247 results about "Cloud modeling" patented technology

The invention discloses a pump station unit state comprehensive evaluation method, and belongs to the technical field of pump station unit detection. The method comprises the following steps: acquiring multi-source monitoring data and generating a standardized monitoring data set; time-varying mutual information between indexes is calculated, a dynamic threshold value is determined in combination with current working condition parameters and a water level difference correction term, and a dynamic association network is constructed; performing physical mechanism characteristic decoupling on the data, including stripping a vibration signal working condition drift component based on a reference curve to obtain a vibration residual error, and calculating an equivalent standard working condition temperature based on a heat balance principle; respectively calculating a first weight based on data statistics and a second weight based on network topology, and adaptively generating a comprehensive coupling weight according to a consistency coefficient of the first weight and the second weight; and finally, judging a health state level by using a cloud model. Through deep fusion of a physical mechanism and data driving, the problems that fault features are difficult to extract and the model robustness is poor under variable working conditions are solved, and accurate evaluation of the unit state is achieved.

The invention discloses a combined electric appliance health analysis method and system based on multi-source data fusion, and the method comprises the steps: cleaning text data, carrying out the semantic mapping of the text data to a preset state quantity index, extracting a basic feature value from image data, and generating a standardized evaluation index set; calculating a weight vector of each evaluation index by constructing a judgment matrix, and performing consistency verification; weibull distribution fitting is carried out based on historical state quantity data, and grading threshold values of the positive degradation index and the negative degradation index are calculated; dividing a state interval based on the normal cloud model and calculating a membership degree vector of each state level; the membership degree vector is converted into a basic probability distribution function, evidence fusion is carried out in combination with the weight vector, and a comprehensive state level is output; and identifying the high-conflict evidence according to the Pignistic probability distance, performing refusion after correction, and outputting a final state level. Deep fusion and intelligent evaluation of multi-source heterogeneous data are realized, and the accuracy and robustness of health state judgment of the combined electric appliance are improved.

The invention discloses a photovoltaic module health state real-time monitoring method, system and device and a medium, and relates to the technical field of photovoltaic energy monitoring and intelligent operation and maintenance, and the method comprises the steps: collecting the health data of a photovoltaic module through a photovoltaic module working state sensor and an environment sensor, and transmitting the health data through a multi-network protocol; a feature importance evaluation method is adopted to construct a priority scheduling model to dynamically adjust the photovoltaic module health data acquisition frequency, anomaly detection is performed on the acquired photovoltaic module health data on an edge device, a graph convolutional network and a time sequence prediction model are fused to construct a cloud model, an anomaly detection result is input into the cloud model, and the cloud model is subjected to feature importance evaluation. Health trend analysis and state prediction are executed through the cloud model; early warning grading processing is carried out according to health trend analysis and state prediction results, a multi-level response mechanism is established through a correlation analysis algorithm, and a maintenance resource linkage process is triggered. According to the method, the real-time performance of key data acquisition and the data transmission efficiency are effectively improved through a priority scheduling scheme based on the information entropy weight.

The invention discloses a civil engineering foundation pit deformation monitoring system based on image analysis, and relates to the technical field of foundation pit deformation monitoring, and the system collects and enhances a multi-modal image, eliminates interference, corrects image distortion, tracks key points, constructs a panoramic image, generates a point cloud, extracts deformation features, and constructs a deformation map. The method comprises the following steps: identifying a construction disturbance event, constructing a deformation chain and performing causal reasoning, identifying a high-risk area, predicting a deformation trend and outputting an early warning, displaying a monitoring result, collecting user feedback and synchronizing system data. A deformation track is accurately extracted through image correction and key point tracking, a three-dimensional deformation map is constructed in combination with point cloud modeling, causal analysis is carried out based on construction disturbance and a deformation path, a high-risk area is predicted and identified by using a time sequence trend, an early warning is given out, user feedback collection and result synchronization are supported, and monitoring intelligence and response efficiency are improved.

The invention discloses a collaborative safety evaluation method and system for a water conservancy project dam and a slope, and relates to the technical field of water conservancy project safety monitoring and evaluation, and the method comprises the steps: constructing a safety evaluation index system based on a structure overall deformation index, a seepage index, a joint crack index and a material response index; based on a safety evaluation index system, a multi-source monitoring data set is acquired, reverse modeling is performed by using a cloud model, a t-Copula function is introduced to describe a tail joint dependence structure of each index under an extreme working condition, and the overall operation state of the structure is judged by calculating a multivariable collaborative membership degree and combining a standard grade cloud picture. According to the method, the limitation on index independence hypothesis in a traditional evaluation method is broken through, the capability of identifying the collaborative instability mode under the extreme working condition is achieved, the robustness of safety evaluation and the sensitivity of early warning are remarkably improved, and the method is suitable for risk identification and intelligent diagnosis of water conservancy project dams and slopes.

The invention discloses a heterogeneous computing power resource allocation method and system, and belongs to the technical field of computing power scheduling. The method comprises the following steps: analyzing an edge task through a content value analysis model, and generating a task label containing real-time, accuracy and exploratory demand scores; based on the task labels, a distributed computing power distribution protocol is utilized to match and distribute computing power resources for the tasks, and primary distribution and execution are completed; in the task execution process, the data value is evaluated through the lightweight evaluation model; if the evaluation value exceeds a dynamic threshold value, a computing power scheduling model agent is triggered to perform centralized secondary distribution, and the agent decides an optimal uploading path based on deep reinforcement learning and adjusts the threshold value so as to efficiently upload high-value data to a headquarter cloud and drive a large model to autonomously evolve. Through the double-track parallel architecture, the resource conflict between the real-time guarantee of the edge task and the evolution of the cloud model is solved, and the efficient and self-adaptive allocation of the computing power resource is realized.

The invention discloses a BIM intelligent assembly system applied to node construction, and belongs to the technical field of node construction. The BIM intelligent assembly system comprises the following modules: a node-level BIM parameterized model library; a factory-end intelligent manufacturing module; the field end intelligent assembly module comprises a reusable node sensing layer composed of UWB, IMU and a visual fusion sensing suite; an edge decision-making layer in which a lightweight Transform model is embedded; the shape memory alloy lock pin and the self-locking and self-adjusting execution layer of the piezoelectric micro-displacement sliding table are integrated on the node connecting plate; and a block chain quality file sub-module. The parameterized node group is directly issued to the steel bar hoop bending robot, the sleeve welding robot and the 3D printing mold, the space coordinates of steel bars, sleeves and embedded parts can be locked within the millimeter-level range in the factory stage, and the six-degree-of-freedom postures of the nodes are captured in real time during on-site hoisting; once deviation is found, the edge AI gives out a posture adjusting instruction immediately, and correction is completed through an SMA lock pin and a piezoelectric micro-displacement sliding table.

The invention relates to an earth pressure balance shield construction safety toughness dynamic evaluation system and method based on an extended cloud model and a network analysis method, and the system comprises an index system construction module which builds a multi-level toughness evaluation system based on a literature measurement and factor analysis method; an entropy weight TOPSIS weight calculation module objectively calculates the initial weight of the index through an information entropy theory; the ANP network weight optimization module corrects and optimizes the global weights of the indexes by constructing an inter-index nonlinear dependency network and a feedback mechanism; and the extended cloud toughness evaluation module quantifies qualitative indexes into membership degrees for different toughness levels based on expectation, entropy, hyper-entropy and other digital characteristics, comprehensively integrates weights and the membership degrees, and finally outputs soil pressure balance shield construction safety toughness levels and targeted optimization strategies. According to the method, multi-dimensional toughness quantitative evaluation and dynamic prevention and control of the construction safety risk of the earth pressure balance shield (EPB) can be realized under the complex stratum condition.

The invention provides a cloud-edge collaborative power quality monitoring method and system. The method comprises the following steps: S1, edge acquisition and preprocessing; s2, performing anomaly detection and event triggering; s3, cloud modeling and recognition: receiving abnormal event data from a plurality of edge nodes, and constructing a space-time diagram model representing the association relationship of each monitoring node in combination with the topological structure and historical data of the industrial park power distribution network; identifying the type, the occurrence position, the influence range and the severity of the power quality abnormal event, and outputting corresponding alarm information; s4, model updating and alarm interpretation: performing model adaptive updating based on data of the abnormal event, performing online training optimization on the power quality anomaly detection model, and issuing updated model parameters to each edge node to improve subsequent detection performance; and carrying out interpretation generation on the alarm information, and generating an interpretable alarm report oriented to operation and maintenance personnel. According to the invention, efficient fusion analysis and intelligent alarm of the multi-source electric energy quality data can be realized.

The invention relates to the technical field of intelligent vehicle-mounted systems, and discloses a vehicle-mounted voice interaction method and system and a readable storage medium, and the method comprises the steps: synchronously collecting initial voice and video data in a vehicle-mounted environment; performing wake-up word detection through a local acoustic model, and based on the detection confidence, extracting a mouth shape visual feature sequence by using a mouth shape recognition model to perform mouth shape verification so as to obtain a wake-up state and sound source positioning information; activating an interaction module at a corresponding position, and performing semantic recognition on the collected interaction voice and video data through a local model and a cloud model respectively; and finally, carrying out fusion cross validation on the local semantic recognition result and the cloud semantic recognition result to generate a final semantic recognition instruction, and executing corresponding operation by the vehicle-mounted system. According to the method, the recognition accuracy, the response speed and the robustness of vehicle-mounted voice interaction in a complex environment are improved, the false wake-up rate is effectively reduced, and the user experience is optimized.

The invention belongs to the technical field of water supply system toughness evaluation and optimization decision making, particularly relates to an urban water supply system toughness evaluation and optimization decision making method and system, and solves the problems that a traditional evaluation method is difficult to deal with uncertainty and large in subjective interference, and an improved method is subjective in weight and lacks dynamic performance and visualization. According to the method, qualitative toughness grade fuzziness and evaluation factor randomness are quantified through a cloud model, index cloudization, a weight cloud model and a multi-level cloud integration mechanism are constructed, toughness grade judgment and cloud parameter optimization are combined, and qualitative-quantitative natural conversion is achieved. According to the method, evaluation scientificity and reliability are improved, decision makers are helped to position short boards and adapt to system time-varying characteristics by means of the visual cloud picture and toughness-impact response corresponding relation, a basis is provided for operation and maintenance improvement and resource allocation, and a stable and adaptive water supply system toughness guarantee system is helped to be constructed.

The invention discloses an integrated three-dimensional space modeling method based on Gaussian splashing and a three-dimensional point cloud. The method comprises the following steps: step 1, converting a scene multi-view image and depth data into the three-dimensional point cloud; 2, constructing a point sequence input set; step 3, inputting the point sequence input set into a PointMamba model; 4, performing Gaussian parameter initialization, performing scale expansion on a local sparse region, and performing direction constraint on a global key region; 5, obtaining a prediction depth map through a binocular parallax matching algorithm, calculating a rendering depth map of the initial Gaussian point set, and adjusting the optimization intensity; and step 6, executing an adaptive encryption operation, and outputting a three-dimensional space model. According to the method, high-precision three-dimensional reconstruction of a complex scene is realized, and the method is suitable for scenes needing high-density point cloud modeling and semantic understanding, such as building scanning, industrial detection and virtual reality.

The invention discloses a radar interference effect evaluation method based on a constraint learning dynamic Bayesian network, is applied to the field of radar interference evaluation, and aims at solving the problem that the accuracy of interference effect evaluation is reduced due to radar detection data missing in a complex electromagnetic environment. Meanwhile, parameter constraints of five types of evaluation indexes and interference effect grades are defined; secondly, constructing a constraint learning dynamic Bayesian network, and learning a conditional probability and a transition probability under a data missing condition; then, proposing a prior constraint expectation maximization algorithm, converting parameter learning into an optimization problem with constraint by combining convex optimization, and overcoming the defects of a traditional expectation maximization algorithm; secondly, a cloud model is introduced to quantify discrete probability distribution into a continuous interference degree value; finally, simulation shows that the method can effectively improve parameter learning stability and evaluation accuracy under the conditions of suppressing and deception jamming and single index deficiency, and provides a reliable scheme for radar jamming effect evaluation in a complex environment.

The invention relates to the technical field of digital twinning and intelligent operation and maintenance of a power system, in particular to a power grid equipment full-life-cycle management cloud platform, which comprises a data acquisition module used for acquiring operation monitoring data flow of target power grid equipment; the state sensing entropy calculation module is used for calculating the state sensing entropy representing the state deviation degree of the digital twin model and the physical entity; the confidence degree evaluation module is used for determining the state confidence degree of the current full-life-cycle management system on the real physical state of the target power grid equipment; the active verification decision module is used for acquiring high-fidelity calibration feedback data; the model calibration module is used for recovering the model fitting authenticity of the system to the target power grid equipment; according to the method, the cognitive problem of seemingly fitting actual rule deviation of the cloud model and the physical entity is solved, and the global optimal decision of power grid full life cycle management under double constraints of safety and economy is realized.

The invention discloses a police target tracking system based on deep learning, and relates to the field of police video monitoring. The system comprises a video sensing and front-end processing unit, an edge intelligent tracking server and a cloud model optimization and command platform, the video sensing and front-end processing unit collects visible light and thermal infrared video streams, the edge intelligent tracking server processes the visible light and thermal infrared video streams, and a stable track with an identity label is generated; and the cloud model optimization and command platform aggregates edge data based on a federated learning framework, iteratively optimizes a global model and realizes command scheduling. According to the system, the tracking robustness, the real-time performance and the multi-target identity keeping accuracy in a complex environment are effectively improved, and the actual combat requirements of police affairs are met.

The invention relates to a multistage self-adaptive plasma excitation and closed-loop feedback processing system, and relates to the field of environmental engineering. After waste gas enters the system, parameters such as odor concentration, temperature and humidity, ozone and the like are detected in real time by a sensor array, and data are fed back to a control center. The method comprises the following steps: decomposing organic high-molecular compounds by pre-ionization, exciting high-density active particles by a main reaction to thoroughly degrade peculiar smell, and decomposing ozone and small-molecular byproducts by using a nano-catalyst in a deep purification stage. The system adopts PID self-adaptive control, parameters can be automatically optimized according to real-time monitoring data, the removal rate is effectively improved, and the energy consumption is reduced. The processed gas is analyzed again through the secondary detection unit, data are uploaded to the cloud model, energy efficiency and processing strategies are continuously optimized through reinforcement learning, and intelligent and closed-loop management is achieved. The device can be widely applied to the field of industrial waste gas treatment, and has the outstanding advantages of efficient removal, low energy consumption, intelligent self-adaption and the like.

The invention relates to the technical field of data processing, and discloses a deep foundation pit monitoring data acquisition method and system. The method comprises the following steps: arranging sensors according to three depth levels of a pit top, a pit wall and a pit bottom of the deep foundation pit to obtain grouped data; calculating foundation pit deformation gradient, supporting structure stress, soil layer stability and underground water seepage parameters, and extracting space weight data; adopting a deep foundation pit space layering Gaussian hybrid clustering algorithm to screen representative monitoring sensors; inputting representative sensor data into the deep foundation pit long short-term memory network to predict foundation pit deformation; and dynamically adjusting the monitoring data acquisition frequency through game theory weight distribution and cloud model evaluation, and generating a data acquisition control instruction. The technical problems that in deep foundation pit monitoring, the data collection efficiency is low, representative sensor selection lacks scientific basis, and misjudgment is prone to occurring in single-index early warning are solved, and the intelligent level and prediction precision of deep foundation pit monitoring data collection are improved.

The invention belongs to the field of power distribution network fault diagnosis, and particularly discloses a power distribution line fault intelligent section positioning method, which does not depend on real-time waveform information in a traditional power supply state, does not adopt an accurate distance measurement method depending on a traveling wave propagation path, and does not depend on a high-frequency injection signal in combination with synchronous multi-point response. A fault feature matrix is constructed, and the AI classification model obtained through training is utilized to realize rapid and accurate identification of a fault section; and meanwhile, a cloud model continuous learning mechanism is designed, so that the system has the intelligent characteristics of self-evolution and self-adaption, and the technical bottleneck of insufficient consideration of intelligence, practicability and robustness in a power-off working condition of a traditional method is broken through.

The invention discloses a computer resource allocation method and system based on big data analysis, and relates to the technical field of computers, and the method comprises the steps: collecting computer resource data in real time, carrying out the preprocessing, and predicting the future resource demands of a computer through a reinforcement learning model based on the preprocessed data; based on future resource requirements, a computer resource allocation strategy is generated by combining a particle swarm optimization algorithm with a cloud model disturbance mechanism and self-adaptive inertia weight adjustment, and a dynamic scheduling mechanism of cloud computing and edge computing is utilized. According to the method, key parameters of the particle swarm optimization algorithm are set and initialized, and adaptive inertia adjustment, cloud model disturbance and an auxiliary particle replacement mechanism are combined, so that the global search capability and diversity control of the particle swarm are enhanced, local optimum is effectively avoided, the quality and convergence stability of a resource allocation solution are improved, and the resource allocation efficiency is improved. Therefore, the accuracy and efficiency of computer resource allocation are remarkably improved.

The invention relates to a cooking control method based on an artificial intelligence model, a control system of cooking equipment comprises a cooking agent, an interaction agent and a cloud, the interaction agent is provided with a terminal model, the cloud is provided with a cloud model, and the cooking agent is electrically connected to the cooking equipment; the cooking equipment is provided with a multi-modal sensor module, and the method comprises the following steps: receiving to-be-processed multi-modal data, and carrying out primary processing on the to-be-processed multi-modal data by a terminal model to obtain target multi-modal data; the cloud model performs fusion analysis processing on the target multi-modal data to generate an analysis result and a control instruction; the cooking agent controls the cooking equipment based on the analysis result and the control instruction. According to the method, the terminal cloud size model collaborative architecture is constructed, the balance between low delay and high precision can be realized, and the scene adaptability is improved by fusing multi-modal sensor data and establishing a dynamic cooking scene understanding model instead of a traditional static preset program rule.

The invention relates to the technical field of intelligent monitoring equipment, in particular to a radar point cloud target detection system and method based on multi-modal space-time fusion. Comprising the following steps: at least two radar transceivers synchronously collect echo signals of a target at different visual angles, and establish a three-dimensional coordinate system; based on the three-dimensional coordinate system of the target points, the target points of each time slice are clustered, a coordinate set of point cloud clustering is obtained, and whether the point cloud gravity center of each detection target is displaced or not is judged; if the detected target is displaced, capturing a space trajectory of a target point based on the barycentric coordinates, and performing gait analysis on the detected target; and if the detection target does not displace, judging the body posture of the detection target. The method has the advantage of low cost, reliable radar point cloud can be realized, the defects that the traditional point cloud needs multi-antenna MIMO and the cost is high are overcome, body point cloud modeling in the walking process is realized, meanwhile, posture body point cloud and vital sign data are jointly verified, and the reliability is enhanced.

The invention relates to the technical field of smart park management and cloud computing, in particular to a smart park safety management method based on cloud edge cooperative computing. According to the method, the multi-source data is acquired through the terminal device which is calibrated periodically, and the lightweight AI model of the edge node performs preliminary analysis according to the screening threshold and uploads the suspected hidden danger data. And the cloud platform performs deep analysis by using the prediction model, evaluates the security situation level and triggers early warning and emergency response. Meanwhile, the cloud model recognition accuracy, the edge model false alarm rate and the actual hidden danger proportion in the uploaded data are comprehensively calculated, the safety management characterization value is generated, the system state is judged based on the safety management characterization value, then the edge screening threshold value and the data uploading strategy are adaptively and dynamically adjusted, and a continuously-optimized management closed loop is formed. The safety management efficiency of the smart park is improved.

The invention discloses a lithium ion battery thermal runaway initial period multi-parameter collaborative early warning method, belongs to the technical field of lithium ion battery safety monitoring, is suitable for ternary NCM and lithium iron phosphate LFP system batteries, and can be widely applied to new energy automobile power batteries, large energy storage power stations and portable electronic equipment power supplies. Aiming at the defects of high false alarm rate of single-parameter early warning, unscientific multi-parameter fusion, poor cross-system adaptation and lack of closed-loop optimization in the prior art, the method generates a unified fusion result through synchronous acquisition and preprocessing of temperature, voltage, CO and multiple parameters in combination with uncertainty of quantitative parameters of a cloud model and a D-S evidence theory; low / middle / high three-level grading early warning is realized, and an emergency mechanism that 0.5 s is triggered to cut off a loop when parameters suddenly change is realized; meanwhile, cross-system self-adaption is supported, cases are stored through a continuous learning module, parameters are optimized, and robustness is ensured through a fault injection test. The method is suitable for multiple scenes and high in industrial compatibility, and provides guarantee for safe use of the lithium ion battery.

The application discloses a multi-part combined modeling identification method, which comprises the following steps: S1, acquiring a three-dimensional model of a sample part, and constructing a cloud model library of the sample part according to the three-dimensional model of the sample part; S2, collecting image data of all parts to be identified, and matching the image data with the three-dimensional model of the sample part in the cloud model library to identify all the parts to be identified; S3, selecting a plurality of associated parts from all the parts to be identified, the plurality of associated parts can be combined into one or more integral parts, and a three-dimensional model group corresponding to the plurality of associated parts is obtained; and S4, combining the three-dimensional model group corresponding to the plurality of associated parts to obtain a three-dimensional combined model, and the three-dimensional combined model is used for identification comparison with the integral part composed of the plurality of associated parts.

The invention discloses a bridge health state intelligent assessment method based on edge calculation, and relates to the technical field of structure health monitoring, and the method comprises the steps: grouping historical bridge sensor data according to environment variables at a cloud, forming a plurality of environment association data groups, and simulating the intervention on the environment variables in the training process of a cloud model; iterative training is carried out until the cloud model converges, and a cloud pre-training model is obtained; calculating a distribution deviation degree between the real-time causal feature and a pre-stored health reference causal feature to obtain a causal feature deviation degree; and when the causal feature deviation degree exceeds a preset causal consistency threshold, generating an assessment result of the damage conclusion and reporting the assessment result to a management platform. Through joint training and causal invariance constraint on cross-environment grouped samples, the cloud model can filter environmental fluctuations and focus on learning causal features related to structural damage.

The invention provides a cloud collaboration-based charging station safety event incremental learning method and system. The method comprises the following steps of: receiving difficulty samples uploaded by edge computing power equipment of a plurality of charging stations; carrying out clustering analysis on the received difficult case samples, and combining the samples belonging to the same potential event type into an incremental learning batch; based on the incremental learning batch, performing incremental training on a teacher model of the cloud under the constraint of an elastic weight consolidation mechanism; migrating target knowledge data corresponding to the teacher model after incremental training to a lightweight student model through a knowledge distillation technology; and calculating a parameter difference between the student model and an edge model currently running on the edge computing power equipment, and generating a difference parameter update package. According to the method, new knowledge of the cloud model is efficiently migrated and refined into a small update package suitable for the edge device, so that the network transmission load is greatly reduced, and frequent, timely and silent edge model iteration becomes possible.

The invention discloses a dynamic projection interaction calibration method and system for an annular science magic space, and belongs to the technical field of projection interaction. The method comprises the steps of obtaining three-dimensional point cloud data of an annular wall surface and spatial pose information of each projector, constructing a wall surface point cloud model, completing projector calibration according to the wall surface point cloud model, and obtaining a pose parameter data set; and based on the data set, mapping each frame of pixel of a preset picture sequence to a corresponding position of the wall surface in real time. And if user interaction is detected, determining an interaction area and executing corresponding logic. In the process, projection feedback images are continuously collected, dynamic calibration and tiny compensation are carried out in combination with pose parameters, calibration projection coordinates and real-time interaction response data are generated, and accordingly, all projectors are controlled to complete real-time and stable projection display on the annular wall surface. According to the scheme, the projection picture is highly consistent with the annular wall surface and user operation, dynamic calibration and real-time interaction are realized, the display precision and stability of projection are improved, and the immersion experience of a user is enhanced.

The invention discloses a seawater cooling system state evaluation method based on a combined weight and a cloud model. The method comprises the following steps: constructing a multi-layer evaluation index system; index weights are determined by comprehensively applying various subjective and objective methods such as an analytic hierarchy process and an improved CRITIC method, and combined weighting is performed through the game theory; establishing a standard cloud model of an evaluation grade based on a 3 principle and a golden section method; actual index data are collected, an index cloud model is constructed after the actual index data are processed through an improved degradation degree formula, and an object layer and target layer comprehensive cloud model is obtained through layer-by-layer upward synthesis; and by calculating the similarity between each layer of cloud model and the standard cloud model, multi-level quantitative evaluation of the health state of the system is realized. According to the method, subjective and objective weights are fused, fuzziness and randomness in cloud model processing evaluation are combined, and scientificity and accuracy of an evaluation result are improved.

The application discloses a kind of AI coordination edge computing's distributed power grid monitoring system, it is related to the technical field of distributed power grid monitoring based on AI model;Including distributed data acquisition module, acquisition multiple operating data, support multi-protocol access;Edge node preprocessing module, local processing original data, classified storage;AI collaborative analysis module, edge and cloud model cooperation, iteration updates model;Abnormal intelligent identification module, constructs rule base and identifies multiple abnormalities, generates alarm;Hierarchical data transmission module, hierarchical transmission data, with link redundancy;Visual management module, show power grid state, support query analysis;Security protection module, encrypted data, hierarchical authorization, detect attack, double backup.The application eliminates power grid monitoring blind area, improves data processing real-time and transmission efficiency;AI collaborative analysis improves the accuracy of abnormal identification, hierarchical disposal improves fault efficiency;Adapt to multiple scenarios, reduce operation and maintenance cost, help power grid safe operation.

This invention discloses a method for generating intelligent shell commands in a Linux environment, comprising: acquiring input natural language in a Linux terminal; determining whether a corresponding ready-made command exists; if it exists, directly executing the corresponding ready-made command; if it does not exist, calling a cloud model to perform semantic parsing on the natural language, identifying the user's intent based on the semantic parsing, and performing knowledge retrieval in conjunction with the context; combining the user's intent and the knowledge retrieval results, calling a large model to generate a shell command, and displaying the shell command to the user; after user confirmation, performing sandbox pre-execution; if pre-execution fails, returning the pre-execution result; if pre-execution succeeds, executing the shell command, and returning the execution result to the user after execution. This invention also discloses an intelligent shell command generation system in a Linux environment. This invention uses natural language to operate the terminal, making it convenient to use.