395 results about "Environmental adaptation" patented technology

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 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 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 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 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 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 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 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 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 charging pile part life cycle prediction and management method, and relates to the technical field of charging piles, and the method comprises the steps: constructing a multi-source data collection system, and obtaining the operation state, environment parameters and user behavior data; key characteristic variables are extracted through principal component analysis, and health state evaluation indexes are generated through a coupling aging model; an improved Transform prediction network is designed to realize accurate prediction of the residual life; and making a dynamic operation and maintenance strategy based on a prediction result and optimizing a maintenance period. The prediction precision can be improved through the environment adaptation factor and the nonlinear mapping layer, the maintenance cost is reduced, and the equipment availability rate is improved.

The invention relates to the technical field of robot control, and discloses a robot hybrid intelligent adaptive control method and system adopting reinforcement learning, and the method comprises the steps: collecting environment perception parameters, and constructing a dynamic environment model; setting and testing a robot execution mechanism dynamic model, and obtaining motion characteristics of the robot execution mechanism; integrating the dynamic model into the dynamic environment model; calculating a task demand parameter and a theoretical control load, and generating a control quantity in combination with motion characteristics; a driving unit action instruction is generated based on the control quantity and control is carried out; and evaluating the execution effect. The system comprises a dynamic environment model construction module, an execution mechanism dynamic model construction module, a model fusion module, a task parameter calculation module, an action instruction generation module, an action control module and an execution effect evaluation module. According to the invention, high-precision self-adaptive control of robot operation in a complex dynamic environment is realized, and environmental adaptability and task execution efficiency are improved.

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 intelligent indoor illumination, and discloses a sunshade and illumination linkage intelligent adjustment method based on environment self-learning, and the method comprises the following steps: constructing an environment feature model representing external light environment features based on collected environment illumination data; based on the intervention behavior of the user, learning to obtain a user preference model reflecting the comfort preference of the user; in combination with the environment feature model and the user preference model, determining a linkage control strategy for the sunshade equipment and the lighting equipment; and according to the linkage control strategy, the sunshade equipment and the lighting equipment are controlled. By adopting a reverse modeling strategy, an environment characteristic model is reversely deduced from actually acquired illumination data, adaptive learning of an external light environment is realized, the implementation threshold and cost of the system are remarkably reduced, and the system can automatically adapt to long-term or temporary changes of a surrounding environment of a building, so that the system is more intelligent and more convenient to use. And the environmental adaptability and robustness of the system are improved.

The invention discloses an unmanned aerial vehicle path planning method, which comprises the following steps: carrying out environment data acquisition and state modeling, and generating an initial task data set containing starting point distribution, end point distribution, a reference random process and original and real-time environment data; modeling and inferring by using a Gaussian process potential force model, and outputting a disturbance force prediction result containing a mean value and uncertainty; on the basis of the starting point, the terminal point, the reference random process and the disturbance force, constructing SRoudinger bridge distribution evolution and multi-objective optimization to obtain an optimal state distribution track; according to the uncertainty and the battery capacity, self-adaptively adjusting the regularization coefficient of the SRosine-Bridge entropy, and updating and optimizing the weight; and mapping the optimal state distribution track into a control input sequence, and triggering re-optimization when the environment suddenly changes. According to the method, Gaussian process potential force modeling and SRoudinger bridge optimization are fused to improve path precision, energy efficiency and environmental adaptability.

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 soft-shell crab molting state monitoring and automatic capturing device, and particularly relates to the technical field of soft-shell crab molting state monitoring and automatic capturing, and the soft-shell crab molting state monitoring and automatic capturing device comprises a monitoring module, an environment self-adaption module, a capturing module, a control module and a remote monitoring module. According to the soft-shell crab molting state monitoring and automatic capturing device, through cooperative application of multi-sensor data fusion and a deep learning algorithm, full-process intelligent monitoring and automatic capturing of the soft-shell crab molting state are achieved; by combining technical means such as environment self-adaptive adjustment, wireless low-power-consumption communication, large-scale multi-pond linkage and night weak light optimization, efficient, stable and safe operation of the system in various breeding scenes is guaranteed, and meanwhile, modular software and hardware design and a distributed cloud edge collaborative architecture have a good application prospect. The device has excellent expansibility, maintenance convenience and intelligent operation and maintenance capability, and the automation degree and production benefits of soft-shell crab culture are greatly improved.

The invention discloses a Gordonia ovorans YDY-4 strain and an application of the Gordonia ovorans YDY-4 strain in degradation of n-hexadecane. The cholesterol-eating Gordonia sp. YDY-4 is named as the cholesterol-eating Gordonia sp. YDY-4, is preserved in the China Center for Type Culture Collection (CCTCC), and has the preservation number of CCTCC M 20251290, and the cholesterol-eating Gordonia sp. YDY-4 is named as the cholesterol-eating Gordonia sp. YDY-4 and is preserved in the China Center for Type Culture Collection (CCTCC). The Gordonia ovorans YDY-4 provided by the invention has the capability of degrading n-hexadecane under the condition that the pH (Potential of Hydrogen) is 7.0 to 9.0, and can grow at 28 to 32 DEG C. The Gordonia ovorans YDY-4 provided by the invention has relatively strong environmental adaptability and n-hexadecane degradation capability, and provides a basis for bioremediation of petroleum polluted environment and application of a microbial enhanced treatment technology.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health, intelligent robots and the like, and discloses a decision-making method, device, equipment and medium based on an environment semantic atlas. Constructing and updating an environment semantic map based on the initial multi-modal fusion feature, fusing the environment semantic map and the initial multi-modal fusion feature to generate a deep fusion feature, determining current action distribution based on the deep fusion feature, and learning transmission mapping through a flow matching network to generate post-mapping action distribution; and generating an action sequence, adjusting the action sequence according to the environment interaction feedback, and updating the flow matching network parameters in combination with the environment interaction feedback and the reward function. According to the method, the environment semantic map and the multi-modal features are fused, the action distribution is optimized and the action sequence is dynamically adjusted in combination with the flow matching network, and the multi-modal decision-making performance and the environment adaptability are improved.

The invention discloses a high-precision phenotype analysis system for plant growth stress response, and relates to the field of agriculture. Comprising a central analysis platform, and the central analysis platform comprises a calculation engine, a stress diagnosis model and a visual interface; the edge calculation stage comprises a data preprocessing module, a feature extraction module and a time sequence compression module; and the sensor array comprises a main control unit, a hyperspectral camera, a thermal infrared camera, a 3DDoF camera and an environment sensor group. Based on a space-time registration mechanism of transfer learning, errors are reduced, the problem of stomatal conductance modeling distortion caused by time difference is solved, a dynamic weight fusion model adjusts feature weights in real time through sensor confidence and environment fitness, illumination abrupt change and temperature and humidity fluctuation interference are eliminated, and the accuracy of stomatal conductance modeling is improved. Compared with a traditional fixed weight model, the stress identification precision is improved.

Some embodiments of the present disclosure provide inventive concepts for estimating a value for an environmental heat stress index at a target microscale location. Meteorological data indicative of parameters such as relative humidity, air temperature, wind characteristics, or atmospheric cloud cover for a geographic area representative of a larger geographic area that includes the target microscale location can be obtained. Localized terrain data specific to the target microscale location can be obtained. A bias correction can be performed on the meteorological data based on the localized terrain data, generating microscale meteorological data that reflects conditions at the target microscale location. The heat stress index value, representing heat-related risk specific to the target microscale location, can be determined using the microscale meteorological data and can be a real-time or forecast value used for providing actionable insights or alerts for health and safety purposes.

The invention discloses a variable impedance constant force control method for a complex environment based on reinforcement learning, which integrates reinforcement learning adaptive compensation, double-loop force position control and a parameter smooth transition mechanism, and does not need to depend on a large amount of manual annotation data to carry out offline calibration. The damping coefficient in the impedance parameters can be dynamically adjusted in the full life cycle of the robot, and continuous optimization and self-adaptive updating are achieved. Compared with a fixed impedance method, the environmental adaptability is remarkably improved; compared with a traditional self-adaptive impedance method, the self-adaptive impedance method has the advantages that higher convergence speed and higher steady-state precision are achieved in a nonlinear and strong disturbance environment by utilizing the autonomous exploration capability of reinforcement learning, so that the robot can quickly converge to expected contact force in a curved surface, an inclined surface, an uneven terrain and a complex environment; the method has the advantages of being high in response speed, small in overshoot, high in steady-state precision and high in environmental adaptability.

The invention provides a lake water body purification and ecological restoration method based on a biotechnology. The method comprises the following steps: S1, collecting and analyzing basic data of a lake water body; s2, screening and culturing dominant microorganisms; s3, making an ecological restoration scheme; s4, putting a microbial agent and planting aquatic plants; s5, monitoring the water body remediation process; and S6, repairing effect evaluation and scheme adjustment. According to the method, the degradation efficiency and the environmental adaptability to target pollutants are remarkably improved, an efficient functional microbial agent is provided for precise repair, a microbial culture optimization model is constructed by utilizing a dynamic programming algorithm, and a nutrient substance adding strategy and a culture period are precisely regulated and controlled in combination with the growth dynamic characteristics of strains. On the premise of ensuring the concentration and activity of the microbial inoculum, the resource consumption and time cost in the culture process are effectively reduced, the economical efficiency and reliability of microbial inoculum production are improved, and a foundation is laid for large-scale engineering application.

The embodiment of the invention discloses a BIM-based building fire evacuation analysis method and system. According to the technical scheme provided by the embodiment of the invention, the path cost is adjusted according to different fire stages of the fire evolution process, and the real-time evacuation path is planned based on the path cost, so that the environmental adaptability of the evacuation path can be remarkably improved, the fire evacuation path planning precision is improved, and the evacuation path can respond to the fire evolution. High-temperature areas, dense smoke areas, structure danger areas and crowded nodes are avoided all the time, the risk of path guiding errors caused by disaster changes is greatly reduced, and the escape success rate is increased.

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.