and particularly relates to the technical field of concrete production quality monitoring, and the method comprises the following steps: constructing an initial control parameter set; environment data is collected and acts on the initial control parameter set to generate an applicable control parameter set; performing mixing production based on the applicable control parameter set, collecting process behavior data, and inputting the process behavior data into a machine learning model to predict compressive strength and setting time; comparing the prediction result with a performance target, if the tolerance is exceeded, determining that the quality is low and correcting control parameters, and if the tolerance is not exceeded, determining that the quality is high and allowing discharging; according to the method, the initial control parameter set is constructed by using the historical weighing deviation and the strength actual measurement result, the applicable control parameter set is dynamically generated in combination with the real-time environment data, the performance result is predicted through the model and compared with the preset target, and a quality judgment and parameter correction closed loop is formed. And the parameter rationality, the environmental adaptability and the quality control intelligent level of concrete production are improved.

The invention discloses a dynamic sea condition ship-machine cooperative attitude control system and method for sea grass transplantation, and belongs to the crossing field of marine ecological restoration and intelligent ship control. The system comprises a mother ship attitude control layer, a communication management layer, an underwater operation end and a sensor network layer, the mother ship attitude control layer, the communication management layer, the underwater operation end and sensor network layer and the mother ship attitude control layer are sequentially connected through lines; the method integrates inertial navigation, visual measurement, event-triggered communication and adaptive filtering technologies, realizes high-precision attitude synchronization and stable operation of the ship and the underwater operation device in sea conditions of four levels and below, and can be widely applied to marine ecological engineering scenes such as seaweed bed restoration, coral transplantation and submerged vegetation reconstruction. And the operation efficiency and the environmental adaptability are effectively improved.

The invention discloses a deep geothermal fluid cause and migration path tracing method, and relates to the technical field of earth scientific exploration, and the method specifically comprises the following steps: A1, collecting a geothermal fluid sample, A2, fusing high-precision three-dimensional seismic data, logging data and drilling data, constructing a multi-scale fracture network model, quantifying fracture connectivity and permeability anisotropy, and calculating the fracture connectivity and permeability anisotropy according to the fracture connectivity and permeability anisotropy. A3, developing a long short-term memory (LSTM) network model, inputting temperature, pressure and chemical field data monitored in real time, dynamically predicting a fluid migration path, and introducing a generative adversarial network (GAN) optimization model, and A4, deploying a distributed optical fiber sensor network. According to the sensing method for the aviation high-speed moving target, through the core-shell structure tracer agent, high fluorescence efficiency and geological compatibility are considered, a complete innovation chain is formed in the aspects of material design, detection method and environmental adaptability of the nano tracer agent, and the precision and sustainability of tracing of a migration path of deep geothermal fluid are remarkably improved.

The invention relates to the technical field of sports artificial intelligence and data management, in particular to a physical exercise AI standing long jump evaluation data management system which comprises a dynamic environment calibration module, a multi-mode identity recognition module, a three-dimensional posture analysis module and the like. The dynamic environment calibration module uses a multispectral sensor array and a deep learning algorithm to establish a dynamic coordinate system without fixing a marker; the multi-mode identity recognition module fuses gait and face features to realize non-contact identity authentication; the three-dimensional attitude analysis module performs high-precision modeling and analysis on the long jump action through a multi-view camera and an advanced algorithm; the intelligent violation determination engine combines rules and an AI model, automatically identifies violation behaviors and can dynamically adjust determination criteria. The method gets rid of the limitation of a traditional evaluation mode, realizes high-precision and intelligent evaluation, effectively solves the problems of poor environmental adaptability, inaccurate identity authentication, unguaranteed data security and the like, and is scientific and reliable in evaluation result and safe and efficient in data management.

The invention discloses a method for predicting plant photosynthetic ability by using plant leaf traits, and relates to the technical field of agricultural science, and the method comprises the following steps: obtaining image data of a target plant leaf in a natural state through a high-resolution imaging device, and synchronously recording environment parameter information during collection; extracting multi-dimensional feature parameters of the leaf based on the image data, wherein the multi-dimensional feature parameters comprise a color index, a texture feature, an area, a thickness estimation value and an edge contour form; and carrying out stress feature identification on the character data by utilizing an association rule of the environmental parameters and the leaf characters. According to the invention, by introducing a stress identification and data correction mechanism, the accuracy of photosynthetic capability prediction is improved; the method has good environmental adaptability and crop universality, and is suitable for various agricultural scenes; and a prediction feedback closed-loop system is constructed, continuous optimization and intelligent adjustment of the model are realized, and the stability, reliability and practical application value of the system are remarkably enhanced.

The invention relates to the technical field of artificial intelligence, in particular to an information interaction method and system for an intelligent robot with a body, and the method comprises the steps: achieving the precise time alignment of multi-source sensor data through a hardware clock synchronization protocol, carrying out the cross-modal feature fusion through a self-adaptive weight distribution mechanism, and constructing a unified environment state expression; analyzing the user instruction in combination with the language model and the dynamic knowledge graph, and generating an execution intention containing environmental adaptation parameters; and generating an action strategy considering a task target and environmental adaptability based on a multi-target optimization algorithm, and converting the action strategy into a control instruction through a hierarchical execution mechanism. The problems that in the prior art, multi-source sensing data synchronization precision is insufficient, and heterogeneous data fusion efficiency is low are effectively solved, and the interaction accuracy, the response speed and the self-adaptive capacity of the robot in the complex dynamic environment are remarkably improved.

The invention relates to the technical field of agricultural seedling optimization decision, and discloses a seedling optimization decision method and system based on a knowledge graph, and the method comprises the steps: constructing a causal graph, and recognizing the causal dependence relation between variables through a PC algorithm; carrying out anti-factual reasoning by adopting docalculus, and predicting the performance of the seedlings under the environment intervention condition; constructing a Bayesian network model, and carrying out uncertainty quantification by adopting variational reasoning; establishing a risk assessment index, and generating a seedling optimization strategy by applying a multi-objective optimization algorithm; performing comprehensive reasoning on the knowledge graph to generate a seedling optimization decision report; according to the method, by introducing the causal reasoning algorithm, the real causal relationship between the seedling characteristics and the environmental factors can be identified, and the method does not only depend on statistical correlation, so that the environmental adaptability prediction is more accurate.

The invention relates to the technical field of aviation composite material detection, and discloses an aviation composite material terrace multi-dimensional flatness digital imaging acceptance system. The system comprises a feature acquisition module, an imaging parameter calibration module, an environment adaptation adjustment module, a deviation threshold setting module, an imaging prediction module and an acceptance feedback control module. The feature acquisition module analyzes the multi-dimensional flatness difference to obtain a feature distribution state value; the imaging parameter calibration module screens an optimal angle and resolution combination to obtain an imaging calibration parameter set; the environment adaptation adjustment module matches the environment factors and the imaging combination to obtain an environment adaptation parameter set; the deviation threshold value setting module sets a threshold value to obtain a deviation load threshold value; the imaging prediction module deduces an imaging change trend to obtain an imaging distribution prediction value; the acceptance feedback control module analyzes errors and adjusts parameters to obtain an acceptance regulation and control scheme. According to the system, through cooperation of multiple modules, the accuracy and adaptability of aviation composite floor flatness acceptance inspection are improved, and many problems existing in traditional detection are solved.

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 provides a three-dimensional modeling method and device based on reinforcement learning and multi-sensor data fusion. The method comprises the following steps: inputting environment data corresponding to a target water area into a trained reinforcement learning model, and outputting a data fusion strategy; based on the fused environment data, constructing a three-dimensional model corresponding to the target water area; the target reward function of the reinforcement learning model at least comprises a precision reward function, an integrity reward function, a fusion strategy optimization reward function and an environmental adaptability reward function; the target reward function is used for optimizing the positioning precision of the intelligent agent, the integrity of the map constructed by the intelligent agent, the positioning precision and the map integrity under different data fusion strategies, and the performance of the reinforcement learning model under different water area environments. According to the three-dimensional modeling method based on reinforcement learning and multi-sensor data fusion provided by the invention, the positioning precision and the navigation efficiency in a changeable water area environment are remarkably improved.

The invention relates to the technical field of Chinese herbal medicine cultivation, in particular to a Chinese herbal medicine cultivation system which comprises an environment monitoring and regulation module, a health monitoring and early warning module, an intelligent scheduling and response module and a data analysis module. The environment monitoring and regulation module monitors environment parameters in a cultivation environment in real time; the health monitoring and early warning module monitors health conditions of Chinese herbal medicines in real time and analyzes diseases and insect pests or nutrition problems; the intelligent scheduling and response module optimizes resources of a cultivation environment; the data analysis module analyzes the growth trend and environmental adaptability of the Chinese herbal medicines based on the optimized growth conditions and cultivation environment resources of the Chinese herbal medicines, and generates cultivation suggestions; according to the method, corresponding measures can be identified and taken as early as possible, so that the outbreak of plant diseases and insect pests is avoided or reduced, and the growth quality and yield of plants are optimized.

The invention discloses a fish underwater behavior identification method, medium and device, and the method comprises the steps: collecting the original video information and environment parameter information of fish, and carrying out the underwater image restoration and foreground segmentation preprocessing of the original video information; performing motion feature extraction on the standardized video information, and constructing a kinematics feature vector by combining fish body key point coordinate information obtained by three-dimensional attitude estimation; performing multi-modal feature fusion on the kinematics feature vectors, and screening out discriminative feature subsets; and performing pattern recognition on the discriminative feature subset by adopting a space-time classifier constructed by a graph convolutional network, performing individual matching and result correction, and finally outputting a comprehensive monitoring report including individual behavior recognition, group statistical analysis and an environment correlation model. According to the method, the influence of an underwater complex environment on behavior recognition is overcome, accurate recognition and intelligent analysis of fish behaviors in a complex culture environment are achieved, and the method has the advantages of being high in recognition precision, high in environmental adaptability and comprehensive in analysis dimension.

The invention provides an intelligent transportation device for fish fry keep-alive transportation and a water treatment process, and the device comprises a heat preservation box body, an environment sensor, a fish fry state indirect monitoring sensor, and a flexible buffer type self-adaptive isolation device, an intelligent environment regulation and control module and a dynamic buffer device which are detachably connected with the heat preservation box body, the intelligent environment regulation and control module comprises a water temperature regulation module, a water quality regulation module and a gradient environment adaptation module, the gradient environment adaptation module automatically regulates values of all parameters according to a preset environment parameter change curve, and meanwhile, the change rate and the water treatment process of the environment parameters are regulated in real time according to data fed back by the water quality regulation module; and the dynamic buffer device dynamically adjusts the damping coefficient of the buffer unit according to the pressure and acceleration data in the cold storage transportation device. The device can accurately match the optimal fish fry transportation environment with the adaptive rhythm of the marine ranching environment, greatly reduces the stress damage of the fish fry in the transportation process, and improves the adaptive capacity of the fish fry to the target environment after transportation.

The invention discloses a self-adaptive control method and system for an unmanned forcible entry robot based on environmental perception, and relates to the related field of robot control, and the method comprises the steps: dynamically obtaining the target information of a target region through a perception device group; performing clustering analysis on the target environment information by taking the target IMU information as a constraint to obtain a target clustering result, and extracting a first clustering cluster; introducing a step-by-step fusion mechanism to perform fusion analysis on the environment information in the first cluster to obtain first sensing information; based on a first mapping relation between the first IMU information and the first perception information, constructing a target environment visual graph of the target area; and performing adaptive control on the target unmanned forcible entry robot according to the target environment visual graph after the rear-end closed-loop optimization. The technical problem that due to the fact that an existing unmanned forcible entry robot is poor in environmental adaptability during control, the working capacity and precision in the complex environment are insufficient is solved, and the technical effects that the environmental adaptability of the robot is enhanced, and the working capacity and precision in the complex environment are improved are achieved.

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.