294 results about "Ambient intelligence" patented technology

The invention relates to an electric power working environment intelligent positioning and risk management and control method based on a three-dimensional model, and the method comprises the following steps: S1, constructing a three-dimensional model of an electric power working environment, and carrying out the spatial layering and equipment marking of the three-dimensional model, and forming a three-dimensional panoramic working image with attributes; s2, marking positions of operating personnel and equipment in real time, and generating dynamic positioning in a power operation scene; s3, recording and predicting the track of the operator, and pre-warning whether the operator enters a safe area or a dangerous area; a camera is used for collecting images of the operating personnel, non-standard behaviors of the operating personnel are recognized in real time through an AI algorithm, and behavior risk indexes of the operating personnel are generated based on detection results; s4, training a risk prediction model based on sensor data, detecting possible risk events, and obtaining an operation risk index of the equipment; and S5, calculating a comprehensive risk index in real time. According to the invention, the comprehensive safety of personnel, equipment and environment is guaranteed while the safety management efficiency is effectively improved.

The invention discloses an elevator control system based on artificial intelligence, which belongs to the technical field of elevator control, and comprises a plurality of layers of architectures such as data acquisition, transmission, processing, intelligent decision, execution control and the like. The data acquisition layer collects multi-source data through a car inside and outside and operation state sensor group; the data processing layer provides support for intelligent decision-making through preprocessing, analysis mining and model training optimization. An elevator dispatching module of the intelligent decision-making layer carries out peak dynamic zoning and flat peak energy-saving dispatching according to information such as passenger flow; the safety protection module monitors and processes hidden dangers in real time; the personalized service module provides customized services based on passenger behaviors and demands. And driving of the control layer, a door machine and a car environment control system are executed, and stable lifting of the car, safe operation of a door and intelligent environment adjustment are achieved correspondingly. Experiments show that the system can remarkably improve the elevator operation efficiency, safety and passenger satisfaction and reduce energy consumption, and has important application value.

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The invention relates to the technical field of urban infrastructure intelligent detection, and discloses an urban drainage pipe network damage detection system based on intelligent analysis. A multi-source data acquisition module acquires pipe network static structure parameters and real-time operation monitoring data through a distributed sensor network; generating a topological characteristic value, a dynamic operation state characteristic value and a dynamic detection threshold set of each damage type; the data preprocessing module filters noise of the monitoring data, eliminates abnormal values and extracts time domain and frequency domain feature vectors; the feature fusion module fuses and generates a multi-dimensional fusion feature matrix based on the topology and operation feature values; the intelligent analysis engine calculates and outputs a damage type diagnosis result through mode matching; the spatial positioning module generates three-dimensional coordinate positioning data of the damaged area in combination with the topological characteristic value; and the dynamic learning module optimizes the detection rule according to the maintenance data. The system realizes intelligent damage detection and accurate positioning, improves the detection accuracy and efficiency, adapts to a complex environment, and is high in intelligent level.

The invention discloses an industrial odor online monitoring system based on big data. The system comprises a data acquisition module, a preprocessing module, a feature fusion module, a multi-source sensing weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module. Data interaction among the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a timestamp is established in a distributed database. Through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and a credible data governance architecture, a new industrial bad smell monitoring normal form with environment intelligent adaptability is constructed. The four technical effects form complementary support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

The invention relates to a machine room environment intelligent regulation and control method fusing multi-source sensing information, and the method comprises the steps: collecting the temperature and humidity data of different regions in a machine room through a temperature and humidity sensor, and collecting the movement track and stay time data of a person in the machine room through a person positioning device, transmitting the collected machine room environment data and personnel activity data to a data fusion module; in the data fusion module, machine room environment data and personnel activity data are aligned according to a time sequence, wrong data and outliers are eliminated, and a data set in a unified format is obtained; a correlation coefficient matrix among the characteristic parameters of the computer room is analyzed through correlation, the characteristic parameters with correlation higher than a preset correlation threshold value are screened out, and the characteristic parameters comprise temperature, humidity, personnel access frequency and staying duration parameters.

The invention discloses an intelligent monitoring method and a monitoring system for an IDC (Internet Data Center) machine room environment, particularly relates to the field of IDC machine room environment monitoring, is used for solving the problems of multi-parameter relevance and real-time early warning, and aims at solving the problems of multi-parameter relevance and real-time early warning by constructing a high-dimensional topological structure, a Bayesian network model and a dynamic threshold adjustment mechanism of the IDC machine room environment. The relevance and the causal relationship among the multi-dimensional parameters are effectively captured, and the analysis and prediction capability on the complex environment data is remarkably improved. And by utilizing a persistent coherence algorithm and random forest regression, nonlinear correlation of parameters is accurately identified, and the limitation of a traditional method in multi-parameter analysis is overcome. Through Granger causal relationship test, a parameter causal sequence is determined, and a basis is provided for accurate prediction of environmental changes. A dynamic threshold adjustment mechanism flexibly adapts to environment changes, false alarm and missing alarm are reduced, it is ensured that the system rapidly identifies abnormities and triggers alarms in a complex environment, and the intelligence and stability of the system are remarkably enhanced.

The invention discloses a laboratory analytical instrument automatic calibration system based on artificial intelligence, and belongs to the technical field of instrument automatic calibration. Comprising the following modules: an intelligent hardware interaction module for realizing plug-and-play of different instruments and accurate conveying of standard substances; the instrument digital twinning module automatically identifies the characteristics of the instrument and simulates the behaviors of the instrument; the multi-mode AI calibration module is used for optimizing calibration parameters and adjusting the calibration process in real time; the environment intelligent compensation module calculates and generates a compensation coefficient in real time, and eliminates the influence of environmental factors on a calibration result; the instrument health management module is used for evaluating the health state of the instrument and predicting the service life of a core component, and realizing conversion from passive maintenance to predictive maintenance; the calibration knowledge graph module is used for realizing automatic extraction, reasoning and application of calibration knowledge through a graph neural network and continuously optimizing a calibration strategy; and the intelligent scheduling and management module is used for generating an optimal calibration plan, coordinating the work of each module and realizing reasonable distribution and efficient utilization of calibration resources.

The invention belongs to the technical field of intelligent control, and particularly relates to an intelligent regulation and control method for an operating environment in an SF6 ring main unit. The method comprises the following steps: firstly, acquiring temperature, gas pressure and humidity data of a plurality of sampling points in the ring main unit, and constructing a time sequence environment data set through time synchronization and missing interpolation processing; analyzing the temperature data, extracting fluctuation characteristics, determining an environment disturbance factor in combination with an electric arc trigger intensity index, and obtaining a cabinet body temperature control safety factor in combination with a dynamically adjusted temperature rise threshold and a gas insulation state; then constructing an environment equivalent thermal inertia model and a hot-pressure covariant model based on historical data, and calculating a thermal stability index of the cabinet body; and finally, whether adjustment is triggered or not is judged according to the thermal stability index and the temperature control safety factor, if yes, a temperature abnormal area is positioned through a convolutional neural network and a clustering method, the temperature control response level is calculated, a temperature control strategy library is called to be matched with adjustment parameters, and accurate control is achieved.

The invention discloses a building environment dynamic regulation and control method and system based on Internet of Things driving, particularly relates to the field of building environment intelligent regulation and control, and is used for solving the problems of multi-region environment dynamic regulation and control and fine management. The environment data of each sub-region of the building is accurately acquired and synchronized, real-time region division is realized in combination with an adaptive clustering algorithm, and the accurate capture of environment features and the reasonability of space division are ensured. On the basis, the independence and interaction strength of the sub-regions are comprehensively evaluated by calculating an environmental independence index, and a scientific basis is provided for refined and coordinated regulation and control. Based on a rule engine and a decision tree reasoning algorithm, an optimal regulation and control strategy is automatically generated, and continuous optimization is performed in combination with a Q learning algorithm, so that the system can adapt to environmental changes, dynamically adjust regulation and control actions, realize intelligent optimization and self-learning of the regulation and control strategy, and remarkably improve the regulation and control efficiency and accuracy of the building environment.

The invention relates to the technical field of phytotron control systems, in particular to a seed seedling phytotron control system and a control method thereof, which integrate multi-dimensional environment perception, intelligent analysis and decision, precise environment regulation and control, a central controller and a data storage and analysis platform. A seedling raising environment is monitored in real time through temperature and humidity, illumination, CO2 concentration, soil moisture and an image sensor, an intelligent analysis decision module preprocesses data, establishes a growth model, generates a control strategy and optimizes a target by using an advanced algorithm, and an accurate environment regulation and control module executes the strategy and regulates temperature, humidity, illumination and the like. The central controller coordinates operation of the modules and realizes man-machine interaction, and the data storage and analysis platform is responsible for storing data and supporting cloud analysis so as to realize efficient seedling culture management and significantly improve the seed emergence rate from 75% of a traditional method to 92%, and greatly reduce seed waste.

The invention relates to the technical field of environment intelligent regulation and control, and discloses a slaughter workshop environment temperature and humidity intelligent regulation and control system. The system comprises a data acquisition subsystem used for performing heterogeneous Internet of Things sensor data acquisition on a slaughter workshop to obtain a slaughter process sensing data set; the directed graph modeling subsystem is used for modeling to obtain a slaughter process flow directed graph model; the environment interference prediction subsystem is used for performing process time sequence environment interference prediction of the first stage to obtain an environment interference prediction result; the multi-objective optimization subsystem is used for carrying out control parameter multi-objective optimization in the second stage to obtain a multi-device cooperative control parameter combination; and the multi-device cooperative control subsystem is used for creating an Internet of Things remote device control instruction and issuing the Internet of Things remote device control instruction to the fresh air system, the dehumidification system and the refrigeration system. According to the invention, the precision, efficiency and robustness of the environment temperature and humidity control of the slaughter workshop are improved.

The invention belongs to the technical field of intelligent control of air conditioning systems, and discloses a split type air conditioner load management system and method. The system comprises a central controller, a health management module, an adaptive integrated control module AICA and an intelligent scene recognition module. The central controller integrates various sensor information such as temperature and humidity, air quality and user physiological data, data processing and intelligent decision making are carried out through an AICA module, and dynamic optimization management of air conditioner loads is achieved. The health management module automatically adjusts the air conditioner operation mode according to the body temperature, heart rate and other physiological indexes of the user, and a comfortable environment is provided. And the intelligent scene recognition module is used for pre-judging user requirements and adjusting the operation of the air conditioner by learning a user behavior mode. The energy efficiency and the intelligent level of air conditioner operation are improved, the method is suitable for air conditioner load management of various buildings, energy can be effectively saved, and the comfort level of users is improved.

The invention discloses a water-cooling energy consumption adaptive regulation and control method suitable for a data center, and the method comprises the following steps: obtaining a multi-dimensional operation parameter set of the data center in real time, and generating a real-time thermal load density distribution diagram reflecting the heat distribution condition in a machine room; calculating the overall thermal deviation degree and the local hot spot intensity, and solving through a dynamic energy efficiency optimization function to obtain a target system supply and return water temperature difference and a target cooling liquid flow; inputting into a hydraulic and thermodynamic simulation model, and generating an optimal multi-device coordinated regulation instruction set; and executing the multi-device coordinated regulation instruction set, and then performing parameter calibration on the hydraulic and thermodynamic simulation model. The method has the following advantages and effects that the thermal environment of the machine room can be sensed in real time, the energy consumption and heat dissipation requirements can be intelligently balanced, and multiple devices in the cooling system can be cooperatively regulated and controlled, so that the overall energy consumption of the cooling system is optimized while the local overheating risk of the machine room is thoroughly eliminated.

The invention discloses a classroom environment intelligent supervision method and system based on data analysis, and the method comprises the steps: obtaining multi-source heterogeneous data in a classroom environment, eliminating the sensor noise of the multi-source heterogeneous data through employing an improved Kalman filtering algorithm, carrying out the time-space alignment of the data after noise elimination, and obtaining the aligned multi-source heterogeneous data; establishing a CRNN three-layer convolutional recurrent neural network model, and inputting the aligned multi-source heterogeneous data into the CRNN three-layer convolutional recurrent neural network model for recognition to obtain a classroom teaching environment state; and carrying out classroom risk early warning and self-adaptive classroom environment regulation and control according to the classroom teaching environment state. The classroom emergency risk can be actively prevented, automatic adjustment can be achieved through an intelligent air conditioner and an illumination system, a safe and comfortable teaching environment is created, and classroom efficiency improvement and teacher and student health guarantee are facilitated.

The invention discloses a dynamic environment navigation method and system based on semantic and geometric fusion information, and relates to the technical field of dynamic environment intelligent navigation, and the method comprises the following steps: obtaining and preprocessing continuous frame images of a dynamic environment; inputting the preprocessed continuous frame images into an improved target detection model, and identifying the type of an object in the dynamic environment; feature points in the preprocessed continuous frame images are extracted and matched, an improved GC-RANSAC algorithm is adopted to distinguish inner and outer points in the matched feature points, and a basic matrix is calculated according to the inner points; screening and determining static feature points in the feature points according to the basic matrix by adopting epipolar constraint and multi-view geometric constraint and combining the recognized object category semantic information; and estimating a camera pose based on the screened static feature points, performing optimization by using a BA optimization algorithm, constructing an environment map according to an optimized result, and performing dynamic environment navigation. According to the invention, accurate positioning and navigation in a dynamic environment can be realized.

The invention discloses a data-driven floating type wind turbine generator model-free control method and system, and the method comprises the steps: obtaining the operation state and external environment parameters of a wind turbine generator, and constructing sample data; inputting the sample data into a pre-trained controller, and outputting a control instruction to control the wind turbine generator; the specific process of obtaining the controller comprises the steps that the simulation operation state / historical operation state and external environment parameters of the wind turbine generator are obtained, and an initial controller is constructed based on the simulation operation state / historical operation state and the external environment parameters; determining a control target, a wind turbine generator operating environment, an intelligent agent, a state space, an action space, a constraint condition and a reward function in the initial controller; and performing continuous interactive learning training on the intelligent agent and the operation environment of the wind turbine generator through a deep reinforcement learning algorithm to obtain a trained controller. The method has the advantages of reducing the modeling difficulty, dynamically correcting the control strategy, improving the power generation efficiency and the like.

The invention relates to the technical field of data processing, and provides an energy storage power station construction environment intelligent monitoring method and system. The method comprises the following steps: constructing a monitoring network of a current construction environment; performing feature dimension disassembly on the data monitored by the monitoring network, and constructing a feature analysis atlas of the current construction environment; according to the feature vector of each node in the feature analysis map of the current construction environment, granularity sensitivity evaluation is carried out, and recommended granularity configuration of each feature is obtained; performing enhancement processing on the matched multi-modal monitoring data acquisition according to the recommendation granularity configuration corresponding to each feature; and carrying out construction environment risk index assessment, and triggering a safety alarm when the risk index exceeds a threshold value. According to the method, data islands among all stages of construction are broken, cross-stage risk linkage analysis and current construction environment risk prediction are realized, passive response is changed into active intervention, and the safety management capability of the whole construction process of the energy storage power station is improved.

The invention relates to the technical field of automobile electronic control, discloses a control system of a three-dimensional interactive automobile atmosphere lamp, and aims at solving the problems that an existing atmosphere lamp is single in function, insufficient in space sense, poor in adaptability and poor in user experience. The system comprises a multi-mode sensing unit which collects environment, operation, passenger behavior and interaction instruction data in real time; the data fusion and situation analysis unit is used for preprocessing the fused data, identifying a prediction situation and generating a situation representation vector; the interaction intention recognition unit is used for analyzing gesture, voice and sight line information of the passenger and recognizing an interaction intention; the three-dimensional light field generation engine is used for dynamically generating a three-dimensional light effect scheme based on the situation representation and the interaction intention; and the distributed light-operated execution network drives the light units to realize a lighting effect scheme. According to the scheme, intelligent dynamic and humanized control over the light environment is achieved, and system intelligence, interaction experience and driving and riding comfort and safety are remarkably improved.

The invention relates to the technical field of cloud computing and identity authentication security, and discloses a cloud environment intelligent identity authentication cross-domain docking method and system.The cloud environment intelligent identity authentication cross-domain docking method comprises the steps that heterogeneous identity data of multiple cloud platforms is collected and preprocessed, and a standardized identity data set is obtained; constructing a unified identity semantic model based on the standardized identity data set and generating a feature embedding vector; performing identity mapping conversion on the feature embedding vector by using a deep neural network to obtain a mapped identity feature vector; performing multi-factor adaptive trust evaluation in combination with the mapped identity feature vector and the real-time behavior data to obtain a dynamic trust evaluation result; generating an adaptive security token based on the dynamic credibility evaluation result; according to the method, the problems of cross-domain isomerism and dynamic trust evaluation of identity authentication in a cloud environment can be solved, and an effective solution is provided for cloud computing security.

The invention provides an urban traffic management intelligent evaluation system and method based on a large language model, and belongs to the technical field of intelligent traffic management. The system comprises a data processing module, an MECA traffic flow analysis module, a feature engineering module, a driving behavior evaluation module, a clustering analysis module, an LLM intelligent decision module, a visualization generation module and a visualization module. The system analyzes the road traffic data collected by the unmanned aerial vehicle, adopts the MECA technology to automatically identify the road type and the traffic environment, intelligently judges the congestion level, evaluates the driving behavior, and combines a big language model to generate a targeted traffic management optimization suggestion. According to the method, the adaptive context sensing technology is innovatively combined with multi-criterion learning, adaptive congestion judgment of different road types is realized, different traffic characteristics of urban expressways, common urban roads and expressways can be accurately recognized, and differentiated management strategies are provided accordingly. The system supports real-time processing of large-scale traffic data, and provides scientific and accurate decision support for urban traffic management departments.

The invention relates to the technical field of intelligent agriculture and agricultural engineering, and particularly provides a facility vegetable cultivation environment intelligent regulation and control system based on the Internet of Things and a multi-objective optimization algorithm. The system comprises a blade microenvironment dynamic sensing subsystem which outputs and generates a blade boundary layer diffusion resistance-wind speed-temperature gradient real-time data set with space coordinates, namely a microenvironment state map; the stomatal conductance collaborative optimization subsystem outputs and generates an optimal airflow parameter combination packet which comprises a target wind speed vector, a temperature gradient threshold value and an expected stomatal conductance gain; and the bionic fluid tissue execution subsystem converts the optimal airflow parameter combination packet into a non-uniform airflow field, and microenvironment reconstruction is realized through a dynamic tuyere array. According to the method, the spanning of the facility environment regulation and control scale from the traditional greenhouse macroscale to the leaf micron scale is realized, the environment regulation and control precision is improved by 2-3 orders of magnitude, and a novel environment control normal form with both the disease prevention effect and the yield increasing potential is provided for facility vegetable production.

The invention relates to the field of large model application, and provides a cloud environment intelligent operation and maintenance method and system based on an MCP protocol. The method comprises the following steps: in response to a natural language task instruction input by a user, generating a structured operation instruction according to an accessed large model; the structured operation instruction is sent to the user side for confirmation, if the user side modifies historical information or to-be-confirmed content, difference points are marked, incremental context is generated, and then the incremental context is sent to the server side to execute specific operation; after a user side submits modification information or makes a new request, correlation analysis is carried out on the large model, only relevant conclusions are reserved or intermediate key results are summarized, the results are submitted to the large model, a next decision is generated, and a natural language task instruction is directly executed and returned to the user side. According to the method, the context length can be greatly reduced, and the memory loss problem of the model in a long task can be relieved.

The invention relates to the technical field of environment intelligent monitoring, and discloses an environment abnormal change intelligent monitoring method based on a cloud platform, and the method comprises the steps: obtaining environment parameter baseline semantic coding features and environment monitoring data time sequence semantic coding features; performing attention interaction analysis based on dynamic feature optimization on the environmental parameter baseline semantic coding features and the environmental monitoring data time sequence semantic coding features to obtain environmental monitoring-environmental parameter baseline semantic interaction coding features; and performing environment abnormal change identification according to the environment monitoring-environment parameter baseline semantic interaction coding features, if the environment abnormal change exists, giving an alarm, otherwise, indicating that the environment is normal. According to the environment abnormal change intelligent monitoring method, time sequence analysis and abnormal change identification are carried out on environment data monitored in real time. The invention further discloses an environment abnormal change intelligent monitoring device and system based on the cloud platform.

The present invention relates to the technical field of multi-tenant environment, and discloses a multi-tenant environment intelligent management method, device, equipment and storage medium. The present invention obtains resource information and tenant information in the multi-tenant environment, constructs an environment simulation map, obtains real-time activity information of each tenant unit, and substitutes it into the environment simulation map to perform multi-dimensional data analysis, and generates a multi-dimensional analysis map including a real-time analysis layer, a historical reference layer and a future estimation layer. According to the multi-dimensional analysis map, resource allocation analysis is performed on each tenant unit, resource allocation countermeasures are formulated, and resource allocation processing is executed. The method realizes accurate resource allocation by dynamically monitoring and analyzing tenant activities, improves resource utilization efficiency and system stability; by combining historical and future data, the system's ability to cope with changes is improved, and user experience is optimized, thereby solving the problem in the prior art that resource allocation in a multi-tenant environment is difficult to achieve a balance between cost and experience.

The invention discloses a distributed environment parameter intelligent early warning method based on general control network flow-oriented computation, and relates to the technical field of intelligent early warning of the environment of an active launching platform, and the method comprises the steps: dividing the environment of the launching platform into a plurality of monitoring points, and collecting environment and key equipment operation state data; calculating priority coefficients of the monitoring points, and judging whether the key safety parameters meet requirements or not; establishing minute-level and hour-level sliding windows under a streaming computation framework, extracting multi-time scale parameter fluctuation characteristics, calculating a stability index, and comparing and analyzing the stability index with a threshold value; for adjacent monitoring nodes, multi-dimensional parameter consistency is calculated, an overall consistency index is obtained, and discrimination of a single-point link fault and a global environment anomaly is realized; and a link tracing log is automatically generated, and a basis and parameter optimization correction are provided for abnormal event tracing. According to the method, efficient monitoring and intelligent early warning of multi-point environment and equipment parameters of the movable launching platform can be realized, and the safety protection accuracy and timeliness are improved.

The invention discloses an intelligent environment sensing system based on a multi-modal sensor, and relates to the technical field of intelligent environment sensing, the reliability of an underground garage environment sensing system is remarkably improved through multi-dimensional innovation, dynamic modal decomposition and a TCN network are combined, and the reliability of the system is improved. Differentiated characteristics of hot vehicles of the fuel vehicles and thermal runaway of the electric vehicles are effectively distinguished, and the misjudgment rate is reduced; a data attenuation mechanism triggered by vibration and an overlapping region linkage rule are introduced, and equipment abnormity and environment sudden change interference are resisted; and meanwhile, a sensing decision verification closed loop is formed by a dynamic learning module, so that the adaptability of the system to a new scene is continuously improved.

The invention belongs to the technical field of automatic control, and particularly relates to a watershed water environment intelligent biological treatment robot system and a customized adding method thereof, and the method comprises the following steps: S1, a cloud control platform fuses space-time water quality data collected by a fixed monitoring station and a robot to form a water quality field topology sudden change event set; s2, the cloud control platform calculates the water quality risk score of each water area unit according to the water quality field topology sudden change event and the static water quality index and sets the robot target density, and the converged robot density distribution and the water quality treatment priority level are obtained; and S3, the cloud control platform generates sampling, adding and inspection tasks according to the converged robot density distribution and the water quality treatment priority level, determines the basic value of the tasks, and allocates the generated tasks to the bid-winning robots for execution. According to the method, key topological characteristics can be captured in time when a pollution structure changes, so that a robot group is quickly concentrated to a high-risk area, and space optimization configuration of treatment resources is realized.

The invention provides an underground pipe gallery environment intelligent monitoring method and system based on multi-mode sensing, and relates to the technical field of monitoring, and the method comprises the steps: obtaining acoustics, pressure and temperature data collected by multiple types of sensors, carrying out the time domain and frequency domain feature extraction, carrying out the matching with a leakage feature mode library, determining a candidate section, and extracting a sound wave propagation time delay inequality; and carrying out constraint solution on the time difference positioning equation set by combining the pressure drop gradient and the temperature abnormal distribution, and determining and outputting the space coordinates of the leakage point. According to the invention, accurate positioning of pipe gallery leakage is realized, and the sensitivity and reliability of the monitoring system are improved.

The invention discloses a portable urban thermal environment intelligent sensing method and equipment based on AR, and the method comprises the steps: collecting a panoramic RGB street scene image of a user visual angle through AR glasses, and synchronously obtaining the time, geographic position and meteorological parameters; mapping the RGB image and the environmental parameters into a panoramic infrared thermal image by using a generative adversarial network, and obtaining the radiation temperature of each pixel; projecting the panoramic heat map to a unit spherical surface taking a user as a center, calculating a long-wave radiation flux through spherical surface angle integration, and calculating a short-wave radiation flux based on a solar radiation model by combining a solar elevation angle and the like; and fusing the total radiation amount to calculate the average radiation temperature, and finally superposing the heat map and / or the average radiation temperature to a real or virtual environment through AR glasses or an intelligent terminal to realize spatialization thermal load visualization.