164 results about "Ambient intelligence" patented technology

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 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 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 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 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 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 coal mine underground environment intelligent sensing and dynamic ventilation linkage control system and method, and is applied to the technical field of coal mine ventilation. The system comprises an environment intelligent sensing module used for collecting environment data of an underground coal mine; the data transmission module is used for realizing data transmission among different modules; the underground data processing module is used for processing and analyzing the environmental data, and performing real-time ventilation early warning and real-time ventilation equipment regulation and control based on the environmental data; the ground linkage control module is used for predicting environment data of a future time node and setting a preset scheduling scheme of ventilation equipment in combination with a ventilation network model simulation result and an underground operation plan; and the dynamic ventilation regulation module is used for regulating the ventilation condition of the underground coal mine based on the preset scheduling scheme and the real-time regulation and control scheme. According to the method, prediction is carried out by collecting the environment data of the underground coal mine, the scheduling scheme is generated in advance according to the operation plan, and the hysteresis problem of a real-time ventilation regulation and control scheme is avoided.

The invention relates to the technical field of ocean dynamic environment intelligent forecasting, and provides a three-dimensional ocean dynamic environment intelligent forecasting method and device and electronic equipment, and the forecasting method comprises the steps: S1000, preparing 1 / 30-degree high-resolution ocean dynamic environment element data (three-dimensional temperature and salt flow and two-dimensional water level), and carrying out the normalization; s2000, selecting a corresponding model input sequence from the training set according to a forecasting strategy of the model; s3000, training a hierarchical feature projection network; and S4000: performing reverse normalization and generating future prediction data: performing reverse normalization on the feature-enhanced output data according to the maximum value and the minimum value of the ocean data, and finally generating a day-by-day prediction product of the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity with 1 / 30-degree high resolution in the future day, namely, the product is obtained by predicting the sea surface height of the C sea, the three-dimensional sea temperature, the salinity, the latitudinal flow velocity and the longitude flow velocity.

The invention provides a data stream real-time scheduling method based on an intelligent agent, and the method comprises the steps: building an intelligent scheduling framework driven by reinforcement learning according to a factory production environment, and the framework comprises an environment, the intelligent agent and a reward function; converting the data stream features and the system state into feature vectors so as to construct a reinforcement learning state space of multi-dimensional features; the intelligent agent perceives the reinforcement learning state space, calls the prediction model to predict a data stream feature change trend and a system state change trend in a future preset time period, and generates a resource pre-allocation scheme based on a prediction result; and the intelligent agent generates at least one scheduling action based on a multi-target scheduling optimization algorithm according to the current production target, the resource pre-allocation scheme and the reinforcement learning state space to form a scheduling strategy. According to the method, resource waste and data processing delay are effectively avoided, the resource utilization rate is remarkably improved, and stable and efficient production of an intelligent factory production environment is guaranteed.

The invention discloses an intelligent monitoring method and system for the microenvironment of a power distribution cabinet, and the method comprises the steps: collecting the multi-parameter data of the microenvironment of the power distribution cabinet in real time through a plurality of sensors disposed in the power distribution cabinet, and enabling the multi-parameter data to at least comprise temperature, humidity, voltage and current; transmitting the multi-parameter data to a processing platform through an internet of things network; on the processing platform, performing data fusion processing on the multi-parameter data to obtain comprehensive environment parameters; based on the comprehensive environmental parameters, performing environmental state evaluation by using a fuzzy logic algorithm, and outputting a security level; based on the time sequence data of the comprehensive environmental parameters, using a time sequence prediction model to carry out fault prediction, and outputting a parameter change trend; and performing anomaly detection based on at least one of the comprehensive environmental parameters, the security level and the parameter change trend, and sending out an alarm signal when an anomaly is detected. The invention aims to realize real-time accurate monitoring of the microenvironment of the power distribution cabinet, early warn various potential risks in time, avoid the situations of terminal strip corrosion, poor contact of a secondary line, short circuit, lightning breakdown, false alarm of a switching signal, false operation of a primary switch and the like caused by the microenvironment problem, improve the power supply reliability and guarantee the safety of a power grid.

The application discloses an interview system construction method based on a large language model multi-agent mechanism and belongs to the field of artificial intelligence dialogue systems.The application implements the method as follows: a seed data set of an interview field prompt-question pair is generated;the seed data set based on ChatGPT is cyclically enhanced to form an updated seed data set;and a training data set is obtained after iteration for n times.The interviewer model is trained so that the interviewer model has an interview question generation capability.A role agent that determines an interview question order is designed.An environment agent that generates a text generation standard is designed.A memory agent that stores questions and answers and makes decisions is designed.The designed interview system is tested.The interview records obtained by the interview system are compared and evaluated to obtain an evaluation result.The interview system uses a large language model and a multi-agent mechanism to realize smooth and natural generation of interview questions, and through the answers of interviewees, interactive generation of more natural and logical interview questions is realized.

The invention relates to an intelligent regulation and control method and system for a health-care environment based on a neural network algorithm, and belongs to the technical field of intelligent regulation and control of the environment, and the regulation and control method comprises the steps: collecting environment sensor data, human physiological data and user interaction data, carrying out the space-time alignment and encryption desensitization processing, and generating an encrypted space-time matrix; respectively calculating a user physiological fitness index, an environment disturbance index and a behavior preference index based on the encrypted space-time matrix, and generating a three-dimensional feature vector; inputting the three-dimensional feature vector and historical sequence data into a neural network model, and outputting environment setting parameters; performing energy-saving adjustment on the environment setting parameters through a multi-objective optimization algorithm, and generating an equipment control instruction set; distributing the equipment control instruction set to an executor, and collecting equipment state data after execution; and calculating an error according to the equipment state data and the user feedback information, and performing parameter adjustment on the neural network model. The method can effectively meet the increasingly complicated health-care environment regulation and control requirements.

The invention discloses a controllable voice generation method and device based on multi-agent dynamic scheduling, and belongs to the technical field of voice synthesis, and the method comprises the steps: constructing a multi-agent voice generation frame comprising a central scheduling module, an identity agent, an emotion agent and an environment agent; a central scheduling module analyzes a user instruction and outputs a structured task plan to drive each agent to generate primary voice output of identity, emotion and environment dimensions, and a cooperation cost matrix between the agents is constructed according to the primary voice output; the optimal execution path of the multiple agents is solved through an optimization algorithm based on the cost matrix, the agents are executed in a cascading mode based on the optimal execution path, and finally sound mixing is completed and high-quality voice is output. According to the method, the naturalness, the semantic consistency and the overall quality of the synthesized voice in a complex scene can be remarkably improved, and the method is suitable for various man-machine interaction application scenes such as intelligent voice assistants, virtual digital humans, immersive entertainment, barrier-free voice services, personalized content creation and the like.

The invention belongs to the technical field of information, and discloses an intelligent sleep environment adjusting method based on artificial intelligence, and the method comprises the steps: synchronously collecting the heart rate variability, respiratory rate and body temperature data of a user, and carrying out the classification and recognition of a sleep state through a long-short-term memory network and a random forest algorithm, and obtaining a sleep state label; judging a deep sleep state according to the sleep state label and a corresponding body temperature data change trend, and further obtaining indoor environment parameters and calculating a deviation value with an ideal sleep environment; a reinforcement learning algorithm is adopted to construct a personalized environment adjustment strategy model, and when the environment parameter deviation exceeds a personalized threshold value, a multi-dimensional environment optimization control scheme is automatically generated. According to the invention, accurate identification of the sleep state of the user and intelligent adjustment of the environmental parameters are realized, the sleep quality is effectively improved, and a comfortable and healthy personalized sleep environment is created for the user.

The invention relates to the technical field of agricultural management, in particular to an intelligent mushroom growth environment monitoring system based on the Internet of Things, which comprises an environment sensing module, an edge computing node, a multi-strategy control execution module, a cloud platform server and a user interaction terminal, the environment sensing module collects growth environment parameters and state data in real time; the edge computing node completes data preprocessing and anomaly diagnosis; the multi-strategy control execution module adjusts the environment according to the instruction; the cloud platform server stores data, trains a model and generates a global instruction; the user interaction terminal realizes data visualization and remote control; the system adopts an improved double-flow convolutional neural network to identify a growth stage, utilizes a collaborative filtering algorithm to optimize cultivation parameters, and has strategies of timing and quantitative control, conflict resolution and the like. The mushroom growing environment can be accurately regulated and controlled, intelligent management is achieved, the labor cost is reduced, the mushroom yield and quality are improved, and intelligent development of the mushroom planting industry is promoted.

The invention relates to a micro-service test environment intelligent arrangement method and system, and belongs to the technical field of micro-service architecture. According to the system, through an AI-driven micro-service topology modeling and arranging technology, a graph neural network is utilized to automatically identify a micro-service dependency relationship and generate a topological graph, and meanwhile, a resource allocation and scheduling strategy is dynamically optimized based on a reinforcement learning algorithm. Besides, the system is equipped with a multi-target resource scheduling engine, supports multi-target optimization of cost, performance and test coverage rate, also has dynamic environment self-healing and optimization functions, can monitor the service health state in real time and automatically repair abnormity, and feeds back and adjusts an arrangement strategy according to a test result. Through the innovative technologies, the construction efficiency, the resource utilization rate and the stability of the micro-service testing environment are remarkably improved, the testing cost is reduced, and an efficient and intelligent solution is provided for testing of the micro-service architecture.

The invention belongs to the field of artificial intelligence and machine learning, aims to solve the problems of high manual generation cost, poor authenticity, low negative sample quality and insufficient scene coverage of multi-modal tool calling data, and comprises four core modules: a tool knowledge base module stores API metadata through PostgreSQL, and generates various user intentions and coherent calling sequences in combination with pre-training LLM; the multi-agent interaction module generates conversation and calling data containing multi-modal input through cooperation of a user, an assistant and an environment agent; the adversarial environment simulation module generates high-quality negative samples in batches by means of three adversarial strategies and automatically marks the high-quality negative samples; and the human feedback iteration module determines convergence by a performance improvement rate according to a model failure case optimization strategy. The system supports local / cloud deployment, adapts to LLM and Agent model training, and is suitable for training and robustness evaluation of dialogue type AI assistants and vertical field intelligent Agents.

The invention relates to the technical field of environment control and multi-system cooperative linkage of flight simulation training equipment, provides a full-motion simulator environment intelligent joint control method, system and equipment and a storage medium, and solves the problems of low intelligent level and poor emergency response efficiency of multi-system cooperative control. The method comprises the following steps: acquiring operation data and basic environment parameters of each subsystem in an environment joint control system in real time through a multi-dimensional environment sensing network, carrying out fusion and state analysis, and identifying security abnormity, smoke simulation triggering or subsystem faults; when the abnormal signal is recognized, generating a corresponding linkage control instruction by using a logic control algorithm, and issuing the instruction to external systems such as a power supply, a gallery bridge, a movement system, a fire alarm system and the like and an IOS teacher platform through a cross-system joint control mechanism; the system receives an external feedback signal in real time, dynamically adjusts a cooperative control strategy, and constructs a closed-loop intelligent joint control system. According to the invention, the intelligent level and the emergency response efficiency of multi-system cooperative control are improved.

This invention provides an active flow control method and system based on intelligent algorithms. The method includes: determining the number of inlet parameter sets for a typical flight state point, constrained by simulation economy and sampling efficiency; performing numerical simulations according to the number of inlet parameter sets to obtain dataset 1, with inlet parameters as independent variables and flow field characteristics as dependent variables; obtaining dataset 2, with flight state as independent variables and flow field characteristics as dependent variables, through numerical simulation for a certain set of inlet parameters; constructing a proxy model of inlet flow field characteristics for a typical flight state point using deep learning based on dataset 1; constructing a proxy model of inlet flow field characteristics for a wide-speed-domain flight state based on the proxy model of inlet flow field characteristics for the flight state point, using a transfer learning algorithm and dataset 2; constructing an environment-agent interaction model based on a deep reinforcement learning algorithm, using the proxy model of inlet flow field characteristics for a wide-speed-domain flight state as the environment model for deep reinforcement learning; setting optimization termination conditions based on the environment-agent interaction model, and obtaining a control sequence with continuously optimal inlet performance under different flight states through deep reinforcement learning. This invention can stably adapt to flight scenarios with a wide speed range and a large airspace, and achieve real-time optimal control of the air intake flow field under a wide speed range, solving the problem of poor generalization ability of traditional methods under different operating conditions.

The application belongs to the technical field of internet of things and intelligent control, and particularly relates to an exhibit environment intelligent regulation and control device based on internet of things, which comprises a multi-dimensional sensing unit, an exhibit state monitoring unit, an intelligent regulation and control execution unit, an internet of things gateway, a cloud platform interaction unit and a local storage module. The exhibit environment intelligent regulation and control device based on internet of things provided in the application has more comprehensive monitoring dimensions, guarantees the double safety of exhibits and personnel, realizes personalized regulation and control of exhibits, protects special material exhibits in a targeted manner, reduces energy consumption and operation cost, has a network interruption emergency mechanism, improves system stability, responds to emergencies in a timely manner, has strong risk prevention capability, realizes multi-terminal linkage and data visualization, and improves management efficiency.

The application discloses a kind of wireless environment intelligent monitoring equipment and its working method, belong to environment intelligent monitoring equipment technical field, including lifting device and temperature and humidity monitor, still including connecting piece, support table, drive arrangement, surround movement device, stretching device, sprinkler and temperature regulating device, connecting piece is set in horizontal plane, lifting device is set in the lower end of connecting piece, support table is set on lifting device, drive arrangement is set in the lower end of support table, surround movement device is set on drive arrangement, stretching device is set on surround movement device, sprinkler is set in the upper end of support table, temperature regulating device is set in the upper end of support table and is connected with sprinkler, the device can drive temperature and humidity monitor to carry out around stretching detection, to improve detection range, and can adapt to different environment, and in the equipment that measures corresponding measure is carried out when monitoring that temperature is too high or too low.

The invention discloses a digital environment infrastructure system and method, and relates to the technical field of the Internet, and the system comprises a demand management module which is connected with a target user terminal, and is used for generating a demand list; the supply management module is used for collecting supply information of suppliers in real time and determining the supply state of each supplier; the supply and demand decision module is used for generating and executing an optimal solution based on the demand list and the supply information of the target supplier; the data management module is used for cleaning data and converting the cleaned data into data assets by using a block chain technology, wherein the data is data acquired and generated by each module; and the digital environment intelligent support management module is used for managing the operation state of each module in the implementation process. The invention aims to solve the problems that data splitting and data collaboration are difficult and cross-platform data element circulation and capitalization are difficult to realize due to the fact that an existing digital scheme mostly focuses on a single link or a specific scene.

The invention discloses an environment intelligent monitoring question-answering platform and method. The environment intelligent monitoring question-answering platform comprises a platform server and an interaction terminal provided with a display unit. The display unit is used for displaying a human-computer interaction interface, the human-computer interaction interface comprises a question input area and a question reply area, the question input area is used for inputting environment monitoring inquiry information containing consultation inquiry information related to environment monitoring, and the question reply area is used for displaying reply content of reply information corresponding to the environment monitoring inquiry information. Wherein the platform server replies the environment monitoring inquiry information by applying an AI inquiry interaction model. The environment intelligent monitoring question-answering platform can be used as an interaction platform for feedback of environmental monitoring data questions, so that quick, convenient, flexible and professional answers can be obtained when consultation inquiry related to environmental monitoring data literatures needs to be carried out.

An integrated working environment intelligent sensing and active alarm protection system and method comprises a shell, a power module, a detection module, a controller and a Bluetooth module are arranged in the shell, an alarm lighting module is arranged on one side of the shell, the detection module is connected with the input end of the controller through an A / D module, and the controller is in communication connection with an intelligent terminal through the Bluetooth module. The detection module is connected with the output end of the controller, the alarm lighting module is connected with the output end of the controller, the detection module is used for detecting the external environment, the controller is used for processing information transmitted by the detection module, the Bluetooth module is used for transmitting the detected information to an intelligent terminal, and the alarm lighting module is used for sending out an alarm signal or providing lighting. The power module is used for providing power for the protection device. According to the invention, active safety monitoring and alarm protection of risk factors on a work site can be realized, and dangers are avoided.

The invention discloses a cloud native fault diagnosis method and system based on large model intelligent agent enhanced fine tuning, and belongs to the technical field of intelligent operation and maintenance and artificial intelligence. The system comprises a data preprocessing module which is used for actively injecting a fault into a cloud native environment and preprocessing multi-modal operation and maintenance data before and after the fault occurs; the scene construction module is used for constructing a fault scene sample based on the preprocessed multi-modal operation and maintenance data; the virtual environment module is used for constructing a fault diagnosis interaction environment; and the agent training module is used for optimizing a diagnosis strategy through model fine tuning and reinforcement learning. According to the method, high-accuracy, high-efficiency and interpretable fault root cause positioning of the cloud native application can be realized.

The application discloses a power distribution cabinet environment intelligent monitoring system and a monitoring method, and relates to the field of power distribution cabinet monitoring. The monitoring system comprises a total control computer, a total control programmable controller and a sub-control unit, the total control computer and the total control programmable controller are connected through a communication network cable to enable the two to communicate with each other, and the total control programmable controller is connected with multiple sub-control units through the communication network cable; the sub-control unit comprises a sub-station controller, a power supply tripping device, a sub-terminal and a power distribution cabinet, and the power distribution cabinet is provided with a fault signal acquisition module, a terminal row, a sensor module and a thermal imaging camera. The application aims to provide a power distribution cabinet environment intelligent monitoring system and a monitoring method, so that the environment in the power distribution cabinet can be detected in real time, and when a safety hazard occurs, maintenance personnel can be timely informed and handled.